The Interaction of Mitochondrial Iron with Manganese Superoxide Dismutase

Superoxide dismutase 2 (SOD2) is one of the rare mitochondrial enzymes evolved to use manganese as a cofactor over the more abundant element iron. Although mitochondrial iron does not normally bind SOD2, iron will misincorporate into Saccharomyces cerevisiae Sod2p when cells are starved for manganese or when mitochondrial iron homeostasis is disrupted by mutations in yeast grx5, ssq1, and mtm1. We report here that such changes in mitochondrial manganese and iron similarly affect cofactor selection in a heterologously expressed Escherichia coli Mn-SOD, but not a highly homologous Fe-SOD. By x-ray absorption near edge structure and extended x-ray absorption fine structure analyses of isolated mitochondria, we find that misincorporation of iron into yeast Sod2p does not correlate with significant changes in the average oxidation state or coordination chemistry of bulk mitochondrial iron. Instead, small changes in mitochondrial iron are likely to promote iron-SOD2 interactions. Iron binds Sod2p in yeast mutants blocking late stages of iron-sulfur cluster biogenesis (grx5, ssq1, and atm1), but not in mutants defective in the upstream Isu proteins that serve as scaffolds for iron-sulfur biosynthesis. In fact, we observed a requirement for the Isu proteins in iron inactivation of yeast Sod2p. Sod2p activity was restored in mtm1 and grx5 mutants by depleting cells of Isu proteins or using a dominant negative Isu1p predicted to stabilize iron binding to Isu1p. In all cases where disruptions in iron homeostasis inactivated Sod2p, we observed an increase in mitochondrial Isu proteins. These studies indicate that the Isu proteins and the iron-sulfur pathway can donate iron to Sod2p.Metal-containing enzymes are generally quite specific for their cognate cofactor. Misincorporation of the wrong metal ion can be deleterious and tends to be a rare occurrence in biology. A prime example of metal ion selectivity is illustrated by the family of manganese- and iron-containing superoxide dismutases (SODs)³. This large family of enzymes utilizes either manganese or iron as cofactors to scavenge superoxide anion. The iron- and manganese-containing forms are highly homologous to one another at primary, secondary, and tertiary levels and have virtually identical metal binding and catalytic sites (1–3). Despite this extensive homology, Mn- and Fe-SODs are only active with their cognate metal. Misincorporation of iron into Mn-SOD or vice versa alters the redox potential of the enzyme''s active site and prohibits superoxide disproportionation (4, 5). Nevertheless, misincorporation of iron into Mn-SOD does occur in vivo (6, 7). The isolated Mn-SOD from Escherichia coli is found as a mixture of manganese- and iron-bound forms (7); binding of manganese is favored under oxidative stress, whereas iron binding is increased under anaerobic conditions (3, 8). It has been proposed that changes in bioavailability of manganese versus iron determine the metal selectivity of Mn-SOD in bacterial cells (3, 8). But is this also true for Fe-SOD? Currently, there is no documentation of manganese misincorporation into Fe-SOD in vivo.Unlike bacteria that co-express Mn- and Fe-SOD molecules in the same cell, eukaryotic mitochondria generally harbor only one member of the Fe/Mn-SOD family, a tetrameric Mn-SOD typically known as SOD2 (9). In some organisms, SOD2 is essential for survival (10–12), and mitochondria have therefore evolved to prevent iron-SOD2 interactions despite high levels of mitochondrial iron relative to manganese. Using a yeast model system, we have shown previously that metal ion mis-incorporation can occur with Saccharomyces cerevisiae Sod2p (7). Specifically, iron binds and inactivates yeast Sod2p when cells are either starved for manganese or have certain disruptions in mitochondrial iron homeostasis. These disruptions include mutations in MTM1, a mitochondrial carrier protein that functions in iron metabolism (7, 13), and mutations in GRX5 or SSQ1, involved in iron-sulfur biogenesis (14). We proposed that these disruptions lead to expansion of a mitochondrial pool of so-called SOD2-reactive iron (7). Currently, it is unknown whether SOD2-reactive iron represents a major shift in the chemistry of bulk mitochondrial iron or whether it is just a small pool of the metal emerging from one or more specific sites.The grx5 and ssq1 mutants that promote iron-SOD2 interactions encode just two of many components of a complex pathway for iron-sulfur biogenesis (15, 16). One of the key components is a well conserved iron-sulfur scaffold protein originally described for bacteria as IscU, also known as mammalian ISCU and S. cerevisiae Isu1p and Isu2p, referred collectively herein as “Isu proteins” (17–22). The iron-sulfur clusters on Isu proteins are labile and can be transferred to target iron-sulfur proteins through the aid of mitochondrial factors including Grx5p and Ssq1p (15, 16). It is not clear whether disruption of the iron-sulfur pathway per se is sufficient to promote iron interactions with yeast Sod2p or whether this effect is specific to grx5, ssq1, and mtm1 mutants.In the current study, we explore the nature of mitochondrial iron that can interact with Sod2p. We find that the changes in mitochondrial metal homeostasis that shift metal binding in yeast Sod2p likewise alter metal cofactor selection in a heterologously expressed Mn-SOD, but not in a Fe-SOD molecule. Through x-ray absorption near edge structure (XANES) and extended x-ray absorption fine structure (EXAFS) analyses of mitochondrial iron, we detected no major change in bulk mitochondrial iron under conditions that promote iron-SOD2 interactions. SOD2-reactive iron appears to represent a small pool of the metal, and we provide evidence that the iron-sulfur scaffold Isu1p can act as an important source of this reactive iron.     

Sgt1 Dimerization Is Negatively Regulated by Protein Kinase CK2-mediated Phosphorylation at Ser361

The kinetochore, which consists of centromere DNA and structural proteins, is essential for proper chromosome segregation in eukaryotes. In budding yeast, Sgt1 and Hsp90 are required for the binding of Skp1 to Ctf13 (a component of the core kinetochore complex CBF3) and therefore for the assembly of CBF3. We have previously shown that Sgt1 dimerization is important for this kinetochore assembly mechanism. In this study, we report that protein kinase CK2 phosphorylates Ser³⁶¹ on Sgt1, and this phosphorylation inhibits Sgt1 dimerization.The kinetochore is a structural protein complex located in the centromeric region of the chromosome coupled to spindle microtubules (1, 2). The kinetochore generates a signal to arrest cells during mitosis when it is not properly attached to microtubules, thereby preventing chromosome missegregation, which can lead to aneuploidy (3, 4). The molecular structure of the kinetochore complex of the budding yeast Saccharomyces cerevisiae has been well characterized; it is composed of more than 70 proteins, many of which are conserved in mammals (2).The centromere DNA in the budding yeast is a 125-bp region that contains three conserved regions, CDEI, CDEII, and CDEIII (5, 6). CDEIII (25 bp) is essential for centromere function (7) and is bound to a key component of the centromere, the CBF3 complex. The CBF3 complex contains four proteins, Ndc10, Cep3, Ctf13 (8–15), and Skp1 (14, 15), all essential for viability. Mutations in any of the CBF3 proteins abolish the ability of CDEIII to bind to CBF3 (16, 17). All of the kinetochore proteins, except the CDEI-binding Cbf1 (18–20), localize to the kinetochores in a CBF3-dependent manner (2). Thus, CBF3 is a fundamental kinetochore complex, and its mechanism of assembly is of great interest.We have previously found that Sgt1 and Skp1 activate Ctf13; thus, they are required for assembly of the CBF3 complex (21). The molecular chaperone Hsp90 is also required to form the active Ctf13-Skp1 complex (22). Sgt1 has two highly conserved motifs that are required for protein-protein interaction: the tetratricopeptide repeat (21) and the CHORD protein and Sgt1-specific motif. We and others have found that both domains are important for the interaction of Sgt1 with Hsp90 (23–26), which is required for assembly of the core kinetochore complex. This interaction is an initial step in kinetochore activation (24, 26, 27), which is conserved between yeast and humans (28, 29).We have recently shown that Sgt1 dimerization is important for Sgt1-Skp1 binding and therefore for kinetochore assembly (30). In this study, we have found that protein kinase CK2 phosphorylates Sgt1 at Ser³⁶¹, and this phosphorylation inhibits Sgt1 dimerization. Therefore, CK2 appears to regulate kinetochore assembly negatively in budding yeast.     

Algorithms for Finding Small Attractors in Boolean Networks

A Boolean network is a model used to study the interactions between different genes in genetic regulatory networks. In this paper, we present several algorithms using gene ordering and feedback vertex sets to identify singleton attractors and small attractors in Boolean networks. We analyze the average case time complexities of some of the proposed algorithms. For instance, it is shown that the outdegree-based ordering algorithm for finding singleton attractors works in time for , which is much faster than the naive time algorithm, where is the number of genes and is the maximum indegree. We performed extensive computational experiments on these algorithms, which resulted in good agreement with theoretical results. In contrast, we give a simple and complete proof for showing that finding an attractor with the shortest period is NP-hard.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32]     

The Wavelet-Based Cluster Analysis for Temporal Gene Expression Data

A variety of high-throughput methods have made it possible to generate detailed temporal expression data for a single gene or large numbers of genes. Common methods for analysis of these large data sets can be problematic. One challenge is the comparison of temporal expression data obtained from different growth conditions where the patterns of expression may be shifted in time. We propose the use of wavelet analysis to transform the data obtained under different growth conditions to permit comparison of expression patterns from experiments that have time shifts or delays. We demonstrate this approach using detailed temporal data for a single bacterial gene obtained under 72 different growth conditions. This general strategy can be applied in the analysis of data sets of thousands of genes under different conditions.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29]     

Splitting the BLOSUM Score into Numbers of Biological Significance

Mathematical tools developed in the context of Shannon information theory were used to analyze the meaning of the BLOSUM score, which was split into three components termed as the BLOSUM spectrum (or BLOSpectrum). These relate respectively to the sequence convergence (the stochastic similarity of the two protein sequences), to the background frequency divergence (typicality of the amino acid probability distribution in each sequence), and to the target frequency divergence (compliance of the amino acid variations between the two sequences to the protein model implicit in the BLOCKS database). This treatment sharpens the protein sequence comparison, providing a rationale for the biological significance of the obtained score, and helps to identify weakly related sequences. Moreover, the BLOSpectrum can guide the choice of the most appropriate scoring matrix, tailoring it to the evolutionary divergence associated with the two sequences, or indicate if a compositionally adjusted matrix could perform better.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29]     

Analysis of Free Energy Signals Arising from Nucleotide Hybridization Between rRNA and mRNA Sequences during Translation in Eubacteria

A decoding algorithm is tested that mechanistically models the progressive alignments that arise as the mRNA moves past the rRNA tail during translation elongation. Each of these alignments provides an opportunity for hybridization between the single-stranded, -terminal nucleotides of the 16S rRNA and the spatially accessible window of mRNA sequence, from which a free energy value can be calculated. Using this algorithm we show that a periodic, energetic pattern of frequency 1/3 is revealed. This periodic signal exists in the majority of coding regions of eubacterial genes, but not in the non-coding regions encoding the 16S and 23S rRNAs. Signal analysis reveals that the population of coding regions of each bacterial species has a mean phase that is correlated in a statistically significant way with species () content. These results suggest that the periodic signal could function as a synchronization signal for the maintenance of reading frame and that codon usage provides a mechanism for manipulation of signal phase.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32]     

Proinsulin and the Genetics of Diabetes Mellitus

Insulin plays a central role in the regulation of vertebrate metabolism. The hormone, the post-translational product of a single-chain precursor, is a globular protein containing two chains, A (21 residues) and B (30 residues). Recent advances in human genetics have identified dominant mutations in the insulin gene causing permanent neonatal-onset DM² (1–4). The mutations are predicted to block folding of the precursor in the ER of pancreatic β-cells. Although expression of the wild-type allele would in other circumstances be sufficient to maintain homeostasis, studies of a corresponding mouse model (5–7) suggest that the misfolded variant perturbs wild-type biosynthesis (8, 9). Impaired β-cell secretion is associated with ER stress, distorted organelle architecture, and cell death (10). These findings have renewed interest in insulin biosynthesis (11–13) and the structural basis of disulfide pairing (14–19). Protein evolution is constrained not only by structure and function but also by susceptibility to toxic misfolding.Insulin plays a central role in the regulation of vertebrate metabolism. The hormone, the post-translational product of a single-chain precursor, is a globular protein containing two chains, A (21 residues) and B (30 residues). Recent advances in human genetics have identified dominant mutations in the insulin gene causing permanent neonatal-onset DM² (1–4). The mutations are predicted to block folding of the precursor in the ER of pancreatic β-cells. Although expression of the wild-type allele would in other circumstances be sufficient to maintain homeostasis, studies of a corresponding mouse model (5–7) suggest that the misfolded variant perturbs wild-type biosynthesis (8, 9). Impaired β-cell secretion is associated with ER stress, distorted organelle architecture, and cell death (10). These findings have renewed interest in insulin biosynthesis (11–13) and the structural basis of disulfide pairing (14–19). Protein evolution is constrained not only by structure and function but also by susceptibility to toxic misfolding.     

Endosomal Trafficking of HIV-1 Gag and Genomic RNAs Regulates Viral Egress

HIV-1 Gag can assemble and generate virions at the plasma membrane, but it is also present in endosomes where its role remains incompletely characterized. Here, we show that HIV-1 RNAs and Gag are transported on endosomal vesicles positive for TiVamp, a v-SNARE involved in fusion events with the plasma membrane. Inhibition of endosomal traffic did not prevent viral release. However, inhibiting lysosomal degradation induced an accumulation of Gag in endosomes and increased viral production 7-fold, indicating that transport of Gag to lysosomes negatively regulates budding. This also suggested that endosomal Gag-RNA complexes could access retrograde pathways to the cell surface and indeed, depleting cells of TiVamp-reduced viral production. Moreover, inhibition of endosomal transport prevented the accumulation of Gag at sites of cellular contact. HIV-1 Gag could thus generate virions using two pathways, either directly from the plasma membrane or through an endosome-dependent route. Endosomal Gag-RNA complexes may be delivered at specific sites to facilitate cell-to-cell viral transmission.The production of infectious retroviral particles is an ordered process that includes many steps (for review see Refs. 1–3). In particular, three major viral components, Gag, the envelope, and genomic RNAs have to traffic inside the cell to reach their assembly site. Viral biogenesis is driven by the polyprotein Gag, which is able to make viral-like particles when expressed alone (4). Upon release, HIV-1⁴ Gag is processed by the viral protease into matrix (MA(p17)), capsid (CA(p24)), nucleocapsid (NC(p7)), p6, and smaller peptides SP1 and SP2. Gag contains several domains that are essential for viral assembly: a membrane binding domain (M) in MA; a Gag-Gag interaction domain in CA; an assembly domain (I) in NC; and a late domain (L) in p6, which recruits the cellular budding machinery. Genomic RNAs are specifically recognized by NC, and they play fundamental roles in viral biogenesis by acting as a scaffold for Gag multimerization (5).It has been demonstrated that retroviruses bud by hijacking the endosomal machinery that sorts proteins into internal vesicles of multivesicular bodies (for review, see Refs. 6, 7). Indeed, these vesicles bud with the same topology as viral particles. Proteins sorted into this pathway are usually destined for degradation in lysosomes, but some can also recycle to the plasma membrane (for review see Refs. 8, 9). They are also frequently ubiquitinated on their cytoplasmic domain (10, 11), allowing their recognition by ESCRT complexes. ESCRT-0 and ESCRT-I recognize ubiquitinated cargo present at the surface of endosomes and recruit other ESCRT complexes (12–14). ESCRT-III is believed to function directly in the formation of multivesicular body intralumenal vesicles (12), even though its mechanism of action is currently not understood. Remarkably, Gag L domains interact directly with components of the multivesicular body-sorting machinery (for review see Ref. 15). HIV-1 Gag uses a PTAP motif to bind Tsg101, a component of ESCRT-I (16–19), and a YPLTSL motif to interact with Alix, a protein linked to ESCRT-I and -III (20–22). Finally, various ubiquitin ligases are also required directly or indirectly during HIV-1 biogenesis (23, 24; for review see Ref. 25).In many cell lines, Gag is found both at the plasma membrane and in endosomes. This has led to the hypothesis that there are several assembly sites for HIV-1 (1, 3). First, Gag can initiate and complete assembly at the plasma membrane. This is thought to occur predominantly in T lymphocytes, and this process is supported by several lines of evidences: (i) disruption of endosomal trafficking with drugs does not prevent viral production (26, 27); (ii) ESCRT complexes can be recruited at the plasma membrane, at sites where Gag accumulates (28–30); (iii) Gag can be seen multimerizing and budding from the plasma membrane in live cells (31). Second, Gag could initiate assembly in endosomes, and then traffic to the cell surface to be released. This is mainly supported by the presence of Gag in endosomes in several cell lines (32–34), including T cells and more strikingly macrophages (32, 35, 36–39). However, we are currently lacking functional experiments addressing the role of this endosomal pool of Gag, and it is still not clear to what extent it contributes to the production of viral particles. Nevertheless, the presence of Gag in endosomes might facilitate recruitment of ESCRT complexes (34, 40), packaging of viral genomic RNAs (32, 41), and incorporation of the envelope (42). It may also be important for polarized budding (43, 44) and to create a viral reservoir in infected cells (45, 46).Despite great progress, the traffic of HIV-1 components is still not fully elucidated. In particular, the transport of the genomic RNAs is poorly understood. In this study, we have used single molecule techniques to investigate the trafficking of HIV-1 RNAs in fixed and live cells, and we show that they are transported on endosomal vesicles. We also obtained functional evidence that Gag and viral RNAs can use at least two trafficking pathways to produce virions, one going directly from the plasma membrane and another one passing through endosomes.     

The Metabolic Status Drives Acclimation of Iron Deficiency Responses in Chlamydomonas reinhardtii as Revealed by Proteomics Based Hierarchical Clustering and Reverse Genetics

Iron is a crucial cofactor in numerous redox-active proteins operating in bioenergetic pathways including respiration and photosynthesis. Cellular iron management is essential to sustain sufficient energy production and minimize oxidative stress. To produce energy for cell growth, the green alga Chlamydomonas reinhardtii possesses the metabolic flexibility to use light and/or carbon sources such as acetate. To investigate the interplay between the iron-deficiency response and growth requirements under distinct trophic conditions, we took a quantitative proteomics approach coupled to innovative hierarchical clustering using different “distance-linkage combinations” and random noise injection. Protein co-expression analyses of the combined data sets revealed insights into cellular responses governing acclimation to iron deprivation and regulation associated with photosynthesis dependent growth. Photoautotrophic growth requirements as well as the iron deficiency induced specific metabolic enzymes and stress related proteins, and yet differences in the set of induced enzymes, proteases, and redox-related polypeptides were evident, implying the establishment of distinct response networks under the different conditions. Moreover, our data clearly support the notion that the iron deficiency response includes a hierarchy for iron allocation within organelles in C. reinhardtii. Importantly, deletion of a bifunctional alcohol and acetaldehyde dehydrogenase (ADH1), which is induced under low iron based on the proteomic data, attenuates the remodeling of the photosynthetic machinery in response to iron deficiency, and at the same time stimulates expression of stress-related proteins such as NDA2, LHCSR3, and PGRL1. This finding provides evidence that the coordinated regulation of bioenergetics pathways and iron deficiency response is sensitive to the cellular and chloroplast metabolic and/or redox status, consistent with systems approach data.The green alga Chlamydomonas reinhardtii has an enormous metabolic versatility (1) and possesses the flexibility to grow in the presence of different carbon sources. It may use carbon dioxide (CO₂) for photoautotrophic, acetate for heterotrophic, and both carbon sources for mixotrophic growth. In this alga CO₂ is fixed via the Calvin Benson Bassham cycle (2), while acetate can be taken up, converted to acetyl-CoA, and enter the glyoxylate cycle where it may be incorporated into C4 acids (3). In addition to the use of acetate as a source of energy and carbon backbone for biosynthetic processes, acetate can control respiration and photosynthesis in conjunction with the light intensity and CO₂ availability (4–6). Moreover, acclimation responses to iron- and copper-deficiencies significantly vary in photoautotrophic versus heterotrophic conditions (7–10), indicating that the metabolic status of the cells influence overall cellular acclimation responses.Transition metals like copper, manganese, and iron possess the ability to donate and accept electrons, making these metals suitable cofactors in enzymes that catalyze redox reactions. In particular, iron is used as a cofactor in numerous biochemical pathways and is therefore an essential nutrient. Cells require relatively high levels of iron because it is present in heme-, iron-sulfur and other proteins that function in respiratory and photosynthetic energy transducing. Correspondingly, in eukaryotic cells, the mitochondrion is a major iron-utilizing compartment. It is well established that iron is transported into mitochondria for heme synthesis and iron-sulfur cluster assembly. This is required for the formation of a functional respiratory electron transport machinery (11). Therefore, mitochondrial metabolism in mammals, fungi and plants is significantly affected under iron deficiency, as demonstrated by a number of studies (12–14). In plants, the chloroplasts are a primary target of iron deficiency. Changes in chloroplast structure, photosynthetic capacity and the composition of thylakoid membranes have been described for plants deprived of iron (15–21).Plants have devised various strategies for acquiring iron (22). Generally, iron deficiency leads to the activation of the iron uptake systems in photosynthetic organisms. For example, the accumulation of the ferroxidase, a component of the high affinity iron uptake system in C. reinhardtii, is very rapidly enhanced when iron becomes limiting (23). Inactivation of IRT1, the most prevalent Fe²⁺ transporter in Arabidopsis thaliana leads to a dramatic iron deficiency that is reflected by chlorosis (24–26). Despite the evolution of elaborate iron-uptake mechanisms in plants, iron deficiency-induced chlorosis remains a major agricultural problem (27, 28).The global impact of iron deficiency on photosynthetic productivity has been also shown in vast ocean regions, which are severely limited for iron (29, 30). Generally, one can conclude that photosynthesis in the oceans and on land can occur in environment where iron availability is restricted.Photosystem I (PSI) is a prime target of iron deficiency as it contains 12 atoms of iron per core complex. In algae, the degradation of PSI is also linked to remodeling of PSI-associated light-harvesting antenna (LHCI) (31–33). Cyanobacteria respond to iron deficiency by degradation of light harvesting phycobilisomes (34) and induction of the “iron-stress-induced” gene isiA. The ISIA protein, which has significant sequence similarity with CP43, a chlorophyll a-binding protein of photosystem II (PSII; (35, 36), forms a ring of 18 molecules around a PSI trimeric reaction center, as shown by electron microscopy (37, 38). The overall reorganization of the PSI complex from 900 kDa into 1.7 MDa complex highlights the large adaptive nature of the cellular response to iron deficiency, which helps to optimize the architecture of the photosynthetic apparatus to conditions in which iron is a limiting factor.The marine diatom Thalassiosira oceanica shows a remarkable retrenchment of cellular metabolism and remodeling of bioenergetic pathways in response to iron availability (39). Low iron triggers a reduction in the level of iron-rich photosynthetic proteins while iron-rich mitochondrial proteins are preserved. Furthermore, iron deprivation causes a remodeling of the photosynthetic machinery resulting in the adjustment of light energy use to an overall decline in the level of photosynthetic electron transport complexes (39). These responses, reported for green algae such as C. reinhardtii (31, 40, 41), are important for minimizing photo-oxidative stress and optimizing photosynthetic function. As observed for T. oceanica, under conditions of low iron availability (in the presence of organic carbon) a hierarchy of iron allocation responses in C. reinhardtii result in the down-regulation of iron-rich photosynthetic complexes while iron-rich mitochondrial complexes remain stable (41). Notably, under photoautotrophic and mixotrophic conditions C. reinhardtii displays distinct iron deprivation responses, suggesting that the cell''s response to iron deficiency is also dependent on trophic conditions (7–9). Thus bioenergetics pathways are remodeled in response to iron availability as well as to the type of carbon source available. Moreover, recent data has indicated that the regulation of iron-induced remodeling of the photosynthetic apparatus is linked to energy metabolism. Depletion of Proton Gradient Regulation Like1 protein (PGRL1) in C. reinhardtii has revealed a decreased efficiency of cyclic electron transfer under low iron conditions resulting in higher vulnerability toward iron deprivation (42).It was our aim to generate a more comprehensive picture of how the proteome of C. reinhardtii varies in response to low iron under distinct trophic conditions and how these changes compare with differences observed for cells grown under photoautotrophic and photoheterotrophic iron replete conditions. Quantitative proteomics in conjunction with a novel hierarchical clustering approach revealed information about the responses of C. reinhardtii to low iron conditions and the iron requirements of photoautotrophic growth. These analyses provide novel insights into the relationships between protein networks required for photosynthesis and iron deprivation-elicited stress responses; these studies are providing the knowledge required for modulating the level of available iron to improve the photosynthetic performance of plants (43, 44).