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<journal-id journal-id-type="publisher-id">Front. Microbiol.</journal-id>
<journal-title>Frontiers in Microbiology</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Microbiol.</abbrev-journal-title>
<issn pub-type="epub">1664-302X</issn>
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<publisher-name>Frontiers Media S.A.</publisher-name>
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<article-id pub-id-type="doi">10.3389/fmicb.2025.1637123</article-id>
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<subj-group subj-group-type="heading">
<subject>Microbiology</subject>
<subj-group>
<subject>Review</subject>
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</article-categories>
<title-group>
<article-title>Proteomic insights into the physiology and metabolism of oleaginous yeasts and filamentous fungi</article-title>
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<contrib-group>
<contrib contrib-type="author">
<name><surname>Gluth</surname><given-names>Austin</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
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<name><surname>Trejo</surname><given-names>Jesse B.</given-names></name>
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<name><surname>Czaijka</surname><given-names>Jeffrey J.</given-names></name>
<xref ref-type="aff" rid="aff3"><sup>3</sup></xref>
<xref ref-type="aff" rid="aff4"><sup>4</sup></xref>
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<name><surname>Deng</surname><given-names>Shuang</given-names></name>
<xref ref-type="aff" rid="aff3"><sup>3</sup></xref>
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<contrib contrib-type="author">
<name><surname>Qian</surname><given-names>Wei-Jun</given-names></name>
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<name><surname>Yang</surname><given-names>Bin</given-names></name>
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<name><surname>Zhang</surname><given-names>Tong</given-names></name>
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<aff id="aff1"><sup>1</sup><institution>Biological Sciences Division, Pacific Northwest National Laboratory</institution>, <addr-line>Richland, WA</addr-line>, <country>United States</country></aff>
<aff id="aff2"><sup>2</sup><institution>Department of Biological Systems Engineering, Washington State University</institution>, <addr-line>Richland, WA</addr-line>, <country>United States</country></aff>
<aff id="aff3"><sup>3</sup><institution>Energy and Environment Directorate, Pacific Northwest National Laboratory</institution>, <addr-line>Richland, WA</addr-line>, <country>United States</country></aff>
<aff id="aff4"><sup>4</sup><institution>Agile BioFoundry, U.S. Department of Energy</institution>, <addr-line>Emeryville, CA</addr-line>, <country>United States</country></aff>
<author-notes>
<fn fn-type="edited-by" id="fn0001">
<p>Edited by: <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/1047529/overview">Yun-Peng Chao</ext-link>, Feng Chia University, Taiwan</p></fn>
<fn fn-type="edited-by" id="fn0002">
<p>Reviewed by: <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/1910155/overview">Thanh Ta</ext-link>, Feng Chia University, Taiwan</p>
<p><ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/3089384/overview">Naghmeh Poorinmohammad</ext-link>, McGill University, Canada</p></fn>
<corresp id="c001">&#x002A;Correspondence: Bin Yang, <email>bin.yang@wsu.edu</email></corresp>
<corresp id="c002">Tong Zhang, <email>tong.zhang@pnnl.gov</email></corresp>
</author-notes>
<pub-date pub-type="epub">
<day>05</day>
<month>09</month>
<year>2025</year>
</pub-date>
<pub-date pub-type="collection">
<year>2025</year>
</pub-date>
<volume>16</volume>
<elocation-id>1637123</elocation-id>
<history>
<date date-type="received">
<day>29</day>
<month>05</month>
<year>2025</year>
</date>
<date date-type="accepted">
<day>11</day>
<month>08</month>
<year>2025</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#x00A9; 2025 Gluth, Trejo, Czaijka, Deng, Qian, Yang and Zhang.</copyright-statement>
<copyright-year>2025</copyright-year>
<copyright-holder>Gluth, Trejo, Czaijka, Deng, Qian, Yang and Zhang</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/">
<p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</p>
</license>
</permissions>
<abstract>
<p>Fungi are vital to the bioeconomy, serving as key producers of food, beverages, biofuels, and medicines, while also acting as essential resource recyclers in ecosystem management. For nearly a century, oleaginous yeast and filamentous fungi have been explored for their proficiency in oleochemicals production and carbon storage. Lipogenesis is one of the most well-studied fungal processes, with substantial progress having been made through reductionist biochemical approaches; however, the physiology and metabolism of fungal systems operating under different conditions arise from the functions of thousands of proteins, for which very little is known outside of model yeast. In this review, we discuss how proteomics provides a valuable analytical approach to contextualize lipogenesis within a complex biological system, where lipid accumulation is fundamentally governed by changes in proteins of multiple pathways. In the past two decades, proteomics has been applied to study stress response to nutrient limitations, metabolism of various carbon and nitrogen sources, the lipid droplet hub of carbon storage, protein post-translational modifications and signaling pathways, as well as oleochemical biosynthesis, thereby advancing our understanding of the oleaginous phenotype. Over 40 studies are reviewed herein to evaluate the impact, critically assess the utility, and propose future applications of proteomics. In the coming years, large systems-level proteomics studies will lay a foundation for marrying modeling and metabolic engineering strategies to optimize oleochemicals production in oleaginous fungi.</p>
</abstract>
<kwd-group>
<kwd>proteomics</kwd>
<kwd>oleaginous</kwd>
<kwd>yeast</kwd>
<kwd>fungi</kwd>
<kwd>bioproducts</kwd>
<kwd>oleochemicals</kwd>
<kwd>lipid production</kwd>
<kwd>stress response</kwd>
</kwd-group>
<counts>
<fig-count count="6"/>
<table-count count="2"/>
<equation-count count="0"/>
<ref-count count="241"/>
<page-count count="25"/>
<word-count count="20616"/>
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<custom-meta-wrap>
<custom-meta>
<meta-name>section-at-acceptance</meta-name>
<meta-value>Microbiotechnology</meta-value>
</custom-meta>
</custom-meta-wrap>
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</front>
<body>
<sec sec-type="intro" id="sec1">
<label>1</label>
<title>Introduction</title>
<p>Ubiquitous in nature, fungi have evolved diverse metabolic capabilities for survival in a spectrum of niches. Certain fungi including single-celled yeast and multicellular filamentous fungi have developed a strategy to store surplus carbon as neutral lipids (triacylglyerides and sterols) in the face of competition or nutrient limitation (<xref ref-type="bibr" rid="ref115">Mason-Jones et al., 2021</xref>). Strains capable of producing &#x2265; 20% of their cell dry weight as lipids are termed &#x201C;oleaginous&#x201D; and have been studied for three-quarters of a century (<xref ref-type="bibr" rid="ref106">Lundin, 1950</xref>; <xref ref-type="bibr" rid="ref85">Kessell, 1968</xref>; <xref ref-type="bibr" rid="ref60">Gill et al., 1977</xref>; <xref ref-type="bibr" rid="ref24">Botham and Ratledge, 1979</xref>; <xref ref-type="bibr" rid="ref75">Higashiyama et al., 1999</xref>; <xref ref-type="bibr" rid="ref170">Song et al., 2001</xref>). Oleaginous fungi have garnered sustained interest because of their potential as secure sources of oleochemicals (i.e., chemicals derived from natural oils) for biofuels, bioplastics, surfactants, and drug delivery vehicles (<xref ref-type="fig" rid="fig1">Figure 1</xref>) (<xref ref-type="bibr" rid="ref2">Abeln and Chuck, 2021</xref>; <xref ref-type="bibr" rid="ref9">Alhattab et al., 2024</xref>). With overfishing depleting marine ecosystems and accelerated deforestation due to land-intensive vegetable oil production, oleaginous fungi also offer cheaper and less land-intensive alternatives for fish feed, palm oil, and cocoa butter (<xref ref-type="bibr" rid="ref21">Blomqvist et al., 2018</xref>; <xref ref-type="bibr" rid="ref188">Tramontin et al., 2019</xref>; <xref ref-type="bibr" rid="ref2">Abeln and Chuck, 2021</xref>; <xref ref-type="bibr" rid="ref27">Brunel et al., 2022</xref>, <xref ref-type="bibr" rid="ref28">2024</xref>; <xref ref-type="bibr" rid="ref229">Zhang X. Y. et al., 2022</xref>; <xref ref-type="bibr" rid="ref166">Sigtryggsson et al., 2023</xref>).</p>
<fig position="float" id="fig1">
<label>Figure 1</label>
<caption>
<p>The potential of oleaginous fungi as part of a sustainable bioeconomy.</p>
</caption>
<graphic xlink:href="fmicb-16-1637123-g001.tif" mimetype="image" mime-subtype="tiff">
<alt-text content-type="machine-generated">Organic feedstocks undergo pretreatment, yielding carbon sources for bioprocesses using oleaginous yeast and filamentousfungi, including species like Yarrowia lipolytica and Mucor circinelloides. The resulting bioproducts include bioplastic, biofuels, skin cream, fragrances, carotenoids, drugs, omega-3 supplements, vegetable oil substitutes, fatty alcohols, organic acids, and fish feed.</alt-text>
</graphic>
</fig>
<p>To develop economically viable bioprocesses using oleaginous fungi, the value of the product, productivity at commercially-relevant scales (g/L/day product), and feedstock cost must be considered (<xref ref-type="bibr" rid="ref16">Banerjee and Singh, 2024</xref>). Additional factors that should be considered include product yield (g product / g carbon source), land usage (tons product / hectare of land harvested for a carbon source), and energy and water inputs (<xref ref-type="bibr" rid="ref16">Banerjee and Singh, 2024</xref>; <xref ref-type="bibr" rid="ref39">Collett et al., 2014</xref>; <xref ref-type="bibr" rid="ref46">Davis et al., 2022</xref>; <xref ref-type="bibr" rid="ref18">Bhatt et al., 2022</xref>; <xref ref-type="bibr" rid="ref158">Sartaj et al., 2023</xref>; <xref ref-type="bibr" rid="ref36">Chopra et al., 2020</xref>). Several bioprocesses target low-cost feedstocks to provide carbon (and other nutrients) for bioproducts synthesis by oleaginous fungi (<xref ref-type="fig" rid="fig1">Figure 1</xref>). Emerging strategies include using algal biomass, plastic wastes, and even carbon/nitrogen-capturing co-cultures to supply resources (<xref ref-type="bibr" rid="ref219">Younes et al., 2020</xref>; <xref ref-type="bibr" rid="ref237">Zhou et al., 2023</xref>; <xref ref-type="bibr" rid="ref118">Mihreteab et al., 2021</xref>; <xref ref-type="bibr" rid="ref218">Yen et al., 2015</xref>; <xref ref-type="bibr" rid="ref34">Cheirsilp et al., 2012</xref>; <xref ref-type="bibr" rid="ref139">Pomraning et al., 2024</xref>). However, the single largest feedstock is lignocellulosic biomass derived from bioenergy crops and agricultural/forestry industry residues (<xref ref-type="bibr" rid="ref20">Bioenergy Technologies Office, 2024</xref>). Lignocellulose is primarily composed of cellulose, hemicellulose, and lignin. Cellulose and hemicellulose are polysaccharides containing glucose, xylose, arabinose, and other monomers, while lignin is a complex heterogeneous polymer containing interlinked phenolics (<xref ref-type="bibr" rid="ref191">Vanholme et al., 2010</xref>). Many oleaginous fungi natively utilize the major constituents of lignocellulose and are resistant to inhibitors from pretreatment steps that depolymerize lignocellulose (e.g., acetic acid, furan, furfural, and vanillin), making them poised for valorization strategies (<xref ref-type="bibr" rid="ref172">Spagnuolo et al., 2019</xref>; <xref ref-type="bibr" rid="ref71">Hassane et al., 2024</xref>).</p>
<p>Though hundreds of strains of oleaginous fungi from various phyla have been identified and studied for their lipid-producing capabilities (<xref ref-type="bibr" rid="ref14">Ayadi et al., 2018</xref>; <xref ref-type="bibr" rid="ref2">Abeln and Chuck, 2021</xref>; <xref ref-type="bibr" rid="ref229">Zhang X. Y. et al., 2022</xref>), only a selection have sequenced genomes and even fewer have an accompanying set of genetic engineering tools. For yeasts, these include certain strains of the ascomycetes <italic>Yarrowia lipolytica</italic> (<xref ref-type="bibr" rid="ref1">Abdel-Mawgoud et al., 2018</xref>), <italic>Lipomyces starkeyi</italic> (<xref ref-type="bibr" rid="ref228">Zhang L. et al., 2022</xref>; <xref ref-type="bibr" rid="ref44">Czajka et al., 2024</xref>), <italic>Debaryomyces hansenii</italic> (<xref ref-type="bibr" rid="ref213">Yaguchi et al., 2017a</xref>; <xref ref-type="bibr" rid="ref175">Strucko et al., 2021</xref>), and <italic>Candida tropicalis</italic> (<xref ref-type="bibr" rid="ref43">Craft et al., 2003</xref>; <xref ref-type="bibr" rid="ref233">Zhang et al., 2020</xref>) as well as the basidiomycetes <italic>Rhodotorula toruloides</italic> (<xref ref-type="bibr" rid="ref222">Yu and Shi, 2023</xref>)<italic>, Rhodotorula glutinis</italic> (<xref ref-type="bibr" rid="ref134">Pi et al., 2018</xref>), and <italic>Cutaneotrichosporon oleaginosum</italic> (<xref ref-type="bibr" rid="ref26">Bracharz et al., 2017</xref>; <xref ref-type="bibr" rid="ref174">Stellner et al., 2023</xref>; <xref ref-type="bibr" rid="ref160">Shaigani et al., 2023</xref>). For filamentous fungi, the mucoromycetes <italic>Mucor circinelloides</italic> (<xref ref-type="bibr" rid="ref122">Nagy et al., 2017</xref>; <xref ref-type="bibr" rid="ref55">Fazili et al., 2022b</xref>) and <italic>Mortierella alpina</italic> (<xref ref-type="bibr" rid="ref69">Hao et al., 2016</xref>; <xref ref-type="bibr" rid="ref156">Sakamoto et al., 2017</xref>; <xref ref-type="bibr" rid="ref231">Zhang et al., 2023</xref>) have been investigated for production of polyunsaturated fatty acids (PUFAs) (<xref ref-type="bibr" rid="ref229">Zhang X. Y. et al., 2022</xref>). Nitrogen limitation is the most popular strategy to induce lipid accumulation, though phosphate, sulfate, or iron limitation can also be used (with combinatorial effects observed by limiting multiple nutrients) (<xref ref-type="bibr" rid="ref65">Granger et al., 1993</xref>; <xref ref-type="bibr" rid="ref210">Wu et al., 2010</xref>, <xref ref-type="bibr" rid="ref211">2011</xref>; <xref ref-type="bibr" rid="ref123">Nambou et al., 2014</xref>; <xref ref-type="bibr" rid="ref49">Dzurendova et al., 2021</xref>; <xref ref-type="bibr" rid="ref209">Wierzchowska et al., 2021</xref>). The earliest systems-level analyses of oleaginous <italic>Y. lipolytica</italic> grown in nitrogen-rich vs. limited conditions were conducted using transcriptomics (<xref ref-type="bibr" rid="ref119">Morin et al., 2011</xref>; <xref ref-type="bibr" rid="ref142">Poorinmohammad and Kerkhoven, 2021</xref>). However, these studies showed no significant changes in the transcriptional profiles of fatty acid biosynthesis genes in response to nitrogen limitation. This suggests that lipogenesis may primarily be regulated at the post-transcriptional level, leading to alterations in protein profiles that ultimately drive differences in lipogenesis and lipid storage mechanisms between oleaginous and non-oleaginous fungi.</p>
<p>Proteins are the major functional units in biological systems, and these functions ultimately give rise to phenotypes. Thus, studying proteins is key to understanding the unique characteristics of oleaginous fungi, and how they regulate lipid production in different environmental conditions. Biochemical tools for characterizing individual fungal proteins and genetically manipulating fungal strains have significantly advanced our understanding of enzyme function and central carbon metabolism (i.e., glycolysis/gluconeogenesis, pentose phosphate pathway or PPP, TCA cycle, and transhydrogenase cycle). However, these reductionist approaches fall short of capturing physiological snapshots of a biological system&#x2019;s response to environmental conditions. In the past two decades, advances in mass spectrometry-based proteomics have enabled systems-level studies of proteins in oleaginous fungi. Unlike transcriptomics, which examines RNA expression, proteomics directly measures protein expression, offering a more precise view of cellular activity. As of writing, more than 40 publications have harnessed proteomics to investigate the physiologies of oleaginous fungi according to Pubmed and Web of Science&#x2122; (Dec, 2024). This body of work encompasses studies involving stress response to nutrient limitation, utilization of different carbon sources, the lipid droplet composition, secreted proteins (the secretome), protein modifications, comparisons among fungi, and integration with other omics approaches such as transcriptomics (transcriptional regulation), lipidomics (lipid profiles), and metabolomics (metabolites analysis) (<xref ref-type="fig" rid="fig2">Figure 2</xref>).</p>
<fig position="float" id="fig2">
<label>Figure 2</label>
<caption>
<p>Summary of proteomics applications in studying the physiologies and metabolisms of oleaginous fungi.</p>
</caption>
<graphic xlink:href="fmicb-16-1637123-g002.tif" mimetype="image" mime-subtype="tiff">
<alt-text content-type="machine-generated">Illustration showing four general aspects of cellular processes that can be explored using proteomics: Metabolism, Survival/Adaptation, Signaling Regulation, and Sub-/Secretomics activities. Metabolism includes nutrient assimilation and oleochemical pathways. Survival/Adaptation involves stress response and intracellular dynamics. Signaling Regulation covers post-translational modifications and signaling pathways. Sub-/Secretomics focus on lipid droplets and transporters. Each area is represented by diagrams and brief descriptions.</alt-text>
</graphic>
</fig>
<p>In this review, we provide a critical summary of proteomics studies in oleaginous fungi and highlight the role of proteomics in advancing our understanding of the oleaginous yeast phenotype. First, we provide an overview of the prevailing theory that explains in part how carbon flux is regulated and re-routed for lipid production in oleaginous fungi. Then, we summarize the overall proteomics workflow, and approaches that have been used to study oleaginous yeast and filamentous fungi. The core of our review focuses on proteomics insights into the unique physiology, metabolism, and stress responses mechanisms of these microorganisms (<xref ref-type="fig" rid="fig2">Figure 2</xref>). Lastly, we address challenges, offer recommendations, and identify unexplored opportunities to bolster proteomics applications for developing hyperlipogenic fungi.</p>
</sec>
<sec id="sec2">
<label>2</label>
<title>Lipid accumulation in oleaginous fungi</title>
<p>The mechanisms of lipid accumulation in oleaginous fungi and their environmental and metabolic triggers have been extensively reviewed (<xref ref-type="bibr" rid="ref17">Beopoulos et al., 2011</xref>; <xref ref-type="bibr" rid="ref4">Adrio, 2017</xref>; <xref ref-type="bibr" rid="ref172">Spagnuolo et al., 2019</xref>; <xref ref-type="bibr" rid="ref2">Abeln and Chuck, 2021</xref>; <xref ref-type="bibr" rid="ref229">Zhang X. Y. et al., 2022</xref>). The current model for oleaginocity implicates triacylglycerides (TAGs) as sinks for carbon that would have otherwise been used for cell growth and division (<xref ref-type="fig" rid="fig3">Figure 3</xref>). When a key nutrient is limiting, flux through anabolic growth-promoting processes is generally downregulated, thereby severely perturbing cellular energy homeostasis and resulting in inhibition of key enzymes such as isocitrate dehydrogenase (ICDH) (<xref ref-type="bibr" rid="ref51">Evans and Ratledge, 1985a</xref>,<xref ref-type="bibr" rid="ref52">b</xref>). Nitrogen limitation leads to a decrease in cellular AMP levels, which is exacerbated by (though not entirely due to) the activity of the nitrogen scavenger and regulator AMP deaminase (<xref ref-type="bibr" rid="ref51">Evans and Ratledge, 1985a</xref>,<xref ref-type="bibr" rid="ref52">b</xref>). As an allosteric positive regulator of ICDH, a decrease in AMP leads to excretion of citrate from the mitochondria. ATP citrate lyase (ACL) then converts citrate into acetyl-CoA and oxaloacetate, fueling lipid biosynthesis (<xref ref-type="bibr" rid="ref149">Ratledge and Wynn, 2002</xref>).</p>
<fig position="float" id="fig3">
<label>Figure 3</label>
<caption>
<p>Theory of carbon re-routing for lipid accumulation in oleaginous fungi as a stress response to nutrient limitation. Green represents enzymes important for routing carbon flux to fatty acid biosynthesis. Red represents the regulatory mechanism linked to nitrogen recycling that leads to inhibition of isocitrate dehydrogenase and mitochondrial citrate buildup. Proteomics studies have elucidated additional pathways and reactions for supplying NADPH and acetyl-CoA for fatty acid biosynthesis. ACC: Acetyl-CoA carboxylase; ACL: ATP citrate lyase; AMPD: AMP deaminase; FAS: Fatty acid synthase; G6PD (GND1): Glucose-6-phosphate dehydrogenase; ICDH: Isocitrate dehydrogenase; ME: Malic enzyme; PGDH (ZWF1): 6-phosphogluconate dehydrogenase; PPP: Pentose phosphate pathway.</p>
</caption>
<graphic xlink:href="fmicb-16-1637123-g003.tif" mimetype="image" mime-subtype="tiff">
<alt-text content-type="machine-generated">Diagram showing metabolic pathways related to nitrogen limitation and redirected carbon flux to lipid accumulation. Glucose undergoes glycolysis to form pyruvate, leading to Acetyl-CoA production. Arrows indicate reactions, with enzymes labeled such as G6PD, ME, ACC, and ICDH. The TCA cycle features intermediates like citrate and &#x03B1;-KG. Citrate buildup connects to lipid synthesis, involving TAGs and FAS pathways. Nitrogen limitation decreases AMP, influencing nitrogen scavenging to produce IMP and NH4+. Background shows a stylized mitochondrion.</alt-text>
</graphic>
</fig>
<p>Fatty acid biosynthesis requires substantial NADPH, which, in oleaginous fungi like <italic>Rhodosporidium toruloides</italic> and <italic>Mucor circinelloides</italic>, is often supplied by malic enzyme (ME) (<xref ref-type="bibr" rid="ref147">Ratledge, 2014</xref>). However, in species such as <italic>Y. lipolytica</italic> and <italic>L. starkeyi</italic>, ME does not produce NADPH, implicating alternative pathways (<xref ref-type="bibr" rid="ref183">Tang et al., 2010</xref>; <xref ref-type="bibr" rid="ref232">Zhang et al., 2013</xref>). The pentose phosphate pathway (PPP) is a well-studied source of NADPH, but other pathways such as the pyruvate-isocitrate-<italic>&#x03B1;</italic>-ketoglutarate cycle (involving NADP<sup>+</sup>-dependent ICDH) and the GABA shunt also play roles (<xref ref-type="bibr" rid="ref147">Ratledge, 2014</xref>; <xref ref-type="bibr" rid="ref101">Liu et al., 2019</xref>; <xref ref-type="bibr" rid="ref73">Henriques et al., 2021</xref>). Moreover, it is clear that there are organism and condition-specific nuances regarding NADPH (and acetyl-CoA) generation. Such nuances have been revealed in recent proteomics studies, and insights beyond the hackneyed role of PPP (unless relevant to xylose utilization) will be explored for oleaginous yeast and filamentous fungi.</p>
</sec>
<sec id="sec3">
<label>3</label>
<title>Proteomics approaches to study oleaginous fungi</title>
<p>A major goal of proteomics is to identify and understand proteins as a function of a biological system&#x2019;s response to environmental stimuli (<xref ref-type="bibr" rid="ref96">Li X. et al., 2021</xref>). Mass spectrometry (MS) is currently the premier platform for analyzing complex mixtures of proteins/peptides (<xref ref-type="bibr" rid="ref5">Aebersold and Goodlett, 2001</xref>; <xref ref-type="bibr" rid="ref6">Aebersold and Mann, 2003</xref>; <xref ref-type="bibr" rid="ref42">Cox and Mann, 2011</xref>; <xref ref-type="bibr" rid="ref164">Shuken, 2023</xref>). There are two general proteomics strategies: the bottom-up approach in which proteins are enzymatically digested into peptides prior to liquid chromatography (LC) MS analysis (LC&#x2013;MS), and the top-down approach in which intact proteins are analyzed directly (<xref ref-type="bibr" rid="ref48">Dupree et al., 2020</xref>). In both cases, peptides/proteins are separated by LC, followed by electrospray ionization (or matrix-assisted laser desorption/ionization, MALDI) to enter MS. Currently, the top-down approach has not been applied to oleaginous fungi and will not be discussed. For bottom-up proteomics studies of oleaginous fungi, data-dependent acquisition (DDA) is commonly used to collect MS spectra, which contain mass-to-charge (m/z) ratios at particular retention times (<xref ref-type="bibr" rid="ref96">Li X. et al., 2021</xref>). A selection of peptides at a particular time are subjected to fragmentation (MS2 or tandem MS/MS) for confident identification of peptide sequences through proteome database searching. Additional details regarding proteomics methodologies are reviewed elsewhere (<xref ref-type="bibr" rid="ref81">Jiang et al., 2024</xref>).</p>
<p>The bottom-up approach can be used to study the cellular proteome and the secretome containing extracellular proteins. To study intracellular proteins, fungi are first lysed via chemical (e.g., HCl), enzymatic (e.g., Zymolyase), and/or mechanical (e.g., bead-beating) means (<xref ref-type="bibr" rid="ref234">Zhao et al., 2024</xref>). For filamentous fungi in particular, homogenization has often been employed for cell lysis due to their thick chitinous cell walls (<xref ref-type="bibr" rid="ref99">Ling et al., 2015</xref>; <xref ref-type="bibr" rid="ref182">Tang et al., 2016</xref>). Following lysis, proteins are solubilized and denatured by a variety of methods tailored to downstream sample processing steps (<xref ref-type="bibr" rid="ref13">Awad and Brueck, 2020</xref>). One common approach involves using high concentrations of denaturing agents such as urea followed by dilution and digestion. For oleaginous fungi in particular, effort has been invested in solubilizing hydrophobic proteins and removing excess lipids using detergents (e.g., SDS and Triton X-100) and chloroform/methanol precipitation (<xref ref-type="bibr" rid="ref103">Liu et al., 2009</xref>; <xref ref-type="bibr" rid="ref113">Martinez-Moya et al., 2011</xref>; <xref ref-type="bibr" rid="ref87">Kim et al., 2021</xref>). Compared to the intracellular proteome, proteins in the secretome are dilute and must be concentrated prior to sample processing using molecular weight cut-off filters or precipitation (<xref ref-type="bibr" rid="ref205">Wei et al., 2013</xref>; <xref ref-type="bibr" rid="ref129">On&#x00E9;sime et al., 2022</xref>). As with older cellular proteomics studies, some secretomics approaches employ SDS-PAGE for separation and in-gel digestion to acquire peptides for MS (<xref ref-type="bibr" rid="ref12">Athenstaedt et al., 2006</xref>; <xref ref-type="bibr" rid="ref205">Wei et al., 2013</xref>; <xref ref-type="bibr" rid="ref38">Ciesielska et al., 2014</xref>).</p>
<p>Proteomics strategies are further categorized according to quantification methodology (<xref ref-type="fig" rid="fig4">Figure 4</xref>). Label-free proteomics refers to workflows without peptide labeling. Relative quantification is achieved by comparing peptide spectrum matches (PSMs) from MS2 or extracted ion chromatography peak areas using MS1 intensity for a given protein across different conditions. Though relatively straightforward, label-free proteomics suffers from the missing data problem&#x2014;peptides detected in one sample may not be detected in the other samples (<xref ref-type="bibr" rid="ref203">Webb-Robertson et al., 2015</xref>). Multiplexed quantitative proteomics typically refers to workflows in which peptides are labeled with isobaric mass tags such as iTRAQ or TMT (<xref ref-type="fig" rid="fig4">Figure 4</xref>) (<xref ref-type="bibr" rid="ref132">Pappireddi et al., 2019</xref>; <xref ref-type="bibr" rid="ref95">Li J. et al., 2021</xref>). Labeling strategies such as stable isotope labeling with amino acids in cell culture (SILAC) (<xref ref-type="bibr" rid="ref130">Ong et al., 2002</xref>) and stable-isotope dimethyl labeling (<xref ref-type="bibr" rid="ref76">Hsu et al., 2003</xref>) also technically facilitate multiplexing, but few have applied these methods to oleaginous fungi (<xref ref-type="table" rid="tab1">Tables 1</xref>, <xref ref-type="table" rid="tab2">2</xref> and <xref ref-type="supplementary-material" rid="SM1">Supplementary Table 1</xref>).</p>
<fig position="float" id="fig4">
<label>Figure 4</label>
<caption>
<p>Overview of bottom-up proteomics approaches used to study oleaginous fungi. The two major quantification methods, label-free and peptide tagging (multiplexed), are shown. For PTM profiling, the selectivity of the enrichment method determines the major type of modified peptide that is enriched (generally one type). Note that many secretomics, lipid droplet proteomics, and early proteomics approaches employed SDS-PAGE and in-gel digestion prior to mass spectrometry analysis.</p>
</caption>
<graphic xlink:href="fmicb-16-1637123-g004.tif" mimetype="image" mime-subtype="tiff">
<alt-text content-type="machine-generated">Flowchart illustrating proteomics workflow. Begins with cell lysis for cellular proteomics and supernatant concentration for secretomics, producing proteins. Sample processing includes three methods: Label-free, Multiplexed, and PTM Profiling. Label-free involves digestion, producing peptides analyzed by LC-MS/MS for MS1 quantification. Multiplexed uses labeled peptides with iTRAQ or TMT, pooled and analyzed by LC-MS/MS for MS2 quantification. PTM Profiling focuses on phosphorylation and acetylation, enriching modified proteins for LC-MS/MS analysis.</alt-text>
</graphic>
</fig>
<p>Protein function is not solely attributed to abundance; rather, post-translational modifications (PTMs), protein&#x2013;protein interactions, and subcellular localization also have important regulatory implications. PTMs such as phosphorylation and acetylation are labile and sub-stoichiometric, thus requiring additional sample processing considerations (e.g., phosphatase inhibitors) to avoid artifacts and enrich low-abundance PTMs. Enrichment strategies mainly include immunoprecipitation by PTM-specific antibodies (e.g., acetylation) and affinity capture techniques based on the chemical properties of PTMs (e.g., phosphorylation) (<xref ref-type="bibr" rid="ref78">James Sanford and Bustamante Smolka, 2022</xref>). Though not summarized in <xref ref-type="fig" rid="fig4">Figure 4</xref>, proteins that localize to the lipid droplet are particularly interesting because of their structural and regulatory roles in TAG/sterol storage, lipid metabolism, and energy homeostasis (<xref ref-type="bibr" rid="ref11">Athenstaedt, 2019</xref>). As with PTMs, additional steps are required to study protein localization and include, for instance, ultracentrifugation in the presence of high concentration salts or density gradients of sucrose or sorbitol (<xref ref-type="bibr" rid="ref12">Athenstaedt et al., 2006</xref>; <xref ref-type="bibr" rid="ref238">Zhu et al., 2015</xref>; <xref ref-type="bibr" rid="ref220">Yu et al., 2017</xref>; <xref ref-type="bibr" rid="ref19">Bhutada et al., 2018</xref>). All the methods summarized thus far have been applied to study oleaginous fungi; this proteomics anthology and the resulting physiological insights are reviewed hereafter.</p>
</sec>
<sec id="sec4">
<label>4</label>
<title>Proteomics insights for oleaginous yeast</title>
<p>Proteomics enables us to decipher the expressed metabolism of oleaginous yeasts in response to environmental stimuli. Characterizing an organism&#x2019;s expressed metabolism under different conditions reveals key enzymes and pathways that coordinate central metabolites for biosynthesis of lipids and other bioproducts, energy production, and redox balance. The proteomes of oleaginous yeast, especially <italic>Y. lipolytica</italic> and <italic>R. toruloides,</italic> have been studied more extensively than filamentous fungi and are reviewed hereafter (<xref ref-type="table" rid="tab1">Table 1</xref> and <xref ref-type="supplementary-material" rid="SM1">Supplementary Table 1</xref>, which includes all oleaginous yeast proteomics studies). Results from other biotechnologically-relevant oleaginous yeasts like <italic>Lipomyces starkeyi</italic> and <italic>Cutaneotrichosporon oleaginosum</italic> (formerly <italic>Cryptococcus curvatus</italic> and <italic>Trichosporon oleaginosus</italic>) are reviewed in the final portion of this section.</p>
<table-wrap position="float" id="tab1">
<label>Table 1</label>
<caption>
<p>Summary for select proteomics studies of oleaginous yeast.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top">Approach</th>
<th align="center" valign="top">Multi-omics</th>
<th align="left" valign="top">Stress</th>
<th align="left" valign="top">Organism</th>
<th align="left" valign="top">Carbon</th>
<th align="left" valign="top">Nitrogen</th>
<th align="left" valign="top">Experimental</th>
<th align="left" valign="top">Lipid titer/content</th>
<th align="center" valign="top">IDs</th>
<th align="left" valign="top">Significant findings summary</th>
<th align="left" valign="top">Ref.</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="middle">Label-free cellular proteomics</td>
<td align="center" valign="middle">G, T</td>
<td align="left" valign="middle">Carbon</td>
<td align="left" valign="middle"><italic>C. oleaginosum</italic></td>
<td align="left" valign="middle">Pretreated<break/>corn stover<break/>1&#x202F;g/L Aromatics<break/>1&#x202F;g/L Glucose</td>
<td align="left" valign="middle">YE<break/>(NH<sub>4</sub>)<sub>2</sub>SO<sub>4</sub></td>
<td align="left" valign="middle">Carbon source conditions compared (mid-exponential phase cells); Batch flasks</td>
<td align="left" valign="middle">0.24&#x202F;g/L, 31.8% for alkaline-pretreated corn stover w/o (NH<sub>4</sub>)<sub>2</sub>SO<sub>4</sub></td>
<td align="center" valign="middle">-</td>
<td align="left" valign="middle">First proteomics investigation of lignin-derived aromatics catabolism in oleaginous yeast. Enzymes of beta-ketoadipate pathway annotated.</td>
<td align="left" valign="middle">
<xref ref-type="bibr" rid="ref212">Yaguchi (2020)</xref>
</td>
</tr>
<tr>
<td align="left" valign="middle">Label-free cellular proteomics</td>
<td align="center" valign="middle">-</td>
<td align="left" valign="middle">Nitrogen</td>
<td align="left" valign="middle"><italic>L. starkeyi</italic></td>
<td align="left" valign="middle">70&#x202F;g/L Glucose</td>
<td align="left" valign="middle">YE<break/>(NH<sub>4</sub>)<sub>2</sub>SO<sub>4</sub></td>
<td align="left" valign="middle">Time course (8, 48, and 96&#x202F;h); Batch bioreactor</td>
<td align="left" valign="middle">0.5&#x202F;g/L, 14%; 12.3&#x202F;g/L, 28%; 30.0&#x202F;g/L, 46%</td>
<td align="center" valign="middle">&#x003E; 250</td>
<td align="left" valign="middle">Early proteomics results demonstrating upregulation of enzymes in PPP for regenerating NADPH and routing TCA derived carbon to fatty acid biosynthesis.</td>
<td align="left" valign="middle">
<xref ref-type="bibr" rid="ref102">Liu et al. (2011)</xref>
</td>
</tr>
<tr>
<td align="left" valign="middle">Multiplexed cellular proteomics</td>
<td align="center" valign="middle">L</td>
<td align="left" valign="middle">-</td>
<td align="left" valign="middle"><italic>R. toruloides</italic></td>
<td align="left" valign="middle">25&#x202F;g/L Glucose</td>
<td align="left" valign="middle">YNB<break/>(NH<sub>4</sub>)<sub>2</sub>SO<sub>4</sub></td>
<td align="left" valign="middle">Timecourse (24, 48, and 72&#x202F;h); Nitrogen-rich vs. limited; Batch flasks</td>
<td align="left" valign="middle">0.97&#x202F;g/L,<break/>10.1% High N;<break/>2.27&#x202F;g/L,<break/>27.5% Low N</td>
<td align="center" valign="middle">&#x003E;23,000<sup>PTM</sup><break/>2,804</td>
<td align="left" valign="middle">First redox proteome of oleaginous yeast. Extensively detailed phosphorylation and protein oxidation patterns regulating lipogenesis and autophagy.</td>
<td align="left" valign="middle">
<xref ref-type="bibr" rid="ref61">Gluth et al. (2025)</xref>
</td>
</tr>
<tr>
<td align="left" valign="middle">Multiplexed cellular proteomics</td>
<td align="center" valign="middle">L, M, T</td>
<td align="left" valign="middle">-</td>
<td align="left" valign="middle"><italic>R. toruloides</italic></td>
<td align="left" valign="middle">10&#x202F;g/L Glucose<break/>10&#x202F;g/L Xylose<break/>10&#x202F;g/L Arabinose<break/>10&#x202F;g/L Coumarate</td>
<td align="left" valign="middle">YNB<break/>(NH<sub>4</sub>)<sub>2</sub>SO<sub>4</sub><break/>AA Mix</td>
<td align="left" valign="middle">Timecourse (varying time points for each carbon source); Batch flasks</td>
<td align="left" valign="middle">-</td>
<td align="center" valign="middle">5,643</td>
<td align="left" valign="middle">Multi-omics data used to improve the metabolic model for lignocellulose-derived carbon sources (xylose, arabinose, and p-coumarate).</td>
<td align="left" valign="middle">
<xref ref-type="bibr" rid="ref87">Kim et al. (2021)</xref>
</td>
</tr>
<tr>
<td align="left" valign="middle">Label-free cellular proteomics</td>
<td align="center" valign="middle">G, T</td>
<td align="left" valign="middle">Nitrogen</td>
<td align="left" valign="middle"><italic>R. toruloides</italic></td>
<td align="left" valign="middle">70&#x202F;g/L Glucose</td>
<td align="left" valign="middle">YE<break/>(NH<sub>4</sub>)<sub>2</sub>SO<sub>4</sub></td>
<td align="left" valign="middle">Timecourse (Seed&#x2020;, 24 and 96&#x202F;h); Batch bioreactor (Technical replicates?)</td>
<td align="left" valign="middle">0.07&#x202F;g/L,<break/>22.8% High N;<break/>0.16&#x202F;g/L,<break/>33.3% Low N</td>
<td align="center" valign="middle">3,108</td>
<td align="left" valign="middle">First systems-level construction of metabolism linked to oleaginous phenotype in this yeast. Found a new class of perilipin-like protein that likely protects lipid droplets.</td>
<td align="left" valign="middle">
<xref ref-type="bibr" rid="ref240">Zhu et al. (2012)</xref>
</td>
</tr>
<tr>
<td align="left" valign="middle">Label-free lipid droplet proteomics</td>
<td align="center" valign="middle">-</td>
<td align="left" valign="middle">-</td>
<td align="left" valign="middle"><italic>Y. lipolytica</italic></td>
<td align="left" valign="middle">20&#x202F;g/L Glucose<break/>5&#x202F;g/L Oleic acid</td>
<td align="left" valign="middle">YE<break/>Peptone<break/>YNB<break/>NH<sub>4</sub>Cl</td>
<td align="left" valign="middle">Timecourse (3 and 24&#x202F;h); YPD vs. minimal medium with oleic acid; Batch flasks</td>
<td align="left" valign="middle">-</td>
<td align="center" valign="middle">&#x003E; 30<sup>&#x2021;,&#x00A7;</sup></td>
<td align="left" valign="middle">RAB GTPases involved in membrane trafficking, autophagy, etc. associated with LD. OIL1 protects LD from lipases.</td>
<td align="left" valign="middle">
<xref ref-type="bibr" rid="ref12">Athenstaedt et al. (2006)</xref>
</td>
</tr>
<tr>
<td align="left" valign="middle">Multiplexed phospho</td>
<td align="center" valign="middle">M</td>
<td align="left" valign="middle">Nitrogen</td>
<td align="left" valign="middle"><italic>Y. lipolytica</italic></td>
<td align="left" valign="middle">25&#x202F;g/L Glucose</td>
<td align="left" valign="middle">YNB<break/>(NH<sub>4</sub>)<sub>2</sub>SO<sub>4</sub></td>
<td align="left" valign="middle">9&#x202F;h; Nitrogen rich vs. limited comparison; Batch flasks</td>
<td align="left" valign="middle">-</td>
<td align="center" valign="middle">1,219<sup>PTM</sup><break/>4,926</td>
<td align="left" valign="middle">Many phosphorylation sites of kinases not conserved in <italic>S. cerevisiae</italic>. RIM11 phosphorylation conserved and regulates glycogen synthesis.</td>
<td align="left" valign="middle">
<xref ref-type="bibr" rid="ref140">Pomraning et al. (2016)</xref>
</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>Full summary of proteomics studies in <xref ref-type="supplementary-material" rid="SM1">Supplementary Table 1</xref>. In the &#x201C;Multi-omics&#x201D; column, the letters correspond to the following: genomics (G), transcriptomics (T), metabolomics (M), and lipidomics (L). For filamentous fungi, PUFA lipid results are provided when specified. Note that the number of unique proteomics IDs is heavily influenced by sample processing, incorporation of offline fractionation to reduce sample complexity, and the MS searching algorithm (i.e., using MaxQuant with match-between-runs boosts IDs but many of them have missing values). &#x2020; Seed culture medium (YPD) or medium for comparison differs substantially from the other media used in the experimentation. &#x2021; Exact number not reported but estimated from differential expression results or inferred from supplementary material. &#x00A7; SDS-PAGE gel bands. <italic>PTM</italic> Number of unique PTM sites (the number of protein IDs is provided below). ~ Exact number not reported but extracted from figure(s).</p>
</table-wrap-foot>
</table-wrap>
<sec id="sec5">
<label>4.1</label>
<title>Yarrowia lipolytica</title>
<p><italic>Yarrowia lipolytica</italic> is a model bio-oil producing ascomycete yeast, for which a number of strains have been isolated and engineered. Few of the wildtype strains are considered oleaginous with much of the genetic engineering conducted on strain W29 (a.k.a. CLIB89 and ATCC 20460) (<xref ref-type="bibr" rid="ref2">Abeln and Chuck, 2021</xref>; <xref ref-type="bibr" rid="ref157">Salvador L&#x00F3;pez et al., 2022</xref>), for example, to confer non-native assimilation of monosaccharides (xylose, galactose, etc.) and disaccharides (fructose, cellobiose, etc.) (<xref ref-type="bibr" rid="ref67">Guo et al., 2015</xref>; <xref ref-type="bibr" rid="ref91">Lazar et al., 2015</xref>; <xref ref-type="bibr" rid="ref92">Ledesma-Amaro et al., 2016</xref>; <xref ref-type="bibr" rid="ref70">Hapeta et al., 2017</xref>). <italic>Y. lipolytica</italic> can utilize hydrophobic substrates (e.g., oleic acid) for growth, <italic>ex novo</italic> lipid synthesis, and bioproduct synthesis (e.g., citric acid), hence its species nomenclature (<xref ref-type="bibr" rid="ref107">Madzak, 2021</xref>). It accomplishes this task by producing a complex extracellular bioemulsifier called liposan, which is comprised of proteins, exopolysaccharides, lipids, and other metabolites (<xref ref-type="bibr" rid="ref129">On&#x00E9;sime et al., 2022</xref>). Proteomics investigations of <italic>Y. lipolytica</italic>, its derivatives, and other oleaginous strains have been conducted using various nutrient sources and stress conditions (<xref ref-type="table" rid="tab2">Table 2</xref> and <xref ref-type="supplementary-material" rid="SM1">Supplementary Table 1</xref>). These proteomic results played key roles in enhancing the understanding of the oleaginous phenotype and are detailed in the following subsections.</p>
<sec id="sec6">
<label>4.1.1</label>
<title>The lipid droplet proteome</title>
<p>The earliest proteomics study of <italic>Y. lipolytica</italic> strain W29 was conducted in 2006 by Athenstaedt et al., who characterized its lipid droplet proteomes using either glucose or oleic acid as the carbon source (<xref ref-type="bibr" rid="ref12">Athenstaedt et al., 2006</xref>). The authors isolated lipid particles and identified 21 proteins, involved in fatty acid activation, lipid and sterol metabolism, and transport processes (<xref ref-type="bibr" rid="ref12">Athenstaedt et al., 2006</xref>). The low number of protein identifications was due to the study being performed at the early stage of proteomics. Interestingly, several Rab GTPases were only observed in the oleic acid condition; these enzymes are involved in trafficking membrane proteins and lipids. They may help facilitate lipid droplet interactions with other organelles (e.g., endosomes and lysosomes) and recruit proteins to a burgeoning lipid droplet (<xref ref-type="bibr" rid="ref100">Liu et al., 2007</xref>; <xref ref-type="bibr" rid="ref25">Bouchez et al., 2015</xref>). The authors also observed a protein without a homolog in <italic>S. cerevisiae</italic>&#x2014;Bhutada et al. keenly noted this and found that deleting this protein (termed OIL1) eliminated the oleaginous phenotype (<xref ref-type="bibr" rid="ref19">Bhutada et al., 2018</xref>). They concluded that OIL1 functioned similarly to perilipins found in higher eukaryotes and protects the lipid droplet from lipase activity.</p>
</sec>
<sec id="sec7">
<label>4.1.2</label>
<title>Molecular mechanisms of nitrogen limitation response</title>
<p>To investigate regulatory mechanisms governing nitrogen limitation-induced lipid accumulation, <xref ref-type="bibr" rid="ref140">Pomraning et al. (2016)</xref> performed a multi-omics analysis of the metabolome, proteome, and phosphoproteome in <italic>Y. lipolytica</italic> under nitrogen-replete and limited conditions. Global proteomics showed an upregulation of proteins associated with proteolysis and downregulation of proteins in <italic>&#x03B2;</italic>-oxidation, amino acid metabolism, and translation after 9 h of nitrogen limitation. Given those results, they tested if inhibiting translation using cycloheximide would result in lipid accumulation and observed lipid droplet growth as a consequence, though to a lesser extent than nitrogen limitation (<xref ref-type="bibr" rid="ref140">Pomraning et al., 2016</xref>). In agreement with earlier transcriptomics work (<xref ref-type="bibr" rid="ref119">Morin et al., 2011</xref>), they did not observe differential expression of ATP-citrate lyase (ACL) and acetyl-CoA carboxylase (ACC) at the protein level, suggesting post-translational regulation of lipogenesis. Using phosphoproteomics, Pomraning et al. identified 599 proteins that were significantly enriched in tyrosine and serine/threonine kinase activity, with 80 and 53 phosphopeptides showing an increase or decrease, respectively, under nitrogen limitation (<xref ref-type="bibr" rid="ref140">Pomraning et al., 2016</xref>). For example, they observed increased phosphorylation levels of ACL and ACC during nitrogen limitation. ACC is negatively regulated by AMP kinase (AMPK or SNF1 in yeast) by phosphorylation (<xref ref-type="bibr" rid="ref161">Shi et al., 2014</xref>), which has been used to engineer constitutively active ACC (<xref ref-type="bibr" rid="ref145">Qiao et al., 2015</xref>). Unless the observed sites are different from those regulated by SNF1, the increase in ACC phosphorylation seems counterintuitive&#x2014;highlighting a challenge with using proteomics for discovery when so much relies on assumptions regarding homology of conserved sequences.</p>
<p>In. <italic>S. cerevisiae</italic>, response to the quantity and quality of nitrogen sources is regulated by nitrogen catabolite repression, which may be partially conserved in <italic>Y. lipolytica</italic> (<xref ref-type="bibr" rid="ref117">Mazurie et al., 2005</xref>; <xref ref-type="bibr" rid="ref90">Lavoie et al., 2009</xref>; <xref ref-type="bibr" rid="ref138">Pomraning et al., 2017</xref>). When high-quality nitrogen like amino acids or ammonium is available, GATA-type transcription factors GLN3 and GAT1 are phosphorylated and remain bound to URE2 in the cytosol. Under poor nitrogen conditions, they localize to the nucleus and activate nitrogen utilization genes. Pomraning et al. did not report changes in phosphorylation for GLN3, GAT1, GCN4, TOR1, or GATA-type transcriptional repressors GZF3 and DAL80, which may be due to phosphoproteomic coverage limitations, the low abundance of phosphorylated proteins, or the selected time points for analysis (<xref ref-type="bibr" rid="ref140">Pomraning et al., 2016</xref>). However, they did observe changes in protein abundances for predicted GATA family transcription factors (e.g., upregulation of GLN3 during nitrogen limitation). They also keenly demonstrated how proteomics can be used to identify DNA motifs that associate transcription factors with up- and downregulated proteins. Genes with a G[AC]TAAGC and [GA]TGAGTCA motifs were enriched for amino acid biosynthesis. The latter may be bound by Gcn4p, which activates expression of genes in response to amino acid starvation (<xref ref-type="bibr" rid="ref140">Pomraning et al., 2016</xref>). Interestingly, genes with the [GA]TGAGTCA motif tended to be downregulated, suggesting the importance of autophagy for scavenging amino acids and nitrogen and/or regulatory responses to ammonium starvation that are unique to <italic>Y. lipolytica</italic>.</p>
</sec>
<sec id="sec8">
<label>4.1.3</label>
<title>Growth rate and lipid accumulation</title>
<p>Disentangling growth-dependent proteome changes during nutrient-limitation from those consequential to lipid accumulation remains a challenge. To explore the relationship between growth rate and lipid accumulation, <xref ref-type="bibr" rid="ref141">Poorinmohammad et al. (2022)</xref> used chemostat <italic>Y. lipolytica</italic> cultures at different dilution rates and performed comparative proteomic analysis. They found that lower growth rates often lead to higher lipid yields and observed upregulation of fatty acid synthase (FAS), ACL, and ACC in low growth rate vs. high growth rate conditions. Using a linear modeling approach and comparisons to a non-lipid accumulating strain, they were able to tease out some enzymes that regulate lipid accumulation independent to some extent of growth rate. Outside of PPP, which had been detailed before proteomics studies (<xref ref-type="bibr" rid="ref202">Wasylenko et al., 2015</xref>), they identified the role of NADPH-producing enzymes formate dehydrogenase (FDH) and isocitrate dehydrogenase (ICDH) in driving lipid biosynthesis. Additionally, downregulation of ER-plasma membrane tethering proteins and upregulation of ER stress proteins (specific targets not provided) suggested the involvement of unfolded protein response (UPR) activation in lipid accumulation. Upregulation of chaperone-mediated autophagy proteins highlighted a possible connection between ER stress, UPR, and selective autophagy in regulating lipid metabolism. Interestingly, they proposed an additional link to oxidative stress according to superoxide dismutase upregulation that may be explained by mitophagy during nitrogen limitation (<xref ref-type="bibr" rid="ref192">Venditti and Di Meo, 2020</xref>). Lastly, Poorinmohammad et al. observed downregulation of high osmolarity glycerol response 1 (HOG1), a mitogen-activated protein kinase known for its role in osmotic stress, which likely plays a role in lipid homeostasis (<xref ref-type="bibr" rid="ref74">Herrero-de-Dios et al., 2020</xref>; <xref ref-type="bibr" rid="ref141">Poorinmohammad et al., 2022</xref>). They knocked out HOG1 which resulted in a 20% increase in lipid production, demonstrating the power of proteomics analysis when paired with chemostat growth studies and metabolic engineering (<xref ref-type="bibr" rid="ref141">Poorinmohammad et al., 2022</xref>).</p>
</sec>
<sec id="sec9">
<label>4.1.4</label>
<title>Impacts of low-cost carbon sources: plastics and lignocellulosic biomass</title>
<p>Hydrophobic substrate utilization by <italic>Y. lipolytica</italic> has translated to growing research interest in hybrid processes to upcycle plastic wastes, few of which focus on testing yeast in general (<xref ref-type="bibr" rid="ref63">Gluth et al., 2022</xref>). <xref ref-type="bibr" rid="ref195">Walker et al. (2023)</xref> studied proteome changes resulting from cultivation on hydrocarbon-rich oil (C11&#x2013;C28 alkanes and alkenes) generated from catalytic decomposition of polyethylene. To grow on this toxic substrate, the yeast allocated a substantial portion of its proteome towards expression of alkane-degrading enzymes including cytochrome P450 oxidases, alcohol/aldehyde dehydrogenases, alcohol oxidases, fatty acid ligases, and <italic>&#x03B2;</italic>-oxidation enzymes. They also noted significant upregulation of an oxysterol-binding protein that may play a role in intracellular hydrocarbon transport (<xref ref-type="bibr" rid="ref59">Fukuda, 2023</xref>).</p>
<p>In another study, lignocellulosic substrate from hydrolysis of switchgrass was used to compare the proteomes of <italic>Y. lipolytica</italic> strain W29 to the isolate strain YB420, which unlike the former is capable of growth and lipid production using xylose (<xref ref-type="bibr" rid="ref194">Walker et al., 2021</xref>). Strain W29 encodes the genes for xylose utilization (xylose reductase, xylitol dehydrogenase, and xylulokinase) (<xref ref-type="bibr" rid="ref152">Rodriguez et al., 2016</xref>); however, they appear to be &#x201C;silent,&#x201D; whereas YB420 upregulated xylitol dehydrogenase, xylulokinase, and PPP enzymes providing NADPH for further xylose assimilation and lipid biosynthesis. As a potential result of redox imbalances, W29 secreted xylitol and degraded lipids as a carbon and energy source in stationary phase.</p>
</sec>
<sec id="sec10">
<label>4.1.5</label>
<title>Thiamine deficiency</title>
<p>Thiamine pyrophosphate (TPP, the active form of vitamin B1) is a coenzyme required for pyruvate dehydrogenase and <italic>&#x03B1;</italic>-ketoglutarate dehydrogenase activity. <italic>Y. lipolytica</italic> cannot natively synthesize thiamine and its absence leads to a downregulation of proteins in lipid biosynthesis and energy metabolism (particularly ATP synthase) (<xref ref-type="bibr" rid="ref196">Walker et al., 2020</xref>). Using genomics, the authors identified the missing thiamine biosynthesis protein 13 (THI13) gene and engineered a strain capable of <italic>de novo</italic> thiamine biosynthesis, which yielded a significant although moderate increase in lipogenesis (~4% vs. ~1% lipid accumulation for the parental strain) in the absence of supplemented thiamine during nitrogen limitation. It follows that all studies using <italic>Y. lipolytica</italic> listed in <xref ref-type="supplementary-material" rid="SM1">Supplementary Table 1</xref> include a source of vitamins&#x2014;a cost that may not be required for other oleaginous yeast (<xref ref-type="bibr" rid="ref127">Nutrition Reviews, 1958</xref>).</p>
</sec>
</sec>
<sec id="sec11">
<label>4.2</label>
<title>Rhodotorula toruloides</title>
<p><italic>Rhodotorula toruloides</italic> (formerly <italic>Rhodosporidium toruloides</italic>) is a model oleaginous basidiomycete yeast with an expansive catalog of studies probing its lipid-accumulating, carotenoid-producing phenotype. Popular oleaginous strains for this yeast include NP11 (haploid derived ultimately from CGMCC 2.1389) (<xref ref-type="bibr" rid="ref98">Li et al., 2007</xref>; <xref ref-type="bibr" rid="ref240">Zhu et al., 2012</xref>) and IFO0880 (now NBRC 0880) (<xref ref-type="bibr" rid="ref41">Coradetti et al., 2018</xref>), both of which naturally utilize xylose for growth and lipid accumulation compared to prominent oleaginous <italic>Y. lipolytica</italic> strains.</p>
<sec id="sec12">
<label>4.2.1</label>
<title>Setting the stage for multi-omics investigations of oleaginicity</title>
<p>In an early seminal multi-omics study, Zhu et al. sequenced the genome of <italic>R. toruloides</italic> and performed comparative transcriptomic and proteomic analyses of cells cultured under nitrogen-limited conditions (<xref ref-type="bibr" rid="ref240">Zhu et al., 2012</xref>). They detailed lipid metabolism (including a novel FAS) and repression of TOR1, a negative regulator of autophagy, in <italic>R. toruloides</italic> under nitrogen-limiting conditions. Consequently, they observed suppression of protein biosynthesis machinery alongside an activation of autophagy-related proteins such as vacuolar proteases and ATPases. Lipid synthesis-related proteins, including key enzymes in fatty acid biosynthesis (ACL1, ACC1, FAS1/2), were elevated when comparing YPD seed cultures to those grown in minimal medium with limited nitrogen. Upon close inspection of their results, abundance changes between the 24&#x202F;h and 96&#x202F;h time points in nitrogen-limited minimal medium were in fact minimal (&#x003C; 2 fold) or insignificant for many enzymes involved in fatty acid synthesis. There were several enzymes in other pathways (e.g., the PPP with GND1 downregulated and ZWF1 upregulated) that were, nevertheless, differentially expressed.</p>
<p>Importantly, their work shows how proteomics provides information about protein expression in organelles, and how said changes in expression may affect their corresponding processes. Zhu et al. noted that a perilipin-like protein (LDP1) was highly expressed during lipid production, suggesting its involvement in lipid droplet stability and formation (<xref ref-type="bibr" rid="ref240">Zhu et al., 2012</xref>). In a follow-up study, the authors characterized this yeast&#x2019;s lipid droplet proteome confirming the localization of LDP1, which was indeed upregulated during nitrogen and, separately, phosphate limitation (<xref ref-type="bibr" rid="ref238">Zhu et al., 2015</xref>). They also observed several metabolic enzymes including GND1 and glyceraldehyde 3-phosphate dehydrogenase that were localized to the lipid droplet yet not directly involved in lipid metabolism. Additionally, the study found an oxylipin-producing enzyme PpoA, which is involved in hormone-like signaling that may be linked to lipid homeostasis and signaling.</p>
</sec>
<sec id="sec13">
<label>4.2.2</label>
<title>Lipid accumulation induced by phosphate limitation</title>
<p>Some waste streams (e.g., those from food processing, distilleries, and anaerobic digestors) are rich in nitrogenous compounds, necessitating other strategies to induce lipid accumulation. This justified Wang et al.&#x2019;s novel proteomics investigations of phosphate (inorganic phosphorus source, Pi) limitation (<xref ref-type="bibr" rid="ref200">Wang et al., 2018</xref>, <xref ref-type="bibr" rid="ref197">2023a</xref>). In their first study (<xref ref-type="bibr" rid="ref200">Wang et al., 2018</xref>), they found that Pi limitation activates the PHO pathway (phosphate homeostasis signaling), which triggers upregulation of Pi transporters, ribosome degradation, and RNA recycling to reclaim phosphate. In addition to key proteins involved in fatty acid biosynthesis, such as ACC1, upregulation of phosphatidate phosphatase and downregulation of DAG kinase routed carbon to lipid accumulation while limiting phospholipid production. Degradation of nucleotides appears to further connect phosphate limitation to lipid accumulation and was demonstrated by upregulation of phosphatases. Pi limitation leads to reduced AMP levels, shifting TCA cycle flux toward citrate accumulation (<xref ref-type="fig" rid="fig5">Figure 5A</xref>).</p>
<fig position="float" id="fig5">
<label>Figure 5</label>
<caption>
<p>Examples of discoveries in <italic>Rhodotorula toruloides</italic> using proteomics. <bold>(A)</bold> Phosphate-limited regulation of lipid accumulation and stress response according to <xref ref-type="bibr" rid="ref200">Wang et al. (2018</xref>, <xref ref-type="bibr" rid="ref197">2023a)</xref>. <bold>(B)</bold> Xylose and arabinose metabolism as annotated by <xref ref-type="bibr" rid="ref87">Kim et al. (2021)</xref>. The light green shading indicates the correct pathway containing the intermediate D-arabinitol. ACC1, Acetyl-CoA carboxylase 1; ACL1, ATP citrate lyase 1; AGK1, Acylglycerol kinase 1; AMPD, AMP deaminase; ATG7, Autophagy-related protein 7; ATG8, Autophagy-related protein 8; ATG11, Autophagy-related protein 11; FAS1, Fatty acid synthase subunit beta; FAS2, Fatty acid synthase subunit alpha; HCD1, Hydroxyacyl-CoA dehydrogenase 1; ME1, Malic enzyme 1; PAH1, Phosphatidate phosphatase 1; PEP4, Proteinase A (vacuolar aspartyl protease); PHM8, Pyrimidine 5&#x2032;-nucleotidase; PHO8, Vacuolar alkaline phosphatase; PHO85, Cyclin-dependent kinase; PYC1, Pyruvate carboxylase 1; RPB1, RNA polymerase II largest subunit; TOR, Target of rapamycin kinase; VMA2, Vacuolar H<sup>+</sup>-ATPase subunit B.</p>
</caption>
<graphic xlink:href="fmicb-16-1637123-g005.tif" mimetype="image" mime-subtype="tiff">
<alt-text content-type="machine-generated">Diagram expemplifying proteomics discoveries in an oleaginous yeast. A: Metabolic processes in &#x002A;R. toruloides&#x002A; under Pi and N limitations. Key pathways include the TCA Cycle, autophagy, and fatty acid metabolism, with indications of upregulation and downregulation.B: Conversion of lignocellulose-derived sugars. L-arabinose and D-xylose undergo enzymatic transformations through alcohol dehydrogenase, sorbose reductase, and other enzymes, integrating into the pentose phosphate pathway.</alt-text>
</graphic>
</fig>
<p>In a follow-up study (<xref ref-type="bibr" rid="ref197">Wang et al., 2023a</xref>), the same group addressed the impacts of Pi limitation on the phosphoproteome, raising important regulatory considerations involving autophagy&#x2014;rather than the PHO pathway directly. Phosphoregulation of autophagy-related proteins (ATG) is controlled by upstream regulators including TORC1, a nutrient-sensing negative regulator of autophagy and positive regulator of ribosome biogenesis, and SNF1, a positive regulator of autophagy. Because ATG9, an important regulator of autophagosome formation, was upregulated in protein abundance, the authors used RNA interference to silence its expression, which led to a&#x202F;~&#x202F;50% decrease in lipid accumulation. Unfortunately, many of these proteins including TOR1 and ATG9 were not observed in either the global proteome (protein abundances) or phosphoproteome (PTM abundances) results, limiting conclusions that can be drawn about significant changes in protein phosphorylation that are independent of changes in protein abundances.</p>
</sec>
<sec id="sec14">
<label>4.2.3</label>
<title>Metabolic network analysis for lignocellulose-relevant carbon sources</title>
<p>To explore lignocellulosic carbon utilization, which includes appreciable quantities of glucose, xylose, and <italic>p</italic>-coumaric acid, Kim et al. reconstructed the metabolic network of <italic>R. toruloides</italic> using transcriptomics, proteomics, and metabolomics data (<xref ref-type="bibr" rid="ref87">Kim et al., 2021</xref>). Importantly, they employed RB-TDNA sequencing to build a library of mutants to test fitness on these carbon sources. Consistent with a previous report that D-arabinitol was among the main fermentation byproducts from xylose in <italic>R. toruloides</italic> (<xref ref-type="bibr" rid="ref77">Jagtap and Rao, 2018</xref>), their findings reveal that <italic>R. toruloides</italic> employs an alternative xylose utilization pathway in which D-xylulose is converted to D-arabinitol by a reductase and/or arabinitol dehydrogenase and subsequently converted to D-ribulose by a D-arabinitol 2-dehydrogenase (<xref ref-type="fig" rid="fig5">Figure 5B</xref>). D-ribulose is converted to D-ribulose-5-phosphate by D-ribulose kinase, which had significant fitness defects in the tested pentose sugar and alcohol media conditions.</p>
<p>A recent study by Al&#x012B;na et al. used genome-scale metabolic modeling to explore metabolic trade-offs in <italic>R. toruloides</italic> related to lipid production using different carbon sources (glucose, xylose, and acetate) (<xref ref-type="bibr" rid="ref150">Re&#x0137;&#x0113;na et al., 2023</xref>). They identified a key metabolic route for acetyl-CoA generation via ACL and the phosphoketolase pathway, which produces acetyl-phosphate and glyceraldehyde-3-phosphate. This pathway provides an engineering opportunity for carbon-efficient production of acetyl-CoA by bypassing CO<sub>2</sub> generation from the pyruvate dehydrogenase step of glycolysis/TCA cycle (<xref ref-type="bibr" rid="ref72">Hellgren et al., 2020</xref>).</p>
</sec>
<sec id="sec15">
<label>4.2.4</label>
<title>Xylose utilization and oxidative stress</title>
<p>Xylose utilization has been studied quite often in <italic>R. toruloides</italic> using proteomics. <xref ref-type="bibr" rid="ref187">Tiukova et al. (2019)</xref> compared glucose vs. xylose-grown cells and observed upregulation of enzymes for xylose assimilation, such as xylitol dehydrogenase and several NADPH-dependent xylose reductases, as expected (<xref ref-type="fig" rid="fig5">Figure 5B</xref>). They also highlighted proteins involved in glutathione metabolism, oxidative stress response, and autophagy that were upregulated during nitrogen limitation in both conditions but also for the specific glucose vs. xylose comparison. The study revealed that peroxisomal <italic>&#x03B2;</italic>-oxidation was more pronounced in cells grown on xylose. Though not explicitly conjectured in their work, it is possible that H<sub>2</sub>O<sub>2</sub> levels as a result of upregulated &#x03B2;-oxidation and competition between antioxidants and xylose reductase for NADPH partially explain those results.</p>
<p><xref ref-type="bibr" rid="ref136">Pinheiro et al. (2020)</xref> further investigated the role of oxidative stress on <italic>R. toruloides</italic>&#x2019; xylose metabolism and its role in enhancing lipid and carotenoid production. Oxidative stress, induced by H<sub>2</sub>O<sub>2</sub> and indicated by a resulting upregulation of catalase, led to a marked increase in lipid content due in part to downregulation of fatty acid &#x03B2;-oxidation. They also reported that D-arabinitol was among the main fermentation byproducts, which agrees with an earlier study and raises questions regarding redox cofactor homeostasis because some sugar alcohols may be secreted to regenerate NAD(P)<sup>+</sup> (<xref ref-type="bibr" rid="ref77">Jagtap and Rao, 2018</xref>). Additionally, autophagy pathways and reduced amino acid biosynthesis under nitrogen depletion further enhance lipid accumulation, showing a tight coupling between oxidative stress responses and metabolic shifts during xylose fermentation.</p>
<p>Xylose is the primary constituent from dilute acid hydrolysis of lignocelluse. This and other pretreatment methods release additional compounds like vanillin, an aldehyde constituent of lignin, that are inhibitory and implicated in oxidative stress response in fungi (<xref ref-type="bibr" rid="ref88">Kim et al., 2014</xref>; <xref ref-type="bibr" rid="ref126">Nguyen et al., 2014</xref>). <xref ref-type="bibr" rid="ref143">Qi et al. (2014)</xref> screened mutant strains with enhanced tolerance to sugarcane bagasse hydrolysate and, in a following study, used proteomics to compare the wild-type strain to one of the mutants (<xref ref-type="bibr" rid="ref144">Qi et al., 2017</xref>). Gene ontology enrichment analysis highlighted distinctions in differential expression of stress response and antioxidant proteins, though the identities of these proteins were not published. Enzymes involved in DNA repair, spliceosome assembly, acetate utilization (e.g., dehydrogenases), and MAPK signaling were upregulated in the mutants. MAPK-related proteins STE20 and SSK2 were highly expressed, perhaps to maintain cell integrity under high-osmolarity hydrolysate cultivation and efficient oxidative phosphorylation by mitigating oxidative stress (<xref ref-type="bibr" rid="ref58">Fuchs and Mylonakis, 2009</xref>) caused by hydrolysate-derived inhibitors. Interestingly, these proteins are upstream regulators of HOG1 in <italic>S. cerevisiae</italic> (<xref ref-type="bibr" rid="ref146">Raitt et al., 2000</xref>)&#x2014;as discussed above, knocking-out HOG1 in <italic>Y. lipolytica</italic> increases lipid production (<xref ref-type="bibr" rid="ref141">Poorinmohammad et al., 2022</xref>). Intriguingly, its role in stress-induced regulation of lipid homeostasis may ultimately be conserved even in unrelated yeast.</p>
<p>Industrially-relevant oleaginous yeast must rapidly assimilate both hexose and pentose sugars derived from low-cost lignocellusic substrates. <italic>R. toruloides</italic> not only grows slower on xylose than glucose but also exhibits a diauxic shift when switching between these sugars (<xref ref-type="bibr" rid="ref208">Wiebe et al., 2012</xref>). <xref ref-type="bibr" rid="ref40">Coradetti et al. (2023)</xref> used proteomics to study diauxic sugar utilization including transport and regulation of carbon catabolite repression, which will help unveil targets to engineer glucose and xylose co-utilizing strains. In their previous study of RB-TDNA mutants discussed above (<xref ref-type="bibr" rid="ref41">Coradetti et al., 2018</xref>; <xref ref-type="bibr" rid="ref87">Kim et al., 2021</xref>), mutations in a zinc binuclear cluster transcription factor (RTO4_12978, or PNT1) greatly decreased fitness on pentose sugars. Proteomic analysis of PNT1 overexpression and deletion mutants grown on xylose confirmed the role of PNT1 in regulating xylose assimilation, but also raised some confounding questions regarding PPP regulation (<xref ref-type="bibr" rid="ref40">Coradetti et al., 2023</xref>). Interestingly, PNT1 overexpression decreases the relative abundance of xylulose phosphoketolase, which breaks down xylulose 5-phosphate to produce acetyl phosphate and glyceraldehyde 3-phosphate. It appears that xylose assimilation regulates how carbon flows through the non-oxidative portion of the PPP&#x2014;bypassing pyruvate dehydrogenase and the TCA to directly produce acetyl-CoA for fatty acid synthesis or dihydroxyacetone phosphate and other PPP anabolic precursors for phospholipids and nucleosides. Their exemplary work demonstrates how regulons can be deduced from proteomics and strain engineering data, providing unexplored targets for optimizing pentose utilization.</p>
</sec>
</sec>
<sec id="sec16">
<label>4.3</label>
<title>Other yeasts of biotechnological value</title>
<sec id="sec17">
<label>4.3.1</label>
<title>Sophorolipid synthesis</title>
<p>Sophorolipids are glycolipid biosurfactants with antimicrobial properties, making them valuable for acne treatments, eco-friendly cleaning solutions, and other food and health applications (<xref ref-type="bibr" rid="ref35">Cho et al., 2022</xref>). The oleaginous yeast <italic>Starmerella bombicola</italic>, isolated from bumblebees, secretes sophorolipids and is a star candidate for metabolic engineering (<xref ref-type="bibr" rid="ref154">Roelants et al., 2024</xref>). Ciesielska et al. performed two proteomics investigations of sophorolipid synthesis in <italic>S. bombicola</italic> (<xref ref-type="bibr" rid="ref37">Ciesielska et al., 2013</xref>, <xref ref-type="bibr" rid="ref38">2014</xref>). In the first, they used an auxotrophic strain to perform SILAC-based characterization of exponentially growing cells vs. those producing sophorolipids in stationary phase (<xref ref-type="bibr" rid="ref37">Ciesielska et al., 2013</xref>). In the first step of sophorolipid biosynthesis, fatty acids are hydroxylated by a membrane-bound cytochrome P450 monooxygenase (Cyp52M1) (<xref ref-type="bibr" rid="ref190">Van Bogaert et al., 2009</xref>), which was only identified in the stationary growth phase. The authors noted that a heme-binding protein DAP1 was upregulated and may enhance Cyp52M1 activity through stabilization. Other enzymes involved in sophorolipid synthesis like UDP-glucosyltransferases were upregulated (see <xref ref-type="fig" rid="fig6">Figure 6</xref> for full metabolic pathway <xref ref-type="bibr" rid="ref154">Roelants et al., 2024</xref>). In the stationary phase, oxidative stress defense enzymes and vacuolar proteins were upregulated, while proteins involved in translation were downregulated. In their second secretomics study, <xref ref-type="bibr" rid="ref38">Ciesielska et al. (2014)</xref> identified an esterase required for the lactonization of secreted sophorolipids and knocked out this enzyme for functional validation, providing another example of how proteomics insights can direct metabolic engineering.</p>
<fig position="float" id="fig6">
<label>Figure 6</label>
<caption>
<p>Metabolic pathways for sophorolipids, carotenoids, and omega-3/6 fatty acids. Proteomics results from multiple studies are summarized. Up/downregulated refers to relative changes to protein abundances for nutrient limited vs. rich conditions. Generalized abundance changes were pulled from references in subsection 4.3 (for sophorolipids and carotenoids) and section 5 (for omega-3/6 fatty acids).</p>
</caption>
<graphic xlink:href="fmicb-16-1637123-g006.tif" mimetype="image" mime-subtype="tiff">
<alt-text content-type="machine-generated">Pathway diagram illustrating the biosynthesis of sophorolipids, polyunsaturated fatty acids (PUFAs), and carotenoids. It starts with acetyl-CoA leading through various enzymes such as desaturases, transferases, and synthases to produce intermediates like stearate, oleate, linoleate, and more. The diagram includes sophorolipid structures and highlights metabolic regulation with different colored boxes indicating products, proteomics discoveries, and upregulation/downregulation.</alt-text>
</graphic>
</fig>
</sec>
<sec id="sec18">
<label>4.3.2</label>
<title>Carotenoid synthesis</title>
<p>The <italic>Rhodotorula</italic> genus includes several carotogenic oleaginous yeast with the characteristic red color, including <italic>R. toruloides</italic>. The mevalonate pathway is used to produce carotenoids from acetyl-CoA. The first step involves acetyl-CoA acetyltransferase to produce acetoacetyl-CoA, which, together with acetyl-CoA, is condensed to form hydroxymethylglutaryl-CoA (HMG-CoA) by the corresponding synthase (<xref ref-type="bibr" rid="ref89">Kot et al., 2016</xref>; <xref ref-type="bibr" rid="ref128">Ochoa-Vi&#x00F1;als et al., 2024</xref>). Then, HMG-CoA is transformed to mevalonic acid by a specific reductase. Several steps involving a kinase, decarboxylase, isomerase, prenyl transferase, phytoene synthase and desaturase, and cyclases ultimately produce a mix of carotenoids. These include carotenes (<italic>&#x03B1;</italic>-, <italic>&#x03B2;</italic>-, and <italic>&#x03B3;</italic>-isomers), torulene, and torularhodin (<xref ref-type="fig" rid="fig6">Figure 6</xref>).</p>
<p>Although most proteomics studies of oleaginous yeast focus on nitrogen availability, oxygen availability also affects lipid accumulation and carotenoid synthesis (<xref ref-type="bibr" rid="ref30">Calvey et al., 2016</xref>; <xref ref-type="bibr" rid="ref121">Mosqueda-Mart&#x00ED;nez et al., 2024</xref>; <xref ref-type="bibr" rid="ref186">Thumkasem et al., 2024</xref>). Fakankun et al. employed proteomics to investigate the effects of dual nitrogen and oxygen limitation on lipogenesis and carotenoid production in <italic>R. diobovata</italic> (<xref ref-type="bibr" rid="ref53">Fakankun et al., 2021</xref>). Samples were analyzed for the late exponential and early stationary phase at which point carotenoid concentrations began to increase. During early stationary phase, upregulation of malic enzyme and PPP enzymes was observed, but an expected increase in lipid biosynthesis was not, indicating a redirection of reducing equivalents away from fatty acid synthesis. Key enzymes in the mevalonate pathway and carotenoid synthesis including acetyl-CoA C-acetyltransferase (also known as thiolase), phosphomevalonate kinase, and phytoene dehydrogenase were upregulated suggesting that acetyl-CoA flux was preferentially directed toward carotenoid biosynthesis. Nevertheless, other studies have shown the importance of oxygen availability for carotenoid production (<xref ref-type="bibr" rid="ref199">Wang and Yu, 2009</xref>; <xref ref-type="bibr" rid="ref186">Thumkasem et al., 2024</xref>).</p>
<p><italic>Xanthophyllomyces dendrorhous</italic> (teleomorph of <italic>Phaffia rhodozyma</italic>) is another oleaginous yeast (<xref ref-type="bibr" rid="ref56">Flores-Cotera et al., 2001</xref>; <xref ref-type="bibr" rid="ref33">Ch&#x00E1;vez-Cabrera et al., 2010</xref>) of biotechnological intrigue for its production of astaxanthin, an antioxidant carotenoid (<xref ref-type="bibr" rid="ref113">Martinez-Moya et al., 2011</xref>). <xref ref-type="bibr" rid="ref112">Martinez-Moya et al. (2015)</xref> conducted a proteomic and metabolomic analysis of how this yeast regulates astaxanthin synthesis according to carbon source (glucose or succinate). When succinate is supplied, carotenogenesis is induced during the exponential phase, whereas for glucose it is induced during the stationary phase&#x2014;demonstrating dependence on not only resource availability over time (e.g., nitrogen) but also carbon source. Their metabolomics results largely follow expectations for carbon source entry points into primary metabolism (glycolysis/PPP for glucose and TCA cycle for succinate). Unfortunately, considerations about how exactly NADPH would be regenerated using succinate (e.g., NADP+ dependent isocitrate dehydrogenase) were not discussed despite its requirement for carotenoid synthesis. Because succinate is transformed into fumarate by succinate dehydrogenase (upregulated in succinate condition; Complex II of the electron transport chain), they propose that respiratory ROS formation is a mechanism by which carotenogenesis is regulated. Though the antioxidant enzyme expression levels do not fully align with this conclusion (e.g., downregulation of peroxiredoxin, TSA2), astaxanthin synthase and cytochrome P450 reductase were significantly upregulated on succinate. This is in addition to phosphoglucomutase, which is involved in trehalose formation and often associated with stress response (<xref ref-type="bibr" rid="ref204">Weeks et al., 2006</xref>).</p>
</sec>
<sec id="sec19">
<label>4.3.3</label>
<title>Additional important chassis for TAG accumulation</title>
<p>Though most proteomics studies have been conducted on <italic>Y. lipolytica</italic> and <italic>R. toruloides</italic>, these are not the only promising lipogenic chassis for metabolic engineering. <italic>L. starkeyi</italic> and <italic>Cutaneotrichosporon oleaginosum</italic> (formerly <italic>Crytococcus curvatus</italic> and <italic>Trichosporon oleaginosus</italic>) are considered top lipid producers, with both naturally utilizing and in some cases co-utilizing (<xref ref-type="bibr" rid="ref64">Gong et al., 2012</xref>; <xref ref-type="bibr" rid="ref217">Yang et al., 2014</xref>; <xref ref-type="bibr" rid="ref224">Yu et al., 2014</xref>) a variety of biomass-derived carbon sources. Studies using these two yeasts are reviewed in this subsection.</p>
<p><xref ref-type="bibr" rid="ref102">Liu et al. (2011)</xref> performed the first timecourse proteomics investigations of oleaginous yeast, including one in which <italic>L. starkeyi</italic> was cultivated in nitrogen-rich vs. deficient media with a high concentration of glucose (<xref ref-type="table" rid="tab2">Table 2</xref>). Many of the primary metabolic processes (e.g., alternative nitrogen source utilization and protein, amino acid, and nucleic acid turnover) discussed in the previous subsections exhibited changes. Increased lipid production was linked to upregulation of key TCA and transhydrogenase cycle enzymes, including citrate synthase (CIT1) to buildup citrate and route carbon towards fatty acid biosynthesis. Acetyl-CoA carboxylases ACC1 (cytosolic) and HFA1 (mitochondrial) were also upregulated in the late culture stage. Additionally, the drastic upregulation of 6-phosphogluconate dehydrogenase (GND2) provides early support for the PPP&#x2019;s role in supplying NADPH for fatty acid biosynthesis. With this and their earlier study (<xref ref-type="bibr" rid="ref103">Liu et al., 2009</xref>)&#x2014;as well as other endeavoring to study protein expression in unconventional microorganisms--it&#x2019;s evident that having a sequenced and well-annotated genome is crucial for maximizing proteomics&#x2019; potential.</p>
<p>Two proteomics research areas related to carbon efficient bioconversion of biomass hydrolysates that have largely been shirked were studied by the Blenner and Brueck labs using <italic>C. oleaginosum</italic>: lignin-derived aromatics utilization (<xref ref-type="bibr" rid="ref214">Yaguchi et al., 2017b</xref>; <xref ref-type="bibr" rid="ref212">Yaguchi, 2020</xref>) and carbohydrate-active enzymes (CAZy) for transport and metabolism of mono-, and importantly, disaccharides (<xref ref-type="bibr" rid="ref57">Fuchs et al., 2021</xref>). Yaguchi harnessed multi-omics and biochemical assays to elucidate catabolic pathways for monoaromatics including phenol, resorcinol, p-hydroxybenzoate, ferulate, and <italic>p</italic>-coumarate, as well as the yeast&#x2019;s growth on an alkaline-pretreated corn stover rich in lignin, aromatics, and acetate (<xref ref-type="bibr" rid="ref212">Yaguchi, 2020</xref>). Yaguchi annotated several transporters, stress response proteins, and metabolic enzymes such as dioxygenases, which perform the ring-cleaving step of aromatics degradation. Fuchs et al. performed a comprehensive proteomics analysis of secreted, cell wall-associated, and cytoplasmatic proteins to uncover enzymes (based on EC, enzyme classes) that cleave and transport dissaccharides (<xref ref-type="bibr" rid="ref57">Fuchs et al., 2021</xref>). These include various glycoside hydrolases (GH) found in different CAZy database (sub)families; however, it&#x2019;s unclear if the CAZy database (<xref ref-type="bibr" rid="ref93">Levasseur et al., 2013</xref>) was specifically used to search their results. Nonetheless, compared to a glucose control, significant differences in the cell-bound and secreted fractions were observed for <italic>&#x03B1;</italic>-galactosidase, <italic>&#x03B2;</italic>-galactosidase, &#x03B2;-glucosidase, &#x03B2;-mannosidase and &#x03B1;-amylase as expected under carbon catabolite derepression.</p>
</sec>
</sec>
</sec>
<sec id="sec20">
<label>5</label>
<title>Proteomics insights for filamentous fungi</title>
<p>Though fewer compared to studies of oleaginous yeast, proteomics investigations of oleaginous filamentous fungi have revealed important insights regarding stress response and metabolic regulation during nutrient limitation. <italic>Mucor circinelloides</italic> and <italic>Mortierella alpina</italic> have been used in these studies due to their ability to produce omega-3 and omega-6 fatty acids (<xref ref-type="bibr" rid="ref229">Zhang X. et al., 2022</xref>). In addition to accumulating TAGs in media with high C: N ratios, <italic>M. circinelloides</italic> has been primarily investigated for native synthesis of &#x03B2;-carotene and significant quantities of <italic>&#x03B3;</italic>-linolenic acid (GLA; <italic>&#x03C9;</italic>-6 C18:3) (<xref ref-type="bibr" rid="ref55">Fazili et al., 2022b</xref>; <xref ref-type="bibr" rid="ref198">Wang et al., 2022</xref>). It has also been engineered to produce the skin and neurological health-promoting antioxidant astaxanthin from <italic>&#x03B2;</italic>-carotene (<xref ref-type="bibr" rid="ref131">Papp et al., 2006</xref>; <xref ref-type="bibr" rid="ref68">Hameed et al., 2017</xref>; <xref ref-type="bibr" rid="ref236">Zhou et al., 2021</xref>; <xref ref-type="bibr" rid="ref3">Ad&#x0131;g&#x00FC;zel and &#x00DC;lger, 2024</xref>). Moreover, <italic>M. circinelloides</italic> secretes cellulases and hemicellulases to improve carbon utilization from pretreated lignocellulose and potentially reduce the cost of enzymatic saccharification (<xref ref-type="bibr" rid="ref205">Wei et al., 2013</xref>; <xref ref-type="bibr" rid="ref7">Al Mousa et al., 2022a</xref>,<xref ref-type="bibr" rid="ref8">b</xref>).</p>
<p><italic>Mortierella alpina</italic> is popular for its ability to produce arachidonic acid (ARA; <italic>&#x03C9;</italic>-6 C20:4), and some strains even produce eicosapentaenoic acid (EPA; <italic>&#x03C9;</italic>-6 20:5) among other economically attractive, bioactive PUFAs (<xref ref-type="bibr" rid="ref215">Yamada et al., 1987</xref>; <xref ref-type="bibr" rid="ref162">Shimiziu et al., 1988</xref>; <xref ref-type="bibr" rid="ref168">Singh and Ward, 1997</xref>; <xref ref-type="bibr" rid="ref177">Takeno et al., 2005</xref>; <xref ref-type="bibr" rid="ref86">Kikukawa et al., 2018</xref>; <xref ref-type="bibr" rid="ref32">Chang et al., 2022</xref>). It is generally regarded as safe with FDA approval for use of its ARA-rich oil in infant formulation (<xref ref-type="bibr" rid="ref148">Ratledge et al., 2010</xref>; <xref ref-type="bibr" rid="ref155">Ryan et al., 2010</xref>). For these reasons, proteomics investigations of oleaginous filamentous fungi have focused exclusively on these particular species (<xref ref-type="table" rid="tab2">Table 2</xref>). Overall, these studies highlight the intricate role of autophagy and alterations to central carbon metabolism that are crucial for understanding lipid accumulation.</p>
<table-wrap position="float" id="tab2">
<label>Table 2</label>
<caption>
<p>Summary for proteomics studies of oleaginous filamentous fungi.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top">Approach</th>
<th align="center" valign="top">Multi-omics</th>
<th align="left" valign="top">Stress</th>
<th align="left" valign="top">Organism</th>
<th align="left" valign="top">Carbon</th>
<th align="left" valign="top">Nitrogen</th>
<th align="left" valign="top">Experimental</th>
<th align="left" valign="top">Lipid titer/content</th>
<th align="center" valign="top">IDs</th>
<th align="left" valign="top">Significant findings summary</th>
<th align="left" valign="top">Ref.</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="middle">Label-free lipid droplet and cellular proteomics</td>
<td align="center" valign="middle">-</td>
<td align="left" valign="middle">Carbon</td>
<td align="left" valign="middle"><italic>M. alpina</italic></td>
<td align="left" valign="middle">80&#x202F;g/L Glucose</td>
<td align="left" valign="middle">YE NaNO3</td>
<td align="left" valign="middle">Timecourse (156 and 192&#x202F;h); Batch flasks</td>
<td align="left" valign="middle">~11&#x202F;g/L, ~40%; 10&#x202F;g/L, ~38%</td>
<td align="center" valign="middle">&#x003E; 411<sup>&#x2021;,&#x00A7;</sup></td>
<td align="left" valign="middle">Fructose metabolic enzymes associated with lipid droplets. Hydratase in PUFA synthesis upregulated during aging.</td>
<td align="left" valign="middle"><xref ref-type="bibr" rid="ref221">Yu et al. (2016</xref>, <xref ref-type="bibr" rid="ref220">2017)</xref></td>
</tr>
<tr>
<td align="left" valign="middle">Label-free lipid droplet and cellular proteomics</td>
<td align="center" valign="middle">M</td>
<td align="left" valign="middle">Carbon</td>
<td align="left" valign="middle"><italic>M. alpina</italic></td>
<td align="left" valign="middle">80&#x202F;g/L Glucose</td>
<td align="left" valign="middle">KNO3 Urea</td>
<td align="left" valign="middle">192&#x202F;h; Control vs. nitrogen source supplemented during aging; Batch</td>
<td align="left" valign="middle">12.7&#x202F;g/L, ~49% (6.48&#x202F;g/L ARA; max using KNO3)</td>
<td align="center" valign="middle">&#x003E; 500<sup>&#x2021;,&#x00A7;</sup></td>
<td align="left" valign="middle">KNO3 stimulated pyruvate-malate cycle and PPP. Antioxidants associated with and differentially regulated in LD proteome.</td>
<td align="left" valign="middle">
<xref ref-type="bibr" rid="ref223">Yu et al. (2018)</xref>
</td>
</tr>
<tr>
<td align="left" valign="middle">Label-free cellular proteomics</td>
<td align="center" valign="middle">M, L</td>
<td align="left" valign="middle">Nitrogen</td>
<td align="left" valign="middle"><italic>M. alpina</italic></td>
<td align="left" valign="middle">30&#x202F;g/L Glucose</td>
<td align="left" valign="middle">Diammonium tartrate YE</td>
<td align="left" valign="middle">Timecourse (many time points); Batch fermentation tanks</td>
<td align="left" valign="middle">~2.9&#x202F;g/L, ~32% max</td>
<td align="center" valign="middle">3,462</td>
<td align="left" valign="middle">Resource reallocation contributes to TAG increase not abundance changes. Explored ubiquitin-mediated proteolysis, ER-associated degradation, and unfold protein response.</td>
<td align="left" valign="middle">
<xref ref-type="bibr" rid="ref104">Lu et al. (2020)</xref>
</td>
</tr>
<tr>
<td align="left" valign="middle">Selective (MALDI-TOF-MS/MS)</td>
<td align="center" valign="middle">-</td>
<td align="left" valign="middle">Nitrogen</td>
<td align="left" valign="middle"><italic>M. alpina</italic>
<break/>
<italic>C. echinulata</italic>
<break/>
<italic>Schizochytrium</italic></td>
<td align="left" valign="middle">Varied glucose concentrations</td>
<td align="left" valign="middle">Varied</td>
<td align="left" valign="middle">Timecourse (varying time points for each organism); Batch flasks</td>
<td align="left" valign="middle">See manuscript</td>
<td align="center" valign="middle">12<sup>&#x00A7;</sup></td>
<td align="left" valign="middle">Homogenization + cold shock is a cheap, optimal method for lipid removal. HSP90 and desaturases upregulated in fungi.</td>
<td align="left" valign="middle">
<xref ref-type="bibr" rid="ref99">Ling et al. (2015)</xref>
</td>
</tr>
<tr>
<td align="left" valign="middle">Selective (MALDI-TOF-MS/MS)</td>
<td align="center" valign="middle">-</td>
<td align="left" valign="middle">-</td>
<td align="left" valign="middle"><italic>M. circinelloides</italic></td>
<td align="left" valign="middle">PDA Cheese-mimicking medium</td>
<td align="left" valign="middle">YE Protein mix</td>
<td align="left" valign="middle">168&#x202F;h; M. circinelloides (cheese contaminant) compared to other fungi; Batch plates</td>
<td align="left" valign="middle">-</td>
<td align="center" valign="middle">494<sup>&#x00A7;</sup></td>
<td align="left" valign="middle">Proteasome, glycogen mobilization, lipid metabolism, purine degradation, and stress response upregulated in M. circinelloideson cheeese mimic.</td>
<td align="left" valign="middle">
<xref ref-type="bibr" rid="ref9001">Morin-Sardin et al. (2017)</xref>
</td>
</tr>
<tr>
<td align="left" valign="middle">Label-free Secretomics</td>
<td align="center" valign="middle">-</td>
<td align="left" valign="middle">Carbon</td>
<td align="left" valign="middle"><italic>M. circinelloides</italic></td>
<td align="left" valign="middle">10&#x202F;g/L Lactose</td>
<td align="left" valign="middle">YE (NH4)2SO4</td>
<td align="left" valign="middle">120&#x202F;h; Qualitative investigation; Batch flasks</td>
<td align="left" valign="middle">-</td>
<td align="center" valign="middle">25<sup>&#x00A7;</sup></td>
<td align="left" valign="middle">Combination of secretome extraction methods boosted coverage. Endoglucanases, polysaccharide deacetylase, and &#x03B2;-glucosidase observed.</td>
<td align="left" valign="middle">
<xref ref-type="bibr" rid="ref205">Wei et al. (2013)</xref>
</td>
</tr>
<tr>
<td align="left" valign="middle">Selective (MALDI-TOF/TOF-MS)</td>
<td align="center" valign="middle">-</td>
<td align="left" valign="middle">Nitrogen</td>
<td align="left" valign="middle"><italic>M. circinelloides</italic></td>
<td align="left" valign="middle">80&#x202F;g/L Glucose</td>
<td align="left" valign="middle">Diammonium tartrate YE</td>
<td align="left" valign="middle">24&#x202F;h; High vs. low-lipid producing strains; Batch fermenters</td>
<td align="left" valign="middle">~2.5&#x202F;g/L, ~21%; ~0.8&#x202F;g/L, ~9%</td>
<td align="center" valign="middle">&#x003E; 800<sup>&#x2021;.&#x00A7;</sup></td>
<td align="left" valign="middle">Enzymes for acetyl-CoA (e.g., from lysine), NADPH, and nitrogen metabolism upregulated in high-lipid strain.</td>
<td align="left" valign="middle">
<xref ref-type="bibr" rid="ref179">Tang et al. (2017)</xref>
</td>
</tr>
<tr>
<td align="left" valign="middle">Selective (MALDI-TOF-MS/MS)</td>
<td align="center" valign="middle">-</td>
<td align="left" valign="middle">Nitrogen</td>
<td align="left" valign="middle"><italic>M. circinelloides</italic></td>
<td align="left" valign="middle">80&#x202F;g/L Glucose</td>
<td align="left" valign="middle">Diammonium tartrate YE</td>
<td align="left" valign="middle">Timecourse (6, 24, and 60&#x202F;h); Batch fermentors</td>
<td align="left" valign="middle">~4.3&#x202F;g/L, ~33%</td>
<td align="center" valign="middle">&#x003E; 800<sup>&#x2021;,&#x00A7;</sup></td>
<td align="left" valign="middle">Glycolysis and PPP upregulated while TCA was downregulated. No change in ME expression. Antioxidants and glutathione metabolisc enzymes upregulated.</td>
<td align="left" valign="middle">
<xref ref-type="bibr" rid="ref182">Tang et al. (2016)</xref>
</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>Refer to legend below <xref ref-type="table" rid="tab1">Table 1</xref>.</p>
</table-wrap-foot>
</table-wrap>
<sec id="sec21">
<label>5.1</label>
<title>Mucor circinelloides</title>
<sec id="sec22">
<label>5.1.1</label>
<title>&#x03B3;-linolenic acid production</title>
<p>The oleaginous <italic>M. circinelloides</italic> strain WJ11 was isolated and sequenced by Tang et al. Under nitrogen limitation, this strain accumulates &#x2265; 36% of its CDW as lipids with a&#x202F;~&#x202F;13% GLA content (<xref ref-type="bibr" rid="ref178">Tang et al., 2015a</xref>, <xref ref-type="bibr" rid="ref184">2015b</xref>). GLA is produced via multiple desaturation reactions: stearic acid (C18:0) is converted to oleic acid (C18:1) by &#x0394;9-desaturase and further desaturated to linoleic acid (C18:2) by &#x0394;12-desaturase and, finally, to GLA by &#x0394;6-desaturase (<xref ref-type="fig" rid="fig6">Figure 6</xref>) (<xref ref-type="bibr" rid="ref230">Zhang Y. et al., 2017</xref>). Two proteomics studies were conducted on <italic>M. circinelloides</italic> WJ11 (<xref ref-type="table" rid="tab1">Table 1</xref>): a time-course experiment in which cultures were sampled at 6&#x202F;h (exponential phase), 24&#x202F;h (rapid lipid production phase), and 60&#x202F;h (stationary phase) (<xref ref-type="bibr" rid="ref182">Tang et al., 2016</xref>) as well as an experiment comparing <italic>M. circinelloides</italic> WJ11 to strain CBS 277.49, a low lipid but high carotenoid-producing strain (<xref ref-type="bibr" rid="ref179">Tang et al., 2017</xref>). In both studies, a modified K &#x0026; R growth medium (<xref ref-type="bibr" rid="ref84">Kendrick and Ratledge, 1992</xref>) was used, which contained 80&#x202F;g/L glucose, 2&#x202F;g/L diammonium tartrate, 1.5&#x202F;g/L yeast extract (~80:1 C: N ratio), and other defined nutrients.</p>
<p>In the timecourse experiment, nitrogen limitation generally led to downregulation of the TCA cycle and amino acid biosynthesis (<xref ref-type="bibr" rid="ref182">Tang et al., 2016</xref>); however, <italic>S</italic>-adenosylmethionine synthase, which participates in methionine degradation, was upregulated and may provide resources for glutathione synthesis. In a later study using genome-scale metabolic modeling and data integration, methionine degradation was highlighted and recapitulates the importance of sulfur amino acid metabolism during nitrogen-limited stress response (<xref ref-type="bibr" rid="ref193">Vongsangnak et al., 2018</xref>). In further support of the role of antioxidants during nitrogen limitation, <italic>S</italic>-formylglutathione hydrolase was upregulated and hydrolyzes <italic>S</italic>-formylglutathione to restore glutathione that had condensed with formaldehyde. Amino acid degradation, demethylation reactions, and tetrahydrofolate (THF)-dependent pathways can generate formaldehyde (<xref ref-type="bibr" rid="ref206">Weimer et al., 1993</xref>; <xref ref-type="bibr" rid="ref47">Degrassi et al., 1999</xref>; <xref ref-type="bibr" rid="ref133">Pham et al., 2023</xref>). Interestingly, they observed upregulation of the antioxidants peroxiredoxin and glutathione peroxidase, which detoxify peroxides (<xref ref-type="bibr" rid="ref137">P&#x00F3;csi et al., 2004</xref>). Strengthening the link between antioxidants and the oleaginous phenotype, peroxiredoxin and catalase were upregulated in strain WJ11 compared to strain CBS 499.25 during nitrogen limitation (<xref ref-type="bibr" rid="ref179">Tang et al., 2017</xref>). A thiazole biosynthetic enzyme involved in thiamine metabolism (also upregulated in WJ11 compared to CBS 499.25), a 14&#x2013;3-3 family protein that promotes cell survival by negatively regulating apoptosis, and heat shock proteins for regulating proper protein folding were all upregulated (<xref ref-type="bibr" rid="ref182">Tang et al., 2016</xref>). These present interesting yet unexplored targets for engineering improved stress response.</p>
<p>Concurrently, pyruvate kinase, glyceraldehyde-3-phosphate dehydrogenase, fructose bisphosphate aldolase, and enolase were upregulated in the timecourse experiment of WJ11; these enzymes function at the intersection of the PPP, glycolysis, and lipid synthesis. For instance, enzymes in the PPP were upregulated to generate NADPH&#x2014;a relationship that had been established prior to proteomics(<xref ref-type="bibr" rid="ref235">Zhao et al., 2015</xref>; <xref ref-type="bibr" rid="ref114">Masi et al., 2021</xref>). Nevertheless, glucose-6-phosphate dehydrogenase was upregulated compared to the lower lipid-producing strain CBS 477.25, reinforcing the role of PPP for the oleaginocity (<xref ref-type="bibr" rid="ref179">Tang et al., 2017</xref>). The expression of ME did not change in both the timecourse experiment and comparative proteomics study (<xref ref-type="bibr" rid="ref182">Tang et al., 2016</xref>, <xref ref-type="bibr" rid="ref179">2017</xref>); however, <italic>M. circinelloides</italic> contains several isoforms of ME with only one being NADP<sup>+</sup>-dependent (<xref ref-type="bibr" rid="ref170">Song et al., 2001</xref>). Because the full set of proteomics data was not published and this enzyme was not listed in the table of presented results, it is unclear which isoform they are referring to (<xref ref-type="bibr" rid="ref182">Tang et al., 2016</xref>). Moreover, studies of ME overexpression in different strains are conflicting (<xref ref-type="bibr" rid="ref226">Zhang et al., 2007</xref>; <xref ref-type="bibr" rid="ref153">Rodr&#x00ED;guez-Fr&#x00F3;meta et al., 2013</xref>; <xref ref-type="bibr" rid="ref54">Fazili et al., 2022a</xref>). Two additional insights related to acetyl-CoA flux were gleaned from the comparative proteomics study. Firstly, aldehyde can be produced from pyruvate and converted to acetate by aldehyde dehydrogenase, thereby providing an alternative source of acetyl-CoA: this enzyme was upregulated in WJ11 compared to CBS 277.49. Secondly, degradation of branched-chain amino acids (e.g., leucine) and lysine provides acetyl-CoA: enzymes that degrade these amino acids were upregulated in WJ11 (<xref ref-type="bibr" rid="ref179">Tang et al., 2017</xref>). As yet another example of proteomics results directing subsequent metabolic engineering studies, Tang et al. overexpressed glucose 6-phosphate dehydrogenase and <italic>&#x03B2;</italic>-isopropylmalate dehydrogenase in CBS 277.49 leading to as much as a 47 and 73% increase in lipid content, respectively (<xref ref-type="bibr" rid="ref180">Tang et al., 2020</xref>).</p>
</sec>
</sec>
<sec id="sec23">
<label>5.2</label>
<title>Mortierella alpina</title>
<sec id="sec24">
<label>5.2.1</label>
<title>Arachidonic acid production</title>
<p>In contrast to the conditions used for lipid production in <italic>M. circinelloides</italic>, ARA production using <italic>M. alpina</italic> is often conducted via a multistage aging process, during which biomass is first generated in a nutrient-replete environment, followed by a transition (or even transfer of mycelia) to a carbon-deficient environment (<xref ref-type="bibr" rid="ref80">Ji et al., 2014</xref>). Jin et al. optimized a fed-batch strategy using strain ME-1 to yield as much as 19.8&#x202F;g/L ARA (<xref ref-type="bibr" rid="ref82">Jin et al., 2008</xref>). Overall, this process is characterized by long cultivation periods, and proteomics sampling was conducted at extended (~200&#x202F;h) time points (<xref ref-type="table" rid="tab1">Table 1</xref>). Three similar studies by Yu et al. harnessed this strategy to produce appreciable quantities of ARA (~ 6&#x202F;g/L) via batch cultivation of strain R807 on 80&#x202F;g/L glucose, 3&#x202F;g/L NaNO<sub>3</sub>, and 10&#x202F;g/L yeast extract (~20:1 C: N ratio), and other nutrients (<xref ref-type="bibr" rid="ref221">Yu et al., 2016</xref>, <xref ref-type="bibr" rid="ref220">2017</xref>, <xref ref-type="bibr" rid="ref223">2018</xref>). In their first study, label-free proteomics was used to study the change of cellular proteins during aging of <italic>M. alpina</italic> for ARA accumulation, revealing a total of 171 significant proteins that are enriched in ROS response (<xref ref-type="bibr" rid="ref221">Yu et al., 2016</xref>). Next, the same group quantified changes in the lipid droplet proteome of <italic>M. alpina</italic> during aging, identifying significant changes in 62 out of the 400 lipid droplet-associated proteins (<xref ref-type="bibr" rid="ref220">Yu et al., 2017</xref>). In a more recent study, a similar analysis of the lipid droplet proteome, as well as the cellular proteome, was performed to study the impact of two nitrogen sources (KNO<sub>3</sub> and urea) on ARA production (<xref ref-type="bibr" rid="ref223">Yu et al., 2018</xref>).</p>
<p>Comparing a nutrient-rich time point to a later time point during carbon starvation, their proteomics results showed a characteristic shift towards autophagy: enzymes involved in amino acid degradation were upregulated in concert with a minor increase in several amino acids suggesting protein turnover (<xref ref-type="bibr" rid="ref221">Yu et al., 2016</xref>). An uncharacterized autophagy-related protein and Rab GTPases were observed in the lipid droplet proteome underlining the role of lipid droplet in lipid and protein trafficking (<xref ref-type="bibr" rid="ref109">Martin and Parton, 2008</xref>; <xref ref-type="bibr" rid="ref207">Welte, 2015</xref>). As expected, carbohydrate catabolic pathways and fatty acid biosynthesis (e.g., ACC) were also generally suppressed while the PPP exhibited insignificant changes in this study (<xref ref-type="bibr" rid="ref221">Yu et al., 2016</xref>). Interestingly, a relative increase in PPP and fatty acid biosynthesis enzymes was found when KNO<sub>3</sub> was supplemented during aging, which may contribute to the slight though significant increase in ARA (<xref ref-type="bibr" rid="ref223">Yu et al., 2018</xref>).</p>
<p>During carbon starvation, lipids from the lipid droplets can be mobilized via <italic>&#x03B2;</italic>-oxidation to provide ATP and acetyl-CoA for TCA cycle anaplerosis (<xref ref-type="bibr" rid="ref50">Enkler and Spang, 2024</xref>). Accordingly, <xref ref-type="bibr" rid="ref220">Yu et al. (2017)</xref> observed aggregates of enlarged mitochondria and shrunken lipid droplets, correlating with upregulation of some &#x03B2;-oxidation enzymes (e.g., acetyl-coA synthetase). During aging, two enzymes involved in fructose, sucrose, mannose, and fucose metabolism were upregulated and localized to lipid droplets, supporting an autophagic or energy balancing role of lipid droplets in antioxidant ascorbate metabolism, cell wall remodeling, or recycling amino/nucleotide sugars, glycoproteins, and/or sucrose stores that accumulated during growth (<xref ref-type="bibr" rid="ref220">Yu et al., 2017</xref>, <xref ref-type="bibr" rid="ref223">2018</xref>). Interestingly, one of these enzymes is GDP-keto-6-deoxymannose 3,5-epimerase/4-reductase that uses either NADH or NADPH (<xref ref-type="bibr" rid="ref151">Ren et al., 2010</xref>). In the cellular proteome, the abundances and measured enzymatic activities for NADP<sup>+</sup>-dependent ME decreased while ICDH increased, suggesting a repressed transhydrogenase cycle and activity of an alternative cytoplasmic NADPH-generating shuttle to supply reducing power for fatty acid biosynthesis and antioxidant defense (<xref ref-type="bibr" rid="ref181">Tang et al., 2021</xref>). Though a thorough explanation is absent, it&#x2019;s curious that glutathione metabolism was significantly downregulated according to KEGG pathway analysis, and that they noted a drastic increase in ROS during aging according to an assay using a fluorogenic probe (<xref ref-type="bibr" rid="ref220">Yu et al., 2017</xref>).</p>
</sec>
<sec id="sec25">
<label>5.2.2</label>
<title>Nitrogen limitation-induced resource reallocation</title>
<p>Lu et al. investigated the response of <italic>M. alpina</italic> to nitrogen limitation using an integrated lipidomics, metabolomics, and proteomics approach (<xref ref-type="bibr" rid="ref104">Lu et al., 2020</xref>). They cultured <italic>M. alpina</italic> strain ATCC 32222 under nitrogen limitation with a medium containing 30.0&#x202F;g/L glucose, 2.0&#x202F;g/L diammonium tartrate, and 1.5&#x202F;g/L yeast extract (~30 C: N ratio) and observed a drop in ARA and TAG accumulation. Enzymes and the corresponding glycolytic metabolites were downregulated during nitrogen limitation, whereas TCA cycle enzymes were generally downregulated with a concomitant accumulation of citrate. ME was downregulated while ACL and phosphoenolpyruvate carboxykinase were upregulated, providing a route from citrate to phosphoenolpyruvate to feed gluconeogenesis. Though they do not explicitly report the expression levels of FAS1/2 nor ACC, they conclude that TAG accumulation was a consequence of resource reallocation, rather than an upregulation of fatty acid biosynthesis enzymes. This is a reasonable conclusion given that succinate increased up to the 144&#x202F;h time point, paralleling an increase in succinate-semialdehyde dehydrogenase and glutamate decarboxylase of the GABA shunt, which provides a TCA cycle entry point for autophagy-derived carbon. According to their lipidomics and proteomics results, an increase in TAGs may be additionally explained by upregulation of phospholipases C and D, which convert phosphatidylcholine and phosphatidylethanolamine into precursor DAGs.</p>
<p>The relative quantities of proteins, peptides, amino acids, and a plethora of other nitrogenous compounds decreased during nitrogen limitation (<xref ref-type="bibr" rid="ref104">Lu et al., 2020</xref>). This is corroborated by an increase in ubiquitin-proteasome system components, including 26S proteasome subunits and ubiquitin-activating enzyme E1 during the early stages of nitrogen deprivation. The importance of autophagic processes are further highlighted by an increase in autophagy-related proteins, vacuolar components, and peroxisomal enzymes&#x2014;including a catalase that detoxifies H<sub>2</sub>O<sub>2</sub> generated as a byproduct of &#x03B2;-oxidation, which was upregulated during nitrogen limitation. Autophagic processes are negatively regulated by TOR (<xref ref-type="bibr" rid="ref201">Wang et al., 2023b</xref>) in nutrient-rich environments: in their results, a homolog of mammalian TOR decreased in abundance over time. A regulatory link between autophagy and lipid metabolism has been established but little is known in oleaginous filamentous fungi (<xref ref-type="bibr" rid="ref167">Singh et al., 2009</xref>). Excitingly, in a follow-up study, they harnessed their proteomics insights to delve into autophagy-regulated lipid metabolism: they showed that overexpression of autophagy-related gene 8 (ATG8) increased fatty acid biosynthesis by ~10% (<xref ref-type="bibr" rid="ref105">Lu et al., 2022</xref>).</p>
</sec>
</sec>
</sec>
<sec id="sec26">
<label>6</label>
<title>Cross-species patterns in proteomic signatures of lipogenesis</title>
<p>Across species like <italic>Yarrowia lipolytica</italic>, <italic>Rhodotorula toruloides</italic>, and <italic>Mucor circinelloides</italic>, differential expression of key lipid biosynthesis enzymes&#x2014;ATP citrate lyase (ACL), acetyl-CoA carboxylase (ACC), and/or fatty acid synthase (FAS)&#x2014;is a common signature under nutrient limitation (<xref ref-type="bibr" rid="ref240">Zhu et al., 2012</xref>; <xref ref-type="bibr" rid="ref140">Pomraning et al., 2016</xref>; <xref ref-type="bibr" rid="ref179">Tang et al., 2017</xref>). Lipid droplet-associated proteins, such as the perilipin-like proteins OIL1 in <italic>Y. lipolytica</italic> (<xref ref-type="bibr" rid="ref19">Bhutada et al., 2018</xref>) and LDP1 in <italic>R. toruloides</italic> (<xref ref-type="bibr" rid="ref238">Zhu et al., 2015</xref>), are consistently upregulated, suggesting a conserved role in regulating lipid droplets and storage lipids. Differential expression of autophagy-related proteins, including ATG8 and ATG9 which are discussed in studies of <italic>R. toruloides</italic> (<xref ref-type="bibr" rid="ref197">Wang et al., 2023a</xref>) and <italic>Mortierella alpina</italic> (<xref ref-type="bibr" rid="ref104">Lu et al., 2020</xref>), also suggests resource recycling as a universal stress response to nutrient limitation. In addition, some components of autophagy-regulated lipid metabolism are conserved (<xref ref-type="bibr" rid="ref201">Wang et al., 2023b</xref>), including the AMP deaminase-isocitrate dehydrogenase (ICDH)-citrate axis that reroutes carbon to lipid biosynthesis under nitrogen limitation. Phosphoregulation of ACL and ACC is another shared feature, though specific phosphorylation sites may differ (<xref ref-type="bibr" rid="ref140">Pomraning et al., 2016</xref>; <xref ref-type="bibr" rid="ref197">Wang et al., 2023a</xref>).</p>
<p>The pentose phosphate pathway (PPP) is a major source of NADPH across fungi. There are additional species-specific sources like NADP<sup>+</sup>-dependent ICDH in <italic>Y. lipolytica</italic> and malic enzyme in <italic>R. toruloides</italic> and <italic>M. circinelloides</italic> (<xref ref-type="bibr" rid="ref147">Ratledge, 2014</xref>). Of course, differing NADPH requirements and regeneration reactions may explain differences in protein expression for the corresponding pathways in central carbon metabolism. However, this is difficult to evaluate without a detailed phyloproteomics investigation or, preferably, direct comparisons within sample sets analyzed using the same proteomics workflow. In addition to fatty acid synthesis, NADPH is required for antioxidant defense against pro-oxidants such as hydrogen peroxide. This and other reactive oxygen species are readily generated during respiration, as a byproduct of purine and amino acid scavenging, as well as &#x03B2;-oxidation (<xref ref-type="bibr" rid="ref165">Sibirny, 2016</xref>; <xref ref-type="bibr" rid="ref135">Picazo and Molin, 2021</xref>; <xref ref-type="bibr" rid="ref116">Mattila et al., 2022</xref>). Though the number and types of antioxidant proteins differ among fungi (<xref ref-type="bibr" rid="ref116">Mattila et al., 2022</xref>), a link between starvation stress response and redox systems was pronounced for many of the studies reviewed herein. In a recent publication, a multi-PTM proteomics and lipidomics approach was used to reveal that nitrogen limitation in <italic>R. toruloides</italic> drives widespread changes in protein thiol oxidation and phosphorylation for stress response signaling, metabolic pathways, and autophagy (<xref ref-type="bibr" rid="ref61">Gluth et al., 2025</xref>).</p>
</sec>
<sec sec-type="discussion" id="sec27">
<label>7</label>
<title>Discussion</title>
<p>MS-based proteomics can quantify thousands of proteins (and modifications to them) in parallel, thereby revealing key cellular pathways that culminate in a specific phenotype. In this review, we demonstrate the power of proteomics for characterizing molecular events that drive oleochemical production in oleaginous fungi. Applications of proteomics in oleaginous fungi in the last decade has pointed to the role of multiple pathways/processes governing oleaginicity including signaling pathways (TOR, AMPK, MAPK, etc.), redox balance (including antioxidant defense), autophagy, nitrogen metabolism (especially pertaining to branched chain amino acids, asparagine and the urea cycle, and purine degradation), transport, cellular trafficking, and energy homeostasis. Comparative proteomics has also identified proteins that can be used as engineering targets to improve lipid content. In addition, subcellular proteomics of lipid droplets has revealed this organelle&#x2019;s multifaceted role in energy and redox homeostasis as well as stress response (<xref ref-type="bibr" rid="ref79">Jarc and Petan, 2019</xref>)&#x2014;even identifying the associated proteins that regulate access to its carbon stores (<xref ref-type="bibr" rid="ref238">Zhu et al., 2015</xref>; <xref ref-type="bibr" rid="ref19">Bhutada et al., 2018</xref>).</p>
<p>Even though this review is comprehensive, we were challenged by the fact that oleaginocity is generally strain-dependent (<xref ref-type="bibr" rid="ref157">Salvador L&#x00F3;pez et al., 2022</xref>), may involve nuances that are not completely conserved or well-annotated, is not represented solely by storage lipids (TAGs) as the primary oleochemical product, and is not always the focus of proteomics investigations. There are studies of oleaginous fungi that were not included here because (1) &#x201C;oleaginous&#x201D; was not explicitly mentioned in the study (<xref ref-type="bibr" rid="ref111">Martinez-Moya et al., 2020</xref>), (2) conditions conducive to lipid production were not studied or oleochemicals were not the focus (<xref ref-type="bibr" rid="ref120">Mor&#x00ED;n et al., 2007</xref>; <xref ref-type="bibr" rid="ref108">Mansour et al., 2009</xref>; <xref ref-type="bibr" rid="ref176">Swennen et al., 2010</xref>; <xref ref-type="bibr" rid="ref125">Navarro et al., 2013</xref>; <xref ref-type="bibr" rid="ref31">Celi&#x0144;ska et al., 2015</xref>; <xref ref-type="bibr" rid="ref216">Yang et al., 2015</xref>; <xref ref-type="bibr" rid="ref227">Zhang H. et al., 2017</xref>; <xref ref-type="bibr" rid="ref45">da Silva et al., 2020</xref>; <xref ref-type="bibr" rid="ref159">Sekova et al., 2021</xref>; <xref ref-type="bibr" rid="ref94">Li X. et al., 2022</xref>), and/or (3) it is unclear if the strain in question is indeed oleaginous. For instance, some strains of <italic>Aspergillus</italic> and <italic>Geotrichum</italic> species are oleaginous, yet their proteomes have not been investigated for lipogenic conditions (<xref ref-type="bibr" rid="ref185">Thammarongtham et al., 2018</xref>; <xref ref-type="bibr" rid="ref66">Grygier et al., 2019</xref>; <xref ref-type="bibr" rid="ref173">Srinivasan et al., 2022</xref>; <xref ref-type="bibr" rid="ref83">Kamilari et al., 2023</xref>; <xref ref-type="bibr" rid="ref71">Hassane et al., 2024</xref>). Another example is the yeast <italic>Debaryomyces hansenii</italic>, which has been studied for its halophilic behavior and could potentially reduce the demand for fresh water during lipid production (<xref ref-type="bibr" rid="ref213">Yaguchi et al., 2017a</xref>; <xref ref-type="bibr" rid="ref124">Navarrete et al., 2022</xref>).</p>
<p>The proteomics field is still progressing along with the appropriate and relevant applications. Proteomics data sharing is of great importance for cross-study comparisons and is gradually improving (<xref ref-type="bibr" rid="ref163">Shome et al., 2024</xref>); however, some of the studies reviewed herein do not have accompanying (or well-annotated and organized) datasets, which severely mars the interpretation of results and stymies a phyloproteomics investigation of carbon storage evolution in fungi. Moreover, some do not explicitly include details for their bioinformatics approaches or erroneously compare intensity values among different proteins despite not using an approach for absolute quantification (<xref ref-type="bibr" rid="ref10">Ankney et al., 2018</xref>). This raises an intriguing opportunity, though: targeted proteomics, in which libraries of peptide standards are used for absolute quantification of proteins, has also not been applied to oleaginous fungi. Overall, integrating proteomics data into genome-scale metabolic models (GEMs) will be essential for predictive design, and future work should prioritize absolute quantification to support accurate kinetic modeling. Quantitative proteomics can refine enzyme-constrained models and improve flux balance predictions (<xref ref-type="bibr" rid="ref179">Tang et al., 2017</xref>; <xref ref-type="bibr" rid="ref87">Kim et al., 2021</xref>). Furthermore, incorporating absolute enzyme quantities with metabolomics data would provide valuable inputs to construct highly accurate metabolic models and engineer optimal lipid production (<xref ref-type="bibr" rid="ref225">Yunus and Lee, 2022</xref>). Artificial Intelligence / Machine Learning (AI/ML) can analyze large proteomic datasets to predict protein functions, interactions, and regulatory networks (<xref ref-type="bibr" rid="ref97">Li F. et al., 2022</xref>; <xref ref-type="bibr" rid="ref169">Smith et al., 2022</xref>). For oleaginous fungi, AI/ML could identify novel lipid biosynthesis regulators by integrating proteomics with transcriptomics and metabolomics (<xref ref-type="bibr" rid="ref15">Ballard et al., 2024</xref>).</p>
<p>Several newer proteomics strategies have yet to be explored in oleaginous fungi but could significantly improve the scalability needed for large-scale studies and the resolution required for investigations that, for example, aim to characterize how different populations of yeast cells respond to the microenvironments which are present even in well-mixed bioreactors (<xref ref-type="bibr" rid="ref23">Boswell et al., 2003</xref>). Regarding the former, data-independent acquisition (DIA) is an emerging technique for label-free quantification and has yet to be applied for proteomics investigations of oleaginous fungi, even though cost savings could be realized by avoiding expensive mass tags and the throughput of modern LC&#x2013;MS/MS systems has been rapidly improving. Single-cell proteomics is another emerging frontier in systems biology that will enable dissection of cell-to-cell heterogeneity in lipid accumulation (<xref ref-type="bibr" rid="ref164">Shuken, 2023</xref>), particularly relevant for filamentous fungi, where hyphal differentiation may lead to varied lipogenic capacities within a culture. This approach could uncover cell-specific proteomic signatures in <italic>Y. lipolytica</italic> or <italic>R. toruloides</italic> under nutrient limitation, identifying subpopulations optimized for lipid accumulation. For example, single-cell proteomics could elucidate why certain cells within a culture exhibit higher TAG storage. Techniques such as SCoPE-MS and NanoPOTS offer new possibilities for such investigations (<xref ref-type="bibr" rid="ref29">Budnik et al., 2018</xref>; <xref ref-type="bibr" rid="ref239">Zhu et al., 2018</xref>; <xref ref-type="bibr" rid="ref110">Martin et al., 2021</xref>).</p>
<p>Looking forward, the burgeoning application of proteomics in synthetic biology is rife with opportunity. Frequently, the rationale for single-cell oil production is tied to the valorization of low-cost organic wastes, especially lignocellulosic feedstocks. However, there are surprisingly few proteomics studies of the fungal oleaginous phenotype using these complex substrates and scaled-up cultivations (&#x003E; 5&#x202F;L). Thus, one direction for future studies would be the analysis of dynamic carbon utilization under bioprocessing-relevant scenarios with engineered vs. wildtype strains. There are currently no metaproteomics studies of oleaginous fungi co-cultured with carbon- and/or nitrogen-fixing microorganisms (algae, cyanobacteria, azitobacter, etc.) (<xref ref-type="bibr" rid="ref22">Bohutskyi et al., 2024</xref>). Additionally, the few quantitative proteomics investigations of PTMs have focused exclusively on phosphorylation, even though methylation, acetylation, ubiquitination, and cysteine oxidation (redox proteomics) also regulate protein function, proteasomal processing, signaling pathways, and transcriptional programs (<xref ref-type="bibr" rid="ref189">Tripodi et al., 2015</xref>; <xref ref-type="bibr" rid="ref171">&#x0160;o&#x0161;tari&#x0107; and van Noort, 2021</xref>). For instance, the OIL1 protein of <italic>Y. lipolytica</italic> protects the lipid droplet from being accessed by lipases&#x2014;it is ubiquitinated (<xref ref-type="bibr" rid="ref19">Bhutada et al., 2018</xref>), raising the question of how much oleaginocity is regulated by the PTMome vs. changes to enzyme abundances. Indeed, this conclusion was reached in a recent publication that describes a novel proteomics approach for profiling multiple types of PTMs (<xref ref-type="table" rid="tab2">Table 2</xref>), specifically protein cysteine oxidation and phosphorylation (<xref ref-type="bibr" rid="ref61">Gluth et al., 2025</xref>). Protein structure confers function; thus, with sophisticated algorithms to link PTM dynamics to protein structural changes at the proteome level, one can envision substantial improvements to metabolic modeling approaches (i.e., more accurate predictions of k<sub>cat</sub> values) (<xref ref-type="bibr" rid="ref97">Li F. et al., 2022</xref>; <xref ref-type="bibr" rid="ref169">Smith et al., 2022</xref>).</p>
<p>Finally, we recommend integrating multi-omics approaches into the field of oleochemicals production and encourage multi-institutional collaborations to actualize this goal in the near future. It is imperative that we understand how genetic/metabolic engineering affects biological systems as a whole&#x2014;especially during the multi-phase, stress-induced fermentations that are applied for oleochemicals production. Employing automated workflows (<xref ref-type="bibr" rid="ref62">Gluth et al., 2024</xref>, <xref ref-type="bibr" rid="ref61">2025</xref>) to screen various engineered and wildtype strains over time and in different conditions will provide a wealth of phenotypic data (i.e., growth, lipid production, etc.) that can be paired with proteomics (including PTMs), transcriptomics, metabolomics, and lipidomics data. Together, these data will facilitate the training of AI/ML models to help us connect phenotypes with the underlying molecular mechanisms. This data can then be leveraged to guide genetic/metabolic engineering strategies for targeted phenotypes (i.e., lipid overproduction). The era defined by integrative multi-omics modeling approaches to direct unintuitive metabolic engineering ventures is upon us.</p>
</sec>
</body>
<back>
<sec sec-type="author-contributions" id="sec28">
<title>Author contributions</title>
<p>AG: Data curation, Visualization, Conceptualization, Writing &#x2013; original draft, Writing &#x2013; review &#x0026; editing. JBT: Visualization, Writing &#x2013; review &#x0026; editing. JJC: Writing &#x2013; review &#x0026; editing, Visualization. SD: Writing &#x2013; original draft, Writing &#x2013; review &#x0026; editing. W-JQ: Writing &#x2013; review &#x0026; editing, Supervision. BY: Supervision, Writing &#x2013; review &#x0026; editing, Writing &#x2013; original draft. TZ: Project administration, Conceptualization, Supervision, Writing &#x2013; review &#x0026; editing, Resources, Writing &#x2013; original draft, Funding acquisition, Visualization.</p>
</sec>
<sec sec-type="funding-information" id="sec29">
<title>Funding</title>
<p>The author(s) declare that financial support was received for the research and/or publication of this article. The authors thank the Predictive Phenomics Initiative (PPI) at Pacific Northwest National Laboratory (PNNL) for funding. A portion of the research was performed on a project award from the Environmental Molecular Sciences Laboratory, a DOE Office of Science User Facility sponsored by the Biological and Environmental Research program under Contract No. DE-AC05-76RL01830. AG was supported by the PNNL-Washington State University (WSU) Distinguished Graduate Research Program (DGRP) Fellowship. The research described in this paper is part of the Predictive Phenomics Initiative at Pacific Northwest National Laboratory and conducted under the Laboratory Directed Research and Development Program.</p>
</sec>
<ack>
<p>The authors thank Dr. Zhangyang Xu (PNNL) for helpful discussions. Portions of the <xref ref-type="fig" rid="fig1">Figures 1</xref>&#x2013;<xref ref-type="fig" rid="fig5">5</xref> were created with <ext-link xlink:href="http://Biorender.com" ext-link-type="uri">Biorender.com</ext-link>.</p>
</ack>
<sec sec-type="COI-statement" id="sec30">
<title>Conflict of interest</title>
<p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
<sec sec-type="ai-statement" id="sec31">
<title>Generative AI statement</title>
<p>The authors declare that no Gen AI was used in the creation of this manuscript.</p>
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<title>Publisher&#x2019;s note</title>
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<sec sec-type="supplementary-material" id="sec33">
<title>Supplementary material</title>
<p>The Supplementary material for this article can be found online at: <ext-link xlink:href="https://www.frontiersin.org/articles/10.3389/fmicb.2025.1637123/full#supplementary-material" ext-link-type="uri">https://www.frontiersin.org/articles/10.3389/fmicb.2025.1637123/full#supplementary-material</ext-link></p>
<supplementary-material xlink:href="Table_1.docx" id="SM1" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document" xmlns:xlink="http://www.w3.org/1999/xlink"/>
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