Efficient downregulation of immunoglobulin μ mRNA with premature translation-termination codons requires the 5′-half of the VDJ exon

Premature translation-termination codons (PTCs) elicit rapid degradation of the mRNA by a process called nonsense-mediated mRNA decay (NMD). NMD appears to be significantly more efficient for mRNAs of genes belonging to the immunoglobulin superfamily, which frequently acquire PTCs during VDJ rearrangment, than for mRNAs of other genes. To identify determinants for efficient NMD, we developed a minigene system derived from a mouse immunoglobulin μ gene (Ig-μ) and measured the effect of PTCs at different positions on the mRNA level. This revealed that PTCs located downstream of the V-D junction in the VDJ exon of Ig-μ minigenes and of endogenous Ig-μ genes elicit very strong mRNA downregulation, whereas NMD efficiency decreases gradually further upstream in the V segment where a PTC was inserted. Interestingly, two PTCs are in positions where they usually do not trigger NMD (<50 nt from the 3′-most 5′ splice site) still resulted in reduced mRNA levels. Using a set of hybrid constructs comprised of Ig-μ and an inefficient substrate for NMD, we identified a 177 nt long element in the V segment that is necessary for efficient downregulation of PTC-containing hybrid transcripts. Moreover, deletion of this NMD-promoting element from the Ig-μ minigene results in loss of strong NM

The role of sign indefinite invariants in shaping turbulent cascades

We highlight a non-canonical yet natural choice of variables for an efficient derivation of a kinetic equation for the energy density in non-isotropic systems, including internal gravity waves on a vertical plane, inertial and Rossby waves. The existence of a second quadratic invariant simplifies the kinetic equation and leads to extra conservation laws for resonant interactions. We analytically determine the scaling of the radial turbulent energy spectrum. Our findings suggest the existence of an inverse energy cascade of internal gravity waves, from small to large scales, in practically relevant scenarios

Metabolic network capacity of Escherichia coli for Krebs cycle-dependent proline hydroxylation

Figure S1. Physiology of recombinant E. coli BL21(DE3)(pLysS) strains bearing pET-24a. Panel A and B show biomass formation (circles), glucose consumption (squares), acetate formation (triangles), and proline consumption (diamonds) during batch cultivation of wildtype (closed symbols) and ΔputA (open symbols) strains at 30°C in M9 medium supplemented with 5 g L−1 glucose in the absence (A) or presence (B) of 5 mM proline, respectively. Figure S2. SDS-PAGE analysis of recombinant E. coli BL21(DE3)(pLysS) (pET_p4h1of) and E. coli BL21 ΔputA (DE3) (pLysS) (pET_p4h1of) at different time points during growth in M9 medium with 5 g L−1 glucose (glc) only or with addition of 5 mM proline (pro) at 30°C. M: protein size marker. Figure S3. Physiology of recombinant E. coli BL21(DE3)(pLysS) strains bearing pET_p4h1of. Biomass formation (circles), glucose consumption (squares), acetate formation (triangles), hyp formation (stars), and proline consumption (diamonds) during batch cultivation of wildtype (closed symbols) and ΔputA (open symbols) strains are shown. Cultivation was performed at 30°C in M9 medium supplemented with 5 g L−1 glucose in the absence (panel A) or presence of 5 mM proline (panels B and C). Table S4. Mass isotopomer distribution of alanine for the wt_pET strain at 30°C in M9 medium supplemented with 5 g L−1 U-13C labeled glucose in the absence or presence of 5 mM proline. Table S5. Reactions of the central carbon metabolism generating or consuming NTP and/or redox equivalents. Table S6. Bacterial strains and plasmids used in this study. Table S7. Correlation factors between OD600 1 and cell dry weight concentration (gCDW L−1) of the strains used in this study

The Dynamics of Mesoscale Winds in the Upper Troposphere and Lower Stratosphere

Spectral analysis is applied to infer the dynamics of mesoscale winds from aircraft observations in the upper troposphere and lower stratosphere. Two datasets are analyzed: one collected aboard commercial aircraft and one collected using a dedicated research aircraft. A recently developed wave–vortex decomposition is used to test the observations’ consistency with linear inertia–gravity wave dynamics. The decomposition method is shown to be robust in the vicinity of the tropopause if flight tracks vary sufficiently in altitude. For the lower stratosphere, the decompositions of both datasets confirm a recent result that mesoscale winds are consistent with the polarization and dispersion relations of inertia–gravity waves. For the upper troposphere, however, the two datasets disagree: only the research aircraft data indicate consistency with linear wave dynamics at mesoscales. The source of the inconsistency is a difference in mesoscale variance of the measured along-track wind component. To further test the observed flow’s consistency with linear wave dynamics, the ratio between tropospheric and stratospheric mesoscale energy levels is compared to a simple model of upward-propagating waves that are partially reflected at the tropopause. For both datasets, the observed energy ratio is roughly consistent with the simple wave model, but wave frequencies diagnosed from the data draw into question the applicability of the monochromatic theory at wavelengths smaller than 10 km

Transition from geostrophic turbulence to inertia–gravity waves in the atmospheric energy spectrum

Midlatitude fluctuations of the atmospheric winds on scales of thousands of kilometers, the most energetic of such fluctuations, are strongly constrained by the Earth’s rotation and the atmosphere’s stratification. As a result of these constraints, the flow is quasi-2D and energy is trapped at large scales—nonlinear turbulent interactions transfer energy to larger scales, but not to smaller scales. Aircraft observations of wind and temperature near the tropopause indicate that fluctuations at horizontal scales smaller than about 500 km are more energetic than expected from these quasi-2D dynamics. We present an analysis of the observations that indicates that these smaller-scale motions are due to approximately linear inertia–gravity waves, contrary to recent claims that these scales are strongly turbulent. Specifically, the aircraft velocity and temperature measurements are separated into two components: one due to the quasi-2D dynamics and one due to linear inertia–gravity waves. Quasi-2D dynamics dominate at scales larger than 500 km; inertia–gravity waves dominate at scales smaller than 500 km