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REVIEW WHITE MATTER AS A TRANSPORT SYSTEM WHITE MATTER: A FEW NUMBERS
T. PAUS, * M. PESARESI AND L. FRENCH Rotman Research Institute, University of Toronto, Toronto, Canada
During evolution, growth of white matter outpaced that of cortical gray-matter (Frahm et al., 1982). In the human brain, white matter (WM) occupies almost half of its volume. Based on post-mortem data, Frahm and colleagues estimated the volume of ‘‘cortical’’1 WM to be 420 cm3, or 42% of the total volume (Frahm et al., 1982). Using magnetic resonance (MR) images collected in 1000 typically developing adolescents participating in the Saguenay Youth Study (Pausova et al., 2007), we obtained similar figures for male (456 ± 48 cm3, 39.9 ± 2.7%; n = 476) and female (392 ± 42 cm3, 38.6 ± 2.5%; n = 509) adolescents. Not surprisingly, most of the space in white matter (87%) is taken up by axons (Wang et al., 2008). In the adult (male) brain, axons form a network that totals an astounding 176,000 km in length (Marner and Pakkenberg, 2003). The length of individual axons varies by four orders of magnitude, from micrometers (inter-neurons) to centimeters (corticospinal neurons). Schu¨z and Braitenberg (2002) have provided intriguing estimates of the number of axons classified into three different groups based on length and location in the human brain: (1) short (30 mm) axons organized in bundles connecting different lobes within a hemisphere (e.g., longitudinal fascicles) and across hemispheres (corpus callosum). As shown in Fig. 1, the estimates suggest that the majority of axons are located within the short group (i.e., within the cerebral cortex), with only 4% of all axons found in the very-long group containing the long-range fiber bundles. They conclude that ‘‘the relation between range and number of cortico-cortical fibers is such that for an increase of one order of magnitude in length, their number goes down by one order of magnitude’’ (Schu¨z and Braitenberg, 2002). Schu¨z and Braitenberg also estimated the volume of WM containing axons of the intermediate length (U-fibers) – a compartment referred to by some as ‘‘superficial white matter’’ (Oishi et al., 2008, 2011). They assumed that it is a 1.5-mm thick sheath of WM located just beneath the surface of the cerebral cortex, the latter estimated at 1600 cm2 based on post-mortem
Abstract—There are two ways to picture white matter: as a grid of electrical wires or a network of roads. The first metaphor captures the classical function of an axon as conductor of action potentials (and information) from one brain region to another. The second one points to the important role of axons in a bi-directional transport of biological molecules and organelles between the cell body and synapse. Given the wide variety of such cargoes, a well-functioning axonal transport is critical for a number of processes, including neurotransmission, metabolism and viability of neurons. This selective review will emphasize the need for considering axonal transport when interpreting functional consequences of inter-individual variations in the structural properties of white matter. We start by describing the space occupied by white matter and techniques used in vivo for its characterization. We then provide examples of key features of maturation and aging of white matter, as well as some of the common abnormalities observed in neurodevelopmental and neurodegenerative disorders. Next, we review work that motivated our focus on axonal diameter, and explain the relationships between transport and cytoskeleton within the axon. We will conclude by describing molecular machinery of axonal transport and genes that may contribute to inter-individual variations in axonal diameter and axonal transport. This article is part of a Special Issue entitled: ‘‘The CNS White Matter’’. Ó 2014 IBRO. Published by Elsevier Ltd. All rights reserved. Key words: MRI, DTI, MTR, cytoskeleton, axonal transport, adolescence. Contents White matter: A few numbers Magnetic resonance imaging of white matter Maturation and aging of white matter Axon, myelin and g ratio Axonal cytoskeleton and axonal transport Axonal cytoskeleton and transport: Genes Conclusion References
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*Corresponding author. Tel: +1-4167852500. E-mail address: [email protected] (T. Paus). Abbreviations: CST, cortico-spinal tract; DTI, diffusion tensor imaging; GO, Gene Ontology; MR, magnetic resonance; MTR, magnetization transfer ratio; MT, magnetization transfer; SNP, single nucleotide polymorphism; WM, white matter.
1 Defined as all WM in the two cerebral hemispheres except for the internal capsule.
http://dx.doi.org/10.1016/j.neuroscience.2014.01.055 0306-4522/Ó 2014 IBRO. Published by Elsevier Ltd. All rights reserved. 1
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Fig. 1. Distribution of the lengths of fibers in the human brain. (A) Number of axons within the gray matter; (B) number of axons in the white matter that follows the gyri and sulci (U fibers); (C) axons constituting long-range fascicles. Note the inverse relationship between the number of fibers in the three categories and their length. From Schu¨z and Braitenberg (2002).
data (Schu¨z and Braitenberg, 2002). With these two assumptions, they arrived at a volume of ‘‘superficial’’ WM being 240 cm3 (or 57% of all WM). We have used our MR-based measurements of cortical area obtained in the Saguenay Youth Study (male adolescents: 1,981 ± 163 cm2, n = 429; female adolescents: 1,740 ± 147 cm2, n = 473) and, with the same assumed thickness of this WM sheath (i.e., 1.5 mm), we have obtained the following figures: 297 cm3 (65% of total WM volume) and 261 cm3 (67%) in male and female adolescents, respectively. In summary, axons are found in three macroscopic compartments, namely in (cortical) gray matter, ‘‘superficial’’ white matter and large (long-range) bundles of fibers. Superficial WM (U-fibers) occupies larger volume than the ‘‘deep’’ (large bundles) WM. Owing to the dramatic differences in their length, axons in the different compartments are likely to differ in other structural (microscopic) features, including axon diameter and g ratio (see Section ‘Axon, myelin and g ratio’). These and other characteristics contribute – in a largely unknown manner – to in vivo measurements of various structural properties of white matter, as obtained with MR imaging.
imaging of myelin water fraction or diffusion spectroscopy – elsewhere (Laule et al., 2007; Paus, 2010; Deoni, 2011; Paus, 2013). T1-weighted images provide an excellent contrast between gray and white matter and, as such, are well suited for classifying brain tissue along these lines. One can use three-dimensional maps of white matter, produced by automatic classification algorithms, for voxel-wise analyses (Paus et al., 1999) or for calculating the volume of white matter in global or regional (e.g., frontal lobe) manner (Perrin et al., 2009). In principle, an absolute volume of white matter reflects the number of axons, their diameter and the thickness of myelin sheath, as well as their spacing (density). DTI provides information about the movement of water molecules in brain tissue (Le Bihan, 1995). This imaging technique allows one to estimate several parameters of water diffusion in live tissue, such as mean diffusivity and fractional anisotropy. The latter parameter reflects the degree of directionality of water diffusion; voxels that contain water moving predominantly along a single direction have higher fractional anisotropy. With conventional MR sequences (low b-values), DTI is sensitive mainly to (fast) diffusion of water within extracellular space (Stanisz, 2003). In white matter, fractional anisotropy and mean diffusivity reflect a variety of microstructural features, including the relative alignment of individual axons, their diameter and thickness of the myelin sheath, as well as axonal density. DTI-derived parameters can be assessed voxelwise using, for example, tract-based statistics (Smith et al., 2006), or by calculating mean values across voxels constituting various WM compartments (see Section ‘White matter: A few numbers’). MT imaging provides an indirect index of myelination. Contrast in MT images reflects the interaction between free water and water bound to macromolecules (McGowan, 1999); the macromolecules of myelin are the dominant source of this signal in white matter (Kucharczyk et al., 1994). This interpretation of magnetization transfer ratio (MTR) is supported by postmortem data that revealed a significant positive correlation between myelin content and MTR (Schmierer et al., 2004, 2008). But as explained below (Section ‘Axon, myelin and g ratio’), axon diameter and other factors are likely to influence mean values of MTR in a scanned voxel. Again, MTR values can be compared in a voxel-wise fashion or by averaging across voxels constituting a given WM compartment.
MAGNETIC RESONANCE IMAGING OF WHITE MATTER Magnetic resonance imaging is a versatile tool for measuring various properties of brain tissue in a living individual. Three MR sequences are most commonly used to characterize structural properties of white matter: (1) T1-weighted images to measure its volume; (2) diffusion tensor imaging (DTI) to assess its ‘‘microstructure’’; and (3) magnetization transfer (MT) imaging to provide an index of myelination. We and others have reviewed in detail these – and other imaging techniques, such as T1, T2 and T2 relaxometry,
MATURATION AND AGING OF WHITE MATTER The first two years of life represent the most dramatic phase of brain growth (Knickmeyer et al., 2008) and myelination (Dean et al., 2013). But more subtle agerelated changes in white matter and its structural properties continue beyond this initial developmental period. From childhood into young adulthood, the overall volume of WM increases in a linear fashion (Lenroot et al., 2007; Perrin et al., 2009; Lebel and Beaulieu, 2011; Tamnes et al., 2011; Brain Development
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Cooperative, 2012). This age-related increase appears to be steeper in male (vs. female) adolescents, a finding likely related to rising levels of testosterone (Perrin et al., 2008). Fractional anisotropy measured in the large fiber-bundles also increases during this period but these age-related changes follow different – mostly nonlinear – trajectories in the different bundles (Lebel and Beaulieu, 2011; Tamnes et al., 2011). The opposite is true about mean diffusivity, which decreases with age during this period (Lebel and Beaulieu, 2011; Tamnes et al., 2011). Age-related changes in MTR (in WM) during adolescence (12–18 years of age) show a clear sexual dimorphism: they increase slightly in female adolescence (Perrin et al., 2009) but decrease in male adolescence, the latter again related to rising levels of testosterone (Perrin et al., 2008). We will discuss the interpretation of these findings in Section ‘Axon, myelin and g ratio’. The aging brain appears to follow opposite trajectories vis-a`-vis WM properties. Thus, volumes of WM decrease dramatically after 60 years of age (Westlye et al., 2010; Fjell et al., 2013). Global values of fractional anisotropy follow a similar trend, with some variations across the different fiber bundles (Westlye et al., 2010; Lebel et al., 2012). These age-related decreases in WM volumes and fractional anisotropy are accompanied by increases in mean diffusivity (Westlye et al., 2010; Lebel et al., 2012). Fig. 2 summarizes the lifespan variations in fractional anisotropy and mean diffusivity, indicating the age at which fractional anisotropy peaks (and mean diffusivity reaches its minimum) and the amount of agerelated change before (maturation) and after (aging) these points. Finally, a handful of studies evaluated MTR in older (healthy) adults and found that it declines with age (Ge et al., 2002; Schiavone et al., 2009). Let us conclude this section by pointing out that – not surprisingly – a large body of literature has reported differences, in WM properties, between typically developing (or aging) individuals and those with various neurodevelopmental and neurodegenerative disorders. A number of meta-analyses have found consistent group differences in global and/or regional volumes and/or microstructure of WM in attention deficit hyperactivity disorder (Castellanos et al., 2002; van Ewijk et al., 2012), autism (Radua et al., 2011; Duerden et al., 2012), schizophrenia (Ellison-Wright and Bullmore, 2009; Olabi et al., 2011; De Peri et al., 2012) and bipolar disorder (Vita et al., 2009; De Peri et al., 2012), as well as in Alzheimer’s Disease (Sexton et al., 2011; Li et al., 2012). Overall, structural properties of white matter vary with age during development and aging. The same properties also deviate from the norm in a number of conditions associated with impaired brain functioning. As pointed out in the opening paragraph, it is often assumed that conduction of action potentials is the key process underlying such structure-function relationships in both health and disease. But it is equally likely that the explanation lies in axonal transport and its variations associated with inter-individual differences in WM properties. Next, we will review findings that inspired this view.
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Fig. 2. Fractional anisotropy (A) and mean diffusivity (B) in a number of fiber tracts as a function of age. ‘‘The age of (A) peak fractional anisotropy, FA, and (B) minimum mean diffusivity, MD, values are shown for each tract in ascending order of the peak/minimum age, along with the magnitude of changes before and after the peak, and the standard error of the age estimate. For each tract, the location of the black vertical line represents the age at peak FA or minimum MD. The gray bar to each side represents the standard error of the age estimate (based on the standard error of the fitting parameters). The color of the bars represents the magnitude of change from 5 years to the peak/minimum (left) and from the peak/ minimum to 83 years (right). Note that the age scale is different for FA and MD, but the color bar is the same. ILF: inferior longitudinal fasciculus; IFO: inferior fronto-occipital fasciculus; SFO: superior fronto-occipital fasciculus; SLF: superior longitudinal fasciculus; ALIC: anterior limb of the internal capsule; CC: corpus callosum’’. From Lebel et al. (2012).
AXON, MYELIN AND G RATIO A nerve fiber consists of an axon and its myelin sheath, the later present in a great majority of fibers constituting white matter in the human brain. The g ratio metric is used to capture the relative proportion of these two main constituents of a WM fiber. This ratio between axon diameter (d) and fiber diameter (D) is illustrated in Fig. 3. In our initial MR-based studies of brain maturation, we noticed large age-related increases in the volume of white
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AXONAL CYTOSKELETON AND AXONAL TRANSPORT
Fig. 3. Schematic representation of an axon and its myelin sheath illustrating g ratio (left) and an electron micrograph of a myelinated axon from the rat corpus callosum (right). Image on the left from Paus and Toro (2009).
matter during male adolescence (Perrin et al., 2008). If this increase were driven by an increasing thickness of the myelin sheath, we should have seen a concomitant increase in MTR. This was not the case: in male adolescents, we observed subtle but significant decreases in the global values of MTR in white matter (Perrin et al., 2008). Rather than interpreting this finding as a decrease in myelination, we have suggested that the apparent decrease in myelin-related (MTR) signal is, in fact, due to an increase in axon diameter; thicker axons ‘‘dilute’’ this signal in a unit of scanned volume. In addition, thicker axons have a relatively thinner myelin sheath (Chatzopoulou et al., 2008), thus decreasing further myelin-to-axon ratio in the scanned volume. Based on the latter observation, we reasoned that axondriven variations in MTR values should be particularly pronounced in long fiber-bundles (Paus and Toro, 2009). We tested this possibility by measuring MTR (and T2 relaxation times) in the longest bundle of fibers found in the human brain, namely the cortico-spinal tract (CST); in adults, the average thickness of the CST fibers is 24 lm (Lassek, 1942; Verhaart, 1950). In young adult males studied with a 7-T MR scanner, we observed lower MTR values (and longer T2 relation time) in the part of the internal capsule likely containing CST, as compared with the adjacent white matter (Herve et al., 2011), a finding consistent with a relatively high g ratio. Furthermore, in male adolescents, we found a consistent age-related decrease of MR signal on T1-weighted images (Herve et al., 2011) and in MTR values (Pangelinan and Paus, unpublished observation) in the same CST region of the internal capsule; again, we interpret these findings as reflecting age-related increases in g ratio during male adolescence. Finally, we verified these MR-based inferences using electron microscopy; we measured axon diameter and thickness of myelin sheath in the splenium of the corpus callosum in adult rats and confirmed that g ratio is higher in males than females (Pesaresi and Paus, unpublished observations). Overall, the above series of in vivo (human) and ex vivo (rat) observations strongly suggest that some of the developmental trajectories and sex differences observed in white matter may be due to radial growth of the axon. In the next section, we will review the relationship between axon morphology (its cytoskeleton) and axonal transport.
The axonal cytoskeleton consists of neurofilaments and microtubules, the former outnumbering the latter 5–10 times (Lee and Cleveland, 1996). Neurofilaments are heteropolymers assembled from four polypeptide subunits; neurofilament light (NF-L), medium (NF-M), heavy (NF-H) and internexin neuronal intermediate filament protein, alpha (a-internexin, INA). Microtubules are heteropolymers consisting of alpha and beta tubulin. Neurofilaments, thanks to their elastic and fibrous properties (Wagner et al., 2007), support the cylindrical structure of an axon and, as such, protect the bore from compressive stress, securing its unobstructed state (Kumar et al., 2002). Microtubules provide the tracks for the movement of cargoes: the plus-end points from the cell body toward the synapse, thus determining direction of axonal transport by motor proteins (see below). Axonal diameter is influenced both by the number of neurofilaments and their spacing (Hoffman et al., 1984, 1987), as well as the length of the C terminus of neurofilament medium (Barry et al., 2012). The number of neurofilaments is regulated by neurofilament synthesis (gene expression) and the amount of neurofilaments undergoing (slow) axonal transport in an anterograde direction (Hoffman et al., 1984; Hoffman et al., 1987). The latter mechanism is regulated, in part, by the amount of the NF-H polypeptide: increasing its expression (beyond certain point) slows transport of neurofilaments in the axons and results in swellings at the proximal part of the axon (Marszalek et al., 1996). Another neurofilament protein, namely a-internexin, appears to play a role not only in the formation of NF heterodimers but also in the neurofilament transport; thus, interactions between the different neurofilament proteins are relevant for their assembly and functionality (Yuan et al., 2003, 2006). Finally, axon diameter is also influenced by the length of the neurofilament medium C terminus (Barry et al., 2012). Phosphorylation of the C terminus appears to be regulated by a protein synthesized by oligodendrocytes, namely myelinassociated glycoprotein (MAG); this ‘‘outside-in’’ signaling pathway provides a cellular mechanism for the coupling between myelination and axonal diameter (Garcia et al., 2003). The axonal cytoskeleton provides the necessary ‘‘infrastructure’’ for unhindered transport of various cargoes between the cell body and the synapse (Mandelkow and Mandelkow, 2002; Goldstein et al., 2008; Kanaan et al., 2013). In this way, cytoskeleton (i.e. neurofilaments and microtubules) and motor proteins are essential contributors to a large number of cellular processes, such as cell metabolism (e.g. transport of mitochondria and glycolytic enzymes) and neurotransmission (e.g. transport of synaptic-vesicle precursors). Cargoes move on the microtubule ‘‘roads’’ either at slow (1 mm/day) or fast (100 mm/day) rates (Mandelkow and Mandelkow, 2002; Shah and Cleveland, 2002). Slow axonal transport mainly moves elements of axonal cytoskeleton and cytosolic proteins
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(Barry et al., 2007), while cargoes necessary for synaptic activity are moved at fast rates (Grafstein and Forman, 1980; Goldstein et al., 2008). Could the number and/or spatial arrangement of neurofilaments and microtubules also influence the rate of axonal transport? This appears to be the case. Murayama and colleagues measured in vivo transport of Mn++ from the eye to the lateral geniculate nucleus in the monkey; Mn++ transport is a good indicator because it is partly dependent on kinesinbased processes (Bearer et al., 2007) and microtubules (Smith et al., 2007). They compared the magnocellular and parvocellular pathways, and found that the rate of transport was higher in the former (large axon diameter) than the latter (small diameter) pathway (Murayama et al., 2006). Motor proteins that generate the motion are the same for both fast and slow transport, namely proteins of kinesin superfamily (45+ kinesins) and dyneins. The two types of motor proteins differ in their involvement in anterograde (plus-end) and retrograde (minus-end) transport, kinesins mediating the former and dyneins mostly the latter. kinesin-1 is a heterotetramer consisting of two heavy chain (KHC) and two light chain (KLC) subunits (DeBoer et al., 2008). Typically, heavychain domains (‘‘heads’’) of a kinesin bind to a microtubule and ‘‘walk’’ along it while hydrolyzing ATP; light chains located on the opposite end of a kinesin contain ‘‘docking’’ elements that bind various cargoes (Fig. 4). Cytoplasmatic dynein (dynein 1) is the only protein from the dynein family involved in axonal transport; as other dyneins, it consists of intermediate, light intermediate, light and heavy chain subunits, the latter containing the motor domain (Vallee et al., 2004). Overall, together with motor proteins, the axonal cytoskeleton (neurofilaments and microtubules) provides a powerful machinery for moving cargoes between the cell body and synapse. Variations in the smooth functioning of this system are likely to have both structural (e.g., thin axons) and functional (e.g., insufficient amount of synaptic vesicle precursors) consequences and, in turn, influence signals measured with structural and functional MR imaging.
AXONAL CYTOSKELETON AND TRANSPORT: GENES As reviewed above, there is a bi-directional relationship between axonal cytoskeleton and axonal transport: cytoskeleton enables the transport and the transport contributes to the construction of cytoskeleton. As pointed out above, many properties of white matter assessed in vivo with MRI may reflect this two-way interaction, as it unfolds in health and disease. How can we evaluate this possibility? We suggest that common genetic variations observed in the general population – in addition to the study of patients with rare (monogenic) disorders – provide an opportunity for identifying molecular pathways underlying inter-individual variations in the various MR-based properties of white matter (Paus, 2013). To facilitate this work, we provide a panel of relevant genes (Table 1). We have assembled this
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Fig. 4. Schematic representation of the regulation of the main motor protein driving anterograde axonal transport: conventional kinesin. Conventional kinesin is composed of heavy chain (kinesin-1, in red) and light chain (KLC, in blue) homodimers. Kinesin-1s use energy derived from ATP hydrolysis to translocate along microtubules (MT). Kinesin light chains play a critical role in the binding of conventional kinesin to transported membrane-bound organelle (MBO) cargoes. Various protein kinases phosphorylate selected subunits of conventional kinesin. The functional consequence of each phosphorylation event is determined in part by the major function of the subunit targeted. For example, phosphorylation of KLCs by GSK3 promotes the detachment of conventional kinesin from membranes, whereas phosphorylation of kinesin-1s by JNK inhibits the binding of conventional kinesin to microtubules. From Morfini et al. (2009).
panel through a combination of approaches, including the knowledge of molecular mechanisms reviewed in the previous section, the use of Gene Ontology (GO) database (Ashburner et al., 2000), and the review of genes expressed in mature (rat) axons (Taylor et al., 2009). The list contains 34 genes including those coding the building blocks of neurofilaments and microtubules, motor proteins, two protein kinases involved in the regulation of conventional kinesin (Morfini et al., 2009), as well as proteins implicated in neurodegenerative diseases with a likely dysfunction of fast axonal transport (Morfini et al., 2009; Kanaan et al., 2013). For each gene, we include the number of single nucleotide polymorphisms (SNPs) genotyped by the Illumina Human660-Quad BeadChip (Illumina, San Diego, CA) and the number of known missense (non-synonymous) mutations reported in the Human Gene Mutation Database as of September 2nd, 2013 (Stenson et al., 2003). In addition to this focused selection of genes, we surveyed the GO database and found 13 relevant genegroups (Table 2).
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Table 1. Genes relevant to axonal cytoskeleton and transport. SNPs, the number of single nucleotide polymorphisms genotyped by the Illumina Human660-Quad BeadChip (Illumina, San Diego, CA); Mutations, the number of known missense (non-synonymous) mutations reported in the Human Gene Mutation Database as of September 2nd, 2013; kb, 1000 base pairs of DNA Gene symbol
Gene name
Chromosome
Gene size (kb)
SNPs
Mutations
Domains
NEFL NEFM NEFH INA TUBA1A TUBA1B TUBA1C TUBB MAP1B MAPT DYNC1I1 DYNC1I2 DYNC1LI1 DYNC1LI2 DYNLT1 DYNLL2 DYNLT3 DYNLRB2 DYNC1H1 KIF1A KIF1B KIF1C KLC1 KLC2 KLC3 KLC4 GSK3B MAPK10 PSEN1 HTT APP APPBP2
Neurofilament, light polypeptide Neurofilament, medium polypeptide Neurofilament, heavy polypeptide internexin neuronal intermediate filament protein, alpha Tubulin, alpha 1a Tubulin, alpha 1b Tubulin, alpha 1c Tubulin, beta class I Microtubule-associated protein 1B Microtubule-associated protein tau Dynein, cytoplasmic 1, intermediate chain 1 Dynein, cytoplasmic 1, intermediate chain 2 Dynein, cytoplasmic 1, light intermediate chain 1 Dynein, cytoplasmic 1, light intermediate chain 2 Dynein, light chain, Tctex-type 1 Dynein, light chain, LC8-type 2 Dynein, light chain, Tctex-type 3 Dynein, light chain, roadblock-type 2 Dynein, cytoplasmic 1, heavy chain 1 Kinesin family member 1A Kinesin family member 1B Kinesin family member 1C Kinesin light chain 1 Kinesin light chain 2 Kinesin light chain 3 Kinesin light chain 4 Glycogen synthase kinase 3 beta Mitogen-activated protein kinase 10 Presenilin 1 Huntingtin Amyloid beta (A4) precursor protein Amyloid beta precursor protein (cytoplasmic tail) binding protein 2 Superoxide dismutase 1, soluble Proteolipid protein 1
8 8 22 10 12 12 12 6 5 17 7 2 3 16 6 17 X 16 14 2 1 17 14 11 19 6 3 4 14 4 21 17
5663 5333 11097 13189 978 3738 8249 5039 102280 133952 325919 60938 44904 30727 8298 6839 8801 9688 86271 106545 170898 30452 72364 10159 10781 15205 41651 438008 87257 169280 290278 83061
0 1 4 1 0 0 0 0 34 6 84 2 2 2 1 0 1 5 11 44 16 3 4 0 1 0 11 90 7 22 51 7
25 3 8 0 33 0 0 3 0 84 0 0 0 0 0 0 0 1 6 3 6 0 0 0 0 0 5 4 236 4 75 0
Neurofilaments Neurofilaments Neurofilaments Neurofilaments Microtubules Microtubules Microtubules Microtubules Microtubules Microtubules Motor protein Motor protein Motor protein Motor protein Motor protein Motor protein Motor protein Motor protein Motor protein Motor protein Motor protein Motor protein Motor protein Motor protein Motor protein Motor protein Regulation Regulation Neurodegeneration Neurodegeneration Neurodegeneration Neurodegeneration
21 X
9309 15794
1 3
180 188
Neurodegeneration Neurodegeneration
SOD1 PLP1
Table 2. Gene Ontology categories relevant to axonal cytoskeleton and transport Name
ID
Genes
Axon cargo transport Anterograde axon cargo transport Retrograde axon cargo transport Axon transport of mitochondrion Regulation of axon diameter Synaptic vesicle transport Anterograde synaptic vesicle transport Synaptic vesicle amine transport Neurofilament cytoskeleton organization Neurofilament cytoskeleton Neurofilament Structural constituent of myelin sheath Axolemma
GO:0008088 GO:0008089 GO:0008090 GO:0019896 GO:0031133 GO:0048489 GO:0048490 GO:0015842 GO:0060052
37 23 6 5 3 63 15 2 8
GO:0060053 GO:0005883 GO:0019911 GO:0030673
12 9 5 11
Definitions and annotated genes for each group can be retrieved from http:// amigo.geneontology.org/.
Overall, this panel of genetic variations in genes involved in axonal cytoskeleton and axonal transport provides a starting point for exploring – in the developing and aging brain – the bi-directional relationship between axonal structure (cytoskeleton) and function (transport).
CONCLUSION Axons are not just wires for conducting electrical signals – they also serve as roads for carrying cargoes between the cell body and the synapse. Subtle deviations in the latter function will affect the maintenance and viability of neurons, as well as fidelity of neurotransmission. It is likely that processes (and genes) influencing axonal transport contribute to inter-individual variations in WM properties observed with MR imaging during the lifespan. Disturbances in axonal transport play a role in the pathophysiology of neurodevelopmental and neurodegenerative disorders. Our understanding of these processes would be facilitated by further refinements of MR imaging techniques that offer higher
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specificity vis-a`-vis axonal compartments. For example, intra-axonal diffusion can be measured in the human brain with diffusion tensor spectroscopy using a metabolite confined to the intra-axonal space and present in relatively high concentrations, namely N-acetyl aspartate (Upadhyay et al., 2008). Other techniques, used in animal models, include diffusion imaging performed at high b-values and thus suitable for tracking slow motion of water protons in intra-axonal space (Barazany et al., 2009), as well as the use of the double pulsed-field gradient method to achieve this at smaller gradients (Komlosh et al., 2013). These techniques need to be further developed, however, in order to make them usable in large normative or clinical samples.
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(Accepted 29 January 2014) (Available online xxxx)
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Neuroscience xxx (2014) xxx–xxx
REVIEW WHITE MATTER AS A TRANSPORT SYSTEM WHITE MATTER: A FEW NUMBERS
T. PAUS, * M. PESARESI AND L. FRENCH Rotman Research Institute, University of Toronto, Toronto, Canada
During evolution, growth of white matter outpaced that of cortical gray-matter (Frahm et al., 1982). In the human brain, white matter (WM) occupies almost half of its volume. Based on post-mortem data, Frahm and colleagues estimated the volume of ‘‘cortical’’1 WM to be 420 cm3, or 42% of the total volume (Frahm et al., 1982). Using magnetic resonance (MR) images collected in 1000 typically developing adolescents participating in the Saguenay Youth Study (Pausova et al., 2007), we obtained similar figures for male (456 ± 48 cm3, 39.9 ± 2.7%; n = 476) and female (392 ± 42 cm3, 38.6 ± 2.5%; n = 509) adolescents. Not surprisingly, most of the space in white matter (87%) is taken up by axons (Wang et al., 2008). In the adult (male) brain, axons form a network that totals an astounding 176,000 km in length (Marner and Pakkenberg, 2003). The length of individual axons varies by four orders of magnitude, from micrometers (inter-neurons) to centimeters (corticospinal neurons). Schu¨z and Braitenberg (2002) have provided intriguing estimates of the number of axons classified into three different groups based on length and location in the human brain: (1) short (30 mm) axons organized in bundles connecting different lobes within a hemisphere (e.g., longitudinal fascicles) and across hemispheres (corpus callosum). As shown in Fig. 1, the estimates suggest that the majority of axons are located within the short group (i.e., within the cerebral cortex), with only 4% of all axons found in the very-long group containing the long-range fiber bundles. They conclude that ‘‘the relation between range and number of cortico-cortical fibers is such that for an increase of one order of magnitude in length, their number goes down by one order of magnitude’’ (Schu¨z and Braitenberg, 2002). Schu¨z and Braitenberg also estimated the volume of WM containing axons of the intermediate length (U-fibers) – a compartment referred to by some as ‘‘superficial white matter’’ (Oishi et al., 2008, 2011). They assumed that it is a 1.5-mm thick sheath of WM located just beneath the surface of the cerebral cortex, the latter estimated at 1600 cm2 based on post-mortem
Abstract—There are two ways to picture white matter: as a grid of electrical wires or a network of roads. The first metaphor captures the classical function of an axon as conductor of action potentials (and information) from one brain region to another. The second one points to the important role of axons in a bi-directional transport of biological molecules and organelles between the cell body and synapse. Given the wide variety of such cargoes, a well-functioning axonal transport is critical for a number of processes, including neurotransmission, metabolism and viability of neurons. This selective review will emphasize the need for considering axonal transport when interpreting functional consequences of inter-individual variations in the structural properties of white matter. We start by describing the space occupied by white matter and techniques used in vivo for its characterization. We then provide examples of key features of maturation and aging of white matter, as well as some of the common abnormalities observed in neurodevelopmental and neurodegenerative disorders. Next, we review work that motivated our focus on axonal diameter, and explain the relationships between transport and cytoskeleton within the axon. We will conclude by describing molecular machinery of axonal transport and genes that may contribute to inter-individual variations in axonal diameter and axonal transport. This article is part of a Special Issue entitled: ‘‘The CNS White Matter’’. Ó 2014 IBRO. Published by Elsevier Ltd. All rights reserved. Key words: MRI, DTI, MTR, cytoskeleton, axonal transport, adolescence. Contents White matter: A few numbers Magnetic resonance imaging of white matter Maturation and aging of white matter Axon, myelin and g ratio Axonal cytoskeleton and axonal transport Axonal cytoskeleton and transport: Genes Conclusion References
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*Corresponding author. Tel: +1-4167852500. E-mail address: [email protected] (T. Paus). Abbreviations: CST, cortico-spinal tract; DTI, diffusion tensor imaging; GO, Gene Ontology; MR, magnetic resonance; MTR, magnetization transfer ratio; MT, magnetization transfer; SNP, single nucleotide polymorphism; WM, white matter.
1 Defined as all WM in the two cerebral hemispheres except for the internal capsule.
http://dx.doi.org/10.1016/j.neuroscience.2014.01.055 0306-4522/Ó 2014 IBRO. Published by Elsevier Ltd. All rights reserved. 1
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Fig. 1. Distribution of the lengths of fibers in the human brain. (A) Number of axons within the gray matter; (B) number of axons in the white matter that follows the gyri and sulci (U fibers); (C) axons constituting long-range fascicles. Note the inverse relationship between the number of fibers in the three categories and their length. From Schu¨z and Braitenberg (2002).
data (Schu¨z and Braitenberg, 2002). With these two assumptions, they arrived at a volume of ‘‘superficial’’ WM being 240 cm3 (or 57% of all WM). We have used our MR-based measurements of cortical area obtained in the Saguenay Youth Study (male adolescents: 1,981 ± 163 cm2, n = 429; female adolescents: 1,740 ± 147 cm2, n = 473) and, with the same assumed thickness of this WM sheath (i.e., 1.5 mm), we have obtained the following figures: 297 cm3 (65% of total WM volume) and 261 cm3 (67%) in male and female adolescents, respectively. In summary, axons are found in three macroscopic compartments, namely in (cortical) gray matter, ‘‘superficial’’ white matter and large (long-range) bundles of fibers. Superficial WM (U-fibers) occupies larger volume than the ‘‘deep’’ (large bundles) WM. Owing to the dramatic differences in their length, axons in the different compartments are likely to differ in other structural (microscopic) features, including axon diameter and g ratio (see Section ‘Axon, myelin and g ratio’). These and other characteristics contribute – in a largely unknown manner – to in vivo measurements of various structural properties of white matter, as obtained with MR imaging.
imaging of myelin water fraction or diffusion spectroscopy – elsewhere (Laule et al., 2007; Paus, 2010; Deoni, 2011; Paus, 2013). T1-weighted images provide an excellent contrast between gray and white matter and, as such, are well suited for classifying brain tissue along these lines. One can use three-dimensional maps of white matter, produced by automatic classification algorithms, for voxel-wise analyses (Paus et al., 1999) or for calculating the volume of white matter in global or regional (e.g., frontal lobe) manner (Perrin et al., 2009). In principle, an absolute volume of white matter reflects the number of axons, their diameter and the thickness of myelin sheath, as well as their spacing (density). DTI provides information about the movement of water molecules in brain tissue (Le Bihan, 1995). This imaging technique allows one to estimate several parameters of water diffusion in live tissue, such as mean diffusivity and fractional anisotropy. The latter parameter reflects the degree of directionality of water diffusion; voxels that contain water moving predominantly along a single direction have higher fractional anisotropy. With conventional MR sequences (low b-values), DTI is sensitive mainly to (fast) diffusion of water within extracellular space (Stanisz, 2003). In white matter, fractional anisotropy and mean diffusivity reflect a variety of microstructural features, including the relative alignment of individual axons, their diameter and thickness of the myelin sheath, as well as axonal density. DTI-derived parameters can be assessed voxelwise using, for example, tract-based statistics (Smith et al., 2006), or by calculating mean values across voxels constituting various WM compartments (see Section ‘White matter: A few numbers’). MT imaging provides an indirect index of myelination. Contrast in MT images reflects the interaction between free water and water bound to macromolecules (McGowan, 1999); the macromolecules of myelin are the dominant source of this signal in white matter (Kucharczyk et al., 1994). This interpretation of magnetization transfer ratio (MTR) is supported by postmortem data that revealed a significant positive correlation between myelin content and MTR (Schmierer et al., 2004, 2008). But as explained below (Section ‘Axon, myelin and g ratio’), axon diameter and other factors are likely to influence mean values of MTR in a scanned voxel. Again, MTR values can be compared in a voxel-wise fashion or by averaging across voxels constituting a given WM compartment.
MAGNETIC RESONANCE IMAGING OF WHITE MATTER Magnetic resonance imaging is a versatile tool for measuring various properties of brain tissue in a living individual. Three MR sequences are most commonly used to characterize structural properties of white matter: (1) T1-weighted images to measure its volume; (2) diffusion tensor imaging (DTI) to assess its ‘‘microstructure’’; and (3) magnetization transfer (MT) imaging to provide an index of myelination. We and others have reviewed in detail these – and other imaging techniques, such as T1, T2 and T2 relaxometry,
MATURATION AND AGING OF WHITE MATTER The first two years of life represent the most dramatic phase of brain growth (Knickmeyer et al., 2008) and myelination (Dean et al., 2013). But more subtle agerelated changes in white matter and its structural properties continue beyond this initial developmental period. From childhood into young adulthood, the overall volume of WM increases in a linear fashion (Lenroot et al., 2007; Perrin et al., 2009; Lebel and Beaulieu, 2011; Tamnes et al., 2011; Brain Development
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Cooperative, 2012). This age-related increase appears to be steeper in male (vs. female) adolescents, a finding likely related to rising levels of testosterone (Perrin et al., 2008). Fractional anisotropy measured in the large fiber-bundles also increases during this period but these age-related changes follow different – mostly nonlinear – trajectories in the different bundles (Lebel and Beaulieu, 2011; Tamnes et al., 2011). The opposite is true about mean diffusivity, which decreases with age during this period (Lebel and Beaulieu, 2011; Tamnes et al., 2011). Age-related changes in MTR (in WM) during adolescence (12–18 years of age) show a clear sexual dimorphism: they increase slightly in female adolescence (Perrin et al., 2009) but decrease in male adolescence, the latter again related to rising levels of testosterone (Perrin et al., 2008). We will discuss the interpretation of these findings in Section ‘Axon, myelin and g ratio’. The aging brain appears to follow opposite trajectories vis-a`-vis WM properties. Thus, volumes of WM decrease dramatically after 60 years of age (Westlye et al., 2010; Fjell et al., 2013). Global values of fractional anisotropy follow a similar trend, with some variations across the different fiber bundles (Westlye et al., 2010; Lebel et al., 2012). These age-related decreases in WM volumes and fractional anisotropy are accompanied by increases in mean diffusivity (Westlye et al., 2010; Lebel et al., 2012). Fig. 2 summarizes the lifespan variations in fractional anisotropy and mean diffusivity, indicating the age at which fractional anisotropy peaks (and mean diffusivity reaches its minimum) and the amount of agerelated change before (maturation) and after (aging) these points. Finally, a handful of studies evaluated MTR in older (healthy) adults and found that it declines with age (Ge et al., 2002; Schiavone et al., 2009). Let us conclude this section by pointing out that – not surprisingly – a large body of literature has reported differences, in WM properties, between typically developing (or aging) individuals and those with various neurodevelopmental and neurodegenerative disorders. A number of meta-analyses have found consistent group differences in global and/or regional volumes and/or microstructure of WM in attention deficit hyperactivity disorder (Castellanos et al., 2002; van Ewijk et al., 2012), autism (Radua et al., 2011; Duerden et al., 2012), schizophrenia (Ellison-Wright and Bullmore, 2009; Olabi et al., 2011; De Peri et al., 2012) and bipolar disorder (Vita et al., 2009; De Peri et al., 2012), as well as in Alzheimer’s Disease (Sexton et al., 2011; Li et al., 2012). Overall, structural properties of white matter vary with age during development and aging. The same properties also deviate from the norm in a number of conditions associated with impaired brain functioning. As pointed out in the opening paragraph, it is often assumed that conduction of action potentials is the key process underlying such structure-function relationships in both health and disease. But it is equally likely that the explanation lies in axonal transport and its variations associated with inter-individual differences in WM properties. Next, we will review findings that inspired this view.
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Fig. 2. Fractional anisotropy (A) and mean diffusivity (B) in a number of fiber tracts as a function of age. ‘‘The age of (A) peak fractional anisotropy, FA, and (B) minimum mean diffusivity, MD, values are shown for each tract in ascending order of the peak/minimum age, along with the magnitude of changes before and after the peak, and the standard error of the age estimate. For each tract, the location of the black vertical line represents the age at peak FA or minimum MD. The gray bar to each side represents the standard error of the age estimate (based on the standard error of the fitting parameters). The color of the bars represents the magnitude of change from 5 years to the peak/minimum (left) and from the peak/ minimum to 83 years (right). Note that the age scale is different for FA and MD, but the color bar is the same. ILF: inferior longitudinal fasciculus; IFO: inferior fronto-occipital fasciculus; SFO: superior fronto-occipital fasciculus; SLF: superior longitudinal fasciculus; ALIC: anterior limb of the internal capsule; CC: corpus callosum’’. From Lebel et al. (2012).
AXON, MYELIN AND G RATIO A nerve fiber consists of an axon and its myelin sheath, the later present in a great majority of fibers constituting white matter in the human brain. The g ratio metric is used to capture the relative proportion of these two main constituents of a WM fiber. This ratio between axon diameter (d) and fiber diameter (D) is illustrated in Fig. 3. In our initial MR-based studies of brain maturation, we noticed large age-related increases in the volume of white
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AXONAL CYTOSKELETON AND AXONAL TRANSPORT
Fig. 3. Schematic representation of an axon and its myelin sheath illustrating g ratio (left) and an electron micrograph of a myelinated axon from the rat corpus callosum (right). Image on the left from Paus and Toro (2009).
matter during male adolescence (Perrin et al., 2008). If this increase were driven by an increasing thickness of the myelin sheath, we should have seen a concomitant increase in MTR. This was not the case: in male adolescents, we observed subtle but significant decreases in the global values of MTR in white matter (Perrin et al., 2008). Rather than interpreting this finding as a decrease in myelination, we have suggested that the apparent decrease in myelin-related (MTR) signal is, in fact, due to an increase in axon diameter; thicker axons ‘‘dilute’’ this signal in a unit of scanned volume. In addition, thicker axons have a relatively thinner myelin sheath (Chatzopoulou et al., 2008), thus decreasing further myelin-to-axon ratio in the scanned volume. Based on the latter observation, we reasoned that axondriven variations in MTR values should be particularly pronounced in long fiber-bundles (Paus and Toro, 2009). We tested this possibility by measuring MTR (and T2 relaxation times) in the longest bundle of fibers found in the human brain, namely the cortico-spinal tract (CST); in adults, the average thickness of the CST fibers is 24 lm (Lassek, 1942; Verhaart, 1950). In young adult males studied with a 7-T MR scanner, we observed lower MTR values (and longer T2 relation time) in the part of the internal capsule likely containing CST, as compared with the adjacent white matter (Herve et al., 2011), a finding consistent with a relatively high g ratio. Furthermore, in male adolescents, we found a consistent age-related decrease of MR signal on T1-weighted images (Herve et al., 2011) and in MTR values (Pangelinan and Paus, unpublished observation) in the same CST region of the internal capsule; again, we interpret these findings as reflecting age-related increases in g ratio during male adolescence. Finally, we verified these MR-based inferences using electron microscopy; we measured axon diameter and thickness of myelin sheath in the splenium of the corpus callosum in adult rats and confirmed that g ratio is higher in males than females (Pesaresi and Paus, unpublished observations). Overall, the above series of in vivo (human) and ex vivo (rat) observations strongly suggest that some of the developmental trajectories and sex differences observed in white matter may be due to radial growth of the axon. In the next section, we will review the relationship between axon morphology (its cytoskeleton) and axonal transport.
The axonal cytoskeleton consists of neurofilaments and microtubules, the former outnumbering the latter 5–10 times (Lee and Cleveland, 1996). Neurofilaments are heteropolymers assembled from four polypeptide subunits; neurofilament light (NF-L), medium (NF-M), heavy (NF-H) and internexin neuronal intermediate filament protein, alpha (a-internexin, INA). Microtubules are heteropolymers consisting of alpha and beta tubulin. Neurofilaments, thanks to their elastic and fibrous properties (Wagner et al., 2007), support the cylindrical structure of an axon and, as such, protect the bore from compressive stress, securing its unobstructed state (Kumar et al., 2002). Microtubules provide the tracks for the movement of cargoes: the plus-end points from the cell body toward the synapse, thus determining direction of axonal transport by motor proteins (see below). Axonal diameter is influenced both by the number of neurofilaments and their spacing (Hoffman et al., 1984, 1987), as well as the length of the C terminus of neurofilament medium (Barry et al., 2012). The number of neurofilaments is regulated by neurofilament synthesis (gene expression) and the amount of neurofilaments undergoing (slow) axonal transport in an anterograde direction (Hoffman et al., 1984; Hoffman et al., 1987). The latter mechanism is regulated, in part, by the amount of the NF-H polypeptide: increasing its expression (beyond certain point) slows transport of neurofilaments in the axons and results in swellings at the proximal part of the axon (Marszalek et al., 1996). Another neurofilament protein, namely a-internexin, appears to play a role not only in the formation of NF heterodimers but also in the neurofilament transport; thus, interactions between the different neurofilament proteins are relevant for their assembly and functionality (Yuan et al., 2003, 2006). Finally, axon diameter is also influenced by the length of the neurofilament medium C terminus (Barry et al., 2012). Phosphorylation of the C terminus appears to be regulated by a protein synthesized by oligodendrocytes, namely myelinassociated glycoprotein (MAG); this ‘‘outside-in’’ signaling pathway provides a cellular mechanism for the coupling between myelination and axonal diameter (Garcia et al., 2003). The axonal cytoskeleton provides the necessary ‘‘infrastructure’’ for unhindered transport of various cargoes between the cell body and the synapse (Mandelkow and Mandelkow, 2002; Goldstein et al., 2008; Kanaan et al., 2013). In this way, cytoskeleton (i.e. neurofilaments and microtubules) and motor proteins are essential contributors to a large number of cellular processes, such as cell metabolism (e.g. transport of mitochondria and glycolytic enzymes) and neurotransmission (e.g. transport of synaptic-vesicle precursors). Cargoes move on the microtubule ‘‘roads’’ either at slow (1 mm/day) or fast (100 mm/day) rates (Mandelkow and Mandelkow, 2002; Shah and Cleveland, 2002). Slow axonal transport mainly moves elements of axonal cytoskeleton and cytosolic proteins
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(Barry et al., 2007), while cargoes necessary for synaptic activity are moved at fast rates (Grafstein and Forman, 1980; Goldstein et al., 2008). Could the number and/or spatial arrangement of neurofilaments and microtubules also influence the rate of axonal transport? This appears to be the case. Murayama and colleagues measured in vivo transport of Mn++ from the eye to the lateral geniculate nucleus in the monkey; Mn++ transport is a good indicator because it is partly dependent on kinesinbased processes (Bearer et al., 2007) and microtubules (Smith et al., 2007). They compared the magnocellular and parvocellular pathways, and found that the rate of transport was higher in the former (large axon diameter) than the latter (small diameter) pathway (Murayama et al., 2006). Motor proteins that generate the motion are the same for both fast and slow transport, namely proteins of kinesin superfamily (45+ kinesins) and dyneins. The two types of motor proteins differ in their involvement in anterograde (plus-end) and retrograde (minus-end) transport, kinesins mediating the former and dyneins mostly the latter. kinesin-1 is a heterotetramer consisting of two heavy chain (KHC) and two light chain (KLC) subunits (DeBoer et al., 2008). Typically, heavychain domains (‘‘heads’’) of a kinesin bind to a microtubule and ‘‘walk’’ along it while hydrolyzing ATP; light chains located on the opposite end of a kinesin contain ‘‘docking’’ elements that bind various cargoes (Fig. 4). Cytoplasmatic dynein (dynein 1) is the only protein from the dynein family involved in axonal transport; as other dyneins, it consists of intermediate, light intermediate, light and heavy chain subunits, the latter containing the motor domain (Vallee et al., 2004). Overall, together with motor proteins, the axonal cytoskeleton (neurofilaments and microtubules) provides a powerful machinery for moving cargoes between the cell body and synapse. Variations in the smooth functioning of this system are likely to have both structural (e.g., thin axons) and functional (e.g., insufficient amount of synaptic vesicle precursors) consequences and, in turn, influence signals measured with structural and functional MR imaging.
AXONAL CYTOSKELETON AND TRANSPORT: GENES As reviewed above, there is a bi-directional relationship between axonal cytoskeleton and axonal transport: cytoskeleton enables the transport and the transport contributes to the construction of cytoskeleton. As pointed out above, many properties of white matter assessed in vivo with MRI may reflect this two-way interaction, as it unfolds in health and disease. How can we evaluate this possibility? We suggest that common genetic variations observed in the general population – in addition to the study of patients with rare (monogenic) disorders – provide an opportunity for identifying molecular pathways underlying inter-individual variations in the various MR-based properties of white matter (Paus, 2013). To facilitate this work, we provide a panel of relevant genes (Table 1). We have assembled this
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Fig. 4. Schematic representation of the regulation of the main motor protein driving anterograde axonal transport: conventional kinesin. Conventional kinesin is composed of heavy chain (kinesin-1, in red) and light chain (KLC, in blue) homodimers. Kinesin-1s use energy derived from ATP hydrolysis to translocate along microtubules (MT). Kinesin light chains play a critical role in the binding of conventional kinesin to transported membrane-bound organelle (MBO) cargoes. Various protein kinases phosphorylate selected subunits of conventional kinesin. The functional consequence of each phosphorylation event is determined in part by the major function of the subunit targeted. For example, phosphorylation of KLCs by GSK3 promotes the detachment of conventional kinesin from membranes, whereas phosphorylation of kinesin-1s by JNK inhibits the binding of conventional kinesin to microtubules. From Morfini et al. (2009).
panel through a combination of approaches, including the knowledge of molecular mechanisms reviewed in the previous section, the use of Gene Ontology (GO) database (Ashburner et al., 2000), and the review of genes expressed in mature (rat) axons (Taylor et al., 2009). The list contains 34 genes including those coding the building blocks of neurofilaments and microtubules, motor proteins, two protein kinases involved in the regulation of conventional kinesin (Morfini et al., 2009), as well as proteins implicated in neurodegenerative diseases with a likely dysfunction of fast axonal transport (Morfini et al., 2009; Kanaan et al., 2013). For each gene, we include the number of single nucleotide polymorphisms (SNPs) genotyped by the Illumina Human660-Quad BeadChip (Illumina, San Diego, CA) and the number of known missense (non-synonymous) mutations reported in the Human Gene Mutation Database as of September 2nd, 2013 (Stenson et al., 2003). In addition to this focused selection of genes, we surveyed the GO database and found 13 relevant genegroups (Table 2).
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Table 1. Genes relevant to axonal cytoskeleton and transport. SNPs, the number of single nucleotide polymorphisms genotyped by the Illumina Human660-Quad BeadChip (Illumina, San Diego, CA); Mutations, the number of known missense (non-synonymous) mutations reported in the Human Gene Mutation Database as of September 2nd, 2013; kb, 1000 base pairs of DNA Gene symbol
Gene name
Chromosome
Gene size (kb)
SNPs
Mutations
Domains
NEFL NEFM NEFH INA TUBA1A TUBA1B TUBA1C TUBB MAP1B MAPT DYNC1I1 DYNC1I2 DYNC1LI1 DYNC1LI2 DYNLT1 DYNLL2 DYNLT3 DYNLRB2 DYNC1H1 KIF1A KIF1B KIF1C KLC1 KLC2 KLC3 KLC4 GSK3B MAPK10 PSEN1 HTT APP APPBP2
Neurofilament, light polypeptide Neurofilament, medium polypeptide Neurofilament, heavy polypeptide internexin neuronal intermediate filament protein, alpha Tubulin, alpha 1a Tubulin, alpha 1b Tubulin, alpha 1c Tubulin, beta class I Microtubule-associated protein 1B Microtubule-associated protein tau Dynein, cytoplasmic 1, intermediate chain 1 Dynein, cytoplasmic 1, intermediate chain 2 Dynein, cytoplasmic 1, light intermediate chain 1 Dynein, cytoplasmic 1, light intermediate chain 2 Dynein, light chain, Tctex-type 1 Dynein, light chain, LC8-type 2 Dynein, light chain, Tctex-type 3 Dynein, light chain, roadblock-type 2 Dynein, cytoplasmic 1, heavy chain 1 Kinesin family member 1A Kinesin family member 1B Kinesin family member 1C Kinesin light chain 1 Kinesin light chain 2 Kinesin light chain 3 Kinesin light chain 4 Glycogen synthase kinase 3 beta Mitogen-activated protein kinase 10 Presenilin 1 Huntingtin Amyloid beta (A4) precursor protein Amyloid beta precursor protein (cytoplasmic tail) binding protein 2 Superoxide dismutase 1, soluble Proteolipid protein 1
8 8 22 10 12 12 12 6 5 17 7 2 3 16 6 17 X 16 14 2 1 17 14 11 19 6 3 4 14 4 21 17
5663 5333 11097 13189 978 3738 8249 5039 102280 133952 325919 60938 44904 30727 8298 6839 8801 9688 86271 106545 170898 30452 72364 10159 10781 15205 41651 438008 87257 169280 290278 83061
0 1 4 1 0 0 0 0 34 6 84 2 2 2 1 0 1 5 11 44 16 3 4 0 1 0 11 90 7 22 51 7
25 3 8 0 33 0 0 3 0 84 0 0 0 0 0 0 0 1 6 3 6 0 0 0 0 0 5 4 236 4 75 0
Neurofilaments Neurofilaments Neurofilaments Neurofilaments Microtubules Microtubules Microtubules Microtubules Microtubules Microtubules Motor protein Motor protein Motor protein Motor protein Motor protein Motor protein Motor protein Motor protein Motor protein Motor protein Motor protein Motor protein Motor protein Motor protein Motor protein Motor protein Regulation Regulation Neurodegeneration Neurodegeneration Neurodegeneration Neurodegeneration
21 X
9309 15794
1 3
180 188
Neurodegeneration Neurodegeneration
SOD1 PLP1
Table 2. Gene Ontology categories relevant to axonal cytoskeleton and transport Name
ID
Genes
Axon cargo transport Anterograde axon cargo transport Retrograde axon cargo transport Axon transport of mitochondrion Regulation of axon diameter Synaptic vesicle transport Anterograde synaptic vesicle transport Synaptic vesicle amine transport Neurofilament cytoskeleton organization Neurofilament cytoskeleton Neurofilament Structural constituent of myelin sheath Axolemma
GO:0008088 GO:0008089 GO:0008090 GO:0019896 GO:0031133 GO:0048489 GO:0048490 GO:0015842 GO:0060052
37 23 6 5 3 63 15 2 8
GO:0060053 GO:0005883 GO:0019911 GO:0030673
12 9 5 11
Definitions and annotated genes for each group can be retrieved from http:// amigo.geneontology.org/.
Overall, this panel of genetic variations in genes involved in axonal cytoskeleton and axonal transport provides a starting point for exploring – in the developing and aging brain – the bi-directional relationship between axonal structure (cytoskeleton) and function (transport).
CONCLUSION Axons are not just wires for conducting electrical signals – they also serve as roads for carrying cargoes between the cell body and the synapse. Subtle deviations in the latter function will affect the maintenance and viability of neurons, as well as fidelity of neurotransmission. It is likely that processes (and genes) influencing axonal transport contribute to inter-individual variations in WM properties observed with MR imaging during the lifespan. Disturbances in axonal transport play a role in the pathophysiology of neurodevelopmental and neurodegenerative disorders. Our understanding of these processes would be facilitated by further refinements of MR imaging techniques that offer higher
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specificity vis-a`-vis axonal compartments. For example, intra-axonal diffusion can be measured in the human brain with diffusion tensor spectroscopy using a metabolite confined to the intra-axonal space and present in relatively high concentrations, namely N-acetyl aspartate (Upadhyay et al., 2008). Other techniques, used in animal models, include diffusion imaging performed at high b-values and thus suitable for tracking slow motion of water protons in intra-axonal space (Barazany et al., 2009), as well as the use of the double pulsed-field gradient method to achieve this at smaller gradients (Komlosh et al., 2013). These techniques need to be further developed, however, in order to make them usable in large normative or clinical samples.
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(Accepted 29 January 2014) (Available online xxxx)
Please cite this article in press as: Paus T et al. White matter as a transport system. Neuroscience (2014), http://dx.doi.org/10.1016/ j.neuroscience.2014.01.055
