We thank Drs. Rami Al-Ouran, Seon-Young Kim, Yanhui (Claire) Hu, Ying-Wooi Wan, Naveen Manoharan, Sasidhar Pasupuleti, Aram Comjean, Dongxue Mao, Michael Wangler, Hsiao-Tuan Chao, Stephanie Mohr, and Norbert Perrimon for their support in the development and maintenance of MARRVEL. We are grateful to Samantha L. Deal and J. Michael Harnish for their input on this manuscript. &#160;
The initial development of MARRVEL was supported in part by the Undiagnosed Diseases Network Model Organisms Screening Center through the NIH Commonfund (U54NS093793) and&#160;through the NIH Office of Research Infrastructure Programs (ORIP)&#160;(R24OD022005).&#160;JW is supported by the NIH Eunice Kennedy Shriver National Institute of Child Health &#38; Human Development (F30HD094503) and The Robert and Janice McNair Foundation McNair MD/PhD Student Scholar Program at BCM. HJB is further supported by the NIH National Institute of General Medical Sciences (R01GM067858) and is an Investigator of the Howard Hughes Medical Institute. ZL is supported by the NIH National Institute of General Medical Science (R01GM120033), National Institute of Aging (R01AG057339), and the Huffington Foundation. SY received additional support from the NIH National Institute on Deafness and other Communication Disorders (R01DC014932), the Simons Foundation (SFARI Award: 368479), the Alzheimer&#8217;s Association (New Investigator Research Grant: 15-364099), Naman Family Fund for Basic Research and Caroline Wiess Law Fund for Research in Molecular Medicine.&#160;

<ol>
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The authors have nothing to disclose.

Critical steps in this protocol include the initial input (steps 1.1-1.3) and subsequent interpretation of the output. The most common reason why search results are negative is because of the many ways that a gene and/or variant can be described. While MARRVEL is updated on a scheduled basis, these updates may cause disconnects between the different databases that MARRVEL links to. Thus, the first step in troubleshooting is invariably checking to see if alternative names of the gene or variant will lead to a successful s...

The use of next-generation sequencing technology is expanding in both research and clinical genetic laboratories1. Whole-exome (WES) and whole-genome sequencing (WGS) analyses reveal numerous rare variants of unknown significance (VUS) in known disease-causing genes as well as variants in genes that are yet to be associated with a Mendelian disease (GUS: genes of uncertain significance). Presented with a list of genes and variants in a clinical sequence report, medical geneticists must manually visit multiple online resources to obtain more information to assess which variant may be responsible for a certain phenotype seen in the patient of interest. This process is time-consuming, and its efficacy is highly dependent on the expertise of the individual. Although several guideline papers have been published2,3, interpretation of WES and WGS requires manual curation since there is yet to be a standardized methodology for variant analysis. For the interpretation of VUS, knowledge on the previously reported genotype-phenotype relationship, mode of inheritance, and allele frequencies in the general population become valuable. In addition, knowledge on whether the variant affects a critical protein domain, or an evolutionarily conserved residue may increase or decrease the likelihood of pathogenicity. To gather all of this information, one typically needs to navigate through 10-20 human and model organism databases since the information is scattered through the World Wide Web.
Similarly, model organism scientists who work on specific genes and pathways are often interested in connecting their findings to human disease mechanisms and wish to take advantage of the knowledge that is being generated in the human genomics field. However, due to the rapid expansion and evolution of data sets regarding the human genome, it has been challenging to identify databases that provide useful information. In addition, since most model organism databases are designed for researchers who work with the specific organism on a daily basis, it is very difficult, for example, for a mouse researcher to search for specific information in a Drosophila database and vice versa. Similar to the variant interpretation searches performed by medical geneticists, identifying useful human and other model organism information is time-consuming and heavily dependent on the background of the model organism researcher. MARRVEL (Model organism Aggregated Resources for Rare Variant ExpLoration)4 is a tool designed for both groups of users to streamline their workflow.
MARRVEL (http://marrvel.org) was designed as a centralized search engine that collects data systematically in an efficient and consistent manner for clinicians and researchers. With information from 20 or more publicly available databases, this program allows users to quickly gather information and access a large number of human and model organism databases without reiterative searches. The search result pages also contain hyperlinks to the original sources of information, allowing individuals to access the raw data and gather additional information provided by the sources.
In contrast to many of the variant prioritization tools that require large sequencing data input in the form of VCF or BAM files and installations of often proprietary/commercial software, MARRVEL operates on any web-browser. It can be used at no cost and compatible with portable devices (e.g. smartphones, tablets) as long as one is connected to the internet. We chose this format since many clinicians and researchers typically need to search one or a few genes and variants at a time. Note that we are developing batch-download and API (application programming interface) features for MARRVEL to eventually allow users to curate hundreds of genes and variants at a time through customized query tools if necessary.
Due to the wide range of applications, in this protocol, we will describe a broadly encompassing approach on how to navigate through different datasets that MARRVEL displays. More targeted examples that are tailored towards specific users&#8217; needs will be described in Representative Results section. It is important to note that the output of MARRVEL still requires a certain level of background knowledge in either human genetics or model organisms to extract valuable information. We refer the readers to the table that lists primary papers that describe the function of each of the original databases that are curated by MARRVEL (Table 1). The following protocol is divided into three sections: (1) How to begin a search, (2) how to interpret MARRVEL human genetics outputs, and (3) how to make use of model organism data in MARRVEL. In the Representative Results section, more focused and specific approaches are described. MARRVEL is being actively updated so please refer to the current website&#8217;s FAQ page for details about data sources. We strongly recommend the users of MARRVEL to sign up in order to receive update notifications through the e-mail submission form at the bottom of the MARRVEL home page.

<table border="0" cellpadding="0" cellspacing="0" style="width:753px;" width="752">
	<colgroup>
		<col />
		<col span="2" />
		<col />
	</colgroup>
	<tbody>
		<tr height="21">
			<td height="21" style="height:21px;width:133px;">Human Genetics</td>
			<td dir="LTR" style="width:169px;">ClinVar</td>
			<td style="width:169px;">PMID: 29165669</td>
			<td dir="LTR" style="width:281px;">https://www.ncbi.nlm.nih.gov/clinvar/</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:133px;">Human Genetics</td>
			<td dir="LTR" style="width:169px;">DECIPHER</td>
			<td style="width:169px;">PMID: 19344873&#160;</td>
			<td dir="LTR" style="width:281px;">https://decipher.sanger.ac.uk/</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:133px;">Human Genetics</td>
			<td dir="LTR" style="width:169px;">DGV</td>
			<td style="width:169px;">PMID:&#160;24174537</td>
			<td dir="LTR" style="width:281px;">http://dgv.tcag.ca/dgv/app/home</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:133px;">Orthology Prediction</td>
			<td dir="LTR" style="width:169px;">DIOPT</td>
			<td style="width:169px;">PMID: 21880147&#160;</td>
			<td dir="LTR" style="width:281px;">https://www.flyrnai.org/cgi-bin/DRSC_orthologs.pl</td>
		</tr>
		<tr height="63">
			<td height="63" style="height:63px;width:133px;">Human Gene/Transcript Nomenclature</td>
			<td dir="LTR" style="width:169px;">Ensembl</td>
			<td style="width:169px;">PMID: 29155950&#160;</td>
			<td dir="LTR" style="width:281px;">https://useast.ensembl.org/</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:133px;">Human Genetics</td>
			<td dir="LTR" style="width:169px;">ExAC&#160;</td>
			<td style="width:169px;">PMID: 27535533</td>
			<td dir="LTR" style="width:281px;">http://exac.broadinstitute.org/</td>
		</tr>
		<tr height="63">
			<td height="63" style="height:63px;width:133px;">Primary Model Organism Databases</td>
			<td dir="LTR" style="width:169px;">FlyBase (Drosophila)</td>
			<td style="width:169px;">PMID:26467478</td>
			<td dir="LTR" style="width:281px;">http://flybase.org</td>
		</tr>
		<tr height="63">
			<td height="63" style="height:63px;width:133px;">Model Organism Database Integration Tools</td>
			<td style="width:169px;">Gene2Function</td>
			<td style="width:169px;">PMID: 28663344</td>
			<td dir="LTR" style="width:281px;">http://www.gene2function.org/search/</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:133px;">Human Genetics</td>
			<td dir="LTR" style="width:169px;">Geno2MP</td>
			<td>N/A</td>
			<td dir="LTR" style="width:281px;">http://geno2mp.gs.washington.edu/Geno2MP/</td>
		</tr>
		<tr height="106">
			<td height="106" style="height:106px;width:133px;">Human Genetics</td>
			<td dir="LTR" style="width:169px;">gnomAD</td>
			<td style="width:169px;">PMID: 27535533</td>
			<td dir="LTR" style="width:281px;">http://gnomad.broadinstitute.org/</td>
		</tr>
		<tr height="45">
			<td height="45" style="height:45px;width:133px;">Gene Ontology</td>
			<td dir="LTR" style="width:169px;">GO Central</td>
			<td style="width:169px;">PMID: 10802651, 25428369&#160;</td>
			<td dir="LTR" style="width:281px;">http://www.geneontology.org/</td>
		</tr>
		<tr height="63">
			<td height="63" style="height:63px;width:133px;">Human Gene/Protein Expression</td>
			<td dir="LTR" style="width:169px;">GTEx</td>
			<td style="width:169px;">PMID: 29019975, 23715323&#160;</td>
			<td dir="LTR" style="width:281px;">https://gtexportal.org/home/</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:133px;">Human Gene Nomenclature</td>
			<td dir="LTR" style="width:169px;">HGNC</td>
			<td style="width:169px;">PMID: 27799471&#160;</td>
			<td dir="LTR" style="width:281px;">https://www.genenames.org/</td>
		</tr>
		<tr height="63">
			<td height="63" style="height:63px;width:133px;">Primary Model Organism Databases</td>
			<td dir="LTR" style="width:169px;">IMPC (mouse)</td>
			<td style="width:169px;">PMID: 27626380</td>
			<td dir="LTR" style="width:281px;">http://www.mousephenotype.org/</td>
		</tr>
		<tr height="63">
			<td height="63" style="height:63px;width:133px;">Primary Model Organism Databases</td>
			<td dir="LTR" style="width:169px;">MGI (mouse)</td>
			<td style="width:169px;">PMID:25348401</td>
			<td dir="LTR" style="width:281px;">http://www.informatics.jax.org/</td>
		</tr>
		<tr height="61">
			<td height="61" style="height:61px;width:133px;">Model Organism Database Integration Tools</td>
			<td dir="LTR" style="width:169px;">Monarch Initiative</td>
			<td style="width:169px;">PMID: 27899636</td>
			<td dir="LTR" style="width:281px;">https://monarchinitiative.org/</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:133px;">Human Variant Nomenclature</td>
			<td style="width:169px;">Mutalyzer</td>
			<td style="width:169px;">PMID: 18000842&#160;</td>
			<td dir="LTR" style="width:281px;">https://mutalyzer.nl/</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:133px;">Human Genetics</td>
			<td dir="LTR" style="width:169px;">OMIM</td>
			<td style="width:169px;">PMID: 28654725</td>
			<td dir="LTR" style="width:281px;">https://omim.org/</td>
		</tr>
		<tr height="63">
			<td height="63" style="height:63px;width:133px;">Primary Model Organism Databases</td>
			<td dir="LTR" style="width:169px;">PomBase (fission yeast)</td>
			<td style="width:169px;">PMID:22039153</td>
			<td dir="LTR" style="width:281px;">https://www.pombase.org/</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:133px;">Literature</td>
			<td dir="LTR" style="width:169px;">PubMed</td>
			<td style="width:169px;">N/A</td>
			<td>https://www.ncbi.nlm.nih.gov/pubmed/</td>
		</tr>
		<tr height="63">
			<td height="63" style="height:63px;width:133px;">Primary Model Organism Databases</td>
			<td dir="LTR" style="width:169px;">RGD (rat)</td>
			<td style="width:169px;">PMID:25355511</td>
			<td dir="LTR" style="width:281px;">https://rgd.mcw.edu/</td>
		</tr>
		<tr height="63">
			<td height="63" style="height:63px;width:133px;">Primary Model Organism Databases</td>
			<td dir="LTR" style="width:169px;">SGD (budding yeast)</td>
			<td style="width:169px;">PMID: 22110037</td>
			<td dir="LTR" style="width:281px;">https://www.yeastgenome.org/</td>
		</tr>
		<tr height="63">
			<td height="63" style="height:63px;width:133px;">Human Gene/Protein Expression</td>
			<td dir="LTR" style="width:169px;">The Human Protein Atlas</td>
			<td style="width:169px;">PMID: 21752111</td>
			<td dir="LTR" style="width:281px;">https://www.proteinatlas.org/</td>
		</tr>
		<tr height="63">
			<td height="63" style="height:63px;width:133px;">Primary Model Organism Databases</td>
			<td dir="LTR" style="width:169px;">WormBase (C. elegans)</td>
			<td style="width:169px;">PMID:26578572</td>
			<td dir="LTR" style="width:281px;">http://wormbase.org</td>
		</tr>
		<tr height="63">
			<td height="63" style="height:63px;width:133px;">Primary Model Organism Databases</td>
			<td dir="LTR" style="width:169px;">ZFIN (zebrafish)</td>
			<td style="width:169px;">PMID:26097180</td>
			<td dir="LTR" style="width:281px;">https://zfin.org/</td>
		</tr>
	</tbody>
</table>

navigating marrvel, a web-based tool that integrates human genomics and model organism genetics information

Through whole-exome/genome sequencing, human geneticists identify rare variants that segregate with disease phenotypes. To assess if a specific variant is pathogenic, one must query many databases to determine whether the gene of interest is linked to a genetic disease, whether the specific variant has been reported before, and what functional data is available in model organism databases that may provide clues about the gene&#8217;s function in human. MARRVEL (Model organism Aggregated Resources for Rare Variant ExpLoration) is a one-stop data collection tool for human genes and variants and their orthologous genes in seven model organisms including in mouse, rat, zebrafish, fruit fly, nematode worm, fission yeast, and budding yeast. In this Protocol, we provide an overview of what MARRVEL can be used for and discuss how different datasets can be used to assess whether a variant of unknown significance (VUS) in a known disease-causing gene or a variant in a gene of uncertain significance (GUS) may be pathogenic. This protocol will guide a user through searching multiple human databases simultaneously starting with a human gene with or without a variant of interest. We also discuss how to utilize data from OMIM, ExAC/gnomAD, ClinVar, Geno2MP, DGV and DECHIPHER. Moreover, we illustrate how to interpret a list of ortholog candidate genes, expression patterns, and GO terms in model organisms associated with each human gene. Furthermore, we discuss the value protein structural domain annotations provided and explain how to use the multiple species protein alignment feature to assess whether a variant of interest affects an evolutionarily conserved domain or amino acid. Finally, we will discuss three different use-cases of this website. MARRVEL is an easily accessible open access website designed for both clinical and basic researchers and serves as a starting point to design experiments for functional studies.

Human geneticists and model organism scientists each use MARRVEL in distinct ways, each with different desired outcomes. Below are three vignettes of possible uses for MARRVEL.
Evaluating pathogenicity of a variant in a dominant disease 
Most of the users that visit MARRVEL use this website to analyze the likelihood that a rare human variant may cause a certain disease. For example, a missense (17:59477596 G&#62;A, p.R20Q) variant in TBX2 was found to segregate i...

Watch this Scientific Journal Video about Navigating MARRVEL, a Web-Based Tool that Integrates Human Genomics and Model Organism Genetics Information at JoVE.com

Navigating MARRVEL, a Web-Based Tool that Integrates Human Genomics and Model Organism Genetics Information

Here, we present a protocol to access and analyze many human and model organism databases efficiently. This protocol demonstrates the use of MARRVEL to analyze candidate disease-causing variants identified from next-generation sequencing efforts.

Through whole-exome/genome sequencing, human geneticists identify rare variants that segregate with disease phenotypes. To assess if a specific variant is ...

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9.6K ビュー.  Baylor College of Medicine. MARRVEL.orgは、主に希少遺伝病に焦点を当てて、ヒト遺伝子の疾患関連性の概要を提供できる情報を集約します。また、モデル生物データベースから生物学的情報を収集します。MARRVELは多くの時間と労力を節約できます。このツールを使用しないと、ユーザーは何時間もかけて異なるデータベースを検索し、MARRVELが数秒で取得できる情報を収集します。MARRVELは世界?...

ビデオ: Navigating MARRVEL, a Web-Based Tool that Integrates Human Genomics and Model Organism Genetics Information

Parallel Measurement of Circadian Clock Gene Expression and Hormone Secretion in Human Primary Cell Cultures

Circadian clocks are functional in all light-sensitive organisms, allowing for an adaptation to the external world by anticipating daily environmental changes. Considerable progress in our understanding of the tight connection between the circadian clock and most aspects of physiology has been made in the field over the last decade. However, unraveling the molecular basis that underlies the function of the circadian oscillator in humans stays of highest technical challenge. Here, we provide a detailed description of an experimental approach for long-term (2-5 days) bioluminescence recording and outflow medium collection in cultured human primary cells. For this purpose, we have transduced primary cells with a lentiviral luciferase reporter that is under control of a core clock gene promoter, which allows for the parallel assessment of hormone secretion and circadian bioluminescence. Furthermore, we describe the conditions for disrupting the circadian clock in primary human cells by transfecting siRNA targeting CLOCK. Our results on the circadian regulation of insulin secretion by human pancreatic islets, and myokine secretion by human skeletal muscle cells, are presented here to illustrate the application of this methodology. These settings can be used to study the molecular makeup of human peripheral clocks and to analyze their functional impact on primary cells under physiological or pathophysiological conditions.

Here, we describe settings to monitor in parallel circadian bioluminescence and the secretory activity of human islet cells and primary myotubes. For this, we employed lentiviral gene delivery of a luciferase core clock reporter, followed by in vitro synchronization and collection of outflow medium by continuous cell perifusion.

ヒト初代細胞培養物における概日時計遺伝子発現やホルモン分泌のパラレル測定

Circadian clocks are functional in all light-sensitive organisms, allowing for an adaptation to the external world by anticipating daily environmental ...

遺伝学

Determination of the Optimal Chromosomal Location(s) for a DNA Element in Escherichia coli Using a Novel Transposon-mediated Approach

The optimal chromosomal position(s) of a given DNA element was/were determined by transposon-mediated random insertion followed by fitness selection. In bacteria, the impact of the genetic context on the function of a genetic element can be difficult to assess. Several mechanisms, including topological effects, transcriptional interference from neighboring genes, and/or replication-associated gene dosage, may affect the function of a given genetic element. Here, we describe a method that permits the random integration of a DNA element into the chromosome of Escherichia coli and select the most favorable locations using a simple growth competition experiment. The method takes advantage of a well-described transposon-based system of random insertion, coupled with a selection of the fittest clone(s) by growth advantage, a procedure that is easily adjustable to experimental needs. The nature of the fittest clone(s) can be determined by whole-genome sequencing on a complex multi-clonal population or by easy gene walking for the rapid identification of selected clones. Here, the non-coding DNA region DARS2, which controls the initiation of chromosome replication in E. coli, was used as an example. The function of DARS2 is known to be affected by replication-associated gene dosage; the closer DARS2 gets to the origin of DNA replication, the more active it becomes. DARS2 was randomly inserted into the chromosome of a DARS2-deleted strain. The resultant clones containing individual insertions were pooled and competed against one another for hundreds of generations. Finally, the fittest clones were characterized and found to contain DARS2 inserted in close proximity to the original DARS2 location.

Determination of the Optimal Chromosomal Locations for a DNA Element in Escherichia coli Using a Novel Transposon-mediated Approach

Here, the power of a transposon-mediated random insertion of a non-coding DNA element was used to resolve its optimal chromosomal position.

新規トランスポゾンを介したアプローチを使用してエシェリヒア属大腸菌DNA 要素の最適な染色体の場所の決定

The optimal chromosomal position(s) of a given DNA element was/were determined by transposon-mediated random insertion followed by fitness selection. In ...

Ultralow Input Genome Sequencing Library Preparation from a Single Tardigrade Specimen

Tardigrades are microscopic animals that enter an ametabolic state called anhydrobiosis when facing desiccation and can return to their original state when water is supplied. The genomic sequencing of microscopic animals such as tardigrades risks bacterial contamination that sometimes leads to erroneous interpretations, for example, regarding the extent of horizontal gene transfer in these animals. Here, we provide an ultralow input method to sequence the genome of the tardigrade, Hypsibius dujardini, from a single specimen. By employing rigorous washing and contaminant exclusion along with an efficient extraction of the 50 ~ 200 pg genomic DNA from a single individual, we constructed a library sequenced with a DNA sequencing instrument. These libraries were highly reproducible and unbiased, and an informatics analysis of the sequenced reads with other H. dujardini genomes showed a minimal amount of contamination. This method can be applied to unculturable tardigrades that could not be sequenced using previous methods.

Contamination during the genomic sequencing of microscopic organisms remains a large problem. Here, we show a method to sequence the genome of a tardigrade from a single specimen with as little as 50 pg of genomic DNA without whole genome amplification to minimize the risk of contamination.

超低入力ヒトゲノム単一 Tardigrade の標本からのライブラリの準備

Tardigrades are microscopic animals that enter an ametabolic state called anhydrobiosis when facing desiccation and can return to their original state ...

Differentiation, Maintenance, and Analysis of Human Retinal Pigment Epithelium Cells: A Disease-in-a-dish Model for BEST1 Mutations

Although over 200 genetic mutations in the human BEST1 gene have been identified and linked to retinal degenerative diseases, the pathological mechanisms remain elusive mainly due to the lack of a good in vivo model for studying BEST1 and its mutations under physiological conditions. BEST1 encodes an ion channel, namely BESTROPHIN1 (BEST1), which functions in retinal pigment epithelium (RPE); however, the extremely limited accessibility to native human RPE cells represents a major challenge for scientific research. This protocol describes how to generate human RPEs bearing BEST1 disease-causing mutations by induced differentiation from human pluripotent stem cells (hPSCs). As hPSCs are self-renewable, this approach allows researchers to have a steady source of hPSC-RPEs for various experimental analyses, such as immunoblotting, immunofluorescence, and patch clamp, and thus provides a very powerful disease-in-a-dish model for BEST1-associated retinal conditions. Notably, this strategy can be applied to study RPE (patho)physiology and other genes of interest natively expressed in RPE.

Differentiation, Maintenance, and Analysis of Human Retinal Pigment Epithelium Cells: A Disease-in-a-dish Model for BEST1 Mutations

Here we present a protocol to differentiate retinal pigment epithelium (RPE) cells from human pluripotent stem cells bearing patient-derived mutations. The mutant cell lines may be used for functional analyses including immunoblotting, immunofluorescence, and patch clamp. This disease-in-a-dish approach circumvents the difficulty of obtaining native human RPE cells.

差別化、保守、および人間の網膜色素上皮細胞の解析: BEST1変異の皿での疾患モデル

Although over 200 genetic mutations in the human BEST1 gene have been identified and linked to retinal degenerative diseases, the pathological mechanisms ...

Novel Sequence Discovery by Subtractive Genomics

Subtractive genomics can be used in any research where the goal is to identify the sequence of a gene, protein, or general region that is embedded in a larger genomic context. Subtractive genomics enables a researcher to isolate a target sequence of interest (T) by comprehensive sequencing and subtracting out known genetic elements (reference, R). The method can be used to identify novel sequences such as mitochondria, chloroplasts, viruses, or germline restricted chromosomes, and is particularly useful when T cannot be easily isolated from R. Beginning with the comprehensive genomic data (R + T), the method uses Basic Local Alignment Search Tool (BLAST) against a reference sequence, or sequences, to remove the matching known sequences (R), leaving behind the target (T). For subtraction to work best, R should be a relatively complete draft that is missing T. Since sequences remaining after subtraction are tested through quantitative Polymerase Chain Reaction (qPCR), R does not need to be complete for the method to work. Here we link computational steps with experimental steps into a cycle that can be iterated as needed, sequentially removing multiple reference sequences and refining the search for T. The advantage of subtractive genomics is that a completely novel target sequence can be identified even in cases in which physical purification is difficult, impossible, or expensive. A drawback of the method is finding a suitable reference for subtraction and obtaining T-positive and negative samples for qPCR testing. We describe our implementation of the method in the identification of the first gene from the germline-restricted chromosome of zebra finch. In that case computational filtering involved three references (R), sequentially removed over three cycles: an incomplete genomic assembly, raw genomic data, and transcriptomic data.

The purpose of this protocol is to use a combination of computational and bench research to find novel sequences that cannot be easily separated from a co-purifying sequence, which may be only partially known.

減法混色のゲノム解析による新規シーケンスの発見

Subtractive genomics can be used in any research where the goal is to identify the sequence of a gene, protein, or general region that is embedded in a ...

Chromogenic In Situ Hybridization as a Tool for HPV-Related Head and Neck Cancer Diagnosis

Human papillomavirus (HPV) infection is a major risk factor for a subtype of oropharyngeal squamous cell carcinoma (OPSCC), which tends to be associated with a better outcome than alcohol- and tobacco-related OPSCC. Chromogenic in situ hybridization (CISH) of HPV viral RNA could allow the semiquantitative evaluation of viral transcripts of the oncogenic proteins E6 and E7 and an in situ visualization with a good spatial resolution. This technique allows the diagnosis of an active infection with the visualization of HPV transcription in the tumoral HPV-infected cells. An advantage of this technique is the avoidance of contamination from nonneoplastic HPV-infected cells adjacent to the tumor. Overall, its good diagnosis performances have it considered to be the gold standard for active HPV infection identification. Since E6 and E7 viral protein interaction with cell proteins pRb and p53 is mandatory for cell transformation, HPV RNA CISH is functionally relevant and acutely reflects active oncogenic HPV infection. This technique is clinically relevant as well since &#34;low&#34; or &#34;high&#34; HPV transcription levels helped the identification of two prognosis groups among HPV-related p16-positive head and neck cancer patients. Here we present the protocol for manual HPV RNA CISH performed on formalin-fixed paraffin-embedded (FFPE) slides with a kit obtained from the manufacturer. Instead of chromogenic revelation, RNA in situ hybridization may also be performed with fluorescent revelation (RNA FISH). It may also be combined with conventional immunostaining.

Human papillomavirus (HPV) RNA chromogenic in situ hybridization is considered to be one of the gold standards for active human papillomavirus infection detection within tumors. It allows the visualization of HPV E6-E7 mRNA expression with localization and semiquantitative evaluation of its signal.

発色性の発色性ハイブリダイゼーションは、HPV関連の頭頸部癌診断のためのツールを提供する

Human papillomavirus (HPV) infection is a major risk factor for a subtype of oropharyngeal squamous cell carcinoma (OPSCC), which tends to be associated ...

Modulation of Tau Subcellular Localization as a Tool to Investigate the Expression of Disease-related Genes

Tau is a microtubule binding protein expressed in neurons and its main known function is related to the maintenance of cytoskeletal stability. However, recent evidence indicated that Tau is present also in other subcellular compartments including the nucleus where it is implicated in DNA protection, in rRNA transcription, in the mobility of retrotransposons and in the structural organization of the nucleolus. We have recently demonstrated that nuclear Tau is involved in the expression of the VGluT1 gene, suggesting a molecular mechanism that could explain the pathological increase of glutamate release in the early stages of Alzheimer&#39;s disease. Until recently, the involvement of nuclear Tau in modulating the expression of target genes has been relatively uncertain and ambiguous due to technical limitations that prevented the exclusion of the contribution of cytoplasmic Tau or the effect of other downstream factors not related to nuclear Tau. To overcome this uncertainty, we developed a method to study the expression of target genes specifically modulated by the nuclear Tau protein. We employed a protocol that couples the use of localization signals and the subcellular fractionation, allowing the exclusion of the interference from the cytoplasmic Tau molecules. Most notably, the protocol is easy and is composed of classic and reliable methods that are broadly applicable to study the nuclear function of Tau in other cell types and cellular conditions.

Tau is a neuronal protein present both in the cytoplasm, where it binds microtubules, and in the nucleus, where it exerts unconventional functions including the modulation of Alzheimer&#39;s disease-related genes. Here, we describe a method to investigate the function of nuclear Tau while excluding any interferences coming from cytoplasmic Tau.

疾患関連遺伝子の発現を調べるツールとしてのタウ小細胞局在の変調

Tau is a microtubule binding protein expressed in neurons and its main known function is related to the maintenance of cytoskeletal stability. However, ...

Preparation of Meiotic Chromosome Spreads from Zebrafish Spermatocytes

Meiosis is the key cellular process required to create haploid gametes for sexual reproduction. Model organisms have been instrumental in understanding the chromosome events that take place during meiotic prophase, including the pairing, synapsis, and recombination events that ensure proper chromosome segregation. While the mouse has been an important model for understanding the molecular mechanisms underlying these processes, not all meiotic events in this system are analogous to human meiosis. We recently demonstrated the exciting potential of the zebrafish as a model of human spermatogenesis. Here we describe, in detail, our methods to visualize meiotic chromosomes and associated proteins in chromosome spread preparations. These preparations have the advantage of allowing high resolution analysis of chromosome structures. First, we describe the procedure for dissecting testes from adult zebrafish, followed by cell dissociation, lysis, and spreading of the chromosomes. Next, we describe the procedure for detecting the localization of meiotic chromosome proteins, by immunofluorescence detection, and nucleic acid sequences, by fluorescence in situ hybridization (FISH). These techniques comprise a useful set of tools for the cytological analysis of meiotic chromatin architecture in the zebrafish system. Researchers in the zebrafish community should be able to quickly master these techniques and incorporate them into their standard analyses of reproductive function.

Nuclear surface spreads are an indispensable tool for studying chromosome events during meiosis. Here we demonstrate a method to prepare and visualize meiotic chromosomes during prophase I from zebrafish spermatocytes.

ゼブラフィッシュの精子細胞からのマイオサイト染色体の広がりの調製

Meiosis is the key cellular process required to create haploid gametes for sexual reproduction. Model organisms have been instrumental in understanding ...

3D Multicolor DNA FISH Tool to Study Nuclear Architecture in Human Primary Cells

A major question in cell biology is genomic organization within the nuclear space and how chromatin architecture can influence processes such as gene expression, cell identity and differentiation. Many approaches developed to study the 3D architecture of the genome can be divided into two complementary categories: chromosome conformation capture based technologies (C-technologies) and imaging. While the former is based on capturing the chromosome conformation and proximal DNA interactions in a population of fixed cells, the latter, based on DNA fluorescence in situ hybridization (FISH) on 3D-preserved nuclei, allows contemporary visualization of multiple loci at a single cell level (multicolor), examining their interactions and distribution within the nucleus (3D multicolor DNA FISH). The technique of 3D multicolor DNA FISH has a limitation of visualizing only a few predetermined loci, not permitting a comprehensive analysis of the nuclear architecture. However, given the robustness of its results, 3D multicolor DNA FISH in combination with 3D-microscopy and image reconstruction is a possible method to validate C-technology based results and to unambiguously study the position and organization of specific loci at a single cell level. Here, we propose a step by step method of 3D multicolor DNA FISH suitable for a wide range of human primary cells and discuss all the practical actions, crucial steps, notions of 3D imaging and data analysis needed to obtained a successful and informative 3D multicolor DNA FISH within different biological contexts.

3D multicolor DNA FISH represents a tool to visualize multiple genomic loci within 3D preserved nuclei, unambiguously defining their reciprocal interactions and localization within the nuclear space at a single cell level. Here, a step by step protocol is described for a wide spectrum of human primary cells.

ヒト初等細胞の核アーキテクチャを研究する3D多色DNA FISHツール

A major question in cell biology is genomic organization within the nuclear space and how chromatin architecture can influence processes such as gene ...

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

Recently, a new implementation of a previously described method for interpreting genome-wide association study (GWAS) data using metabolic pathway analysis has been developed and released. The Pathway Association Study Tool (PAST) was developed to address concerns with user-friendliness and slow-running analyses. This new user-friendly tool has been released on Bioconductor and Github. In testing, PAST ran analyses in less than one hour that previously required twenty-four or more hours. In this article, we present the protocol for using either the Shiny application or the R console to run PAST.

By running the Pathway Association Study Tool (PAST), either through the Shiny application or through the R console, researchers can gain a deeper understanding of the biological meaning of their genome-wide association study (GWAS) results by investigating the metabolic pathways involved.