Archive for the ‘Inside NIF’ Category

Neuronify the first neuroscience app on my phone

Posted on November 23rd, 2016 in Anita Bandrowski, Inside NIF | No Comments »

So I never ever put apps on my phone because they are a waste of space, also I have an old sad phone so a couple of apps just decimate up my memory.

This all changed at the 2016 SfN meeting last week when I talked to some lovely grad students from Norway, who showed me Neuronify. Ok I know what you are thinking, how can an app for something neuroscience-related actually be useful? I was certainly a skeptic, but no longer.

You have got to try it!

I am an electrophysiologist, by training, and always wondered why anyone in their right mind would ever simulate data. Isn’t that like making things up?
Then I started simulating data on some nice Cray supercomputers, because it was fun, but it is a little inconvenient to lug one of those to class or a dinner date.

Neuronify solves this problem, at least for relatively simple simulations, allowing students to quickly see how neurons behave in a network while doing what they do all day anyway, i.e., play with their phones.

I can’t say enough about how much fun I am having with my phone, now that I can simulate Tritonia swimming central pattern generator circuits, without having to find snails!

http://cinpla.org/neuronify/

Neuronify RRID:SCR_014755

What is the identity of your Cell Line?

Posted on April 12th, 2016 in Data Spotlight, Inside NIF, News & Events | No Comments »

The SciCrunch portals now contain a data source that will help people figure out if their cell lines have been reported to be contaminated and the Resource Identification Portal at scicrunch will start asking authors to check this source at the time of publication.

Screen Shot 2016-04-11 at 11.00.21 AM

Members of the International Cell Line Authentication Committee (ICLAC) have been working with ExPASy on Cellosaurus, a comprehensive data registry for cell lines and cell line information. Cellosaurus assigns a cell line identifier to each cell line, cross links these identifiers to products available at any of the ~20 cell lines stock centers which make them available and adds notes where concerns have been raised about a cell line.

NIH has recently announced a set of reproducibility principles that target cell line authentication as an important part of research reproducibility and expect that most grants, starting in May 2016, will include a new attachment that explains the authentication of key biological resources, including cell lines.

For these reasons, we are proud to announce that Resource Identification Portal will now include Cellosaurus the core database of annotated cell lines and hope that authors begin to identify their cell lines by the RRID in the coming months, helping to keep track of this key biological resource.

NIF’s top 10 brain regions and cells

Posted on December 2nd, 2014 in Anita Bandrowski, Inside NIF | No Comments »

Back by popular demand,

NIF Brings you the top brain region and cellular search terms, most of which actually lost to the weirdest search term for this month: lettuce! No, we have no idea why people are looking for lettuce on our site, but apparently we have a very nice scanning electron microscopic image of it.

Top 10 Brain Regions
hippocampus
cerebellum
Forebrain
Frontal Lobe
Brainstem
Epithalamus
Fusiform Gyrus
Cerebral Crus
anterior cingulate cortex

Top 10 Cells
glioblastoma
Retinal Ganglion Cell
cholinergic neuron
Medium Spiny Neuron
Photoreceptor Cell
Bergmann glia
Cone Cell
neuron
Satellite Cell
Amacrine Cell

Big Data vs Small Data: Is it really about size?

Posted on October 31st, 2014 in Anita Bandrowski, Curation, Data Spotlight, Inside NIF, Interoperability | No Comments »

We have been hearing for some time that when it comes to data, it is all about size. The bigger is better mantra has been all over the press, but is it really size that matters?

There are the so called “Big Data” projects such as the Allen Brain Atlas, which generates data, sans hypothesis, over the whole brain for thousands of genes. This is great because the goal of the project is to generate consistent data and not worry about which disease will or will not be impacted by each data point. That may be a great new paradigm for science, but there are not many projects like this “in the wild”.

Most data is being generated in the world of science can be considered small, i.e., would fit on a personal computer, and there are a LOT of labs out there generating this sort of data. So the question that we addressed in the recent the Big Data issue of Nature Neuroscience, is whether small data could organize to become big data? If such a thing is desirable, then what would be the steps to accomplish this lumping?

Here are the principles that we have extracted from working on NIF that we think will really help small data (from Box 2):

Discoverable. Data must be modeled and hosted in a way that they can be discovered through search. Many data, particularly those in dynamic databases, are considered to be part of the ‘hidden web’, that is, they are opaque to search engines such as Google. Authors should make their metadata and data understandable and searchable, (for example, use recognized standards when possible, avoid special characters and non-standard abbreviations), ensure the integrity of all links and provide a persistent identifier (for example, a DOI).

Accessible. When discovered, data can be interrogated. Data and related materials should be available through a variety of methods including download and computational access via the Cloud or web services. Access rights to data should be clearly specified, ideally in a machine-readable form.

Intelligible. Data can be read and understood by both human and machine. Sufficient metadata and context description should be provided to facilitate reuse decisions. Standard nomenclature should be used, ideally derived from a community or domain ontology, to make it machine readable.

Assessable. The reliability of data sources can be evaluated. Authors should ensure that repositories and data links contain sufficient provenance information so that a user can verify the source of the data.

Useable. Data can be reused. Authors should ensure that the data are actionable, for example, that they are in a format in which they can be used without conversion or that they can readily be converted. In general, PDF is not a good format for sharing data. Licenses should make data available with as few restrictions as possible for researchers. Data in the laboratory should be managed as if it is meant to be shared; many research libraries now have data-management programs that can help.

 

Do you know what you don’t know? A gap analysis of Neuroscience Data.

Posted on October 17th, 2013 in Anita Bandrowski, Data Spotlight, Inside NIF, NIFarious Ideas | No Comments »

My thesis adviser, a colorful spirit and one whose wisdom will long be missed, used to say that undergraduate or professional students differed from graduate students in that they were asked to learn what was known about a subject, while graduate students were asked to tackle the unknown.

We, in higher education, are essentially seeking to find out what is not known and start to come up with new answers. How does one find out what is not known? In fact, is it possible to do that? Don’t most graduate students or postdocs add onto a lab’s existing body of knowledge? Adding to the unknown by building on the known? If this is how we work then does this create a very skewed version of the brain? How would we even know what is truly unknown?

Now we enter the omics era, where we try to find out all things about a set of things. We no longer want to know about a gene, we want to know about all of the genes, the genome of an organism. We want to account for all things of the type DNA and figure out which parts do what. In neuroscience, this tends to be a little more difficult. Mainly because we do not have a finite list of things that we can account for. We have a large quantity of species with brains, or at least ganglia, we have billions of cells and many more connections between them in a single human brain. The worst part is that these connections are not even static so a wiring diagram is only good for a few minutes for a single brain and then the brain reorganizes some of these connections.

Is the hope for an “omics” approach to neuroscience?

Well, the space is not infinite and has been studied over the last 100+ years so we have some ways of getting at the problem. We have a map!
Can we use this map to figure out some basic information about what we do and do not study? Well, the short answer at least for some things seems to be yes!

The Neuroscience Information Framework (neuinfo.org) project has been aggregating data of various sorts that is useful to neuroscientists, and also a set of vocabularies for all of the brain parts, the map of the nervous system. So we can start to look at which labels are used for tagging data, and which are found in the literature? Are all parts of the brain equally represented by relatively even amounts of data or papers or are there hot spots and cold spots for data?

Below is a heat map generated using the Kepler tool for data sources vs brain regions across the canonical brain regions (a hierarchy built to resemble what one may find in a graduate level text book of neuroanatomy).
Screen Shot 2013-10-17 at 1.28.39 PM

Albeit the heat map is very hard to read (the darker the green the more data, you can generate your own by clicking on the graph icon in NIF), there is little doubt that all brain regions are not equal, and some have very little data, while others have a plethora of data begging the question: Are there popular brain regions and not-so-popular brain regions?

Screen Shot 2013-10-28 at 8.38.30 AM

Indeed, there are brain region annotations that are found more often, when looking at data and much like pop stars, they tend to have shorter names. The most popular data label is actually brain, and the least popular appears to be the Oculomotor nerve root. This is starting to tell us that most data is just labeled as “brain vs kidney”, but can we do better as neuroscientists? In fact, we can break down the labels into major regions like hindbrain, midbrain and forebrain and add up all of the data that fit into each of these. Note most of the data are attributed to the forebrain, housing some of the most popular brain regions such as the cerebral cortex and the hippocampus, but the hindbrain also comes back with some reasonable data, mainly for the cerebellum. It turns out that adding up all the data labels for midbrain regions results in an awkward sense that the midbrain may be completely non-essential to brain research. On the other hand, removing the midbrain appears to be essential to life, so why do neuroscientists not know much or at least publish much about the midbrain?

Screen Shot 2013-10-17 at 3.57.58 PM

So if you are hiding a big pile of data about the midbrain in your desk drawer, I would like to formally ask you to share it with NIF (just email info@neuinfo.org) so that I can stop thinking of the midbrain as the tissue equivalent of fly-over country.

 

NIF Hackathon Summary

Posted on September 16th, 2013 in Inside NIF | No Comments »

San Diego, Calif., Aug. 19, 2013 — Approximately 30 programmers from around the country gathered recently for an intensive, two-day Hack-a-thon to improve the University of California, San Diego’s Neuroscience Information Framework —  a government-sponsored research portal into all things neuroscience.

Hackers at the NIF Hack-a-thon
Hackers at the Neuroscience Information Database Hack-a-thon earlier this month made improvements to NIF, which serves as a “data warehouse” for neuroscientific research.

NIF is a web-based, neuroscience resource housed at the Qualcomm Institute, the UC San Diego division of the California Institute for Telecommunications and Information Technology. It compiles and categorizes data, web pages, and literature in an accessible, searchable fashion to meet the needs of any biomedical researcher. Essentially, NIF is a “data warehouse” and acts similarly to the National Library of Medicine’s publication search engine, PubMed. But Anita Bandowski, a neuroscientist and project lead of NIF, notes that “the NIF registry accounts for databases, software tools and other forms of ‘intellectual output’ that are not publications accessible by PubMed.”

Developed in a collaborative effort by neuroscientists and computer scientists, NIF uses “concept-based searches” to gather information. For example, a concept-based search in NIF for the enzyme “Akt” also displays information about “PKB,” because these two names, created by different scientific groups, identify the same gene.  A Google search for “Akt,” in contrast, would create a list of topics related to that term and would miss other relevant data pertaining to “PKB.”

“Think of the data as individual towns, with their own road infrastructure, along a freeway system,” explained Bandrowski “NIF puts in the ‘big roads’ to connect the data.”

The Hack-a-thon, she continued, attempted to map certain local terms from various database “roads” to the general concept “freeways.” “What the Hack-a-thon did was to generate the ‘freeway off-ramps’ so these communities could be linked.”

During the Hack-a-thon event, the “hackers” (who, unlike malicious hackers, ‘hack something together’) worked closely with NIF programmers to incorporate data, software tools and vocabularies into the NIF system.

Trish Whetzel, one of the project lead managers said the Hack-a-thon “tied together existing code from other people into one project and acted as a personal resource to ask each other questions during this coding process.”

According to Bandrowski, the Hack-a-thon had three main goals. The first goal centered on the “input,” or uploading of data into NIF (i.e. hackers who had their own data and wanted to make it available). For example, data for the Monarch Initiative, which focuses on disease phenotypes and genotypes, were added during this event.

The second goal focused on “output,” or how the data in NIF could be accessed more efficiently. Here, the hackers tried to incorporate the best methods of extracting information from NIF.

The third goal emphasized “ontologies,” which are the semantic structures generated by programmers to group search terms and index data and information through the use of synonyms. Ontologies serve as the foundation for the concept-based searches. For example, searching for “Parkinson’s disease” in NIF also produces data related to “paralysis agitans,” which is an older term that refers to the disease.

One of the challenges the programmers encountered was identifying the synonyms that would link all data related to a certain concept and ensure that all of the data was relevant. For example, prior to the Hack-a-thon, entering “GOAT” into the NIF search engine (an acronym for ghrelin o-acyltransferase, a protein) not only produced information about that protein, but also information about the animal by the same name.

Bandrowski and Whetzel noted that the improvements made during the Hack-a-thon have important implications for the scientific community. Using NIF, scientists inputted large databases and information as resources for the rest of the community — ”conceptual communities” that weren’t necessarily connected.

“The strength of this system is the data integration,” added Whetzel. “Common annotations are created across the database to make more sense of the information in that database.”

Story by Christine Gould

by Tiffany Fox, (858) 246-0353, tfox@ucsd.edu

http://www.calit2.net/newsroom/article.php?id=2213

Top 25 of June

Posted on July 3rd, 2013 in Anita Bandrowski, Data Spotlight, Force11, Inside NIF | No Comments »

It appears that the top search term is: database and the top database (no pun intended) is the Registry (of databases). Certainly a very interesting month.
NIF Registry
NIF Annotations
Podcasts
Open Source Brain
Human Brain Atlas
AntibodyRegistry
Allen Brain Atlas
YPED
BioNumbers
Research Crossroads
ModelDB
NeuroLex Neurons
dkCOIN
Integrated Software
Gene Ontology Annotations
Integrated Animals
Gensat
BrainInfo
DrugBank
ABCD Brain Regions
Allen Mouse Brain Atlas
OneMind Biobanks
OMIM
MGI Transgenes

The top 25 search terms are:
database
cerebellum
cre
consciousness podcast
dkcoin
hippocampus
nida
“Gene Ontology Tools”
Frontal Lobe
open source brain
antibody
Diencephalon
dinosaurs podcast
Gene:cre
Marijuana OR THC OR cannabis OR Cannabinoid
“Drug Related Gene Database”
Habenula
mgi
NIDA
Cerebellum
Upk1b
“YPED”
autism
cell
cocaine addiction

Cannabis in NIF’s Data Holdings

Posted on June 13th, 2013 in Anita Bandrowski, Data Spotlight, Inside NIF, NIFarious Ideas | No Comments »

NIF was asked to give the National Institutes on Drug Abuse a report of the state of the data holdings for one abused substance: Cannabis. The report is included below. The data reflect the state of NIF in early 2013, following the links will potentially lead users to updated numbers.

Within NIF many sources have information about cannabis or the endocanabinoid system (we did not include an analysis of the literature). These results have been broken down by the number, below (for an interactive graph click here and then select the Graph Filters Box).

1

Genes

Looking at genes we find the two human genes CNR1 and CNR2, which are endocanabinoid receptors have these counterparts (CNRIP1,  Cnr1,  cnrip1,  Cnr2,  Cnrip1,  and cnr1) in many other species including, mammals, birds, fish and tunicates. This indicates that the gene family is quite widespread.

Three clusters of genes have emerged based on the Homologene clustering algorithm. These gene families are for CNR1, CNR2 and the interacting protein called CNRIP1.

 

Drugs

The endocanabinoid receptors have several drugs (largely derived from THC) that interact with the receptors.

Drugbank, a leading source of drug information tells us that two small molecule drugs have been used to affect the endocannabinoid system.

Nabilone has been used largely as an anti-anxiety agent or an antiemetic. Nabilone is a cannabinoid with therapeutic uses. It is an analog of dronabinol (also known as tetrahydrocannabinol or THC), the psychoactive ingredient in cannabis. It is reserved for use in individuals who do not respond to the more commonly used anti-emetics.

Dronabinol has also been used as an antiemetic, but also analgesic, non-narcotic psychotropic drug and a hallucinogen. Marinol may have complex effects on the central nervous system (CNS), including cannabinoid receptors. Dronabinol may inhibit endorphins in the emetic center, suppress prostaglandin synthesis, and/or inhibit medullary activity through an unspecified cortical action.

The NIMH Chemical Synthesis and Drug Supply Program lists three more specific drugs including two specific antagonists and ChEBI lists 12 variants of THC, which have less associated data, but may be useful as highly experimental substances that may have some specificity as agonists or antagonists, see below.

Screen Shot 2013-06-13 at 2.27.39 PM
ChEBI compounds can be found below.

Structure

ChEBI ID

Chemical Formula

Name

CHEBI:219639 C21H27F3O2 6,6,9-Trimethyl-3-(5,5,5-trifluoro-pentyl)-6a,7,10,10a-tetrahydro-6H-benzo[c]chromen-1-ol (5′-F3-delta8-THC)
CHEBI:219603 C21H29FO2 3-(5-Fluoro-pentyl)-6,6,9-trimethyl-6a,7,10,10a-tetrahydro-6H-benzo[c]chromen-1-ol (5′-F-delta8-THC)
CHEBI:164237 C21H30O2 6,6,9-Trimethyl-3-pentyl-6a,7,8,10a-tetrahydro-6H-benzo[c]chromen-1-ol (delta9-THC)
CHEBI:566631 C37H54O4 alpha-Cadinyl delta9-Tetrahydrocannabinolate
CHEBI:566632 C37H54O4 gama-Eudesmyl delta9-Tetrahydrocannabinolate
CHEBI:566609 C32H46O4 alpha-Terpenyl delta9-Tetrahydrocannabinolate
CHEBI:566610 C32H46O4 beta-Fenchyl delta9-Tetrahydrocannabinolate
CHEBI:566618 C32H46O4 alpha-Fenchyl delta9-Tetrahydrocannabinolate
CHEBI:566619 C32H46O4 epi-Bornyl delta9-Tetrahydrocannabinolate
CHEBI:566620 C32H46O4 Bornyl delta9-Tetrahydrocannabinolate
CHEBI:566630 C32H46O4 4-Terpenyl delta9-Tetrahydrocannabinolate
CHEBI:566636 delta9-tetrahydrocannabinolic acid A

Several of these compounds have been tested in various brain regions against the two known canabinoid receptors and from the Ki database we find that there is more data for CB1 (345), than the CB2 (173). Results are also organized by species and brain region with rat (296 results) and human (192) tested most frequently, followed by mouse (18), zebra finch (8) and newt (5) data.

 

Pathways

Wiki pathways, a collaborative platform from UCSF, lists the following pathways for CNR1 and CNR2:

Pathway Name

  • GPCRs, Class A Rhodopsin-like (11)
  • Small Ligand GPCRs (11)
  • GPCRs, Other (3)

Gene Symbol

  • CNR2 (7)
  • CNR1 (6)

Organism

  • Mus musculus (5)
  • Pan troglodytes (5)
  • Rattus norvegicus (5)
  • Homo sapiens (4)
  • Bos taurus (2)
  • Danio rerio (2)
  • Gallus gallus (2)

 

MultiMedia Information

Scientists studying endo- and exo-cannabinoids have written hundreds of blogs. Most of these focus on cannabis risks or the treatment of various disorders. A quick survey of these blogs indicates that there are links to Fragile X syndrome, multiple sclerosis, depression and decreases in intelligence on standardized tests. In addition, many scientists study the risk of motor vehicle accidents and possible interactions of mothers’ canabinoid exposure and the proclivity of the offspring toward opiates, but surprisingly some have also reported a Marijuana-Borne Salmonella Outbreak and poisoning of workers from herbicides used to kill the plants.

A few video talks and interviews with leading researchers are also available (see several examples below).

_____________________________________________________________________________

NIHVideo Molecular Dissection of Cannabis Sensitivity in the Developing Brain Tibor Harkany, PhD, Department of Medical Biochemistry and Biophysics, Karolinska Institute
NIHVideo New Developments in Cannabinoid Research: The Path from Plant to Modern Prescription Medicine Guy, Geoffrey W. National Institutes of Health (U.S.)
The Guardian: Science Videos Cannabis ‘more harmful to under-18s than adults’ – video
NIHVideo Brain Stress Systems and Addiction Koob, George F. National Institutes of Health (U.S.)

 

Funding Sources

Not surprisingly, NIDA funds most research on cannabis, but a few current grants are also given by NIMH, Alcohol, and NINDS. The breakdown of the number of recent grants by institute follows: national institute on drug abuse (375), national institute of mental health (26), national institute on alcohol abuse and (26), national institute on aging (11),  and many others.

From older grants (Research Crossroads dataset covering both federal and foundation grants) we find that many institutes and foundation have given out grants related to the cannabinoids, including:

  • National Institute on Drug Abuse(NIDA) (2937)
  • National Center for Research Resources(NCRR) (152)
  • National Institute of Neurological Disorders and Stroke(NINDS) (90)
  • National Institute on Alcohol Abuse and Alcoholism(NIAAA) (64)
  • National Institute of Mental Health(NIMH) (57)
  • National Institute of General Medical Sciences(NIGMS) (52)
  • National Cancer Institute(NCI) (33)
  • National Eye Institute(NEI) (33)
  • National Institute of Allergy and Infectious Diseases Extramural Activities(NIAID) (15)
  • National Institute of Child Health & Human Development(NICHD) (15)
  • Medical Research Council (UK) (12)
  • National Heart, Lung, and Blood Institute(NHLBI) (12)
  • CORDIS (11)
  • National Institute of Environmental Health Sciences(NIEHS) (9)
  • National Center for Complementary and Alternative Medicine(NCCAM) (7)
  • National Institute of Diabetes and Digestive and Kidney Diseases (7)
  • National Institute on Aging(NIA) (7)
  • Fogarty International Center(FIC) (5)

Our search also reveals that even private funders like the American Diabetes Association also gave out a grant focusing on the therapeutic effects of endogenous cannabinoids in diabetic retinopathy.

 

Diseases and Clinical Studies

There are few diseases directly associated with Marijuana, however Pubmed health mentions four, including:

Marijuana intoxication

Aspergillosis, which is an infection or allergic response due to the Aspergillus fungus. Aspergillosis is caused by a fungus (Aspergillus), which is commonly found growing on dead leaves, stored grain, compost piles, or in other decaying vegetation. It can also be found on marijuana leaves. Although most people are often exposed to aspergillus, infections caused by the fungus rarely occur in people who have a normal immune system. The rare infections caused by aspergillus include pneumonia and fungus ball (aspergilloma).

Lung cancer – non-small cell, which mentions that “research shows that smoking marijuana may help cancer cells grow, but there is no direct link between the drug and developing lung cancer.”

Paraquat poisoning, which describes paraquat (dipyridylium) as a highly toxic weed killer once promoted by the United States for use in Mexico to destroy marijuana plants. Research found that this herbicide was dangerous to workers who applied it to the plants. This article discusses the health problems that can occur from swallowing or breathing in Paraquat.

NIF searches across two sources of clinical data, and the US based ClinicalTrials.gov contains information about 294 clinical trials, but the European based EU Clinical Trials Register finds only 27 additional trials.  Below you can find the main conditions, interventions and sponsors for the clinical trials, with numbers indicating the number of clinical trial results.

Conditions

  • Marijuana Dependence (15)
  • Marijuana Abuse (13)
  • Multiple Sclerosis (11)
  • Cannabis Dependence (10)
  • Healthy (8)
  • Marijuana Smoking (6)
  • Pain (5)
  • Schizophrenia (4)
  • Schizophrenia;Schizoaffective Disorder (4)
  • Substance-Related Disorders

Intervention

  • Behavioral: Behavior Therapy (6)
  • Drug: Dronabinol (6)
  • Drug: Cannabis (4)
  • Drug;Drug: Sativex®;Placebo (4)
  • Drug: GW-1000-02 (3)
  • Drug: Nabilone (3)
  • Drug: Smoked Cannabis (3)
  • Drug;Drug: GW-1000-02;Placebo (3)

Sponsored By

  • National Institute on Drug Abuse (NIDA); NIH (23)
  • GW Pharmaceuticals Ltd.; Industry (15)
  • New York State Psychiatric Institute;National Institute on Drug Abuse (NIDA); Other;NIH (9)
  • Center for Medicinal Cannabis Research; Other (7)
  • National Institute of Mental Health (NIMH); NIH (6)
  • Yale University; Other (6)

 

Inference Data

The Clinical Toxogenomics Database (CTD) includes data about genes, pathways and diseases that have been found to be statistically associated with cannabinoids. The data are not from direct assertions, so they should be taken with a certain degree of skepticism. However, possibly interesting interactions with the following genes, diseases and pathways have been asserted.

Genes:

  • AKT1 (580)
  • TNF (414)
  • ABCB1 (400)
  • SCARB1 (146)
  • IL1B (144)
  • IFNG (140)
  • CNR1 (139)
  • VEGFA (110)
  • FDFT1 (104)
  • HMOX1 (100)
  • PTGS2 (97)

Diseases

  • Marijuana Abuse (1512)
  • Breast Neoplasms (74)
  • Prostatic Neoplasms (69)
  • Stomach Neoplasms (55)
  • Lung Neoplasms (47)
  • Carcinoma, Hepatocellular (45)
  • Myocardial Ischemia (43)
  • Schizophrenia (43)
  • Cocaine-Related Disorders (39)
  • Liver Cirrhosis, Experimental (37)

Pathways

  • Signal Transduction (11)
  • Immune System (9)
  • Disease (8)
  • Membrane Trafficking (6)
  • Metabolism (6)
  • Neuronal System (5)
  • Glioma (4)
  • Hemostasis (4)
  • Muscle contraction (4)
  • Neurotrophin signaling pathway (4)

 

2

 

Microarray and Gene Expression Data

The Gene Expression Omnibus (GEO) contains data from 5 studies having to do with cannabinoids.

One of those studies was analyzed in significant detail by Gemma, which tells us that the study which specifically targeted an animal model for cutaneous contact hypersensitivity, showed that mice lacking both known cannabinoid receptors display exacerbated allergic inflammation. The study looked at CNR knockout mice and the main experimental factors were dinitrofluorobenzene vs. Control_group, and the Cnr1-/-/Cnr2-/- vs. C57BL/6J (knockout vs control).

The drug related gene database, reports on several studies including a heroin withdrawl / cannabidol withdrawl interaction study showing some interactions in the caudoputamen. The brain regions, experimental conditions and organisms most commonly studied are listed below:

Brain Region

  • Anterior prefrontal cortex (556)
  • Dorsolateral caudoputamen (8)
  • Medial caudoputamen (8)
  • Mid-lateral caudoputamen (8)
  • CA1 stratum lacunosum moleculare (5)
  • CA1 stratum oriens (5)
  • CA1 stratum radiatum (5)
  • CA3 stratum lucidum (5)
  • CA3 stratum oriens (5)
  • CA3 stratum radiatum (5)

Exp vs Control

  • Cocaine + THC + PCP vs. Control (139)
  • Cocaine vs. Control (139)
  • PCP vs. Control (139)
  • THC vs. Control (139)
  • Delta9 THC vs. 1:1:18 solution of ethanol, emulphor, and saline (17)
  • Alpha7 nicotinic acetylcholine + cannabinoid receptor 1 vs. Alpha7 nicotinic acetylcholine (9)

Organism

  • Human: , 13-64 years Adolescent – Adult human (556)
  • Sprague Dawley Rat: Male, Adult 200-250 g (43)
  • Long Evans rat Rat: Male, 230-250 g at the beginning of the experiment (30)
  • Mouse: , (17)
  • Sprague Dawley Rat: Male, Adult rat 380-410 g (2)

Protocol Type

  • dna microarray (558)
  • immunohistochemistry (47)
  • in situ hybridization / immunohistochemistry (18)
  • in situ hybridization / double in situ hybridization (16)
  • in situ hybridization /double in situ hybridization (9)

 

In mice, the brain structures that express cnr1 and cnr2, based on data from the mouse genome informatics database, the alen brain atlas and gensat are:

  • hypothalamus (10)
  • olfactory bulb (10)
  • thalamus (10)
  • cerebellum (9)
  • cerebral cortex (9)
  • midbrain (9)
  • pons (8)
  • amygdala (7)
  • hippocampus (7)
  • pallidum (7)
  • hippocampal formation (6)
  • lateral septal complex (6)
  • anterior olfactory nucleus (5)
  • basal ganglia (5)
  • corpus striatum (5)
  • diencephalon (5)
  • entorhinal cortex (5)
  • globus pallidus (5)
  • hindbrain (5)
  • inferior colliculus (5)

Most common assays are:

  • rt-pcr (976)
  • bac-cre recombinase driver (38)
  • rna in situ hybridization (34)
  • rna in situ (26)

The age of the organism:

  • postnatal week 6-8 (732)
  • postnatal (244)
  • adult (53)
  • embryonic day 14.5 (26)
  • p7 (19)

 

Brain Volume and Brain Activation Foci Data

Based on a publication in 2010, the cerebellar volume of marijuana abusers of 18 years of age appears to be significantly smaller than the norm. The norm is established by looking at the volumes reported from many publications.

The brain activation foci from SumsDB involved in marijuana use or abuse are shown as gray dots on the brain below. Each gray dot represents a coordinate from a study cataloged by SumsDB that involves cannabis. These have been pulled from the WebCaret software, from the laboratory of David VanEssen, and are accessible by clicking the “View on Brain” button within the SumsDB data result. Below, we are showing several views of the same result set, because not all points are visible from any one view of the brain, suggesting that there is no unified brain region involved cannabis abuse rather many regions are involved.

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Medial View

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Posterior View

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Imaging Data

The Cell Image Library has several data sets from Margaret I. Davis, mainly of interneurons expressing EGFP from the 5HT3 receptor promoter (Tg(Htr3a-EGFP)DH30Gsat, www.gensat.org) colabelled for the CB1 cannabinoid receptor.

The image below is from the pyramidal cell layer in hippocampal CA1.

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The figures show the distribution of interneurons expressing EGFP from the 5HT3 receptor promoter (Tg(Htr3a-EGFP)DH30Gsat, www.gensat.org) in the dorsal hippocampus colabelled for the CB1 cannabinoid receptor (red) and counterstained with DAPI (blue) to show the cell layers. In this experiment EGFP expression was amplified with chicken anti-GFP (Abcam, 1:2000); cell bodies and fibers are present throughout all layers of the hippocampus but enriched in the hilus and stratum lacunosum moleculare (see associated images). CB1 immunoreactivity (L15 rabbit polyclonal 1:200, K. Mackie) is prominent in the terminals of basket cells synapsing in the pyramidal cell layer. CB1 is also enriched in axons with distinct intensities in the inner and outer molecular layer of the dentate gyrus. CB immunoreactivity is also present in the stratum radiatum and stratum lacunosum moleculare.

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The data above can be complemented by 168 images gathered by Gensat from this mouse, generated by that project.

 

Resources

Animals, antibodies, databases as well as a game useful for teaching undergraduates are available from various sources.

The mouse line generated by Gensat (images above) is now available from the MMRRC resource, where two founder lines of knock out mice are available. The International Mouse Resource Center (IMSR) lists several other mice that may be available to the research community.

The Antibody Registry, our in house antibody aggregator, which aims to serve the scientific community by providing a set of unique identifiers for commercial and non-commercial antibody reagents, lists 242 antibody reagent offerings related to cannabinoids from over 10 vendors.

 

The NIF registry does not encode many resources that are specifically devoted to cannabinoids, but does note that several resources mention cannabis in their description including:
University of California at San Diego, Center for Medicinal Cannabis Research Resource Type: institutional portal
The Center for Medicinal Cannabis Research (CMCR) will conduct high quality scientific studies intended to ascertain the general medical safety and efficacy of cannabis products and examine alternative forms of cannabis administration. The Center will be seen as a … See full record: nif-0000-10503

Mouse Party Resource Type: training material, video
Mouse Party is an interactive website that teaches how various drugs disrupt the synapse by taking a look inside the brains of mice on drugs! Every drug of abuse has its own unique molecular mechanism. Where applicable, this presentation primarily depicts how drugs… See full record: nif-0000-00429

 

SPROUTS- Structural Prediction for Protein Folding Utility System Resource Type: database, data analysis service
SPROUTS is a database of predicted protein folding related data. It was designed to gather all the results from a study concerning the comparison between tools devoted to the prediction of stability changes upon point mutations. The second aim of this database is…  See full record: nif-0000-03491

Universal Virus Database Resource Type: web accessible database
The ICTVdB is a dynamic database containing information about viruses of animals, plants, bacteria, and fungi. Though initially designed for taxonomic (or classification) research, the ICTVdB has evolved to become a major reference resource and research tool. The … [more] See full record: nif-0000-21213

Psychoactive Drug Screening Program Ki Database Resource Type: database, data repository
Database of information on the abilities of drugs to interact with an expanding number of molecular targets. It serves as a data warehouse for published and internally-derived Ki, or affinity, values for a large number of drugs and drug candidates at an expanding n… [more] See full record: nif-0000-01866

Subviral RNA Database Resource Type: database
The Subviral RNA database facilitates the research and analysis of viroids, satellite RNAs, satellite viruses, the human hepatitis delta virus, and related RNA sequences. It integrates a large number of Subviral RNA sequences, their respective RNA motifs, analysis … [more] See full record: nif-0000-03507

How long does it take to get a resource into NIF? The case of the open source brain.

Posted on June 4th, 2013 in Anita Bandrowski, Data Spotlight, Force11, Inside NIF, Interoperability | 2 Comments »

Believe it or not, there really is a project called open source brain, and it is a wonderful community of hackers that attempts to do very novel things with open source models, mainly in a format called NeuroML.

What is the open source brain?

Well, it takes models, converts them into cool visualizations and then allows users to manipulate them in their browser, with functionality similar to google body. The hope is to strap some significant computational power from the Neuroscience Gateway’s massive clusters so that the pretty pictures can be fully functional, but for now, this is a great way of exploring three-dimensional neurons and connectivity.

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But the reason I am blogging about this project is not because of the “ooohh-aaaahhh” factor that nice graphics usually have on me, but also because this source came to NIF in an interesting way, by human flying from London on his way to another meeting. Unfortunately last week we did not know about the Open Source Brain, but Padraig knew about NIF and wanted to register the project, hoping to integrate his data or at least “get the process started”.

At 10:30 am we were sufficiently caffeinated to begin and created a registry entry, from which we obtained an identifier.

The identifier was then used to create a sitemap entry in the DISCO database (essentially anyone who has logged in to the NeuroLex can click a button at the bottom of a curated registry entry can actually do this).

Then we added an “interop” file, which instructs our crawler to put data the xml data output by open source brain into our local data warehouse making sure to specify appropriate tables and columns.

Then we went to lunch, came back after fighting much larger crowds at the indian place than were expected before finals, and created the “view” of the data (basically, wrote a sql statement and used our concept mapping tool to define what data would be displayed).

By 3:30 pm we had a view deployed. Well ok, we did have to import the data twice because we messed up the file once, and this deployment was the beta server and we had to wait to update to production until Friday night, but that is still pretty darn fast in my opinion.

The question for many people who have data has been how much effort will it take to make my data interoperable with community resources and for the first time ever, we can report …. it will only take a couple of hours (we should insert many caveats here).

We have A LOT of neuroscience information, and would like to share….

Posted on May 14th, 2013 in Curation, Inside NIF, Jonathan Cachat | No Comments »

Over the past 4 years, the Neuroscience Information Framework systematically scanned the literature, internet and social buzz for all things neuroscience (& biomedical science). This tedious bookkeeping has resulted in the largest, most comprehensive catalog of neuroscience-relevant information ever amassed – with the added bonus of semantically enhanced search functions. And now, we would like to share it with you via myNIF…but before those details…

What do we mean “neuroscience information”?

Neuroscience information includes data, resources, literature, grants, multimedia, social buzz, a lexicon and more..

Data: Over 140 independent databases (i.e. CCDB, Grants.gov, GENSAT) are deeply indexed and semantically mapped by NIF – representing over 400 million pieces of data. These data are considered part of the “hidden web”, not indexed by major search engines because do so requires specialized database query statements for retrieving data within, rather than on the surfaces of pages surrounding the database. NIF has developed technologies to regularly re-crawl and update data content, index it, and provide search within the contents of these databases simultaneously. Moreover, data resulting from a search can be exported with a single click into standard data formats for desired, subsequent analysis. This can simply save  you time – if you need to know what type of serotonin receptors have been classified in zebrafish (Danio rerio) – searching NIF for ‘zebrafish serotonin receptor’ provides results from authoritative data providers (HomoloGene, EntrezGene) which can be compared instantly, rather than visiting each site separately, and comparing through notes, multiple windows, or several downloads. In addition to this primary information , the results also include related, and sometimes very helpful information about zebrafish and serotonin – signaling pathways, antibodies, and grant information.

Resources: Need to find a software analysis package for microarray data? NIF can recommend 41 options, as well as 100+ unique organizations, centers, labs and websites that  have similar interests. Looking for non-governmental funding of ALS research? Here are 7. What about a tissue bank with Alzheimer’s disease CNS tissue samples available for researchers? NIF is aware of around 88 worth a look. All of this to convey that a resource is object or entity, with a website, that provides potential value to neuroscience research or the researchers. Importantly, this catalog of resources indexed by NIF is maintained at NeuroLex, a semantic mediawiki website. Homologous to Wikipedia, in that any one can contribute their resource or favorite resources, but endowed with reasoning capabilities permitting logical reasoning on relationships between data (i.e. list all GABAergic Neurons).