Cell Metabolism – Updates

Autophagy —

Autophagy occurs when cells need to degrade their constituents. This occurs in most normal cells to prevent the accumulation of protein aggregates and defective cellular substructures. During starvation, high temperature, low oxygen, hormonal stimulation or intracellular stress (damaged organelles, accumulation of mutant proteins, microbial invasion) there is activation of signalling pathways that increase autophagy.

The enzyme TOR kinase is a sensor of nutrient status and a major regulator of cell growth; negatively regulating autophagy acting through autophagy-execution proteins. Various signalling pathways, such as those involved in the control of cell growth, DNA-damage repair, programmed cell death (apoptosis) and immunity also induce autophagy.

After the cell receives the appropriate signal, the autophagy-execution proteins trigger a cascade of reactions leading to membrane rearrangements forming a double-membrane-bound vesicle called an autophagosome. An isolation membrane forms, which surrounds the cytoplasmic contents to be degraded, creating a sac the autophagosome. This vesicle then fuses with a lysosome (or a vacuole in yeast), with the release of lysosomal digestive enzymes into the lumen of the resulting autolysosome. The sequestered cytoplasmic contents are degraded inside the autolysosome into free nucleotides, amino acids and fatty acids, which are reused by the cell to maintain macro-molecular synthesis and to fuel energy production. The nutrient recycling and housekeeping functions of autophagy allows cell survival, although in certain circumstances autophagy may promote cell death

Autophagy may also stop routine protein synthesis to allow the synthesis of essential proteins when external nutrients are limited. During starvation, autophagy comes into play to ensure that the cell has sufficient amino acids to synthesize the proteins that are essential for its survival. However the synthesis of specific stress-response proteins, including autophagy-execution proteins are turned on. A coordinated strategy. and autophagy is activated.

The autophagy-specific genes are encode proteins that are components of kinase complexes, which regulate the activity of proteins and lipids through the addition of a phosphate group. Alternatively, they encode components of protein-conjugation systems, which attach to each other or to membrane lipids to form the membrane of the autophagosome.

Autophagy and apoptosis are usually been classified as different forms of programmed cell death. Whereas apoptosis invariably leads to cell death, autophagy (despite its frequent occurrence in dying cells) commonly contributes to cell survival. There is a complex, and not fully understood, relationship between autophagy and apoptosis that may vary depending on the biological context. The two pathways are regulated by common factors; they share common components; they can exert overlapping functions; and one pathway may regulate and modify the activity of the other.

Autophagy and apoptosis may occur in the same cell, both when autophagy is trying to keep cells alive and when it contributes to cell death. In some circumstances, e.g. starvation and treatment with certain DNA-damaging agents, autophagy delays the onset of apoptosis.

Review in Nature Levine Nature 2007, vol 446, 745-7

Chaos and the Cell —

When I was taught anatomy, we the heirs of the EB Jamieson tradition were left in no doubt that muscles, nerves, arteries and veins occupied fixed positions in the body. Once transplantation became common this was shown not to be the case. There were variables eg two renal arteries.

Similarly biochemistry has been taught as a fixed system. The implication was that the same fundamental processes went on in each cell in the body. It would appear that the biochemistry of cells is much more random than hither too believed.

Pearson The cellular hullabaloo Nature vol 453 pp 150-153

Identical genes in seemingly identical cells do different tasks. This variation becomes more pronounced as the overall organ becomes older.

The two copies of DNA in the cell are constantly changing shape and structure whilst proteins attach and detach. Such proteins may be activating or suppressing activity. Chaos theory in action. A constant tension between randomness and correction. The switching on and off of processes in individual cells in an organ may be very different. The more fluctuations there are the greater need for suppressing or correcting repressor protein activity. Quite a waste of energy. This activity and flexibility of genes is not universal , some genes are rigid in their activity others are very flexible.

Perhaps this system which has many options at any one time is very flexible in its response to the environment eg nutrition and metabolism.

Chaos Theory —

One of the best programmes on the BBC is Melvyn Braggs In our Time. Full of knowledge and wisdm. There is also a wonderful newsletter that comes from his broadcast.

This programe on 29th May talks of chaos theory, of great relevance to biology.

Marcus du Sautoy pointed out that probability comes into play when picking numbers for the National Lottery. The sequence of numbers: ‘1, 2, 3, 4, 5, 6’ are just as likely to come up as any other combination of numbers. Numbers are more likely to come up in clusters because randomness likes to cluster things together (so often you will see clusters of things in a random sample); so if you pick say 42 followed by 43 (e.g. consecutive numbers), there may be slightly more chance of winning a prize
Physical systems that are deterministic yet impossible to predict (ie: requiring probabilistic predictions) is the study of chaotic systems or ‘Chaos Theory’. Chaotic systems were not totally random, crazy and unpredictable but actually rather simpler than they might appear. They follow meta rules, structures and patterns that you can observe, extract and use. So in that sense it is deterministic (ie: run it twice under same conditions and the same outcomes will happen) as opposed to random (which by definition means running the same thing under the same conditions and getting different outcomes).

However, chaotic systems are characterised by something popularly called the butterfly effect – that if a butterfly beats its wings in Rio you get hurricanes in Tokyo. This is better referred to as ‘extreme sensitivity to initial conditions’. A small effect early on leads to vast differences later on. To accurately predict the future of a chaotic system, a measurement of what it is doing now (and upon which the prediction is based) must be unbelievably accurate – just as a butterfly’s wings can create storms, so a small error in initial measurement can lead to vast errors in predicted outcomes.

And what does this have to do with nutrition. Metabolism is in part chaos theory in action. The food supply chain certainly is. Choice of food and marketing.

Intracellular Cooperation —

Cooperativity is the basis for a variety of microscopic events, such as the simultaneous chelation of metal ions, the temporal coordination of protein folding and the concerted function of biomolecular assemblies. At the macroscale, cooperation between scientific laboratories, organizations and countries is required to advance research in a timely manner and to coordinate conferences and funding initiatives. The whole process of nutrition to food science to farming to sales in shops to cooking food to eating to metabolising the food is another important Cooperativity process.

Cooperativity is most usually applied to small-molecule properties and enzyme behavior. The cooperative function of hemoglobin was first documented in 1925 by Gilbert Adair. Similarly, the term ‘chelate’ was first coined in 1920, with subsequent investigations into host molecules such as clathrates and cryptands.

Today Cooperativity encompasses a range of scientific systems and is an umbrella term for processes such as preorganization, avidity, allostery and some types of assembly. Cooperativity can also be more broadly defined as a process for which intermediates are disfavoured (resulting, for example, in a two-state conformational change).

However as knowledge increases on processes it becomes less clear that a particular process is cooperative as compared to simply proceeding along a downward energetic trajectory or occurring at an observed rate. Similarly, according to this broad definition, complicated processes such as cytokinesis can be considered cooperative in that in the absence of perturbants or disruptive mutations, halting at an intermediate state is disfavored. As our mechanistic insight into biological systems grows, scientists must be mindful not to apply the term too loosely to processes that are simply coordinated in space or time (like cell division or signaling cascades), but rather they must look for those systems that are directly energetically linked.

These diverse examples accentuate the importance of looking at cooperativity from new angles, in allowing us to formulate hypotheses about increasingly complex systems and in helping to provide a firm scientific grounding for discussions about larger scale phenomena such as emergent properties

A recent edition of Nature Chemical Biology is a exciting review of processes and concepts which are central to so much in nutrition. Chemical biology itself provides an important reflection of cooperativity, since the field has grown through collaboration and openness to new ideas and approaches. This issue, features pieces exploring molecular, cellular and organismal cooperativity, providing further thought as to how the mechanisms of seemingly divergent systems intersect.

Editorial Nature Chemical Biology 4, 433 (2008

Capturing cooperativity


In biology, mathematical systems analysis was until recently nearly invisible in the dazzling light of twentieth-century discoveries. But it has emerged from the shadows in the field of systems biology, a subject buoyed by immense data sets, conveyed by heavy computing power, and addressing seemingly incomprehensible forms of complexity. If systems biology has heroes, one of them is Reinhart Heinrich, a former professor at the Humbotdt University in Berlin, who died on 23 October, aged 60. His most famous accomplishment was metabolic control theory, published in 1974 with Tom Rapoport and formulated independently by I lenrik Kacser and lames A. Burns in Edinburgh, UK.

From the 1930s to the 1960s, biochemists were busy describing metabolic pathways, just as molecular biologists today are feverishly trying to inventory the cell’s gene-transcription and signalling circuits. The basic kinetic features of the enzymes in the major pathways were studied in great detail and with exceeding care. It seemed self-evident that, knowing the properties of each element, the behaviour of a pathway at vivo could simply be understood as the sum of its enzymes.

One assertion, drilled into the head of every biochemist, was the concept of the rate-limiting step. In this view, the flux through a pathway was determined by the slowest reaction, in the way a bucket brigade fighting a fire would be limited by the speed of the slowest member. Yet this concept, as shown by the metabolic control theory, was theoretically flawed, practically incomplete and often wrong, as many efforts to genetically engineer enzymes by changing ‘rate-limiting steps’ would ultimately show.

Metabolic control theory introduced the concept of control coefficients — dimensionless quantities indicating how the flux of a pathway depended on a given step. Only a pathway where every control coefficient except one was zero would have a rate-limiting step, since the flux of that pathway would depend only on that step. Several pathways in fact had rate-limiting steps, but that often reflected the structure of the pathway, and could not simply be deduced from the maximum rates of the individual enzymes, their Michaelis constants or their displacement from equilibrium. Many pathways were instead networks, with the fluxes distributed in a self-governing way among its various branches.

Heinrich went on to apply this theory to the real case of glycolysis in red blood cells

Warburg Effect Explained —

Otto Warburg demonstrated the difference between metabolism in cancer ceils and that in normal adult tissue. Cancer cells take up glucose at higher rates than normal tissue but use a smaller fraction of this glucose for oxidative phosphorylation. This effect is known as aerobic glycolysis or the Warburg effect Lewis Cantley and colleagues now report that the human M2 (fetal) isoform of pyruvate kinase (PKM2), an enzyme that is involved in glycolysis, is a phosphotyrosine-binding protein and promotes the Warburg effect.

Phosphotyrosine-peptide binding is specific to the M2 isoform. PKM2 contains a 56-amino-acid stretch, which forms an allsteric pocket unique to PKM2 that allows binding of its activator, fructose-1,6-bisphosphate (FBP). Binding of phosphotyrosine peptides toPKM2 results in release of the allosteric activator FBP and subsequent inhibition of enzymatic activity.

Tyrosine phosphorylation can regulate the activity of PKM2 in cells,. Of the the different pyruvate kinase isoforms, this effect is specific to the M2 isoform and requires the phosphotyrosine-peptide binding capability.

Cancer cell lines exclusively express PKM2, and knockdown of PKM2 expression in cancer cells results in reduced glycolysis and decreased cell proliferation. Further analysis of M2KE-mutant cells revealed reduced lactate production and increased oxygen consumption compared with wild-type cells. This finding indicates that tyrosine kinase regulation of PKM2 activity is involved in mediating the Warburg effect in tumour cells.

But how do tumour cells achieve this altered metabolic phenotype? The authors reasoned that tumour tissue switches pyruvvate kinase expression from an adult isoform to the embryonic M2 isoform.

In a breast cancer tumour model PKMi is the primary- isoform before tumour development, whereas PKM2 is the primary isoform in four independent tumours. In vivo, PKM2 expression was found to provide a selective growth advantage for tumour.

The specialised metabolism of tumour cells is critical for tumorogenesis.

Kritikou 2008, Metabolism Warburg revisited Nature Reviews cancer vol 8 p 247A


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