Sage Journals: Discover world-class research

Abstract

Dysfunctions at the level of RNA processing have recently been shown to play a fundamental role in the pathogenesis of many neurodegenerative diseases. Several proteins responsible for these dysfunctions (TDP-43, FUS/TLS, and hnRNP A/Bs) belong to the nuclear class of heterogeneous ribonucleoproteins (hnRNPs) that predominantly function as general regulators of both coding and noncoding RNA metabolism. The discovery of the importance of these factors in mediating neuronal death has represented a major paradigmatic shift in our understanding of neurodegenerative processes. As a result, these discoveries have also opened the way toward novel biomolecular screening approaches in our search for therapeutic options. One of the major hurdles in this search is represented by the correct identification of the most promising targets to be prioritized. These may include aberrant aggregation processes, protein-protein interactions, RNA-protein interactions, or specific cellular pathways altered by disease. In this review, we discuss these four major options together with their various advantages and drawbacks.

Keywords

RNA binding proteins TDP-43 ALS FTLD hnRNP review

Introduction

In eukaryotes, proper regulation of gene expression requires the coupling of many processes that include transcription, pre–messenger RNA (mRNA) splicing, mRNA editing, surveillance, stability/transport, and finally translation.¹ All these processes were initially considered functionally separate units, with each process regulated by a distinct set of factors. However, research in the past two decades has uncovered a very high degree of coupling, and it is now commonly accepted that nuclear events occurring very early in nascent RNAs can condition their ultimate fate in the cytoplasm.^2–4 Most important, this conditioning is not unidirectional, and we are also just starting to understand the many ways in which gene expression can be modulated by dynamic changes in chromatin structure driven by small and noncoding RNAs.^5–7 The practical consequence of this extremely complex situation is that RNA processing represents a crucial regulatory node in the control of gene expression. This is especially true in higher organisms and in those organs that display the highest complexity in terms of developmental plasticity, protein isoform production, and noncoding RNA expression.

In keeping with this view, RNA processing events have been shown to play a major role in the regulation of brain development and normal functioning.^8–11 This higher complexity also means that defects at the level of RNA metabolism can often represent a major cause of neuronal and synaptic impairment, eventually leading to disease. Indeed, a connection between RNA metabolism and neurodegeneration has always been known to be important for monogenic diseases that involve the nerve system, such as neurofibromatosis.¹² At a more general level, high-throughput analyses have recently shown that alterations in general RNA processing events can be observed in all the most common forms of neurodegenerative diseases such as amyotrophic lateral sclerosis (ALS), frontotemporal lobar degeneration (FTLD), Parkinson disease (PD), and Alzheimer disease (AD).^13–16

Widespread alterations in RNA metabolic pathways usually include several overlapping effects. First, there is a physiological decrease in RNA processing efficiency that comes with the normal aging process.¹⁷ In disease-affected individuals, on top of these natural changes, there are also disease-specific losses in RNA processing efficiency. It is these specific losses that are currently thought to considerably accelerate and worsen the natural decline.

The possible causes for this RNA-related damage can occur in many different ways, such as the formation of aberrant RNA foci, bidirectional transcription, or the production of toxic RNAs and their resulting proteins.¹⁸ In many cases, these losses also originate from impairments of macromolecular complexes important for RNA processing, such as the spliceosome¹⁹ or the microRNA processing systems.²⁰

In most of these cases, functional alterations of these machineries can derive from changes in the primary sequence, intracellular localization, or aggregation status of various RNA binding proteins (RBPs) that participate in the assembly and activity of these complexes. The identification of these RBPs and the way they affect RNA processing functions have therefore become a major target of recent research in neurodegeneration.²¹ Once identified, the RBPs responsible for the dysfunctions have immediately become the preferential targets of biomolecular screening analyses aimed at evaluating their possible use as new biomarkers, to follow disease onset/progression, or as promising therapeutic targets to slow/reverse disease.

RBP Proteins in Neurodegeneration

RBP proteins play an essential role in providing the connection between all the gene expression steps that lead to the production of a mature RNA.²² Most important, RBPs tend to assemble in distinct multimeric complexes that control different steps of specific RNA life cycles.^23,24 In this way, by varying their specific RBP composition, the various macromolecular systems form a bridge among the different steps and, at the same time, display different functional properties.

In terms of numbers and quantity, the RBPs responsible for creating the “backbone” of these multimeric complexes belong for the most part to the heterogeneous ribonucleoprotein (hnRNP) family.²⁵ From a structural point of view, all hnRNPs contain one or more RNA binding domains that allow specific binding to RNA sequences. As determined in many studies, they also contain specific sequences of variable composition, such as arginine-glycine-glycine (RGG) boxes that mediate their biological functions.^26,27 This fundamental role played by hnRNPs is especially highlighted in neurons, where there is an extremely high level of differential RNA expression that is mainly generated through the cooperation of these ubiquitous hnRNP proteins with other tissue-specific RNA binding factors.^28–30

At present, the most studied hnRNP proteins that have been shown to play a major and direct role in neurodegenerative processes are represented by TDP-43,^31,32 FUS/TLS,^33,34 FET proteins,³⁵ and very recently two hnRNP A/B family members.³⁶ These proteins, it is important to note, do not act alone in mediating neurodegeneration. For example, the recent description of the presence of hexanucleotide expansions in the C9orf72 gene associated with disease^37,38 has also raised the possibility that the effect of pathological RNA foci may contribute to sequester additional RBPs and thus contribute to influence the onset and progression of disease. At the moment, very little is known about the interplay between all these various factors and the way they may affect disease. Nonetheless, these findings have sparked extensive research in the connection between RBPs and neurodegeneration. Indeed, they are likely to become a major theme in future research on this topic, as discussed in several recent reviews.^39,40

Most important, these findings have triggered novel screening, diagnostic, and especially therapeutic possibilities, schematically summarized in Figure 1 , and have already provided a whole new spectrum of targets that are expected to improve our fight against ALS, frontotemporal dementia, Alzheimer disease, and other neurodegenerative diseases. From the moment of their discovery, in fact, knowing which RBPs are involved in a particular neurodegenerative disease has allowed population screening studies to look for mutation carriers and establish genotype-phenotype correlations^41,42 ( Fig. 1 ). In parallel, their eventual aberrant presence in easily available patient samples such as plasma, peripheral blood mononuclear cells (PBMCs), or cerebrospinal fluid (CSF) has also been used to try and set up better diagnostic criteria^43–45 ( Fig. 1 ). Most important, knowing the identity of the responsible RBPs has allowed the development of model systems with the aim of mimicking the pathological features observed in affected individuals.^46–50 The development of these models, together with a better knowledge of the biological functions of proteins involved, are key factors that pave the way to hypothesize novel therapeutic options ( Fig. 1 ).

Figure 1.

Targeting RNA binding proteins in neurodegenerative diseases. This figure shows the possible pathways through which the study of RNA binding proteins such as TDP-43, FUS/TLS, or the heterogeneous ribonucleoprotein (hnRNP) A/B factors involved in neurodegeneration can provide novel diagnostic, screening, and therapeutic approaches to researchers. The various arrows indicate what are the preferential study areas that should be targeted and which aspects of our knowledge will benefit the most from these kinds of studies.

Finally, the characterization of the RNA and protein interactors of different hnRNPs might be helpful for defining the extent of overlaps among the regulated pathways. In this context, for example, recent studies have found that a subset of transcripts harboring several intronic binding sites for both TDP-43 and FUS/TLS contain exceptionally long introns and encode for proteins crucial for normal neuronal function.⁵¹

There are four major areas where efforts could be directed in future screening studies (schematically summarized in Fig. 2 ). First, the foremost major feature of these proteins to be targeted might be the pathological features displayed by these RBP proteins in disease (i.e., aggregation, aberrant cytoplasmic localization, etc.). Another potential therapeutic approach might consist of targeting specific protein-protein and RNA-protein interactions that may become altered in the disease state ( Fig. 2 ). Finally, rather than focusing on single events (that may be difficult to pinpoint accurately at least in this early stage of research, as will be further discussed), one could also hypothesize to target the major metabolic pathways that become impaired following the aberrant localization/expression of the various RBPs ( Fig. 2 ).

Figure 2.

Possible strategies to target RNA binding proteins (RBPs) involved in neurodegeneration. This figure provides a schematic overview of the various options that can be used to target various aspects of RBP properties. These range from targeting the aggregates formed by these proteins to affecting their protein binding, RNA binding, or autoregulatory properties.

Since TDP-43 is one of the oldest known RBPs directly involved in neurodegeneration, there is a wealth of information regarding its major biological properties. Much is now known regarding its global RNA binding/protein binding properties and the pathways that can become predominantly affected following its aberrant aggregation in the cytoplasm of neuronal cells.^45,52 Therefore, in this work, particular attention will be devoted to this protein when discussing the various approaches described above. Nonetheless, because of their biological and structural similarities, most of the considerations that will be made regarding TDP-43 will generally apply to all RBP proteins of similar composition/activity.

Targeting Aberrant RBP Aggregation

Aggregation is one of the most distinguishing features of many neurodegenerative diseases. As a result, aggregate formation should represent one of the most obvious and preferred targets for the development of novel therapeutic effectors. Unfortunately, the events that regulate this process are still hard to define, in terms of their biological significance and of the factors/pathways that trigger this process.⁵³

Aggregation involves several successive steps ranging from protein misfolding, oligomerization, and finally the eventual formation of ordered or amorphous fibrils/filaments (for a detailed review on the subject, the reader is referred to Bartolini and Andrisano⁵⁴). Based on these experimental findings, and depending on the aggregated protein under study, many strategies have been developed to try and stop this process. These strategies include all the various steps of aggregate formation: from approaches aimed at inhibiting basic protein production levels, inhibiting all the various intermediate steps that lead to fibril or filament formation, or achieving the resolubilization of already formed, mature aggregates.⁵⁴

To develop a successful therapeutic strategy against any aggregation process, therefore, it is important to obtain information regarding the composition of the aggregates, the genetic or environmental factors that mediate their formation, and especially their significance with regard to the pathological process. As will be described below, this is not an easy or straightforward process even for well-studied proteins such as TDP-43.

The most common sign of ALS/FTLD pathology is the presence in the patient’s neurons of nuclear and cytoplasmic TDP-43 aggregates that are ubiquitinated, hyperphosphorylated, and cleaved to generate C-terminal fragments (CTFs).⁵⁵ The presence of the cytoplasmic aggregates is often accompanied by nuclear clearance of the protein, an observation that may help to explain the RNA processing defects observed in aggregate-bearing cells (see below for more discussion on this subject).

At present, the direct pathological connection of the aggregates with human disease is still unclear.^56,57 Indeed, aggregates may be considered to cause a “loss-of-function” effect by sequestering active TDP-43, could be inherently toxic (“gain of function”), or could even represent a neutral marker of disease (although based on recent results from cellular and animal models, this third possibility is rather unlikely, at least for TDP-43 and FUS/TLS). Regarding the pathology, it should also be considered that loss- and gain-of-function effects are not mutually exclusive and may both contribute to disease, as recently reviewed.^58,59 Strictly speaking, in case of TDP-43 disease-associated mutations, the presence of aggregates may not even be required to result in neurodegeneration.^60,61 Finally, another very important function of the TDP-43 aggregates in disease could be their ability to form “cross-seeding” units that allow the disease to spread from cell to cell and neighboring brain regions in a prion-like manner,⁶² as hypothesized for many other RBP proteins.⁶³

Obviously, providing an answer to these questions may have huge consequences on future therapeutic approaches if we consider that the presence of TDP-43 aggregates remains to this date the most distinguishing feature of the human pathology.

At present, the major problem of investigations in this direction is represented by our inability to mimic human aggregates in a satisfactory manner. In cell cultures, TDP-43 aggregates have been successfully obtained using TDP-43 variants that lack particular functional domains such as NLS signals, the C-terminal tail, or by directly transfecting C-terminal fragments.^64–67 The major drawback of these approaches is that results cannot be easily compared with each other as the various studies often use different cell lines and transfection conditions (in addition, most studies rely on transient transfections whose differing efficiency may also lead to variable results). Researchers in this field are very much aware of these limitations, and as a consequence, a lot of effort has gone to establish a model system of TDP-43 pathology in various animal models, especially rodents.^46,68,69 Unfortunately, most models obtained so far do not display TDP-43 aggregates,^46,70 and even those that do^71,72 generally fail to satisfactorily mimic human pathology.^73,74

More recently, several unconventional approaches have also been attempted. For example, it has been shown that in cell lines and primary rat neuronal cultures, the introduction of tandem repeats carrying the 331-369 Q/N region of TDP-43 coupled to a reporter protein (enhanced green fluorescent protein [EGFP]) can trigger the formation of aggregates that can sequester endogenous TDP-43 in the cytoplasm and display most of the characteristics of human aggregates.⁷⁵ Furthermore, formation of these aggregates can also be enhanced by pathological missense mutations that interfere with hnRNP A/B binding to this Q/N-rich region.⁷⁶ The advantage of such systems, beside their ability to trigger TDP-43 aggregation, is that they achieve this aim without affecting basal TDP-43 expression levels.

Following the development of a satisfactory model of aggregation, the successive step is then to find/develop compounds that are capable of affecting its formation/solubilization. At present, this is still very much a process of trial and error, and several compounds have already been tested for an effect on TDP-43 aggregation, as recently reviewed by us.⁴⁵ At the moment, the major drawback in screening for effective molecules capable of slowing down or solubilizing TDP-43 aggregates comes from the fact that compounds that work well in a particular model may fail to have any protective effect in a different model, thus making their validation more difficult for more advanced pharmaceutical studies. Just to provide an example, methylene blue has been described to be effective in a transient cellular model of TDP-43 aggregation⁷⁷ and in two Danio rerio and Caenorhabditis elegans models of TDP-43 toxicity⁷⁸ but had no protective effect in a mouse transgenic model expressing a TDP-43 carrying the disease-associated G348C mutation.⁷⁹ The reasons for these discrepancies are currently unknown. One possibility is that insufficient exposures were achieved with the compounds used or that in simpler organisms, life span and neuroprotection may be uncoupled from each other.

Taken together, these observations suggest that RBP aggregate model systems should always be thoroughly validated to make sure that they accurately reflect, as much as possible, the human situation. Only when this aim will be achieved, the use of biomolecular screening approaches will stand the best chance for identifying compounds worthy to be carried forward toward a more advanced clinical setting. Second, it should also be very important to deeply characterize the molecular regions of the RBP protein of interest that are involved in the aggregation process. This will allow the “intelligent” design of small molecules capable of interfering with this process in a highly specific manner, hopefully leaving unaffected the protein’s normal functions.

Targeting Protein-Protein Interactions

The modulation of protein-protein interactions (PPIs) has recently become a reality in the biomolecular screening field.⁸⁰ In particular, several strategies have been recently developed to improve our ability to modulate these interactions by using a variety of compounds commonly referred to as PPI modulators (PPIMs).^81,82 At the practical level, modulation by PPIMs can be achieved using two major strategies. The first option is to create a steric hindrance at the regions of the proteins involved in the reciprocal interactions (and in this case, they are called orthosteric PPIMs). The second option, on the other hand, is for the PPIM to bind away from the surface directly responsible for the interaction but in a critical position to induce/prevent conformational changes required for the PPI interaction to take place. In this case, the PPIMs are referred to as allosteric PPIMs.

As previously described, many hnRNP proteins have the ability to connect with each other and with other factors to form multifunctional complexes. The best-characterized binding partners of TDP-43 are several members of the hnRNP A/B protein family that have been shown to be very important for the splicing inhibitory properties of TDP-43^27,83 (see also below). In addition to hnRNP proteins, many others have been proposed to be possible, and proteomic studies performed principally in culture cell lines such as HeLa⁸⁴ and HEK-293T⁸⁵ have shown that TDP-43 potentially displays several hundred potential interactors that could well explain its different functional properties within the cell. In addition to these very focused studies, there is also already a huge wealth of information coming from general studies aimed at elucidating the interactome profile of human proteins.

Therefore, by focusing on direct (physical) and indirect (functional) associations derived from published high- and low-throughput screening analyses in different databases,^86–88 many TDP-43 interactors at the protein level can be outlined ( Table 1 ). The studies of direct associations are of great interest because in several cases, they have allowed the identification of the domains crucial for the interaction with TDP-43. In this context, the HRDC (spanning the residues 503–583) domain of human exosome component 10,⁷³ the exonuclease domain (encompassing residues 1–280) of Xrn2/Rat1,⁷³ the UBQLN UBA domain,⁸⁹ the POZ/BTB domain of NAC1,⁹⁰ and the C-terminal tail of hnRNP A2 spanning residues 288 to 341⁷⁵ all represent some paradigmatic example.

Table 1.

TDP-43 Protein-Protein Interactions.

Source Gene Symbol	Source Gene ID	Target Gene Symbol	Target Gene ID	Source Organ	Target Organ	Assay	Throughput	Reference
TARDBP	23435	EXOSC10	5394	Human	Human	Two-hybrid	High	73
TARDBP	23435	XRN2	22803	Human	Human	Two-hybrid	High	73
NSFL1C	55968	TARDBP	23435	Human	Human	Two-hybrid	High	114
TARDBP	23435	SETDB1	9869	Human	Human	Two-hybrid	High	114
TARDBP	23435	ZHX1	11244	Human	Human	Two-hybrid	High	114
UBC	7316	TARDBP	23435	Human	Human	Copurification	Low	115
HNRNPA1	3178	TARDBP	23435	Human	Human	Affinity capture–MS	High	116
KHDRBS2	202559	TARDBP	23435	Human	Human	Affinity capture–MS	High	116
TARDBP	23435	UBC	7316	Human	Human	Affinity capture–Western	Low	116
TARDBP	23435	SQSTM1	8878	Human	Human	Colocalization	Low	117
UBQLN1	29979	TARDBP	23435	Human	Human	Reconstituted complex	Low	89
UBQLN1	29979	TARDBP	23435	Human	Human	Two-hybrid	Low	89
TARDBP	23435	UBC	7316	Human	Human	Affinity capture–Western	Low	65
TARDBP	23435	VCP	7415	Human	Human	Affinity capture–Western	Low	91
VCP	7415	TARDBP	23435	Human	Human	Affinity capture–Western	Low	91
SUMO2	6613	TARDBP	23435	Human	Human	Affinity capture–MS	High	118
TARDBP	23435	UBC	7316	Human	Human	Affinity capture–Western	Low	119
TARDBP	23435	UBC	7316	Human	Human	Reconstituted complex	Low	119
TARDBP	23435	UBC	7316	Human	Human	Colocalization	Low	119
GTF2E1	2960	TARDBP	23435	Mouse	Mouse	Two-hybrid	High	120
GTF2E2	2961	TARDBP	23435	Mouse	Mouse	Two-hybrid	High	120
LDB1	8861	TARDBP	23435	Mouse	Mouse	Two-hybrid	High	120
MED16	10025	TARDBP	23435	Mouse	Mouse	Two-hybrid	High	120
TARDBP	23435	TARDBP	23435	Mouse	Mouse	Two-hybrid	High	120
NHP2L1	4809	TARDBP	23435	Mouse	Human	Affinity capture–MS	High	121
FUS	2521	TARDBP	23435	Human	Human	Reconstituted complex	Low	122
TARDBP	23435	FUS	2521	Human	Human	Affinity capture–Western	Low	122
TARDBP	23435	PABPN1	8106	Human	Human	Affinity capture–Western	Low	122
TARDBP	23435	HSP90AA1	3320	Human	Human	Affinity capture–Western	Low	123
TARDBP	23435	HSPA4	3308	Human	Human	Affinity capture–Western	Low	123
TARDBP	23435	UBC	7316	Human	Human	Affinity capture–Western	Low	123
UBC	7316	TARDBP	23435	Human	Human	Affinity capture–MS	High	124
CAND1	55832	TARDBP	23435	Human	Human	Affinity capture–MS	High	125
CUL3	8452	TARDBP	23435	Human	Human	Affinity capture–MS	High	125
TARDBP	23435	UBC	7316	Mouse	Mouse	Affinity capture–Western	Low	126
SUMO2	6613	TARDBP	23435	Human	Human	Affinity capture–MS	Low	127
TARDBP	23435	UBC	7316	Human	Human	Affinity capture–Western	Low	62
KIAA0101	9768	TARDBP	23435	Human	Human	Affinity capture–MS	High	128
TARDBP	23435	UBC	7316	Mouse	Mouse	Affinity capture–Western	Low	129
TARDBP	23435	APP	351	Human	Human	Reconstituted complex	High	130
UBC	7316	TARDBP	23435	Human	Human	Affinity capture–MS	Low	131
UBC	7316	TARDBP	23435	Human	Human	Affinity capture–MS	High	132
IRAK1	3654	TARDBP	23435	Human	Human	Affinity capture–MS	High	133
IRAK2	3656	TARDBP	23435	Human	Human	Affinity capture–MS	High	133
IRAK2	3656	TARDBP	23435	Human	Human	Reconstituted complex	Low	133
UBC	7316	TARDBP	23435	Human	Human	Affinity capture–MS	High	134
UBC	7316	TARDBP	23435	Human	Human	Affinity capture–MS	High	135
PSMA3	5684	TARDBP	23435	Human	Human	Affinity capture–MS	High	136
SUMO3	6612	TARDBP	23435	Rat	Rat	Affinity capture–MS	High	137
UBC	7316	TARDBP	23435	Human	Human	Affinity capture–MS	High	138
HNRNPA1	3178	TARDBP	23435	Human	Human	Affinity capture–MS	High	139
UBC	7316	TARDBP	23435	Human	Human	Affinity capture–MS	High	140
SQSTM1	8878	TARDBP	23435	Human	Human	Affinity capture–Western	Low	141
TARDBP	23435	SQSTM1	8878	Human	Human	Reconstituted complex	Low	141
TARDBP	23435	SQSTM1	8878	Human	Human	Affinity capture–Western	Low	141
UBD	10537	TARDBP	23435	Human	Human	Affinity capture–MS	High	142
AHCYL1	10768	TARDBP	23435	Human	Human	Cofractionation	High	143
ATIC	471	TARDBP	23435	Human	Human	Cofractionation	High	143
HNRNPK	3190	TARDBP	23435	Human	Human	Cofractionation	High	143
HNRNPR	10236	TARDBP	23435	Human	Human	Cofractionation	High	143
NUBP2	10101	TARDBP	23435	Human	Human	Cofractionation	High	143
PCBP1	5093	TARDBP	23435	Human	Human	Cofractionation	High	143
PSMA6	5687	TARDBP	23435	Human	Human	Cofractionation	High	143
SF3B1	23451	TARDBP	23435	Human	Human	Cofractionation	High	143
SRPRB	58477	TARDBP	23435	Human	Human	Cofractionation	High	143
SYNCRIP	10492	TARDBP	23435	Human	Human	Cofractionation	High	143
TARDBP	23435	U2AF2	11338	Human	Human	Cofractionation	High	143
TARDBP	23435	ARHGEF28	64283	Human	Human	Affinity capture–Western	Low	144
NAC1	112939	TARDBP	23435	Mouse	Mouse	Affinity capture–Western	Low	145
NAC1	112939	TARDBP	23435	Human	Human	Reconstituted complex	Low	145
TARDBP	23435	NAC1	112939	Human	Human	Reconstituted complex	Low	145
TARDBP	23435	NAC1	112939	Mouse	Mouse	Affinity capture–Western	Low	145
TARDBP	23435	TNFRSF14	8764	Human	Human	Affinity capture–MS	High	146
TARDBP	23435	ARF6	382	Human	Human	Affinity capture–MS	High	146

The methods for physical interactions are classified according to the BioGRID experimental evidence codes (http://wiki.thebiogrid.org/doku.php/experimental_systems).

MS, mass spectrometry.

Finally, when evaluating a protein interaction profile, it is also important to consider ad hoc studies. For example, different mutants of VCP (R95G, R155H, R155C, R191Q, and A232E) have been found to be colocalized with TDP-43 in the nucleus, leading to a more granular distribution of TDP-43, an observation that suggests an interplay between the two proteins.⁹¹

The comparison of two proteomic studies specific for TDP-43 with the other high- or low-throughput proteomic studies reported in Table 2 highlights the interaction of TDP-43 with hnRNPs (in particular, A1, K, and R) and with FUS/TLS ( Fig. 3A ). These observations not only strengthen the hypothesis that these associations are important for the proposed TDP-43 functions in RNA metabolism but also suggest that alterations of any of the partners recruited in these interactions can perturb common molecular pathways in which these proteins are involved, leading to neurodegenerative disease.

Table 2.

Cellular Pathways Affected by TDP-43 Protein-Protein Interactions.^a

Term	No.	%	Genes	p Value
Nucleus	33	79	FUS, NHP2L1, U2AF2, KIAA0101, SYNCRIP, UBQLN1, SUMO3, EXOSC10, CUL3, SUMO2, GTF2E1, SF3B1, GTF2E2, HNRNPK, SQSTM1, NUBP2, TARDBP, PCBP1, PABPN1, SETDB1, NACC1, KHDRBS2, LDB1, NSFL1C, ZHX1, HNRNPR, HNRNPA1, PSMA6, VCP, MED16, PSMA3, UBC, CAND1, XRN2	4.87E-14
Phosphoprotein	33	79	FUS, U2AF2, KIAA0101, SYNCRIP, UBQLN1, CUL3, SF3B1, GTF2E1, APP, HNRNPK, ATIC, SQSTM1, TARDBP, PCBP1, HSPA4, PABPN1, SETDB1, IRAK1, NACC1, HSP90AA1, KHDRBS2, LDB1, ZHX1, NSFL1C, HNRNPA1, PSMA6, VCP, PSMA3, UBC, UBD, AHCYL1, CAND1, XRN2	1.66E-07
Acetylation	22	52	PABPN1, HSP90AA1, KHDRBS2, NHP2L1, U2AF2, SYNCRIP, UBQLN1, HNRNPA1, HNRNPR, SUMO3, SF3B1, SUMO2, GTF2E1, HNRNPK, PSMA6, VCP, ATIC, NUBP2, PSMA3, UBC, HSPA4, CAND1, XRN2	1.16E-08
Alternative splicing	22	52	FUS, PABPN1, SETDB1, IRAK1, HSP90AA1, LDB1, U2AF2, NSFL1C, SYNCRIP, UBQLN1, HNRNPR, HNRNPA1, EXOSC10, CUL3, APP, HNRNPK, MED16, SQSTM1, PSMA3, AHCYL1, CAND1, XRN2	0.07
Cytoplasm	18	43	PABPN1, NACC1, HSP90AA1, SYNCRIP, UBQLN1, HNRNPA1, HNRNPR, EXOSC10, SUMO3, SUMO2, HNRNPK, PSMA6, VCP, NUBP2, SQSTM1, PCBP1, PSMA3, UBC, HSPA4, ARF6	2.42E-04
RNA binding	12	29	FUS, PABPN1, EXOSC10, KHDRBS2, HNRNPK, NHP2L1, U2AF2, TARDBP, PCBP1, SYNCRIP, HNRNPA1, HNRNPR	1.14E-08
Ubl conjugation	11	26	SETDB1, SUMO3, CUL3, IRAK1, SUMO2, HNRNPK, PSMA6, LDB1, TARDBP, UBC, SYNCRIP, HNRNPA1	3.15E-07
mRNA processing	10	24	PABPN1, SF3B1, HNRNPK, NHP2L1, U2AF2, TARDBP, SYNCRIP, HNRNPA1, HNRNPR, XRN2	3.15E-09
Transcription regulation	10	24	SETDB1, GTF2E1, GTF2E2, NACC1, KHDRBS2, MED16, TARDBP, ZHX1, CAND1, XRN2	0.02
Transcription	10	24	SETDB1, GTF2E1, GTF2E2, NACC1, KHDRBS2, MED16, TARDBP, ZHX1, CAND1, XRN2	0.03
mRNA splicing	8	19	SF3B1, HNRNPK, NHP2L1, U2AF2, TARDBP, SYNCRIP, HNRNPA1, HNRNPR	2.66E-07
Ribonucleoprotein	8	19	HNRNPK, NHP2L1, PCBP1, UBC, SYNCRIP, SRPRB, HNRNPA1, HNRNPR	1.85E-06
Isopeptide bond	8	19	SETDB1, SUMO3, CUL3, SUMO2, HNRNPK, PSMA6, UBC, SYNCRIP, HNRNPA1	4.48E-06
Spliceosome	7	17	SF3B1, HNRNPK, NHP2L1, U2AF2, SYNCRIP, HNRNPA1, HNRNPR	1.96E-07
Methylation	5	12	FUS, PABPN1, KHDRBS2, HNRNPK, HNRNPA1	0.002
Host-virus interaction	5	12	GTF2E1, HNRNPK, PSMA3, SYNCRIP, HNRNPA1	0.003
Ubl conjugation pathway	5	12	SUMO3, CUL3, SUMO2, VCP, UBD, CAND1	0.02
Viral nucleoprotein	4	10	HNRNPK, SYNCRIP, HNRNPA1, HNRNPR	3.16E-05
Repressor	4	10	SETDB1, NAC1, TARDBP, ZHX1	0.07
Protein degradation	3	7	PSMA6, PSMA3, UBC	7.41E-04
RNA binding	3	7	FUS, HNRNPK, UBC	0.002
Proteasome	3	7	PSMA6, PSMA3, UBQLN1	0.01
Neurodegeneration	3	7	FUS, APP, TARDBP	0.01
Amyotrophic lateral sclerosis	2	5	FUS, TARDBP	0.02
Proteinase	2	5	PSMA6, PSMA3	0.04
Threonine protease	2	5	PSMA6, PSMA3	0.04

Functional enrichment analysis was performed using DAVID Bioinformatics Resources (Frederick, MD).

Figure 3.

Overlapping comparison of various TDP-43 protein-protein interactions shown as a Venn diagram and schematic diagram of the cellular pathways affected by TDP-43 protein-protein interactions. The data sets analyzed in A include recent proteomic studies performed in HeLa⁸⁴ and HEK-293T⁸⁵ together with the hits reported later in Table 2 that mostly derive from a literature analysis of general protein-protein interactions of the eukaryotic proteome. The number of overlapping transcripts is reported, and their list is provided in the respective boxes. The four common hits are highlighted in bold/underlined. B shows a schematic representation of the pathways principally affected according to TDP-43 protein-protein interactions. The pathways that could represent the most likely targets for therapeutic intervention are highlighted in bold/within a box.

It is also to be expected that many of these interactions will probably be protective against aberrant aggregation processes by helping to maintain the protein’s localization and solubility. Of course, the challenge currently lies in identifying and characterizing these protective interactions. Indeed, recent evidence in this direction has been obtained with regard to the interaction of TDP-43 with hnRNP A/Bs through its aggregation-prone Q/N-rich region.^75,76,92 It is therefore expected that effectors capable of stabilizing this interaction might protect against the aberrant processing of this protein ( Fig. 2 ), and further studies will be aimed in this direction. In a specular but opposite manner, it has also been reported that the affinity for FUS/TLS of a mutated TDP-43 is much higher than for the wild-type protein,⁸⁴ suggesting that this interplay may be important for disease. Hence, small molecules capable of interfering with this aberrant interaction might prove beneficial to reduce mutated TDP-43 pathology.

Finally, a growing body of evidence also suggests that several of the TDP-43 interactors so far validated can also be independently implicated in the pathogenesis of similar disorders since a recent report has found that mutations in hnRNPA2/B1 and hnRNPA1 can also lead to multisystem proteinopathy and ALS.³⁶ Therefore, the hypothesis that an alteration in the protein-protein interaction profile of a single protein might affect eventual disease-associated functions of its partners is far more than a simple speculation.

Targeting RNA-Protein Interactions

Several high-throughput screening studies have targeted TDP-43 to identify its preferred RNA targets. These studies have been performed in variety of cell lines such as HeLa,⁹³ HEK293E,^94,95 mouse Neuro2a cells,⁹⁶ induced pluripotent stem cells (iPSCs) derived from patients with ALS,⁹⁷ and transgenic mouse models overexpressing wild-type and mutant versions of human TDP-43.⁹⁸ In addition, immunoprecipitation analysis coupled with deep sequencing has also been recently used to identify TDP-43 mRNA targets in normal mouse brain⁷⁴ and in the cytoplasm of NSC-34 cells.⁹⁹ The aim of these studies was to identify disease-specific RNA processing events that become altered in disease and could therefore be rescued using the various methodologies that have been recently developed in the RNA processing field^100,101 ( Fig. 2 ).

As with the protein-protein interaction section, the major problem in targeting RNA-protein interactions of TDP-43 is limited by our ability to exactly identify which events should be targeted among the several hundreds that are normally described to occur for any RBP of interest. As this particular topic has already been discussed elsewhere in detail, the reader is referred to the recent review by Buratti et al.¹⁰²

Nonetheless, just to give an example of the complexity of results when trying to address this issue, Table 3 provides an overview of the major RNA-protein interaction networks of TDP-43. In particular, TDP-43–dependent changes of gene expression have been functionally validated for five targets, and the mRNA regions targeted by this factor have been mapped. In addition, at least 25 additional alterations of TDP-43–dependent alternative splicing have been characterized so far. However, it should be noticed that only in 4 of 25 cases (CFTR, ApoAII, SC35, SKAR/POLDIP3, and SMN2), the regions required for these TDP-43–induced splicing variations have been found. Moreover, looking in deeper detail, the physical TDP-43–RNA interaction has been specifically mapped only in three genes (CFTR, SKAR/POLDIP3, and ApoAII), and in all the remaining cases, it has yet to be determined whether the involvement of TDP-43 is direct or indirect. For this reason, it is very likely that therapeutic interventions based on modulating the interaction of TDP-43 with specific RNAs are rather far in the future.

Table 3.

Validated TDP-43 RNA-Protein Interactions.

Gene Expression
Source Gene Symbol	Source Gene ID	Target Gene Symbol	Target Gene ID	Source Target Organisms	Target Region		Regulation Effect	Reference
TARDBP	23435	CDK6	1021	Human	3′-UTR		Inhibition	93
TARDBP	23435	HDAC6	10013	Human	CDS		Enhancing	94
TARDBP	23435	NFL	4747	Human	3′-UTR		Enhancing	147
Tardbp	230908	Vegfa	22339	Mouse	3′-UTR		Enhancing	99
Tardbp	230908	Grn	14824	Mouse	3′-UTR		Enhancing	99
Pre-mRNA Splicing
Source Gene Symbol	Source Gene ID	Target Gene Symbol	Target Gene ID	Source Target Organisms	Target Exon	Binding Site	Splicing Effect	Reference
TARDBP	23435	CFTR	1080	Human	9	IVS8	Inhibition	148
TARDBP	23435	ApoAII	336	Human	3	IVS2	Inhibition	149
TARDBP	23435	SC35	6427	Human	3	E3	Inhibition	150
TARDBP	23435	SMN2	6607	Human	7	NBD	Enhancing	151
TARDBP	23435	SKAR/POLDIP3	84271	Human	3	IVS3	Enhancing	95,152
Tardbp	230908	Sort1	20661	Mouse	18	NBD	Inhibition	153
Tardbp	230908	Sema3f	20350	Mouse	5	NBD	Inhibition	153
Tardbp	230908	Atp2b2	11941	Mouse	6-8	NBD	Inhibition	153
Tardbp	230908	Eif4h	22384	Mouse	5	NBD	Inhibition	153
Tardbp	230908	Dnajc5	13002	Mouse	4bis	NBD	Inhibition	153
Tardbp	230908	Kcnd3	56543	Mouse	5	NBD	Inhibition	153
Tardbp	230908	Pdp1	381511	Mouse	1	NBD	Inhibition	153
Tardbp	230908	Ddef2	211914	Mouse	25	NBD	Inhibition	153
Tardbp	230908	Mpzl1	68481	Mouse	5	NBD	Inhibition	153
Tardbp	230908	Ppp3ca	19055	Mouse	13	NBD	Enhancing	153
Tardbp	230908	Dclk1	13175	Mouse	16	NBD	Enhancing	153
Tardbp	230908	Rbm33	381626	Mouse	6	NBD	Enhancing	153
Tardbp	230908	Kcnip2	80906	Mouse	2	NBD	Enhancing	153
Tardbp	230908	Kcnip2	80906	Mouse	2+3	NBD	Enhancing	153
Tardbp	230908	Dst	13518	Mouse	96	NBD	Enhancing	153
Tardbp	230908	Tsn	22099	Mouse	5	NBD	Enhancing	153
Tardbp	230908	Poldip3	73826	Mouse	3	NBD	Enhancing	153
TARDBP	23435	MIAT	440823	Human	10	NBD	Enhancing	154
TARDBP	23435	MEF2D	4209	Human	11	NBD	Enhancing	154
TARDBP	23435	BIM	10018	Human	3	NBD	Enhancing	154

IVS, intervening sequence; NBD, binding site not determined.

The modulation of autoregulatory processes of RBPs might represent another potential therapeutic target. These processes often involve the presence of a negative feedback loop controlled by the binding of the RBP protein of interest to its own pre-mRNAs in the nucleus.^11,103 Since these mechanisms are very efficient in raising/lowering protein production levels, any alteration in an RBP autoregulatory process may play an important role in a pathological setting, as already discussed by us elsewhere.¹⁰⁴ The reason is that pathological aggregates may act as a protein “sink” of the various RBP proteins, lowering their intracellular levels. This would predictably trigger an increasing rate of RBP production to overcome any “loss-of-function” effects in the nucleus, likely resulting in eventually toxic gain-of-function effects on the cellular metabolism. Even if successfully compensated, the increase in energetic expenditure to overcome the continuous loss of RBP from the nucleus may well lead to a certain degree of cell suffering if protracted for a long period. This could be especially harmful to neurons that have an extremely long life span and might help to explain why RBP alterations preferentially affect these types of cells.

Recently, autoregulation of TDP-43 expression levels has been shown to be dependent on its ability to bind several sequences¹⁰⁵ in the 3′-UTR region of its own mRNA. This region has been called TDPBR¹⁰⁶ and spans a normally silent intron that contains the major polyadenylation site (PAS) used by the TARDBP gene. Binding of TDP-43 to this region induces increased processing of the intron with the consequent use of suboptimal PAS sites that are retained in the nucleus and rapidly degraded.¹⁰⁷ As a result of this mechanism, a reduction of TDP-43 cellular mRNA levels can occur very rapidly in the presence of high TDP-43 concentrations.

From a biomolecular target point of view, therefore, one could hypothesize that these TDP-43 binding sequences in the 3′-UTR may represent a promising target to modulate the production of TDP-43 within cells, especially in a pathological setting to avoid the overexpression of this factor. Accessibility of these sequences by TDP-43 could thus be modulated using antisense oligonucleotides (AONs)¹⁰⁰ or other small RNA technologies¹⁰⁸ that are being developed just for this purpose. In particular, the development of high-throughput screening technologies for TDP-43 binding will be useful to identify compounds capable of modulating its binding to specific RNA targets.¹⁰⁹

Targeting TDP-43 Affected Pathways

One possible solution to overcome the overabundance of likely targets that originate from the very complex network of protein-protein and RNA-protein interactions described above might be to take a step backward and try to determine which cellular pathways are most affected following RBP dysregulation in disease.

Significant progress has been made in the neurodegeneration field using global techniques to identify cell signaling or metabolic networks involved in disease origin and progression, especially for ALS, AD, and PD, as recently reviewed elsewhere.¹¹⁰ One of the major drawbacks of these approaches is that they often maintain a high degree of uncertainty with regard to the various candidates/hypotheses under investigation until the validation stage. Nonetheless, it is possible to reasonably guess which pathways may be preferentially affected following the aggregation/removal of each RBP factor.

Based on the protein-protein interactions reported in Table 1 for TDP-43, it is possible to identify the general pathways that might be affected if the protein becomes sequestered in the aggregates. In particular, Table 2 shows the SwissProt/PIR Protein Sequence Database keywords enriched in our set of TDP-43 protein-protein interactors. As reported in this table, 80% of the interactors were nuclear (p = 4.9 × 10⁻¹⁴) and associated with phosphoprotein (p = 1.6 × 10⁻⁷). More than 50% were associated with acetylation (p = 1 × 10⁻⁸), 26% with Ubl conjugation (p = 3 × 10⁻⁷), and 12% with the Ubl conjugation pathway (p = 0.02), respectively. In addition, 52%, 29%, 24%, and 19% were correlated with alternative splicing (p = 0.07), RNA binding (p = 1 × 10⁻⁸), mRNA processing (p = 3 × 10⁻⁹), and mRNA splicing (p = 3 × 10⁻⁷), respectively.

Taken together, this enrichment analysis supports the known functions of TDP-43 in the regulation of several steps of mRNA processing. In addition, if we consider its association with the ubiquitin pathway, one might be tempted to speculate that TDP-43 might have other still unmapped nonproteolysis-related roles such as, differentiation, inflammation, biogenesis of organelles, trafficking, and kinase activation. Figure 3B highlights in particular those pathways that are not necessarily the most prominent but might be targeted in a more profitable manner for specific therapeutic approaches (see options highlighted in Fig. 2).

Future Aims

In conclusion, when studying the role of RBP proteins in neurodegenerative diseases, several major areas can be preferentially targeted to develop novel therapeutic or diagnostic approaches:

Aberrant aggregation processes of these factors represent the most prominent characteristic of disease. To maximize chances of success, priority should be given to obtain animal and cellular models that accurately mimic the pathology observed in patients. These models can then be used as ideal substrates to perform screening analysis to identify the genetic and environmental agents capable of modifying disease course and progression.

Additional targets can be identified through the characterization of the biological properties of each RBP factor of interest (and the way they become modified in the disease state). In particular, it is important to focus specific RNA processing events or protein-protein interactions that affect neuronal viability and synaptic functionality. In recent years, this approach has yielded several possible targets that are currently being validated.¹¹¹ In addition to these properties, this characterization should also involve a careful appraisal of how the RBP factor of interest relates to other factors or important disease modifiers. For example, the presence of nonpathological Atxn repeats may contribute to influence the onset and progression of disease in ALS and FTLD TDP-43 pathologies.^112,113 The drawback of these approaches is that it is often very difficult to determine exactly which of the abundant RBP interactions plays a major role during disease.

An alternative to targeting specific events that are altered following aggregation could be to try and affect in a more general manner the pathways controlled by each individual factors. For example, molecules capable of generally improving the efficiency of autophagic or proteome clearance systems would certainly be beneficial in avoiding the build-up of the various aggregates.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by AriSLA (TARMA), Thierry Latran Foundation (REHNPALS), and University of Trieste-Finanziamento per Ricercatori di Ateneo.

References

Maniatis

Reed

An Extensive Network of Coupling among Gene Expression Machines. Nature 2002, 416, 499–506.

Kornblihtt

A. R.

Coupling Transcription and Alternative Splicing. Adv. Exp. Med. Biol. 2007, 623, 175–189.

Graveley

B. R.

Coordinated Control of Splicing and Translation. Nat. Struct. Mol. Biol. 2005, 12, 1022–1023.

Braunschweig

Gueroussov

Plocik

A. M.

. Dynamic Integration of Splicing within Gene Regulatory Pathways. Cell 2013, 152, 1252–1269.

Khan

D. H.

Jahan

Davie

J. R.

Pre-mRNA Splicing: Role of Epigenetics and Implications in Disease. Adv. Biol. Regul. 2012, 52, 377–388.

Luco

R. F.

Misteli

More Than a Splicing Code: Integrating the Role of RNA, Chromatin and Non-coding RNA in Alternative Splicing Regulation. Curr. Opin. Genet. Dev. 2011, 21, 366–372.

Sabin

L. R.

Delas

M. J.

Hannon

G. J.

Dogma Derailed: The Many Influences of RNA on the Genome. Mol. Cell 2013, 49, 783–794.

Norris

A. D.

Calarco

J. A.

Emerging Roles of Alternative Pre-mRNA Splicing Regulation in Neuronal Development and Function. Front. Neurosci. 2012, 6, 122.

Mattick

J. S.

The Central Role of RNA in Human Development and Cognition. FEBS Lett. 2011, 585, 1600–1616.

10.

Grabowski

Alternative Splicing Takes Shape during Neuronal Development. Curr. Opin. Genet. Dev. 2011, 21, 388–394.

11.

Yap

Makeyev

E. V.

Regulation of Gene Expression in Mammalian Nervous System through Alternative Pre-mRNA Splicing Coupled with RNA Quality Control Mechanisms. Mol. Cell Neurosci., in press.

12.

Buratti

Baralle

Exon Skipping Mutations in Neurofibromatosis. Methods Mol. Biol. 2012, 867, 65–76.

13.

Renoux

A. J.

Todd

P. K.

Neurodegeneration the RNA Way. Prog. Neurobiol. 2011, 97, 173–179.

14.

Mills

J. D.

Janitz

Alternative Splicing of mRNA in the Molecular Pathology of Neurodegenerative Diseases. Neurobiol. Aging 2012, 33, 1012.e11–24.

15.

Gagliardi

Milani

Sardone

. From Transcriptome to Noncoding RNAs: Implications in ALS Mechanism. Neurol. Res. Int. 2012, 2012, 278725.

16.

Youmans

K. L.

Wolozin

TDP-43: A New Player on the AD Field?

Exp. Neurol. 2012, 237, 90–95.

17.

Tollervey

J. R.

Wang

Hortobagyi

. Analysis of Alternative Splicing Associated with Aging and Neurodegeneration in the Human Brain. Genome Res. 2011, 21, 1572–1582.

18.

Belzil

V. V.

Gendron

T. F.

Petrucelli

RNA-Mediated Toxicity in Neurodegenerative Disease. Mol. Cell Neurosci., in press.

19.

Tsuiji

Iguchi

Furuya

. Spliceosome Integrity Is Defective in the Motor Neuron Diseases ALS and SMA. EMBO Mol. Med. 2013, 5, 221–234.

20.

Eacker

S. M.

Dawson

T. M.

Dawson

V. L.

Understanding MicroRNAs in Neurodegeneration. Nat. Rev. Neurosci. 2009, 10, 837–841.

21.

Colombrita

Onesto

Tiloca

. RNA-Binding Proteins and RNA Metabolism: A New Scenario in the Pathogenesis of Amyotrophic Lateral Sclerosis. Arch. Ital. Biol. 2011, 149, 83–99.

22.

Dreyfuss

Kim

V. N.

Kataoka

Messenger-RNA-Binding Proteins and the Messages They Carry. Nat. Rev. Mol. Cell Biol. 2002, 3, 195–205.

23.

Swanson

M. S.

Dreyfuss

Classification and Purification of Proteins of Heterogeneous Nuclear Ribonucleoprotein Particles by RNA-Binding Specificities. Mol. Cell Biol. 1988, 8, 2237–2241.

24.

Pinol-Roma

Choi

Y. D.

Matunis

M. J.

. Immunopurification of Heterogeneous Nuclear Ribonucleo-protein Particles Reveals an Assortment of RNA-Binding Proteins. Genes Dev. 1988, 2, 215–227.

25.

Glisovic

Bachorik

J. L.

Yong

. RNA-Binding Proteins and Post-Transcriptional Gene Regulation. FEBS Lett. 2008, 582, 1977–1986.

26.

Cartegni

Maconi

Morandi

. hnRNP A1 Selectively Interacts through Its Gly-Rich Domain with Different RNA-Binding Proteins. J. Mol. Biol. 1996, 259, 337–348.

27.

Buratti

Brindisi

Giombi

. 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. J. Biol. Chem. 2005, 280, 37572–37584.

28.

Bekenstein

Soreq

Heterogeneous Nuclear Ribonucleo-protein A1 in Health and Neurodegenerative Disease: From Structural Insights to Post-Transcriptional Regulatory Roles. Mol. Cell Neurosci., in press.

29.

Ule

Jensen

K. B.

Ruggiu

. CLIP Identifies Nova-Regulated RNA Networks in the Brain. Science 2003, 302, 1212–1215.

30.

Zubovic

Baralle

F. E.

Mutually Exclusive Splicing Regulates the Nav 1.6 Sodium Channel Function through a Combinatorial Mechanism That Involves Three Distinct Splicing Regulatory Elements and Their Ligands. Nucleic Acids Res. 2012, 40, 6255–6269.

31.

Neumann

Sampathu

D. M.

Kwong

L. K.

. Ubiquitinated TDP-43 in Frontotemporal Lobar Degeneration and Amyotrophic Lateral Sclerosis. Science 2006, 314, 130–133.

32.

Arai

Hasegawa

Akiyama

. TDP-43 Is a Component of Ubiquitin-Positive Tau-Negative Inclusions in Frontotemporal Lobar Degeneration and Amyotrophic Lateral Sclerosis. Biochem. Biophys. Res. Commun. 2006, 351, 602–611.

33.

Kwiatkowski

T. J.

Jr. Bosco

D. A.

Leclerc

A. L.

. Mutations in the FUS/TLS Gene on Chromosome 16 Cause Familial Amyotrophic Lateral Sclerosis. Science 2009, 323, 1205–1208.

34.

Vance

Rogelj

Hortobagyi

. Mutations in FUS, an RNA Processing Protein, Cause Familial Amyotrophic Lateral Sclerosis Type 6. Science 2009, 323, 1208–1121.

35.

Mackenzie

I. R.

Neumann

FET Proteins in Frontotemporal Dementia and Amyotrophic Lateral Sclerosis. Brain Res. 2012, 1462, 40–43.

36.

Kim

H. J.

Kim

N. C.

Wang

Y. D.

. Mutations in Prion-Like Domains in hnRNPA2B1 and hnRNPA1 Cause Multisystem Proteinopathy and ALS. Nature 2013, 495, 647–673.

37.

Rademakers

Neumann

Mackenzie

I. R.

Advances in Understanding the Molecular Basis of Frontotemporal Dementia. Nat. Rev. Neurol. 2012, 8, 423–434.

38.

Polymenidou

Lagier-Tourenne

Hutt

K. R.

. Misregulated RNA Processing in Amyotrophic Lateral Sclerosis. Brain Res. 2012, 1462, 3–15.

39.

Ugras

S. E.

Shorter

RNA-Binding Proteins in Amyotrophic Lateral Sclerosis and Neurodegeneration. Neurol. Res. Int. 2012, 2012, 432780.

40.

Hanson

K. A.

Kim

S. H.

Tibbetts

R. S.

RNA-Binding Proteins in Neurodegenerative Disease: TDP-43 and Beyond. Wiley Interdiscip. Rev. RNA 2012, 3, 265–284.

41.

Pesiridis

G. S.

Lee

V. M.

Trojanowski

J. Q.

Mutations in TDP-43 Link Glycine-Rich Domain Functions to Amyotrophic Lateral Sclerosis. Hum. Mol. Genet. 2009, 18, R156–R162.

42.

Lanson

N. A.

Jr. Pandey

U. B.

FUS-Related Proteinopathies: Lessons from Animal Models. Brain Res. 2012, 1462, 44–60.

43.

Kovacs

G. G.

Botond

Budka

Protein Coding of Neurodegenerative Dementias: The Neuropathological Basis of Biomarker Diagnostics. Acta Neuropathol. 2010, 119, 389–408.

44.

Grossman

Biomarkers in Frontotemporal Lobar Degeneration. Curr. Opin. Neurol. 2010, 23, 643–648.

45.

Buratti

Baralle

F. E.

TDP-43: Gumming Up Neurons through Protein-Protein and Protein-RNA Interactions. Trends Biochem. Sci. 2012, 37, 237–247.

46.

Gendron

T. F.

Petrucelli

Rodent Models of TDP-43 Proteinopathy: Investigating the Mechanisms of TDP-43–Mediated Neurodegeneration. J. Mol. Neurosci. 2011, 45, 486–499.

47.

Wegorzewska

Baloh

R. H.

TDP-43–Based Animal Models of Neurodegeneration: New Insights into ALS Pathology and Pathophysiology. Neurodegener. Dis. 2011, 8, 262–274.

48.

Kabashi

Brustein

Champagne

. Zebrafish Models for the Functional Genomics of Neurogenetic Disorders. Biochim Biophys. Acta 2011, 1812, 335–345.

49.

Romano

Feiguin

Buratti

Drosophila Answers to TDP-43 Proteinopathies.

J. Amino Acids 2012, 2012, 356081.

50.

Braun

R. J.

Buttner

Ring

. Nervous Yeast: Modeling Neurotoxic Cell Death. Trends Biochem. Sci. 2010, 35, 135–144.

51.

Lagier-Tourenne

Polymenidou

Hutt

K. R.

. Divergent Roles of ALS-linked Proteins FUS/TLS and TDP-43 Intersect in Processing Long Pre-mRNAs. Nat. Neurosci. 2012, 15, 1488–1497.

52.

Buratti

Baralle

F. E.

The Multiple Roles of TDP-43 in Pre-mRNA Processing and Gene Expression Regulation. RNA Biol. 2010, 7, 420–429.

53.

Y. R.

King

O. D.

Shorter

. Stress Granules as Crucibles of ALS Pathogenesis. J. Cell Biol. 2013, 3, 361–372.

54.

Bartolini

Andrisano

Strategies for the Inhibition of Protein Aggregation in Human Diseases. Chembiochem 2010, 11, 1018–1035.

55.

Buratti

Baralle

F. E.

The Molecular Links between TDP-43 Dysfunction and Neurodegeneration. Adv. Genet. 2009, 66, 1–34.

56.

Rothstein

J. D.

TDP-43 in Amyotrophic Lateral Sclerosis: Pathophysiology or Patho-Babel?

Ann. Neurol. 2007, 61, 382–384.

57.

Baloh

R. H.

TDP-43: The Relationship between Protein Aggregation and Neurodegeneration in Amyotrophic Lateral Sclerosis and Frontotemporal Lobar Degeneration. FEBS J. 2011, 278, 3539–3549.

58.

Halliday

Bigio

E. H.

Cairns

N. J.

. Mechanisms of Disease in Frontotemporal Lobar Degeneration: Gain of Function versus Loss of Function Effects. Acta Neuropathol. 2012, 124, 373–382.

59.

Z. S.

Does a Loss of TDP-43 Function Cause Neurodegeneration?

Mol. Neurodegener. 2012, 7, 27.

60.

Arnold

E. S.

Ling

S. C.

Huelga

S. C.

. ALS-linked TDP-43 Mutations Produce Aberrant RNA Splicing and Adult-Onset Motor Neuron Disease without Aggregation or Loss of Nuclear TDP-43. Proc. Natl. Acad. Sci. U. S. A. 2013, 110, E736–745.

61.

Janssens

Wils

Kleinberger

. Overexpression of ALS-Associated p.M337V Human TDP-43 in Mice Worsens Disease Features Compared to Wild-type Human TDP-43 Mice. Mol. Neurobiol., in press.

62.

Furukawa

Kaneko

Watanabe

. A Seeding Reaction Recapitulates Intracellular Formation of Sarkosyl-Insoluble Transactivation Response Element (TAR) DNA-Binding Protein-43 Inclusions. J. Biol. Chem. 2011, 286, 18664–18672.

63.

King

O. D.

Gitler

A. D.

Shorter

The Tip of the Iceberg: RNA-Binding Proteins with Prion-Like Domains in Neurodegenerative Disease. Brain Res. 2012, 1462, 61–80.

64.

Ayala

Y. M.

Zago

D’Ambrogio

. Structural Determinants of the Cellular Localization and Shuttling of TDP-43. J. Cell Sci. 2008, 121, 3778–3785.

65.

Igaz

L. M.

Kwong

L. K.

Chen-Plotkin

. Expression of TDP-43 C-terminal Fragments In Vitro Recapitulates Pathological Features of TDP-43 Proteinopathies. J. Biol. Chem. 2009, 284, 8516–8524.

66.

Nonaka

Kametani

Arai

. Truncation and Pathogenic Mutations Facilitate the Formation of Intracellular Aggregates of TDP-43. Hum. Mol. Genet. 2009, 18, 3353–3364.

67.

Zhang

Y. J.

Y. F.

Cook

. Aberrant Cleavage of TDP-43 Enhances Aggregation and Cellular Toxicity. Proc. Natl. Acad. Sci. U. S. A. 2009, 106, 7607–7612.

68.

Roberson

E. D.

Mouse Models of Frontotemporal Dementia. Ann. Neurol. 2012, 72, 837–849.

69.

Tsao

Jeong

Y. H.

Lin

. Rodent Models of TDP-43: Recent Advances. Brain Res. 2012, 1462, 26–39.

70.

Wegorzewska

Bell

Cairns

N. J.

. TDP-43 Mutant Transgenic Mice Develop Features of ALS and Frontotemporal Lobar Degeneration. Proc. Natl. Acad. Sci. U. S. A. 2009, 106, 18809–18814.

71.

Wils

Kleinberger

Janssens

. TDP-43 Transgenic Mice Develop Spastic Paralysis and Neuronal Inclusions Characteristic of ALS and Frontotemporal Lobar Degeneration. Proc. Natl. Acad. Sci. U. S. A. 2010, 107, 3858–3863.

72.

Tsai

K. J.

Yang

C. H.

Fang

Y. H.

. Elevated Expression of TDP-43 in the Forebrain of Mice Is Sufficient to Cause Neurological and Pathological Phenotypes Mimicking FTLD-U. J. Exp. Med. 2010, 207, 1661–1673.

73.

Lehner

Sanderson

C. M.

A Protein Interaction Framework for Human mRNA Degradation. Genome Res. 2004, 14, 1315–1323.

74.

Narayanan

R. K.

Mangelsdorf

Panwar

. Identification of RNA Bound to the TDP-43 Ribonucleoprotein Complex in the Adult Mouse Brain. Amyotroph. Lateral Scler. 2012, 14, 252–260.

75.

Budini

Buratti

Stuani

. Cellular Model of TAR DNA-binding Protein 43 (TDP-43) Aggregation Based on Its C-terminal Gln/Asn-rich Region. J. Biol. Chem. 2012, 287, 7512–7525.

76.

Budini

Romano

Avendano-Vazquez

S. E.

. Role of Selected Mutations in the Q/N Rich Region of TDP-43 in EGFP-12xQ/N-induced Aggregate Formation. Brain Res. 2012, 1462, 139–150.

77.

Yamashita

Nonaka

Arai

. Methylene Blue and Dimebon Inhibit Aggregation of TDP-43 in Cellular Models. FEBS Lett. 2009, 583, 2419–2424.

78.

Vaccaro

Patten

S. A.

Ciura

. Methylene Blue Protects against TDP-43 and FUS Neuronal Toxicity in C. elegans and D. rerio. PLoS One 2012, 7, e42117.

79.

Audet

J. N.

Soucy

Julien

J. P.

Methylene Blue Administration Fails to Confer Neuroprotection in Two Amyotrophic Lateral Sclerosis Mouse Models. Neuroscience 2012, 209, 136–143.

80.

Morelli

Hupp

Searching for the Holy Grail: Protein-Protein Interaction Analysis and Modulation. EMBO Rep. 2012, 13, 877–879.

81.

Morelli

Bourgeas

Roche

Chemical and Structural Lessons from Recent Successes in Protein-Protein Interaction Inhibition (2P2I). Curr. Opin. Chem. Biol. 2011, 15, 475–481.

82.

Rechfeld

Gruber

Hofmann

. Modulators of Protein-Protein Interactions: Novel Approaches in Targeting Protein Kinases and Other Pharmaceutically Relevant Biomolecules. Curr. Topics Med. Chem. 2011, 11, 1305–1319.

83.

D’Ambrogio

Buratti

Stuani

. Functional Mapping of the Interaction between TDP-43 and hnRNP A2 In Vivo. Nucleic Acids Res. 2009, 37, 4116–4126.

84.

Ling

S. C.

Albuquerque

C. P.

Han

J. S.

ALS-associated Mutations in TDP-43 Increase Its Stability and Promote TDP-43 Complexes with FUS/TLS. Proc. Natl. Acad. Sci. U. S. A. 2010, 107, 13318–13323.

85.

Freibaum

B. D.

Chitta

R. K.

High

A. A.

. Global Analysis of TDP-43 Interacting Proteins Reveals Strong Association with RNA Splicing and Translation Machinery. J. Proteome Res. 2010, 9, 1104–1120.

86.

Chatr-Aryamontri

Breitkreutz

B. J.

Heinicke

. The BioGRID Interaction Database: 2013 Update. Nucleic Acids Res. 2013, 41, D816–D823.

87.

Kerrien

Aranda

Breuza

. The IntAct Molecular Interaction Database in 2012. Nucleic Acids Res. 2012, 40, D841–D846.

88.

Davis

A. P.

Murphy

C. G.

Johnson

. The Comparative Toxicogenomics Database: Update 2013. Nucleic Acids Res. 2013, 41, D1104–D1114.

89.

Kim

S. H.

Shi

Hanson

K. A.

. Potentiation of Amyotrophic Lateral Sclerosis (ALS)–Associated TDP-43 Aggregation by the Proteasome-Targeting Factor, Ubiquilin 1. J. Biol. Chem. 2009, 284, 8083–8092.

90.

Scofield

Korutla

Jackson

T. G.

. NAC1, a POZ/BTB Protein Binds to TDP-43 and Has a Potential Role in Amyotrophic Lateral Sclerosis. Neuroscience 2012, 277, 44–54.

91.

Gitcho

M. A.

Strider

Carter

. VCP Mutations Causing Frontotemporal Lobar Degeneration Disrupt Localization of TDP-43 and Induce Cell Death. J. Biol. Chem. 2009, 284, 12384–12398.

92.

Fuentealba

R. A.

Udan

Bell

. Interaction with Polyglutamine Aggregates Reveals a Q/N-rich Domain in TDP-43. J. Biol. Chem. 2010, 285, 26304–26314.

93.

Ayala

Y. M.

Misteli

Baralle

F. E.

TDP-43 Regulates Retinoblastoma Protein Phosphorylation through the Repression of Cyclin-dependent Kinase 6 Expression. Proc. Natl. Acad. Sci. U. S. A. 2008, 105, 3785–3789.

94.

Fiesel

F. C.

Voigt

Weber

S. S.

. Knockdown of Transactive Response DNA-binding Protein (TDP-43) Downregulates Histone Deacetylase 6. EMBO J. 2010, 29, 209–221.

95.

Fiesel

F. C.

Weber

S. S.

Supper

. TDP-43 Regulates Global Translational Yield by Splicing of Exon Junction Complex Component SKAR. Nucleic Acids Res. 2012, 40, 2668–2682.

96.

Bose

J. K.

Huang

C. C.

Shen

C. K.

Regulation of Autophagy by Neuropathological Protein TDP-43. J. Biol. Chem. 2011, 286, 44441–44448.

97.

Egawa

Kitaoka

Tsukita

. Drug Screening for ALS Using Patient-Specific Induced Pluripotent Stem Cells. Sci. Transl. Med. 2012, 4, 145ra104.

98.

Igaz

L. M.

Kwong

L. K.

Lee

E. B.

. Dysregulation of the ALS-associated Gene TDP-43 Leads to Neuronal Death and Degeneration in Mice. J. Clin. Invest. 2011, 121, 726–738.

99.

Colombrita

Onesto

Megiorni

. TDP-43 and FUS RNA-binding Proteins Bind Distinct Sets of Cytoplasmic Messenger RNAs and Differently Regulate Their Post-transcriptional Fate in Motoneuron-like Cells. J. Biol. Chem. 2012, 287, 15635–15647.

100.

Kole

Krainer

A. R.

Altman

RNA Therapeutics: Beyond RNA Interference and Antisense Oligonucleotides. Nat. Rev. Drug Discov. 2012, 11, 125–140.

101.

Muntoni

Wood

M. J.

Targeting RNA to Treat Neuromuscular Disease. Nat. Rev. Drug Discov. 2011, 10, 621–637.

102.

Buratti

Romano

Baralle

F. E.

TDP-43 High Throughput Screening Analyses in Neurodegeneration: Advantages and Pitfalls. Mol. Cell Neurosci., in press.

103.

Buratti

Baralle

F. E.

TDP-43: New Aspects of Autoregulation Mechanisms in RNA Binding Proteins and Their Connection with Human Disease. FEBS J. 2011, 278, 3530–3538.

104.

Budini

Buratti

TDP-43 Autoregulation: Implications for Disease. J. Mol. Neurosci. 2011, 45, 473–479.

105.

Bhardwaj

Myers

M. P.

Buratti

. Characterizing TDP-43 Interaction with its RNA Targets. Nucleic Acids Res. 2013, 41, 5062–5074.

106.

Ayala

Y. M.

De Conti

Avendano-Vazquez

S. E.

. TDP-43 Regulates Its mRNA Levels through a Negative Feedback Loop. EMBO J. 2011, 30, 277–288.

107.

Avendano-Vazquez

S. E.

Dhir

Bembich

Autoregulation of TDP-43 mRNA Levels Involves Interplay between Transcription, Splicing, and Alternative PolyA Site Selection. Genes Dev. 2012, 26, 1679–1684.

108.

Benchaouir

Goyenvalle

Splicing Modulation Mediated by Small Nuclear RNAs as Therapeutic Approaches for Muscular Dystrophies. Curr. Gene Ther. 2012, 12, 179–191.

109.

Cassel

J.A.

Blass

B.E.

Reitz

A.B.

. Development of a Novel Nonradiometric Assay for Nucleic Acid Binding to TDP-43 Suitable for High-throughput Screening Using AlphaScreen Technology. J. Biomol. Screen. 2010, 15, 1099–1106.

110.

Cooper-Knock

Kirby

Ferraiuolo

. Gene Expression Profiling in Human Neurodegenerative Disease. Nat. Rev. Neurol. 2012, 8, 518–530.

111.

Gitler

A. D.

TDP-43 and FUS/TLS Yield a Target-rich Haul in ALS. Nat. Neurosci. 2012, 15, 1467–1469.

112.

Elden

A. C.

Kim

H. J.

Hart

M. P.

. Ataxin-2 Intermediate-length Polyglutamine Expansions Are Associated with Increased Risk for ALS. Nature 2010, 466, 1069–1075.

113.

Hart

M. P.

Gitler

A. D.

ALS-associated Ataxin 2 PolyQ Expansions Enhance Stress-induced Caspase 3 Activation and Increase TDP-43 Pathological Modifications. J. Neurosci. 2012, 32, 9133–9142.

114.

Stelzl

Worm

Lalowski

. A Human Protein-Protein Interaction Network: A Resource for Annotating the Proteome. Cell 2005, 122, 957–968.

115.

Falsone

S. F.

Gesslbauer

Rek

A Proteomic Approach towards the Hsp90-dependent Ubiquitinylated Proteome. Proteomics 2007, 7, 2375–2383.

116.

Jeronimo

Forget

Bouchard

. Systematic Analysis of the Protein Interaction Network for the Human Transcription Machinery Reveals the Identity of the 7SK Capping Enzyme. Mol. Cell 2007, 27, 262–274.

117.

Hiji

Takahashi

Fukuba

. White Matter Lesions in the Brain with Frontotemporal Lobar Degeneration with Motor Neuron Disease: TDP-43–Immunopositive Inclusions Co-localize with p62, but Not Ubiquitin. Acta Neuropathol. 2008, 116, 183–191.

118.

Golebiowski

Matic

Tatham

M. H.

. System-wide Changes to SUMO Modifications in Response to Heat Shock. Sci. Signal. 2009, 2, ra24.

119.

Urushitani

Sato

Bamba

. Synergistic Effect between Proteasome and Autophagosome in the Clearance of Polyubiquitinated TDP-43. J. Neurosci. Res. 2010, 88, 784–797.

120.

Ravasi

Suzuki

Cannistraci

C. V.

. An Atlas of Combinatorial Transcriptional Regulation in Mouse and Man. Cell 2010, 140, 744–752.

121.

Hutchins

J. R.

Toyoda

Hegemann

. Systematic Analysis of Human Protein Complexes Identifies Chromosome Segregation Proteins. Science 2010, 328, 593–599.

122.

Kim

S. H.

Shanware

N. P.

Bowler

M. J.

. Amyotrophic Lateral Sclerosis–Associated Proteins TDP-43 and FUS/TLS Function in a Common Biochemical Complex to Co-regulate HDAC6 mRNA. J. Biol. Chem. 2010, 285, 34097–34105.

123.

Zhang

Y. J.

Gendron

T. F.

Y. F.

. Phosphorylation Regulates Proteasomal-mediated Degradation and Solubility of TAR DNA Binding Protein-43 C-terminal Fragments. Mol. Neurodegener. 2010, 5, 33.

124.

Danielsen

J. M.

Sylvestersen

K. B.

Bekker-Jensen

. Mass Spectrometric Analysis of Lysine Ubiquitylation Reveals Promiscuity at Site Level. Mol. Cell Proteomics 2011, 10, M110 003590.

125.

Bennett

E. J.

Rush

Gygi

S. P.

. Dynamics of Cullin-RING Ubiquitin Ligase Network Revealed by Systematic Quantitative Proteomics. Cell 2010, 143, 951–965.

126.

Sharma

Burre

Sudhof

T. C.

CSPalpha Promotes SNARE-Complex Assembly by Chaperoning SNAP-25 during Synaptic Activity. Nat. Cell Biol. 2011, 13, 30–39.

127.

Bruderer

Tatham

M. H.

Plechanovova

. Purification and Identification of Endogenous PolySUMO Conjugates. EMBO Rep. 2011, 12, 142–148.

128.

Emanuele

M. J.

Ciccia

Elia

A. E.

. Proliferating Cell Nuclear Antigen (PCNA)–Associated KIAA0101/PAF15 Protein Is a Cell Cycle–Regulated Anaphase-Promoting Complex/Cyclosome Substrate. Proc. Natl. Acad. Sci. U. S. A. 2011, 108, 9845–9850.

129.

van Eersel

Y. D.

Gladbach

. Cytoplasmic Accumulation and Aggregation of TDP-43 upon Proteasome Inhibition in Cultured Neurons. PLoS One 2011, 6, e22850.

130.

Olah

Vincze

Virok

. Interactions of Pathological Hallmark Proteins: Tubulin Polymerization Promoting Protein/p25, Beta-amyloid, and Alpha-synuclein. J. Biol. Chem. 2011, 286, 34088–34100.

131.

Zhou

Lin

. Shotgun Proteomics and Network Analysis of Ubiquitin-related Proteins from Human Breast Carcinoma Epithelial Cells. Mol. Cell Biochem. 2012, 359, 375–384.

132.

Wagner

S. A.

Beli

Weinert

B. T.

. A Proteome-wide, Quantitative Survey of In Vivo Ubiquitylation Sites Reveals Widespread Regulatory Roles. Mol. Cell Proteomics 2011, 10, M111 013284.

133.

Wang

Berman

. Mapping a Dynamic Innate Immunity Protein Interaction Network Regulating Type I Interferon Production. Immunity 2011, 35, 426–440.

134.

Kim

Bennett

E. J.

Huttlin

E. L.

. Systematic and Quantitative Assessment of the Ubiquitin-modified Proteome. Mol. Cell 2011, 44, 325–340.

135.

Lee

K. A.

Hammerle

L. P.

Andrews

P. S.

. Ubiquitin Ligase Substrate Identification through Quantitative Proteomics at Both the Protein and Peptide Levels. J. Biol. Chem. 2011, 286, 41530–41538.

136.

Fedorova

O. A.

Moiseeva

T. N.

Nikiforov

A. A.

. Proteomic Analysis of the 20S Proteasome (PSMA3)–Interacting Proteins Reveals a Functional Link between the Proteasome and mRNA Metabolism. Biochem. Biophys. Res. Commun. 2011, 416, 258–265.

137.

Yang

Thompson

J. W.

Wang

. Analysis of Oxygen/Glucose-Deprivation-Induced Changes in SUMO3 Conjugation Using SILAC-based Quantitative Proteomics. J. Proteome Res. 2012, 11, 1108–1117.

138.

Altun

Kramer

H. B.

Willems

L. I.

. Activity-based Chemical Proteomics Accelerates Inhibitor Development for Deubiquitylating Enzymes. Chem. Biol. 2011, 18, 1401–1412.

139.

East

Dirac-Svejstrup

A. B.

. DBIRD Complex Integrates Alternative mRNA Splicing with RNA Polymerase II Transcript Elongation. Nature 2012, 484, 386–389.

140.

Udeshi

N. D.

Mani

D. R.

Eisenhaure

. Methods for Quantification of In Vivo Changes in Protein Ubiquitination following Proteasome and Deubiquitinase Inhibition. Mol. Cell Proteomics 2012, 11, 148–159.

141.

Tanji

Zhang

H. X.

Mori

. p62/Sequestosome 1 Binds to TDP-43 in Brains with Frontotemporal Lobar Degeneration with TDP-43 Inclusions. J. Neurosci. Res. 2012, 90, 2034–2042.

142.

Aichem

Kalveram

Spinnenhirn

. The Proteomic Analysis of Endogenous FAT10 Substrates Identifies p62/SQSTM1 as a Substrate of FAT10ylation. J. Cell Sci. 2012, 125, 4576–4585.

143.

Havugimana

P. C.

Hart

G. T.

Nepusz

. A Census of Human Soluble Protein Complexes. Cell 2012, 150, 1068–1081.

144.

Keller

B. A.

Volkening

Droppelmann

C. A.

. Co-aggregation of RNA Binding Proteins in ALS Spinal Motor Neurons: Evidence of a Common Pathogenic Mechanism. Acta Neuropathol. 2012, 124, 733–747.

145.

Scofield

M. D.

Korutla

Jackson

T. G.

. Nucleus Accumbens 1, a Pox Virus and Zinc Finger/Bric-a-brac Tramtrack Broad Protein Binds to TAR DNA-binding Protein 43 and Has a Potential Role in Amyotrophic Lateral Sclerosis. Neuroscience 2012, 227, 44–54.

146.

Ewing

R. M.

Chu

Elisma

. Large-scale Mapping of Human Protein-Protein Interactions by Mass Spectrometry. Mol. Syst. Biol. 2007, 3, 89.

147.

Volkening

Leystra-Lantz

Yang

. Tar DNA Binding Protein of 43 kDa (TDP-43), 14-3-3 Proteins and Copper/Zinc Superoxide Dismutase (SOD1) Interact to Modulate NFL mRNA Stability: Implications for Altered RNA Processing in Amyotrophic Lateral Sclerosis (ALS). Brain Res. 2009, 1305, 168–182.

148.

Buratti

Dork

Zuccato

. Nuclear Factor TDP-43 and SR Proteins Promote In Vitro and In Vivo CFTR Exon 9 Skipping. EMBO J. 2001, 20, 1774–1784.

149.

Mercado

P. A.

Ayala

Y. M.

Romano

. Depletion of TDP 43 Overrides the Need for Exonic and Intronic Splicing Enhancers in the Human ApoA-II Gene. Nucleic Acids Res. 2005, 33, 6000–6010.

150.

Dreumont

Hardy

Behm-Ansmant

. Antagonistic Factors Control the Unproductive Splicing of SC35 Terminal Intron. Nucleic Acids Res. 2010, 38, 1353–1366.

151.

Bose

J. K.

Wang

I. F.

Hung

. TDP-43 Overexpression Enhances Exon 7 Inclusion during the Survival of Motor Neuron Pre-mRNA Splicing. J. Biol. Chem. 2008, 283, 28852–28859.

152.

Shiga

Ishihara

Miyashita

. Alteration of POLDIP3 Splicing Associated with Loss of Function of TDP-43 in Tissues Affected with ALS. PLoS One 2012, 7, e43120.

153.

Polymenidou

Lagier-Tourenne

Hutt

K. R.

. Long Pre-mRNA Depletion and RNA Missplicing Contribute to Neuronal Vulnerability from Loss of TDP-43. Nat. Neurosci. 2011, 14, 459–468.

154.

Tollervey

J. R.

Curk

Rogelj

. Characterizing the RNA Targets and Position-dependent Splicing Regulation by TDP-43. Nat. Neurosci. 2011, 14, 452–458.

Targeting RNA Binding Proteins Involved in Neurodegeneration

Abstract

Keywords

Introduction

RBP Proteins in Neurodegeneration

Targeting Aberrant RBP Aggregation

Targeting Protein-Protein Interactions

Targeting RNA-Protein Interactions

Targeting TDP-43 Affected Pathways

Future Aims

Footnotes

Declaration of Conflicting Interests

Funding

References