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Bioenergetics and dynamics of ciliary responses and systems biology of phototaxis in Chlamydomonas reinhardtii

2011· article· en· W24326189 on OpenAlexfundno aff
Suphatra Adulrattananuwat

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldNeuroscience
TopicPhotoreceptor and optogenetics research
Canadian institutionsnot available
FundersTeck ResourcesInternational Zinc AssociationCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorCopper Development AssociationInternational Copper AssociationNickel Producers Environmental Research Association
KeywordsPhototaxisChlamydomonas reinhardtiiBioenergeticsDynamics (music)BiologyPhysicsCell biologyBotanyGenetics

Abstract

fetched live from OpenAlex

The goal of this dissertation is to understand how a eukaryotic cell makes decisions. Chlamydomonas reinhardtii, a biciliated unicellular green alga, is used as our model organism. This organism has the ability to track the light using its photoreceptor called rhodopsin, which overlays the eyespot. The organism makes decisions to swim toward, away from, perpendicular to or to ignore the light using its slender arm-like structures called cilia. It can integrate several external inputs such as ion concentration and light intensity, and then process this information to adjust the steering of its cilia corresponding to its environment. We investigated how red light (670 nm) influences cell behavior. Most studies were done with a single cell held on a micropipette making it possible to observe the cilia behavior over a long time. The cell is illuminated with near-infrared light (peak at 870 nm) to avoid photoreceptor excitation. Ciliary movement is monitored using a quadrant photodiode detector (Chapter 2). Interpreted ciliary behavior parameters are the beating frequency (BF) and the stroke velocity (SV). Pulse stimuli were used to stimulate the mutant strain 806 (agg1), a negatively phototactic cell, whose beating frequency is in the same range as wild type. The "step-up" red light from the dark increases the beating frequency as an exponential function, y(t) = a*[1-exp(-t/b)] where y is a beating frequency, a is an amplitude and b is a time constant. On the other hand, the "step-down" red light drops the beating frequency transiently and recovers to its normal beating frequency of about 50 Hz in about 10 s. The 40 s duration pulse gave the maximum transient drop of the beating frequency. Using multi-sinusoidal red-light stimuli, I compared the behavior of the double mutant (cpc1-2) relative to the single mutant 806 (cpc1-2 was backcrossed to strain 806 so it is a single mutant with respect to 806). The mutant misses the part of the central-pair complex containing the enolase enzyme, one of the ciliary glycolytic enzymes that produces ATP in the cilia. Under a high constant-intensity of red light, the BF fluctuation is less than 2%. In the dark, BF of cpc1-2 is about 30 Hz which is lower than 806 probably due to less ATP being available. However, BF can be increased to the 806 level of 50 Hz by exposure to red light. A simple hypothesis is that red-light photosynthesis of the chloroplast makes ATP more available in the cell. In any case, sinusoidal red-light response of cpc1-2 shows that part of the early signal processing is approximately linear. In this case, cells respond to a decrease in light intensity by differentiating the red light signal. Our hypothesis is that the cell creates this signal to avoid futile usage of ATP. The transfer function describing this step is, G(s) = a*s*exp(-&tau*s) where &tau = 0.40 sec. In addition to this linear part, both strains have non-linear or approximately full-wave rectified signal processing of another red light created signal with a simple delay in time described by the transfer function, G(s) = a*exp(-&taustrain*s) where &tau806 = 1.18 sec and &taucpc1-2 = 0.37 sec. The longer delay time of 806 is likely due to the slow conversion of 3-phosphoglycerate (3PG) to adenosine triphosphate (ATP), in the glycolytic pathway, which is absent in the mutant. We hypothesize that the slow synthesis is due to the positive Gibbs free energy of two steps in the ciliary glycolytic pathway between 3PG and production of ATP and pyruvate. Furthermore, the beating frequency of red-light sinusoidal responses is stabilized by negative feedback. However, in the frequency range from 10 to 100 Hz in both strains that stabilizing negative feedback becomes positive and the BF jumps to a new state. In addition, I also studied how external ion concentration such as Ca2+, H+, and K+, and red light affect phototaxis of positively and negatively-phototactic cells (1117 and 806 respectively). I have tracked a cell population using the cell-tracking system for 10 s after stimulating them with green light (Chapter 4). Increasing [Ca2+]ext with a red light background enhances the motion of cells in the same and the opposite direction respectively according to cells' phototactic behavior under normal condition (pCa4 and pH 6.8). Increasing the pH tends to induce cells to move away from the light while increasing the [K+]ext gave the opposite results. Changing external ion concentration such as H+ and K+ affects the cell's membrane potential. Changing Ca2+ concentration affects both membrane potential and likely triggers internal signaling proteins such as IP3 and cAMP. Therefore, we hypothesize that cells may integrate these and potentially other signals to decide its phototaxis. Finally, I developed a technique that can be used to measure changes of the electric field across the plasma membrane of the cell in response to rhodopsin excitation. Rhodopsin excitation is thought to cause transmembrane ion influxes resulting in changes in the electric field across the plasma membrane. These electric field signals are then sensed in the cilia to enable phototactic steering of the cell (Chapter 5).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.088
GPT teacher head0.312
Teacher spread0.224 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations1
Published2011
Admission routes1
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