Bioinformatic Analysis Of Androgenetic Alopecia Microarray Data

[InBeforeTheCure]

In this thread, I'm unloading the results of a few tools I've run on three microarray datasets. The first, from Chew et. al, compares static measurements of dermal papilla cell gene expression in balding vs. non-balding scalp (GSE66663). The second, again from Chew et. al, is time series data from balding and non-balding DPCs treated with 1nM or 10nM DHT (GSE66664). And the third, from Cotsarelis et. al, is a comparison of bald scalp vs. haired scalp as a whole (GSE36169). I won't go into much depth in this post (there's too much information, so an in depth analysis would be a dissertation and not a forum post ), but instead will mostly throw the results out there for discussion by the community.

Analysis of Static DP Expression
First, I filtered out all probesets with detection less than 1 in either all bald replicates or all non-bald replicates. I aggregated the probesets corresponding to the same gene and normalized for total mRNA signal for each replicate. Then I calculated average fold change between balding and non-balding DPCs for each gene as well as log2 average fold change.

First we can run gene ontology (GO) on the upregulated and downregulated genes to find biological processes overrepresented in the upregulated and downregulated genes.

List of major categories associated with upregulated genes (log2 fold change of 0.5 or more, i.e. genes expressed at ~1.41x or higher):

Spoiler

- DNA strand elongation involved in DNA replication
- DNA replication initiation
- telomere maintenance via recombination
- metaphase plate congression
- mitotic prometaphase
- spindle assembly
- G1/S transition of mitotic cell cycle
- DNA synthesis involved in DNA repair
- mitotic sister chromatid segregation
- sister chromatic cohesion
- regulation of mitotic nuclear division
- mitotic cell cycle checkpoint
- double-strand break repair
- regulation of microtubule cytoskeleton organization
- mitotic anaphase
- regulation of response to DNA damage stimulus
- cell division
- DNA damage checkpoint
- G2/M transition of mitotic cell cycle
- negative regulation of mitotic cell cycle phase transition
- regulation of signal transduction by p53 class mediator
- response to ionizing radiation
- DNA packaging
- positive regulation of cell cycle process
- regulation of cysteine-type endopeptidase activity involved in apoptotic process
- apoptotic signaling pathway
- angiogenesis
- urogenital system development
- morphogenesis of an epithelium
- tube morphogenesis
- regulation of DNA metabolic process
- reproductive structure development
- cellular response to lipid
- negative regulation of cellular component organization
- regulation of cellular protein localization
- single organism reproductive process
- regulation of cellular component biogenesis
- positive regulation of developmental process
- positive regulation of cellular component organization
- organonitrogen compound biosynthetic process
- movement of cell or subcellular component
- protein complex assembly
- regulation of cell proliferation
- generation of neurons
- positive regulation of signal transduction
- cell development
- regulation of phosphorylation
- positive regulation of molecular function
- animal organ development
- positive regulation of nucleobase-containing compound metabolic process
- positive regulation of gene expression
- regulation of multicellular organismal development
- regulation of protein modification process
- multi-organism process
- cellular protein metabolic process

A lot of categories related to cell cycle and DNA damage/repair, as you can see. A few studies find that balding DPCs are more prone to cellular senescence than non-balding cells(1)(2). One study finds expression of DNA damage markers in balding DPCs(3).

List of major categories associated with downregulated genes (log2 fold change of -0.5 or less, i.e. genes expressed at ~0.71x or lower):

Spoiler

- response to interferon-beta
- phagosome acidification
- type I interferon signaling pathway
- cholesterol biosynthetic process
- negative regulation of viral genome replication
- antigen processing and presentation of exogenous peptide antigen via MHC class I
- regulation of macroautophagy
- multicellular organismal macromolecule metabolic process
- defense response to virus
- transition metal ion transport
- vacuole organization
- response to interferon-gamma
- carboxylic acid catabolic process
- cellular lipid catabolic process
- cellular response to insulin stimulus
- cellular response to nutrient levels
- membrane lipid metabolic process
- extracellular matrix organization
- positive regulation of innate immune response
- vacuolar transport
- organonitrogen compound catabolic process
- response to oxygen levels
- negative regulation of immune system process
- oxidation-reduction process
- monocarboxylic acid metabolic process
- regulation of cell migration
- positive regulation of apoptotic process
- negative regulation of cell proliferation
- organophosphate metabolic process
- positive regulation of signal transduction
- negative regulation of cell death
- apoptotic process
- protein transport
- vesicle-mediated transport
- negative regulation of multicellular organismal process
- negative regulation of cellular protein metabolic process
- negative regulation of signal transduction
- positive regulation of cellular protein metabolic process
- cellular protein localization
- protein complex subunit organization
- phosphate-containing compound metabolic process
- regulation of protein modification process
- intracellular signal transduction
- regulation of catalytic activity
- regulation of cellular component organization
- regulation of development process
- single-organism localization
- developmental process

Many immune system categories among the downregulated genes, such as those related to the interferon pathway and histocompatibility/antigen processing. As you'll see later, immune-related categories are actually upregulated in balding scalp as a whole according to the Cotsarelis data. In particular, we run into the interferon pathway again and again through several separate analyses.

Next we'll use TFactS to infer transcription factors that may be responsible for changes in gene expression in balding DPCs, which performs extremely well compared to other methods(4):

To evaluate the ability of TFactS to detect the relevant TFs, we submitted the genes reported by the authors as showing a significant response in their respective microarray analysis. When regulated genes were not listed in the paper, we reanalyzed the raw data obtained from GEO database and we selected genes significantly regulated >2-fold.

Even though these studies were based on very different biological systems, the results summarized in Table 1 (details in Supplementary Data in supplementary file 1) show that TFactS identified all (18/18) of the relevant TFs. For example, Terragni et al. (32) showed that inhibition of the AKT pathway provokes the activation of FOXO3 and the inhibition of NF-κB. Consistently, TFactS identified FOXO3 as regulated (Pval≃0.00e+0) and activated (Pval = 1.40e−4) and NF-κB as regulated (Pval≃0.00e+0) and inhibited (Pval=1.16e−3).
…
To compare the different tools, we have used similar settings for all of them in terms of statistical cutoff and promoter length. Lists of enriched transcription factors generated using these tools were ranked according to P-values or FWER P-value (GSEA). We considered only significant results (nominal P-value) and limited the number of TFs in the output lists to maximum 100, even though such long lists are not suitable for experimental validation. Using these parameters, CRSD and CORE_TF found 12 out of 18 expected TFs, TFM-Explorer 8, oPOSSUM 7 and GSEA 2. Three TFs were absent from JASPAR profiles used by oPOSSUM. When used with TFactS sign-less catalog instead of ‘c3' signatures, GSEA performed better (5/18). We did not use other GSEA gene set signatures as they are not centered on TFs. Detailed results and methods are shown in Supplementary Data supplementary file 2. Compared to TFactS, these tools produced much longer lists of regulated TFs, but it is not clear whether these represent background or previously unrecognized regulations. In summary, TFactS was able to identify expected transcription factor regulations, which, at least in some cases, were not found by tools based on PWM or consensus motifs, using standard settings.

There's both a signed mode, which specifies higher vs. lower activity of the transcription factor, and an unsigned mode which has a larger dataset but does not specify direction.

The unsigned analysis (statistically significant results after multi-testing correction shown):

Spoiler

regulated TFs
Transcription Factor P.value E.value Q.value FDR control (B-H) Intersection Target genes Random Control(%)
MYC 0.000e+0 0.000e+0 0.000e+0 2.976e-4 121 553 100
TFAP2A 0.000e+0 0.000e+0 0.000e+0 5.952e-4 34 115 71
TCF7L2 0.000e+0 0.000e+0 0.000e+0 8.929e-4 20 61 31
NFKB1 0.000e+0 0.000e+0 0.000e+0 1.190e-3 38 141 88
JUN 0.000e+0 0.000e+0 0.000e+0 1.488e-3 28 131 85
USF1 0.000e+0 0.000e+0 0.000e+0 1.786e-3 25 108 64
FOXO1 0.000e+0 0.000e+0 0.000e+0 2.083e-3 39 161 88
GLI1 0.000e+0 0.000e+0 0.000e+0 2.381e-3 29 124 69
GLI2 0.000e+0 0.000e+0 0.000e+0 2.679e-3 31 107 62
AR 0.000e+0 0.000e+0 0.000e+0 2.976e-3 17 60 31
STAT3 0.000e+0 0.000e+0 0.000e+0 3.274e-3 19 69 48
SMAD3 0.000e+0 0.000e+0 0.000e+0 3.571e-3 18 63 37
SMAD4 0.000e+0 0.000e+0 0.000e+0 3.869e-3 14 45 15
SP3 0.000e+0 0.000e+0 0.000e+0 4.167e-3 29 132 77
SP1 0.000e+0 0.000e+0 0.000e+0 4.464e-3 112 428 100
SMAD1 0.000e+0 0.000e+0 0.000e+0 4.762e-3 10 22 5
RELA 0.000e+0 0.000e+0 0.000e+0 5.060e-3 26 84 52
STAT1 0.000e+0 0.000e+0 0.000e+0 5.357e-3 23 61 36
SREBF1 0.000e+0 0.000e+0 0.000e+0 5.655e-3 22 51 16
REL 0.000e+0 0.000e+0 0.000e+0 5.952e-3 11 23 10
FLI1 0.000e+0 0.000e+0 0.000e+0 6.250e-3 11 28 10
TP53 0.000e+0 0.000e+0 0.000e+0 6.548e-3 34 148 89
CTNNB1 0.000e+0 0.000e+0 0.000e+0 6.845e-3 86 306 100
CREB1 0.000e+0 0.000e+0 0.000e+0 7.143e-3 38 211 97
ETS1 1.000e-5 1.680e-3 3.339e-7 7.440e-3 26 136 87
ATF1 1.000e-5 1.680e-3 3.339e-7 7.738e-3 16 59 32
SREBF2 1.000e-5 1.680e-3 3.339e-7 8.036e-3 12 35 10
RARA 1.000e-5 1.680e-3 3.339e-7 8.333e-3 16 60 25
EGR1 1.000e-5 1.680e-3 3.339e-7 8.631e-3 20 91 58
USF2 2.000e-5 3.360e-3 6.247e-7 8.929e-3 19 86 47
NFIC 2.000e-5 3.360e-3 6.247e-7 9.226e-3 13 45 15
FOXO3 6.000e-5 1.008e-2 1.709e-6 9.524e-3 17 78 49
ETV4 6.000e-5 1.008e-2 1.709e-6 9.821e-3 10 31 9
ATF2 6.000e-5 1.008e-2 1.709e-6 1.012e-2 11 37 8
HIF1A 1.000e-4 1.680e-2 2.766e-6 1.042e-2 12 45 15
YY1 1.200e-4 2.016e-2 3.227e-6 1.071e-2 12 46 17

The signed analysis (p < 0.05 shown):
TFs predicted to be more active:

Spoiler

activated TFs
Transcription Factor P.value E.value Q.value FDR control (B-H) Intersection Target genes Random Control(%)
CTNNB1 2.190e-3 1.971e-1 1.107e-1 5.556e-4 45 300 0
GLI2 2.460e-3 2.214e-1 1.107e-1 1.111e-3 20 104 0
MYC 9.760e-3 8.784e-1 2.928e-1 1.667e-3 30 197 0
REL 2.747e-2 2.472e+0 6.181e-1 2.222e-3 2 3 0

TFs predicted to be less active:

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inhibited TFs
Transcription Factor P.value E.value Q.value FDR control (B-H) Intersection Target genes Random Control(%)
SREBF1 0.000e+0 0.000e+0 0.000e+0 5.556e-4 17 50 0
SREBF2 4.610e-3 4.149e-1 2.074e-1 1.111e-3 9 34 0
CREBBP 8.290e-3 7.461e-1 2.205e-1 1.667e-3 3 5 0
BRCA1 9.800e-3 8.820e-1 2.205e-1 2.222e-3 2 2 0
CTNNB1 1.738e-2 1.564e+0 3.128e-1 2.778e-3 41 300 0
SMAD4 2.177e-2 1.959e+0 3.266e-1 3.333e-3 8 36 0
CDKN1A 2.747e-2 2.472e+0 3.532e-1 3.889e-3 2 3 0
FOXO1 4.085e-2 3.676e+0 3.893e-1 4.444e-3 23 160 0
NFKB1 4.275e-2 3.848e+0 3.893e-1 5.000e-3 11 63 0
SP1 4.326e-2 3.893e+0 3.893e-1 5.556e-3 25 178 0

Keep in mind that for the signed lists, I've bolded all results with p < 0.05 with no multi-testing correction. Therefore, probability of false positives is higher in these lists. We see CTNNB1 (beta-catenin) in both the activated and inhibited lists, which indicates that its target genes may be altered, perhaps through binding to AR, which one study has shown to be enhanced in Androgenetic Alopecia (5). We also see different subunits of NFkB (REL, NFKB1) in both groups. Others:

Gli2 – mediator of hedgehog signaling
MYC is upregulated 1.55x at the transcriptional level.

In the repressed list:
SREBF1 (a.k.a. SREBP1) and SREBF2 (a.k.a. SREBP2), which are involved in lipid and sterol synthesis.
CREBBP – acetyltransferase which acts as a general coactivator, is downregulated at the transcriptional level.
BRCA1 – involved in DNA repair (actually upregulated at transcriptional level).
SMAD4 – mediator of TGFbeta/BMP signaling
CDKN1A (a.k.a. p21) – cell cycle inhibitor, highly downregulated at transcriptional level
FOXO1 – regulates metabolism, resistance to stress, longevity
SP1 – wide variety of functions

Lastly for the static data, we can run SDREM(6) to attempt to reverse-engineer upstream signaling pathways that may account for changes in gene expression. SDREM is intended for time-series data, but we'll just treat it like the dermal papilla cells went from the non-balding state to the balding state all in one step.

Red = source node (which I specified as AR), green = target nodes (inferred downstream transcription factors), blue = internal nodes connecting the sources to the targets

There's a lot going on here, so it's helpful to break the nodes into rough categories:

Immune system – IRF9, TBK1, IRF8, IRF2, IRF3, IRF4, IRF5, IRF6, IRF7, RELA, SPI1, STAT1, STAT3, TAL1, XBP1
Development – CDX2, CTNNB1, EGFR, SOX8, ONECUT1, SMAD3, SMAD4, SOX18, PRDM1, SOX2, SOX4, TCF3, ALX1
Histone acetyltransferases – NCOA2, CREBBP, EP300, NCOA3, NCOA1, KAT2B
Histone deacetylases – HDAC1, HDAC2, HDAC4
Nuclear corepressors – NCOR1, NCOR2
Nuclear receptors – ESR1, NR5A2, NR6A1, NR3C1, AR (source), PPARG, RARA, VDR
Stress response/resistance – FOXO1, HIF1A
Chaperones – HSP90AA1
Cell Cycle – JUN, RB1, TP53, CDK1
Ubiquitination/proteasome – MDM2, UBC
Sumoylation – PIAS4, UBE2I, SUMO1, PIAS1
Kinases – SRC
DNA Repair – BRCA1
Other – DAXX, SIN3A, PML, SMARCA4, SP1, ZBTB16


(continued in next post)

 

[InBeforeTheCure]

Most of the target nodes fit into just two categories - immune system (especially IRFs - interferon regulatory factors) and development (especially SOX genes). We can also run GO on the inferred nodes, using the proteins in the entire SDREM protein-protein-interaction dataset as the background set so as not to bias the results:

Spoiler

- N-terminal peptidyl-lysine acetylation
- fungiform papilla formation
- regulation of production of miRNAs involved in gene silencing by miRNA
- positive regulation of pri-miRNA transcription from RNA polymerase II promoter
- positive regulation of protein sumoylation
- eyelid development in camera-type eye
- glial cell fate commitment
- negative regulation of telomerase activity
- positive regulation of protein import into nucleus, translocation
- positive regulation by host of viral transcription
- negative regulation of peptidyl-lysine acetylation
- positive regulation of transcription from RNA polymerase II promoter involved in cellular response to chemical stimulus
- beta-catenin-TCF complex assembly
- androgen receptor signaling pathway
- positive regulation of peptidyl-lysine acetylation
- type I interferon signaling pathway
- interferon-gamma-mediated signaling pathway
- regulation of nitric-oxide synthase activity
- lactation
- positive regulation of histone modification
- regulation of transcription from RNA polymerase II promoter in response to stress
- positive regulation of reactive oxygen species biosynthetic process
- regulation of histone acetylation
- mammary gland epithelium development
- response to estrogen
- cellular response to hydrogen peroxide
- positive regulation of type I interferon production
- DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
- embryonic pattern specification
- negative regulation of muscle cell differentiation
- skin epidermi development
- pancreas development
- somatic stem cell population maintenance
- transforming growth factor beta receptor signaling pathway
- regulation of DNA binding
- response to UV
- regulation of fibroblast proliferation
- ureteric bud development
- histone acetylation
- regulation of smooth muscle cell proliferation
- response to estradiol
- regulation of myeloid cell differentiation
- defense response to virus
- protein sumoylation
- liver development
- cell maturation
- circadian rhythm
- regulation of cytokine-mediated signaling pathway
- digestive tract development
- cellular response to oxygen levels
- chromatin remodeling
- negative regulation of transcription from RNA polymerase II promoter
- response to alkaloid
- placenta development
- cellular response to lipopolysaccharide
- myeloid cell differentiation
- transcription initiation from RNA polymerase II promoter
- morphogenesis of a branching epithelium
- epithelial tube morphogenesis
- positive regulation of hemopoiesis
- response to alcohol
- limb development
- response to purine-containing compound
- epithelial cell development
- positive regulation of DNA metabolic process
- lymphocyte differentiation
- heart morphogenesis
- regulation of gene expression, epigenetic
- regulation of innate immune response
- regulation of cysteine-type endopeptidase activity involved in apoptotic process
- regulation of leukocyte differentiation
- response to mechanical stimulus
- positive regulation of proteolysis
- positive regulation of sequence-specific DNA binding transcription factor activity
- cellular response to peptide hormone stimulus
- cellular response to external stimulus
- positive regulation of apoptotic process
- regulation of epithelial cell proliferation
- angiogenesis
- negative regulation of cell proliferation
- response to hypoxia
- negative regulation of apoptotic process
- regulation of apoptotic signaling pathway
- positive regulation of cell development
- response to drug
- positive regulation of cellular component biogenesis
- positive regulation of cell proliferation
- regulation of catabolic process
- positive regulation of intracellular signal transduction
- embryonic morphogenesis
- regulation of cellular response to stress
- chordate embryonic development
- cell proliferation
- negative regulation of signal transduction
- regulation of MAPK cascade
- regulation of cell adhesion
- regulation of neurogenesis
- negative regulation of multicellular organismal process
- positive regulation of protein phosphorylation
- regulation of anatomic structure morphogenesis
- apoptotic process
- central nervous system development
- multi-organism reproductive process

Analysis of Time-Series DP Expression
For the time-series data, Chew et. al cultured dermal papilla cells from balding and non-balding scalp for 24 hours, then exposed them to either 10nM or 1nM of DHT for a further 48 hours. We have four different time series:
- balding DPCs treated with 10nM DHT
- balding DPCs treated with 1nM DHT
- non-balding DPCs treated with 10nM DHT
- non-balding DPCs treated with 1nM DHT

Four measurements are missing – the 1nM balding replicate C at 0h, the 10nM non-balding replicate B at 1h, the 1nM non-balding replicate C at 0h, and the 1nM non-balding replicate C at 3h. I first filtered for the same probesets used in the static expression data and then treated them the same as before. Expression values are log2-transformed with respect to 0h to track changes in gene expression over time. We can use DREM with the TFacts dataset to infer transcription factors that may be controlling splits in the expression of clusters of genes.

10nM balding:

1nM balding:

10nM non-balding:

1nM non-balding:

Thin lines are individual genes, thick lines represent clusters of genes that the program determines.

TFs that show up often as controlling splits in response to DHT:
NFkB subunits (REL, RELA, NFKB1)
CEBPs
MYC
JUN
FOXOs
SP1
SMAD1

Notice how the response of the non-balding cells is more complex than the response of the balding cells. I do fear it may be overfitting those though…might have to change some of the DREM settings around. Also notice the one gene that shoots up way above the others at the first measurement (i.e. within the first 15 minutes) of the non-balding graph? That's EGR1, one of the "immediate early genes". It's also induced in the balding cells, but less strongly.

Analysis of Cotsarelis Data
Finally, we compare bald scalp vs. non-balding scalp as a whole to get some insight into what might be happening in the epithelial cells of the hair follicle as well as the microenvironment. I processed this data in the same way as the static Chew data.

Major GO categories in genes upregulated in balding scalp:

Spoiler

- protection from natural killer cell mediated cytotoxicity
- antigen processing and presentation of exogenous peptide antigen via MHC class I, TAP-independent
- lipoxin metabolic process
- intramembranous ossification
- lipoxygenase pathway
- antigen processing and presentation of endogenous antigen
- type I interferon signaling pathway
- interferon-gamma-mediated signaling pathway
- response to interferon-beta
- eicosanoid biosynthetic process
- antigen processing and presentation of exogenous peptide antigen via MHC class I, TAP-dependent
- collagen catabolic process
- chemokine-mediated signaling pathway
- negative regulation of viral genome replication
- complement activation, classical pathway
- T cell costimulation
- positive regulation of smooth muscle cell proliferation
- maternal process involved in female pregnancy
- extracellular matrix disassembly
- antigen processing and presentation of exogenous peptide antigen via MHC class II
- response to glucocorticoid
- positive regulation of leukocyte chemotaxis
- T cell receptor signaling pathway
- defense response to virus
- unsaturated fatty acid metabolic process
- regulation of wound healing
- monocarboxylic acid biosynthetic process
- cellular response to transforming growth factor beta stimulus
- cell chemotaxis
- response to ketone
- regulation of lymphocyte proliferation
- cellular response to tumor necrosis factor
- response to lipopolysaccharide
- response to nutrient
- inflammatory response
- regulation of angiogenesis
- skin development
- response to mechanical stimulus
- positive regulation of innate immune response
- leukocyte migration
- positive regulation of cytokine production
- regulation of endopeptidase activity
- response to acid chemical
- blood vessel development
- wound healing
- cell activation
- skeletal system development
- response to inorganic substance
- response to organonitrogen compound
- cell adhesion
- negative regulation of developmental process
- negative regulation of multicellular organismal process
- positive regulation of cell differentiation
- positive regulation of developmental process
- positive regulation of intracellular signal transduction
- regulation of cell development
- tissue development
- anatomical structure morphogenesis
- cell differentiation
- regulation of biological quality
- positive regulation of macromolecule metabolic process
- positive regulation of cellular metabolic process

Lots of immune system categories, many of which were downregulated in the balding dermal papilla cells specifically. Assuming the Chew data is typical, this suggests immunosuppression of balding dermal papilla cells, but greater immune infiltration of higher layers (i.e. the epithelial hair follicle).

Major GO categories in genes downregulated in balding scalp:

Spoiler

- hair follicle development
- odontogenesis
- canonical Wnt signaling pathway
- developmental growth involved in morphogenesis
- cardiac chamber morphogenesis
- kidney epithelium development
- cardiac muscle tissue development
- cell growth
- epithelial cell development
- negative regulation of cell adhesion
- muscle organ development
- angiogenesis
- ossification
- morphogenesis of an epithelium
- extracellular matrix organization
- regulation of epithelial cell proliferation
- neuron projection morphogenesis
- response to steroid hormone
- cell morphogenesis involved in neuron differentiation
- cellular response to growth factor stimulus
- positive regulation of cell development
- regulation of neuron differentiation
- tube development
- cell migration
- sensory organ development
- negative regulation of cell proliferation
- response to wounding
- regulation of anatomical structure morphogenesis
- enzyme linked receptor protein signaling pathway
- regulation of cell migration
- positive regulation of cell proliferation
- negative regulation of catalytic activity
- positive regulation of multicellular organismal process
- positive regulation of signal transduction
- regulation of apoptotic process
- cell adhesion
- regulation of cellular component organization
- response to external stimulus

"Hair follicle development"…Of course, right?

Let's run TFacts on this data. Unsigned results, statistically significant after multi-testing correction:

Spoiler

regulated TFs
Transcription Factor P.value E.value Q.value FDR control (B-H) Intersection Target genes Random Control(%)
NFKB1 0.000e+0 0.000e+0 0.000e+0 4.032e-4 24 141 33
GLI2 0.000e+0 0.000e+0 0.000e+0 8.065e-4 16 107 15
ETS1 0.000e+0 0.000e+0 0.000e+0 1.210e-3 16 136 24
RELA 0.000e+0 0.000e+0 0.000e+0 1.613e-3 17 84 12
SMAD3 0.000e+0 0.000e+0 0.000e+0 2.016e-3 19 63 3
SP1 0.000e+0 0.000e+0 0.000e+0 2.419e-3 42 428 89
SMAD7 0.000e+0 0.000e+0 0.000e+0 2.823e-3 7 15 1
SMAD4 0.000e+0 0.000e+0 0.000e+0 3.226e-3 11 45 4
ESR1 0.000e+0 0.000e+0 0.000e+0 3.629e-3 10 54 5
STAT1 0.000e+0 0.000e+0 0.000e+0 4.032e-3 13 61 12
CTNNB1 0.000e+0 0.000e+0 0.000e+0 4.435e-3 51 306 66
CREB1 0.000e+0 0.000e+0 0.000e+0 4.839e-3 27 211 36
ATF2 1.000e-5 1.240e-3 3.668e-6 5.242e-3 8 37 1
ATF3 1.000e-5 1.240e-3 3.668e-6 5.645e-3 5 11 0
EGR1 1.000e-5 1.240e-3 3.668e-6 6.048e-3 12 91 20
ETV4 2.000e-5 2.480e-3 6.474e-6 6.452e-3 7 31 4
SPI1 2.000e-5 2.480e-3 6.474e-6 6.855e-3 12 93 22
SMAD1 3.000e-5 3.720e-3 8.254e-6 7.258e-3 6 22 1
JUN 3.000e-5 3.720e-3 8.254e-6 7.661e-3 14 131 13
TFAP2A 3.000e-5 3.720e-3 8.254e-6 8.065e-3 13 115 9
FOXO1 7.000e-5 8.680e-3 1.834e-5 8.468e-3 15 161 39
STAT3 1.700e-4 2.108e-2 4.252e-5 8.871e-3 9 69 5
POU1F1 2.900e-4 3.596e-2 6.938e-5 9.274e-3 9 74 11
TCF7L2 3.800e-4 4.712e-2 8.712e-5 9.677e-3 8 61 8

Signed results, activated:

Spoiler

activated TFs
Transcription Factor P.value E.value Q.value FDR control (B-H) Intersection Target genes Random Control(%)
SMAD3 0.000e+0 0.000e+0 0.000e+0 7.692e-4 12 55 0
CTNNB1 2.000e-4 1.300e-2 6.500e-3 1.538e-3 26 300 1
STAT1 4.400e-4 2.860e-2 9.533e-3 2.308e-3 6 25 0
RELA 2.870e-3 1.866e-1 4.664e-2 3.077e-3 6 35 0
STAT3 4.780e-3 3.107e-1 6.214e-2 3.846e-3 7 51 0
NFKB1 1.507e-2 9.796e-1 1.633e-1 4.615e-3 7 63 0
STAT2 2.257e-2 1.467e+0 1.899e-1 5.385e-3 3 15 0
EP300 2.337e-2 1.519e+0 1.899e-1 6.154e-3 2 6 0

We see some indication of overactive interferon pathways again with STAT1 in the list. Also SMAD3, a mediator of the TGFbeta pathway, is inferred to be more active than in haired scalp.

Signed results, inhibited:

Spoiler

inhibited TFs
Transcription Factor P.value E.value Q.value FDR control (B-H) Intersection Target genes Random Control(%)
SMAD7 0.000e+0 0.000e+0 0.000e+0 7.692e-4 6 10 0
REL 5.080e-3 3.302e-1 1.257e-1 1.538e-3 2 3 0
CTNNB1 5.800e-3 3.770e-1 1.257e-1 2.308e-3 22 300 0
SMAD4 1.584e-2 1.030e+0 2.574e-1 3.077e-3 5 36 0
GLI2 2.845e-2 1.849e+0 3.699e-1 3.846e-3 9 104 0
TCF7L2 4.050e-2 2.633e+0 4.387e-1 4.615e-3 6 61 0

We also see beta-catenin in both lists again.

I've also uploaded a RAR with files containing:
- gene expression data
- GO output files
- top path edges and path nodes output from SDREM (for use in Cytoscape)
- TFacts results

You can find it here.

Any thoughts or ideas about any of this are much appreciated.

 

[BaldingBeast666]

So male pattern baldness is basically an auto-immune issue?

 

[Armando Jose]

So male pattern baldness is basically an auto-immune issue?

Probably yes, more certain in the last step of the multifactorial problem of common baldness.

 

[distracted]

Probably yes, more certain in the last step of the multifactorial problem of common baldness.

If that's the case do you have any confidence that JAK would be effective for Androgenetic Alopecia? Since if Androgenetic Alopecia is an auto-immune issue it may be more similar to AA than initially thought.

 

[opti]

how about topical corticosteroids?

 

[Jeca Tatu]

Now we are talking!

Thank you very very much fellow biologist @InBeforeTheCure

 

[InBeforeTheCure]

So male pattern baldness is basically an auto-immune issue?

Probably yes, more certain in the last step of the multifactorial problem of common baldness.

Immune infiltration has been reported with male pattern baldness. Jaworsky et. al, 1992:

Summary Hair-bearing, transitional, and alopecic scalp from three males and one female with progressive pattern alopecia were examined. Ultrastructural studies disclosed measurable thickening of the follicular adventitial sheaths of transitional and alopecic zones compared with those in the nonalopecic zones. This finding was associated with mast cell degranulation and fibroblast activation within the fibrous sheaths. Immunohistochemically, control biopsies were devoid of follicular inflammation (n= 3), while transitional regions consistently showed the presence of activated T-cell infiltrates about the lower portions of follicular infundibula. These infiltrates were associated with the induction of class II antigens on the endothelial linings of venules within follicular adventitia and with apparent hyperplasia of follicular dendritic cells displaying the CDl epitope. Inflammatory cells infiltrated the region of the follicular bulge, the putative source of stem cells in cycling follicles. The data suggest that progressive fibrosis of the perifollicular sheath occurs in lesions of pattern alopecia, and may begin with T-cell infiltration of follicular stem cell epithelium. Injury to follicular stem cell epithelium and/or thickening of adventitial sheaths may impair normal pilar cycling and result in hair loss.

Characterization of inflammatory infiltrates in male pattern alopecia: implications for pathogenesis

I've uploaded the full text here for anyone who wants to read it. Now, this doesn't mean that male pattern baldness progression is dependent on immune infiltration -- I doubt that it is. And I'm not sure if inflammation/infiltration is there in all cases of male pattern baldness (does anyone know?).

If that's the case do you have any confidence that JAK would be effective for Androgenetic Alopecia? Since if Androgenetic Alopecia is an auto-immune issue it may be more similar to AA than initially thought.

There have been several clinical trials run on JAK inhibitors and AFAIK not a single report of hair growth in anyone with Androgenetic Alopecia, so it doesn't look that way. And actually, according to the Jaworsky paper, the pattern of immune infiltration in Androgenetic Alopecia is quite different from AA and more like certain forms of irreversible immune-driven hair loss (i.e. around the bulge and higher rather than the bulb as in alopecia areata).

...

how about topical corticosteroids?

Doesn't work for Androgenetic Alopecia according to a study done in 1960.

Now we are talking!

Thank you very very much fellow biologist @InBeforeTheCure

Thanks. I'm not a biologist though.

 

[Armando Jose]

If that's the case do you have any confidence that JAK would be effective for Androgenetic Alopecia? Since if Androgenetic Alopecia is an auto-immune issue it may be more similar to AA than initially thought.

It depends from we consider as effective, .... personally I think that a intense regrowth of hair in persons who have for years common baldness is very difficult or impossible (except by hair transplant), if we think in stopping the hairloss process JAK can be effective.

 

[Armando Jose]

Hi,
I find a interesting study
"Acne is an inflammatory disease and alterations of sebum composition initiate acne lesions"
where point to sebaceous gland with immunity

Sebaceous glands and innate immunity
Follicular keratinocytes and sebocytes, the major components of
the pilosebaceous unit, may act as immune-active cells capable
of microbia recognition and abnormal lipid presentation.13
Innate immunity molecules such as toll-like receptor (TLR)-2
and TLR-4, CD1d and CD14 are expressed in human keratinocytes
14 and SZ95 sebocytes.13,15 Acting that way, keratinocytes
and sebocytes may be activated by Propionibacterium acnes (P.
acnes) and recognize altered lipid content in sebum, followed by
the production of proinflammatory cytokines. In addition, antimicrobial
peptides, such as defensin-1, defensin-2 and cathelicidin,
are expressed and are active in the sebaceous gland.16–19
Human b-defensin-2 is expressed upon exposure to lipopolysaccharides
and P. acnes17 and up-regulated by sebum free fatty
acids.18

I like these studies talking about changes in composition of sebum related to certains skin problems, it is important not the quantity but quality of sebum.
"Current evidence indicates that sebum composition
(lipid quality) and not quantity plays a central role in the development
of acne"

 

[InBeforeTheCure]

Hi,
I find a interesting study
"Acne is an inflammatory disease and alterations of sebum composition initiate acne lesions"
where point to sebaceous gland with immunity

Sebaceous glands and innate immunity
Follicular keratinocytes and sebocytes, the major components of
the pilosebaceous unit, may act as immune-active cells capable
of microbia recognition and abnormal lipid presentation.13
Innate immunity molecules such as toll-like receptor (TLR)-2
and TLR-4, CD1d and CD14 are expressed in human keratinocytes
14 and SZ95 sebocytes.13,15 Acting that way, keratinocytes
and sebocytes may be activated by Propionibacterium acnes (P.
acnes) and recognize altered lipid content in sebum, followed by
the production of proinflammatory cytokines. In addition, antimicrobial
peptides, such as defensin-1, defensin-2 and cathelicidin,
are expressed and are active in the sebaceous gland.16–19
Human b-defensin-2 is expressed upon exposure to lipopolysaccharides
and P. acnes17 and up-regulated by sebum free fatty
acids.18

I like these studies talking about changes in composition of sebum related to certains skin problems, it is important not the quantity but quality of sebum.
"Current evidence indicates that sebum composition
(lipid quality) and not quantity plays a central role in the development
of acne"

But if you look at the stains in the Jaworsky paper, the sebaceous glands are unaffected. T-cell infiltration is around the bulge and lower infundibulum (close to the surface, but not so much upper infundibulum right near the surface). My best guess for the pathway responsible for upregulation of HLA genes, with reference to these results, would be something like Secretion of cytokines like interleukins and TGFbeta from DP --> ROS --> p38 and/or JNK --> ATF/CREB1/SP1 --> upregulation of HLA genes and maybe NFkB --> upregulation of HLA genes.

 

[Armando Jose]

But if you look at the stains in the Jaworsky paper, the sebaceous glands are unaffected. T-cell infiltration is around the bulge and lower infundibulum (close to the surface, but not so much upper infundibulum right near the surface). My best guess for the pathway responsible for upregulation of HLA genes, with reference to these results, would be something like Secretion of cytokines like interleukins and TGFbeta from DP --> ROS --> p38 and/or JNK --> ATF/CREB1/SP1 --> upregulation of HLA genes and maybe NFkB --> upregulation of HLA genes.

InBeforeTheCure,

Thank for your point, it is very interesting.

My idea concurs exactly with it. I think that exist an inward flow of sebum in the hair (the outflow to the surface we all know), parting from sebaceous gland to dermal papilla. This is the key in common baldness, when this flow is interrupted the sebum goes to rancidity, increase viscosity and start the real problem, as secretion of cytokynes, and also it can affect the travel of stem cells from bulge to DP in the next hair cycle (this is an explanation of miniaturization before hairloss, less DP then less thick of hair shaft).

The problem with stains is that the normal protocol with hematoxylin… eliminate the fats (sebum), it is necessary to see them the use of red/congo/black etc colorants to fit the sebum.

This is my idea but I can be wrong. Only a simple experiment can bring the light.

 

[InBeforeTheCure]

and also it can affect the travel of stem cells from bulge to DP in the next hair cycle (this is an explanation of miniaturization before hairloss, less DP then less thick of hair shaft).

What do you mean by "stem cells from bulge to DP"? There have been one or two experiments showing DP cells coming from the dermal sheath, but I haven't seen any showing bulge stem cells traveling to the DP...?

 

[Armando Jose]

What do you mean by "stem cells from bulge to DP"? There have been one or two experiments showing DP cells coming from the dermal sheath, but I haven't seen any showing bulge stem cells traveling to the DP...?

http://www.jidonline.org/cms/attachment/2039323078/2052953717/gr4.jpg
full text of Cotsarelis
http://www.jidonline.org/article/S0022-202X(15)32987-0/fulltext

 

[Swoop]

Great job @InBeforeTheCure.

 

[baldboys]

also armando jose is just spilling the crazyest nonsense ever. Hell, are any of you even in the medical field?

 

[InBeforeTheCure]

http://www.jidonline.org/cms/attachment/2039323078/2052953717/gr4.jpg
full text of Cotsarelis
http://www.jidonline.org/article/S0022-202X(15)32987-0/fulltext

Interesting paper, but I see nothing about bulge stem cells repopulating the DP in there.

Great job @InBeforeTheCure.

Thanks man. All of this essentially makes a lot of predictions, so do you know of any experiments that confirm or refute things in the mess of results? For example, some predictions might be decreased NF-kB and STAT activity in bald DPCs but increased activity in other places, rearrangement of beta-catenin target genes, higher TGFbeta activity, inhibition of SREBP1 in DPCs, maybe involvement of p38 and/or JNK, and of course many many others. There are experiments showing increased production of TGFbeta in DPCs in response to DHT* of course, but I'm at a loss for much else -- clearly there's a lot we don't know, or my Googling skills just need work.

* This paper shows that while DHT induces TGFbeta at 21% oxygen, dependent on oxidative stress, it actually inhibits it at 2% (physiological) oxygen -- and potently inhibits it at supraphysiological DHT (100 nM), but only really in the balding cells. Weird.

 

[Armando Jose]

Interesting paper, but I see nothing about bulge stem cells repopulating the DP in there.

Maybe this work of Yahoda point out it better
Cell Movement in the Hair Follicle Dermis More Than a Two-Way Street?

https://www.google.es/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwibktaC4M3OAhVJDcAKHRRpD9QQFggeMAA&url=http://replicel.com/wp-content/uploads/2015/02/Jahoda-comment-on-our-paper-JID-2003.pdf&usg=AFQjCNHswjUAsMldk5TiizDx976jqF2-NA&sig2=y_Z2WprBPpUwvYn6HnyKqA&bvm=bv.129759880,d.d24


or this
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4006068/
SnapShot: Hair Follicle Stem Cells

http://onlinelibrary.wiley.com/doi/10.1111/exd.12416/abstract
Modulating hair follicle size with Wnt10b/DKK1 during hair regeneration

Thymosin beta 4 induces hair growth via stem cell migration and differentiation.
http://www.ncbi.nlm.nih.gov/pubmed/17947589

Here a diagram

http://www.biospectrum.co.kr/design/bspectrum0/2013img/05_tech09_02.jpg