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A necessary step in the origin of life was the generation of a discrete barrier, a boundary layer, that serves to separate the living non-equilibrium reaction system from the rest of the universe. So what is the structure of this barrier (plasma) membrane? How is it built and how does it work?
When a new cell is formed its plasma membrane is derived from the plasma membrane of the progenitor cell. As the cell grows, new molecules must be added into the membrane to enable it to increase its surface area. Biological membranes are composed of two general classes of molecules, proteins (which we will discuss in much greater detail in the next section of the course) and lipids. It is worth noting explicitly here that, unlike a number of other types of molecules we will be considering, such as proteins, nucleic acids, and carbohydrates, lipids are not a structurally coherent group, that is they do not have one particular basic structure. Structurally diverse molecules, such as cholesterol and phospholipids, are both considered lipids. While there is a relatively small set of common lipid types, there are many different lipids found in biological systems and the characterization of their structure and function(s) has led to a new area of specialization known as lipidomics165.
All lipids have two distinct domains: a hydrophilic (circled in red in this figure domain characterized by polar regions and one or more hydrophobic/hydroapathetic domains that are usually made up of C and H and are non-polar. Lipids are amphipathic. In aqueous solution, entropic effects will drive the hydrophobic/hydroapathetic parts of the lipid out of aqueous solution. But in contrast to totally non-polar molecules, like oils, the hydrophobic/hydroapathetic part of the lipid is connected to a hydrophilic domain that is soluble in water. Lipid molecules deal with this dichotomy by associating with other lipid molecules in multimolecular structures in which the interactions between the hydrophilic parts of the lipid molecule and water molecules are maximized and the interactions between the lipid’s hydrophobic/hydroapathetic parts and water are minimized. Many different multi-molecular structures can be generated that fulfill these constraints. The structures that form depend upon the details of the system, including the shapes of the lipid molecules and the relative amounts of water and lipid present, but the reason these structures self- assemble is because their formation leads to an increase in the overall entropy of the system, a somewhat counterintuitive idea. For example, in a micelle the hydrophilic region is in contact with the water, while the hydrophobic regions are inside, away from direct contact with water. This leads to a more complete removal of the hydrophobic domain of the lipid from contact with water than can be arrived at by a purely hydrophobic oil molecule, so unlike oil, lipids can form stable structures in solution. The diameter and shape of the micelle is determined by the size of its hydrophobic domain. As this domain gets longer, the center of the micelle becomes more crowded. Another type of organization that avoids “lipid-tail crowding” is known as a bilayer vesicle. Here there are two layers of lipid molecules, pointing in opposite directions. The inner layer surrounds a water-filled region, the lumen of the vesicle, while the outer layer interacts with the external environment. In contrast to the situation within a micelle, the geometry of a vesicle means that there is significantly less crowding as a function of lipid tail length. Crowding is further reduced as a vesicle increases in size to become a cellular membrane. Micelles and vesicles can form a colloid-like system with water, that is they exist as distinct structures that can remain suspended in a stable state. We can think of the third type of structure, the planar membrane, as simply an expansion of the vesicle to a larger and more irregular size. Now the inner layer faces the inner region of the cell (which is mostly water) and the opposite region faces the outside world. For the cell to grow, new lipids have to be inserted into both inner and outer layers of the membrane; how exactly this occurs typically involves interactions with proteins. For example, there are proteins that can move a lipid from the inner to the outer domain of a membrane (they flip the lipid between layers, and are known as flipases); while but the details are beyond our scope here you might be able to generate a plausible mechanism. A number of distinct mechanisms are used to insert molecules into membranes, but they all involve a pre-existing membrane – this is another aspect of the continuity of life. Totally new cellular membranes do not form, membranes are built on pre-existing membranes. For example, a vesicle (that is a spherical lipid bilayer) could fuse into or emerge from a planar membrane. These processes are typically driven by thermodynamically favorable reactions involving protein-based molecular machines. When the membrane involved is the plasma (boundary) membrane, these processes are known as exocytosis and endocytosis, respectively. These terms refer explicitly to the fate of the material within the vesicle. Exocytosis releases that material from the vesicle interior into the outside world, whereas endocytosis captures material from outside of the cell and brings it into the cell. Within a cell, vesicles can fuse and emerge from one another.
As noted above, there are hundreds of different types of lipids, generated by a variety of biosynthetic pathways catalyzed by proteins encoded in the genetic material. We will not worry too much about all of these different types of lipids, but we will consider two generic classes, the glycerol-based lipids and cholesterol, because considerations of their structures illustrates general ideas related to membrane behavior. In bacteria and eukaryotes, glycerol-based lipids are typically formed from the highly hydrophilic molecule glycerol combined with two or three fatty acid molecules. Fatty acids contain a long chain hydrocarbon with a polar (carboxylic acid) head group. The nature of these fatty acids influences the behavior of the membrane formed. Often these fatty acids have what are known as saturated hydrocarbon tails. A saturated hydrocarbon contains only single bonds between the carbon atoms of its tail domain. While these chains can bend and flex, they tend to adopt a more or less straight configuration. In this straight configuration, they pack closely with one another, which maximizes the lateral (side to side) van der Waals interactions between them. Because of the extended surface contact between the chains, lipids with saturated hydrocarbon chains are typically solid around room temperature. On the other hand, there are cases where the hydrocarbon tails are “unsaturated”, that is they contain double bonds (–C=C–) in them. These are typically more fluid and flexible. This is because unsaturated hydrocarbon chains have permanent kinks in them (because of the rigid nature and geometry of the C=C bonds), so they cannot pack as regularly as saturated hydrocarbon chains. The less regular packing means that there is less interaction area between the molecules, which lowers the strength of the van der Waals interactions between them. This in turn, lowers the temperature at which these bilayers change from a solid (no movement of the lipids relative to each other within the plane of the membrane) to a liquid (much freer movements). Recall that the strength of interactions between molecules determines how much energy is needed to overcome a particular type of interaction. Because these van der Waals intermolecular interactions are relatively weak, changes in environmental temperature influence the physical state of the membrane. The liquid-like state is often referred to as the fluid state. The importance of membrane state is that it can influence the behavior and activity of membrane proteins. If the membrane is in a solid state, proteins embedded within the membrane will be immobile; if is in the liquid state, these proteins will move by diffusion, that is, by thermally driven movement, within the plane of the membrane. In addition, since lipids and proteins are closely associated with one another in the membrane, the physical state of the membrane can influence the activity of embedded proteins, a topic to which we will return.
Cells can manipulate the solid-to-liquid transition temperature of their membrane by altering the membrane’s lipid composition. For example, by altering the ratio of saturated to unsaturated chains present. This level of control involves altering the activities of the enzymes involved in saturation/desaturation reactions. That these enzymes can be regulated implies a feedback mechanism, by whicheither temperature or membrane fluidity acts to regulate metabolic processes. This type of feed back mechanism is part of what is known as the homeostatic and adaptive system of the cell (and the organism) and is another topic we will return to toward the end of the course.
There are a number of differences between the lipids used in bacterial and eukaryotic organisms and archaea166. For example, instead of hydrocarbon chains, archaeal lipids are constructed of isoprene (CH2=C(CH3)CH=CH2) polymers linked to the glycerol group through an ether (rather than an ester) linkage. The bumpy and irregular shape of the isoprene groups (compared to the relatively smooth saturated hydrocarbon chains) means that archaeal membranes will tend to melt (go from solid to liquid) at lower temperatures167. At the same time the ether linkage is more stable (requires more energy to break) than the ester linkage. It remains unclear why it is that while all organisms use glycerol-based lipids, the bacteria and the eukaryotes use hydrocarbon chain lipids, while the archaea use isoprene-based lipids. One speculation is that the archaea were originally adapted to live at higher temperatures, where the greater stability of the ether linkage would provide a critical advantage.
At the highest temperatures, thermal motion might be expected to disrupt the integrity of the membrane, allowing small charged molecules (ions) and hydrophilic molecules through168. Given the importance of membrane integrity, you will (perhaps) not be surprised to find “double-headed” lipids in organisms that live at high temperatures, known as thermophiles and hyperthermophiles. These lipid molecules have a two distinct hydrophilic glycerol moieties, one located at each end of the molecule; this enables them to span the membrane. The presumption is that such lipids act to stabilize the membrane against the disruptive effects of high temperatures - important since some archaea live (happily, apparently) at temperatures up to 110 ºC169. Similar double-headed lipids are also found in bacteria that live in high temperature environments.
That said, the solid-fluid nature of biological membranes, as a function of temperature, is complicated by the presence of cholesterol and structurally similar lipids. For example, in eukaryotes the plasma membrane can contain as much as 50% (by number of lipid molecules present) cholesterol. Cholesterol has a short bulky hydrophobic domain that does not pack well with other lipids a hydrocarbon chain lipid (left) and cholesterol (right)). When present, it dramatically influences the solid-liquid behavior of the membrane. The diverse roles of lipids is a complex subject that goes beyond our scope here170.
Full-length cDNAs from chicken bursal lymphocytes to facilitate gene function analysis
A large number of cDNA inserts were sequenced from a high-quality library of chicken bursal lymphocyte cDNAs. Comparisons to public gene databases indicate that the cDNA collection represents more than 2,000 new, full-length transcripts. This resource defines the structure and the coding potential of a large fraction of B-cell specific and housekeeping genes whose function can be analyzed by disruption in the chicken DT40 B-cell line.
Known Issues with SCALE 6.1
Since SCALE 6.1 was released on July 22, 2011, end users and the SCALE development team have identified a few issues that impact the performance of the code package. Several important issues are addressed with the SCALE 6.1.2 Update (https://www.ornl.gov/scale/scale/scale-613-update), which is recommended for all SCALE users. These and other issues that are not corrected in the update are detailed below. Where possible, these known issues will be corrected in the next release of SCALE and possibly in a patch to the current release. Possible user corrections or workarounds are noted below.
KENO V.a Requires Cuboidal Outermost Region to Enable the Use of Albedo Boundary Conditions
In all versions of SCALE, the Monte Carlo code KENO V.a only implements the use of non-vacuum albedo boundary conditions (e.g., mirror, periodic, white) when the outermost geometry region of the model is a cuboidal region. This limitation is noted in the user documentation in the section on Albedo data, where it is stated that “Albedo boundary conditions are applied only to the outermost region of a problem. In KENO V.a this geometry region must be a rectangular parallelepiped.”
It was recently discovered that—beginning with the release of SCALE 6.1 in 2011—KENO V.a will accept non-compliant input that specifies albedo boundary conditions for non-cuboidal outer shapes and will then attempt to complete the calculation. For example, a user can specify a cylinder as the outermost region and add a mirror boundary condition on the top or bottom to effectively double the volume of the system considered. A user could also add a mirror boundary condition to both the top and the bottom of the cylinder to simulate a bounding case of an infinite system. While these scenarios are accepted and perform as expected in KENO-VI, KENO V.a requires the addition of a cuboidal region (typically an empty void region) to enable the use of these albedo boundary conditions.
For calculations using KENO V.a in SCALE 6.1–6.2.2 with non-compliant input in which albedo boundary conditions are applied but without the required cuboidal outermost region, the calculation will proceed without warning, and an underestimation of k-eff often results. The magnitude of underestimation in k-eff can vary widely, depending on the system modeled and the desired boundary conditions, but it can exceed several percent in k-eff.
It is strongly recommended that users who rely on albedo boundary conditions in KENO V.a review their input models to ensure that the outermost region is a cube or cuboid, per the documentation requirement. Note that input models that were generated and applied with SCALE 6 and earlier versions that included the check for the cuboidal outer boundary will continue to produce the expected results with SCALE 6.1–6.2.2.
In testing the extent of this issue by placing mirror boundary conditions on non-cuboidal outer shapes, it was found that cylinders oriented along the x-, y-, or z-axis most often produce non-conservative results without warning. The calculation will terminate prior to completion for cases in which a sphere is the outermost shape. The calculation will terminate with an error message for cases in which a hemicylinder or hemisphere is the outermost shape. The calculation performs as expected for cases in which a cube or cuboid is the outermost shape.
This issue applies to all SCALE 6.1–6.2.2 sequences that implement KENO V.a, including CSAS5, TSUNAMI-3D-K5, T5-DEPL, and STARBUCS. No other SCALE sequences are impacted by this issue. The error condition for the attempted use of albedo boundary conditions on non-cuboidal outer shapes in KENO V.a will be restored in the pending release of SCALE 6.2.3, thus preventing users from inadvertently entering non-compliant input.
ORIGEN Input Concentrations for Stream Blending Calculation
An error was reported in the stream blending option of ORIGEN in SCALE 6.1. Other previous versions are not affected. As documented in the user manual, the NGO parameter indicates the type of subcase that follows the current calculation so that appropriate data are retained. When the blended compositions from previous subcases are requested as the starting concentrations in the current subcase (KBLEND = -1), the value of NGO in the preceding subcase should request the blended concentrations using NGO = -1. However, this option results in some streams being omitted from the starting concentrations in the blended case. The use NGO = 1 in the prior subcase, instead of NGO = -1, avoids this issue and the concentrations from all blended streams are retained. However, this use of NGO is inconsistent with the user manual.
It is important that users verify that the blended stream compositions by printing the concentrations of the streams before and after blending. The blending example case shown in Section F7.6.5 of the SCALE Manual has been modified to function correctly. The use of NGO = -1 in this example results in the fuel compositions being omitted from the glass matrix composition. The revised input should be as follows:
Date Identified: 05/20/2014 Date Resolved: 08/13/2014
Problems with SCALE 6.1 Installer with Java 7
The IZPack installer distributed with SCALE 6.1 was created using Java 6. With Java 7 now being deployed by many IT departments to address issues with Java 6, the behavior of the SCALE 6.1 installer has changed.
The instructions provided in the Scale6.1_Readme file states:
To begin installation of SCALE 6.1 for Windows, double-click the scale-6.1-setup.jar file on the DVD. Linux and Mac systems will not allow the first installation disk to eject if the install program is running from the DVD. For Linux or Mac, copy the scale-6.1-setup.jar to your local disk and double-click the local version or issue the following command java –jar scale-6.1-setup.jar in the location where the installer .jar file was copied.
Revised instructions for use with Java 7 are:
To begin installation of Scale 6.1 for Windows, Linux, and Mac systems copy the scale-6.1-setup.jar to your local disk and issue the following command from the Command Prompt (DOS Window) or Terminal:
java –jar scale-6.1-setup.jar -direct in the location where the installer .jar file was copied. The -direct option resolves issues associated with Java 7 but is not available when double-clicking the .jar file for installation.
Date Identified: 05/08/2013 Date Resolved: 09/13/2013
NEWT Mesh Generator Issue for Hexagonal Geometries
An issue in NEWT’s automatic mesh generation routines was recently identified for hexagonal-array geometries. In certain instances, NEWT will place an incorrect material in a computational cell of the problem. The issue can be identified by inspecting the “newtmatl.ps” file that is generated when the NEWT parameter option “drawit=yes” is enabled. When viewing this file, some computational cells may appear as a different color compared to adjacent cells, as shown in the figure below.
In order to circumvent this issue, it is recommended that users construct hexagonal geometries by placing fuel pins using holes rather than an array. Using holes should eliminate the issue and provide a computational speedup due to a reduction in computational cells versus using an array.
Critical Spectrum Calculations with NEWT
An issue was identified in NEWT that causes few-group homogenization calculations to fail in critical spectrum mode when using the user specified critical buckling value or critical height. There is currently no workaround for this issue, but it will be corrected in the pending SCALE 6.1.2 patch.
ORIGEN Irradiation Calculations
An issue in ORIGEN for irradiation calculations that will occasionally cause large masses fission products to be produced from non-fissile materials. This error only affects SCALE 6.1 (and 6.1.1) calculations with time steps of 5-35 days. When the error is encountered and hydrogen exists in the system, large masses of fission products with A>162 are produced (
1E8 grams) and are easily identified. When hydrogen does not exist in the system, the error may be more difficult to detect as it only affects the transitions for a small set of fission products with A>162. Users can work around this issue by modify the time steps, and it will be corrected in the pending SCALE 6.1.2 patch.
Possible Inaccurate Implicit Sensitivities with TSUNAMI
An error has been identified that affects some TSUNAMI sensitivity analysis calculations where implicit sensitivities for some nuclides may not be accurately computed. The issue was found in BONAMIST, used to generate the implicit sensitivity data, when examining a MOX pin-cell benchmark. The error has been observed to impact the sensitivity for U-238 in this test case, but has not been show to impact critical experiments or realistic application systems. Users who performed recommended direct perturbation calculations would observe the discrepancy in any previous calculations. This issue will be corrected in the SCALE 6.1.2 patch. A possible user workaround for this issue is to change the order of nuclides in the input read compositions data block.
For the MOX fuel pin test case, the following results were observed with a significant difference in the U-238 implicit contribution leading to a significant change in the sensitivity coefficient. As show in the figure below, the differences occur in the resonance region for U-238, but Pu-239 is largely unaffected.
Sensitivity of keff to U-238 Total Cross Section for MOX Fuel Pin Test Case
A more typical result is shown below for critical experiment MIX-COMP-THERM-001-001, where only small differences are observed.
Sensitivity of keff to U-238 Total Cross Section for MIX-COMP-THERM-001-001
Possible Incorrect Selection of an Axial Burnup Profile in STARBUCS Burnup Credit Criticality Calculations
STARBUCS has the option to use axial burnup profiles that are dependent on assembly average burnup and provides three default axial burnup profiles (i.e., the NAX=-18 input option) applicable to an assembly averageburnup as follows: (1) burnup less than 18 GWd/MTU (2) burnup greater than or equal to 18 GWd/MTU and less than 30 GWd/MTU and (3) burnup greater than or equal to 30 GWd/MTU. It has been noticed that for assembly average burnup values at which the axial burnup profile changes (i.e., 18 and 30 GWd/MTU in the case of the STARBUCS default burnup profiles), and depending on the number of libraries per cycle (NLIB) provided in the burnup history data or in the search parameter data, STARBUCS may select an incorrect burnup profile. For the search parameter data block specification: POWER=50.0 NLIB=7 BU=30, STARBUCS may select the axial burnup profile that is applicable to the burnup range [18 – 30) GWd/MTU in place of the axial burnup profile that is applicable to an assembly average burnup of 30 GWd/MTU. This problem is caused by a rounding error, which will be corrected in a future release. Currently, the STARBUCS internal calculation NLIB*BU/NLIB does not always produces the required precision for the assembly average burnup values of 18 and 30 GWd/MTU to enable the selection of the intended axial burnup profile. To avoid this error, the assembly average burnup values being used as the boundaries for the burnup intervals that define different axialburnup profiles should be increased by a very small amount. For example, if the assembly average values for loading curve analyses are 18 and 30 GWd/MTU, the following input specifications in the read search data block produce correct selection of the intended default axial burnup profiles:
BU=17.999 18.001 29.999 30.001 end
Note that the STARBUCS output file provides information about the axial burnup profile selected for an average assembly burnup value, such as:
axial profile from database
assembly avg burnup 18.000 gwd/mtu, profile 2
Error in ENDF/B-VII.0 Decay Data
An error in the nuclear decay data for 234Th has been identified in ENDF/B-VII.0, which is used for the SCALE decay library. A review of the problem indicates that the error was introduced in the evaluated ENDF/B-VII.0 decay sub-library released by the National Nuclear Data Center (NNDC) in December 2006. The NNDC has confirmed the problem and recently released an updated decay library with ENDF/B-VII.1. Currently, ORNL is working closely with NNDC to identify the nature and extent of the nuclear data evaluation problem and is preparing a patch for the ENDF/B-VII.0-based decay library distributed with SCALE 6.1. It is important to note that ORNL has performed extensive validation using the ENDF/B-VII.0-based decay library in SCALE 6.1 and has NOT identified any discrepancies for benchmark problems involving irradiated fuel isotopic compositions, decay heat, and source terms. The error has been observed for problems involving the decay of 238 U. As an example, the gamma ray spectra calculated using SCALE 6.0 (ENDF/B-VI decay data) and SCALE 6.1 (ENDF/B-VII.0 decay data) are shown in the figure below. The spectrum obtained using ENDF/B-VII.0 data is significantly over estimated, caused primarily by incorrect production of 234 Pa from 234 Th decay. Additional information on the error in ENDF/B-VII.0 and decay evaluation improvements for ENDF/B-VII.1 are posted on the NNDC website (http://www.nndc.bnl.gov/exfor/endfb7.1_decay.jsp).
Date Identified: March 18, 2012
Minor Issues Identified with Fixed-Source Monte Carlo Capabilities
A few minor issues were identified with the SCALE fixed-source Monte Carlo code Monaco and an associated utility, especially related to seldom-used optional features. These features will be corrected in a pending patch for SCALE. These features should be used with caution until the patch is applied.
- When specifying the special distribution pwrNeutronAxialProfileReverse or pwrGammaAxialProfileReverse for a spatial source distribution, the un-reversed profile is erroneously returned.
Discrepancy Observed with Small Number Densities with 44-Group ENDF/B-V Data and CENTRM
An issue has been identified that can lead to non-conservative keff values when using the 44-group ENDF/B-V data with CENTRM for high-leakage models with trace-element number densities below
10-9 atoms/barn-cm when running SCALE 5.1 – SCALE 6.1. The effect on the 238-group ENDF/B-V,VI, and VII libraries is minimal. There is no effect on continuous-energy Monte Carlo calculations.
In the dozens of test cases examined thus far, the discrepancy is only realized in cases that meet ALL of the following conditions:
- The number density of at least one nuclide has a small fractional concentration of 10 -8 or less relative to the total mixture number density. Typically this corresponds to an absolute concentration less than
- Continuous-energy KENO calculations do not use CENTRM and are not affected.
- The impact for all 238-group calculations examined thus far is small, on the order of a few pcm.
- Eigenvalues and isotopic concentrations computed for the 44-group ENDF/B-V depletion cases examined are not significantly affected, as these are low-leakage systems [reflected lattice geometries]. For most cases that meet all of the above criteria, including burned fuel criticality safety calculations that include small concentrations of fission products, the discrepancy introduces an error on the order of 100 pcm.
- In a contrived case that artificially introduces a trace material into a plutonium nitrate system, a discrepancy of
- The SCALE Team is developing a patch that corrects this issue.
- Users should examine calculations to determine if they meet the criteria provided above.
- The eigenvalue for suspect systems should be examined using a different library, such as the 238-group ENDF/B-V to determine if a particular system is impacted.
- Users should install the SCALE 6.1 patch when it is available and repeat any suspect calculations.
Acknowledgement: This issue was first identified by SCALE user Dale Lancaster
Optional Output Edit in STARBUCS
In STARBUCS burnup credit loading curve search calculations, an optional input prt=short may be used within the READ SEARCH input block to restrict the final output to contain only relevant information for a burnup loading curve calculation. In SCALE 6.1, this optional input causes the calculation to crash.
Users should only use the default parameter prt=long, which retains all SCALE output information for the last step of the iterative fuel enrichment search process. As prt=long is the default option in STARBUCS, there is no need for this input option to be specified in a STARBUCS input file.
MacOS System Requirements
The SCALE 6.1 Readme states that the system will operate on Mac OSX version 10.5 or newer, where Mac OSX 10.6 or newer is actually required to properly execute SCALE 6.1.
The symptoms are such that the SCALE runtime will execute and a job banner will be produced, but the executable modules will fail.
If messages are turned on (-m flag on the batch6.1 command) the following message will be reported:
'dyld: unknown required load command 0x80000022'
The solution is to upgrade to Mac OSX 10.6 or newer.
Windows ORIGEN and OPUS Sample Problems
There has been an issue identified when running the ORIGEN and OPUS sample problems on Windows.
Specifically, the sample problems' shell script uses an invalid path when attempting to copy needed resources into the working directory. Without these needed resources, both sample problems fail to produce the expected results.
The fix is simple. For the origen.input and opus.input files, located in
copy z:scale_stagingdataarplibsw17_e40.arplib ft33f001
copy %DATA%arplibsw17_e40.arplib ft33f001
Unable to access jarfile . ScaleDiff.jar
There has been an issue identified where when running the sample problems,
the ScaleDiff.jar file is not found producing an 'Unable to access jarfile . ScaleDiff.jar' message.
The issue is due to not having the source code installed.
The ScaleDiff-Samples.xml zip file contains the following:
Do the following to update your Scale6.1 install
1. Extract the contents into your Scale6.1 directory. You will be prompted to ‘copy and replace’ your samples.xml file.
2. Move the Scale6.1ScaleDiff.jar file into your Scale6.1cmds directory. You will be prompted ‘copy and replace’ your ScaleDiff.jar file.
The updated Scale6.1samples.xml, and Scale6.1cmdsScaleDiff.jar files should be available to verify Scale as detailed in the readme file.
Table_of_content_*.txt: no such file or directory
When running the sample problems an error may occur similar to the following,
C:Scale6.1Windows_amd64ingrep: table_of_content_*.txt: No such file or directory
This is due to a typo in the scalesamples.xml file.
'table_of_content_*' should be 'table_of_contents_*'. Notice the extra 's'.
Edit your Scalesamples.xml file, find 'table_of_content_*' and replace with 'table_of_contents_*'.
ORIGEN 200-group cross section library
A problem was identified in the energy-group boundaries of the ORIGEN 200-neutron-group cross-section library, origen.rev02.jeff200g. The boundaries were generated with constant lethargy instead of the boundaries of the SCALE 200-group transport library. Use of this library is currently not recommended, as it will produce erroneous results. An update to the library will be available soon.
ORIGEN natural isotopic abundances
The natural isotopic abundances for several elements in the ORIGEN library are incorrect. The abundances have been corrected and an updated library will be available soon. The use of natural isotopic abundances (NEX1=4) for input element concentrations enter in gram units may result in incorrect isotopic concentrations for Mg, Ge, Kr, Sr, and Te. If atom units (gram atoms) are used, incorrect isotopic concentration may occur for F, Na, Mg, Al, P, Sc, Mn, Co, Ge, As, Kr, Sr, Y, Nb, Rh, Te, I, Cs, Pr, Tb, Ho, Tm, and Au.
Problem with thermal energy cutoff in continuous-energy KENO calculations
Internal testing of continuous-energy calculations with KENO has revealed a considerable non-conservative change in keff, on the order of 20%, for cases involving BeO. Users who properly validate continuous-energy KENO calculations for these systems would notice a strong systematic bias for bound BeO cases prior to use in safety calculations. Nevertheless, users should not use be-beo in continuous-energy KENO calculations.
Note that multigroup calculations in KENO are not affected by this issue, and updates to the continuous-energy data for bound BeO will be available soon.
Scale continuous energy neutron cross-section libraries are based on ENDF/B-VI Release 8 and ENDF/B-VII Release 0. While most of the neutron cross sections are for nuclides that are assumed to be free (not bound in a molecule), some nuclide cross sections are for bound nuclei that are commonly referred to as s(a,b) cross sections or thermal kernels. Hydrogen bound in water or Be in BeO are some example nuclei that have bound thermal cross sections. Scale continuous-energy neutron cross-section libraries were generated by processing the ENDF thermal kernel data for incident neutron energies of 5.05eV or below. To provide flexibility in analysis without the need to regenerate the cross section library, KENO was designed to implement a user-selectable value for the thermal cutoff for s(a,b) treatment, with default neutron cutoff energy of 3eV. Above this cutoff the effects of thermal motion of the molecule are assumed to be negligible.
As a result of a recent internal testing, it was discovered that KENO does not apply the thermal cutoff value to the use of s(a,b) treatment. If the evaluation does not have data up to 5.05eV, the short collision time method is used to extend the incoherent inelastic scattering data up to 5.05eV. Coherent elastic scattering is generated only for the energy range specified in the ENDF file. It was discovered that for Be in BeO, the coherent elastic and incoherent inelastic scattering cross sections extended beyond 3eV but did not have the same upper cut-off value. When KENO ignores the default thermal cut-off value of 3eV, it tries to sample from both coherent elastic and incoherent inelastic and obtains the wrong cross section between the cut-off values of these reactions.
BxdPRE silencing activity depends on the chromosomal context
In previous studies, PREs were found to repress expression of reporter genes in only about half of the transgene insertion sites [38,39,40,41, 58, 59]. To better understand the context-dependent factors that impact PRE activity, we used the ΦC31 site-specific integration system  to generate independent Drosophila transgenic lines. For this purpose, we selected five attP sites that had previously been shown to provide a context in which the expression of a white reporter is not subject to obvious repression or activation by the surrounding chromatin neighborhood (Additional file 1: Table S1). Three attP sites are on the 2nd chromosome (22A, 51C, and 58A), while two are on the 3rd chromosome (68E and 96E). As a PRE, we selected a well-characterized 656-bp bxdPRE element from the Ubx regulatory region [30, 31, 61, 62] (bxd construct, Fig. 1a). To evaluate the silencing activity of the bxdPRE as a reporter, we used a white gene with its eye tissue-specific enhancer (E). The bxdPRE was flanked by
1 kb “neutral” spacers derived from coding regions of the eGFP and RFP genes and by terminators of transcription (SV40 terminator upstream and yellow gene terminator downstream) to reduce the influence of potential transcription from surrounding genomic sequences. To better assess the effects of the PRE, we also inserted a control construct at each site, which has all of these components except for bxdPRE (E-w construct, Fig. 1a). The transgene constructs were inserted in w - attP lines lacking a functional white gene. The silencing activity of the bxdPRE was assessed by the reduction in eye pigmentation as pigmentation is known to be directly correlated with the level of white gene transcription [62, 63].
Su(Hw) binding sites induce bxdPRE silencing activity. a Map of transgenes. Labels: “attB” – attB site required for transgene integration into the attP insertion site “bxdPRE” – bxdPRE silencing element “T,” terminators of transcription white -marker gene “E” - enhancer of the white gene, “4xSu” – four binding sites for Su(Hw) protein. b The eye phenotypes of flies that have the control “E-w” transgene integrated into the different attP sites: 22A (Bloomington Drosophila Stock Center (BDSC) #24481), 51C (BDSC #24482), 58A (BDSC #24484), 68E (BDSC #24485), 96E (BDSC #24487). P/+, hemizygous P/P, homozygous adult flies. c The eye phenotypes of flies that have the “bxd” transgene integrated into the different attP sites: 22A, 51C, 58A, 68E, and 96E. d Ph enrichment at bxdPRE in “bxd” transgenes integrated into the different attP insertion sites. Diagrams summarize the results of X-ChIP with Ph antibody or with IgG from a non-immunized animal as a negative control. X-ChIP was analyzed by real-time PCR with primers specific to transgene bxdPRE—the region indicated by underlined number 2 above the “bxd” transgene map. The X-ChIP experiments were performed with chromatin isolated from the heads of homozygous adult flies. The ordinate shows the percentage of target sequences in the immunoprecipitated material relative to the input DNA and normalized to the positive control—a sequence adjacent to the endogenous bxdPRE in BX-C (bxdPRE-Genome). The line with bxd transgene is designated by its attP insertion site and is indicated on the abscissa. Vertical lines indicate SDs. e The eye phenotypes of flies with the “bxd,” “Su-bxd,” or “Su” transgenes at the 96E attP site
Shown in Fig. 1b, c are the eye color phenotypes of hemizygous and homozygous flies for each of the five insertion sites. In the five lines carrying the E-w control transgene, there is no evidence of repression in either hemizygotes or homozygotes (Fig. 1b). In contrast, different degrees of repression are observed in 4 of the 5 lines with the bxd transgene (Fig. 1c). Line 22A is a classic example of PSS. It shows little or no evidence of silencing as a hemizygote, while as a homozygote there is a strong and almost uniform reduction in pigmentation. Though PSS is observed for line 58A, it differs from line 22A in that only a small sector of the eye shows a significant loss of pigmentation as a homozygote. Unlike 22A and 58A, weak silencing is observed in line 51С as a hemizygote. However, PSS is also evident as silencing is clearly enhanced when the flies are homozygous for the insert. For line 68E, eye pigmentation is greatly reduced in both hemizygous and homozygous flies. Finally, the bxdPRE is unable to induce silencing at 96E insertion site, suggesting that this chromatin environment renders the PRE inactive. In all cases, flies within a given line have similar eye pigmentation phenotypes. Since PSS is thought to be dependent upon homolog pairing, the different properties of the five insertion sites could be due to differences in the strength of local homolog pairing. To investigate this possibility, we took advantage of a recent genome-wide study that measured interaction frequencies between homologs at the embryo stage . However, analysis of the available Hi-C data did not show any significant correlations (Additional file 2).
Silencing is expected to be accompanied by the association of PcG proteins with the bxdPRE. To confirm that this is the case, we isolated chromatin from adult heads of homozygous bxdPRE transgene flies and performed chromatin immunoprecipitation (X-ChIP) with antibodies against Ph, which is a core component of the PRC1 complex (Fig. 1d). To compare Ph association in different lines, we calculated the extent of enrichment relative to an internal positive control. For this purpose, we used primers to a sequence immediately adjacent to the endogenous bxdPRE (hereafter referred to as bxdPRE-Genome) that is known to be enriched in PcG/TrxG proteins. Figure 1c shows that Ph is associated with the transgenic bxdPRE in the four lines that show silencing of the white gene. Moreover, the extent of association correlates well with the level of repression observed in each line. Consistent with the lack of silencing of white in the 96E insertion, Ph is not found to be associated with its bxdPRE sequence.
Multimerized sites for Su(Hw) boundary induce bxdPRE silencing at 96E
PREs are often located near other transcriptional regulatory elements. As noted above, the PREs in the four Abd-B regulatory domains are positioned close to the boundary elements for each domain. Another example is one of the PREs for the even-skipped (eve) gene that is located next to the distal boundary of the eve locus homie . These observations led us to wonder whether boundary elements might be able to augment the silencing activities of PREs.
To explore this possibility, we selected the 96E attP since the bxdPRE is unable to silence white at this insertion site. As the test boundary, we used an artificial element consisting of multimerized binding sites for the polydactyl zinc finger protein Su(Hw) rather than an endogenous boundary. Endogenous boundaries contain binding sites for many different proteins, and some are known to be required for PRE activity. For example, the GAF protein is implicated not only in insulation but also in PcG-dependent silencing .
The Su(Hw) protein is responsible for the boundary activity of the insulator element associated with the gypsy transposon [66, 67]. The gypsy transposon has 12 binding sites for the Su(Hw) protein [68, 69] however, previous studies have shown that a multimer consisting of only four copies of the third Su(Hw) binding site from the gypsy insulator is sufficient for boundary activity in transgene reporter assays  and in the context of BX-C . This 4xSu(Hw) multimer was placed on the distal side of the bxdPRE (Fig. 1a: Su-bxd construct) so that the PRE is between it and the white gene. In this position, the 4xSu(Hw) multimer would not be able to insulate white from PRE-dependent silencing [54, 57, 72]. To assess the effects of the multimer alone, we inserted a control transgene containing 4xSu(Hw) but not the PRE (Fig. 1a: Su construct). Figure 1e shows that combining 4xSu(Hw) with the bxdPRE has a dramatic effect on white expression. Silencing of white is evident in hemizygotes, while strong PSS is observed when the transgene is homozygous. In contrast, the 4xSu(Hw) multimer alone has no effect on eye pigmentation either as a hemizygote or a homozygote. Thus, the presence of the 4xSu(Hw) multimer can induce the establishment of silencing by the bxdPRE in a chromosomal location that is not conducive to PcG-dependent silencing.
Su(Hw) binding facilitates recruitment of PcG/TrxG and PRE DNA-binding proteins to the bxdPRE at 96E
As shown above, Ph is recruited to bxdPRE insertions that are able to repress white expression but is not found associated with the bxdPRE at 96E. If the addition of the 4xSu(Hw) multimer induces PcG-dependent repression, it should also facilitate the recruitment of Ph to the 96E bxdPRE. To test this prediction, we used ChIP to examine Ph association at 5 sites in the Su-bxd and bxd transgenes: (1) the distal end of the spacer sequence upstream of bxdPRE, (2) bxdPRE, (3) the distal end of the spacer sequence downstream of bxdPRE, (4) the white transcription unit, and (5) the white promoter (Fig. 2a). As a negative control, we used the Ras64B coding region, while the bxdPRE-Genome region was used as a positive internal control.
Su(Hw) boundary induces the recruitment of PcG proteins to the bxdPRE. (a) Maps of the “bxd” and “Su-bxd” transgene constructs. The numbers above the maps (1, 2, 3, 4, 5, 6) indicate the regions amplified by qPCR in X-ChIP experiments. The X- ChIPs were performed with chromatin isolated from heads of adult flies homozygous for the “bxd” or “Su-bxd” transgenes at 96E insertion site. The X-ChIPs were performed with specific antibodies or with IgG. The specific antibodies: (b) Ph, (c) Sfmbt, (d) Pho, (e) Combgap, (f) Trx, (g) CBP. The ordinate shows the percentage of target sequences in the immunoprecipitated material relative to the input DNA and normalized to bxdPRE-Genome. The transgene specific regions and negative genome control (ras - coding part of Ras64B gene) are indicated on the abscissa. Other designations are as in Fig. 1
As would be predicted from the activation of silencing, there is a substantial increase in Ph association with the bxdPRE in the Su-bxd transgene as compared to the bxd transgene (Fig. 2b). While Ph is enriched at the bxdPRE, its association with the four other sites in the Su-bxd transgene is essentially the same as in the bxd transgene or the negative Ras64B control. This result is consistent with previous ChIP experiments in which we found that Ph (as well as several other PcG proteins: see below) associates with the bxdPRE element in the transgene construct, but not with other sequences even though white expression is silenced . Like silencing, the recruitment of Ph requires a combination of the bxdPRE and the 4xSu(Hw) multimer as Ph is not associated with the control transgene carrying only the 4xSu(Hw) multimer (Additional file 1: Figure S1b). Consistent with the idea that the presence of Su(Hw) protein bound to its target sites is responsible for the acquisition of silencing activity and the recruitment of Ph, we find that Su(Hw) associates with the 4x multimer not only in the Su transgene but also the Su-bxd transgene (Additional file 1: Figure S1c).
Biochemical and genetic studies have shown that, like other PREs, the silencing activity of the bxdPRE depends upon several DNA-binding proteins that help recruit the PRC1 and PRC2 complexes. Thus, one explanation for the inability of the bxdPRE element alone to silence white at 96E is that these DNA-binding proteins are unable to associate with the bxdPRE. In this model, these proteins would be able to access their recognition sequences in the PRE when the 4xSu(Hw) multimer is included in the transgene, but not when it is absent. The DNA-binding proteins known or thought to be important for bxdPRE silencing include Pho, which binds PREs together with its partner Sfmbt (the PhoRC complex) as well as the Combgap DNA-binding protein. An alternative model is that the bxdPRE at 96E is unable to silence because it is occupied by TrxG proteins and these factors block association of Ph and other PcG proteins and/or their function. In this case, the presence of the 4xSu(Hw) multimer could shift the balance to favor of the recruitment of PcG complexes.
To test these two models, we used antibodies against Pho, Sfmbt, Combgap, and two TrxG proteins, Trx and CBP, for ChIP experiments. In the bxd transgene, we observe only background levels of Pho, Sfmbt, and Combgap in ChIPs for the PRE and other sequences in the transgene (Fig. 2c–e). In contrast, all three of these proteins are detected at the bxdPRE in the Su-bxd transgene. These results are consistent with the predictions of the first model. We infer from these finding that the presence of the 4xSu(Hw) multimer induces the association of key DNA-binding proteins with the bxdPRE.
The second model predicts that TrxG proteins will be associated with the transgene containing the bxdPRE alone, but will be displaced by PcG proteins when the 4xSu(Hw) multimer is present. However, like the PcG proteins, the Trx and CBP are recruited to the bxdPRE only when the 4xSu(Hw) multimer is present (Fig. 2f, g). Thus, the boundary induces the association of PRE DNA-binding proteins as well as both PcG and TrxG factors to the bxdPRE. It remains to be determined whether this is actual co-occupancy or whether it reflects instead a heterogeneity in the population such that some PREs are occupied by PcG proteins while others are occupied by TrxG proteins.
The Su(Hw) multimer augments bxdPRE silencing activity at different chromosomal sites
Genome-wide ChIPs indicate there is a minor Su(Hw) peak that overlaps the attP site at 96E, while there are two larger peaks on either side of the attP
10 kb away (Additional file 3, data from [73, 74]). This raises the possibility that the 4xSu(Hw) multimer is able to rescue the silencing activity of the bxdPRE at this particular attP site only because of the endogenous Su(Hw) protein. For this reason, we asked whether the 4xSu(Hw) multimer is able to augment bxdPRE silencing at the four other attP sites which do not have peaks for Su(Hw) nearby (Additional file 3). To control for the effects of the Su(Hw) binding sites on transcriptional activity, the Su transgene was inserted at these other attP sites as well. Shown in Fig. 3 is a comparison of the silencing activity of the bxdPRE with and without the 4xSu(Hw) multimer (Fig. 3a,b) and 4xSu(Hw) multimer alone (Fig. 3c). For inserts at 22A, 51C, and 58A, silencing of white in hemizygotes is enhanced when 4xSu(Hw) is included next to bxdPRE in the transgene (Fig. 3b). While there is no obvious effect on the silencing of inserts at 68E (Fig. 3b), the bxdPRE already strongly silences on its own as either a hemizygote or homozygote (Fig. 3a). In homozygotes, the 22A insertion containing 4xSu(Hw) and bxdPRE is lethal, while the PSS observed for the 58A insert is clearly stronger when the 4xSu(Hw) multimer is present (Fig. 3b). Although there is no obvious difference between hemizygotes and homozygotes for the Su-bxd transgene inserted at 51C, silencing is substantially greater when the 4xSu(Hw) multimer is present (Fig. 3b). For the control construct, the Su(Hw) multimer alone, there is no evidence for silencing in any of these lines either in hemi- or homozygotes (Fig. 3c). Taken together, these findings indicate that the 4xSu(Hw) multimer can augment the silencing activity of the bxdPRE in different chromosomal environments.
Su(Hw) multimer can stimulate bxdPRE silencing activity at different insertion sites. 22A, 51C, 58A and 68E – designates the chromosome position of attP insertion sites. Phenotypes of eyes of adult flies with (a) bxd, (b) Su-bxd, and (c) Su transgenes in hemizygotes (P/+) and homozygotes (P/P)
Binding sites for architectural proteins CTCF and Pita can induce bxdPRE silencing
Next, we wondered whether induction of PRE repressing activity is unique to Su(Hw) or whether other polydactyl zinc finger proteins that have chromosome architectural functions are able to induce PRE silencing. To test this, we linked the bxdPRE to either a Drosophila 4xCTCF multimer or a 5xPita multimer. Like 4xSu(Hw), both of these multimers have insulating activity in BX-C boundary replacement experiments [71, 75,76,77]. Figure 4 shows that combining either 4xCTCF or 5xPita with the bxdPRE induces silencing activity in hemizygotes and PSS in homozygotes. Control experiments show that white expression is not affected when the multimers are included in the transgene alone. Thus, multimerized sites for three different chromosomal architectural proteins can induce the silencing activity of the bxdPRE.
CTCF and Pita multimers induce bxdPRE silencing activity. Transgenes were integrated into 96E attP insertion site. Designations: “4xCTCF”––four binding sites for CTCF protein “5xPita”—five binding sites for Pita protein. Other designations are as on Fig. 1
Su(Hw) architectural protein induces enPRE silencing activity
We also used the same strategy to test the silencing activity of another well-defined PRE, the 181 bp enPRE (PSE2) from the engrailed locus, in different chromosomal environments. In previous P-element transgene experiments, this element was shown to repress white expression as a hemizygote and/or homozygote and maintain the parasegmental expression pattern of a Ubx reporter [33, 38, 46, 59, 78,79,80,81,82]. Surprisingly, however, repression of white by the enPRE in hemizygote flies is weak or nonexistent at all five attP insertion sites and there is no evidence of PSS when the inserts are homozygous (en construct, Fig. 5a, b) Moreover, Ph binding is not detected at the enPRE in homozygous insertions of this construct (Fig. 5c).
Su(Hw) multimer can induce enPRE silencing. a Map of “en” and “Su-en” transgenes. Designations: “en”—enPRE element. Other labels are as in Fig. 1. b The eye phenotypes of flies with “en” and “Su-en” transgenes integrated into different attP insertion sites. c The results of X-ChIP with chromatin isolated from adult heads of homozygous lines with “en” or “Su-en” transgenes integrated into different attP insertion sites. X-ChIP was performed with Ph and Sfmbt antibodies or with an IgG control followed by real-time PCR with primers specific to transgenic enPRE. The ordinate shows the percentage of target sequences in the immunoprecipitated material relative to the input DNA and normalized relative to bxdPRE-Genome. The lines with enPRE transgene are designated by the attP insertion site as indicated on the abscissa. Other designations are as in Fig. 2
Since the lack of silencing was unexpected, we tested whether silencing activity could be induced by linking the 4xSu(Hw) multimer to the enPRE (Su-en construct, Fig. 5a). For this purpose, we chose the 68E attP integration site as it was most permissive for bxdPRE silencing. Figure 5b shows that the eye pigmentation of hemizygous Su-en flies is similar to that observed for en flies however, silencing is observed in flies homozygous for the 68E insert. This is opposite of that observed for the en transgene at 68E where the eye color in homozygotes becomes darker not lighter. ChIP experiments provide further evidence that 4xSu(Hw) is able to activate PcG-dependent silencing. While Ph and Sfmbt are not associated with the enPRE in the en transgene, both are recruited to the enPRE when it is linked to 4xSu(Hw) (Fig. 5c).
Increasing the distance between the bxdPRE and the boundary element disrupts silencing in hemizygotes
How do the architectural proteins help establish PRE activity? Two different mechanisms could be in play. The architectural proteins could facilitate the establishment of PcG silencing by locally displacing nucleosomes and/or by recruiting chromatin remodeling complexes so that key DNA-binding factors and PcG complexes are able to assemble on the PRE. Alternatively, the architectural proteins could enhance PRE activity by helping target the transgene from an active chromosomal compartment to a PcG-silenced chromosomal compartment . Since the effects of nucleosome displacement and chromatin remodeling are expected to be limited to closely linked sequences, the former model predicts that the impact of architectural proteins on the establishment of PcG silencing will decrease as the distance between the multimers and the bxdPRE is increased. In the latter model, relatively small changes in distance (<10 kb) should have little or no effect on the ability of the boundary to target the transgene to a PcG-silenced compartment.
To test these two models, we increased the distance between the 4xSu(Hw) or 4xCTCF multimers and the bxdPRE by 1 kb and 3 kb (Fig. 6a). For the distance of 1 kb, we inserted each multimer upstream of the left eGFP coding sequence spacer (the Su-1kb-bxd and CTCF-1kb-bxd constructs). To adjust the distance between the multimers and the PRE to 3 kb, we introduced an additional 2 kb spacer derived from the Escherichia coli LacZ coding sequence (the Su-3kb-bxd and CTCF-3kb-bxd constructs). A 1-kb distance would be sufficient for approximately five nucleosomes, while 3 kb would correspond to about fifteen nucleosomes. All of the constructs were then introduced into the 96E attP site. The results of this analysis are shown in Fig. 6. In hemizygotes (P/+) increasing the distance between the multimer and the bxdPRE adversely impacts silencing activity. For the 4xSu(Hw) multimer, a distance of 1 kb is sufficient to substantially reduce silencing (compare with bxdPRE alone). In the case of the 4xCTCF multimer, the disruption of silencing is greater when it is located 3 kb away from the bxdPRE than it is at a 1-kb distance however, even at 1 kb, silencing is reduced compared to the control CTCF-bxd construct. These findings argue in favor of the first model, namely that the 4xSu(Hw) and 4xCTCF multimers act in cis to facilitate the assembly of silencing complexes on the PRE (Fig. 6c).
Impact of distance between the boundary and the bxdPRE on PcG-dependent silencing. All transgenes were integrated in 96E insertion site. a Su-1kb-bxd, Su-3kb-bxd—the 4xSu sites are placed at the distance of 1 or 3 kb from the bxdPRE. CTCF-1kb-bxd, CTCF-3kb-bxd—the 4xCTCF sites are placed at the distance of 1 or 3 kb from the bxdPRE. Eye phenotypes in hemizygous (P/+) and homozygous (P/P) flies are shown. b The phenotypes of trans-heterozygotes. To generate the Su-1kb-bxd/CTCF-1kb-bxd or Su-3kb-bxd/CTCF-3kb-bxd trans-heterozygotes, Su-1kb-bxd and CTCF-1kb-bxd or Su-3kb-bxd and CTCF-3kb-bxd homozygous flies were crossed with each other. Hypothetical models of interactions in hemizygotes (c), homozygotes, and trans-heterozygotes (d) are shown. Red ovals—enhancer-associated activator proteins blue and pink ovals—Polycomb and Trithorax group proteins, respectively
Boundaries located at a distance from the bxdPRE can induce PSS
While silencing activity in hemizygotes is significantly compromised when the multimers are moved away from the bxdPRE, this is not true for PSS. As shown in Fig. 6 (P/P), the bxdPRE represses white expression even when the boundary multimers are located 3 kb away. A plausible explanation for this result is that pairing of the 4xSu(Hw) or 4xCTCF multimers in trans would tend to stabilize pairing interactions between the PREs on each homolog, and this interaction facilitates the PSS-dependent assembly of functional silencing complexes.
Boundary pairing in Drosophila depends upon specific interactions between proteins associated with each element [56, 84,85,86]. A classic example of specificity comes from the boundary bypass assay. In this assay, an upstream regulatory element (enhancer or silencer) is separated from a reporter gene by a spacer DNA that is flanked by two boundary elements (endogenous or artificial). If the boundaries flanking the spacer DNA can pair with other, the upstream regulatory element is brought into close proximity with the reporter and can either activate (enhancer) or repress (silencer) its expression [48, 55, 57, 87, 88]. This is what is found when the spacer DNA is flanked by either Su(Hw) or CTCF multimers . On other hand, bypass is not observed when the spacer DNA is flanked by a heterologous combination of Su(Hw) multimers and CTCF multimers . Thus, if boundary-boundary interactions between homologs are required to induce PRE repression, then PSS should not be observed in the two sets of mixed pairs: Su-1kb-bxd trans to CTCF-1kb-bxd or Su-3kb-bxd trans to CTCF-3kb-bxd. Figure 6b shows that this prediction is correct: silencing depends on boundary pairing in trans and is not observed in heterologous combinations. A possible model is shown in Fig. 6d.
Binding sites of architectural proteins leads to local decrease of histone H3 enrichment
The finding that the boundary element must be closely linked to the bxdPRE for silencing in hemizygotes and enhanced PSS in homozygotes suggests that the boundary has a local effect on chromatin structure that enables PcG factors to gain access to the PRE and assemble functional silencing complexes. If this suggestion is correct, then the chromatin organization of the bxdPRE should be altered when it is closely linked to a boundary element. To test this prediction, we analyzed histone H3 association with six unique sequences (1n-6n) located at different distances upstream and downstream of the BamH1 site used to insert test DNAs (Fig. 7a). The distance between the midpoints of the 3n and 4n sequences and the BamH1 site are 75 bp and 83 bp, respectively. The midpoints of 2n and 5n are 187 bp and 233 bp from the BamH1 site, respectively, while those for 1n and 3n are 301 and 365 bp.
Multimerized binding sites for the three polydactyl zinc finger architectural proteins induces a local reduction in histone H3 occupancy. a Maps of transgene constructs used for the histone H3 ChIPs: E-w, bxd, Su, CTCF, Pita, Su-bxd, CTCF-bxd, Pita-bxd. The location of the sequences amplified by qPCR in X-ChIP experiments are indicated above the E-w construct map (1n, 2n, 3n, 4n, 5n, 6n). They are the same for all of the constructs tested. b X-ChIPs were performed with chromatin isolated from heads of adult flies homozygous for transgenes inserted at 96E. X-ChIPs were performed with histone H3-specific antibodies or with the IgG control. The ordinate shows the percentage of target sequences in the immunoprecipitated material relative to the input DNA. The abscissa indicates the transgene construct and amplified primer pair. The ras (coding part of Ras64B gene) was used as an internal control
In the control E-w transgene, histone H3 association as measure by ChIP is nearly the same in all regions tested (1n-6n) and is equivalent to that observed for the control genomic sequence in the coding region of the Ras64B gene (ras) (Fig. 7b). The inclusion of the bxdPRE in the transgene (bxd) has no apparent effect on histone H3 occupancy and the profile across the six sequences is similar to that of the E-w control. A different result is obtained for Su, CTCF, or Pita transgenes containing the multimerized binding sites for the boundary proteins. In all three cases, there is a reduction in histone H3 occupancy in the sequences located immediately next to the multimers (Fig. 7b). This finding indicates that the multimerized sites for these three chromosomal architectural proteins generate a region that is depleted in nucleosomes. This effect is local and does not extend to sequences located more distant (1n, 2n, 5n, and 6n) from the multimerized sites.
We next examined the Su-bxd, CTCF-bxd, and Pita-bxd transgenes (Fig. 7b). As was observed for the transgenes containing only the multimerized binding sites, histone association with the two sequences, 3n and 4n, that immediately flank the multimer-bxdPRE combination is reduced. In each case, the reduction is approximately the same as that observed for the corresponding multimer alone. Importantly, this is true for 4n, which is separated from the multimers by the 650 bp bxdPRE. These results provide strong support for the idea that closely linked boundary elements can induce alterations in the association of nucleosomes with the PRE. Interestingly, this is not the only alteration in nucleosome association evident in these transgenes. In all three of the multimer-bxdPRE combinations, we found that histone H3 association with the more distant sequences (1n, 2n, 5n, and 6n) is enhanced relative to the various control transgenes (E-w, bxd and Su, CTCF, and Pita). Since enhanced association is not observed with the inactive bxdPRE or with any of the multimers alone, it seems likely that this effect is due to the recruitment of functional PcG complexes to the bxdPRE. It remains to be determined whether this alteration in histone H3 association reflects an increase in nucleosome density in the flanking DNA regions or an increase in the extent of compaction.
Interplay between PREs and boundary contributes to silencing
The studies in the previous sections indicate that boundaries can enhance PRE silencing by two different mechanisms. One takes place in cis and requires a close linkage of the boundary and PRE. This mechanism locally alters the pattern of histone association and facilitates the recruitment of factors critical for PcG repression. The other takes place in trans and is mediated by boundary:boundary pairing interactions. In this second mechanism, boundaries appear to provide a “spot weld” that holds the homologs in close proximity. This facilitates PRE:PRE interactions and results in PSS even when the boundary multimers are separated from the PREs. We undertook several additional experiments to further explore this “pairing” mechanism.
Boundary pairing can induce silencing in trans: When 4xSu(Hw) or 4xCTCF is placed next to the bxdPRE, the PRE can assemble a functional silencing complex and repress white in hemizygotes. However, the bxdPRE would not be expected to efficiently silence white in trans unless the homologs are tightly paired in the immediate neighborhood. To test this expectation, we generated trans-heterozygotes between the starting transgene E-w (the eye enhancer—white gene control construct which has all of the elements in the bxd construct except bxdPRE) and either Su-bxd or CTCF-bxd (Fig. 8a). While the bxdPRE silences the white gene in cis (Fig. 8a-II, Su-bxd/+ and CTCF-bxd/+ - compare with E-w/+ in Fig. 8a-I), it does not efficiently silence the white gene in trans (Fig.8a-III, Su-bxd/E-w, and CTCF-bxd/E-w: compare with E-w/+ and E-w/E-w in Fig. 8a-I). In these two combinations, the eye color phenotype is indistinguishable from that observed in E-w /+ or E-w/E-w. A different result is obtained when both transgenes have a copy of the same multimer (Su-bxd/Su or CTCF-bxd/CTCF, Fig. 8a-IV—compare with Su-bxd/E-w and CTCF-bxd/E-w, Fig.8a-III). While silencing is not as effective as when the two transgenes not only have identical multimers but also a copy of the bxdPRE (see Fig. 8a-V, Su-bxd/Su-bxd, CTCF-bxd/CTCF-bxd), the level of white expression is clearly reduced compared to that observed when the multimer is not present in the E-w transgene. This finding indicates that boundary:boundary pairing interactions can promote trans silencing by a PRE. To confirm that pairing interactions between the boundaries in the two transgenes provide a “spot weld” that facilitates trans silencing activity, we tested heterologous multimer combinations that do not pair with each other. As shown in Fig. 8a-VI, silencing is not observed with the Su-bxd/CTCF combination, nor is it observed with the converse CTCF-bxd/Su combination.
Induction of PRE silencing in trans by boundary:boundary pairing. a Phenotypes of eyes of transgenic flies. Trans-heterozygotes were obtained by crossing the corresponding homozygote flies. All transgenes used for crosses were integrated at 96E. b III, IV, V, VI—hypothetical chromatin interactions formed in a-III, a-IV, a-V, a-VI, respectively
Boundary pairing is not necessary when both PREs are active: Placing multimerized binding sites for zinc finger architectural proteins next to the bxdPRE induces the assembly of functional silencing complexes in hemizygous flies and enhances PSS in homozygous flies. If the PREs on both homologs are activated by multimerized binding sites, then boundary:boundary pairing interactions would not be expected to be required for generating the synergistic trans interactions between PREs on each homolog that are responsible for PSS. To test this prediction, we generated trans combinations of bxdPRE transgenes that have closely linked multimerized Su(Hw), CTCF, or Pita-binding sites. Figure 9a-II shows that the silencing of white in the three trans-heterologous combinations of multimerized binding sites is close to that observed when both transgenes have multimerized binding sites for the same protein.
PRE dependent trans-activation of silencing. All transgenes used for crosses were integrated in 96E insertion site. a Eye phenotypes of transgenic flies. b I, II, III, IV—the hypothetical chromatin interactions formed in a-I, a-II, a-III, a-IV, respectively
An inactive PRE can be transactivated by an active PRE: The bxdPRE insertion in the 22A attP site is a classic example of PSS. Little or no silencing is observed in hemizygotes, while silencing is quite efficient in homozygotes. The PSS phenomenon suggests that PRE:PRE interactions in trans can synergizes, promoting the assembly of functional PcG silencing complexes on both PREs. To test this idea for 96E, we generated trans combinations between the inactive PRE in the bxd transgene and transgenes in which bxdPRE is activated by closely linked 4xSu(Hw), 4xCTCF, or 5xPita sites. Consistent with prediction, the white reporter in the homolog carrying the bxdPRE (only) transgene is repressed when the other homolog has the boundary-bxdPRE (compare eyes in Fig. 9a-III with those in Fig. 9a-IV). However, silencing is not equivalent to that observed when the bxdPREs on both homologs are activated by multimerized binding sites (Fig. 9a-I).
Our studies probe the molecular mechanism of LD. We identified laforin orthologues in specific protists and further showed that Hs-laforin and plant SEX4 are functional equivalents. Our results provide compelling evidence that a laforin-like activity is required to regulate the metabolism of amylopectin-like material across multiple kingdoms. Additionally, they demonstrate the nature of this activity that is, the dephosphorylation of the carbohydrate itself, thus providing a molecular explanation for LD. Although there are examples of DSPs that dephosphorylate nonproteinacious substrates (such as phosphate and tensin homologue, the myotubularin family, and Sac domain phosphatases that dephosphorylate the inositol head group of phospholipids Chung et al., 1997 Maehama and Dixon, 1998 Guo et al., 1999 Hughes et al., 2000 Taylor et al., 2000 Robinson and Dixon, 2006), ours is the first example of a family of phosphatases that dephosphorylate complex carbohydrates.
We demonstrate that laforin is not merely restricted to the genomes of vertebrates but is well conserved in the protists T. gondii, E. tenella, T. thermophila, P. tetraurelia, and C. merolae. Laforin's evolutionary lineage shows that it originated in a primitive red alga during early eukaryotic evolution. Despite its early origin, laforin was only maintained by organisms that synthesize floridean starch (such as the aforementioned five protists) and organisms that inhibit the production of insoluble carbohydrates (i.e., all vertebrates). Organisms that no longer performed either of these processes lost laforin. Conversely to laforin, we show that although SEX4 contains similar domains as laforin, its lineage differs in that SEX4 is conserved in all land plants as well as in C. reinhardtii, a close descendent of primitive green algae. Despite their different lineages, Hs-laforin performs the same function as the plant protein SEX4 thus, we propose that laforin and SEX4 are functional equivalents.
It must be noted that although laforin and SEX4 share a common function and similar domains, they are not orthologous proteins. They are not orthologues because (1) although they share similar CBMs, the CBMs belong to different classes and differ considerably with respect to the primary amino acids that are important for binding carbohydrates, and (2) the DSP and CBM of laforin and SEX4 are arranged in opposite orientations. Thus, it is likely that red and green algae independently evolved a phosphatase via convergent evolution that utilizes a similar mechanism to regulate insoluble carbohydrate metabolism.
Despite the independent means by which laforin and SEX4 evolved, they both dephosphorylate the same carbohydrate substrate and constitute a unique family of phosphatases. In addition, we demonstrate that endogenous Cm-laforin localizes around the floridean starch granules. Although most studies thus far suggest a carbohydrate substrate for laforin and SEX4, it is possible that they bind their respective amylopectin-like material (insoluble glycogen and starch, respectively) and dephosphorylate a proteinacious substrate. This proteinacious substrate would likely be involved in regulating carbohydrate metabolism, a process controlled by multiple levels of phosphorylation (Roach, 2002). Although the overall carbohydrate machinery differs substantially between mammals and plants, both systems contain common phosphoproteins that share conserved functions (Preiss et al., 1983 Vikso-Nielsen et al., 2002 Coppin et al., 2005). These proteins would be likely substrate candidates for laforin and SEX4. To address this hypothesis, we tested the majority of the mammalian candidates, but none of them served as a substrate for laforin (Worby et al., 2006 our unpublished data).
It is interesting that laforin and SEX4 are functional equivalents that dephosphorylate a complex carbohydrate and that the mutation of either gene results in the accumulation of insoluble carbohydrates in vertebrates and plants, respectively. Our understanding of the metabolism of insoluble carbohydrates in vertebrate systems is still in its infancy. In contrast, the plant community has made substantial progress in understanding the metabolism of starch (Smith et al., 2005 Zeeman et al., 2007). In plants, it is clear that the phosphorylation of glucose residues within starch is required for its proper accumulation and degradation (Blennow et al., 2002 Smith et al., 2005 Zeeman et al., 2007). In A. thaliana, glucan water dikinase (Ritte et al., 2002) and phosphoglucan water dikinase (Baunsgaard et al., 2005 Kotting et al., 2005) phosphorylate glucose monomers within amylopectin at the C6 and C3 position (Ritte et al., 2006), respectively. As with SEX4, mutations in the genes encoding glucan water dikinase and phosphoglucan water dikinase also yield a starch excess phenotype (Yu et al., 2001 Baunsgaard et al., 2005 Kotting et al., 2005). Phosphorylation is necessary for both starch accumulation and degradation however, the timing of these phosphorylation and dephosphorylation events is unknown (Smith et al., 2005 Zeeman et al., 2007). Intriguingly, although glycogen, the soluble storage carbohydrate in vertebrates, contains little to no phosphate, detrimental insoluble carbohydrates like LBs are highly phosphorylated, just like amylopectin in plant starch (Schnabel and Seitelberger, 1968 Sakai et al., 1970). Therefore, it appears logical that laforin and SEX4 evolved to perform the critical role of dephosphorylating insoluble carbohydrates to allow their proper degradation.
This basic function of insoluble carbohydrate metabolism provides an intriguing explanation for both the existence of a laforin-like activity in protists and plants and the role of laforin in preventing LD. In protists and plants, carbohydrate dephosphorylation would be necessary for the utilization of insoluble carbohydrates as an energy source. When this activity is absent, these organisms accumulate unusable starch as in the sex4 mutants. In vertebrates, laforin would dephosphorylate nascent insoluble carbohydrates to inhibit the formation of detrimental LBs. In the absence of laforin, these nascent molecules increase in size and number and eventually cause LD.
Our work clearly demonstrates that a laforin-like activity is necessary for the proper metabolism of insoluble carbohydrates. This activity is required throughout multiple kingdoms and regulates an overlooked aspect of carbohydrate metabolism. It is striking that protists and plants have provided new insights into a human neurodegenerative disease involving aberrant carbohydrate metabolism that was described almost 100 yr ago by Lafora and Gluck (Lafora, 1911 Lafora and Gluck, 1911).
Spatial transcriptomics provides the coordinates of each transcript without any information on the transcript cell of origin (Lee, 2017 ). Here, we present JSTA, a new method to convert raw measurements of transcripts and their coordinates into spatial single-cell expression maps. The key distinguishing aspect of our approach is its ability to leverage existing scRNAseq-based reference cell type taxonomies to simultaneously segment cells, classify cells into (sub)types, and assign mRNAs to cells. The unique integration of spatial transcriptomics with existing scRNAseq information to improve the accuracy of image segmentation and enhance the biological applications of spatial transcriptomics, distinguishes our approach from other efforts that regardless of their algorithmic ingenuity are bounded by the available information in the images themselves. As such, JSTA is not a generalist image segmentation algorithm rather a tool specifically designed to convert raw spatial transcriptomic data into single cell-level spatial expression maps. We show the benefits of using a dedicated analysis tool through the insights it provides into spatial organization of distinct (sub)types in the mouse hippocampus and the hundreds of newly discovered cell (sub)type-specific spDEGs. These insights into the molecular- and cellular-level structural architecture of the hippocampus demonstrate the types of biological insights provided by highly accurate spatial transcriptomics.
The promise of single cell and spatial biology lends itself to intense focus on technological and computational development and large-scale data collection efforts. We anticipate that JSTA will benefit these efforts while at the same time benefit from them. On the technology side, we have demonstrated the performance of JSTA for two variants of spatial transcriptomics, MERFISH and osmFISH. However, the algorithm is extendable and could be applied to other spatial transcriptomic approaches that are based on in situ sequencing (Lee et al, 2014 Lee et al, 2015 Turczyk et al, 2020 ), subcellular spatial barcoding (Ståhl et al, 2016 Salmén et al, 2018 ), and potentially any other spatial “omics” platforms (Gerdes et al, 2013 Lin et al, 2015 Goltsev et al, 2018 Keren et al, 2018 Lin et al, 2018 Lundberg & Borner, 2019 ). Additionally, cell segmentation results from JSTA can be used as input for other tools such as GIOTTO (Dries et al, 2021 ) and TANGRAM (preprint: Biancalani et al, 2020 ) to facilitate single cell and spatial transcriptomic data analysis. The benefits of JSTA are evident even with a small number of measured genes. This indicates that it is applicable to a broad range of platforms across all multiplexing capabilities. JSTA is limited by its ability to harmonize technical differences between spatial transcriptomic data modalities and the scRNAseq reference. Harmonization between datasets is an active area of research, and JSTA will benefit from these advances (preprint: Lopez et al, 2019 Stuart et al, 2019 Welch et al, 2019 Abdelaal et al, 2020 Tran et al, 2020 ). JSTA relies on initial seed identification (nuclei or cell centers), and incorrect identification can lead to split or merged cells. JSTA currently does not split or merge cells, but this postprocessing step could be added to further improve segmentation (Chaudhuri & Agrawal, 2010 Surut & Phukpattaranont, 2010 Correa-Tome & Sanchez-Yanez, 2015 Gamarra et al, 2019 ). On the data side, as JSTA leverages external reference data, it will naturally increase in its performance as both the quality and quantity of reference cell type taxonomies improve (HuBMAP Consortium, 2019 ). We see JSTA as a dynamic analysis tool that could be reapplied multiple times to the same dataset each time external reference data is updated to always provide highest accuracy segmentation, cell (sub)type classification, spDEG identification.
Due to the nascent status of spatial transcriptomics, there are many fundamental questions related to the interplay between cell (sub)types and other information gleaned from dissociative technologies and tissue and organ architecture (Trapnell, 2015 Mukamel & Ngai, 2019 ). Our results show that strong codependency between spatial position and transcriptional state of a cell in the hippocampus, these results mirror findings from other organs (Halpern et al, 2017 Moor et al, 2018 Egozi et al, 2020 ). This codependency supports the usefulness of the reference taxonomies that were developed without the use of spatial information. Agreements between cell type taxonomies developed solely based on scRNAseq and other measurement modalities, i.e., spatial position, corroborate the relevance of the taxonomical definitions created for mouse brain (Yuste et al, 2020 ). At the same time, the spatial measurements demonstrate the limitation of scRNAseq. We discovered many spatial expression patterns within most cell (sub)types that prior to these spatial measurements would have been considered biological heterogeneity or even noise but in fact they represent key structural features of brain organization. High accuracy mapping at the molecular and cellular level will allow us to bridge cell biology with organ anatomy and physiology pointing toward a highly promising future for spatial biology.
LATERAL ORGAN BOUNDARIES defines a new family of DNA-binding transcription factors and can interact with specific bHLH proteins
Conserved in a variety of evolutionarily divergent plant species, LOB DOMAIN (LBD) genes define a large, plant-specific family of largely unknown function. LBD genes have been implicated in a variety of developmental processes in plants, although to date, relatively few members have been assigned functions. LBD proteins have previously been predicted to be transcription factors, however supporting evidence has only been circumstantial. To address the biochemical function of LBD proteins, we identified a 6-bp consensus motif recognized by a wide cross-section of LBD proteins, and showed that LATERAL ORGAN BOUNDARIES (LOB), the founding member of the family, is a transcriptional activator in yeast. Thus, the LBD genes encode a novel class of DNA-binding transcription factors. Post-translational regulation of transcription factors is often crucial for control of gene expression. In our study, we demonstrate that members of the basic helix-loop-helix (bHLH) family of transcription factors are capable of interacting with LOB. The expression patterns of bHLH048 and LOB overlap at lateral organ boundaries. Interestingly, the interaction of bHLH048 with LOB results in reduced affinity of LOB for the consensus DNA motif. Thus, our studies suggest that bHLH048 post-translationally regulates the function of LOB at lateral organ boundaries.
Subcellular localization of LOB and…
Subcellular localization of LOB and results of selection and amplification binding (SAAB) assay.…
LOB and other LBD proteins…
LOB and other LBD proteins specifically bind the LBD motif. ( A )…
LOB binds the LBD motif as a dimer and is a transcriptional activator…
LOB interacts with members of…
LOB interacts with members of the bHLH family of transcription factors. ( A…
bHLH048 specifically reduces the binding…
bHLH048 specifically reduces the binding affinity of the LD for the LBD motif.…
What is Code Biology?
Codes and conventions are the basis of our social life and from time immemorial have divided the world of culture from the world of nature. The rules of grammar, the laws of government, the precepts of religion, the value of money, the rules of chess etc., are all human conventions that are profoundly different from the laws of physics and chemistry, and this has led to the conclusion that there is an unbridgeable gap between nature and culture. Nature is governed by objective immutable laws, whereas culture is produced by the mutable conventions of the human mind.
In this millennia-old framework, the discovery of the genetic code, in the early 1960s, came as a bolt from the blue, but strangely enough it did not bring down the barrier between nature and culture. On the contrary, a protective belt was quickly built around the old divide with an argument that effectively emptied the discovery of all its revolutionary potential. The argument that the genetic code is not a real code because its rules are the result of chemical affinities between codons and amino acids and are therefore determined by chemistry. This is the ‘Stereochemical theory’, an idea first proposed by George Gamow in 1954, and re-proposed ever since in many different forms (Pelc and Welton 1966 Dunnil 1966 Melcher 1974 Shimizu 1982 Yarus 1988, 1998 Yarus, Caporaso and Knight 2005). More than fifty years of research have not produced any evidence in favour of this theory and yet the idea is still circulating, apparently because of the possibility that stereochemical interactions might have been important at some early stages of evolution (Koonin and Novozhilov 2009). The deep reason is probably the persistent belief that the genetic code must have been a product of chemistry and cannot possibly be a real code. But what is a real code?
The starting point is the idea that a code is a set of rules that establish a correspondence, or a mapping, between the objects of two independent worlds (Barbieri 2003). The Morse code, for example, is a mapping between the letters of the alphabet and groups of dots and dashes. The highway code is a correspondence between street signals and driving behaviours (a red light means ‘stop’, a green light means ‘go’, and so on).
What is essential in all codes is that the coding rules, although completely compatible with the laws of physics and chemistry, are not dictated by these laws. In this sense they are arbitrary, and the number of arbitrary relationships between two independent worlds is potentially unlimited. In the Morse code, for example, any letter of the alphabet could be associated with countless combinations of dots and dashes, which means that a specific link between them can be realized only by selecting a small number of rules. And this is precisely what a code is: a small set of arbitrary rules selected from a potentially unlimited number in order to ensure a specific correspondence between two independent worlds.
This definition allows us to make experimental tests because organic codes are relationships between two worlds of organic molecules and are necessarily implemented by a third type of molecules, called adaptors, that build a bridge between them. The adaptors are required because there is no necessary link between the two worlds, and a fixed set of adaptors is required in order to guarantee the specificity of the correspondence. The adaptors, in short, are the molecular fingerprints of the codes, and their presence in a biological process is a sure sign that that process is based on a code.
This gives us an objective criterion for discovering organic codes and their existence is no longer a matter of speculation. It is, first and foremost, an experimental problem. More precisely, we can prove that an organic code exists, if we find three things: (1) two independents worlds of molecules, (2) a set of adaptors that create a mapping between them, and (3) the demonstration that the mapping is arbitrary because its rules can be changed, at least in principle, in countless different ways.
Two outstanding examples
In protein synthesis, a sequence of nucleotides is translated into a sequence of amino acids, and the bridge between them is realized by a third type of molecules, called transfer-RNAs, that act as adaptors and perform two distinct operations: at one site they recognize groups of three nucleotides, called codons, and at another site they receive amino acids from enzymes called aminoacyl-tRNA-synthetases. The key point is that there is no deterministic link between codons and amino acids since it has been shown that any codon can be associated with any amino acid (Schimmel 1987 Schimmel et al. 1993). Hou and Schimmel (1988), for example, introduced two extra nucleotides in a tRNA and found that that the resulting tRNA was carrying a different amino acid. This proved that the number of possible connections between codons and amino acids is potentially unlimited, and only the selection of a small set of adaptors can ensure a specific mapping. This is the genetic code: a fixed set of rules between nucleic acids and amino acids that are implemented by adaptors. In protein synthesis, in conclusion, we find all the three essential components of a code: (1) two independents worlds of molecules (nucleotides and amino acids), (2) a set of adaptors that create a mapping between them, and (3) the proof that the mapping is arbitrary because its rules can be changed.
The signal transduction codes
Signal transduction is the process by which cells transform the signals from the environment, called first messengers, into internal signals, called second messengers. First and second messengers belong to two independent worlds because there are literally hundreds of first messengers (hormones, growth factors, neurotransmitters, etc.) but only four great families of second messengers (cyclic AMP, calcium ions, diacylglycerol and inositol trisphosphate) (Alberts et al. 2007). The crucial point is that the molecules that perform signal transduction are true adaptors. They consists of three subunits: a receptor for the first messengers, an amplifier for the second messengers, and a mediator in between (Berridge 1985). This allows the transduction complex to perform two independent recognition processes, one for the first messenger and the other for the second messenger. Laboratory experiments have proved that any first messenger can be associated with any second messenger, which means that there is a potentially unlimited number of arbitrary connections between them. In signal transduction, in short, we find all the three essential components of a code: (1) two independents worlds of molecules (first messengers and second messengers), (2) a set of adaptors that create a mapping between them, and (3) the proof that the mapping is arbitrary because its rules can be changed (Barbieri 2003).
A world of organic codes
In addition to the genetic code and the signal transduction codes, a wide variety of new organic codes have come to light in recent years. Among them: the sequence codes (Trifonov 1987, 1989, 1999), the Hox code (Paul Hunt et al. 1991 Kessel and Gruss 1991), the adhesive code (Redies and Takeichi 1996 Shapiro and Colman 1999), the splicing codes (Barbieri 2003 Fu 2004 Matlin et al. 2005 Pertea et al. 2007 Wang and Burge 2008 Barash et al. 2010 Dhir et al. 2010), the signal transduction codes (Barbieri 2003), the histone code (Strahl and Allis 2000 Jenuwein and Allis 2001 Turner 2000, 2002, 2007 Kühn and Hofmeyr 2014), the sugar code (Gabius 2000, 2009), the compartment codes (Barbieri 2003), the cytoskeleton codes (Barbieri 2003 Gimona 2008), the transcriptional code (Jessell 2000 Marquard and Pfaff 2001 Ruiz i Altaba et al. 2003 Flames et al. 2007), the neural code (Nicolelis and Ribeiro 2006 Nicolelis 2011), a neural code for taste (Di Lorenzo 2000 Hallock and Di Lorenzo 2006), an odorant receptor code (Dudai 1999 Ray et al. 2006), a space code in the hippocampus (O’Keefe and Burgess 1996, 2005 Hafting et al. 2005 Brandon and Hasselmo 2009 Papoutsi et al. 2009), the apoptosis code (Basañez and Hardwick 2008 Füllgrabe et al. 2010), the tubulin code (Verhey and Gaertig 2007), the nuclear signalling code (Maraldi 2008), the injective organic codes (De Beule et al. 2011), the molecular codes (Görlich et al. 2011 Görlich and Dittrich 2013), the ubiquitin code (Komander and Rape 2012), the bioelectric code (Tseng and Levin 2013 Levin 2014), the acoustic codes (Farina and Pieretti 2014), the glycomic code (Buckeridge and De Souza 2014 Tavares and Buckeridge 2015) and the Redox code (Jones and Sies 2015).
The living world, in short, is literally teeming with organic codes, and yet so far their discoveries have only circulated in small circles and have not attracted the attention of the scientific community at large.
Code Biology is the study of all codes of life with the standard methods of science. The genetic code and the codes of culture have been known for a long time and represent the historical foundation of Code Biology. What is really new in this field is the study of all codes that came after the genetic code and before the codes of culture. The existence of these codes is an experimental fact – let us never forget this – but also more than that. It is one of those facts that have extraordinary theoretical implications.
The first is the role that the organic codes had in the history of life. The genetic code was a precondition for the origin of the first cells, the signal transduction codes divided the descendants of the common ancestor into the primary kingdoms of Archaea, Bacteria and Eukarya, the splicing codes were instrumental to the origin of the nucleus, the histone code provided the rules of chromatin, and the cytoskeleton codes allowed the Eukarya to perform internal movements, including those of mitosis and meiosis (Barbieri 2003, 2015). The greatest events of macroevolution, in other words, were associated with the appearance of new organic codes, and this gives us a completely new understanding of the history of life.
The second great implication is the fact that the organic codes have been highly conserved in evolution, which means that they are the great invariants of life, the sole entities that have been perpetuated while everything else has been changed. Code Biology, in short, is uncovering a new history of life and bringing to light new fundamental concepts. It truly is a new science, the exploration of a vast and still largely unexplored dimension of the living world, the real new frontier of biology.
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