text:
Astrocyte Function in Alcohol Reward and Addiction

Glial cells, particularly astrocytes, play essential roles in the regulation of neurotransmission, metabolism, and supply of energy substrates for synaptic transmission. One astrocyte can receive inputs from several hundreds of synapses, and synchronized neuronal activity correlates with astrocyte calcium signaling. Astrocyte pathology is a common feature of ethanol exposure in both humans and animal models, and brief alcohol intake is sufficient to cause long-lasting changes in astrocyte gene expression, activity and proliferation. Recent research also suggests that astrocytes shape the rewarding sensation of ethanol, and might be involved in modulating alcohol consumption. Considering the role of astrocytes in regulating glutamate homeostasis, a crucial component of alcohol abuse disorders, the astrocyte might be an important target for the development of new pharmacological treatments of alcoholism.

The astroglial cell, or astrocyte, is the most numerous cell type of the glial cell family. Astrocytes give structural and metabolic support to surrounding neurons and are pivotal for neuronal functioning and signal processing in the CNS [1,2] (Figure 1). The end feet of astrocytic processes encapsulate blood vessels and participate in building up the blood brain barrier [3,4]. The contact with blood vessels also enables the astrocytes to provide neurons with lactate under anaerobic conditions [1,5] (Figure 1). The astrocyte was first acknowledged as the primary cell type responsible for potassium buffering [6], and combined with the clearance of amino acids from the extracellular space, astrocytes warrants a high signal to noise ratio, and reduced receptor desensitization [7,8]. One single astrocyte can enwrap several neuronal somata and receive inputs from thousands of synapses, enabling them to sense and integrate synaptic activity [9,10]. Activation of astrocytes also appears to be a crucial component for the induction of synaptic plasticity mechanisms, and both long-term potentiation (LTP) and long-term depression (LTD) in the hippocampus may be regulated by astrocytes [11,12].
Astrocytes enwrap both presynaptic and postsynaptic terminals, forming the tripartite synapse. Astrocytic end feet encapsulate blood vessels, enabling the astrocyte to give metabolic support to surrounding neurons. Astrocytic clearance of neuroactive substances is crucial for synaptic transmission. The excitatory amino acid glutamate (Glu) is rapidly metabolized to its non-excitable precursor glutamine (Gln), released back to the extracellular space, and taken up by neurons for synthesis of glutamate or GABA. See text for further details.

Astrocytic transporters
One of the most prominent functions of the astrocyte is the clearance of glutamate, and astrocytic glutamate transporters EAAT2 (GLT-1) and EAAT1 (GLAST) are the most abundant transporters for removal of glutamate in the brain [13]. Impaired astrocytic glutamate clearance appears to contribute to neuronal dysfunction and degeneration in Huntington's disease, major depressive disorder and Alzheimer's disease [14][15][16][17], and might also play a role in alcohol abuse [18]. Following reuptake, glutamate is converted by glutamine synthetase to its non-excitable precursor glutamine, which is released back to the extracellular space [19] (Figure 1). This astrocyte-dependent glutamate-glutamine cycle is required to maintain active neurotransmission at excitatory terminals, but may also play a vital role for GABAergic neurotransmission [20,21]. Inhibitory transmission is further regulated by astrocytes through glycine transporters GlyT1 and GlyT2, and GABA transporters GAT-1 and GAT-3 [22,23], while adenosine is transported through the equilibrative nucleoside transporter 1 and 3 (ENT1, ENT3) [24,25].

Gap junction channels
Astrocytes are highly interconnected through gap junction channels, built up by the protein connexin [43]. These gap junction channels concede bidirectional diffusion of charged and uncharged small molecules allowing Ca 2+ waves to spread through the astrocytic syncytium [44]. Functional gap junctions might also exist between astrocytes and oligodendrocytes and to some extent neurons [45][46][47]. Coupling at gap junctions is regulated by intracellular ion homeostasis and influenced by neuronal activity and released neuroactive substances such as growth hormones, glutamate, 5-HT, ATP, and potassium [48-53] (Figure 2). Connexins also form plasma membrane channels, hemi channels, through which gliotransmitters can be released [54,55]. Connexin-43 hemi channels are necessary for fear memory consolidation and has been suggested as pharmacological targets for treatment of post-traumatic stress disorder, but dysregulations of hemi channel properties could also be critical during homeostatic imbalances [54,56]. Importantly, the connectivity of the astrocyte network, as well as individual cells, appears to be brain region selective, where astrocytes in the striatum are highly interconnected and primarily categorized as passive, while a larger proportion of astrocytes in the hippocampus are classified as complex [2,49,57].

The Astrocyte during Acute and Chronic Alcohol Exposure
Ethanol effects on signal transduction appear to vary considerable with development, differentiation and functional activity [58]. Many membrane proteins, such as ion channels, transporters, receptors, enzymes and G-proteins are affected by ethanol in ways that lead to long-lasting changes in activity and gene expression [59], and astrocytes appear to be important target cells. Brief ethanol-exposure is sufficient to alter astrocytic gene expression and to restrict proliferation [60,61], and may acutely impair astrocytic regulation of neurotransmission. Ethanol elevates intracellular calcium in a subset of astrocytes and reduces gap junction coupling in a brain region selective manner, which could selectively affect signal processing [62-64]. Considering that gap junction coupling equalizes [Na + ] i , the decline in connectivity might also reduce the capacity for amino acid uptake and potassium buffering, as well as lead to failure in coordinating other physiological responses that involves sodium [65,66]. Diminished immunoreactive puncta, and reduced levels of the gap junction protein connexin 43, is also seen in post mortem studies of subjects with alcohol abuse disorder, supporting a role for altered connectivity or hemi channelbased communication in the pathophysiology of alcoholism [67].

Astrocytic cell swelling
Ethanol induces cell swelling in a subset of astrocytes, which could lead to changes in size and geometry of the extracellular space and thereby altered volume transmission [64, [68][69][70]. Swelling of the end feet of astrocytic perivascular processes may also lead to altered cerebral blood flow [71]. Cell swelling is rapidly followed by regulatory volume decrease and the release of organic osmolytes, of which the amino acid taurine is of especial interest [72][73][74][75]. Taurine has previously been shown to increase accumbal dopamine levels, and appears to be a key component in ethanol induced dopamine release [76][77][78]. Considering the role of dopamine in mediating the rewarding sensation of drugs of abuse, it is thus possible that astrocytes are indirectly involved in ethanol-induced reinforcement [65, 79,80]. Rapid swelling and regulatory volume decrease is facilitated by astrocytic water channels built up by the protein aquaporin-4 [81,82], and reduced aquaporin-4 expression has in fact also been linked to excessive alcohol consumption [83]. Interestingly, aquaporin-4 expression is depressed by dopamine, while genetic deletion of aquaporin-4 reduces accumbal dopamine [81]. Antagonists targeting dopamine D1 and D2 receptors has also been shown to increase astrocyte calcium excitability, synchrony and gap junction coupling [84], indicating that astrocytes may monitor extra synaptic dopamine levels.

Astrocytic protein expression and morphological transformations
Changes in the expression of the astrocyte specific cytoskeletal protein glial fibrillary acidic protein (GFAP) have been detected in several psychiatric conditions such as Parkinson's disease, schizophrenia, depression and Alzheimer's disease [17, [85][86][87][88][89], but it is not clear if this is in response to the injury, or part of the cause of the disorder. Voluntary intake of alcohol, as well as forced exposure, increases both GFAP and GFAP mRNA expression [90][91][92][93], while withdrawal reduces the number of GFAP positive cells [94,95]. Alcohol also affects astrocyte morphology and induces rearrangements of actin filaments, which may further impair Ca 2+ signaling [64,96]. Post mortem analysis of human alcoholics reports reduced GFAP expression and altered astrocyte morphology [97]. These prominent changes in astrocyte morphology are not exclusive to alcoholics with liver pathology, and may persist despite the cessation of alcohol consumption [98,99].

Astrocytic amino acid transporters and ethanol
Astrocytic amino acid transporters are crucial regulators of neurotransmission, and ethanol may cause glutamatergic dysfunction by affecting astrocytic proteins responsible for maintaining extracellular homeostasis [100]. Over all, chronic ethanol exposure causes long-lasting alterations in the expression of glutamatergic receptors and transporters, producing a hyperglutamatergic state [101][102][103][104]. Pharmacological modulation of astrocytic clearance of glutamate might thus indirectly mitigate behavioral responses caused by ethanol exposure. Supporting this theory, selective up regulation of the glutamate transporter GLT-1 in the nucleus accumbens shell reduces voluntary ethanol consumption, and impairs methamphetamine and morphine induced conditioned place preference (CPP) [105,106]. However, mice with homozygous GLAST deletion show a pronounced reduction of voluntary alcohol consumption, and inhibited ethanolinduced CPP [107]. In addition, blocking central astrocytic glutamate uptake selectively attenuates ethanol binge drinking behavior in mice [108]. The role of glutamate transporters might thus depend on the brain region targeted, the amount of alcohol consumed, and/or the transporter subtype manipulated. A differential role for astrocyte glutamate transporters is also supported by the selective correlation between GLT-1 and alcohol consumption [106]. Interestingly, changes in adenosine levels, caused by ethanol exposure, may also indirectly alter GLT-1 expression, further affecting alcohol consumption [25,83].
Astrocytes also express transporters for glycine, which has been implemented in the rewarding properties of ethanol through the potentiation of NMDA receptors and activation of glycine receptors [109][110][111]. Especially, glycine receptors could be important regulators of ethanol induced reinforcement as antagonists targeting glycine receptors both blocks the dopamine-elevating property of ethanol in vivo, as well as prevents ethanol-induced changes in striatal neurotransmission ex vivo [79,111,112]. In addition, inhibition of the glycine transporter GlyT1 persistently reduces ethanol intake and relapse-like alcohol drinking in rats [113][114][115][116], and might thus be a pharmacological target for the treatment of alcohol abuse disorders. However, even though it was recently shown that the GlyT1 transporter inhibitor Org25935 robustly reduces the number of days with heavy drinking, the effect was not significant as compared to placebo [117]. More research is thus required in order to fully elucidate the role of extra synaptic glycine levels in the pathophysiology of alcohol abuse and addiction.

Conclusion
Astrocytes sense and integrate neuronal functioning and activation of astrocytes coincides with synchronized synaptic firing and plasticity. Due to their pivotal role in monitoring extra synaptic transmission, the astrocyte appears to be an important target during both the acute and chronic phase of ethanol exposure and may play a key role in alcohol abuse disorders. Finding ways to modulate astrocyte function might thus lead to novel pharmacological interventions for treatment of alcohol abuse disorders. Especially, restoring ethanol-induced effects on amino acid transporters, and reinstating glutamate homeostasis, might be efficient methods for inhibiting drug seeking, and heavy drinking. However, additional studies are required in order to define ethanol-induced changes in astrocyte function, and to determine the role of astrocytes in the pathophysiology of relapse and alcohol addiction.


extfieldsofstudy: Chemistry Medicine

s2fieldsofstudy: Biology Medicine Chemistry

text:
Overexpression of Lin28 Decreases the Chemosensitivity of Gastric Cancer Cells to Oxaliplatin, Paclitaxel, Doxorubicin, and Fluorouracil in Part via microRNA-107

Higher Lin28 expression is associated with worse pathologic tumor responses in locally advanced gastric cancer patients undergoing neoadjuvant chemotherapy. However, the characteristics of Lin28 and its mechanism of action in chemotherapy resistance is still unclear. In this study, we found that transfection of Lin28 into gastric cancer cells (MKN45 and MKN28) increased their resistance to the chemo-drugs oxaliplatin (OXA), paclitaxel (PTX), doxorubicin (ADM), and fluorouracil (5-Fu) compared with gastric cancer cells transfected with a control vector. When knockdown Lin28 expression by Lin28 small interfering RNA (siRNA) was evaluated in vitro, we found that the resistance to chemo-drugs was reduced. Furthermore, we found that Lin28 up-regulates C-myc and P-gp and down-regulates Cylin D1. Finally, we found that miR-107 is a target microRNA of Lin28 and that it participates in the mechanism of chemotherapy resistance. Our results suggest that the Lin28/miR-107 pathway could be one of many signaling pathways regulated by Lin28 and associated with gastric cancer chemo-resistance.

Introduction
Gastric cancer is a common clinical digestive system malignant tumor with a mortality rate that ranks third in the world [1]. Our country is an area that is prone to gastric cancer. In recent years, neo-adjuvant chemotherapy has been administered to gastric cancer patients to prolong survival. Nonetheless, the 5-year survival rate of those with advanced gastric cancer is still lower than 30% [2]. Chemotherapy resistance is the leading cause of the failure of gastric cancer and other tumor treatments. Therefore, the mechanism of chemotherapy resistance of gastric cancer is currently a major concern in gastric cancer studies.
Lin28 is a highly conserved RNA-binding protein that has been shown to participate in inducing pluripotent stem cells and in maintaining stem cell-like cells in cancer. Recent studies have reported that aberrant activation of Lin28 correlates well with the prognosis of different cancers, including breast, ovarian, lung, colon, and liver cancer [3][4][5][6]. Our previous study showed that Lin28 expression is also associated with poor survival in gastric cancer patients [7], and another study demonstrated that the expression of Lin28 is correlated with paclitaxel resistance in human breast cancer cells [8]. Moreover, Lin28 expression is associated with pathologic tumor response in locally advanced gastric cancer patients undergoing neo-adjuvant chemotherapy. However, the molecular mechanism underlying these findings remains unclear [9].
Lin28 promotes tumorigenesis by repressing tumor suppressor miRNAs such as the let-7 family, which is associated with drug resistance [8]. However, as a target miRNA of Lin28, miR-107 is rarely mentioned as a drug resistance factor in gastric cancer. MiR-107 expression is decreased in a variety of malignancies, such as colon cancer, head and neck cancer, pancreatic cancer and gastric cancer [10][11][12][13]. MiR-107 regulates downstream genes which are involved in cell differentiation and proliferation, metabolism, stress responses and the formation of blood vessels [10][11][12][13][14]. In this study, we investigated the Lin28/miR-107 pathway and its role in gastric cancer cell resistance to chemotherapy. We may have identified a new potential target for chemotherapy-resistant gastric cancer.

Cell culture and plasmid transfection
The human gastric cancer cell lines MKN-28 and MKN-45 were obtained as a gift from Professor Sung Hoon Noh (Yonsei University College of Medicine, Seoul, Korea). The cells were cultured in RPMI 1640 medium (Sigma-Aldrich, St. Louis, MO, USA) supplemented with 10% fetal bovine serum (MP Biomedicals, Costa Mesa, CA, USA) in a humidified atmosphere containing 5% CO2 at 37°C. Oxaliplatin, paclitaxel, doxorubicin, and fluorouracil were purchased from Sigma (St. Louis, MO). The plasmid pcDNA3.1-Lin28 was transfected with Xtreme GENE HP DNA Transfection Reagent (Roche Applied Science, Mannheim, Germany) according to the manufacturer's protocol. Stable Lin28-expressing clones were further selected and cultured with the appropriate neomycin-containing RPMI 1640 medium.
Cells were seeded in 96-well plates at a density of 5 Ã 10 3 cells per well. After treatment, the cells were incubated with 5 mg/ml MTT for 4 h. Then, the medium was removed and 150 ml of sterilized DMSO solution was added, followed by incubation at 37°C for 4 h. The OD value was obtained at the wavelengths 570 nm and 630 nm. The OD value (570 nm to 630 nm) can indirectly reflect the cell number and activity according to the following formula: survival rate = dosed group OD570-630/no dose group OD570-630. A cell survival/drug concentration curve was then generated.

Flow Cytometric Assay
Cells (2 Ã 10 5 per well) were plated in a six-well plate and treated with paclitaxel. After 72 h, cells were fixed in 70% ethanol and stained with propidium iodide (PI). The DNA content was analyzed with an Epics Profile II flow cytometer (Beckman Coulter, Fullerton, CA) with Multicycle software (Phoenix Flow Systems, San Diego, CA). All the experiments were repeated at least twice. PI was also used in conjunction with Annexin V (BD Biosciences, San Jose, CA) to determine if cells were viable, apoptotic, or necrotic.

MiRNA Transfection and Detection
Cells were plated in six-well culture plates and transfected with 50 nM pre-miR-107 purchased from Applied Biosystems (Carlsbad, CA) or control pre-miRNA according to the manufacturer's protocol. Quantitative Real-time Polymerase Chain Reaction (PCR) was performed using the TaqMan MicroRNA Reverse Transcription Kit and the Fast Real-Time PCR System (Applied Biosystems, Carlsbad, CA) according to the manufacturer's protocols. The fold change in miR-107 microRNA levels was calculated and normalized to a hsa-mir-423 loading control.

Xenograft Assay and Treatment
Briefly, groups of six BALB/c mice were injected subcutaneously with 1 Ã 10 6 MKN45/Lin28, MKN45/Vector, or MKN45 cells in the right flank. The tumor volume was calculated using the formula: V = 1/2 Ã a Ã b 2 , where a and b are the longest and the shortest diameters of the tumor mass (in millimeters), respectively. When the tumors reached approximately 80 mm 3 , the mice were treated with injection of ADM (50mg/kg) and OXA (5uM/kg) every week for 2 weeks. Quantitative changes in body weight and tumor volume were measured every three days by a single individual using a balance and Vernier calipers. No anesthetics were used in the mouse experiments.

Statistical analysis
All the experiments were performed three times with triplicate samples. Analysis of variance and Student's t-test were used to compare the values of the test and control samples. A P-value less than 0.05 was deemed to be statistically significant. Statistica 6.1 software was used for all statistical analyses. The significance was evaluated by the student t-test.

In vitro testing of chemotherapy sensitivity changes in gastric cancer cells stably expressing Lin28
Two strains of gastric cancer cells with negative Lin28 expression (MKN45 and MKN28) were selected and cultured. We identified gastric cancer cells with stable Lin28 expression from resistant transfected clones (MKN45/Lin28/C2 and MKN28/Lin28/C3) using western blotting and q-PCR (Fig 1). The selected MKN45/Lin28C2, MKN45/Vector, MKN45, MKN28/Lin28C3, MKN28/Vector, and MKN28 cells were tested for chemotherapy drug susceptibility. We selected the following four commonly used clinical chemotherapy drugs for treatment of gastric cancer: OXA, PTX, ADM, and 5-Fu. Treatment with OXA, PTX, and ADM lasted for 48 hours, while 5-Fu treatment lasted for 72 hours. After Lin28 transfection, MKN45 and MKN28 cells had a decreased sensitivity to OXA, PTX, ADM, and 5-Fu that was most pronounced for OXA and PTX. The IC50 values were more than twice that of the control group (Fig 1).
On the basis of the MTT assay, we used flow cytometry to detect the differences in apoptosis between Lin28-positive and Lin28-negative cells after chemotherapy PTX exposure. The MKN45/Lin28, MKN45/Vector and MKN45 cell groups received 0 μM and 0.1 μM PTX treatment for 24 hours, and double staining was performed to detect cell apoptosis. The apoptotic rate of transfected Lin28 cells was lower than that of non-transfected cells (p<0.05). In addition, western blots of protein collected from cells treated with 0.1 μM PTX showed that apoptosis was reduced in MKN45/Lin28 cells as shown by decreases in the activation of caspase 9 and caspase 3 (Fig 2).
Transient transfection of siRNA-Lin28 should inhibit Lin28 expression levels in MKN45/ Lin28 and MKN28/Lin28 cells 48 hours after transfer according to the experimental guidelines of the reagent manufacturer. Lin28 protein expression was detected, and the associated drug sensitivity was tested. Lin28 RNA expression in gastric cancer cells was detected by q-PCR, while protein expression was detected by western blotting (Fig 3).
An MTT assay was performed on the four cell types (MKN45/Lin28/si-control, MKN45/ Lin28/si-Lin28, MKN28/Lin28/si-control, and MKN28/Lin28/si-Lin28 cells) that were transfected with Lin28 siRNA for 48 hours to assess chemotherapy sensitivity. We chose to use the chemotherapy drugs that had the most pronounced effects in the pre-testing: OXA, PTX and ADM. We selected a concentration gradient of 5-6 according to the preliminary results of preliminary experiments. Interference by siRNA reduced the expression of Lin28, and Lin28-positive cell lines had an increased sensitivity to chemotherapeutic drugs (Fig 3).
We next detected the sensitivity of cells to different chemotherapy drugs after down-regulation of Lin28. Cells were divided into 4 groups, MKN45/Lin28/si-control, MKN45/Lin28/si-Lin28, MKN28/Lin28/si-control and MKN28/Lin28/si-Lin28, and were tested with the chemotherapy drugs OXA and PTX respectively. The chemotherapy drug concentrations (OXA 3.2 μg/mL and PTX 1 μM) were selected according to the results of preliminary experiments. The number of apoptotic MKN45/Lin28 and MKN28/Lin28 cells was less than that of MKN45/Lin28/si-Lin28 and MKN28/Lin28/si-Lin28 cells (p<0.05). This result indicates that down-regulation of Lin28 expression can increase cell apoptosis (Fig 3).

The molecular mechanisms by which Lin28 changes in chemotherapy sensitivity
We detected drug resistance-related gene expression levels by q-PCR in MKN45/Lin28 and MKN45/vector cells.P-gp, and C-myc RNA levels were markedly increased and Cyclin D1 RNA level were decreased, while other Bcl-2,CDK6,NF-kB,TOP II did not show significant difference between MKN45/Lin28 and MKN45/vector cells.Also these drug resistance-related gene expressions were investigated by western blot in MKN45/Lin28 and MKN45/Vector cells, which showed consistent trend that P-gp and C-myc protein expression increased while Cyclin D1 decreased. However, Bcl-2 expression decreased, NF-kB expression did not change, and expression of Cyclin D1 and the protein kinase CDK6 was reduced (Fig 4).
After Lin28 expression was inhibited by transfection with Lin28 siRNA, the RNA levels of P-gp, C-myc and Cyclin D1 were reassessed. After reduction in the RNA level of Lin28, P-gp and C-myc were significantly down-regulated, while Cyclin D1 levels significantly increased.  Detection of P-gp, C-myc, and Cyclin D1 expression by western blotting revealed that after inhibition of Lin28 expression, P-gp and C-myc protein expression was significantly down-regulated, while the expression of Cyclin D1 was significantly upregulated (Fig 4).

MiR-107 is the target microRNA of Lin28
While testing the Lin28 RNA and miR-107 levels in gastric cancer cell lines, we found mutual inhibition of Lin28 and miR-107 in the MKN45 and AGS cell lines. Additionally, miR-107 was down-regulated in MKN45/Lin28 and MKN28/Lin28 cells. Knockdown of the expression of Lin28 reversed this inhibition. In a follow-up experiment, Lin28-positive cells, MKN45/Lin28 and MKN28/Lin28, were transfected with pre-mir-107 and were subsequently referred to as MKN45/Lin28/pre-miR-107 and MKN28/Lin28/pre-miR-107 cells. Transfected pre-miR-control cells were called MKN45/Lin28/pre-miR-Control and MKN28/Lin28/pre-miR-Control. Anti-miR-107 or control transfections into Lin28-negative cells, MKN45/Vector, yielded MKN45/Vector/anti-miR-107 and MKN45/Vector/anti-miR-Control cells, respectively. Fortyeight hours after transfection, RNA was collected and subjected to q-PCR to detect the differences in miR-107 expression in MKN45/Lin28, MKN28/Lin28, MKN45/Vector, and MKN28/Vector cells. The effect of knockdown of Lin28 expression by Lin28 siRNA was also assessed. After transfection with Lin28, miR-107 expression was significantly down-regulated, while after knockdown of Lin28 expression, the expression of miR-107 increased. Forty-eight hours after transfection of pre-miR-107, Lin28 RNA levels were significantly decreased, and 96 hours after transfection, Lin28 protein expression was still lower than that of the non-transfected control group. This finding indicates that miR-107 may regulate the expression of Lin28 (Fig 5).

MiR-107 participates in Lin28-mediated chemo-drug resistance
The chemotherapy sensitivity of 4 cell groups, MKN45/Lin28/pre-miR-control, MKN45/ Lin28/pre-miR-107, MKN28/Lin28/pre-miR-control and MKN28/Lin28/pre-miR-107, was tested using the MTT method 48 hours after transfection with pre-miR-107. We chose chemotherapy drugs that had the most pronounced effects during pre-testing, OXA, PTX and ADM, and selected a concentration gradient of 5-6 according to preliminary results from preliminary experiments. The results suggest that the introduction of pre-miR-107 into Lin28-positive cells can increase cell sensitivity to chemotherapy. Apoptosis testing showed that the number of MKN45/Lin28 apoptotic cells increased after transfection with pre-miR-107 (p<0.05). MTT testing of MKN45/Lin28/anti-miR-control and MKN45/Lin28/anti-miR-107 chemotherapy sensitivity showed that inhibition of expression of miR-107 in MKN45 reduced the sensitivity to PTX (Fig 5).
By western blotting and q-PCR, we detected the drug resistance-related gene and protein expression levels in MKN45/Lin28 cells that had been transfected with pre-miR-107, including those of C-myc, P-gp, and Cyclin D1. The results showed that 48 hours after transfection with miR-107, the RNA and protein expression of C-myc and P-gp decreased (p<0.01), while the expression of Cyclin D1 increased (p<0.05) (Fig 5).

Lin28 associated chemo-drug resistance in vivo
After 2 cycles of chemotherapy, we found that the xenograft tumors of MKN45/Lin28 cells did not reduce in volume and became bigger than those of other groups (MKN45/Vector and MKN45). In both the MKN45/Lin28 and MKN45/Vector groups, one of the mice died, so chemotherapy was stopped. The tumors were excised and their volumes analyzed. We found that the MKN45/Lin28 xenograft tumors were bigger than the other tumors after ADM and OXA chemotherapy (p<0.01) (Fig 6).

Discussion
In the present study, we investigated the association of Lin28 expression with chemo-sensitivity in gastric cancer cells. We found that transfection with Lin28 reduces the sensitivity of the gastric cancer cell lines MKN45 and MKN28 to four types of chemotherapeutic reagents (OXA, PTX, ADM, and 5-Fu). Using siRNA interference technology to knockdown Lin28 expression could reverse chemo-resistance. We also showed that miR-107 could down-regulate Lin28 expression, while is also served as one of the potential target microRNAs that is regulated by Lin28. Overexpression of Lin28 up-regulated C-myc and P-gp, while down-regulated Cyclin D1, this phenomenons could reverse by Lin28 siRNA or transfection pre-miR-107. This might be critical mechanisms by which Lin28 decreased chemosensitivity in gastric cancer cells. To our knowledge, this is the first study that demonstrates Lin28/miR-107 signaling pathway might be involved in the chemo-drug resistance in gastric cancer.
Chemotherapy is one of major treatment for gastric cancer patients, but more than 50% of patients will progress to chemotherapy resistance. The possible mechanisms for chemo-resistance in gastric cancer are currently considered to include the trans-membrane transport of ABC transporters, glutathione S transferase, the activity of DNA topoisomerase, cancer gene p53 mutation and apoptosis related pathways. A recent study found that microRNA plays an important role in gastric cancer chemotherapy drug resistance [15]. In a tumor cell resistance model, it was observed that cancer stem cells also play an important role in resistance to chemotherapy. The C-myc gene is one of the surface markers of cancer stem cells but also an oncogene associated with indefinite proliferation, infinite immortalization, and the promotion of cell division [16]. One study reported that 40% of patients with gastric cancer have C-myc gene overexpression and that higher C-myc expression is associated with worse prognosis [16][17][18]. Studies have also reported a correlation between the C-myc gene and apoptosis. C-myc expression may lead to decreased tumor cell apoptosis. The cell apoptotic rate and sensitivity to induced factors are dependent on C-myc protein levels [19]. Moreover, C-myc overexpression is associated with radiation and chemotherapy resistance. Furthermore, C-myc can up-regulated P-gp, which can induce effluxing of chemotherapeutic reagents led to chemotherapy resistance [20]. Cell cycle arrest can affect the response of cancer cells to certain chemotherapy drugs (such as PTX), which plays a role in G2/S phase reduced drug sensitivity. The cell cycle Lin28/miR-107 Promote Chemo-Drug Resistance in GC protein Cyclin D has three subtypes, D1, D2, and D3, and is a cell cycle initiation factor. Its suppression by transforming growth factor -β1 (TGF-β1) prevents cells from entering the cell cycle such that they remain in a resting state [21]. Cyclin D specifically combines with CDK4/6 to phosphorylate and inactivate retinoblastoma protein (pRb), thus contributing to G1/S transition and the initiation of DNA synthesis [21]. In the current study, we found Lin28 were not relevant with Bcl-2, NF-kB and TOPO II in gastric cancer cells. The partnership between Lin28 and microRNAs has been well established, such as let-7, miR-200c, miR-143 [4,22]. Lin28 overexpression down-regulates microRNA, while microRNA can also negatively affect the expression of Lin28. Lin28 together with TUT4 constitutes a negative regulator, which influences maturation and homeostasis of microRNAs [4,22]. By regulating downstream genes, microRNA is involved in cell differentiation and proliferation, cell metabolism, stress responses and the formation of blood vessels. Cheng et al [23] found that after inhibiting miR-107, the growth of HeLa cervical cancer cells and A549 lung cancer cells was promoted. Marijn et al [24] found that the expression of miR-107 in drug-resistant ovarian cancer cells was down-regulated. Our study found that Lin28 can also restrain the maturation of miR-107, and introduction of miR-107 increase gastric cancer cell chemo-sensitivity to OXA, PTX, and ADM. After transfection of anti-miR-107, this sensitivity decreased. Pre-miR-107 caused a decrease in the expression of Lin28, illustrating that the Lin28/miR-107 pathway may be involved in the chemosensitivity changes of gastric cancer.
In summary, we showed that Lin28 could inhibit the expression of miR-107, thereby up-regulating C-myc, P-gp and down-regulating Cyclin D1, subsequently result in chemo-resistance of gastric cancer cells. The Lin28/miR-107 pathway might be served as one of many signaling pathways that is associated with gastric cancer chemo-resistance. Our study may provide new insights into the gastric cancer chemoresistance mechanism. Lin28/miR-107 may be a new potential therapeutic targets for chemotherapy-resistant gastric cancer.
Supporting Information S1 Table. Minimal data for all these figures.


extfieldsofstudy: Biology Medicine

s2fieldsofstudy: Biology Chemistry

text:
Distinct cholesterogenic and lipidogenic gene expression patterns in ovarian cancer - a new pool of biomarkers

Cancer cells display different metabolic requirements compared to nonmalignant cells imposed by their need for rapid proliferation. Alterations in cellular metabolic pathways of lipid and cholesterol synthesis have been linked to tumorigenesis and cancer progression but have not been exploited in clinical diagnosis. Here, the expression of genes related to cholesterol/lipid metabolism was measured with semiquantitative and real-time RT-PCR in RNA isolated from normal, benign and cancer ovarian tissues. We found that both SREBF2 and its target gene DHCR7 are downregulated in ovarian cancer tissues. On the contrary, SREBF1c and its target SCD1 were upregulated. The steroidogenesis regulator PDE8B was found downregulated. Oncomine analysis supported these findings, and further revealed that in ovarian cancers, the SREBF1-regulated lipidogenic pathway is activated while the SREBF2-regulated cholesterogenic pathway is repressed based on expression profiles of HMGCR and DHCR7. In conclusion, we show that ovarian cancer cells display distinct lipidogenic and cholesterogenic gene expression profiles with potential applications in the development of new biomarkers and/or treatment of ovarian cancer. Reduced cholesterol and enhanced lipid synthesis and SCD1 expression may provide an explanation for the previously reported increased membrane fluidity of ovarian cancer cells, a finding that merits further investigation.

INTRODUCTION
Ovarian cancer is a neoplastic growth originating from various parts of the ovary, although most ovarian cancers derive from ovarian surface epithelial cells. Since ovarian cancer constitutes a main cause of mortality in women, understanding its pathophysiology and translation into novel therapeutics is a major concern [1]. Considering that malignant cell proliferation highly depends on nutrients, energy and synthetic activity ensuring duplication of all macromolecules during cell division, it is reasonable that metabolic activities in cycling cells, like cancer cells, are fundamentally different than in nonproliferating cells [2].
Recent studies highlighted that tumors display distinct metabolic programs, and altered cholesterol/ lipid metabolism is emerging as an important process in cancer [3][4][5][6][7]. Clendening et al. [7] showed that HMGCR (3-hydroxy-3-methylglutaryl-CoA reductase) and other genes encoding enzymes or regulatory proteins of the mevalonate (MVA) pathway exhibit aberrantly high expression in breast cancers and overexpression of HMGCR or its novel transcript variant lacking exon 13 promoted cellular transformation indicating that HMGCR represents a metabolic oncogene [7]. In breast cancers, upregulation of genes involved in the MVA pathway is directly related to mutant p53 that in turn, interacts with SREBPs to induce expression of cholesterogenic/lipogenic genes [5]. In addition, alteration of lipid metabolism is increasingly recognized as a hallmark of cancer [6]. Indeed, it has been shown that specific pathways are shared between atherosclerosis and cancer pointing to a common transcriptional program [6]. Co-culturing ovarian cancer cells with adipocytes induced lipolysis in adipocytes and transport of lipids to cancer cells that exhibited increased β-oxidation. This may provide an www.impactjournals.com/Genes&Cancer explanation for the observed omental metastasis of ovarian cancer [8]. Moreover, ectopic expression of SREBF1 in MCF10A non-tumorigenic breast cancer cells enhanced lipogenesis in stem-like cells and promoted cell growth and mammosphere formation [4].
To our knowledge no study has been performed to investigate the expression of genes encoding the cholesterogenic and lipidogenic enzymes and the expression of their master regulators SREBF1 and SREBF2 in ovarian cancer or any other type of cancer. To examine whether lipid and cholesterol synthesis pathways are altered in ovarian cancer, we analyzed the expression of SREBF1c, SREBF2, SCD1, DHCR7, LDLR and PDE8B in a pilot study. SREBF1c is the isoform transcribed from the SREBF1 gene encoding the transcription factor mainly responsible for the expression of lipidogenic genes, while SREBF2 encodes the transcription factor of the genes encoding the enzyme of the MVA pathway including the DHCR7, that catalyzes the production of cholesterol from 7-dehydrocholesterol and is the causative gene for Smith-Lemli-Opitz syndrome (SLOS, OMIM #270400) [9]. SCD1 is the rate-limiting step of monounsatured fatty acid synthesis [10]. PDE8B is a cAMP-specific phosphodiesterase that controls steroidogenesis, a cholesterol biotransformation process [11]. LDLR is the major protein involved in hypercholesterolemia [12]. We enriched our expression pattern analysis by analyzing microarray data with oncomine and incorporating the expression levels of HMGCR, INSIG1, LDLRAP1 and FASN. Our data showed for the first time that the SREBF1 and the SREBF2 regulated pathways are altered in ovarian cancer with the former exhibiting up-regulation and the latter down-regulation.

RESULTS
For analysis of gene expression we used 10 normal ovary tissue specimens (average age 52.7 ± 5.7 years, median 51 years), 6 benign (average age 46.3 ± 12.4 years, median 44.5) and 15 ovarian cancer specimens (average 56.6 ± 13.9, median 57). Figure 1 depicts the genes that were chosen for expression level analysis. PDE8B is the only gene from our list that is not regulated by SREBPs. As shown in Figure 2A, 2B and in Table 1, up-regulation of SCD1 expression was clearly observed in ovarian cancer compared to normal and benign tissues. Contrary, SREBF2 and its target DHCR7 as well as PDE8B were found downregulated. Expression of LDLR was statistically not significantly different between normal and cancer tissues (p=0.584, t-test). SREBF1c was up-regulated in cancer samples compared to normal samples ( Figure 2A, and B, Table 1) although the p value was slightly larger than 0.05 (0.0878, t-test, and 0.0894, Fisher exact test) which may be due to a rather low number of analyzed samples. Up-regulation of SREBF1c is in accordance with the up-regulation of its target SCD1. Indeed, hierarchical clustering indicated that SREBF2 and DHCR7 are clustered together in cancer cells as well as SREBF1 and SCD1. Interestingly, PDE8B clusters together with SREBF2 and DHCR7, although PDE8B has not been described to be associated with the MVA pathway or controlled by SREBFs.
We could not find any correlation between gene expression with either stage or grade of cancer. For the panel of tissue specimens analyzed here, distribution of gene expression levels did not show correlation with disease stage or grade. For example, PDE8B gene expression with the cutoff value shown in Table 1, 7/8 cancer samples were below cutoff for stage III and 5/6 for stages I+II (p=1.000) (one cancer sample for which grade and stage were not known was excluded from the statistical analysis). According to grade, 7/8 cancer samples with grade III were below the cutoff and 5/6 for grades I+II. The same results were obtained for the other genes (data not shown).
Oncomine was applied to analyze the wellestablished microarray datasets of [13] encompassing 10 normal and 185 ovarian cancer tissue samples. We have enriched the analysis by incorporating additional genes including LDLRAP1, LDL, FASN, HMGCR and INSIG1. Our results shown in Figure 2A are in accordance with the microarray data ( Figure 3A) with the exception of SREBF2 for which no correlation could be found in the Bonome dataset (p=0.854) and LDLR, which was found up-regulated in ovarian carcinomas. LDLRAP1 that is necessary for the uptake of cholesterol by LDLR [12] was also found up-regulated in ovarian carcinomas. Upregulation of SREBF1 was confirmed in the microarray data and was coordinated with increased FASN and SCD1 expression, the later validated both in our experimental set and in the microarray data. Microarray data analysis confirmed that HMGCR is strongly repressed in ovarian carcinomas, while induction of SREBF1 is in accordance with our experimental data showing increased SREBF1c in ovarian clinical cancer specimens. Our integrated data point to suppression of the MVA pathway and coordinated activation of the lipogenic pathway in ovarian cancer.
Finally, INSIG1 that is regulated by both SREBFs [14] was not found deregulated in ovarian cancer compared to normal samples.
A heatmap generated by Treeview depicts the profile of clinical samples ( Figure 3B). It clusters together genes with common expression profiles and confirms that the expression of SREBF1, SCD1 and FASN is coordinately up-regulated in ovarian cancers. Moreover, PDE8B clustered together with HMGCR and DHCR7 indicating that its altered expression (down-regulation) is correlated with the expression of MVA pathway genes.

DISCUSSION
Whereas alterations to metabolism of glucose, amino acids, and fatty acids have been extensively studied in cancer [15], cholesterol metabolism is a relatively understudied field [16]. Maintenance of cholesterol/lipid homeostasis is fundamental for the growth of eukaryotic cells including mammalian cells. Currently, it has been demonstrated that cancer cells display altered cholesterol/ lipid metabolism [5,6]. Here, we show that genes involved in cholesterol/lipid biotransformation are altered in ovarian cancers.
We found that suppression of SREBF2 in ovarian cancers results in down-regulation of cholesterogenic target genes such as the DHCR7, while induction of SREBF1 is associated with increased expression of lipid synthesis genes such as the SCD1, which was found highly elevated. In accordance, Nie et al. [17] showed by immunohistochemistry that SREBP1 protein levels are elevated in ovarian cancers. SCD1 has a protective effect in cancer cells that de novo synthesize high amounts of saturated fatty acids since saturated fatty acids trigger programmed cell death through a process referred as lipoapoptosis [10]. Indeed, ablation of SCD1 in lung cancer cells reduces their ability to grow xenografts in immunocompromized mice and SCD1 overexpression correlates with genetic predisposition for liver cancer in rodents [10]. A recent immunohistochemical study showed that SCD1 is elevated in breast, prostate, lung, renal, and ovarian cancers [18,19]. Up-regulated expression of SCD1 in hepatocellular carcinoma has been associated with resistance to chemotherapy-induced apoptosis [20]. Further, we have found that PDE8B is strongly downregulated in ovarian cancer. As the PDE8B is a negative regulator of steroidogenesis [11], its down-regulation, is expected to drive the synthesis of steroids that are essential in supporting the growth of ovarian cancer cells.
Previously, microarray profiling of ovarian epithelial cells exposed to progesterone detected up-regulated expression of gene transcripts encoding for enzymes of the MVA pathway, LDLR, and FASN [21]. This gene set was suggested to mediate the protective role of progesterone against ovarian cancer development. In support of these findings, our gene expression analysis and the analysis of Bonome microarray data [13] showed strong downregulation of cholesterogenic genes in ovarian cancer, including HMGCR. In contrast, in other types of cancer, MVA pathway was shown to drive cellular transformation. Previous studies involving analysis of tissue microarrays showed that patients with ovarian tumors expressing high levels of HMGCR had a significantly favorable prognosis [22]. Pitfalls of this study include lack of correlation with normal tissue expression and a later study that demonstrated non-specific crossreactivity of the antibody [7].
Aberrant regulation of cholesterol and lipid metabolism may significantly impact on cell membrane fluidity, since their metabolic products are components of cellular membranes. Previously it was observed that in SKOV-3 ovarian adenocarcinoma cells, decrease in membrane fluidity reduces aggressiveness of these cells [23]. Consistently, we found that the cholesterol pathway is repressed in ovarian cancer cells. Reduced cholesterol is likely responsible for decreased membrane fluidity corroborated by increased contents of monounsatured fatty acids produced by elevated SCD1. Recently, it has been reported that in breast cancer, cholesterol metabolites can either promote (e.g. 27-hydroxycholesterol) or inhibit tumor formation (e.g. dendrogenin A) [24]. This may also hold true for ovarian cancer and could account for the differential expression of cholesterogenic and lipidogenic genes, a fact that merits further investigation.
Recently, lipid profiles emerged as an alternative approach to stratify breast cancers thus, opening new ways to be exploited in molecular cancer diagnostics [25]. Based on lipid profiles determined by Raman microspectroscopy and multivariate statistical techniques, an algorithm was developed to distinguish breast cancer cells with enhanced metastatic ability and to monitor epithelialto-mesenchymal transition [25]. However, analysis of lipids is more complex and requires sophisticated instrumentation as opposed to mRNA quantification, making its application for routine monitoring rather difficult.
In summary, this is the first report describing altered expression profiles of key genes involved in cholesterol and lipid biosynthesis/biotransformation in ovarian cancer. These expression profiles reveal that lipidogenic and cholesterogenic pathways are differentially regulated in ovarian cancer impacting on the metabolome and hence the biology of ovarian cancer cells. The currently accepted general concept is that cholesterol and sterols synthesis has evolved symbiotically with fatty acid synthesis in order to form structural components of cell membranes (they both require the same carbon acetyl-CoA and hydrogen NADPH sources, and are under the control of SREBPs) [26]. However, our data indicate that under certain conditions (e.g. ovarian cancer) there are ways to overcome this concerted regulation and instead differentially regulate one pathway relative to the other. Further, our results may provide novel targets for pharmacological intervention. Towards this direction, it was demonstrated that targeting SCD1 for inhibition increases apoptosis of renal carcinoma cells in vitro and in vivo [19] and lung cancer cells in vivo [27], while in ovarian cancer, FASN inhibitors induce cell cycle blockage and stimulate apoptosis. Thus, FASN is considered a metabolic marker of cell proliferation [28].

Patients and clinical specimens
Tissue specimens were collected by Dr D Katsaros at the University of Turin, Italy and have been described previously [29]. All tissues were obtained after patients' written consent under a general tissue collection protocol approved by the Institutional Review Board and the University of Turin. Samples were snap-frozen in liquid N 2 and then stored at -80 o C until use.

RNA extraction and reverse transcription
Total RNA extraction was carried out with RNeasy (Qiagen) and RNAs were treated with DNase according to manufacturer's instructions. RNA integrity was confirmed by agarose gel electrophoresis. 500 ng of RNA were reverse-transcribed with Superscript II (Invitrogen) using an oligo-dT primer and the cDNAs were recovered in 21 μl.

Bioinformatic analysis
Microarray data were retrieved from Oncomine and statistically analyzed with Origin 8.0. Further, the microarray gene expression data were normalized against the median of normal gene expression and used for hierarchical clustering. Hierarchical clustering was performed with Cluster and visualization of results with TreeView (http://rana.lbl.gov/EisenSoftware. htm). Hierarchical clustering was also performed in the experimental dataset. For this, expression levels from clinical specimens were converted to log 2 and normalized against the mean of normal expression.


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Polyubiquitination of Transforming Growth Factor β-activated Kinase 1 (TAK1) at Lysine 562 Residue Regulates TLR4-mediated JNK and p38 MAPK Activation

Toll-like receptor 4 (TLR4) plays an important role in innate immunity by eliciting inflammation. Upon receptor engagement, transforming growth factor β-activated kinase 1 (TAK1) is an essential mediator that transmits a signal from the receptor to downstream effectors, IκB kinase (IKK) and the mitogen-activated protein kinases (MAPKs), which control the production of inflammatory cytokines. However, the association between phosphorylation and ubiquitination of TAK1 is not yet clear. Here, we examined the crosstalk between phosphorylation and polyubiquitination of TAK1 and further investigated the mechanism of distinct activation of MAPKs and IKK. Inhibition of TAK1 phosphorylation enhanced Lys63-linked polyubiquitination of TAK1. Conversely, ubiquitin modification was counteracted by phospho-mimic TAK1 mutant, T(184,187)D. Moreover, using LC-MS analysis, Lys562 of TAK1 was identified as a novel Lys63-linked ubiquitination site and as the key residue in the feedback regulation. Mutation of Lys562 of TAK1 leads to a decrease in TAK1 phosphorylation and specific inhibition of the MAPK pathway, but has no effect on formation of the TAK1-containing complex. Our findings demonstrate a feedback loop for phosphorylation and ubiquitination of TAK1, indicating a dynamic regulation between TAK1 polyubiquitiantion and phosphorylated activation, and the molecular mechanism by which IKK and MAPKs are differentially activated in the TLR4 pathway.

regulates signals in a cell-specific and receptor-specific manner 3 . TAK1-deficient mouse embryonic fibroblasts (MEFs) or B cells lose the response to TLR ligands, IL-1β or TNF, and fail to activate MAPKs and NF-κ B 4 . However, Map3k7 deletion in neutrophils enhances activation of NF-κ B and MAPKs after LPS stimulation 5 . The molecular mechanisms underlying the opposing roles of TAK1 in different cell types remain unknown.
TLR4 is the most-studied of the TLRs. In general, TLR4 signaling is mediated by recruitment of adaptor proteins, such as MyD88 and toll-interleukin-1 receptor (TIR) domain-containing adaptor inducing interferon-beta (TRIF), to the cytoplasmic portions of the receptors and classified as either MyD88-dependent or TRIF-dependent 6,7 . In MyD88-dependent signaling, a two-stage signaling mechanism is responsible for spatial and temporal separation of MAPK and IKK-NF-κ B signaling 8 . Upon TLR4 engagement, a receptor-associated complex containing MyD88, IL-1 receptor-associated kinase (IRAK) 1, IRAK4, TRAF6, TRAF3, cIAP1, cIAP2, TAK1 and IKKγ is formed. Activation of TAK1 and its downstream targets, the MAPKs, requires translocation of the MyD88-associated complex in to the cytoplasm in a manner that is dependent on cIAP1-or cIAP2-mediated TRAF3 degradation. Phosphorylation-dependent activation of TAK1 subsequently activates MAPK kinase (MKK) 4, MKK7 or MKK6, which are upstream kinases of JNK and p38 MAPK 9,10 . Notably, although it has been suggested that TRAF6 interaction with TAK1 may be responsible for the activation of IKK and MAPK pathways 11 , IKK activation is independent of degradative TRAF3-regulated complex translocation, implying different mechanisms for IKK and MAPKs activation in TLR4 signaling. Our recent study suggested that IKK activation might depend on TAK1 protein as a platform for complex formation, but not depend on TAK1 activation 8 . In TNF signaling, it has also been reported that TAK1 activates IKKα and IKKβ due to the dissociation of IKKα and IKKβ from TAK1-containing complex 12 .
In addition to translocation to the cytosol, activation of TAK1 is tightly regulated through its binding partners and protein modifications. TAK1 activation requires the formation of complexes with its binding proteins TAK1-binding protein (TAB) 1, TAB2 or TAB3 [13][14][15] . Post-translational modifications of TAK1 via phosphorylation and polyubiquitination have also be proposed to regulate TAK1 kinase activation 3 . The phosphorylation of Thr184 and Thr187 residues located within the TAK1 activation loop is required for TAK1 kinase activity 16,17 , and it has been suggested that "unanchored" Lys63-linked polyubiquitin chains synthesized by TRAF6 trigger TAK1 autophosphorylation on Tyr187 18 . Binding of TAK1 with TAB1 activates TAK1 via autophosphorylation at Ser192 within the kinase domain 19 . Phosphorylation at Ser412 in a PKA-dependent manner is required for full activation of TAK1 20,21 . As a relatively rapid and transient signaling process, polyubiquitin chains are added to specific lysine residues of TAK1 by TRAF6 or tripartite motif 8 (TRIM8) 11,22 , and trigger TAK1 activation. On the other hand, deubiquitinating enzymes, such as CYLD or ubiquitin specific protease 4 (USP4), can remove the ubiquitin chains from TAK1 and suppress the signal activation 23,24 . The Lys209 residue of TAK1 has been identified as the site for Lys63-linked ubiquitin chains in response to IL-1beta 25 . Lys63-linked polyubiquitination at the Lys158 residue has been reported in response to IL-1β , TNF, TGFβ or Helicobacter pylori, and regulates the kinase activity of TAK1 [26][27][28][29] . Lys63-linked ubiquitin modification at the Lys34 residue of TAK1 in response to different inflammatory stimuli has also been demonstrated 30,31 . In addition, Lys72 of TAK1 has been reported to be a Lys48-linked ubiquitination site that downregulates TNF-mediated TAK1 signaling 32 . Moreover, proteomic analyses in murine tissues reveal that Lys563 of TAK1 is ubiquitinated 33 . These data demonstrate that TAK1 is modified by ubiquitin chains at multiple sites.
Biochemical and genetic evidence accumulated so far leaves no doubt that TAK1 plays a major role in the activation of NF-kappaB and the MAPKs in the TLR4 pathway, which is critical for innate immunity. However, the molecular mechanisms underlying TAK1-mediated signal activation, in particular the crosstalk between phosphorylation and ubiquitination, still await elucidation. In this study, we examined the role of TAK1 phosphorylation in activating the IKK and MAPK pathways, and in its interplay with polyubiquitination. Using mass spectrometry, we identified the Lys562 residue of TAK1 as a novel Lys63-linked ubiquitination site that forms a regulatory feedback loop with phosphorylation, and demonstrated that the polyubiqutination of TAK1 at Lys562 is critical for the activation of the MAPKs, but not IKKs, in the TLR4 pathway.

Results
TAK1, but not phosphorylated-TAK1, is required for IKK activation. In agreement with previous reports 4,8 , we found that the activation of the MAPKs and IKK was inhibited (Fig. 1A) and the mRNA of proinflammatory cytokines such as TNF and IL6, was diminished in mouse TAK1-knock down RAW264.7 cells stimulated with LPS (Fig. 1C,D). The knockdown efficiency of TAK1 in RAW264.7 cells was about 70% for mRNA level (Fig. 1B) and protein level (Fig. 1A). This indicated that the expression of TAK1 is required for the activation of the MAPKs and IKK, which are critical upstream mediators of proinflammatory cytokine production. Similar results was observed in human TAK1-knock down THP-1 cells (Supplmentary Fig. 1) to confirm that the outcomes of TAK1-silencing were not cell type-specific or due to off-target effect of RNA interference (RNAi). Meanwhile, treatment of TAK1 inhibitor, 5Z-7-oxozeanenol, that inhibits TAK1 kinase activity resulted to the blockage of IKK and MAPKs activation, and the inhibition of cytokine induction, including TNF and IL-6 (Supplementary Figure 2), implying that enzyme activity is critical. However, by reconstituting TAK1-silenced RAW264.7 Scientific RepoRts | 5:12300 | DOi: 10.1038/srep12300 Figure 1. TAK1, but not phosphorylated-TAK1, is required for IKK activation. A, TAK1 is required for IKK and MAPK activation. TAK1 in RAW264.7 cells was knocked down with lentiviral-based shRNA. After treatment with LPS (100 ng/mL) for the indicated times, cells were lysed and subjected to immunoblot analysis. B, mRNA level is decreased in Map3K7 knocked-down RAW264.7 cells. RAW264.7 cells tranduced with control or shRNA targeting to TAK1 were collected and the total RNA was extracted with TRIzol, reverse transcribed, and analyzed for TAK1 mRNA with Q-PCR. C and D, TAK1 is required for TLR4mediated TNF and IL-6 production. Control and TAK1-silenced RAW264.7 cells were stimulated with LPS for 2 h, and total RNA was extracted. After reverse transcription, TNF and IL-6 mRNA were analyzed with Q-PCR. E, phosphorylated-TAK1 is not required for IKK activation. TAK1-silenced RAW264.7 cells were reconstituted with Flag-tagged TAK1 wild-type (WT) or T(184,187)A mutant. The cells were incubated with LPS for the indicated times, lysed and kinase activation was analyzed by immunoblotting. The western blots were quantified with densitometry, the relative amount was calculated and the band with the highest intensity was set as 1. The densitometry data was presented as mean values from three independent experiments. Results of Q-PCR are averages ± SD of three separate experiments (*, significant difference).
Scientific RepoRts | 5:12300 | DOi: 10.1038/srep12300 cells with TAK1 that was mutated in the activation loop (T184 and T187), MAPK signaling was blocked, but the IKK pathway was still intact as compared with wild-type TAK1 (Fig. 1E).
Lys63-linked polyubiquitination of TAK1 is enhanced by blocking TAK1 phosphorylation. Lys63-linked polyubiquitination of TAK1 is required for the activation of TAK1 and the downstream IKK and MAPKs pathways 34 . It has also been shown that crosstalk between phosphorylation and ubiquitination occurs at several levels 35 . To further investigate the association between TAK1 phosphorylation and polyubiquitnation, either TAK1 wild-type (WT) or mutants that mutated in the activation loop T(184,187)A or substrate binding site D156A 36 were overexpressed in HEK293T cells. Surprisingly, polyubiquitination of TAK1 as detected by in vivo ubiquitination assay was enhanced when T(184,187) A-or D156A-TAK1 was expressed ( Fig. 2A). Treatment with TAK1 inhibitor, 5Z-7-oxozeanenol, produced a similar result (Fig. 2B). The enhancement of polyubiquitination was more significant in the A, TAK1 polyubiquitnation is enhanced by blocking TAK1 phosphorylation. Flag-tagged TAK1 wild-type (WT), T(184,187)A, or D156A mutants were co-expressed in HEK293T cells with HA-tagged Ub WT, K48R, or K63R mutants. The cells were lysed, immunoprecipitated with anti-Flag antibody, extensively washed, and gel separated. Polyubiquitination was detected with anti-HA antibody. B, TAK1 polyubiquitination is enhanced by inhibiting TAK1 activation. Flag-tagged WT TAK1 was co-expressed in HEK293T cells with HA-tagged Ub WT, K48R, or K63R mutants. After treatment with TAK1 inhibitor 5Z-7-oxozeanenol (100 nM) for 30 min, the cells were lysed, immunoprecipitated, and ubiquitination was detected with anti-HA antibody. C, in response to LPS, TAK1 polyubiquitnation is enhanced in TAK1 T(184,187)A mutant. TAK1-silenced RAW264.7 cells were reconstituted with Flag-tagged TAK1 wild-type (WT) or T(184,187)A mutant. The cells were incubated with LPS for the indicated times, lysed, immunoprecipitated with anti-Flag antibody, extensively washed, and gel separated. Polyubiquitination was detected with anti-Lys63-linked Ub antibodies. D, non-Lys48-linked polyubiquitination of TAK1 is blocked by TAK1 phosphorylation. Flagtagged TAK1 wild-type (WT), T(184,187)A, or T(184,187)D mutants were co-expressed in HEK293T cells with HA-tagged K48R Ub mutant. The cells were collected, and analyzed using in vivo ubiquitination assay. E, enhancement of TAK1 polyubiquitination induced by TAK1 inhibition is not at the Lys158 residue. Flagtagged TAK1 wild-type (WT), T(184,187)A, or T(184,187)A, K158R mutants were co-expressed in HEK293T cells with HA-tagged Ub K48R mutant. The cells were collected and analyzed for polyubiquitination. The result are represented from at least three independent experiments.
It has been reported that Lys158 residue of TAK1 is a Lys63-linked ubiquitination site, and is critical for TAK activation 28 . Therefore, we next examined whether the polyubiquitiantion is at Lys158 residue of TAK1. Overexpression of TAK1 mutant, T(184,187)A, K158R, in HEK293T cells did not block the increase in polyubiquitination (Fig. 2E), indicating that Lys158 is not the site of modification resulted from inhibiting TAK1 activation.
Lys562 residue of TAK1 is a Lys63-linked ubiquitination site. Since the enhanced polyubiquitination caused by inhibition of TAK1 activation was not at Lys158, it is very likely that TAK1 contains multiple ubiquitination sites in response to signal activation. Flag-tagged TAK1 and ubiquitin were co-expressed in HEK293T cells to force ubiquitination. After immunoprecipitating ubiquitinated TAK1 with anti-Flag antibody, the sample was separated by SDS-PAGE and visualized by Coomassie blue (Fig. 3A). In order to analyze ubiquitinated TAK1 by MS, the gel region containing ubiquitin-modified protein was excised, digested with trypsin, and analyzed by LC-MS/MS. A database search of the MS/ MS spectra revealed that TAK1 was the predominant protein identified in the samples. Representative spectra that demonstrate that the Lys562 residue of TAK1 was identified to be modified with ubiquitin with 60% sequence coverage are shown in Fig. 3B. Comparison of the C-terminal protein sequence of TAK1 in different species showed that the Lys562 residue was highly conserved (Fig. 3C).
The result from mass analysis that Lys562 residue of TAK1 as an ubiquitination site was further confirmed with in vivo ubiquitination assay. Because TAK1 residue 563 next to Lys562 is also a lysine and also reported as an ubiquitination site 33 , K(562,563)R mutant was used to prevent non-specific modification. Mutants in the nearby lysine residues Lys545 and 546, which are conserved across various species (Fig. 3C) were used as negative controls. TAK1 WT, K(562,563)R and K(545,546)R mutants were overexpressed in HEK293T cells, respectively. Polyubiquitination was significantly decreased in K(562,563)R mutant, in particular when K48R ubiquitin was co-transfected ( Fig. 4A). This result indicated that the Lys562 or Lys563 residue of TAK1 is the ubiquitination site and the modification is mainly non-Lys48-linked ubiquitination. To confirm the mode of polyubiquitination, Lys63-linked ubiquitination was diminished in TAK1-silenced RAW264.7 or THP-1 cells reconstituted with TAK1 K(562,563) R mutant in response to LPS stimulation ( Fig. 4B and Supplementary Figure 5A). Furthermore, the role of TAK1 Lys562 or Lys563 residue as an ubiquitination site was investigated. With in vitro ubiquitination assay, TRAF6-mediated Lys63-linked polyubiquitination of TAK1 was detected in WT and K562R, but not in K(562,563)R mutant (Supplementary Figure 5B and C). Although the polyubiquitination of K562R TAK1 was still modified with polyubiquitination, it might be due to non-specific reaction on nearby Lys563 residue. In vivo ubiquitination assay was used for further examination. WT, K562R, K563R and K(562,563)R mutants were overexpressed in HEK293T cells, respectively. Polyubiquitination was significantly decreased in K562R and K(562,563)R, but not in K563R mutant (Fig. 4C). This result supported the finding from mass analysis that the Lys562 residue of TAK1 is Lys63-linked ubiquitination site. Additionally, in response to Pam3CSK4 stimulation, Lys63-linked ubiquitination was diminished in TAK1-silenced RAW264.7 reconstituted with TAK1 K(562,563)R mutant (Supplementary Figure 5D), indicating the modification at Lys562 residue is not TLR4-specific.
Next, whether the Lys562 residue is the site of polyubiquitination when TAK1 activation is blocked was examined. Again, K(562,563)R mutant showed decreased non-Lys48-linked polyubiquitination comparing with WT, and T(184,187)A mutant enhanced non-Lys48-linked polyubiquitination. When TAK1 mutant T(184,187)A, K(562,563)R was co-expressed with ubiquitin WT or K48R mutant in HEK293T cells, the enhancement in polyubiquitination was blocked (Fig. 4D). However, when TAK1 mutant T(184,187)A, K(562,563)R was co-expressed with K63R Ub mutant, the enhancement in polyubiquitination was not inhibited, indicating Lys562 residue of TAK1 might not be involved in the increase of Lys48-linked ubiquitination with TAK1 inhibition, and Lys72 residue of TAK1 might be the site responding to the Lys48-linked ubiquitination 32 . Meanwhile, the accumulation of non-Lys48-linked polyubiquitination resulted from treatment of TAK1 inhibitor was decreased in HEK293T cells expressed with K(562,563)R TAK1 mutant and K48R Ub mutant (Fig. 4E). These data suggest that the Lys562 residue is the site of over-modification with Lys63-linked ubiquitin when TAK1 activation is inhibited.
Scientific RepoRts | 5:12300 | DOi: 10.1038/srep12300 Since non-Lys48-linked polyubiquitination was still able to be detected in K562R or K(562,563)R mutated TAK1 as shown in Fig. 4C,D, the remaining ubiquitination might by at Lys158 residue. With triple mutation of TAK1 K(158,562,563)R, polyubiquitination enhanced by T(184,187)A mutant can be further blocked comparing to TAK1 K(562,563)R, indicating that polyubiquitination at Lys158 residue of TAK1 is independent to Lys562 residue (Fig. 4F).
Polyubiquitination of TAK1 at Lys562 is required for the activation of TAK1 and the MAPKs, but not IKK upon LPS stimulation. The results above suggested that Lys562 of TAK1 is a novel non-Lys48-linked ubiquitination site. Next, to dissect the role of Lys562 in TAK1-mediated signaling TAK1-silenced RAW264.7 cells were reconstituted with TAK1 WT or K(562,563)R mutant, and TAK1-mediated signal activation, including IKK, p38 and JNK, in TLR4 pathway was analyzed. Unlike the Lys158 site of TAK1 that has been reported to be required for TAK1-mediated IKK and MAPK activation in response to TNF stimulation 28 , mutation in Lys562 residue of TAK1 led to impairment in TAK activation, and downstream MAPKs, but not IKK signaling in response to LPS stimulation ( Fig. 5A and Supplementary Figure 6A). To confirm the activation of the MAPKs and IKK pathway, AP-1 and NF-κ B luciferase reporter assay were performed. TAK1 knock-down HEK293 cells were reconstituted with WT and K(562,563)R mutant TAK1 with or without overexpression of TLR4/MD2, and the result showed decreased AP-1 transcription activity (Fig. 5B), but no significant difference in NF-κ B transcription activity (Fig. 5C). Furthermore, similar results were observed in RAW264.7 cells stimulated with IL-1 and TNF (Supplementary Figure 6B and C). Also, the AP-1 reporter assay with TAK1 K562R, or K(562,563)R mutants were decreased when TLR2 was overexpressed (Supplementary Figure 6D). These data suggest that in response to various ligand stimulation, ubiquitination of TAK1 on Lys562 is important for TAK1 activation, and, thus, critical for MAPK, but not IKK, activation.  Mutation at Lys562 of TAK1 does not disrupt the interaction of TAK1 with TRAF6 or Tab1. We previously demonstrated that MyD88-dependent TAK1 signaling in TLR4 proceeds via a two stage mechanism that involves receptor-induced assembly of a multiprotein complex, containing MyD88, TRAF6, IKKγ /NEMO, TAK1 and TRAF3, and degradative TRAF3-mediated translocation of the MyD88-associated signaling complex to the cytosol 8 . In addition, TAK1 is constitutively associated with TAB1 and activated by autophosphorylation 19 . Also in a previous report 26 , polyubiquitination of TAK1 at Lys158 residue was found critical for binding with IKKγ /NEMO. Therefore, next, the role of the ubiquitinated Lys562 residue in complex formation was examined. To investigate the interaction between TAK1 and TRAF6, Flag-tagged TAK1 WT, T(184,187)A, or K(562,563)R mutants and Myc-tagged TRAF6 were co-expressed in HEK293T cells. After immunoprecipitating, the TAK1-containing complex was detected. The result showed that the binding between TAK1 and TRAF6 is independent of phosphorylation in the activation loop or ubiquitination at Lys562 (Supplementary Figure 7 and Fig. 6A). To explore the association between TAK1 and TAB1, HEK293T cells were co-expressed with Flag-tagged TAK1 WT, or K(562,563)R mutants and TAB1. After immunoprecipitating TAK1 with anti-Flag antibody, the TAK1-TAB1 complex was detected with anti-Tab1 antibody. The result indicated that the polyubiquitination at Lys562 residue located within the TAB2-binding domain is not involved in the interaction between TAK1 and TAB1 (Fig. 6B). By reconstituting TAK1-silenced RAW264.7 cells with TAK1 WT or K(562,563)R, the amount of coprecipitated TRAF6 or TAB1 in TAK1-containing complex was not changed in response to LPS engagement (Fig. 6C), suggesting that Lys562 of TAK1 did not interfere with interaction of TAK1 with TRAF6 or TAB1.
Inhibiting polyubiquitination of TAK1 at Lys562 residue leads to a decline in cytokine production upon LPS stimulation. TAK1 mediates IKK and MAPK activation which in turn induce inflammatory cytokines, such as TNF and IL-6. Next, the role of Lys562 in TAK1-mediated gene expression was investigated. TAK1-silenced RAW264.7 cells were reconstituted with either TAK1 WT, T(184,187) A, K562R, or K(562,563)R mutants, and TAK1-induced mRNA expression, including that of TNF and IL6, in response to LPS stimulation was analyzed with RT-qPCR (Fig. 7A,B). It was shown previously that Lys562 residue of TAK1 is critical for MAPKs activation. Meanwhile, MAPKs pathway has been indicated to regulate inflammatory cytokines, including TNF and IL-6 through multiple mechanisms 37-39 . Therefore, it was no surprise that the mRNA level of TNF and IL-6 was decreased in cells expressing  3). B and C, expression of the TAK1 K(562,563)R mutant lead to declined TLR4mediated AP-1 reporter activity, but had no effect on NF-κ B reporter activity. Flag-tagged TAK1 wild-type (WT) or K(562,563)R mutant, Renilla-Luc plasmid and either AP-1 or NF-κ B reporter were co-expressed in HEK293T cells with or without TLR4-MD2 for 24 h. Relative luciferase activity was measured and normalized with the Renilla activity. Results are averages ± SD of three separate experiments (*, significant difference).
Scientific RepoRts | 5:12300 | DOi: 10.1038/srep12300 K562R or K(562,563)R mutants. In TAK1-silenced THP-1 cells, TNF and IL-6 mRNA production in response to LPS was also decreased with reconstitution of K(562,563)R mutant (Supplementary Figure  8A and B). Besides, T(184,187)A mutant that inhibits MAPK signaling also caused inhibition in TNF and IL-6 mRNA production (Fig. 7A,B). To determinate the protein level of cytokines, TAK1-knock down RAW264.7 cells were reconstituted with TAK1 WT, T(184,187)A, K562R, or K(562,563)R mutants. The production of cytokines, such as TNF and IL-6, was detected by ELISA (Fig. 7C,D). In line with the finding that mutation in Lys562 site of TAK1 had a decreased level of mRNA expression of inflammatory cytokines, K562R or K(562,563)R mutant led to the decline of TNF and IL-6 production in response to TLR4 signaling. Furthermore, in line with previous result that Lys562 residue was also critical in TLR2-mediated MAPK activation (Supplementary Figure 6D), the decreased IL-6 mRNA expression could be observed in TAK1-knocked down RAW264.7 cells reconstituted with T(184,187)A or K(562,563)R TAK1 mutants in response to Pam3CSK4 stimulation (Supplementary Figure 8C).

Discussion
TLR4 signaling is a double-edged sword that protects the host from infection but also causes tissue damage, and therefore requires careful regulation and fine tuning 40 . Through transcriptional regulation of the IKK and MAPK pathways, engagement of TLR4 can induce different levels of inflammatory cytokines and result in different biological outcomes 41 . The basis for this selectivity and specificity remains the biggest challenge in completing understanding of how TLR4 can be engaged in inflammatory signaling. Hitherto, it was also unclear how the two major effectors, IKK and MAPK are differentially regulated. Previously, we demonstrated that the translocation of a multiprotein complex containing MyD88, TRAF6, and TAK1 from the cell membrane to the cytosol compartment where TAK1 is activated is critical for TLR4-mediated MAPKs, but not IKK, activation. In addition to the location of the complex, post-translational modification is also an important factor for signal transduction. Polyubiquitination of TAK1 has been shown to be correlated with activation of TAK1 and is essential for the activation of downstream effectors IKK and the MAPKs upon different immune stimulation.
In this report, we examined the crosstalk between phosphorylation and polyubiquitination of TAK1 and investigated the mechanism through which the distinct activation of MAPKs and IKK is regulated. Although TAK1 inhibitor, 5Z-7-oxozeaenol, that inhibits kinase activity leads to inhibition of both IKK and MAPKs pathways, the results of the reconsititution of TAK1-silenced cells with TAK1 WT or mutant that blocks phosphorylation at Thr184 and Thr187 of the activation loop, suggest that phosphorylated TAK1 is important for MAPKs signaling, but less critical for the IKK pathway in response to LPS. We assume that the basal kinase activity of TAK1 is enough for IKK activation, which needs for further investigation. Moreover, TAK1 phosphorylation status is able to modulate the level of polyubiquitination. By inhibiting TAK1 activation with T(184,187)A mutant or specific inhibitor, 5Z-7-oxozeanenol, TAK1 polyubiquitination is enhanced. Conversely, ubiquitin modification can be counteracted by the TAK1 phospho-mimic mutant, T(184,187)D. These results reveal a feedback loop for phosphorylation and ubiquitination of TAK1, and indicate dynamic regulation of TAK1 polyubiquitiantion and phosphorylation activation. Moreover, Lys562 of TAK1 was identified as a Lys63-linked ubiquitination site and the key residue in the feedback regulation. Mutation in Lys562 of TAK1 leads to inhibition of TAK1 and subsequent MAPK pathway activation. It has been reported by different groups that TAK1 is modified with Lys63-linked polyubiquitination at specific residues, including Lys34, Lys158 and Lys209, in response to different stimuli. Those reports inferred that regulation of TAK1 occurs in a cell-type specific and ligand specific manner, and indicated the existence of multiple ubiquitination sites. Unlike the Lys63-linked ubiquitination sites 25,28,30 , the Lys562 residue of TAK1 was identified with mass spectrometry as direct evidence for covalent conjugation between TAK1 and ubiquitin, and was confirmed with in vivo ubiquitination assay to be modified Figure 7. Inhibition of polyubiquitination of TAK1 at Lys562 residue leads to a decline in cytokine production. A and B, TAK1 is required for TLR4-mediated induction of TNF and IL-6 mRNA. TAK1silenced RAW264.7 cells were reconstituted with Flag-tagged TAK1 wild-type (WT), T(184,187)A, K562R or K(562,563)R mutant. After treatment with LPS for indicated times, the cells were collected and the total RNA was extracted, reverse transcribed, and analyzed for TNF and IL-6 mRNA with Q-PCR. C and D, TAK1 is required for TLR4-mediated TNF and IL-6 production. TAK1-silencing RAW264.7 cells were reconstituted with Flag-tagged TAK1 wild-type (WT), T(184,187)A, K562R or K(562,563)R mutant. After incubating with LPS for 24 h, the medium was collected and analyzed for TNF and IL-6 level with ELISA. Results are averages ± SD of three separate experiments (*, significant difference).
Scientific RepoRts | 5:12300 | DOi: 10.1038/srep12300 with a Lys63-linked polyubiquitin chain. However, the role of TAK1 Lys562 does not conflict with the other ubiquitination sites. Although mutation in the Lys158 residue is unable to reverse the enhancement of polyubiquitination caused by blocking of TAK1 phosphorylation, TAK1 K158R mutant leads to inhibition of TAK1 and the downstream effector, NF-κ B pathway 28 . Therefore, in TAK1-mediated signaling, Lys158 of TAK1, which regulates both IKK and MAPK activation, might be more crucial than Lys562. However, Lys562 residue of TAK1 acts as a gatekeeper to trigger TAK1 to activate the MAPKs, and differentially regulates activation of the IKK and MAPK pathways.
In summary, our study demonstrates that the Lys562 residue of TAK1 is a novel Lys63-linked ubiquitinated site that is involved in feedback regulation of TAK1 phosphorylation. As shown in the working model in Figure 8, in response to LPS stimulation, TAK1 undergoes K63-linked ubiquitin modification at Lys562, which is critical for activation of phosphorylation of TAK1 and downstream effectors, JNK and p38. Meanwhile, the activated TAK1, in turn, stops the polyubiquitination at Lys562. Conversely, inhibiting the phosphorylation of TAK1, disregulates the modification at Lys562 and leads to the accumulation of polyubiquitination. Importantly, this report highlights a hitherto unknown feedback regulation loop between polyubiquitination and phosphorylation of TAK1 that dynamically modulates TAK1 activation, and shows how post-translational modification regulates the selection of MAPK activation in TLR4 signaling.  Cell culture and treatment. Human HEK293T, human THP-1 and mouse RAW264.7 cells were obtained from American Type Culture Collection (Manassas, VA). The cells were grown in Dulbecco's modified Eagle's medium (DMEM) containing 10% fetal bovine serum (FBS), 1 mM sodium pyruvate, and 1% (v/v) penicillin-streptomycin solution, which were from Biological Industries (Beit-Haemek, Israel). For ligand stimulation, cells were washed once with PBS, and incubated with 100 ng/mL LPS, 100 ng/mL Pam3CSK4, 0.1 μ g/mL TNF or 0.1 μ g/mL IL-1β , respectively, for the indicated times. When TAK1 inhibitor was used, the cells were pre-treated with 100 nM 5Z-7-oxozeaenol for 30 minutes.

Reagents
Plasmids. The lentiviral vector, pLU-PL3, for protein expression was a gift from Andrei V.
Budanov (Virginia Commonwealth University). pEF-HA-Ub was kindly provided by Ze' ev Ronai (Sanford-Burnham) for in vivo ubiquitination assay. pDUO-mMD2/TLR4 was purchased from Invivogen (San Diego, CA), and CMV-Flag-mouse TLR2 was kindly provided by Li-Chung Hsu (National Taiwan University). AP-1 Luc reporter vector from Michael Karin (University of California, San Diego), and pGL4.32[luc2P/NF-κ B-RE/Hygro] vector and pRL-TK Renilla luciferase control reporter vector purchased from Promega were for luciferase reporter assay. The lentivirus-based specific Map3K7 gene knockdown construct in the pLKO_TRC2 vector, and the packaging plasmids, pCMVΔ 8.91 and pMD.G, were from the National RNAi Core Facility (Institute of Molecular Biology/Genomics Research Center, Academia Sinica, Taiwan). The target sequence for 3′ UTR region of mouse Map3K7 is as follow: 5′ -AGAGAAGACAAACCATTATAA-3′ . The target sequence for 3′ UTR region of human MAP3K7 is as follow: 5′ -CGGAACCTTTAGGGATAGTTC-3′ . Expression vectors encoding specific proteins were constructed by standard recombinant DNA technology. Site-directed mutagenesis was conducted using QuikChange mutagenesis kit from Agilent (Santa Clara, CA). All mutations were confirmed by DNA sequencing.
Lentivirus production and infection. Lentiviral packaging was as previously described 42 . The 293T cells were transfected with the lentivirus-based constructs along with packaging plasmids, pMD.G and pCMVΔ 8.91 by using T-pro NTRII (T-Pro Biotechnology, Taiwan). Virus-containing medium was collected at 48, and 72 hours post-transfection. RAW264.7 or THP-1 cells were infected with lentiviral-containing medium at a multiplicity of infection (MOI) of 10-25 in the presence of 5 mg/ml polybrene (Sigma). After 24 hours, the virus-containing medium was replaced with selection medium containing 5mg/ml puromycin (EMD Millipore). After cell growth was stable, cells were used in subsequent experiments.
Immunoblotting. Cells were lysed in ice-cold RIPA lysis buffer containing 50 mM Tris-HCl, pH 8.0, 150 mM NaCl, 1% Nonidet P-40, 0.5% deoxycholate, 0.1% SDS, 1 mM phenylmethyl sulfonyl fluoride and 20 μ g/ml of aprotinin. After centrifugation, cell extracts were resolved by SDS-PAGE and analyzed by immunoblotting. The membranes were probed with the indicated antibodies. Blots were visualized by chemiluminescence reagent (Thermo, Rockford, IL) exposed on X-ray film (Fujifilm). For densitometric quantification, the Western blot images were analyzed with ImageJ 1.48 (Windows version of NIH Image, http://rsb.info.nih.gov/nih-image/). The relative amount was calculated and the band with the highest intensity was set as 1. The data was presented as mean values from duplicated experiments.
Immunoprecipitation. Cell lysates were prepared by ice-cold lysis buffer, and immunoprecipitated with the indicated antibodies in immunoprecipitation buffer containing 10 mM Tris-HCl, pH 7.0, 150 mM NaCl, and 0.2% Nonidet P-40 overnight at 4 °C. Whenever protein ubiquitination was analyzed, 20 mM N-ethylmaleimide (NEM, Sigma) was added into the lysis buffer. All samples were incubated with protein G-Sepharose (Roche) for 2 hours at 4 °C. The beads were washed three times with ice-cold lysis buffer or immunoprecipitation buffer, separated by SDS-PAGE and analyzed by immunoblotting with indicated antibodies. Blots were developed with chemiluminescence reagent.
In vivo ubiquitination assays. Flag-tagged protein and HA-tagged ubiquitin were coexpressed in HEK293T cells. 24 hours post transfection, cells were lysed with RIPA lysis buffer containing protease inhibitors and 20 mM NEM (Sigma). The lysate was immunoprecipitated with Flag antibody overnight at 4 °C, and pulled-down with protein G beads. The samples were washed three times with RIPA lysis buffer, gel-separated and analyzed by immunoblotting.
In vitro ubiquitination assays. Flag-tagged TAK1 WT, K562R, or K(562,563)R mutants and Myc-tagged TRAF6 were overexpressed in HEK293T cells, respectively. The tagged proteins were immunoprecipitated with anti-Flag or anti-Myc antibodies. The Flag-tagged proteins were eluted from beads by incubating with 3 × Flag peptide at 4 degrees for 30 mins. The supernatant containing TAK1 proteins was collected and incubated with or without Myc-TRAF6 immunoprecipitated beads in the present of ATP, UBE1, E2 UbcH enzyme set, and either ubiquitin K63 only or ubiquitin K48R mutant (Boston Biochem) for 1 hour. The supernatant and beads were isolated and the reactions were stopped by adding sample buffer and heating at 95 degrees for 2 mins. The supernatant containing TAK1 protein and the beads containing TRAF6 were subjected for immunoblotting analysis.
Scientific RepoRts | 5:12300 | DOi: 10.1038/srep12300 Real-time quantitative polymerase chain reaction. Total cellular RNA from 1 × 10 5 cells was isolated with TRIzol (Invitrogen), and used to synthesize first-strand cDNA with iScript cDNA synthesis kit (Bio-Rad). mRNA amounts were quantified by real-time quantitative polymerase chain reaction (RT-qPCR) 43 with the StepOnePlus system (Applied Biosystems). Primer sequences are available upon request. All values were normalized to the level of cyclophilin A (PPLA) mRNA expression.
The enzyme-linked immunosorbent assay (ELISA). Cells (3 × 10 5 ) were seeded in a 12-well culture plate overnight. After stimulating with the indicated ligands for 24 hours, the cytokine-containing medium was collected, and the protein level was measured with Mouse TNF-alpha DuoSet ELISA Development kit (R&D, Minneapolis, MN) or Mouse IL-6 ELISA set (BD Pharmingen, San Jose, CA).
Luciferase reporter assays. Cells were seeded into a 96-well culture plate, and co-transfected with either AP-1 reporter or NF-κ B reporter pGL4.32, pRL-TK and the indicated expression vectors for 24 hours. The luciferase activity was measured with Dual-Glo luciferase assay system (Promega) following the manufacturer's instructions.
Sample preparation for mass analysis. Flag-tagged TAK1 and HA-tagged ubiquitin were co-expressed in HEK293T cells. After 24 hours, cells were collected and underwent in vivo ubiquitination assay. Cell lysate with 25 mg total protein was incubated with 250 μ l Flag-agarose (Sigma) at 4 o C overnight. Samples were gel-separated, and stained with Coomassie blue. Protein samples containing ubiquinated-TAK1 were recovered from the gel, and subjected to enzyme digestion according to the following procedure. After thermal denaturation at 95 °C for 5 min, 10 μ L of protein samples (0.1 mg/ mL) were reduced through the addition of DTT to a final concentration of 10 mM and incubated at 50 o C for 30 min. Alkylation was performed by adding IAA to a final concentration of 20 mM. After 30 min of incubation at room temperature in the dark, a second aliquot of DTT was added to quench the reaction. Trypsin solution (20 ng/μ L) was added to protein samples (enzyme mixture to sample 1:50, w/w) and the reaction was incubated at 25 o C for 12 h. The enzymatic digestions were quenched through the addition of a 10 μ L formic acid (10%). Digested peptides were dried by vacuum concentrator (Savant SPD131DDA, Thermo Fisher) prior to mass analysis.
Liquid chromatography-mass spectrometry. Mass spectral data were acquired using a Thermo LTQ-FT mass spectrometer (Thermo Fisher, Santa Clara, CA) equipped with a nanoelectrospray ion source (New Objective, Woburn, MA), an Agilent 1100 Series binary high-performance liquid chromatography pump (Agilent Technologies, Palo Alto, CA), and a Famos autosampler (LC Packings, San Francisco, CA). The vacuum-dried digested samples were reconstituted with mobile phase A buffer and 5 μ L of samples were injected on to a self-packed pre-column (150 μ m I.D. × 30 mm, 5 μ m, 100 Å) at a flow rate of 10 μ L/min. Chromatographic separation was performed on a self-packed reversed phase C18 nano-column (75 μ m I.D. × 200 mm, 3 μ m, 100 Å) using 0.1% formic acid in water as mobile phase A and 0.1% formic acid in 80% acetonitrile as mobile phase B operated at the flow rate of 300 nL/min. Separation was attained using a solvent gradient ramping from 10% to 40% of mobile phase B in 45 min. The mass scanning range was set from m/z 350 to 2,000 for the survey full-scan MS mode and data dependent MS/MS acquisition. The ten most abundant ions detected in a full-scan were subjected to a MS/MS experiment performed in a LTQ mass spectrometer. Ion accumulation (automatic gain control target number) and maximal ion accumulation time for a full-scan and MS/MS were set at 1 × 10 6 ions, 1000 ms, and 5 × 10 4 ions, 200 ms. Ions were fragmented by use of collision induced dissociation (CID) with the normalized collision energy set to 35%, activation Q at 0.3 and activation time at 30 ms. For data analysis, all MS/MS spectra were converted to mzXML and mgf format from the experimental RAW file using MM File Conversion Tools (http://www.massmatrix.net) 44 , and then analyzed by MassMatrix for MS/MS ion search 45 . The search parameters in MassMatrix including the error tolerance of precursor ions and the MS/MS fragment ions in spectra were 10 ppm and 0.6 Da and the enzyme was assigned as trypsin with three missed cleavages allowed. The variable post-translational modifications in the search parameters were assigned to include the oxidation of methionine, carbamidomethylation of cysteine, phosphorylation of serine/threonine/tyrosine, and acetylation of lysine.