A Genetic Strategy for Probing the Functional Diversity of Magnetosome Formation

Model genetic systems are invaluable, but limit us to understanding only a few organisms in detail, missing the variations in biological processes that are performed by related organisms. One such diverse process is the formation of magnetosome organelles by magnetotactic bacteria. Studies of model magnetotactic α-proteobacteria have demonstrated that magnetosomes are cubo-octahedral magnetite crystals that are synthesized within pre-existing membrane compartments derived from the inner membrane and orchestrated by a specific set of genes encoded within a genomic island. However, this model cannot explain all magnetosome formation, which is phenotypically and genetically diverse. For example, Desulfovibrio magneticus RS-1, a δ-proteobacterium for which we lack genetic tools, produces tooth-shaped magnetite crystals that may or may not be encased by a membrane with a magnetosome gene island that diverges significantly from those of the α-proteobacteria. To probe the functional diversity of magnetosome formation, we used modern sequencing technology to identify hits in RS-1 mutated with UV or chemical mutagens. We isolated and characterized mutant alleles of 10 magnetosome genes in RS-1, 7 of which are not found in the α-proteobacterial models. These findings have implications for our understanding of magnetosome formation in general and demonstrate the feasibility of applying a modern genetic approach to an organism for which classic genetic tools are not available

Identifying exoplanets with deep learning. IV. Removing stellar activity signals from radial velocity measurements using neural networks

Funding: This project has received funding from the European Research Council (ERC) under the European Unions Horizon 2020 research and innovation program (SCORE grant agreement No. 851555). A.C.C. acknowledges support from the Science and Technology Facilities Council (STFC) consolidated grant No. ST/R000824/1 and UKSA grant ST/R003203/1. R.D.H. is funded by the UK Science and Technology Facilities Council (STFC)’s Ernest Rutherford Fellowship (grant number ST/V004735/1). M.P. acknowledges financial support from the ASI-INAF agreement No. 2018-16-HH.0. A.M. acknowledges support from the senior Kavli Institute Fellowships.Exoplanet detection with precise radial velocity (RV) observations is currently limited by spurious RV signals introduced by stellar activity. We show that machine-learning techniques such as linear regression and neural networks can effectively remove the activity signals (due to starspots/faculae) from RV observations. Previous efforts focused on carefully filtering out activity signals in time using modeling techniques like Gaussian process regression. Instead, we systematically remove activity signals using only changes to the average shape of spectral lines, and use no timing information. We trained our machine-learning models on both simulated data (generated with the SOAP 2.0 software) and observations of the Sun from the HARPS-N Solar Telescope. We find that these techniques can predict and remove stellar activity both from simulated data (improving RV scatter from 82 to 3 cm s−1) and from more than 600 real observations taken nearly daily over 3 yr with the HARPS-N Solar Telescope (improving the RV scatter from 1.753 to 1.039 m s−1, a factor of ∼1.7 improvement). In the future, these or similar techniques could remove activity signals from observations of stars outside our solar system and eventually help detect habitable-zone Earth-mass exoplanets around Sun-like stars.Publisher PDFPeer reviewe

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A genetic strategy for probing the functional diversity of magnetosome formation.

Model genetic systems are invaluable, but limit us to understanding only a few organisms in detail, missing the variations in biological processes that are performed by related organisms. One such diverse process is the formation of magnetosome organelles by magnetotactic bacteria. Studies of model magnetotactic α-proteobacteria have demonstrated that magnetosomes are cubo-octahedral magnetite crystals that are synthesized within pre-existing membrane compartments derived from the inner membrane and orchestrated by a specific set of genes encoded within a genomic island. However, this model cannot explain all magnetosome formation, which is phenotypically and genetically diverse. For example, Desulfovibrio magneticus RS-1, a δ-proteobacterium for which we lack genetic tools, produces tooth-shaped magnetite crystals that may or may not be encased by a membrane with a magnetosome gene island that diverges significantly from those of the α-proteobacteria. To probe the functional diversity of magnetosome formation, we used modern sequencing technology to identify hits in RS-1 mutated with UV or chemical mutagens. We isolated and characterized mutant alleles of 10 magnetosome genes in RS-1, 7 of which are not found in the α-proteobacterial models. These findings have implications for our understanding of magnetosome formation in general and demonstrate the feasibility of applying a modern genetic approach to an organism for which classic genetic tools are not available

Sensitivity of low-degree solar p modes to active and ephemeral regions:frequency shifts back to the Maunder Minimum

We explore the sensitivity of the frequencies of low-degree solar p-modes to near-surface magnetic flux on different spatial scales and strengths, specifically to active regions with strong magnetic fields and ephemeral regions with weak magnetic fields. We also use model reconstructions from the literature to calculate average frequency offsets back to the end of the Maunder minimum. We find that the p-mode frequencies are at least three times less sensitive (at 95% confidence) to the ephemeral-region field than they are to the active-region field. Frequency shifts between activity cycle minima and maxima are controlled predominantly by the change of active region flux. Frequency shifts at cycle minima (with respect to a magnetically quiet Sun) are determined largely by the ephemeral flux, and are estimated to have been $0.1\,\rm \mu Hz$ or less over the last few minima. We conclude that at epochs of cycle minimum, frequency shifts due to near-surface magnetic activity are negligible compared to the offsets between observed and model frequencies that arise from inaccurate modelling of the near-surface layers (the so-called surface term). The implication is that this will be the case for other Sun-like stars with similar activity, which has implications for asteroseismic modelling of stars.Comment: 5 pages, 3 figures, accepted for publication in Letters of Monthly Notices of the Royal Astronomical Societ

Electron-dense particles are found in some mutants.

<p>TEM of WT and mutant cells show WT-like particles in some mutants (<i>fmpB</i>, <i>fmpA</i>) and unusual-looking particles in others (<i>mad1</i>, <i>mad2</i>). Scale bar 200 nm for whole cell images, 50 nm for enlargements.</p

<i>mad1</i> mutants produce paramagnetic, multi-domain magnetite crystals.

<p>A) <i>mad1</i> mutant crystal. Lattice measurements indicate that the crystal is magnetite. Several crystal domains are present. Scale bar 5 nm, inset scale bar 50 nm. B) Distribution of magnetic field magnitudes recorded from WT and <i>mad1</i> mutant RS-1 cells using nitrogen-vacancy (NV) diamond-based magnetic imaging in a 1.2 mT external bias field. The WT cells produce magnetic fields on the order of 0.6 µT, whereas only a small fraction (∼10%) of the mutants produce magnetic fields distinguishable from zero in these measurements. C) Magnetic field images of WT (left panel) and <i>mad1</i> mutant (right panel) cells recorded with a 20.5 mT external bias field. The superimposed black outlines indicate the cell boundaries, as determined from optical transmission images. The fields shown here are one scalar component of the total vector magnetic field, projected along the direction of the bias field (indicated by the arrow labeled B₀). The WT cells show field patterns consistent with randomly oriented permanent dipoles; cells labeled 1 and 2 are examples of dipoles with clearly distinguishable orientations, the latter at a significant angle to the external bias field. The mutant cells show weaker field patterns in general, and dipoles are preferentially aligned parallel to the direction of B₀ (negative lobe on the left, positive lobe on the right), consistent with a paramagnetic response to the external field. Scale bars 10 µm.</p

The RS-1 MAI.

<p>TEM of WT (A) and MAI deletion (B) cells. Scale bar 200 nm. C) Depiction of groups I, II, III, and IV of the MAI. Pink squares represent the repeats surrounding the island. Dark grey represents areas of transposons and repeats. Dashed box labeled MamAB is the region detailed in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004811#pgen-1004811-g001" target="_blank">figure 1C</a>. The nucleotide positions of the beginning and end of the island are indicated. The areas of the island missing in deletions 1, 2, and 3 are also shown. Below, the genes that make up groups I, II, III, and IV are represented by arrows, with the beginning and ending gene names indicated for each group. The ending nucleotide position is given in the case of <i>mamI1</i>, which is not assigned a gene number. Genes that were hit in the screen are indicated in purple.</p

RS-1 is a representative of a group of bacteria that are phylogenetically and phenotypically distinct from the magnetotactic α-proteobacteria.

<p>A) 16S phylogenetic tree of magnetotactic bacteria. B) AMB-1 and RS-1 magnetite crystals visualized by TEM. Scale bar 100 nm. C) The <i>mamAB</i> gene clusters of AMB-1 and RS-1 shown in the context of the MAI. Pink squares represent the repeats surrounding each island. Purple arrows represent the genes that are conserved between the two.</p