The life context: cells, nutrients and signals

G.P.  Pescarmona

Department of Genetics, Biology and Biochemistry

University of Torino

 

Running title:

Cells, nutrients, signals

 

ABSTRACT

The current trend in biology is to collect more and more data as fast as possible; their number is so overwhelming that it is almost impossible to fit them into a coherent picture starting from data analysis. The only possibility to make experimental results meaningful is to try to fit them into a general model of a living organism, starting from the assumption that life in itself is very simple and that actual complexity arises from the interaction of organisms with numberless and chaotic environments. The aim of this paper is to define the basic factors ruling all forms of life and their interactions with the environment.

 

1. INTRODUCTION

One of the most intriguing problems of the current biological research is the fact that the more information is collected about genes and metabolic pathways in different organisms, the more difficult it is to fit it into a coherent picture. When glucose catabolism was simply described in mammals as glycolysis in anaerobiosis and Krebs cycle associated to mitochondrial electron transport in aerobiosis, it was easy to distinguish between tissues like muscles with high aerobic metabolism and erythrocytes with an almost complete dependence from glycolysis for energy supply. But when we started to include all the branches and the connections of these pathways to aminoacid metabolism not only in mammals, which are reasonably similar, but also in very different species, we realized that glycolysis was no longer a kind of highway for glucose breakdown but a part of an intricate network of fluxes, whose entity and direction was largely dependent on the type of organism and on the surrounding environment. The use of a reductionistic approach to the description of innumerable living organisms has led to a huge collection of data, barely comparable.

To make a long story short, at the present day the risk is that the more we know, the less we understand. One of the possible approaches to overcome this difficulty is the use of models to simulate the behavior, at different levels of complexity, of living systems. The advantage of models is that they are much faster to test and cheaper to run than classical in vivo or in vitro experiments; apparently, they are not as sound as "real" experiments, but all scientists know how many constraints are imposed on experiments to make them reproducible and how often, more or less consciously, the results of this kind of experiments are biased by the selection, out of the available results, of those judged more sound on the basis of many factors. These factors include the original working hypothesis of the authors, the prevailing opinions in the specific scientific sector, and the opinions of the referee to whom the paper is sent for review. That means that it is very difficult to get new information to support original hypotheses; experiments are expensive and time-consuming, and the judgement about the feasibility of the projects, their funding and the acceptance of the results depends on the opinions of well-established scientists, who would find hardly acceptable a drastic change of the current opinion.

On the contrary, models may be tested even with a relatively low budget, due to the low cost of computing time. Modeling in biology is still a young science and therefore the most common approach in building models is to create models able to reproduce experiments. This approach is based on the hypothesis that experiments are neutral, but it is now quite clear that experimental results are biased by many factors, such as the type of organism used, its age, the number of organisms tested (the response of one cell to an agonist is completely different from the average response of 50.000 cells to the same agonist), the local conditions, and so on. Moreover, if the final result of the experiment is known in advance, it is quite easy to manipulate the value assigned to the initial parameters in the model to fit almost perfectly the experimental results. But, as we said before, there are now so many experimental data that people are more and more confused about their meaning and the idea of creating models with the aim of reproducing experimental results simply makes no sense. Modeling makes sense if it allows a strong reduction in the number of the parameters involved in the models and focuses on the dynamical interactions between them. The core of the model is the relationship between the parameters; the search has to be focused on the robustness of the set of relationships, that is to say that a sound model should be able, with minor adjustments, to predict biological events independently of the scale (molecular or cellular or population scale). The model construction, according to this approach, is not influenced by the final results of any specific experiments, but it is based on a set of rules, based on known or argued relationships between parameters. This approach has been widely used in Artificial Intelligence (A. I.) and it is defined as a "deep model". Models developed in this way can be checked for either their intrinsic behavior (sensitivity to the initial parameters, sensitivity to the environment, chaotic behavior, etc) or for their ability to predict real world events. In the case of some lack of correspondence between the calculated and the expected results, if the model is sound, criticism may be advanced against both the model and the experiment, trying to find out which one is the most biased.

 

2. A "DEEP MODEL" OF LIFE

There are so many forms of life in our present environment that it seems very hard to choose the properties that they share and can be used to establish a common model. In the past naturalists classified organisms according to their shape, nutrition, reproduction mode and so on. This approach was based on the analysis of the end products of evolution. Looking into the genome we now know that these features depend on an intricate exchange of genetic information between both individuals and species and that genomic data correlate only poorly with phenotype similarities, making this approach too complex and unrealistic. We have therefore followed a different approach [1,2] defining some very simple rules ("deep model" [3]) that can be applied to the behavior of any living system, from the very early forms of life to the most complex societies. Once defined, rules can be checked for their applicability and falsifiability in any known environment, from the most primitive environments with few molecules (CO, CH4, NH3, SH2) to the recent environmental conditions with thousands of new molecules polluting water and atmosphere.

All life forms in our environment share some properties and can be described as:

Dissipative: life takes place only in environments with an excess of energy: in our case, in the sunlight.

Evolutionary: formation of evolutionary trees for any molecule and organism mediated by irreversible bifurcation followed by selection. The driving forces of bifurcation and selection depend on the environment and can be considered “local”.

Cyclic: at any level from the molecules to the species, precise feedback mechanisms can be identified that regulate the number of the objects involved in the equilibrium.

Oscillating: in any self-regulatory cyclic system the number of any item is changing in time with a periodicity that depends on the size of the system – from seconds for chemical reactions to years for prey/predator relationships – and the time the feedback signals need to diffuse across the system.

Competitive: as biological systems tend to expand exponentially in a finite environment, they become – sooner or later – limited in their growth due to a shortage in some essential factor (“nutrient”). The competition for the limiting nutrient will locally drive the selection.

The relationships between different properties vary and can be described as:

·        Hierarchical, when a property is not possible unless the other(s) is (are) present. This is the case of the dissipative behavior that is a prerequisite for any other property of living systems.

·        Complementary, when properties describe, from different points of view, the same set of events. Cyclicity of a metabolic pathway is a structural feature and does not depend on time, whereas the concentration of metabolites involved oscillates with time. When the concentration of a metabolite reaches its lowest value, it usually becomes limiting

and therefore competition arises between users of the same molecule.

 

 

Fig.1: Schematic representation of the main properties of living systems

 

3. DETAILED DESCRIPTION AND EXAMPLES OF LIVING SYSTEMS PROPERTIES

1) Living systems are dissipative [4].

Dissipative systems do not conform to the second law of classical thermodynamics because they are not closed systems, but they continuously receive energy from the outside. On the earth crust excess energy comes from sunlight. Photosynthetic organisms (plants, bacteria, algae) use radiant energy to move electrons from more to less electronegative elements leading to the present atmosphere, which is completely different from the original one surrounding earth before life.

The most striking difference is the continuous increase of gaseous N2 and O2 in the atmosphere over the past 4 billion years [5]. The large availability of a strong oxidant like O2 allows survival of all the forms of life depending on respiration for energy production.

 

 

The whole picture of the different forms of life possible in an environment fuelled by sunlight is summarized in Fig.2.

 

Fig. 2: The life context on the earth crust (redrawn from Morowitz H.J.[6])

 

In the case of sunlight and life on earth surface, the concept of dissipative system is quite intuitive, but how can we describe in both qualitative and quantitative ways dissipative systems, from molecular to social level?

The answer is that dissipative systems, as they are continuously getting energy from the environment and using it to do something (it doesn’t matter what), are always asymmetric. Indeed they cannot create new atoms (the energy required for nuclear fusion is too high); they can just build up and destroy molecules, move them around, move themselves and modify their own shape.

As a matter of fact, in order to describe properly any dissipative system we have to identify and quantify first all its intrinsic asymmetries and then to evaluate which are the environmental asymmetries that can supply enough energy to drive the intrinsic ones.

Ionic asymmetry is shared by all living organisms; it involves both cations like Na+, K+, Ca++ and H+ and anions like Cl-, Pi (HPO4--) and protein carboxyl groups [7].

As biological membranes are not fully impermeable to ions, the ion gradients between the inner and outer compartments would spontaneously disappear. Their persistence (with a very small shift from the average value) means that gradients are sustained by an active process driven by different types of ion-sensitive- ATPases [8].

ATP synthesis requires a supply of nutrients, both reducing (carbon skeletons: monosac-charides, fatty acids (FA), aminoacids (AA) or the light-dependent reduced cofactors in photosynthetic organisms) and oxidizing (O2, nitrate , sulfate). As electrons spontaneously flow from less electronegative (C, H) to more electro-negative atoms like oxygen, life is possible wherever we have asymmetry in the electron distribution: more electrons than expected from electronegativity on C or H (e.g. CH4 instead of CO2 and H2O), less on oxygen (e.g. O2 instead of H2O and CO2). For any given organism it is always possible to identify the local conditions (reducing and oxidizing nutrients availability) necessary for its life. In in vitro cell growth (a good model of a closed system until the growth medium is changed), initial conditions are characterized by plenty of nutrients, but after a few days the nutrient will be broken down and waste products will accumulate leading to cell death. This happens in closed systems, while in natural systems organisms have developed motility and they move in the direction of new sources when nutrients are locally scarce [9]. After billions of years organisms have adapted themselves to almost any environment, and have developed every type of machinery (enzymes, organelles) to take advantage of the locally available nutrients [10]; indeed no form of life is possible in the absence of nutrients in whatsoever form. Identification of nutrient type, concentration and diffusion rate in the environment is therefore the first step in the definition of any form of life.

Actually, the lack of one or more nutrients (e.g. ischemia or hypoxia) leads to disruption of intracellular ion gradients with a significant increase of cytoplasmic H+ and/or Ca++ that activate degradation of cellular components and induce cell death by necrosis or apoptosis [11]; through this process atoms of the dying cell are made available to the healthier cells.

To cope with their continuous energy requirement, in the presence of a space- or time- dependent local lack of nutrients, cells have evolved two specific functions:

·        Motility to move in the direction of nutrient source. In this function we can include motility of suspended cells (bacteria), adhering cells (eukaryotic) and hyphae formation by fungi [12].

·        Nutrients storage (glycogen, fatty acids) to cope with the problem of unpredictability of feeding and work requirements.

 

1) Living systems are evolutionary.

Many evidences show that living organisms are unstable and can change with time. The time course of modifications depends on many factors linked to both the environment and to the type of organism. The highest rates of change are displayed by viruses; months or a few years are sufficient for the creation and selection of new variants [13]. A hostile environment is a good reason for change: a group of Spirochetas (Pillotina Calotermitidis) living in the termite intestines have not changed in the past 1 billion years (the termites supposedly did not change their diet during this time) [14]. On the other hand, in the past few years the introduction of chemotherapy rapidly induced the rise of new variants of parasites resistant to the therapy (Plasmodium falciparum and chloroquine, most bacteria and antibiotics, HIV and ziduvidine) [15].

Why only dissipative systems can be evolutionary? Because evolution is based on the selection of the fittest in a changing environment. To cope with these external changes organisms have to change themselves, and they can do that in two ways:

·        To change randomly, an energetically very expensive process based on the random synthesis of new molecules followed by selection of the fittest

·        To change according to a blueprint,  synthesizing only the molecules fitting the new environment

As a matter of fact a blueprint requires forecasts on the future environment; to get this kind of information organisms should develop sensors and that would require further blueprints, which would require additional information and so on. Evolution without forecasts is the only possible strategy to adapt to a changing environment. This approach requires excess nutrients for both energy production and the synthesis of new structures.

Energy production: glucose and fatty acids (FA) can be stored and used for the synthesis of ATP necessary for movement; the bigger the stores, the larger the space the organisms can wander into looking for new nutrients. Since without O2 one molecule of glucose produces 2ATP and FA are not metabolized at all, whereas with O2 38 ATP are produced and FA are fully burnt, the terrestrial life, which requires long trips in search of food, evolved only when atmospheric pO2 approached its present value.

New structures synthesis: aminoacids are essential as building blocks for synthesis of both proteins and purine/pyrimidine bases of nucleic acids. These processes require also a lot of ATP. Nucleotides and aminoacids are stored in a more dynamic state than glucose and FA: they are stored as mRNA and nascent proteins respectively. The mRNA and protein synthesis is a process continuously going on, but the percentage of proteins effectively reaching the functional state is very low and hence all the intermediate molecules (nascent mRNA and proteins) can be quantified as intracellularly available stores [16].

In synthesis, plenty of ATP, glucose, FA and scarcity of aminoacids will stimulate cell motility and hence migration, while protein evolution will be slow due to the relative lack of aminoacids. In the presence of excess AA, new and new proteins can be synthesized, with a run up of evolutionary process in situ. In any case the choice between movement and protein evolution is driven by the local type and concentration of nutrients.

In the case of protein evolution the birth of cell clones with different isoforms of a specific protein usually will be followed by irreversible selection of the clone with the highest fitness to the environment. Selection depends usually on the fact that nutrients are always limited in any environment: the population with the highest fitness and the highest reproduction rate will eliminate, sooner or later, all the other less fit populations. All the evolutionary trees, either in temporal (cytochrome C in different species, millions of years [17]) or in spatial terms (virus variety in different geographical areas [18]) can be considered features representative of a dissipative form of life in an environment with limited resources. The liveliest description of dissipative systems is the Pinball (Fig. 3).

 


Fig. 3: The Pinball, a comprehensive metaphor of life.

 


It is always possible to play again, provided you have enough coins (sun, nutrients, oxygen -  they depend on the type of life). But once the ball is at the top it has only one aim: to go down. The pathway doesn’t matter. Life behaves in the same way: it has to go on, it doesn’t matter where and how. The winners survive and eventually reproduce, the losers disappear. In the bifurcation of every evolutionary tree only the result of winning players can be read. The winning choice is valid as long as the local conditions are the same. When they change, a set of new bifurcations will take place as well as a new selection. Life is an endless pinball game.

The driving forces of bifurcation and selection depend on the environment and can be considered “local”.

 

2) Living systems are cyclic.

If we look at the current forms of life we get the impression that evolution, in the time scale of man life span, is actively at work as far as some proteins of bacteria and viruses are concerned, but most of the basic cellular metabolisms are stable. How do dissipative and evolutionary systems stabilize instead of going on with an endless change? The main reason for this stability is the existence, at any level from the molecules to the species, of feedback mechanisms downregulating specific steps of the system. Usually, some of the end products inhibit the first step of the corresponding metabolic pathway (e.g. heme inhibits ALA-synthetase, estrogens inhibit cholesterol synthesis etc.). This mechanism of downregulation of the first step of a series of events obeys the same general rules independently of the scale and of the molecules involved. Suitable examples of cyclic behavior are: a) calcium influx at cellular level [19], b) TSH release from hypophysis  [20], and c) a predator/prey system [21].

A further constraint to an endless evolution is the lack of atoms: on the earth crust the number of atoms is fixed (atom number can change in environments with temperatures of millions of degrees) and competition for them strongly limits the evolution of new molecules and metabolic cycles.

 

3) Living systems are oscillating.

In any self-regulatory cyclic system the number of any item is changing with time with a periodicity that depends on the size of the system: seconds for chemical reactions at the cellular level (NADH, ATP/ADP, Ca++) and heartbeat; a few minutes for oxygen consumption; from minutes to hours for blood hormones; months for the menstrual cycle; years for prey/predator relationships. The period length depends not only on the geometrical size of the system but also on the diffusion rate of the feedback signals across the different compartments of the system [22].

 

4) Living systems are competitive.

As biological systems tend to expand exponentially in a finite environment they – sooner or later – become limited in their growth by the scarcity of some essential factor (“nutrient”) and the competition for the limiting nutrient will locally drive the selection. Since life started to evolve billions years ago, all possible nutrient sources have been exploited and life is now possible only at the expense of other forms of life.

 

Complex cyclic and oscillating systems also evolve with time exactly as molecules do, in order to become as fit as possible to the environment. This fitness can be evaluated measuring the behavior of the system with time: the most studied example is the heartbeat, but calcium oscillations, breathing or metabolic rates can be studied as well.  Heartbeat series show a chaotic behavior that can be quantified with a fractal number [23]; the higher the number the lower the predictability of the characteristics of the single heartbeat. This poor reproducibility is typical of an unstable system (at the chaos edge) which is very sensitive to even the smallest change of local conditions (rest, fatigue, digestion, emotions) and therefore is indicative of a good degree of fitness (remember that at molecular level proteins usually work at a substrate concentration near Km in order to get maximal dependence of reaction rate from the environment). This instability is lost and the heartbeat or breathing becomes oscillating almost independently from the environment in the presence of diseases (e.g. Cheyne-Stokes breathing) [24].

A second form of fitness for oscillating systems is the strong reduction of the oscillations of the parameter under control. A good example is the serum Ca++; the range of oscillation of Ca++ in serum is very narrow (from 2.05 to 2.8 mM) but this stability is kept in a dynamic way. When Ca++ is high, it is stored in bones, and when it lowers immediately parathormone (PTH) is released activating Ca++ release from bone [25].

As a consequence, serum Ca++ oscillations with time are barely detectable, but PTH oscillations are huge and display the same chaotic behavior of heartbeat. Stability of Ca++ availability improves the reliability of all signaling pathways using Ca++ as a second messenger. The costs of a huge store, like bone, and of a continuous turnover of PTH are compensated by the evolutionary advantage of a more efficient signaling.

Stability in biological systems is therefore constitutively different from stability of rocks and diamonds: rocks and diamonds are characterized by stable multiple bonds with electrons perfectly shared by adjacent atoms and therefore not breakable unless very high temperatures are used. In living organisms stability is attained through very expensive processes planned to keep the internal milieu constant and to make enzyme activity reproducible. Enzyme activity depends on substrate concentration, temperature, metal ions, pH. Multiple systems have been worked out to allow the control of these key factors: the H+ exchanger, Ca++ channels, Na+ channels, Ca++ ATPases, glucose carriers, and so on [26]. Moreover, many of these proteins can exist in phosphorylated and not phosphorylated forms. The need for multiple forms of proteins exerting very similar effects can be explained assuming that in order to survive cells must be sensitive to the environment, that is to say, to substrates, ions or H+ concentration changes. Protein interaction with ligand is expressed through Kb or Km.

 


 


Fig.4: Protein activity (carriers, enzymes, receptors) depends on the number of ligand molecules bound to the protein. Kb (carriers) and Km (enzymes) define the affinity of the protein for the ligand. V is the enzymatic reaction rate.

 

Kb (or Km) is, by definition, the ligand concentration at which 50% of the protein is in the bound form and at which very small changes in ligand concentration strongly affect the binding and consequently the protein activity (see Fig. 4). Any system gets its highest responsivity to environmental ligand changes near its Kb or Km and in a world where the concentration of molecules changes, the proteins must also change their Kb to fit local conditions. Biological systems have evolved different solutions to put up with this variability:

a)     Phosphorylation. Ca++ ATPase e.g. exists in a nonphosphorylated form (Km for Ca++ = 10-7M) and in a phosphorylated form (Km for Ca++ = 10-6M). Quiescent cells have an average Ca++ concentration of 10-7 and the ATPase is fitted to manage small random changes in this range. A stimulated cell phosphorylates the enzyme via protein kinase A or C, converting it to a form sensitive to small changes of Ca++ in the range of 10-6, typical of activated cells [27].

b)    Multiple forms with different Km or pH dependent activity. Good examples are proteases, DNAases etc, involved in apoptosis or digestion.  Protein kinases and protein phosphatases are the enzymes that exist in the highest number of isoforms; so far they are only partially characterized. A fruitful approach to the characterization of their exact function should start from the definition of the conditions of maximal sensitivity (pH, Mg++, ATP, Ca++, Na+, K+ etc) to environmental factors. The advantage of protein phosphorylation is the partial lack of specificity of these enzymes that allows them to control multiple metabolic pathways simultaneously [28].

The Kb curve is one of the few parameters useful to describe the properties of a system based on chemical reactions, as it is the case with all living organisms. Chemical reaction rates depend on temperature, reactant concentrations and protein/ligand affinity. Temperature can be easily measured and evaluated; the strong dependence of chemical reactions on temperature explains why keeping constant the body temperature has been the choice of so many organisms: the gain in predictability of chemical reactions is worth to have a costly central heating.

Concentration is usually symmetric in dilute aqueous media like sea or synthetic culture media, but in most environments, where more cells or species live and the diffusivity of reactants is not free, gradients of almost every molecule can be described. Every time we see a shape arising in a biological system we have to wonder which molecular gradient is underlying it. In some cases the answer is easy: sunlight for the tree branches, water, nitrogen and iron for the roots, but what about mammal-ian lungs, so exquisitely fitted to exchange oxygen, but shaped during the fetal life in the absence of air? [29] Gradient evaluation requires the knowledge of many factors including, for each molecule, synthesis and degradation rates, diffusion from different environments, and spatial localization of metabolic steps.

Affinity is the ability of molecules to bind to each other. In order to react, molecules have to meet in a proper way and this goal may be attained either in a statistical way, by increasing the average speed of molecules (high temperature), or in a structural way, by stretching the shape of the molecules to increase their reciprocal fitness.

As living systems exist only at relatively low temperatures (mostly between 20 °C and 37 °C) their high metabolic efficiency can be achieved only by increasing the molecular fitness [30].

Tricks available to do that rely primarily on the different electronegativities of atoms that regulate both polarity and acidity of molecules; small localized differences in charge can change the tertiary structure of proteins and hence their Kb to specific ligands including binding between subunits of a functional unit (homo- and heterodimers e.g. of growth factor receptors).

All biochemical systems from the simplest to the most complex can be described in terms of binding kinetics identified by a Kb, correspond-ing to the substrate concentration at which the system displays the maximal instability and sensitivity, and by a slope identifying the concentration range in which the system can be regulated.

Some examples:

·        The behavior of acids (strong and weak) depends on the affinity of the conjugated base (A-) for H+. The Kb in this case is called Ka. pKa of a group also depends on the micro-environment and proteins can supply many different microenviron-ments to the same group modulating its pKa. Changes of protein shape can affect  pKa, but also protonation  of a weak acid group can change protein structure [31].

·        The hemoglobin dissociation curve and its dependence on pO2, pH, 2,3-DPG and type of globin [32].

·        The allosteric behavior of enzymes [33]

·        The shift of Kb or Km of many proteins (enzymes, carrier etc.) upon phosphorylation [34]

 

4. CELLS

Organisms (from bacteria to nations) are delimited by dynamic boundaries (cell membrane, skin, borders) where essential nutrients or goods are exchanged with the environment (including the whole of all other organisms) [35]. The working hypothesis is that rules valid at the microscopic level (cell) will be applicable to upper levels too, since complex organisms are made of cells and societies are made of organisms. We can define the cell as the simplest living unit, able to duplicate itself and to exchange nutrients with the environment (competition for nutrients). Chemical composition (characterized from the structural and molecular points of view), energy requirements and boundary properties are the parameters that must be taken into account for a correct modeling of cell behavior. Chemical composition identifies all atoms and molecules necessary for the synthesis of structural molecules (essential "nutrients" including some aminoacids, vitamins, ions), energy requirements identifies the daily uptake of oxidant (e.g. O2) and reducing (e.g. glucose) agents necessary for energy metabolism. Cells have an additional property: each of them differs from the other objects of the same class and from itself at a different time. The limits of the changes allowed to the single parameter (remember that cells are identified by dynamic boundaries) without leaving a specific class have to be included in the cell class definition. Living objects are self-made and their structure strongly depends on both the genetic background (that affects the general properties of the system) and the local supply of energy and molecules required as building blocks for the new organism. The possibility for the cell of changing with time depends strongly on the energy supply and on the conservation of all its asymmetries (ions, H+, membrane phospholipids, etc) even when stressed by environmental stimuli. Since at the microscopic level stimuli from the environment are randomly distributed, any single cell is always different from its previous self and from the neighboring cell with a different history. We have previously described how evolutionary living systems, when adapted to their environment, are apparently stable, but this stability has a high energy cost.

Cell morphology follows the same rule; for 200 years from its foundation by the French researcher Bichat, histology has described hundreds of different types of cells. It was a morphological classification based on reproducibility of cell shapes in the same organ of different individuals and shape differences between cells of different organs in the same individual. Now we know that even if the vast majority of the cells of an organ is differentiated and displays the expected features (according to the textbooks), it is always possible to find out a small number of undifferentiated, totipotent cells in any tissue and that this pool of cells is a reservoir for the replacement of dying cells [36]. If we combine this information with the fact that genetic information is exactly the same in all the cells of an individual, it becomes clear that all the morphological patterns we know depend on the local conditions where the cells live. Stability and reproducibility of cell shapes is not due to some hidden information of the genome or some blueprint but to the stability and the reproducibility of the milieu surrounding the cells.

Let's try to describe why liver is liver as a function of its local conditions. It is the heaviest organ in the body because it collects 100% of the nutrients we introduce with the diet. It gets 50% venous blood very rich in any kind of nutrients (portal vein) and 50% fully oxygenated blood (hepatic artery); a lot of nutrients but 50% less oxygen than the other tissues. Moreover, hepatic cells are not directly stimulated by nerves and do not require high rate of ATP synthesis to perform heavy-duty mechanical work like muscle. ATP is therefore utilized for synthetic processes: glycogen is produced and stored, cholesterol, triglycerides and proteins are produced and poured into the blood. The entire liver enzymatic machine is not evenly distributed in the liver, but at the microscopic level there is a clear zonation of enzymes and cytochromes according to the distance from the cell to the artery. The oxygen gradient is responsible for this behavior. Up to now the liver structure and function can be explained on the basis of local conditions, determined by nutrient availability and gradients. To fill the picture we can evaluate the molecular mechanisms (nuclear factors, Ca++, cAMP e.g.) of the induction of the different proteins but we do not have to invoke new mechanisms in addition to nutrient and oxygen gradients [37].

Liver is also sensitive to hormones like insulin, glucagon, and growth hormone (GH), which modify its function. Hormones themselves are not nutrients (usually present in 10-3 M concentration) as they exert their effect at very low concentrations (10-9M) and cannot modify substantially the cell composition. Insulin release from pancreas tells the body cells that in the next half an hour there will be plenty of glucose. Insulin cannot be used for energy metabolism, but can increase the number of glucose carriers on the cell plasma membrane, improving the efficiency of glucose uptake. Insulin has no effect unless it activates glucose uptake. Glucose without insulin and insulin without glucose are both threatening to cell life.

The half-life of small molecules, like cofactors or metabolites, is in the range from minutes to hours, the half life of structural molecules, like proteins from hours to months [38]; the metabolic state of a cell (ATP/ADP ratio, NAD/NADH ratio, glycogen stores) is representative of the past of the cell life in terms of minutes or hours, the proteins pattern in terms of hours or months, according to the protein. As in many cases, the life of cells is orders of magnitude longer than that of their components. Surviving in a changing environment has a high energy cost: the more rapidly the environment is changing the shorter the half-life of cell components to fit the changes.

This continuous turnover of molecules requires an incessant uptake of new molecules from the outside, making feeding the most prominent activity of the cell. Feeding includes also respiration (O2 uptake) and in a way reproduction; feeding indeed requires a set of complex strategies like acid digestion, receptor- mediated uptake, and movement in the direction of the nutrient source. The splitting of one cell into more cells allows exploratory movements in search of food in different directions, substantially increasing the probability of survival of the species.

 

5. NUTRIENTS

Local availability of nutrients plays a fundamental role in cell life.

Factors affecting nutrient diffusion (medium viscosity, temperature, blood flow, angiogenesis, etc) will affect the growth and shape of the organisms as well as the energy supply. Cells can actively move in search of nutrients: in bacteria a rotating flagellum drives the cell in the right direction, while in eukaryotic cells nutrient uptake is mediated in most cases by endocytosis coupled to vesicle acidification, a process that is also responsible for the movement in the direction of the nutrient source [39]. Movement involves a) secretion of proteases able to digest surrounding proteins and to decrease medium viscosity (metallo-proteases) [40] b) adhesion to extracellular matrix c) cytoskeleton rearrangement and d) directional acid vesicle flow [41].

Details of the mechanisms involved in movement show that they follow the same rules: to use the local tools and the available nutrients to get the best result with the minimal effort. Strategies differ from case to case. Bacteria living as separate organisms in aqueous solutions usually move using their flagellum as a propeller, but under certain conditions the rely for movement on the Brownian motion of water  and the use of their flagellum  as a rudder. In both cases their movements will be driven by nutrient gradients[42].

As a whole, the winning strategies of living organisms (remember that only winners survive and we have no information on the strategies of the losers) share a common feature: they are strictly dependent on and adapted to a perennially changing environment. As this adaptability cannot depend on a blueprint based on forecasts, it has to be based on a very efficient network of information about both the environment and the other populations living there.

 

6. SIGNALS

Think global, act local

In a complex environment like ours even an outstanding improvement in factors involved in local competition for nutrients cannot give a strong evolutionary advantage to a cell. To increase their fitness cells have had to learn to get information about their present and future environment. We call signals all (physical or chemical) events able to transmit information to a cell. Signaling requires at least two components:

a)     a physical (e.g. sound) or chemical (e.g. hormone) event that it is released into the environment in specific conditions and that we can call a sign (S)

b)    a cellular structure able to react with (S)  modifying the cell behavior according to the concentration and direction of (S). These structures are called Receptors (R).

Modeling of complex systems requires a correct definition of all parameters involved in signaling. They include:

a)     all  local conditions required for the release of (S) n 

b)    diffusion rate of  (S) n  in the specific environment

c)     local distribution and characterization of  different (R) n 

d)    signal transduction  pathways at cellular level, whose degree of signal amplification depends on local conditions

e)     combination of all previous factors to get all or most of the information  spread along the whole signaling network influenced by  (S) n 

A proper combination of the aforementioned factors leads to thousands of different patterns of information about local and distant availability of essential nutrients delivered to cells or groups of cells with different metabolic requirements. The diffusion rate of  (S) n in this context plays a major role as a delay in the signal transmission (common and unpredictable in complex systems) can induce signal oscillations out of phase with nutrient oscillations resulting in misleading information.

(S) n  are classified as sound, light, hormones, cytokines, chemokines, growth factors, and so on, according to their major effect, but they share a common property: the ability to give information about the existence of specific environments or about the availability of one or more nutrients. The minimal information content carried by any sign is that all the conditions required for its synthesis are present.

The exact definition of the role of any molecule involved in signalling (cytokines, hormones etc.) should always include the search of all the factors necessary for its synthesis (Fig. 5).

 

 


 Fig. 5: The search for all factors required for the synthesis of a sign should be always thoroughly performed.

 


For example, thyroid hormones depend on oxygen supply, estrogens on heme and oxygen, insulin on intestinal glucose absorption, LPS  (lipopolysaccharide) on bacterial infections, nitric oxide (NO) requires arginine, oxygen, heme, NADPH, GSH [43]. This set of information is what we can define as the "meaning" of the signal  (S) n. As far as the target cells are concerned, only skeletal muscles and adipocytes will get from insulin the information that glucose will increase in the next ten minutes. All other cells just will sense the local glucose increase.

Modeling of the living state has therefore to take into account modeling of both nutrients and signal molecules independently (uptake, diffusion, binding constants), with their adequate time scales.  What is important is to avoid excessive emphasis on the role of signals in models. Cell life requires energy and essential nutrients for work performances or duplication; signals can freely modulate cell activity in the presence of unlimited nutrients, but can never overcome the lack of some essential nutrient. Models based on cells + nutrients are possible and meaningful and can validly mimic local effects; modeling of complex organisms or environments requires also the incorporation of signals into the model. Models solely based on cell/signal interactions are meaningless.

 

Modeling a complex environment including both nutrients and signals requires previous assignment of molecules to one of the two classes of molecules.

Nutrients (glucose, aminoacids, fatty acids) are in the range of 10-3 M, while signs (chemotactic factors, hormones, cytokines, odors, growth factors) are comprised between 10-6 and 10-9 M.

Every sign (S) should be investigated to identify all the conditions necessary for its synthesis (precursors, pO2, pH) and that have to be included as information carried by the (S) molecule.

As transfer of information takes place only when cells have proper receptors (R) also receptors have to be treated as signs. Signs carry information from the near or far environment, while receptors carry infor-mation about the inside of the cell: nutrients and energy stores, filling of calcium stores, ATP and cyclic nucleotides level, and so on.

Signaling is possible only when all the requirements for (S) and  (R) synthesis are fulfilled.

Additional factors that have to be checked are:

·        Binding constants (Kb) of receptors for their ligand; they are usually in the same range of prevailing concentration of the ligand/sign in body fluids (between 10-6 and 10-9M).

·        Degree of linearity of the signal as a function of ligand concentration. Role of homodimers and heterodimers, dimers or tetramers, allosteric effectors.

·        Signal amplification; it depends on the asymmetries of ions (Na+, K+, Ca++) in the different cell compartments (calciosomes, mitochondria, nuclear membrane). The properties of both passive and active ionic channels spanning the different membranes have to be taken into account.

As in the post-genomic era the structure of all proteins is known, it is also possible to try to answer some questions about the evolution of signaling systems.

What is born before, the sign or the receptor?

From the logical point of view the sign should always come earlier [44], but if we want also some additional biological evidence we can just check the AA composition of signs and their corresponding receptors. The AA pattern is different in environments with more or less oxygen. Usually the aminoacid percentage in proteins corresponds to the prevailing AA pattern when the protein was created in the evolution. As pO2 has been continuously growing during evolution, AA composition can give some reasonable hint about the age of proteins and can help us to identify which one came earlier.

One striking example of applicability of this approach is that of proteins working as protease inhibitors [45]. They usually have a structure rich in aminoacids frequent when pO2 is low and usually their synthesis is induced when tissues are hypoxic: in this situation they act as antiapoptotic agents and seem to play an intelligent role. But when billions of years ago pO2 was low and what we call protease inhibitors were the prevailing proteins, no evolution at all was possible. The increase in the number of signs is a prerequisite for the development of information exchange between molecules and between cells. The combination of a protein with two proteases of different specificity can produce tens of signals (Fig. 6).

But in the presence of protease inhibitors the number of signs will be drastically reduced. In a world based on signaling protease inhibitors have to be quiescent; they are strongly anti-

evolutionary and become useful only when cells are dying.


 

 


Fig. 6: Role of proteases in the evolution of signaling. Splitting of one protein into many peptides diffusing with different rates and interacting with different receptors dramatically increases the signalling power of the system.

 

 

A similar approach can be applied to many organisms and cell types, allowing a better comprehension of the living systems "intelligence" based on a powerful hierarchical system of information, whose features can be better understood if interpreted according to the approach described in this paper.

 

7. APPENDIX

Life in a pond

Life of a bacterial colony in an isolated pond can be a good field application to check the correctness of the approach to the description of a living system detailed in the previous sections.

Let us consider:

·        An isolated pond with a cyclic nutrient supply (representative e.g. of seasonal variations in nutrient availability, Fig. 7), allowing description of cell life under both high and low nutrient conditions.

·        a single cell population whose behavior, as a function of nutrients, can be described by a logistic curve, with bacterial growing rates dependent on

Fig. 7: Nutrient behavior in an isolated pond with cyclic feeding and growing organisms

 

 

Fig. 8: Logistic curve of bacterial growth in a closed environment

 

      nutrient availability (Fig. 8).

·        an oversimplified life style (for a bacterium) with lack of resistance forms (like spores e.g.) to overcome starvation. Cells can reproduce until nutrients are available, afford starvation for a while, and then die. This assumption makes the model more general and applicable also to eukaryotic cells. The cell count in this case will display an oscillating behavior very similar to that of nutrients but slightly shifted to the right (dotted line), a typical predator/prey behavior (Fig.9).

But we know that long-lived systems, even if they are structurally oscillating, display very reduced oscillations. How did they get that?

 

Fig. 9: Time course of cell number (dotted line) as a function of nutrient availability

 

Let's try to build up a possible scenario leading to oscillation smoothening, given an organism fully described by its general properties (dissipative, evolutionary etc.).

Our dissipative bacteria feed and grow while there are enough nutrients and then die. But we know that protein synthesis is not a straightforward process, much more mRNA and many more proteins start to be synthesized than actually get to a mature form. Both nascent mRNA and proteins can undergo splicing and rearrangement. New and new proteins in search of their function are produced by well-fed cells. The fate of these orphan proteins without a well-defined role for the cell producing them may be very different from case to case. Albumin produced by liver cells is discarded by an overfed liver (it takes up 100% of what we eat) and becomes a nutrient for all other body cells, even if it is a waste for the liver. Another possibility is that the orphan protein becomes a membrane protein; it will not be discarded but it will enter the basic structure of the membrane.

Different cells can develop different types of soluble or membrane proteins; the absolute number of molecules produced in any case will depend on the nutritional status of the cells. It will be higher when the nutrients are more abundant. Let us assume that one of these proteins dimerizes (we know plenty of homodimeric membrane proteins) and that dimers behave as active protein kinases or as calcium channels leading to a local calcium influx. There are hundreds of protein kinases activated either directly or indirectly by calcium. In case one of them phosphorylates a carrier for a specific nutrient (glucose, aminoacids, phosphate) and the phosphorylation lowers the Kb of the carrier for the nutrient (dotted line: native form, open circles: phosphorylated form) we would have a well-defined evolutionary advantage for the clone derived from this cell (Fig. 10).

 

Fig. 10: Nutrient uptake rate as a function of extracellular concentration for two carriers with different Kb

 

Indeed, at lower substrate concentrations, these cells are able to take up nutrients at a faster rate (open circles) and on the short or long run, depending on the difference between the two Kb's, they will be able to eliminate all

other cells that are less effective (continuous line) in feeding at low nutrient concentrations

 

 

Fig. 11: Cells with lower Kb (open circles) take up more nutrients than cells with higher Kb (continuous line)

 

These cells nevertheless are still subject to death by starvation; they are able to sense the nutrient (the dimer is a "sign" of nutrient abundance) and to transform this information into a behavioral change (the "sign" is used as a "signal" via protein phosphorylation and Kb change), but they are still unable to sense the future lack of nutrients. Which molecule can tell a cell that in the near future nutrients will decrease? Any molecule produced by a living cell; live cells (don’t forget they are dissipative organisms) include in their definition a continuous nutrient consumption and therefore any molecule produced and released by these cells contains the information of a future constant nutrient breakdown. Plenty of these molecules have been described in the control of bacterial growth and are grouped under the name of "quorum sensing", a "sign" of the number of living cells [46].

Different mechanisms can be devised to transform this "sign" into a "signal" and some of them have already been characterized [47].

 

 

 


 


Fig. 12: Biphasic effect of a ligand on a dimeric receptor. Activation at low concentration, inhibition at high concentration

 

One of the simplest models relies on the behavior of intrinsic membrane proteins working e.g. as calcium channels in the homodimeric form and inactive in monomeric form. In the simplest form the model requires

as a first step the already described evolution of a dimeric protein kinase or calcium channel giving an evolutionary advantage to the cells

bearing it. Further evolution (it doesn’t matter how much time it requires) can produce monomers able to bind with high affinity (lower Kb) a protein secreted (the ligand) by the same cell. Low concentrations of the secreted protein will increase the percentage of dimers giving to these cells an additional advantage in feeding rate over the cells with membrane

proteins not binding the ligand (dimer formation leads to nutrient uptake activation).

The final result will be a further shift of the Kb curve to the left. The effect of this shift in an environment with a limited and oscillating supply of nutrients could be dangerous for the survival of the cell population due to the lengthening of the period of food deprivation (Fig. 13: dotted line versus continuous line, the time on abscissa is representative of the interval between two successive nutrient supplies).

 

Fig. 13: Cell population with higher uptake rate wins the competition for nutrients, but dies sooner and has no evolutionary advantage

 

But as every monomer has a binding site for the ligand, high ligand concentrations will lead to a competition of ligand molecules for monomers, decreasing the dimer formation and the corresponding nutrient uptake activation. The final result will be a net reduction of the cell replication rate (Fig. 14: dotted line after time 5).

 

Fig. 14: Cell population with biphasic uptake rate wins the competition for nutrients and survives longer evolving toward an almost stationary state

 

The overall effect will be to postpone cell death due to lack of nutrients and to reduce the amplitude of the cell number oscillations. Provided constant oscillations in nutrient supply (whose period corresponds to the length of the oscillation shown in the previous figures) and enough time for an evolutionary process to take place, this mechanism will lead to the development of a ligand/receptor coupling with a Kb that permits a very fine regulation of the population size with barely detectable oscillations.

This very simple kind of dimeric receptor displays a biphasic behavior as a function of ligand concentration; activation at low concentrations and inhibition at high concentrations. This feature, mimicking an “intelligent” behavior, clearly offers a strong evolutionary advantage in adaptation to environments with a limited supply of nutrients, thus explaining why this kind of dimeric structure is currently used by the vast majority of membrane receptors.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          The model can easily be adapted to mixed cell populations, assuming that the receptor evolves in one population and the ligand in a different one [48]: the result will be the behavior typical of a complex organism, where the cell growth in different organs is finely tuned by reciprocal interactions.

 

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