Allosteric inhibition of a stem cell RNA-binding protein by an intermediary metabolite
The RNA-binding protein Musashi-1 ( MSI1 ) is expressed in stem and progenitor cells of neural and epithelial lineage . In the central nervous system , MSI1 is expressed in astrocytes and committed glial and neural progenitor cells , but not in mature neurons and oligodendrocytes ( Figure 1A; Kaneko et al . , 2000; Dobson et al . , 2008 ) . In histological studies of neural and epithelial tissues , MSI1 is routinely used as a marker for stem and progenitor cells ( Johnson et al . , 2010 ) . Analysis in mice and primary cells shows that MSI1 regulates neural development . Msi1−/− knockout mice are uncoordinated , ataxic , develop hydrocephaly , and die within 1–2 months after birth ( Sakakibara et al . , 2002 ) . Their brains are small , contain an expansion of early lineage progenitor cells , and display fewer mature cell types than normal ( Sakakibara et al . , 2002 ) . Embryonic neurospheres cultured from Msi1−/− mouse brains have a reduced capacity to differentiate into mature neurons and oligodendrocytes ( Sakakibara et al . , 2002 ) . In primary oligodendrocyte progenitor cells , MSI1 promotes progenitor cell survival and prevents differentiation into mature oligodendrocytes ( Dobson et al . , 2008 ) . The phenotype and expression pattern reveal that MSI1 plays an early role in regulating neurogenesis and gliogenesis . 10 . 7554/eLife . 02848 . 003Figure 1 . MSI1 is inhibited by monounsaturated fatty acids . ( A ) Pattern of MSI1 ( blue ) in the CNS . ( B–D ) EMSA and FP of MSI1 binding to RNA aptamer CCCR005 ( AGCGUUAGUUAUUUAGUUCG ) . EMSA data ( red line ) were fit to the Hill equation where all shifted species were fit as an aggregate . FP data ( black line ) were fit to a two-site binding model . ( E and F ) Assay scheme for the inhibitor screen ( E ) and F–EMSA dose responses with hits identified from the small molecule screens ( E ) and oleic and elaidic acid ( F ) . Each gel is one representative experiment of at least three independent experiments . No compound and no protein lanes identify the position of bound and free RNA migration , respectively . DOI: http://dx . doi . org/10 . 7554/eLife . 02848 . 00310 . 7554/eLife . 02848 . 004Figure 1—figure supplement 1 . ( A ) Coomassie-stained SDS page gel shows that recombinant MSI1 is purified to greater than 95% over a 3-column purification protocol . ( B ) MSI1 displays decreased affinity for an RNA aptamer upon addition of oleic acid . Apparent dissociation constants were determined by plotting fluorescence polarization as a function of MSI1 protein concentration and fitting the data to the Hill equation . 0 µM oleic acid: Kd , app = 16 . 3 ± 1 . 2 nM; 1 µM oleic acid: Kd , app = 18 . 1 ± 2 . 6 nM; 10 µM oleic acid: Kd , app = 40 . 5 ± 3 . 5 nM; 0 µM oleic acid: Kd , app > 2000 nM . ( C ) CMC determination by N-phenyl-1-naphthylamine ( NPN ) fluorescence in equilibration buffer ( pH 8 . 0 ) . Segmented linear regression was used to determine the breakpoint between baseline and micelle-associated NPN fluorescence . The value of the CMC presented is the average and standard deviation from three experiments . DOI: http://dx . doi . org/10 . 7554/eLife . 02848 . 00410 . 7554/eLife . 02848 . 005Figure 1—figure supplement 2 . RNA binding specificity and inhibition by specific fatty acids is conserved in MSI2 . ( A ) Sequence alignment shows 83% conservation between MSI1 and MSI2 within the RRM domains . Regions that correspond to α-helices , β-sheets , and intervening loops as defined by NMR spectroscopy are diagramed above the alignment . ( B ) MSI2 binds the MSI1 RNA aptamer CCCR005 with similar affinity to that of MSI1 by both FP and F–EMSA . The no protein control lane defines the position of free RNA . Data are the average and standard deviation of three independent experiments . ( C ) MSI2 is specifically inhibited by oleic acid and eicosenoic acid in FP and F–EMSA dose response experiments . No compound and no protein controls define the position of bound and free RNA respectively . Data are the average and standard deviation of three independent experiments . DOI: http://dx . doi . org/10 . 7554/eLife . 02848 . 005 MSI1 contains two RNA recognition Motifs ( RRMs ) and is homologous to Drosophila melanogaster Musashi , a post-transcriptional regulatory protein that guides external sensory bristle patterning in flies ( Sakakibara et al . , 1996 ) . In vitro SELEX experiments identified a series of aptamer sequences that bind to MSI1 ( Imai et al . , 2001 ) . Visual inspection identified a consensus sequence ( G/A ) U1–3AGU that was present in most but not all of the aptamers . A number of MSI1 targets have been identified by co-immunoprecipitation , including NUMB , a repressor of NOTCH signaling . Numb transcripts harbor MSI1 consensus elements in the 3′-UTR ( Imai et al . , 2001 ) . MSI1 interacts with the Numb 3′-UTR in vitro , and Numb mRNA co-immunoprecipitates with MSI1 in transiently transfected NIH 3T3 cells . Overexpression of MSI1 in NIH 3T3 cells decreases NUMB protein levels without affecting Numb mRNA and reduces the expression of a luciferase reporter in a 3′-UTR dependent manner ( Imai et al . , 2001 ) . Together , the results show that MSI1 negatively regulates Numb mRNA translation . In contrast , MSI1 acts as a translational activator in Xenopus laevis oocytes , where it modulates cell cycle progression by regulating mRNA encoding the proto-oncogene Mos ( Charlesworth et al . , 2006 ) . MSI1 also promotes proliferation of numerous cancers of the brain and epithelial tissues ( Toda et al . , 2001; Hemmati et al . , 2003; Yokota et al . , 2004; Sanchez-Diaz et al . , 2008; Sureban et al . , 2008 ) . MSI1 depletion in medulloblastoma and colorectal tumors results in decreased proliferation and increased apoptosis ( Sanchez-Diaz et al . , 2008; Sureban et al . , 2008 ) . In colorectal tumors , MSI1 depletion is accompanied by inhibition of Notch-1 and upregulation of p21WAF1 , a MSI1 target involved in cell cycle regulation ( Battelli et al . , 2006; Sureban et al . , 2008 ) . Musashi-2 ( MSI2 ) is 69% identical to MSI1 protein and is expressed in a partially overlapping set of tissues ( Sakakibara et al . , 2002 ) . MSI2 regulates hematopoesis and is involved in acute myeloid leukemia ( Ito et al . , 2010; Kharas et al . , 2010 ) . In myeloid leukemia cells , MSI2 is highly expressed , and depletion results in decreased proliferation and increased apoptosis ( Kharas et al . , 2010 ) . The crisis phase of myeloid leukemia is marked by low NUMB expression ( Ito et al . , 2010 ) . Loss of MSI2 restores NUMB expression and impairs the blast crisis phase of myeloid leukemia ( Ito et al . , 2010 ) . Ultimately , MSI2 expression levels are directly correlated with poor prognosis in myeloid leukemia patients ( Kharas et al . , 2010 ) . Because of the importance of Musashi family proteins in stem and cancer cell proliferation , we sought to identify a small molecule inhibitor of MSI1 RNA-binding activity . After screening more than 30 , 000 compounds , we identified four inhibitors , one of which is the intermediary metabolite oleic acid . Here , we characterize the specificity and mechanism of oleic acid inhibition and identify a novel regulatory target that enables MSI1 to act as a metabolite sensor .
To screen for small molecule inhibitors of MSI1 RNA-binding activity , we developed an in vitro assay pipeline amenable to high throughput measurements . First , we tested the ability of a purified , his6-tagged MSI1 dual RRM construct ( amino acids 7–192 , Figure 1—figure supplement 1A ) to bind a fragment of a previously identified SELEX aptamer ( CCCR005 ) ( Imai et al . , 2001 ) using two quantitative assays: fluorescence electrophoretic mobility shift ( F–EMSA ) and fluorescence polarization ( FP , Figure 1B–D ) ( Pagano et al . , 2011 ) . MSI1 binds with high affinity to the aptamer fragment , which contains two copies of the consensus sequence . Next , we optimized the FP assay for use in high throughput screens . We used this assay to screen two small molecule libraries: the 1280-compound Sigma Library of Pharmacologically Active Compounds ( LOPAC ) and the 30 , 000-compound Chembridge library ( Figure 1E; Table 1A ) . Inhibitors identified in the screen were validated by dose response measurements using both the FP and F–EMSA assays ( Figure 1E , F ) . 10 . 7554/eLife . 02848 . 006Table 1 . Structure-activity relationship analysis demonstrates specificity of inhibitionDOI: http://dx . doi . org/10 . 7554/eLife . 02848 . 006ACompound nameCIDScreen scoreFP Ki , app ( µM ) F–EMSA Ki , app ( µM ) Chembridge 7409829284250 . 04515 ± 2 . 854 ± 22Aurintricarboxylic Acid ( ATA ) 22590 . 0530 . 23 ± 0 . 031 . 5 ± 0 . 14GW76473397731−0 . 0286 . 5 ± 0 . 421 ± 0 . 8Oleic Acid445639−0 . 0051 . 2 ± 0 . 41 . 4 ± 0 . 7BCompound nameStructureCodeFP Ki , app ( µM ) F–EMSA Ki , app ( µM ) Oleic acid18:1 ω-91 . 2 ± 0 . 41 . 4 ± 0 . 7Eicosenoic acid20:1 ω-91 . 2 ± 0 . 41 . 7 ± 0 . 6Erucic acid22:1 ω-90 . 64 ± 0 . 20 . 82 ± 0 . 03Nervonic acid24:1 ω-947 ± 3023 ± 8Palmitoleic acid16:1 ω-75 . 3 ± 0 . 513 ± 0 . 9Linoleic acid18:2 ω-6 , 92 . 2 ± 0 . 21 . 2 ± 0 . 03Arachidonic acid20:4 ω-6 , 9 , 12 , 153 . 0 ± 0 . 21 . 1 ± 0 . 3Oleoyl-CoA ( 18:1 ω-9 ) 8 . 1 ± 0 . 34 . 0 ± 0 . 2Erucyl-CoA ( 18:1 ω-9 ) 4 . 1 ± 0 . 90 . 62 ± 0 . 2Ricinoleic acid ( 18:1 ω-9 ) No inh . 18 ± 9Oleamide ( 18:1 ω-9 ) No inh . No inh . Ethyl oleate ( 18:1 ω-9 ) No inh . No inh . 4-Methylumbelliferyl Oleate ( 18:1 ω-9 ) No inh . No inh . Elaidic acid18:1 ( trans ) No inh . No inh . Stearic acid18:0No inh . No inh . Palmitic acid16:0No inh . No inh . Myristic acid14:0No inh . No inh . ( A ) Small molecule screen hits . Compound ID ( CID ) refers to each compound's LOPAC identification number . Screen scores were calculated by normalizing the polarization value of each compound to the no protein and no compound controls , as described in the supplemental methods . After the screen was complete , compounds that scored as hits were confirmed by FP and F–EMSA dose response experiments . Apparent inhibition constants ( Ki , app ) are the average and standard deviation of at least three independent experiments . ( B ) The code = carbon number:number of double bonds , followed by the position of the double bonds from the aliphatic end of the fatty acid . Where a fatty acid is modified , the parental fatty acid numerical code is given in parentheses for comparison purposes . FP and F-EMSA dose response results are reported as the average and standard deviation of at least three independent experiments . Four candidate inhibitors were identified . The weakest inhibitor was the Chembridge compound 7409829 ( Ki , app , FP = 15 ± 2 . 8 µM , Ki , app , F–EMSA = 54 ± 22 µM ) . The most potent inhibitor was aurintricarboxylic acid ( ATA , Ki , app , FP = 230 ± 30 nM , Ki , app , F–EMSA = 1 . 55 ± 0 . 14 µM ) , a compound that readily polymerizes in aqueous solution to form a polyanion . This compound has been identified in many high throughput small molecule assays as a non-specific inhibitor of protein–nucleic acid interactions ( Lam et al . , 1995 ) . The next inhibitor was GW7647 , a PPARα agonist ( Berger and Moller , 2002 ) . PPARα is a nuclear hormone receptor that is activated by long chain unsaturated fatty acids ( Gottlicher et al . , 1992; Bocos et al . , 1995; Forman et al . , 1997; Kliewer et al . , 1997 ) . The Ki , app for this compound was 6 . 5 ± 0 . 4 µM by FP and 21 ± 0 . 8 µM by F–EMSA . The final inhibitor was oleic acid , an eighteen-carbon monounsaturated fatty acid with one double bond located nine carbons from the aliphatic—omega—end of the molecule ( 18:1 , ω-9 ) . The apparent Ki of this compound was 1 . 2 ± 0 . 4 µM by FP and 1 . 4 ± 0 . 7 µM by F–EMSA , and we observed decreased affinity for the RNA aptamer with increasing concentrations of oleic acid ( Figure 1F , Figure 1—figure supplement 1B ) . The Ki , app is approximately two orders of magnitude below the critical micelle concentration ( CMC ) in our equilibration buffer ( pH 8 . 0 ) as measured by N-phenyl-1-naphthylamine ( CMC ≥ 75 ± 8 µM , Figure 1—figure supplement 1C ) . Oleic acid has been screened in 754 bioassays reported in the PubChem database . Of these , oleic acid scored as positive in 4% of the assays . It should be noted that this figure overestimates the hit rate because it includes multiple bioassays that target the same protein . For example , oleic acid scored as a positive in eleven bioassays targeting fatty acid binding proteins ( FABP ) and seven assays that target membrane-associated potassium channels . Both are known to be sensitive to fatty acids ( Capaldi et al . , 2006; Boland and Drzewiecki , 2008 ) . Other proteins responsive to oleic acid include fatty acid synthase , estrogen synthase , factor VIIa complex , and enterotoxin . It also scored positive in a screen for membrane permeant biomolecules . In total , only 14 unique proteins are responsive to oleic acid . This suggests that oleic acid inhibition is specific . Oleic acid is the most abundant fatty acid in body fat and is produced by mature oligodendrocytes during myelination ( Martinez and Mougan , 1998 ) . Cells can produce almost any fatty acid by modifying existing fatty acids through precise metabolic pathways . Because oleic acid is structurally related to a large number of fatty acids , we obtained a library of fatty acids and analogs to assess the specificity of inhibition ( Figure 1F; Table 1 ) . First , we measured inhibition by longer omega-9 monounsaturated fatty acids using the FP and F–EMSA dose response assays . Eicosenoic acid ( 20:1 , ω-9 ) inhibited MSI1 with a potency similar to oleic acid ( Ki , app , FP = 1 . 2 ± 0 . 4 µM , Ki , app , F–EMSA = 1 . 7 ± 0 . 6 µM ) . Erucic acid ( 22:1 , ω-9 ) was a stronger inhibitor ( Ki , app , FP = 640 ± 150 nM , Ki , app , F–EMSA = 820 ± 30 nM ) , and nervonic acid ( 24:1 , ω-9 ) inhibited more weakly ( Ki , app , FP = 47 ± 25 µM , Ki , app , F–EMSA = 23 ± 8 µM ) . Second , we assessed truncations and modifications of the aliphatic end of the fatty acid . Removing two carbons ( palmitoleic acid , 16:1 , ω-7 ) had a moderate effect on inhibition ( Ki , app , FP = 5 . 3 ± 0 . 5 µM , Ki , app , F–EMSA = 12 ± 0 . 9 µM ) . The presence of a hydroxyl group at carbon 12 ( ricinoleic acid , 12-hydroxy-oleic acid ) had a stronger effect; inhibition is barely detectable by FP and reduced 15-fold in F–EMSA measurements ( Ki , app , F–EMSA = 18 ± 9 ) . Third , we assessed esterification or other modification of the carboxylate group . Oleamide , ethyl oleate , and 4-methylumbelliferyl oleate failed to inhibit RNA-binding . In contrast , the presence of a Coenzyme A ( CoA ) substituent was apparently tolerated , although we noted some deacylation of acyl-CoA stocks by thin layer chromatography . Fourth , we assessed the requirement for the ω-9 double bond . Stearic ( 18:0 ) , palmitic ( 16:0 ) , and myristic ( 14:0 ) acids failed to inhibit MSI1 RNA-binding activity , indicating that the ω-9 double bond is required . Surprisingly , the orientation of the double bond is also critical . Elaidic acid ( 18:1 , ω-9 trans ) has the same molecular weight as oleic acid , and nearly identical refractive index and molar aqueous solubility ( ∼10 mM at pH 8 . 0 ) , but its ω-9 double bond is trans rather than cis . Elaidic acid did not inhibit MSI1 ( Figure 1F ) . Linoleic ( 18:2 , ω-9 , ω-6 ) and arachidonic ( 20:4 , ω-12 , ω-9 , ω-6 , ω-3 ) polyunsaturated fatty acids also inhibit MSI1 , but with a weaker apparent inhibition constant ( Linoleic acid: Ki , app , FP = 2 . 2 ± 0 . 2 µM , Ki , app , F–EMSA = 1 . 2 ± 0 . 03 µM; Arachidonic acid: Ki , app , FP = 3 . 0 ± 0 . 2 µM , Ki , app , F–EMSA = 1 . 1 ± 0 . 3 µM ) . We conclude that omega-9 cis unsaturated fatty acids between 18 and 22 carbons specifically inhibit MSI1 RNA-binding activity and identify erucic acid as the most potent inhibitor ( Table 1 ) . MSI2 was inhibited with similar specificity ( Figure 1—figure supplement 2 ) . In principle , inhibitory fatty acids could inhibit by interacting with either MSI1 or its RNA target . We hypothesized that if the fatty acid bound to the RNA-binding domain of MSI1 , the interaction might alter the local environment of its single tryptophan ( W29 ) , leading to a change in the intrinsic fluorescence ( Vivian and Callis , 2001 ) . To test this hypothesis , we titrated oleic acid or elaidic acid ( trans-oleic acid ) into recombinant MSI1 protein or an n-acetyl-L-tryptophanamide ( NATA ) control and measured tryptophan fluorescence . Titration of oleic acid , but not elaidic acid , strongly quenched tryptophan fluorescence and altered the emission intensity curve shape from 300 to 400 nm ( Figure 2A , Figure 2—figure supplement 1A–D ) . Neither compound affected the fluorescence of NATA . In our buffer system , the maximal emission for both NATA and MSI1 is 350 nm ( Figure 2—figure supplement 1A–D ) . We fit the emission at 350 nm to a quadratic , bimolecular association model in order to determine the apparent dissociation constant . The Kd , app of oleic acid was 2 . 6 ± 1 µM , essentially identical to the Ki , app determined by FP and F–EMSA dose response experiments ( Table 1A; Figure 2A ) . Our data are consistent with inhibition resulting from a direct association of oleic acid with the MSI1 protein . 10 . 7554/eLife . 02848 . 007Figure 2 . MSI1 inhibition is allosteric . ( A ) MSI1 tryptophan fluorescence at 350 nm as a function of oleic and elaidic acid . The Kd , app , Ki , app , and Hill parameters are the average and standard deviation of three independent replicates . ( B ) Ribbon model of MSI1 RRM1 ( top ) . Space-filling model of MSI1 RRM1 ( middle ) and RRM2 ( bottom ) ( Nagata et al . , 1999; Miyanoiri et al . , 2003 ) . Left , β-sheet surface , right , α-helical surface . Conserved phenylalanines and W29 are green . Lysine and arginine residues are blue . A hydrophobic pocket exists on the RRM1 α-helical surface . ( C ) CD spectra of MSI1 RRM1 in the presence of oleic ( top ) or elaidic acid ( bottom ) . ( D ) Envelope traces of the van Holde-Weischet analysis for analytical ultracentrifugation experiments of MSI1 alone ( top ) , with oleic acid ( middle ) and with elaidic acid ( bottom ) . The predominant species sediments where monomeric MSI1 would be expected , and there is no significant change in the sedimentation profile after addition of oleic or elaidic acid . Data are representative traces from one of three independent experiments . DOI: http://dx . doi . org/10 . 7554/eLife . 02848 . 00710 . 7554/eLife . 02848 . 008Figure 2—figure supplement 1 . ( A–D ) The MSI1 tryptophan fluorescence spectrum changes as a function of inhibitor titration . The tryptophan fluorescence emission spectrum is strongly quenched upon oleic acid ( A ) and erucic acid ( C ) but not elaidic acid ( B ) addition . ( D ) A tryptophan-containing control N-acetyl-tryptophanamide ( NATA ) does not change upon compound addition . Data from one representative experiment for each compound concentration are shown . ( E ) Difference spectra calculated for the far-UV CD spectra of MSI-1 collected at increasing oleic acid concentrations relative to the spectrum in the absence of oleic acid . Different colors correspond to different concentration of oleic acid: red is 3 µM , purple is 6 µM , blue is 12 . 5 µM , green is 25 µM , and black is 50 µM . The inset shows the same difference spectra calculated for the CD spectra of MSI1 with increasing elaidic acid concentration relative to the spectrum collected in the absence of elaidic acid . The same color scheme applies to the different elaidic acid concentrations . DOI: http://dx . doi . org/10 . 7554/eLife . 02848 . 00810 . 7554/eLife . 02848 . 009Figure 2—figure supplement 2 . ( A and B ) Recombinant His6-tagged MSI1 RRM1 binds RNA aptamer CCCR005 ( A ) and is inhibited by oleic acid ( B ) . FP data are reported as the average and standard deviation of three independent experiments . ( C ) 2D 1H-15N HSQC spectra of MSI1 free ( red ) and bound to RNA ( blue ) at pH 7 and 25°C . Selected resonances are labeled and enlarged in the insets . Crosspeaks are colored according to the increasing concentration of added RNA . ( D–F ) NMR titration of oleic acid ( D ) The cross peak intensities ( I ) from the 2D 1H-15N HSQC spectra of MSI1 are plotted for four representative residues ( Q30 black , R82 green , K98 red , A100 blue ) as a function of the concentration of oleic acid added to sample of 133 µM MSI1 at pH 7 and 25°C . The intensities are normalized relative to the measured intensity in the absence of oleic acid , I0 . The intensity ratios were fitted to straight lines . ( E ) The cross peak intensities for all assigned residues of MSI1 in the 2D NMR spectrum after adding 0 . 22 molar equivalents of oleic acid . I0 is the intensity without oleic acid addition and I0 . 22 is the intensity after addition of 0 . 22 molar equivalents of oleic acid . Each point represents the I0 . 22/I0 for a single residue . The average of all intensities at I0 . 22 ( solid line ) and ±1 standard deviation ( dashed lines ) indicate that some residues lose more intensity than others . A number of residues display normalized intensity values that are higher ( blue ) or lower ( red ) than 1 standard deviation from average . ( F ) Space-filling model of MSI1 RRM1 ( Nagata et al . , 1999; Miyanoiri et al . , 2003 ) . Residues with intensities below 1 SD from the average are red . Residues with intensities higher than 1 SD from the average are blue . DOI: http://dx . doi . org/10 . 7554/eLife . 02848 . 009 The NMR structures of MSI1 RRM1 and RRM2 in the absence and presence of RNA show that both domains adopt the canonical RRM fold in which an anti-parallel beta sheet is buttressed by two alpha helices ( Figure 2B; Nagata et al . , 1999; Miyanoiri et al . , 2003; Ohyama et al . , 2011 ) . In RRM domain proteins , the beta sheet surface typically forms the RNA-binding platform ( Figure 2B; Kielkopf et al . , 2004; Clery et al . , 2008 ) . In the RNA-bound structure , several conserved amino acids located on the beta sheet surface and loops make sequence-specific contacts to the RNA ( Ohyama et al . , 2011 ) . Notably , W29 directly contacts RNA by stacking on the first purine nucleotide . Our data reveal that fatty acid binding causes W29 fluorescence quenching , suggesting that binding changes the environment surrounding this amino acid . As such , fatty acid binding may inhibit MSI1 RNA-binding by an allosteric mechanism . To assess whether oleic acid association induces a change in MSI1 secondary structure , we collected circular dichroism spectra as a function of fatty acid treatment . We observe a decrease in mean residue ellipticity centered around wavelength 220 nm upon treatment with oleic acid but not elaidic acid ( Figure 2C , Figure 2—figure supplement 1E ) . To determine whether the spectral changes correspond to a change in oligomerization state , we performed velocity sedimentation analytical ultracentrifugation experiments ( Demeler et al . , 2011 ) . We observe no significant change in the velocity sedimentation profile of MSI1 by van Holde-Weischet analysis after adding either oleic acid or elaidic acid , and the predominant species remains monomeric ( Figure 2D ) ( Demeler and van Holde , 2004; Demeler et al . , 2011 ) . We conclude that oleic acid binding alters the secondary structure of MSI1 , but this transition does not appear to induce aggregation at concentrations below the CMC . After examining the published NMR structures , we were intrigued to note a hydrophobic cavity on the alpha-helical surface of RRM1 , opposite the RNA-binding surface ( Figure 2B ) and adjacent to W29 ( Nagata et al . , 1999; Miyanoiri et al . , 2003 ) . MSI1 RRM2 does not have this feature . We hypothesized that this cavity comprises the fatty acid binding site . To test this idea , we first asked whether RRM1 is sufficient for ω-9 fatty acid inhibition . We purified his6-tagged MSI1 RRM1 ( amino acids 7-103 ) and used FP and F–EMSA to determine whether RRM1 is sufficient for both RNA binding and inhibition by oleic acid . RRM1 bound to the aptamer RNA with an apparent Kd of 75 . 2 ± 10 nM by FP ( Figure 2—figure supplement 2A ) . Addition of oleic acid but not elaidic acid inhibits RNA-binding activity with similar Ki , app compared to the full RNA-binding domain ( Figure 2—figure supplement 2B ) . Thus , RRM1 is sufficient for RNA recognition and fatty acid inhibition . Next , we prepared an 15N labeled sample of RRM1 for NMR spectroscopy , collected an 15N-1H HSQC spectrum , and titrated aptamer RNA to identify amide proton chemical shift differences associated with RNA binding ( Figure 2—figure supplement 2C ) . The majority of the chemical shift differences map to the β-sheet surface and the loops , which is typical for RRM proteins . Notably , backbone amide protons corresponding to F23 , G64 , and F65 , display large chemical shift changes upon RNA binding . In contrast , chemical shifts changes on the α helical face are small . The data are consistent with the model where the β-sheet surface recognizes RNA , and that binding involves a structural transition including F23 , G64 , and F65 ( Ohyama et al . , 2011 ) . Next , we titrated oleic acid or elaidic acid into the sample of MSI1 ( Figure 2—figure supplement 2D–F ) . We expected to observe chemical shift changes associated with oleic acid binding to the protein . The addition of increasing amounts of oleic acid did not result in significant changes in chemical shifts but in considerable reduction of the peak intensities and in line broadening . The largest loss of signal upon addition of substoichiometric concentrations of oleic acid was observed for W29 , Q30 , L36 , C49 , L50 , R53 , S60 , G62 , V74 , T89 , and K98 ( Figure 2 , Figure 2—figure supplement 2E , F ) . The observed loss of signal is likely due to chemical exchange between the free and bound state . As expected , we detected a strong loss in signal intensity across the entire protein when the concentration of oleic acid exceeded the CMC ( 75 ± 8 µM , Figure 1—figure supplement 1C and Figure 2—figure supplement 2D–F ) . The observed general loss of MSI1 signal is probably due to precipitation of the protein–oleic acid complex , or it could be attributed to the interaction of MSI1 with oleic acid micelles , or both . We note that a precipitant forms in the NMR tube at elevated oleic acid concentration . Because the CMC of oleic acid is below the concentration needed to make a saturated MSI1 sample ( Figure 1—figure supplement 1C ) , we cannot form sufficient MSI1-oleic acid complex for NMR structural studies . Because the high concentrations needed for NMR spectroscopic studies precluded direct measurement of the MSI1 fatty acid interface , we performed computational docking calculations with eicosenoic acid or oleic acid with MSI1 RRM1 ( Table 1; Figure 3; Friesner et al . , 2004 , 2006 ) . In the docked models , both compounds insert the ω-9 end into the cavity , form extensive contacts with the protein surface , and position their carboxy termini adjacent to a positively charged arginine side chain ( R53 or R61 , dependent on the model , Figure 3A , B , Figure 2—figure supplement 2D–F , Figure 3—figure supplement 1A ) . The docked model explains the specificity of inhibition . Amide and ester derivatives lose the negative charge and cannot form charge–charge interactions with arginine ( oleamide , ethyl oleate ) . Shorter omega-7 fatty acids will not fill the hole ( palmitoleic acid ) . Modifications of the omega end limit insertion into the hole ( ricinoleic acid ) . 18 carbons enable the carboxylate to reach R53 ( oleic acid ) , ( Figure 3A , B , Figure 3—figure supplement 1A ) . 24 carbons position the group beyond the arginine ( nervonic acid ) . Trans orientation of the double bond limits surface contact and prevents orientation of the carboxylate towards R53 ( elaidic acid ) . Finally , saturation of the double bond would require a large entropic penalty in order to adopt the necessary conformation ( stearic acid ) . 10 . 7554/eLife . 02848 . 010Figure 3 . ( A and B ) Model of RRM1 bound to oleic acid ( yellow ) calculated by Schrödinger GLIDE ( Friesner et al . , 2004; Friesner et al . , 2006 ) . ( C ) Overlay of the oleic acid bound MD simulation ( gray and red ) with the apo-state NMR structure ( gray and blue ) ( Ohyama et al . , 2011 ) . ( D ) RNA contact residues ( red ) in loop 5 , helix 2 , and strand 4 of the β-sheet are perturbed in oleic acid-bound molecular dynamics simulation ( Ohyama et al . , 2011 ) . ( E and F ) A representative snapshot from the MD simulation of MSI1 bound to oleic acid ( white ) compared to the MSI1-RNA NMR structure ( blue ) ( Ohyama et al . , 2011 ) . Panel ( E ) shows the Gua 1 binding pocket . In the oleic-bound state , the open conformation of loop 5 ( L5 ) orients K88 such that K88 cannot contact Gua 1 . W29 is stacked against Q30 and unavailable for stacking against Gua 1 . Interaction with the side chain of R61 stabilizes the conformation of W29 in the oleic-bound state . Panel ( F ) highlights the different conformations of residues that interact with Gua 1 , Ura 2 , Ade 3 , and Gua 4; represented in grey , orange , red , and purple , respectively . ( G ) Difference of the mean Lipari-Szabo order parameters by residue between the apo and oleic acid-bound states of MSI1 . The Lipari-Szabo order parameters for the backbone NH bond vectors , S2 , were calculated to quantify the backbone flexibility of the free and oleic acid-bound form of MSI1 . The difference of the order parameters , ΔS2 = S2apo−S2MSI−OA , indicates that MSI1-oleic acid complex is more flexible than apo MSI1 , with the few exceptions mostly observed at the N-terminus . The secondary structural elements are highlighted at the top . Error bars are calculated from the standard deviation among trajectories . DOI: http://dx . doi . org/10 . 7554/eLife . 02848 . 01010 . 7554/eLife . 02848 . 011Figure 3—figure supplement 1 . Mutational analysis supports molecular dynamics simulation and docked model predictions . ( A ) MSI1 RRM1 overlays from two NMR models demonstrate the highly dynamic nature of loop 3 . Arginine 53 ( model 1 ) and arginine 61 ( model 3 ) are predicted to interact with the caboxylate of oleic acid in the respective models . ( B ) Space-filling model of MSI1 RRM1 with mutated residues colored . Orange residues indicate mutations with modest ( <twofold ) or no reduction in inhibition by oleic acid . Red residues displayed a larger ( >twofold ) reduction in inhibition by oleic acid . ( C ) Table of mutant RNA-binding and inhibition data . Data are the average of three independent fluorescence polarization experiments . All proteins were purified and tested during the same time period and using the same reagents , enabling comparison of wild-type and mutant variants . DOI: http://dx . doi . org/10 . 7554/eLife . 02848 . 011 To further assess how fatty acid binding alters the dynamics of amino acids that contribute to RNA recognition , we performed molecular dynamics ( MD ) simulations of the MSI1 RRM1 motif with and without oleic acid . The NMR structure of MS1 RRM1 served as the starting configuration for the free state ( Nagata et al . , 1999; Miyanoiri et al . , 2003 ) . Three different models derived by docking oleic acid provided the starting configurations for the oleic acid-bound state ( Friesner et al . , 2004 , 2006 ) . Each of the four simulations was equilibrated for 1 ns and data were collected during a subsequent 30 ns trajectory . Upon oleic acid binding , MSI1 underwent a transition to a more open state characterized by an increase in solvent accessible surface area ( SASA ) and radius of gyration ( Figure 4A ) . Direct visualization of the structure's time evolution showed that binding of oleic acid is associated with stabilization of the C-terminus of α-helix 1 , fraying of α-helix 2 at both the N- and C-termini and , in one trajectory , formation of an additional β-sheet in loop 5 ( Figure 3C , D; Video 1 ) . The computed probability of each residue to be in a secondary structural element ( Figure 4C ) supports our observations . Notably , the β-sheet that forms in loop 5 is absent in the MSI1 apo structure but is present in the structure of MSI1 bound to RNA ( Figure 3D; Ohyama et al . , 2011 ) . 10 . 7554/eLife . 02848 . 012Figure 4 . Molecular dynamics studies ( A and B ) represent the normalized histograms of the protein solvent accessible surface area ( SASA ) and radius of gyration ( rgyr ) illustrating the structural transition undergone by the MSI1 upon oleic acid binding . ( A ) SASA distributions calculated from the MD trajectories of MSI1 bound to oleic acid and of MSI1 in the apo state are represented in red and black , respectively . ( B ) Radius of gyration distributions calculated from the MD trajectories of MSI-1 bound to oleic acid and of MSI1 in the apo state are represented in red and black , respectively . ( C ) The probability of being in an α-helix or β-sheet is shown for each residue of MSI1 . The probabilities calculated for each residue from the MD trajectories of MSI1 free and bound to oleic acid are shown in black and red , respectively . Oleic acid binding is associated with stabilization of the C-terminus of α-helix 1 , fraying of α-helix 2 , at both the N- and C-termini , extension of sheet 2 , as well as the formation of an additional β-sheet at loop 5 ( L5 ) . ( D and E ) Normalized histograms of the distance between G35 , located on α-helix 1 , and L85 , located on loop 5 , calculated from the MD trajectories of oleic acid-bound MSI-1 and of apo MSI1 are shown in red and black , respectively . The distribution of distances between the Cα atoms of G35 and L85 is depicted in ( D ) . The distribution of distances between the Cα of G35 and the Cδ2 of L85 is shown in ( E ) . The green lines show the values of these distances observed in the NMR structure of MSI1 bound to RNA ( PDB ID 2RS2 ) . In the oleic-bound state , loop 5 is restricted in approaching α-helix 1 due to the steric hindrance of the oleic acid . ( F and G ) Normalized histograms of two representative side chain distances of W29 and Q30 . Histograms calculated over the MD trajectories of oleic acid-bound MSI1 are shown in red , those of apo MSI1 in black . The distance between W29 Cε2 and Q30 Cγ is shown in ( F ) . The distance between W29 Cζ2 and Q30 Nε2 is shown in ( G ) . The green lines show these distances observed in the NMR structure of MSI1 bound to RNA ( PDB ID 2RS2 ) . In the oleic-bound state , the side chain of W29 is stacked against the side chain of Q30 . This conformation of W29 is not observed in either the free or RNA-bound states of MSI1 . DOI: http://dx . doi . org/10 . 7554/eLife . 02848 . 01210 . 7554/eLife . 02848 . 013Video 1 . This video is a representative segment of an MD trajectory of MSI1 bound to oleic acid . DOI: http://dx . doi . org/10 . 7554/eLife . 02848 . 013 Analysis of the structure and dynamics of MSI1 bound to oleic acid and its comparison to the RNA-bound structure suggests a mechanism of inhibition . Oleic acid binding stabilizes β-strand formation in loop 5 and alters its position relative to helix 1 . The distance between the C-terminus of α-helix 1 and loop 5 in the free and oleic acid-bound state is greater than that observed in the RNA-bound state ( Figures 3D , Figure 4 ) . The presence of oleic acid blocks loop 5 from approaching the C-terminus of α-helix 1 . When loop 5 is in the more open conformation , K88 is not in position to interact with the first purine nucleotide of the consensus , in this case Gua1 . In addition , binding of oleic acid causes W29 to stack against the side chain of Q30 . ( Figure 3E , F , Figure 4 ) . This observation is in agreement with the NMR signal intensity loss observed for certain residues during oleic acid titration ( Figure 2—figure supplement 2D–F ) , as well as the strong quenching of MSI1 tryptophan fluorescence measured upon addition of oleic acid ( Figure 2A , Figure 2—figure supplement 1A–D ) . W29 directly stacks with the first RNA nucleotide , stabilizing the interaction between MSI1 and RNA . Stacking is eliminated in the presence of oleic acid ( Figure 3E , F ) . Finally , strand 4 is more flexible in the oleic acid-bound state than in free MSI1 ( Figure 3E , F ) . Strand 4 contains several residues that directly contact RNA . The simulations suggest that oleic acid weakens these interactions . In contrast to our initial hypothesis , we do not observe a dramatic change in the dynamics of F65 in the presence of oleic acid over the time course of the simulations . The circular dichroism and analytical ultracentrifugation data ( Figure 2C , D , Figure 2—figure supplement1E ) are consistent with a net gain in secondary structure upon oleic acid binding , as predicted by the MD simulation ( Figure 3 , Figure 4 ) . In addition , the change in tryptophan fluorescence observed upon oleic acid but not elaidic acid treatment is consistent with the changes in tryptophan solvent exposure observed in the simulation . To test features of the model , we mutated residues predicted to interact with oleic acid ( Figure 3—figure supplement 1 ) and measured the relative inhibition constant ( Krel , app ) by dose response FP . We observed no change in fatty acid inhibition when Q30 was mutated to a glutamate ( Krel , app = 0 . 9 , Figure 3—figure supplement 1 ) . Mutation of H83 to leucine also had no effect on fatty acid inhibition . Mutation of H83 to phenylalanine reduced inhibition by almost twofold . H83 lines the hydrophobic pocket . Mutation of this residue to phenylalanine is predicted to make the pocket narrower . Mutation of G64 to alanine reduced inhibition by 3 . 1-fold . G64 forms the floor of the hydrophobic pocket , mutation to an alanine is expected to make the pocket more shallow . Mutation of G35 to aspartate and glutamate results in a twofold and sevenfold reduction of inhibition , respectively . This is possibly due to the addition of a negative charge near the mouth of the hydrophobic cavity . To test whether the R53 and/or R61 , which are predicted to interact with the carboxylate end of oleic acid , are important for inhibition , we made single glutamate mutations at each position ( R53E or R61E ) . Oleic acid inhibition of the R53E mutant is reduced threefold , while R61E is reduced fivefold . Mutating both arginines ( R53E/R61E ) increases this effect to 35-fold . This indicates that R53 and R61 are both important for inhibition , and act in a partially redundant way . A triple mutant ( R53E/R59E/R61E ) weakens inhibition by a similar amount ( 27-fold ) . The R53E mutant has a small effect on RNA-binding , while the R61E mutant has a strong 18-fold effect . The interaction of R61 with oleic acid would preclude its interaction with RNA , and thus may contribute to the mechanism of inhibition . Together , the MD simulations and experimental data are consistent with an allosteric model of inhibition , wherein fatty acid binding induces a change in conformation that modifies the secondary structure of RRM1 and perturbs the position of amino acids required for RNA recognition . Additional work will be necessary to determine whether fatty acid inhibition works via the mechanism suggested by the computational docking and MD simulations or through an alternative mechanism . To study the role of fatty acids in regulating MSI1 in a cell culture model , we investigated the effect of treating the immortalized rat oligodendrocyte progenitor cell line CG-4 with oleic acid ( Louis et al . , 1992 ) . CG-4 cells maintain normal precursor cell morphology , can be readily transfected , and can be induced to differentiate into mature oligodendrocytes by withdrawal of growth factors ( Louis et al . , 1992; Franklin et al . , 1995 ) . Mature oligodendrocytes produce myelin , a lipid-rich membrane structure that insulates neuronal axons to both protect them and to aid in saltatory impulse conductance throughout the CNS ( Griffiths et al . , 1998 ) . The lipid and fatty acid profiles of mature oligodendrocyte-rich white matter differ from that of the grey matter ( Figure 5–figure supplement 3 , Martinez and Mougan , 1998 ) . CG-4 cells also shift lipid profiles upon differentiation , as detected by quantitative lipidomics mass spectrometry ( Figure 5—figure supplement 1 ) . As with primary OPCs , MSI1 is expressed strongly in the precursor state but decreases upon differentiation ( OPCs: 1 . 0 ± 0 . 3 , oligodendrocytes: 0 . 15 ± 0 . 01 , p-value=0 . 0035 ) . Treatment of CG-4 OPCs with oleic acid strongly inhibited the rate of cell proliferation ( Figure 5 ) , matching the published phenotype of MSI1 knock down in primary OPCs ( Dobson et al . , 2008 ) . In contrast , treating HEK293T cells—which do not express MSI1—with the same concentration of oleic acid had little or no effect on the rate of proliferation . Treatment with stearic acid , which does not inhibit MSI1 , had no effect on proliferation rate in either cell type ( Figure 5A ) . The data suggest that reduction of MSI1 activity by oleic acid limits proliferation , but we cannot rule out that the fatty acid modulates other cellular pathways that contribute to proliferation . 10 . 7554/eLife . 02848 . 014Figure 5 . MSI-1 regulates stearoyl-CoA desaturase . ( A ) HEK293T ( dashed ) and CG-4 ( solid ) cell proliferation as a function of oleic acid or stearic acid treatment ( red = treated , black = untreated ) . The data are the average and standard deviation of at least three biological replicates . ( B ) There are seven MSI1 consensus sites in the 3′-UTR of Scd-1 mRNA . The Kd , app is the average and standard deviation of at least three experiments . ( C ) Scd-1 transcripts co-immunoprecipitate with anti-MSI1 antibodies . The data were quantified using a FUJI FLA-5000 imager . ( D ) Western analysis of SCD expression in HEK293T cells . The data were quantified using the LICOR Odyssey system relative to non specific bands ( ** and * , Figure 5—figure supplement 2B ) to control for loading . The average and standard deviation of at least three independent experiments is shown . ( E–H ) Lipidomics analysis of HEK293T cells ± MSI1 expression . Source data are included in Figure 5—source data 1 . ( E ) Volcano plot of lipidomics data . Dashed lines denote fold-changes of ±1 . 5 and ±3 . Red data points indicate lipids that are significantly changed upon MSI1 expression . ( F ) Scatter plot of lipidomics data . Data are reported as nMoles per million cells . Red data points indicate lipids that are significantly changed upon MSI1 expression ( FDR = 0 . 05 ) . ( G ) Fold-changes of the total cholesterol esters and two TAG classes in which 38 of the 54 significantly changing lipids are categorized . Each class changes significantly with MSI1 overexpression ( p<0 . 05 ) . ( H ) Fold-changes for the four lipids that comprise the total cholesterol esters class . All display significant changes with MSI1 expression ( FDR = 0 . 05 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 02848 . 01410 . 7554/eLife . 02848 . 015Figure 5—source data 1 . Lipidomics data files . DOI: http://dx . doi . org/10 . 7554/eLife . 02848 . 01510 . 7554/eLife . 02848 . 016Figure 5—figure supplement 1 . Dose response for oleic acid treatment in cell culture . HEK293T ( dashed ) and CG-4 ( solid ) cell proliferation as a function of increasing oleic acid ( red = 50 µM , grey = 5 µM , black = 0 µM ) . The data are the average and standard deviation of at least three biological replicates . DOI: http://dx . doi . org/10 . 7554/eLife . 02848 . 01610 . 7554/eLife . 02848 . 017Figure 5—figure supplement 2 . ( A ) MSI1 binds each of 7 SCD1 3′ UTR fragments containing the MSI1 consensus sequence ( G/A ) U1–3AGU with variable affinity . FP and F–EMSA binding experiments are reported as the average and standard deviation of three independent experiments . ( B ) The SCD1 antibody is specific for full-length SCD1 and the associated proteolysis products . Two independent shRNA constructs reduced full-length SCD1 and associated proteolysis products compared to non-transfected or control shRNA-transfected HEK293T cells . ( C ) qRT-PCR of SCD1 mRNA . The data are the mean and SD of the fold-change in SCD1 mRNA from five independent experiments . Each experiment comprised three technical replicates normalized to tubulin or GTF2i . DOI: http://dx . doi . org/10 . 7554/eLife . 02848 . 01710 . 7554/eLife . 02848 . 018Figure 5—figure supplement 3 . Lipidomics analysis of undifferentiated and differentiated CG4 oligodendrocyte progenitor cells . ( A ) Scatter plot of lipidomics data for differentiated vs undifferentiated CG4 cells . Data are reported as nMoles per million cells . Red data points indicate lipids that are significantly different after differentiation ( FDR = 5% ) . ( B ) Volcano plot of lipidomics data . Dashed lines denote fold-changes of ±1 . 5 and ±3 . DOI: http://dx . doi . org/10 . 7554/eLife . 02848 . 018 We hypothesized that MSI1 might control the expression of enzymes required to make long chain monounsaturated fatty acids . In humans , non-dietary oleic acid is produced from stearic acid by stearoyl-CoA desaturase ( SCD ) . Expression of SCD is tightly controlled at the transcriptional , post-transcriptional , and post-translational levels . In rodents , there are four SCD genes , Scd1-4 . We used FP and F–EMSA assays to test the ability of recombinant MSI1 to bind each of seven putative consensus sites present in the Scd1 3′-UTR ( Figure 5B ) . MSI1 binds to all seven sites by FP and F–EMSA ( Figure 5—figure supplement 2 ) . Site 2 and site 7 bind to MSI1 with comparable affinity to the selected aptamer . The slightly decreased binding affinity measured for these 7 fragments is likely due to the fact that our aptamer CCCR005 contains two MSI1 binding sites , while each Scd1 3′-UTR fragment contains only a single MSI1 binding site . Transcripts encoding SCD1 co-immunopreciptated with MSI1 from CG-4 cell extracts using two independent antibodies ( Figure 5—figure supplement 2C ) . Similar results were obtained with Numb mRNA , a positive control . Overexpression of MSI1 in HEK293T cells , which do not endogenously express MSI1 , increased the amount of SCD and SCD proteolysis products by 2 . 5 ± 0 . 35-fold ( p-value=0 . 018 , Figure 5D , Figure 5—figure supplement 2B ) . We were unable to detect SCD expression in the rat CG-4 cells using available antibodies . qRT-PCR analysis of HEK293T mRNA shows a slight but non-significant increase in SCD1 transcript abundance upon MSI1 overexpression ( Figure 5—figure supplement 2C ) . Next , we sought to assay downstream effects of SCD regulation by MSI1 in cells . We reasoned that because SCD plays an integral role in lipogenesis , changes in SCD activity would result in a shift in the cellular lipid profile . To this end , we performed quantitative lipidomics mass spectroscopy analysis . CG-4 cell proliferation halts upon MSI1 knockdown ( Dobson et al . , 2008 ) . For this reason , we performed these experiments in HEK293T cells with and without MSI1 expression . We observed statistically significant changes ranging from 2- to 32-fold in 54 of the 312 lipids assayed ( FDR = 0 . 05 , Figure 5E , F ) . We noted positive changes in 50 of 54 significant data points , indicating that MSI1 expression stimulates production of certain lipids . 38 of the significantly changing lipids fell into one of three cholesterol and triacylgylcerol ( TAG ) classes: cholesterol esters , 16:0 acyl-containing TAG , and 16:1 acyl-containing TAG ( Figure 5G , H ) . Cholesterol esters and TAGs are made using SCD products , and have been shown to be more abundant in mice overexpressing SCD1 , and less abundant in liver SCD1 knockout mice ( Miyazaki et al . , 2000; Attie et al . , 2002 ) . This indicates that SCD activity is upregulated in the presence of MSI1 . Together , our binding data , co-immunoprecipitation , western blots , and lipidomics profiling results show that stearoyl-CoA desaturase expression is increased in the presence of MSI1 , possibly through direct association with consensus motifs in the 3′-UTR of SCD transcripts . We note that of 2107 annotated 3′ UTRs for genes involved with lipid , cholesterol , fatty acid biosynthesis , and SREBP activity , 1275 contain at least one MSI1 binding element . While the presence of a binding element does not confer regulation , MSI1 regulation of lipid metabolism may be more complex than simple direct regulation of SCD . Indeed , a survey of 64 genes that associate with MSI1 in a RIP-ChIP experiment reveals that 8 are annotated to be involved with lipid metabolic processes ( de Sousa Abreu et al . , 2009 ) .
Our data reveal that the long chain ω-9 fatty acids oleic acids between 18 and 22 carbons in length are allosteric inhibitors of MSI1 RNA binding activity . Our data also show that SCD , the enzyme that catalyzes the ω-9 desaturation , is a MSI1 regulatory target . The results are consistent with a model where MSI1 controls cellular proliferation through a feedback loop that includes SCD and its enzymatic products , such as oleic acid ( Figure 6 ) . 10 . 7554/eLife . 02848 . 019Figure 6 . Model of SCD regulation by MSI1 , and subsequent downstream consequences of SCD activity changes . DOI: http://dx . doi . org/10 . 7554/eLife . 02848 . 019 Oleic acid is the precursor for synthesis of longer chain fatty acids such as eicosanoic , erucic , and nervonic acid , signaling molecules such as arachidonic acid , endocannabinoids , and prostaglandins , and membrane phospholipids . Oleic acid is abundant in the lipid-rich myelin membranes produced by mature oligodendrocytes ( Martinez and Mougan , 1998 ) , and CG-4 OPCs show global changes in the lipid profile upon differentiation . OPCs express MSI1 , but mature myelinating oligodendrocytes do not ( Dobson et al . , 2008 ) . In the oligodendrocyte lineage , oleic acid , and MSI1 levels are anti-correlated , which suggests a possible biological role for MSI1 inhibition by fatty acids ( Martinez and Mougan , 1998; Dobson et al . , 2008 ) . Another MSI1 inhibitor identified in our screen was the PPARα agonist GW7647 . PPARs are nuclear hormone receptor proteins that regulate cellular processes including fatty acid metabolism , differentiation , cell cycling , and inflammation ( Berger and Moller , 2002 ) . PPARγ agonists accelerate oligodendrocyte maturation ( De Nuccio et al . , 2011 ) . Both GW7647 and oleic acid function as PPAR agonists , and both inhibit MSI1 RNA-binding activity , suggesting that MSI1 and PPARs possibly regulate gene expression in a reciprocal fashion . We have identified the metabolic enzyme SCD as regulatory target of MSI1 . The two human isoforms of SCD , SCD1 and SCD5 , catalyze the conversion of saturated fatty acids ( SFAs ) into monounsaturated fatty acids ( MUFAs ) ( Zhang et al . , 1999; Beiraghi et al . , 2003; Minville-Walz et al . , 2010 ) . MUFAs are then used in the synthesis of numerous lipids , including phospholipids , di- and triacylglycerols , cholesterol esters , and signaling molecules such as eicosanoids . SCD is therefore an essential enzyme to normal cellular proliferation , metabolism , and signaling . It is also possible that MSI1 may be regulating other factors involved in lipid , fatty acid , and cholesterol metabolism . The Penalva group identified 64 putative MSI1 targets using a RIP-ChIP analysis in HEK293T cells ( de Sousa Abreu et al . , 2009 ) . Of these putative targets , eight are annotated to play a role in lipid , fatty acid , or cholesterol metabolic processes . These include three variants of the fatty acid elongase ELOVL5 , two variants of the glycolipid biosynthetic factor PIGF , two variants of Lamin B receptor ( LBR ) , and aminoadipate-semialdehyde dehydrogenase-phosphopantetheinyl transferase ( AASDHPPT ) . SCD has been implicated in a number of disease states , including obesity , diabetes , hyperlipidemia , and cancer . Obese and diabetic animals produce abnormally high levels of TAGs and cholesterol esters ( Coleman and Lee , 2004 ) . These energy storage molecules are also elevated in a hyperlipidemia mouse model that overexpresses SCD ( Attie et al . , 2002 ) . Conversely , SCD1 knockout mice display impaired biosythesis of both TAGs and cholesterol esters ( Miyazaki et al . , 2000 ) . SCD is upregulated in tumor cells , including colonic and esophageal carcinoma ( Li et al . , 1994 ) . Cancerous cells use newly synthesized lipids primarily for phospholipid production , which are used in new membranes ( Swinnen et al . , 2000 ) . Additionally , new evidence suggests that SCD1 activates the oncogenic Akt and AMPK signaling pathways ( Scaglia and Igal , 2008; Scaglia et al . , 2009 ) . SCD regulates SFA and MUFA homeostasis , and therefore plays an integral role in lipid signaling pathways . SFAs serve as proinflammatory factors by acting as ligands for immune receptors such as those in the Toll-like receptor family ( Shi et al . , 2006; Nguyen et al . , 2007 ) . MUFA products of SCD are further modified to become polyunsaturated fatty acids , such as arachidonic acid , which are converted into eicosanoids by cyclooxygenases ( James et al . , 2000 ) . Eicosanoids serve as proinflammatory and immune signaling molecules ( Liu et al . , 2011 ) . Recent data also link SCD to Wnt signaling through mediation of the palmitic/palmitoleic acid conversion , as active Wnt proteins require conjugation of palmitoleic acid ( Rios-Esteves and Resh , 2013 ) . The balance between SFAs and MUFAs must be tightly controlled , and while SCD is not the only enzyme involved in the process , it does serve as an essential gatekeeper in the conversion of dietary SFA to MUFA . Several examples of functional interactions between transcription factors and fatty acids have been published . For example , fatty acids associate with membrane-bound sterol responsive element binding protein ( SREBP ) to inhibit a cleavage event that produces the activated form of SREBP , a transcription factor involved in lipid homeostasis ( Wang et al . , 1994 ) . Free fatty acids have been shown to stimulate proinflammatory cytokine expression while decreasing anti-inflammatory cytokine expression in adipocytes ( Bradley et al . , 2008 ) . Interestingly , treatment of various tumor lines with oleic acid results in transcriptional inhibition of the Her-2/neu ( erbB-2 ) oncogene through PEA3 , although the precise mechanism of regulation remains unknown ( Menendez et al . , 2006 ) . In yeast cells , fatty acids bind oleate-activated transcription factors ( OAFs ) to effect transcription of genes responsible for fatty acid metabolism , glucose metabolism , stress response , and other related processes ( Gurvitz and Rottensteiner , 2006 ) . Although numerous transcription factors are regulated by fatty acids , to our knowledge , MSI1 is the first example of a fatty acid-responsive RNA binding protein . Integration of metabolite sensing and post-transcriptional regulation is widespread in bacteria , but relatively few examples have been found in eukaryotes ( Winkler and Breaker , 2003; Roth and Breaker , 2009 ) . Riboswitches regulate gene expression at the RNA level in response to intermediary metabolites . Small molecule metabolites bind mRNA transcripts , usually in the 5′ UTR , to induce a structural change that interferes with the transcriptional or translational machinery ( Winkler and Breaker , 2005 ) . In bacteria , riboswitches sensitive to a number of metabolites have been characterized , including guanine , adenine , coenzyme B12 , glycine , lysine , and thyamine pyrophosphate ( TPP ) , among others ( Grundy et al . , 2003; Mandal et al . , 2003 , 2004; Sudarsan et al . , 2003; Vitreschak et al . , 2003; Mandal and Breaker , 2004 ) . Although most riboswitches have been identified within the bacterial mRNA 5′ UTR , TPP riboswitches , have also been identified in plants and fungi ( Kubodera et al . , 2003; Sudarsan et al . , 2003 ) . While riboswitches comprise an essential cis-regulatory mechanism in bacteria , other mechanisms of coupling metabolic state to post-transcriptional gene regulation may occur in eukaryotes . An increasing number of protein-metabolite-RNA interactions have now been identified in bacteria and eukaryotes . One example is the Bacillus subtilis tryptophan RNA-binding attenuation protein ( TRAP ) . Increasing intracellular tryptophan levels induce TRAP multimerization , which enables mRNA recognition and subsequent translational repression of targets ( Gollnick et al . , 1990; Antson et al . , 1995; Babitzke et al . , 1995; Yakhnin et al . , 2004 ) . Intriguingly , several metabolic enzymes have been proposed to ‘moonlight’ as RNA-binding proteins in ‘RNA/Enzyme/Metabolite ( REM ) ’ networks ( Ciesla , 2006; Hentze and Preiss , 2010; Castello et al . , 2012 ) . A notable example is cytosolic aconitase , which demonstrates mutually exclusive enzymatic and RNA-binding functionality , depending upon cellular iron levels ( Hentze and Argos , 1991; Rouault et al . , 1991 ) . A number of metabolite-sensitive enzymes have also demonstrated RNA-binding activity , including GAPDH , glutamate dehydrogenase , thymidylate synthase , and dihydrofolate reductase ( Ryazanov , 1985; Chu et al . , 1991 , 1993; Preiss et al . , 1993; Dollenmaier and Weitz , 2003 ) . This growing body of research suggests that metabolite-mediated post-transcriptional regulation is much more prevalent than previously thought . Our results show that MSI1 N-terminal RRM1 acts as a metabolite sensor , the first example of such activity observed for the most abundant RNA-binding motif in eukaryotic genomes . A survey of RRM structures in the protein data bank reveals several with a surface cavity on the alpha helical face , which may comprise metabolite-binding pockets . We predict that a network of RNA regulatory proteins act as metabolite sensors , possibly replacing the bacterial riboswitch regulation that appears to have been largely lost in eukaryotes .
DNA encoding the mouse MSI1 RNA binding domain fragment ( amino acids 7–192 ) was amplified from the mammalian gene collection ( MGC ) full-length ORF clone 100014969 ( Invitrogen ) using gene specific primers ( forward primer: 5′-cgcgcggatcccagcccggcctcgcctcccc-3′; reverse primer: 5′-gcgcgaagcttcggggacatcacctcctttg-3′ ) . This fragment was digested using BamHI and HindIII restriction enzymes and subcloned into a modified version of pET-22b vector ( Life Technologies , Grand Island , NY ) in which the pelB leader sequence was replaced with a His6-Gly tag followed by a TEV protease site to make pET-22HT-MSI1 ( 7–192 ) . Mutant versions of the MSI1 RNA binding domain were prepared by site-directed mutagenesis using QuikChange ( Stratagene ) . The human MSI2 RNA binding domain fragment ( amino acids 8–193 ) was amplified from MGC full-length ORF clone 3505639 using gene specific primers ( forward primer: 5′-cgcgcggatccggcacctcgggcagcgccaa-3′; reverse primer: 5′-gcgcgaagctttcatgggaacatgacttctttcg-3′ ) . This fragment was cloned into the BamHI and HindIII restriction sites of pET-22HT to make pET-22HT-MSI2 ( 8–193 ) . The MSI1 RRM1 plasmid pET-22HT-MSI1 ( 7–103 ) was prepared by site-directed mutagenesis using QuikChange ( Stratagene ) to replace M104 with an ochre stop codon . Full-length mouse MSI1 was amplified from MGC full-length ORF clone 100014969 ( Life Technologies ) using forward primer 5′-cgcgcggatccatggagactgacgcgcccca-3′ and reverse primer 5′-ccgggcggccgctcagtggtacccattggtgaa-3′ . The resulting fragment was subcloned into pCDH-CMV-MCS-EF1-Puro ( System Biosciences ) using the BamHI and NotI restriction enzymes to make pCDH-CMV-MSI1 ( FL ) . H6-TEV-MSI-1 ( 7-192 ) , H6-TEV-MSI-1 ( 7-104 ) , and H6-TEV-MSI-2 ( 8-294 ) were expressed and purified from Escherichia coli BL21 ( DE3 ) cells . Liquid cultures grown at 37°C were induced for 3 hr during mid-log phase with 1 mM isopropyl β-D-1-thiogalactopyranoside ( IPTG ) . Cells were pelleted , resuspended in lysis buffer ( 50 mM NaH2PO4 , 300 mM NaCl , 20 mM Imidazole , 5 mM β-Mercaptoethanol [BME] ) , and lysed using a microfluidizer ( IDEX Health and Science ) . Soluble lysate was applied to a Ni-NTA column ( Qiagen ) , washed with wash buffer ( 50 mM NaH2PO4 , 300 mM NaCl , 50 mM Imidazole , 5 mM BME ) , and eluted with elution buffer ( 50 mM NaH2PO4 , 300 mM NaCl , 300 mM Imidazole , 5 mM BME ) . Fractions were analyzed by SDS-page and those containing recombinant MSI1 or MSI2 were pooled and dialyzed overnight into S buffer ( 50 mM MOPS pH 6 . 0 , 20 mM NaCl , 2 mM DTT ) . Pooled fractions were applied to a HiTrap SP cation exchange column ( GE Healthcare ) and eluted using a gradient of 0 . 1 M–1 M NaCl in S Buffer . Fractions containing MSI1 or MSI2 were pooled and dialyzed overnight into Q buffer ( 50 mM Tris pH 8 . 8 , 20 mM NaCl , 2 mM DTT ) prior to loading a HiTrap Q anion exchange column ( GE Healthcare ) . The protein was eluted over a gradient from 0 . 1 to 1 M NaCl in Q buffer . Fractions containing MSI were pooled and dialyzed using Spectra/Por 7 25 kD ( MSI1 ) or 10 kD ( MSI2 ) molecular weight cutoff tubing ( Spectrum laboratories ) overnight into storage buffer ( 50 mM Tris pH 8 . 0 , 20 mM NaCl , 2 mM DTT ) . The yield of >95% pure MSI1 or MSI2 is typically 20 mg per liter of culture ( Figure 1—figure supplement 1 ) . Synthetic RNA oligonucleotides were ordered from IDT and 3′ end-labeled with fluorescein 5-thiosemicarbazide ( Life Technologies ) according to the method of Reines and Cantor ( Reines and Cantor , 1974; Pagano et al . , 2007; Farley et al . , 2008 ) . Briefly , 5 × 10−10 mol of RNA were incubated with 100 mM NaOAc , pH 5 . 1 , and 5 nmol of NaIO4 for 90 min at room temperature then ethanol precipitated . The RNA was resuspended in 50 µl of 1 mM fluorescein-5-thiosemicarbazide in 100 mM NaOAc , pH 5 . 1 . After incubating overnight at 4°C , the RNA was separated from unreacted label by ethanol precipitation and subsequently passaged through a Pierce centrifuge column packed with Spehadex G-25 resin ( GE Healthcare ) . RNA CCCR005 was a truncated form of the SELEX aptamer S8-13 identified by the Okano lab ( Imai et al . , 2001 ) . In preparation for the small molecule screen , fCCCR005 ( AGCGUUAGUUAUUUAGUUCG/36-FAM/ ) was ordered pre-labeled from IDT . Fluorescence polarization ( FP ) , also known as fluorescence anisotropy , and Fluorescence electrophoretic mobility shift assays ( F–EMSA ) were used to measure the binding affinity of recombinant MSI1 to fluorescein-labeled RNA aptamers . Assays were conducted as described in Pagano et al . ( 2011 ) . Briefly , 2 nM fluorescein-labeled RNA was incubated with varying concentrations of recombinant purified MSI1 protein in equilibration buffer ( 37 . 5 mM Tris pH 8 . 0 , 75 mM NaCl , 0 . 0075% igepal , 0 . 0075 mg/ml tRNA ) for 3 hr . Fluorescence polarization was determined with a Victor V3 plate reader using a 480 ± 31 nm excitation filter and a 535 ± 40 nm emission filter . After measuring FP , the samples were mixed with 6 ×x bromocresol green loading dye ( 0 . 15% ( wt/vol ) Bromocresol green , 30% ( vol/vol ) glycerol ) and run on a 5% native polyacrylamide gel at 120 V for 75 min at 4°C . Wet gels were scanned with a Typhoon FLA 9000 Biomolecular imager ( GE healthcare ) using a 473 nm laser and a long-pass cut-off filter ( 510 nm ) . The fraction of bound RNA was determined by quantifying lower ( free ) and upper ( bound ) band intensities using MultiGauge and ImageGauge software ( Fujifilm ) . For RNA sequences with two shifted species , the bands were quantified together . Polarization values or the fraction of bound RNA were plotted as a function of protein concentration and fit to the Hill Equation 1 to determine the apparent dissociation constant ( Kd ) and the apparent Hill coefficient ( n ) . The upper ( m ) and lower ( b ) values were also fit in order to define the assay window . ( 1 ) φ=b+ ( m−b ) [11+ ( Kd/[Pt] ) n] For RNA sequences where a bi-phasic transition was observed , the FP data were fit using a two-site model ( Equation 2 ) to determine both apparent dissociation constants ( Kd1 and Kd2 ) and the fraction ( F ) of signal that corresponds to each transition . ( 2 ) φ=[F ( m−b ) ( PtPt−Kd1 ) ]+[ ( 1−F ) ( m−b ) ( PtPt+Kd2 ) ]+b The small molecule screen was performed at the UMass Medical School small molecule screening core facility using a variation of the FP assay described above . 100 nM recombinant MSI1 protein and 2 nM fluorescein-labeled RNA aptamer CCCR005 were added to each assay well of 384-well black plates ( Corning ) using a μFill liquid dispenser ( BioTek ) . Compounds dissolved in DMSO from the LOPAC and Chembridge libraries of small molecules were spotted in each assay well to a final concentration of 384 µM using a Tecan genesis workstation 150 . 64 wells of each plate were reserved for controls , including 32 wells that included no protein and no compound ( free RNA ) , and 32 wells that included protein , RNA , and DMSO ( no compound ) . The plates were equilibrated at 25°C prior to collecting polarization and fluorescence intensity data for each well using a Victor V2V plate reader ( Perkin Elmer ) . The Z′ score , a measure of signal-to-noise , was calculated for each plate using the average ( µ ) and standard deviation ( σ ) of the control wells ( Equation 3 ) . ( 3 ) Z1=1−[3 ( σ1+σ2 ) μ1−μ2] Plate reads with a Z′ of <0 . 5 were repeated . The average Z′ of all plates was 0 . 7 ± 0 . 2 . The polarization values ( mP ) of each well were normalized against the assay window using the mean polarization values of the no protein control ( µ2 ) and no compound control ( µ1 ) wells to generate an assay score ( Equation 4 ) . ( 4 ) Score=mP−μ2μ1−μ2 Hits were classified as wells with a score of 0 . 1 or less where the fluorescence intensity remained within twofold of the control average to eliminate false positives due to compound fluorescence or quenching . Dose response experiments to assess inhibition activity were performed using a modified FP and F–EMSA protocol . A constant concentration of sub-saturating protein was equilibrated with 2 nM fluorescein-labeled RNA and varying concentrations of compound in equilibration buffer . The FP and F–EMSA data were collected as above and fit to a sigmoidal dose response equation to determine the IC50 ( Equation 5 ) . ( 5 ) φ=b+ ( m−b ) [11+ ( IC50/[Pt] ) n] The apparent inhibition constant was calculated using the Lin and Riggs conversion ( Equation 6 ) , which corrects for the equilibrium dissociation constant of MSI1 for the labeled RNA as well as the concentration of labeled RNA and protein used in the experiment ( Lin and Riggs , 1972; Ryder and Williamson , 2004 ) . ( 6 ) Ki , app=2 ( Kd ) ( IC50 ) 2P−R−2Kd To directly assay the association of fatty acids and MSI1 protein , 6 µM MSI1 or N-acetyl-tryptophanamide ( NATA ) was incubated with varying concentrations of compound . Equilibrated reactions were excited at 280 nm , then steady-state fluorescence emission spectra were recorded between 295 and 400 nm in 1 nm intervals using a T-format Fluorolog fluorimeter ( Horiba ) . 350 nm emission data were normalized and fit to a quadratic bimolecular association curve ( Equation 7 ) to determine the apparent dissociation constant , where C is the total compound concentration , P is the total protein concentration , and m and b represent the maximal and minimal signal , respectively . ( 7 ) φ=b+ ( m−b ) [C+P+Kd− ( C+P+Kd ) −4 ( CP ) 2C] The ligand molecules were downloaded from the PubChem database ( pubchem . ncbi . nlm . nih . gov ) and prepared for docking using the LigPrep module in Maestro ( Schrödinger , LLC ) . The target protein structures were downloaded from the PDB ( www . rcsb . org ) and prepared for docking in Maestro ( Schrödinger , LLC ) using the Protein preparation wizard . Glide ( Schrödinger , LLC ) was used to generate the receptor grid for subsequent docking and scoring the docked ligands in Standard Precision ( SP ) mode . The pose with best Glide score from each ligand/receptor docking run was selected for further analysis ( Friesner et al . , 2004; Halgren et al . , 2004; Friesner et al . , 2006 ) . Labeling with 15N was performed by growing cells in isotopically enriched M9 medium , 1 g 15NH4Cl per liter . 2D 1H-15N HSQC spectra were collected using samples of U-15N MSI1 in 90% H2O/10%D2O buffer solution of 50 mM Tris at pH 7 . 0 . 2D 1H-15N HSQC spectra were collected for each incremental addition of the unlabeled ligand ( either aptamer RNA , oleic acid , or elaidic acid ) to the 15N MSI1 sample to determine amide proton chemical shift changes upon titration of the ligand . All experiments were performed at 600 MHz on a Varian Inova spectrometer equipped with a triple-resonance cold probe at 298 K . Data processing was performed using NMRPipe ( Delaglio et al . , 1995 ) and sparky ( Goddard and Kneller ) software . We performed molecular dynamics ( MD ) simulations of the RRM1 domain of MSI1 ( residues 20–96 ) free and bound to oleic acid . We modeled the unknown structure of MSI1 bound to oleic acid by starting from the NMR solution structure of MSI1 RRM1 ( Miyanoiri et al . , 2003; Ohyama et al . , 2011 ) and docking the oleic acid ligand using the GLIDE software package from Schrödinger , LLC ( Friesner et al . , 2006 ) , followed by energy minimization and equilibration . All structures were solvated and neutralized in a TIP3P water box ( Jorgensen et al . , 1986 ) . Energy minimization and MD simulations were subsequently carried out using the NAMD software package ( Phillips et al . , 2005 ) and using the version 27 CHARMM potential energy function ( MacKerell et al . , 1998 ) . The particle mesh Ewald method ( Darden et al . , 1993; Essmann et al . , 1995 ) was used to treat electrostatic interactions and periodic boundary conditions were applied throughout . The SHAKE algorithm ( Ryckaert et al . , 1977 ) was applied throughout the simulation to constrain the hydrogen atom bond lengths at their equilibrium values and an integration time step of 2 fs was used . After an initial energy minimization , the system was simulated in the isothermal-isobaric ensemble . Non-bonded interactions were calculated every time step using a cut-off distance of 12 Å . After equilibration in the isothermal-isobaric ensemble , an additional stage of equilibration was performed in the microcanonical ensemble . We then collected three independent 30 ns constant-NVE production runs of MSI-1 bound to oleic acid and one for MSI-1 in the free-state at an average temperature of 298 K . Visualization and secondary structure analysis was performed in VMD , using the STRIDE method ( Frishman and Argos , 1995 ) . In addition , we employed a number of measures to characterize the structural and dynamical changes between the free and bound state of MSI-1 , including the radius of gyration , the solvent accessible surface area ( SASA ) , distance between backbone and/or side chain atoms of residues G35-L85 and W29-Q30 , and Lipari and Szabo order parameters ( Lipari and Szabo , 1982; Humphrey et al . , 1996 ) . These quantities were calculated using VMD ( Humphrey et al . , 1996 ) software along with bespoke programs previously described elsewhere ( Morgan and Massi , 2010 ) . Far-UV circular dichroism ( CD ) spectra were collected using 10 µM MSI1 in 50 mM Tris pH 8 . 0 , 100 mM NaCl , and 0 . 1% TFE on a Jasco-810 spectropolarimeter ( Jasco Inc . , Easton , MD ) . Spectra were collected from 215 to 260 nm in a 0 . 2-cm path length quartz cuvette using a scan rate of 20 nm min−1 and a response time of 8 s . The sample temperature for all CD measurements was maintained at 293 K . Sedimentation velocity analyses were conducted using a Beckman Optima XL-I analytical ultracentrifuge in the University of Massachusetts Medical School Ultracentrifuge Facility . Data were analyzed with UltraScan III version 1 . 0 ( Demeler et al . , 2011 ) . Sedimentation velocity experiments were performed with 45 µM MSI1 in storage buffer ( 50 mM Tris-HCl pH 8 . 0 , 20 mM NaCl , 2 mM DTT ) . Measurements were made at 20°C using an AN60ti rotor at 20 , 000 rpm and 280 nm in intensity mode . Partial specific volumes were estimated on the basis of peptide sequence with UltraScan and found to be 0 . 7280 cm3/g for the MSI1 RBD . Data were analyzed by two-dimensional spectrum analysis with simultaneous removal of time and radially invariant noise ( Demeler et al . , 2009; Brookes et al . , 2010 ) . Noise and diffusion-corrected , model-independent sedimentation coefficient distributions were generated using the enhanced van Holde–Weischet analysis ( Demeler et al . , 1997; Demeler and van Holde , 2004 ) .
Gene expression and metabolism are coupled at numerous levels . Cells must sense and respond to nutrients in their environment , and specialized cells must synthesize metabolic products required for their function . Pluripotent stem cells have the ability to differentiate into a wide variety of specialized cells . How metabolic state contributes to stem cell differentiation is not understood . In this study , we show that RNA-binding by the stem cell translation regulator Musashi-1 ( MSI1 ) is allosterically inhibited by 18–22 carbon ω-9 monounsaturated fatty acids . The fatty acid binds to the N-terminal RNA Recognition Motif ( RRM ) and induces a conformational change that prevents RNA association . Musashi proteins are critical for development of the brain , blood , and epithelium . We identify stearoyl-CoA desaturase-1 as a MSI1 target , revealing a feedback loop between ω-9 fatty acid biosynthesis and MSI1 activity . We propose that other RRM proteins could act as metabolite sensors to couple gene expression changes to physiological state .
When an embryo is developing , stem cells must divide and develop into many specialized types of cells . However , if cell division doesn't stop , or if it restarts later in life , it can cause tumors to form . Musashi-1 is a protein that binds to molecules of RNA and helps to promote cell growth during development: mice that lack this protein have serious brain defects and die shortly after birth . Musashi-1 is usually turned off in adult cells that are not dividing . Sometimes , however , it remains active and contributes to the growth of cancers in the brain and the gut . Reducing Musashi-1 levels in colon tumors slows their growth and causes the cancer cells to die . To find a compound that would switch off Musashi-1 , Clingman et al . screened more than 30 , 000 compounds and identified four inhibitors . One of these was oleic acid , a fatty acid that is found in olive oil and other animal and plant oils . Oleic acid interacts with Musashi-1 in a way that changes the shape of the protein . These changes mean that Musashi-1 is no longer able to regulate the genes that control cell proliferation . Clingman et al . also found that Musashi-1 promotes the activity of a particular enzyme that makes fatty acids; molecules that are needed in large quantities when cells are dividing . Musashi-1 appears to act as a ‘nutrient sensor’ , turning down the activity of this enzyme in cells when levels of oleic acid are high , and turning up enzyme activity when oleic acid levels are low . The findings of Clingman et al . further reveal how our diets can affect gene expression , and may aid the development of new treatments against cancer .