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Do any cells change in size or mass as mammals grow?


That is to say, are there cells that, between infancy and adulthood, get larger? Or is all growth done entirely via cell division? I'm wondering if it is safe to assume that the approximate number of cells per unit mass in a mammal will remain fairly constant throughout its lifespan.


I'm wondering if it is safe to assume that the approximate number of cells per unit mass in a mammal will remain fairly constant throughout its lifespan.

Not exactly. When a tissue is put under stress, it can respond in four main ways:

  1. Hypertrophy - individual cells get larger. E.g. stressed muscle cells get bigger.
  2. Atrophy - invidivual cells get smaller. E.g. naturally in the thymus during development.
  3. Hyperplasia - increased cell division to produce more cells. E.g. mammary gland cells in pregnancy
  4. Metaplasia - one type of specialised cell is replaced with another, usually more durable one, E.g. columnar epithelium in the respiratory tract of a smoker being replaced with squamous epithelium.

The first three responses to stress can change both cell size (auxetic response) and number (multiplicative response), meaning you can't really say that cells per unit mass stays constant throughout life.

Muscle tissue grows predominantly by hypertrophy, meaning that the muscle gains during puberty are likely to be more as a result of cells getting larger than the development of new ones.

Both auxetic and multiplicative growth occurs as the body develops. In addition to this, accretionary growth also occurs - where connective tissues such as bone and cartilage increase in size.


This is not my field so I am sure there are other examples, but certain neurons will definitely be larger in adulthood than in infancy. There are motor neurons that connect the spine to, for example, the toes. These will grow in length as an animal grows. So, in a human infant they will be a few centimeters long and can reach lengths of over a meter in an adult.


Large-brained mammals live longer

Many mammals have brains substantially larger than expected for their body size, but the reasons for this remain ambiguous. Enlarged brains are metabolically expensive and require elongated developmental periods, and so natural selection should have favoured their evolution only if they provide counterbalancing advantages. One possible advantage is facilitating the construction of behavioural responses to unusual, novel or complex socio-ecological challenges. This buffer effect should increase survival rates and favour a longer reproductive life, thereby compensating for the costs of delayed reproduction. Here, using a global database of 493 species, we provide evidence showing that mammals with enlarged brains (relative to their body size) live longer and have a longer reproductive lifespan. Our analysis supports and extends previous findings, accounting for the possible confounding effects of other life history traits, ecological and dietary factors, and phylogenetic autocorrelation. Thus, these findings provide support for the hypothesis that mammals counterbalance the costs of affording large brains with a longer reproductive life.


Mammals shrink at faster rates than they grow: Research helps explain large-scale size changes and recovery from mass extinctions

Just how big can mammals get and how fast can they get there? These are questions examined by an international team of researchers exploring increases in mammal size after the dinosaurs became extinct 65 million years ago.

Research published in the journal Proceedings of the National Academy of Sciences shows it took about 10 million generations for terrestrial mammals to hit their maximum mass: that's about the size of a cat evolving into the size of an elephant. Sea mammals, such as whales took about half the number of generations to hit their maximum.

The team, including Dr. Jessica Theodor of the University of Calgary, also discovered it took only about one hundred thousand generations for very large decreases, such as extreme dwarfism, to occur.

"Our research demonstrates, for the first time, a large-scale history of mammal life in terms of the pace of growth. This is significant because most research focuses on microevolution, which are changes that occur within a specific species," says Theodor, co-author of the study and an associate professor of biology at the University of Calgary.

The research team looked at 28 different types of mammals from the four largest continents (Africa, Eurasia, and North and South America) and all ocean basins for during the last 70 million years. For example, one group would include the mammals related to an elephant, another group would include carnivorous mammals.

Researchers were surprised to learn how quickly body-size decreased: the rate is more than 10 times faster than the increases.

"Many of the species which shrunk, such as the dwarf mammoth, dwarf hippo and dwarf hominids, found in the Indonesian island of Flores, became extinct," says Theodor, whose area of expertise are the artiodactyls, hoofed mammals which include in the present day, cows, pigs, sheep, camels, hippos and whales.

"What caused their dwarfism? They may have needed to be small to survive in their environment or perhaps food was scarce and a small stature would require less nutrients," adds Theodor.

This research will help scientists to better understand mammal evolution: what conditions allow a certain mammals to thrive and grow bigger and what conditions would slow the pace of growth and potentially contribute to extinction.

Researchers used generations instead of time in their study because species have different life spans. A mouse only lives for about two years and elephant for 80. They created a metric using the relationship between body size and generation time in living mammals and used it to reconstruct the life history of the extinct forms.


Flowering plants, new teeth and no dinosaurs: New study sheds light on the rise of mammals

IMAGE: Well-preserved fossils -- like this Yanoconodon allini (Specimen No.: NJU P06001 Formation: Yixian Age: 122.2-124.6 million years ago Provenance: China) -- enabled the team to infer ecology of these extinct. view more

A new study published April 30 in the Proceedings of the National Academy of Sciences identified three factors critical in the rise of mammal communities since they first emerged during the Age of Dinosaurs: the rise of flowering plants, also known as angiosperms the evolution of tribosphenic molars in mammals and the extinction of non-avian dinosaurs, which reduced competition between mammals and other vertebrates in terrestrial ecosystems.

Previously, mammals in the Age of Dinosaurs were thought to be a relatively small part of their ecosystems and considered to be small-bodied, nocturnal, ground-dwelling insectivores. According to this long-standing theory, it wasn't until the K-Pg mass extinction event about 66 million years ago, which wiped out all non-avian dinosaurs, that mammals were then able to flourish and diversify. An astounding number of fossil discoveries over the past 30 years has challenged this theory, but most studies looked only at individual species and none has quantified community-scale patterns of the rise of mammals in the Mesozoic Era.

Co-authors are Meng Chen, a University of Washington alumnus and current postdoctoral researcher at Nanjing University Caroline Strömberg, a University of Washington biology professor and curator of paleobotany at the UW's Burke Museum of Natural History & Culture and Gregory Wilson, a UW associate professor of biology and Burke Museum curator of vertebrate paleontology. The team created a Rubik's Cube-like structure identifying 240 "eco-cells" representing possible ecological roles of mammals in a given ecospace. These 240 eco-cells cover a broad range of body size, dietary preferences, and ways of moving of small-bodied mammals. When a given mammal filled a certain type of role or eco-cell, it filled a spot in the 'Rubik's Cube.' This method provides the first comprehensive analysis of evolutionary and ecological changes of fossil mammal communities before and after K-Pg mass extinction.

"We cannot directly observe the ecology of extinct species, but body size, dietary preferences and locomotion are three aspects of their ecology that can be relatively easily inferred from well-preserved fossils," said Chen. "By constructing the ecospace using these three ecological aspects, we can visually identify the spots filled by species and calculate the distance among them. This allows us to compare the ecological structure of extinct and extant communities even though they don't share any of the same species."

The team analyzed living mammals to infer how fossil mammals filled roles in their ecosystems. They examined 98 small-bodied mammal communities from diverse biomes around the world, an approach that has not been attempted at this scale. They then used this modern-day reference dataset to analyze five exceptionally preserved mammal paleocommunities ? two Jurassic Period and two Cretaceous Period communities from northeastern China, and one Eocene Epoch community from Germany. Usually Mesozoic Era mammal fossils are incomplete and consist of fragmentary bones or teeth. Using these remarkably preserved fossils enabled the team to infer ecology of these extinct mammal species, and look at changes in mammal community structure during the last 165 million years.

The team found that, in current communities of present-day mammals, ecological richness is primarily driven by vegetation type, with 41 percent of small mammals filling eco-cells compared to 16 percent in the paleocommunities. The five mammal paleocommunities were also ecologically distinct from modern communities and pointed to important changes through evolutionary time. Locomotor diversification occurred first during the Mesozoic, possibly due to the diversity of microhabitats, such as trees, soils, lakes and other substrates to occupy in local environments. It wasn't until the Eocene that mammals grew larger and expanded their diets from mostly carnivory, insectivory and omnivory to include more species with diets dominated by plants, including fruit. The team determined that the rise of flowering plants, new types of teeth and the extinction of dinosaurs likely drove these changes.

Before the rise of flowering plants, mammals likely relied on conifers and other seed plants for habitat, and their leaves and possibly seeds for food. By the Eocene, flowering plants were both diverse and dominant across forest ecosystems. Flowering plants provide more readily available nutrients through their fast-growing leaves, fleshy fruits, seeds and tubers. When becoming dominant in forests, they fundamentally changed terrestrial ecosystems by allowing for new modes of life for a diversity of mammals and other forest-dwelling animals, such as birds.

"Flowering plants really revolutionized terrestrial ecosystems," said Strömberg. "They have a broader range of growth forms than all other plant groups ? from giant trees to tiny annual herbs ? and can produce nutrient-rich tissues at a faster rate than other plants. So when they started dominating ecosystems, they allowed for a wider variety of life modes and also for much higher 'packing' of species with similar ecological roles, especially in tropical forests."

Tribosphenic molars ? complex multi-functional cheek teeth ? became prevalent in mammals in the late Cretaceous Period. Mutations and natural selection drastically changed the shapes of these molars, allowing them to do new things like grinding. In turn, this allowed small mammals with these types of teeth to eat new kinds of foods and diversify their diets.

Lastly, the K-Pg mass extinction event that wiped out all dinosaurs except birds 66 million years ago provided an evolutionary and ecological opportunity for mammals. Small body size is a way to avoid being eaten by dinosaurs and other large vertebrates. The mass extinction event not only removed the main predators of mammals, but also removed small dinosaurs that competed with mammals for resources. This ecological release allowed mammals to grow into larger sizes and fill the roles the dinosaurs once had.

"The old theory that early mammals were held in check by dinosaurs has some truth to it," said Wilson. "But our study also shows that the rise of modern mammal communities was multifaceted and depended on dental evolution and the rise of flowering plants."

For more information contact Andrea Godinez with the UW Burke Museum at [email protected]

Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.


The Mammalian Brain

Basic Plan

Mammals are the most widespread group of vertebrates having conquered a large variety of ecological niches on land, water, and air. There are around 5,500 mammalian species today classified in 18 orders. Three subgroups of mammals are clearly distinguished among living mammals. Monotremata (Prototheria), is a group of egg-laying mammals that live in Australasia and represented today by only two species of echidna and a species of platypus (Figure 1). Marsupialia (Metatheria) are pouched mammals living today in the Americas and Australasia and classified in 260 species, the most representative of which are the kangaroos and the opossums. Placentalia (Eutheria) is the largest group, with around 4,300 species divided in 18 orders that have been clustered in four major branches: Xenarthra, encompassing anteaters, armadillos, and sloths Afrotheria, a group including elephants and tenrecs, Laurasiatheria, with bats, cats, cows and whales and Euarchontoglires, a group composed of rodents, primates, flying lemurs and rabbits (Figures 1, 3).

Figure 1. Phylogenetic tree of mammalian evolution. The schematic phylogenetic tree has been based on phylogenetic trees built by Goffinet (2017) and Rowe (2017). Red lines mark the mass extinction events. In every lineage two examples of lissencephalic and gyrencephalic brains are shown. Extinct lineages show examples of species that have been described from fossils specimens. Drawings of Therapsid Proburnetia viatkensis Tatarinov species and Cynodont Kayentatherium wellesi Kermack species were performed by the artist Nobu Tamura (http://spinops.blogspot.com/) and reproduced with permission.

Beyond the very well-known characteristics that distinguish mammals from other vertebrates such as hair, breast-feeding, jaws, dentition, etc., the mammalian brain allows this successful group to sense the world in a unique way. In fact, Mammals have evolved a series of innovations regarding the way they can read sensory clues, including a highly developed sense of smell and the ability to better detect and discriminate airborne sounds. On the other hand it has been hypothesized that mammals at some point became nocturnal and as a consequence they lost their ability to see color (Walls, 1942 Land and Osorio, 2003). Thus, these changes in the sensory system have also impacted in the brain centers that process sensory information. Beyond the diversity and specialization of the mammalian brain in different lineages a basic organization of the mammalian brain is characterized by a well-developed forebrain that contains a six-layered neocortex located dorsally. In fact, at the beginning of development, shortly after its closure, the neural tube forms rostrally three primary vesicles namely prosencephalon (forebrain), mesencephalon (midbrain), and rhombencephalon (hindbrain). These primary vesicles later develop into five secondary brain vesicles: whereas mesencephalon stays undivided, the prosencephalon splits to render the telencephalon and diencephalon, and the rhombencephalon is subdivided into the metencephalon and myelencephalon. From the telencephalon are developed the cerebral cortex together with several subcortical structures, including the hippocampus, basal ganglia, limbic system and the olfactory bulbs. Whereas the cerebral cortex primarily derives from the dorsal part of the telencephalon, the ventral telencephalon is composed of the ganglionic eminences (GE) from where interneurons that express the inhibitory neurotransmitter GABA originate and later migrate to the developing cortex (Gelman and Marín, 2010 Faux et al., 2012).

The cerebral cortex can be subdivided either into: isocortex and allocortex based on histological criteria homogenetic and heterogenetic based on layer development timelines or neocortex, paleocortex and archicortex based on evolutionary criteria. The archicortex consists of the hippocampal formation, which is located ventromedially related to the neocortex. This part of the cortex is involved in learning and memory. The paleocortex consists of the olfactory bulbs, limbic structures (amygdala), piriform cortex and secondary olfactory cortex and it is located ventrolaterally in relation to the neocortex.

The isocortex or neocortex in mammals is located dorsally and comprises the phylogenetically youngest cortical areas and it is characterized by a six-layered structure that develops during fetal stages and maintains this lamination pattern in adulthood. The neocortex mainly deals with sensory information beyond olfactory input that is processed at the piriform cortex. The neocortex is organized in regions specialized for different functions: these areas include primary visual (V1), somatosensory (S1), and auditory areas (A1). In addition there are other areas in the neocortex such as motor areas, secondary somatosensory, visual and other areas that vary from lineage to lineage.

Information from fossils (endocasts) and extant mammals is used to describe the basic brain of early mammals and protomammals. The fossil evidence indicates that early mammals had little neocortex relative to brain size and that piriform cortex and other areas dedicated to olfaction were more developed. Thus, the olfactory bulbs were quite large since early mammals had a very well-developed sense of smell. Regarding other areas of the brain, it is very probable that ancestral mammals lacked a corpus callosum that connects both cerebral hemispheres since although this structure is present in all placental mammals it is not found in monotremes or marsupials (Aboitiz and Montiel, 2003 Mihrshahi, 2006 Kaas, 2013). On the other hand, in the basal ganglia, the striatum is present in all tetrapods and receives dopaminergic projections from the diencephalum and/or the tegmentum, thus we suppose that basal ganglia were present in ancestral mammals. Moreover, other structures such as the nucleus accumbens, pallidum (globus pallidus) were also present as in all tetrapods.

The Emergence of the Mammalian Brain: Comparison to Other Tetrapods Brains

What is different about the mammalian cortex compared to other tetrapods? In the reptiles the homologous forebrain region to the neocortex is the dorsal cortex but it possesses three layers of which only one possesses the neuronal bodies of pyramidal neurons and interneurons (Figure 2) (Aboitiz et al., 2002 Bruce, 2010 Molnár, 2011). In addition, reptiles and birds (sauropsids) possess a big structure in the telencephalon called the dorsal ventricular ridge (DVR) where many sensory inputs like visual, somatosensory and auditory, are processed and in this ways covers many of the functions of the mammalian neocortex (Figure 2). Several hypotheses have been proposed to explain the origin of the DVR of birds and reptiles but they are outside the reach of this review (see Striedter, 2005 Medina, 2007 Butler et al., 2011 Montiel et al., 2016 Puelles et al., 2017). In birds, although they have a large dorsal cortex, it is organized in nuclei and not in layers (Dugas-Ford et al., 2012). The dorsal cortex is called “Wulst” or hyperpallium (Reiner et al., 2004). There is almost no doubt that the Wulst is the homologous region to the dorsal cortex in reptiles and also to neocortex in mammals. However, it is small in the majority of birds compared to the mammalian neocortex and it has been suggested that it is the very big DVR in birds that plays many of the functions of the cortex in mammals (Figure 2). Since the Wulst process mainly visual and some somatosensorial inputs, it is more developed in those birds that have improved visual capacities (Striedter, 2005).

Figure 2. Cortex across amniota. (A) Schematics of coronal sections at the forebrain in amniotes. On the left a drawing of the developing mammalian forebrain (based on the mouse) indicating the location of the neocortex (NCx), medial cortex (MC), lateral cortex (LC), and ventral telencephalic structures such as the lateral and medial ganglionic eminences (LGE and MGE). In the middle and at the right schematics of the reptile and bird forebrains showing dorsal cortex (DC), medial cortex, lateral cortex, hyperpallium or Wulst (W), and subpallial structures as the dorsal ventricular ridge (DVR). The approximate location of the striatum is also indicated (ST). Colors indicate brain regions that are homologous among the different vertebrate lineages. Rectangles in mammal and reptile brains indicate approximate location of the layers schematic shown in (C). (B) A Nissl stained coronal section of the adult macaca rhesus forebrain is shown. The rectangle indicates the approximate location of the magnification shown at the right. Magnification shows layers of the neocortex. (C) Schematic of the six layers of the neocortex in the adult mammalian neocortex. Next, a drawing shows the three layers of the dorsal cortex in a reptile. (D) Representational drawings of the developing neocortex of a gyrencephalic primate and a lissencephalic rodent where the germinative zones and cellular types are indicated. Next to it, the different cellular types of the adult and the embryonic developing neocortex are indicated. Macaque rhesus (Macaca mulatta) brain slices are from BrainMaps: An Interactive Multiresolution Brain Atlas http://brainmaps.org.

It is proposed that the stem amniotes from which mammals and present day reptiles and birds originated had a cerebral cortex in the telencephalon. In fact, a basic plan for the organization of this amniote cortex has been proposed (Puelles et al., 2016, 2017): this cortex is divided in a ventral part and three dorsal fields that includes medial, lateral, and dorsal components. Whereas, the medial part in sauropsids corresponds in mammals to the hippocampal formation, the lateral cortex coincides with the piriform cortex and the dorsal cortex corresponds to the neocortex (Puelles et al., 2016, 2017).

How the Neocortex Is Made in Mammals?

Before analyzing the genetic pathways that could underlie the evolution of the six-layered neocortex, I will summarize briefly how the cortex develops in mammals compared to sauropsids and birds. In mammals the cortex is composed approximately of 80% of excitatory glutamatergic neurons that are generated in situ through the proliferation and migration of progenitor cells. In addition, the cortex possesses GABAergic cortical interneurons that originate in the ganglionic eminences and that migrate to the cortex (Gelman and Marín, 2010 Faux et al., 2012). The neocortex develops through a process called neurogenesis from a single layer of neural progenitor cells (NPCs) that cover the lateral ventricles and that are present in early stages of brain development as neuroepithelial cells (NECs). This layer of progenitor cells that covers the lateral ventricles is known as ventricular zone (VZ) (Figure 2). In early stages of development NEC divide symmetrically to amplify the progenitor pool and then, at the onset of neurogenesis NECs acquire glia markers and are from this stage named as apical radial glia cells (aRG). Then, aRG can divide symmetrically or asymmetrically to give origin either to more aRG or to three other cell types: (i) basal radial glia (bRG), (ii) intermediate progenitors (IPs), or (iii) neurons (for a review of cell types see Florio and Huttner, 2014 Goffinet, 2017) (Figure 2).

IPs migrate into a new layer or proliferative zone called the Subventricular Zone (SVZ). In the SVZ, IPs divide symmetrically to generate more IPs, before differentiating into neurons. Early born neurons, in turn migrate through the intermediate zone (IZ) to form first the preplate and later the cortical plate (CP). Neurons are organized in the CP forming layers that are deposited during development in an inside to outside manner in which layers VI and V are formed first and then IV, III and II (for a review see Rakic, 2009). Layer I, that consist mainly of Cajal-Retzius neurons, is an exception to this inside-outside pattern since these cortical cells are born earlier (around mouse embryonic days 10�.5) and migrate to form this layer (Germain et al., 2010). Layer I is called the molecular layer and contains very few neurons and together with layer II or external granular layer, and layer III which is the external pyramidal layer constitute the supragranular layers. The supragranular layers are the primary origin and termination of intracortical connections that permits communication between one portion of the cortex and other regions (Swenson, 2006). Layer IV or internal granular layer receives thalamocortical connections, mainly from specific thalamic nuclei. Layer V called the internal pyramidal layer and layer VI known as the multiform/fusiform layer constitute the infragranular layers, which function is to connect the cerebral cortex with subcortical regions. Each cortical layer contains different cell types, for instance the pyramidal cells are the main neuronal type within layers III and V (Figure 2).

In reptiles, like the turtles, it has been described that they possess a VZ where cell division occurs, but not SVZ has been found (Cheung et al., 2007). In diapsids, like the gecko, it has been shown that NE cells divide first symmetrically and then asymmetrically to generate neurons (Nomura et al., 2013a). In addition, neurogenesis in the cortex of turtles and lizards obeys an outside-to-inside gradient (Goffinet et al., 1986). In birds (particularly in the chick), it has been shown that they have a clearly distinguished SVZ where cell divisions occur at E8 and E10. This SVZ is present in pallial and subpallial structures like the DVR and basal ganglia but not in the dorsal cortex (Cheung et al., 2007).

Evolution of the Six-Layered Neocortex in Mammals: When, How, and Where?

To clearly establish when the first animal to be called mammal appeared on Earth depends on the definition of mammals. Mammals possess many distinctive characters but in the fossil record it is possible to find many animals that show a few but not all the characters that define mammals. The history of mammals is a very rich one and it starts very early on with the appearance of a lineage of reptiles that showed some of the distinctive mammalian characters. Here I will revise this story very briefly but excellent reviews and books on the matter can be found (Kemp, 2005 Kielan-Jaworowska et al., 2005 Rowe, 2017).

Early reptiles, now usually called “stem amniotes,” originated from amphibians about 320 million years ago in the late Carboniferous (Colbert et al., 2001 Benton, 2015 Benton et al., 2015) and soon (around 305 mya) divided into two major clades, the sauropsid or diapsid clade and the synapsid clade. From the sauropsid clade originated modern reptiles and birds, while the synapsid clade, led to the appearance of early mammals 񾊀 mya (Figure 1). Stem synapsids are conformed by two groups: pelycosaurs and therapsids (Figure 1). It is known that after the Permian-Triassic mass extinction 80% of terrestrial vertebrates disappeared but some therapsids survived, particularly the dicynodonts and the cynodonts (Kemp, 2005) and from this last group it is documented that the stem mammals evolved 񾉀 mya (Figure 1).

Thus, during the first part of the Mesozoic era the first animals that are named mammals appeared. These early mammals (or Mammaliaformes) were very small, shrew-like insectivores that were mostly nocturnal or lived underground. As mentioned before, these habits did not require three color vision, which led to the loss of opsins at some point during the evolution of mammals whereas trichromatic color vision was conserved in diapsids (Rowe et al., 2011). From this group, the egg-laying prototherians splitted very early on around 200 mya, whereas the metatherians or marsupials diverged more recently, around 150 mya from the lineage leading to Eutherian or placental mammals (Figure 1). For many years, until around 66 mya, mammals were small animals like mice, rats or shrews and some of them a little larger like cats or dogs. When dinosaurs started to disappear, around 66 mya, mammals rapidly diverged and occupied a diversity of ecological niches (Figure 1). This adaptive radiation led to the appearance of a great diversity of mammals from all the mammalian orders, some of which inhabit the Earth today.

Regarding the appearance of the six layered neocortex it is known that all therian mammals, including placentals and marsupials possess a six layered neocortex. In fact, it has been shown that marsupials display an organized SVZ, determined by the presence of basal progenitor cells and a pattern of expression of genes that resembles the one found in eutherian mammals, implying that the SVZ emerged prior to the Eutherian-Metatherian divergence (Cheung et al., 2010).

In addition, it is now known that monotremes that splitted from the mammalian lineage very early on (around 200 mya Figure 1) after the appearance of what are called stem mammals, have a six-layered neocortex (Krubitzer et al., 1995) and also the presence of a SVZ has been described (Ashwell and Hardman, 2012). This indicated that a six-layered neocortex was already present before the split between monotremes and therian mammalian lineages. Then, the question is: did synapsids have six-layered neocortex? Undoubtedly, to answer this question we have to analyze only fossil evidence. From reconstructions performed using brain endocasts and braincases it looks like there was no great development of the telencephalon (Kemp, 2005), thus the answer to the above question is probably not. However, very recently Laaß and Kaestner have reported what seems to be the earliest evidence of a structure analogous to the mammalian neocortex in the fossorial anomodont (Therapsid) Kawingasaurus fossilis from the late Permian of Tanzania (Laaß and Kaestner, 2017). This finding is striking because in all therapsids the telencephalon is apparently quite narrow and does not show any clear signs of enlargement (Hopson, 2001 Kielan-Jaworowska et al., 2005 Kemp, 2009 Rowe et al., 2011). However, the authors of this finding concluded that the appearance of this neocortex-like structure is the result of convergent evolution (Laaß and Kaestner, 2017).

Thus, although this cannot be certainly established the appearance of a six-layered neocortex should have happened between the emergence of stem-mammals from therapsids (around 250 mya) and the evolution of monotremes (around 200 mya) (Figure 1).

In addition, regarding cynodonts there is a lot of discussion among specialist about the evolution of the brain in this group but the general agreement is that although it was very small compared to mammals there was some tendency to an increased size (Kemp and Parrington, 1979 Quiroga, 1980 Kemp, 2005 Kielan-Jaworowska et al., 2005).

Regarding Mammaliaformes, in addition to the general shape of the endocast that suggest an enlarged telecenplalon (Kemp and Parrington, 1979 Quiroga, 1980 Kermack and Kermack, 1984 Kielan-Jaworowska, 1986) and also the presence of a neocortex (Allman, 1999 Kielan-Jaworowska et al., 2005) there is also indirect evidence that the emergence of Mesozoic mammals marks the origin of the neocortex (Rowe, 2017). In fact, it has been suggested that the presence of a special kind of hair follicles called guard hairs involved in mechanoreception found in fossils from China (Ji et al., 2006) indicates the presence of somatosensory regions in the neocortex (Rowe, 2017).

Thus, it is apparent from the evidence analyzed so far that the expansion from a three- to a six-layered neocortex took place at some point in a Mammaliaformes in the lineage leading to the emergence of the common ancestor of all present day mammals. The emergence of a six-layered neocortex required the evolution of a developmental mechanism leading to increase neural production during embryonic neurogenesis. As mentioned before, in the mammalian embryonic cortex aRGs are the main type of progenitor cells, they form in the ventricular zone where they undergo mitosis to generate daughter cells that can take two different pathways: to leave the cell cycle and differentiate as neurons in a mechanisms known as direct neurogenesis or remain as progenitors an re-enter the cell cycle. In fact, aRGs give rise to two types of basal progenitors that migrate to build the subventricular zone (SVZ): bRGs and bIPs. These basal progenitors in turn divide to generate neurons in a two-step process known as indirect neurogenesis (Figure 2). Direct neurogenesis produces neurons in a fast way but also exhausts the progenitor pool rapidly. This is the mechanism that mainly produces neurons in the dorsal cortex of reptiles and birds. These diapsid derived vertebrates do not possess a SVZ in the homolog region of the neocortex, where indirect neurogenesis occurs in mammals (see above). Thus, it is possible that the evolution of this two-step mechanism of neurogenesis or indirect neurogenesis could be the key step in the evolution of the six-layered neocortex.

Moreover, this two-step neurogenesis mechanism that occurs in the SVZ could underlie the amplification of the number of neurons produced by increasing the pace and by lengthening the period of neurogenesis that is the raw material for the expansion of the cerebral cortex in diverse mammalian lineages.

Cortical Folding in Mammals

The size of the neocortex varies remarkably among mammalian species. The extension of the surface area of the neocortex, results in a pattern of folds that characterizes many mammals. For excellent comprehensive reviews on the matter see (Albert and Huttner, 2015 Striedter et al., 2015 Borrell, 2018 Kroenke and Bayly, 2018 Llinares-Benadero and Borrell, 2019). Cortical folding is the result of developmental mechanisms that lead to an extension increase of cortical layers which outcome is a pattern of gyri and sulci. Cortical folding has been described only in mammals. Species without cortical folding are called lissencephalic and species displaying folded brains are named gyrencephalic. Gyrification correlates with neocortical enlargement (Reillo and Borrell, 2012 Lewitus et al., 2013) and it is not the result of a particular evolutionary trend in some mammalian groups, as it is present in all mammalian orders (Figure 1). It has been postulated that folding appeared as an evolutionary solution to the problem of increasing cortical surface area without increasing the volume of the crania (Zilles et al., 2013). However, this hypothesis has been challenged by studies focusing on developmental mechanisms (Borrell, 2018). Cortical folding has been associated with the splitting of the SVZ and the appearance of the outer SVZ (oSVZ) in several gyrencephalic species (Reillo et al., 2011). In fact, the seminal finding by Smart et al. (2002) that in rhesus monkeys the SVZ was splited into two distinctive proliferative layers, i.e., oSVZ and inner SVZ (iSVZ) led to the identification of the oSVZ, as the principal source of cortical neurons in primates (Dehay et al., 2015). The oSVZ in rhesus monkeys and humans is populated by a particular kind of progenitor cell that is collectively known as basal Radial Glia (bRGCs). These progenitors were first described in the developing human neocortex (Fietz et al., 2010 Hansen et al., 2010) and then in other gyrencephalic mammals, such as ferret, cat and sheep (Reillo et al., 2011). In contrast, in the lissencephalic mouse, the SVZ is undifferentiated and a few bRGCs have been found (Wang et al., 2011). Thus, cortical folding has been also linked to a higher abundance of bRGCs in gyrencephalic vs. lissencephalic species (Wang et al., 2011 Pilz et al., 2013). Moreover, increasing the number of bRGCs in the mouse embryonic cortex through genetic manipulations leads to the appearance of folds (Stahl et al., 2013 Florio et al., 2015 Ju et al., 2016 Wang et al., 2016). Although, some lissencephalic mammals such as the marmoset and rats display a small oSVZ (Kelava et al., 2012 Martínez-Cerdeño et al., 2012). The presence of oSVZ-like structures in several placental mammals orders had led to propose that this structure appeared in an ancestor of placental mammals before the divergence of most groups and that was later lost in some species like mice (Dehay et al., 2015).

Regarding the genetic programs underlying cortical folding, several genes have been involved in different mechanisms and at different stages. Many of them were identified in people exhibiting cortical folding anomalies, such as polymicrogyria and lissencephaly. In fact, patients carrying mutations in genes such LIS1, doublecortin (DCX), and cyclin-dependent kinase 5 (CDK5) show lissencephaly (Pilz et al., 1998 Kerjan and Gleeson, 2007 Magen et al., 2015). Genetic manipulations in animal models such as the ferret that displays a gyrencephalic brain, have allowed to show that in fact CDK5 knockout in the ferret cerebral cortex in vivo impairs cortical folding (Shinmyo et al., 2017). Moreover, ferrets lacking DCX lack cortical folds (Kou et al., 2015). As mentioned before, genes affecting the generation and amplification of bRGCs are key factors in the formation of cortical folds. For instance, loss of function of the protein Trnp1 and activation of the SHH signaling pathways increased the number of bRGCs and led to the appearance of cortical folding in mice (Stahl et al., 2013 Wang et al., 2016). It has also been shown that extracellular matrix components such as HAPLN1, Lumican, and Collagen I induce folding of the cortical plate in human fetal neocortex explant systems suggesting that extracellular matrix components play a role in the folding of the human neocortex (Long et al., 2018).

On the other hand, it was early suggested that cortical folding is determined by hydraulic pressure from the cerebrospinal fluid and blood vessels acting on a limited cranial volume (Welker, 1990). Although these early theories were discarded due to the lack of experimental evidence, it has been suggested more recently that cortical folding results from internal or external biomechanical forces (Kroenke and Bayly, 2018). In fact, computational and mathematical models combined with experimental approaches have been developed in order to explain the biomechanical forces that govern folding. In order to simplify computational models the developing brain is represented before the emergence of sulci and gyri, as a structure consisting of two zones: the inner zone composed by the tissue between the cortical plate and the ventricle and the outer zone, conformed by the cortical plate (Kroenke and Bayly, 2018). Then, two main hypothesis have been proposed to establish if the mechanical forces inducing folding arise from the outer or the inner zone: (i) 𠇋uckling due to differential expansion” that proposes that the tangential expansion of the outer zone relative to the inner zone is the main force inducing folding (Xu G. et al., 2010 Bayly et al., 2014) and (ii) 𠇊xon tension” that suggests that such forces emerge from axons in the inner zone (Richman et al., 1975 Van Essen, 1997). Another theory has been recently developed to explain the expansion of supragranular layers in primates (Nowakowski et al., 2016). This theory, named “Supragranular Cortex Expansion Hypothesis,” proposes that primate cortical neurogenesis progresses in two stages. During early neurogenesis, basal fibers of ventricular radial glia contact the pial surface and newborn neurons migrate along ventricular as well as outer radial glia fibers. In late neurogenesis, newborn neurons reach the cortical plate only along outer radial glia fibers that do not contact the ventricular surface. In this second stage the scaffold formed by radial glia is broken and there is a discontinuous scaffold formed by two morphologically and molecularly distinct radial glia subtypes: ventral RG and outer RG. This model proposes that the tangential and radial expansion of the supragranular neuronal layers in primates is only dependent in neurogenic divisions of outer RG cells leading to a disproportionate expansion of supragranular cortex relative to infragranular cortex (Nowakowski et al., 2016).

Although these theories based on genetics or biomechanical forces into the determination of cortical folding appear to build upon contrasting ideas, a combination of early events determined by molecular genetic programs that set the cellular composition of the cortex and later events determined by the regional varying mechanical forces seem to better explain the appearance of gyri and sulci in the brain cortex of mammals.

Certainly, the impressive amount of knowledge that has accumulated in the last years related to mechanisms underlying cortical folding has shed light on the evolution of this salient characteristic unique to mammals. In fact, there is clear evidence that the most recent ancestor to all mammals already exhibited a gyrencephalic brain (O'Leary et al., 2013 Lewitus et al., 2014). Thus, it is possible to speculate that in the ancestor of all extant mammalian lineages there were already molecular mechanisms that make it possible to generate a gyrencephalic brain.

Definitely the availability of more comparative studies among vertebrates and new advances in technologies promise to render a better understanding of the evolution of this complex mammalian feature. Moreover, as it will be discussed below, several hominoid-specific genes have been recently linked to the regulation of cortical folding in humans.

Interneurons Origin, Development, and Evolution

As mentioned before, during development the neocortex is populated by two main groups of neurons: excitatory projection neurons and inhibitory interneurons, that are mainly generated outside the cortex. In fact, inhibitory interneurons that mainly express GABA are originated in the medial and caudal ganglionic eminences and in the preoptic area and then migrate first tangentially in two streams over long distances into the cerebral cortex and then radially inside the cortex in order to become integrated into the various cortical layers (Buchsbaum and Cappello, 2019). The tangential migration of interneurons is regulated by multiple factors and although a deep review of them is not within the reach of this review, I will briefly mention some of the key factors involved in this important process of neocortical development. Excellent recent reviews on the matter are available (Faux et al., 2012 Hu et al., 2017 Lim et al., 2018). It has been shown that connexin 43 and Sox6 play important roles in the switch between tangential migration and radial migration (Azim et al., 2009 Batista-Brito et al., 2009 Elias et al., 2010). Another important factor controlling the correct path of migrating interneurons is the CXCL12/CXCR signaling pathway that seem to play a dual role, first attracting interneurons to the neocortex and then guiding their tangential migration until the correct radial signal is received (Faux et al., 2012). Once in the cortex, radial migration and lamination seem to be influenced by cues provided by pyramidal cells. Thus, neuregulin 3 (Nrg3) expressed by pyramidal cells, facilitates the dispersion of cortical interneurons in the laminar dimension of the cortex (Bartolini et al., 2017). The correct lamination of interneurons in the CP is controlled by intrinsic and extrinsic factors. Among the extrinsic factors, reelin seems to also play a role in the layering of these neurons since abnormal lamination has been observed when reelin signaling is disrupted (Hevner et al., 2004 Hammond et al., 2006 Pla et al., 2006 Yabut et al., 2007). However, it is not clear if it is due to reelin signaling (Hammond et al., 2006) or to the location of pyramidal neurons (Pla et al., 2006). Among the intrinsic factors it has been suggested that the time of generation, the site of origin and also the cell-intrinsic genetic programs that they display influence not only on the final destination of interneurons in the cortex but also on the type of inhibitory cell that they become. Regarding the site of origin it has been suggested that interneurons arising from a common progenitor preferentially form clusters in the cortex (Brown et al., 2011 Ciceri et al., 2013) but this view has been recently challenged (Mayer et al., 2015). On the other hand, using single-cells transcriptome analyses, Mi et al. (2018) showed that shortly after the interneurons become postmitotic in their site of origin, their diversity is already evident due to the distinctive transcriptional programs that they display, and this transcriptional signature underlies their final differentiation in the developing cortex. Tangential migration by inhibitory interneurons from the subpallium to the pallium is a process highly conserved among vertebrates. There is evidence that suggests that the migratory pathways of neocortical GABAergic interneurons are mainly conserved among mammals (Tanaka and Nakajima, 2012). However, the site of origin may differ among species, because interneurons appear to be generated within the neocortex in addition to the ganglionic eminences in cynomolgus monkeys and humans (Letinic et al., 2002 Petanjek et al., 2009 Hansen et al., 2010 Jakovcevski et al., 2011 Yu and Zecevic, 2011). However, we are still far from understanding lineage-specific differences among mammals and vertebrates that can illuminate our knowledge about the complex mechanisms underlying interneurons development and evolution.


Methods

Data used in Figs. 2 ⇑ –4 are given in SI Tables 2–4. We required that at least six data points be available for a cell type to be included in our analyses. Data were taken from the references that are listed therein. The methods for obtaining and measuring each cell type are detailed in these original references. When different studies measured values for the same cell type, we combined the data into a single dataset. When multiple values of a given cell type were reported for a given species, the geometric mean of values was used for analysis. We did not, however, average values for Fisher 344 and Sprague–Dawley rats because these rats may be considerably different metabolically and because the original source did not average these values. We also excluded data for cerebellar Purkinje neurons from Friede (15), which largely disagreed with more recent values from (12, 13), because, as stated there, these measures were meant to facilitate interspecies comparisons but not to establish absolute values for cell volume. Redoing the statistics and figures with the average for rats discussed above or the data from Friede has almost no influence on the exact numbers in Table 1 and does not affect any of our conclusions.

For adipocytes, data from 15 depots throughout the body were provided by C. M. Pond (The Open University, Milton Keynes, U.K.). Because of differing resource availability for the sampled mammals, exponents for cell size were variable across depots. In this article, we use only adipocytes from the dorsal wall of the abdomen (specifically from a depot sometimes called the retroperitoneal that includes perirenal adipose tissue and extends along the inner wall of the abdomen into the pelvis) because they are representative of general storage adipose (C. M. Pond, personal communication).

Allometric exponents were determined by using ordinary least squares (OLS) regression on ln-ln plots of the data. See Methods in ref. 38 for more information. CIs and P values were computed by using Mathematica (Wolfram Research, Inc., Champaign, IL).


Ankle and foot evolution gave mammals a leg up

Reconstruction of a Paleocene mammal that lived around 65 million years ago. Credit: Sarah Shelley

The evolution of ankle and foot bones into different shapes and sizes helped mammals adapt and thrive after the extinction of the dinosaurs, a study suggests.

The evolution of ankle and foot bones into different shapes and sizes helped mammals adapt and thrive after the extinction of the dinosaurs, a study suggests.

A surge of evolution following the mass extinction 66 million years ago enabled mammals to diversify and prosper during a period of major global change, researchers say.

Analysis of bones that form part of the ankle and the heel of the foot reveal that mammals during this time—the Paleocene Period—were less primitive than previously thought.

Edinburgh paleontologists made the discovery by comparing the anatomy of Paleocene mammals with species from the earlier Cretaceous Period and those that exist today.

They analyzed foot and ankle bone measurements—which provide insights into animals' lifestyle and body size—of 40 Paleocene species. The team contrasted the results with data from living mammal species and mammals that existed during the Cretaceous Period.

Their findings show that Paleocene mammals had stockier, more muscular builds than those from the Cretaceous or present day.

The animals' joints were also very mobile, supported by ligaments and tendons—rather than bony features as in some living mammals—which the team hypothesize enabled them to adapt and evolve more rapidly following the extinction.

Surviving devastation

Many species' ankles and feet closely resembled those of ground-dwelling and burrowing mammals that exist today, indicating that these lifestyles were key to surviving and thriving after the mass extinction, which was caused by an asteroid impact.

The ability to dig underground, for example, is likely to have helped mammals survive the initial devastation, while a loss of tree habitats after the extinction period may have favored ground-dwelling species, the team says.

The study is published in the journal Proceedings of the Royal Society B.

"At the core of our study, we wanted to figure out what Paleocene mammals were doing in terms of their anatomy and how this related to aspects of their lifestyle and evolution in the wake of the dinosaur extinction. Paleocene mammals have this tendency to combine unusual mish-mashes of anatomy but are often seen as 'archaic' and unspecialised precursors to living mammal groups. What we found was this incredible diversity—they're adapting and evolving their robustly built bodies in ways that are different to living mammals. Our results show one of the many ways mammals were able to adapt and thrive following the catastrophic devastation of the end-Cretaceous extinction," says Dr. Sarah Shelley, School of GeoSciences.


CHARACTERISTICS OF MAMMALIAN GROWTH

Study of Animal Growth: Autonomous versus Regulated Growth

Examination of growth properties has historically involved two major approaches. The first aims to determine whether a tissue compartment is subject to a size “set point” by characterizing its response to cell number alteration. Compensation by changes in cell growth, proliferation, or survival is evidence of “regulated” growth: the compartment size is “sensed” and correspondingly adjusted. Lack of a set point indicates that growth is driven by an “autonomous” program, which is insensitive to compartment size. Even then, final size is not necessarily fixed, as the execution of the program might be modulated by intrinsic and external factors. This type of approach was initially limited to surgical ablation and engraftment, but the advent of genetic manipulation has considerably increased its scope and value by allowing precisely targeted cell number alterations in almost any tissue in a temporally controlled manner and with the added possibility of cell lineage tracking. The second approach aims to determine the nature of signals regulating growth by characterizing compartment response to a change in the external or intrinsic environment. Heterotypic and heterochronic graft, parabiosis, and nutrient restriction could all be included in this group, along with alteration of cell microenvironment via gene expression and protein function manipulation. Taken together, these approaches have succeeded in uncovering major principles of organ-size determinations, as we will discuss below.

Whole Body Size Control in Mammals

Growth Control during Early Embryonic Development

Because conformation of the animal body plan is a major constraint size for all organs, it is useful to review what is known about how body size is determined in mammals. Control of cell number in preimplantation embryos was among the first features of size determination to be studied. Blastomere ablation or embryo scission up to the third cleavage stage results in smaller blastocysts containing fewer cells. However, increased proliferation is observed starting shortly after implantation, at embryonic day 7.5 in mice, resulting in normalization of embryo size by midorganogenesis (E10.5) (Tarkowski and Wróblewska 1967 Rands 1986). Conversely, postimplantation mouse embryos, resulting from morula aggregation, grow more slowly and their size is normalized by E7.5 (Lewis and Rossant 1982). Hence, embryo size in mammals is governed by an autonomous growth program early in development, but then switches to a program whereby size is tightly regulated to a predetermined set point, at least until mid- or late organogenesis.

Control of Late Embryonic and Postnatal Growth by Regulative and Autonomous Mechanisms

The existence of “extrinsic” (regulative) mechanisms modulating late embryonic and postnatal growth was also established decades ago, based on the observation that a transient deficit of amino acids results in accelerated “catch-up” growth in mammals after normal nutrition is restored (Osborne and Mendel 1914 Tanner 1963). The observation of the effect of pituitary gland ablation on bodily growth in 1909 led to the discovery of growth hormone (GH) in the 1920s and its purification in the 1940s (Aschner 1909 Kopchick 2003). GH deficiency results in slowed postnatal growth and reduced body size, whereas transgenic animals overexpressing GH display increased growth and size (Palmiter et al. 1982 Hull and Harvey 1999). However, linear growth rates in GH-deficient animals still decline with age at a pace identical to that of age-matched controls (Lupu et al. 2001). Similarly, GH-treated animals display normal age-related growth deceleration if the ratio of GH to body mass is kept constant (Mathews et al. 1988). This indicates that age-related growth deceleration is not controlled by GH levels, whereas the phenomenon of catch-up growth further suggests that growth deceleration is driven by how much growth has already taken place, rather than by the chronological age of the animal.

The question of whether late embryonic size may also be subject to “set point control” requires similar methods of unbiased, body-wide cell ablation and assessing for catch-up growth. Although such an approach has not been taken experimentally, a similar situation has been found to result naturally from mutation of the pericentrin gene in humans. The pericentrin protein is a component of centrosomes and loss of its function results in an increased frequency of abnormal mitoses, leading to cell arrest or death, thus reducing growth efficiency. Pericentrin mutations cause primordial dwarfism, a rare condition characterized by slowed growth, starting during gestation. Organ and body size are reduced, but proportions are preserved except for some craniofacial bones (Delaval and Doxsey 2010). Continually impaired cell proliferation and loss are, therefore, not compensated for during late embryonic and postnatal growth in humans, arguing against the existence of a mammalian set point for body size.

Vertebrate Organ-Growth Properties

Organ Size Is Determined by Extrinsic and Intrinsic Cues

The existence of overlapping mechanisms of size regulation in vertebrate organs was also first revealed by early work, most notably the studies of amphibian limb growth performed by Harrison in the 1920s (Harrison 1924). Harrison took advantage of the differences in growth in two closely related species of salamander, Ambystoma punctatum (now maculatum) and Ambystoma tigrinum. These species are similar in size at the time of metamorphosis, but punctatum larvae develop their limbs earlier and reach smaller adult sizes than tigrinum. Harrison cross-transplanted limb buds between the species to surprising results: punctatum limbs grafted on tigrinum larvae grew to be smaller than corresponding donor limbs, whereas tigrinum buds grafted in punctatum larvae reached gigantic proportions. Harrison concluded that size was determined by integration of a limb-intrinsic “potential,” which was greater in tigrinum, and a systemic “regulator” more active in punctatum (Harrison 1924).

Size-Control Mechanisms Differ across Mammalian Organs

Adult organs in mammals display dramatically varying degrees of plasticity. Liver mass, for example, is tightly regulated to a set point. Surgical ablation of liver lobes results in rapid growth of the remaining lobes, bringing back the organ within 5% of its original mass in a few days. Liver transplantation to a recipient larger than the donor also induces growth, resulting in rapid normalization of liver–body ratio. Conversely, large-for-host transplanted liver mass decreases as a result of increased apoptosis (Fausto et al. 2006). In animals with linked blood circulation (parabiosis), partial hepatectomy induces compensatory growth of both livers, showing that liver size is influenced by systemic factors still unidentified (Bucher et al. 1951 Moolten and Bucher 1967). In contrast, the thymus, kidney, gut, and cartilage growth plates do not alter their growth on transplantation (Lui and Baron 2011). Regenerative capacity is very limited in most mammalian organs however, responses depend on the nature of the injury. For example, obstruction of the main pancreatic duct results in acute inflammation and pancreatic tissue destruction, but full regeneration occurs once the obstruction is resolved. Partial resection of the pancreas, in contrast, elicits no response. Resected small intestine does not increase in length even in juvenile individuals instead, enteric villi increase their length to compensate for lost absorption surface (Stanger 2008).

Developing and Mature Organ Size Is Controlled by Different Processes

Differences in organ plasticity arise early during organ development. Cell ablation via targeted expression of the diphtheria toxin in liver progenitor cells of transgenic mice results in compensatory proliferation and restoration of normal liver by the time of birth. In contrast, ablation of pancreatic progenitors using the same approach results in a proportional reduction of pancreas size. The two organ primordia, therefore, show different size regulation despite the fact that both arise from a shared pool of progenitors in the early foregut (Stanger et al. 2007). Further complicating this picture, some organs display different plasticity during embryonic and postnatal growth. The early limb bud, for instance, is capable of regeneration on partial ablation. However, it rapidly loses as development proceeds, indicating a switch from regulated to autonomous growth (Müller et al. 1999). In others, both types of regulation seem to co-exist: while the hematopoietic bone marrow exhibits a high degree of plasticity, the number of hematopoietic stem cells is determined by an autonomous mechanism (Müller-Sieburg et al. 2000).

Characterizing Mammalian Organ Growth at the Cell Level: Morphogenetic versus Homeostatic Growth?

Organ Growth Is Driven by Two Distinct Cellular Processes

Although the results discussed above paint a complex picture, a look at the cellular mechanisms driving organ growth might provide some insight into the process of organ-size regulation. Most tissue compartments in vertebrates are constituted around a self-renewing stem-cell population, giving rise to one or more differentiated cell types. Stem cells reside in a variable number of anatomically discrete niches acting as independent morphogenetic units. Enteric crypts, pancreatic acini, skeletal growth plates, and the entire hematopoietic cell compartment could be considered each a morphogenetic unit. In some cases, such as the liver, differentiated cells are capable of self-renewal without the need for a stem cell (Yanger et al. 2013). However, liver lobes behave as a discrete morphogenetic unit as they are anatomically isolated from each other. It is, therefore, reasonable to consider organ size to be the coordinated outcome of two modes of growth control: “morphogenetic growth,” involving creation of morphogenetic units, and “homeostatic growth,” resulting from increase in morphogenetic unit size (Fig. 1).

Response of mammalian organs to cell ablation during morphogenesis and homeostasis. Organs are represented as clusters of independent morphogenetic (see text for details) units each containing stem (black dots) and differentiated cells (white dots). (A) Morphogenetic unit number is determined during embryonic development. Progenitor pool size can be determined by an autonomous program (upper path) that cannot compensate for progenitor-cell loss, or by ongoing intrinsic and/or extrinsic cues (dotted circle) allowing for compensatory proliferation (lower path) in response to cell ablation. (B) Mature morphogenetic unit size is, in most cases, limited by extrinsic and intrinsic cues and might regenerate as long as stem cells are spared (top). Lost morphogenetic units cannot, in most cases, be recreated. Instead, the remaining units may undergo compensatory growth. Total organ size is then limited by external and/or internal cues determining a “set point” (bottom).

Morphogenetic growth is largely an autonomous process. Morphogenetic unit formation in mammals occurs mostly during embryonic development. The number of units produced in organ primordia is dependent on progenitor pool size, which is regulated in the early embryo, but becomes autonomously determined in specific organ lineages. The reason for this is not fully understood, but growth and differentiation themselves may act as factors restricting progenitor pool plasticity. This is illustrated in the limb bud in which growth is driven by signals from the apical ectodermal ridge that are, in turn, disrupted by growth (as further discussed below) (Cohn et al. 1995 Tanaka et al. 1997). In some cases, however, new morphogenetic unit formation might extend beyond embryonic development. This is the case in the intestine in which new crypts are produced by fission of existing ones. This phenomenon likely plays a role in the elongation of the bowel during postnatal growth, but it is not observed in response to injury, suggesting that it might be under control of GH levels, an autonomous program, or both (Cummins et al. 2006).

Homeostatic growth can be autonomous or regulated. Morphogenetic unit size, on the other hand, is clearly regulated in most mature organs, but the mode of size determination varies, arguably as result of evolutionary adaptation to the function of each organ. Organs with functions requiring rapid adaptation and variable functional output, such as the liver, muscles, or the hematopoietic stem-cell compartment, are tightly regulated to a set point, which supposes the existence of “thermostats” by which the organ can sense physiological stress. The size of organs that do not need to meet changing demands is determined either autonomously or by interaction with a “scaffold” compartment the size of which is, in turn, autonomously determined. This paradigm is illustrated by the acinar pancreas, which regenerates as long as the subjacent ductal compartment is maintained (Stanger 2008). Size of the ductal compartment, in turn, is set early during development and its subsequent growth is not regulated. Consistent with this idea, the mass of the endocrine β-cell compartment, which plays a critical role in metabolic regulation, is subject to regulation and compensatory growth, despite sharing the same progenitor pool as the acinar and duct compartments (Bouwens and Rooman 2005).


MATERIALS AND METHODS

The models

Overview

Figure 1 is a flow diagram that shows qualitatively how we connect macroclimate, microclimates, individual properties, population level effects, and community attributes. The macroclimate–microclimate connection is achieved in part by general climate data available through the National Oceanic and Atmospheric Administration (NOAA). The microclimate model has been described for a variety of habitats that range from southwestern deserts ( Mitchell et al., 1975 Porter et al., 1973) to Santa Fe Island in the Galapagos ( Christian et al., 1983) to Michigan bogs ( Kingsolver, 1979). It is a one-dimensional finite difference model that simultaneously solves the heat and mass balance equations for the ground surface and below. It also calculates wind speed and temperature profiles from the ground surface to two meter reference height, where meteorological data are typically measured. Clear sky solar radiation is calculated from basic principles ( McCullough and Porter, 1971).

Microclimate calculations for heterogeneous environments can determine percent of thermally available habitat and temperature dependent feeding frequency ( Grant and Porter, 1992). Grant and Porter showed that item feeding frequency was a linear function of the thermally available percent of the habitat (the percentage that allows the animal to stay within its preferred temperature range, thereby avoiding significant thermoregulatory heat stress costs). A summation of a day's preferred activity times over a month and over the year yields total annual activity time.

Total annual activity time is a key variable linking individual energetics with population and community level phenomena. Annual activity time for a terrestrial vertebrate was first calculated from basic principles in 1973 ( Porter et al., 1973). By “basic principles” we mean equations derived from thermodynamic principles that do not involve regression equations. Total annual activity time can be used to calculate key life history variables, such as survivorship, growth, and reproductive potential ( Adolph and Porter, 1993 Adolph and Porter, 1996), that are used to calculate population dynamics.

Survivorship (mortality) probability/hour is affected by activity time, which is affected by temperature dependent habitat selection. Climate change may affect survivorship, partly by modifying predation probabilities that change with seasonal changes in overlap of predator and prey preferred activity time ( Porter et al., 1973 Porter and James, 1979) and partly due to climate stress ( Porter and Gates, 1969).

Growth and reproduction potential depend on mass and energy intake and expenditures. The difference between intake and expenditure is the capital available for growth or reproduction. We are in a strong position to calculate mass/energy expenditures. Intake of mass and energy is more challenging. Intake depends on item feeding frequency and handling time. Handling time depends on the size of food “packages” and morphology of the feeding apparatus. Calculations in this paper assumed no shortage of food and that the mass flow through the gut scales with mass ( Calder, 1984) and meets the body size/climate imposed metabolic demand. The mass flow absorbed over a day is assumed sufficient to meet basic thermoregulatory requirements for the day plus a user defined multiplier (up to 7) above the minimal metabolism needed to maintain core temperature in the current climate. This was done to try to establish an upper bound for absorbed mass for different sizes of animals.

Different sizes of animals may represent different trophic levels in the community Only some of the connections between a species' individual energetics, population dynamics and community attributes are shown in Figure 1. Other species within the habitat may influence temperature dependent behavior by competing with the arbitrarily chosen animal species represented here, thereby affecting their numbers (Ives et al., 1999). The reader may imagine multiple layers of this graph for individual species interconnected vertically to allow for explicit multiple species descriptions.

Model cross section

Figure 2 represents a diagrammatic cross section through an arbitrarily chosen part of an animal. This could represent a torso whose geometry may be approximated by a cylinder, sphere, or ellipsoid, or even a cross section through an appendage, if the heat loss by respiration is removed. There may or may not be a porous insulation beyond the skin. Figure 2 shows what would be needed for heat and mass transfer calculations. Data needed are the mean length of the fibers (hair or hair-like elements in feathers), fiber density as a function of depth, fiber diameter, and the depth of the insulation. Length and depth of the fibers are usually different unless the fibers extend outward normal to the skin. Solar reflectivity and transmissivity of the fibers also must be known if the animal is diurnal and exposed to sunlight. The environmental conditions that specify the climate boundary conditions for an individual include solar radiation, infrared fluxes from the sky and ground, air temperature, wind speed, and relative humidity of the air passing over the animal. These values are calculated based on the animal's average height above ground and the microclimate calculations for environment conditions above ground. The microclimate equations have been described ( Mitchell et al., 1975 Porter et al., 1973).

Most of the equations describing porous media heat flux without convection through the fur are described ( Conley and Porter, 1986 Porter et al., 1994). Heat and mass flux equations describing flow through fur are complex ( Stewart et al., 1993 Budaraju et al., 1994, 1997). Solar radiation was incorporated in the model used here by assuming that solar radiation is absorbed very close to the fur/feather–air interface, which is usually the case for bird feathers and dark, dense fur (Porter, unpublished data). Absorbed solar radiation heats the fiber elements, which then emit infrared radiation outward toward the sky and inward through the porous insulation. The watts of absorbed solar radiation were treated as an additional source of thermal radiation from the sky for the half of the animal exposed to the sky. Thus, the diffuse infrared radiation equations already in model were also used for incorporating absorbed solar radiation in the model.

The porous media model is only part of the animal model used to calculate metabolic heat production that will maintain core temperature given the internal and external morphology of the animal, including its insulation ( Porter et al., 1994). The radial dimension of an animal is calculated from its weight and geometry. An iterative searching routine named Zbrent guesses the metabolic heat production needed to maintain any specified core temperature ( Press et al., 1986). Zbrent finds the unique metabolic heat production that satisfies the heat and mass balance equations (Appendix, Porter et al., 1994) given the body allometry, dimensions, specified core temperature, insulation properties, and environmental conditions. Because the equations are interconnected, relatively few variables determine these solutions ( Porter et al., 1994).

Inside the body

The type of food in the gut determines the relative proportions of carbohydrates, proteins and lipids that are absorbed by the body. A healthy body will utilize these absorbed molecules as substrates. The demand for energy and the substrates being oxidized determine the amount of oxygen needed. The oxygen consumption is associated with heat generation. The proportion of the substrates oxidized determines the amount of carbon dioxide produced and hence the respiratory quotient. The oxygen demand specifies the moles of air that must pass through the respiratory system to meet the demand. Thus, the type of food in the gut affects indirectly the amount of incoming respiratory air, which in turn affects the water balance in the respiratory system in the heat generation-ventilation-gut coupled model described below.

Heat generation models

Figure 3 shows how the current model of distributed heat generation throughout the body creates a parabolic temperature profile from the body core to skin. The equations describing uniform heat generation for rectangular (slab), cylindrical, spherical, and ellipsoid geometry ( Porter et al., 1994) all show that the internal heat generation and the temperature gradient from core to skin are functions of the body radius squared. The model solves the heat and mass balance equations ( Porter et al., 1994) for heat generation needed to maintain core temperature by iterative guessing the solution for each hour of simulation throughout a 24 hr daily cycle. The coupled equations of heat and mass transfer simultaneously yield solutions for water balance, gut absorbed food requirements, hours of activity time and discretionary mass and energy available for growth or reproduction or fat deposition as described below.

Earlier metabolic heat generation models, such as a slab approximation, assumed a heat source only at the center of the animal ( Porter and Gates, 1969 Porter et al., 1973). This assumption creates a simple linear temperature profile from core to skin ( Fig. 3, Porter et al., 1994), but not shown here. This type of construct frequently uses the term “thermal conductance,” the reciprocal of” thermal resistance.” Thermal conductance is a linear model of heat transfer commonly used in many biological publications referring to animal heat transfer. Unfortunately it is only relevant in the context of non-heat generating materials.

A cylindrical geometry with a heat source only at the center (axis) does not mathematically allow for the heat source only at the axis, since it is undefined there ( Bird et al., 1960). A central heated region is required. Simple conduction (but not added heat generation by the conducting tissues) of heat radially from the perimeter of the core region yields a logarithmic temperature profile. This logarithmic profile has different heat generation requirements to maintain a specified core temperature in the center region than a model using distributed heat production from the core to the skin.

Respiration

An important addition to the current model is the distributed respiratory water loss, which represents lungs that span most of the body cavity. This innovation gives much better agreement of predicted metabolic rates with measured values.

Figure 4 shows the system diagram for the lung molar balance model. A dashed line labeled 1 represents the entrance surface to the respiratory system. The dashed line labeled 2 represents the exit surface from the respiratory system. Moles of nitrogen, oxygen, water, and carbon dioxide enter the respiratory system. The moles of air entering are calculated from the product of the moles of oxygen needed for the current guess for heat generation requirements times the sum of the percent composition of the components of air divided by the percent of oxygen in the air, which may change in burrows. Thus, the current iterative guess for metabolic heat production specifies how many moles of oxygen are needed to meet the metabolic demand from the respiratory system. The type of diet (carbohydrate/protein/lipid) specifies the joules of heat produced from the oxidation of a mole of oxygen ( Schmidt-Nielsen, 1979). The oxygen extraction efficiency of the respiratory system and the properties of air determine how many moles of air are needed per unit time by the respiratory system. The amount of carbon dioxide added to the respiratory system air is calculated from the respiratory quotient, RQ, which is the ratio of moles of carbon dioxide produced per mole of oxygen consumed ( Schmidt-Nielsen, 1979).

The RQ changes with different substrates oxidized. The respiration model uses the RQ for carbohydrates, proteins, or lipids, or a combination of the three, to calculate the amount of carbon dioxide that flows out of the respiratory surfaces. The user-specified proportions of carbohydrate, protein, and lipid in the food consumed thus ultimately determine the RQ. Thus, the metabolic oxygen demand to maintain core temperature and the current properties of air specify the volume of airflow and the amount of water added to saturate the respiratory system air. At expiration, the user specified temperature difference between the air in contact with nasal surfaces as air exits at surface 2 and the free stream external air (1–3°C) is used to calculate the amount of water recovered by condensation on the nasal surfaces. The calculated skin temperature of the body would not be relevant for estimating nasal air temperature at exit because of the different convective environment inside the nares vs. the outer skin covered with fur or feathers. Since we were trying to estimate maximum recovery rates as an upper bound, we used experimental data summarized from the literature ( Welch, 1980) for the calculations and used a 3°C difference between exit air temperature and local external (free stream) air temperature.

Temperature regulation model

Another important addition to the model was temperature regulation responses. Sensitivity analyses of the model done by increasing air and radiation temperatures revealed that the calculated skin temperature, which is a function of the specified core temperature, must not exceed core temperature. If it does exceed the core temperature, metabolic heat production must be dissipated by evaporation of respiratory water to achieve steady state. The molar balance model for the lungs just described clearly showed a limited capacity for heat dissipation by water vaporization in the lungs, which is consistent with experimental data ( Welch and Tracy, 1977 Welch, 1980). A user specified minimum core–skin temperature difference was added to the model. The value used in our calculations was 0.5°C. If an iterative solution for heat generation given the specified core temperature produced a skin temperature less than the minimum core–skin difference, a three-level hierarchy of physiological responses was invoked.

First, flesh thermal conductivity increases to the maximum value measured in the literature. That was never sufficient to increase the core-skin temperature gradient, since it only serves to increase skin temperature.

Second, the percentage of the skin surface assumed covered with tiny water drops increases up to 100 percent of the skin surface area to cool the skin. The amount of cooling is constrained by air temperature, wind speed, relative humidity, and the boundary layer thickness at the skin. The latter is a function of body characteristic dimension, insulation properties, and wind properties defined in Nusselt and Reynolds numbers ( Bird et al., 1960). The Nusselt number is simply a nondimensional ratio of the heat transfer coefficient times a characteristic dimension (often defined as the distance a fluid such as air travels when passing over the object of interest) divided by the thermal conductivity of the fluid. The Reynolds number is also a nondimensional ratio. It is the product of the fluid density, velocity, and the characteristic dimension divided by the dynamic viscosity of the fluid. The Nusselt number is often plotted against the Reynolds number. The regression of the data plotted is a relationship that allows for the calculation of the heat transfer coefficient (used to calculate convective heat loss) for any value of Reynolds numbers variables, such as changing characteristic dimension (body size).

Third, failing all else, the core temperature is allowed to rise in 0.1°C increments until a stable solution of the equation is found that allows a 0.5°C temperature difference between core and skin. This approach causes a rise in metabolic rate at high temperatures that is observed experimentally ( Schmidt-Nielsen, 1979). It also mimics the rise in core temperature that is observed experimentally ( Schmidt-Nielsen, 1979). No regressions are needed to emulate the experimental data.

The gut

Figure 4 also shows the system diagram for the molar balance gut model. It is related to the well-known batch reactor and plug flow model originally developed in chemical engineering and subsequently applied to animal digestive systems ( Penry and Jumars, 1987). The model used here allows for any type of ingested food consisting of user specified proportions of carbohydrates, lipids, proteins and water content. The food can enter the gut any time during activity time in any amount, subject to the constraint that the volume of food ingested per day may not exceed the wet mass of the animal. The energy value of absorbed carbohydrates, lipids, and proteins is well known ( Schmidt-Nielsen, 1979). Details of the model are in the Appendix.

Temperature dependent feeding

Figure 5 shows how these animal models respond to different temperatures. The metabolic rate of an endotherm changes with increasing environmental temperature in a distorted U-shaped curve ( Bucher et al., 1986 Kleiber, 1975 Morris and Kendeigh, 1981 Schmidt-Nielsen, 1979 Scholander, 1940). It is commonly assumed from a physiological perspective that the capacity to absorb food is independent of environmental temperature because of the relatively constant body temperatures that endotherms usually maintain. This is in contrast to the temperature dependent digestion of ectotherms ( Waldschmidt et al., 1987).

However, the temperature dependent foraging behavior and appetite levels of endotherms are frequently ignored, although they have been considered with respect to domestic animals ( Kleiber, 1975). Recent seed tray experiments under natural foraging conditions show that desert rodents are extremely sensitive to substrate temperatures that affect willingness to forage (Mitchell et al., ms), and similar results have been reported for free ranging raccoons ( Berris, 1998). Predation risk and competition also influence foraging costs. Birds and mammals may compete for the same resource ( Brown et al., 1997). Predation risk and competition can be expressed in terms of energetic cost ( Brown et al., 1994).

Thus, the difference between temperature dependent foraging (mass and chemical energy intake) and temperature dependent metabolic costs (mass and chemical energy expenditure) yields temperature dependent discretionary mass and energy intake. Discretionary mass and energy intake is the oval area in Figure 5 bordered by intake and expenditure rates. Climate and type of food available are important constraints on fitness that can now be calculated from basic principles. As we shall soon see, body size and diet are additional important constraints on fitness in different climates.

Porous insulation

Fur vs. feathers

Figure 6 shows schematically the difference between fur and plumage density as a function of distance above the skin. The densities of hair elements are greatest near the skin. The density of feather elements, in contrast, is lowest near the skin and greatest at the feather–air interface. The consequence of this difference in density profiles is that air can penetrate at least the outer parts of fur more easily. Feathers, however, seal off the plumage–air interface, creating a conduction–radiation environment that is easier to analyze.

This is true, irrespective of whether the fur or plumage elements are normal to the skin or at an angle relative to the skin. Changing angle relative to the skin modifies density profiles for either type of porous insulation, but does not change the general shape of the density profile with depth. In reality, parts of the skin of birds do have plumaceous elements near the skin. Individual plumages will vary between taxa and between different locations on the body. The density will also vary with degree of elevation of contour feathers, degree of density at the same level of the rachis/vane junction, and the presence and density of down feathers. However, vertical serial sections of crow feathers embedded in plastic under vacuum have shown that the greatest density of elements is at the plumage–air interface (Porter, unpublished data).

Fortunately, in low wind environments, changes in individual element density with height do not have a significant impact on porous insulation heat loss, unless the fibrous elements are either very sparse or extremely dense across a wide range of body sizes (Fig. 25 in Porter et al., 1994). At very low individual element density, the porous insulation becomes very open, allowing substantial convective and radiant heat transfer from the skin. In contrast, at very high individual element density, the effective thermal conductivity of the porous insulation approaches that of keratin, rather than air. This amounts to an increase in thermal conductivity by a factor of about eight, thus increasing heat loss. Sensitivity of heat loss due to density changes with depth in fur in a conduction-radiation heat exchange is very small ( Kowalski, 1978 McClure and Porter, 1983). Kowalski used a measured density of fur as a function of depth for cow fur, which is described by a hyperbolic tangent function ( Fig. 6).

If fur density has an optical thickness ( Porter et al., 1994) less than 0.001, the fur is so sparse that it is assumed to be transparent to infrared radiation, and conduction heat transfer along the fibers is negligible. Under these conditions, the model assumes the functional equivalent of bare skin. For example, a user of the model can explore the consequences of changing insulation or removing insulation merely by altering the input data file, the depth and density of fur or plumage. If it is set to zero, or very low density, the program automatically checks to be sure that a porous model is appropriate and changes to a bare skin model, if necessary.

Finite elements and flow through the fur

Moderate and high wind environments can force penetration of air through fur. Thus, it was important to develop a basic principles model that would permit calculation of velocity and temperature profiles in a porous medium with nonlinear coordinates on a round body. Integration of the profiles allows for calculations of heat energy and mass transfer from basic principles, a task first accomplished only recently ( Stewart et al., 1993 Budaraju et al., 1994, 1997). We will briefly review the basic features of this sophisticated model.

Figure 7 shows the finite element model in cylindrical (radial and angular) coordinates from an end view. These curvilinear boxes are used to compute the mass and heat flows in the radial and angular directions relative to the skin and to the direction of incoming air for variable densities of keratin elements projecting from the skin of an endotherm. This concept is also appropriate for birds, under conditions where air penetrates the feathers. Examples include resting birds with fluffed feathers or flightless birds like kiwis, whose plumage strongly resembles mammalian fur. In principle, this model would also be useful for the pulsing (changing angular orientation relative to the skin) feather conditions of active bird flight.

Appendages

Figure 8 shows an appendage model for birds developed for three large ratites, the rhea, Rhea americana, the cassowary, Casuarius casuarius, and the ostrich, Struthio camelus. These appendages are largely bare and constitute a significant percentage of the surface area of the standing bird. Appendage dimensions were measured relative to torso dimensions using photographs of the birds of known height and weight from the side, and the front. We measured only the exposed areas of appendages. The portions of the legs covered by torso feathers were assumed to be part of the volume of the torso for heat transfer calculations.

The same cross-section model shown in Figure 2, but without porous insulation and respiratory water loss, was used to calculate heat loss in the radial dimension from these appendages. Heat loss from the bottom of the appendages in contact with the ground was assumed to be negligible. Total calculated heat loss was the sum computed from the torso plus the heat losses from the appendages.

The regression equations that were fitted to the appendage dimensions, areas, and volumes are listed in the Appendix. The appendage dimensions were computed from regressions based on body weight. Appendage volumes were then computed, added and the total subtracted from the total volume of the bird based upon its weight. The difference was the torso volume. The torso length, width, and height were calculated from the dimension ratios of the feathered torso. The calculated torso dimensions then were used to compute the effective torso skin area and plumage depths. Plumage depths agreed well with the few data available.


Do any cells change in size or mass as mammals grow? - Biology

The amount of satellite cells present within in a muscle depends on the type of muscle. Type I or slow-twitch oxidative fibers, tend to have a five to six times greater satellite cell content than Type II (fast-twitch fibers), due to an increased blood and capillary supply (2). This may be due to the fact that Type 1 muscle fibers are used with greatest frequency, and thus, more satellite cells may be required for ongoing minor injuries to muscle.

Immunology
As described earlier, resistance exercise causes trauma to skeletal muscle. The immune system responds with a complex sequence of immune reactions leading to inflammation (3). The purpose of the inflammation response is to contain the damage, repair the damage, and clean up the injured area of waste products.
The immune system causes a sequence of events in response to the injury of the skeletal muscle. Macrophages, which are involved in phagocytosis (a process by which certain cells engulf and destroy microorganisms and cellular debris) of the damaged cells, move to the injury site and secrete cytokines, growth factors and other substances. Cytokines are proteins which serve as the directors of the immune system. They are responsible for cell-to-cell communication. Cytokines stimulate the arrival of lymphocytes, neutrophils, monocytes, and other healer cells to the injury site to repair the injured tissue (4).

The three important cytokines relevant to exercise are Interleukin-1 (IL-1), Interleukin-6 (IL-6), and tumor necrosis factor (TNF). These cytokines produce most of the inflammatory response, which is the reason they are called the “inflammatory or proinflammatory cytokines” (5). They are responsible for protein breakdown, removal of damaged muscle cells, and an increased production of prostaglandins (hormone-like substances that help to control the inflammation).

Growth Factors
Growth factors are highly specific proteins, which include hormones and cytokines, that are very involved in muscle hypertrophy (6). Growth factors stimulate the division and differentiation (acquisition of one or more characteristics different from the original cell) of a particular type of cell. In regard with skeletal muscle hypertrophy, growth factors of particular interest include insulin-like growth factor (IGF), fibroblast growth factor (FGF), and hepatocyte growth factor (HGF). These growth factors work in conjunction with each other to cause skeletal muscle hypertrophy.

Insulin-Like Growth Factor
IGF is a hormone that is secreted by skeletal muscle. It regulates insulin metabolism and stimulates protein synthesis. There are two forms, IGF-I, which causes proliferation and differentiation of satellite cells, and IGF-II, which is responsible for proliferation of satellite cells. In response to progressive overload resistance exercise, IGF-I levels are substantially elevated, resulting in skeletal muscle hypertrophy (7).

Fibroblast Growth Factor
FGF is stored in skeletal muscle. FGF has nine forms, five of which cause proliferation and differentiation of satellite cells, leading to skeletal muscle hypertrophy. The amount of FGF released by the skeletal muscle is proportional to the degree of muscle trauma or injury (8).

Hepatocyte Growth Factor
HGF is a cytokine with various different cellular functions. Specific to skeletal muscle hypertrophy, HGF activates satellite cells and may be responsible for causing satellite cells to migrate to the injured area (2).
Hormones in Skeletal Muscle Hypertrophy
Hormones are chemicals which organs secrete to initiate or regulate the activity of an organ or group of cells in another part of the body. It should be noted that hormone function is decidedly affected by nutritional status, foodstuff intake and lifestyle factors such as stress, sleep, and general health. The following hormones are of special interest in skeletal muscle hypertrophy.

Growth Hormone
Growth hormone (GH) is a peptide hormone that stimulates IGF in skeletal muscle, promoting satellite cell activation, proliferation and differentiation (9). However, the observed hypertrophic effects from the additional administration of GH, investigated in GH-treated groups doing resistance exercise, may be less credited with contractile protein increase and more attributable to fluid retention and accumulation of connective tissue (9).

Cortisol
Cortisol is a steroid hormone (hormones which have a steroid nucleus that can pass through a cell membrane without a receptor) which is produced in the adrenal cortex of the kidney. It is a stress hormone, which stimulates gluconeogenesis, which is the formation of glucose from sources other than glucose, such as amino acids and free fatty acids. Cortisol also inhibits the use of glucose by most body cells. This can initiate protein catabolism (break down), thus freeing amino acids to be used to make different proteins, which may be necessary and critical in times of stress.
In terms of hypertrophy, an increase in cortisol is related to an increased rate of protein catabolism. Therefore, cortisol breaks down muscle proteins, inhibiting skeletal muscle hypertrophy (10).

Testosterone
Testosterone is an androgen, or a male sex hormone. The primary physiological role of androgens are to promote the growth and development of male organs and characteristics. Testosterone affects the nervous system, skeletal muscle, bone marrow, skin, hair and the sex organs.
With skeletal muscle, testosterone, which is produced in significantly greater amounts in males, has an anabolic (muscle building) effect. This contributes to the gender differences observed in body weight and composition between men and women. Testosterone increases protein synthesis, which induces hypertrophy (11).

Fiber Types and Skeletal Muscle Hypertrophy
The force generated by a muscle is dependent on its size and the muscle fiber type composition. Skeletal muscle fibers are classified into two major categories slow-twitch (Type 1) and fast-twitch fibers (Type II). The difference between the two fibers can be distinguished by metabolism, contractile velocity, neuromuscular differences, glycogen stores, capillary density of the muscle, and the actual response to hypertrophy (12).

Type I Fibers
Type I fibers, also known as slow twitch oxidative muscle fibers, are primaritly responsible for maintenance of body posture and skeletal support. The soleus is an example of a predominantly slow-twitch muscle fiber. An increase in capillary density is related to Type I fibers because they are more involved in endurance activities. These fibers are able to generate tension for longer periods of time. Type I fibers require less excitation to cause a contraction, but also generate less force. They utilize fats and carbohydrates better because of the increased reliance on oxidative metabolism (the body’s complex energy system that transforms energy from the breakdown of fuels with the assistance of oxygen) (12).
Type I fibers have been shown to hypertrophy considerably due to progressive overload (13,15). It is interesting to note that there is an increase in Type I fiber area not only with resistance exercise, but also to some degree with aerobic exercise (14).

Type II Fibers
Type II fibers can be found in muscles which require greater amounts of force production for shorter periods of time, such as the gastrocnemius and vastus lateralis. Type II fibers can be further classified as Type IIa and Type IIb muscle fibers.

Type IIa Fibers
Type IIa fibers, also known as fast twitch oxidative glycolytic fibers (FOG), are hybrids between Type I and IIb fibers. Type IIa fibers carry characteristics of both Type I and IIb fibers. They rely on both anaerobic (reactions which produce energy that do not require oxygen), and oxidative metabolism to support contraction (12).
With resistance training as well as endurance training, Type IIb fibers convert into Type IIa fibers, causing an increase in the percentage of Type IIa fibers within a muscle (13). Type IIa fibers also have an increase in cross sectional area resulting in hypertrophy with resistance exercise (13). With disuse and atrophy, the Type IIa fibers convert back to Type IIb fibers.

Type IIb Fibers
Type IIb fibers are fast-twitch glycolytic fibers (FG). These fibers rely solely on anaerobic metabolism for energy for contraction, which is the reason they have high amounts of glycolytic enzymes. These fibers generate the greatest amount of force due to an increase in the size of the nerve body, axon and muscle fiber, a higher conduction velocity of alpha motor nerves, and a higher amount of excitement necessary to start an action potential (12). Although this fiber type is able to generate the greatest amount of force, it is also maintains tension for a shortesst period of time (of all the muscle fiber types).
Type IIb fibers convert into Type IIa fibers with resistance exercise. It is believed that resistance training causes an increase in the oxidative capacity of the strength-trained muscle. Because Type IIa fibers have a greater oxidative capacity than Type IIb fibers, the change is a positive adaptation to the demands of exercise (13).

Conclusion
Muscular hypertrophy is a multidimensional process, with numerous factors involved. It involves a complex interaction of satellite cells, the immune system, growth factors, and hormones with the individual muscle fibers of each muscle. Although our goals as fitness professionals and personal trainers motivates us to learn new and more effective ways of training the human body, the basic understanding of how a muscle fiber adapts to an acute and chronic training stimulus is an important educational foundation of our profession.


Table 1. Structural Changes that Occur as a Result of Muscle Fiber Hypertrophy
Increase in actin filaments
Increase in myosin filaments
Increase in myofibrils
Increase in sarcoplasm
Increase in muscle fiber connective tissue
Source: Wilmore, J.H. and D. L. Costill. Physiology of Sport and Exercise (2nd Edition).Champaign, IL: Human Kinetics, 1999.

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9. Frisch, H. Growth hormone and body composition in athletes. Journal of Endocrinology Investigation 22: 106-109, 1999.

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