bluuebunny/crossref_metadata_2025_split huggingface dataset:
TransferRNAin Decoding and the Wobble Hypothesis
Authors: Tuite Mick F , von der Haar Tobias 
Published: November 2016

AbstractTranslation of the genetic code stored in messenger ribonucleic acid (RNA) requires significantly fewer transfer RNAs (35–45) than there are codons (61, amino acid specifying). This is achieved through an increased flexibility in the allowable base‐pair interactions between the messenger RNA and the transfer RNA involving the third position of the codon and the first position of the corresponding anticodon. The rules governing this RNA:RNA interaction were originally summarised in Crick's ‘wobble hypothesis’. Covalent modification of the first base of an anticodon of a transfer RNA can profoundly affect the degree of flexibility in its base‐pairing potential by either extending or restricting such interactions. Recent studies suggest that the rate at which a codon is processed by the ribosome is influenced by whether or not decoding of that codon is via wobble base interactions. Yet, in spite of this flexibility and different rates of processing, decoding by transfer RNAs is achieved with considerable accuracy.Key ConceptsThe genetic code is decoded via transient interactions between messenger RNA (mRNA) and a series of ‘adaptor’ RNA molecules called transfer RNAs (tRNAs).The mRNA–tRNA interaction occurs on the ribosome via the complementary base pairing between the three‐base anticodon of the tRNA and the three‐base codon in the mRNA.A greater degree of flexibility of allowable base‐pair interactions between mRNA and tRNA allows most organisms to have far fewer tRNA species than there are codons.Specific non‐Watson–Crick base‐pair interactions occur between the third base of a codon and the first base of the anticodon of a tRNA during decoding, the so‐called wobble.Wobble base pairing enables the decoding of two or more codons by the same tRNA.Certain modified bases, for example inosine (I) can extend or restrict the degree of flexibility in the range of mRNA:tRNA interactions.Codons that are decoded by wobble base interactions are processed at a slower rate in the ribosome.Certain antibiotics, for example streptomycin can promote misreading at the wobble third position of a codon.The genetic code assignments are not universally conserved particularly in organellar genes, for example mitochondrial genes.
GAS5

Gene
Authors: Aly Mahmoud Khamis , Hirose Tetsuro 
Published: November 2016

Abstract

Growth arrest‐specific 5 (
GAS5
) is a nonprotein‐coding small nucleolar ribonucleic acid (snoRNA) host gene. Ten box C/D snoRNAs are encoded within the 11 introns of the human
GAS5
gene, whereas the spliced exons have little protein‐coding potential.
GAS5
has been classified as a member of the 5′ oligopyrimidine tract (5′ TOP) gene family. Growth‐dependent translation mediated by the 5′ TOP sequence determines the level of the
GAS5
spliced transcript (
GAS5
long noncoding RNA (lncRNA)) by a mechanism of translation‐linked RNA degradation.
GAS5
lncRNA plays a critical role in arresting cell growth and inducing apoptosis. The decreased expression of
GAS5
in multiple cancers suggests that
GAS5
acts as a tumour suppressor.
GAS5
lncRNA controls the gene expression of critical cell cycle and apoptosis regulators.
GAS5
lncRNA suppresses steroid‐responsive transcription by direct association with several steroid receptors and acts as a molecular decoy.
GAS5
lncRNA also sequestrates a specific functional microRNA.

Key Concepts

GAS5
is a nonprotein‐coding small nucleolar RNA host gene.

A translation‐linked mRNA decay pathway controls growth arrest‐specific accumulation of
GAS5
by way of its 5′‐terminal oligopyrimidine tract (5′ TOP).

GAS5
functions to arrest cell growth and induce apoptosis.

GAS5
controls expression of genes for critical regulators of the cell cycle and apoptosis.

GAS5
binds to steroid hormone receptors through its 3′‐terminal stem‐loop structure to suppress steroid‐responsive transcription.

GAS5
sequestrates a specific functional microRNA.
Criminal Responsibility and Genetics
Authors: Bostock Jennifer , Adshead Gwen 
Published: November 2016

Abstract

What potential influence would a genetic predisposition to crime have on traditional, moral and legal accounts of responsibility? What do we mean by crime? Is there any truth in the claims that genetics may predispose some individuals to criminal activity? And what about the most serious of criminal activity, homicide? Could a genetic defence affect conviction or sentencing? Does this evidence, and should it affect jurors' perceptions of guilt and innocence? What, if anything, does genetic evidence say about how we determine risk, punishment and moral responsibility? This article addresses these questions by exploring some of the legal cases that have attempted to use genetic evidence as mitigation in homicide trials. The authors conclude that despite advances in genetic science and attempts to forward genetic defences, our key understanding of legal and moral responsibility remains unchanged.

Key Concepts

Criminal, antisocial and violent activity are not synonymous terms.

There is no evidence for a ‘criminal gene’.

There is a question regarding the usefulness of evidence of genetic predispositions to violent criminal activity.

The role of genetic evidence in mitigation against homicide is limited and unsuccessful.

Genetic defence evidence may impact upon jurors' perceptions of guilt or length of sentence.

The quest to find a scientific answer to the question of what causes violent criminal activity may result from the desire to find ways of preventing this activity.

Science cannot determine whether an individual is morally responsible for his/her actions.
Genome Databases
Authors: Castrignanò Tiziana , Chillemi Giovanni 
Published: November 2016

Abstract

All the instructions needed by a cell to direct its activities are contained in its genome. The crucial advances in biology we are witnessing have an impact on genome databases, as the genome is the natural reference frame for mapping information about genes and proteins. Both primary databases, containing the exponentially growing raw information produced by the genomic initiatives, and secondary databases, containing functional annotations of genes and proteins, are the primary entry points for an increasing number of research communities, from biologists to geneticists, from clinical researchers to pharmacologists and more. The elaboration and extraction of new information from this huge amount of genome data is a key challenge that is met by interdisciplinary teams, including high‐throughput data analysts and high‐performance computing technologists.

Key Concepts

Genome databases are the official repositories of the ever‐growing amount of genomic sequences.

The genome represents a natural framework for mapping the biological data of an organism.

Genome browsers provide integrated and customisable views of the information.

Genome databases and their associated tools are the primary entry points for accessing biological information for an increasing number of species.

As more data on individual genomes become available, genome databases allow the reconstruction of whole ‘genetic landscapes’ of a pathology, population or species.

Future challenges are not only related to the sheer size of the data, but also to the need of protecting sensitive information without hampering the exploitation of data for new discoveries.
Lod Score
Authors: Thompson Elizabeth A 
Published: November 2016

AbstractLod scores are test statistics used to detect genetic linkage, which is the dependence in descent of deoxyribonucleic acid (DNA) at locations that are nearby in the genome. Lod scores are based on likelihood ratios and hence require parametric models for both trait and genetic marker phenotypes. The classical two‐locus lod score is introduced, and its use in linkage detection is described. Given a genetic marker map, a multilocus map‐specific lod score may be used to map causal trait loci against that framework map. Issues of ascertainment and trait‐model uncertainty are briefly described. A basic assumption of lod score analysis is that dependence between trait and marker data is only through the dependence in DNA descent. Modern genetic marker data allow accurate estimates of shared genome descending to currently observed individuals. This descent may then be used in trait data analyses, providing new approaches to lod score analyses.Key ConceptsDNA that is at locations on the same chromosome is genetically linked.DNA at linked locations tends to cosegregate from parents to offspring.Linkage analysis is the determination of locations of DNA‐affecting traits of interest from evidence of this cosegregation in defined pedigrees.Genetic markers at known positions in the genome provide a framework against which DNA‐affecting traits of interest may be mapped.The lod score is a statistic that measures the strength of dependence in segregation between DNA affecting a trait and DNA at locations of specified markers.The lod score is based on likelihood, and so requires a parametric model for trait data and genetic marker data.The classical lod score is robust to ascertainment through trait phenotypes.Modern genetic marker data permit inference of shared descent of genome, even in the absence of a specified pedigree structure.The lod score may be based on this inferred shared genome among individuals with observed trait phenotypes.
Bayes' Factors
Authors: Johnson Valen E 
Published: November 2016

AbstractBayes' factors play a pivotal role in Bayesian hypothesis testing, representing the factor through which the prior odds between competing hypotheses are transformed to the posterior odds between the hypotheses. Mathematically, Bayes' factors are defined as the ratio of the marginal probability assigned to the data by one hypothesis to the marginal probability assigned to data by the other hypothesis. In many parametric statistical tests, hypotheses are defined by the specification of prior densities on unknown parameters. When the prior density on a parameter concentrates on a single value under both the null and alternative hypotheses (resulting in two ‘simple’ or ‘point’ hypotheses), the Bayes factor equals the likelihood ratio. More generally, Bayes' factors represent the ratio of an averaged likelihood function, averaged with respect to the different prior densities assigned to the unknown parameter under each hypothesis. Inconsistencies in the limiting behaviour of Bayes' factors may arise when hypotheses are defined with respect to prior densities that have overlapping support.Key ConceptsBayes' factors are used in Bayesian hypothesis tests and are the key factors through which experimental data determine the posterior probability assigned to scientific hypotheses.Bayes' factors represent the ratio of the probability assigned by competing hypotheses to a common set of data.When testing whether one of the two simple hypotheses is true, the Bayes factor equals the likelihood ratio between the hypotheses.Bayes' factors equal the posterior odds divided by the prior odds between hypotheses.The natural logarithm of a Bayes' factor is called the weight of evidence.
Brain Tumours
Authors: López Giselle Y , Cummings Thomas J , Bigner Darell D , McLendon Roger E 
Published: November 2016

Abstract

Brain tumour genetics allow for improved classification and development of therapeutic options for primary brain tumours. Tumours with specific genetic alterations and molecular profiles are also associated with certain inherited familial cancer syndromes, including Li–Fraumeni syndrome, neurofibromatosis, tuberous sclerosis and Turcot syndrome. Advances in genetic profiling, including karyotypic analyses, genome‐wide sequencing, epigenetic studies and gene expression studies, have greatly improved the understanding of the genetic basis for these tumours. Genome‐wide sequencing identified three key pathways as mutated in the majority of primary glioblastomas, namely, the p53, retinoblastoma and receptor tyrosine kinase pathways. These studies have also identified characteristic genetic alterations in many tumours, such as
IDH1/2
mutations in oligodendrogliomas and progressive astrocytomas,
TERT
promoter mutations in primary glioblastomas and oligodendrogliomas and
BAF47
loss in atypical teratoid/rhabdoid tumours. Further analysis may prove key to identifying likely the therapeutic targets.

Key Concepts

The most common primary malignant brain tumour of adults is glioblastoma, a grade IV astrocytoma with an average survival of approximately 1 year following diagnosis.

The specific genetic alterations present in brain tumours are critical to prognosis and treatment decisions; new diagnostic criteria for brain tumours now integrate molecular classification with the previous histologic classification.

Genome‐wide expression analyses have identified distinct expression patterns in primary glioblastoma patients which predict survival and may eventually be used to target therapies.

Three key pathways altered in the majority of primary glioblastomas include the receptor tyrosine kinase, p53 and retinoblastoma signalling pathways.

Mutations in IDH1 or IDH2 are the most common abnormality in both oligodendrogliomas and progressive astrocytomas, making these tumours genetically distinct from primary glioblastoma.

The most characteristic chromosomal abnormality in oligodendrogliomas is complete allelic losses of chromosomes 1p and 19q.

Gene expression microarray data has made possible the classification of medulloblastomas into four subgroups: Sonic hedgehog pathway subgroup, Wnt pathway subgroup, group 3 and group 4.

Many paediatric brain tumours are characterised by alterations in genes involved in histone/chromatin modifications.
Theory of Cooperation
Authors: Gardner Andy , Griffin Ashleigh S , West Stuart A 
Published: November 2016

Abstract

Cooperation is defined as any adaptation that has evolved, at least in part, to increase the reproductive success of the actor's social partners. Inclusive fitness theory reveals that cooperation can be favoured by natural selection owing to either direct fitness benefits (mutually beneficial cooperation) or indirect fitness benefits (altruistic cooperation). Direct fitness benefits can arise as a simple by‐product of cooperation, or else owing to the existence of enforcement mechanisms, which may be fixed or conditioned according to the individual's cooperative behaviour. Indirect fitness benefits can arise when cooperation occurs between genetically similar individuals, as a consequence of limited dispersal, kin discrimination or greenbeard mechanisms. These theoretical mechanisms are illustrated with empirical examples, from laboratory experiments to field studies.

Key Concepts

The function of Darwinian adaptation is to maximise the organism's inclusive fitness.

Inclusive fitness describes how well an organism transmits copies of its genes to future generations.

Direct fitness is the part of inclusive fitness that comes from the organism's own reproductive success.

Indirect fitness is the part of inclusive fitness that comes from the reproductive success of the organism's genetic relatives.

Cooperation is any adaptation whose function is, at least in part, to increase the reproductive success of a social partner.

Cooperation is mutually beneficial if the actor also benefits and altruistic if the actor suffers a net loss of reproductive success.

Mutually beneficial cooperation is favoured by direct fitness benefits.

Direct fitness benefits can arise as a by‐product or owing to enforcement mechanisms.

Altruistic cooperation is favoured by indirect fitness benefits.

Indirect fitness benefits can arise as a consequence of limited dispersal, kin discrimination or greenbeard mechanisms.
Microbial Inhibitors of Apoptosis
Authors: Häcker Georg 
Published: November 2016

Abstract

Infection with microbial agents (bacteria, fungi, viruses and parasites) is a challenge to a host cell. The host cell's response to infection is complex and entails changes in the activity of a number of intracellular signalling pathways, commonly including the apoptotic machinery. Microbes, if they have evolved the capacity to infect mammals or other complex hosts, have to be able to deal with such defence systems. The lifestyle of microbes varies substantially – viruses, for instance, depend on an intact cell, whereas many bacteria grow on surfaces and are not sensitive to host cell apoptosis. What we know about microbial interference with host cell apoptosis is in accordance with these lifestyles. Viruses often directly target and inhibit the apoptosis machinery with specific proteins, whereas other microbes frequently also interfere with host apoptosis, but this interaction is more frequently indirect.

Key Concepts

Microbes (bacteria, fungi, viruses and parasites) can vary widely in terms of growth requirements and intimacy of association with host cells.

Apoptosis may be induced in cells infected with microbes as a defence mechanism.

A number of viruses carry genes whose products can directly interfere with the host cell's apoptosis apparatus.

Activation of host cells by microbes may enhance survival of the host cell or a responding immune cell.

Activation of antiapoptotic pathways in immune cells by microbial components is a regulatory mechanism of the immune response.
Identifying Genes Underlying Human Inherited Disease
Authors: Natanzon Yanina , Stein Catherine M , Cooke Bailey Jessica N 
Published: November 2016

AbstractThe field of genetic epidemiology seeks to identify the genetic risk factors underlying human disease. First, one must assess whether the trait has an underlying genetic influence, by estimating familial relative risk and/or conducting segregation analysis. In order to discover genes influencing human disease, both family‐ and population‐based studies are designed, and linkage and association methods are used to analyse the relationship between the trait(s) of interest and genetic markers. In general, linkage analysis is more powerful for detecting rare genes with large effects, whereas association analysis is more powerful for detecting common alleles with smaller effects. For the genetics of complex traits, additional factors must be considered, including rare variants, interactions with other genes and the environment, parent‐of‐origin effects and structural variants. Finally, casualty needs to be established beyond statistical implication of association with disease.Key ConceptsGenetic epidemiologists design both family‐ and population‐based studies to find genes influencing disease risk.Before conducting a gene‐mapping study, one must first ascertain whether the trait is influenced by genetic effects.Linkage and association analysis are two complementary methods used to analyse the relationship between trait(s) and genetic markers.The approach used (linkage vs association) depends on the study design, and these approaches depend on the genetic parameters (rare vs common alleles and large vs small effects).Other factors – rare variants, gene–gene and gene–environment interaction, existence of structural variants and parent‐of‐origin effects – further complicate the search for disease genes.
Studying Genomic and Epigenetic Aberrations by Microarray Profiling
Authors: Walker Logan C , Nones Katia , Patch Ann‐Marie , Waddell Nicola 
Published: November 2016

Abstract

Genomic (genetic and epigenetic) changes are important for interindividual variation and can cause or influence a person's predisposition for a wide range of human diseases, including cancer. An important tool for the study of these genetic and epigenetic changes is the microarray. For example, high‐density single nucleotide polymorphism (SNP) arrays have been used extensively to study inherited traits using genome‐wide association studies (GWASs). SNP arrays have also been commonly used to profile copy number changes which occur in and are specific to cancer cells. The same microarray technology can be used to assay deoxyribonucleic acid (DNA) methylation status across the genome. Despite the emergence of next‐generation sequencing, microarrays are a relatively low cost and high‐throughput platform that will continue to play a role in unravelling the complexity of human diseases.

Key Concepts

Genomic variation exists between individuals.

Microarrays can assay SNPs, copy number and methylation changes.

Microarrays have been used to study human diseases, inherited genetic traits and cancer.

Compared to single nucleotide polymorphisms, the contribution of inherited copy number variants to human development and disease remains relatively understudied.

Microarray analysis remains simpler and cheaper than next-generation sequencing, but lack the resolution of next‐generation sequencing.
Molecular Genetics of Craniosynostosis
Authors: Lattanzi Wanda 
Published: November 2016

AbstractCraniosynostosis (CS) is a relatively prevalent congenital malformation, due to the premature ossification of calvarial sutures that articulate skull bones, leading to a variable alteration of craniofacial landmarks and shape. CS is extremely heterogeneous in both clinical and a etiological aspects. It occurs as an isolated defect (i.e. nonsyndromic) in most cases, while it is syndromic in less than one fourth of cases. A genetic aetiology (i.e. mutations inFGFR,TWIST1,EFNB1,MSX, andERFgenes, among others) is known for most syndromes. Recent studies helped clarifying the genetic basis of new CS syndromes and nonsyndromic forms. The discovery of new genes (includingRUNX2,ALX4,TCF12andZIC1) enabled deciphering the complex signalling network that orchestrates craniofacial development. In this light, the entire CS disease spectrum can be regarded as a nosological continuum, with variable degree of severity, in which a strong genetic component provides the background for environmental factors that contribute to the aetiopathogenesis of the birth defect.Key ConceptsCraniosynostosis is a frequent congenital defect with a high degree of clinical and genetic heterogeneity.Nonsyndromic forms are more frequent, although their aetiology is still poorly understood.Many developmental syndromes include craniosynostosis in their phenotype.Different closely interacting genes (FGFR,TWIST1,MSX2,ERF,TCF12, among others) are involved in the molecular genetics of the best characterised and more frequent syndromic craniosynostoses.Most genes involved in the etiopathogenesis of craniosynostosis interact within a common gene network.