scholarly journals Genome plasticity, a key factor of evolution in prokaryotes

2018 ◽  
Author(s):  
Itamar Sela ◽  
Yuri I. Wolf ◽  
Eugene V. Koonin
Keyword(s):  
Scaling Laws ◽  
Statistical Tests ◽  
Prokaryotic Genome ◽  
Functional Class ◽  
Gene Gain ◽  
Genome Plasticity ◽  
Microbial Genomes ◽  
Number Of Genes ◽  
Key Factor ◽  
Functional Classes

In prokaryotic genomes, the number of genes that belong to distinct functional classes shows apparent universal scaling with the total number of genes [1–5] (Fig. 1). This scaling can be approximated with a power law, where the scaling power can be sublinear, near-linear or super-linear. Scaling laws are robust under various statistical tests [4], across different databases and for different gene classifications [1–5]. Several models aimed at explaining the observed scaling laws have been proposed, primarily, based on the specifics of the respective biological functions [1, 5–8]. However, a coherent theory to explain the emergence of scaling within the framework of population genetics is lacking. We employ a simple mathematical model for prokaryotic genome evolution [9] which, together with the analysis of 34 clusters of closely related microbial genomes [10], allows us to identify the underlying forces that dictate genome content evolution. In addition to the scaling of the number of genes in different functional classes, we explore gene contents divergence to characterize the evolutionary processes acting upon genomes [11]. We find that evolution of the gene content is dominated by two factors that are specific to a functional class, namely, selection landscape and genome plasticity. Selection landscape quantifies the fitness cost that is associated with deletion of a gene in a given functional class or the advantage of successful incorporation of an additional gene. Genome plasticity, that can be considered a measure of evolvability, reflects both the availability of the genes of a given functional class in the external gene pool that is accessible to the evolving microbial population, and the ability of microbial genomes to accommodate these genes. The selection landscape determines the gene loss rate, and genome plasticity is the principal determinant of the gene gain rate.

scholarly journals Extensive repetitive DNA facilitates prokaryotic genome plasticity

2003 ◽  
Vol 100 (23) ◽  
pp. 13579-13584 ◽  
Author(s):  
R. A. Aras ◽  
J. Kang ◽  
A. I. Tschumi ◽  
Y. Harasaki ◽  
M. J. Blaser
Keyword(s):  
Repetitive Dna ◽  
Prokaryotic Genome ◽  
Genome Plasticity

scholarly journals Cardinality of sets of closed functional classes in weak 3-valued logics

2013 ◽  
Vol 19 ◽  
pp. 334-343
Author(s):  
Н.Н. Преловский
Keyword(s):  
Functional Class ◽  
Functional Classes

This paper proves that sets of closed functional classes in 3-valued logics of Bochvar $B_3$ and Hallden $H_3$ contains a continuum of different closed classes. It is also proven that both of these logics contain a closed functional class which has no basis.

scholarly journals Spatiospectral brain networks reflective of improvisational experience

2021 ◽  
Author(s):  
Josef Faller ◽  
Andrew Goldman ◽  
Yida Lin ◽  
James R. McIntosh ◽  
Paul Sajda
Keyword(s):  
Large Scale ◽  
Time Window ◽  
Network Connectivity ◽  
Functional Class ◽  
Network Activity ◽  
Oddball Paradigm ◽  
Auditory Oddball ◽  
Scalp Eeg ◽  
Musical Structures ◽  
Functional Classes

AbstractMusical improvisers are trained to categorize certain musical structures into functional classes, which is thought to facilitate improvisation. Using a novel auditory oddball paradigm (Goldman et al., 2020) which enables us to disassociate a deviant (i.e. musical cord inversion) from a consistent functional class, we recorded scalp EEG from a group of musicians who spanned a range of improvisational and classically trained experience. Using a spatiospectral based inter and intra network connectivity analysis, we found that improvisers showed a variety of differences in connectivity within and between large-scale cortical networks compared to classically trained musicians, as a function of deviant type. Inter-network connectivity in the alpha band, for a time window leading up to the behavioural response, was strongly linked to improvisation experience, with the default mode network acting as a hub. Spatiospectral networks post response were substantially different between improvisers and classically trained musicians, with greater inter-network connectivity (specific to the alpha and beta bands) seen in improvisers whereas those with more classical training had largely reduced inter-network activity (mostly in the gamma band). More generally, we interpret our findings in the context of network-level correlates of expectation violation as a function of subject expertise, and we discuss how these may generalize to other and more ecologically valid scenarios.

Activity of EGFR inhibition in atypical (non-V600E) BRAF-mutated metastatic colorectal cancer.

2019 ◽  
Vol 37 (4_suppl) ◽  
pp. 596-596 ◽  
Author(s):  
Benny Johnson ◽  
Jonathan M. Loree ◽  
Van K. Morris ◽  
Arvind Dasari ◽  
Shubham Pant ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Metastatic Colorectal Cancer ◽  
Kinase Activity ◽  
Statistical Tests ◽  
Class Ii ◽  
Rank Test ◽  
Class Iii ◽  
Primary Tumors ◽  
Egfr Inhibition ◽  
Functional Classes

596 Background: Atypical BRAF mutations (a BRAF) represent a rare molecular subtype of metastatic colorectal cancer (mCRC), distinct from BRAFV600E (class I). Preclinical data categorizes a BRAF into class II (intermediate-high kinase activity without RAS dependency) and III (low kinase activity, RAS dependent), however the clinical impact regarding these functional classes is unknown. Methods: We retrospectively analyzed 2,084 mCRC patients (pts) at MD Anderson Cancer Center (MDACC) to identify a BRAF and BRAFV600E. Clinicopathologic features were compared by chi-square or fisher’s exact test. Overall survival (OS) calculated utilizing Kaplan-Meier method and log-rank test. Statistical tests were two-sided. Results: a BRAF occurred in 36 mCRC pts (1.7%; 95% CI 1.2-2.4): 22 class III, 10 class II, 4 unclassified. The most common class II and class III BRAF codons were 469 (60%) and 594 (59%), respectively . Median OS (mOS) for a BRAF mCRC was 39.4 months (mo), without difference between class III and II. 19/36 (53%) were left sided primary tumors and 24/36 (67%) were microsatellite stable. BRAFV600E occurred in 221 mCRC pts (10.6%; 95% CI 9.3-12.0) with a mOS of 21.0 mo. In contrast to BRAFV600E which is mutually exclusive with RAS mutations, 12 pts with a BRAF were RAS mutants (class III, 7/21 [33%], class II 5/10 [50%]). Among a BRAF RAS wt pts, 11 (50%) received anti-EGFR monoclonal antibodies (mAb) (class III 7/14 [50%], class II 3/5 [60%]). There were no responses, and only three pts (all class III) achieved stable disease as best response. Median time on therapy was 4 months. Class II RAS wt pts treated with anti-EGFR mAb had mOS of 31.7 mo versus 46.8 mo for those not exposed (HR 2.0; 95% CI 0.3-15.9). Class III RAS wt pts treated with anti-EGFR mAB had mOS of 44.2 mo versus 45.7 mo for those not treated (HR 0.80; 95% CI 0.2-2.6). Conclusions: a BRAF mCRC appear to manifest improved clinical outcomes as previously reported. Despite this, the efficacy of anti-EGFR therapy appears limited in class II and III patients. Future efforts are needed to establish the predictive impact of functional classes on anti-EGFR efficacy and to design novel therapeutic strategies for a BRAF mCRC.

scholarly journals TWO PLUS TWO DOES NOT EQUAL THREE: STATISTICAL TESTS FOR MULTIPLE GENOME COMPARISON

2008 ◽  
Vol 06 (01) ◽  
pp. 1-22 ◽  
Author(s):  
NARAYANAN RAGHUPATHY ◽  
ROSE HOBERMAN ◽  
DANNIE DURAND
Keyword(s):  
Comparative Genomics ◽  
Large Scale ◽  
Statistical Tests ◽  
Gene Clusters ◽  
Genome Comparison ◽  
Pairwise Comparisons ◽  
Multiple Genome ◽  
Number Of Genes ◽  
Multiple Species ◽  
Combine Evidence

Gene clusters that span three or more chromosomal regions are of increasing importance, yet statistical tests to validate such clusters are in their infancy. Current approaches either conduct several pairwise comparisons or consider only the number of genes that occur in all of the regions. In this paper, we provide statistical tests for clusters spanning exactly three regions based on genome models of typical comparative genomics problems, including analysis of conserved linkage within multiple species and identification of large-scale duplications. Our tests are the first to combine evidence from genes shared among all three regions and genes shared between pairs of regions. We show that our tests of clusters spanning three regions are more sensitive than existing approaches, and can thus be used to identify more diverged homologous regions.

scholarly journals On the total number of genes and their length distribution in complete microbial genomes

2001 ◽  
Vol 17 (8) ◽  
pp. 425-428 ◽  
Author(s):  
M SKOVGAARD ◽  
L JENSEN ◽  
S BRUNAK ◽  
D USSERY ◽  
A KROGH
Keyword(s):  
Length Distribution ◽  
Microbial Genomes ◽  
Number Of Genes

A DNA polymorphism from five inbred strains of the mouse identifies a functional class of domesticus-type Y chromosome that produces the same phenotypic distribution of gonadal hermaphrodites

Genome ◽  
1991 ◽  
Vol 34 (1) ◽  
pp. 96-104 ◽  
Author(s):  
Fred G. Biddle ◽  
Brenda A. Eales ◽  
Yutaka Nishioka
Keyword(s):  
Inbred Strain ◽  
Y Chromosome ◽  
Dna Polymorphism ◽  
Low Frequency ◽  
Inbred Strains ◽  
Functional Class ◽  
Low Frequencies ◽  
Testis Differentiation ◽  
Y Chromosomes ◽  
Functional Classes

The wild-derived CLA inbred strain of the house mouse contains a domesticus-type Y chromosome that lacks a 2.3-kb TaqI band with fragment 1 of the AC11 probe. The CLA Y chromosome also causes a low frequency of XY gonadal hermaphrodites when backcrossed to the C57BL/6J strain (F.G. Biddle and Y. Nishioka. 1988. Genome, 30: 870–878). A similar domesticus-type Y chromosome, lacking the 2.3-kb TaqI band has now been found in the four historical inbred strains AKR/J, MA/MyJ, PL/J, and RF/J. When backcrossed to C57BL/6J, these four Y chromosomes cause low frequencies of gonadal hermaphrodites similar to the CLA Y and phenotypic distributions of types of gonad are indistinguishable from that with the CLA Y. The absence of the 2.3-kb TaqI band appears to be a polymorphism among domesticus-type Y chromosomes that identifies one of the three functional classes that, so far, can be distinguished only by their effects on testis differentiation in backcross test fetuses with the C57BL/6J strain. Three other historical inbred strains, BUB/BnJ, ST/bJ, and SWR/J, with a domesticus-type Y chromosome but containing the 2.3-kb TaqI band, were also assayed. They permit normal testis development in backcross test fetuses with C57BL/6J.Key words: mouse, Y chromosome, gonadal hermaphrodites, primary sex determination.

scholarly journals Genome Plasticity a Key Factor in the Success of Polyploid Wheat Under Domestication

Science ◽  
2007 ◽  
Vol 316 (5833) ◽  
pp. 1862-1866 ◽  
Author(s):  
J. Dubcovsky ◽  
J. Dvorak
Keyword(s):  
Genome Plasticity ◽  
Polyploid Wheat ◽  
Key Factor

scholarly journals Correlated variability in primate superior colliculus depends on functional class

2021 ◽  
Author(s):  
Leor N Katz ◽  
Gongchen Yu ◽  
James P Herman ◽  
Richard J Krauzlis
Keyword(s):  
Working Memory ◽  
Superior Colliculus ◽  
Functional Class ◽  
Population Coding ◽  
Spike Count ◽  
Behavioral Task ◽  
Primate Brain ◽  
Functional Classes ◽  
Saccade Task ◽  
Neuronal Tuning

Correlated variability (spike count correlations, rSC) in a population of neurons can constrain how information is read out, depending on behavioral task and neuronal tuning. Here we tested whether rSC also depends on neuronal functional class. We recorded from populations of neurons in macaque superior colliculus (SC), a structure that contains well-defined functional classes. We found that during a guided saccade task, different classes of neurons exhibited differing degrees of rSC. "Delay class" neurons displayed the highest rSC, especially during the delay epoch of saccade tasks that relied on working memory. This was only present among Delay class neurons within the same hemisphere. The dependence of rSC on functional class indicates that subpopulations of SC neurons occupy distinct circuit niches with distinct inputs. Such subpopulations should be accounted for differentially when attempting to model or infer population coding principles in the SC, or elsewhere in the primate brain.

scholarly journals Balrog: A universal protein model for prokaryotic gene prediction

2020 ◽  
Author(s):  
Markus J. Sommer ◽  
Steven L. Salzberg
Keyword(s):  
High Throughput Sequencing ◽  
Prokaryotic Genome ◽  
Amino Acid Sequences ◽  
Specific Gene ◽  
Universal Model ◽  
Gene Finding ◽  
Convolutional Network ◽  
Protein Coding ◽  
Microbial Genomes ◽  
Protein Coding Genes

AbstractLow-cost, high-throughput sequencing has led to an enormous increase in the number of sequenced microbial genomes, with well over 100,000 genomes in public archives today. Automatic genome annotation tools are integral to understanding these organisms, yet older gene finding methods must be retrained on each new genome. We have developed a universal model of prokaryotic genes by fitting a temporal convolutional network to amino-acid sequences from a large, diverse set of microbial genomes. We incorporated the new model into a gene finding system, Balrog (Bacterial Annotation by Learned Representation Of Genes), which does not require genome-specific training and which matches or outperforms other state-of-the-art gene finding tools. Balrog is freely available under the MIT license at https://github.com/salzberg-lab/Balrog.Author summaryAnnotating the protein-coding genes in a newly sequenced prokaryotic genome is a critical part of describing their biological function. Relative to eukaryotic genomes, prokaryotic genomes are small and structurally simple, with 90% of their DNA typically devoted to protein-coding genes. Current computational gene finding tools are therefore able to achieve close to 99% sensitivity to known genes using species-specific gene models.Though highly sensitive at finding known genes, all current prokaryotic gene finders also predict large numbers of additional genes, which are labelled as “hypothetical protein” in GenBank and other annotation databases. Many hypothetical gene predictions likely represent true protein-coding sequence, but it is not known how many of them represent false positives. Additionally, all current gene finding tools must be trained specifically for each genome as a preliminary step in order to achieve high sensitivity. This requirement limits their ability to detect genes in fragmented sequences commonly seen in metagenomic samples.We took a data-driven approach to prokaryotic gene finding, relying on the large and diverse collection of already-sequenced genomes. By training a single, universal model of bacterial genes on protein sequences from many different species, we were able to match the sensitivity of current gene finders while reducing the overall number of gene predictions. Our model does not need to be refit on any new genome. Balrog (Bacterial Annotation by Learned Representation of Genes) represents a fundamentally different yet effective method for prokaryotic gene finding.

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