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Super-resolution kinetochore tracking reveals the mechanisms of human sister kinetochore directional switching.

Burroughs NJ, Harry EF, McAinsh AD - Elife (2015)

Bottom Line: Here, we combine super-resolution tracking of kinetochores with automated switching-point detection to analyse sister switching dynamics over thousands of events.We discover that switching is initiated by both the leading (microtubules depolymerising) or trailing (microtubules polymerising) kinetochore.Surprisingly, trail-driven switching generates an overstretch of the chromatin that relaxes over the following half-period.

View Article: PubMed Central - PubMed

Affiliation: Warwick Systems Biology Centre, Warwick Mathematics Institute, University of Warwick, Coventry, United Kingdom.

ABSTRACT
The congression of chromosomes to the spindle equator involves the directed motility of bi-orientated sister kinetochores. Sister kinetochores bind bundles of dynamic microtubules and are physically connected through centromeric chromatin. A crucial question is to understand how sister kinetochores are coordinated to generate motility and directional switches. Here, we combine super-resolution tracking of kinetochores with automated switching-point detection to analyse sister switching dynamics over thousands of events. We discover that switching is initiated by both the leading (microtubules depolymerising) or trailing (microtubules polymerising) kinetochore. Surprisingly, trail-driven switching generates an overstretch of the chromatin that relaxes over the following half-period. This rules out the involvement of a tension sensor, the central premise of the long-standing tension-model. Instead, our data support a model in which clocks set the intrinsic-switching time of the two kinetochore-attached microtubule fibres, with the centromeric spring tension operating as a feedback to slow or accelerate the clocks.

No MeSH data available.


Related in: MedlinePlus

Variability in inter-sister distance across switching events.(A–C) Distribution of the inter-sister distance at specified times: (A) 4 s prior to event, (B) at switching event and (C) 4 s after the switching event (times are marked in Figure 4A). Events separated into lead initiated directional switch (LIDS) (black, n=4900) and trail initiated directional switch (TIDS) (red, n=3143). Separation of medians (shown as dashed lines) is 36, 2 and–107 nm, respectively (LIDS – TIDS inter-sister distance), corresponding to 23, 1.5 and 70% of the distribution SD. (D–F). Distribution of the inter-sister distance at specified times: (D) 4 s prior to event, (E) at event and (F) 4 s after event conditioned on previous event type (times marked in Figure 4B). Note median time order changes as time series in Figure 4B. Coherent runs are restricted to 12–40 s, sample sizes as (B). Kruskal–Wallis test on homogeneity is p=2.9 × 10–19, 0.0057, 1.4 × 10–113 for 4 s prior to event, at event and 4 s after event, respectively.DOI:http://dx.doi.org/10.7554/eLife.09500.015
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fig4s2: Variability in inter-sister distance across switching events.(A–C) Distribution of the inter-sister distance at specified times: (A) 4 s prior to event, (B) at switching event and (C) 4 s after the switching event (times are marked in Figure 4A). Events separated into lead initiated directional switch (LIDS) (black, n=4900) and trail initiated directional switch (TIDS) (red, n=3143). Separation of medians (shown as dashed lines) is 36, 2 and–107 nm, respectively (LIDS – TIDS inter-sister distance), corresponding to 23, 1.5 and 70% of the distribution SD. (D–F). Distribution of the inter-sister distance at specified times: (D) 4 s prior to event, (E) at event and (F) 4 s after event conditioned on previous event type (times marked in Figure 4B). Note median time order changes as time series in Figure 4B. Coherent runs are restricted to 12–40 s, sample sizes as (B). Kruskal–Wallis test on homogeneity is p=2.9 × 10–19, 0.0057, 1.4 × 10–113 for 4 s prior to event, at event and 4 s after event, respectively.DOI:http://dx.doi.org/10.7554/eLife.09500.015

Mentions: Although we have detected clear signatures in the switching choreographies (Figure 4), these reflect regulatory and mechanical processes of a highly stochastic system. This stochasticity is evident on many scales. First, kinetochores are known to display a range of stochasticity in their trajectories, from near deterministic oscillations to the near random (Jaqaman et al., 2010; Magidson et al., 2011). Second, the switching times are stochastic; the duration of a coherent run has a large variability with a coefficient of variation (standard deviation [SD]/mean) of 0.45, similar for both runs terminated by an LIDS (mean time and SD 29.6 ± 12.7 s) or a TIDS (29.2 ± 13.6 s, Figure 4—figure supplement 1). Thus, although we have invoked a clock mechanism as a switching time regulator, it is inherently stochastic. This stochasticity could stem from both the number and depolymerisation/polymerisation state of individual microtubules that make up the K-fibre. The fraction of microtubules that are in a polymerising state within a K-fibre is highly variable among kinetochores (Armond et al., 2015), indicating that growing and shrinking K-fibres are unlikely to be composed of fully coherent microtubules. Third, the signatures in Figure 4 are a mean behaviour, while variability in the inter-sister stretch throughout the dynamics is in fact large. This can be seen at the population level of trajectories, where the inter-sister distance distributions for LIDS and TIDS only show marginal separation before the switching event (–4 s), are hardly separated at the event, while separation increases after the event (+4 s) (Figure 4—figure supplement 2A–C). The inter-sister distance distribution over a switching event is in fact far from bimodal; a mixture of two Gaussian distributions requires the respective means to be separated by at least 2 SDs to be bimodal, the largest we observe is 70% at 4 s post event (Figure 4—figure supplement 2A–C). The separation of these distributions does not improve even on further categorising by the prior event (i.e. prior LIDS or TIDS; Figure 4—figure supplement 2D–F). Therefore, we have to conclude that the signatures shown in Figure 4 are not a universal behaviour but only detectable on averaging; that is, the actual switching process is highly stochastic. It may be that analysis of the most deterministic trajectories will reduce this stochasticity in the switching dynamics and signatures. However, directional switching may be a composite process that integrates over multiple signals, that is, tension may not be the only determinant; the stochasticity in our signatures would then be due to measuring only one of these determinants. It remains unknown to what extent the stochasticity in switching time and switching type explains the observed diversity in kinetochore trajectory dynamics, or whether other sources of variability exist.


Super-resolution kinetochore tracking reveals the mechanisms of human sister kinetochore directional switching.

Burroughs NJ, Harry EF, McAinsh AD - Elife (2015)

Variability in inter-sister distance across switching events.(A–C) Distribution of the inter-sister distance at specified times: (A) 4 s prior to event, (B) at switching event and (C) 4 s after the switching event (times are marked in Figure 4A). Events separated into lead initiated directional switch (LIDS) (black, n=4900) and trail initiated directional switch (TIDS) (red, n=3143). Separation of medians (shown as dashed lines) is 36, 2 and–107 nm, respectively (LIDS – TIDS inter-sister distance), corresponding to 23, 1.5 and 70% of the distribution SD. (D–F). Distribution of the inter-sister distance at specified times: (D) 4 s prior to event, (E) at event and (F) 4 s after event conditioned on previous event type (times marked in Figure 4B). Note median time order changes as time series in Figure 4B. Coherent runs are restricted to 12–40 s, sample sizes as (B). Kruskal–Wallis test on homogeneity is p=2.9 × 10–19, 0.0057, 1.4 × 10–113 for 4 s prior to event, at event and 4 s after event, respectively.DOI:http://dx.doi.org/10.7554/eLife.09500.015
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fig4s2: Variability in inter-sister distance across switching events.(A–C) Distribution of the inter-sister distance at specified times: (A) 4 s prior to event, (B) at switching event and (C) 4 s after the switching event (times are marked in Figure 4A). Events separated into lead initiated directional switch (LIDS) (black, n=4900) and trail initiated directional switch (TIDS) (red, n=3143). Separation of medians (shown as dashed lines) is 36, 2 and–107 nm, respectively (LIDS – TIDS inter-sister distance), corresponding to 23, 1.5 and 70% of the distribution SD. (D–F). Distribution of the inter-sister distance at specified times: (D) 4 s prior to event, (E) at event and (F) 4 s after event conditioned on previous event type (times marked in Figure 4B). Note median time order changes as time series in Figure 4B. Coherent runs are restricted to 12–40 s, sample sizes as (B). Kruskal–Wallis test on homogeneity is p=2.9 × 10–19, 0.0057, 1.4 × 10–113 for 4 s prior to event, at event and 4 s after event, respectively.DOI:http://dx.doi.org/10.7554/eLife.09500.015
Mentions: Although we have detected clear signatures in the switching choreographies (Figure 4), these reflect regulatory and mechanical processes of a highly stochastic system. This stochasticity is evident on many scales. First, kinetochores are known to display a range of stochasticity in their trajectories, from near deterministic oscillations to the near random (Jaqaman et al., 2010; Magidson et al., 2011). Second, the switching times are stochastic; the duration of a coherent run has a large variability with a coefficient of variation (standard deviation [SD]/mean) of 0.45, similar for both runs terminated by an LIDS (mean time and SD 29.6 ± 12.7 s) or a TIDS (29.2 ± 13.6 s, Figure 4—figure supplement 1). Thus, although we have invoked a clock mechanism as a switching time regulator, it is inherently stochastic. This stochasticity could stem from both the number and depolymerisation/polymerisation state of individual microtubules that make up the K-fibre. The fraction of microtubules that are in a polymerising state within a K-fibre is highly variable among kinetochores (Armond et al., 2015), indicating that growing and shrinking K-fibres are unlikely to be composed of fully coherent microtubules. Third, the signatures in Figure 4 are a mean behaviour, while variability in the inter-sister stretch throughout the dynamics is in fact large. This can be seen at the population level of trajectories, where the inter-sister distance distributions for LIDS and TIDS only show marginal separation before the switching event (–4 s), are hardly separated at the event, while separation increases after the event (+4 s) (Figure 4—figure supplement 2A–C). The inter-sister distance distribution over a switching event is in fact far from bimodal; a mixture of two Gaussian distributions requires the respective means to be separated by at least 2 SDs to be bimodal, the largest we observe is 70% at 4 s post event (Figure 4—figure supplement 2A–C). The separation of these distributions does not improve even on further categorising by the prior event (i.e. prior LIDS or TIDS; Figure 4—figure supplement 2D–F). Therefore, we have to conclude that the signatures shown in Figure 4 are not a universal behaviour but only detectable on averaging; that is, the actual switching process is highly stochastic. It may be that analysis of the most deterministic trajectories will reduce this stochasticity in the switching dynamics and signatures. However, directional switching may be a composite process that integrates over multiple signals, that is, tension may not be the only determinant; the stochasticity in our signatures would then be due to measuring only one of these determinants. It remains unknown to what extent the stochasticity in switching time and switching type explains the observed diversity in kinetochore trajectory dynamics, or whether other sources of variability exist.

Bottom Line: Here, we combine super-resolution tracking of kinetochores with automated switching-point detection to analyse sister switching dynamics over thousands of events.We discover that switching is initiated by both the leading (microtubules depolymerising) or trailing (microtubules polymerising) kinetochore.Surprisingly, trail-driven switching generates an overstretch of the chromatin that relaxes over the following half-period.

View Article: PubMed Central - PubMed

Affiliation: Warwick Systems Biology Centre, Warwick Mathematics Institute, University of Warwick, Coventry, United Kingdom.

ABSTRACT
The congression of chromosomes to the spindle equator involves the directed motility of bi-orientated sister kinetochores. Sister kinetochores bind bundles of dynamic microtubules and are physically connected through centromeric chromatin. A crucial question is to understand how sister kinetochores are coordinated to generate motility and directional switches. Here, we combine super-resolution tracking of kinetochores with automated switching-point detection to analyse sister switching dynamics over thousands of events. We discover that switching is initiated by both the leading (microtubules depolymerising) or trailing (microtubules polymerising) kinetochore. Surprisingly, trail-driven switching generates an overstretch of the chromatin that relaxes over the following half-period. This rules out the involvement of a tension sensor, the central premise of the long-standing tension-model. Instead, our data support a model in which clocks set the intrinsic-switching time of the two kinetochore-attached microtubule fibres, with the centromeric spring tension operating as a feedback to slow or accelerate the clocks.

No MeSH data available.


Related in: MedlinePlus