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9.9: Types of Signals - Biology

9.9: Types of Signals - Biology


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There are four categories of chemical signaling found in multicellular organisms: paracrine signaling, endocrine signaling, autocrine signaling, and direct signaling across gap junctions (Figure 1). Not all cells are affected by the same signals.

Paracrine Signaling

Signals that act locally between cells that are close together are called paracrine signals. Paracrine signals move by diffusion through the extracellular matrix. These types of signals usually elicit quick responses that last only a short amount of time. In order to keep the response localized, paracrine ligand molecules are normally quickly degraded by enzymes or removed by neighboring cells. Removing the signals will reestablish the concentration gradient for the signal, allowing them to quickly diffuse through the intracellular space if released again.

One example of paracrine signaling is the transfer of signals across synapses between nerve cells. A nerve cell consists of a cell body, several short, branched extensions called dendrites that receive stimuli, and a long extension called an axon, which transmits signals to other nerve cells or muscle cells. The junction between nerve cells where signal transmission occurs is called a synapse. A synaptic signal is a chemical signal that travels between nerve cells. Signals within the nerve cells are propagated by fast-moving electrical impulses. When these impulses reach the end of the axon, the signal continues on to a dendrite of the next cell by the release of chemical ligands called neurotransmitters by the presynaptic cell (the cell emitting the signal). The neurotransmitters are transported across the very small distances between nerve cells, which are called chemical synapses (Figure 2). The small distance between nerve cells allows the signal to travel quickly; this enables an immediate response, such as, Take your hand off the stove!

When the neurotransmitter binds the receptor on the surface of the postsynaptic cell, the electrochemical potential of the target cell changes, and the next electrical impulse is launched. The neurotransmitters that are released into the chemical synapse are degraded quickly or get reabsorbed by the presynaptic cell so that the recipient nerve cell can recover quickly and be prepared to respond rapidly to the next synaptic signal.

Endocrine Signaling

Signals from distant cells are called endocrine signals, and they originate from endocrine cells. (In the body, many endocrine cells are located in endocrine glands, such as the thyroid gland, the hypothalamus, and the pituitary gland.) These types of signals usually produce a slower response but have a longer-lasting effect. The ligands released in endocrine signaling are called hormones, signaling molecules that are produced in one part of the body but affect other body regions some distance away.

Hormones travel the large distances between endocrine cells and their target cells via the bloodstream, which is a relatively slow way to move throughout the body. Because of their form of transport, hormones get diluted and are present in low concentrations when they act on their target cells. This is different from paracrine signaling, in which local concentrations of ligands can be very high.

Autocrine Signaling

Autocrine signals are produced by signaling cells that can also bind to the ligand that is released. This means the signaling cell and the target cell can be the same or a similar cell (the prefix auto- means self, a reminder that the signaling cell sends a signal to itself). This type of signaling often occurs during the early development of an organism to ensure that cells develop into the correct tissues and take on the proper function. Autocrine signaling also regulates pain sensation and inflammatory responses. Further, if a cell is infected with a virus, the cell can signal itself to undergo programmed cell death, killing the virus in the process. In some cases, neighboring cells of the same type are also influenced by the released ligand. In embryological development, this process of stimulating a group of neighboring cells may help to direct the differentiation of identical cells into the same cell type, thus ensuring the proper developmental outcome.

Direct Signaling Across Gap Junctions

Gap junctions in animals and plasmodesmata in plants are connections between the plasma membranes of neighboring cells. These water-filled channels allow small signaling molecules, called intracellular mediators, to diffuse between the two cells. Small molecules, such as calcium ions (Ca2+), are able to move between cells, but large molecules like proteins and DNA cannot fit through the channels. The specificity of the channels ensures that the cells remain independent but can quickly and easily transmit signals. The transfer of signaling molecules communicates the current state of the cell that is directly next to the target cell; this allows a group of cells to coordinate their response to a signal that only one of them may have received. In plants, plasmodesmata are ubiquitous, making the entire plant into a giant, communication network.


9.9: Types of Signals - Biology

Journey inside a cell as you follow proteins and learn about cellular interactions. This 3-D animation brings to life the inner workings of a fibroblast cell as it responds to external signals. Created by Cold Spring Harbor Laboratory and Interactive Knowledge, Inc.

Duration: 14 minutes, 15 seconds

(00:36) Hey wait up for me. OW. Are you Ok? Yeah, I think I just fell over a root. Oh my knee.

(00:55) Your body is an incredible living system made up of billions of cells. We're following blood cells as they're propelled through a blood vessel toward the boy's injured knee. If you are hurt, your cells work together to repair the damage. They communicate using their own language of chemical signals.

(01:20) Now we've arrived at the wound site. Blood cells are flowing out of the broken blood vessel ahead. Moments after an injury, these blood cells and cell fragments start to form a mesh-like clot. Many different types of cells are involved in tissue repair. The flat, light-colored cells are fibroblasts. Early in the healing process, fibroblasts multiply and produce proteins that help to repair the damage. The smaller dark cell fragments between the fibroblasts are platelets. When activated, platelets release a stream of protein messengers, called growth factors, to stimulate cell growth and tissue repair. To see how a growth factor from a platelet signals a nearby fibroblast cell, we need to swoop in close to the rippling fibroblast surface.

(02:45) Fibroblasts, like all your cells, have a fluid, outer membrane that regulates the flow of molecules in and out. The gray structures sticking out of the cell membrane are receptors for incoming signals. When the growth factor from the platelet (shown in purple and blue) encounters a matching receptor, it binds to it. A second receptor protein joins in, making the growth factor fit like a key in a lock. The binding of the growth factor causes the receptor to change shape. This change in the protein conducts the signal through the membrane and into the cell's interior &ndash the cytoplasm. You'll see this better from inside the cell . . .

(03:46) Beneath the cell membrane, you see the grey receptor ends surrounded by pink fibers &ndash these structures help to give the cell its shape &ndash and a range of messenger proteins that will carry the signal through the cytoplasm. While active &ndash as shown by the yellow flashes of light &ndash the ends of the receptor interact with the messenger proteins.

(04:25) Now we'll watch the action again from our position in the cell's cytoplasm. The growth factor binds the receptor proteins outside the cell, drawing the receptor ends together. The signal is transmitted through the cell membrane, and each new protein is activated in turn. If you look closely, you can see the proteins change shape as they become activated with the signal. Each step of a pathway is under tight control to ensure that the correct message is relayed. For example, as this white protein accepts the signal, the blue protein comes in to deactivate the red one. Now let's follow the white protein on its journey through the cytoplasm, toward the center of the cell.

(03:56) As we follow the signal to the nucleus, you'll see it passed from messenger to messenger. The first exchange is with this brown protein, the second will be with a purple protein in a few seconds time. Although we're only following one path, a single cell has many different ways to transmit signals through the cytoplasm. The fibers (shown in green) are part of the cytoskeleton. Like the pink fibers you saw before, these give the cell shape and help to organize its contents. Crawling along the fibers are motor proteins that reshape the cytoskeleton and help this fibroblast cell to move. On our way, we will encounter other structures in the cytoplasm, known as organelles. On the right, you see glowing organelles, called mitochondria, which generate energy for the cell. The activated protein passes by a network of membranes (here in light brown) known as the endoplasmic reticulum.

(06:57) The protein is transported into the nucleus through a pore in the nuclear membrane. The nucleus contains tightly wound coils of DNA (shown in green). The protein messenger passes the signal to two other molecules that team up to locate a specific gene along the DNA. In this case, the gene carries the information to make a growth factor. Other molecules then unwind a small section of the DNA molecule and allow an enzyme called RNA polymerase (shown in brown) to make an RNA copy of the gene. The "copy," called messenger RNA (here in light green), is packaged with a set of carrier proteins and leaves the nucleus. The cell will use this copy to make the growth factor. Now we'll follow the messenger RNA copy back out of the nucleus to see how a new protein is made.

(08:18) On the left is the endoplasmic reticulum (which we've seen before) and on the right is a new structure called the Golgi apparatus (which we'll visit again later). Straight ahead are more of the glowing mitochondria. In the cytoplasm, the messenger RNA is released from its carrier proteins and binds to a complex called a ribosome. Here, the ribosome, a huge molecule, is shown as a multicolored structure. The ribosome reads the information encoded in the RNA and assembles a protein from amino acids found in the cell. Many ribosomes can operate at the same time to make multiple copies of the protein. The ribosomes anchor on the outer membrane of the endoplasmic reticulum. If you look carefully, you can see the ghostly shapes of the newly made proteins accumulating on the inner side of the membrane. Once the job is done the ribosomes and RNA part company.

(10:00) The newly made proteins leave the endoplasmic reticulum wrapped in a layer of membrane called a vesicle. They travel toward the Golgi apparatus (on the right) where the proteins are modified and sorted for transport. The loops of the Golgi are busy with protein traffic moving in and out. The vesicle fuses with the membrane at one end of the Golgi and a new vesicle containing the modified proteins is pinched off the other side. The new vesicle transports the proteins through the cytoplasm &ndash delivering the proteins to where they are needed. Some proteins are used inside the cell. Others, like these growth factors, must be exported. Here, the vesicle fuses with the cell membrane, dumping the proteins outside the cell.

(11:48) The released growth factors will communicate with other cells to continue the healing process. These growth factors will attract more fibroblasts to the wound site and remodel the clot for better healing. Other proteins produced by this signaling pathway will tell the fibroblast cell to grow and divide, making many new cells to heal the wound. With the cooperation of many different cells, damage to the injured knee can be quickly repaired. Every day, your cells communicate and cooperate to keep you healthy. They act and interact they grow, divide and die all through the amazing language of cell signals.

Cell Signals, nucleus, cell journey, cytoplasm, growth factor, 3d animation


Contents

In different tumor subtypes, cells within the tumor population exhibit functional heterogeneity and tumors are formed from cells with various proliferative and differentiation capacities. [4] This functional heterogeneity among cancer cells has led to the creation of multiple propagation models to account for heterogeneity and differences in tumor-regenerative capacity: the cancer stem cell (CSC) and stochastic model. However, certain perspectives maintain that this demarcation is artificial, since both processes act in complementary manners as far as actual tumor populations are concerned. [1] Importantly it is observed that whereas in the healthy human esophageal epithelium the proliferative burden is met by a stochastically dividing basal epithelium. Upon its transition to the precancerous Barrett’s esophagus epithelium, however, a small dedicated stem cell compartment appears that supports proliferation of the epithelium while concomitantly evidence for a stochastically dividing compartment contributing to the maintenance of the tissue disappears. Hence, at least for certain neoplastic tissues, dedicated stem cell compartments maintain and enlarge the size of the transformed compartment [5]

The cancer stem cell model Edit

The cancer stem cell model, also known as the Hierarchical Model proposes that tumors are hierarchically organized (CSCs lying at the apex [6] (Fig. 3).) Within the cancer population of the tumors there are cancer stem cells (CSC) that are tumorigenic cells and are biologically distinct from other subpopulations [7] They have two defining features: their long-term ability to self-renew and their capacity to differentiate into progeny that is non-tumorigenic but still contributes to the growth of the tumor. This model suggests that only certain subpopulations of cancer stem cells have the ability to drive the progression of cancer, meaning that there are specific (intrinsic) characteristics that can be identified and then targeted to destroy a tumor long-term without the need to battle the whole tumor. [8]

Stochastic model Edit

In order for a cell to become cancerous it must undergo a significant number of alterations to its DNA sequence. This cell model suggests these mutations could occur to any cell in the body resulting in a cancer. Essentially this theory proposes that all cells have the ability to be tumorigenic making all tumor cells equipotent with the ability to self-renew or differentiate, leading to tumor heterogeneity while others can differentiate into non-CSCs [7] [9] The cell's potential can be influenced by unpredicted genetic or epigenetic factors, resulting in phenotypically diverse cells in both the tumorigenic and non-tumorigenic cells that compose the tumor. According to the “stochastic model” (or “clonal evolution model”) every cancer cell in a tumor could gain the ability to self-renew and differentiate to the numerous and heterogeneous lineages of cancer cells that compromise a tumor [10]

These mutations could progressively accumulate and enhance the resistance and fitness of cells that allow them to outcompete other tumor cells, better known as the somatic evolution model. [7] The clonal evolution model, which occurs in both the CSC model and stochastic model, postulates that mutant tumor cells with a growth advantage outproliferate others. Cells in the dominant population have a similar potential for initiating tumor growth. [11] (Fig. 4).

[12] These two models are not mutually exclusive, as CSCs themselves undergo clonal evolution. Thus, the secondary more dominant CSCs may emerge, if a mutation confers more aggressive properties [13] (Fig. 5).

Tying CSC and stochastic models together Edit

A study in 2014 argues the gap between these two controversial models can be bridged by providing an alternative explanation of tumor heterogeneity. They demonstrate a model that includes aspects of both the Stochastic and CSC models. [9] They examined cancer stem cell plasticity in which cancer stem cells can transition between non-cancer stem cells (Non-CSC) and CSC via in situ supporting a more Stochastic model. [9] [14] But the existence of both biologically distinct non-CSC and CSC populations supports a more CSC model, proposing that both models may play a vital role in tumor heterogeneity. [9]

The cancer stem cell immunology model Edit

This model suggests that immunological properties may be important for understanding tumorigenesis and heterogeneity. As such, CSCs can be very rare in some tumors, [15] but some researchers found that a large proportion of tumor cells can initiate tumors if transplanted into severely immunocompromised mice, [16] and thus questioned the relevance of rare CSCs. However, both stem cells [17] and CSCs [18] possess unique immunological properties which render them highly resistant towards immunosurveillance. Thus, only CSCs may be able to seed tumors in patients with functional immunosurveillance, and immune privilege may be a key criterion for identifying CSCs. [19] Furthermore, the model suggests that CSCs may initially be dependent on stem cell niches, and CSCs may function there as a reservoir in which mutations can accumulate over decades unrestricted by the immune system. Clinically overt tumors may grow if: A) CSCs lose their dependence on niche factors (less differentiated tumors), B) their offspring of highly proliferative, yet initially immunogenic normal tumor cells evolve means to escape immunosurveillance or C) the immune system may lose its tumorsuppressive capacity, e.g. due to ageing. [19]

The existence of CSCs is under debate, because many studies found no cells with their specific characteristics. [15] Cancer cells must be capable of continuous proliferation and self-renewal to retain the many mutations required for carcinogenesis and to sustain the growth of a tumor, since differentiated cells (constrained by the Hayflick Limit [20] ) cannot divide indefinitely. For therapeutic consideration, if most tumor cells are endowed with stem cell properties, targeting tumor size directly is a valid strategy. If CSCs are a small minority, targeting them may be more effective. Another debate is over the origin of CSCs - whether from dysregulation of normal stem cells or from a more specialized population that acquired the ability to self-renew (which is related to the issue of stem cell plasticity). Confounding this debate is the discovery that many cancer cells demonstrate a phenotypic plasticity under therapeutic challenge, altering their transcriptomes to a more stem-like state to escape destruction. [ citation needed ]

The first conclusive evidence for CSCs came in 1997. Bonnet and Dick isolated a subpopulation of leukemia cells that expressed surface marker CD34, but not CD38. [21] The authors established that the CD34 + /CD38 − subpopulation is capable of initiating tumors in NOD/SCID mice that were histologically similar to the donor. The first evidence of a solid tumor cancer stem-like cell followed in 2002 with the discovery of a clonogenic, sphere-forming cell isolated and characterized from adult human brain gliomas. Human cortical glial tumors contain neural stem-like cells expressing astroglial and neuronal markers in vitro. [22] Cancer stem cells isolated from adult human gliomas were shown to induce tumours that resembled the parent tumour when grafted into intracranial nude mouse models. [23]

In cancer research experiments, tumor cells are sometimes injected into an experimental animal to establish a tumor. Disease progression is then followed in time and novel drugs can be tested for their efficacy. Tumor formation requires thousands or tens of thousands of cells to be introduced. Classically, this was explained by poor methodology (i.e., the tumor cells lose their viability during transfer) or the critical importance of the microenvironment, the particular biochemical surroundings of the injected cells. Supporters of the CSC paradigm argue that only a small fraction of the injected cells, the CSCs, have the potential to generate a tumor. In human acute myeloid leukemia the frequency of these cells is less than 1 in 10,000. [21]

Further evidence comes from histology. Many tumors are heterogeneous and contain multiple cell types native to the host organ. Tumour heterogeneity is commonly retained by tumor metastases. This suggests that the cell that produced them had the capacity to generate multiple cell types, a classical hallmark of stem cells. [21]

The existence of leukemia stem cells prompted research into other cancers. CSCs have recently been identified in several solid tumors, including:

  • Brain [24]
  • Breast [25]
  • Colon [26]
  • Ovary [27][28]
  • Pancreas [29]
  • Prostate [30][31]
  • Melanoma [32][33][34][35]
  • Multiple Myeloma [36][37]
  • Non-melanoma skin cancer [38][39]

Mechanistic and mathematical models Edit

Once the pathways to cancer are hypothesized, it is possible to develop predictive mathematical models, [40] e.g., based on the cell compartment method. For instance, the growths of abnormal cells can be denoted with specific mutation probabilities. Such a model predicted that repeated insult to mature cells increases the formation of abnormal progeny and the risk of cancer. [41] The clinical efficacy of such models [42] remains unestablished.

The origin of CSCs is an active research area. The answer may depend on the tumor type and phenotype. So far the hypothesis that tumors originate from a single "cell of origin" has not been demonstrated using the cancer stem cell model. This is because cancer stem cells are not present in end-stage tumors.

Origin hypotheses include mutants in developing stem or progenitor cells, mutants in adult stem cells or adult progenitor cells and mutant, differentiated cells that acquire stem-like attributes. These theories often focus on a tumor's "cell of origin".

Hypotheses Edit

Stem cell mutation Edit

The "mutation in stem cell niche populations during development" hypothesis claims that these developing stem populations are mutated and then reproduce so that the mutation is shared by many descendants. These daughter cells are much closer to becoming tumors and their numbers increase the chance of a cancerous mutation. [43]

Adult stem cells Edit

Another theory associates adult stem cells (ASC) with tumor formation. This is most often associated with tissues with a high rate of cell turnover (such as the skin or gut). In these tissues, ASCs are candidates because of their frequent cell divisions (compared to most ASCs) in conjunction with the long lifespan of ASCs. This combination creates the ideal set of circumstances for mutations to accumulate: mutation accumulation is the primary factor that drives cancer initiation. Evidence shows that the association represents an actual phenomenon, although specific cancers have been linked to a specific cause. [44] [45]

De-differentiation Edit

De-differentiation of mutated cells may create stem cell-like characteristics, suggesting that any cell might become a cancer stem cell. In other words, fully differentiated cell undergoes mutations or extracellular signals that drive it back to a stem-like state. This concept has been demonstrated most recently in prostate cancer models, whereby cells undergoing androgen deprivation therapy appear to transiently alter their transcriptome to that of a neural crest stem-like cell, with the invasive and multipotent properties of this class of stem-like cells. [ citation needed ]

Hierarchy Edit

The concept of tumor hierarchy claims that a tumor is a heterogeneous population of mutant cells, all of which share some mutations, but vary in specific phenotype. A tumor hosts several types of stem cells, one optimal to the specific environment and other less successful lines. These secondary lines may be more successful in other environments, allowing the tumor to adapt, including adaptation to therapeutic intervention. If correct, this concept impacts cancer stem cell-specific treatment regimes. [46] Such a hierarchy would complicate attempts to pinpoint the origin.

CSCs, now reported in most human tumors, are commonly identified and enriched using strategies for identifying normal stem cells that are similar across studies. [47] These procedures include fluorescence-activated cell sorting (FACS), with antibodies directed at cell-surface markers and functional approaches including side population assay or Aldefluor assay. [48] The CSC-enriched result is then implanted, at various doses, in immune-deficient mice to assess its tumor development capacity. This in vivo assay is called a limiting dilution assay. The tumor cell subsets that can initiate tumor development at low cell numbers are further tested for self-renewal capacity in serial tumor studies. [49]

CSCs can also be identified by efflux of incorporated Hoechst dyes via multidrug resistance (MDR) and ATP-binding cassette (ABC) Transporters. [48]

Another approach is sphere-forming assays. Many normal stem cells such as hematopoietic or stem cells from tissues, under special culture conditions, form three-dimensional spheres that can differentiate. As with normal stem cells, the CSCs isolated from brain or prostate tumors also have the ability to form anchor-independent spheres. [50]

Recent years have seen an advent of genetic approaches to identify cancer stem cells in experimental rodents. In such studies, following the induction of cancer (usually through the application of mutagens), a genetic cassette is activated resulting in the expression of an easily identifiable marker, for instance green fluorescent protein (GFP). This overcomes the limitations of traditional approaches (e.g. the classic Bromodeoxyuridine (BrdU) labeling technique has been used to identify slow-cycling cells in animals) as genetic approaches are cell cycle independent and can be used for in vivo pulse-chase labeling to identify quiescent/slow-cycling cells. [51] This strategy, for instance, was instrumental for identifying the so-called Lgr5+ compartment as a cancer stem cell compartment in liver cancer and showing its potential as a viable therapeutic target. [52]

CSCs heterogeneity is a pool of differentiated and undifferentiated tumour cells that are replenished by cells possessing both tumour and stem cell like properties and having phenotypic and metabolic heterogeneity inside the single tumour mass. There are two theories to explain the phenotypic and metabolic heterogeneity of CSCs clonal variation and cancer stem cell theory. While former theory dictates the role of genetic, epigenetic and micro environment where tumour cell resides to acquire undifferentiated tumorigenic traits. The latter theory focus more on the malignancy traits acquired by stem cells where these undifferentiated and highly tumorigenic stem cells repopulate the differentiated tumour mass. [53]

CSCs have been identified in various solid tumors. Commonly, markers specific for normal stem cells are used for isolating CSCs from solid and hematological tumors. Markers most frequently used for CSC isolation include: CD133 (also known as PROM1), CD44, ALDH1A1, CD34, CD24 and EpCAM (epithelial cell adhesion molecule, also known as epithelial specific antigen, ESA). [54]

CD133 (prominin 1) is a five-transmembrane domain glycoprotein expressed on CD34 + stem and progenitor cells, in endothelial precursors and fetal neural stem cells. It has been detected using its glycosylated epitope known as AC133.

EpCAM (epithelial cell adhesion molecule, ESA, TROP1) is hemophilic Ca 2+ -independent cell adhesion molecule expressed on the basolateral surface of most epithelial cells.

CD90 (THY1) is a glycosylphosphatidylinositol glycoprotein anchored in the plasma membrane and involved in signal transduction. It may also mediate adhesion between thymocytes and thymic stroma.

CD44 (PGP1) is an adhesion molecule that has pleiotropic roles in cell signaling, migration and homing. It has multiple isoforms, including CD44H, which exhibits high affinity for hyaluronate and CD44V which has metastatic properties.

CD24 (HSA) is a glycosylated glycosylphosphatidylinositol-anchored adhesion molecule, which has co-stimulatory role in B and T cells.

CD200 (OX-2) is a type 1 membrane glycoprotein, which delivers an inhibitory signal to immune cells including T cells, natural killer cells and macrophages.

ALDH is a ubiquitous aldehyde dehydrogenase family of enzymes, which catalyzes the oxidation of aromatic aldehydes to carboxyl acids. For instance, it has a role in conversion of retinol to retinoic acid, which is essential for survival. [55] [56]

The first solid malignancy from which CSCs were isolated and identified was breast cancer and they are the most intensely studied. Breast CSCs have been enriched in CD44 + CD24 −/low , [57] SP [58] and ALDH + subpopulations. [59] [60] Breast CSCs are apparently phenotypically diverse. CSC marker expression in breast cancer cells is apparently heterogeneous and breast CSC populations vary across tumors. [61] Both CD44 + CD24 − and CD44 + CD24 + cell populations are tumor initiating cells however, CSC are most highly enriched using the marker profile CD44 + CD49f hi CD133/2 hi . [62]

CSCs have been reported in many brain tumors. Stem-like tumor cells have been identified using cell surface markers including CD133, [63] SSEA-1 (stage-specific embryonic antigen-1), [64] EGFR [65] and CD44. [66] The use of CD133 for identification of brain tumor stem-like cells may be problematic because tumorigenic cells are found in both CD133 + and CD133 − cells in some gliomas and some CD133 + brain tumor cells may not possess tumor-initiating capacity. [65]

CSCs were reported in human colon cancer. [26] For their identification, cell surface markers such as CD133, [26] CD44 [67] and ABCB5, [68] functional analysis including clonal analysis [69] and Aldefluor assay were used. [70] Using CD133 as a positive marker for colon CSCs generated conflicting results. The AC133 epitope, but not the CD133 protein, is specifically expressed in colon CSCs and its expression is lost upon differentiation. [71] In addition, CD44 + colon cancer cells and additional sub-fractionation of CD44 + EpCAM + cell population with CD166 enhance the success of tumor engraftments. [67]

Multiple CSCs have been reported in prostate, [72] lung and many other organs, including liver, pancreas, kidney or ovary. [55] [73] In prostate cancer, the tumor-initiating cells have been identified in CD44 + [74] cell subset as CD44 + α2β1 + , [75] TRA-1-60 + CD151 + CD166 + [76] or ALDH + [77] cell populations. Putative markers for lung CSCs have been reported, including CD133 + , [78] ALDH + , [79] CD44 + [80] and oncofetal protein 5T4 + . [81]

Metastasis is the major cause of tumor lethality. However, not every tumor cell can metastasize. [82] This potential depends on factors that determine growth, angiogenesis, invasion and other basic processes.

Epithelial-mesenchymal transition Edit

In epithelial tumors, the epithelial-mesenchymal transition (EMT) is considered to be a crucial event. [83] EMT and the reverse transition from mesenchymal to an epithelial phenotype (MET) are involved in embryonic development, which involves disruption of epithelial cell homeostasis and the acquisition of a migratory mesenchymal phenotype. [84] EMT appears to be controlled by canonical pathways such as WNT and transforming growth factor β. [85]

EMT's important feature is the loss of membrane E-cadherin in adherens junctions, where β-catenin may play a significant role. Translocation of β-catenin from adherens junctions to the nucleus may lead to a loss of E-cadherin and subsequently to EMT. Nuclear β-catenin apparently can directly, transcriptionally activate EMT-associated target genes, such as the E-cadherin gene repressor SLUG (also known as SNAI2). [86] Mechanical properties of the tumor microenvironment, such as hypoxia, can contribute to CSC survival and metastatic potential through stabilization of hypoxia inducible factors through interactions with ROS (reactive oxygen species). [87] [88]

Tumor cells undergoing an EMT may be precursors for metastatic cancer cells, or even metastatic CSCs. [89] [82] In the invasive edge of pancreatic carcinoma, a subset of CD133 + CXCR4 + (receptor for CXCL12 chemokine also known as a SDF1 ligand) cells was defined. These cells exhibited significantly stronger migratory activity than their counterpart CD133 + CXCR4 − cells, but both showed similar tumor development capacity. [90] Moreover, inhibition of the CXCR4 receptor reduced metastatic potential without altering tumorigenic capacity. [91]

Two-phase expression pattern Edit

In breast cancer CD44 + CD24 −/low cells are detectable in metastatic pleural effusions. [25] By contrast, an increased number of CD24 + cells have been identified in distant metastases in breast cancer patients. [92] It is possible that CD44 + CD24 −/low cells initially metastasize and in the new site change their phenotype and undergo limited differentiation. [93] The two-phase expression pattern hypothesis proposes two forms of cancer stem cells - stationary (SCS) and mobile (MCS). SCS are embedded in tissue and persist in differentiated areas throughout tumor progression. MCS are located at the tumor-host interface. These cells are apparently derived from SCS through the acquisition of transient EMT (Figure 7). [94]

CSCs have implications for cancer therapy, including for disease identification, selective drug targets, prevention of metastasis and intervention strategies.

Treatment Edit

CSCs are inherently more resistant to chemotherapeutic agents. There are 5 main factors that contribute to this: [95]

1. Their niche protects them from coming into contact with large concentrations of anti-cancer drugs. 2. They express various transmembrane proteins, such as MDR1 and BCRP, that pump drugs out of the cytoplasm. 3. They divide slowly, like adult stem cells tend to do, and are thus not killed by chemotherapeutic agents that target rapidly replicating cells via damaging DNA or inhibiting mitosis. 4. They upregulate DNA damage repair proteins. 5. They are characterized by an overactivation of anti-apoptotic signaling pathways.

After chemotherapy treatment, surviving CSCs are able to repopulate the tumor and cause a relapse. Additional treatment targeted at removing CSCs in addition to cancerous somatic cells must be used to prevent this.

Targeting Edit

Selectively targeting CSCs may allow treatment of aggressive, non-resectable tumors, as well as prevent metastasis and relapse. The hypothesis suggests that upon CSC elimination, cancer could regress due to differentiation and/or cell death. [ citation needed ] The fraction of tumor cells that are CSCs and therefore need to be eliminated is unclear. [96]

Studies looked for specific markers [25] and for proteomic and genomic tumor signatures that distinguish CSCs from others. [97] In 2009, scientists identified the compound salinomycin, which selectively reduces the proportion of breast CSCs in mice by more than 100-fold relative to Paclitaxel, a commonly used chemotherapeutic agent. [98] Some types of cancer cells can survive treatment with salinomycin through autophagy, [99] whereby cells use acidic organelles such as lysosomes to degrade and recycle certain types of proteins. The use of autophagy inhibitors can kill cancer stem cells that survive by autophagy. [100]

The cell surface receptor interleukin-3 receptor-alpha (CD123) is overexpressed on CD34+CD38- leukemic stem cells (LSCs) in acute myelogenous leukemia (AML) but not on normal CD34+CD38- bone marrow cells. [101] Treating AML-engrafted NOD/SCID mice with a CD123-specific monoclonal antibody impaired LSCs homing to the bone marrow and reduced overall AML cell repopulation including the proportion of LSCs in secondary mouse recipients. [102]

A 2015 study packaged nanoparticles with miR-34a and ammonium bicarbonate and delivered them to prostate CSCs in a mouse model. Then they irradiated the area with near-infrared laser light. This caused the nanoparticles to swell three times or more in size bursting the endosomes and dispersing the RNA in the cell. miR-34a can lower the levels of CD44. [103] [104]

A 2018 study identified inhibitors of the ALDH1A family of enzymes and showed that they could selectively deplete putative cancer stem cells in several ovarian cancer cell lines. [105]

The design of new drugs for targeting CSCs requires understanding the cellular mechanisms that regulate cell proliferation. The first advances in this area were made with hematopoietic stem cells (HSCs) and their transformed counterparts in leukemia, the disease for which the origin of CSCs is best understood. Stem cells of many organs share the same cellular pathways as leukemia-derived HSCs.

A normal stem cell may be transformed into a CSC through dysregulation of the proliferation and differentiation pathways controlling it or by inducing oncoprotein activity.

BMI-1 Edit

The Polycomb group transcriptional repressor Bmi-1 was discovered as a common oncogene activated in lymphoma [106] and later shown to regulate HSCs. [107] The role of Bmi-1 has been illustrated in neural stem cells. [108] The pathway appears to be active in CSCs of pediatric brain tumors. [109]

Notch Edit

The Notch pathway plays a role in controlling stem cell proliferation for several cell types including hematopoietic, neural and mammary [110] SCs. Components of this pathway have been proposed to act as oncogenes in mammary [111] and other tumors.

A branch of the Notch signaling pathway that involves the transcription factor Hes3 regulates a number of cultured cells with CSC characteristics obtained from glioblastoma patients. [112]

Sonic hedgehog and Wnt Edit

These developmental pathways are SC regulators. [113] [114] Both Sonic hedgehog (SHH) and Wnt pathways are commonly hyperactivated in tumors and are necessary to sustain tumor growth. However, the Gli transcription factors that are regulated by SHH take their name from gliomas, where they are highly expressed. A degree of crosstalk exists between the two pathways and they are commonly activated together. [115] By contrast, in colon cancer hedgehog signalling appears to antagonise Wnt. [116]

Sonic hedgehog blockers are available, such as cyclopamine. A water-soluble cyclopamine may be more effective in cancer treatment. DMAPT, a water-soluble derivative of parthenolide, induces oxidative stress and inhibits NF-κB signaling [117] for AML (leukemia) and possibly myeloma and prostate cancer. Telomerase is a study subject in CSC physiology. [118] GRN163L (Imetelstat) was recently started in trials to target myeloma stem cells.

Wnt signaling can become independent of regular stimuli, through mutations in downstream oncogenes and tumor suppressor genes that become permanently activated even though the normal receptor has not received a signal. β-catenin binds to transcription factors such as the protein TCF4 and in combination the molecules activate the necessary genes. LF3 strongly inhibits this binding in vitro, in cell lines and reduced tumor growth in mouse models. It prevented replication and reduced their ability to migrate, all without affecting healthy cells. No cancer stem cells remained after treatment. The discovery was the product of "rational drug design", involving AlphaScreens and ELISA technologies. [119]


Ragulator is a GEF for the rag GTPases that signal amino acid levels to mTORC1

The mTOR Complex 1 (mTORC1) pathway regulates cell growth in response to numerous cues, including amino acids, which promote mTORC1 translocation to the lysosomal surface, its site of activation. The heterodimeric RagA/B-RagC/D GTPases, the Ragulator complex that tethers the Rags to the lysosome, and the v-ATPase form a signaling system that is necessary for amino acid sensing by mTORC1. Amino acids stimulate the binding of guanosine triphosphate to RagA and RagB but the factors that regulate Rag nucleotide loading are unknown. Here, we identify HBXIP and C7orf59 as two additional Ragulator components that are required for mTORC1 activation by amino acids. The expanded Ragulator has nucleotide exchange activity toward RagA and RagB and interacts with the Rag heterodimers in an amino acid- and v-ATPase-dependent fashion. Thus, we provide mechanistic insight into how mTORC1 senses amino acids by identifying Ragulator as a guanine nucleotide exchange factor (GEF) for the Rag GTPases.

Copyright © 2012 Elsevier Inc. All rights reserved.

Figures

Figure 1. HBXIP and C7orf59 are components…

Figure 1. HBXIP and C7orf59 are components of an expanded Ragulator complex

A) Recombinant epitope-tagged…

Figure 2. HBXIP and C7orf59 are necessary…

Figure 2. HBXIP and C7orf59 are necessary for TORC1 activation by amino acids and localization…


Lactate Is a Natural Suppressor of RLR Signaling by Targeting MAVS

RLR-mediated type I IFN production plays a pivotal role in elevating host immunity for viral clearance and cancer immune surveillance. Here, we report that glycolysis, which is inactivated during RLR activation, serves as a barrier to impede type I IFN production upon RLR activation. RLR-triggered MAVS-RIG-I recognition hijacks hexokinase binding to MAVS, leading to the impairment of hexokinase mitochondria localization and activation. Lactate serves as a key metabolite responsible for glycolysis-mediated RLR signaling inhibition by directly binding to MAVS transmembrane (TM) domain and preventing MAVS aggregation. Notably, lactate restoration reverses increased IFN production caused by lactate deficiency. Using pharmacological and genetic approaches, we show that lactate reduction by lactate dehydrogenase A (LDHA) inactivation heightens type I IFN production to protect mice from viral infection. Our study establishes a critical role of glycolysis-derived lactate in limiting RLR signaling and identifies MAVS as a direct sensor of lactate, which functions to connect energy metabolism and innate immunity.

Keywords: MAVS RLR signaling glucose metabolism interferon lactate.


Compartmentalization of telomeric protein complexes

Structural, temporal and developmental variation greatly impact on the assembly and disassembly of the various sub-complexes that make up the dynamic telomere interactome. While numerous studies have been carried out to elucidate protein-protein interactions and telomere localizations of multiple factors within the interactome (for example, TRF1, TIN2 and TRF2), surprisingly little is known regarding the subcellular localization and regulated targeting of core telomere proteins.

Proteins of the telosome have been found in cellular locations other than the telomeres. For example, TRF2 and RAP1 have been shown to associate with the Epstein-Barr virus origin of replication [90], and TRF2 can be recruited to intra-satellite double-strand breaks when the damage level is high [91]. The growth status of human cells may influence the localization of TIN2 [92]. In growth-arrested epithelial cells, TIN2 was found to migrate into non-telomeric domains that contained the protein HP1, a marker of heterochromatin. It is possible that different complexes may form under these different conditions.

Recent studies have indicated for the first time the importance of nuclear export and spatial control of telomeric proteins in telomere maintenance in mammalian cells, as endogenous TIN2, TPP1 and POT1 have been found to localize in both the cytoplasm and the nucleus [93]. In addition, as determined by bimolecular fluorescence complementation assays [93, 94], different pairs of telomeric proteins appear to interact with each other in different cellular compartments. Whereas TIN2-TRF2 interaction takes place exclusively in the nucleus (including at telomeres), TIN2-TPP1 and TPP1-POT1 interactions occur in both the cytoplasm and nucleus. These results suggested telomere protein subcomplex formation in the cytoplasm. Interestingly, a nuclear export signal (NES) has been identified on TPP1 that directly controls the amount of TPP1 and POT1 in the nucleus. This NES resides next to the POT1-recruitment domain on TPP1, raising the possibility that interaction and nuclear localization of the TPP1-POT1 complex may be linked.

Binding of TIN2 to TPP1 promotes nuclear localization of TPP1 and POT1, by a mechanism yet to be determined [93]. The finding that TIN2 promotes nuclear retention of TPP1 and POT1 suggests that TIN2 plays a dual role in telosome assembly. While acting as a molecular tether for telosome subunits, TIN2 also ensures nuclear targeting and assembly of the entire complex. It would be of great interest to determine whether there exist other signaling pathways that control the nuclear import and export of telomeric complexes. Unexpectedly, disrupting TPP1 nuclear export can result in telomeric DNA damage response and telomere length disregulation [93]. This underlines the importance of spatial control of telomeric complexes, such that too much TPP1 in the nucleus may be detrimental to cells, and TPP1 nuclear export may regulate the concentration of TPP1-POT1 in the nucleus. These findings suggest that coordinated interactions among TIN2, TPP1 and POT1 in the cytoplasm could regulate the assembly and function of the telosome in the nucleus.


Materials and Methods

Human cell culture

Flp-In 293 T-REx (Thermo Fisher Scientific) and HEK (human embryonic kidney) cells were grown in Dulbecco's modified Eagle's medium (DMEM) high glucose 5 g/l (Sigma-Aldrich) supplemented with 10% heat inactivated fetal bovine serum (FBS). The parental cell line was grown with the addition of 100 μg/ml Zeocin (Invitrogen) and 15 μg/ml blasticidin (Thermo Fisher Scientific). After generation of stable cell lines, Zeocin™ was replaced by 100 μg/ml hygromycin. Stable cell lines were seeded at a density of 1.6e4 cells/cm 2 , allowed to attach to the culture dish for 24 h, and then induced by adding 1 μg/ml tetracycline (Sigma-Aldrich) in ethanol directly to the medium. After 24 additional hours, 50 μM biotin (Sigma-Aldrich) of a 10 mM stock solution in water was added.

NTR-BirA* and cargo-BirA* plasmid and stable cell line generation

For the generation of plasmids for genomic integration into the Flp-In T-Rex cells, the Gateway Technology (Invitrogen) was used. Expression clones were generated by combining a destination vector (pcDNA5-pDEST-BirA*-FLAG-N-term or pcDNA5-pDEST-BirA*-FLAG-C-term) with an entry clone. The following entry clones were purchased from the human ORFeome collection either as cDNA or as entry clone: IMA1 (BC005978), IMA5 (BC002374), IMA6 (BC047409), KPNB1 (BC003572), IPO4 (BC136759), IPO5 (BC001497), IPO11 (BC033776), IPO13 (BC008194), TNPO1 (BC040340.1), TNPO2 (BC072420), XPO1 (BC032847), XPO2 (BC109313), XPO7 (BC030785), RAN (BC014901), NTF2 (BC002348.2), NXT1 (BC003410), NXT2 (BC014888.1), APC2 (BC011656), CSN4 (BC093007), DKC1 (BC009928), EIF3D (BC093686), EXOSC10 (BC073788), HAUS2 (BC010903), INTS11 (BC007978), RPAC2 (BC000889), RPC3 (BC002586), SRC2 (BC114383). Entry clones were generated using the donor vector pDONR. Purchased entry clones or cDNAs with sequence disagreements with current sequences available at ENSEMBL were changed using the QuickChange II Site-Directed Mutagenesis Kit (Agilent). Required attB sites to generate an entry clone were added by PCR using the Phusion High-Fidelity DNA Polymerase (Thermo Fisher Scientific). Stable cell lines were generated using X-tremeGENE 9 DNA Transfection Reagent (Sigma-Aldrich).

BioID affinity purification (AP)

For each experiment, 4e7 snap-frozen cells were used. The AP was performed as previously described (Coyaud et al, 2015 ). Instead of a protease inhibitor mixture, 1 mg/ml aprotinin and 0.5 mg/ml leupeptin was used. 1 μg of trypsin (Mass Spectrometry Grade, Promega) was added and incubated at 37°C for 16 h shaking at 500 rpm. Subsequently, 0.5 μg of trypsin was added and the on-bead digest continued for additional 2 h. The beads were transferred to a spin column, and the digested peptides were eluted with two times 150 μl of 50 mM ammonium bicarbonate. To remove the biotinylated peptides still bound to the beads, 150 μl of 80% ACN and 20% TFA was added, briefly mixed, and eluted this step was done twice. The ACN/TFA elutions were merged. The samples were dried using a vacuum centrifugation. The elutions were resuspended in 200 μl buffer A. The desalting and clean-up of the samples were carried out using Micro Spin Columns (Harvard Apparatus).

Protein identification by mass spectrometry and label-free quantification

The shot-gun MS experiments were performed as previously described (Mackmull et al, 2015 ). The elutions of biotinylated peptides were measured using the same settings but with a stepwise gradient lasting 90 min. For the quantitative label-free analysis, raw files from a Orbitrap Velos Pro instrument (Thermo) were analyzed using MaxQuant (version 1.5.3.28 Cox & Mann, 2008 ). MS/MS spectra were searched against the Human Swiss-Prot entries of the UniProt KB (database release 2016_09, 19,594 entries) using the Andromeda search engine (Cox et al, 2011 ). The protein sequences of BirA* and streptavidin were added to the database. The search criteria were set as follows: Full tryptic specificity was required (cleavage after lysine or arginine residues, unless followed by proline) three missed cleavages were allowed oxidation (M), acetylation (protein N-term), and biotinylation (K) were applied as variable modifications, if applicable, mass tolerance of 20 ppm (precursor) and 0.5 Da (fragments). The retention times were matched between runs, using a time window of 3 min. The reversed sequences of the target database were used as decoy database. Peptide and protein hits were filtered at a false discovery rate of 1% using a target-decoy strategy (Elias & Gygi, 2007 ). Additionally, only protein groups identified by at least two unique peptides were retained. The intensity per protein of the proteinGroups.txt output of MaxQuant was used for further analysis. All comparative analyses were performed using R version 3.2.2. (R Core Team, 2012 ). The R packages MSnbase (Gatto & Lilley, 2012 ) was used for processing proteomics data, and the included package imputeLCMD was used for imputing missing values based on the definitions for missing at random (MAR) and missing not at random (MNAR) values. MNAR were defined for each pairwise comparison as values that were (i) missing in four out of four, or three out of four biological replicates in one sample group, and (ii) present in at least three out of four biological replicates in the second sample group. Because of their non-random distribution across samples, these values were considered as underlying biological difference between sample groups. MNAR values were computed using the method “MinDet” by replacing values with minimal values observed in the sample. MAR values were consequently defined for each pairwise comparison as values that were missing in one out of four biological replicates per sample group. MAR values were imputed based on the method “knn” (k-nearest neighbors Gatto & Lilley, 2012 ). All the other cases (e.g., protein groups that had two or fewer values in both sample groups) were filtered out because of the lack of sufficient information to perform robust statistical analysis. The data were quantile normalized to reduce technical variations (Gatto & Lilley, 2012 ). Protein abundance variation was evaluated using the Limma package (Smyth et al, 2005 ). Differences in protein abundances were statistically determined using the Student's t-test (one-sided) with variances moderated by Limma's empirical Bayes method.

The pairwise comparisons of the AP of NTRs to the control data set were used to define the NIP and background proteome. The comparisons were done one-sided and separately for the NIP and the background proteome. The Sime's adjusted P-values were calculated for each protein using the R cherry package (Goeman & Solari, 2011 ). The minimum Sime's P-value per protein, which defines if this protein is significant in the NIP or background proteome, was adjusted using the method of Benjamini and Hochberg. This idea was adapted from (Lun & Smyth, 2014 ): The minimum Sime's P-value will be small if the protein of interest is truly significantly differentially abundant in at least one of the comparisons to the control conditions under consideration.

The specificity score per protein in each experiment was calculated by multiplying all significant P-values (P-values < 0.01) of the selected protein obtained in the pairwise comparisons (P-values of those pairwise comparisons where the protein was identified as enriched). Subsequently, an average adjusted fold change (FC) was calculated by using all FC corresponding to the previously considered P-values. The Fisher transformation was used to define a P-value per experiment. The Fisher P-values were adjusted using the method of Benjamini and Hochberg.

Network analysis

In order to identify clusters of proteins displaying specific interaction with NTRs, we applied a network propagation approach similar to the one described in (Vanunu et al, 2010 ). First, we mapped all the proteins quantified in our experiments to the human STRING protein–protein interaction network (v10, combined score > 0.7, 15,478 nodes). The network was then converted into an adjacency matrix and normalized using Laplacian transformation. Specificity scores where propagated to adjacent nodes by network propagation using the sharing coefficient (α) of 0.5 and 30 iterations. We observed that the standard network propagation algorithm suffered from gene-specific biases created by their network neighborhood (“topology bias”). For example, genes with many neighbors will generally tend to accumulate higher scores independent of their initial specificity scores. Therefore, we devised an additional step of topology bias-correction after the standard network propagation (Appendix Fig S3). We computed each node's topology bias by applying the mean initial score from each sample to all the nodes in the network and then propagating scores using the same parameters (α = 0.5 and iterations = 30). If there was no topology bias, all nodes should have the same scores after this procedure. Resulting propagated scores were used as correction factors for each node and thus subtracted from the original propagated scores. For each NTR sample, proteins were ranked according to their smoothed, topology bias corrected scores, and the top 2% proteins for each sample were used for the identification of highly interconnected subnetworks using the Cytoscape (Cline et al, 2007 ) App MCODE (Bader & Hogue, 2003 ).

Gene Ontology enrichment analysis

Gene Ontology enrichment analysis was performed on ranked list of proteins using log-transformed P-values or specificity scores using GOrilla (Eden et al, 2009 ) followed by GO term redundancy reduction performed by REVIGO (Supek et al, 2011 ).

Staining of NTR-BirA* cell lines

Cells were grown directly on glass slides, previously coated with poly-lysine, in PBS for 4 h. The cells were induced and treated with biotin as described above. Between each of the incubation steps at room temperature, the glass slides were washed twice with PBS. First, the cells were fixed with 2% PFA in PBS for 15 min and then permeabilized with 0.4% triton in PBS for additional 15 min. Blocking was performed using 2% BSA and 2% FBS in PBS for 1 h. To visualize the nuclear envelope, cells were incubated with anti-FLAG (1:500, Sigma-Aldrich, #F1804) in blocking buffer for 1 h. As a secondary antibody, an anti-mouse conjugated to Alexa Fluor 488 (1:1,000, Life Technologies, #A21204) was used also for 1 h. All following steps were done with minimum light exposure. Streptavidin covalently bound to Alexa Fluor 647 (1:1,000, Thermo Fisher Scientific, #S21374) in 0.1% BSA in PBS was used to incubate the cells for 10 min. To preserve the stained cells, all glass slides were mounted upside down on a microscope slide using one drop of mounting medium (Thermo Fisher Scientific), dried over night at room temperature, and afterward stored at −20°C.

Depletion of NTRs by siRNA

Cells were allowed to attach to the dish for 24 h and then transfected with IMA1-specific siRNA (#s7922), IMA5-specific siRNA (#s223980), IPO11-specific siRNA (#s27652), IPO4-specific siRNA (#s36154), IPO5-specific siRNA (#s7935), TNPO2-specific siRNA (#s26880), GAPDH-specific siRNA (#4390849), and negative control no. 1 siRNA (#4390843) purchased from Ambion by Life Technologies. 25 pmol of siRNA (final concentration of 10 nM) was transfected using 7.5 μl of lipofectamine RNAiMAX (Thermo Fisher), according to the manufacturer's protocol. The cells were incubated with the different siRNAs for 72 h. Each treatment was performed in three biological replicates.

Quantitative PCR (qPCR)

Total RNA was extracted using the RNeasy Plus Mini Kit (Qiagen) following the manufacturer's protocol. For cDNA synthesis, the QuantiTect Reverse Transcription Kit (Qiagen) was used. Quantitative real-time PCR (qRT–PCR) was used to examine the relative expression of IMA1 (5′-ttatcctggatgccatttcaa-3′, 5′-agcctccacattcttcaatca-3′), IMA5 (5′-gctagtactgtgccgcttcc-3′, 5′-gcaggtacagattgcagtcatc-3′), IPO11 (5′-caaacggtttccatggatct-3′, 5′-ctgtgtctcccactgcttca-3′), IPO4 (5′-cacctctcagcccagttca-3′, 5′-ctcagggacagccctgtaag-3′), IPO5 (5′-tgggacagatggctacagatt-3′, 5′-acgttgattgccttggtctt-3′), TNPO2 (5′-atcctggatggcaacaagag-3′, 5′-ttcccaaaggcaaagacaag-3′), and normalized to GAPDH (5′-ggtctcctctgacttcaaca-3′, 5′-agccaaattcgttgtcatac-3′). For qRT–PCR analysis, 25 ng of cDNA was used in a 20-μl reaction consisting of 11 μl of SYBR ® Green PCR Master Mix (Applied Biosystems), 10 μM forward and reverse primer, and water. Thermocycling was carried out using the StepOne™ (Applied Biosystems) and each sample was measured in technical duplicate. Relative mRNA levels were calculated using the ΔΔCt method (Livak & Schmittgen, 2001 ). Significant changes were assessed by applying a Welch two sample t-test on the ΔCt values for treatment and control samples (Yuan et al, 2006 ).

Mutation of motifs

Predicted motifs by DILIMOT or cNLS Mapper were removed using the Q5 Site-Directed Mutagenesis Kit [New England Biolabs (NEB)] following the manufacturer's protocol and replaced with a flexible linker (5′-ggtggcggaggtagcggaggcggtggatcg-3′). Transient transfected cells were generated using X-tremeGENE 9 DNA Transfection Reagent (Sigma-Aldrich).

Image analysis

Image analysis was performed using CellProfiler (Carpenter et al, 2006 ). Nuclei were segmented using CellProfiler's automated maximum correlation thresholding (MCT) algorithm (Padmanabhan et al, 2010 ). To ensure that the segmented nuclei only cover pixels inside the nucleoplasm, the nuclear masks were shrunk by 3 pixels. Next, a ring of 20 pixel width around each nucleus was generated marking the cytoplasm. The area covered by both the cytoplasm and the nucleus was termed cell. The mean intensity of the protein was measured in the cell, nucleus, and cytoplasm region. The measured intensity in the cell was used to filter out cells that had too low protein expression (< 0.08) in the transient transfected cells. The ratio of the mean intensities in the nucleus and cytoplasm was the final readout of the analysis.

Data availability

The data set generated in this study is available in the following database:


1 Answer 1

Short answer
Axons can be over a meter long, but dendrites are never that long. Distance in the body is covered by axons. The dendritic part of skin receptors is generally considered to be the receptor part and the receptor part only. The elongated structure leading to the soma, as well as the axonal part to the spinal cord are generally considered to be one and the same axon, the soma being attached to the axon in this case.

Background
The sensory receptors in the skin have their cell bodies located in the dorsal root ganglia situated adjacent of the spinal cord (Bourinet et al., 2014). In the case of pain receptors it is shown in Fig. 1. The same basic structure holds for the touch receptors in the skin.


Fig. 1. Signaling of pain receptors in the skin. source: Bourinet et al. (2014)

The cell bodies are located in the dorsal root ganglia of the spinal cord. Action potentials are sent from the dendritic region in the skin to the cell body in the ganglion. From there on it is transmitted to spinal interneurons in the spinal cord and up to the brain.

The nomenclature of these sensory neurons is generally as follows: in the cell body the axon splits in two. The afferent part that transmits the pulses from the skin to the cell body and the part that transmits the signal from the soma away from the cell body to the spinal cord are both considered as a part of the axon. The dendritic region is generally considered to be the nerve ending (Fig. 2C). So the axons of peripheral sensory neurons, as well as motor neurons for that matter, can indeed be over a meter long (Lodish et al., 2000).


Fig. 2. Types of neurons. Arrows indicate the direction of conduction of action potentials in axons. (A) Multipolar interneurons. The dendrites receive signals at synapses with several hundred other neurons, and there is a single long branching axon. (B) A motor neuron innervating a muscle cell. Typically, motor neurons have a single long axon extending from the cell body to the effector cell. (C) A sensory neuron in which the axon branches just after it leaves the cell body. The peripheral branch carries the nerve impulse from the receptor cell to the cell body, which is located in the dorsal root ganglion near the spinal cord the central branch carries the impulse from the cell body to the spinal cord or brain. Both branches are structurally and functionally axons, except at their terminal portions, even though the peripheral branch conducts impulses toward, rather than away from, the cell body. source: Lodish et al. (2000)


North American river otter News

North American river otters, also called Canadian otters, have long, muscular, streamlined bodies with short legs and fully webbed feet bearing non-retractable claws. Their small heads widen to long necks and shoulders, and they have flattened, well-muscled tails. These otters have brown-to-gray fur, and their undersides are a lighter, silvery shade. Their dense, short under-fur is overlain by darker, coarse guard hairs that help repel water.

The river otter's eyes and ears are located high on its head for surface swimming. A third eyelid, or nictitating membrane, protects the eye and allows the otter to see when swimming underwater. The otter's ears and nostrils close underwater.

River otters have long, stiff and highly sensitive facial whiskers that aid in locating and capturing prey. The otters typically capture prey in their mouths but occasionally use their thumbs and forepaws to grasp and manipulate prey. Like other carnivores, their teeth are well adapted for grinding and crushing.

The tail is highly muscular and comprises up to 40 percent of the otter's total body length. With the tail's strong, undulating movement, a river otter propels itself through the water as fast as 8 miles (13 kilometers) per hour and can easily dive to 36 feet (11 meters) or more. River otters use their powerful hind feet to help with propulsion and their small, dexterous front feet for paddling through the water.

Adult river otters weigh 10 to 33 pounds (4.5 to 15 kilograms) and are about 2.5 to 5 feet (76 to 152 centimeters) in length. Females are roughly one-third the size of males.

River otters are found throughout most of North America from the Rio Grande to Canada and Alaska, except for in arid deserts and the treeless Arctic. They live in riparian zones, often in the same areas as beavers. Their aquatic habitats can be both marine and fresh water: streams, rivers, lakes, ponds and marshes.

They prefer unpolluted water with a minimal human disturbance. An extremely adaptable animal, otters tolerate hot and cold climates, as well as high elevations and lowland coastal waters.

River otters exhibit a variety of vocalizations, ranging from whistles and buzzes to twitters, staccato chuckles, chirps and growls. When threatened or frightened, they emit a hair-raising scream that can be heard up to 1.5 miles (2.4 kilometers) across the water.

River otters leave scent marks on vegetation within their home range. Scent marking is done by either urinating/defecating or by emitting a strong, musky odor from the paired scent glands near the base of the tail.

River otters eat mostly aquatic organisms, including fish, frogs, crayfish, turtles, insects and some small mammals. They hunt singly or in pairs and although otters generally forage in water, they are equally at home on land, sometimes traveling between 10 and 18 miles (16 and 29 kilometers) in search of food.

North American river otters get their boundless energy from their very high metabolism, which also requires that they eat a great deal during the day. At the Smithsonian's National Zoo, they eat a prepared meat diet and several types of fish. They also receive mice, carrots, hard-boiled eggs, clams, crayfish, dry kibble, crickets and live fish for variety and enrichment.

A North American river otter's home range can be as large as 30 square miles (78 square kilometers), but a typical territory is 3 to 15 square miles (4.8 to 24 square kilometers). That home range shrinks drastically during breeding and rearing season.

While river otters tend to live alone or in pairs, they often socialize in groups and are known for their playful behavior. Their long, agile bodies enable them to quickly twist , turn, roll and dive, and they are frequently seen sliding or burrowing in the mud or snow. There is evidence that river otters' play activities strengthen social bonds, improve hunting techniques and scent mark territories. They spend a significant portion of the day scent marking territory by urinating, defecating, scratching and rubbing their scent glands on rocks and trees.

Information about river otter breeding and reproductive and social behavior is varied due to the difficulty of studying these animals in the wild. Some studies indicate that river otters pair for only a few months during the breeding season and have no further strong bonds. Other research maintains that river otters mate for life.

Different studies have placed the breeding season in winter, late spring and summer. What is clear is that there is a delayed implantation of nine to 11 months, with actual gestation taking about 60 days. Otter births occur most frequently in March or April.

Female otters prepare dens that they keep scrupulously clean. The den is usually dug into the bank of a stream but can be an old beaver's lodge, muskrat house or hollow tree. Young are born between April and May and arrive silky black, blind, toothless and totally helpless. They weigh about 4 to 6 ounces (113 to 170 grams) at birth and measure 8 to 11 inches (20 to 28 centimeters). The male otter is generally chased away until the young are weaned and old enough to leave the riverbank, at which time they may return and help raise the pups.

River otters stay in family groups during the summer and early fall. Pups grow rapidly and emerge from the den at about 2 months of age. At this point, they eat solid food but are not completely weaned for another month or two. While young otters swim naturally, the mother must coax them into the water for their first swim. They remain as a family unit for seven to eight months or until the birth of a new litter. Otters reach sexual maturity at 2 to 3 years of age.

Provided it survives its first year of life, a typical North American river otter will live to the age of 12, with some surviving longer. The oldest living river otter on record was 27 years old.

North American river otters are likely the most numerous of the otter species. Because they are at the top of their food chain, they have few predators. However, water pollution, uncontrolled trapping and severe habitat loss have reduced the number of river otters.

For years, river otters have been hunted for their fur, and their pelts are still an important source of income for many people in Canada. As recently as the mid-1980s, more than 30,000 pelts were harvested annually. Today, accidental trappings in beaver traps constitute the most otter fatalities.

Regionally extinct throughout the Midwest and heavily populated areas in the east, several states have recently begun reintroduction programs. It is encouraging to note that with these conservation programs, regulations on trapping and the improvement of water quality, the river otters are finally making a comeback in certain wetland areas.

Because they have a low tolerance for polluted water, river otters are considered by some naturalists to be a good indicator, or "keystone," species of the quality of aquatic habitats. They are found at the top of the food chain, and there is some evidence that their birth rates are reduced when pollution levels—including toxic chemicals, heavy metals, pesticides and agricultural wastes—build up.

River otters have been blamed for decimating game fish populations and are seen as a pest to eradicate by many game fishermen. Recent research, however, indicates that otters prefer slower moving, easier to catch fish, such as suckers and catfish found along river bottoms, and pose no threat to game fish.


Causes of Cancer

Science Photo Library - STEVE GSCHMEISSNER/Getty Images

Cancer results from the development of abnormal properties in normal cells that enable them to grow excessively and spread to other locations. This abnormal development can be caused by mutations that occur from factors such as chemicals, radiation, ultraviolet light, and chromosome replication errors. These mutagens alter DNA by changing nucleotide bases and can even change the shape of DNA. The altered DNA produces errors in DNA replication and protein synthesis. These changes influence cell growth, cell division, and cell aging.

Viruses also have the ability to cause cancer by altering cell genes. Cancer viruses change cells by integrating their genetic material with the host cell's DNA. The infected cell is regulated by the viral genes and gains the ability to undergo abnormal new growth. Several viruses have been linked to certain types of cancer in humans. The Epstein-Barr virus has been linked to Burkitt's lymphoma, the hepatitis B virus has been linked to liver cancer, and the human papillomaviruses have been linked to cervical cancer.


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