Direct visualization of degradation microcompartments at the ER membrane

To promote the biochemical reactions of life, cells can compartmentalize molecular interaction partners together within separated non-membrane-bound regions. It is unknown whether this strategy is used to facilitate protein degradation at specific locations within the cell. Leveraging in situ cryo-electron tomography to image the native molecular landscape of the unicellular alga Chlamydomonas reinhardtii, we discovered that the cytosolic protein degradation machinery is concentrated within similar to 200-nm foci that contact specialized patches of endoplasmic reticulum (ER) membrane away from the ER-Golgi interface. These non-membrane-bound microcompartments exclude ribosomes and consist of a core of densely clustered 265 proteasomes surrounded by a loose cloud of Cdc48. Active proteasomes in the microcompartments directly engage with putative substrate at the ER membrane, a function canonically assigned to Cdc48. Live-cell fluorescence microscopy revealed that the proteasome clusters are dynamic, with frequent assembly and fusion events. We propose that the microcompartments perform ER-associated degradation, colocalizing the degradation machinery at specific ER hot spots to enable efficient protein quality control

Chlorophyll biogenesis sees the light.

Upon first exposure to light, plants initiate the synchronized biogenesis of chlorophyll and thylakoid membranes. Two new studies have revealed a molecular view of the light-dependent step of chlorophyll synthesis within the membranes of developing angiosperm chloroplasts

The state of oligomerization of Rubisco controls the rate of synthesis of the Rubisco large subunit in <em>Chlamydomonas reinhardtii</em>.

Ribulose 1,5-bisphosphate carboxylase/oxygenase (Rubisco) is present in all photosynthetic organisms and is a key enzyme for photosynthesis-driven life on Earth. Its most prominent form is a hetero-oligomer in which small subunits (SSU) stabilize the core of the enzyme built from large subunits (LSU), yielding, after a chaperone-Assisted multistep assembly process, an LSU8SSU8 hexadecameric holoenzyme. Here we use Chlamydomonas reinhardtii and a combination of site-directed mutants to dissect the multistep biogenesis pathway of Rubisco in vivo. We identify assembly intermediates, in two of which LSU are associated with the RAF1 chaperone. Using genetic and biochemical approaches we further unravel a major regulation process during Rubisco biogenesis, in which LSU translation is controlled by its ability to assemble with the SSU, via the mechanism of control by epistasy of synthesis (CES). Altogether this leads us to propose a model whereby the last assembly intermediate, an LSU8-RAF1 complex, provides the platform for SSU binding to form the Rubisco enzyme, and when SSU is not available, converts to a key regulatory form that exerts negative feedback on the initiation of LSU translation

Multishot tomography for high-resolution in situ subtomogram averaging

Cryo-electron tomography (cryo-ET) and subtomogram averaging (STA) can resolve protein complexes at near atomic resolution, and when combined with focused ion beam (FIB) milling, macromolecules can be observed within their native context. Unlike single particle acquisition (SPA), cryo-ET can be slow, which may reduce overall project throughput. We here propose a fast, multi-position tomographic acquisition scheme based on beam-tilt corrected beam-shift imaging along the tilt axis, which yields sub-nanometer in situ STA averages

Charting the native architecture of Chlamydomonas thylakoid membranes with single-molecule precision.

Thylakoid membranes scaffold an assortment of large protein complexes that work together to harness the energy of light. It has been a longstanding challenge to visualize how the intricate thylakoid network organizes these protein complexes to finely tune the photosynthetic reactions. Previously, we used in situ cryo-electron tomography to reveal the native architecture of thylakoid membranes (Engel et al., 2015). Here, we leverage technical advances to resolve the individual protein complexes within these membranes. Combined with a new method to visualize membrane surface topology, we map the molecular landscapes of thylakoid membranes inside green algae cells. Our tomograms provide insights into the molecular forces that drive thylakoid stacking and reveal that photosystems I and II are strictly segregated at the borders between appressed and non-appressed membrane domains. This new approach to charting thylakoid topology lays the foundation for dissecting photosynthetic regulation at the level of single protein complexes within the cell

Author Correction: Deep learning improves macromolecule identification in 3D cellular cryo-electron tomograms (Nature Methods, (2021), 18, 11, (1386-1394), 10.1038/s41592-021-01275-4).

In the version of this Article initially published, there was an error in Fig. 2b. The image labeled “Segmentation target” was a duplicate of Fig. 2a; the image has been replaced with the correct version. In the Fig. 4 caption for panels “b,c, Score maps…,” the text “(25 Å)” has been removed from the end of the sentence. For the final table in the online Methods, under “Evaluation,” the data are unchanged but have been reorganized for clarity. Finally, the two callouts to “Fig. 4” in Extended Data Fig. 5 caption should instead have referred to “Extended Data Fig. 4” and have now been corrected. The changes have been made to the online version of the article

Deep learning improves macromolecule identification in 3D cellular cryo-electron tomograms.

Cryogenic electron tomography (cryo-ET) visualizes the 3D spatial distribution of macromolecules at nanometer resolution inside native cells. However, automated identification of macromolecules inside cellular tomograms is challenged by noise and reconstruction artifacts, as well as the presence of many molecular species in the crowded volumes. Here, we present DeepFinder, a computational procedure that uses artificial neural networks to simultaneously localize multiple classes of macromolecules. Once trained, the inference stage of DeepFinder is faster than template matching and performs better than other competitive deep learning methods at identifying macromolecules of various sizes in both synthetic and experimental datasets. On cellular cryo-ET data, DeepFinder localized membrane-bound and cytosolic ribosomes (roughly 3.2 MDa), ribulose 1,5-bisphosphate carboxylase–oxygenase (roughly 560 kDa soluble complex) and photosystem II (roughly 550 kDa membrane complex) with an accuracy comparable to expert-supervised ground truth annotations. DeepFinder is therefore a promising algorithm for the semiautomated analysis of a wide range of molecular targets in cellular tomograms