Robustness and back-up systems

New Evidence On The Robustness Of Metabolic Networks

Biological systems are constantly evolving in ways that increase their fitness for survival amidst environmental fluctuations and internal errors. Now, in a study of cell metabolism, a Northwestern University research team has found new evidence that evolution has produced cell metabolisms that are especially well suited to handle potentially harmful changes like gene deletions and mutations.

You Can Be Replaced: Immune Cells Compensate For Defective DNA Repair Factor

Genetic instability can lead to multiple problems, including cell death and many forms of cancer. Therefore, it is absolutely critical for cells to have both the means to constantly survey genes for damage and the mechanisms to repair broken DNA. Currently, there are six well characterized classical non-homologous end-joining (C-NHEJ) factors that repair double strand breaks (DSBs) in mammalian cells.Lymphocytes, a type of immune cell, use a kind of genetic shuffling called variable, diversity, joining V(D)J recombination. This gene shuffling occurs during lymphocyte development and helps to produce diverse immune system cells that can recognize all sorts of different foreign substances, called antigens, that might pose a threat to the organism. Previous work in mice has shown that deficiency of C-NHEJ factors results in a severely compromised immune system, because of incomplete V(D)J recombination, along with increased sensitivity to cellular ionizing radiation (IR) and genomic instability.

Nice to know cell intelligence and evolution from a front-loaded state provide for robust systems with back-up. Preadaptation is good for the future.

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

Life's toolkits

Life has a genetic toolkit to build a wide variety of forms from just a few basic, simple and elegant body plans.

Take this into consideration and take a look at how stem cells become specialized.
Many Paths, Few Destinations: How Stem Cells Decide What They'll Become.

Quote:

How does a stem cell decide what specialized identity to adopt -- or simply to remain a stem cell? A new study suggests that the conventional view, which assumes that cells are "instructed" to progress along prescribed signaling pathways, is too simplistic. Instead, it supports the idea that cells differentiate through the collective behavior of multiple genes in a network that ultimately leads to just a few endpoints -- just as a marble on a hilltop can travel a nearly infinite number of downward paths, only to arrive in the same valley.

Quote:

The findings, published in the May 22 issue of Nature, give a glimpse into how that collective behavior works, and show that cell populations maintain a built-in variability that nature can harness for change under the right conditions. The findings also help explain why the process of differentiating stem cells into specific lineages in the laboratory has been highly inefficient.

Quote:

"Nature has created an incredibly elegant and simple way of creating variability, and maintaining it at a steady level, enabling cells to respond to changes in their environment in a systematic, controlled way," adds Chang, first author on the paper.

Quote:

The landscape analogy and collective "decision-making" are concepts unfamiliar to biologists, who have tended to focus on single genes acting in linear pathways. This made the work initially difficult to publish, notes Huang. "It's hard for biologists to move from thinking about single pathways to thinking about a landscape, which is the mathematical manifestation of the entirety of all the possible pathways," he says. "A single pathway is not a good way to understand a whole process. Our goal has been to understand the driving force behind it."

So stem cells have a built-in toolkit that responds to random changes, enabling then to respond to changes in their environment in a systematic and controlled way, ultimately leading to just a few endpoints. The toolkit harnesses random variation and selection to reach the same destination.
The stem cells are front-loaded (provided with a toolkit) to develop along a certain path while harnessing random variation and selection.


Key Regulator Of DNA Mutations Identified

Quote:

As a general rule, your DNA is not something you want rearranged. But there are exceptions – especially when it comes to fighting infections. Since the number of microbes in the world far surpasses the amount of human DNA dedicated to combat them, specialized cells in the immune system have adopted an ingenious, if potentially disastrous, strategy for making antibodies. These cells, called B lymphocytes, intentionally mutate their own DNA to ward off invaders they have never seen before.

B lymphocytes have a toolkit that regulates mutations for the purpose of generating antibodies. Thus, here we have another toolkit that harnesses random variation and selection to intentionally generate variety for the purpose of producing novel antibodies.

How many more toolkits that harness quantum randomness and selection to generate controlled variety will we discover?


Genetic toolkits in action:
New Evidence That Ancient Choanoflagellates' Form Evolutionary Link Between Single-celled And Multi-celled Organisms
Evolutionary Origin Of Mammalian Gene Regulation Is Over 150 Million Years Old
Marsupials And Humans Share Same Genetic Imprinting That Evolved 150 Million Years

Intelligence

Intelligence is associated with a property of mind.
From wiki:
Intelligence
From the first sentence:

Quote:

Intelligence (also called intellect) is an umbrella term used to describe a property of the mind that encompasses many related abilities, such as the capacities to reason, to plan, to solve problems, to think abstractly, to comprehend ideas, to use language, and to learn.

Artificial intelligence
From this article a few essential traits of intelligence are considered:
1) Deduction, reasoning, problem solving
2) Knowledge representation
3) Planning
4) Learning
5) Natural language processing
6) Motion and manipulation
7) Perception
8) Social intelligence
9) Creativity
10) General intelligence

However, there is no universally accepted definition of intelligence.
So let's take what we do know about intelligence (the 10 criteria above) and compare the systems and machinery within cells to any intelligent AI system.

1) Deduction, reasoning, problem solving
Cells:
Deduction: No
Reasoning: No
Problem solving: Yes. E.g. (from Nature;Vol 446;12 April 2007: Quantum path to photosynthesis)

Quote:

Elsewhere in this issue, Engel et al. (page 782) take a close look at how nature, in the form of the green sulphur bacterium Chlorobium tepidum, manages to transfer and trap light’s energy so effectively. The key might be a clever quantum computation built into the photosynthetic algorithm.

Quote:

The process is analogous to Grover’s algorithm in quantum computing, which has been proved to provide the fastest possible search of an unsorted information database.

And in the same issue: Evidence for wavelike energy transfer through quantum coherence in photosynthetic systems

Quote:

When viewed in this way, the system is essentially performing a single quantum computation, sensing many states simultaneously and selecting the correct answer, as indicated by the efficiency of the energy transfer.

Who knows what other kinds of quantum computing we will discover in organisms? Perhaps a clever quantum “trick” together with coulombic interactions in the bifurcated electron transfer of bc1-like complexes through the Q-cycle? Microtubles, centrioles etc.?

AI:
Deduction: No
Reasoning: No
Problem solving: Yes. (not quantum mechanically)

2) Knowledge representation
Cells:
Default reasoning and the qualification problem: No?
Unconscious knowledge: Perhaps? Stored in any or all of the cellular codes?
The breadth of common sense knowledge: No.
AI:
Default reasoning and the qualification problem: No
Unconscious knowledge: Yes. The software contains the stored information
The breadth of common sense knowledge: No


3) Planning
Cells: Possibly yes!
Predictive Behavior Within Microbial Genetic Networks

Quote:

We question whether homeostasis alone adequately explains microbial responses to environmental stimuli, and explore the capacity of intra-cellular networks for predictive behavior in a fashion similar to metazoan nervous systems. We show that in silico biochemical networks, evolving randomly under precisely defined complex habitats, capture the dynamical, multidimensional structure of diverse environments by forming internal models that allow prediction of environmental change. We provide evidence for such anticipatory behavior by revealing striking correlations of Escherichia coli transcriptional responses to temperature and oxygen perturbations—precisely mirroring the co-variation of these parameters upon transitions between the outside world and the mammalian gastrointestinal-tract. We further show that these internal correlations reflect a true associative learning paradigm, since they show rapid decoupling upon exposure to novel environments.

Emphasis mine.

Microarray transcriptional profiling was employed to determine whether gene expression correlates with the observed global cellular state and physiological responses. And indeed it does.
From the study it was determined that anticipatory transcriptional reprogramming occurs in response to aerobic and anaerobic environmental changes and these anticipatory transcriptional reprogramming events are as a result an “associative learning” paradigm. Is this an example of harnessing random variation and selection that allow for predictive transcriptional reprogramming in response to environmental change that gives the illusion of foresight? Creativity?

It should also be interesting to determine how big a part riboswitches play in this phenomenon.

AI:Yes if programmed to.

4) Learning
Cells: Yes, see "planning".
AI: Yes, certain artificial neural networks are capable of this.

5) Natural language processing
Cells: Yes and no. Yes because cells are able to communicate and process information from themselves and other cells (autocrine, paracrine, endocrine etc). No, cells do not consciously talk to exchange concepts and ideas.
AI: Yes and no. Yes because certain programs can interpret human language and systems of various platforms can communicate (Linux to Mac etc). No, AI does not consciously talk to exchange concepts and ideas.

6) Motion and manipulation
Cells: Yes, with the possibility that tubulin and other structural components of cells acting as quantum computers, motion and manipulation is directed, not stochastic, in even the simplest organisms.
Movement of organisms without a nervous system.
Also here:
Interesting site about cell intelligence and movement.
AI: Yes

7) Perception
Cells: Yes, cells communicate with the environment through surface receptors and relays information through signal transduction which in turn affects gene expression and protein activity which it turn results in predictive cell responses. Information from the environment is also processed via the multiples codes, e.g. histone code, ribosomal code and the standard genetic code.
AI: Yes

8) Social intelligence
Cells: Yes, even bacteria interact with other bacteria and can even mimic a multicellular organism through quorum sensing.
AI: Perhaps? AI neural networks?

9) Creativity
Cells: Perhaps? Harnessing random variation and selection to adapt?
AI: Perhaps? An example?

10) General intelligence
Cells: No (Only in humans so far)
AI: No


At present, even traditionally viewed simple cells outsmart our best efforts at AI.


I don't know where to put the following:
Cell's 'Quality Control' Mechanism Discovered
Is this an example of a non-passive selection system to remove mutated proteins from the population, even if the mutated proteins are functional? Perhaps a system that preserves a set of proteins? Unconscious knowledge (2)? Constrained creativity (9)?
This mechanism (system?) is not limited to eukaryotic cells. The ERdj5 enzyme operates in eukaryotes. The DnaJ enzyme is a homlogous chaperone protein in bacteria that carries out virtually the same function. Also known as heat shock 40 proteins (HSP40).
The DNAJ gene family.
DnaJ is also found in primitive eubacteria, indicating that the system was present VERY early on during evolution.
Eubacteria:

Quote:

Most eubacteria are gram positive, and they are generally less structurally complex than other bacteria.

Articles:
ERdj4 and ERdj5 Are Required for Endoplasmic Reticulum-associated Protein Degradation of Misfolded Surfactant Protein C
ERdj5 is required as a disulfide reductase for degradation of misfolded proteins in the ER.

Seems interesting nonetheless.