Amyotrophic lateral sclerosis (ALS), also commonly known as Lou Gehrig’s disease, is a progressive neuromuscular disorder. ALS is the most common form of motor neuron disease (MND) and is characterised by progressive degeneration of both upper and lower motor neurons. Symptoms associated with the disease include muscle atrophy, limb paralysis, dysphagia, dysarthria, and respiratory failure. The annual incidence rate of ALS in the European population is approximately 2–3 per 100,000 people [1
], with the highest incidence occurring in individuals >55 years of age. Prognosis is poor, with median survival time from onset to death of around 20–48 months [2
]. ALS can be divided broadly into two categories, familial ALS (fALS) involving patients with a family history of disease, and sporadic ALS (sALS), involving patients with no previous family history. Cases are split approximately 90%–95% sALS and 5%–10% fALS [3
With improving technologies in genomic analysis and large-scale international collaborations, the understanding of genetic factors in fALS has improved significantly [5
]. Studies have been able to identify multiple ALS-associated genes, and from the discovery of the first ALS-associated gene SOD1
, more than 20 genes have been attributed to the disease [6
]. Pathogenic mutations have been identified in genes such as ALS2
, and C9orf72
]. Multiple cell types have been postulated to play a role in pathology, such as muscle satellite cells, mitochondria, astrocytes, and microglia [10
Riluzole and edaravone are currently the only two approved drug therapeutics available in the treatment of ALS and are observed to have a modest efficacy. Clinical trials involving riluzole treatment in ALS populations have indicated an improvement in survival of ALS patients compared to placebo controls [14
]. Subsequent reviews have identified that riluzole can extend the median survival time of ALS patients by around 2–3 months [16
]. The cost effectiveness of the drug, as well as the ability to treat the functional consequences of disease, such as muscle strength and bulbar or limb function, have been extensively studied [17
]. Clinical trials involving edaravone treatment in ALS populations were originally unable to identify any statistically significant benefit between treatment groups and placebo groups [18
]. Post hoc analysis subsequently identified a subpopulation of ALS patients with well-defined early stage ALS that appeared to respond to treatment with a reduction in the deterioration of their ALS Functional Rating score (ALSFRS-R) compared to placebo [19
]. The efficacy of edaravone was only apparent in a subset of ALS patients with early stage ALS, and it is believed that any benefits observed would not translate to the whole ALS population [21
]. The overall limited efficacy of both these approved ALS drugs highlights the necessity of developing more effective therapeutics.
Biomarkers are laboratory-measurable biological characteristics that can be attributed to a certain physiological or pathological process. Within ALS, the early diagnosis of patients remains difficult due to the phenotypic overlap between the disease and various oer motor neuron diseases. Biomarkers have the potential to provide more accurate diagnoses of ALS patients and to help stratify the ALS patient population into groups of responders to therapeutics, to act as prognostic markers in disease progression, and to lead us toward possible drug targets. The value of biomarkers in ALS has been demonstrated in previous publications. For example, studies have shown that cerebrospinal fluid (CSF) diagnostic assays can provide a suitable diagnostic tool for distinguishing ALS patients from control groups in their respective study populations. A study involving 41 ALS patients and 33 neurological disease controls was able to define a diagnostic assay with 87.5% sensitivity and 91.2% specificity, using the top five differentially expressed proteins between the two test groups (IL-10, IL-6, GM-CSF, IL-2, and IL-15) [22
]. A separate study identified phosphorylated neurofilament heavy chain and complement C3 (pNFH/C3) as optimal biomarkers for a diagnostic assay when measuring levels of cytoskeletal proteins and inflammatory markers in CSF samples of ALS subjects, disease controls, and healthy controls. The diagnostic assay was able to identify ALS patients with 87.3% sensitivity and 94.6% specificity when applied to a study population consisting of 71 ALS subjects, 52 disease controls, and 40 healthy subjects [23
]. However, CSF samples are relatively invasive and expensive to obtain compared to blood assays.
Multiple prognostic biomarkers have been identified to predict disease severity, originating from blood, muscle, CSF, and genetic factors [24
]. Higher pNFH levels in plasma, serum, and CSF samples have been associated with a faster decline in the ALSFRS-R score, suggesting a more rapid disease progression [25
]. Multiple inflammatory markers have been suggested to provide viable prognostic markers in ALS. Increased levels of wide-range C-reactive protein (wrCRP) [26
], high mobility group box 1 (HMGB1) autoantibody, increased granzyme B, and increased CSF IL-8 levels [22
] have all been implicated in correlating with a more rapid decline in the ALSFRS-R score. The number of genetic alterations associated with ALS continues to grow. Prognostically, the presence of some genetic variants indicates susceptibility to and increased severity of ALS. The most frequently known genetic cause of ALS is the hexanucleotide repeat (GGGGCC) expansion in the first intron of C9ORF72
]. Patients exhibiting this repeat expansion are characterised by a lower age of onset and a shorter survival time [29
]. Numerous other genetic variants have been associated with reduced survival in the ALS population, including SLC11A2
, and ZNF512B
, among many others [31
]. Whereas prognostic and diagnostic biomarkers have been identified in ALS, their usefulness in terms of directing drug development is currently minimal.
Research investigating the molecular basis of ALS aims to elucidate molecular mechanisms involved in disease pathology with the hope of yielding insights into defining viable therapeutic targets. In ALS, multiple molecular mechanisms have been suggested. Glutamate excitotoxicity is believed to cause neuronal death either via abnormally high presynaptic neuron activity, impaired response to excitatory stimuli in the postsynaptic neuron, or a combination of both [34
], and is believed to be a therapeutic target of riluzole. Examples include increased extracellular glutamate levels due to reduced clearance via glutamate transporters such as the excitatory amino acid transporter 2 (EAAT2
) expressed on astrocytes [35
], or increased synaptic release of glutamate via endoplasmic reticulum stress (ER) [37
]. Mitochondrial dysfunction and death is considered an important component in ALS pathogenesis. Super oxide dismutase (SOD1
) mutations have been observed to result in the accumulation of mutant SOD1 aggregates in mitochondria, causing mitochondrial damage and subsequent motor neuron death [38
]. Morphological abnormalities in mitochondria and the presence of fusion and fission proteins have all been documented to disrupt normal mitochondria function in ALS [40
]. Reactive oxygen species (ROS
) accumulation has been linked to ALS due to reduced clearance by mutant SOD1 [41
]. Protein toxicities in the form of TAR DNA-binding protein 43 (TDP-43), fused in sarcoma (FUS), optineurin (OPTN), and ubiquilin-2 (UBQLN2) aggregate in motor neuron and glial cells [42
]. This list of mechanisms implicated in ALS is far from exhaustive, and many more mechanisms have been suggested. Considering the multitude of genes identified as associated with fALS and the varying molecular mechanisms described, ALS in many cases may be a multigenic systemic disease. As a result, applying a personalized approach to molecular data could provide a benefit to ALS patients if their specific mechanism of disease can be determined and therapies relevant to these disease mechanisms can be applied. The use of molecular interaction networks can aid in the understanding of disease mechanisms associated with ALS and can identify genes and biological pathways that may be perturbed by these mechanisms, to provide possible therapeutic targets.
Personalized medicine is the concept of tailoring treatment to the individual patient. With advancements in “omics” technologies (e.g., genome, proteome, metabolome), the ability to comprehensively define an individual’s omics profile, has illustrated how biologically heterogeneous we are [47
]. Understanding how a person’s omics profile can influence their disease phenotype, response to treatment, and prognosis can allow clinicians to identify the most efficient and beneficial treatment plan for patients. Heritable factors and the variability of the underlying cause of disease can attribute to varying drug responses observed in patient populations [48
An example of how personalised medicine approaches at the molecular level can be implemented to improve patient care is in cystic fibrosis (CF). Mutations in the CF transmembrane conductance regulator (CFTR
) gene have been identified as the root cause for the disease [49
]. Mutations in the CFTR
gene result in a dysfunctional CFTR protein. In the airway epithelial cells, this results in viscous mucous secretions due to inadequate chloride transport. This results in chronic infection and inflammation, leading to respiratory failure [52
]. In CF, there are currently three approved drugs that target specific genetic mutations in the CF population. Ivacaftor targets patients with at least one allele of the G551D CFTR
gating mutation [53
], lumacaftor/ivacaftor and tezacaftor/ivacaftor are combination treatments in the treatment of the most common CFTR
mutation, Phe508del [54
]. This example highlights the potential benefit of treating patients based on their personal genomic profile. Although the treatments are not generalizable across the whole population, they are able to produce significant benefits to the strata of patients they target [57
]. Since many genes are implicated in ALS, a single therapeutic target to treat the whole patient population may not be feasible. With this in mind, a personalized medicine approach may be needed to effectively develop therapeutics for the ALS population.
Within living organisms, a single protein or other biomolecule will rarely act alone to effect a given function. Instead, there is a complex series of interactions between multiple biomolecules that contributes to a biological process [60
]. These interactions can include protein-protein binding, gene co-expression, RNA interactions, and many other types of molecular functional association. Functional associations can be represented using molecular interaction networks (MINs), with nodes denoting molecules and edges denoting the interactions between nodes. Biological networks have been identified as scale-free networks that follow a power law distribution [61
]. This indicates that biological networks are not randomly connected, but instead exhibit a definite architecture related to biological processes contained within the interactions. Studying the structure and topology of MINs can help identify biomolecules that are involved in biological processes and elucidate which biomolecules or processes are dysfunctional in disease [62
]. Identifying disease-implicated modules in networks will aid in narrowing the search for effective drug targets or disease biomarkers [65
]. With a network view of a disease phenotype, multitarget therapies can be implemented to target dysfunctional processes that may be impractical to resolve with a single drug therapeutic [66
]. Since ALS may in many cases be a multigenic complex systemic disease, MIN approaches are particularly relevant, as they could potentially identify one or more network clusters that are consistently associated with pathology but within which only a smaller subset of genes or other biomolecules are affected in any one patient (Figure 1
In this review, we highlight the current applications of MINs in ALS and how further approaches might be applied toward personalized medicine in future analyses.
3. Molecular Data and Analyses Applied to the Study of Motor Neurons
A critical event in the pathology of ALS is the death of motor neurons (MNs). In this field, there is a developing debate as to whether motor neuron degeneration is a cell-autonomous process or if it depends on pathologic processes emerging in other cell and tissue types. The dying forward hypothesis suggests ALS pathology initiates MN degeneration, and any subsequent muscle atrophy is a downstream effect [99
], whereas the dying back hypothesis suggests initiating pathology occurs at the muscle and neuromuscular junction, and subsequent motor neuron degeneration is a downstream effect [101
]. Regardless of the direction of mechanism of pathology in ALS, motor neurons are pivotal in the disease mechanism and have the potential to provide biomarkers of the disease.
Transcriptomic analysis has been applied in many studies involving motor neurons, mainly to determine DEGs between control and disease cohorts. Examples of studies implementing transcriptomic analysis and MINs concurrently are less frequent. One such study compared the gene coexpression profiles of the ALS model of spinal motor neurons (spMN, derived from iPSCs) to fetal spinal tissues and adult spinal tissues [102
]. The study was able to identify that the gene coexpression profile of the spMN more closely matched the fetal spMN than adult spMNs. This may indicate that to develop a more precise ALS model, the maturation and aging process needs to be replicated. Another study involving gene coexpression, specifically in the C9ORF72
GGGGCC-expanded repeat mutation population, was able to identify coexpressed modules of genes that were differentially expressed between control and ALS MNs. The study was able to define six statistically significant GO terms associated with C9ORF72
ALS pathophysiology, including the cholesterol biosynthetic process and the regulation-of-glucose metabolic process [75
]. The authors noted that these findings support the involvement of endoplasmic reticulum (ER) stress, a process previously implicated in ALS. The ER is responsible for the correct folding of proteins, and aggregations of misfolded proteins are often observed in ALS [103
GWA studies have led to the association of many genetic variants associated with motor neuron dysfunction in ALS. Mutations in the fused in sarcoma/translated in liposarcoma (FUS/TLS
) gene on chromosome 16 in fALS [44
] are believed to contribute to toxic protein aggregations in neuronal cytoplasm, similarly to the mechanism exhibited by mutant SOD1
protein aggregates in ALS [104
]. Mutations in the TARDBP
gene that encodes the protein TDP-43
have been identified in fALS families, with TDP-43
aggregation observed in upper and lower motor neurons [107
]. An application not yet explored for ALS genetic variant data is to include these variants into MINs such as gene coexpression or PPI networks. Examples of how this approach could be beneficial include a study of Alzheimer’s disease in which genetic variants were incorporated into a tissue-specific PPI network [111
]. This revealed a strong indirect connection between large numbers of proteins from known AD loci, indicating a common pathological pathway. Furthermore, a study taking the genetic variants identified in sporadic autism cases was able to identify a large interconnected hub of genetic variant-associated proteins [112
]. Of the 126 listed autism-related variants, 49 connected at a high degree within the PPI network. Downstream analysis of this highly connected cluster was able to identify a previously implemented pathway involving beta-catenin.
In ALS, the majority of proteomic studies of the motor neurons have involved the analysis and comparison of the proteome of the CSF between disease and control. All proteins accumulating in CSF are not necessarily specific to upper motor neuron tissue. The accumulation of neurofilaments in CSF is suggestive of motor neuron degradation [113
]. Differential neurofilament markers between control and disease have been outlined as potential diagnostic biomarkers [85
]. In a clinical setting, these biomarker panels may be of limited use for diagnostics due to the presence of ALS mimic phenotypes. A study was able to identify extremely high levels of CSF neurofilaments as suggestive of ALS, but not conclusively so [115
]. Such data could be used in MIN analyses of PPI networks. A study compared the DEGs from brain biopsies in schizophrenia, bipolar disorder, and major depression [116
]. These DEGs were mapped to a PPI network to characterize mutual protein interactions associated with these diseases. An approach such as this in ALS would be beneficial to clearly understand any differential pathology at the motor neuron level between ALS and ALS mimic conditions.
Metabolomic analyses in ALS, similarly to the study of the proteome, have been focused on the CSF rather than MN cells. The study of the metabolome between control and ALS populations has potential diagnostic value [117
]. One study was able to identify a diagnostic profile for ALS from differential metabolite quantities compared to controls, which performed reasonably well (78.9% sensitivity, 76.5% specificity) in characterizing a test cohort [118
]. Whether these metabolite markers would be generalizable across ALS populations is yet to be seen. Specific metabolome data on MNs in ALS is currently lacking, and with improving coverage of metabolomics pathways, MN-specific analysis may prove beneficial in dysfunctional pathway identification.
Personalized medicine provides the opportunity for improved treatment strategies in disease. Utilizing omics data is vital in establishing biomarkers for improved diagnosis, prognosis, and therapeutic targets. Molecular interaction networks represent a powerful aid in the analysis of omics data. ALS is a severe and fatal neuromuscular disease, and the lack of truly effective treatment underlines the need to incorporate cutting-edge approaches to discover improved therapeutics. Currently in ALS research, vast amounts of omics and other experimental data are being generated. However, the application of MINs is minimal. This review has highlighted MIN approaches in tissues involved in ALS that could provide greater insight into the pathological processes at play in ALS. The application of these approaches not only to motor neurons but also to other cell types such as muscle and astrocytes, or to specific subcellular structures such as mitochondria, should be a priority to help refine our understanding of underlying cellular molecular mechanisms in ALS.
Asides from approaches using MINs, which have been the subject of this review, several studies have applied mathematical modelling to attempt to understand the pathology in terms of relationships between high-order disease concepts such as “genetic damage”, “necro-apoptosis”, “calcium homeostasis”, “cellular respiration”, and other processes [141
], whereas others have mathematically modelled clinical features in order to better predict disease progression in each patient [143
]. MIN approaches may ultimately feed into higher order and clinical models, for example if they are used to better define molecular pathways or molecular biomarkers.
At the molecular level, the increasing availability of high-throughput data from experimental models and from large-population genomic and functional genomic studies, together with the growth of molecular interaction datasets and the optimization of network-clustering algorithms, may provide an opportunity to take a fresh look at the mechanistic processes underlying cellular pathology in ALS. Molecular changes at the level of network modules could be described per individual, lending itself to personalized medicine machine-learning approaches to model the relationships between dysregulated pathways. The clarification of pathological processes involved in ALS will guide the identification of biomarkers and potential drug targets in the future.