extfieldsofstudy: Medicine Biology

text:
The TOR signaling pathway regulates starvation-induced pseudouridylation of yeast U2 snRNA

Pseudouridine (Ψ) has been identified in various types of RNAs, including mRNA, rRNA, tRNA, snRNA, and many other noncoding RNAs. We have previously shown that RNA pseudouridylation, like DNA and protein modifications, can be induced by stress. For instance, growing yeast cells to saturation induces the formation of Ψ93 in U2 snRNA. Here, we further investigate this inducible RNA modification. We show that switching yeast cells from nutrient-rich medium to different nutrient-deprived media (including water) results in the formation of Ψ93 in U2 snRNA. Using gene deletion/conditional depletion as well as rapamycin treatment, we further show that the TOR signaling pathway, which controls cell entry into stationary phase, regulates Ψ93 formation. The RAS/cAMP signaling pathway, which parallels the TOR pathway, plays no role in this inducible modification.

INTRODUCTION
Pseudouridylation (Ψ) is the most abundant post-transcriptional modification found in various types of RNAs, including mRNA, tRNA, rRNA, spliceosomal snRNA, and a number of other noncoding RNAs (Reddy and Busch 1988;Bjork 1995;Massenet et al. 1998;Ofengand and Fournier 1998;Yu et al. 2005;Grosjean 2009). For instance, there are ∼100 and ∼50 Ψs in mammalian and Saccharomyces cerevisiae rRNAs, respectively (Ofengand and Fournier 1998). Likewise, eukaryotic spliceosomal snRNAs contain a large number of Ψs, and many of them are conserved across species. For example, vertebrate U2 snRNA contains 13 Ψs (Reddy and Busch 1988;Massenet et al. 1998;Karijolich et al. 2009), three of which are also found in the S. cerevisiae U2 branch site recognition region (Massenet et al. 1998).
RNA pseudouridylation can be catalyzed by two distinct molecular mechanisms, which are either RNA-dependent or RNA-independent (Ganot et al. 1997;Ni et al. 1997;Massenet et al. 1999;Ma et al. 2003Ma et al. , 2005. RNA-independent pseudouridylation is catalyzed by stand-alone protein enzymes, which recognize the substrate and catalyze the Uto-Ψ conversion (Massenet et al. 1999;Hoang and Ferré-D'Amaré 2001;Ma et al. 2003). In contrast, a family of protein-RNA complexes, known as box H/ACA sno (small nucleolar) or sca (small Cajal body-specific) RNPs (Ganot et al. 1997;Ni et al. 1997;Huttenhofer et al. 2001;Darzacq et al. 2002;Zhao et al. 2002;Ma et al. 2005;Kiss et al. 2010), is responsible for RNA-dependent pseudouridylation. In each box H/ACA RNP, there is a unique box H/ ACA RNA and four core proteins (Cbf5/Nap57/Dyskerin, Nhp2, Gar1, and Nop10) . The RNA component (box H/ACA RNA) forms a unique "hairpin-hingehairpin-tail" structure ( Fig. 1). In each of the two hairpins, there is an internal loop (also called pseudouridylation pocket) that base pairs with the substrate RNA. Thus, the target uridine is identified and subsequently converted to Ψ by the catalytic component Cbf5 (Fig. 1). The three Ψs of S. cerevisiae U2, located at positions 35, 42, and 44, are introduced by either an RNA-dependent or RNA-independent mechanism. Specifically, snR81, a box H/ACA RNP, catalyzes Ψ42 formation (Ma et al. 2005), and stand-alone enzymes Pus7 and Pus1 are responsible for the formation of Ψ35 and Ψ44, respectively (Massenet et al. 1999;Ma et al. 2003). Pseudouridylation at these three sites occurs constitutively.
We previously showed that, in addition to the three conserved Ψs discussed above, additional pseudouridines can be introduced into S. cerevisiae U2 snRNA under stress conditions (Wu et al. 2011;Ge and Yu 2013). Specifically, pseudouridylation occurs at position 93 (and to a lesser extent, at position 56 as well) when cells are grown to saturation; Ψ56 can also be induced by heat-shock. While the formation of Ψ56 is catalyzed by Pus7 in an RNA-independent manner, Ψ93 formation is catalyzed by snR81 (a box H/ACA RNP) in an RNA-dependent manner (Wu et al. 2011). The inducible nature of U2 pseudouridylation strongly suggests that this modification plays a regulatory role in pre-mRNA splicing, in which U2 snRNA participates, and indeed this proves to be true (Wu et al. 2011).
In the current work, we have studied inducible yeast U2 pseudouridylation at position 93 that is catalyzed by snR81 box H/ACA RNP, and demonstrated that Ψ93 formation is indeed induced by nutrient-deprivation and that the induction is regulated by the TOR pathway, a signaling pathway that plays an important role in determining how cells coordinate growth in response to changes in environmental conditions.

RESULTS
The formation of Ψ93 is induced by nutrient-deprivation We previously showed that growing S. cerevisiae cells to saturation resulted in pseudouridylation in U2 snRNA at positions 56 and 93 (and to a lesser extent, position 91 as well) (Wu et al. 2011), suggesting that the formation of Ψ56 and Ψ93 (and Ψ91 as well) was induced by nutrient-deprivation. In order to confirm this nutrient-mediated induction, we decided to carry out further experiments, focusing on position 93 of U2, given that the formation of Ψ93 in S. cerevisiae U2 is much clearer and more efficient compared to the formation of Ψ56 and Ψ91. After growing yeast cells to log phase (nutrient-rich) in complete SD medium, we collected the cells and equally split them into four aliquots. Two of the aliquots were transferred to new complete SD medium; one was allowed to grow only to log phase (with dilution with new medium during growth) and the other to saturation phase (with no dilution). For the other two aliquots, one was transferred to a nutrient-deprived medium (synthetic medium lacking glucose) and the other to pure water. As expected, during the course of a 1-h continuous incubation, no cell growth was observed in SD medium lacking glucose or in water. Cells were subsequently collected, and pseudouridylation assay (CMC modification followed by primer extension) was performed.
As shown in Figure 2, Ψ93 was not detected in cells that were grown to log phase in complete SD medium (lanes 1 and 2). In contrast, Ψ93 was detected in cells grown to saturation, as expected (lanes 7 and 8). Importantly, Ψ93 (and to a lesser extent, Ψ91 as well) was also detected in cells incubated in synthetic medium lacking glucose (lanes 3 and 4) FIGURE 1. snR81 box H/ACA RNP-catalyzed pseudouridylation. The secondary structure (5 ′ hairpin-H box Hinge-3 ′ -hairpin-ACA tail) and the sequences of two internal loops (pseudouridylation pockets) of snR81 box H/ACA RNA, along with their substrate sequences (paired with the internal loop sequences) are shown. The two sets of four core proteins (Cbf5, Nhp2, Nop10, and Gar1) that bind to box H/ ACA RNA are also depicted. Under normal conditions, the 5 ′ pseudouridylation pocket within the 5 ′ hairpin of snR81 RNA guides (via basepairing) the modification of U2 snRNA at position 42 (indicated), and the 3 ′ pseudouridylation pocket within the 3 ′ hairpin guides the modification of 25S rRNA at position 1051 (indicated). Under stress (nutrient-deprivation), the 3 ′ pseudouridylation pocket not only guides the modification of 25S rRNA at position 1051, but also directs the modification of U2 snRNA at position 93 (indicated at far right). Dash (-), base-pairing; cross (×), mismatch. Induced formation of Ψ93 (as well as Ψ91) under different nutrient-deprivation conditions. Log-phase cells were either kept at log phase (lanes 1 and 2), grown to saturation (lanes 7 and 8), transferred to synthetic medium lacking glucose and incubated for another hour (lanes 3 and 4), or transferred to pure water and incubated for another hour (lanes 5 and 6). Cells were then collected and cellular RNAs recovered. Pseudouridylation assay (CMC-modification followed by primer extension) was subsequently performed. Signals corresponding to the three constitutively formed pseudouridines (Ψ35, Ψ42, and Ψ44) as well as inducibly formed Ψ93 and Ψ91 are indicated by arrows. and in pure water (lanes 5 and 6). These results indicate that Ψ93 is indeed induced by nutrient-deprivation.

Blocking the TOR signaling pathway by rapamycin results in Ψ93 formation
The fact that pseudouridylation can be induced by nutrientdeprivation ( Fig. 2) prompted us to carry out further investigations to determine what signaling pathway is involved.
It is well established that nutrient-starvation (e.g., cells grown to saturation) signals the shutdown of two parallel pathways, the TOR (target of rapamycin) and the RAS/ cAMP pathways, thereby leading to the entry of cells into stationary phase (Broach 1991;Schmelzle and Hall 2000;Herman 2002;Schmelzle et al. 2004). It is thus likely that the formation of Ψ93 is controlled by either or both pathways. Since the TOR pathway can be readily blocked by rapamycin, we first tested the TOR pathway using this drug.
We treated yeast cells with rapamycin, and subsequently isolated total RNAs for pseudouridylation assay (CMC modification followed by primer extension). As shown in Figure  3, a clear Ψ93 signal was observed when rapamycin-treated cells were allowed to grow for 12 h to reach an OD 600 of ∼3 (far from saturation in YPD medium) (lane 3). We also carried out parallel control experiments where cells were treated with tunicamycin (an endoplasmic reticulum stress inducer) or DMSO (vehicle for rapamycin and tunicamycin) in exactly the same way (grown for 12 h); no or a very low level of Ψ93 was detected in tunicamycin-treated cells (OD 600 of ∼2; far from saturation), and a slightly higher level of Ψ93 was detected in cells cultured in DMSO medium (OD 600 of ∼8; close to but not at saturation) compared to the level of Ψ93 in rapamycin-treated cells (Fig. 3, cf. lanes 1 and 5 with lane 3). These results strongly suggest that the TOR signaling pathway is involved in induction of Ψ93 formation.

Depletion of the Tor proteins results in Ψ93 formation even under nutrient-rich conditions
To further confirm the TOR-dependent Ψ93 formation, we used deletion strains coupled with conditional depletion to check for dependence of the TOR pathway.
It is known that there are two complementary TOR genes (TOR1 and TOR2) in S. cerevisiae. In order to check for dependence of the TOR signaling pathway, both TOR1 and TOR2 would need to be deleted. However, it is well established that, although tor1-deletion alone is viable, deletion of both TOR1 and TOR2 leads to lethality (Loewith et al. 2002). To overcome this problem, we took advantage of the availability of a yeast strain, JK350-21a, in which both chromosomal TOR1 and chromosomal TOR2 are deleted and a plasmid (pJK15)-borne TOR2 is expressed when cultured in Gal medium (its expression is under the control of the Gal promoter) (Loewith et al. 2002). We grew the strain in Gal medium (YPGal) to log phase (OD 1.0), then transferred some cells to glucose medium (YPD, starting OD of 0.03), and then allowed the cells to grow for an additional 19 h, reaching an OD of 1.2, or 30 h to an OD of 1.3, thus depleting Tor2 while keeping the medium nutritionally rich. U2 RNA from these cells was then assayed for pseudouridylation. As shown in Figure 4A, while no Ψ93 was observed in U2 RNA isolated from log-phase cells (OD of 1) cultured in Gal-medium (lanes 1 and 2), a significant escalation of Ψ93 (and to a lesser extent, Ψ91 as well) was detected when cells were switched to glucose medium and grown to an OD of 1.2 or 1.3 (lanes 3-6). The level of Ψ93 (and Ψ91) in Tor2-depleted cells was comparable to that in Tor2-intact cells when they were grown in Gal-medium to saturation (OD 12) (lanes 7 and 8).
We also used another yeast strain, JK350-18a, in which only chromosomal TOR2 is replaced by the plasmid-borne TOR2 (pJK15), whose expression is controlled by the Gal promoter. We cultured this strain in exactly the same way as we did for strain JK350-21a, where both chromosomal TOR1 and TOR2 are replaced by the plasmid-borne TOR2 (pJK15) (see above). As shown in Figure 4A, when the FIGURE 3. Ψ93 formation induced by rapamycin treatment. Yeast cells were treated with DMSO alone (lanes 1 and 2), rapamycin dissolved in DMSO (lanes 3 and 4), or tunicamycin dissolved in DMSO (lanes 5 and 6) for ∼12 h, and then harvested. Pseudouridylation assay was then performed. At the moment of cell harvest, DMSO-treated cells reached an OD 600 of ∼10, whereas rapamycin-and tunicamycin-treated cells reached an OD 600 of only ∼2-3. medium is still rich in nutrients, depletion of Tor2 alone (wild-type TOR1 background) was not sufficient to induce the formation of Ψ93 (lanes 11-14), just as seen in log-phase cells (lanes 9 and 10).
To ensure that Tor2 was indeed depleted after medium switch, we performed Western blotting using an anti-Tor2 antibody. As shown in Figure 4B, at the time of medium switch (lanes 1 and 5) or when cells were grown continuously in YPGal to saturation (lane 4), there was a clear Tor2 signal; 19 and 30 h after medium switch, no Tor2 was detected (lanes 2,3,6, and 7). In contrast, a similar level of actin, an internal loading control, was detected across all samples (lanes 1-7).
We also measured the level of snR81, a box H/ACA RNA responsible for directing the formation of Ψ93, in cells before and after Gal-to-Glu medium switch. Our primer-extension results showed that the snR81 level, relative to U2 (an internal loading control), was essentially the same under all culturing conditions (before and after medium switch) (Fig. 4C), suggesting that the TOR pathway regulates something else (rather than the level of the guide RNA) that is important for the induced pseudouridylation.
Depletion of the Ras proteins has no effect on induced formation of Ψ93 Since the RAS/cAMP signaling pathway, which is considered to function in parallel with the TOR pathway, is thought to also respond to nutrient-deprivation (Broach 1991;Schmelzle and Hall 2000;Herman 2002;Schmelzle et al. 2004; Ramachandran and Herman 2011), we next tested the possible interrelationship between the formation of Ψ93 and the shut-down of the RAS/cAMP pathway. Given that there are two interchangeable Ras proteins, Ras1 and Ras2, we created a conditional Ras-depletion strain where the chromosomal RAS1 was deleted and the original RAS2 promoter was replaced by a GAL promoter. Thus, in the new strain, only RAS2 is expressed when cultured in galactose-containing medium.
As expected, when the new strain was grown to the log phase (OD 1.2) in galactose-containing medium (nutrientrich), no Ψ93 formation was detected (Fig. 5, lanes 1 and  2); in contrast, when cells were further grown in the galactose-containing medium to saturation (OD 15 [nutrientstarvation]), Ψ93 was detected (Fig. 5, lanes 7 and 8).  1 and 2). Cells were transferred to glucose medium and allowed to grow to OD of 1.2 (lanes 3 and 4) or 1.3 (lanes 5 and 6), or were allowed to continue to grow in galactose medium to OD 12 (lanes 7 and 8). In addition, another strain (tor2Δ pTOR2 [under the control of PGal]), where only chromosomal TOR2 is replaced by a plasmid borne and Gal-controlled TOR2, was grown in exactly the same way as above. Lanes 9 and 10, cells grown in galactose medium to OD 1.1; lanes 11 and 12, cells grown in glucose medium to OD 2.0 after switch from galactose medium to glucose medium; lanes 13 and 14, cells grown in glucose medium to OD 4.4 after switch from galactose medium to glucose medium. Upon completion of cell culture, total RNA was recovered and pseudouridylation assay conducted. Signals representing constitutively formed pseudouridines (Ψ35, Ψ42, and Ψ44) and inducibly formed Ψ93 and Ψ91 are indicated. (B) Upon completion of cell culture, total protein was also isolated and Western blot against Tor2 and Act1 (control) performed. (C) Total RNA described in A was also used for Northern analysis to monitor the level of snR81 box H/ACA RNA relative to the level of U2 snRNA (control). However, when Ras2 was depleted in nutrient-rich medium, resulting from the change of medium (from galactose-containing medium to glucose-containing medium), no Ψ93 was detected (Fig. 5, lanes 3-6). These results indicate that, unlike the TOR pathway, the RAS/cAMP pathway is not involved in the regulation of Ψ93 formation.

DISCUSSION
We have shown that switching yeast cells from a nutrient-rich medium to a nutrient-deprived medium or water (thus leading to entry of cells into stationary phase) induces the formation of Ψ93 in U2 snRNA. We have further shown that upon depletion of both Tor1 and Tor2 or blocking the TOR pathway by introducing rapamycin, the TOR pathway inhibitor, pseudouridylation, is also induced at position 93 of U2 even under nutrient-rich conditions, indicating that the TOR signaling pathway, which controls cell entry into stationary phase (Broach 1991;Schmelzle and Hall 2000;Herman 2002;Schmelzle et al. 2004), also controls induced formation of Ψ93. Using a similar approach, we have also tested the interrelationship between the RAS/cAMP signaling pathway and the formation of Ψ93, and found that the RAS/cAMP pathway plays no role in Ψ93 formation.
It is well documented that there are two TOR complexes in the cell, TORC1 and TORC2, but only one, TORC1, is the target of rapamycin (Loewith et al. 2002). Thus, the fact that introduction of rapamycin into cells induces the formation of Ψ93 indicates that it is TORC1 (or inactivation of TORC1) that is responsible for this induced modification. It is also well established that there are two forms of TORC1 complex (containing either Tor1 or Tor2) that are functionally indistinguishable (Loewith et al. 2002). Consistently, we have shown that in order to induce the formation of Ψ93, both Tor1 and Tor2 have to be depleted, arguing for the functional redundancy of the two forms of TORC1 in regulating the formation of Ψ93. The fact that inactivation of TORC1 leads to Ψ93 formation also suggests that U2 pseudouridylation at position 93 (and perhaps at other starvation-inducible sites as well) is the default pathway that is inhibited or kept in check by the TOR signaling pathway in fast growing cells (nutrient-rich environment). This implies that pseudouridylation at the inducible sites disfavors pre-mRNA splicing (part of gene expression), an important process for growing cells. In this regard, we have shown that Ψ93 does indeed have an inhibitory effect on splicing (Wu et al. 2011).
It is known that the 3 ′ pseudouridylation pocket of snR81, which guides the induced formation of Ψ93, is also naturally responsible for the formation of Ψ1051 in 25S rRNA (a constitutive pseudouridylation site) under both saturation and nutrient-rich conditions ( Fig. 1; Ma et al. 2005). Interestingly, the sequence flanking position 93 of U2 is not exactly the same as that of position 1051 of 25S rRNA (Fig. 1). In fact, while the 3 ′ pseudouridylation pocket of snR81 forms perfect base-pairing interactions with the sequence flanking position 1051 of 25S rRNA, it matches imperfectly with the sequence surrounding position 93 of U2. In the latter case, there are two mismatches in the middle of the 5 ′ base-pairing duplex (Fig. 1). Such an imperfect pairing will certainly weaken the interaction between the guide RNA (snR81) and the substrate (sequence surrounding position 93 of U2). According to what we know about the RNA-guided pseudouridylation mechanism (Xiao et al. 2009), position 93 would not be considered a snR81 substrate. Then how can snR81 direct pseudouridylation at position 93 under starvation conditions?
It is possible that the enzyme (snR81) is overexpressed under nutrient-deprivation conditions (triggered by inactivation of the TOR pathway). An elevated level of snR81 could allow the imperfect site (position 93 of U2) to be recognized and modified. However, our Northern analysis indicates that the level of snR81 does not change after switching from nutrient-rich to nutrient-deprived conditions (Fig.   FIGURE 5. The RAS/cAMP signaling pathway is not involved in the regulation of Ψ93 formation. Yeast strain (ras1Δ GAL-RAS2), where the chromosomal RAS1 was deleted and the chromosomal RAS2 promoter was replaced by a GAL promoter, was cultured in exactly the same way as in Figure 4A. Lanes 1 and 2, cells grown in galactose medium to OD 1.5; lanes 3 and 4, cells grown in glucose medium to OD 1.0 after transferring some cells from galactose medium to glucose medium; lanes 5 and 6, cells grown in glucose medium to OD 1.5 after transferring some cells from galactose medium to glucose medium; lanes 7 and 8, cells grown in galactose medium to saturation (OD 15). Upon completion of cell culture, total RNA was isolated and pseudouridylation assay performed. Signals representing constitutively formed pseudouridines (Ψ35, Ψ42, and Ψ44) and inducibly formed Ψ93 are indicated. 4C), suggesting that the notion that overexpression of snR81 drives induced modification is not true. However, although the level of snR81 does not change, our result cannot rule out the possibility that the enzyme/substrate relocalizes upon induction (inactivation of the TOR pathway), such that snR81 (normally resides in or travels between nucleoli and Cajal bodies) becomes concentrated at the site of its substrate (U2), allowing position 93 to be modified. This possibility is yet to be tested. Finally, we also consider another possibility, where inactivation of the TOR pathway triggers the expression or redistribution of a protein(s) (or chaperone) that helps snR81 RNP (or substrate U2) restructure, thus increasing the binding affinity between the guide RNA and the imperfect substrate and permitting efficient pseudouridylation at the new site (position 93).
Our results have unambiguously demonstrated that the TOR signaling pathway regulates starvation-inducible pseudouridylation at position 93 of U2. Interestingly, besides Ψ93, there are a number of additional starvation-inducible pseudouridylation sites in U2 (e.g., position 56 and possibly position 91 as well, this study) and other spliceosomal snRNAs (G Wu and YT Yu, unpubl.), and even in mRNA (Carlile et al. 2014;Schwartz et al. 2014;Li et al. 2015). It is quite possible that pseudouridine formation at these positions is also regulated by the TOR pathway. Our current work opens the door to investigation into this possibility.

Yeast culture
To examine inducible pseudouridine formation in a nutrient-deprivation state, a freshly streaked BY4741 colony was picked and inoculated into 100 mL of YPD or synthetic complete medium with dextrose (SD). When OD 600 of the culture reaches 1.0, cells were spun down at 6000g. Cells were then transferred to a synthetic medium lacking dextrose (the same as SD except that there is no dextrose), or pure water at an OD of 1.0, and allowed to grow for another hour. For depletion of Tor2 (from a plasmid-borne PGal promoter) in the tor1Δ tor2Δ background, a colony of freshly streaked JK350-21a was inoculated into YPGal (synthetic complete medium with galactose) medium. When the culture reached an OD 600 of 1.0, cells were spun down at 6000g, and then transferred to YPD medium at a starting OD of 0.03. Cells were harvested 19 h later at an OD of 1.2 or 30 h later at an OD of 1.3. For depleting Tor2 (from a plasmid-borne PGal promoter) in the tor2Δ background, a colony of JK350-18a was inoculated firstly into YPGal medium. Cells were spun down at an OD of 1.0 and transferred to YPD medium at a starting OD of 0.002. Cells were harvested 19 h later at an OD of 2.0, or 30 h later at an OD of 4.4. For depletion of Ras2 (from a PGal promoter) in ras1Δ background, strain ras1Δ PGAL-RAS2 was inoculated into YPGal medium and grown to an OD of 1.5. Cells were spun down and transferred to YPD at an OD of 0.03, and harvested 19 h later at an OD of 1.0, or 30 h later at an OD of 1.5 (diluted with new YPD when necessary).

Drug treatment of yeast culture
A colony from freshly streaked BY4741 was inoculated into YPD medium and grown to early log phase. Cells were then used to inoculate new cultures at an OD of 0.5. DMSO, rapamycin (1 μg/mL), or tunicamycin (10 μg/mL) was added to the new cultures (YPD medium). Cells were allowed to grow for 12 h, and at that point, the cell cultures, which were treated with DMSO, rapamycin, or tunicamycin, reached an OD of 8.0, 3.0, or 2.0, respectively.

RNA extraction
RNA was isolated using TRIzol (Invitrogen). Briefly, ∼30 OD cells were spun down, washed with cold PBS twice, and resuspended in 1 mL of TRIzol with ∼100 µL of 0.5 mm glass beads. Yeast cells were then lyzed by vigorous vortexing. After centrifugation to remove cell debris and glass beads, the supernatant was mixed with 200 µL of chloroform and vortexed for 1 min. After centrifugation to separate the phenol and aqueous phases, the clear upper phase was transferred to a new tube and an equal volume of isopropanol was used to precipitate the RNA. The RNA was then washed with 70% ethanol and briefly air-dried. RNA was resuspended in ddH 2 O and immediately used for subsequent experiments.

CMC-primer extension assay
Pseudouridines were detected by the CMC-primer extension assay, as previously described (Wu et al. 2011). Briefly, 10 µg of total cellular RNA was incubated with CMC (Sigma, cat. no. 29469) in reaction buffer (50 mM Bicine, 0.15 M CMC, 4 mM EDTA, and 7 M urea, pH 8.3) at 37°C for 20 min. RNA was then precipitated with 2.5 volume of ethanol and washed with 70% ethanol, and then resuspended in sodium carbonate (50 mM, pH 10.4) and incubated for 2 h at 37°C. RNA was then once again precipitated and cleaned up using ethanol. The resulting RNA pellet was suspended in annealing buffer (50 mM Tris-Cl pH 8.3, 60 mM NaCl, 10 mM DTT) and was heated at 90°C for 3 min in the presence of a 5 ′ 32 P-labeled primer. The primer used for mapping Ψ93 in U2 snRNA was 5 ′ -CTCTTCCCGTCCATTTTATTATTTTG-3 ′ . After heat-denaturation, the RNA-oligo mix was immediately chilled on ice, and 1.5 units of AMV reverse transcriptase and reaction buffer [9 mM Tris-HCl Ph 8.3, 10 mM NaCl, 1.8 mM DTT, 5 mM Mg (OAc) 2 , and 0.3 mM of each dNTP] were added. The reaction was carried out at 42°C for 20 min. Reaction products were resolved on an 8% polyacrylamide, 8 M urea gel (acrylamide:bis = 19:1, EM science) and visualized via autoradiography.


ChREBP promotes the differentiation of leukemia-initiating cells to inhibit leukemogenesis through the TXNIP/RUNX1 pathways

Targeting leukemia-initiating cells (LICs) is the key to eradicating leukemia and preventing its relapse. Recent studies have indicated that metabolic regulation may play a critical role in the maintenance of stemness in LICs, although the detailed mechanisms are poorly understood. Herein, we provide intriguing evidence showing that a glucose-responsive transcription factor, carbohydrate responsive element binding protein (ChREBP), served as a tumor suppressor rather than an oncogene, as previously described, to inhibit the development of acute myeloid leukemia by promoting the differentiation of LICs. Using an MLL-AF9-induced murine leukemia model, we demonstrated that the deletion of ChREBP resulted in the blockage of the differentiation of LICs and significantly reduced survival in ChREBP-null leukemic mice. However, ChREBP was not required for the normal repopulation abilities of hematopoietic stem cells. ChREBP promoted leukemia cell differentiation through the direct inhibition of RUNX1 or the transactivation of TXNIP to downregulate the RUNX1 level and ROS generation. Moreover, knockdown of ChREBP in human leukemia THP1 cells led to markedly enhanced proliferation and decreased differentiation upon PMA treatment. Collectively, we unraveled an unexpected role of ChREBP in leukemogenesis, which may provide valuable clues for developing novel metabolic strategies for leukemia treatment.

In the past decades, multiple oncogenic pathways have been found to be involved in metabolic alterations during cancer development. For example, PI3K/AKT/ mTOR signaling is one of the most commonly altered pathways in human cancers, including leukemia and lymphoma. Studies have shown that the dysregulation of many genes that are involved in glycolysis or oxidative phosphorylation, such as glucose transport protein 1 (GLUT1), glucose-6-phosphate dehydrogenase (G6PD) and isocitrate dehydrogenase 1/2 (IDH1/2), leads to a high risk of the occurrence of leukemia or other tumors [8][9][10][11]. Although it is well known that cancer cells tend to use aerobic glycolysis even under normoxic conditions (Warburg's effect) [12], studies by Eleni D. Lagadinou and colleagues have illustrated that, instead of glycolysis, LICs utilize oxidative phosphorylation as the main energy source through the upregulation of BCL-2 [13]. More effort is required to unravel the relationship between LIC stemness and the metabolism of different nutrients.
Carbohydrate response element binding protein (ChREBP) is a large transcription factor containing approximately 850 amino acids that is highly conserved among species [14]. ChREBP contains several critical functional domains, such as a DNA-binding motif of the bHLH/ZIP type and proline-rich regions. ChREBP has been reported to be a key transcription factor that regulates both glycolysis and lipogenesis in hepatocytes [15,16]. A high glucose level leads to the dephosphorylation of ChREBP, followed by its translocation into the nucleus and binding to the promoters of target genes related to glycolysis and lipogenesis, including L-PK, ACC, FAS, ACC1, SCD1 and TXNIP [17]. Evidence has shown that ChREBP promotes glycolysis and proliferation in both normal cells and solid tumors. For example, ChREBP is critical for the glucose-stimulated proliferation of pancreatic β-cells [18]. Knockdown of ChREBP caused a remarkable decrease in glycolysis, lipogenesis and the synthesis of nucleic acids but an increase in oxidative phosphorylation and the ROS level to inhibit the growth of colorectal cancer cell lines [19]. It is well known that certain ROS levels may be critical for many physiological activities. However, a high level of ROS may enhance the differentiation or reduce the proliferation of cancer cells [20,21]. How ChREBP controls the ROS levels and the metabolic functions of ChREBP in LICs remain largely unknown.
Herein, we demonstrate that ChREBP is highly upregulated in mouse AML cells (as well as LICs) and acts as a tumor suppressor, rather than as an oncogene as previously reported, to inhibit the differentiation of LICs from a MLL-AF9-induced murine AML model. ChREBP deletion led to notably accelerated leukemia progression. ChREBP collaborated with TXNIP and RUNX1 to promote LIC differentiation. This study unravels a differential role of ChREBP in leukemia development compared to that in other solid tumors, which may open a new avenue for the treatment of leukemia or other types of cancers.

results chrebP serves as a tumor suppressor to inhibit leukemogenesis in a murine AMl model
ChREBP has been found to be a key regulator of glycolysis and lipogenesis in hepatocytes. Interestingly, we demonstrated that ChREBP was also highly expressed in mouse YFP + leukemia cells or YFP + Mac-1 + c-Kit + LICs from a MLL-AF9-induced AML model, as determined by RT-PCR ( Figure 1A). The LICs expressed much higher levels of ChREBP compared to normal HSCs, as evaluated by quantitative RT-PCR, although their ChREBP expression level was lower than that in the total bone marrow (BM) leukemia cells (YFP + cells, Figure 1B). Next, we measured the frequencies of YFP + leukemia cells in the peripheral blood of WT and ChREBP-null (ChREBP +/+ and ChREBP -/hereafter) recipient mice (Supplementary Figure 1A-1B), which only expressed myeloid cell markers (Mac-1 and Gr-1, Supplementary Figure 1C-1D), but not lymphoid cell markers (CD3 and B220, Supplementary Figure 1E-1F). However, there were no significant differences in the percentages of YFP + leukemia cells in the peripheral blood, the sizes of the leukemic spleens and livers, the YFP + Mac-1 + c-Kit + LIC frequencies and the survival between the recipients receiving WT and ChREBP-null cells (Supplementary Figure 1A-1B and 1G-1K), We noticed that there was a slightly increased frequency of ChREBP-null YFP + Mac-1 + Gr-1leukemia cells in the peripheral blood, although the increase was not significant (Supplementary Figure 1C-1D).
To evaluate the roles of ChREBP in leukemogenesis, we conducted a secondary transplantation with WT and ChREBP-null primary leukemia cells. Although we did not observe significant changes in the frequencies of YFP + leukemia cells in the peripheral blood at 5 weeks posttransplantation ( Figure 1C-1D), the recipients of MLL-AF9-transduced ChREBP-null cells had a significantly reduced survival upon secondary transplantation ( Figure 1E). Consistently, a subsequent third transplantation experiment also exhibited that ChREBP-null leukemic mice died much faster compared to WT controls ( Figure 1F). In contrast, we revealed that ChREBP was not required for normal hematopoiesis, as determined by a competitive reconstitution analysis ( Figure 1G), which indicates that ChREBP may be an ideal target for LICs. Due to the slight phenotypic changes in the primary recipient mice, we decided to focus on the phenotypes in the secondary recipient mice hereafter.

chrebP promotes the differentiation of lIcs
To further confirm the changes in the differentiation of ChREBP-null AML cells, we first examined the frequencies of YFP + Mac-1 + Gr-1leukemia cells in the BM of the mice upon primary transplantation, which was significantly increased compared to the controls (17.75 ± 2.54% vs 6.85 ± 1.72%, Figure 2A). This change in the Gr-1 expression levels, which represent the extent of myeloid differentiation, indicated that differentiation was blocked in ChREBP-null leukemia cells. Wright-Giemsa staining further revealed that many more immature blast cells appeared in ChREBP-null recipients than in WT counterparts ( Figure 2B-2C). Moreover, there was an approximately 2-fold higher frequency of YFP + Mac-1 + Gr-1leukemic cells in both the peripheral blood ( Figure 2D-2E) and the BM ( Figure 2F-2G) of ChREBPnull recipients upon secondary transplantation. This was consistent with the more immature blast cells found in the recipients of ChREBP-null leukemia cells ( Figure 2H-2I).
Meanwhile, the phenotypic LIC frequencies in the recipients of ChREBP-null leukemia cells were markedly increased ( Figure 2J-2K). An in vitro functional assay using colony-forming units further demonstrated that there were more large colonies (diameter > 500 mm) and a notably increased cell number in colonies derived from ChREBP-null leukemia cells isolated from secondary recipients, indicating their enhanced clonogenic potential ( Figure Figure 2E-2F). These results suggest that ChREBP may contribute to enhanced LIC differentiation, a decreased LIC pool and delayed leukemogenesis.

chrebP controls the differentiation of lIcs through tXnIP
Because ChREBP has been well known to be involved in glycolysis and lipogenesis in hepatocytes and may be involved in the regulation of differentiation in LICs, as reported here, we next tried to identify the potential targets related to the cells' phenotypes. Surprisingly, we did not find notable changes in several glycolysis-related genes (GLUT1, PKM2), as measured by quantitative RT-PCR in ChREBP-null LICs ( Figure 3A). Consistently, the ATP level and lactate production, which are indicative of glycolysis (extracellular acidification rate, ECAR), remained unchanged, as determined with the Seahorse XF96 extracellular flux analyzer (Supplementary Figure 3A-3B). However, RUNX1 and GATA2 (but not PU.1), which are two critical transcription factors for the inhibition of differentiation, were dramatically increased upon ChREBP deletion ( Figure 3A). Moreover, several known targets important for lipogenesis, including FAS, ACC1, SCD1 and TXNIP (but not ACL), were markedly downregulated in ChREBP-null LICs. Many studies indicate that lipogenesis is required for the growth of cancer cells, which contradicts the marked decrease of FAS, ACC1 and SCD1 (genes that enhance lipogenesis) and accelerated leukemia development upon ChREBP deletion reported here. Interestingly, we found that TXNIP (a critical gene that inhibits lipogenesis [5]) was downregulated in ChREBP-null LICs. TXNIP has been reported to be involved in many cellular and physiological processes in addition to its function in the negative regulation of lipogenesis [22,23]. For example, TXNIP can serve as an inhibitor for the activity of thioredoxin [4,23], a mediator of glucose metabolism [5,25], a tumor suppressor in T-cell leukemia or other  [26][27][28] or a critical regulator of the differentiation of natural killer cells [29]. Taken together, all these clues led us to speculate that TXNIP may be a potential target of ChREBP to suppress leukemia development.
To ask whether TXNIP regulates the differentiation of LICs, we further evaluated TXNIP expression levels by western blotting and demonstrated that the TXNIP levels were strikingly reduced in ChREBP-null LICs ( Figure 3B). Because the increased expression of TXNIP may lead to enhanced ROS levels, which is a potent driver of differentiation [30], we measured the ROS levels in leukemia cells by staining with DCFDA. Consistently, both ChREBP-null YFP + BM leukemia cells and LICs had relatively lower ROS levels compared to the WT controls ( Figure 3C-3D). To confirm whether TXNIP is a direct downstream target for ChREBP, we overexpressed TXNIP in ChREBP-null leukemia cells and transplanted them into recipient mice. Our results clearly displayed that the mice transplanted with the TXNIP-overexpressing, ChREBPnull AML cells developed leukemia much more slowly than those transplanted with the ChREBP-null control cells, which were comparable to their WT counterparts ( Figure 3E). Meanwhile, the overexpression of TXNIP efficiently rescued the phenotypes in ChREBP-null leukemic mice, as shown by the decreased frequencies of YFP + peripheral blood leukemia cells, YFP + Mac-1 + GR-1undifferentiated leukemia cells, and YFP + Mac-1 + c-Kit + LICs ( Figure 3F-3H) as well as the increased percentages of mature leukemia cells in the BM (Supplementary Figure 4A-4B). Consistently, RUNX1, but not GATA2, was remarkably upregulated in ChREBP-null leukemia cells, which could be partially suppressed by the ectopic expression of TXNIP (Supplementary Figure 4C). Collectively, these results indicate that ChREBP suppresses the leukemogenic potential of AML cells through TXNIP, which may contribute to the enhanced differentiation of LICs through RUNX1.

chrebP transactivates tXnIP to enhance differentiation by inhibiting runX1
To figure out how TXNIP is regulated by ChREBP, we first constructed a lentiviral luciferase reporter (plenti-TXNIP-GFP) containing a conserved ChREBP-binding site ( Figure 4A) and demonstrated a dose-dependent activation of TXNIP in THP1 cells with exogenous supplementation of glucose (glucose can effectively induce ChREBP activation, as reported previously) ( Figure 4B). ChREBP also directly bound to the promoter of TXNIP, as demonstrated by chromatin Immunoprecipitation (ChIP) assay in THP1 cells (Supplementary Figure 5A), or as determined using a luciferase reporter in THP1 or 293T cells ( Figure 4C-4D). We further confirmed the activation of TXNIP by detecting its protein levels by western blotting at 12 h and 24 h posttreatment ( Figure 4E). In contrast with the above data, the ChREBP-mediated transactivation of TXNIP was totally abrogated when ChREBP was knocked down in THP1 cells (Supplementary Figure 5B and Figure 4E). Similar to our results in ChREBP-null mice, knockdown of ChREBP with specific shRNAs in THP1 cells led to a significant upregulation of RUNX1 ( Figure 4F). Interestingly, we also found that ChREBP, but not TXNIP, directly bound to the RUNX1 promoter, as determined using a luciferase assay ( Figure 4G-4H). However, neither ChREBP nor TXNIP bound to the GATA2 promoter (Supplementary Figure 5C-5D). These results suggest that ChREBP mainly collaborates with TXNIP and RUNX1 to mediate their function in LIC differentiation.
To further evaluate whether ChREBP has a similar effect on the differentiation of THP1 cells as that in mouse LICs, we knocked down ChREBP in THP1 cells and examined the morphologic changes after treatment with PMA, which can potently induce the differentiation of THP1 cells. As shown in Figure 4I-4J, much lower frequencies of differentiated THP1 cells were observed in ChREBP-knockdown cells, indicating that ChREBP promotes differentiation in myeloid leukemia cells. Consistently, levels of Mac-1 and CD14, two markers for myeloid differentiation, were remarkably decreased in ChREBP-knockdown THP1 cells upon PMA treatment ( Figure 4K-4N). We also noticed that the downregulation of ChREBP in THP1 cells resulted in a notable expansion in vitro, even two days after knockdown (Supplementary Figure 5E). Similar effects were observed in other ChREBP-knockdown leukemia cell lines, including U937 and HL60 (Supplementary Figure 5F-5G). In contrast, the overexpression of ChREBP caused a marked delay in the growth of THP1 cells (Supplementary Figure 5H-5I).

tXnIP suppresses the proliferation and promotes the differentiation of a mouse leukemia cell line
To ask whether TXNIP has a direct impact on the proliferation of leukemia cells, we overexpressed TXNIP in C1498 cells, which is a myeloid leukemia cell line, and demonstrated that the overexpression of TXNIP dramatically inhibited the expansion of C1498 cells in vitro ( Figure 5A-5B). As expected, RUNX1 was significantly downregulated and the ROS level was slightly increased upon TXNIP overexpression ( Figure 5C-5D). TXNIPoverexpressing C1498 cells were prone to differentiation, as shown by Wright-Giemsa staining ( Figure 5E-5F). Flow cytometric analysis also showed a notably increased percentage of Mac-1 + or Gr-1 + differentiated cells ( Figure 5G-5H). These data suggest that TXNIP enhances the differentiation of leukemia cells. In summary, as shown in the working model ( Figure 5I), ChREBP directly suppresses RUNX1 expression or transactivates TXNIP to further downregulate the expression of RUNX1 and enhance the ROS levels to promote the differentiation of LICs, which remarkably delays leukemogenesis.

dIscussIon
In this study, we provide intriguing evidence that ChREBP serves as a tumor suppressor, rather than as an oncogene, in leukemia development. This is similar to some other transcription factors, such as EZH2, which can function as both a tumor suppressor and an oncogene in different types of cancers [31][32][33]. A genome-wide analysis of ChREBP targets also reveals that ChREBP may act as either a transcriptional repressor or an activator [34].
In this study, we showed that ChREBP was highly upregulated in YFP + leukemia cells and LICs compared to normal BM cells and HSCs, indicating that ChREBP served as an oncogene. However, we also noticed that the ChREBP expression level was lower in LICs compared to the total YFP + leukemia cells, which suggests that ChREBP may be a tumor suppressor. Thus far, most evidence indicates that ChREBP acts as an oncogene in many solid tumors, such as liver and colon cancers [19]. However, very few reports also indicate that a high expression level of ChREBP may be related with the increased survival of patients with certain types of breast cancers, indicating its inhibitory role in some tumors [35], which is similar to what we reported here. Currently, we do not find any reports related to the relationship between ChREBP mutations and human cancers. An in-depth analysis and more evidence are required to confirm the differential roles of ChREBP in different types of cancers.
ChREBP has been reported to be involved in many physiological or pathological activities, including glycolysis, lipogenesis, amino acid metabolism and cell motility [36][37][38][39][40]. TXNIP, a critical downstream target of ChREBP and a negative regulator of lipogenesis, has numerous functions in glucose and lipid metabolism, redox status and NK cell differentiation. Although we currently do not have much evidence to show that lipogenesis may play a critical role in leukemogenesis through TXNIP, we demonstrated that TXNIP could enhance ROS generation and downregulate the expression levels of RUNX1 to promote the differentiation of LICs. It has been suggested that ROS levels fine-tune the cell fates of many tumors. Low levels of ROS may be required for cell proliferation, differentiation and survival, whereas excessively high levels of ROS may lead to enhanced oxidative stress and cell death. Our study suggests that ChREBP transactivates TXNIP and enhances the production of ROS to promote leukemia differentiation and delay leukemogenesis, although the detailed mechanisms await further investigation. To our knowledge, this is the first body of evidence showing that ChREBP/TXNIP/RUNX1 signaling plays an essential role in the suppression of leukemogenesis. We also speculate that some other metabolic genes, similar to ChREBP, may have dual functions in the initiation, development or relapse of different types of tumors. RUNX1 and GATA2 have been considered two critical transcription factors for stemness of HSCs [41][42][43][44]. The levels of RUNX1 and GATA2 are finetuned during hematopoiesis as well as leukemogenesis. Interestingly, there exist dose effects of both RUNX1 and GATA2 in leukemia development because either too low or too high levels of these proteins significantly influence the initiation, maintenance and relapse of leukemia [45]. Notably, our data indicate that ChREBP directly binds to the RUNX1 promoter, but not to the GATA2 promoter, to promote the differentiation of LICs. TXNIP can further suppress RUNX1, but not GATA2, expression. Meanwhile, both ChREBP and TXNIP effectively inhibit the mRNA levels of RUNX1 and GATA2, although the underlying mechanism remains largely unknown. In summary, we provide interesting and important clues showing that ChREBP may play differential roles in different types of cancers. ChREBP acts as a tumor suppressor to promote the differentiation of LICs but does not promote the differentiation of normal HSCs. ChREBP and its downstream molecules, including TXNIP, RUNX1 and GATA2, may be ideal therapeutic targets for certain types of leukemia.

Mice
The ChREBP-null mice were kindly provided by Dr.

retroviral infection and transplantation
The MSCV-MLL-AF9-IRES-YFP-encoding plasmid [46] and the packaging plasmid pCL-ECO were used to co-transfect 293T cells using the calcium phosphate transfection method. MLL-AF9-expressing retroviruses were collected 48-72 h after transfection. Linfetal liver cells were isolated from WT and ChREBP-null mice and infected with MLL-AF9 retroviruses by spinoculation in the presence of 4 μg/mL Polybrene. The cells were cultured overnight in DMEM with 10% FBS, 20 ng/mL SCF, 20 ng/mL IL-3 and 10 ng/mL IL-6, followed by another round of spin infection. The infected cells (300,000) were transplanted into lethally irradiated (1,000 rad) C57BL/6 mice by retro-orbital injection. YFP + bone marrow leukemia cells from primary transplanted mice were further isolated and injected into recipient mice for serial transplantation. For the rescue experiment, the retroviral plasmid MSCV-TXNIP-IRES-mCherry was used to transfect 293T cells, and the resulting retroviral supernatant was collected for spin infection with ChREBP-null leukemia cells, followed by retro-orbital injection into recipient mice.

Flow cytometry
Peripheral blood was collected by retro-orbital bleeding, and bone marrow cells were isolated from the femurs and tibias of leukemic mice. Flow cytometry and cell cycle analyses were performed as we described previously [47]. Briefly, leukemia cells were stained with anti-mouse Mac-1-APC, anti-mouse Gr-1-PE, antimouse CD3-APC, anti-mouse B220-PE or anti-mouse c-Kit-PE monoclonal antibodies (eBioscience). The cell cycle stages were evaluated with either Ki-67/7-AAD staining (BD Pharmingen) or a 5-bromo-2ʹ-deoxyuridine (BrdU) incorporation assay. For the analysis of apoptosis, leukemia cells were stained with PE-conjugated anti-Annexin V and 7-AAD (BD Pharmingen) according to the manufacturer's protocol. For the measurement of ROS, the cells were incubated with 1 µM 5-(and-6)-carboxy-2ʹ,7ʹdichlorofluorescein diacetate (carboxy-DCFDA, Invitrogen) for 30 minutes at 37°C, followed by flow cytometric analysis. For the examination of the BrdU incorporation assay, leukemic mice were subjected to three intraperitoneal injections of BrdU (Sigma; 3 mg/24 hours) in PBS. The BM cells were fixed, permeabilized and denatured, followed by antibody staining with anti-BrdU-APC according to the manufacturer's instructions (BD Pharmingen).

Western blotting
Equal numbers of bone marrow leukemia cells or leukemia cell lines were collected for further immunoblotting. Whole cell lysates were electrophoresed on 8-10% sodium dodecyl sulfate polyacrylamide gels and transferred onto polyvinylidene difluoride membranes (Millipore). The membranes were blocked with 5% non-fat milk/TBS and incubated with primary antibodies at 4°C overnight. The following antibodies were used for blotting: anti-TXNIP (Proteintech), anti-ChREBP (Nova Biologicals), anti-β-actin (Sigma), anti-HA (Sigma) and anti-StrepII (Genescript). differentiation analysis of tHP1 or c1498 cells THP1 cells infected with shRNA targeting ChREBP were treated with 100 ng/mL of phorbol 12-myristate 13-acetate (PMA) for 48 h as previously described [48]. Myeloid differentiation was monitored by flow cytometric analysis with antibodies against human Mac-1 and CD14 (eBiosciences) or Wright-Giemsa staining. In the other experiment, a mouse myeloid leukemia cell line, C1498 (kindly provided by Fubin Li at Shanghai Jiao Tong University School of Medicine), was induced with PMA for differentiation, followed by examination with Wright-Giemsa staining and flow cytometric analysis.

lentivirus construction, infection and cell proliferation assays
The lentiviral vector GIPZ was used to express shRNAs designed to target ChREBP (sequences listed in Supplementary Table 1). Using the calcium phosphate transfection method, lentivirus constructs together with the packaging plasmids pSPAX2 and pMD2G (4:3:1) were mixed and transfected into 293T cells. The supernatant, which contained lentiviruses, was harvested 48 h and 72 h later. Lentiviruses were used for subsequent infections of leukemia cell lines, including THP1, U937 and HL60 cells. Cells (200,000) treated with shRNAs targeting ChREBP or a scrambled control were cultured in 12-well plates and counted at the indicated time points.

Quantitative rt-Pcr and colony-forming unit assays
Total bone marrow cells, HSCs, YFP + leukemia cells or YFP + Mac-1 + c-Kit + LICs were sorted by flow cytometry for the isolation of total RNA. First-strand cDNA was reverse transcribed using M-MLV reverse transcriptase (Promega Inc.). The PCR reactions were performed according to the manufacturer's protocol. The mRNA level was normalized to the level of the β-actin RNA transcripts. The primer sequences used are shown in Supplementary  Table 1. For the colony-forming unit assays, the indicated numbers of cells from AML mice were plated in methylcellulose (M3534, Stem Cell Technologies) according to the manufacturer's instructions. The numbers of colonies were calculated 8-10 days after culture.

Glycolysis assays
The ATP content was determined using an ATP Bioluminescence Assay Kit HS II (Roche) according to the manufacturer's protocols. Lactate generation was measured using the Seahorse XF96 extracellular flux analyzer as previously described, with minor modifications [49]. Briefly, three replicate wells of 3 × 10 5 WT or ChREBP-null AML cells per well were seeded in 96-well XF96 plates coated with BD Cell-Tak (BD Biosciences) in unbuffered DMEM and incubated at 37°C for pH stabilization. Analyses were performed both under basal conditions and after the injection of oligomycin (2 µM), glucose (10 mM) and 2-DG (100 mM).

luciferase reporter assays
The transcriptional activation of TXNIP by ChREBP was evaluated using a constructed lentiviral luciferase reporter vector (kindly provided by Guoqiang Chen at Shanghai Jiao Tong University School of Medicine), plenti-TXNIP-GFP, which contains the conserved ChREBP-binding site, as previously described [50]. THP1 cells were infected with plenti-TXNIP-GFP lentiviruses, FACS-purified, and treated with 5, 10 and 25 mM glucose for 12 h or 24 h. Following the incubation, the luciferase activity was measured using a luciferase reporter system (GloMax ® Multi Instrument). Alternatively, the luciferase activity of THP1 cells or 293T cells overexpressing ChREBP and plenti-TXNIP-GFP was measured. To evaluate the transcriptional activation of RUNX1 and GATA2 by ChREBP or TXNIP, the promoter regions of RUNX1 and GATA2 were incorporated into a pGL4 vector and analyzed with a luciferase reporter system as described above.

chromatin immunoprecipitation assays
Chromatin immunoprecipitation (ChIP) assays were performed as described previously [51]. A total of 5 × 10 6 ChREBP-overexpressing (with a StrepII tag) THP1 cells were collected and cross-linked by adding formaldehyde at a final concentration of 1%. The samples were sonicated with a Q700 sonicator six times (5 sec on and 10 sec off for each round of sonication). ChREBP was immunoprecipitated with Strep-Tactin beads (IBA). DNA fragments were purified using a Qiagen PCR purification kit and quantified by semi-quantitative PCR with primers for the TXNIP promoter, listed in Supplementary Table 1.

statistics
The data are expressed as the mean ± SEM. The data were analyzed with Student's t test, and the statistical significance was set at p < 0.05. The survival rates of the two groups were analyzed using a log-rank test.

conFlIcts oF Interest
The authors declare no competing financial interests.


A Combination of CD28 (rs1980422) and IRF5 (rs10488631) Polymorphisms Is Associated with Seropositivity in Rheumatoid Arthritis: A Case Control Study

Introduction The aim of the study was to analyse genetic architecture of RA by utilizing multiparametric statistical methods such as linear discriminant analysis (LDA) and redundancy analysis (RDA). Methods A total of 1393 volunteers, 499 patients with RA and 894 healthy controls were included in the study. The presence of shared epitope (SE) in HLA-DRB1 and 11 SNPs (PTPN22 C/T (rs2476601), STAT4 G/T (rs7574865), CTLA4 A/G (rs3087243), TRAF1/C5 A/G (rs3761847), IRF5 T/C (rs10488631), TNFAIP3 C/T (rs5029937), AFF3 A/T (rs11676922), PADI4 C/T (rs2240340), CD28 T/C (rs1980422), CSK G/A (rs34933034) and FCGR3A A/C (rs396991), rheumatoid factor (RF), anti–citrullinated protein antibodies (ACPA) and clinical status was analysed using the LDA and RDA. Results HLA-DRB1, PTPN22, STAT4, IRF5 and PADI4 significantly discriminated between RA patients and healthy controls in LDA. The correlation between RA diagnosis and the explanatory variables in the model was 0.328 (Trace = 0.107; F = 13.715; P = 0.0002). The risk variants of IRF5 and CD28 genes were found to be common determinants for seropositivity in RDA, while positivity of RF alone was associated with the CTLA4 risk variant in heterozygous form. The correlation between serologic status and genetic determinants on the 1st ordinal axis was 0.468, and 0.145 on the 2nd one (Trace = 0.179; F = 6.135; P = 0.001). The risk alleles in AFF3 gene together with the presence of ACPA were associated with higher clinical severity of RA. Conclusions The association among multiple risk variants related to T cell receptor signalling with seropositivity may play an important role in distinct clinical phenotypes of RA. Our study demonstrates that multiparametric analyses represent a powerful tool for investigation of mutual relationships of potential risk factors in complex diseases such as RA.

Introduction
The aim of the study was to analyse genetic architecture of RA by utilizing multiparametric statistical methods such as linear discriminant analysis (LDA) and redundancy analysis (RDA).

Results
HLA-DRB1, PTPN22, STAT4, IRF5 and PADI4 significantly discriminated between RA patients and healthy controls in LDA. The correlation between RA diagnosis and the explanatory variables in the model was 0.328 (Trace = 0.107; F = 13.715; P = 0.0002). The risk

Introduction
Genetic factors have a substantial role in development of rheumatoid arthritis [RA] accounting for 50-60% of disease susceptibility [1]. For the past four decades, the strongest genetic association with RA has been attributed to human leukocyte antigen (HLA) region at chromosome 6p21, particularly to HLA-DRB1 locus [2]. Recently, 101 non-HLA loci have been confirmed in trans-ethnic meta-analysis of RA [3]. In the population-specific genetic risk model, the 100 RA risk loci outside of the major histocompatibility complex (MHC) region [4] explained 5.5% and 4.7% of heritability in Europeans and Asians, respectively. Lately, RA has been divided into two clinical phenotypes based on the presence or absence of rheumatoid factor (RF) and antibodies against citrullinated proteins (ACPA) [5,6]. These two clinical subtypes appear to have distinct genetic aetiologies [7]. Significant differences have been found in frequency of risk alleles in the HLA region and in PTPN22, CCR6, CD40, RASGRP1 and TAGAP genes between ACPA-positive and ACPA-negative RA patients [8,9].
Traditionally, genetic markers have been considered independent risk factors in majority of studies in RA. Although, this univariate approach has been successful in identifying alleles with relatively strong associations with the disease or its subtypes, interactions occurring in complex biological systems can be overlooked [10]. It remains unclear whether or not a combination of known genetic loci confers higher risk for RA development, clinical outcome or response to therapy compared to their simple additive effects. To solve this kind of question, multiparametric approaches may represent a potential tool enabling analysis of complex relationships such as those in the multifactor RA pathogenesis. The multiparametric methods have been used mainly in studies investigating predictive genetic tests in RA. In a pioneering study, McClure and colleagues found that a combination of five confirmed risk loci significantly increased an association with RA compared to the presence of any risk allele alone [11]. Subsequently, several other reports outlined predictive models for RA using HLA alleles, SNPs and clinical factors generating an aggregate weighted genetic risk score formed from the product of individual-locus odds ratios (ORs) [12,13]. Recently, validated environmental factors such as tobacco smoking and gene-environment interactions were added to the RA risk modelling [14,15]. These studies demonstrate that combining risk factors has a potential to provide a clinically relevant prediction with respect to disease onset [15]. The receiver operating characteristic curve analysis was adopted in studies to evaluate the performance of predictive genetic testing [16]. Various other methods have been used to combine multiple predictors for the ROC curve analysis. Among these, the most commonly used have been the allele counting methods and logistic regression [17,18]. In order to elucidate the genetic architecture of RA, the main goal of our study was to study interactions of known genetic risk factors with serologic and clinical parameters by utilizing multiparametric statistical methods: the multivariate linear discriminant analysis (LDA) and the redundancy analysis (RDA). These multivariate ordination analyses have been already used in genome-wide association studies for correction of population stratification [19,20]. The LDA and, in particular RDA, enables to quantify dependence between two groups of variables; independent and dependent (e.g. genes and clinical parameters) compared to the principal component analysis. Unlike regression methods, the LDA and RDA allow working with all variables with unknown correlations among them, reducing risk of finding false positive result based on a specific sample selection instead of on real differences in the whole population. Also, interpretation of such results is substantially improved, as not only relations between dependent and independent variables but also inside these groups are detected.

Patients and Controls
A total of 1393 volunteers, 499 patients with RA and 894 healthy controls were included in the study. All RA patients fulfilled the 2010 ACR-EULAR classification criteria for RA [21]. The patients were recruited from the National Institute of Rheumatic Diseases, Piestany and from ten outpatient rheumatology clinics in Slovakia. Control subjects were recruited from collaborating institutes and 485 DNA samples of geographically-and sex-matched healthy individuals were obtained from DNA bank of the Slovak Medical University in Bratislava. All subjects gave informed written consent and the study was approved by the Ethics Committee of the National Institute of Rheumatic Diseases, Piestany, Slovakia in agreement with the ethical guidelines of the Declaration of Helsinki as revised in 2000. Disease activity score 28 (DAS28) was calculated from number of swollen and tender joints and erythrocyte sedimentation rate [22]. The health assessment questionnaire disability index (HAQ-DI) was recorded using the standardized form [23]. Rheumatoid factor (RF), anti-citrullinated peptides antibodies (ACPA), C-reactive protein (CRP) and erythrocyte sedimentation rate (ESR) were measured in accredited hospital laboratories. IgM-rheumatoid factor was measured by nephelometry (CSL Behring, Germany), IgA-RF by ELISA (Hycor, Indianapolis, IN, USA), and ACPA by ELISA (Phadia, Freiburg, Germany). RA patients were considered RF positive when IgM or IgA-RF was positive. RA patients were considered seropositive when either RF or ACPA were above a specific threshold as given by the manufacturer. Clinical characteristics of the cohort are shown in Table 1.

SNP Genotyping
rs34933034) and FCGR3A A/C (rs396991) was performed using TaqMan 1 SNP Genotyping Assays (Applied Biosystems) and TaqMan 1 Genotyping PCR Master Mix (Applied Biosystems) on ABI 7900HT Fast Real-Time PCR System (Applied Biosystems). The results were analysed by Primer Express™ software (Applied Biosystems).

Statistical Analysis
Statistical analysis of obtained data was performed using SPSS v11.5 software (SPSS Inc., Chicago, IL, USA). All SNPs were individually tested for association with RA by Chi square test (χ 2 ). Odds ratio with 95% confidence interval was calculated using logistic regression. Multivariable ordination analysis was performed in Canoco 4.5 software (Microcomputer Power, NY, USA). The categorical data were coded as dummy-variables (i.e. each category as new variable with 0/1 values). All variables were Z-normalized. As the analysis cannot operate with blank fields in the table, those variables and subjects with the most missing values had to be excluded from the analysis. Before the main analysis, the detrended correspondence analysis (DCA) or detrended correspondence canonical analysis (DCCA) were performed to assure that PCA, LDA or RDA could be applied according to the 1 st ordinal axis gradient length. PCA was performed for each group of variables to see relations among variables in the group. Monte-Carlo permutation test with 999 permutations was applied as a method of forward selection of significant dependent variables in LDA and RDA. This approach helped us to avoid a problem with possible correlations among numerous independent variables (e.g. genes), as the best fitting variable was chosen by the method as the first; and the second (and further) variables were selected only if their presence improved the model. In the final model, the variables with significant influence were supplemented with variables correlated with them (including remaining stages of the variable). These ones do not explain any new variability of the model, so their influence is negligible, but they can help us to compare our results to previously published observations. First, LDA was performed to see, which genetic factors separate patients with RA from healthy controls. Second, RDA was performed under similar conditions, however, healthy controls and RA patients were split up into two groups according to the presence of antibodies. Third, another RDA was performed with genetic factors and smoking as independent variables, and with clinical parameters associated with RA diagnosis as dependent ones. Age and sex were included as covariates.

Analysis of Genetic Factors Associated with RA
All genes analysed in the study were in Hardy-Weinberg equilibrium. The presence of SE coding alleles in HLA-DRB1 gene was significantly higher in RA patients compared to controls ( Table 2). The most frequent SE alleles in patient group were DRB1 Ã 01:01 (13.4%), DRB1 Ã 04:01 (11.9%), DRB1 Ã 04:04 (3.8%) (data not shown). Among 11 studied SNPs, risk allele and genotype frequencies of PTPN22, STAT4, IRF5 and PADI4 genes were significantly higher in RA patients compared to controls (Table 2). LDA model was used to classify our cohort based on the genotype frequencies of studied SNPs. A major discriminant function was generated based on the variances seen in the data using LDA. This discriminant function was assigned to 1st ordinal axis of LDA diagram and the centroids for diagnosis were plotted in this matrix together with the significant genetic determinants (Fig 1). Based on these discriminant functions, the model predicted to which of the study group individual subject belongs with an overall accuracy of 10.7%. The correlation coefficient between RA diagnosis and explanatory variables in the model was 0.328 (Trace = 0.107; F = 13.715; P = 0.0002). These genetic determinants were selected as significant in the following order representing their importance in the model: SE 0 > IRF5 TT > SE 1 + SE 2 > STAT4 GG > PADI4 CC + PADI4 TT > PTPN22 CC.
In total, the presence of RA significantly correlated with genetic determinants PADI4 and SE alleles. As shown in Fig 1, at least one SE allele and two risk alleles (T) in PADI4 gene were the most important combination associated with RA diagnosis in our cohort. There was a strong prevalence of the non-risk genotype in genes PADI4, STAT4, IRF5 and PTPN22 and lack of SE coding alleles in control groups.

Analysis of Genetic Factors Associated with Autoantibody Status in RA
An association analysis between antibody status and genetic markers was performed in RA patients. The RDA, which estimated the effect of SNPs on the presence of antibodies, found a significant model explaining 17.9% of variability in data (1st ordinal axis 17.5% and 2nd ordinal axis 0.4%) (Fig 2). The correlation between categories RF status, ACPA status and genetic determinants on the 1st ordinal axis is 0.468, and on the 2nd ordinal axis 0.145 (Trace = 0.179; F = 6.135; P = 0.001). RF and ACPA negative RA patients were more likely to have SE 0 genotype. On the other hand, CD28 CC and IRF5 CC genotypes were associated with both RF and ACPA positivity, while CTLA4 AG genotype associated preferentially with RF positivity.

Analysis of Genetic Factors Associated with Clinical Outcomes
In general, RDA analysis with forward selection performed in RA patients showed that ACPA positivity, AFF3 gene, in particular AFF3 TT genotype and smoking significantly associate with higher disease activity defined by clinical parameters DAS28, CRP, ESR, TJC, SJC and HAQ-DI. The explanatory variables explain 6.1% of variability among RA patients (Trace = 0.061; F = 3.482; P = 0.0020). In particular, higher CRP levels were associated with the presence of AFF3 TT genotype, whereas higher DAS28 and higher number of swollen and tender joints were correlated with ACPA positivity (Fig 3).

Discussion
The aim of the presented study was to analyse possible interactions among genetic, serologic and clinical data in a cohort of Slovak patients with RA. LDA and RDA, multiparametric statistical methods, together with conventional association analyses of known RA risk gene variants were applied. As expected, among eleven gene loci previously associated with RA, risk variants  in IRF5, STAT4, PADI4 and PTPN22 genes and the presence of SE alleles in HLA-DRB1 were found to be significantly more frequent in RA patients compared to controls in our study. In line with the latter, the LDA model demonstrated that at least one SE allele and two risk alleles (T) in the PADI4 gene is the most significant determinant of RA diagnosis. On the other hand, the presence of non-risk genotypes in genes PADI4, STAT4, IRF5 and PTPN22 and the lack of SE coding alleles as determinants of non-RA phenotype in our study may suggest a cluster of protective gene variants preventing RA development. It remains unclear whether or not the gene cluster can be regarded RA protective in truly mechanistic way.
Our results suggest that only HLA-DRB1 and PADI4 are predisposing to RA, whereas the other four genotypes were dominant in the healthy population. Based on this analysis, the presence of non-risk genotypes in IRF5, STAT4, PADI4 and PTPN22 indicates more strongly the exclusion of RA diagnosis than would the risk genotype indicate its confirmation. The presence of at least one shared epitope coding allele in HLA-DRB1 gene among RA predisposing factors is not surprising since the HLA region, and particularly the HLA-DRB1 locus, is considered the strongest genetic factor contributing to the increased susceptibility of RA. PADI4 is, as one of the isoenzymes responsible for post-translational conversion of arginine to citrulline, one of the most interesting genes associated with RA. However, its association with disease remains rather disputable. The association of PADI4 with RA was confirmed in European as well as Japanese populations [25] showing also high specificity for the disease. On the other hand, it failed to be confirmed as RA-associated gene variant in a large British-French-Spanish cohort, calling its association in European population in question [26,27]. Our results support the association of SNP in PADI4 gene with RA in a population of European origin. Although there might be a correlation of PADI4 with ACPA positivity, the presence of both components in significant LDA model in our study indicates their own contribution to data variability and thus proves them as two separate factors.
The presence or absence of seropositivity appears to be fundamental with regard to a clinical phenotype of RA [5,6]. Therefore, we performed an analysis investigating which genes from the studied group are associated with RF and/or ACPA positivity in our RA cohort. We found a significant correlation of RA seropositivity with IRF5 and CD28 risk allele homozygosity. As shown on the RDA diagram [Fig 2], the risk variants of IRF5 and CD28 genes are common determinants for both RF-and ACPA-positivity. In other words, the presence of both risk gene variants has a significant association with seropositivity. Moreover, the RDA shows that RFpositivity is associated with the CTLA4 risk variant in heterozygous form. The association of IRF5 gene, including the rs10488631 SNP analysed in this study was mainly described in seronegative RA [28,29]. In other studies, however, this SNP was associated with the disease exclusively in ACPA positive RA patients and in autoantibody positive RA replication cohort [30,31]. Interestingly, the absence of SE alleles associated with ACPA negativity was more significant than the association of two SE alleles with seropositivity. It has been shown that SE is strongly associated with both RF status and presence of ACPA [6,32]. More specifically, ACPA play a vital role in the causal pathway between SE and joint erosions [33].
IRF5 is a latent transcription factor expressed in lymphocytes, macrophages and dendritic cells. IRF5 is involved in expression regulation of IFN-α genes and mediates Toll-like receptor (TLR) signal transduction leading to production of proinflammatory cytokines [34]. CTLA-4 and CD28 are functionally as well as structurally related, as both bind the same counter-receptor, the two B7 family members (CD80 and CD86), that are present on antigen presenting cells, but the molecules have opposing effects on T cell activation playing an important role in the T cell receptor (TCR) signalling regulation [35]. The CTLA4 gene was repeatedly found associated with a diagnosis of RA in genome-wide association studies [31,36,37], however, various SNPs in this gene were analysed. In case of CT60 SNP included in this study, the results are contradictory [38,39]. Among these CTLA4 SNPs, the CT60 variant appears to have the highest functional relevance; the G allele is associated with lower mRNA and protein levels of soluble CTLA4 that could result in increased T cell activation. The allelic distribution of CTLA4 gene was very similar in our cohort (A 61.7; G 38.3%) indicating highest prevalence of heterozygotes among genotypes (AA 38%, AG 47% and GG 15%). Lower significance of homozygotic states of CTLA4 in the final model could be contributed to lower statistical power because of their smaller counts. Surprisingly, CTLA4 and CD28 genes did not show association with RA in our study when analysed as independent risk factors. The lack of independent association of AFF3, CD28, CTLA4, IRF5 and TNAIP3 genes in our cohort might be partially explained by observations of Viatte and co-authors [40], who described the association of these SNPs with RA only in ACPA positive patients. This, together with the fact that the association analysis in this study was done on the whole group of patients regardless the antibody status, with 34.5% of ACPA negative patients from 499 cases analysed, might rationalize why these genes did not reach statistical significance.
In the present study, the RDA was used to investigate a combination of factors associated with the disease activity in RA expressed by DAS28, CRP, ESR, swollen and tender joint counts and HAQ-DI. Our results demonstrate that from all factors analysed, ACPA positivity and the homozygous state of AFF3 risk alleles most significantly influenced the disease activity in our cohort of patients. The link of serologic status to different disease outcomes, with more severe and erosive disease observed in RF and ACPA positive patients is well known [41][42][43]. In terms of disease activity, to our best knowledge, AFF3 locus has been previously associated with the rate of joint destruction [44] but no other clinical parameters. AFF3 gene encodes a tissue-restricted nuclear transcriptional activator that is preferentially expressed in lymphoid tissue. Interestingly, recent studies showed an association of AFF3 locus with triglyceride levels [45] and the end-stage renal disease [46] as a major complication of diabetes. These findings suggest even more plausible clinical implications of AFF3 for RA since serum lipid alterations are monitored and managed in RA patients to minimize the long-term risk of cardiovascular disease and diabetes [47][48][49]. The association of AFF3 locus with RA was first identified by Barton and co-authors [50]. Although the association of SNP in AFF3 gene [rs11676922] was previously found in meta-analysis [30] we did not confirm it in Slovak cohort. This might be influenced by stronger association of AFF3 SNP in ACPA positive patients [40] and therefore undetected in our mixed cohort with respect to its size (as mentioned above). Several other studies reporting an association of AFF3 gene with RA analysed different SNPs in this gene [50,51]. In RDA analysis performed in our study, all factors entered the analysis equivalently as explanatory variables and their appearance in final model after forward selection indicates their own significant contribution to data variability, i.e. strong impact on parameters of clinical manifestation. Thus, our results not only confirm the central role of ACPA as a determinant of RA severity and or inflammatory status, but suggest significant contribution of risk variants in AFF3 gene. Our results support the view that even if some genes, analysed as independent factors, may not provide sufficient risk stratification, their involvement within a prediction/ explanatory model may help to identify clinically relevant high-and low-risk groups [15].
In this study, we confirmed an association of PTPN22, STAT4, IRF5 and PADI4 with RA in Slovak population. A study of PTPN22, STAT4, OLIG3/TNFAIP3, TRAF1/C5 and HLA-DRB1 Ã 04 in Slovak population was previously reported by Stark [52]. Unlike that study, in which authors used osteoarthritis patients as controls, our cohort consisted of gender-and geographically matched healthy individuals. Furthermore, seven additional SNPs with known association with RA were investigated and the first of its kind comprehensive analysis of HLA-DRB1 allele prevalence in Slovak population was performed in our study.
In general, genome-wide association studies-established SNPs have helped identify cellular pathways relevant to RA pathogenesis, however, their modest effect limits their predictive value [53]. Several reasons exist for a failure to replicate GWAS findings. First, it could be that this study had insufficient power to detect the modest effect sizes of selected SNPs. Second, for some non-replicated SNPs, there are differences in terms of the allele frequencies and LD patterns. Third, the magnitude of the effect of a risk allele might be altered in combination with other genetic and non-genetic factors [54,55]. We performed the present study on the genetically homogeneous Slovak population, which shares a common genetic and cultural background and a common environment and is characterized by good genealogical and clinical records and low migration rates, thus contributing to an increased reliability of the data collected. Interestingly, an analysis of individual SNPs, in which association with RA was confirmed such as in PADI4, PTPN22, STAT4 or IRF5, were also found in the multiparametric LDA model suggesting that these genes represent independent risk factors for RA.

Conclusion
This study shows replication data of previously identified genetic risk factors of RA in Slovak population. In single as well as multiparametric analyses, HLA-DRB1, PTPN22, STAT4, IRF5 and PADI4 significantly discriminated between RA patients and healthy controls. IRF5, CD28 and CTLA4 were associated with seropositivity in RA patients. Moreover, risk alleles in AFF3 gene together with the presence of ACPA, were associated with higher clinical severity of RA. The association of multiple risk variants related to T cell receptor signalling with seropositivity may suggest an important role in distinct clinical phenotypes of RA. Our study demonstrates that multiparametric analyses represent a powerful tool for investigation of mutual relationships of potential risk factors in complex diseases such as RA.


extfieldsofstudy: Biology Medicine

s2fieldsofstudy: Biology Engineering

text:
Synergistic effect of quorum sensing genes in biofilm development and PAHs degradation by a marine bacterium

ABSTRACT Quorum sensing (QS) is a prevalently found intercellular signaling system in bacteria. QS system bestows behavioral coordination ability in bacteria at high population density. QS via acylated homoserine lactone (AHL) is extensively conserved in Gram-negative bacteria and plays crucial role in regulating many biological processes. The role of QS genes coding for AHL synthase enzyme (lasI and rhlI) was established in bioremediation of polycyclic aromatic hydrocarbons (PAHs) viz. phenanthrene and pyrene. AHL producing biofilm forming marine bacterium Pseudomonas aeruginosa N6P6 was isolated by selective enrichment on PAHs. AHL production was confirmed using AHL bioreporters and GC-MS analysis. Biofilm development and its architecture was significantly (P < 0.05) affected by alterations in lasI/rhlI expression. The lasI/rhlI gene expression pattern significantly influences biofilm formation and subsequent degradation of PAHs. The integrated density of Pseudomonas aeruginosa N6P6 biofilm was highest for 48 h old biofilm and the PAHs (phenanthrene and pyrene) degradation was also found maximum (85.6 % and 47.56 %) with this biofilm. A significant positive correlation (P < 0.05) was observed between lasI expression and PAHs degradation. The role of QS genes in biofilm formation and degradation of PAHs was validated by blocking the transcription of lasI/rhlI by a QS inhibitor (QSI) tannic acid. Further, application of such QS positive isolates in PAHs contaminated sites could be a promising strategy to improve the PAHs bioremediation.

Introduction
Density sensing mechanism mediated by N-acyl-homoserine lactone (AHL) based quorum sensing (QS) endowed in gram-negative bacteria empowers them with enhanced bioremediation potential. Various key features regulated by QS genes alleviating the biodegradation of polycyclic aromatic hydrocarbons (PAHs) are biofilm formation, biosurfactant production, horizontal gene transfer and catabolic gene expression. 1 Bacterial biofilm has been recognized as an impeccable agent in bioremediation technology. QS via AHLs regulates biofilm architecture, development and maturation. 2 Diffusible autoinducers (AIs) such as AHLs aid in biofilm formation. Marine bacteria under the genera Pseudoalteromonas, Thalassomonas, Vibrio and Pseudomonas often form dense biofilm with the help of these AIs. Among the known QS positive isolates, Pseudomonas aeruginosa has been explored for its QS associated properties. P. aeruginosa is found in diverse environments such as clinical, terrestrial, and aquatic set-ups and show tremendous potential to degrade various xenobiotics. 3 The QS system of P. aeruginosa is very well conserved consisting of lasI/R and rhlI/R genes coding for the Lux family transcriptional activators. 4 The bacterial communication system mediated via AHL involves binding of AHL to transcriptional factor which further initiates the signaling cascades to regulate appropriate gene(s). In P. aeruginosa, the synthesis of AHL signal molecule i.e. N-(3-oxododecanoyl)-L-homoserine lactone (3O-C 12 -HSL) is regulated by lasI/R. In order to become an active transcription factor, lasR requires the released AHL molecule (3O-C 12 -HSL). The transcription of various genes involved in the synthesis of biosurfactant, EPS and biofilm development relies on this signaling molecule 3O-C 12 -HSL. Only multimeric form of LasR has the potential to bind DNA, whereas the formation of multimeric LasR is dependent on 3O-C 12 -HSL. 5,6 RhlI and RhlR proteins constitute the second QS system in P. aeruginosa. Both the QS system work synchronously and constitute a regulatory cascade. The rhlI/R mediated QS system always works under the control of the lasI/R QS system. RhlI synthase facilitates the synthesis of short chain AHL such as N-butyryl-Lhomoserine lactone (C 4 -HSL) via the transcriptional regulator, RhlR. 7 RhlR often forms complex with C 4 -HSL and promotes the expression of several other genes (e.g rhamnosyltransferase, aminopeptidase, endoproteinase etc.). Both the 3O-C 12 -HSL and C 4 -HSL signaling molecule diffuse freely from the bacterial cells and initiates the communication cascade. 8 However, 3O-C 12 -HSL has high molecular weight and it diffuses at a significantly slower rate in comparison to C 4 -HSL.
Among the phenotypes regulated by QS system, biofilm formation holds the premier position. On the basis of comparison made between the biofilm structure of wild type P. aeruginosa strain and isogenic rhlI, lasI and lasIrhlI mutant, an initial relationship between biofilm formation and QS was established. 9 Structured biofilm was formed in wild-type and rhlI mutant, whereas, undifferentiated and flat biofilm was observed in lasI and lasIrhlI mutant. The flat biofilm formed in the lasIrhlI mutant strains were prone to various stress conditions and were susceptible to the surfactants such as sodium dodecyl sulfate. 9 On the other hand, the structured biofilms formed by the wild type P. aeruginosa was found to be resistant. Bacterial motility (Swimming, swarming and twitching motility) in case of P. aeruginosa enhances biofilm mediated bioremediation by many folds as actively growing cells sense the pollutant and colonises. However, QS mutants lack swarming motility and exhibits reduced biofilm formation and bioremediation. 10 PAHs comprising of 2 or more fused aromatic rings are of great concern because of their persistent nature. 11 PAHs are hydrophobic in nature which is a major limiting factor for their degradation in the aquatic environment. 12 However, biofilm forming marine bacteria are effective xenobiotic degraders. They have potential to strive in harsh and fluctuating environment and the biofilms provide an appropriate aid for enhancing the degradation of hydrophobic PAH in both aqueous and terrestrial environment. 13,14 The extracellular polymeric substances (EPS) secreted by bacterial biofilm composed of many hydrophobic cores, which can increase the solubility of PAHs in the aqueous environment. Besides, bacteria from marine environment with QS system which regulate biofilm formation can further enhance degradation of PAH by increasing the cell density around the vicinity of the PAH compounds. 15 The focus of our study was to establish a relationship between QS, biofilm formation and bioremediation of PAH. QS positive marine bacterium P. aeruginosa N6P6 was used in the present study. The bacterium can form dense biofilm after 48 h and it harbours both rhl and las QS system. The expression profiling of rhlI and lasI, encoding AHL synthase, was studied under different phase of biofilm growth and PAH stress such as phenanthrene and pyrene. AHLs produced by this bacterium were characterized by various chromatographic techniques. The effect of QS gene(s) expression on biofilm growth and its consequent impact on PAHs (phenanthrene and pyrene) degradation was monitored. We applied a potent QS inhibitor (QSI), tannic acid, for further validating the role of QS in biofilm formation and bioremediation. Tannic acid is a phenolic compound obtained from plants. It has multiple phenol groups linked together and hence often called as polyphenol. The presence of many phenols together constituting characteristically different structures makes them physically, chemically and biologically inimitable and endows them with many unique properties (metabolic, toxic, therapeutic, etc.). 16 In the present study, the transcription of lasI and rhlI was directly inhibited by tannic acid by some unknown mechanism, which potentially affects the expression and function of Lux protein. 17 Results and discussion AHL characterization and genetic basis of AHL production QS has been reported to regulate various phenotypes in P. aeruginosa N6P6. Biofilm formation is one such QS regulated phenotype having crucial role in degradation of PAHs. P. aeruginosa N6P6 synthesizes 2 different types of AHL molecules, which was confirmed through TLC and GC-MS analysis. Both TLC (via overlaying the QS bioreporters) and GC-MS analysis revealed the presence of C 4 HSL and 3OC 12 -HSL [compared with standard AHLs mass spectra (1 mg/ ml stock was prepared in methanol)].
Chromobacterium violaceum CV026 (CECT 5999), a violacein-negative, mini-Tn5 mutant was used as a bioreporter for detecting short chain AHLs. 18 Pigment production can be restored in the strain by incubating it with AHLs positive isolates. Similarly, for detecting long chain AHL, Agrobacterium tumefaciens NTLR4 (ATCC-BAA2240) was used. A. tumefaciens NTLR4 harbours a b-galactosidase gene driven by a promoter traI. Long chain AHL signaling molecules induce the expression of b-galactosidase. In the presence AHLs, b-galactosidase is induced and it cleaves X-Gal resulting in the formation a blue precipitate. 19 The AHL production was further confirmed by comparing P. aeruginosa N6P6 crude AHL extract with standard AHLs retention time through HPLC analysis. The genetic basis of AHL production in P. aeruginosa N6P6 was confirmed by amplifying the quorum sensing (QS) genes (lasI and rhlI) coding for AHL synthase with the aid of gene-specific primers.
To understand the expression pattern of AHL synthase during planktonic and biofilm mode of growth, semi-quantitative PCR was performed. During biofilm mode of growth, the expression of lasI gene increased with time. The expression of rhlI remained constant throughout the observed time frame. QS genes play crucial role in the synthesis of EPS and biofilm maturation. For validating the role of QS genes in biofilm formation and development, RNA was extracted from biofilm cultures at different time interval (15-72 h) and relative expression of QS genes was studied with qRT-PCR. lasI expression increased with increase in biofilm growth and declines slightly for 72 h old biofilm. Development of mature biofilm follows a cascade of events i.e., attachment to substratum, colonization, EPS production, primary and secondary maturation and ultimately dispersion. EPS are the most crucial component of the biofilm. lasI regulates the expression of several gene(s) involved in the production of glucose rich EPS-matrix. The slight decrease in lasI expression at this point indicates that lasI might not have any essential role once a biofilm community has been established. The other reason for decreased expression of 72 h old biofilm of P. aeruginosa N6P6 might be the initiation of dispersion phase of biofilm.
The trend observed in rhlI expression was completely different from lasI expression pattern. rhlI gene remained positively upregulated for 15 h, 48 h and 72 h, but down regulation was observed for 24 h old biofilm. To correlate the QS genes expression with biofilm development, the biofilm surface architecture of 15 h, 24 h and 48 h old P. aeruginosa N6P6 biofilm was studied with the help of fluorescence microscopy. Biofilm growth and lasI expression follows similar trend i.e. biofilm growth and lasI expression increases from 15 to 48 h followed by reduction in biofilm growth and lasI expression at 72 h. P. aeruginosa N6P6 biofilm surface architecture turned more rough and heterogeneous with time. However, a significant positive correlation (P > 0.05) was not established between P. aeruginosa N6P6 biofilm growth and expression of rhlI gene. The fold difference between lasI and rhlI gene expression level was least for 15 h old biofilm and was maximum for 24 h old biofilm specifying the significance of both these genes in initial biofilm development and maturation. Large difference between lasI and rhlI gene expression level resulted in topologically smooth surface biofilm at this point of time. A very structured biofilm was observed at 48 h and 72 h old biofilm. The upregulation of rhlI gene at 48 h and 72 h. might be a reason behind the metamorphosis of smooth surface biofilm to distinct 3-dimensional architecture.

Role of QS genes in biofilm growth and PAHs degradation
After establishing the relationship between biofilm development and QS genes expression, our next aim was to establish relationship between QS genes expression, biofilm growth and PAHs degradation. Two representative members of PAH family, phenanthrene and pyrene were selected for the degradation study. The efficiency of 48 h old and 72 h old biofilm in degrading phenanthrene was almost same. 21.5%, 54.2%, 85.6% and 85.7% phenanthrene degradation was achieved in 7 d by 15 h, 24 h, 48 h and 72 h old biofilm respectively. Day wise analysis of phenanthrene degradation indicated that biofilm growth and QS gene (lasI and rhlI) expression had significant effect on phenanthrene degradation. Similar trend was also observed during pyrene degradation. 48 h old biofilm was more effective in degrading pyrene as compared to other. A significant positive correlation (P < 0.05) was observed between biofilm growth, lasI expression and pyrene degradation.
Further, to understand P. aeruginosa N6P6 QS gene expression pattern under PAH stress, the bacterium was cultured in PAH substituted media. Under phenanthrene stress, the expression of lasI was 3 fold higher than rhlI in P. aeruginosa N6P6 biofilm culture. In contrast, 9 fold higher expression of rhlI gene than lasI was observed for biofilm culture grown under pyrene stress. Cells embedded in the biofilm are often released if PAH compounds are present in the growth medium. The availability of hydrophobic compounds in the growth medium might play crucial role in the production of emulsifiers. Production of emulsifying agents enhances the solubility of a PAH molecule and makes it available to the bacterial cells encased in biofilm matrix.

Tannic acid as potent QS inhibitor
To confirm whether biofilm formation is only time depended factor or it actually rely on QS genes (i.e., with time the cells aggregate and form biofilm or QS based genetic mechanism governs the phenomenon), a QS inhibitor tannic acid was used for validating the role of QS in biofilm formation and PAHs degradation. Tannic acid blocks the transcription of QS genes, hence the production of AHL synthase is also inhibited. 16 Different concentrations of tannic acid were tested to find the threshold concentration for complete inhibition of AHL production in P. aeruginosa N6P6. AHLs were extracted from P. aeruginosa N6P6 culture after treatment with different concentration of tannic acid. The extracted AHLs were incubated with bioreporter Chromobacterium violaceum CV026 (CECT 5999) for 24 h. In the presence of AHL, CV026 produces deep violet pigment. In the present experimental study, 0.3 mg/ml of tannic acid completely inhibited AHL production in P. aeruginosa N6P6 without interfering with its growth.
To ascertain the non-toxic effect of tannic acid on P. aeruginosa N6P6 growth, the bacterium was grown in the presence of tannic acid at a threshold concentration. Tannic acid showed no toxic effect on the growth of the bacterium. However, other phenotypes regulated by QS genes viz. biofilm formation, pyocyanin production etc. were observed to be inhibited totally in the presence of tannic acid. Tannic acid treated biofilm was stained with aqueous solution of acridine orange (0.02 %) were analyzed with fluorescence microscope (Olympus, 1X71, Japan) under 20X magnification. Fluorescence micrograms were processed to construct biofilm 3D surface plot using IMAGE J interactive 3D surface plot tool. Tannic acid treatment caused a decrease in biofilm density by 83.2%. Flat biofilm without any structural heterogeneity was observed in the presence of 0.3 mg/ml tannic acid, whereas highly structured biofilm was formed by P. aeruginosa N6P6 in absence of tannic acid. The biofilm growth declined in the growth medium supplemented with tannic acid. The biofilm growth retardation becomes more prominent with increasing concentration of tannic acid and ultimately biofilm appears as straight line with no surface coverage (Fig. 1). Biofilm growth was quantified as raw integrated density calculated using IMAGE J. The raw integrated density reduces by around 94% on addition of 0.1mg/ml tannic acid. Further increase in tannic acid concentration completely inhibited the biofilm growth resulting in significant reduction in integrated density (P < 0.0001; One way ANOVA) (Fig. 2). In the similar manner, pyocyanin production was also reduced by 64.2% compared to the untreated P. aeruginosa N6P6 culture.
The centered aim of this study was to understand the involvement of the QS genes in PAHs bioremediation. It was observed that treatment with 0.3 mg/ml tannic acid resulted 90% reduction in lasI and 56.6 % reduction in rhlI gene expression. Such reduction in QS genes expression caused a decrease in PAHs degradation. 18.6% and 15.38 % reduction in phenanthrene and pyrene degradation was observed respectively in presence of tannic acid. PAHs degradation by planktonic culture of P. aeruginosa N6P6 was also decreased by 23% when tannic acid was added in the degradation medium. EPS production plays crucial role in degradation of PAHs by acting as biosurfactant to enhance its solubility. rhlI gene expression controls the EPS production resulting in partial solubilisation of PAHs in aqueous environment. Solubilisation of PAHs makes it easily available to bacterial cells for ensuing degradation. For analyzing the effect of QS inhibitor tannic acid on EPS production, confocal laser scanning microscopic (CLSM) studies were performed for tannic treated and untreated biofilm after staining with 5 mmol l ¡1 Syto9 and 100 mg ml ¡1 of ConA-TRITC (concanavalin A-tetramethylrhodamine isothiocyanate). 14 Syto9 binds to the cells staining them green whereas, ConA-TRITC binds to the EPS portion of biofilms staining them red. Tannic acid (0.3 mg/ml) treatment reduces the EPS production considerably as the red stained portions diminishes completely in the treated biofilm (Fig. 3). The z stack images (Fig. 3 D) clearly reveal the reduction in EPS production and overall reduction in biofilm thickness on treatment with QS inhibitor tannic acid.
Yong and Zhang 21 reported similar trend of decreased aromatic xenobiotic degradation by a QS mutant isolate. A significant decrease in the degradation of an aromatic xenobiotic was observed when rhl QS system of Pseudomonas aeruginosa CGMCC1.860 was deleted. Catechol 2,3-dioxygenase a key enzyme responsible for degradation of various xenobiotics was also repressed on deletion of rhl QS system of Pseudomonas aeruginosa CGMCC1.860. Sarabhai et al. 22 also reported similar effects of ellagic acid (a type of tannin) in inhibiting QS genes expression. Ellagic acid treatment resulted in 89 and 90% reduction in lasI and rhlI expression in P. aeruginosa PAO1. Biofilms are natural emulsifiers that disperse the oil and provide greater surface area for growth. Such decrease in PAHs degradation indicates that QS genes play crucial role in the regulation of catabolic genes responsible for PAHs degradation. To comprehend the role of QS genes in catabolic gene expression and regulation, further experiments with directed real time PCR expression analysis is needed. To expedite the bioremediation of PAHs, a combinatorial approach comprising of QS and PAHs catabolic genes will be studied in future in this potential marine bacterium.

Conclusion
The findings from the present study suggest that lasI gene encoding long chain-AHL and rhlI gene  encoding short chain-AHL regulates biofilm formation and development and EPS production in P. aeruginosa N6P6. These factors along with intracellular signaling molecules significantly enhance the bioremediation performance. The QS mediated intracellular signaling process operates at society level; hence the activities regulated via QS process might play crucial role in enhancing bioremediation of natural contaminated sites. Bacterial biofilm is one such structured community, envisioned in nature till date. Unique chemical composition and communication capabilities of biofilm provide resistance to various stresses and make it the best aspirant for restoring the contaminated environment.

Disclosure of potential conflicts of interest
No potential conflicts of interest were disclosed.