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Bacterial growth and metabolic rates are often closely related. However, under antibiotic selection, a paradox in this relationship arises: antibiotic efficacy decreases when bacteria are metabolically dormant, yet antibiotics select for resistant cells that grow fastest during treatment. That is, antibiotic selection counterintuitively favors bacteria with fast growth but slow metabolism. Despite this apparent contradiction, antibiotic resistant cells have historically been characterized primarily in the context of growth, whereas the extent of analogous changes in metabolism is comparatively unknown. Here, we observed that previously evolved antibiotic-resistant strains exhibited a unique relationship between growth and metabolism whereby nutrient utilization became more efficient, regardless of the growth rate. To better understand this unexpected phenomenon, we used a simplified model to simulate bacterial populations adapting to sub-inhibitory antibiotic selection through successive bottlenecking events. Simulations predicted that sub-inhibitory bactericidal antibiotic concentrations could select for enhanced metabolic efficiency, defined based on nutrient utilization: drug-adapted cells are able to achieve the same biomass while utilizing less substrate, even in the absence of treatment. Moreover, simulations predicted that restoring metabolic efficiency would re-sensitize resistant bacteria exhibiting metabolic-dependent resistance; we confirmed this result using adaptive laboratory evolutions ofEscherichia coliunder carbenicillin treatment. Overall, these results indicate that metabolic efficiency is under direct selective pressure during antibiotic treatment and that differences in evolutionary context may determine both the efficacy of different antibiotics and corresponding re-sensitization approaches.

Enterobacter hormaecheiDVZ29 was isolated from a sediment trap incubated in an¹²⁹I plume at the Hanford Site (Washington State, USA). A whole genome sequencing of the strain resulted in 32 contigs and revealed that the genome is 4.90 Mb, with a G + C content of 55.61%.

Executive Summary

Will recipients of Deferred Action for Childhood Arrivals (DACA) leave the United States if a legal challenge succeeds in ending this important program? The fate of over 600,000 current DACA recipients may depend on answers to this question in one expert report and one online survey question, which a Texas District Court found to show “a quantifiable percentage” would leave. Had the District Court in its DACA ruling not credited these sources, Texas’ lawsuit could have collapsed, and DACA would persist. The issue of whether DACA recipients will leave if DACA is ended will matter as this case is litigated, most likely to be decided by the Supreme Court. Drawing on research on immigrant return, DACA recipients, survey questions, priming, and intention-behavior gap, this paper argues that this expert report and the single online survey question used by the District Court cannot support the inferences regarding the return of DACA recipients to their birth countries. Rather, relevant research points toward DACA recipients staying in the United States, even if DACA were terminated. The paper recommends offering DACA recipients and long term undocumented residents a short path to legal status and citizenship, reassessing whether these two sources can support the conclusions drawn by the District Court; and analyzing the benefits accruing from DACA to Texas and all Texans, including US citizen children of DACA recipients, in assessing the claimed injury to the state.

The power spectrum of the non-linearly evolved large-scale mass distribution recovers only a minority of the information available on the mass fluctuation amplitude. We investigate the recovery of this information in 2D ‘slabs’ of the mass distribution averaged over ≈100 h−1 Mpc along the line of sight, as might be obtained from photometric redshift surveys. We demonstrate a Hamiltonian Monte Carlo method to reconstruct the non-Gaussian mass distribution in slabs, under the assumption that the projected field is a point-transformed Gaussian random field, Poisson-sampled by galaxies. When applied to the Quijote N-body suite at z = 0.5 and at a transverse resolution of 2 h−1 Mpc, the method recovers ∼30 times more information than the 2D power spectrum in the well-sampled limit, recovering the Gaussian limit on information. At a more realistic galaxy sampling density of 0.01 h3 Mpc−3, shot noise reduces the information gain to a factor of 5 improvement over the power spectrum at resolutions of 4 h−1 Mpc or smaller.

The astronomical transient AT2018cow is the closest example of the new class of luminous, fast blue optical transients (FBOTs). Liverpool telescope RINGO3 observations of AT 2018cow are reported here, which constitute the earliest polarimetric observations of an FBOT. At $5.7\, \mathrm{days}$ post-explosion, the optical emission of AT2018cow exhibited a chromatic polarization spike that reached $\sim 7{{\ \rm per\ cent}}$ at red wavelengths. This is the highest intrinsic polarization recorded for a non-relativistic explosive transient and is observed in multiple bands and at multiple epochs over the first night of observations, before rapidly declining. The apparent wavelength dependence of the polarization may arise through depolarization or dilution of the polarized flux, due to conditions in AT 2018cow at early times. A second ‘bump’ in the polarization is observed at blue wavelengths at $\sim 12\, \mathrm{days}$. Such a high polarization requires an extremely aspherical geometry that is only apparent for a brief period (<1 d), such as shock breakout through an optically thick disk. For a disk-like configuration, the ratio of the thickness to radial extent must be $\sim 10{{\ \rm per\ cent}}$.

Conjugative plasmids often encode antibiotic resistance genes that provide selective advantages to their bacterial hosts during antibiotic treatment. Previous studies have predominantly considered these established genes as the primary benefit of antibiotic-mediated plasmid dissemination. However, many genes involved in cellular metabolic processes may also protect against antibiotic treatment and provide selective advantages. Despite the diversity of such metabolic genes and their potential ecological impact, their plasmid-borne prevalence, co-occurrence with canonical antibiotic resistance genes, and phenotypic effects remain widely understudied. To address this gap, we focused on Escherichia coli, which can often act as a pathogen, and is known to spread antibiotic resistance genes via conjugation. We characterized the presence of metabolic genes on 1,775 transferrable plasmids and compared their distribution to that of known antibiotic resistance genes. We found high abundance of genes involved in cellular metabolism and stress response. Several of these genes demonstrated statistically significant associations or disassociations with known antibiotic resistance genes at the strain level, indicating that each gene type may impact the spread of the other across hosts. Indeed, in vitro characterization of 13 statistically relevant metabolic genes confirmed that their phenotypic impact on antibiotic susceptibility was largely consistent with in situ relationships. These results emphasize the ecological importance of metabolic genes on conjugal plasmids, and that selection dynamics of E. coli pathogens arises as a complex consequence of both canonical mechanisms and their interactions with metabolic pathways.