A closer look at the hits generates 2 new hypotheses

Blog post by Dr. Tamara Maiuri

Forgive me readers for I have been busy, it has been 3 months since my last blog post. During this time, we published a very exciting story identifying a lead compound and how we think it works in HD. Also during this time, I have gone through the list of huntingtin interactors identified in my HDSA-funded project to find drug targets that are relevant to the process of DNA repair. We now have some interesting results!

As explained in my last post, the original aim to identify huntingtin interactors from human cells was not technically feasible and mouse cells were used instead. Several hundred protein-protein interactions were detected, then the list was further refined by only considering high and medium confidence hits. We now have a list of 314 proteins that interact with huntingtin reproducibly across biological replicates. See the end of this post for the lists of ROS-dependent interactors (Tables 1-3), proteins that interacted with huntingtin only in untreated cells (Table 4), and proteins found to be modified by poly ADP-ribose (PARylated proteins; Table 5). These tables have also been deposited to Zenodo.

Blog Post 9 fig


Indeed, the most notable finding of the ROS-dependent interactome analysis was the high degree of overlap with datasets of “PARylated” proteins. PAR is of interest to us because it is generated in response to DNA damage, and acts to recruit DNA repair proteins to damage sites.

This result led us to pursue two hypotheses with the aim of identifying drug targets relevant to DNA repair:

Huntingtin is a PAR-binding protein: Huntingtin may use PAR binding to interact with PARylated proteins. If so, this is likely to be the mechanism by which huntingtin interacts with chromatin and assembles DNA repair factors in its role as a scaffold, and this may be dysregulated in HD.

PAR signaling is dysregulated in HD: Regardless of whether huntingtin physically binds PAR, it is still possible that we have uncovered a previously unexplored aspect of HD pathology: there is strong genetic evidence that DNA repair genes influence disease [1-3], huntingtin participates in the process of DNA repair [4], and elevated levels of damaged DNA are common to HD tissues and models [4-8].

Old spider web. Shallow DOF.
© Bolotov | Stock Free Images

In response to DNA damage, PAR chains are the first scaffold generated for DNA repair factor assembly: a sticky web bound by proteins involved in DNA repair. So it’s possible that in HD, excess DNA damage accumulates due to sub-optimal huntingtin function in the repair process, leading to Poly ADP-Ribose Polymerase (PARP) hyperactivation and elevated PAR levels.

High PAR levels could not only interfere with huntingtin function if it binds PAR, but also cause an “energy crisis” in high-energy-consuming neurons [9-11]. In fact, energy deficits have been frequently observed across HD models and in HD patients [12]. Similarly, PARP hyperactivation is linked to cerebellar ataxia [13], and PARP inhibition has been reported to improve phenotypes in a HD mouse model, although by a different mechanism [14,15].

In the coming blog posts, I will share preliminary data that suggests huntingtin can bind PAR in a test tube, and that PAR is at least partially responsible for huntingtin recruitment to chromatin. We have also detected increased amounts of PAR and of chromatin-bound huntingtin in cells from an HD patient. Stay tuned!


  1. Genetic Modifiers of Huntington’s Disease (GeM-HD) Consortium. Identification of Genetic Factors that Modify Clinical Onset of Huntington’s Disease. Cell. 2015;162: 516–526.
  2. Bettencourt C, Hensman-Moss D, Flower M, Wiethoff S, Brice A, Goizet C, et al. DNA repair pathways underlie a common genetic mechanism modulating onset in polyglutamine diseases. Ann Neurol. 2016;79: 983–990.
  3. Moss DJH, Pardiñas AF, Langbehn D, Lo K, Leavitt BR, Roos R, et al. Identification of genetic variants associated with Huntington’s disease progression: a genome-wide association study. Lancet Neurol. 2017;16: 701–711.
  4. Maiuri T, Mocle AJ, Hung CL, Xia J, van Roon-Mom WMC, Truant R. Huntingtin is a scaffolding protein in the ATM oxidative DNA damage response complex. Hum Mol Genet. 2017;26: 395–406.
  5. Bogdanov MB, Andreassen OA, Dedeoglu A, Ferrante RJ, Beal MF. Increased oxidative damage to DNA in a transgenic mouse model of Huntington’s disease. J Neurochem. 2001;79: 1246–1249.
  6. Kovtun IV, Liu Y, Bjoras M, Klungland A, Wilson SH, McMurray CT. OGG1 initiates age-dependent CAG trinucleotide expansion in somatic cells. Nature. 2007;447: 447–452.
  7. Enokido Y, Tamura T, Ito H, Arumughan A, Komuro A, Shiwaku H, et al. Mutant huntingtin impairs Ku70-mediated DNA repair. J Cell Biol. 2010;189: 425–443.
  8. Askeland G, Dosoudilova Z, Rodinova M, Klempir J, Liskova I, Kuśnierczyk A, et al. Increased nuclear DNA damage precedes mitochondrial dysfunction in peripheral blood mononuclear cells from Huntington’s disease patients. Sci Rep. 2018;8: 9817.
  9. Morales J, Li L, Fattah FJ, Dong Y, Bey EA, Patel M, et al. Review of poly (ADP-ribose) polymerase (PARP) mechanisms of action and rationale for targeting in cancer and other diseases. Crit Rev Eukaryot Gene Expr. 2014;24: 15–28.
  10. Andrabi SA, Umanah GKE, Chang C, Stevens DA, Karuppagounder SS, Gagné J-P, et al. Poly(ADP-ribose) polymerase-dependent energy depletion occurs through inhibition of glycolysis. Proc Natl Acad Sci U S A. 2014;111: 10209–10214.
  11. Fouquerel E, Goellner EM, Yu Z, Gagné J-P, Barbi de Moura M, Feinstein T, et al. ARTD1/PARP1 negatively regulates glycolysis by inhibiting hexokinase 1 independent of NAD+ depletion. Cell Rep. 2014;8: 1819–1831.
  12. Dickey AS, La Spada AR. Therapy development in Huntington disease: From current strategies to emerging opportunities. Am J Med Genet A. 2017; doi:10.1002/ajmg.a.38494
  13. Hoch NC, Hanzlikova H, Rulten SL, Tétreault M, Komulainen E, Ju L, et al. XRCC1 mutation is associated with PARP1 hyperactivation and cerebellar ataxia. Nature. 2017;541: 87–91.
  14. Cardinale A, Paldino E, Giampà C, Bernardi G, Fusco FR. PARP-1 Inhibition Is Neuroprotective in the R6/2 Mouse Model of Huntington’s Disease. PLoS One. 2015;10: e0134482.
  15. Paldino E, Cardinale A, D’Angelo V, Sauve I, Giampà C, Fusco FR. Selective Sparing of Striatal Interneurons after Poly (ADP-Ribose) Polymerase 1 Inhibition in the R6/2 Mouse Model of Huntington’s Disease. Front Neuroanat. 2017;11: 61.


Table 1: Hit Proteins unique to H2O2-treated cells
Hit Protein (human homolog) Molecular Function Biological Function Interactions (UniprotKB, BioGrid) Disease Connections
TPT1 metal ion binding; protein binding; RNA binding


calcium binding and microtubule stabilization


TP53, XRCC5, XRCCC6, HSPB1, EF1D, PCH2, RHEB, EF1D-2, T22D1, AT1A1 Charcot Marie tooth, Distal hereditary motor neuropathy type II, several cancers
NDUFV1 metal ion binding; nucleotide binding; protein binding ATP synthesis
SLIRP RNA-binding RNA-binding protein that acts as a nuclear receptor corepressor PNMA1, C102B, MTUS2, K1C40, LPPRC Leigh Syndrome French Canadian Type
HINT2 nucleotide binding


steroid biosynthesis, apoptosis 33 interactors, including APP, TOP3A
ACAD9 nucleotide binding mitochondrial complex I assembly 79 interactors, including many mitochondrial proteins
PMPCB metal ion binding cleaves presequences (transit peptides) from mitochondrial protein precursors 77 interactors, including many mitochondrial proteins
IDH3G metal ion binding nucleotide binding tricarboxylic acid cycle 28 interactors, including KPNA2, HNRNPK, PQBP1
OAT protein binding amino acid biosynthesis 44 interactors, including SOD1, SOD2, PARK7, HDAC5, SIRT7, CDK2, FBXO6, FUS Parkinson’s disease
ACOT10 hydrolase activity acyl-CoA metabolic process
GCAT acetyltransferase activity amino acid metabolism 12 interactors, including ATXN3, MDM2, FBXO6 Spinocerebellar ataxia 3
DKC1 protein binding; RNA binding ribosome biogenesis, telomere maintenance RUVB1, HMBX1, NAF1 Hoyeraal Hreidaarsson syndrome
CHST2 nucleotide binding carbohydrate metabolism
HIBADH nucleotide binding amino acid catabolism 10 interactors, including BRCA1, SOD1
PPIF protein binding protein folding, mitochondrial permeability TP53, CKLF5, ABI2, BANP Li-Fraumeni syndrome, several cancers
MRPS33 mitochondrial translation 36 interactors, including several RNA-binding proteins
HAT1 histone acetyltransferase activity DNA packaging RBBP4, H4, VPR, REL, MEOX2, BACD2, ITF2 Pitt-Hopkins syndrome
PPM1G metal ion binding; protein binding; phosphatase activity cell cycle arrest TERF1, XRCC5, XRCC6, TAT, YBOX1
VAT1 metal ion binding negative regulation of mitochondrial fusion 25 interactors, including TP53, H2AFX, SOD1, PARK2, CDK2


Table 2: Hit Proteins unique to MMS-treated cells
Hit Protein (human homolog) Molecular Function Biological Function Interactions (UniprotKB, BioGrid) Disease Connections
TXNDC17 antioxidant activity redox reactions RUFY1, TINF2, EXOS8 Dyskeratosis congenita, Pontocerebellar hypoplasia
MRPS21 RNA binding structural molecule activity mitochondrial translation 38 interactors, including mitochondrial translation proteins, RNA-binding proteins
HMGA1 DNA binding; protein binding base excision repair, nucleosome disassembly ORC6, ANM6 Meier-Gorlin syndrome
MRPS26 RNA binding DNA damage response, mitochondrial translation 69 interactors, including mitochondrial translation proteins, RNA binding proteins
PEBP1 enzyme regulator activity;

nucleotide binding; protein binding;

RNA binding

MAPK cascase 41 interactrs, including SOD1, SOD2, RAF1, LOX15, CFL1, PABPC3
CDC37 enzyme regulator activity;

protein binding

co-chaperone, mitophagy 246 interactions, including IKKA, IKKB, APOE, PSN1, LRKK2 Alzheimer’s disease, frontotemporal dementia, amyotrophic lateral sclerosis, Parkinson’s disease
TXNL1 antioxidant activity redox homeostasis 64 interactors, including proteasomal proteins
NARS nucleotide binding tRNA synthesis 67 interactors, including ATXN1, HDAC2, XRCC6, XRCC5 Spinocerebellar ataxia 1
FDPS metal ion binding;

RNA binding

cholesterol biosynthesis 50 interactors, including ATXN 1, G6PD, CREB3, PABPC1, PSME4 Spinocerebellar ataxia 1
PSME4 enzyme regulator activity;

protein binding

DNA repair, histone degradation 45 interactors, including proteasomal proteins, FDPS
Table 3: Hit Proteins common to H2O2- and MMS-treated cells
Hit Protein (human homolog) Molecular Function Biological Function Interactions (UniprotKB, BioGrid) Disease Connections
FAM135A hydrolase activity lipid metabolism 10 interactors, including EZR, RAB5A, RAB9A
CAVIN1 protein binding;

RNA binding

caveolae formation, transcription 115 interactors, including T10IP, CAV1, CAVN2, CAVN3, RCOR1, RAB5A, RAB7A, RAB5C Neurodegeneration syndrome
Table 4: Hit Proteins specific to untreated cells
Hit Protein (human homolog) Molecular Function Biological Function Interactions (UniprotKB, BioGrid) Disease Connections
TCEB1 protein binding translation 154 interactors, including APE1, FUS, SQSTM1, POU5F1, OTUB1, TOP2A, CENPC
RPS14 RNA binding;

structural molecule activity

translation 254 interactors, including MDM2 and many ribosomal proteins
BRK1 protein binding actin and microtubule organization 25 interactors, including BRAP, PFDN1 Hermansky-Pudlak syndrome
RPL24 protein binding;

RNA binding;

structural molecule activity

translation 165 interactors, including many ribosomal proteins
RPL15 protein binding;

RNA binding;

structural molecule activity

translation 197 interactors, including many ribosomal proteins
RAD23B DNA binding;

protein binding

DNA damage recognition, nucleotide excision repair 125 interactors, including HMGB1, MLH1, XPC, ATXN3, POU5F1, ERCC3, PUF60, G6PD, EWSR1, BRCA1 Xeroderma pigmentosum, Spinocerebellar ataxia 3, Verheij syndrome
NACA DNA binding;

protein binding

protein transport, transcription 55 interactors, including EWSR1, SOD1, BRCA1, PARK2, MDM2, H2AFX, APLP1 Parkinson’s disease
CAND1 protein binding ubiquitin conjugation 708 interactors, including many cullins X-linked syndromic mental retardation
PAICS nucleotide binding;

protein binding

purine biosynthesis 94 interactors, including BRCA1, FUS, 53BP1
EWSR1 metal ion binding;

protein binding;

RNA binding

transcription 650 interactors, including FUS, ATPF2, PABPN1, SOD1 Ewing sarcoma, Mitochondrial complex V deficiency nuclear 1, Amyotrophic lateral sclerosis, Frontotemporal dementia
TPM3 protein binding;

structural molecule activity

actin filament organization 133 interactors, including TP53, PARK7, PARK2, BRCA1 Dystonia, Parkinson’s disease
FHL1 metal ion binding;

protein binding

differentiation 35 interactors, including EWSR1 Emery-Dreifuss muscular dystrophy
HNRNPK DNA binding;

protein binding;

RNA binding

transcriptional regulation of TP53 DNA damage response 281 interactors, including TP53, MDM2, PABPC1, FUS, XRCC6, BRCA1, HMGB1, TOP1, PARK2 Amyotrophic lateral sclerosis, Li Fraumeni syndrome, Parkinson’s disease
RPL31 protein binding;

RNA binding;

structural molecule activity

translation 159 interactors, including BRCA1, EWSR1, APP, TP53 Diamond-Blackfan anemia
YBX1 DNA binding;

protein binding;

RNA binding

transcription, mRNA processing 290 interactors, including TP53, BRCA1, FUS, PCNA, H2AFX, APE1, EWSR1
LMNA protein binding;

structural molecule activity

nuclear assembly, chromatin organization 645 interactors, including FUS, PARP1, H2AFX Emery-Dreifuss muscular dystrophy, Charcot Marie tooth disease, Hutchinson-Gilford progeria syndrome, LMNA-related congenital muscular dystrophy
SNRPC metal ion binding;

protein binding;

RNA binding

mRNA splicing 90 interactors, including EWSR1, BARD1
FABP5 transporter activity fatty acid transport 40 interactors, including SOD1, POU5F1
RPL23A protein binding;

RNA binding;

structural molecule activity

translation 239 interactors, including TP53, BRCA1, MDM2, PARK2, many ribosomal proteins
UBA1 nucleotide binding;

protein binding;

RNA binding


ubiquitin conjugation, response to DNA damage 147 interactors, including FUS, BRCA1, SOD1, PABPC1 X-linked spinal muscular atrophy, Giant axonal neuropathy
RPL27 RNA binding;

structural molecule activity

translation 157 interactors, including TP53, BRCA1, PABPC1, many ribosomal proteins
RPL7 DNA binding;

protein binding;

RNA binding;

structural molecule activity


translation 230 interactors, including TP53, BRCA1, PABPC1, many ribosomal proteins
H3F3C DNA binding;

protein binding

nucleosome assembly 24 interactors, including FAN1, DNAJC11
RPL36 RNA binding;

structural molecule activity

translation 138 interactors, including BRCA1, many ribosomal proteins
Table 5: PARylated proteins




The N6-furfuryladenine Hypothesis in HD

The summer of 2018 marks the third manuscript to come out of the lab defining the role of huntingtin in DNA damage repair, and a defective signaling pathway in HD caused by the proximity of expanded glutamine tracts to the kinase substrate site in huntingtin N17, the first 17 amino acids of huntingtin.

Since 2011, we have defined this site as hypo-phosphorylated in HD.  Since 2011, academic and industrial labs have reported that huntingtin is under-phosphorylated at many sites, and in all model and samples taken from humans.


(Data from Hung et al., 2018, showing hypo-phosphorylation in human HD fibroblasts from clinic). Here’s Claudia:



With the help of a donor gift of a Nikon A1 confocal microscope, the CFI/OIT Leaders Opportunity Fund for a super-resolution SIM device, Laura Bowie set on to her PhD thesis work with CIHR doctoral Scholarship to set up an unbiased screening protocol in which a robotic stage took random shots around a  96 well plate well, with a library of natural compounds applied. This was supposed to be a pilot screen of only 130, to test out the microscopy acquisition and then use non-supervised machine sorting to sort the images, so from front-to-back, there is no possible investigator bias.

Here’s Laura:

Truant Bowie 2018 3 (002)

However, we got hits, most of them were anti-oxidants, which lead to our 2016 discovery that huntingtin was a ROS sensor via a single methionine at position eight, and this oxidation proceeded phosphorylation. The other hits affected the NFKb/ IKK kinase pathways, pathways we discovered in 2011. But we got one lone hit, distant and distinct in 3D prinicipal component analysis space: N6-furfuryladenine.


The rest, is now recent history.

The net results: YAC128 mice get better, and brain levels of huntingtin drop. Huntingtin hypo-phosphorylation is now restored to normal, with sub-micromolar levels required.

This was all outstanding mouse work from Melanie Alpaugh and Simonetta Sipione at the University of Alberta.

But what is N6-FFA? 

An astute eye will find an adenosine core, the same core of nature’s energy molecule, ATP, but count the positions around the rings from nitrogen 1, and at nitrogen 6 you will find a furfuryl ring -the 5 -sided ring with double carbon bonds and an oxygen. In DNA, this is what happens when you oxidize DNA, and this isn’t good. Instead of pairing with thymine (A-T), it can pair with guanine (A-G) -that’s not supposed to happen. So, the DNA gets fixed. In dividing cells, it get fixed by mismatch repair, which sees the N6-FFA:G mismatch and removes it. But in brains, neurons don’t divide, so they rely on Base Excision Repair or Nucleotide Excision Repair, in which a single nucleotide is removed and replaced, or a small patch of DNA. This is how oxidized guanines are removed and throw out of the body as garbage. We can even detect N6FFA in human pee.

N6FFA -Reduce, Reuse, Recycle

Neurons are weird cells. They don’t work like most other cells. They don’t divide, they are huge and spindly and they are energy hogs. Over half the energy in your body is used in the brain at rest, and it doesn’t really slow down or take a break. This means the brain takes huge amounts of fuel, and burns a lot of ATP. Neurons also rely heavily of recycling nucleotides, or salvaging, because at times, they run out of fuel and ATP is just not around. So the brain makes energy like other cells by oxidative phosphorylation and glycolysis (blow dust off the old biochemistry textbook). What happens if we don’t have the enzymes to salvage nucleotides? We get severe diseases, usually fatal in children. All this burning means pollution, in the form of reactive oxygen species, or ROS. Neurons need to get rid of ROS, or ROS will go nuts and react with everything around it, especially DNA.

Enter Nick Hertz

This is Nick.


In 2013, the Kevan Shokat lab at UCSF, with student Nick Hertz, did a screen to find molecules that could be used by a mutant form of Pink1 kinase, a form found in families with familial Parkinson’s disease.  (digression, Nick Hertz is the great Grandson of Gustav Hertz, who won the 1926 Nobel Prize with James Franck in Physics). What Nick discovered was that N6FFA can be salvaged to form weird triphosphate, with that adenosine core, and this triphosphate can be used by mutant Pink1 to restore it’s activity lost in Parkinson disease. We know this is also true to HD because a version of N6FFA that cannot be salvaged doesn’t work to fix mutant huntingtin hypo-phosphorylation. so, N6FFA is not the active molecule, it is a pro-drug, a compound that gets converted to an active form. This is a “neo-substrate” for a kinase, not ATP or GTP. This is an aspect of drug discovery called pharmacokinetics, or what the body does to the drug. Pharmacodynamics is what the drug does to the body. Both are essential to understand to make a lead like N6FFA into a drug. With David Litchfield, (another uncool biochemist) at  Western University, he could show that the huntingtin kinase, CK2, can also use this “neo-substrate”, while the Shokat lab thinks there are only two kinases that can do this: CK2 and Pink1. (digression #2, Litchfield was a graduate of McMaster Biochemistry Department).

“But why not just use ATP?!?” – several reviewers and pharma executives

Somehow in the last 40 years, biochemistry became uncool. We don’t teach it in the detail of the past because science is just moving too damn fast and we have more and more stuff to teach. It’s too bad, because biochemistry is what led to drugs that worked 40 years ago, and still work today.  It’s also complex, with steps and pathways and feedback loops and big wall charts no one looks at. The human neuron can undergo energy crisis, times where all the ATP is burned up, under periods of stress. Stress like high ROS levels due to human aging, which only increase as the brain gets older. DNA damage triggers an even called PARylation – PAR is poly-ADP-ribose, these are chains that grow out of sites of DNA damage that act like nets catching PAR-binding proteins, and when they form, they need to get removed or they will inhibit energy metabolism by draining NAD+, halting glycolysis, and ATP release from the energy plants of the neuron, the mitochondria. So, sorry “experts” but ATP is not universal and always abundant, and energy deficits is a long-standing observation in HD, which makes the comment from an HD expert rather bizarre.

There is defective DNA repair in HD (2017)

There is defective DNA repair in HD (2018)

The point is, there is defective DNA repair in HD.

To read more about PAR and PARylation, see the work in real-time with Dr. Maiuri.

What does this say about the Amyloid hypothesis in HD?

tombThe amyloid-like hypothesis in HD is fraught with problems. There is no explanation as to why aggregates of huntingtin can accumulate for decades, when neuronal protein half-lives don’t exceed 20 days.

Plus, the whole AD drug thing.

Regions with aggregates don’t map to regions of pathology in HD, and while there are phenotypes of disease in human cell from HD patients, we can’t find aggregates unless we force them to happen in tiny fragments overexpressed.  This is explained  by much hand -waving in the research community for 25 years, typically based around synthetic hyper-allele models of disease, that usually involves cutting off 97% of the protein.


The effect of N6FFA, and HD Genome-Wide Association Studies, suggests that aggregates are an effect, not cause in HD. DNA damage and energy crisis proceed protein misfolding. Misfolding is just a symptom of a sick neuron, particularly a sick ER. When we restored this signaling, mutant huntingtin inclusions disappear.

So, by unbiased microscopy screening, we hit the same answers as by unbiased GWAS in HD 2015. and again in 2017. Maybe hypothesis-driven research was not the best approach for HD.


So N6FFA is a drug? 

No. N6FFA is a lead. As a compound, it has very poor pharmacological properties for dosing in humans, but, derivatives are looking very promising to overcome these hurdles already, we just need to make sure we don’t gain any toxicity (N6FFA is a natural human metabolite). This is dull, pedantic work that needs to be done and tested in more animal models before we plan to trial in humans.

We also present the N6FFA hypothesis in a Youtube video abstract:





Next steps in the identification of ROS-related huntingtin protein-protein interactions

Blog post by Dr. Tamara Maiuri

In my last real-time report of the HDSA-funded project to identify of oxidation-related huntingtin protein-protein interactions, I was happy to report the successful purification of huntingtin and its interacting proteins from mouse cells. I was quite optimistic that the experiment would work using cells from an HD patient. This turned out not to be the case. Despite growing large amounts of cells, there was simply not enough starting material. Although we want to answer our questions about HD using human sources of information, it is just not technically possible with patient fibroblasts.

The good news is that I was able to generate two more replicates of the experiment in mouse cells. The total list of proteins identified by mass spectrometry can be found on Zenodo, and further refinement of the data was done by quantifying the intensity of each peptide (bit of protein) to give us a better sense of the most abundant hits. This has also been deposited on Zenodo.

Sifting through the data is taking some time—being a scaffold, huntingtin interacts with several hundred proteins. We are also in the final revision stages of a few manuscripts for which experiments have been prioritized (one manuscript describes how we turned HD patient skin cells into a tool for the HD research community—a pre-print can be read on Bioarchive). I will post a more detailed analysis in the coming weeks, but here are some general conclusions from the most reproducible results:

Stable interactions:

The proteins that interact with huntingtin in cells treated with DNA damaging agents also interact with huntingtin in untreated cells. This could be because

  • The treatment didn’t work, or the untreated cells are under an unintended form of stress
  • Huntingtin transiently “samples” interactions with many proteins in unstressed conditions, which it binds more tightly upon stress. In this case, the cross-linking step may cause us to capture weak interactions
  • Some of the interactions may be non-specific artifacts of the experimental set-up

These possibilities will be tested by following up on interesting hits in our human fibroblast system.

A connection to poly ADP ribose:

Many of the proteins that interact with huntingtin are also found in data sets of “PARylated” and “PAR-binding” proteins (see references below). Poly ADP ribose, or PAR, is a small biomolecule that plays a role in the process of DNA repair (among many other cellular processes). When the DNA repair protein “PARP1” notices some damaged DNA, it starts to attach chains of PAR to nearby proteins. This forms a sort of net to recruit other DNA repair factors. The overlap between our list of huntingtin interacting proteins and PARylated/PAR-binding proteins suggests that huntingtin may also bind PAR, just like many other DNA repair proteins. In fact, I have preliminary results suggesting it does just that. I will post them soon!


Data sets of PARylated and PAR-binding proteins:

Gagné J-P, Isabelle M, Lo KS, Bourassa S, Hendzel MJ, Dawson VL, et al. Proteome-wide identification of poly(ADP-ribose) binding proteins and poly(ADP-ribose)-associated protein complexes. Nucleic Acids Res. 2008;36: 6959–6976.

Jungmichel S, Rosenthal F, Altmeyer M, Lukas J, Hottiger MO, Nielsen ML. Proteome-wide identification of poly(ADP-Ribosyl)ation targets in different genotoxic stress responses. Mol Cell. 2013;52: 272–285.

Zhang Y, Wang J, Ding M, Yu Y. Site-specific characterization of the Asp- and Glu-ADP-ribosylated proteome. Nat Methods. 2013;10: 981–984.


Let’s Fix Peer Review

Scientists are the smartest idiots I know. 


If one explains the current system of peer review to a non-scientist, the response is typically, “that’s insane, I thought you guys were supposed to be smart”.

To recap:

When we apply for a grant or want to publish our science, we secretly get the work reviewed by our peers, some of which are competing with us for precious funding, or a bizarre version of fame. Under the veil of anonymity, a reviewer can write anything, included false statements, or incorrect statements to justify a decision. The decision is most often, “do not fund” or “reject”, even if the review is based off of inaccuracies, lack of expertise, or even blatant slander. There are no rules, there are no repercussions. There are few integrity guidelines, or oversight, nor rules of ethics in the review process for the most part. It can lead to internet trolling at a level of high art. In funding decisions, these mistakes can be missed by inattentive panels, but were definitely missed in the CIHR reform scheme before panels were re-introduced. We still have a problem of reviewers self-identifying expertise they simply do not have.

Scientists have to follow strict rules of ethics when submitting data, including conflicts of interest, research ethics, etc.  No such rules are often formally stated in the review process and can vary widely between journals.

This system is historic, back to an era when biomedical research was a fraction of the size it is today, and journal Editors were typically active scientists. The community was small. But as science rapidly expanded in the 90s, so did scientific publishing, and soon editors became professional editors, with some never running a lab or research program. Then, came the digital revolution, and journals were no longer being read on paper and the pipeline to publish increased exponentially.

What drove the massive expansion of journals? Money.  Big money. And like many historic industries, it’s thriving, mostly based off free slave labor.

CELL was sold it to Elsevier press in 1999. While the sales number was never formally revealed, it was rumored to exceed $US100M. No person who reviewed for this journal received a thin dime. The analogy would be hiring workers to build a road, pay them nothing, insist the road get paved in under 14 days, then charge them to use the road. Why? for the prestige of being associated with a road (which is fundamentally no different than any other road).

What CELL and Nature started, mushroomed wildly in the next 20 years, with journals starting up weekly, now numbering in the thousands, on a simple business model: hire Editors, accept submission, get three reviews, charge to publish, with numbers in the thousands of dollars per manuscript.  It’s a money making machine, based off free labor. Why not? Scientists are idiots, they work for free, they do hard work just based off ideology.

What happens under the veil of anonymity? Papers are trivially reviewed, either quickly dismissed or accepted without much scientific input, which has driven a lot of fraud or just bad science, that when revealed often leads to the question: how did the reviewers not see this? It gets worse, “knowledge leaders” in some fields can manipulate the process, hold up or block manuscripts while post-docs race to reproduce the data as their own, as outlined in a public letter to the Editors of Nature in 2010. Journals return comments to authors, but many take secret comments from reviewers directly to the Editors, without author knowledge. Why? Horror stories abound about revision after revision, then a final rejection after a year or more because the Editor lost interest. Meanwhile, careers and lives are stalled. This is very problematic when a field of research becomes dogmatic, and truly innovative theories or approaches are presented: to accept this work means having to remove dogma, and this can mean invalidation of “knowledge leaders” entire publication records.

These publications can set careers or lack of them can ruin careers and gain or lose funding. PDFs get hired by institutions that look like they can walk on water based on their CVs, only to drown in a few steps as an independent investigator.

Often overheard at symposium by senior scientists: ” we had a problem with reviewer #2, so I called the Editor and sorted it out”. Called? How? No journal lists phone numbers for Editors, what magic Rolodex does this involve?

We have a system in place that is used because it is historic. It’s not working, it’s not fair, it benefits fraud, and it’s bad for science. This failure needs to be addressed with a series of ethical guidelines and transparency, because the process has been corrupted and failure is now so common, there are entire websites dedicated to it. Suggestions:

  1. Editors need to be active scientists. The Journal of Biological Chemistry is an excellent example.
  2. Reviewers and academic editors need to be paid. The Public Library of Online Science (PLoS) sounds like an altruistic organization to disseminate scientific knowledge, but executive compensations can exceed $330,000-$540,000 a year. Clearly, PloS feels expertise and talent should be rewarded, which is fair, but not when it comes to reviewers who put in hours of work to review manuscripts. The same reviewers then have to pay $1500+ to publish, and the journal decided to just stop copy editing manuscripts, leading to sloppy publications. The line between legitimate journals and “predatory” journals is blurring. This is not unique to PloS. Scientific publishing is a massive profit business. The NY times revealed a “shocking” number of $500 for page charges in “predatory” journals. Yet, many established journals charge $500-600 per color figure alone. I cannot think of another profession that requires so many years of expertise under draconian standards that has so little value applied to our time. Try getting a free 3 hours from a lawyer, accountant, or consultant. Good luck with that, or look out for what you get.Dr._Nick_S28_billboard_gag
  3. Reviewers need to be scored. By both Editors and submitting authors. We recently were reviewed at a leading cell biology journal, and while the paper was not accepted for publication, we received deeply detailed, outstanding reviews from all three reviewers. Their intent was obvious: address these criticisms and this will be better work. We were also reviewed at two leading magazines recently, and what we got back were late reviews, of 5-7 lines or less with terms like “unconvincing”, or simply incorrect statements, with no chance to respond (at 18 years as PI, I have not received the magic Editorial Rolodex). Review without scientific justification. These scores should be tied to ORICID. Editors should be able to flag scientific misconduct to home institutions or funding agencies, for reviewers behaving inappropriately. Low scoring reviewers should be asked to justify this score to home institutions. Scoring then allows justification to pay good reviewers, and insist on sincere efforts to avoid trivial reviews.
  4. No more gatekeeping. Famous journals do not review most of their submissions, with most rejections coming from the desk of non-expert, non-scientist Editors looking for name and institutional recognition and trendy buzzwords. The issue is they simply have too many submissions if they are regarded as “high impact” journals. Yet, regardless of the science, they will not decline submission from high profile institutions, with fear the institution’s senior scientists will not submit. What is compounding this problem is that relative new or lesser known journals are doing this in an attempt to boost “impact” based on trendy subjects, which just demonstrates what their priorities are: not science, but gaming the impact factor metrics. This would require a new system, onto point 5….
  5. No more direct submissions. Manuscripts should be openly submitted to free access sites like BioRxiv, and go live in hours, and journal Editors can bid to authors to send to review in a clearing house type model. What this does is allow the Editors to judge impact based on comments from the community if they lack direct expertise. As it stands, the current process is stochastic and decision to review is based on often one opinion. It can now take months before a paper is even reviewed, as journals can sit on the decision to send to review up to a month or more (remember, they don’t have to follow any rules). It can take half a day at time just to submit a manuscript. The cycles of submission and editorial desk rejection can suck half a year out of the publication process -this does nothing for science.
  6. One manuscript and reference format. One journal format. Pick one, any one. the current need for software to deal with 1000s of journal reference styles for 1000s of  journals is asinine. It’s like trying to do science in 1000 different standards of measurement. We picked the metric system and moved on.
  7. Manuscript and funding agency reviews should be public, as this is publicly funded. This allows readers to know exactly how well a manuscript or grant was reviewed, and if a journals press hype matches actual scientific opinion, and if any obvious bias occurred in the review process. This would help with the media coverage of manuscripts as the journalists almost entirely rely on PR hype.
  8. All Reviews should be addressable by authors before decision. This is particularly a problem on grant panels that lack expertise, they can rank and score based on reviewer errors, but this cannot be addressed until the next competition. Same problem with rejection after first submission at journals. There should be a brief ability to respond to reviews before a decision is made. The current system relies on pure chance that our work is reviewed properly. We might as well have a lottery, especially in Canada where biomedical research grants can be reviewed, scored, and ranked  by non-scientists (seriously).
  9. Reviewers should discuss and unmask prior to decision, after reading the responses.  Currently some journals do unmask reviewers to each other and allow discussion (EMBO J., Current Biology, eLife…). Nothing is more discouraging than spending hours on review to improve a manuscript, only to have another reviewer dismiss the manuscript with an obvious minimal effort and comments like, “unconvincing” plus secret comments to the editor I cannot see. I don’t see the point to unblinding reviewers to authors, this will just discourage participation and fear of vindictive authors.
  10. Define Misconduct in the Scientific Review Process.  There needs to be repercussions for unethical activity.
  11. Have a Higher bar for Authorship. Many clinicians have networks that yield their names on hundreds of manuscripts, for zero effort on the actual work, and it’s very likely they never read the manuscripts. This is simply not ethical, and unfair to authors with real effort on manuscripts. This is a real problem in funding agencies that then use reviewers that count papers, coming to the conclusion a good scientist publishes seriously every two to three weeks of their lives.
  12. Keep individual manuscript metrics, ban journal impact metrics. Journal impact scores can be gamed, and are gamed, and make no sense. It’s like saying a single driver of a Honda is more intelligent, because on average, Honda drivers have a high IQ, and thus, driving a Honda makes you smarter.  Using metrics like impact factors or H-index to judge careers is lazy, incompetent administration. You drive a Honda? Hired! We denied tenure? Not my fault, he/she drove a Honda!
  13. Retracted manuscripts due to figure fraud should reveal who reviewed the manuscript. Maybe these guys will pay attention next time. Or, maybe if we paid them, this would happen a lot less. It’s very likely if we got to see the reviews of these manuscripts, we would see that they were trivially reviewed.
  14. Canada needs an Office of Research Integrity. For a variety of reasons, fraudsters can flourish in the Canadian system, as funding institutions defer fraud investigations to home institutions, who have a perverse incentive to bury any conclusions. The US has the ORI, independent of any institution, and in some countries, scientific fraud is legally regarded as fraud of the public trust and can result in civil action or jail time. If Canadian science wants serious public support, as the Naylor report recommends, it should come with equally serious scientific integrity.

ROS-dependent huntingtin interactions in mouse striatal cells

Blog post by Dr. Tamara Maiuri

Well it’s been a long haul, but I’m happy to say I finally have a list of proteins that interact with the huntingtin protein (expanded versus normal) under conditions of reactive oxygen species (ROS) stress. This is the very first step to achieving the goal of the project: to identify drug targets that are relevant to the process of DNA repair, which, through powerful genetic studies, has been repeatedly implicated in the progression of HD.

This first step was not without its obstacles. The goal at the outset was to identify proteins out of real HD patient cells, a more relevant system than cells from an HD mouse model. Unfortunately, it’s nearly impossible to grow up enough cells to yield the protein needed for mass spectrometry. My solution to this problem was to treat cells in batches, snap freeze them, and store them for processing once I had enough.

After working out the conditions for cross-linking and fractionation, inducing oxidative stress, and pulling huntingtin-associated proteins out of HD patient cells, I started growing up batches of cells. On the day I harvested the largest batch yet, the ROS inducer, 3NP, didn’t show the tell-tale signs of working (floating cells, larger cell pellet). When I tested a sample for interacting DNA repair proteins, I found almost no interaction. That was a bad day. This batch cannot be used–it amounted to a waste of time and resources. I spent a few weeks trying to figure out what went wrong with the 3NP, but no dice.

At this point, it was time to consider options and cut losses: we need to move on with this project. So, I considered switching to mouse striatal cells. They may not be as accurate a model as human fibroblasts, but we can get a list of huntingtin-interacting proteins from mouse cells and verify them in human cells. I revisited the other ROS sources tested in the past, and decided on H2O2 (see the optimization experiment on Zenodo).

It was much faster growing up enough striatal cells for mass spec analysis. The experiment wasn’t perfect–the untreated HD cells showed undue signs of stress, and so this will have to be repeated to be sure of our results. But we now have a list! Here are the preliminary results, in a nut shell:

After eliminating likely false positives (ribosomal proteins, chaperones, cytoskelton), there are:

  • 92 proteins that interact with huntingtin under basal conditions and are released upon ROS stress
    • 36 of these are inappropriately maintained by expanded huntingtin
  • 38 new interactions formed upon ROS stress
    • 29 of which do not happen with expanded huntingtin
  • 52 proteins that interact with expanded, but not normal, huntingtin upon ROS stress

Of note, HMGB1 was identified in the immunoprecipitates from H2O2-treated cells (both Q7 and Q111), which we have previously identified as a huntingtin interacting protein. Further, the list of proteins from H2O2-treated Q111 cells includes FEN1, PCNA, Formin1 (the actin binding protein required for DNA repair), CENPJ, PARP8, and many other DNA repair proteins. The full list and experimental conditions are available on Zenodo.

The good news is that we now know these conditions worked very well in the mass spec analysis, and it may be feasible to grow up enough human cells after all. Since our TruHD-Q43Q17 cells (from a patient with 43 CAG repeats) grow the fastest, I started with those. Last week, I sent samples from the TruHD-Q43Q17 cells treated with a DNA damaging agent called MMS for mass spec analysis. It will take a few more weeks to get enough TruHD-Q21Q18 cells (from a spousal control). Stay tuned for the results!

This project is funded by the HDSA Berman/Topper HD Career Advancement Fellowship. 

Update: Measuring DNA repair capacity and visualizing huntingtin in HD patient cells

Blog post by Dr. Tamara Maiuri

Image credit: Justinas

Previously I described a method to measure DNA repair capacity in cells: the GFP reactivation assay. This worked nicely in mouse striatal cells, with HD cells consistently showing about half the repair capacity of wild type cells. I have since tried it in cells from HD patients, using a different method to measure the GFP signal (microscopy instead of flow cytometry). The results were similar: a lower repair capacity was seen in HD cells (see experiment on Zenodo). The difference wasn’t as big as with mouse striatal cells, which is to be expected from clinically relevant CAG lengths compared to a model system that exaggerates the effect of expanded huntingtin. But the experiment was done twice with very similar results each time. In the coming weeks I will test whether this small but consistent difference is exacerbated by treating cells with DNA damaging agents. I will also make sure we’re measuring DNA damage pathways, and not some other phenomenon, by knocking down or inhibiting PARP. Stay tuned…

I also previously reported a way to visualize huntingtin protein at sites of DNA damage: stable cell lines expressing an inducible, huntingtin-specific YFP-tagged intrabody. I’m happy to say that the stable cell lines are growing, albeit slowly. If the growth rates recover, we will have available TruHD-Q21Q18, TruHD-Q41Q17, TruHD-Q43Q17, and TruHD-Q50Q40 cell lines in which huntingtin protein can be visualized in real time by addition of doxycycline to the media. The slow growth may be because of the combined toxicity of nucleofection and G418 selection, or due to leaky expression of the intrabody, which interferes with cell division. I’m currently testing the first idea by lowering the G418 concentration. If this doesn’t work, I may have to use alternate methods of detecting endogenous huntingtin. Fingers crossed!


Visualizing real huntingtin protein in cells from an HD patient

Blog post by Dr. Tamara Maiuri

I am still busily collecting cells to be sent for mass spec for our goal of obtaining a list of proteins that interact with huntingtin upon oxidative DNA damage. Unfortunately I’ve run into a few road blocks, which I will blog about in the coming weeks (hopefully with a resolution!).

Meanwhile, I’ve been working on methods to assess the hit proteins for their physiological relevance as potential drug targets. Last time I described one such approach: the GFP reactivation assay. Since then, data from 3 experiments have been combined and look promising. While repair efficiency varies from experiment to experiment, mouse HD cells consistently show approximately half the repair efficiency of normal cells (an average of 44.8% over 3 experiments). This is a readout we can use to test the effects of manipulating our hit proteins.

Another approach involves measuring how long huntingtin hangs around at sites of DNA damage, and whether expanded huntingtin lingers too long. We know expanded huntingtin has no trouble reaching damaged DNA, so maybe the problem is that it can’t get off, inappropriately gluing down all the proteins it is scaffolding.

To test this hypothesis, we first need a way to visualize huntingtin protein at sites of DNA damage. While most researchers use overexpression (getting cells to generate protein from externally supplied DNA) to visualize their protein of interest, this is very difficult with huntingtin because of its huge size. To get around this, many HD researchers express small fragments of the huntingtin protein. Overexpression of any protein can have pitfalls because it’s impossible to know if the overexpressed protein is behaving the way it would under normal expression levels in the cell. This is especially true if you’re only using a fragment of the protein—what if the fragment doesn’t fold into the same shape that it would as a whole? What if the missing parts of the protein interact with other important proteins?

Exciting new technologies now allow us to track the behaviour of endogenous huntingtin protein (the huntingtin existing naturally in the cell). We put to use two different intracellular antibodies, or “intrabodies” that recognize and bind the huntingtin protein. We tagged these intrabodies with yellow fluorescent protein (YFP) to generate “chromobodies”. This allowed us to follow their interaction with endogenous huntingtin in live cells. Indeed, we could watch the endogenous huntingtin protein being recruited to sites of DNA damage.

This tool is not without its drawbacks. While it doesn’t seem to interfere with huntingtin’s recruitment to damaged DNA, it must interfere with its role in cell proliferation. We know this because we can’t stably express the chromobody in cells over time. When we watch cells expressing the chromobody try to divide, they just die (unlike the cells expressing only YFP, which happily multiply).

This roadblock can be hurdled using an inducible system: the cells carry the DNA expressing the chromobody, but it isn’t turned on to generate protein until you add a drug called doxycycline. So I first cloned the chromobody into an inducible vector (cloning experiment deposited to Zenodo). When co-transfected with the doxycycline-responsive Tet3G transcriptional activator, it showed beautiful induction by doxycycline in mouse striatal cells (induction experiment deposited to Zenodo).

But we want to work with cells from HD patients. It’s harder to get DNA into these cells, but we can do it with electroporation. To avoid this labour-intensive process every time I want to do an experiment, I’m making HD patient cell lines that stably express the inducible chromobody and doxycycline-responsive Tet3G activator. The Tet3G vector carries a drug resistance gene, so I can select the cells with the drug G418. A simple experiment (deposited to Zenodo) showed that the optimal concentration for G418 selection in fibroblasts is 50 ug/mL.

At this point, my luck ran out. The beautiful induction I saw in mouse striatal cells did not happen in HD patient fibroblasts. From the first few failed attempts, I learned the following:

  • If cells become too sparse during the G418 selection process, they die. Need to transfect a larger number of cells so that they can be downsized during the selection process and still maintain confluency >50% for cell health.
  • Transfection of fibroblasts is an issue. Need to use electroporation, and co-transfect H2B-mCherry to identify transfected cells
  • Transfection of pTRE-nucHCB2 (inducible chromobody), pEF1a-Tet3G (doxycycline-responsive transcriptional activator), and H2B-mCherry is far more toxic than the equivalent microgram quantity of sonicated salmon sperm DNA. Need to use pTRE-nucHCB2, sssDNA, and H2B-mCherry as the untransfected control (that is, -Tet3G) in order to compare rates of selection by G418 in untransfected versus transfected cells
  • In contrast to striatal cells, fibroblasts don’t seem to be inducing expression of nucHCB2 with doxycycline

After ruling out protein turnover, FBS concentration in the media, and different preps of DNA, the only difference between the nice result in mouse striatal cells and the confusing result in human fibroblasts is the method of transfection (the easier, polymeric method for striatal cells versus the more tedious—and expensive—electroporation method for fibroblasts). But this couldn’t possibly be the problem… could it? Only one way to find out: I set up a direct comparison experiment. To my great surprise, the striatal cells induced when transfected by the polymeric method, but not by electroporation! The experiment is posted on Zenodo.

At this point I recalled a suggestion made a few weeks prior, by Claudia Hung, a student in the lab: she asked whether the size of the plasmids could explain the results. I really didn’t think so at the time, but now that idea might make sense! The Tet3G vector is pretty large (7.9 kb), and sure enough, difficulty transfecting large vectors by electroporation is well documented (once you look for it!). This study by Lesueur et al explains that simply giving the cells a chance to recover from the electroporation before plating them can greatly enhance cell viability and transfection efficiency. This was my next move. There was a glimmer of hope in the results: the longer recovery time resulted in induction in a few cells. After taking a closer look at the Lesueur et al study, in which they used much larger amounts of DNA, I tried increasing the amount of DNA.

Eureka! Finally, after months of trouble shooting, I found conditions in which we can induce expression of a huntingtin-specific chromobody in cells from an HD patient (see the results on Zenodo). Next week I will be electroporating cells from HD patients who have different CAG lengths in their huntingtin genes, and selecting them in G418 to get stable cell lines. The result will be a panel of cell lines with different-sized huntingtin expansions, in which we can visualize the natural huntingtin protein by dropping in doxycycline—a great tool for our lab and HD researchers around the world.

If you’ve made it this far through this tedious blog post, thanks for reading. You now have a sense of the tiny incremental steps it takes to move a project forward. This is only one facet of a much larger goal, and each facet has its own set of obstacles. But with careful, calculated perseverance we can get through each road block and move our understanding of HD forward. This work is funded by the HDSA Berman/Topper HD Career Development Fellowship.