Deep-Sea Archaea Fix and Share Nitrogen in Methane-Consuming Microbial Consortia

Nitrogen-fixing (diazotrophic) microorganisms regulate productivity in diverse ecosystems; however, the identities of diazotrophs are unknown in many oceanic environments. Using single-cell–resolution nanometer secondary ion mass spectrometry images of ^(15)N incorporation, we showed that deep-sea anaerobic methane-oxidizing archaea fix N_2, as well as structurally similar CN^–, and share the products with sulfate-reducing bacterial symbionts. These archaeal/bacterial consortia are already recognized as the major sink of methane in benthic ecosystems, and we now identify them as a source of bioavailable nitrogen as well. The archaea maintain their methane oxidation rates while fixing N_2 but reduce their growth, probably in compensation for the energetic burden of diazotrophy. This finding extends the demonstrated lower limits of respiratory energy capable of fueling N_2 fixation and reveals a link between the global carbon, nitrogen, and sulfur cycles

Collision-Induced Non-Adiabatic Transitions Between The Ion-Pair States Of Molecular Iodine: A Challenge For Experiment And Theory

The ion-pair states of molecular iodine provide a unique system for studying the efficiency, selectivity, and mechanisms of collision-induced non-adiabatic transitions. Non-adiabatic transitions between the first-tier ion-pair states in collisions with molecular partners and rare gases are analyzed and discussed. The qualitative features of the rate constants and product state distributions under single collision conditions are summarized and interpreted in terms of appropriate theoretical approaches. Two mechanisms for the non-adiabatic transitions are clearly identified. The first, operative for collisions involving molecular partners possessing permanent or transition electrostatic moments, is highly selective. It connects the initially prepared level in the E 0(g)(+) electronic state with the near-resonant vibronic level of the D 0(u)(+) state with a minimum change of the total angular momentum. In an extreme quasi-resonant case when the gap between initial and final rovibronic level is less than 1 cm(-1), this mechanism has a giant cross section, 40 times that of a gas kinetic collision. An electrostatic model, which includes the coupling of the giant E-D transition dipole moment with a moment of the colliding partner and the semiclassical Born approximation, provides a plausible interpretation of this mechanism. A second mechanism is shown to govern collisions with rare gas atoms. It results in population of several ion-pair states and broad distributions over rovibronic levels. This mechanism is successfully interpreted by quantum scattering calculations based on the diatomics-in-molecule diabatic potential energy surfaces and coupling matrix elements. The calculations provide good agreement with experimental measurements and reveal different mechanisms for the population of different electronic states. Unexplained features of the non-adiabatic dynamics and directions of future work are outlined

A Panel Data Analysis of the Effects of Macroeconomic Variables on Income Inequality in Latin American Countries

This paper investigates the relationship between inflation, trade, unemployment, education, and economic growth on income inequality in the South American OECD countries (Chile, Costa Rica, Colombia, Mexico, Argentina, Brazil, and Peru). While Argentina, Brazil, and Peru are not official OECD countries, they have a working relationship with OECD and have taken the first steps toward initiation in OECD. The variable that represents income inequality is the Gini Index World Bank estimator, and the variable that represents economic growth is GDP. This paper uses a panel data set from 2006 to 2020. The results of this study show that trade percentage, the unemployment percentage, and labor education increase income inequality, while inflation and GDP decrease income inequality

Chitin mixed in potting soil alters lettuce growth, the survival of zoonotic bacteria on the leaves and associated rhizosphere microbiology

Chitin is a promising soil amendment for improving soil quality, plant growth, and plant resilience. The objectives of this study were twofold. First, to study the effect of chitin mixed in potting soil on lettuce growth and on the survival of two zoonotic bacterial pathogens, Escherichia colt O157:H7 and Salmonella enterica on the lettuce leaves. Second, to assess the related changes in the microbial lettuce rhizosphere, using phospholipid fatty acid (PLFA) analysis and amplicon sequencing of a bacterial 16S rRNA gene fragment and the fungal ITS2. As a result of chitin addition, lettuce fresh yield weight was significantly increased. S. enterica survival in the lettuce phyllosphere was significantly reduced. The E. coli O157:H7 survival was also lowered, but not significantly. Moreover, significant changes were observed in the bacterial and fungal community of the lettuce rhizosphere. PLFA analysis showed a significant increase in fungal and bacterial biomass. Amplicon sequencing showed no increase in fungal and bacterial biodiversity, but relative abundances of the bacterial phyla Acidobacteria, Verrucomicrobia, Actinobacteria, Bacteroidetes, and Proteobacteria and the fungal phyla Ascomycota, Basidiomycota, and Zygomycota were significantly changed. More specifically, a more than 10-fold increase was observed for operational taxonomic units belonging to the bacterial genera Cellvibrio, Pedobacter, Dyadobacter, and Streptomyces and to the fungal genera Lecanicillium and Mortierella. These genera include several species previously reported to be involved in biocontrol, plant growth promotion, the nitrogen cycle and chitin degradation. These results enhance the understanding of the response of the rhizosphere microbiome to chitin amendment. Moreover, this is the first study to investigate the use of soil amendments to control the survival of S. enterica on plant leaves

Development and quantitative analyses of a universal rRNA-subtraction protocol for microbial metatranscriptomics

Metatranscriptomes generated by pyrosequencing hold significant potential for describing functional processes in complex microbial communities. Meeting this potential requires protocols that maximize mRNA recovery by reducing the relative abundance of ribosomal RNA, as well as systematic comparisons to identify methodological artifacts and test for reproducibility across data sets. Here, we implement a protocol for subtractive hybridization of bacterial rRNA (16S and 23S) that uses sample-specific probes and is applicable across diverse environmental samples. To test this method, rRNA-subtracted and unsubtracted transcriptomes were sequenced (454 FLX technology) from bacterioplankton communities at two depths in the oligotrophic open ocean, yielding 10 data sets representing ~350 Mbp. Subtractive hybridization reduced bacterial rRNA transcript abundance by 40–58%, increasing recovery of non-rRNA sequences up to fourfold (from 12% to 20% of total sequences to 40–49%). In testing this method, we established criteria for detecting sequences replicated artificially via pyrosequencing errors and identified such replicates as a significant component (6–39%) of total pyrosequencing reads. Following replicate removal, statistical comparisons of reference genes (identified via BLASTX to NCBI-nr) between technical replicates and between rRNA-subtracted and unsubtracted samples showed low levels of differential transcript abundance (<0.2% of reference genes). However, gene overlap between data sets was remarkably low, with no two data sets (including duplicate runs from the same pyrosequencing library template) sharing greater than 17% of unique reference genes. These results indicate that pyrosequencing captures a small subset of total mRNA diversity and underscores the importance of reliable rRNA subtraction procedures to enhance sequencing coverage across the functional transcript pool.Agouron InstituteGordon and Betty Moore FoundationUnited States. Dept. of Energy. Office of ScienceNational Science Foundation (U.S.) (NSF Science and Technology Center Award EF0424599

Microbial metatranscriptomics in a permanent marine oxygen minimum zone

Simultaneous characterization of taxonomic composition, metabolic gene content and gene expression in marine oxygen minimum zones (OMZs) has potential to broaden perspectives on the microbial and biogeochemical dynamics in these environments. Here, we present a metatranscriptomic survey of microbial community metabolism in the Eastern Tropical South Pacific OMZ off northern Chile. Community RNA was sampled in late austral autumn from four depths (50, 85, 110, 200 m) extending across the oxycline and into the upper OMZ. Shotgun pyrosequencing of cDNA yielded 180 000 to 550 000 transcript sequences per depth. Based on functional gene representation, transcriptome samples clustered apart from corresponding metagenome samples from the same depth, highlighting the discrepancies between metabolic potential and actual transcription. BLAST-based characterizations of non-ribosomal RNA sequences revealed a dominance of genes involved with both oxidative (nitrification) and reductive (anammox, denitrification) components of the marine nitrogen cycle. Using annotations of protein-coding genes as proxies for taxonomic affiliation, we observed depth-specific changes in gene expression by key functional taxonomic groups. Notably, transcripts most closely matching the genome of the ammonia-oxidizing archaeon Nitrosopumilus maritimus dominated the transcriptome in the upper three depths, representing one in five protein-coding transcripts at 85 m. In contrast, transcripts matching the anammox bacterium Kuenenia stuttgartiensis dominated at the core of the OMZ (200 m; 1 in 12 protein-coding transcripts). The distribution of N. maritimus-like transcripts paralleled that of transcripts matching ammonia monooxygenase genes, which, despite being represented by both bacterial and archaeal sequences in the community DNA, were dominated (> 99%) by archaeal sequences in the RNA, suggesting a substantial role for archaeal nitrification in the upper OMZ. These data, as well as those describing other key OMZ metabolic processes (e.g. sulfur oxidation), highlight gene-specific expression patterns in the context of the entire community transcriptome, as well as identify key functional groups for taxon-specific genomic profiling.Agouron InstituteGordon and Betty Moore FoundationUnited States. Dept. of Energy (Office of Science)Comisión Nacional de Investigación Ciencia y Tecnología (Chile) (Program Fondap

PhosBoost: Improved phosphorylation prediction recall using gradient boosting and protein language models.

Protein phosphorylation is a dynamic and reversible post-translational modification that regulates a variety of essential biological processes. The regulatory role of phosphorylation in cellular signaling pathways, protein-protein interactions, and enzymatic activities has motivated extensive research efforts to understand its functional implications. Experimental protein phosphorylation data in plants remains limited to a few species, necessitating a scalable and accurate prediction method. Here, we present PhosBoost, a machine-learning approach that leverages protein language models and gradient-boosting trees to predict protein phosphorylation from experimentally derived data. Trained on data obtained from a comprehensive plant phosphorylation database, qPTMplants, we compared the performance of PhosBoost to existing protein phosphorylation prediction methods, PhosphoLingo and DeepPhos. For serine and threonine prediction, PhosBoost achieved higher recall than PhosphoLingo and DeepPhos (.78, .56, and .14, respectively) while maintaining a competitive area under the precision-recall curve (.54, .56, and .42, respectively). PhosphoLingo and DeepPhos failed to predict any tyrosine phosphorylation sites, while PhosBoost achieved a recall score of .6. Despite the precision-recall tradeoff, PhosBoost offers improved performance when recall is prioritized while consistently providing more confident probability scores. A sequence-based pairwise alignment step improved prediction results for all classifiers by effectively increasing the number of inferred positive phosphosites. We provide evidence to show that PhosBoost models are transferable across species and scalable for genome-wide protein phosphorylation predictions. PhosBoost is freely and publicly available on GitHub

Resistin and adenylyl cyclase-associated protein 1 (CAP1) regulate the expression of genes related to insulin resistance in BNL CL.2 mouse liver cells

© 2019 The Author(s) Resistin is an adipokine produced in white adipose tissue that is thought to modulate insulin sensitivity in peripheral tissues (such as liver, skeletal muscle or adipose tissue). Human and murine resistin molecules share only about 60% sequence homology. [1] Contrary to humans, in which resistin is secreted mostly by macrophages, Park and Ahima 2013 resistin in rodents is produced primarily by the mature adipocytes of the white adipose tissue. Although resistin can bind to toll-like receptor 4 (TLF4) activating proinflammatory responses in human and rodents, [3–8] the inflammatory actions of resistin in human monocytes were found to be mediated by resistin binding to adenylyl cyclase-associated protein 1 (CAP1). [9] In this study, we aimed to investigate the in vitro effects of resistin on the expression of various genes related to insulin resistance in mouse liver cells. Using BNL CL.2 cells, we investigated the effect of resistin in untransfected or CAP1 siRNA-transfected cells on the expression of 84 key genes involved in insulin resistance