SDSS-IV MaStar: Data-driven Parameter Derivation for the MaStar Stellar Library

The Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) Stellar Library (MaStar) is a large collection of high-quality empirical stellar spectra designed to cover all spectral types and ideal for use in the stellar population analysis of galaxies observed in the MaNGA survey. The library contains 59,266 spectra of 24,130 unique stars with spectral resolution R ∼ 1800 and covering a wavelength range of 3622–10,354 Å. In this work, we derive five physical parameters for each spectrum in the library: effective temperature (T eff), surface gravity ( log g ), metallicity ([Fe/H]), microturbulent velocity ( log ( v micro ) ), and alpha-element abundance ([α/Fe]). These parameters are derived with a flexible data-driven algorithm that uses a neural network model. We train a neural network using the subset of 1675 MaStar targets that have also been observed in the Apache Point Observatory Galactic Evolution Experiment (APOGEE), adopting the independently-derived APOGEE Stellar Parameter and Chemical Abundance Pipeline parameters for this reference set. For the regions of parameter space not well represented by the APOGEE training set (7000 ≤ T ≤ 30,000 K), we supplement with theoretical model spectra. We present our derived parameters along with an analysis of the uncertainties and comparisons to other analyses from the literature.

Resistance to miltefosine results from amplification of the RTA3 floppase or inactivation of flippases in Candida parapsilosis

Flippases and floppases are two classes of proteins that have opposing functions in the maintenance of lipid asymmetry of the plasma membrane. Flippases translocate lipids from the exoplasmic leaflet to the cytosolic leaflet, and floppases act in the opposite direction. Phosphatidylcholine (PC) is a major component of the eukaryotic plasma membrane and is asymmetrically distributed, being more abundant in the exoplasmic leaflet. Here we show that gene amplification of a putative PC floppase or double disruption of two PC flippases in the pathogenic yeast Candida parapsilosis results in resistance to miltefosine, an alkylphosphocholine drug that affects PC metabolism that has recently been granted orphan drug designation approval by the US FDA for treatment of invasive candidiasis. We analysed the genomes of 170 C. parapsilosis isolates and found that 107 of them have copy number variations (CNVs) at the RTA3 gene. RTA3 encodes a putative PC floppase whose deletion is known to increase the inward translocation of PC in Candida albicans. RTA3 copy number ranges from 2 to >40 across the C. parapsilosis isolates. Interestingly, 16 distinct CNVs with unique endpoints were identified, and phylogenetic analysis shows that almost all of them have originated only once. We found that increased copy number of RTA3 correlates with miltefosine resistance. Additionally, we conducted an adaptive laboratory evolution experiment in which two C. parapsilosis isolates were cultured in increasing concentrations of miltefosine over 26 days. Two genes, CPAR2_303950 and CPAR2_102700, gained homozygous protein-disrupting mutations in the evolved strains and code for putative PC flippases homologous to S. cerevisiae DNF1. Our results indicate that alteration of lipid asymmetry across the plasma membrane is a key mechanism of miltefosine resistance. We also find that C. parapsilosis is likely to gain resistance to miltefosine rapidly, because many isolates carry loss-of-function alleles in one of the flippase genes.

New Delhi metallo-beta-lactamase facilitates the emergence of cefiderocol resistance in Enterobacter cloacae .

Cefiderocol is a promising novel siderophore cephalosporin for the treatment of multi-drug resistant Gram-negative bacilli and with stability against degradation by metallo-β-lactamases. Nonetheless, the emergence of cefiderocol in metallo-β-lactamase-producing Enterobacterales during therapy has been reported on more than one occasion. To understand the underlying mechanisms and factors facilitating the resistance development, we conducted an in vitro evolution experiment using clinical E. cloacae isolates via serial passaging under cefiderocol pressure. In this study, we show that the presence of the New-Delhi metallo-β-lactamase (NDM) facilitates the emergence of resistance via non-synonymous mutations of the CirA catecholate siderophore receptor. Inhibition of metallo-β-lactamase activity using dipicolinic acid prevented the emergence of cefiderocol-resistant mutants successfully. This finding implies that caution should be taken, when using cefiderocol for the treatment of infections caused by metallo-β-lactamase- producing bacteria.

Final Targeting Strategy for the Sloan Digital Sky Survey IV Apache Point Observatory Galactic Evolution Experiment 2 North Survey

The Apache Point Observatory Galactic Evolution Experiment 2 (APOGEE-2) is a dual-hemisphere, near-infrared (NIR), spectroscopic survey with the goal of producing a chemodynamical mapping of the Milky Way. The targeting for APOGEE-2 is complex and has evolved with time. In this paper, we present the updates and additions to the initial targeting strategy for APOGEE-2N presented in Zasowski et al. (2017). These modifications come in two implementation modes: (i) “Ancillary Science Programs” competitively awarded to Sloan Digital Sky Survey IV PIs through proposal calls in 2015 and 2017 for the pursuit of new scientific avenues outside the main survey, and (ii) an effective 1.5 yr expansion of the survey, known as the Bright Time Extension (BTX), made possible through accrued efficiency gains over the first years of the APOGEE-2N project. For the 23 distinct ancillary programs, we provide descriptions of the scientific aims, target selection, and how to identify these targets within the APOGEE-2 sample. The BTX permitted changes to the main survey strategy, the inclusion of new programs in response to scientific discoveries or to exploit major new data sets not available at the outset of the survey design, and expansions of existing programs to enhance their scientific success and reach. After describing the motivations, implementation, and assessment of these programs, we also leave a summary of lessons learned from nearly a decade of APOGEE-1 and APOGEE-2 survey operations. A companion paper, F. Santana et al. (submitted; AAS29036), provides a complementary presentation of targeting modifications relevant to APOGEE-2 operations in the Southern Hemisphere.

Emerging Adaptive Strategies Under Temperature Fluctuations in a Laboratory Evolution Experiment of Escherichia Coli

Generalists and specialists are types of strategies individuals can employ that can evolve in fluctuating environments depending on the extremity and periodicity of the fluctuation. To evaluate whether the evolution of specialists or generalists occurs under environmental fluctuation regimes with different levels of periodicity, 24 populations of Escherichia coli underwent laboratory evolution with temperatures alternating between 15 and 43°C in three fluctuation regimes: two periodic regimes dependent on culture's cell density and one random (non-periodic) regime with no such dependency, serving as a control. To investigate contingencies on the genetic background, we seeded our experiment with two different strains. After the experiment, growth rate measurements at the two temperatures showed that the evolution of specialists was favored in the random regime, while generalists were favored in the periodic regimes. Whole genome sequencing demonstrated that several gene mutations were selected in parallel in the evolving populations with some dependency on the starting genetic background. Given the genes mutated, we hypothesized that the driving force behind the observed adaptations is the restoration of the internal physiology of the starting strains' unstressed states at 37°C, which may be a means of improving fitness in the new environments. Phenotypic array measurements supported our hypothesis by demonstrating a tendency of the phenotypic response of the evolved strains to move closer to the starting strains' response at the optimum of 37°C, especially for strains classified as generalists.

The distribution of [α/Fe] in the Milky Way disc

Using a sample of red giant stars from the Apache Point Observatory Galactic Evolution Experiment (APOGEE) Data Release 16, we infer the conditional distribution p([α/Fe] | [Fe/H]) in the Milky Way disk for the α-elements Mg, O, Si, S, and Ca. In each bin of [Fe/H] and Galactocentric radius R, we model p([α/Fe]) as a sum of two Gaussians, representing ‘low-α’ and ‘high-α’ populations with scale heights z1 = 0.45 kpc and z2 = 0.95 kpc, respectively. By accounting for age-dependent and z-dependent selection effects in APOGEE, we infer the [α/Fe] distributions that would be found for a fair sample of long-lived stars covering all z. Near the Solar circle, this distribution is bimodal at sub-solar [Fe/H], with the low-α and high-α peaks clearly separated by a minimum at intermediate [α/Fe]. In agreement with previous results, we find that the high-α population is more prominent at smaller R, lower [Fe/H], and larger |z|, and that the sequence separation is smaller for Si and Ca than for Mg, O, and S. We find significant intrinsic scatter in [α/Fe] at fixed [Fe/H] for both the low-α and high-α populations, typically ∼0.04-dex. The means, dispersions, and relative amplitudes of this two-Gaussian description, and the dependence of these parameters on R, [Fe/H], and α-element, provide a quantitative target for chemical evolution models and a test for hydrodynamic simulations of disk galaxy formation. We argue that explaining the observed bimodality will probably require one or more sharp transitions in the disk’s gas accretion, star formation, or outflow history in addition to radial mixing of stellar populations.

Spatial structure affects evolutionary dynamics and drives genomic diversity in experimental populations of Pseudomonas fluorescens

Most populations live in spatially structured environments and that structure has the potential to impact the evolutionary dynamics in a number of important ways. Theoretical models tracking evolution in structured environments using a range of different approaches, suggest that local interactions and spatial heterogeneity can increase the adaptive benefits of motility, impact both the rate and extent of adaptation, and increase the probability of parallel evolution. We test these general predictions in a microbial evolution experiment tracking phenotypic and genomic changes in replicate populations of Pseudomonas fluorescens evolved in both well-mixed and spatially-structured environments, where spatial structure was generated through the addition of semi-solid agar. In contrast to the well-mixed environment, populations evolved in the spatially-structured environment adapted more slowly, retained the ability to disperse more rapidly, and had a greater putatively neutral population genomic diversity. The degree of parallel evolution measured at the gene-level, did not differ across these two types of experimental environments, perhaps because the populations had not evolved for long enough to near their fitness optima. These results confirm important general impacts of spatial structure on evolutionary dynamics at both the phenotypic and genomic level.

Learning from your mistakes: a novel method to predict the response to directional selection

Predicting how populations respond to selection is a key goal of evolutionary biology. The field of quantitative genetics provides predictions for the response to directional selection through the breeder’s equation. However, differences between the observed responses to selection and those predicted by the breeder’s equation occur. The sources of these errors include omission of traits under selection, inaccurate estimates of genetic variance, and nonlinearities in the relationship between genetic and phenotypic variation. A key insight from previous research is that the expected value of these prediction errors is often not zero, in which case the predictions are systematically biased. Here, we propose that this prediction bias, rather than being a nuisance, can be used to improve the predictions. We use this to develop a novel method to predict the response to selection, which is built on three key innovations. First, the method predicts change as the breeder’s equation plus a bias term. Second, the method combines information from the breeder’s equation and from the record of past changes in the mean, to estimate the bias and predict change using a Kalman filter. Third, the parameters of the filter are fitted in each generation using a machine-learning algorithm on the record of past changes. We apply the method to data of an artificial selection experiment of the wing of the fruit fly, as well as to an in silico evolution experiment for teeth. We find that the method outperforms the breeder’s equation, and notably provides good predictions even when traits under selection are omitted from the analysis and when additive genetic variance is estimated inaccurately. The proposed method is easy to apply since it only requires recording the mean of the traits over past generations.

Multi-plasmid clash in a bacterial community: plasmid viability depends on the ecological setting of hosts

Plasmids are genetic elements that disperse horizontally between different strains and species of bacteria and a major factor in the dissemination of virulence factors and antibiotic resistance. Understanding the ecology of plasmids has a notable anthropocentric value and therefore the interactions between bacterial hosts and individual plasmids have been studied in detail. However, bacterial systems often carry multiple genetically distinct plasmids, but dynamics of these multi-plasmid "clashes" has remained unstudied. Here, we set to investigate the survival of 11 mobilizable or conjugative plasmids in five different ecological settings. The key incentive was to determine whether plasmid dynamics are reproducible and whether there are trade-offs in plasmid fitness that stem from the ecological situation of their initial hosts. Growth rates and maximum population densities increased in all communities and treatments over the 42-day evolution experiment although plasmid contents at the end varied notably. We show that large multi-resistance conferring plasmids are unfit when the community also contains smaller plasmids with fewer resistance genes. This suggests that restraining the use to few antibiotics can make bacterial communities sensitive to others. The hosts also appear to react to the presence of multiple genetically different plasmids by enhancing fimbriae production instead of alleviating costs of individual plasmids. In general, the survivors of the here-studied multi-plasmid clash are significantly affected by the presence or absence of antibiotic selection and plasmid-free hosts of varying fitness. Therefore, these trade-offs in different settings can explain for example why some resistance plasmids have an advantage during a rapid proliferation of antibiotic sensitive pathogen whereas others dominate in alternative situations.