Abundance Profiles Research Articles

The integrated metallicity profile of the Milky Way

The heavy element abundance profiles of galaxies place stringent constraints on galaxy growth and assembly history. As the Milky Way is currently the only spiral galaxy in which we can measure temporally resolved chemical abundances, it enables insights into the origin of metallicity gradients and their correlation with the growth history of galaxies. However, until now, these abundance profiles have not been translated into the integrated-light measurements that are needed to compare the Milky Way with the general galaxy population. Here we report the measurement of the light-weighted, integrated stellar metallicity profile of our Galaxy. We find that the integrated stellar metallicity profile of the Milky Way has a ∧-like broken shape, with a mildly positive gradient inside a Galactocentric radius of 7 kpc and a steep negative gradient outside. This broken integrated metallicity profile of the Milky Way is not unique but is not common among Milky Way-mass star-forming galaxies observed in the MaNGA survey and simulated in the TNG50 cosmological simulation. Our results suggest that the Milky Way might not have a typical metallicity distribution for a galaxy of its mass, and thus offers valuable insight into the rich variety of galaxy enrichment processes.

Inferring microbial co-occurrence networks from amplicon data: a systematic evaluation.

Microbes commonly organize into communities consisting of hundreds of species involved in complex interactions with each other. 16S ribosomal RNA (16S rRNA) amplicon profiling provides snapshots that reveal the phylogenies and abundance profiles of these microbial communities. These snapshots, when collected from multiple samples, can reveal the co-occurrence of microbes, providing a glimpse into the network of associations in these communities. However, the inference of networks from 16S data involves numerous steps, each requiring specific tools and parameter choices. Moreover, the extent to which these steps affect the final network is still unclear. In this study, we perform a meticulous analysis of each step of a pipeline that can convert 16S sequencing data into a network of microbial associations. Through this process, we map how different choices of algorithms and parameters affect the co-occurrence network and identify the steps that contribute substantially to the variance. We further determine the tools and parameters that generate robust co-occurrence networks and develop consensus network algorithms based on benchmarks with mock and synthetic data sets. The Microbial Co-occurrence Network Explorer, or MiCoNE (available at https://github.com/segrelab/MiCoNE) follows these default tools and parameters and can help explore the outcome of these combinations of choices on the inferred networks. We envisage that this pipeline could be used for integrating multiple data sets and generating comparative analyses and consensus networks that can guide our understanding of microbial community assembly in different biomes. IMPORTANCE Mapping the interrelationships between different species in a microbial community is important for understanding and controlling their structure and function. The surge in the high-throughput sequencing of microbial communities has led to the creation of thousands of data sets containing information about microbial abundances. These abundances can be transformed into co-occurrence networks, providing a glimpse into the associations within microbiomes. However, processing these data sets to obtain co-occurrence information relies on several complex steps, each of which involves numerous choices of tools and corresponding parameters. These multiple options pose questions about the robustness and uniqueness of the inferred networks. In this study, we address this workflow and provide a systematic analysis of how these choices of tools affect the final network and guidelines on appropriate tool selection for a particular data set. We also develop a consensus network algorithm that helps generate more robust co-occurrence networks based on benchmark synthetic data sets.

Microbiome maps: Hilbert curve visualizations of metagenomic profiles.

Abundance profiles from metagenomic sequencing data synthesize information from billions of sequenced reads coming from thousands of microbial genomes. Analyzing and understanding these profiles can be a challenge since the data they represent are complex. Particularly challenging is their visualization, as existing techniques are inadequate when the taxa number is in the thousands. We present a technique, and accompanying software, for the visualization of metagenomic abundance profiles using a space-filling curve that transforms a profile into an interactive 2D image. We created Jasper, an easy to use tool for the visualization and exploration of metagenomic profiles from DNA sequencing data. It orders taxa using a space-filling Hilbert curve, and creates a "Microbiome Map", where each position in the image represents the abundance of a single taxon from a reference collection. Jasper can order taxa in multiple ways, and the resulting microbiome maps can highlight "hot spots" of microbes that are dominant in taxonomic clades or biological conditions. We use Jasper to visualize samples from a variety of microbiome studies, and discuss ways in which microbiome maps can be an invaluable tool to visualize spatial, temporal, disease, and differential profiles. Our approach can create detailed microbiome maps involving hundreds of thousands of microbial reference genomes with the potential to unravel latent relationships (taxonomic, spatio-temporal, functional, and other) that could remain hidden using traditional visualization techniques. The maps can also be converted into animated movies that bring to life the dynamicity of microbiomes.

Metabolomics-Guided Identification of a Distinctive Hepatocellular Carcinoma Signature.

Hepatocellular carcinoma (HCC) is a major contributor to cancer-related morbidity and mortality burdens globally. Given the fundamental metabolic activity of hepatocytes within the liver, hepatocarcinogenesis is bound to be characterized by alterations in metabolite profiles as a manifestation of metabolic reprogramming. HCC and adjacent non-tumoral liver specimens were obtained from patients after HCC resection. Global patterns in tissue metabolites were identified using non-targeted 1H Nuclear Magnetic Resonance (1H-NMR) spectroscopy whereas specific metabolites were quantified using targeted liquid chromatography-mass spectrometry (LC/MS). Principal component analysis (PCA) within our 1H-NMR dataset identified a principal component (PC) one of 53.3%, along which the two sample groups were distinctively clustered. Univariate analysis of tissue specimens identified more than 150 metabolites significantly altered in HCC compared to non-tumoral liver. For LC/MS, PCA identified a PC1 of 45.2%, along which samples from HCC tissues and non-tumoral tissues were clearly separated. Supervised analysis (PLS-DA) identified decreases in tissue glutathione, succinate, glycerol-3-phosphate, alanine, malate, and AMP as the most important contributors to the metabolomic signature of HCC by LC/MS. Together, 1H-NMR and LC/MS metabolomics have the capacity to distinguish HCC from non-tumoral liver. The characterization of such distinct profiles of metabolite abundances underscores the major metabolic alterations that result from hepatocarcinogenesis.

Integrated genome-centric metagenomic and metaproteomic analyses unravel the responses of the microbial community to ammonia stress

Ammonia is a major inhibitor in anaerobic digestion of nitrogen-rich organic wastes. In this study, integrated genome-centric metagenomic and metaproteomic analyses were used to identify the key microorganisms and metabolic links causing instability by characterizing the process performance, microbial community, and metabolic responses of key microorganisms during endogenous ammonia accumulation. The identification of 89 metagenome-assembled genomes and analysis of their abundance profile in different operational phases permitted the identification of key taxa (Firmicutes and Proteobacteria) causing poor performance. Metabolic reconstruction indicated that the key taxa had the genetic potential to participate in the metabolism of C2C5 volatile fatty acids (VFAs). Further investigation suggested that during Phase I, the total ammonia nitrogen (TAN) level was maintained below 2000 mg N/L, and the reactor showed a high methane yield (478.30 ± 33.35 mL/g VS) and low VFAs concentration. When the TAN accumulated to > 2000 mg N/L, acid accumulation, mainly of acetate, began to occur, and the methane yield gradually decreased to 330.44 mL/g VS (Phase II). During this phase, the VFA degradation functions of the community were mainly mediated by Firmicutes. Approximately 61.54% of significant differentially expressed proteins (DEPs) related to acetate metabolism in Firmicutes were down-regulated, which led to an increase in acetate concentration to 4897.91 ± 1558.96 mg/L. However, the reactor performance showed spontaneous recovery without any interference (Phase III), during which Firmicutes gradually adapted to the high ammonia conditions. Approximately 75% of the significant DEPs related to acetate metabolism of Proteobacteria were also up-regulated in Phase III compared with Phase II; thus, VFA-related metabolic functions of the community were enhanced, which resulted in a decrease in the total VFA concentration to 195.39 mg/L. When the TAN increased above 4000 mg N/L, the system gradually showed acid accumulation dominated by propionate, accompanied by a second decrease in methane yield (Phase IV). During this phase, the number of up-regulated and down-regulated proteins related to acetate metabolism of Firmicutes and butyrate/valerate metabolism of Proteobacteria was comparable with that of Phase III, indicating that the metabolic functions related to acetate, butyrate, and valerate of the microbial community were not significantly affected. However, for propionate metabolism, the expression activity of fumarate hydratase from Firmicutes and Proteobacteria was severely inhibited by ammonia, as shown by down-regulation ratios of 63.64% and 85.71%, respectively. No protein with the same function that was not inhibited by ammonia could be detected, and the fumarate degradation function of the microbial community was severely damaged, leading to blocked propionate metabolism and irreversible deterioration of reactor performance. This study has provided a new perspective on the microecological mechanisms of ammonia inhibition.

The patchiness of soil 13C versus the uniformity of 15N distribution with geomorphic position provides evidence of erosion and accelerated organic matter turnover

Farming on hillslopes often affects the accumulation and loss of soil organic matter (SOM) depending on slope position and cropping patterns. Most hillslope studies focus on soil movement to characterize SOM turnover under erosive conditions. In this study, we trace erosion and characterize agronomic practices erosive impacts on SOM translocation and transformation along geomorphic positions. To achieve this, we assessed the horizontal distribution (upper 15 cm) and vertical distribution (to 100 cm profiles) of soil δ15N and δ13C isotope abundance individually. We mapped the spatial distribution of δ13C, δ15N, and SOM turnover indices as a novel approach to tracing erosion and degradation of SOM in the field. Except for tillage (conventional vs. reduced tillage), other individual agricultural practices (residue removal with no cover crop vs. retaining residuals, cover cropping, and fertilizer 0, 40, and 80 kg ha-1 nitrogen) caused no significant shifts in δ15N and δ13C values in topsoil (0–15 cm). Among the evaluated factors, topography and depth predicted soil δ15N and δ13C profiles. Trends in δ13C vs. δ15N showed a wider range of δ13C values in topsoil of upslope plots under reduced tillage, while in the depositional location, conventional tillage had the same effect. This suggests erosion under reduced tillage occurred. Erosion and accelerated decomposition gradually slowed δ13C enrichment with soil depth. Digital soil mapping approach depicted low continuity of δ13C vs. high continuity of δ15N with geomorphic position We attributed the intermediate δ13C values, and steeper slope of δ13C against logarithm of soil organic carbon (SOC) across the slope to erosion and high SOM turnover, particularly of recently added plant inputs. Current results support the prediction of intensive vs. conservation practices’ effects on upslope soil stability and the fate of SOM in both topsoil and at depth of sloping farmlands.

The lipidomic profiles of donkey semen present greater diversity and abundance than horse semen

While donkeys and horses belong to the same family and are able to cross-mate to produce hybrids, there are many unique features of either species regarding their sexual behavior, semen characteristics, and tolerance to cryopreservation; therefore, a better understanding of the physiology of either species can enhance semen processing and fertility. Lipids play a paramount role in the fluidity and flexibility of the sperm plasma membrane and are intrinsically related to sperm motility and plasma membrane integrity. This study aimed to determine the lipid profile of donkey and horse semen. Semen from 19 jacks and 25 stallions was collected by artificial vagina, and gel-free semen was assessed for motility, morphology, concentration, and volume. An aliquot of gel-free semen (50 million sperm/mL) was placed in cryovials and then plunged into liquid nitrogen until lipidomic analysis. The lipidomic profile of the gel-free semen was performed with MALDI-MS with no addition of an extender. The analyses were performed with MetaboAnalyst 2.0 and the discriminating analyses of PLS-DA to demonstrate differences between horses and donkeys. The lipid types that were differentially expressed were analyzed by ANOVA followed by Tukey test. The Volcano plot was used to compare species, using the criteria values of P<0.05 andfold-change (FC) ≥1.5. A total of 101 ion lipids were found in horse semen and 105 in donkey semen. Five ion-lipids with mass charge (mz) 762, 822, 865, 867, and 879 were uniquely present in horse semen, but not identified in the databases (www.lipidmaps.org), whereas 9-ion-lipid were present in donkey semen (m/z 709.4, 731, 753, 756, 778, 796, 798, 811 and 880) with only 709.4 being identified (Phosphatidylglycerol). The donkeys showed 18 ion-lipid with greater relative abundance (P<0.05 and fold-change>1.5), 12 identified in the database (4 triacylglycerols, 2 phosphatidylglycerols, 2 phosphatidylcholine, 2 phosphatidylinositols and 2 sphingolipids); whereas the horse had ten ion-lipid with greater abundance (P<0.05 and fold-change >1.5) but only 4 identified (2 diacylglycerols, 1 triacylglycerol and 1 phosphatidylglycerol). Among these identified lipids, 10 have one or more double bonds, making them mono- or polyunsaturated, while in horses only 1 was polyunsaturated. This may be one of the underlying reasons for lower tolerance to cryopreservation/cooling of donkey semen. In conclusion, the lipidomic profile of donkey and stallion semen differ with regard to relative abundance and diversity.

Effect of salt concentration on Chinese soy sauce fermentation and characteristics

The effect of salt concentration on soy sauce fermentation was studied. Four groups of soy sauce samples with 8%, 12%, 16%, and 20% salt concentrations were prepared and their physicochemical parameters, non-volatile metabolites (organic acids, free amino acids, and biogenic amines), volatile metabolites, and microbial abundance were analyzed during the whole fermentation. The sensory characteristics of the final products were also assessed. Salt concentration significantly affected the microbial abundance and metabolite profiles during soy sauce fermentation, resulting in different characteristics among samples. The RT-qPCR analysis showed that the microbial abundance in lower salted soy sauce was higher. Correspondingly, the accumulation of total acid and the decrease of reducing sugar were observed in lower salted soy sauce, aggravating the sourness of soy sauce. The levels of free amino acids and biogenic amines (detected by HPLC) in lower salted soy sauce were higher, resulting in a taste of umami but making unsafe soy sauce. Additionally, HS-SPME/GC-MS analysis showed that several distinctive volatile compounds were absent in lower salted soy sauce. Sensory evaluation revealed that the highest total score for the quality of soy sauce was obtained through fermentation under 16% salt concentration. This study helped deepen our understanding of the effect of salt on soy sauce fermentation and provide a theoretical data reference for low salt soy sauce development.

Assessment of the differential profile of breast intratumoral bacteria in treatment responders and nonresponders.

580 Background: Despite improvements in breast cancer therapy, many patients do not respond to treatment: the response rate varies between 31 and 80% [Walks et al., 2019]. The tumor microenvironment contains a microbiome which is both cancer type-specific and different to surrounding normal tissue [Nejman et al., 2020]. It plays an important role in tumour microenvironment crosstalk with tumor cells and has been implicated in disease pathogenesis, being recognised in the latest hallmarks of cancer [Hanahan et Weinberg, 2011]. It has also successfully predicted treatment response [Chen et al., 2022; Hermida et al., 2022]. Here, we use conventional microbiome analysis alongside our proprietary mechanistically-powered technology platform to compare the intratumoral bacteria between responders and non-responders. We hypothesise that there are significant differences between those groups regarding composition and mechanistic function of the bacteria. Methods: The tumor microbiome can be deduced from existing sequencing data of tumor biopsy samples. Using 34 tumor DNA samples from 5 breast cancer studies, we compared the intratumoral bacterial profiles of responders to non-responders of various treatments. We performed microbiome analysis in R, including alpha diversity, compositional abundance profiles, and PERMANOVA (permutational analysis of variance). Additionally, BioCorteX CarbonMirror version dated 2023-02-14 was used to infer mechanistic links between bacterial species and the up- and downregulation of genes implicated in the hallmarks of cancer. Results: The results show significant differences in diversity and compositional profile between responders and non-responders: Non-responders have significantly higher alpha diversity ( p&lt;0.05) and a higher proportion of Proteobacteria. PERMANOVA analysis revealed that while responders are more likely to have commensal bacteria, non-responder tumour microbiomes are more likely to harbor pathogenic species such as Mycoplasmopsis fermentans. Mechanistic analysis showed that for all responders the tumor microbiome is consistently promoting tumor suppressor genes and downregulating proto-oncogenes. In non-responders, however, the tumor microbiome is upregulating and downregulating both with unclear consistency. Conclusions: Our results show microbiome differences between responders and non-responders which were mechanistically implicated in tumor pathogenesis. Further analysis divided by the hallmarks of cancer is required to fully understand the non-responder microbiome links. The reported findings could make the tumor microbiome a useful biomarker aiding patient stratification for both prognosis and treatment choice. They also highlight a potentially meaningful mechanistic link between tumour microenvironment and treatment response that could be leveraged as a novel therapeutic avenue.

Systematic and Comprehensive Analysis of tRNA-Derived Small RNAs Reveals Their Potential Regulatory Roles and Clinical Relevance in Sarcoidosis.

The pathogenesis of sarcoidosis, which involves several systems, is unclear, and its pathological type is non-caseating epithelioid granulomas. tRNA-derived small RNA (tsRNA) is a novel class of short non-coding RNAs with potential regulatory functions. However, whether tsRNA contributes to sarcoidosis pathogenesis remains unclear. Deep sequencing technology was used to identify alterations in tsRNA relative abundance profiles between patients with sarcoidosis and healthy controls and quantitative real-time polymerase chain reaction (qRT-PCR) was used to validate. The clinical parameters were analysis to evaluate the clinical feature correlations initially. Target prediction and bioinformatics analysis of validated tsRNA were conducted to explore the mechanisms of tsRNAs in sarcoidosis pathogenesis. A total of 360 tsRNAs were identified for exact matches. Among them, the relative abundance of three tRNAs (tiRNA-Glu-TTC-001, tiRNA-Lys-CTT-003, and tRF-Ser-TGA-007) was markedly regulated in sarcoidosis. The levels of various tsRNAs were significantly correlated with age, the number of affected systems, and calcium levels in the blood. Additionally, target prediction and bioinformatics analyses revealed that these tsRNAs may play roles in chemokine, cAMP, cGMP-PKG, retrograde endorphin, and FoxO signalling pathways. The related genes, APP, PRKACB, ARRB2, and NR5A1 finding may participate in the occurrence and development of sarcoidosis through immune inflammation. This study provides novel insights to explore tsRNA as a novel and efficacious pathogenic target of sarcoidosis.

Anthocyanin-rich blue potato meals protect against polychlorinated biphenyl-mediated disruption of short-chain fatty acid production and gut microbiota profiles in a simulated human digestion model.

Polychlorinated biphenyls (PCBs) are ubiquitous environmental pollutants associated with a wide variety of adverse human health outcomes. PCB 126 and PCB 153 are among the most prevalent congeners associated with human exposure. Emerging studies have suggested that PCB exposure leads to lower gut microbial diversity although their effects on microbial production of health promoting short-chain fatty acids (SCFAs) has been scarcely studied. Blue potatoes are rich in anthocyanins (ACNs), which is a class of polyphenols that promote the growth of beneficial intestinal bacteria such as Bifidobacterium and Lactobacillus and increase the generation of SCFAs. A batch-culture, pH-controlled, stirred system containing human fecal microbial communities was utilized to assess whether human gut microbiota composition and SCFA production are affected by: (a) PCB 126 and PCB 153 exposure; and (b) ACN-rich digests in the presence and absence of the PCB congeners. Anthocyanin-rich blue potato meals (11.03 g) were digested over 12 h with and without PCB 126 (0.5 mM) and PCB 153 (0.5 mM) using an in vitro simulated gut digestion model involving upper gastrointestinal digestion followed by metabolism by human fecal microbiota. Fecal digests were collected for analysis of gut microbial and SCFA profiles. Polychlorinated biphenyl-exposed fecal samples showed a significant (p < 0.05) decrease in species richness and a significantly (p < 0.05) different microbial community structure. PCB treatment was associated with an increased (p < 0.05) relative abundance of Akkermansia, Eggerthella, and Bifidobacterium and a decreased (p < 0.05) relative abundance of Veillonella, Streptococcus, and Holdemanella. ACN digests counteracted the altered abundances of Akkermansia and Bifidobacterium seen with the PCB treatment. PCB exposure was associated with a significant (p < 0.05) decrease in total SCFA and acetate concentrations. ACN digests were associated with significantly (p < 0.05) higher SCFA and acetate concentrations in the presence and absence of PCBs. Human fecal matter exposed to PCB 126 and PCB 153 led to decreased abundance and altered gut microbiota profiles as well as lowered SCFA and acetate levels. Importantly, this study showed that prebiotic ACN-rich potatoes counteract PCB-mediated disruptions in human gut microbiota profiles and SCFA production.

Virome Profiling of Chickens with Hepatomegaly Rupture Syndrome Reveals Coinfection of Multiple Viruses.

Liver diseases seriously challenge the health of chickens raised on scaled farms and cause tremendous economic losses to farm owners. The causative agents for liver diseases are still elusive, even though various pathogens, such as the hepatitis E virus, have been reported. In the winter of 2021, a liver disease was observed on a chicken farm in Dalian, China, which increased chicken mortality by up to 18%. We conducted panvirome profiling of the livers, spleens, kidneys, and recta of 20 diseased chickens. The viromic results revealed coinfection of multiple viruses, including pathogenic ones, in these organs. The viruses were highly identical to those detected in other provinces, and the vaccine and field strains of avian encephalomyelitis virus (AEV) and chicken infectious anemia virus (CIAV) cocirculated on the farm. In particular, the liver showed higher abundance of AEV and multiple fowl adenoviruses than other organs. Furthermore, the liver also contracted avian leukemia virus and CIAV. Experimental animals with infected liver samples developed minor to medium lesions of the liver and showed a virus abundance profile for AEV across internal organs similar to that in the original samples. These results suggest that coinfection with multiple pathogenic viruses influences the occurrence and development of infectious liver disease. The results also highlight that strong farm management standards with strict biosafety measures are needed to minimize the risk of pathogenic virus introduction to the farm.

Edge and modular significance assessment in individual-specific networks

Individual-specific networks, defined as networks of nodes and connecting edges that are specific to an individual, are promising tools for precision medicine. When such networks are biological, interpretation of functional modules at an individual level becomes possible. An under-investigated problem is relevance or ”significance” assessment of each individual-specific network. This paper proposes novel edge and module significance assessment procedures for weighted and unweighted individual-specific networks. Specifically, we propose a modular Cook’s distance using a method that involves iterative modeling of one edge versus all the others within a module. Two procedures assessing changes between using all individuals and using all individuals but leaving one individual out (LOO) are proposed as well (LOO-ISN, MultiLOO-ISN), relying on empirically derived edges. We compare our proposals to competitors, including adaptions of OPTICS, kNN, and Spoutlier methods, by an extensive simulation study, templated on real-life scenarios for gene co-expression and microbial interaction networks. Results show the advantages of performing modular versus edge-wise significance assessments for individual-specific networks. Furthermore, modular Cook’s distance is among the top performers across all considered simulation settings. Finally, the identification of outlying individuals regarding their individual-specific networks, is meaningful for precision medicine purposes, as confirmed by network analysis of microbiome abundance profiles.

Development and validation of a proteomic correlation profiling technique to detect and identify enzymes involved in metabolism of drugs of concern.

It is important to be able to detect previously unknown and unsuspected enzymes involved in drug metabolism, in order to predict the variation of pharmacological or toxicological effect caused by pharmacokinetic variance. We investigated the use of proteomic correlation profiling (PCP) as a technique to identify the enzymes involved in metabolism of drugs of concern. By evaluating the metabolic activities of each enzyme (including isoforms of cytochrome P450, uridine 5' diphospho‑glucuronosyltransferase, and hydrolases, plus aldehyde oxidase and carbonyl reductase) on their typical substrates using a panel of human liver samples, we were able to show the validity of PCP for this purpose. Pearson or Spearman's rank correlation coefficient and P values were calculated for the association between the protein abundance profile of each protein and the metabolic rate profile of each typical substrate. For the 18 enzymatic activities examined, 13 of the enzymes reported to be responsible for the reactions had correlation coefficients higher than 0.7, and were ranked first to third. For the remaining five activities, the responsible enzymes had correlation coefficients lower than 0.7 and lower rankings. The reasons for this were diverse, including confounding resulting from low protein abundance ratios, artificially high correlations of other enzymes due to limited sample numbers, the presence of inactive enzyme forms, and genetic polymorphisms. Overall, PCP was able to identify the majority of responsible drug-metabolizing enzymes across several enzyme classes (oxidoreductase, transferase, hydrolase); use of this methodology could allow more timely and accurate identification of unknown drug-metabolizing enzymes. Significance Statement Proteomic correlation profiling using samples from individual human donors was proven to be a useful methodology for the identification of enzymes responsible for drug-metabolism. This methodology could accelerate the identification of unknown drug-metabolizing enzymes in the future.

The stellar chemical abundances of simulated massive galaxies at z = 2

ABSTRACT We analyse the stellar abundances of massive galaxies (log M*/M⊙ &amp;gt; 10.5) at redshift, z = 2, in the IllustrisTNG simulation with the goal of guiding the interpretation of current and future observations, particularly from JWST. We find that the effective size, Re, of galaxies strongly affects the abundance measurements: both [Mg/H] and [Fe/H] are anticorrelated with Re, while the relative abundance [Mg/Fe] slightly increases with Re. The α enhancement as tracked by [Mg/Fe] traces the formation time-scale of a galaxy weakly, and mostly depends on Re. Aperture effects are important: measuring the stellar abundances within 1 kpc instead of within Re can make a large difference. These results are all due to a nearly universal, steeply declining stellar abundance profile that does not scale with galaxy size – Small galaxies appear metal-rich because their stars live in the inner part of the profile where abundances are high. The slope of this profile is mostly set by the gas-phase abundance profile and not substantially modified by stellar age gradients. The gas-phase abundance profile, in turn, is determined by the strong radial dependence of the gas fraction and star-formation efficiency. We develop a simple model to describe the chemical enrichment, in which each radial bin of a galaxy is treated as an independent closed-box system. This model reproduces the gas-phase abundance profile of simulated galaxies, but not the detailed distribution of their stellar abundances, for which gas and/or metal transport are likely needed.

Early-time spectroscopic modelling of the transitional Type Ia Supernova 2021rhu with tardis

ABSTRACT An open question in SN Ia research is where the boundary lies between ‘normal’ Type Ia supernovae (SNe Ia) that are used in cosmological measurements and those that sit off the Phillips relation. We present the spectroscopic modelling of one such ‘86G-like’ transitional SN Ia, SN 2021rhu, that has recently been employed as a local Hubble Constant calibrator using a tip of the red-giant branch measurement. We detail its modelling from −12 d until maximum brightness using the radiative-transfer spectral-synthesis code tardis. Please check and correct this paper accordingly. We base our modelling on literature delayed-detonation and deflagration models of Chandrasekhar mass white dwarfs, as well as the double-detonation models of sub-Chandrasekhar mass white dwarfs. We present a new method for ‘projecting’ abundance profiles to different density profiles for ease of computation. Due to the small velocity extent and low outer densities of the W7 profile, we find it inadequate to reproduce the evolution of SN 2021rhu as it fails to match the high-velocity calcium components. The host extinction of SN 2021rhu is uncertain but we use modelling with and without an extinction correction to set lower and upper limits on the abundances of individual species. Comparing these limits to literature models we conclude that the spectral evolution of SN 2021rhu is also incompatible with double-detonation scenarios, lying more in line with those resulting from the delayed-detonation mechanism (although there are some discrepancies, in particular a larger titanium abundance in SN 2021rhu compared to the literature). This suggests that SN 2021rhu is likely a lower luminosity, and hence lower temperature, version of a normal SN Ia.

Egg-laying by female Aedes aegypti shapes the bacterial communities of breeding sites

BackgroundAedes aegypti, the main arboviral mosquito vector, is attracted to human dwellings and makes use of human-generated breeding sites. Past research has shown that bacterial communities associated with such sites undergo compositional shifts as larvae develop and that exposure to different bacteria during larval stages can have an impact on mosquito development and life-history traits. Based on these facts, we hypothesized that female Ae. aegypti shape the bacteria communities of breeding sites during oviposition as a form of niche construction to favor offspring fitness.ResultsTo test this hypothesis, we first verified that gravid females can act as mechanical vectors of bacteria. We then elaborated an experimental scheme to test the impact of oviposition on breeding site microbiota. Five different groups of experimental breeding sites were set up with a sterile aqueous solution of larval food, and subsequently exposed to (1) the environment alone, (2) surface-sterilized eggs, (3) unsterilized eggs, (4) a non-egg laying female, or (5) oviposition by a gravid female. The microbiota of these differently treated sites was assessed by amplicon-oriented DNA sequencing once the larvae from the sites with eggs had completed development and formed pupae. Microbial ecology analyses revealed significant differences between the five treatments in terms of diversity. In particular, between-treatment shifts in abundance profiles were detected, showing that females induce a significant decrease in microbial alpha diversity through oviposition. In addition, indicator species analysis pinpointed bacterial taxa with significant predicting values and fidelity coefficients for the samples in which single females laid eggs. Furthermore, we provide evidence regarding how one of these indicator taxa, Elizabethkingia, exerts a positive effect on the development and fitness of mosquito larvae.ConclusionsOvipositing females impact the composition of the microbial community associated with a breeding site, promoting certain bacterial taxa over those prevailing in the environment. Among these bacteria, we found known mosquito symbionts and showed that they can improve offspring fitness if present in the water where eggs are laid. We deem this oviposition-mediated bacterial community shaping as a form of niche construction initiated by the gravid female.

MKMR: a multi-kernel machine regression model to predict health outcomes using human microbiome data.

Studies have found that human microbiome is associated with and predictive of human health and diseases. Many statistical methods developed for microbiome data focus on different distance metrics that can capture various information in microbiomes. Prediction models were also developed for microbiome data, including deep learning methods with convolutional neural networks that consider both taxa abundance profiles and taxonomic relationships among microbial taxa from a phylogenetic tree. Studies have also suggested that a health outcome could associate with multiple forms of microbiome profiles. In addition to the abundance of some taxa that are associated with a health outcome, the presence/absence of some taxa is also associated with and predictive of the same health outcome. Moreover, associated taxa may be close to each other on a phylogenetic tree or spread apart on a phylogenetic tree. No prediction models currently exist that use multiple forms of microbiome-outcome associations. To address this, we propose a multi-kernel machine regression (MKMR) method that is able to capture various types of microbiome signals when doing predictions. MKMR utilizes multiple forms of microbiome signals through multiple kernels being transformed from multiple distance metrics for microbiomes and learn an optimal conic combination of these kernels, with kernel weights helping us understand contributions of individual microbiome signal types. Simulation studies suggest a much-improved prediction performance over competing methods with mixture of microbiome signals. Real data applicants to predict multiple health outcomes using throat and gut microbiome data also suggest a better prediction of MKMR than that of competing methods.

First report on metagenomics and their predictive functional analysis of fermented bamboo shoot food of Tripura, North East India.

Moiya pansung, mileye amileye, moiya koshak, and midukeye are naturally fermented bamboo shoot foods of Tripura. The present study aimed to reveal the whole microbial community structure of naturally fermented moiya pangsung, mileye amileye, moiya koshak, and midukeye along with the prediction of microbial functional profiles by shotgun metagenomic sequence analysis. The metataxonomic profile of moiya pangsung, mileye amileye, moiya koshak, and midukeye samples showed different domains, viz., bacteria (97.70%) followed by the virus (0.76%), unclassified (0.09%), eukaryotes (1.46%) and archaea (0.05%). Overall, 49 phyla, 409 families, 841 genera, and 1,799 species were found in all the fermented bamboo shoot samples collected from different places of Tripura. Firmicutes was the most abundant phylum (89.28%) followed by Proteobacteria (5.13%), Bacteroidetes (4.38%), Actinobacteria (1.02%), and Fusobacteria (0.17%). Lactiplantibacillus plantarum was the most abundant species in moiya pangsung, mileye amileye, moiya koshak, and midukeye followed by Lactococcus lactis, Levilactobacillus brevis, Leuconostoc mesenteroides, Weissella paramesenteroides, Leuconostoc kimchii, Pediococcus pentosaceus, Leuconostoc gasicomitatum, and Lacticaseibacillus casei. A few phyla of fungus were found, viz., Ascomycota, Basidiomycota, and Glomeromycota, where Ascomycota was present in high abundance. Functional analysis of moiya pangsung, mileye amileye, moiya koshak, and midukeye metagenome revealed the genes for the synthesis and metabolism of a wide range of bioactive compounds including, various essential amino acids, and conjugated amino acids. The abundance profile and predictive analysis of fermented bamboo shoots revealed a huge plethora of essential microorganisms and KEGG analysis revealed genes for amino acid metabolism, pectin degradation, lipid metabolism, and many other essential pathways that can be essential for the improvement of nutritional and sensory qualities of the fermented bamboo shoot products.

Effects of Peripheral Nerve Injury on the Induction of c-Fos and Phosphorylated ERK in the Brainstem Trigeminal Sensory Nuclear Complex.

Sequential changes in brainstem and spinal cord neurons after traumatic injury to peripheral nerves are related to neuropathic pain symptoms. This study was conducted to elucidate the influence of nerve insult on stimulus-induced c-Fos expression and ERK phosphorylation by brainstem neurons. The brainstem trigeminal sensory nuclear complex (BTSNC) was examined for neuronal profiles immunolabeled with c-Fos and phosphorylated ERK (p-ERK) antibodies elicited by stimulation of the tongue with capsaicin after lingual or inferior alveolar nerve (IAN) injury. Abundant neuronal profiles immunolabeled for c-Fos and p-ERK elicited by capsaicin were distributed in the spinal trigeminal nucleus caudalis (Vc) without nerve injury. The spinal trigeminal nucleus oralis (Vo) contained limited numbers of these neuronal profiles after stimulation of the tongue. A significant reduction of these neuronal profiles in the ipsilateral Vc was detected after lingual nerve injury. After IAN injury, an increased number of neuronal profiles immunolabeled for c-Fos elicited by capsaicin was noted, while that of p-ERK was left unchanged in the ipsilateral Vc. On the both sides of the Vo, an increased number of capsaicin-induced neuronal profiles immunolabeled for c-Fos and p-ERK was detected after lingual or IAN injury. Differential effects of lingual or IAN injury on stimulus-induced c-Fos expression and ERK phosphorylation by Vo and Vc neurons may be involved in the complex nature of symptoms of trigeminal neuralgia.