Electron transport phosphorylation in rumen butyrivibrios: unprecedented ATP yield for glucose fermentation to butyrate.

From a genomic analysis of rumen butyrivibrios (Butyrivibrio and Pseudobutyrivibrio sp.), we have re-evaluated the contribution of electron transport phosphorylation (ETP) to ATP formation in this group. This group is unique in that most (76%) genomes were predicted to possess genes for both Ech and Rnf transmembrane ion pumps. These pumps act in concert with the NifJ and Bcd-Etf to form a electrochemical potential (ΔμH(+) and ΔμNa(+)), which drives ATP synthesis by ETP. Of the 62 total butyrivibrio genomes currently available from the Hungate 1000 project, all 62 were predicted to possess NifJ, which reduces oxidized ferredoxin (Fdox) during pyruvate conversion to acetyl-CoA. All 62 possessed all subunits of Bcd-Etf, which reduces Fdox and oxidizes reduced NAD during crotonyl-CoA reduction. Additionally, 61 genomes possessed all subunits of the Rnf, which generates ΔμH(+) or ΔμNa(+) from oxidation of reduced Fd (Fdred) and reduction of oxidized NAD. Further, 47 genomes possessed all six subunits of the Ech, which generates ΔμH(+) from oxidation of Fdred. For glucose fermentation to butyrate and H2, the electrochemical potential established should drive synthesis of ∼1.5 ATP by the F0F1-ATP synthase (possessed by all 62 genomes). The total yield is ∼4.5 ATP/glucose after accounting for three ATP formed by classic substrate-level phosphorylation, and it is one the highest yields for any glucose fermentation. The yield was the same when unsaturated fatty acid bonds, not H(+), served as the electron acceptor (as during biohydrogenation). Possession of both Ech and Rnf had been previously documented in only a few sulfate-reducers, was rare in other rumen prokaryotic genomes in our analysis, and may confer an energetic advantage to rumen butyrivibrios. This unique energy conservation system might enhance the butyrivibrios' ability to overcome growth inhibition by unsaturated fatty acids, as postulated herein

A Study of Certain Green Manure Crops in Making Rock Phosphate Available in Soils

At the present time the world is in the midst of the greatest conflict ever staged. The byword on the lips of every thoughtful American is, Food will win the war. In order that food may be conserved it must first be produced. Therefore, increased crop production must be stimulated. We must produce larger crops upon a given area, that is, intensive farming should be practiced

Relationship between urinary energy and urinary nitrogen or carbon excretion in lactating Jersey cows

Measurement of urinary energy (UE) excretion is essential to determine metabolizable energy (ME) supply. Our objectives were to evaluate the accuracy of using urinary N (UN) or C (UC) to estimate UE and ultimately improve the accuracy of estimating ME. Individual animal data (n = 433) were used from 11 studies with Jersey cows at the University of Nebraska–Lincoln, where samples were analyzed after drying (n = 299) or on an as-is basis (n = 134). Dried samples resulted in greater estimated error variance compared with as-is samples, and thus only as-is samples were used for final models. The as-is data set included a range (min to max) in dry matter intake (11.6–24.6 kg/d), N intake (282–642 g/d), UE excretion (1,390–3,160 kcal/d), UN excretion (85–220 g/d or 20.6–59.5% of N intake), and UC excretion (130–273 g/d). As indicated by a bias in residuals between observed and predicted ME as dietary crude protein (CP; range of 14.9–19.1%) increased, the National Research Council dairy model did not accurately predict ME of diets, as dietary CP varied. The relationship between UE (kcal/d) and UN (g/d) excretion was linear and had an intercept of 880 ± 140 kcal. Because an intercept of 880 is biologically unlikely, the intercept was forced through 0, resulting in linear and quadratic relationships. The regressions of UE (kcal/d) on UN (g/d) excretion were UE = 14.6 ± 0.32 × UN, and UE = 20.9 ± 1.0 × UN − 0.0357 ± 0.0056 × UN2. In the quadratic regression, UE increased, but at a diminishing rate as UN excretion increased. As UC increased, UE linearly and quadratically increased. However, error variance was greater for regression with UC compared with UN as explanatory variables (8.42 vs. 7.42% of mean UE). The use of the quadratic regression between UN and UE excretion to predict ME resulted in a slope bias in ME predictions as dietary CP increased. The linear regression between UE and UN excretion removed slope bias between predicted ME and CP, and thus may be more appropriate for predicting UE across a wider range of dietary CP. Using equations to predict UE from UN should improve our ability to predict diet ME in Jersey cows compared with calculating ME directly from digestible energy

Evaluation of the National Research Council (2001) dairy model and derivation of new prediction equations. 1. Digestibility of fiber, fat, protein, and nonfiber carbohydrate

Evaluation of ration balancing systems such as the National Research Council (NRC) Nutrient Requirementsseries is important for improving predictions of animal nutrient requirements and advancing feeding strategies. This work used a literature data set (n = 550) to evaluate predictions of total-tract digested neutral detergent fiber (NDF), fatty acid (FA), crude protein (CP), and nonfiber carbohydrate (NFC) estimated by the NRC (2001) dairy model. Mean biases suggested that the NRC (2001) lactating cow model overestimated true FA and CP digestibility by 26 and 7%, respectively, and under-predicted NDF digestibility by 16%. All NRC (2001) estimates had notable mean and slope biases and large root mean squared prediction error (RMSPE), and concordance (CCC) ranged from poor to good. Predicting NDF digestibility with independent equations for legumes, corn silage, other forages, and nonforage feeds improved CCC (0.85 vs. 0.76) compared with the re-derived NRC (2001) equation form (NRC equation with parameter estimates re-derived against this data set). Separate FA digestion coefficients were derived for different fat supplements (animal fats, oils, and other fat types) and for the basal diet. This equation returned improved (from 0.76 to 0.94) CCC compared with the re-derived NRC (2001) equation form. Unique CP digestibility equations were derived for forages, animal protein feeds, plant protein feeds, and other feeds, which improved CCC compared with the re-derived NRC (2001) equation form (0.74 to 0.85). New NFC digestibility coefficients were derived for grain-specific starch digestibilities, with residual organic matter assumed to be 98% digestible. A Monte Carlo cross-validation was performed to evaluate repeatability of model fit. In this procedure, data were randomly subsetted 500 times into derivation (60%) and evaluation (40%) data sets, and equations were derived using the derivation data and then evaluated against the independent evaluation data. Models derived with random study effects demonstrated poor repeatability of fit in independent evaluation. Similar equations derived without random study effects showed improved fit against independent data and little evidence of biased parameter estimates associated with failure to include study effects. The equations derived in this analysis provide interesting insight into how NDF, starch, FA, and CP digestibilities are affected by intake, feed type, and diet composition

Methane prediction equations including genera of rumen bacteria as predictor variables improve prediction accuracy

Methane (CH) emissions from ruminants are of a significant environmental concern, necessitating accurate prediction for emission inventories. Existing models rely solely on dietary and host animal-related data, ignoring the predicting power of rumen microbiota, the source of CH. To address this limitation, we developed novel CH prediction models incorporating rumen microbes as predictors, alongside animal- and feed-related predictors using four statistical/machine learning (ML) methods. These include random forest combined with boosting (RF-B), least absolute shrinkage and selection operator (LASSO), generalized linear mixed model with LASSO (glmmLasso), and smoothly clipped absolute deviation (SCAD) implemented on linear mixed models. With a sheep dataset (218 observations) of both animal data and rumen microbiota data (relative sequence abundance of 330 genera of rumen bacteria, archaea, protozoa, and fungi), we developed linear mixed models to predict CH production (g CH/animal·d, ANIM-B models) and CH yield (g CH/kg of dry matter intake, DMI-B models). We also developed models solely based on animal-related data. Prediction performance was evaluated 200 times with random data splits, while fitting performance was assessed without data splitting. The inclusion of microbial predictors improved the models, as indicated by decreased root mean square prediction error (RMSPE) and mean absolute error (MAE), and increased Lin’s concordance correlation coefficient (CCC). Both glmmLasso and SCAD reduced the Akaike information criterion (AIC) and Bayesian information criterion (BIC) for both the ANIM-B and the DMI-B models, while the other two ML methods had mixed outcomes. By balancing prediction performance and fitting performance, we obtained one ANIM-B model (containing 10 genera of bacteria and 3 animal data) fitted using glmmLasso and one DMI-B model (5 genera of bacteria and 1 animal datum) fitted using SCAD. This study highlights the importance of incorporating rumen microbiota data in CH prediction models to enhance accuracy and robustness. Additionally, ML methods facilitate the selection of microbial predictors from high-dimensional metataxonomic data of the rumen microbiota without overfitting. Moreover, the identified microbial predictors can serve as biomarkers of CH emissions from sheep, providing valuable insights for future research and mitigation strategies.Te authors gratefully acknowledge funding for this project from the USDA National Institute of Food and Agriculture (Award number: 2014-67003-21979). Te animal and microbial data originated from a study funded by the Pastoral Greenhouse Gas Research Consortium (www.pggrc.co.nz)

Brain-behaviour modes of covariation in healthy and clinically depressed young people.

Understanding how variations in dimensions of psychometrics, IQ and demographics relate to changes in brain connectivity during the critical developmental period of adolescence and early adulthood is a major challenge. This has particular relevance for mental health disorders where a failure to understand these links might hinder the development of better diagnostic approaches and therapeutics. Here, we investigated this question in 306 adolescents and young adults (14-24 y, 25 clinically depressed) using a multivariate statistical framework, based on canonical correlation analysis (CCA). By linking individual functional brain connectivity profiles to self-report questionnaires, IQ and demographic data we identified two distinct modes of covariation. The first mode mapped onto an externalization/internalization axis and showed a strong association with sex. The second mode mapped onto a well-being/distress axis independent of sex. Interestingly, both modes showed an association with age. Crucially, the changes in functional brain connectivity associated with changes in these phenotypes showed marked developmental effects. The findings point to a role for the default mode, frontoparietal and limbic networks in psychopathology and depression.Wellcome Trus

Effects of metal-on-metal wear on the host immune system and infection in hip arthroplasty

Methods We reviewed the available literature on the influence of degradation products of MOM bearings in total hip arthroplasties on infection risk. Results Wear products were found to influence the risk of infection by hampering the immune system, by inhibiting or accelerating bacterial growth, and by a possible antibiotic resistance and heavy metal co-selection mechanism. Interpretation Whether or not the combined effects of MOM wear products make MOM bearings less or more prone to infection requires investigation in the near future

Schizotypy-related magnetization of cortex in healthy adolescence is colocated with expression of Schizophrenia-related genes

Background: Genetic risk is thought to drive clinical variation on a spectrum of schizophrenia-like traits, but the underlying changes in brain structure that mechanistically link genomic variation to schizotypal experience and behavior are unclear. Methods: We assessed schizotypy using a self-reported questionnaire and measured magnetization transfer as a putative microstructural magnetic resonance imaging marker of intracortical myelination in 68 brain regions in 248 healthy young people (14–25 years of age). We used normative adult brain gene expression data and partial least squares analysis to find the weighted gene expression pattern that was most colocated with the cortical map of schizotypy-related magnetization. Results: Magnetization was significantly correlated with schizotypy in the bilateral posterior cingulate cortex and precuneus (and for disorganized schizotypy, also in medial prefrontal cortex; all false discovery rate–corrected ps < .05), which are regions of the default mode network specialized for social and memory functions. The genes most positively weighted on the whole-genome expression map colocated with schizotypy-related magnetization were enriched for genes that were significantly downregulated in two prior case-control histological studies of brain gene expression in schizophrenia. Conversely, the most negatively weighted genes were enriched for genes that were transcriptionally upregulated in schizophrenia. Positively weighted (downregulated) genes were enriched for neuronal, specifically interneuronal, affiliations and coded a network of proteins comprising a few highly interactive “hubs” such as parvalbumin and calmodulin. Conclusions: Microstructural magnetic resonance imaging maps of intracortical magnetization can be linked to both the behavioral traits of schizotypy and prior histological data on dysregulated gene expression in schizophrenia