Why the genomic LOCATION of individual SNPs is FUNCTIONAL?

Abstract Background The genome of Vibrionaceae bacteria, which consists of two circular chromosomes, is replicated in a highly ordered fashion. In fast-growing bacteria, multifork replication results in higher gene copy numbers and increased expression of genes located close to the origin of replication of Chr 1 (ori1). This is believed to be a growth optimization strategy to satisfy the high demand of essential growth factors during fast growth. The relationship between ori1-proximate growth-related genes and gene expression during fast growth has been investigated by many researchers. However, it remains unclear which other gene categories that are present close to ori1 and if expression of all ori1-proximate genes is increased during fast growth, or if expression is selectively elevated for certain gene categories. Results We calculated the pangenome of all complete genomes from the Vibrionaceae family and mapped the four pangene categories, core, softcore, shell and cloud, to their chromosomal positions. This revealed that core and softcore genes were found heavily biased towards ori1, while shell genes were overrepresented at the opposite part of Chr 1 (i.e., close to ter1). RNA-seq of Aliivibrio salmonicida and Vibrio natriegens showed global gene expression patterns that consistently correlated with chromosomal distance to ori1. Despite a biased gene distribution pattern, all pangene categories contributed to a skewed expression pattern at fast-growing conditions, whereas at slow-growing conditions, softcore, shell and cloud genes were responsible for elevated expression. Conclusion The pangene categories were non-randomly organized on Chr 1, with an overrepresentation of core and softcore genes around ori1, and overrepresentation of shell and cloud genes around ter1. Furthermore, we mapped our gene distribution data on to the intracellular positioning of chromatin described for V. cholerae, and found that core/softcore and shell/cloud genes appear enriched at two spatially separated intracellular regions. Based on these observations, we hypothesize that there is a link between the genomic location of genes and their cellular placement.

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A Novel NFIX-STAT6 Gene Fusion in Solitary Fibrous Tumor: A Case Report

International Journal of Molecular Sciences ◽

10.3390/ijms22147514 ◽

2021 ◽

Vol 22 (14) ◽

pp. 7514

Author(s):

David S. Moura ◽

Juan Díaz-Martín ◽

Silvia Bagué ◽

Ruth Orellana-Fernandez ◽

Ana Sebio ◽

...

Keyword(s):

Solitary Fibrous Tumor ◽

Gene Fusion ◽

Wide Spectrum ◽

Bioinformatic Analysis ◽

Fusion Partner ◽

Rna Seq ◽

Genomic Location ◽

Nuclear Factor I ◽

New Gene ◽

Fibrous Tumor

Solitary fibrous tumor is a rare subtype of soft-tissue sarcoma with a wide spectrum of histopathological features and clinical behaviors, ranging from mildly to highly aggressive tumors. The defining genetic driver alteration is the gene fusion NAB2–STAT6, resulting from a paracentric inversion within chromosome 12q, and involving several different exons in each gene. STAT6 (signal transducer and activator of transcription 6) nuclear immunostaining and/or the identification of NAB2–STAT6 gene fusion is required for the diagnostic confirmation of solitary fibrous tumor. In the present study, a new gene fusion consisting of Nuclear Factor I X (NFIX), mapping to 19p13.2 and STAT6, mapping to 12q13.3 was identified by targeted RNA-Seq in a 74-year-old female patient diagnosed with a deep-seated solitary fibrous tumor in the pelvis. Histopathologically, the neoplasm did not display nuclear pleomorphism or tumor necrosis and had a low proliferative index. A total of 378 unique reads spanning the NFIXexon8–STAT6exon2 breakpoint with 55 different start sites were detected in the bioinformatic analysis, which represented 59.5% of the reads intersecting the genomic location on either side of the breakpoint. Targeted RNA-Seq results were validated by RT-PCR/ Sanger sequencing. The identification of a new gene fusion partner for STAT6 in solitary fibrous tumor opens intriguing new hypotheses to refine the role of STAT6 in the sarcomatogenesis of this entity.

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Elimination of Introns at the Drosophila suppressor-of-forked Locus by P-Element-Mediated Gene Conversion Shows That an RNA Lacking a Stop Codon is Dispensable

Genetics ◽

10.1093/genetics/143.1.345 ◽

1996 ◽

Vol 143 (1) ◽

pp. 345-351

Author(s):

Carol J Williams ◽

Kevin O'Hare

Keyword(s):

X Chromosome ◽

Stop Codon ◽

Alternative Polyadenylation ◽

Gene Replacement ◽

P Element ◽

Wild Type ◽

Genomic Location ◽

Chromosomal Breaks ◽

White Locus ◽

Cleavage Stimulation Factor

Abstract The suppressor of forked [su(f)] locus affects the phenotype of mutations caused by transposable element insertions at unlinked loci. It encodes a putative 84-kD protein with homology to two proteins involved in mRNA 3′ end processing; the product of the yeast RNA14 gene and the 77-kD subunit of human cleavage stimulation factor. Three su(f) mRNAs are produced by alternative polyadenylation. The 2. 6 and 2.9-kb mRNAs encode the same 84-kD protein while a 1.3-kb RNA, which terminates within the fourth intron, is unusual in having no stop codon. Using P-element-mediated gene replacement we have copied sequences from a transformation construct into the su(f) gene creating a su(f) allele at the normal genomic location that lacks the first five introns. This allele is viable and appears wild type for su(f) function, demonstrating that the 1.3-kb RNA and the sequences contained within the deleted introns are dispensable for su(f) function. Compared with studies on gene replacement at the white locus, chromosomal breaks at su(f) appear to be less efficiently repaired from ectopic sites, perhaps because of the location of su(f) at the euchromatin/heterochromatin boundary on the X chromosome.

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The chicken ubiquitin gene contains a heat shock promoter and expresses an unstable mRNA in heat-shocked cells

Molecular and Cellular Biology ◽

10.1128/mcb.6.12.4602-4610.1986 ◽

1986 ◽

Vol 6 (12) ◽

pp. 4602-4610

Author(s):

U Bond ◽

M J Schlesinger

Keyword(s):

Heat Shock ◽

Chicken Embryo ◽

Actinomycin D ◽

Genomic Library ◽

Protein Coding ◽

Noncoding Regions ◽

Genomic Location ◽

Embryo Fibroblasts ◽

The Stability ◽

Chicken Embryo Fibroblasts

A chicken genomic library was screened to obtain genomic clones for ubiquitin genes. Two genes that differ in their genomic location and organization were identified. One gene, designated Ub I, contains four copies of the protein-coding sequence arranged in tandem, while the second gene, Ub II, contains three. The origin of the two major mRNAs that are induced after heat shock in chicken embryo fibroblasts was determined by generating DNA probes from the 5'-and 3'-noncoding regions of the two genes. Both mRNAs are transcribed from Ub I, the larger being the unspliced precursor of the smaller. A 674-base-pair intron was located within the 5'-noncoding region of Ub I. The second gene, Ub II, does not appear to code for an RNA species in normal or heat-shocked chicken embryo fibroblasts. The expression of ubiquitin mRNA during heat shock and recovery was examined. Addition of actinomycin D before heat shock completely abolished the response of ubiquitin mRNA to the stress. Analysis of the stability of the mRNA during recovery revealed that the mRNA accumulated during the heat shock is rapidly degraded with a half-life of approximately 1.5 h, suggesting a specialized but transient role for ubiquitin during heat shock.

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Identification of Predictive Pathways for Non-Hodgkin Lymphoma Prognosis

Cancer Informatics ◽

10.4137/cin.s6315 ◽

2010 ◽

Vol 9 ◽

pp. CIN.S6315 ◽

Cited By ~ 6

Author(s):

Xuesong Han ◽

Yang Li ◽

Jian Huang ◽

Yawei Zhang ◽

Theodore Holford ◽

...

Keyword(s):

Hodgkin Lymphoma ◽

Predictive Power ◽

B Cell Lymphoma ◽

Separate Analysis ◽

Nucleotide Polymorphisms ◽

Clinical Factors ◽

Single Nucleotide ◽

Kegg Pathways ◽

Non Hodgkin Lymphoma ◽

Individual Snps

Despite decades of intensive research, NHL (non-Hodgkin lymphoma) still remains poorly understood and is largely incurable. Recent molecular studies suggest that genomic variants measured with SNPs (single nucleotide polymorphisms) in genes may have additional predictive power for NHL prognosis beyond clinical risk factors. We analyzed a genetic association study. The prognostic cohort consisted of 346 patients, among whom 138 had DLBCL (diffuse large B-cell lymphoma) and 101 had FL (follicular lymphoma). For DLBCL, we analyzed 1229 SNPs which represented 122 KEGG pathways. For FL, we analyzed 1228 SNPs which represented 122 KEGG pathways. Unlike in existing studies, we targeted at identifying pathways with significant additional predictive power beyond clinical factors. In addition, we accounted for the joint effects of multiple SNPs within pathways, whereas some existing studies drew pathway-level conclusions based on separate analysis of individual SNPs. For DLBCL, we identified four pathways, which, combined with the clinical factors, had medians of the prediction logrank statistics as 2.535, 2.220, 2.094, 2.453, and 2.512, respectively. As a comparison, the clinical factors had a median of the prediction logrank statistics around 0.552. For FL, we identified two pathways, which, combined with the clinical factors, had medians of the prediction logrank statistics as 4.320 and 3.532, respectively. As a comparison, the clinical factors had a median of the prediction logrank statistics around 1.212. For NHL overall, we identified three pathways, which, combined with the clinical factors, had medians of the prediction logrank statistics as 5.722, 5.314, and 5.441, respective. As a comparison, the clinical factors had a median of the prediction logrank statistics around 4.411. The identified pathways have sound biological bases. In addition, they are different from those identified using existing approaches. They may provide further insights into the biological mechanisms underlying the prognosis of NHL.

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Transcription initiation defines kinetoplast RNA boundaries

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1808981115 ◽

2018 ◽

Vol 115 (44) ◽

pp. E10323-E10332 ◽

Cited By ~ 5

Author(s):

François M. Sement ◽

Takuma Suematsu ◽

Liye Zhang ◽

Tian Yu ◽

Lan Huang ◽

...

Keyword(s):

Antisense Rna ◽

Transcription Initiation ◽

Rna Binding ◽

Trna Genes ◽

Pentatricopeptide Repeat ◽

Mrna Stabilization ◽

Guide Rnas ◽

Genomic Location ◽

Specific Sequences ◽

Binding Subunit

Mitochondrial genomes are often transcribed into polycistronic RNAs punctuated by tRNAs whose excision defines mature RNA boundaries. Although kinetoplast DNA lacks tRNA genes, it is commonly held that in Trypanosoma brucei the monophosphorylated 5′ ends of functional molecules typify precursor partitioning by an unknown endonuclease. On the contrary, we demonstrate that individual mRNAs and rRNAs are independently synthesized as 3′-extended precursors. The transcription-defined 5′ terminus is converted into a monophosphorylated state by the pyrophosphohydrolase complex, termed the “PPsome.” Composed of the MERS1 NUDIX enzyme, the MERS2 pentatricopeptide repeat RNA-binding subunit, and MERS3 polypeptide, the PPsome binds to specific sequences near mRNA 5′ termini. Most guide RNAs lack PPsome-recognition sites and remain triphosphorylated. The RNA-editing substrate-binding complex stimulates MERS1 pyrophosphohydrolase activity and enables an interaction between the PPsome and the polyadenylation machinery. We provide evidence that both 5′ pyrophosphate removal and 3′ adenylation are essential for mRNA stabilization. Furthermore, we uncover a mechanism by which antisense RNA-controlled 3′–5′ exonucleolytic trimming defines the mRNA 3′ end before adenylation. We conclude that mitochondrial mRNAs and rRNAs are transcribed and processed as insulated units irrespective of their genomic location.

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dbVar structural variant cluster set for data analysis and variant comparison

F1000Research ◽

10.12688/f1000research.8290.1 ◽

2016 ◽

Vol 5 ◽

pp. 673 ◽

Cited By ~ 10

Author(s):

Lon Phan ◽

Jeffrey Hsu ◽

Le Quang Minh Tri ◽

Michaela Willi ◽

Tamer Mansour ◽

...

Keyword(s):

Genomic Sequence ◽

Chromosomal Rearrangements ◽

Copy Number Variations ◽

Structural Variant ◽

Variant Type ◽

External Data ◽

Cluster Set ◽

Genomic Location ◽

Complex Chromosomal Rearrangements ◽

Genomic Regions

dbVar houses over 3 million submitted structural variants (SSV) from 120 human studies including copy number variations (CNV), insertions, deletions, inversions, translocations, and complex chromosomal rearrangements. Users can submit multiple SSVs to dbVAR that are presumably identical, but were ascertained by different platforms and samples, to calculate whether the variant is rare or common in the population and allow for cross validation. However, because SSV genomic location reporting can vary – including fuzzy locations where the start and/or end points are not precisely known – analysis, comparison, annotation, and reporting of SSVs across studies can be difficult. This project was initiated by the Structural Variant Comparison Group for the purpose of generating a non-redundant set of genomic regions defined by counts of concordance for all human SSVs placed on RefSeq assembly GRCh38 (RefSeq accession GCF_000001405.26). We intend that the availability of these regions, called structural variant clusters (SVCs), will facilitate the analysis, annotation, and exchange of SV data and allow for simplified display in genomic sequence viewers for improved variant interpretation. Sets of SVCs were generated by variant type for each of the 120 studies as well as for a combined set across all studies. Starting from 3.64 million SSVs, 2.5 million and 3.4 million non-redundant SVCs with count >=1 were generated by variant type for each study and across all studies, respectively. In addition, we have developed utilities for annotating, searching, and filtering SVC data in GVF format for computing summary statistics, exporting data for genomic viewers, and annotating the SVC using external data sources.

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Snake venom NAD glycohydrolases: primary structures, genomic location, and gene structure

PeerJ ◽

10.7717/peerj.6154 ◽

2019 ◽

Vol 7 ◽

pp. e6154 ◽

Cited By ~ 2

Author(s):

Ivan Koludarov ◽

Steven D. Aird

Keyword(s):

Snake Venom ◽

Active Site ◽

Disulfide Bonds ◽

Venom Gland ◽

Catalytic Residue ◽

Nad Glycohydrolase ◽

Adp Ribosyl Cyclase ◽

Genomic Location ◽

Cyclic Adp Ribose ◽

Very High

NAD glycohydrolase (EC 3.2.2.5) (NADase) sequences have been identified in 10 elapid and crotalid venom gland transcriptomes, eight of which are complete. These sequences show very high homology, but elapid and crotalid sequences also display consistent differences. As in Aplysia kurodai ADP-ribosyl cyclase and vertebrate CD38 genes, snake venom NADase genes comprise eight exons; however, in the Protobothrops mucrosquamatus genome, the sixth exon is sometimes not transcribed, yielding a shortened NADase mRNA that encodes all six disulfide bonds, but an active site that lacks the catalytic glutamate residue. The function of this shortened protein, if expressed, is unknown. While many vertebrate CD38s are multifunctional, liberating both ADP-ribose and small quantities of cyclic ADP-ribose (cADPR), snake venom CD38 homologs are dedicated NADases. They possess the invariant TLEDTL sequence (residues 144–149) that bounds the active site and the catalytic residue, Glu228. In addition, they possess a disulfide bond (Cys121–Cys202) that specifically prevents ADP-ribosyl cyclase activity in combination with Ile224, in lieu of phenylalanine, which is requisite for ADPR cyclases. In concert with venom phosphodiesterase and 5′-nucleotidase and their ecto-enzyme homologs in prey tissues, snake venom NADases comprise part of an envenomation strategy to liberate purine nucleosides, and particularly adenosine, in the prey, promoting prey immobilization via hypotension and paralysis.

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Opportunities and limits of combining microbiome and genome data for complex trait prediction

10.1101/2020.10.05.325977 ◽

2020 ◽

Author(s):

Miguel Pérez-Enciso ◽

Laura M. Zingaretti ◽

Yuliaxis Ramayo-Caldas ◽

Gustavo de los Campos

Keyword(s):

Complex Traits ◽

Variance Components ◽

Complex Trait ◽

Parameter Estimates ◽

Phenotypic Variance ◽

Genome Data ◽

Individual Snps ◽

Simulation Strategy ◽

Microbiome Data ◽

Trait Prediction

AbstractThe analysis and prediction of complex traits using microbiome data combined with host genomic information is a topic of utmost interest. However, numerous questions remain to be answered: How useful can the microbiome be for complex trait prediction? Are microbiability estimates reliable? Can the underlying biological links between the host’s genome, microbiome, and the phenome be recovered? Here, we address these issues by (i) developing a novel simulation strategy that uses real microbiome and genotype data as input, and (ii) proposing a variance-component approach which, in the spirit of mediation analyses, quantifies the proportion of phenotypic variance explained by genome and microbiome, and dissects it into direct and indirect effects. The proposed simulation approach can mimic a genetic link between the microbiome and SNP data via a permutation procedure that retains the distributional properties of the data. Results suggest that microbiome data could significantly improve phenotype prediction accuracy, irrespective of whether some abundances are under direct genetic control by the host or not. Overall, random-effects linear methods appear robust for variance components estimation, despite the highly leptokurtic distribution of microbiota abundances. Nevertheless, we observed that accuracy depends in part on the number of microorganisms’ taxa influencing the trait of interest. While we conclude that overall genome-microbiome-links can be characterized via variance components, we are less optimistic about the possibility of identifying the causative effects, i.e., individual SNPs affecting abundances; power at this level would require much larger sample sizes than the ones typically available for genome-microbiome-phenome data.Author summaryThe microbiome consists of the microorganisms that live in a particular environment, including those in our organism. There is consistent evidence that these communities play an important role in numerous traits of relevance, including disease susceptibility or feed efficiency. Moreover, it has been shown that the microbiome can be relatively stable throughout an individual’s life and that is affected by the host genome. These reasons have prompted numerous studies to determine whether and how the microbiome can be used for prediction of complex phenotypes, either using microbiome alone or in combination with host’s genome data. However, numerous questions remain to be answered such as the reliability of parameter estimates, or which is the underlying relationship between microbiome, genome, and phenotype. The few available empirical studies do not provide a clear answer to these problems. Here we address these issues by developing a novel simulation strategy and we show that, although the microbiome can significantly help in prediction, it will be difficult to retrieve the actual biological basis of interactions between the microbiome and the trait.

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