Saturday, August 16, 2008

Putting cytosine deamination to work

The effect of cytosine deamination on a random pool of amino acids and how it might facilitate evolution has been described. Cytosine deamination also does not result in any stop codon formation. Bollenbach et al. (2007) briefly describes a few more optimal features of the genetic code as discussed in more detail by Itzkovitz and Alon (2007).
These include:
1) Quote:
They (Itzkovitz and Alon) compared the actual genetic code with an ensemble of all other codes that are equally optimized with respect to mistranslation or mutation (for more on this statistical approach, see also Alff-Steinberger 1969; Haig and Hurst 1991; Freeland and Hurst 1998). Assuming that the usage frequencies of the different amino acids are fixed, while their codon assignments vary in the ensemble, they find that the actual code is far better than other possible codes in minimizing the number of amino acids incorporated until translation is interrupted after a frameshift error occurred. This new observation by Itzkovitz and Alon could therefore be seen as reviving the basis for Crick’s theory of a comma-less code, modified by the constraints imposed on the code by the need to be robust to other kinds of translation errors and mutations. Another possible interpretation of their result is that the amino acid usage has adjusted to reduce the effects of frameshift errors; alternative genetic codes would have had a different amino acid usage coadapted to them. It has been shown previously that amino acid usage is rather malleable, and, for example, influenced by GC content (Knight et al. 2001b).
2) Quote:
Itzkovitz and Alon suggest another, quite unanticipated, type of optimality: the code is highly optimal for encoding arbitrary additional information, i.e., information other than the amino acid sequence in protein-coding sequences. Optimality for encoding additional information is particularly important and relevant given the known signals contained in the nucleotide sequence of coding regions. These include RNA splicing signals, which are encoded in the nucleotide sequence together with the amino acid sequence of the prospective protein (Cartegni et al. 2002), as well as signals recognized by the translation apparatus.
Bollenbach et al. (2007) also briefly mentions how the code could have evolved:
1) Quote:
(1) the code has evolved under selection pressure to optimize certain functions such as minimization of the impact of mutations (Sonneborn 1965) or translation errors (Woese 1965a); Random mutation is a source of variability, yet selection pressure is believed to have selected for a system to put constraints on variability. Why?

2) Quote:
(2) the number of amino acids in the code has increased over evolutionary time according to evolution of the pathways for amino acid biosynthesis (Wong 1975)
Why was selection so strong in removing the other variants with fewer codons? Is there evidence of organisms using only 5, 6, 9, 13, 18 etc. amino acid codons? Bollenbach et al. (2007) also points out the following:
Quote:
The discovery of variant codes (Barrell et al. 1979; Fox 1987; Knight et al. 2001a) made the connection between evolvability and universality even more puzzling. On one hand, they prove that the genetic codes can evolve; on the other hand, if they could easily evolve, why are all variations minor? It was recently proposed that extensive horizontal gene transfer during early evolution can account for both evolution toward optimality and the near universality of the genetic code (Vetsigian et al. 2006).
3) Quote:
(3) direct chemical interactions between amino acids and short nucleic acid sequences originally led to corresponding assignments in the genetic code (Woese et al. 1966b).
Bollenbach et al. (2007) concludes with the following:
Quote:
As we learn more about the functions of the genetic code, it becomes ever clearer that the degeneracy in the genetic code is not exploited in such a way as to optimize one function, but rather to optimize a combination of several different functions simultaneously. Looking deeper into the structure of the code, we wonder what other remarkable properties it may bear. While our understanding of the genetic code has increased substantially over the last decades, it seems that exciting discoveries are waiting to be made.
The vertebrate immune system exploits these optimal features of the genetic code by "putting cytosine deamination to work". Antibody diversification is crucial in limiting the frequency of environmentally acquired infections and thereby increasing the fitness of the organism. Initial diversification of antibodies is achieved by assembling variable (V), diversity (D) and joining (J) gene segments (V(D)J recombination) by non-homologous recombination. Further diversification is carried out by somatic hypermutation (SHM) and Class Switch Recombination. Central to the initiation to these diversification processes is the activation-induced cytosine deaminase (AID) protein. AID deaminates cytosine to uracil in single stranded DNA (ssDNA - arising during gene transcription) and is dependent on active gene transcription of the various antibody genes. The induced mutation is resolved by at least 4 pathways (Figure 4):
1) Copying of the base by high-fidelity polymerases during DNA replication.
2) Short-Patch Base Excision Repair (SP-BER) by uracil-DNA glycosylase removal and subsequent repair of the base.
3) Long-Patch Base Excision Repair (LP-BER)
4) Mismatch repair (MMR)

Figure 1: Activation induced cytosine deamination and the pathways involved in resolving the induced mutation. 1) Normal DNA replication results in a C:G→T:A transition. 2) Successful SP-BER resolves the mutation, however the recruitment of error-prone translesion polymerases results (e.g. REV1) in transversions (REV1; C:G→G:C) and transition. 3) LP-BER can also resolve the mutation, however recruitment of low-fidelity polymerases (e.g. Pol n) also causes transition and transversion mutations. 4) MMR repair can also resolve the mutation, however the recruitment of low-fidelity polymerases through this pathway is a major cause of A:T transitions.

AID causes somatic hypermutation and its activity is limited to the certain genetic regions of the immune system. When the system runs unchecked, mutations might be introduced into proto-oncogenes, resulting in possible cancerous growth. The system is controlled (Figure 2). The activity and gene expression of AID is controlled. The type of error-repair pathway and the subsequent recruitment of various low-fidelity polymerases determine the type of mutations after the repair process and these also seem to be controlled. Current research focuses on the mechanisms of control of downstream repair pathways and why this system is selectively targeted to the small region of antibody genes.

Figure 2: Controlled variability of somatic hypermutation.

Thus, the immune system exploits the properties the genetic code for the purpose of controlled variability. Is the system limited to vertabrates or can similar systems be found in other organisms. Cytosine deamninases are found in bacteria as well. Error-prone repair systems are also present. Will we discover an active system in bacteria that exploits the properties of the genetic code for the purpose of controlled variability under selective pressure? Will RecA
and LexA play a part?

References:
Peled JU, Kuang FL, Iglesias-Ussel MD, Roa S, Kalis SL, Goodman MF et al. The biochemistry of somatic hypermutation. Annu Rev Immunol. 2008;26:481-511.

Teng G, Papavasiliou FN. Immunoglobulin somatic hypermutation. Annu Rev Genet. 2007;41:107-20.

Goodman MF, Scharff MD, Romesberg FE. Abstract AID-initiated purposeful mutations in immunoglobulin genes. Adv Immunol. 2007;94:127-55.

Basu U, Chaudhuri J, Phan RT, Datta A, Alt FW. Regulation of activation induced deaminase via phosphorylation. Adv Exp Med Biol. 2007;596:129-37

Tuesday, August 12, 2008

Cell cycle signaling network

DNA replication, DNA repair, cell division signaling and programmed cell death

The cell cycle is a highly regulated process and "takes micromanagement to the extreme". Various positive- and negative-feedback systems ensure that cells divide in a controlled manner. The process consists of a sequence of events by which a growing cell duplicates all its components and divides into two daughter cells, each with sufficient machinery to repeat the process. In eukaryotic cells, one round of cell division consists of two “gap” phases termed G1- and G2-, an S-phase during which duplication of all DNA happen, and an M-phase where proper segregation of duplicated chromosomes and chromatid separation occur. During each of these phases, regulatory signaling pathways monitor the successful completion of events in each phase before proceeding to the next phase. These regulatory pathways are commonly referred to as cell cycle checkpoints. Cell cycle checkpoints are activated in response the following (Figure 1):
  • Cellular damage
  • Exogenous cellular stress signals
  • Lack of availability of nutrients, hormones and essential growth factors.

During the G1 phase many signals intervene to influence cell division and the deployment of a cell’s developmental program (Figure 1). Crucial "decisions" are made to pass the G1 restriction point as commitment to replicate DNA and divide is irreversible until the next G1 phase. Failure to meet the correct conditions results in a failed attempt to divide. Signaling events converge to affect the phosphorylation status of the retinoblastoma protein (pRB) family (pRB, p107, and p130). Cyclin dependent kinases (CDKs) play a crucial role in pRB phosphorylation status and their activity is in turn controlled by cell stress and growth inhibitory signaling pathways. Sufficient phosphorylation (hyper-phosphorylation) of pRB causes it to dissociate from the elongation factor 2 (e2F) family of transcription factors. Dissociated e2F transcription factors mediate the transcription and activity of genes required for DNA replication during the S-phase.

As soon as the restriction point (G1/S transition checkpoint) is passed, initiation of DNA replication takes place at multiple sites on the chromosomes, called the origins of replication. The origin recognition complex (ORC) marks the position of replication origins in the genome and serves as the landing pad for the assembly of a multiprotein, pre-replicative complex (pre-RC) at the origins, consisting of ORC, cell division cycle 6 (Cdc6), Cdc10-dependent transcript (Cdt1), mini-chromosome maintenance (MCM) proteins, clamp-loaders, sliding clamps, helicases, DNA polymerases etc. The MCM proteins serve as key participants in the mechanism that limits eukaryotic DNA replication to once-per-cell-cycle and its binding to the chromatin marks the final step of pre-RC formation. Once the replisome is assembled, the transition to DNA replication is irreversibly completed and the cell enters the S-phase.

After successful completion of DNA replication the mitosis promoting factor (MPF) complex forms and plays a crucial role in nuclear envelope breakdown, centrosome separation, spindle assembly, chromosome condensation and Golgi fragmentation during mitosis. Cells only enter mitosis (G2/M transition) after the completion of the above events.

When a cell is unable to address the above circumstances, cell division is permanently halted and the cell either enters senescence or programmed cell death is activated (Figure 1). Programmed cell death (particularly apoptosis) removes potentially hazardous cells from a population of cells, resulting in the controlled destruction of the cells designated for destruction. Two checkpoints during the cell cycle exist.

  1. The DNA structure checkpoint
  2. The spindle checkpoint

The DNA structure checkpoint operates between the G1/S transition, the S-phase and the G2/M transition (Figure 1). The DNA structure checkpoint during the G1/S and G2/M transitions ensure that DNA damage is minimal while the S-phase DNA structure checkpoint also recognizes and deals with replication intermediates, stalled replication forks and unreplicated DNA. Whenever the criteria are not met during a checkpoint, a cell will not proceed to the next phase. Various signaling networks are activated and operate to ensure these criteria are met. DNA structure checkpoint signaling has the same pattern during any phase of the cell cycle (Figure 1):

  • Detection: Sensor proteins include proliferating cell nuclear antigen (PCNA)-like and replication factor C (RFC)-like protein complexes (see Sliding clamps, clamp-loaders and helicases), which are able to bind to damaged DNA to form a scaffold for downstream repair proteins. The Rad50/Mre11/NBS1 complex is also loaded onto damaged DNA sites and mediates downstream checkpoint and repair proteins.
  • Signal transduction: Activated sensor proteins in turn activate several signaling proteins which in turn activates DNA repair mechanisms and downstream effector proteins that controls cell cycle checkpoint signal transduction and programmed cell death signaling. Some examples include, ataxia telangiectasia mutated (ATM), ataxia telangiectasia and Rad3 related (ATR) p53 binding protein (53bp), the topoisomerase binding protein TopBP1, mediator of DNA damage checkpoint (MDC1), breast cancer 1 (BRCA 1) etc.
  • Effect: Downstream of the signal transducers include the the effector serine/threonine protein kinases CHK1 and CHK2. CHK’s transfer the signal of DNA damage to the phosphotyrosine phosphatases and cell division cycle proteins Cdc25A, Cdc25B, and Cdc25C as well the tumor-suppressor p53. Cdc25A controls the G1/S and S-phase transition (prevents pRB dissociation through dephosphorylation of pRB proteins) while Cdc25B and Cdc25C control the G2/M transition (both upregulating Wee1 and Myt1 by phosphorylation, which together control Cdc2/CyclinB activity). Tumor supressor p53 protein activity links DNA damage to programmed cell death.

Figure 1: Dynamic control of cell cycle events through cell signaling, checkpoints, nutrient availability and extracellular stress.

The spindle assembly checkpoint is a molecular system that ensures accurate segregation of mitotic chromosomes and functions during the M-phase of cell division. The spindle checkpoint depends on the activity of two systems.

  1. The 26S proteasome (APC/C-cdc20 complex) for the degradation of cyclin B.
  2. The anaphase promoting complex/cyclosome (APC/C-cdh1 complex) for the degradation of cyclins and securin

How are these for provocative sounding titles:
Voges D, Zwickl P, Baumeister W. The 26S proteasome: a molecular machine designed for controlled proteolysis. Annu Rev Biochem. 1999;68:1015-68.
Peters JM. The anaphase promoting complex/cyclosome: a machine designed to destroy. Nat Rev Mol Cell Biol. 2006 Sep;7(9):644-56.

Cyclin B is ubiquitinylated and degraded by the the 26S proteasome (APC/C-cdc20 complex) which in turn results in the activation of the APC/C-cdh1 complex. The APC/C-cdc20 complex is controlled by the mitotic checkpoint complex (MCC) which detects tubulin and kinetochore integrity. The APC/C-cdh1 complex mediates the degradation of securin resulting in chromosome segregation.

There is a considerable amount of cross-talk between DNA repair mechanisms, programmed cell cycle signaling pathways, cell death pathways (autophagy, apoptosis, mitotic catastrophe etc.) and other cell stress signaling pathways. All these intricately interwoven pathways serve to ensure accurate cell division and removal of faulty cells from a population through programmed cell death. The problem comes when one of the checkpoints or programmed cell death pathways become corrupted and causes uncontrolled cell division in multicellular organisms. Cancer is one of the outcomes of abrogated cell death signaling and uncontrolled cell division. Programmed cell death is however not limited to multicellular organisms as bacteria also contain the necessary pathways to self destruct.

E.g.:
Engelberg-Kulka H, Amitai S, Kolodkin-Gal I, Hazan R. Bacterial programmed cell death and multicellular behavior in bacteria. PLoS Genet. 2006 Oct;2(10):e135.

Rice KC, Bayles KW. Molecular control of bacterial death and lysis. Microbiol Mol Biol Rev. 2008 Mar;72(1):85-109.