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W2034196924 abstract "This Essay explores the notion that specialized cells have unique vulnerabilities to environmental contingencies that microRNAs help to counteract. Given the ease with which new microRNAs evolve, they may serve as ideal facilitators for the emergence of new cell types. This Essay explores the notion that specialized cells have unique vulnerabilities to environmental contingencies that microRNAs help to counteract. Given the ease with which new microRNAs evolve, they may serve as ideal facilitators for the emergence of new cell types. Invariant laws of nature impact the general forms and functions of organisms; they set the channels in which organic design must evolve. But the channels are so broad relative to the details that fascinate us! The physical channels do not specify arthropods, annelids, mollusks, and vertebrates, but, at most, bilaterally symmetrical organisms based upon repeated parts … When we set our focus upon the level of detail that regulates most common questions about the history of life, contingency dominates and the predictability of general form recedes to an irrelevant background… Stephen Jay Gould, Wonderful Life: The Burgess Shale and the Nature of History. Penguin Books, 1989. (pp. 289–290). Much is puzzling about microRNAs (miRNAs). They are highly accurate markers of cell identity; their profiles unambiguously distinguish among cellular phenotypes, including embryonic stem cells, a vast variety of precursor cells, terminally differentiated cells, and tumor types, even among closely related cancers (Lu et al., 2005Lu J. Getz G. Miska E.A. Alvarez-Saavedra E. Lamb J. Peck D. Sweet-Cordero A. Ebert B.L. Mak R.H. Ferrando A.A. et al.Nature. 2005; 435: 834-838Crossref PubMed Scopus (7734) Google Scholar). Furthermore, in surveying many miRNA profiling studies, the expression differences among certain miRNAs in various cell types are often orders-of-magnitude in contrast to the low variation of most miRNAs following environmental influences that do not change cell identity. Although there is a strong correlation between cell identity and patterns of miRNA expression, this does not mean that there are strong phenotypic effects when an individual miRNA is suppressed or knocked out. In fact the effects of miRNAs on protein levels are generally modest (Guo et al., 2010Guo H. Ingolia N.T. Weissman J.S. Bartel D.P. Nature. 2010; 466: 835-840Crossref PubMed Scopus (2861) Google Scholar), and short-circuiting nearly all miRNA biogenesis by inactivating Dicer can have surprisingly modest effects on differentiation and patterning; however, contrary experiments have also been reported (reviewed in Fineberg et al., 2009Fineberg S.K. Kosik K.S. Davidson B.L. Neuron. 2009; 64: 303-309Abstract Full Text Full Text PDF PubMed Scopus (263) Google Scholar). Although many miRNAs are highly conserved, some over the entire period of bilaterian evolution, other miRNAs are only found along a single evolutionary branch, indicating the ease with which new miRNAs are invented (Kosik, 2009Kosik K.S. Nat. Rev. Neurosci. 2009; 10: 754-759Crossref PubMed Scopus (42) Google Scholar). Finally, among the puzzling features of miRNAs are the overall increase in their variety as a function of evolutionary time, the lack of conservation of some targets, and the poorly understood relationship between targets and phenotypes. The perspective put forth here is that miRNAs serve as a reservoir to assist cells in coping with environmental contingencies. For instance, cells may at times face short-term oxygen deprivation, but a cell that is more dependent on aerobic respiration will require its own adaptive response. If miRNAs are available for environmental contingencies, then their response must be honed for the needs of specific cell types. Evolutionary change begins with mutations—not specialized cells. The ease of miRNA invention suggests that new miRNAs will create conditions for expanding cell diversity because the presence of a specific miRNA may offset vulnerabilities of specialized cells to environmental contingencies. The complete list of constituent molecules within a cell—its transcripts, proteins, lipids, metabolites, and a host of other molecules—occupy a parameter space within a range of values, which define the “cell state.” As markers of cell identity, miRNAs encode a representation of multiple cell states that all correspond to a single identity. That is, many different states comprise a single identity because cells must retain their identities in the face of both environmental changes and internal noise that can result in large variations in molecular composition. Presumably, protein levels in cells fall within certain boundaries below which there is an insufficient amount of the protein to achieve function and above which toxicity emerges. miRNAs are good candidates for setting boundary conditions upon coding transcripts to restrict protein levels within a range of values that maintain cell identity in the face of homeostatic compensatory changes. Thus, miRNAs have properties, which can hierarchically link the many parameter settings of the cell state to a phenotypic singularity known as cell identity. Cells undergoing developmental or malignant transformation reset their boundary conditions across a specified collective threshold of multiple parameters, which define a new identity. Shifting the miRNA profile during development or relaxing controls over onco-miRNAs and tumor suppressor miRNAs are associated with morphing a cell toward a new identity (Figure 1). Changes in cell identity usually occur in the context of mitosis during stem cell differentiation, reprogramming, oncogenesis, metaplasia, or pathological response to injury. Usually controls over the cell cycle are closely linked to the emergence of a new identity, a point most recently confirmed in several studies that enhance the generation of induced pluripotent stem cells by modulating cell-cycle regulators p53, p21, and p16(Ink4a)/p19(Arf) (reviewed in Puzio-Kuter and Levine, 2009Puzio-Kuter A.M. Levine A.J. Nat. Biotechnol. 2009; 27: 914-915Crossref PubMed Scopus (31) Google Scholar). The reverse and forward arrows of change in cell identity are not symmetric. Reprogramming a somatic cell to a stem cell is a rare event but potentially possible in any cell. On the other hand, pluripotency is easily lost. Beyond a defined set of growth factors required for sustaining stem cells, pluripotency exists as a state of “freedom” from other extrinsic factors (Silva and Smith, 2008Silva J. Smith A. Cell. 2008; 132: 532-536Abstract Full Text Full Text PDF PubMed Scopus (348) Google Scholar) that promote differentiation. To maintain pluripotency, the cell must minimize not only the effects of extrinsic signals but also intrinsically random fluctuations that can initiate unintended differentiation. The intermediate states through which cells travel to reach new identities are lined with traps. The concept of steering between these danger zones is called “canalization” and was introduced by C.H. Waddington, and it has been proposed that miRNAs guide a cell past epigenetic traps toward its phenotype in the face of environmental variation (Hornstein and Shomron, 2006Hornstein E. Shomron N. Nat. Genet. 2006; 38: S20-S24Crossref PubMed Scopus (447) Google Scholar). Although chromatin organization may account for the height of the barriers to identity changes (Chi and Bernstein, 2009Chi A.S. Bernstein B.E. Science. 2009; 323: 220-221Crossref PubMed Scopus (42) Google Scholar), relatively subtle balances in the constituents of a protein complex accompany differentiation. An example of this shift mediated by miRNAs occurs in vertebrate nervous system development. The development of the vertebrate nervous system provides an example of the influence of miRNAs over epigenetic factors. As precursor cells lose multipotency, a subunit switch occurs in the mammalian SWI/SNF complex, which mediates ATP-dependent chromatin remodeling (Yoo et al., 2009Yoo A.S. Staahl B.T. Chen L. Crabtree G.R. Nature. 2009; 460: 642-646Crossref PubMed Scopus (442) Google Scholar). During development, the BAF53a and BAF45a subunits within the neural-progenitor-specific complexes swap out in favor of the homologous BAF53b and BAF45b subunits to form neuron-specific complexes found in postmitotic neurons. miR-9∗ and miR-124 mediate this dynamic shift in subunit composition by binding to sequences in the 3′ untranslated region of BAF53a mRNA, repressing protein expression, and presumably changing the kinetic balance of subunits that drive complex assembly. The control elements over gene expression and the networks that link them are often discussed in terms of their role in sharpening the output and making the system robust. Because miRNAs target multiple mRNAs, they can exert distributed control over broad target fields of functionally related mRNAs as opposed to focusing their control on a small number of genes in a “final common pathway.” These networks are often specialized for specific cell types. For example, miR-21 regulates diverse mRNAs that collectively control apoptosis and proliferation, and the dysregulation of miR-21 is associated with many types of cancer (Papagiannakopoulos et al., 2008Papagiannakopoulos T. Shapiro A. Kosik K.S. Cancer Res. 2008; 68: 8164-8172Crossref PubMed Scopus (560) Google Scholar). miRNAs, including nonhomologous miRNAs, are often physically clustered in the genome, and these sets of miRNAs may target mRNAs with related biological functions at short distances in their protein-protein interaction map (Kim et al., 2009Kim Y.K. Yu J. Han T.S. Park S.Y. Namkoong B. Kim D.H. Hur K. Yoo M.W. Lee H.J. Yang H.K. Kim V.N. Nucleic Acids Res. 2009; 37: 1672-1681Crossref PubMed Scopus (391) Google Scholar). The mouse miRNA cluster, mmu-mir-183-96-182, targets Irs1, Rasa1, and Grb2, all of which are located in the insulin-signaling pathway, and these miRNAs coordinate the control of this signal transduction process (Xu and Wong, 2008Xu J. Wong C. RNA. 2008; 14: 1276-1283Crossref PubMed Scopus (80) Google Scholar). The wide variation in the glucose needs of cells suggests that the specific workings of this pathway probably differ among cell types. These specific examples have been generalized to show that coordinated miRNA targeting of closely connected genes is prevalent across pathways (Tsang et al., 2010Tsang J.S. Ebert M.S. van Oudenaarden A. Mol. Cell. 2010; 38: 140-153Abstract Full Text Full Text PDF PubMed Scopus (178) Google Scholar). Target capture by an miRNA depends on the expression level of the miRNA, the levels of all the target mRNAs, including pseudogene decoy targets (Poliseno et al., 2010Poliseno L. Salmena L. Zhang J. Carver B. Haveman W.J. Pandolfi P.P. Nature. 2010; 465: 1033-1038Crossref PubMed Scopus (1699) Google Scholar), and the affinities between them. Thus the network effects of miRNAs can only be interpreted in a particular cell if the copy numbers of all mRNA targets are known. Small changes within an miRNA/mRNA target network may broaden random variation around a threshold and, as described for the intestinal specification network in C. elegans (Raj et al., 2010Raj A. Rifkin S.A. Andersen E. van Oudenaarden A. Nature. 2010; 463: 913-918Crossref PubMed Scopus (434) Google Scholar), give rise to a variable ON/OFF expression pattern of a “master” regulatory gene within a population of cells. Disrupting a network in this manner thus leads to cell population variation and has the potential to expand the phenotypic repertoire of an organism's cells. miRNAs often operate in feedforward and feedback loops. Genome-scale mapping in C. elegans has revealed 23 such loops within the transcription circuitry (Martinez et al., 2008Martinez N.J. Ow M.C. Barrasa M.I. Hammell M. Sequerra R. Doucette-Stamm L. Roth F.P. Ambros V.R. Walhout A.J. Genes Dev. 2008; 22: 2535-2549Crossref PubMed Scopus (177) Google Scholar) including a miRNA/transcription feedback loop that sets up left-right asymmetry (Johnston et al., 2005Johnston Jr., R.J. Chang S. Etchberger J.F. Ortiz C.O. Hobert O. Proc. Natl. Acad. Sci. USA. 2005; 102: 12449-12454Crossref PubMed Scopus (216) Google Scholar). The mediation of pluripotency exit by miR-145 operates as a double-negative feedback loop with the transcription factor Oct4 (Xu et al., 2009Xu N. Papagiannakopoulos T. Pan G. Thomson J.A. Kosik K.S. Cell. 2009; 137: 647-658Abstract Full Text Full Text PDF PubMed Scopus (901) Google Scholar). The operation of this loop may generate bistability through which the cell reaches a single identity unless it crosses a barrier at which point it inevitably transitions to an alternative identity. Identity transitions via bistable states achieve discrete identities and avoid intermediate states. miR-145 continues to operate in differentiation at further stages of mesoderm development in regulating smooth muscle cell fate (Cordes et al., 2009Cordes K.R. Sheehy N.T. White M.P. Berry E.C. Morton S.U. Muth A.N. Lee T.H. Miano J.M. Ivey K.N. Srivastava D. Nature. 2009; 460: 705-710Crossref PubMed Scopus (1169) Google Scholar). Interestingly, miR-145 in smooth muscle cells maintains its functional vector toward differentiation but switches some targets through which it acts. Whether degraded or maintained as a stable duplex the miRNA is consumed, and thus its action is distinct from the catalytic effects of many protein regulators of gene expression. Thus the two limbs of the transcriptional feedback loops operate quite differently: transcription factors regulate transcription of the primary miRNA and miRNAs stoichiometrically regulate the translation of the mRNA that encodes the transcription factor. Wu and colleagues (Wu et al., 2009Wu C.I. Shen Y. Tang T. Genome Res. 2009; 19: 734-743Crossref PubMed Scopus (108) Google Scholar) have proposed that miRNAs keep the system close to the mean and set expression boundaries of transcription factors, which are otherwise noisy. The mean number of copies of different proteins in a cell might have a set point, which lies at different distances from the level of toxicity. When the range of protein levels in a cell fluctuates far from the point of toxicity, the fluctuation is better tolerated and miRNA regulation becomes extraneous. Only the extremes of protein copy number variation within an infrequently occurring long tail jeopardize the cell. However, if the mean copy number of the protein is close to the point of toxicity—and indeed, optimal function may require that the protein set point is close to the toxic level—then tight regulation is necessary, and this might be achieved by miRNAs. In this context, a modest effect of miRNAs on protein levels (Guo et al., 2010Guo H. Ingolia N.T. Weissman J.S. Bartel D.P. Nature. 2010; 466: 835-840Crossref PubMed Scopus (2861) Google Scholar) will be highly significant. PTEN appears to be an example of a gene under exquisitely fine regulation—it is targeted by numerous miRNAs—and fine changes in its dosage are critical to its cancer-forming potential (Alimonti et al., 2010Alimonti A. Carracedo A. Clohessy J.G. Trotman L.C. Nardella C. Egia A. Salmena L. Sampieri K. Haveman W.J. Brogi E. et al.Nat. Genet. 2010; 42: 454-458Crossref PubMed Scopus (404) Google Scholar). Information on the turnover of miRNAs is just emerging. Often the pairing of the prokaryotic small RNAs with a target mRNA exposes both molecules to rapid degradation (Masse et al., 2003Masse E. Escorcia F.E. Gottesman S. Genes Dev. 2003; 17: 2374-2383Crossref PubMed Scopus (553) Google Scholar). Some miRNA/mRNA duplexes appear to be highly stable as long as the identity of the cell is stable. On the other hand, in neurons (and perhaps other specialized settings will show similar phenomena), miRNA turnover is rapid. For example, the miR-183/96/182 cluster, miR-204, and miR-211 decay rapidly during dark adaptation and are transcriptionally upregulated in light-adapted retinas (Krol et al., 2010Krol J. Busskamp V. Markiewicz I. Stadler M.B. Ribi S. Richter J. Duebel J. Bicker S. Fehling H.J. Schübeler D. et al.Cell. 2010; 141: 618-663Abstract Full Text Full Text PDF PubMed Scopus (344) Google Scholar). Indeed, the specialized requirements of neurons, particularly with regard to plasticity, may utilize the miRNA system for regulation at a faster timescale than in other cells. When a small number of mRNAs are locally activated, the RISC through its component protein, MOV10 (also known as Armitage in Drosophila and SDE3 in Arabidopsis thaliana), can derepress otherwise silenced local translation (Banerjee et al., 2009Banerjee S. Neveu P. Kosik K.S. Neuron. 2009; 64: 871-884Abstract Full Text Full Text PDF PubMed Scopus (185) Google Scholar). The adaptation of miRNAs for rapid local regulation contributes to fundamental neuronal properties such as control over local translation at the synapse and hence has facilitated cell specialization. The RISC allows both the constitutive maintenance of cell identity by silencing mRNAs that are not part of the specialized cell's repertoire as well as the holding of mRNAs of an alternative identity in reserve (Lim et al., 2005Lim L.P. Lau N.C. Garrett-Engele P. Grimson A. Schelter J.M. Castle J. Bartel D.P. Linsley P.S. Johnson J.M. Nature. 2005; 433: 769-773Crossref PubMed Scopus (3784) Google Scholar), perhaps for less frequent contingencies. Maintaining a large pool of stable miRNA/mRNA duplexes rather than triggering duplex degradation at the moment of binding allows an entire control layer to lie poised for the rapid release of a networked set of mRNAs to undergo translation and achieve a smooth and coordinated identity transition. Like apoptosis, in which the cell systematically destroys itself in a highly controlled sequence of events to prevent triggering inflammatory reactions, changes in cell identity require an orderly transition so that residua from a parental cell do not create toxic interactions with an emerging daughter cell while sustaining cell function during the transition. The mapping of an miRNA profile onto cell identity—a many onto one mapping—corresponds to a phenotypic singularity within the repertoire of all possible cellular identities that the organism is capable of producing. How the miRNA profile undergoes the sweeping coordinated changes associated with a new cell identity is poorly understood. Is there a global disassembly of RISCs and loss of pre-existing miRNAs while new miRNA transcription ramps up to fill RISCs or induce their assembly with a distinct set of miRNAs? XRN is a candidate for mediating this transition. In C. elegans, active turnover is mediated by the 5′ to 3′ exoribonuclease XRN-2 to modulate activity of the mature miRNA (Chatterjee and Grosshans, 2009Chatterjee S. Grosshans H. Nature. 2009; 461: 546-549Crossref PubMed Scopus (262) Google Scholar) and XRN is necessary for regeneration in planarian (Rouhana et al., 2010Rouhana L. Shibata N. Agata K. Dev. Biol. 2010; 341: 429-443Crossref PubMed Scopus (83) Google Scholar). Heuristically, an entry point to this issue is cell transition states. Because nature is so effective in establishing discrete identities for cells, such states are not always easy to observe. Developmentally, cells have two strategies by which they can morph into another cell type. These strategies are distinguished by “mitosis required” or “mitosis optional” properties. The mitosis required option utilizes precursors that travel through stages that progressively narrow the potential of the cell within a lineage tree until a terminal identity is achieved. Reaching a terminal identity requires passage through each discrete precursor in a Waddington landscape. Progression toward terminal differentiation through a set of precursors can scale the number of cells produced to the morphology of the organism and position them correctly. For example, the kinetics of neuron generation in the development of the mouse cerebral cortex can be modeled by determining the proportion of neuroepithelial cells that exit versus re-enter the cell cycle over the 6 day neuronogenetic interval of 11 cell cycles (Caviness et al., 2003Caviness Jr., V.S. Goto T. Tarui T. Takahashi T. Bhide P.G. Nowakowski R.S. Cereb. Cortex. 2003; 13: 592-598Crossref PubMed Scopus (156) Google Scholar). In Drosophila, neuroectodermal cells have a single fate decision at the time of cell division: differentiate into neuroblasts, which specify neural fate through their progeny, the ganglion mother cells, or specify epidermal differentiation (Doe, 2008Doe C.Q. Development. 2008; 135: 1575-1587Crossref PubMed Scopus (299) Google Scholar). In the case of reprogramming one can reverse the arrow of differentiation; however, mitosis remains a requirement for successful reprogramming. The many control points over mitosis operate within a complex circuitry that includes multiple miRNAs as is apparent in many studies that implicate miRNAs in cancer. Changes in cell identity also occur without cell division or very limited cell division through transition states without discrete precursors. For example, when zebrafish endothelial cells egress from the aortic ventral wall they become hematopoietic stem cells (Kissa and Herbomel, 2010Kissa K. Herbomel P. Nature. 2010; 464: 112-115Crossref PubMed Scopus (611) Google Scholar). Direct conversion of cells has been achieved repeatedly in the laboratory: the transcription factor CEBP can convert B lymphocytes to macrophages (Xie et al., 2004Xie H. Ye M. Feng R. Graf T. Cell. 2004; 117: 663-676Abstract Full Text Full Text PDF PubMed Scopus (717) Google Scholar), Math1 can reprogram inner ear support cells to hair cells (Izumikawa et al., 2005Izumikawa M. Minoda R. Kawamoto K. Abrashkin K.A. Swiderski D.L. Dolan D.F. Brough D.E. Raphael Y. Nat. Med. 2005; 11: 271-276Crossref PubMed Scopus (534) Google Scholar), and MyoD, a transcription factor that specifies the skeletal muscle lineage, can convert cultured embryonic fibroblasts, chondroblasts, and retinal epithelial cells into contracting muscle cells (Vierbuchen et al., 2010Vierbuchen T. Ostermeier A. Pang Z.P. Kokubu Y. Südhof T.C. Wernig M. Nature. 2010; 463: 1035-1041Crossref PubMed Scopus (2071) Google Scholar). A method known as direct reprogramming or lineage reprogramming introduces sets of transcription factors into differentiated cells that determine the identity of the reprogrammed cell. Three factors—Ascl1, Brn2 (also called Pou3f2), and Myt1l—are sufficient to convert fibroblasts into neurons (Zhou et al., 2008Zhou Q. Brown J. Kanarek A. Rajagopal J. Melton D.A. Nature. 2008; 455: 627-632Crossref PubMed Scopus (1539) Google Scholar). In addition to in vitro approaches, pancreatic exocrine cells have been converted to beta-cells in vivo by the addition of three factors, Ngn3, Pdx1, and Mafa (Lessard et al., 2007Lessard J. Wu J.I. Ranish J.A. Wan M. Winslow M.M. Staahl B.T. Wu H. Aebersold R. Graef I.A. Crabtree G.R. Neuron. 2007; 55: 201-215Abstract Full Text Full Text PDF PubMed Scopus (485) Google Scholar). Changes in cell identity are closely linked to transcription factors, and therefore, within the many feedback loops involving miRNAs, transcription factors have a “dominant” role. Oncogenic changes in cell identity are also dominated by transcription factors that operate in feedback or feedforward loops with miRNAs. For example, activation of the c-Myc oncogenic transcription factor induces Lin-28 and Lin-28B, which negatively regulate let-7 biogenesis by preventing both Drosha- and Dicer-mediated let-7 processing (Chang et al., 2009Chang T.C. Zeitels L.R. Hwang H.W. Chivukula R.R. Wentzel E.A. Dews M. Jung J. Gao P. Dang C.V. Beer M.A. et al.Proc. Natl. Acad. Sci. USA. 2009; 106: 3384-3389Crossref PubMed Scopus (309) Google Scholar). Thus, a Myc-Lin-28B-let-7 regulatory circuit appears to reinforce Myc-mediated oncogenesis. The Lin-28-let-7 core circuitry also operates in a positive feedback loop through NF-κB, which activates Lin-28 to create a link between inflammation and cell transformation (Iliopoulos et al., 2009Iliopoulos D. Hirsch H.A. Struhl K. Cell. 2009; 139: 693-706Abstract Full Text Full Text PDF PubMed Scopus (1064) Google Scholar). The two strategies for increasing the variety of specialized cells during development have important differences. Lineage reprogramming may reduce the dangers of the mitotic state with its risk of cancer and directly preserve the epigenetic marks of the starting cell type. But without expansion in cell number, growth of the organism is restricted. Importantly, growth of the organism is not strictly a matter of size; in the case of the brain, for example, massively parallel neuronal networks confer emergent properties to the organism including sapience. The widespread developmental strategy of utilizing precursor pools as discrete cellular intermediates toward the genesis of a mature organism requires the establishment of a series of precursor cell identities along a path of progressively narrowing potential until the cell reaches a terminal identity. The ability of miRNAs to capacitate cellular phenotypy permits the emergence of large numbers of precursor cell types capable of honing developmental processes toward highly specialized identities and precise cell numbers. Many of the puzzling features of miRNAs could be explained if they adapt cells to environmental contingencies. The environment that cells face is many times more complex than the biological adaptations available within the genome. Among the adaptive responses of cells to an environmental contingency is the up- or downregulation of proteins. The properties of miRNAs to adjust protein levels, their dispensability under basal conditions, their conservation, as well as the ease with which new miRNAs appear over evolutionary time all suggest that they are suited for environmental contingencies. Among the many contingencies organisms face is famine. The response to limited glucose is mediated by insulin, which lies in a pathway that is highly interconnected to miRNAs (Xu and Wong, 2008Xu J. Wong C. RNA. 2008; 14: 1276-1283Crossref PubMed Scopus (80) Google Scholar). One developmental response to limited glucose at the organismal level is a reduction in body size, and in Drosophila this adaptation appears to be mediated by miR-8 and its target USH (u-shaped) (Hyun et al., 2009Hyun S. Lee J.H. Jin H. Nam J. Namkoong B. Lee G. Chung J. Kim V.N. Cell. 2009; 139: 1096-1108Abstract Full Text Full Text PDF PubMed Scopus (220) Google Scholar). Flies lacking miR-8 are both defective in insulin signaling in the fat body (the counterpart of liver and adipose tissue) and smaller in size. In humans, a miR-8 homolog, miR-200, and a USH homolog, FOG2, mediate the same pathway. Another example is the response of the heart to stress and hypothyroidism through expression of the cardiac-specific miR-208 (van Rooij et al., 2007van Rooij E. Sutherland L.B. Qi X. Richardson J.A. Hill J. Olson E.N. Science. 2007; 316: 575-579Crossref PubMed Scopus (1309) Google Scholar). The miR-143/145 locus nicely illustrates the paradox that specific miRNAs, which are part of a cell's unique profile, do not result in the loss of the cell's identity when knocked out, but they do impair the cell under certain contingencies. miR-143/145 knockout mice have impaired neointima formation in response to vascular injury and have reduced vascular tone (Xin et al., 2009Xin M. Small E.M. Sutherland L.B. Qi X. McAnally J. Plato C.F. Richardson J.A. Bassel-Duby R. Olson E.N. Genes Dev. 2009; 23: 2166-2178Crossref PubMed Scopus (527) Google Scholar). Hornstein and Shomron, 2006Hornstein E. Shomron N. Nat. Genet. 2006; 38: S20-S24Crossref PubMed Scopus (447) Google Scholar point to the example of miR-1 in D. melanogaster in the context of a discussion on canalization. miR-1 is a highly conserved muscle-specific miRNA that does not affect muscle differentiation in D. melanogaster when knocked out. The phenotype only emerges during a rapid growth phase (Sokol and Ambros, 2005Sokol N.S. Ambros V. Genes Dev. 2005; 19: 2343-2354Crossref PubMed Scopus (324) Google Scholar). This example also makes the point that different cells require different responses to the same environmental contingency. In this case, rapid growth in muscle requires different regulatory circuits than rapid growth in other cell types. Cells adapted to an environmental event retain a genetic memory of the event. When the frequency of an environmental contingency falls below a certain level, the selection pressure on the adaptive response is diminished. However, genetic memory is extended by weakly embedding the miRNA contingency response within a genetic circuitry (that is, a network in which a single miRNA targets multiple mRNAs to tune a complex function). Whereas purifying selection operates on the miRNA's role in the genetic circuitry, the miRNA remains in the absence of the contingency and is available to facilitate variation (Kirschner and Gerhart, 2005Kirschner M. Gerhart J.C. The Plausibility of Life. Yale University Press, London2005Google Scholar). miRNAs, as part of modular networks, can potentially speed evolutionary processes and facilitate novelty (Parter et al., 2008Parter M. Kashtan N. Alon U. PLoS Comput. Biol. 2008; 4: e1000206Crossref PubMed Scopus (98) Google Scholar). Given the very different cell responses to the same contingency, one can pose the “chicken and egg” question. Did specialized cells give rise to miRNA diversification or did miRNAs permit cell specialization? Although framing of the question as an either/or belies the complexity of the answer, miRNAs have many properties that are consistent with a role in fostering cell specialization. Chief among these properties is the ease with which they can be invented through a reservoir of 70 nucleotide hairpin structures in the genome, duplication at different chromosomal loci, and formation of miRNA families with different expression levels. Thus, miRNAs may underlie the vast expansion of specialized cells during early metazoan evolution and support the numerous discrete precursor cell types that have accompanied cell specialization. At the base of the animal kingdom lies the phylum Porifera, a sister group to the animal kingdom with an approximately 650 million year fossil record. The few generic cell types in the largest class of sponge species, the Demosponges, bear little homology to cells found in the rest of the animal kingdom. On the other hand, cnidaria, an extraordinarily diversified phylum whose members, like the sponge, are also derived from two germ layers, has acquired many metazoan cell types including neurons. Thus, the common ancestor of the sponge and all other animals represents a critical evolutionary node when animal phenotypy arose. At this same node, miRNAs characteristic of animals also arose (Christodoulou et al., 2010Christodoulou F. Raible F. Tomer R. Simakov O. Trachana K. Klaus S. Snyman H. Hannon G.J. Bork P. Arendt D. Nature. 2010; 463: 1084-1088Crossref PubMed Scopus (217) Google Scholar). Interestingly, the role of miRNAs in evolution of complex multicellularity may extend beyond animals. Among the eukaryotic groups that evolved complex multicelluarity, miRNAs are also present in red/green algae and brown algae (Cock et al., 2010Cock J.M. Sterck L. Rouzé P. Scornet D. Allen A.E. Amoutzias G. Anthouard V. Artiguenave F. Aury J.M. Badger J.H. et al.Nature. 2010; 465: 617-621Crossref PubMed Scopus (574) Google Scholar). The miRNA machinery exists in the Demosponge, Amphimedon queenslandica; however, only eight miRNAs have been detected, none of which bear any orthology to those in bilateria, and the size of both the mature miRNA and its precursor is distinct from other metazoans (Grimson et al., 2008Grimson A. Srivastava M. Fahey B. Woodcroft B.J. Chiang H.R. King N. Degnan B.M. Rokhsar D.S. Bartel D.P. Nature. 2008; 455: 1193-1197Crossref PubMed Scopus (474) Google Scholar). In contrast, the cnidarian Nematostella vectensis (starlet sea anemone) possesses a larger repertoire of more conventional miRNA genes, at least one of which is conserved in bilateria (Prochnik et al., 2007Prochnik S.E. Rokhsar D.S. Aboobaker A.A. Dev. Genes Evol. 2007; 217: 73-77Crossref PubMed Scopus (108) Google Scholar). The “long fuse” transition to metazoan cell diversity rests upon a core gene set present in the sponge ancestor (Sakarya et al., 2007Sakarya O. Armstrong K.A. Adamska M. Adamski M. Wang I.F. Tidor B. Degnan B.M. Oakley T.H. Kosik K.S. PLoS ONE. 2007; 2: e506Crossref PubMed Scopus (192) Google Scholar). Although sponges lack the phenotypic features of cell types seen in the animal kingdom as well as many of the corresponding subcellular features of animal cells such as synapses and adherens junctions, they do have gene sets that characterize animal cell types, and many of these genes are expressed (Conaco and K.S.K., unpublished data). Poriferan gene sets were exapted (Sakarya et al., 2007Sakarya O. Armstrong K.A. Adamska M. Adamski M. Wang I.F. Tidor B. Degnan B.M. Oakley T.H. Kosik K.S. PLoS ONE. 2007; 2: e506Crossref PubMed Scopus (192) Google Scholar) in a manner that gave rise to an extraordinary diversity of cells and a variety of organisms over vast differences in scale. Positioned within the biological hierarchy at a point where phenotypes emerge from gene networks, miRNAs, acting broadly on numerous transcription factors and other genes already present in the metazoan ancestor, very likely contributed to the emergence of animal phenotypy. My thanks go to T. Papagiannakopoulos, M. Srivastava, B. Shraiman, M. Khammash, S. Goyal, P. Neveu, and K. Foltz, whose comments greatly improved this manuscript." @default.
W2034196924 created "2016-06-24" @default.
W2034196924 creator A5016853030 @default.
W2034196924 date "2010-10-01" @default.
W2034196924 modified "2023-10-16" @default.
W2034196924 title "MicroRNAs and Cellular Phenotypy" @default.
W2034196924 cites W1676204041 @default.
W2034196924 cites W1966498224 @default.
W2034196924 cites W1966969776 @default.
W2034196924 cites W1978337436 @default.
W2034196924 cites W1983804343 @default.
W2034196924 cites W1987248329 @default.
W2034196924 cites W1991843764 @default.
W2034196924 cites W1996630410 @default.
W2034196924 cites W2017718158 @default.
W2034196924 cites W2028444170 @default.
W2034196924 cites W2034203463 @default.
W2034196924 cites W2040648064 @default.
W2034196924 cites W2042531263 @default.
W2034196924 cites W2056147363 @default.
W2034196924 cites W2064827935 @default.
W2034196924 cites W2067156314 @default.
W2034196924 cites W2072810552 @default.
W2034196924 cites W2079236594 @default.
W2034196924 cites W2082187624 @default.
W2034196924 cites W2088931058 @default.
W2034196924 cites W2094902161 @default.
W2034196924 cites W2095377293 @default.
W2034196924 cites W2099782860 @default.
W2034196924 cites W2102643936 @default.
W2034196924 cites W2105493567 @default.
W2034196924 cites W2107268184 @default.
W2034196924 cites W2109505983 @default.
W2034196924 cites W2110209356 @default.
W2034196924 cites W2110612987 @default.
W2034196924 cites W2111783800 @default.
W2034196924 cites W2111990378 @default.
W2034196924 cites W2118105142 @default.
W2034196924 cites W2126008912 @default.
W2034196924 cites W2126780067 @default.
W2034196924 cites W2128613900 @default.
W2034196924 cites W2130979840 @default.
W2034196924 cites W2133968430 @default.
W2034196924 cites W2136541538 @default.
W2034196924 cites W2141884202 @default.
W2034196924 cites W2142879643 @default.
W2034196924 cites W2151777177 @default.
W2034196924 cites W2152310279 @default.
W2034196924 cites W2166356074 @default.
W2034196924 cites W2169123200 @default.
W2034196924 cites W2170139221 @default.
W2034196924 cites W2171559697 @default.
W2034196924 cites W4236316592 @default.
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