GcatWiki - User contributions [en]

Talk:Jenna Reed

2018-03-11T18:59:45Z

Jereed:

== CMT: 2/8/18 ==

2/8/18

Bio343 HTSeq Results

Male Mutant 25 and 24 = paired reads, Male mouse 2078, C201R 15 and 14 = paired reads, Male mouse 2073, C201R 3 and 2 = paired reads, Male mouse 2079, C201R

Male WT 21 and 20 = paired reads, Male mouse 2076, WT 19 and 18 = paired reads, Male mouse 2075, WT 17 and 16 = paired reads, Male mouse 2074, WT

Female Mutant 13 and 12 = paired reads, Female mouse 2072, C201R 11 and 10 = paired reads, Female mouse 2071, C201R 9 and 8 = paired reads, Female mouse 2070, C201R

Female WT 23 and 22 = paired reads, Female mouse 2077, WT 7 and 6 = paired reads, Female mouse 2081, WT 5 and 4 = paired reads, Female mouse 2080, WT

Gene Cards Gene Weaver GO rilla FNTM String NCBI

2/8/18 Loaded data from "Histories Shared with Me"

NefL >>> deltaCMT 1F 2E NefH >>> deltaALS Sphk2 >>> kinase

Male v Female
MaleWT v MaleCMT
FemaleWT v FemaleCMT
Gm4210 NefM (neurofilament medium) Close with NefL, and Gm2410 Calca >>> calcium regulator

About this data: Took the spinal cord of the mice and sent it off for RNASeq 2-fold change

-0.7 = 1.62 fold change
-0.8 = 1.74 fold change
-7.56 = 128fold change, p=10^-14
What's our fold-change cutoff? JAX had a strict cutoff and got only 20 genes Lack of signal because of CMT could cause repression of transcription (because usually ligand binding induces transcription) RNASeq data will always have differential expression from natural variation Is it enough differential expression that it could be caused by the disease?

== CMT: 2/13/18 ==

2/13/18

mut/WT log2 500/1000 = 0.5 Anything less than 1 is negative

DESeq2 >>> run htseq-count data with features After doing a DESeq2 run, two files will appear in the history First one is tabular. Download that, open in Text Wrangler, then copy and paste that in excel Second one is a pdf. Download and open that to see data visualizations

54 & 55 >>> DESeq2 for "Female_MUTvWT" 56 & 57 >>> DESeq2 for "Male_MUTvWT" 58 & 59 >>> DESeq2 for "MUT_MALEvFEMALE" 60 & 61 >>> DESeq2 for "WT_MALEvFEMALE" 62 & 63 >>> DESeq2 for "MUT_v_WT"

Research method for today: look at what genes are significant in each dataset and see where there is overlap between datasets

In excel sheet: DESeq2 data for all 5 comparisons P-value < 0.05 highlighted in yellow P-adjusted <0.05 highlighted in green List of just the p-adjusted <0.05 genes for all 5 comparisons together

General observations:

of genes with p-adj < 0.05
Female_MUTvWT: 72

Sphk2 >>> p-adj is less than 0.05 for both MUT_MALEvFEMALE and WT_MALEvFEMALE Sphingosine Kinase 2 Paralog with Sphk1, which codes for the other enzyme that has the same function Encodes for an enzyme that catalyzes the phosphorylation of sphingosine into sphingosine 1-phosphate sphingosine 1-phosphate = important in cell migration, proliferation, apoptosis Implicated in some cancers (breast cancer proliferation, chemoresistance) Related pathways: Calcium signaling pathway (could be connection to CMT2D), Metabolism

Tsix >>> p-adj is less than 0.05 for both MUT_MALEvFEMALE and WT_MALEvFEMALE

== CMT: 2/15/18: GeneWeaver Lecture ==

Why GeneWeaver?
Integrative functional genomics
Goal is to integrate the genetic investigation of humans and animal models
Data repository has a number of different types of data
Microarrays
Published data
Annotations
GWAS
QTL
Tools
Can do set-set matches
Can match your set of gene with your other set of genes or with another set of genes in the database
ODE IDs are a reference to find the consilience among associations of biomolecular entities and related concenpts
http://beta.geneweaver.org/ >>> new version
Types of functional genomics data
Mapping Data
QTL Positional candidates (mouse, rat)
GWAS Candidate (from original studies)
Expression Data
DRG (Drug Related Genes) >>> from literature
ABA (Allen Brain Atlas) >>> mice
CTD (Comparative Toxicogenomics Database) >>> from 9 different species
Functional Annotations
GO (Gene Ontology Annotations) > human, house
MP (Mammalian Phenotype Ontology)
HP (Human phenotype ontology)
OMIM (Online Mendelian Inheritance in Man)
MeSH (Medical Subject Headings)
Pathway Data
KEGG (Kyoto Encyclopedia of Genes and Genomes)
MSigDB (Molecular Signatures Databases)
PC (Pathway Commons)

How do we use GeneWeaver?
Tier III = data curated from literature (gold standard)
Tier I and II = public resource data (II is pre-processed somehow)
Search
On results, "+" give you more basic info (description, authors, etc.) or gene set
Clicking on gene set name will allow you to view the gene set
Gene list on bottom of gene set entry
Gene Symbol >>> can be changed to show identifier numbers for datasets
Homology shows which species there is a homologous gene in
Linkouts can take you to entry in each database
Under a search, you can select genesets, then hit "Add Selected to Project" (at top of search results) and you can add it to an existing project or create a new project
Top Toolbar: Analyze GeneSet
Can select all genesets in a project, or can expand project (+) and select specific sets

Analyzing a GeneSet
HiSim Graph
Usually default parameters are fine
On resulting graph
Can zoom in or out
Right = 4 genesets
Hover mouse over it to get a little data info
Moving right to left, you'll encounter nodes (intersection between our genesets)
Hovering over the node will show you what genes interact between the two genesets
Furthest left nodes show the most connected genes (genes that are in the most genesets)
Left clicking on a node will make it disappear so that we only show what we do care about (left clicking on it again will make it reappear)
Right clicking (or shift+left click) on the node will make a more detailed node summary page appear
Visualization
Classic = more descriptive without clicking on things
Modern = easier to see connections
Contains my projects and projects that have been shared with me
GeneSet Graph
Shows us which genes are connected to which datasets
Left to right = less connected gene sets to most connected gene sets
Color lines match color of gene set boxes
Difference between this and HiSim Graph
HiSim = emphasizes the sets and the relationship between the sets
GeneSet = highlights the genes
Different ways of visualizing the same thing
Tool Options >>> MinDegree
Allows you to limit it so that the graph only shows genes that have X number of connections
e.g. if you change the MinDegree to 4 and hit "Re-Run Tool," the graph will only show genes that are in at least 4 of the data sets
Jaccard Similarity Status
Top Toolbar
Manage Genesets > Manage Projects
Can view/edit all your projects
Little arrow with dots on points = share project
Can share project with a group that you're part of
Top Toolbar >>> Manage GeneSets >>> Upload GeneSet
Give set a descriptive name
Figure Label = shorter version of GeneSet Name (something that will fit on an analysis label)
Score Type: no way to upload a gene set with two different score types
Can upload gene set twice, each one with a different score type, then threshold the gene sets
Choose access restriction and you can share which groups you want
Select species (Mus musculus)
Gene Identifier: can't use a mix of gene identifiers, but some databases have conversion tools
Gene List: File Upload only takes plain text
If you copy and paste from excel, it should automatically format properly
Annotations should automatically be generated when you create your set
Once you've created your gene sets, you can share it with groups and begin analysis

== CMT: 2/22/18 ==

Final Paper: 5-10 pages, including figures
Zotero style = Cell
Figures embedded in text
Have figure legends (not in a text box)
Suggested writing order
Title > 7
Authors (you first, partner second) > 6
Abstract (200-word limit) > 5
Intro > 4
Methods > 1
Results > 2 (figures first, then writing)
Discussion > 3
References > 8 (Zotero)

GO-rilla:
MUTvWT (ranked by p-value)
Nefm (medium polypeptide)
Nefh (heavy polypeptide)
Nefl (light polypeptide)
Calca (calcitonin/calcitonin-related polypeptide, alpha
Slc31a1 (solute carrier family 31, member 1)
Tnnt1 (troponin t1, skeletal, slow)
Atp1a1 (atpase, na+/k+ transporting, alpha 1 polypeptide)
Myh7 (myosin, heavy polypeptide 7, cardiac muscle, beta)

p = 10-7-10-9

When sorted by fold change: MUTvWT
intermediate filament-based process
Prph - peripherin

Genes of Interest:
Nefm
Nefh
Nefl

If I need them (in order of use):
Calca
Atp1a1
Prph
Tnnt1
Slc31a1 & Myh7

Nefl, Nefm, & Nefh:
Intermediate filament-based process
Intermediate filament cytoskeleton organization
Neurofilament cytoskeleton organization
Intermediate filament organization
Intermediate filament bundle assembly
Neurofilament bundle assembly
Axon development
Calca
Regulation of muscle contraction (not shown on figure)
Regulation of anatomical structure size (not shown on figure)
Atp1a1
Regulation of muscle contraction

== CMT: 2/27/18: Notes for Results & Conclusion ==

Nefl: neurofilament light polypeptide
GeneCards:
Has not been connected to ALS, but has been associated with CMT
Related pathways: RET Signaling & Post-NMDA receptor activation events
RET Signaling: (Takahashi, 2001)
Works with glial cell line-derived neurotrophic factor (GDNF)
Promotes neuron survival
Related neurotrophic factors could be used as therapies for neurodegenerative diseases
Post-NMDA Receptor Activation Events
Involved in calcium ion influx
Establishes long-term synaptic changes
Appear to be more related to central nervous system rather than peripheral
NMDA agonists are treatment Alzheimer's in US and Europe
OMIM
Implicated in CMT2E (pretty similar phenotype to 2D) and CMT1F, so may play a role here
(Lin et al., 2009)
Previously connected motor neuron degradation with the binding of a destabilizing protein (190RhoGEF) to Nefl mRNA
Result was downregulation of Nefl mRNA in neurons
Nefm co-expression increased in response

Nefm: neurofilament medium polypeptide
GeneCards:
Associated with ALS
GO annotations: related to structural molecule activity and structural constituents of the cytoskeleton

Nefh: neurofilament heavy polypeptide
GeneCards:
Associated with ALS and CMT previously
GO annotations: Related to protein kinase binding and microtubule binding
OMIM
Collard et al. (1995) hypothesized that axonal degeneration is caused by blocking the transport of molecules needed for axonal maintenance by road-blocking the transport network (may be part of ALS pathogenesis)
Rebelo et al. (2016)
Implicated in CMT2CC
Frameshift mutations in NEFH resulted in aggregation of protein, which could block the transport for axonal maintenance molecules
Elder et al. (1998)
Created Nefh knockout mice to study connection between neurofilament levels and axon size
Resulted in slight reduction of neurofilament levels, yet a drastic reduction in axon diameter
Supports the idea that the issue is about disrupting transport on the intracellular network rather than a degeneration of the network itself
Shows that the Nefh gene plays a strong role in axonal development (and reduced axon size is part of the CMT2D phenotype, which is why it may be important to study)
Need more info on how Nefh may affect axon size
Rebelo et al. (2016): looked at NEFH mutations and saw aggregation of abnormal proteins. Found similar mutation in NEFL, which they note is known to be related to CMT. Perhaps the mutant GARS proteins bind to NEFH proteins and cause them to aggregate because they can't be used (similar to the NEFL explanation)

All 3 of these genes are under-expressed in our research mice. Under-expression means that the integrity of the axoskeleton around the neurons is not good, so intracellular transport to and from these neurons may be affected

As seen with Nefl, interference with the mRNA causes downregulation of the mRNA, which could explain why the gene is showing under-expression in our mice

In the Lin et al. (2009) study, Nefm expression increased to compensate for the downregulation of Nefl, but perhaps in CMT2D, it interferes with all three types of neurofilament mRNA
Could the mutant GARS proteins have gained the function to bind with the neurofilament mRNA (like the destabilizing protein mentioned in Lin et al., 2009)?
Research may also support this explanation for Nefh

Discussion
Nefl: Potential toxic gain-of-function where the mutant GARS proteins interfere with neurofilament mRNA by binding to it, resulting in downregulation of the neurofilament genes
Nefh: blocks transport on intracellular network (may be similar mechanism to Nefl issue)
Need more info on how Nefh may affect axon size
Each of the focus genes above have orthologs in mice, meaning researchers could study these genes in relation to Gars fairly easily
May have a section connecting to ALS, as I think the potential mechanisms I'm discussing have a role in ALS.
Reiterate need to research better treatment options

Talk:Jenna Reed

2018-03-11T18:57:40Z

Jereed:

== 2/8/18 ==

2/8/18

Bio343 HTSeq Results

Male Mutant 25 and 24 = paired reads, Male mouse 2078, C201R 15 and 14 = paired reads, Male mouse 2073, C201R 3 and 2 = paired reads, Male mouse 2079, C201R

Male WT 21 and 20 = paired reads, Male mouse 2076, WT 19 and 18 = paired reads, Male mouse 2075, WT 17 and 16 = paired reads, Male mouse 2074, WT

Female Mutant 13 and 12 = paired reads, Female mouse 2072, C201R 11 and 10 = paired reads, Female mouse 2071, C201R 9 and 8 = paired reads, Female mouse 2070, C201R

Female WT 23 and 22 = paired reads, Female mouse 2077, WT 7 and 6 = paired reads, Female mouse 2081, WT 5 and 4 = paired reads, Female mouse 2080, WT

Gene Cards Gene Weaver GO rilla FNTM String NCBI

2/8/18 Loaded data from "Histories Shared with Me"

NefL >>> deltaCMT 1F 2E NefH >>> deltaALS Sphk2 >>> kinase

Male v Female
MaleWT v MaleCMT
FemaleWT v FemaleCMT
Gm4210 NefM (neurofilament medium) Close with NefL, and Gm2410 Calca >>> calcium regulator

About this data: Took the spinal cord of the mice and sent it off for RNASeq 2-fold change

-0.7 = 1.62 fold change
-0.8 = 1.74 fold change
-7.56 = 128fold change, p=10^-14
What's our fold-change cutoff? JAX had a strict cutoff and got only 20 genes Lack of signal because of CMT could cause repression of transcription (because usually ligand binding induces transcription) RNASeq data will always have differential expression from natural variation Is it enough differential expression that it could be caused by the disease?

== 2/13/18 ==

2/13/18

mut/WT log2 500/1000 = 0.5 Anything less than 1 is negative

DESeq2 >>> run htseq-count data with features After doing a DESeq2 run, two files will appear in the history First one is tabular. Download that, open in Text Wrangler, then copy and paste that in excel Second one is a pdf. Download and open that to see data visualizations

54 & 55 >>> DESeq2 for "Female_MUTvWT" 56 & 57 >>> DESeq2 for "Male_MUTvWT" 58 & 59 >>> DESeq2 for "MUT_MALEvFEMALE" 60 & 61 >>> DESeq2 for "WT_MALEvFEMALE" 62 & 63 >>> DESeq2 for "MUT_v_WT"

Research method for today: look at what genes are significant in each dataset and see where there is overlap between datasets

In excel sheet: DESeq2 data for all 5 comparisons P-value < 0.05 highlighted in yellow P-adjusted <0.05 highlighted in green List of just the p-adjusted <0.05 genes for all 5 comparisons together

General observations:

of genes with p-adj < 0.05
Female_MUTvWT: 72

Sphk2 >>> p-adj is less than 0.05 for both MUT_MALEvFEMALE and WT_MALEvFEMALE Sphingosine Kinase 2 Paralog with Sphk1, which codes for the other enzyme that has the same function Encodes for an enzyme that catalyzes the phosphorylation of sphingosine into sphingosine 1-phosphate sphingosine 1-phosphate = important in cell migration, proliferation, apoptosis Implicated in some cancers (breast cancer proliferation, chemoresistance) Related pathways: Calcium signaling pathway (could be connection to CMT2D), Metabolism

Tsix >>> p-adj is less than 0.05 for both MUT_MALEvFEMALE and WT_MALEvFEMALE

== 2/15/18: GeneWeaver Lecture ==

Why GeneWeaver?
Integrative functional genomics
Goal is to integrate the genetic investigation of humans and animal models
Data repository has a number of different types of data
Microarrays
Published data
Annotations
GWAS
QTL
Tools
Can do set-set matches
Can match your set of gene with your other set of genes or with another set of genes in the database
ODE IDs are a reference to find the consilience among associations of biomolecular entities and related concenpts
http://beta.geneweaver.org/ >>> new version
Types of functional genomics data
Mapping Data
QTL Positional candidates (mouse, rat)
GWAS Candidate (from original studies)
Expression Data
DRG (Drug Related Genes) >>> from literature
ABA (Allen Brain Atlas) >>> mice
CTD (Comparative Toxicogenomics Database) >>> from 9 different species
Functional Annotations
GO (Gene Ontology Annotations) > human, house
MP (Mammalian Phenotype Ontology)
HP (Human phenotype ontology)
OMIM (Online Mendelian Inheritance in Man)
MeSH (Medical Subject Headings)
Pathway Data
KEGG (Kyoto Encyclopedia of Genes and Genomes)
MSigDB (Molecular Signatures Databases)
PC (Pathway Commons)

How do we use GeneWeaver?
Tier III = data curated from literature (gold standard)
Tier I and II = public resource data (II is pre-processed somehow)
Search
On results, "+" give you more basic info (description, authors, etc.) or gene set
Clicking on gene set name will allow you to view the gene set
Gene list on bottom of gene set entry
Gene Symbol >>> can be changed to show identifier numbers for datasets
Homology shows which species there is a homologous gene in
Linkouts can take you to entry in each database
Under a search, you can select genesets, then hit "Add Selected to Project" (at top of search results) and you can add it to an existing project or create a new project
Top Toolbar: Analyze GeneSet
Can select all genesets in a project, or can expand project (+) and select specific sets

Analyzing a GeneSet
HiSim Graph
Usually default parameters are fine
On resulting graph
Can zoom in or out
Right = 4 genesets
Hover mouse over it to get a little data info
Moving right to left, you'll encounter nodes (intersection between our genesets)
Hovering over the node will show you what genes interact between the two genesets
Furthest left nodes show the most connected genes (genes that are in the most genesets)
Left clicking on a node will make it disappear so that we only show what we do care about (left clicking on it again will make it reappear)
Right clicking (or shift+left click) on the node will make a more detailed node summary page appear
Visualization
Classic = more descriptive without clicking on things
Modern = easier to see connections
Contains my projects and projects that have been shared with me
GeneSet Graph
Shows us which genes are connected to which datasets
Left to right = less connected gene sets to most connected gene sets
Color lines match color of gene set boxes
Difference between this and HiSim Graph
HiSim = emphasizes the sets and the relationship between the sets
GeneSet = highlights the genes
Different ways of visualizing the same thing
Tool Options >>> MinDegree
Allows you to limit it so that the graph only shows genes that have X number of connections
e.g. if you change the MinDegree to 4 and hit "Re-Run Tool," the graph will only show genes that are in at least 4 of the data sets
Jaccard Similarity Status
Top Toolbar
Manage Genesets > Manage Projects
Can view/edit all your projects
Little arrow with dots on points = share project
Can share project with a group that you're part of
Top Toolbar >>> Manage GeneSets >>> Upload GeneSet
Give set a descriptive name
Figure Label = shorter version of GeneSet Name (something that will fit on an analysis label)
Score Type: no way to upload a gene set with two different score types
Can upload gene set twice, each one with a different score type, then threshold the gene sets
Choose access restriction and you can share which groups you want
Select species (Mus musculus)
Gene Identifier: can't use a mix of gene identifiers, but some databases have conversion tools
Gene List: File Upload only takes plain text
If you copy and paste from excel, it should automatically format properly
Annotations should automatically be generated when you create your set
Once you've created your gene sets, you can share it with groups and begin analysis

== 2/22/18 ==

Final Paper: 5-10 pages, including figures
Zotero style = Cell
Figures embedded in text
Have figure legends (not in a text box)
Suggested writing order
Title > 7
Authors (you first, partner second) > 6
Abstract (200-word limit) > 5
Intro > 4
Methods > 1
Results > 2 (figures first, then writing)
Discussion > 3
References > 8 (Zotero)

GO-rilla:
MUTvWT (ranked by p-value)
Nefm (medium polypeptide)
Nefh (heavy polypeptide)
Nefl (light polypeptide)
Calca (calcitonin/calcitonin-related polypeptide, alpha
Slc31a1 (solute carrier family 31, member 1)
Tnnt1 (troponin t1, skeletal, slow)
Atp1a1 (atpase, na+/k+ transporting, alpha 1 polypeptide)
Myh7 (myosin, heavy polypeptide 7, cardiac muscle, beta)

p = 10-7-10-9

When sorted by fold change: MUTvWT
intermediate filament-based process
Prph - peripherin

Genes of Interest:
Nefm
Nefh
Nefl

If I need them (in order of use):
Calca
Atp1a1
Prph
Tnnt1
Slc31a1 & Myh7

Nefl, Nefm, & Nefh:
Intermediate filament-based process
Intermediate filament cytoskeleton organization
Neurofilament cytoskeleton organization
Intermediate filament organization
Intermediate filament bundle assembly
Neurofilament bundle assembly
Axon development
Calca
Regulation of muscle contraction (not shown on figure)
Regulation of anatomical structure size (not shown on figure)
Atp1a1
Regulation of muscle contraction

== 2/27/18: Notes for Results & Conclusion ==

Nefl: neurofilament light polypeptide
GeneCards:
Has not been connected to ALS, but has been associated with CMT
Related pathways: RET Signaling & Post-NMDA receptor activation events
RET Signaling: (Takahashi, 2001)
Works with glial cell line-derived neurotrophic factor (GDNF)
Promotes neuron survival
Related neurotrophic factors could be used as therapies for neurodegenerative diseases
Post-NMDA Receptor Activation Events
Involved in calcium ion influx
Establishes long-term synaptic changes
Appear to be more related to central nervous system rather than peripheral
NMDA agonists are treatment Alzheimer's in US and Europe
OMIM
Implicated in CMT2E (pretty similar phenotype to 2D) and CMT1F, so may play a role here
(Lin et al., 2009)
Previously connected motor neuron degradation with the binding of a destabilizing protein (190RhoGEF) to Nefl mRNA
Result was downregulation of Nefl mRNA in neurons
Nefm co-expression increased in response

Nefm: neurofilament medium polypeptide
GeneCards:
Associated with ALS
GO annotations: related to structural molecule activity and structural constituents of the cytoskeleton

Nefh: neurofilament heavy polypeptide
GeneCards:
Associated with ALS and CMT previously
GO annotations: Related to protein kinase binding and microtubule binding
OMIM
Collard et al. (1995) hypothesized that axonal degeneration is caused by blocking the transport of molecules needed for axonal maintenance by road-blocking the transport network (may be part of ALS pathogenesis)
Rebelo et al. (2016)
Implicated in CMT2CC
Frameshift mutations in NEFH resulted in aggregation of protein, which could block the transport for axonal maintenance molecules
Elder et al. (1998)
Created Nefh knockout mice to study connection between neurofilament levels and axon size
Resulted in slight reduction of neurofilament levels, yet a drastic reduction in axon diameter
Supports the idea that the issue is about disrupting transport on the intracellular network rather than a degeneration of the network itself
Shows that the Nefh gene plays a strong role in axonal development (and reduced axon size is part of the CMT2D phenotype, which is why it may be important to study)
Need more info on how Nefh may affect axon size
Rebelo et al. (2016): looked at NEFH mutations and saw aggregation of abnormal proteins. Found similar mutation in NEFL, which they note is known to be related to CMT. Perhaps the mutant GARS proteins bind to NEFH proteins and cause them to aggregate because they can't be used (similar to the NEFL explanation)

All 3 of these genes are under-expressed in our research mice. Under-expression means that the integrity of the axoskeleton around the neurons is not good, so intracellular transport to and from these neurons may be affected

As seen with Nefl, interference with the mRNA causes downregulation of the mRNA, which could explain why the gene is showing under-expression in our mice

In the Lin et al. (2009) study, Nefm expression increased to compensate for the downregulation of Nefl, but perhaps in CMT2D, it interferes with all three types of neurofilament mRNA
Could the mutant GARS proteins have gained the function to bind with the neurofilament mRNA (like the destabilizing protein mentioned in Lin et al., 2009)?
Research may also support this explanation for Nefh

Discussion
Nefl: Potential toxic gain-of-function where the mutant GARS proteins interfere with neurofilament mRNA by binding to it, resulting in downregulation of the neurofilament genes
Nefh: blocks transport on intracellular network (may be similar mechanism to Nefl issue)
Need more info on how Nefh may affect axon size
Each of the focus genes above have orthologs in mice, meaning researchers could study these genes in relation to Gars fairly easily
May have a section connecting to ALS, as I think the potential mechanisms I'm discussing have a role in ALS.
Reiterate need to research better treatment options

Talk:Jenna Reed

2018-03-11T18:50:52Z

Jereed:

Talk:Jenna Reed

2018-03-11T18:46:05Z

Jereed:

Jenna Reed

2018-02-14T02:47:48Z

Jereed: Blanked the page

Talk:Jenna Reed

2018-02-14T02:47:32Z

Jereed:

Talk:Jenna Reed

2018-02-14T02:46:45Z

Jereed: /* 2/8/18 */ new section

2/8/18

Bio343 HTSeq Results

Male Mutant
25 and 24 = paired reads, Male mouse 2078, C201R
15 and 14 = paired reads, Male mouse 2073, C201R
3 and 2 = paired reads, Male mouse 2079, C201R

Male WT
21 and 20 = paired reads, Male mouse 2076, WT
19 and 18 = paired reads, Male mouse 2075, WT
17 and 16 = paired reads, Male mouse 2074, WT

Female Mutant
13 and 12 = paired reads, Female mouse 2072, C201R
11 and 10 = paired reads, Female mouse 2071, C201R
9 and 8 = paired reads, Female mouse 2070, C201R

Female WT
23 and 22 = paired reads, Female mouse 2077, WT
7 and 6 = paired reads, Female mouse 2081, WT
5 and 4 = paired reads, Female mouse 2080, WT

Gene Cards
Gene Weaver
GO rilla
FNTM
String
NCBI

2/8/18
Loaded data from "Histories Shared with Me"

NefL >>> deltaCMT 1F 2E
NefH >>> deltaALS
Sphk2 >>> kinase
Male v Female
MaleWT v MaleCMT
FemaleWT v FemaleCMT
Gm4210
NefM (neurofilament medium)
Close with NefL, and Gm2410
Calca >>> calcium regulator

About this data: Took the spinal cord of the mice and sent it off for RNASeq
2-fold change
-0.7 = 1.62 fold change
-0.8 = 1.74 fold change
-7.56 = 128fold change, p=10^-14
What's our fold-change cutoff?
JAX had a strict cutoff and got only 20 genes
Lack of signal because of CMT could cause repression of transcription (because usually ligand binding induces transcription)
RNASeq data will always have differential expression from natural variation
Is it enough differential expression that it could be caused by the disease?

== 2/13/18 ==

2/13/18

mut/WT log2 500/1000 = 0.5 Anything less than 1 is negative

DESeq2 >>> run htseq-count data with features After doing a DESeq2 run, two files will appear in the history First one is tabular. Download that, open in Text Wrangler, then copy and paste that in excel Second one is a pdf. Download and open that to see data visualizations

54 & 55 >>> DESeq2 for "Female_MUTvWT" 56 & 57 >>> DESeq2 for "Male_MUTvWT" 58 & 59 >>> DESeq2 for "MUT_MALEvFEMALE" 60 & 61 >>> DESeq2 for "WT_MALEvFEMALE" 62 & 63 >>> DESeq2 for "MUT_v_WT"

Research method for today: look at what genes are significant in each dataset and see where there is overlap between datasets

In excel sheet: DESeq2 data for all 5 comparisons P-value < 0.05 highlighted in yellow P-adjusted <0.05 highlighted in green List of just the p-adjusted <0.05 genes for all 5 comparisons together

General observations:

of genes with p-adj < 0.05
Female_MUTvWT: 72

Sphk2 >>> p-adj is less than 0.05 for both MUT_MALEvFEMALE and WT_MALEvFEMALE Sphingosine Kinase 2 Paralog with Sphk1, which codes for the other enzyme that has the same function Encodes for an enzyme that catalyzes the phosphorylation of sphingosine into sphingosine 1-phosphate sphingosine 1-phosphate = important in cell migration, proliferation, apoptosis Implicated in some cancers (breast cancer proliferation, chemoresistance) Related pathways: Calcium signaling pathway (could be connection to CMT2D), Metabolism

Tsix >>> p-adj is less than 0.05 for both MUT_MALEvFEMALE and WT_MALEvFEMALE

== 2/8/18 ==

2/8/18

Bio343 HTSeq Results

Male Mutant 25 and 24 = paired reads, Male mouse 2078, C201R 15 and 14 = paired reads, Male mouse 2073, C201R 3 and 2 = paired reads, Male mouse 2079, C201R

Male WT 21 and 20 = paired reads, Male mouse 2076, WT 19 and 18 = paired reads, Male mouse 2075, WT 17 and 16 = paired reads, Male mouse 2074, WT

Female Mutant 13 and 12 = paired reads, Female mouse 2072, C201R 11 and 10 = paired reads, Female mouse 2071, C201R 9 and 8 = paired reads, Female mouse 2070, C201R

Female WT 23 and 22 = paired reads, Female mouse 2077, WT 7 and 6 = paired reads, Female mouse 2081, WT 5 and 4 = paired reads, Female mouse 2080, WT

Gene Cards Gene Weaver GO rilla FNTM String NCBI

2/8/18 Loaded data from "Histories Shared with Me"

NefL >>> deltaCMT 1F 2E NefH >>> deltaALS Sphk2 >>> kinase

Male v Female
MaleWT v MaleCMT
FemaleWT v FemaleCMT
Gm4210 NefM (neurofilament medium) Close with NefL, and Gm2410 Calca >>> calcium regulator

About this data: Took the spinal cord of the mice and sent it off for RNASeq 2-fold change

-0.7 = 1.62 fold change
-0.8 = 1.74 fold change
-7.56 = 128fold change, p=10^-14
What's our fold-change cutoff? JAX had a strict cutoff and got only 20 genes Lack of signal because of CMT could cause repression of transcription (because usually ligand binding induces transcription) RNASeq data will always have differential expression from natural variation Is it enough differential expression that it could be caused by the disease?

Talk:Jenna Reed

2018-02-14T02:46:32Z

Jereed: /* 2/13/18 */ new section

Jenna Reed

2018-02-14T02:45:46Z

Jereed:

2/13/18

mut/WT log2 500/1000 = 0.5 Anything less than 1 is negative

DESeq2 >>> run htseq-count data with features
After doing a DESeq2 run, two files will appear in the history
First one is tabular. Download that, open in Text Wrangler, then copy and paste that in excel
Second one is a pdf. Download and open that to see data visualizations

54 & 55 >>> DESeq2 for "Female_MUTvWT"
56 & 57 >>> DESeq2 for "Male_MUTvWT"
58 & 59 >>> DESeq2 for "MUT_MALEvFEMALE"
60 & 61 >>> DESeq2 for "WT_MALEvFEMALE"
62 & 63 >>> DESeq2 for "MUT_v_WT"

Research method for today: look at what genes are significant in each dataset and see where there is overlap between datasets

In excel sheet:
DESeq2 data for all 5 comparisons
P-value < 0.05 highlighted in yellow
P-adjusted <0.05 highlighted in green
List of just the p-adjusted <0.05 genes for all 5 comparisons together

General observations:
# of genes with p-adj < 0.05
Female_MUTvWT: 72

Sphk2 >>> p-adj is less than 0.05 for both MUT_MALEvFEMALE and WT_MALEvFEMALE
Sphingosine Kinase 2
Paralog with Sphk1, which codes for the other enzyme that has the same function
Encodes for an enzyme that catalyzes the phosphorylation of sphingosine into sphingosine 1-phosphate
sphingosine 1-phosphate = important in cell migration, proliferation, apoptosis
Implicated in some cancers (breast cancer proliferation, chemoresistance)
Related pathways: Calcium signaling pathway (could be connection to CMT2D), Metabolism

Tsix >>> p-adj is less than 0.05 for both MUT_MALEvFEMALE and WT_MALEvFEMALE

Talk:Jenna Reed

2018-02-14T02:44:31Z

Jereed:

Talk:Jenna Reed

2018-02-14T02:44:07Z

Jereed:

Jenna Reed

2018-02-14T02:41:30Z

Jereed: 2/13/18

2/13/18

mut/WT log2 500/1000 = 0.5 Anything less than 1 is negative

DESeq2 >>> run htseq-count data with features
After doing a DESeq2 run, two files will appear in the history
" First one is tabular. Download that, open in Text Wrangler, then copy and paste that in excel
" Second one is a pdf. Download and open that to see data visualizations

54 & 55 >>> DESeq2 for "Female_MUTvWT"
56 & 57 >>> DESeq2 for "Male_MUTvWT"
58 & 59 >>> DESeq2 for "MUT_MALEvFEMALE"
60 & 61 >>> DESeq2 for "WT_MALEvFEMALE"
62 & 63 >>> DESeq2 for "MUT_v_WT"

Research method for today: look at what genes are significant in each dataset and see where there is overlap between datasets

In excel sheet:
" DESeq2 data for all 5 comparisons
o P-value < 0.05 highlighted in yellow
o P-adjusted <0.05 highlighted in green
" List of just the p-adjusted <0.05 genes for all 5 comparisons together

General observations:
"
" # of genes with p-adj < 0.05
o Female_MUTvWT: 72

Sphk2 >>> p-adj is less than 0.05 for both MUT_MALEvFEMALE and WT_MALEvFEMALE
" Sphingosine Kinase 2
o Paralog with Sphk1, which codes for the other enzyme that has the same function
" Encodes for an enzyme that catalyzes the phosphorylation of sphingosine into sphingosine 1-phosphate
o sphingosine 1-phosphate = important in cell migration, proliferation, apoptosis
o Implicated in some cancers (breast cancer proliferation, chemoresistance)
" Related pathways
o Calcium signaling pathway
" Could be connection to CMT2D
o Metabolism

Tsix >>> p-adj is less than 0.05 for both MUT_MALEvFEMALE and WT_MALEvFEMALE

Talk:Jenna Reed

2018-02-13T14:43:06Z

Jereed: Created page with "2/8/18 Bio343 HTSeq Results Male Mutant 25 and 24 = paired reads, Male mouse 2078, C201R 15 and 14 = paired reads, Male mouse 2073, C201R 3 and 2 = paired reads, Male mouse..."