Hepatic SEL1L-HRD1 ER-associated degradation regulates systemic iron homeostasis via ceruloplasmin.

Iron homeostasis is critical for cellular and organismal function and is tightly regulated to prevent toxicity or anemia due to iron excess or deficiency, respectively. However, subcellular regulatory mechanisms of iron remain largely unexplored. Here, we report that SEL1L-HRD1 protein complex of endoplasmic reticulum (ER)-associated degradation (ERAD) in hepatocytes controls systemic iron homeostasis in a ceruloplasmin (CP)-dependent, and ER stress-independent, manner. Mice with hepatocyte-specific Sel1L deficiency exhibit altered basal iron homeostasis and are sensitized to iron deficiency while resistant to iron overload. Proteomics screening for a factor linking ERAD deficiency to altered iron homeostasis identifies CP, a key ferroxidase involved in systemic iron distribution by catalyzing iron oxidation and efflux from tissues. Indeed, CP is highly unstable and a bona fide substrate of SEL1L-HRD1 ERAD. In the absence of ERAD, CP protein accumulates in the ER and is shunted to refolding, leading to elevated secretion. Providing clinical relevance of these findings, SEL1L-HRD1 ERAD is responsible for the degradation of a subset of disease-causing CP mutants, thereby attenuating their pathogenicity. Together, this study uncovers the role of SEL1L-HRD1 ERAD in systemic iron homeostasis and provides insights into protein misfolding-associated proteotoxicity.

Two pathways to understanding electron transfer in reaction centers from photosynthetic bacteria: A comparison of Rhodobacter sphaeroides and Rhodobacter capsulatus mutants.

The rates, yields, mechanisms and directionality of electron transfer (ET) are explored in twelve pairs of Rhodobacter (R.) sphaeroides and R. capsulatus mutant RCs designed to defeat ET from the excited primary donor (P*) to the A-side cofactors and re-direct ET to the normally inactive mirror-image B-side cofactors. In general, the R. sphaeroides variants have larger P+HB- yields (up to ∼90%) than their R. capsulatus analogs (up to ∼60%), where HB is the B-side bacteriopheophytin. Substitution of Tyr for Phe at L-polypeptide position L181 near BB primarily increases the contribution of fast P* â†’ P+BB- â†’ P+HB- two-step ET, where BB is the "bridging" B-side bacteriochlorophyll. The second step (∼6-8 ps) is slower than the first (∼3-4 ps), unlike A-side two-step ET (P* â†’ P+BA- â†’ P+HA-) where the second step (∼1 ps) is faster than the first (∼3-4 ps) in the native RC. Substitutions near HB, at L185 (Leu, Trp or Arg) and at M-polypeptide site M133/131 (Thr, Val or Glu), strongly affect the contribution of slower (20-50 ps) P* â†’ P+HB- one-step superexchange ET. Both ET mechanisms are effective in directing electrons "the wrong way" to HB and both compete with internal conversion of P* to the ground state (∼200 ps) and ET to the A-side cofactors. Collectively, the work demonstrates cooperative amino-acid control of rates, yields and mechanisms of ET in bacterial RCs and how A- vs. B-side charge separation can be tuned in both species.

Shared Genomic Regions Underlie Natural Variation in Diverse Toxin Responses.

Phenotypic complexity is caused by the contributions of environmental factors and multiple genetic loci, interacting or acting independently. Studies of yeast and Arabidopsis often find that the majority of natural variation across phenotypes is attributable to independent additive quantitative trait loci (QTL). Detected loci in these organisms explain most of the estimated heritable variation. By contrast, many heritable components underlying phenotypic variation in metazoan models remain undetected. Before the relative impacts of additive and interactive variance components on metazoan phenotypic variation can be dissected, high replication and precise phenotypic measurements are required to obtain sufficient statistical power to detect loci contributing to this missing heritability. Here, we used a panel of 296 recombinant inbred advanced intercross lines of Caenorhabditis elegans and a high-throughput fitness assay to detect loci underlying responses to 16 different toxins, including heavy metals, chemotherapeutic drugs, pesticides, and neuropharmaceuticals. Using linkage mapping, we identified 82 QTL that underlie variation in responses to these toxins, and predicted the relative contributions of additive loci and genetic interactions across various growth parameters. Additionally, we identified three genomic regions that impact responses to multiple classes of toxins. These QTL hotspots could represent common factors impacting toxin responses. We went further to generate near-isogenic lines and chromosome substitution strains, and then experimentally validated these QTL hotspots, implicating additive and interactive loci that underlie toxin-response variation.