Can intestinal absorption of dietary protein be improved through early exposure to plant-based diet?

Nutritional Programming (NP) has been studied as a means of mitigating the negative effects of dietary plant protein (PP), but the optimal timing and mechanism behind NP are still unknown. The objectives of this study were: 1) To determine whether zebrafish (Danio rerio) can be programmed to soybean meal (SBM) through early feeding and broodstock exposure to improve SBM utilization; 2) To determine if NP in zebrafish affects expression of genes associated with intestinal nutrient uptake; 3) To determine if early stage NP and/or broodstock affects gene expression associated with intestinal inflammation or any morphological changes in the intestinal tract that might improve dietary SBM utilization. Two broodstocks were used to form the six experimental groups. One broodstock group received fishmeal (FM) diet (FMBS), while the other was fed ("programmed with") SBM diet (PPBS). The first ((+) Control) and the second group ((-) Control) received FM and SBM diet for the entire study, respectively, and were progeny of FMBS. The last four groups consisted of a non-programmed (FMBS-X-PP and PPBS-X-PP) and a programmed group (FMBS-NP-PP and PPBS-NP-PP) from each of the broodstocks. The programming occurred through feeding with SBM diet during 13-23 dph. The non-control groups underwent a PP-Challenge, receiving SBM diet during 36-60 dph. During the PP-Challenge, both PPBS groups experienced significantly lower weight gains than the (+) Control group. NP in early life stages significantly increased the expression of PepT1 in PPBS-NP-PP, compared to PPBS-X-PP. NP also tended to increase the expression of fabp2 in the programmed vs. non-programmed groups of both broodstocks. The highest distal villus length-to-width ratio was observed in the dual-programmed group, suggesting an increase in surface area for nutrient absorption within the intestine. The results of this study suggest that NP during early life stages may increase intestinal absorption of nutrients from PP-based feeds

Site and Strain-Specific Variation in Gut Microbiota Profiles and Metabolism in Experimental Mice

The gastrointestinal tract microbiota (GTM) of mammals is a complex microbial consortium, the composition and activities of which influences mucosal development, immunity, nutrition and drug metabolism. It remains unclear whether the composition of the dominant GTM is conserved within animals of the same strain and whether stable GTMs are selected for by host-specific factors or dictated by environmental variables.The GTM composition of six highly inbred, genetically distinct strains of mouse (C3H, C57, GFEC, CD1, CBA nu/nu and SCID) was profiled using eubacterial -specific PCR-DGGE and quantitative PCR of feces. Animals exhibited strain-specific fecal eubacterial profiles that were highly stable (c. >95% concordance over 26 months for C57). Analyses of mice that had been relocated before and after maturity indicated marked, reproducible changes in fecal consortia and that occurred only in young animals. Implantation of a female BDF1 mouse with genetically distinct (C57 and Agoutie) embryos produced highly similar GTM profiles (c. 95% concordance) between mother and offspring, regardless of offspring strain, which was also reflected in urinary metabolite profiles. Marked institution-specific GTM profiles were apparent in C3H mice raised in two different research institutions.Strain-specific data were suggestive of genetic determination of the composition and activities of intestinal symbiotic consortia. However, relocation studies and uterine implantation demonstrated the dominance of environmental influences on the GTM. This was manifested in large variations between isogenic adult mice reared in different research institutions

An investigation of horizontal transfer of feed introduced DNA to the aerobic microbiota of the gastrointestinal tract of rats

Background: Horizontal gene transfer through natural transformation of members of the microbiota of the lower gastrointestinal tract (GIT) of mammals has not yet been described. Insufficient DNA sequence similarity for homologous recombination to occur has been identified as the major barrier to interspecies transfer of chromosomal DNA in bacteria. In this study we determined if regions of high DNA similarity between the genomes of the indigenous bacteria in the GIT of rats and feed introduced DNA could lead to homologous recombination and acquisition of antibiotic resistance genes. Results: Plasmid DNA with two resistance genes (nptII and aadA) and regions of high DNA similarity to 16S rRNA and 23S rRNA genes present in a broad range of bacterial species present in the GIT, where constructed and added to standard rat feed. Six rats, with a normal microbiota, were fed DNA containing pellets daily over four days before sampling of the microbiota from the different GI compartments (stomach, small intestine, cecum and colon). In addition, two rats were included as negative controls. Antibiotic resistant colonies growing on selective media were screened for recombination with feed introduced DNA by PCR targeting unique sites in the putatively recombined regions. Conclusions: The analyses showed that extensive ingestion of DNA (100 \ub5g plasmid) per day did not lead to increased proportions of kanamycin resistant bacteria, nor did it produce detectable transformants among the aerobic microbiota examined for 6 rats (detection limit <1 transformant per 1.1 x 108 cultured bacteria). The key methodological challenges to HGT detection in animal feedings trials are identified and discussed

Combination of searches for heavy spin-1 resonances using 139 fb−1 of proton-proton collision data at √s = 13 TeV with the ATLAS detector

A combination of searches for new heavy spin-1 resonances decaying into diferent pairings of W, Z, or Higgs bosons, as well as directly into leptons or quarks, is presented. The data sample used corresponds to 139 fb−1 of proton-proton collisions at √ s = 13 TeV collected during 2015–2018 with the ATLAS detector at the CERN Large Hadron Collider. Analyses selecting quark pairs (qq, bb, tt¯, and tb) or third-generation leptons (τν and τ τ ) are included in this kind of combination for the frst time. A simplifed model predicting a spin-1 heavy vector-boson triplet is used. Cross-section limits are set at the 95% confdence level and are compared with predictions for the benchmark model. These limits are also expressed in terms of constraints on couplings of the heavy vector-boson triplet to quarks, leptons, and the Higgs boson. The complementarity of the various analyses increases the sensitivity to new physics, and the resulting constraints are stronger than those from any individual analysis considered. The data exclude a heavy vector-boson triplet with mass below 5.8 TeV in a weakly coupled scenario, below 4.4 TeV in a strongly coupled scenario, and up to 1.5 TeV in the case of production via vector-boson fusion

Search for resonant production of dark quarks in the dijet final state with the ATLAS detector

This paper presents a search for a new Z′ resonance decaying into a pair of dark quarks which hadronise into dark hadrons before promptly decaying back as Standard Model particles. This analysis is based on proton-proton collision data recorded at $$\sqrt{s}$$ s = 13 TeV with the ATLAS detector at the Large Hadron Collider between 2015 and 2018, corresponding to an integrated luminosity of 139 fb−1. After selecting events containing large-radius jets with high track multiplicity, the invariant mass distribution of the two highest-transverse-momentum jets is scanned to look for an excess above a data-driven estimate of the Standard Model multijet background. No significant excess of events is observed and the results are thus used to set 95% confidence-level upper limits on the production cross-section times branching ratio of the Z′ to dark quarks as a function of the Z′ mass for various dark-quark scenarios

Study of High-Transverse-Momentum Higgs Boson Production in Association with a Vector Boson in the qqbb Final State with the ATLAS Detector

This Letter presents the first study of Higgs boson production in association with a vector boson (V = W or Z) in the fully hadronic qqbb final state using data recorded by the ATLAS detector at the LHC in proton-proton collisions at √s = 13 TeV and corresponding to an integrated luminosity of 137fb^{-1}. The vector bosons and Higgs bosons are each reconstructed as large-radius jets and tagged using jet substructure techniques. Dedicated tagging algorithms exploiting b-tagging properties are used to identify jets consistent with Higgs bosons decaying into b¯b. Dominant backgrounds from multijet production are determined directly from the data, and a likelihood fit to the jet mass distribution of Higgs boson candidates is used to extract the number of signal events. The VH production cross section is measured inclusively and differentially in several ranges of Higgs boson transverse momentum: 250–450, 450–650, and greater than 650 GeV. The inclusive signal yield relative to the standard model expectation is observed to be μ = 1.4 ^{+1.0}_{-0.9} and the corresponding cross section is 3.1 ± 1.3(stat.)^{+1.8}_{-1.4}(syst) pb

Search for new phenomena with top-quark pairs and large missing transverse momentum using 140 fb−1 of pp collision data at \sqrt{s} = 13 TeV with the ATLAS detector

A search is conducted for new phenomena in events with a top quark pair and large missing transverse momentum, where the top quark pair is reconstructed in final states with one isolated electron or muon and multiple jets. The search is performed using the Large Hadron Collider proton-proton collision data sample at a centre-of-mass energy of \sqrt{s} = 13 TeV recorded by the ATLAS detector that corresponds to an integrated luminosity of 140 fb^{−1}. An analysis based on neural network classifiers is optimised to search for directly produced pairs of supersymmetric partners of the top quark (stop), and to search for spin-0 mediators, produced in association with a pair of top quarks, that decay into dark-matter particles. In the stop search, the analysis is designed to target models in which the mass difference between the stop and the neutralino from the stop decay is close to the top quark mass. This new search is combined with previously published searches in final states with different lepton multiplicities. No significant excess above the Standard Model background is observed, and limits at 95% confidence level are set. Models with neutralinos with masses up to 570 GeV are excluded, while for small neutralino masses models are excluded for stop masses up to 1230 GeV. Scalar (pseudoscalar) dark matter mediator masses as large as 350 (370) GeV are excluded when the coupling strengths of the mediator to Standard Model and dark-matter particles are both set to one. At lower mediator masses, models with production cross-sections as small as 0.15 (0.16) times the nominal predictions are excluded. Results of this search are also used to set constraints on effective four-fermion contact interactions between top quarks and neutrinos

Accuracy versus precision in boosted top tagging with the ATLAS detector

Abstract The identification of top quark decays where the top quark has a large momentum transverse to the beam axis, known as top tagging, is a crucial component in many measurements of Standard Model processes and searches for beyond the Standard Model physics at the Large Hadron Collider. Machine learning techniques have improved the performance of top tagging algorithms, but the size of the systematic uncertainties for all proposed algorithms has not been systematically studied. This paper presents the performance of several machine learning based top tagging algorithms on a dataset constructed from simulated proton-proton collision events measured with the ATLAS detector at √ s = 13 TeV. The systematic uncertainties associated with these algorithms are estimated through an approximate procedure that is not meant to be used in a physics analysis, but is appropriate for the level of precision required for this study. The most performant algorithms are found to have the largest uncertainties, motivating the development of methods to reduce these uncertainties without compromising performance. To enable such efforts in the wider scientific community, the datasets used in this paper are made publicly available.</jats:p