Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease that afflicts approximately 1 in 400 people worldwide (Alonso et al., 2009). Diagnosis is predicated on symptoms of motor neuron (MN) loss from the primary motor cortex (upper MNs), brainstem and spinal cord (lower MNs), which results in progressive weakness and loss of motor control, and eventually paralysis and death (Rowland and Shneider, 2001). While predominantly a motor disorder, many ALS patients experience cognitive and behavioral changes, and ~10–15% of ALS patients meet formal criteria for a diagnosis of frontotemporal dementia (FTD; Lattante et al., 2015; Ling et al., 2013). FTD is a heterogeneous condition marked by the degeneration of neurons in the frontal cortex, and patients exhibit a range of cognitive, behavioral and language deficits (Seelaar et al., 2011).

The co-occurrence of ALS and FTD is genetically determined in some cases, most often by the dominantly inherited C9ORF72 (C9) hexanucleotide expansion (DeJesus-Hernandez et al., 2011; Renton et al., 2011). The presence of a single copy of expanded C9 increases sharply the chance that an ALS-afflicted individual will be diagnosed with FTD, from 10 to ~50% (Byrne et al., 2012). While consensus on precisely how the expanded sequence acts as a pathological unit has remained elusive, we have shown that the repeat-containing RNA can directly sequester the RNA-binding protein (RBP) hnRNP H, leading to dysregulation of pre-mRNA splicing (Conlon et al., 2016). Generally, our and others’ evidence for proteinaceous aggregates, seeded by RNAs and which are prone to undergo phase transitions (Fay et al., 2017; Jain and Vale, 2017; Schwartz et al., 2013), may offer a bridge from aggregation - as a classical observation of neurodegenerative disease (Ginsberg et al., 1998; Ross and Poirier, 2004) - to a biophysical mechanism at the origins of ALS/FTD, in cases both with and without disease-associated mutations (Alberti and Hyman, 2016; Conlon and Manley, 2017; Mackenzie et al., 2017; Murakami et al., 2015).

Histopathologic similarities also link ALS and FTD on this disease spectrum. In almost all ALS and over half of FTD patients, the distributions of pathological cytoplasmic accumulation of the hnRNP-like DNA/RNA binding protein TDP-43 tend to correlate with areas of neurodegeneration (Baloh, 2011). Although the observed aggregates of TDP-43 come in several forms (Scotter et al., 2015), translocation and nuclear clearance events are of particular interest, as they have suggested a loss-of-function (LOF) mechanism (Vanden Broeck et al., 2014), predicted to impact mRNA processing and transport (Humphrey et al., 2017; Ling et al., 2015; Polymenidou et al., 2011; Tollervey et al., 2011). While TDP-43 aggregation could reflect the downstream effects of cell death rather than the cause, the existence of ALS-causing mutations in the gene that encodes TDP-43 (TARDBP) suggests that the protein has a deterministic role in the ALS/FTD spectrum (Scotter et al., 2015). Between the extremes of bystander and instigator, it is possible that changes to TDP-43 reflect an important indicator of upstream processes that influence RBPs more pervasively.

In all ALS/FTD cases with TDP-43 pathology, aggregation may be driven by any number of factors that impact the protein’s ability to form stoichiometrically correct interactions with itself or with other nuclear proteins and RNAs. For example, disease-linked changes affecting the solubility properties of other RBPs (Harrison and Shorter, 2017), including hnRNP H (Conlon et al., 2016), have the potential to affect solubility of RBPs they interact with, like TDP-43 (Freibaum et al., 2010; Ling et al., 2010), although whether this occurs in ALS/FTD patient brains is unknown. Extending this idea, downstream changes to the transcriptome, particularly splicing patterns, may result from combinatorial deficiencies of several RBPs (Huelga et al., 2012; Mohagheghi et al., 2016), making it difficult to pinpoint the initiating event or distinguish specific effects of TDP-43 LOF.

In this study, we have investigated whether the biochemical ‘signature’ we described previously for C9ALS might extend to a broader spectrum of ALS/FTD patients. For this, we analyzed splicing and RBP solubility in 50 postmortem human brains from patients with sporadic ALS, FTD with motor neuron disease (MND) and ALS-FTD (collectively, sALS/FTD), all of which were negative for known disease mutations, including C9. Unexpectedly, we identified splicing dysregulation of known hnRNP H targets (Conlon et al., 2016) in a large fraction of patients, and used the extent of splicing dysregulation to divide the patients into two groups, like-control and like-C9. Notably, we found that the like-C9 patient brains also had higher amounts of insoluble hnRNP H, the amount of which directly correlated with the magnitude of splicing dysregulation. Importantly, several other abundant RBPs linked to ALS, including TDP-43, showed the same patterns of insolubility as hnRNP H. Consistent with this, we found pervasive splicing dysregulation genome wide in like-C9 brains. Intriguingly, the like-control cases with lowest hnRNP H/TDP-43 insolubility displayed almost no splicing dysregulation, despite the ubiquitous presence of histopathological TDP-43 inclusions. Finally, and importantly, we show that like-C9 patients, similar to actual C9ALS patients, were much more prone to develop dementia than were like-control patients. Together, our data define unanticipated biochemical similarities between a large fraction of sALS/FTD cases and C9ALS/FTD, suggesting a common mechanism underlying disease pathogenesis.

In this study, we have characterized a diverse cohort of ALS/FTD patient brains and identified a signature of graded splicing dysregulation that correlates in magnitude with insolubility of several abundant and disease-relevant RBPs. Our work provides evidence that aberrant alternative splicing is a common molecular feature of C9ALS/FTD and sporadic ALS/FTD patients, and importantly, that these changes reflect underlying biochemical changes that manifest in RBP aggregation. A unified vision of what causes ALS and FTD at the molecular levels has remained highly speculative despite great advances in our understanding of the genetics that contribute to this disease spectrum (Taylor et al., 2016). Thus, our work is significant in that it provides a compelling case for the existence of a shared biochemical mechanism that occurs in ALS, FTD and ALS/FTD, and which can be rationalized in the context of other known disease mutations and pathologies.

RNA splicing defects have previously been reported in C9ALS (Conlon et al., 2016) and sporadic ALS/FTD (Ling et al., 2015; Prudencio et al., 2015). However, in the absence of mechanistic insights into the origins of these defects in sporadic patients, the assumed culprit has been TDP-43 deficiency, reflecting the almost universal observation of TDP-43 histopathology (Polymenidou et al., 2011; Lagier-Tourenne et al., 2012; Ling et al., 2015). Through stratification of patient samples based on biochemical insolubility of hnRNP H, and by extension TDP-43, we found that TDP-43 pathology is not sufficient to confer detectable splicing differences relative to neurologically normal controls or SOD1 ALS patients. Thus, our data suggest a model (Figure 6—figure supplement 5) in which TDP-43 is just one of several affected RBPs. Our finding that biochemical insolubility of TDP-43 correlates with splicing defects as well as with insolubility of several other RBPs casts doubt on the TDP-43-centric view of ALS (Lagier-Tourenne and Cleveland, 2009; Scotter et al., 2015). Given the decoupling of visible pathology from biochemical insolubility, with only the latter correlated with splicing dysregulation, our results strongly suggest that ALS/FTD should be considered a multiRBP proteinopathy rather than a TDP-43 proteinopathy.

The fact that approximately half of sporadic ALS/FTD patient brains do not display significant splicing defects or insoluble protein seemingly contradicts our assertion that this is the common mechanism underlying disease. One possibility is that an entirely distinct mechanism drives neurodegeneration in these patients, perhaps one similar to SOD1ALS in which soluble hnRNP H and splicing profiles resemble neurologically normal controls. Alternatively, the range of abnormality in sALS patients we observed may reflect what we detected in C9ALS/FTD (Conlon et al., 2016). Specifically, some C9 patients displayed severe splicing changes, while others were indistinguishable from normal controls. One possible explanation for this is that in both C9s and sporadic cases, some patients display these changes only on a limited scale. For example, splicing and insolubility changes may be confined to only the most vulnerable cell types in patients (sporadic and C9) that were modestly different from control. As a result, changes in splicing or solubility that occur in a minority subpopulation of cells could be diluted in bulk studies by more numerous transcripts derived from unaffected cell types. A future goal will be to try to investigate whether such specific splicing events indeed exist that are unique or disproportionately relevant to affected neuronal populations.

Another interpretation of our data is that detectable splicing changes and highly insoluble protein levels may only become evident in the later stages of a progressive coalescence of RBPs (Lin et al., 2015; Murakami et al., 2015; Patel et al., 2015). In such a model, splicing changes, while undoubtedly contributory, may not be the sole, or critical, event driving disease onset or progression, but rather a consequence of perturbations in the equilibrium of soluble versus aggregated RBPs (Figure 6—figure supplement 5). Many studies of ALS/FTD disease mechanisms have focused on a complex equilibrium between differentially concentrated RBPs in physically distinct phases. Importantly, many factors, including mutations known to cause disease (Kim et al., 2013; Murakami et al., 2015; Patel et al., 2015; Martinez et al., 2016; Fay et al., 2017; Mackenzie et al., 2017), are capable of influencing the balance between protein concentrations in each phase over time (Ramaswami et al., 2013). Our model (Figure 6—figure supplement 5) proposes that all ALS/FTD patients fall at varying points beyond a hypothetical ‘toxicity boundary’ that would be analogous to the phase boundaries that describe physiological granule formation (Weber and Brangwynne, 2012; Elbaum-Garfinkle et al., 2015), only unidirectional. The magnitude of this imbalance, or position past a theoretical tipping point, would thus be related to increased abundance of biochemically insoluble protein and decreased levels of functional RBPs, leading to aberrant splicing.

The above model depicts RBP insolubility as lying upstream of splicing defects. However, it is worth considering the possibility that splicing changes lead to, or at least contribute to, insolubility. Notably, two events found to affect the RBPs hnRNP D and hnRNP A1 (exons 7 and 8, respectively) have been shown to cause inclusion of glycine-tyrosine (GY)-rich intrinsically disordered regions (IDRs) in their respective proteins (Gueroussov et al., 2017). In our analysis, we observed increased inclusion of hnRNP D exon 7, leading to an isoform with an extended GY-rich region that makes increased contacts with other hnRNPs, thereby potentially promoting overly stable RBP complexes. Inclusion of hnRNP A1 exon 8 also extends its IDR, although the difference in the number of GY-repeats is not as strong as for hnRNP D (Gueroussov et al., 2017). Somewhat surprisingly, hnRNP A1 exon 8 was found in our data to be more skipped as RBP insolubility increased, which seems to contradict a recent study suggesting that reduced levels of TDP-43 promote inclusion of this exon (Deshaies et al., 2018). In contrast, the loss of soluble FUS, which regulates its own transcript, may explain FUS splicing defects in our data. Normally, FUS protein represses inclusion of exon 7, leading to a FUS isoform that undergoes nonsense-mediated decay in a self-regulating loop (Zhou et al., 2013). Thus, it may be that reduced levels of soluble FUS lead to the observed increase in exon 7 inclusion, thereby resulting in excess levels of FUS that either seed RBP aggregates or ‘pile on’ to existing ones. Such a mechanism, whereby splicing changes affecting one or a few RBPs act as ‘drivers’ of RBP aggregation, can also be envisioned for other events in our analysis, for example, hnRNP H1, which we found undergoes differential splicing in its gly-rich IRD. Regardless of whether insolubility is upstream of splicing changes, or splicing changes precipitate insolubility, these events, and likely others, highlight how the two processes may reinforce one another in a vicious cycle, ultimately leading to the severe insolubility/splicing dysregulation we have documented in the like-C9 ALS and FTD cases.

If all ALS-FTD indeed converges on the same mechanism then what factors dictate the onset and progression of disease at the clinical level? This is not straightforward, even when a known mutation underlies disease. In the case of C9, understanding how a single genetic polymorphism can lead to two different disorders, or a combination of the two, is perplexing. Further complicating matters, the C9 expansion has been associated with scarce disease incidence in a wide spectrum of neurodegenerative and psychiatric conditions, including Huntington’s Disease, Parkinson’s Disease, and Schizophrenia (Hensman Moss et al., 2014; Lesage et al., 2013; McLaughlin et al., 2017). It is unclear if these different patterns of neuronal degeneration reflect intra-patient variation in toxic burden across cell types/brain regions (Mackenzie et al., 2014), or if particular cell-types have inherently lower tolerance to toxic threats that are uniform across an individual (Nijssen et al., 2017). It may be that a combination of burden mosaicism and varying toxicity thresholds of distinct cell types converge to affect disease progression, such that mutation alone cannot predict the disease course of a given individual (Van Mossevelde et al., 2017). In our C9 model, these hypothetical burdens may be manifested and/or enhanced by transcriptional activity of the expanded locus (Liu et al., 2014), heightened ratio of aggregated to soluble hnRNP H, and increasingly severe splicing changes. For sALS/FTD, we know the latter two occur, but as of yet, what triggers this remains a mystery.

The variation in disease severity implied in our model also suggests a natural progression of disease from ALS to FTD along this clinical spectrum. Our results suggest that FTD patients are further past this hypothetical boundary than are patients with pure ALS, as reflected by the highest values of RBP insolubility, and most global splicing changes. Given that all of the patients in our cohort with FTD also displayed MN loss, but not all ALS patients displayed signs of frontal cortex deterioration, we propose that the distinct neuronal populations affected by this disease spectrum have differential vulnerabilities to the alterations in granule dynamics that define the toxicity boundary. The heightened sensitivity of spinal MNs implied by this gradient parallels what is known about SMA, a disease where subtle alterations in the concentration of the protein SMN cause highly specific degeneration of spinal MNs, with further increasing deficiency in SMN resulting in wider systemic dysfunction (Tu et al., 2017). In both cases, disease mechanisms have been suggested to converge on perturbations of granule dynamics (Zhang et al., 2006; Shan et al., 2010; Ishihara et al., 2013) and RNA splicing (Tsuiji et al., 2013; Zhang et al., 2008).

The observation that defects in mRNA splicing may have relevance to ALS was made 20 years ago, through examination of the disease-relevant EAAT2 gene product (Lin et al., 1998). In retrospect, our findings shed new light on the observation that splicing changes could be found region-specifically, and in most, but not all, sporadic patients. In addition to providing a potential mechanistic basis for this observation, our data can fit it into a broader mechanistic context that correlates changes in mRNA splicing with changes in RBP solubility. While the ubiquity of TDP-43 pathology to ALS/FTD, and the protein’s known roles in RNA processing, brought RNA biology to the forefront of research on these diseases, we now conclude that RNA processing defects are not simply correlated to visible pathology. Nonetheless, the microscopically invisible fluctuations in RBP concentration that we have described biochemically imply that this complex spectrum of disease states can be reached through many paths, some genetically mediated, some potentially driven by environment and experience. Our discovery that a large fraction of all ALS/FTD is characterized by a ‘like-C9’ signature and reflects a ‘multiRBP proteinopathy’ thus provides a unifying theme for these complex and lethal diseases.

All raw RNASeq data from Target ALS samples are immediately made publicly available. Access to the data can be requested by emailing ALSData@nygenome.org. All RNASeq data from the ALS Consortium are made immediately available to all members of the Consortium and with other Consortia with whom we have a reciprocal sharing arrangement. Data have been deposited in the National Center for Biotechnology Information Gene Expression Omnibus (GEO) (accession no. GSE116622).

1. 1. James L Manley

   (2018) Data from: Unexpected similarities between C9ORF72 and sporadic forms of ALS/FTD suggest a common disease mechanism

   Publicly available at the NCBI Gene Expression Omnibus (accession no: GSE116622).

   http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE116622

1. 1. Alberti S
   2. Hyman AA

   (2016) Are aberrant phase transitions a driver of cellular aging?

   BioEssays 38:959–968.

   https://doi.org/10.1002/bies.201600042

   • PubMed
   • Google Scholar
2. 1. Alonso A
   2. Logroscino G
   3. Jick SS
   4. Hernán MA

   (2009) Incidence and lifetime risk of motor neuron disease in the United Kingdom: a population-based study

   European Journal of Neurology 16:745–751.

   https://doi.org/10.1111/j.1468-1331.2009.02586.x

   • PubMed
   • Google Scholar
3. 1. Appocher C
   2. Mohagheghi F
   3. Cappelli S
   4. Stuani C
   5. Romano M
   6. Feiguin F
   7. Buratti E

   (2017) Major hnRNP proteins act as general TDP-43 functional modifiers both in Drosophila and human neuronal cells

   Nucleic Acids Research 45:8026–8045.

   https://doi.org/10.1093/nar/gkx477

   • PubMed
   • Google Scholar
4. 1. Baloh RH

   (2011) TDP-43: the relationship between protein aggregation and neurodegeneration in amyotrophic lateral sclerosis and frontotemporal lobar degeneration

   FEBS Journal 278:3539–3549.

   https://doi.org/10.1111/j.1742-4658.2011.08256.x

   • PubMed
   • Google Scholar
5. 1. Buratti E
   2. Brindisi A
   3. Giombi M
   4. Tisminetzky S
   5. Ayala YM
   6. Baralle FE

   (2005) TDP-43 binds heterogeneous nuclear ribonucleoprotein A/B through its C-terminal tail: an important region for the inhibition of cystic fibrosis transmembrane conductance regulator exon 9 splicing

   The Journal of Biological Chemistry 280:37572–37584.

   https://doi.org/10.1074/jbc.M505557200

   • PubMed
   • Google Scholar
6. 1. Byrne S
   2. Elamin M
   3. Bede P
   4. Shatunov A
   5. Walsh C
   6. Corr B
   7. Heverin M
   8. Jordan N
   9. Kenna K
   10. Lynch C
   11. McLaughlin RL
   12. Iyer PM
   13. O'Brien C
   14. Phukan J
   15. Wynne B
   16. Bokde AL
   17. Bradley DG
   18. Pender N
   19. Al-Chalabi A
   20. Hardiman O

   (2012) Cognitive and clinical characteristics of patients with amyotrophic lateral sclerosis carrying a C9orf72 repeat expansion: a population-based cohort study

   The Lancet Neurology 11:232–240.

   https://doi.org/10.1016/S1474-4422(12)70014-5

   • PubMed
   • Google Scholar
7. 1. Conlon EG
   2. Lu L
   3. Sharma A
   4. Yamazaki T
   5. Tang T
   6. Shneider NA
   7. Manley JL

   (2016) The C9ORF72 GGGGCC expansion forms RNA G-quadruplex inclusions and sequesters hnRNP H to disrupt splicing in ALS brains

   eLife 5:e17820.

   https://doi.org/10.7554/eLife.17820

   • PubMed
   • Google Scholar
8. 1. Conlon EG
   2. Manley JL

   (2017) RNA-binding proteins in neurodegeneration: mechanisms in aggregate

   Genes & Development 31:1509–1528.

   https://doi.org/10.1101/gad.304055.117

   • PubMed
   • Google Scholar
9. 1. DeJesus-Hernandez M
   2. Mackenzie IR
   3. Boeve BF
   4. Boxer AL
   5. Baker M
   6. Rutherford NJ
   7. Nicholson AM
   8. Finch NA
   9. Flynn H
   10. Adamson J
   11. Kouri N
   12. Wojtas A
   13. Sengdy P
   14. Hsiung GY
   15. Karydas A
   16. Seeley WW
   17. Josephs KA
   18. Coppola G
   19. Geschwind DH
   20. Wszolek ZK
   21. Feldman H
   22. Knopman DS
   23. Petersen RC
   24. Miller BL
   25. Dickson DW
   26. Boylan KB
   27. Graff-Radford NR
   28. Rademakers R

   (2011) Expanded GGGGCC hexanucleotide repeat in noncoding region of C9ORF72 causes chromosome 9p-linked FTD and ALS

   Neuron 72:245–256.

   https://doi.org/10.1016/j.neuron.2011.09.011

   • PubMed
   • Google Scholar
10. 1. Deshaies JE
    2. Shkreta L
    3. Moszczynski AJ
    4. Sidibé H
    5. Semmler S
    6. Fouillen A
    7. Bennett ER
    8. Bekenstein U
    9. Destroismaisons L
    10. Toutant J
    11. Delmotte Q
    12. Volkening K
    13. Stabile S
    14. Aulas A
    15. Khalfallah Y
    16. Soreq H
    17. Nanci A
    18. Strong MJ
    19. Chabot B
    20. Vande Velde C

    (2018) TDP-43 regulates the alternative splicing of hnRNP A1 to yield an aggregation-prone variant in amyotrophic lateral sclerosis

    Brain 141:1320–1333.

    https://doi.org/10.1093/brain/awy062

    • PubMed
    • Google Scholar
11. 1. Dobin A
    2. Gingeras TR

    (2015) Mapping RNA-seq reads with STAR

    Current Protocols in Bioinformatics 51:11–19.

    https://doi.org/10.1002/0471250953.bi1114s51

    • PubMed
    • Google Scholar
12. 1. Elbaum-Garfinkle S
    2. Kim Y
    3. Szczepaniak K
    4. Chen CC
    5. Eckmann CR
    6. Myong S
    7. Brangwynne CP

    (2015) The disordered P granule protein LAF-1 drives phase separation into droplets with tunable viscosity and dynamics

    PNAS 112:7189–7194.

    https://doi.org/10.1073/pnas.1504822112

    • PubMed
    • Google Scholar
13. 1. Fay MM
    2. Anderson PJ
    3. Ivanov P

    (2017) ALS/FTD-Associated C9ORF72 repeat RNA promotes phase transitions in vitro and in cells

    Cell Reports 21:3573–3584.

    https://doi.org/10.1016/j.celrep.2017.11.093

    • PubMed
    • Google Scholar
14. 1. Fisette JF
    2. Toutant J
    3. Dugré-Brisson S
    4. Desgroseillers L
    5. Chabot B

    (2010) hnRNP A1 and hnRNP H can collaborate to modulate 5' splice site selection

    Rna 16:228–238.

    https://doi.org/10.1261/rna.1890310

    • PubMed
    • Google Scholar
15. 1. Freibaum BD
    2. Chitta RK
    3. High AA
    4. Taylor JP

    (2010) Global analysis of TDP-43 interacting proteins reveals strong association with RNA splicing and translation machinery

    Journal of Proteome Research 9:1104–1120.

    https://doi.org/10.1021/pr901076y

    • PubMed
    • Google Scholar
16. 1. Ghetti B
    2. Oblak AL
    3. Boeve BF
    4. Johnson KA
    5. Dickerson BC
    6. Goedert M

    (2015) Invited review: Frontotemporal dementia caused by microtubule-associated protein tau gene (MAPT) mutations: a chameleon for neuropathology and neuroimaging

    Neuropathology and Applied Neurobiology 41:24–46.

    https://doi.org/10.1111/nan.12213

    • PubMed
    • Google Scholar
17. 1. Ginsberg SD
    2. Galvin JE
    3. Chiu TS
    4. Lee VM
    5. Masliah E
    6. Trojanowski JQ

    (1998) RNA sequestration to pathological lesions of neurodegenerative diseases

    Acta Neuropathologica 96:487–494.

    https://doi.org/10.1007/s004010050923

    • PubMed
    • Google Scholar
18. 1. Gueroussov S
    2. Weatheritt RJ
    3. O'Hanlon D
    4. Lin ZY
    5. Narula A
    6. Gingras AC
    7. Blencowe BJ

    (2017) Regulatory expansion in mammals of multivalent hnRNP assemblies that globally control alternative splicing

    Cell 170:324–339.

    https://doi.org/10.1016/j.cell.2017.06.037

    • PubMed
    • Google Scholar
19. 1. Harrison AF
    2. Shorter J

    (2017) RNA-binding proteins with prion-like domains in health and disease

    Biochemical Journal 474:1417–1438.

    https://doi.org/10.1042/BCJ20160499

    • PubMed
    • Google Scholar
20. 1. Hensman Moss DJ
    2. Poulter M
    3. Beck J
    4. Hehir J
    5. Polke JM
    6. Campbell T
    7. Adamson G
    8. Mudanohwo E
    9. McColgan P
    10. Haworth A
    11. Wild EJ
    12. Sweeney MG
    13. Houlden H
    14. Mead S
    15. Tabrizi SJ

    (2014) C9orf72 expansions are the most common genetic cause of Huntington disease phenocopies

    Neurology 82:292–299.

    https://doi.org/10.1212/WNL.0000000000000061

    • PubMed
    • Google Scholar
21. 1. Hua Y
    2. Zhou J

    (2004) Survival motor neuron protein facilitates assembly of stress granules

    FEBS Letters 572:69–74.

    https://doi.org/10.1016/j.febslet.2004.07.010

    • PubMed
    • Google Scholar
22. 1. Huelga SC
    2. Vu AQ
    3. Arnold JD
    4. Liang TY
    5. Liu PP
    6. Yan BY
    7. Donohue JP
    8. Shiue L
    9. Hoon S
    10. Brenner S
    11. Ares M
    12. Yeo GW

    (2012) Integrative genome-wide analysis reveals cooperative regulation of alternative splicing by hnRNP proteins

    Cell Reports 1:167–178.

    https://doi.org/10.1016/j.celrep.2012.02.001

    • PubMed
    • Google Scholar
23. 1. Humphrey J
    2. Emmett W
    3. Fratta P
    4. Isaacs AM
    5. Plagnol V

    (2017) Quantitative analysis of cryptic splicing associated with TDP-43 depletion

    BMC Medical Genomics 10:38.

    https://doi.org/10.1186/s12920-017-0274-1

    • PubMed
    • Google Scholar
24. 1. Ishihara T
    2. Ariizumi Y
    3. Shiga A
    4. Kato T
    5. Tan CF
    6. Sato T
    7. Miki Y
    8. Yokoo M
    9. Fujino T
    10. Koyama A
    11. Yokoseki A
    12. Nishizawa M
    13. Kakita A
    14. Takahashi H
    15. Onodera O

    (2013) Decreased number of gemini of coiled bodies and U12 snRNA level in amyotrophic lateral sclerosis

    Human Molecular Genetics 22:4136–4147.

    https://doi.org/10.1093/hmg/ddt262

    • PubMed
    • Google Scholar
25. 1. Izquierdo JM
    2. Valcárcel J

    (2007) Two isoforms of the T-cell intracellular antigen 1 (TIA-1) splicing factor display distinct splicing regulation activities. Control of TIA-1 isoform ratio by TIA-1-related protein

    The Journal of Biological Chemistry 282:19410–19417.

    https://doi.org/10.1074/jbc.M700688200

    • PubMed
    • Google Scholar
26. 1. Jain A
    2. Vale RD

    (2017) RNA phase transitions in repeat expansion disorders

    Nature 546:243–247.

    https://doi.org/10.1038/nature22386

    • PubMed
    • Google Scholar
27. 1. Kim HJ
    2. Kim NC
    3. Wang YD
    4. Scarborough EA
    5. Moore J
    6. Diaz Z
    7. MacLea KS
    8. Freibaum B
    9. Li S
    10. Molliex A
    11. Kanagaraj AP
    12. Carter R
    13. Boylan KB
    14. Wojtas AM
    15. Rademakers R
    16. Pinkus JL
    17. Greenberg SA
    18. Trojanowski JQ
    19. Traynor BJ
    20. Smith BN
    21. Topp S
    22. Gkazi AS
    23. Miller J
    24. Shaw CE
    25. Kottlors M
    26. Kirschner J
    27. Pestronk A
    28. Li YR
    29. Ford AF
    30. Gitler AD
    31. Benatar M
    32. King OD
    33. Kimonis VE
    34. Ross ED
    35. Weihl CC
    36. Shorter J
    37. Taylor JP

    (2013) Mutations in prion-like domains in hnRNPA2B1 and hnRNPA1 cause multisystem proteinopathy and ALS

    Nature 495:467–473.

    https://doi.org/10.1038/nature11922

    • PubMed
    • Google Scholar
28. 1. Lagier-Tourenne C
    2. Cleveland DW

    (2009) Rethinking ALS: the FUS about TDP-43

    Cell 136:1001–1004.

    https://doi.org/10.1016/j.cell.2009.03.006

    • PubMed
    • Google Scholar
29. 1. Lagier-Tourenne C
    2. Polymenidou M
    3. Hutt KR
    4. Vu AQ
    5. Baughn M
    6. Huelga SC
    7. Clutario KM
    8. Ling SC
    9. Liang TY
    10. Mazur C
    11. Wancewicz E
    12. Kim AS
    13. Watt A
    14. Freier S
    15. Hicks GG
    16. Donohue JP
    17. Shiue L
    18. Bennett CF
    19. Ravits J
    20. Cleveland DW
    21. Yeo GW

    (2012) Divergent roles of ALS-linked proteins FUS/TLS and TDP-43 intersect in processing long pre-mRNAs

    Nature Neuroscience 15:1488–1497.

    https://doi.org/10.1038/nn.3230

    • PubMed
    • Google Scholar
30. 1. Lattante S
    2. Ciura S
    3. Rouleau GA
    4. Kabashi E

    (2015) Defining the genetic connection linking amyotrophic lateral sclerosis (ALS) with frontotemporal dementia (FTD)

    Trends in Genetics 31:263–273.

    https://doi.org/10.1016/j.tig.2015.03.005

    • PubMed
    • Google Scholar
31. 1. Lefebvre S
    2. Burlet P
    3. Liu Q
    4. Bertrandy S
    5. Clermont O
    6. Munnich A
    7. Dreyfuss G
    8. Melki J

    (1997) Correlation between severity and SMN protein level in spinal muscular atrophy

    Nature Genetics 16:265–269.

    https://doi.org/10.1038/ng0797-265

    • PubMed
    • Google Scholar
32. 1. Lesage S
    2. Le Ber I
    3. Condroyer C
    4. Broussolle E
    5. Gabelle A
    6. Thobois S
    7. Pasquier F
    8. Mondon K
    9. Dion PA
    10. Rochefort D
    11. Rouleau GA
    12. Dürr A
    13. Brice A
    14. French Parkinson’s Disease Genetics Study Group

    (2013) C9orf72 repeat expansions are a rare genetic cause of parkinsonism

    Brain 136:385–391.

    https://doi.org/10.1093/brain/aws357

    • PubMed
    • Google Scholar
33. 1. Li YI
    2. Knowles DA
    3. Humphrey J
    4. Barbeira AN
    5. Dickinson SP
    6. Im HK
    7. Pritchard JK

    (2018) Annotation-free quantification of RNA splicing using LeafCutter

    Nature Genetics 50:151–158.

    https://doi.org/10.1038/s41588-017-0004-9

    • PubMed
    • Google Scholar
34. 1. Lin CL
    2. Bristol LA
    3. Jin L
    4. Dykes-Hoberg M
    5. Crawford T
    6. Clawson L
    7. Rothstein JD

    (1998) Aberrant RNA processing in a neurodegenerative disease: the cause for absent EAAT2, a glutamate transporter, in amyotrophic lateral sclerosis

    Neuron 20:589–602.

    https://doi.org/10.1016/S0896-6273(00)80997-6

    • PubMed
    • Google Scholar
35. 1. Lin Y
    2. Protter DS
    3. Rosen MK
    4. Parker R

    (2015) Formation and maturation of Phase-Separated liquid droplets by RNA-Binding proteins

    Molecular Cell 60:208–219.

    https://doi.org/10.1016/j.molcel.2015.08.018

    • PubMed
    • Google Scholar
36. 1. Ling JP
    2. Pletnikova O
    3. Troncoso JC
    4. Wong PC

    (2015) TDP-43 repression of nonconserved cryptic exons is compromised in ALS-FTD

    Science 349:650–655.

    https://doi.org/10.1126/science.aab0983

    • PubMed
    • Google Scholar
37. 1. Ling SC
    2. Albuquerque CP
    3. Han JS
    4. Lagier-Tourenne C
    5. Tokunaga S
    6. Zhou H
    7. Cleveland DW

    (2010) ALS-associated mutations in TDP-43 increase its stability and promote TDP-43 complexes with FUS/TLS

    PNAS 107:13318–13323.

    https://doi.org/10.1073/pnas.1008227107

    • PubMed
    • Google Scholar
38. 1. Ling SC
    2. Polymenidou M
    3. Cleveland DW

    (2013) Converging mechanisms in ALS and FTD: disrupted RNA and protein homeostasis

    Neuron 79:416–438.

    https://doi.org/10.1016/j.neuron.2013.07.033

    • PubMed
    • Google Scholar
39. 1. Liu EY
    2. Russ J
    3. Wu K
    4. Neal D
    5. Suh E
    6. McNally AG
    7. Irwin DJ
    8. Van Deerlin VM
    9. Lee EB

    (2014) C9orf72 hypermethylation protects against repeat expansion-associated pathology in ALS/FTD

    Acta Neuropathologica 128:525–541.

    https://doi.org/10.1007/s00401-014-1286-y

    • PubMed
    • Google Scholar
40. 1. Mackenzie IR
    2. Bigio EH
    3. Ince PG
    4. Geser F
    5. Neumann M
    6. Cairns NJ
    7. Kwong LK
    8. Forman MS
    9. Ravits J
    10. Stewart H
    11. Eisen A
    12. McClusky L
    13. Kretzschmar HA
    14. Monoranu CM
    15. Highley JR
    16. Kirby J
    17. Siddique T
    18. Shaw PJ
    19. Lee VM
    20. Trojanowski JQ

    (2007) Pathological TDP-43 distinguishes sporadic amyotrophic lateral sclerosis from amyotrophic lateral sclerosis with SOD1 mutations

    Annals of Neurology 61:427–434.

    https://doi.org/10.1002/ana.21147

    • PubMed
    • Google Scholar
41. 1. Mackenzie IR
    2. Frick P
    3. Neumann M

    (2014) The neuropathology associated with repeat expansions in the C9ORF72 gene

    Acta Neuropathologica 127:347–357.

    https://doi.org/10.1007/s00401-013-1232-4

    • PubMed
    • Google Scholar
42. 1. Mackenzie IR
    2. Nicholson AM
    3. Sarkar M
    4. Messing J
    5. Purice MD
    6. Pottier C
    7. Annu K
    8. Baker M
    9. Perkerson RB
    10. Kurti A
    11. Matchett BJ
    12. Mittag T
    13. Temirov J
    14. Hsiung GR
    15. Krieger C
    16. Murray ME
    17. Kato M
    18. Fryer JD
    19. Petrucelli L
    20. Zinman L
    21. Weintraub S
    22. Mesulam M
    23. Keith J
    24. Zivkovic SA
    25. Hirsch-Reinshagen V
    26. Roos RP
    27. Züchner S
    28. Graff-Radford NR
    29. Petersen RC
    30. Caselli RJ
    31. Wszolek ZK
    32. Finger E
    33. Lippa C
    34. Lacomis D
    35. Stewart H
    36. Dickson DW
    37. Kim HJ
    38. Rogaeva E
    39. Bigio E
    40. Boylan KB
    41. Taylor JP
    42. Rademakers R

    (2017) TIA1 mutations in amyotrophic lateral sclerosis and frontotemporal dementia promote phase separation and alter stress granule dynamics

    Neuron 95:808–816.

    https://doi.org/10.1016/j.neuron.2017.07.025

    • PubMed
    • Google Scholar
43. 1. Markmiller S
    2. Soltanieh S
    3. Server KL
    4. Mak R
    5. Jin W
    6. Fang MY
    7. Luo EC
    8. Krach F
    9. Yang D
    10. Sen A
    11. Fulzele A
    12. Wozniak JM
    13. Gonzalez DJ
    14. Kankel MW
    15. Gao FB
    16. Bennett EJ
    17. Lécuyer E
    18. Yeo GW

    (2018) Context-Dependent and Disease-Specific diversity in protein interactions within stress granules

    Cell 172:590–604.

    https://doi.org/10.1016/j.cell.2017.12.032

    • PubMed
    • Google Scholar
44. 1. Martinez FJ
    2. Pratt GA
    3. Van Nostrand EL
    4. Batra R
    5. Huelga SC
    6. Kapeli K
    7. Freese P
    8. Chun SJ
    9. Ling K
    10. Gelboin-Burkhart C
    11. Fijany L
    12. Wang HC
    13. Nussbacher JK
    14. Broski SM
    15. Kim HJ
    16. Lardelli R
    17. Sundararaman B
    18. Donohue JP
    19. Javaherian A
    20. Lykke-Andersen J
    21. Finkbeiner S
    22. Bennett CF
    23. Ares M
    24. Burge CB
    25. Taylor JP
    26. Rigo F
    27. Yeo GW

    (2016) Protein-RNA networks regulated by normal and ALS-Associated mutant HNRNPA2B1 in the nervous system

    Neuron 92:780–795.

    https://doi.org/10.1016/j.neuron.2016.09.050

    • PubMed
    • Google Scholar
45. 1. McLaughlin RL
    2. Schijven D
    3. van Rheenen W
    4. van Eijk KR
    5. O'Brien M
    6. Kahn RS
    7. Ophoff RA
    8. Goris A
    9. Bradley DG
    10. Al-Chalabi A
    11. van den Berg LH
    12. Luykx JJ
    13. Hardiman O
    14. Veldink JH
    15. Project MinE GWAS Consortium
    16. Schizophrenia Working Group of the Psychiatric Genomics Consortium

    (2017) Genetic correlation between amyotrophic lateral sclerosis and schizophrenia

    Nature Communications 8:14774.

    https://doi.org/10.1038/ncomms14774

    • PubMed
    • Google Scholar
46. 1. Mohagheghi F
    2. Prudencio M
    3. Stuani C
    4. Cook C
    5. Jansen-West K
    6. Dickson DW
    7. Petrucelli L
    8. Buratti E

    (2016) TDP-43 functions within a network of hnRNP proteins to inhibit the production of a truncated human SORT1 receptor

    Human Molecular Genetics 25:534–545.

    https://doi.org/10.1093/hmg/ddv491

    • PubMed
    • Google Scholar
47. 1. Murakami T
    2. Qamar S
    3. Lin JQ
    4. Schierle GS
    5. Rees E
    6. Miyashita A
    7. Costa AR
    8. Dodd RB
    9. Chan FT
    10. Michel CH
    11. Kronenberg-Versteeg D
    12. Li Y
    13. Yang SP
    14. Wakutani Y
    15. Meadows W
    16. Ferry RR
    17. Dong L
    18. Tartaglia GG
    19. Favrin G
    20. Lin WL
    21. Dickson DW
    22. Zhen M
    23. Ron D
    24. Schmitt-Ulms G
    25. Fraser PE
    26. Shneider NA
    27. Holt C
    28. Vendruscolo M
    29. Kaminski CF
    30. St George-Hyslop P

    (2015) ALS/FTD Mutation-Induced phase transition of FUS liquid droplets and reversible hydrogels into irreversible hydrogels impairs RNP granule function

    Neuron 88:678–690.

    https://doi.org/10.1016/j.neuron.2015.10.030

    • PubMed
    • Google Scholar
48. 1. Nazim M
    2. Masuda A
    3. Rahman MA
    4. Nasrin F
    5. Takeda JI
    6. Ohe K
    7. Ohkawara B
    8. Ito M
    9. Ohno K

    (2017) Competitive regulation of alternative splicing and alternative polyadenylation by hnRNP H and CstF64 determines acetylcholinesterase isoforms

    Nucleic Acids Research 45:1455–1468.

    https://doi.org/10.1093/nar/gkw823

    • PubMed
    • Google Scholar
49. 1. Nijssen J
    2. Comley LH
    3. Hedlund E

    (2017) Motor neuron vulnerability and resistance in amyotrophic lateral sclerosis

    Acta Neuropathologica 133:863–885.

    https://doi.org/10.1007/s00401-017-1708-8

    • PubMed
    • Google Scholar
50. 1. Patel A
    2. Lee HO
    3. Jawerth L
    4. Maharana S
    5. Jahnel M
    6. Hein MY
    7. Stoynov S
    8. Mahamid J
    9. Saha S
    10. Franzmann TM
    11. Pozniakovski A
    12. Poser I
    13. Maghelli N
    14. Royer LA
    15. Weigert M
    16. Myers EW
    17. Grill S
    18. Drechsel D
    19. Hyman AA
    20. Alberti S

    (2015) A Liquid-to-Solid phase transition of the ALS protein FUS accelerated by disease mutation

    Cell 162:1066–1077.

    https://doi.org/10.1016/j.cell.2015.07.047

    • PubMed
    • Google Scholar
51. 1. Polymenidou M
    2. Lagier-Tourenne C
    3. Hutt KR
    4. Huelga SC
    5. Moran J
    6. Liang TY
    7. Ling SC
    8. Sun E
    9. Wancewicz E
    10. Mazur C
    11. Kordasiewicz H
    12. Sedaghat Y
    13. Donohue JP
    14. Shiue L
    15. Bennett CF
    16. Yeo GW
    17. Cleveland DW

    (2011) Long pre-mRNA depletion and RNA missplicing contribute to neuronal vulnerability from loss of TDP-43

    Nature Neuroscience 14:459–468.

    https://doi.org/10.1038/nn.2779

    • PubMed
    • Google Scholar
52. 1. Prudencio M
    2. Belzil VV
    3. Batra R
    4. Ross CA
    5. Gendron TF
    6. Pregent LJ
    7. Murray ME
    8. Overstreet KK
    9. Piazza-Johnston AE
    10. Desaro P
    11. Bieniek KF
    12. DeTure M
    13. Lee WC
    14. Biendarra SM
    15. Davis MD
    16. Baker MC
    17. Perkerson RB
    18. van Blitterswijk M
    19. Stetler CT
    20. Rademakers R
    21. Link CD
    22. Dickson DW
    23. Boylan KB
    24. Li H
    25. Petrucelli L

    (2015) Distinct brain transcriptome profiles in C9orf72-associated and sporadic ALS

    Nature Neuroscience 18:1175–1182.

    https://doi.org/10.1038/nn.4065

    • PubMed
    • Google Scholar
53. 1. Ramaswami M
    2. Taylor JP
    3. Parker R

    (2013) Altered ribostasis: RNA-protein granules in degenerative disorders

    Cell 154:727–736.

    https://doi.org/10.1016/j.cell.2013.07.038

    • PubMed
    • Google Scholar
54. 1. Reber S
    2. Stettler J
    3. Filosa G
    4. Colombo M
    5. Jutzi D
    6. Lenzken SC
    7. Schweingruber C
    8. Bruggmann R
    9. Bachi A
    10. Barabino SM
    11. Mühlemann O
    12. Ruepp MD

    (2016) Minor intron splicing is regulated by FUS and affected by ALS-associated FUS mutants

    The EMBO Journal 35:1504–1521.

    https://doi.org/10.15252/embj.201593791

    • PubMed
    • Google Scholar
55. 1. Renton AE
    2. Majounie E
    3. Waite A
    4. Simón-Sánchez J
    5. Rollinson S
    6. Gibbs JR
    7. Schymick JC
    8. Laaksovirta H
    9. van Swieten JC
    10. Myllykangas L
    11. Kalimo H
    12. Paetau A
    13. Abramzon Y
    14. Remes AM
    15. Kaganovich A
    16. Scholz SW
    17. Duckworth J
    18. Ding J
    19. Harmer DW
    20. Hernandez DG
    21. Johnson JO
    22. Mok K
    23. Ryten M
    24. Trabzuni D
    25. Guerreiro RJ
    26. Orrell RW
    27. Neal J
    28. Murray A
    29. Pearson J
    30. Jansen IE
    31. Sondervan D
    32. Seelaar H
    33. Blake D
    34. Young K
    35. Halliwell N
    36. Callister JB
    37. Toulson G
    38. Richardson A
    39. Gerhard A
    40. Snowden J
    41. Mann D
    42. Neary D
    43. Nalls MA
    44. Peuralinna T
    45. Jansson L
    46. Isoviita VM
    47. Kaivorinne AL
    48. Hölttä-Vuori M
    49. Ikonen E
    50. Sulkava R
    51. Benatar M
    52. Wuu J
    53. Chiò A
    54. Restagno G
    55. Borghero G
    56. Sabatelli M
    57. Heckerman D
    58. Rogaeva E
    59. Zinman L
    60. Rothstein JD
    61. Sendtner M
    62. Drepper C
    63. Eichler EE
    64. Alkan C
    65. Abdullaev Z
    66. Pack SD
    67. Dutra A
    68. Pak E
    69. Hardy J
    70. Singleton A
    71. Williams NM
    72. Heutink P
    73. Pickering-Brown S
    74. Morris HR
    75. Tienari PJ
    76. Traynor BJ
    77. ITALSGEN Consortium

    (2011) A hexanucleotide repeat expansion in C9ORF72 is the cause of chromosome 9p21-linked ALS-FTD

    Neuron 72:257–268.

    https://doi.org/10.1016/j.neuron.2011.09.010

    • PubMed
    • Google Scholar
56. 1. Ross CA
    2. Poirier MA

    (2004) Protein aggregation and neurodegenerative disease

    Nature Medicine 10:S10–S17.

    https://doi.org/10.1038/nm1066

    • PubMed
    • Google Scholar
57. 1. Rowland LP
    2. Shneider NA

    (2001) Amyotrophic lateral sclerosis

    New England Journal of Medicine 344:1688–1700.

    https://doi.org/10.1056/NEJM200105313442207

    • PubMed
    • Google Scholar
58. 1. Schwartz JC
    2. Wang X
    3. Podell ER
    4. Cech TR

    (2013) RNA seeds higher-order assembly of FUS protein

    Cell Reports 5:918–925.

    https://doi.org/10.1016/j.celrep.2013.11.017

    • PubMed
    • Google Scholar
59. 1. Scotter EL
    2. Chen HJ
    3. Shaw CE

    (2015) TDP-43 proteinopathy and ALS: insights into disease mechanisms and therapeutic targets

    Neurotherapeutics 12:352–363.

    https://doi.org/10.1007/s13311-015-0338-x

    • PubMed
    • Google Scholar
60. 1. Seelaar H
    2. Rohrer JD
    3. Pijnenburg YA
    4. Fox NC
    5. van Swieten JC

    (2011) Clinical, genetic and pathological heterogeneity of frontotemporal dementia: a review

    Journal of Neurology, Neurosurgery & Psychiatry 82:476–486.

    https://doi.org/10.1136/jnnp.2010.212225

    • PubMed
    • Google Scholar
61. 1. Sephton CF
    2. Cenik C
    3. Kucukural A
    4. Dammer EB
    5. Cenik B
    6. Han Y
    7. Dewey CM
    8. Roth FP
    9. Herz J
    10. Peng J
    11. Moore MJ
    12. Yu G

    (2011) Identification of neuronal RNA targets of TDP-43-containing ribonucleoprotein complexes

    Journal of Biological Chemistry 286:1204–1215.

    https://doi.org/10.1074/jbc.M110.190884

    • PubMed
    • Google Scholar
62. 1. Shan X
    2. Chiang PM
    3. Price DL
    4. Wong PC

    (2010) Altered distributions of Gemini of coiled bodies and mitochondria in motor neurons of TDP-43 transgenic mice

    PNAS 107:16325–16330.

    https://doi.org/10.1073/pnas.1003459107

    • PubMed
    • Google Scholar
63. 1. Taylor JP
    2. Brown RH
    3. Cleveland DW

    (2016) Decoding ALS: from genes to mechanism

    Nature 539:197–206.

    https://doi.org/10.1038/nature20413

    • PubMed
    • Google Scholar
64. 1. Thomas MG
    2. Loschi M
    3. Desbats MA
    4. Boccaccio GL

    (2011) RNA granules: the good, the bad and the ugly

    Cellular Signalling 23:324–334.

    https://doi.org/10.1016/j.cellsig.2010.08.011

    • PubMed
    • Google Scholar
65. 1. Tollervey JR
    2. Curk T
    3. Rogelj B
    4. Briese M
    5. Cereda M
    6. Kayikci M
    7. König J
    8. Hortobágyi T
    9. Nishimura AL
    10. Zupunski V
    11. Patani R
    12. Chandran S
    13. Rot G
    14. Zupan B
    15. Shaw CE
    16. Ule J

    (2011) Characterizing the RNA targets and position-dependent splicing regulation by TDP-43

    Nature Neuroscience 14:452–458.

    https://doi.org/10.1038/nn.2778

    • PubMed
    • Google Scholar
66. 1. Tsuiji H
    2. Iguchi Y
    3. Furuya A
    4. Kataoka A
    5. Hatsuta H
    6. Atsuta N
    7. Tanaka F
    8. Hashizume Y
    9. Akatsu H
    10. Murayama S
    11. Sobue G
    12. Yamanaka K

    (2013) Spliceosome integrity is defective in the motor neuron diseases ALS and SMA

    EMBO Molecular Medicine 5:221–234.

    https://doi.org/10.1002/emmm.201202303

    • PubMed
    • Google Scholar
67. 1. Tu WY
    2. Simpson JE
    3. Highley JR
    4. Heath PR

    (2017) Spinal muscular atrophy: factors that modulate motor neurone vulnerability

    Neurobiology of Disease 102:11–20.

    https://doi.org/10.1016/j.nbd.2017.01.011

    • PubMed
    • Google Scholar
68. 1. Van Mossevelde S
    2. van der Zee J
    3. Cruts M
    4. Van Broeckhoven C

    (2017) Relationship between C9orf72 repeat size and clinical phenotype

    Current Opinion in Genetics & Development 44:117–124.

    https://doi.org/10.1016/j.gde.2017.02.008

    • PubMed
    • Google Scholar
69. 1. Vanden Broeck L
    2. Callaerts P
    3. Dermaut B

    (2014) TDP-43-mediated neurodegeneration: towards a loss-of-function hypothesis?

    Trends in Molecular Medicine 20:66–71.

    https://doi.org/10.1016/j.molmed.2013.11.003

    • PubMed
    • Google Scholar
70. 1. Weber SC
    2. Brangwynne CP

    (2012) Getting RNA and protein in phase

    Cell 149:1188–1191.

    https://doi.org/10.1016/j.cell.2012.05.022

    • PubMed
    • Google Scholar
71. 1. Zhang H
    2. Xing L
    3. Rossoll W
    4. Wichterle H
    5. Singer RH
    6. Bassell GJ

    (2006) Multiprotein complexes of the survival of motor neuron protein SMN with Gemins traffic to neuronal processes and growth cones of motor neurons

    Journal of Neuroscience 26:8622–8632.

    https://doi.org/10.1523/JNEUROSCI.3967-05.2006

    • PubMed
    • Google Scholar
72. 1. Zhang Z
    2. Lotti F
    3. Dittmar K
    4. Younis I
    5. Wan L
    6. Kasim M
    7. Dreyfuss G

    (2008) SMN deficiency causes tissue-specific perturbations in the repertoire of snRNAs and widespread defects in splicing

    Cell 133:585–600.

    https://doi.org/10.1016/j.cell.2008.03.031

    • PubMed
    • Google Scholar
73. 1. Zhou Y
    2. Liu S
    3. Liu G
    4. Oztürk A
    5. Hicks GG

    (2013) ALS-associated FUS mutations result in compromised FUS alternative splicing and autoregulation

    PLoS Genetics 9:e1003895.

    https://doi.org/10.1371/journal.pgen.1003895

    • PubMed
    • Google Scholar

Author details

1. Erin G Conlon

   Department of Biological Sciences, Columbia University, New York, United States

   Contribution

   Conceptualization, Formal analysis, Investigation, Visualization, Methodology, Writing—original draft

   Competing interests

   No competing interests declared

2. Delphine Fagegaltier

   Center for Genomics of Neurodegenerative Disease, New York Genome Center, New York, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Visualization, Writing—review and editing

   Competing interests

   No competing interests declared

3. Phaedra Agius

   New York Genome Center, New York, United States

   Contribution

   Data curation, Formal analysis, Visualization, Writing—review and editing

   Competing interests

   No competing interests declared

4. Julia Davis-Porada

   Department of Biological Sciences, Columbia University, New York, United States

   Contribution

   Investigation, Writing—review and editing

   Competing interests

   No competing interests declared

5. James Gregory

   Center for Genomics of Neurodegenerative Disease, New York Genome Center, New York, United States

   Contribution

   Methodology, Writing—review and editing

   Competing interests

   No competing interests declared

6. Isabel Hubbard

   Center for Genomics of Neurodegenerative Disease, New York Genome Center, New York, United States

   Contribution

   Investigation

   Competing interests

   No competing interests declared

7. Kristy Kang

   Center for Genomics of Neurodegenerative Disease, New York Genome Center, New York, United States

   Contribution

   Investigation

   Competing interests

   No competing interests declared

8. Duyang Kim

   Center for Genomics of Neurodegenerative Disease, New York Genome Center, New York, United States

   Contribution

   Resources

   Competing interests

   No competing interests declared

9. The New York Genome Center ALS Consortium

   Contribution

   Resources, Funding acquisition

   Competing interests

   No competing interests declared

   1. Hemali Phatnani, Center for Genomics of Neurodegenerative Diseases, New York Genome Center, New York, United States
   2. Justin Kwan, Department of Neurology, University of Maryland School of Medicine, University of Maryland ALS Clinic, Baltimore, United States
   3. Dhruv Sareen, Cedars-Sinai Department of Biomedical Sciences, Board of Governors Regenerative Medicine Institute and Brain Program, Cedars-Sinai Medical Center, Los Angeles, United States
   4. James R Broach, Department of Biochemistry and Molecular Biology, Penn State Institute for Personalized Medicine, The Pennsylvania State University, Hershey, United States
   5. Zachary Simmons, Department of Neurology, The Pennsylvania State University, Hershey, United States
   6. Ximena Arcila-Londono, Department of Neurology, Henry Ford Health System, Detroit, United States
   7. Edward B Lee, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States
   8. Vivianna M Van Deerlin, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States
   9. Neil A Shneider, Department of Neurology, Center for Motor Neuron Biology and Disease, Institute for Genomic Medicine, Columbia University, New York, United States
   10. Ernest Fraenkel, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, United States
   11. Lyle W Ostrow, Department of Neurology, Johns Hopkins School of Medicine, Baltimore, United States
   12. Frank Baas, Department of Neurogenetics, Academic Medical Centre, Amsterdam and Leiden University Medical Center, Leiden, Netherlands
   13. Noah Zaitlen, Department of Medicine, Lung Biology Center, University of California, San Francisco, San Francisco, United States
   14. James D Berry, ALS Multidisciplinary Clinic, Neuromuscular Division, Department of Neurology, Harvard Medical School, Boston, United States
   15. Andrea Malaspina, Centre for Neuroscience and Trauma, Blizard Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
   16. Pietro Fratta, Institute of Neurology, National Hospital for Neurology and Neurosurgery, University College London, London, United Kingdom
   17. Gregory A Cox, The Jackson Laboratory, Bar Harbor, United States
   18. Leslie M Thompson, Department of Psychiatry and Human Behavior and Department of Biological Chemistry, School of Medicine, University of California, Irvine, Irvine, United States
   19. Steve Finkbeiner, Taube/Koret Center for Neurodegenerative Disease Research, Roddenberry Center for Stem Cell Biology and Medicine, Gladstone Institute, San Francisco, United States
   20. Efthimios Dardiotis, Department of Neurology and Sensory Organs, University of Thessaly, Thessaly, Greece
   21. Timothy M Miller, Department of Neurology, Washington University in St. Louis, St. Louis, United States
   22. Siddharthan Chandran, Centre for Clinical Brain Sciences, Anne Rowling Regenerative Neurology Clinic, Euan MacDonald Centre for Motor Neurone Disease Research, University of Edinburgh, Edinburgh, United Kingdom
   23. Suvankar Pal, Centre for Clinical Brain Sciences, Anne Rowling Regenerative Neurology Clinic, Euan MacDonald Centre for Motor Neurone Disease Research, University of Edinburgh, Edinburgh, United Kingdom
   24. Eran Hornstein, Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, Israel
   25. Daniel J MacGowan, Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, United States
   26. Terry Heiman-Patterson, Center for Neurodegenerative Disorders, Department of Neurology, the Lewis Katz School of Medicine, Temple University, Philadelphia, United States
   27. Molly G Hammell, Cold Spring Harbor Laboratory, Cold Spring Harbor, United States
   28. Nikolaos A Patsopoulos, Computer Science and Systems Biology Program, Ann Romney Center for Neurological Diseases, Department of Neurology, Brigham and Women’s Hospital, Harvard Medical School, Boston, United States
   29. Joshua Dubnau, Department of Anesthesiology, Stony Brook University, Stony Brook, United States
   30. Avindra Nath, Section of Infections of the Nervous System, National Institute of Neurological Disorders and Stroke, NIH, Bethesda, United States

10. Hemali Phatnani

    Center for Genomics of Neurodegenerative Disease, New York Genome Center, New York, United States

    Contribution

    Conceptualization, Funding acquisition, Methodology, Writing—review and editing

    Competing interests

    No competing interests declared

11. Neil A Shneider

    Department of Neurology, Columbia University Medical Center, New York, United States

    Contribution

    Conceptualization, Resources, Supervision, Funding acquisition, Methodology, Project administration, Writing—review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-3223-7366

12. James L Manley

    Department of Biological Sciences, Columbia University, New York, United States

    Contribution

    Conceptualization, Supervision, Funding acquisition, Methodology, Writing—original draft

    For correspondence

    jlm2@columbia.edu

    Competing interests

    Senior editor, eLife

    "This ORCID iD identifies the author of this article:" 0000-0002-8341-1459

• 8,633

  views

• 1,213

  downloads

• 50

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.