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Do archaea undergo the same horizontal gene transfer processes as bacteria?

Do archaea undergo the same horizontal gene transfer processes as bacteria?


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Do archaea also undergo processes like conjugation, trasformation, etc.? If not, do they have their own horizontal gene transfer methods? Can bacteria conjugate with archaea?


Wagner A, Whitaker RJ, Krause DJ, Heilers JH, van Wolferen M, van der Does C, Albers SV (2017) Mechanisms of gene flow in archaea. Nat Rev Microbiol 15: 492-501.

The exchange of genetic material is a major driving force for genome evolution across the tree of life and has a role in archaeal speciation, adaptation and maintenance of diversity.


Soucy SM, Huang J, Gogarten JP (2015) Horizontal gene transfer: building the web of life. Nat Rev Genet 16: 472-482.

Horizontal gene transfer (HGT) is the sharing of genetic material between organisms that are not in a parent-offspring relationship. HGT is a widely recognized mechanism for adaptation in bacteria and archaea.


Fuchsman CA, Collins RE, Rocap G, Brazelton WJ (2017) Effect of the environment on horizontal gene transfer between bacteria and archaea. PeerJ 29: e3865.

Potential hotspots of horizontal gene transfer between archaea and bacteria include hot springs, marine sediments, and oil wells.


Identification of constraints influencing the bacterial genomes evolution in the PVC super-phylum

Horizontal transfer plays an important role in the evolution of bacterial genomes, yet it obeys several constraints, including the ecological opportunity to meet other organisms, the presence of transfer systems, and the fitness of the transferred genes. Bacteria from the Planctomyctetes, Verrumicrobia, Chlamydiae (PVC) super-phylum have a compartmentalized cell plan delimited by an intracytoplasmic membrane that might constitute an additional constraint with particular impact on bacterial evolution. In this investigation, we studied the evolution of 33 genomes from PVC species and focused on the rate and the nature of horizontally transferred sequences in relation to their habitat and their cell plan.

Results

Using a comparative phylogenomic approach, we showed that habitat influences the evolution of the bacterial genome’s content and the flux of horizontal transfer of DNA (HT). Thus bacteria from soil, from insects and ubiquitous bacteria presented the highest average of horizontal transfer compared to bacteria living in water, extracellular bacteria in vertebrates, bacteria from amoeba and intracellular bacteria in vertebrates (with a mean of 379 versus 110 events per species, respectively and 7.6% of each genomes due to HT against 4.8%). The partners of these transfers were mainly bacterial organisms (94.9%) they allowed us to differentiate environmental bacteria, which exchanged more with Proteobacteria, and bacteria from vertebrates, which exchanged more with Firmicutes. The functional analysis of the horizontal transfers revealed a convergent evolution, with an over-representation of genes encoding for membrane biogenesis and lipid metabolism, among compartmentalized bacteria in the different habitats.

Conclusions

The presence of an intracytoplasmic membrane in PVC species seems to affect the genome’s evolution through the selection of transferred DNA, according to their encoded functions.


Background

Various methods for computing a consensus tree for a given set of phylogenetic trees have been proposed [1]. The most known types of consensus trees are the strict consensus tree, the majority consensus tree and the extended majority consensus tree [1, 2]. The strict consensus tree contains only the edges that are common to all input trees. The majority consensus tree contains the edges that are present in more than 50% of the input trees, although higher percentages may also be considered. According to the extended majority rule, the consensus tree includes all of the majority edges to which compatible residual edges are added gradually, starting with the most frequent ones. Extended majority consensus trees are the most frequently used consensus trees in evolutionary biology because they are usually much better resolved (i.e. have lower mean degree of internal nodes) than strict and majority consensus trees [2].

The output of most conventional consensus tree algorithms is a single consensus tree [1]. However, in many practical situations it is much more appropriate to infer several consensus trees. In biology, it is often risky to group phylogenetic trees corresponding to different sets of genes. Each gene has its own evolutionary history which can substantially differ from evolutionary histories of other genes. For example, some individual genes or gene clusters (e.g. operons) affected by specific horizontal gene transfer events will display different evolutionary patterns than the rest of genes under study [3–8]. The evolutionary history of such genes or gene clusters will be depicted by phylogenetic trees having different topologies from that of the species tree which represents the evolution of genes that did not undergo gene transfers. Furthermore, the homogeneity of a given set of genes can be also affected by ancient duplication events causing the emergence of paralogous alleles.

There are several computational tools for analyzing and visualizing sets of incompatible phylogenetic trees, including SplitsTree [9], Dendroscope [10] and DensiTree [11]. These programs allow for inferring different kinds of phylogenetic networks which can be viewed as alternatives to multiple consensus trees. Holland et al. [12] were among the first to discuss a consensus building approach using splits network. Holland et al. compared gene trees of yeast genomes and demonstrated that consensus networks can be useful to depict hidden contradictory signals existing in species phylogenies.

Thus, the question whether a unique consensus tree or multiple consensus trees best characterize a given set of phylogenies arises as an alternative to phylogenetic network reconstruction approaches. If the given phylogenies are topologically congruent, they should be combined into a single consensus tree. However, if these phylogenies encompass conflicting genetic signals, they should be organized into multiple consensus trees, each of which accounts for a specific evolutionary pattern [13–15]. Figure 1 shows four phylogenetic trees T1, T2, T3 and T4 with seven leaves. Here, the solution consisting of two majority-rule consensus trees, T12 and T34, seems to be much more appropriate than the solution consisting of a single majority consensus tree, T1234, i.e. a star tree here, given by the traditional majority consensus approach.

Four phylogenetic trees T1, T2, T3 and T4 defined on the same set of seven leaves. Their majority-rule consensus tree is a star tree T1234. The majority-rule consensus trees, T12 and T34, constructed for the pairs of topologically close trees: T1 and T2, and T3 and T4, respectively

In this paper we describe a new algorithm for determining clusters of homogeneous trees which can be combined in order to infer multiple consensus trees. The idea of building multiple consensus trees was originally formulated by Maddison [16] who found that consensus trees of some subsets of a given set of trees may differ and that they are usually better resolved than the consensus tree of the whole set. Then, Stockham et al. [17] proposed two variants of a tree clustering algorithm based on k-means, which were meant to infer a set of strict consensus trees (called characteristic trees) minimizing the information loss. However, these methods were very expensive in terms of the running time because the consensus trees had to be determined for each set of clusters in all intermediate partitioning solutions tested by k-means. Bonnard et al. [13] described a method, called Multipolar Consensus, to display all the splits of a given set of phylogenetic trees having a support above a predefined threshold, using a minimum possible number of consensus trees. The authors indicated that biologically relevant secondary signals, which would be normally absent in a classical consensus tree, can be captured by the Multipolar Consensus method thus providing a convenient exploratory tool for phylogenetic analysis. This method allows one to display more secondary evolutionary signals than it is proposed by the extended majority rule consensus without making possible arbitrary choices which are usually made in this consensus method. In his recent paper, Guénoche [14] has presented a method for partitioning phylogenetic trees into one cluster (K=1, when given gene trees are homogeneous) or several clusters (K>1, when given gene trees are divergent). A generalized partition score, computed over a set of tree partitions, is calculated by the Guénoche method in order to determine the number of clusters, K, in which a given set of gene trees should be partitioned. Guénoche validated his method on both simulated data, i.e. random sets of trees organized in different topological groups, and real data, i.e. a set of non homogeneous gene trees of 30 E.coli strains assumed to be affected by horizontal gene transfers. The MCT (Multiple Consensus Trees) program developed by the author remains one of the rare pieces of software for inferring multiple consensus trees, available for the research community.

We will describe a new tree clustering method that relies on specific versions of the Silhouette (SH) [18] and Caliński-Harabasz (CH) [19] indices adapted for tree clustering with k-medoids. These cluster validity indices will be used to determine the best partitioning obtained over multiple random starts of k-medoids [20] when the number of clusters is fixed and then to select the optimal number of clusters for a given set of trees.


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Speculations and Conclusions

The old idea that protein folding is better preserved than sequence throughout evolution ( Schulz and Schirmer 1979 ) is fully accepted nowadays. The A domain of AAMY sequences is an excellent example of the preservation of folding—the TIM barrel—with a simultaneous general loss of sequence similarity. In fact, the gene sequences have diverged throughout evolution from the “first AAMY” gene in such a way that only the four segments indicated in table 4 are easily identified when AAMYs that belong to unrelated clusters are compared. As we have also reported, the structural features of the helix that joins the conserved residues Gly (LA2) and Asp (βA3) are also extraordinarily well preserved.

Our results suggest a general hypothesis for the evolution of domain A in AAMYs which involves two waves of evolutionary events. In the first wave, a limited set of genes strongly diverged from their common ancestral, or first AAMY, gene. These “first-generation” AAMYs probably had very low SI between them, had different lengths, and were the precursors of some of the unrelated clusters shown in figure 2 . Therefore, the only characteristics that the first-generation AAMYs preserved from the original gene were (1) the four segments indicated in table 4 , (2) the 3-D structure, and (3) the characteristics of helix αA2. Some examples of these first-generation AAMYs are the common ancestor for AII/BV/EI, that for BVIII/EIII, and that for BIV/EII. Each of these first-generation AAMYs would further evolve in a second wave to produce the sequences that form each cluster (each first-generation AAMY gives a different cluster). These “second-generation” sequences diverge only slightly when compared with those of the first wave and make up the contemporaneous AAMYs. During this second wave, HGT could generate the interkingdom similarities between clusters (AII/BV/EI, BIV/EII, and BVIII/EIII). In this sense, Mazodier and Davies (1991) suggested that the sequence similarity between the AAMY from StrLm (P09794 cluster BVIII) and those from mammalian and invertebrates (cluster EIII) found by Long et al. (1987) may be proof of natural gene transfer between distantly related organisms. Mazodier and Davies (1991) suggested that the HGT direction would be from Eukaryota to Streptomyces.

The clustering tree for Bacteria shows two characteristics that are not found in the trees for Archaea and Eukaryota: (1) the scattering in different clusters of AAMY sequences either from the same species or from closely related species (based on taxonomic grounds) and its opposite (the grouping of AAMYs from very distantly related organisms), and (2) that poorly related AAMY sequences frequently coexist in the genomes of some species and are included in different clusters. Following the above-mentioned general hypothesis for the evolution of domain A in AAMYs, we suggest that the evolutionary events that gave rise to this special distribution of bacterial AAMYs occurred during the first wave. This hypothesis is based on the fact that the sequences affected by these evolutionary phenomena are fully coherent with the characteristics of the rest of the sequences in the cluster. Therefore, it proves that they share the same common ancestor.

We may wonder which evolutionary events are responsible for the fact that the classification of the bacterial AAMY genes is not coherent with the current classification of the species. Probably, the first AAMY gene evolved to give rise to the limited set of first-generation AAMYs in two different ways: (1) gene duplications followed by independent parsimonious evolution, and (2) HGT. In addition to AAMY, the GH-13 superfamily comprises another 18 enzyme activities. There is functional ( Kuriki and Imanaka 1999 ) and sequence-based evidence (del-Rio, Morett, and Soberon 1997 Garcia-Vallvé, Palau, and Romeu 1999 ) that the “first” GH-13 in a genome can give rise to a set of paralogs through massive gene duplication. A posteriori, these sequences can evolve by independent parsimonious evolution and acquisition of subtly different specificities to obtain the rest of GH-13 in the genome. Nevertheless, new glycoside hydrolase genes may also be acquired by a genome in a radically different way: by HGT from an exogen organism ( Mazodier and Davies 1991 Garcia-Vallvé, Palau, and Romeu 1999 Garcia-Vallvé, Romeu, and Palau 2000 ). Figure 2B shows that poorly related AAMY sequences coexist in the genomes of some bacterial species. Most probably, the same is also true for other bacteria, but for the moment, only one gene has been characterized. Therefore, the use of completed bacterial genomes could help us to discover if there is more than one AAMY gene and to test their mutual evolutionary relationships, i.e., to determine whether they are paralogs or one of them has arrived by HGT. Since function assignment for genome-derived sequences is usually obtained by sequence comparison with proteins of known function and not from biochemical analysis, such a study should not be restricted to AAMYs and therefore should consider all GH-13 genes. Only 11 out of 25 completed bacterial genomes (Aquifex aeolicus, Bacillus subtilis, Chlamydia muridarum, Chlamydia pneumoniae, Chlamydia trachomatis, Deinococcus radiodurans, Escherichia coli, Haemophilus influenzae, Mycobacterium tuberculosis, Synechocystis sp., and Thermotoga maritima) have at least one GH-13 (93 genes). According to Garcia-Vallvé, Palau, and Romeu (1999) , GH-13 sequences in E. coli and B. subtilis seem to be the product of the gene duplication of a common ancestor not arrived at by HGT to their genomes (and the same is valid for the GH-13 of the rest of completed bacterial genomes S. Garcia-Vallvé, personal communication). The bacterial genomes, where poorly related AAMY sequences coexist (i.e., AerHy, PseSp, XanCa, StrLi, TheVu, and StreBo), have not yet been completed, and therefore only partial information is known. Once completed, it would be of interest to study whether these genes are paralogs or the result of HGT. Such information would allow us to fill in the gaps of the story of AAMY's evolution.


Materials and Methods

Protein sequences deposited as of April 26, 1999, were imported from the Swiss-All database (formed by SwissProt, TrEMBL, and updates Bairoch and Apweiler 1999 ) using the European Bioinformatic Institute Sequence Retrieval System (http://srs.ebi.ac.uk/). We considered only well-identified AAMY sequences (neither putative nor probable nor hypothetical) that belonged to glycoside hydrolases from family 13 (GH-13) and fully contained the characteristic A and B domains. All sequences were shortened to produce informationally similar segments that corresponded to the strict (β/α)8 barrel plus B domain (A+B segments) structure (see fig. 1 ). Therefore, neither the N-terminal tail nor domain C was considered. The amino acid segments of reference used to define the A+B sequences were obtained by analyzing the AAMY Protein Data Bank (PDB) structures (Berman et al. 2000). More specifically, we took the PDB sequences from the first residue in βA1 to the last residue in βA8 as our reference. The starting and finishing points for A+B segments in noncrystallized AAMYs were easily obtained by local alignments against the most similar PDB-derived segments. Redundancy for identical A+B sequences obtained from the same species was removed. The remaining sequences constituted our “full sample,” which is made up of 144 sequences (7 from Archaea, 48 from Bacteria, and 89 from Eukaryota) from 87 different species. All taxonomic classifications of AAMY sources were done according to the Taxonomy database (http://www3.ncbi.nlm.nih.gov/Taxonomy/).

In a second stage, all the A+B segments were used to produce two different sets of sequences, one corresponding to the strict domain A and the other corresponding to domain B. Once again, the boundary between domains was obtained by visually inspecting all crystallized AAMYs. The sequence for the B domain goes from the highly conserved His residue located after βA3 to four residues downstream of the Asp that binds to Ca 2+ (just before αA3, the α-helix preceding the highly conserved βA4). Domain A was obtained by joining the two subsegments on the left and right of domain B. For some sequences (e.g., Q05884 [StrLi]), the C-terminal boundary of domain B was difficult to find. In these special cases, we used the PSIpred server (http://insulin.brunel.ac.uk/psipred) to predict the secondary structure for the corresponding A+B sequences and identify where αA3 began. This server was chosen because at the latest Critical Assessment of Techniques for Protein Structure Prediction (CASP3 http://PredictionCenter.llnl.gov/casp3/), it was the most accurate method of secondary-structure prediction tested, achieving an overall three-state accuracy of 77% across 24 prediction targets. The PSIpred server was used (1) to find the locations of β-strands in domain A of sequences with an overall low similarity with crystallized AAMYs and (2) to confirm the locations of secondary structures inferred from alignments with sequences from crystallized structures. Two prediction methods offered by the server were used extensively to test the coherence of the secondary-structure assignments. These were PSIpred for predicting protein secondary structure and GenTHREADER for predicting protein tertiary structure by fold recognition.

To avoid bias in our results due to highly similar isozymes of a given species in the full sample, we defined an AAMY “representative sample.” The new sample therefore contained 7 sequences from Archaea, 44 from Bacteria, and 61 from Eukaryota and was made up of AAMYs from different biological species and isozymes with similarity indices (SI) below 95% for both the A and the B domains. The results presented in this paper—when not specifically indicated—are from the representative sample. The set of AAMY sequences that form this sample is shown in figure 2 , in which sequences may be identified by their Swiss-All accession numbers and the abbreviations for the names of the species are made up of the first three letters of the genus name followed by the first two letters of the species name (e.g., AerHy for Aeromonas hydrophila). Results from the full sample may be obtained as supplementary material from our web site (http://argo.urv.es/∼pujadas/AAMY/AAMY_01).

GH-13 sequences from completed bacterial genomes (http://www.ebi.ac.uk/genomes) were obtained from the CAZy database (http://afmb.cnrs-mrs.fr/∼pedro/CAZY/ghf_13.html). Analysis of a possible horizontal gene transfer (HGT) mechanism for the evolution of GH-13 in completed bacterial genomes was provided by S. Garcia-Vallvé (personal communication) and obtained by the method of Garcia-Vallvé, Palau, and Romeu (1999) .

Crystallographic data retrieval, as well as sequence- and structure-derived information, were taken from the database and links in the Structure Explorer (http://www.rcsb.org/pdb/). The PDB entries for AAMY were as follows: 1BSI ( Rydberg et al. 1999 ), 1B2Y ( Qian et al. 1994 ), 1CPU (G. D. Brayer et al., personal communication), 1HNY ( Brayer, Luo, and Withers 1995 ), and 1SMD ( Ramasubbu et al. 1996 ) from Homo sapiens 1BVN ( Wiegand, Epp, and Huber 1995 ), 1DHK ( Bompard-Gilles et al. 1996 ), 1JFH ( Qian et al. 1997 ), 1OSE ( Gilles et al. 1996 ), 1PIF ( Machius et al. 1996 ), 1PIG ( Machius et al. 1996 ), and 1PPI ( Qian et al. 1994 ) from Sus scrofa 1JAE ( Strobl et al. 1998a ), 1TMQ ( Strobl et al. 1998b ), and 1VIW ( Nahoum et al. 1999 ) from Tenebrio molitor 1AMY ( Kadziola et al. 1994 ), 1AVA ( Vallee et al. 1998 ), and 1BG9 ( Kadziola et al. 1998 ) from Hordeum vulgare 2AAA ( Boel et al. 1990 ) from Aspergillus niger 2TAA ( Matsuura et al. 1984 ) and 6TAA and 7TAA ( Brzozowski and Davies 1997 ) from Aspergillus oryzae 1BLI ( Machius et al. 1998 ), 1BPL ( Machius, Wiegand, and Huber 1995 ), and 1VJS ( Hwang et al. 1997 ) from Bacillus licheniformis 1BAG ( Fujimoto et al. 1998 ) from Bacillus subtilis and 1AQH, 1AQM ( Aghajari et al. 1998a ) and 1B0I ( Aghajari et al. 1998b ) from Pseudoalteromonas haloplanctis. X-ray diffraction resolutions and R factors for these structures ranged from 1.6 to 3.2 Å and from 0.151 to 0.208, respectively. Although 1BVZ from Thermoactinomyces vulgaris ( Kamitori et al. 1999 ) is considered an AAMY in its PDB file, a FASTA search (http://www2.ebi.ac.uk/fasta3/) showed that this enzyme is a neopullulanase (E.C. 3.2.1.135) whose sequence matches the NEPU_THEVU (Q08751) SwissProt entry exactly.

Multiple-sequence alignments were carried out for protein sequences of the working database with the CLUSTAL V algorithm ( Higgins and Sharp 1989 ) and the commercial program MEGALIGN, version 3.16, from the Lasergene software package (1997 DNASTAR, Inc., London, England) running in a Power Macintosh. Initial dendrograms and SIs were calculated by applying available MEGALIGN subroutines that calculated the SI parameter between two sequences (i and j) based on the method of Wilbur and Lipman (1983) with a gap penalty of 3, a K-tuple of 1, five top diagonals, and a window size of 5. The SI was calculated as the number of exactly matching residues in this alignment minus a “gap penalty” for every gap introduced. The result was then expressed as a percentage of the length of the shorter sequence. Multiple-alignment parameters (fixed and floating gap penalties) both had a value of 10. The protein weight matrix was PAM 250. We calculated the phylogenetic trees by the neighbor-joining method ( Saitou and Nei 1987 ) with 1,000 bootstrap replicates and a seed value of 111 with the CLUSTAL X program, version 1.8 ( Thompson et al. 1997 ). Unrooted trees were drawn with NJPLOT ( Perrière and Gouy 1996 ).

Hydrogen bonds involved in helix-capping interactions at the N- and C-terminal ends of αA2, along with distances between donors and acceptors, were analyzed using HBPLUS ( McDonald and Thornton 1994 ). The capping interactions were visually analyzed with the program Rasmol ( Sayle and Milner-White 1995 ) using a Silicon Graphics Indigo 2 XZ workstation. The DSSP algorithm included in Rasmol was used to determine the limits of β-strands which were not included in the PDB files (e.g., some of the β-strands in the TIM barrel of 1AQH), although their presence was obvious in the visualization.


Publications by authors named "Andrew Moore"

Division of Pulmonary and Critical Care Medicine, Stanford University School of Medicine, Stanford, CA, USA.

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International Association for the Study of Pain Presidential Task Force on Cannabis and Cannabinoid Analgesia: research agenda on the use of cannabinoids, cannabis, and cannabis-based medicines for pain management.

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Pain 2021 Jul162(Suppl 1):S117-S124

Pain Research, Department of Surgery & Cancer, Faculty of Medicine, Imperial College London, United Kingdom.

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Pragmatic but flawed: the NICE guideline on chronic pain.

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Lancet 2021 May397(10289):2029-2031

Research Department of Clinical, Educational and Health Psychology, University College London, London, UK.

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Adhesion-mediated mechanosignaling forces mitohormesis.

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Cell Metab 2021 May 17. Epub 2021 May 17.

Center for Bioengineering and Tissue Regeneration, Department of Surgery, University of California, San Francisco, San Francisco, CA 94143, USA Department of Bioengineering and Therapeutic Sciences and Department of Radiation Oncology, Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, and The Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA 94143, USA. Electronic address:

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What a privilege to have been a true editor.

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Bioessays 2021 Jun43(6):e2100111

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Actin mixes up mitochondria for inheritance.

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Nature 2021 May 14. Epub 2021 May 14.

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Tumor cell invasion into Matrigel: optimized protocol for RNA extraction.

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Biotechniques 2021 Jun 1070(6):327-335. Epub 2021 May 10.

Department of Onco-haematology, Gene & Cell Therapy, Bambino Gesù Children's Hospital-IRCCS, Rome, 00146, Italy.

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Is it worth writing covering letters anymore? Yes, but not for the reason you'd imagine.

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Bioessays 2021 May43(5):e2100085

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Phylogeny of the Bacillus altitudinis Complex and Characterization of a Newly Isolated Strain with Antilisterial Activity.

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J Food Prot 2021 Apr 1. Epub 2021 Apr 1.

The University of Tennessee Assistant Professor Food Science 2600 River Drive UNITED STATES Knoxville TN 37996.

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Actin cables and comet tails organize mitochondrial networks in mitosis.

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Nature 2021 Mar 3591(7851):659-664. Epub 2021 Mar 3.

Department of Physiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.

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ZAP-70 constitutively regulates gene expression and protein synthesis in chronic lymphocytic leukemia.

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Blood 2021 Feb 22. Epub 2021 Feb 22.

University of Cambridge, Cambridge, United Kingdom.

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The cost of superficial values in a life-threatening pandemic: How globalization grates against evolution….

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Bioessays 2021 Mar43(3):e2100026

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Genomic Background Governs Opposing Responses to Nalidixic Acid upon Megaplasmid Acquisition in .

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mSphere 2021 02 176(1). Epub 2021 Feb 17.

School of Plant Sciences, University of Arizona, Tucson, Arizona, USA.

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ACE2 abrogates tumor resistance to VEGFR inhibitors suggesting angiotensin-(1-7) as a therapy for clear cell renal cell carcinoma.

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Sci Transl Med 2021 Jan13(577)

Division of Hematology-Oncology and Cancer Biology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA.

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Cigarette Smoke Activates NOTCH3 to Promote Goblet Cell Differentiation in Human Airway Epithelial Cells.

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Am J Respir Cell Mol Biol 2021 0464(4):426-440

Department of Medicine, Section of Pulmonary, Critical Care and Sleep Medicine, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma and.

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Reliability and diagnostic performance of a new blood ketone and glucose meter in humans.

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J Int Soc Sports Nutr 2021 Jan 718(1). Epub 2021 Jan 7.

Department of Kinesiology, Augusta University, 3109 Wrightsboro Road, Augusta, GA, 30909, USA.

Background: Accurate and reliable monitoring of blood ketone and glucose levels is useful for athletes adhering to a ketogenic diet who want to verify that they are in a state of ketosis and, therefore, accruing performance adaptations. However, the cost of devices and testing materials may prohibit their use. More affordable field testing systems are available, but their accuracy and reliability remain in question. The objectives of this study were to evaluate the agreement between a previously validated ketone and glucose meter (Meter 1 - Precision Xtra) and a more affordable meter that has not been validated (Meter 2 - Keto-Mojo), and also to assess the diagnostic performance of Meter 2 for identifying nutritional ketosis.

Methods: Thirteen participants (7 females and 6 males 21.6 ± 3.0 years old) visited the laboratory three times in this randomized, double-blind cross-over design study. Ketone and glucose levels were measured with Meter 1 and Meter 2 twice before and twice after ingestion of a racemic ketone, natural ketone, or maltodextrin supplement. Intraclass correlation coefficient (ICC) estimates and their 95% confidence intervals were calculated to evaluate interrater reliability for Meter 1 and Meter 2. Bland-Altman plots were constructed to visually assess the agreement between devices. Area under the ROC curve analysis was performed to evaluate the diagnostic ability of Meter 2 to detect nutritional ketosis at a threshold ketone level of 0.5 mM as identified by Meter 1.

Results: Reliability between the meters was excellent for measuring ketones (ICC = .968 .942-.981) and good for measuring glucose (ICC = .809 .642-.893), though the Bland-Altman plot revealed substantial differences in agreement for measuring glucose. Area under the ROC curve (Area = 0.913 0.828-0.998) was excellent for diagnosing nutritional ketosis.

Conclusions: Both Meter 1 and Meter 2 displayed excellent agreement between each other for ketone measurement. Meter 2 also displayed an excellent level of accuracy for diagnosing nutritional ketosis at a threshold value of 0.5 mM, making it an effective and affordable alternative to more expensive testing devices.



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