The genetic code gives precise instructions on how to translate codons into amino acids. Due to the degeneracy of the genetic code & mdash;18 out of 20 amino acids are encoded for by more than... Show moreThe genetic code gives precise instructions on how to translate codons into amino acids. Due to the degeneracy of the genetic code & mdash;18 out of 20 amino acids are encoded for by more than one codon & mdash;more information can be stored in a basepair sequence. Indeed, various types of additional information have been discussed in the literature, e.g., the positioning of nucleosomes along eukaryotic genomes and the modulation of the translating efficiency in ribosomes to influence cotranslational protein folding. The purpose of this study is to show that it is indeed possible to carry more than one additional layer of information on top of a gene. In particular, we show how much translation efficiency and nucleosome positioning can be adjusted simultaneously without changing the encoded protein. We achieve this by mapping genes on weighted graphs that contain all synonymous genes, and then finding shortest paths through these graphs. This enables us, for example, to readjust the disrupted translational efficiency profile after a gene has been introduced from one organism (e.g., human) into another (e.g., yeast) without greatly changing the nucleosome landscape intrinsically encoded by the DNA molecule. Show less
Azadi Chegeni, F.; Thallmair, S.; Ward, M.E.; Perin, G.; Marrink, S.J.; Baldus, M.; ... ; Pandit, A. 2022
The xanthophyll cycle in the antenna of photosynthetic organisms under light stress is one of the most well-known processes in photosynthesis, but its role is not well understood. In the... Show moreThe xanthophyll cycle in the antenna of photosynthetic organisms under light stress is one of the most well-known processes in photosynthesis, but its role is not well understood. In the xanthophyll cycle, violaxanthin (Vio) is reversibly transformed to zeaxanthin (Zea) that occupies Vio binding sites of light-harvesting antenna proteins. Higher monomer/trimer ratios of the most abundant light-harvesting protein, the light-harvesting complex II (LHCII), usually occur in Zea accumulating membranes and have been observed in plants after prolonged illumination and during high-light acclimation. We present a combined NMR and coarse-grained simulation study on monomeric LHCII from the npq2 mutant that constitutively binds Zea in the Vio binding pocket. LHCII was isolated from 13C-enriched npq2 Chlamydomonas reinhardtii (Cr) cells and reconstituted in thylakoid lipid membranes. NMR results reveal selective changes in the fold and dynamics of npq2 LHCII compared with the trimeric, wild-type and show that npq2 LHCII contains multiple mono- or digalactosyl diacylglycerol lipids (MGDG and DGDG) that are strongly protein bound. Coarse-grained simulations on npq2 LHCII embedded in a thylakoid lipid membrane agree with these observations. The simulations show that LHCII monomers have more extensive lipid contacts than LHCII trimers and that protein-lipid contacts are influenced by Zea. We propose that both monomerization and Zea binding could have a functional role in modulating membrane fluidity and influence the aggregation and conformational dynamics of LHCII with a likely impact on photoprotection ability. Show less
The trinuclear copper center (TNC) of laccase reduces oxygen to water with very little overpotential. The arrangement of the coppers and ligands in the TNC is known to be from many crystal... Show moreThe trinuclear copper center (TNC) of laccase reduces oxygen to water with very little overpotential. The arrangement of the coppers and ligands in the TNC is known to be from many crystal structures, yet information about possible dynamics of the ligands is absent. Here, we report dynamics at the TNC of small laccase from Streptomyces coelicolor using paramagnetic NMR and electron paramagnetic resonance spectroscopy. Fermi contact-shifted resonances tentatively assigned to histidine Hd1 display a two-state chemical exchange with exchange rates in the order of 100 s1 . In the electron paramagnetic resonance spectra, at least two forms are observed with different gz-values. It is proposed that the exchange processes reflect the rotational motion of histidine imidazole rings that coordinate the coppers in the TNC. Show less
A living cell's interior is one of the most complex and intrinsically dynamic systems, providing an elaborate interplay between cytosolic crowding and ATP-driven motion that controls cellular... Show moreA living cell's interior is one of the most complex and intrinsically dynamic systems, providing an elaborate interplay between cytosolic crowding and ATP-driven motion that controls cellular functionality. Here, we investigated two distinct fundamental features of the merely passive, non-biomotor-shuttled material transport within the cytoplasm of Dictyostelium discoideum cells: the anomalous non-linear scaling of the mean-squared displacement of a 150-nm-diameter particle and non-Gaussian distribution of increments. Relying on single-particle tracking data of 320,000 data points, we performed a systematic analysis of four possible origins for non-Gaussian transport: 1) sample-based variability, 2) rarely occurring strong motion events, 3) ergodicity breaking/aging, and 4) spatiotemporal heterogeneities of the intracellular medium. After excluding the first three reasons, we investigated the remaining hypothesis of a heterogeneous cytoplasm as cause for non-Gaussian transport. A, to our knowledge, novel fit model with randomly distributed diffusivities implementing medium heterogeneities suits the experimental data. Strikingly, the non-Gaussian feature is independent of the cytoskeleton condition and lag time. This reveals that efficiency and consistency of passive intracellular transport and the related anomalous scaling of the mean-squared displacement are regulated by cytoskeleton components, whereas cytoplasmic heterogeneities are responsible for the generic, non-Gaussian distribution of increments. Show less
Yu, M.; Silva, T.C.; Opstal, A. van; Romeijn, S.; Every, H.A.; Jiskoot, W.; ... ; Ottens, M. 2019
In this study, we developed a microfluidics method, using a so-called H-cell microfluidics device, for the determination of protein diffusion coefficients at different concentrations, pHs, ionic... Show moreIn this study, we developed a microfluidics method, using a so-called H-cell microfluidics device, for the determination of protein diffusion coefficients at different concentrations, pHs, ionic strengths, and solvent viscosities. Protein transfer takes place in the H-cell channels between two laminarly flowing streams with each containing a different initial protein concentration. The protein diffusion coefficients are calculated based on the measured protein mass transfer, the channel dimensions, and the contact time between the two streams. The diffusion rates of lysozyme, cytochrome c, myoglobin, ovalbumin, bovine serum albumin, and etanercept were investigated. The accuracy of the presented methodology was demonstrated by comparing the measured diffusion coefficients with literature values measured under similar solvent conditions using other techniques. At low pH and ionic strength, the measured lysozyme diffusion coefficient increased with the protein concentration gradient, suggesting stronger and more frequent intermolecular interactions. At comparable concentration gradients, the measured lysozyme diffusion coefficient decreased drastically as a function of increasing ionic strength (from zero onwards) and increasing medium viscosity. Additionally, a particle tracing numerical simulation was performed to achieve a better understanding of the macromolecular displacement in the H-cell microchannels. It was found that particle transfer between the two channels tends to speed up at low ionic strength and high concentration gradient. This confirms the corresponding experimental observation of protein diffusion measured via the H-cell microfluidics. Show less
Cytotoxic T lymphocyte (CTL)-mediated killing involves the formation of a synapse with a target cell, followed by delivery of perforin and granzymes. Previously, we derived a general functional... Show moreCytotoxic T lymphocyte (CTL)-mediated killing involves the formation of a synapse with a target cell, followed by delivery of perforin and granzymes. Previously, we derived a general functional response for CTL killing while considering that CTLs form stable synapses (i.e., single-stage) and that the number of conjugates remains at steady state. However, the killing of target cells sometimes requires multiple engagements (i.e., multistage). To study how multistage killing and a lack of steady state influence the functional response, we here analyze a set of differential equations as well as simulations employing the cellular Potts model, in both cases describing CTLs that kill target cells. We find that at steady state the total killing rate (i.e., the number of target cells killed by all CTLs) is well described by the previously derived double saturation function. Compared to single-stage killing, the total killing rate during multistage killing saturates at higher CTL and target cell densities. Importantly, when the killing is measured before the steady state is approached, a qualitatively different functional response emerges for two reasons: First, the killing signal of each CTL gets diluted over several targets and because this dilution effect is strongest at high target cell densities; this can result in a peak in the dependence of the total killing rate on the target cell density. Second, the total killing rate exhibits a sigmoid dependence on the CTL density when killing is a multistage process, because it takes typically more than one CTL to kill a target. In conclusion, a sigmoid dependence of the killing rate on the CTLs during initial phases of killing may be indicative of a multistage killing process. Observation of a sigmoid functional response may thus arise from a dilution effect and is not necessarily due to cooperative behavior of the CTLs. Show less
