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What is the minimum functional biological light sensor?

What is the minimum functional biological light sensor?


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As a follow up to this question regarding the evolution of the eye, it was suggested that primitive eyes only needed to evolve a light sensor and could perhaps use the existing biochemical cascade infrastructure.

What is the minimum of this biological light sensor? Please provide a detailed biochemical description of how many proteins are involved and their interactions which can be "hooked up" to the existing biochemical cascade infrastructure. (to the best of our knowledge)


The minimum biological light sensor is just an existing signaling molecule that, by chance, also has some change in its activity due to light. There are several ways this could happen.

One extremely simple example would be based on a G-protein coupled receptor (GPCR). GPCRs are a family of related signal transduction proteins (the largest family) that sit on the surface of a cell's membrane. Each one is sensitive to a molecule or small group of related molecules. When exposed to the molecule it is sensitive to, the GPCR sets of a series of chemical cascades that trigger some activity in the cell. For example, it can serve as a signal for the cell to move towards the place where the chemical was detected, or move away.

Now, imagine that, just by chance, the molecule the GPCR is sensitive to has some sensitivity to light, in that when exposed to light the molecule either has a larger or smaller chance (even slightly) of binding to the GPCR. This is not improbable, a ton of biological molecules have this characteristic, in fact many have substantial responses to light even though they are not involved in light-sensing pathways. A good example is called "photoisomerization", where the same molecule can have more than one shape, and switches between two shapes when exposed to light.

This would be a biological light sensor. Not necessarily a very sensitive one, but once you have such a basic light sensor, natural selection would take over and lead to stronger and stronger effects.

In fact, this is pretty much how our eyes work today. It uses a GPCR whose target is a molecule that changes its shape when exposed to light, which makes it no longer able to bind to the receptor, shutting of the signaling cascade. What is more, this molecule is not only used in sensing light, it is a precursor to a molecule needed for gene transcription and signaling.

In fact, you can splice light-sensitive GPCRs from bacteria (which are different than those in animals) into non-light-sensitive animal cells, and those molecules will automatically integrate with existing GPCR signaling cascades, rendering the cell light sensitive.


Thermoplasmonic sensor for the detection of phase transitions in nanoscale materials

The work was conducted under the auspices of the Russian Science Foundation the project "Synthesis and research of a new class of nanocomposite ceramics with degenerate dielectric constant for optoplasmonic applications" is headed by Professor Sergey Kharintsev (KFU's Institute of Physics).

Professor Kharintsev, the first co-author, comments, "Under the influence of light, collective oscillations of electrons can be excited in metallic nanostructures, and as a result the electric field in the vicinity of the nanostructures strongly increases. The field of physics that studies the effects of generation and propagation of such electromagnetic excitations is called plasmonics. Its most striking achievements include optical visualization of single molecules and diagnostics of their vibrations with a spatial resolution of 0.16 nm (for example, the size of a water molecule is 0.3 nm). In practice, the achievements of plasmonics are widely used in the development of highly sensitive biomedical sensors, in the creation of new generation solar cells, in the development of the element base of nanophotonics and optoelectronics, in particular, light filters, polarizers, modulators, waveguides, etc."

Strong heating of nanostructures under plasmon resonance conditions underlies a number of unique applications of thermoplasmonics in biology and medicine. This is the basis of photothermal cancer therapy methods. It made possible to create a thermoplasmonic biosensor with a record sensitivity of 0.22 pmol per liter for the detection of SARS-CoV-2 (COVID-19 virus), as well as reusable protective masks in which copper nanoparticles are heated by sunlight to temperatures at which viruses die.

"We have developed an optical sensor, which is a metasurface composed of an ordered array of metallic titanium nitride (TiN) nanoantennas, each 500 times smaller than a human hair. By scanning ordered nanoantennas with a focused laser beam, they can be successively heated to a predetermined temperature (up to several hundred degrees) in less than one microsecond. Using such an optical sensor, we were able to determine for the first time the local glass transition temperature of a polymer with a spatial resolution of 200 nanometers," continues Kharintsev. "I would like to draw the attention of biologists, chemists and physicists to the fact that the functional properties of the created thermoplasmonic sensor go far beyond the scope of the mentioned application. This nanophotonic device can be used to study, for example, size effects in spatially limited (in three directions) 0D polymers. Using the sensor, it is possible to detect the glass transition temperature of spatially inhomogeneous polymer films, including multicomponent polymer mixtures. A thermoplasmonic optical sensor can be used to study structural changes and phase transitions, such as to determine the local crystallization (melting) temperature of nanoobjects. Attention should be paid to the possibility of using a thermoplasmonic sensor in the study of biological reactions on single cells (for instance, in the study of denaturation of individual proteins)."

As the interviewee noted, the team plans to use thermoplasmonic sensors in ultrafast calorimetry, another research priority of Kazan University chemists.

Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.


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Schmitz, O., Katayama, M., Williams, S. B., Kondo, T. & Golden, S. S. Science 289, 765–768 (2000).

Davis, S. J., Vener, A. V. & Vierstra, R. D. Science 286, 2517–2520 (1999).

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RESULTS

To study the behaviour of the luxCDABE cassette which appears often as an operon in natural and synthetic systems, we first constructed a circuit that regulates the luxCDABE cassette with an inducible Plux promoter using the quorum-sensing transcriptional activator LuxR (Figure 2A). LuxR was expressed under a constitutive promoter, located on a low-copy-number plasmid (LCP), and induced by acyl homoserine lactone (AHL). The AHL-LuxR complex activates the promoter Plux (Figure 2A), which regulates both luciferase (a heterodimer of luxA and luxB) and the enzymes required for the production of its substrate aliphatic aldehyde (luxC, luxD, and luxE). The luxCDABE operon was located on a high-copy-number plasmid (HCP). The circuit activity, which is measured by the transfer function between AHL and the bioluminescent signal, is determined by a cascade of Plux activity and the metabolic reactions of the luxCDABE cassette (Figure 2B). Then, the luxCDABE operon was replaced by GFP, whose expression level is directly proportional to the activity of Plux promoter. The measured transfer functions of AHL-GFP and AHL-bioluminescence are well-matched by Hill-functions (Figure 2B).

Studying the luxCDABE operon characteristics. (A) The luxCDABE operon is regulated by the Plux promoter located on a high-copy-number plasmid (HCP). LuxR is regulated by a constitutive promoter located on a low-copy-number plasmid (LCP) and is induced by acyl-homoserine-lactone (AHL). (B) Measured transfer function between AHL input concentration and either a green fluorescent protein (GFP, right) or bioluminescent (left) outputs. The dashed lines are fitted to Hill functions (for GFP according to |$frac<><>$| and for bioluminescence according to |$frac<<>>>><<>> + 30>>$|⁠ ). (C) The bioluminescent signal and the GFP signal comparison, relative to the same levels of AHL concentration. The GFP level regulated by Plux promoter are proportional to lux enzymes (D) Schematic model showing the linear behaviour of a system with paradoxical components compared to the non-linear behaviour of the Michaelis-Menten model. The average and errors (s.e.m.) shown in the figures are derived from three experiments.

Studying the luxCDABE operon characteristics. (A) The luxCDABE operon is regulated by the Plux promoter located on a high-copy-number plasmid (HCP). LuxR is regulated by a constitutive promoter located on a low-copy-number plasmid (LCP) and is induced by acyl-homoserine-lactone (AHL). (B) Measured transfer function between AHL input concentration and either a green fluorescent protein (GFP, right) or bioluminescent (left) outputs. The dashed lines are fitted to Hill functions (for GFP according to |$frac<><>$| and for bioluminescence according to |$frac<<>>>><<>> + 30>>$|⁠ ). (C) The bioluminescent signal and the GFP signal comparison, relative to the same levels of AHL concentration. The GFP level regulated by Plux promoter are proportional to lux enzymes (D) Schematic model showing the linear behaviour of a system with paradoxical components compared to the non-linear behaviour of the Michaelis-Menten model. The average and errors (s.e.m.) shown in the figures are derived from three experiments.

Since the five genes of the luxCDABE operon are regulated by the same Plux promoter, we can assume that the expression levels of these genes are proportional to the same input ( ⁠|$x$|⁠ ). As we mentioned above, luciferase is formed by the binding of LuxA and LuxB ( ⁠|$ = frac<><<<>>>>$|⁠ , where |$<>>$| is dissociation constant), and therefore, we can approximate the level of luciferase concentration as a square function of input |$x ( <propto > )$|⁠ . The reductase, transferase and synthetase, are produced from the luxC, luxD, and luxE genes independently, and therefore their concentrations can be approximated as a linear function with input |$x$| ( ⁠|$ propto x$|⁠ ). Thus, and according to Equation ( 7), the bioluminescence signal can also be approximated by a power-law function with the input |$x$| ( ⁠|$I propto )$|⁠ , where |$1 < n < 2$|⁠ . To gain deeper insight into the biophysical model of the wild type circuit, we compared the bioluminescent signal and the GFP signal relative to the same levels of AHL concentration (Figure 2C). The resulting transfer function, that describes the behavior of the luxCDABE operon without the contribution of the Plux/LuxR system, is fitted by a power-law relation ( ⁠|$I propto GF>$|⁠ ). Based on our analysis, the bioluminescent signal of the wild type circuit is ‘tailored’ to respond in a dose-dependent manner to changes in the input concentrations with an amplification factor (gain) that cannot be achieved by fluorescent protein reporters. Furthermore, the design principle of the wild type luxCDABE cassette, based on the regulation of paradoxical components with the same signal ( 36) (e.g. increasing both the forward and reverse reaction rates of the same metabolic pathway, Figure 2D), allows to convert non-linear relations (e.g. Michaelis–Menten model or Hill-function, Figure 2D) to linear or power-law relations, where saturation is reached for very high input levels only.

In addition to using the luxCDABE cassette as a reporter, we modified its gene structure to build circuits that can perform logic computations. To this end, the luxCDABE cassette was split into two sub-circuits that were regulated by two different input chemicals. The first sub-circuit received Arabinose and regulated the luxAB genes involved in the forward reaction, and the second sub-circuit received AHL and regulated the luxCDE genes involved in the reverse reaction (Figure 3A). The transcription factor AraC is produced by a constitutive promoter, located on a LCP. Arabinose binds to AraC, forming a complex which activates the PBAD promoter. LuxR is regulated by a constitutive promoter located on a LCP and is induced by acyl-homoserine-lactone (AHL) to activate Plux. The PBAD and Plux promoters, located on a medium-copy-number plasmid (MCP) and an HCP respectively, regulate the luciferase and the complex enzymes required to produce bioluminescence. Re-arranging Equation ( 7) shows that the bioluminescent signal can compute the minimum between |$frac<<>><<>>$| and |$frac<< >><<>>$| levels |$left(I propto MIN left<><>,frac<><>> ight> ight)$|⁠ , (see Section 3 of the Supplementary Data for additional information). Minimum function, which receives continuous physical inputs and displays a vague output over an output dynamic range, is widely used in fuzzy logic to implement conjunction ( 37). Typically, when the inputs are ‘0’ and ‘1’, the MIN fuzzy lattice acts as an AND Boolean gate (Figure 3B). Given that the bioluminescent enzymes were regulated by inducible promoters which convert the continuous levels of Arabinose and AHL to two-discrete states (‘0’, ‘1’), the design of the MIN fuzzy lattice (Figure 3A) can also implement an AND logic gate (Figure 3C).

A synthetic MIN fuzzy lattice (soft minimum function) in living cells implemented by splitting the luxCDABE cassette. (A) The luxCDE-luxAB spitting design. The luxCDE is regulated by the Plux promoter located on a high copy number plasmid (HCP). LuxR is regulated by a constitutive promoter located on a low copy number plasmid (LCP) and is induced by acyl-homoserine-lactone (AHL) to activate Plux. The luxAB is regulated by the PBAD promoter located on a medium copy number plasmid (MCP). AraC is regulated by a constitutive promoter located on a LCP and is induced by Arabinose (Arab) to activate PBAD. (B) Model simulations of the synthetic MIN fuzzy lattice. The model consists of an Arab- |$$| transfer function |$ = frac<<>>>><<1 + Arab/<>>>>$|⁠ , an AHL- |$$| transfer function |$ = frac<<>>>><<1 + AHL/<>>>>$|⁠ , and a minimum function |$Out = frac <<cdot >> <<+ >>$|⁠ , where |$<>>$| and |$<>>$| have units of concentration and are proportional to the dissociation constants of binding Arab-AraC and PBAD, AHL-LuxR and Plux. (C) The measured AHL/Arabinose transfer function and bioluminescence output for the MIN fuzzy lattice. (D) Model of the synthetic AND logic gate based on LuxA-LuxB interaction. The model consists of several transfer functions: |$LuxA = frac<<>>>><<1 + Arab/<>>>>$|⁠ , |$LuxB = frac<<>>>><<1 + AHL/<>>>>$|⁠ , and |$Out = frac<><<<>>>>$|⁠ , where |$<>>$| is binding dissociation constant. (E) The luxA-luxB splitting design. LuxB is regulated by the Plux promoter located on a MCP. LuxR is regulated by a constitutive promoter located on a LCP and is induced by AHL. LuxA is regulated by the PBAD promoter located on a MCP. AraC is regulated by a constitutive promoter located on a LCP and is induced by Arabinose. The luxCDE genes are regulated by a constitutive promoter located on an HCP. (F) The measured AHL/Arabinose transfer function and bioluminescence output for an AND gate based on the LuxA-LuxB interaction. (G) Performance comparison of the two logic AND gates. The output dynamic range (ODR) was calculated as the lowest value of the logarithmic transform of the ratio between the ‘1’ levels and ‘0’ levels: |$ODR = minleft < ]>>>> <<]>>>>,log frac <<]>>>> <<]>>>>,< m>frac <<]>>>> <<]>>>>> ight>.$| All experimental data represent the average of three experiments. The average and errors (s.e.m.) shown in the figures are derived from three experiments.

A synthetic MIN fuzzy lattice (soft minimum function) in living cells implemented by splitting the luxCDABE cassette. (A) The luxCDE-luxAB spitting design. The luxCDE is regulated by the Plux promoter located on a high copy number plasmid (HCP). LuxR is regulated by a constitutive promoter located on a low copy number plasmid (LCP) and is induced by acyl-homoserine-lactone (AHL) to activate Plux. The luxAB is regulated by the PBAD promoter located on a medium copy number plasmid (MCP). AraC is regulated by a constitutive promoter located on a LCP and is induced by Arabinose (Arab) to activate PBAD. (B) Model simulations of the synthetic MIN fuzzy lattice. The model consists of an Arab- |$$| transfer function |$ = frac<<>>>><<1 + Arab/<>>>>$|⁠ , an AHL- |$$| transfer function |$ = frac<<>>>><<1 + AHL/<>>>>$|⁠ , and a minimum function |$Out = frac <<cdot >> <<+ >>$|⁠ , where |$<>>$| and |$<>>$| have units of concentration and are proportional to the dissociation constants of binding Arab-AraC and PBAD, AHL-LuxR and Plux. (C) The measured AHL/Arabinose transfer function and bioluminescence output for the MIN fuzzy lattice. (D) Model of the synthetic AND logic gate based on LuxA-LuxB interaction. The model consists of several transfer functions: |$LuxA = frac<<>>>><<1 + Arab/<>>>>$|⁠ , |$LuxB = frac<<>>>><<1 + AHL/<>>>>$|⁠ , and |$Out = frac<><<<>>>>$|⁠ , where |$<>>$| is binding dissociation constant. (E) The luxA-luxB splitting design. LuxB is regulated by the Plux promoter located on a MCP. LuxR is regulated by a constitutive promoter located on a LCP and is induced by AHL. LuxA is regulated by the PBAD promoter located on a MCP. AraC is regulated by a constitutive promoter located on a LCP and is induced by Arabinose. The luxCDE genes are regulated by a constitutive promoter located on an HCP. (F) The measured AHL/Arabinose transfer function and bioluminescence output for an AND gate based on the LuxA-LuxB interaction. (G) Performance comparison of the two logic AND gates. The output dynamic range (ODR) was calculated as the lowest value of the logarithmic transform of the ratio between the ‘1’ levels and ‘0’ levels: |$ODR = minleft < ]>>>> <<]>>>>,log frac <<]>>>> <<]>>>>,< m>frac <<]>>>> <<]>>>>> ight>.$| All experimental data represent the average of three experiments. The average and errors (s.e.m.) shown in the figures are derived from three experiments.

The implementation of AND logic gates in living cells often employs the interaction of two cooperative components such as tRNA and mRNA ( 29), protein-protein interactions ( 38), or protein-DNA interactions ( 39). The output of such circuits is activated only when both components are produced. On the other hand, we created an AND logic gate that exploits the paradoxical components which are already present in the biochemical reactions, by controlling independently the forward and reverse rates using two different inputs. To compare the performance of the two designs, we built an AND logic gate based on the LuxA–LuxB protein interaction (Figure 3D). Our strategy was to split the luxAB into two parts that are regulated by two different inputs (Figure 3E). The luxA gene is regulated by the PBAD promoter located on a MCP which receives Arabinose, and the luxB gene is regulated by the Plux promoter located on a MCP which receives AHL. Both AraC and LuxR are produced by a constitutive promoters. The luxCDE genes are constitutively produced and located on an HCP. The measured transfer function showed that when both luxA and luxB were induced by high AHL and Arabinose, a high bioluminescent signal was achieved (Figure 3F). In cases where only one of the luxA or luxB components was induced at high levels, a low bioluminescence signal was achieved. This transfer function can be approximated as an AND logic gate with an output dynamic range of 0.32 orders of magnitude (Figure 3G). However, it has a poor performance compared to the first design, which is based on the luxAB-luxCDE interaction, with an output dynamic range of more than one order of magnitude (Figure 3G). These results can be described by a minimal biochemical model (Figure 3B and D), which incorporates the promoter activities and bioluminescent signals (Equation 7). For simplicity, we assumed that all the promoters are identical and their activity can be described by the Michaelis Menten equation |$left(<<1 + x>>> ight)$|⁠ . Indeed, AND logic gates that exploit a MIN fuzzy lattice (Figure 3B) showed a better classification with a narrower dynamic range of logic states compared to the AND logic gate that is based on protein-protein interactions (Figure 3D). Further analysis that compared the performance of the two AND logic gates is provided in Section 4 of the Supplementary Data ( Supplementary Figures S4–S6 ).

Genetic circuits with tunable detection thresholds (Figure 1B) can be used to discretize continuous input values into multiple distinct outputs, acting as comparators ( 30, 35). Such circuits have recently attracted widespread attention ( 25) because they can detect graded environmental inputs, and reliably report outputs with two states. The measured signals of biosensors are often encoded by two logic states instead of analog levels, in order to tolerate noise and compensate for distortion of biological signals. Consequently, combining comparators with detection thresholds ranging from low to high levels, results in a system that can convert graded environmental signals to digital signals with high sensitivity. In such a case, only comparators that have detection thresholds lower than the input level are activated and display high outputs (Section 5, Supplementary Figure S7 of the Supplementary Data). Circuits with programmable detection thresholds are also useful for biological systems where the detection threshold is mismatched with application and design requirements ( 19, 40). As described above, controlling the reverse reaction in the luxCDABE cassette independently from the forward reaction (Figure 3A), allowed us to build a system with a programmable detection threshold using AHL (Figure 4A, Supplementary Figure S2 ). The estimated detection threshold of the transfer function increases as AHL concentrations increase (inset of Figure 4A). However, the experimental results showed that this design also affected the bioluminescence signal, when AHL concentrations changed. To reduce this dependency, we optimized the expression level of the lux enzymes by a further splitting of the operon. In the new circuit, only luxA and luxC were controlled by inducible promoters (PBAD/Arabinose, Plux/AHL, respectively), while luxB, luxD, and luxE were controlled by constitutive promoters (Figure 4B). The new strategy resulted in a transfer function with a programmable detection threshold (Figure 4C, Supplementary Figure S3 ), that increased with AHL concentration (Inset of Figure 4C), while the fold change of the ON/OFF ratio was well maintained in contrast to the first design (Figure 4D, Supplementary Tables S6, S7 ).

Design of a synthetic comparator with a programmable detection threshold in living cells by splitting the luxCDABE cassette. (A) Measured transfer function between Arabinose input concentration and bioluminescent signal, for varying acyl-homoserine-lactone (AHL) levels. These data were extracted from Figure 3C. The dots represent the experimental results and the dashed lines fit Hill function curves (see Section 2, Supplementary Table S5 of the Supplementary Data for parameters used for fitting). The inset shows the relationship between the detection threshold ( ⁠|$<>>$|⁠ ) and AHL concentration. |$<>>$| was calculated as the input value corresponding to half of the output value. (B) Design by further splitting the luxCDABE cassette. The luxC is regulated by the Plux promoter located on a high copy number plasmid (HCP). LuxR is regulated by a constitutive promoter located on a low copy number plasmid (LCP) and is induced by AHL to activate Plux. The luxA is regulated by the PBAD promoter located on a medium copy number plasmid (MCP). AraC is regulated by a constitutive promoter located on a LCP and is induced by Arabinose to activate PBAD. The luxD is regulated by a constitutive promoter located on MCP. The luxB and luxE are regulated by a constitutive promoter located on an HCP. (C) Measured transfer function between Arabinose input concentration and bioluminescence signal for varying AHL levels. The dots represent the experimental results and the dashed lines fit Hill function curves (see Section 2, Supplementary Table S5 of the Supplementary Data for parameters used for fitting). The inset shows the relationship between the detection threshold ( ⁠|$<>>$|⁠ ) and AHL concentrations. The |$<>>$| was calculated as the input value corresponding to half of the output value. (D) Fold change comparison (ON/OFF ratio) for luxCDE-luxAB (data based on A, Supplementary Table S6 ) and the new design by further luxA-luxC splitting of the luxCDABE cassette (data based on C, Supplementary Table S7 ). All experimental data represent the average of three experiments. Further statistical analysis is provided in Section 2 of the Supplementary Data.

Design of a synthetic comparator with a programmable detection threshold in living cells by splitting the luxCDABE cassette. (A) Measured transfer function between Arabinose input concentration and bioluminescent signal, for varying acyl-homoserine-lactone (AHL) levels. These data were extracted from Figure 3C. The dots represent the experimental results and the dashed lines fit Hill function curves (see Section 2, Supplementary Table S5 of the Supplementary Data for parameters used for fitting). The inset shows the relationship between the detection threshold ( ⁠|$<>>$|⁠ ) and AHL concentration. |$<>>$| was calculated as the input value corresponding to half of the output value. (B) Design by further splitting the luxCDABE cassette. The luxC is regulated by the Plux promoter located on a high copy number plasmid (HCP). LuxR is regulated by a constitutive promoter located on a low copy number plasmid (LCP) and is induced by AHL to activate Plux. The luxA is regulated by the PBAD promoter located on a medium copy number plasmid (MCP). AraC is regulated by a constitutive promoter located on a LCP and is induced by Arabinose to activate PBAD. The luxD is regulated by a constitutive promoter located on MCP. The luxB and luxE are regulated by a constitutive promoter located on an HCP. (C) Measured transfer function between Arabinose input concentration and bioluminescence signal for varying AHL levels. The dots represent the experimental results and the dashed lines fit Hill function curves (see Section 2, Supplementary Table S5 of the Supplementary Data for parameters used for fitting). The inset shows the relationship between the detection threshold ( ⁠|$<>>$|⁠ ) and AHL concentrations. The |$<>>$| was calculated as the input value corresponding to half of the output value. (D) Fold change comparison (ON/OFF ratio) for luxCDE-luxAB (data based on A, Supplementary Table S6 ) and the new design by further luxA-luxC splitting of the luxCDABE cassette (data based on C, Supplementary Table S7 ). All experimental data represent the average of three experiments. Further statistical analysis is provided in Section 2 of the Supplementary Data.

A synthetic crosstalk-compensating circuit for bacterial biosensors. (A) A synthetic gene circuit (left) and schematic model (center) for katG-based bacterial biosensor. Time courses (right) of katG promoter in response to 25 mg/l of peroxide (H2O2) and 2.5 mg/l of nalidixic acid (NA), alone and in combination. The dots represent experimental results and the dashed lines are simulation results (Equation 8) with simulation parameters: τ1= 20 min, τeff= 20 min, τD1 = 0 min, τD2= 0 min, β = 0.02, c = 2000 (see Section 2, Supplementary Table S9 of the Supplementary Data for parameters used for fitting). (B) A synthetic gene circuit (left) and schematic model (center) for recA-based bacterial biosensor. Time courses (right) of recA promoter in response to 25 mg/l of H2O2 and 2.5 mg/l of NA, alone and in combination. The dots represent experimental results and the dashed lines are simulation results (Equation 8). Simulation parameters for the fitting of NA response were τ1= 60 min, τeff= 25 min, τD1= 60 min, τD2= 100 min, β = 0.1, c = 9000. Simulation parameters for the fitting of H2O2 response were τ1= 30 min, τeff= 30 min, τD1= 0 min, τD2= 40 min, β = 0.25, c = 9000 (see Section 2, Supplementary Table S9 of the Supplementary Data for parameters used for fitting). (C) A schematic model of the crosstalk-compensating circuit. The model consists of recA and katG promoters, and an incoherent type-1 feedforward loop, which is built from a time-delay, a repressor, and an AND logic gate. (D) The crosstalk-compensating circuit is segmented into ‘sender’ and ‘receiver’ parts carried by two bacterial strains. Acyl-homoserine-lactone (AHL) quorum-sensing molecules were used to wire between the sender and the receiver. The sender included the katG promoter that regulates LuxI located on a MCP to produce AHL. There are three plasmids in the receiver. On the LCP, the promoter Pluxlbl (which is activated by the AHL-LuxR complex and has a very low basal level ( 10)) regulates the expression of TetR, and the promoter Plux regulates the expression of LuxR. On the MCP, the promoter recA regulates the expression of the luxC, luxD and luxE. On the HCP, the PtetO promoter regulates the expression of the LuxA and LuxB. AHL produced from the sender binds LuxR and activates Plux and Pluxlbl. The TetR repressor is located on the LCP and binds PtetO located on the HCP, inhibiting the activity of luciferase. (E) Measured transfer function of the receiver circuit using externally added AHL. The inset shows NIMPLY logic gate (NOT-IMPLY) that is implemented by the receiver circuit with externally supplied AHL. (F) Time courses of the crosstalk-compensating circuit response in the presence of 25 mg/l of peroxide (H2O2) and 2.5 mg/l of nalidixic acid (NA) alone and in combination. The sender and receiver circuits carried by two different bacterial strains were mixed together at a ratio of 1 to 10, respectively. (G) Simulated time courses of proteins production by the crosstalk-compensating circuit in the presence of NA or H2O2 or both. The average and errors (s.e.m.) shown in the figures are derived from three experiments.

A synthetic crosstalk-compensating circuit for bacterial biosensors. (A) A synthetic gene circuit (left) and schematic model (center) for katG-based bacterial biosensor. Time courses (right) of katG promoter in response to 25 mg/l of peroxide (H2O2) and 2.5 mg/l of nalidixic acid (NA), alone and in combination. The dots represent experimental results and the dashed lines are simulation results (Equation 8) with simulation parameters: τ1= 20 min, τeff= 20 min, τD1 = 0 min, τD2= 0 min, β = 0.02, c = 2000 (see Section 2, Supplementary Table S9 of the Supplementary Data for parameters used for fitting). (B) A synthetic gene circuit (left) and schematic model (center) for recA-based bacterial biosensor. Time courses (right) of recA promoter in response to 25 mg/l of H2O2 and 2.5 mg/l of NA, alone and in combination. The dots represent experimental results and the dashed lines are simulation results (Equation 8). Simulation parameters for the fitting of NA response were τ1= 60 min, τeff= 25 min, τD1= 60 min, τD2= 100 min, β = 0.1, c = 9000. Simulation parameters for the fitting of H2O2 response were τ1= 30 min, τeff= 30 min, τD1= 0 min, τD2= 40 min, β = 0.25, c = 9000 (see Section 2, Supplementary Table S9 of the Supplementary Data for parameters used for fitting). (C) A schematic model of the crosstalk-compensating circuit. The model consists of recA and katG promoters, and an incoherent type-1 feedforward loop, which is built from a time-delay, a repressor, and an AND logic gate. (D) The crosstalk-compensating circuit is segmented into ‘sender’ and ‘receiver’ parts carried by two bacterial strains. Acyl-homoserine-lactone (AHL) quorum-sensing molecules were used to wire between the sender and the receiver. The sender included the katG promoter that regulates LuxI located on a MCP to produce AHL. There are three plasmids in the receiver. On the LCP, the promoter Pluxlbl (which is activated by the AHL-LuxR complex and has a very low basal level ( 10)) regulates the expression of TetR, and the promoter Plux regulates the expression of LuxR. On the MCP, the promoter recA regulates the expression of the luxC, luxD and luxE. On the HCP, the PtetO promoter regulates the expression of the LuxA and LuxB. AHL produced from the sender binds LuxR and activates Plux and Pluxlbl. The TetR repressor is located on the LCP and binds PtetO located on the HCP, inhibiting the activity of luciferase. (E) Measured transfer function of the receiver circuit using externally added AHL. The inset shows NIMPLY logic gate (NOT-IMPLY) that is implemented by the receiver circuit with externally supplied AHL. (F) Time courses of the crosstalk-compensating circuit response in the presence of 25 mg/l of peroxide (H2O2) and 2.5 mg/l of nalidixic acid (NA) alone and in combination. The sender and receiver circuits carried by two different bacterial strains were mixed together at a ratio of 1 to 10, respectively. (G) Simulated time courses of proteins production by the crosstalk-compensating circuit in the presence of NA or H2O2 or both. The average and errors (s.e.m.) shown in the figures are derived from three experiments.

The crosstalk-compensating circuit was further segmented into ‘sender’ and ‘receiver’ circuits carried by two different bacterial strains, using quorum sensing molecules (AHL) for wiring ( 43) (Figure 5D). The sender circuit specifically detected H2O2 using the katG promoter and produced AHL molecules by regulating LuxI protein expression. The receiver circuit collected the diffusing AHL molecules and expressed the TetR repressor, which was regulated by Plux and LuxR. The receiver circuit was comprised of an AND gate, which was implemented by the luxAB-luxCDE interaction, where the luxCDE was activated by the recA promoter and luxAB were regulated by PtetO and repressed by TetR. The delay between the recA and katG responses was programmed by the kinetics of TetR expression and the dynamics of AHL diffusion. Initially, we tested the receiver circuit with externally supplied AHL (Figure 5E). The measurements taken at the steady state showed that a high bioluminescent signal was measured when the AHL concentration was low, and vice versa. This circuit can also act as NOT IMPLY logic gate (inset Figure 5E). Then, we tested the sender and receiver circuits carried by two different strains, at various ratios of the strains. The optimal ratio was one volume of cells carrying the sender circuit to ten volumes of cells carrying the receiver circuit. The experimental results indicated that the number of measured peaks for the bioluminescent signal was proportional to the number of types of chemicals (Figure 5F). For example, two separated peaks were measured when NA and H2O2 were present in the culture. We also built a simplified biochemical model that thoroughly captured the kinetics of the crosstalk-compensating circuit (Figure 5G). The model was based on Equations ( 7 and 8), using a consistent set of model parameters. The time delay ( ⁠|$< au _>$|⁠ ) between the two parallel antagonistic regulation paths of the luxCDABE cassette was incorporated into the kinetics of TetR (TetR |$ propto$| AHL |$( >> )$|⁠ ) and the luxAB level was proportional to PtetO activity |$left( = frac<1><<1 + <><<0.2>>> ight)>^2>>> ight)$|⁠ . Further analysis that compared the performance of the recA-based bacterial biosensor and the crosstalk-compensating circuit, including stochastic behaviour, is provided in Section 7 ( Supplementary Figure S10 ) of the Supplementary Data. In conclusion, while bacterial biosensors have a slow response compared to chemical sensors, they can be programmed to provide sensitivity for a very wide-range of chemicals and analytes.


Plant Tissue Culture: Laboratory Requirements

In this article we will discuss about the basic laboratory requirements for plant tissue culture.

Laboratory Requirements:

‘Plant tissue culture’ or in vitro cultivation of plant parts needs some basic requirements:

(a) Cultivation should be done under aseptic conditions.

(b) The isolated plant part should get an appropriate environment which will help to divide the cell and to get an expression of internal potential.

Basic facilities for plant tissue culture operations involving any type of in vitro proce­dures must include certain essential elements:

(a) Washing and storage facilities

(b) Media preparation, sterilisation and storage room

(c) Transfer area for aseptic manipulations

(d) Culture rooms or incubators for maintenance of cultures under controlled condi­tions of temperature, light and humidity

(e) Observation or data collection area

Washing and Storage Facilities:

An area with large sink (lead lined to resist acids and alkalis) and draining area is necessary with provision for running water, draining-boards or racks and ready access to a deionized, distilled and double-distilled apparatus.

Space should also be available to set up drying ovens, washing machines, plastic or steel buckets for soaking labware, acid or deter­gent baths, pipette washers, driers and cleaning brushes. For storage of washed and dried labware, the laboratory should be provided with dustproof cupboards or storage cabinets.

Media Preparation Room or Space:

This part is the central section of the laboratory where most of the activities are performed i.e., media preparation and sterilisation of media and glassware’s needed for culture. There should be sufficient working bench as well as storage space.

The following items are essential in the room (Fig. 16.2A-D):

(i) Different types of glassware

(ii) Different kinds of balances

(iv) Hot plates and Stirrer

(vii) Autoclave and Hot air oven

(xi) Refrigerator and Freezer

(xii) Storage cabinet (Dust-free)

Tissue culture techniques can only be successfully carried out in a very clean labora­tory having dry atmosphere with protection against air-borne microorganisms. For this pur­pose a sterile dust-free room/cabinet is needed for routine transfer and manipulation work.

The ‘laminar air flow cabinet’ (Fig. 16.2C) is the most common accessory used for aseptic manipulations now-a-days. The cabinet may be designed with horizontal air flow or vertical air flow where the air is forced into the cabinet through a bacterial HEPA (High Efficiency Particulate Air) filter. The air flows over the working bench at a constant rate which prevents the particles (microorganisms) from settling on the bench.

Before ope­ration in the laminar air flow cabinet, the interior of the cabinet is sterilised with the ultraviolet (UV) germicidal light and wiping the floor of cabinet with 70% alcohol. Inoculation chamber, a specially designed air tight glass chamber fitted with UV light, may also be used as transfer area.

Plant tissue cultures should be incubated under conditions of well-controlled tempe­rature, illumination, photoperiod, humidity and air circulation. Incubation culture rooms, commercially available incubator cabinets, large plant growth chambers and walk-in- environmental rooms satisfy these requirements.

Culture rooms are constructed with proper air-conditioning perforated shelves (Fig. 16.2D) to support the culture vessels, fitted with fluorescent tubes having a timing device to maintain the photoperiod, black curtains may be used to maintain total darkness.

For the suspension cultures, gyratory shakers are used. Air conditioners and heaters are used to maintain the temperature around 25 ± 2°C and humidity is maintained by uniform forced air-ventilation. The light­ing is also done in a measured amount i.e., 40-200 fc (foot-candle).

Data Collection Area:

The growth and development of tissues cultured in vitro are generally monitored by observing cultures at regular intervals in the culture room or incubators where they have been maintained under controlled environmental conditions.

Arrangement should be there where the observations can be done under aseptic conditions using microscope. Special facilities are required for germplasm conservation i.e., cryopreservation accessories should be there.

Transplantation Area:

Plants regenerated from in vitro tissue culture are transplanted to soil in pots. The pot­ted plants are ultimately transferred to greenhouse but prior to transfer the tissue culture grown plants are allowed for acclimatization under well humid condition and controlled temperature and under controlled entry of sunlight.


Conclusions

The knowledge of structural biological changes associated due to biomolecular interactions represents the milestone to assess and discover biological process.

Currently new insights are achieved by using SPR technology to measure kinetic parameters (rates of complex association and dissociation) driving biochemical and biological reactions, such as afβinity and speciβicity of molecular interactions, establishing an “afβinity ranking” in which it is possible to discriminate weak binders, stable binders or no binders [36].

A new frontier in this βield is the realization of a miniaturized and multichannel, portable SPR devices [37].


Biological Systems Engineering

For the undergraduate curriculum in biological systems engineering leading to the degree bachelor of science. The Biological Systems Engineering program is accredited by the Engineering Accreditation Commission of ABET, http://www.abet.org/.

Biological Systems Engineering integrates life sciences with engineering to solve problems related to, or using, biological systems. These biological systems may include microbes, plants, animals, humans and/or ecosystems. Biological systems engineers have a worldview shaped by an understanding of fundamental principles of engineering and life-sciences. They use their understanding of engineering to analyze organisms or ecosystems, and their knowledge of biological systems to inspire and inform their designs. They approach engineering design from a biological systems perspective, appreciating the complexity of biological systems and developing solutions that accommodate and anticipate the adaptability of biological systems.

Goal: To educate students to solve problems related to biorenewables production and processing, water quality, environmental impacts of the bioeconomy, food processing, and biosensors, and in so doing to prepare students for professional practice and post-graduate educational opportunities.

Program Educational Objectives: Three to five years after graduation, our graduates will be using the knowledge, skills, and abilities from their biological systems engineering degree to improve the human condition through successful careers in a wide variety of fields. They will be effective leaders, collaborators, and innovators who address environmental, social, technical, and business challenges. They will be engaged in life-long learning and professional development through self-study, continuing education, or graduate/professional school.

Well-qualified juniors and seniors in biological systems engineering who are interested in graduate study may apply for concurrent enrollment in the Graduate College to simultaneously pursue a bachelor of science degree in biological systems engineering and a master of science degree in agricultural engineering. Under concurrent enrollment, students are eligible for assistantships and simultaneously take undergraduate and graduate courses.

A concurrent bachelor of science and master of business administration program is also offered by the department.

The department also offers a bachelor of science curriculum in agricultural engineering. See College of Engineering. Additionally, the department offers bachelor of science curricula in agricultural systems technology and in industrial technology. See College of Agriculture and Life Sciences.

The department also participates in interdepartmental majors in environmental science, sustainable agriculture, biorenewable resources and technology, human computer interaction, and toxicology (see Index).


Abstract

Conspectus

Bioluminescence is widely used for real-time imaging in living organisms. This technology features a light-emitting reaction between enzymes (luciferases) and small molecule substrates (luciferins). Photons produced from luciferase–luciferin reactions can penetrate through heterogeneous tissue, enabling readouts of physiological processes. Dozens of bioluminescent probes are now available and many are routinely used to monitor cell proliferation, migration, and gene expression patterns in vivo.

Despite the ubiquity of bioluminescence, traditional applications have been largely limited to imaging one biological feature at a time. Only a handful of luciferase–luciferin pairs can be easily used in tandem, and most are poorly resolved in living animals. Efforts to develop spectrally distinct reporters have been successful, but multispectral imaging in large organisms remains a formidable challenge due to interference from surrounding tissue. Consequently, a lack of well-resolved probes has precluded multicomponent tracking. An expanded collection of bioluminescent probes would provide insight into processes where multiple cell types drive physiological tasks, including immune function and organ development.

We aimed to expand the bioluminescent toolkit by developing substrate-resolved imaging agents. The goal was to generate multiple orthogonal (i.e., noncross-reactive) luciferases that are responsive to unique scaffolds and could be used concurrently in living animals. We adopted a parallel engineering approach to genetically modify luciferases to accept chemically modified luciferins. When the mutants and analogs are combined, light is produced only when complementary enzyme–substrate partners interact. Thus, the pairs can be distinguished based on substrate selectivity, regardless of the color of light emitted. Sequential administration of the luciferins enables the unique luciferases to be illuminated (and thus resolved) within complex environments, including whole organisms.

This Account describes our efforts to develop orthogonal bioluminescent probes, crafting custom luciferases (or “biological flashlights”) that can selectively process luciferin analogs (or “batteries”) to produce light. In the first section, we describe synthetic methods that were key to accessing diverse luciferin architectures. The second section focuses on identifying complementary luciferase enzymes via a combination of mutagenesis and screening. To expedite the search for orthogonal enzymes and substrates, we developed a computational algorithm to sift through large data sets. The third section features examples of the parallel engineering approach. We identified orthogonal enzyme–substrate pairs comprising two different classes of luciferins. The probes were vetted both in cells and whole organisms. This expanded collection of imaging agents is applicable to studies of immune function and other multicomponent processes. The final section of the Account highlights ongoing work toward building better bioluminescent tools. As ever-brighter and more selective probes are developed, the frontiers of what we can “see” in vivo will continue to expand.


Imaging O2 dynamics and microenvironments in the seagrass leaf phyllosphere with magnetic optical sensor nanoparticles

Eutrophication leads to epiphyte blooms on seagrass leaves that strongly affect plant health, yet the actual mechanisms of such epiphyte-induced plant stress remain poorly understood. We used magnetic optical sensor nanoparticles in combination with luminescence lifetime imaging to map the O2 concentration and dynamics in the heterogeneous seagrass phyllosphere under changing light conditions. By incorporating magnetite into the sensor nanoparticles, it was possible to image the spatial O2 distribution under flow over seagrass leaf segments in the presence of a strong magnetic field. Local microniches with low leaf surface O2 concentrations were found under thick epiphytic biofilms, often leading to anoxic microhabitats in darkness. High irradiance led to O2 supersaturation across most of the seagrass phyllosphere, whereas leaf microenvironments with reduced O2 conditions were found under epiphytic biofilms at low irradiance, probably driven by self-shading. Horizontal micro-profiles extracted from the O2 images revealed pronounced heterogeneities in local O2 concentration over the base of the epiphytic biofilm, with up to 52% reduction in O2 concentrations in areas with relatively thick (>2 mm), compared with thin (≤1 mm), epiphyte layers in darkness. We also present evidence of enhanced relative internal O2 transport within leaves with epiphyte overgrowth, compared with bare seagrass leaves, in light as a result of limited mass transfer across thick outward diffusion pathways. The local availability of O2 was still markedly reduced in the epiphyte-covered leaves, however. The leaf phyllosphere is thus characterized by a complex microlandscape of O2 availability that strongly affects microbial processes occurring within the epiphytic biofilm, which may have implications for seagrass health, as anoxic microhabitats have been shown to promote the microbiological production of reduced toxic compounds, such as nitric oxide.


Results and Discussion

The anatomical localization of the activated cortical regions was done on the basis of the published ranges of Talaraich coordinates for the different areas and known anatomical landmarks. Fig. 1 illustrates averaged activity maps for all five subjects in the stereotaxic space of Talairach and Tournoux (27) for each of the three experimental tasks: BM, FG, and NRM, each contrasted with the baseline. Several clusters were identified across tasks, and Table 1 lists the Talairach coordinates and the z maps of the peak activations of these areas. Both the BM and NRM tasks strongly activated a region in the ventral lateral occipital cortex (Brodmann areas 19/37), which involves the hMT+ (22, 29–32) and the cuneus. The activity in the cuneus was centered on an area corresponding to the human cortical area V3A (33), with the most intense activity seen in the BM task (−22, −84, +24). These areas were not prominently activated in the face discrimination task (Exp. 3).

Comparison of activity across the three experiments. The red lines through the top sagittal slice indicate the position of the axial slices in the figure. A schematic of the three stimuli used is shown on the left. On the right are displayed correlated group z maps showing statistically significant neural activity for each experiment. To generate these z maps for each experiment, a t test was performed to contrast each experiment's task with the control (letter discrimination at fixation). Results from individual subjects were combined to generate group results (see data analysis). The color scale represents the z score of the activation (4 < z < 10). The figure illustrates bilateral activations in the lingual and fusiform gyri in all three tasks. The Face-Gender (FG) discrimination task produces significantly stronger activity in these areas than the other two tasks, and BM had stronger activation than NRM (see Table 1). BM and NRM produce selectively strong activation in the hMT+ and the cuneus, and these areas are not significantly activated on the face-gender discrimination task (Table 1).

Intense activity during FG occurred in the middle posterior fusiform gyrus and the lingual gyrus, which is consistent with specific-face activation areas reported by a large number of functional imaging studies of face perception (23, 34–38). However, strong responses in the fusiform and lingual gyri were also elicited by the BM discrimination task and to a lesser extent by the NRM discrimination task.

In addition to the activation in the fusiform gyrus for both the FG and BM discrimination tasks, support for the hypothesis that the ventral pathway is involved in both tasks is illustrated in Fig. 2. The contrasts performed were between BM and baseline and between FG and baseline. Strong activation during both BM and FG discriminations was observed in the inferior, middle, and superior temporal gyri, roughly corresponding to BA 20, 21, 22, and 38. The BA 38 is a region in the superior temporal gyrus (STG) presumably corresponding to the human analogue of the monkey area STP (3, 39, 40). No activation in BA 38 was seen during the non-rigid direction discrimination task (NRM). Bilateral activation of STG selective to BM (41) was also reported in an fMRI study that contrasted activity during observation of a point-light display portraying a man running with a display of just random motion. The STG region was also activated in a positron-emission tomography (PET) study of visual perception of biologically possible motion contrasted with biologically impossible motion (42).

Brain activity selective to biological motion and faces: emphasis on ventral stream. A schematic of the BM and face-gender (FG) discrimination stimuli used is shown on the left. On the right are displayed correlated group z maps showing statistically significant neural activity for each experiment. To generate these z maps for each experiment, a t test was performed to contrast each experiment's task with the control. Results from individual subjects were combined to generate group results (see Data Analysis). The color scale represents the z score of the activation (4 < z < 10). The figure shows that BM and face-gender tasks produce activations in the STG and BA 22 and 38. This finding supports the hypothesis that the ventral pathway is involved in both tasks.

Fig. 3 illustrates areas of activation along the dorsal pathway, common to the BM and NRM motion tasks but not to the FG task. The contrasts performed were again between baseline and either BM or NRM. As shown in Fig. 1, the strongest activity was in hMT+. The direction discrimination of the point-light walker is a directionally complex locally non-rigid task. To perform it, subjects must discriminate the overall direction of the non-rigid motion pattern of the walker while walking left or right on a treadmill. When subjects are asked to discriminate the point-light walker from a non-walker (the dots still move left or right), the task involves figural recognition. Significant overlap of activation for these two tasks was found in the medial occipital and temporal gyri.

Brain Activity selective to BM and NRM: emphasis on dorsal stream. The red lines through the sagittal slice (upper left) indicate the position of the axial slices in the figure. On the right are displayed group z maps showing statistically significant neural activity for each experiment (BM, face-gender, and NRM). To generate these z maps for each experiment, a t test was performed to contrast each experiment's task with the control. Results from individual subjects were combined to generate group results (see Data Analysis). The color scale represents the z score of the activation (4 < z < 10). The slices corresponding to BM and NRM illustrate activity in the BA 37/19 including the hMT+, BA 39, and BA 7. This finding supports the hypothesis that activation in the dorsal pathway is common to the BM and NRM experiments but not the face-gender discrimination experiment.

Fig. 4A shows activation specific only to the BM task in the lateral occipital cortex, a region corresponding to the area KO (Table 1) described as selective to kinetic boundaries (30, 43, 44). We suggest that, in the BM stimulus, kinetic boundaries (corresponding to the structure of the point-light walker) resulted from the integration of the differences in local direction of point-light motions with the goal of determining whether these together constitute the outline of a human silhouette walking. This area might correspond to the motion-defined objects area V3B (45).

Brain activity selective to BM. (A) The red line through the top sagittal slice indicates the position of the axial slice in the figure. On the upper right are displayed group z maps showing statistically significant neural activity for each experiment (BM, face-gender, and NRM). To generate these z maps for each experiment, a t test was performed to contrast each experiment's task with the control. Results from individual subjects were combined to generate group results (see Data Analysis). The color scale represents the z score of the activation (4 < z < 10). Bilateral activations selective to BM discrimination only were observed in KO (x = 28 y = −84, and x = −28 y = −82) and the lateral cerebellum. Mostly in the right hemisphere, the figure illustrates significant activation in the inferior frontal gyrus corresponding to BA47 (x = 44 y = 20). On the bottom left, the lines through the cerebellum in the sagittal slice indicate the angulation of the coronal cut. To the right, the three coronal slices (arranged vertically) show that only BM had activity in the lateral cerebellum. (B) Three axial slices illustrating activation for BM only (single subject). These slices illustrate activity is in KO, lateral cerebellum, and bilaterally in BA 21 (+54, −16, −16 −54, −16, −16) and in the right hemisphere activity in BA 38 (+54, +10, −16). To obtain this image, the sum of z maps from FG and from NRM were subtracted from the sum of z maps from BM and then divided by the square root of the total number of z maps. The resulting group z map, representing activation from BM only, was superimposed on the high resolution MRI in Talairach space.

Recently, it has been reported that the lateral occipital cortex and, thus, probably KO are activated by small kinetic stimuli (like the ones presented here) with or without a contour present. However, when subjects performed the NRM discrimination task, the area KO was not activated. Therefore, we suggest that it is the task, not just the stimulus, that determines the activation areas. This hypothesis is further reinforced by the fact that BM discrimination produced only selective activation in the various areas of the cerebellum. As in previous studies of BM, we observed activity in the posterior lobe of the cerebellum (46) and in the anterior cerebellum, near the midline (47). Based on recent evidence from functional neuroimaging studies of the cerebellum in a variety of cognitive tasks, in particular in judgment of motor activity (48) or verb-generation (49) in response to visually presented nouns, Grossman (47) put forward an intriguing hypothesis. He suggested that, in the point-light walker displays, subjects might label the action portrayed in the BM sequence, thus reproducing activation through cognitive channels. In our study, we found additional strong activity in the lateral cerebellum (Fig. 4 bottom) for the BM recognition task. We suggest that the integration of the non-rigidly moving dots into a recognizable form (a walker) requires visual spatial attention. This hypothesis is supported by the fact that the strongest cerebellar activation was found in the lateral cerebellum, the area QuPO (Table 1), which was previously shown to be selective for visual spatial attention (28, 50). This area of the cerebellum was activated only during the BM task. However, common to all three tasks, there was a distributed network of significant activity involving the anterior cingulate, the frontal eye field, and the superior parietal lobule. These areas have been reported to be involved in directed attention (51), which is necessary for performing all of the tasks in this study.

Significant activation selective to BM was also found in the right inferior frontal gyrus corresponding to BA 45 and 47 (shown in Fig. 4A), which were reported previously in positron-emission tomography studies as being selectively active in visual tasks comparing apparent physically possible and impossible human movements (42) and in tasks comparing meaningful human actions and observations of stationary (hands ref. 52). Strong activation specific to BM recognition were found only in BA 21 and 38, area KO, and the lateral cerebellum. Fig. 4B illustrates these BM-specific activations in a single subject after contrasting (BM − NRM) − FG).

General Discussion.

We investigated the specific neuronal substrate of BM perception by comparing brain activity elicited by BM discrimination (BM task), to activity corresponding to discrimination of overall direction of motion in the BM stimuli (NRM task), but without the requirement of processing BM and with face-gender discrimination (FG task). We found that, in the BM task, brain activation was distributed across several cortical areas within the dorsal and the ventral visual processing streams (Figs. 1, 2, and 3). It has been suggested that these streams converge in the STG, areas of STS including a region probably corresponding to the human homologue of the monkey STP. STP is also involved in face discrimination. Previous functional neuroimaging studies in normal human subjects demonstrated involvement of STS and probably STP in BM perception. Bonda et al. (46) in a positron-emission tomography study reported that the lower bank of the right posterior STS was selectively activated for movements of human actors in point-lights displays. STS was also reported active while observers viewed animation sequences of eye and mouth movements (53) or viewed a video of a face silently mimicking pronunciation of numbers and while they repeated silently these numbers. In an fMRI study, Howard et al. (41) reported that optic flow and point-light BM displays activated discrete and distinct regions bilaterally in STS, an area which also responds to auditory stimuli. They suggest that this area (Talairach coordinates +43 −19 +12 (BA41) and −47 −26 +17 (BA 42) within STG may correspond to the human homologue of the macaque STP situated anterior to the hMT+. We did not find activation in these Brodmann areas in any of the three tasks, but strong and specific responses to BM and FG were observed in other regions of the STG and the BA 22 and 38.

The differences in brain activity between the BM and NRM tasks bring further support to the suggestion that the subject's “task,” not just the visual display (the visual stimuli were identical in the BM and NRM tasks), strongly contributes to the pattern of activation (54). Fig. 4 A and B demonstrated that, in the BM only, subjects need to link the pattern of the dynamic point-lights to determine whether the dots portray a man or not. We suggest that, to accomplish this, they use spatial attention (mediated by the lateral cerebellum), which facilitates the spatial integration of the dots into the overall kinetic form of a walker. Subjects' self reports of their percept during the two tasks support this conjecture. When asked to describe what they saw in the BM display, all subjects reported that they had a vivid percept of a human-figure walking. However, none of the subjects detected the walker in the NRM task, although the stimuli in the two experiments were absolutely identical.


Watch the video: Αισθητήρες θερμοκρασίας καυσαερίων της NTK θέσεις τοποθέτησης (May 2022).