PIIS2589004224010393-html.html

Enzymatic depletion of circulating glutamine is immunosuppressive in cancers

Monish Kumar

, Ankita Leekha

, Suman Nandy

, Rohan Kulkarni

, Melisa Martinez-Paniagua

, K

M Samiur Rahman Sefat

, Richard C Willson

, Navin Varadarajan

William A. Brookshire Department of Chemical and Biomolecular Engineering, University of

Houston, Houston, TX 77204

†

To whom correspondence should be addressed:

Navin Varadarajan,

Department of Chemical and Biomolecular Engineering,

University of Houston

Houston, Texas, TX 77204, USA

Tel: +1-713-743-1691;

E-mail: nvaradar@central.uh.edu

Lead contact: Navin Varadarajan

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Summary

Although glutamine addiction in cancer cells is extensively reported, there is controversy

on the impact of glutamine metabolism on the immune cells within the tumor microenvironment

(TME). To address the role of extracellular glutamine, we enzymatically depleted circulating

glutamine using PEGylated

Helicobacter pylori

Gamma-glutamyl transferase (PEG-GGT) in

syngeneic mouse models of breast and colon cancers. PEG-GGT treatment inhibits growth of

cancer cells

in vitro

, but

in vivo

it increases myeloid derived suppressor cells (MDSC) and has no

significant impact on tumor growth. By deriving a glutamine depletion signature, we analyze

diverse human cancers within the TCGA and illustrate that glutamine depletion is not associated

with favorable clinical outcomes and correlates with accumulation of MDSC. Broadly, our results

help clarify the integrated impact of glutamine depletion within the TME and advance PEG-GGT

as an enzymatic tool for the systemic and selective depletion (no asparaginase activity) of

circulating glutamine in live animals.

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Introduction

Tumorigenesis requires cancer cells to adapt cellular metabolism to utilize scarce nutrients

that can sustain cellular proliferation

. One of the most frequently documented metabolic changes

in proliferating cancer cells is their addiction to glutamine, the most abundant amino acid in

circulation (500 µM)

. Glutamine is a carbon source by directly fueling the TCA cycle, and a

nitrogen source by enabling nucleotide and protein synthesis

3-5

. This metabolic adaptation of

cancer cells to utilize glutamine has been extensively documented in several preclinical and clinical

studies, and glutamine metabolism is an attractive therapeutic target since cancer cells are

dependent on glutamine

. Indeed, the extensive pathways involved in glutamine metabolism

including glutamine transporters, glutaminases, and aminotransferases have all been targeted as

anti-tumor therapeutics

7-9

Despite extensive

in vitro

data supporting the growth arrest of cancer cell lines in the

absence of glutamine, the translation of drugs targeting glutamine metabolism has been largely

unsuccessful. Small molecules used to prevent glutamine uptake by inhibiting the primary

glutamine transporters, ASCT2, SNAT2, and SNAT1, have suffered from poor affinity, lack of

specificity, and toxicity

10-12

. For example, V-9302 was reported as a high affinity inhibitor of ASCT2,

but other studies suggest that it preferentially inhibits SNAT2 and LAT1, the latter being the

primary transporter for essential amino acid uptake

13,14

. The naturally occurring glutamine analog

6-diazo-5-oxo-L-norleucine (DON) inhibits multiple enzymes that utilize glutamine, including

mitochondrial glutaminases but has translated poorly as a therapeutic primarily due to a narrow

therapeutic index leading to gastrointestinal toxicity

. Although newer, safer prodrugs of DON

like JHU-083 have been developed, their efficacy derives from not only an anti-proliferative effect

directly on the tumor cells but also by inducing a strong anti-tumor immune response

16,17

. An

improved understanding of the role of the immune system and the remarkable success of

immunotherapies

18,19

has renewed focus on the role of extracellular glutamine within the tumor

microenvironment (TME)

20,21

. Several studies have shown that targeting glutamine metabolism

directly inhibits tumor growth and enhances the anti-tumor response mediated by CD8

cells

16,17,22,23

. By contrast, other reports suggest that interfering with glutamine metabolism either

directly impacts the effector function and proliferation of CD8

T cells, or acts indirectly by

upregulating PDL1 expression on cancer cells and functioning as a metabolic checkpoint that

licenses the function of type 1 conventional dendritic cells in activating CD8

T cells

24-26

. A

fundamental question that needs to be answered is what is the integrated impact of inhibiting

glutamine metabolism of both the cancer cells and the immune system?

To answer this fundamental question about the pro and anti-tumor function of glutamine

in vivo

, here, we used an enzymatic approach since enzymes can facilitate the highly specific

depletion of extracellular glutamine. We show that

Helicobacter pylori (H. pylori)

GGT (hp-GGT) as

an enzyme has no asparaginase activity and that PEGylated-hp-GGT (PEG-GGT) can be used to

efficiently deplete circulating glutamine while preserving asparagine in living animals. Using

syngeneic models of breast and colon cancers, we show that depleting circulating glutamine does

not inhibit tumor growth

in vivo,

and this is primarily due to the enrichment of myeloid derived

suppressive cells (MDSC). Based on the RNA-Seq with PEG-GGT treated cancers in mice, we derive

a signature for glutamine depletion. Using this glutamine depletion score, we analyzed TCGA and

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Results

H. pylori

GGT is a high affinity glutaminase with no asparaginase activity

The glutaminases used to date for

in vivo

glutamine depletion have concomitant

asparaginase activity

27,28

, motivating our efforts to find a glutaminase with no asparaginase activity

to uncouple the effects of asparagine depletion. Although bacterial glutaminases with no

asparaginase activities exist, these enzymes have low catalytic efficiencies (k

cat

~10

-3

-1

/µM)

because of their high K

(2-30 mM) and are inhibited by the product glutamate

29,30

. To utilize

more efficient enzymes that function efficiently at physiologically relevant glutamine

concentrations (500 µM), we targeted the gamma-glutamyl transpeptidase (GGT) class of

enzymes. GGTs are a class of enzymes that can either transfer the



-glutamyl moiety from a

compound to an acceptor substrate or hydrolyze the



-glutamyl moiety

31,32

(

Figure 1.A

). In

contrast to the asparaginase-glutaminases which have similar kinetics for glutamine and

asparagine hydrolysis, we did not find reports of GGT mediated asparaginase activity in literature.

We specifically focused on the

Helicobacter pylori (H. pylori)

GGT (hp-GGT) to facilitate glutamine

depletion (

Figure 1.B

). As the reported K

of hp-GGT for glutamine hydrolysis is 12 ± 2 µM

which is 40 fold lower than physiological glutamine concentration, it is a highly efficient

glutaminase (k

cat

=1.8 ± 0.2 s

-1

/µM) and this activity is comparable to that of potent

Acinetobacter glutaminasificans

asparaginase-glutaminase (k

cat

=1.1 ± 0.2 s

-1

/µM)

. This

glutaminase activity of hp-GGT is unlike mammalian membrane bound GGTs which have a 100-

fold higher reaction rate for transpeptidation over hydrolysis and function as transferases rather

than hydrolases under physiological conditions

To produce hp-GGT recombinantly, we expressed and purified the codon optimized wild-

type hp-GGT gene in

E. coli

Rosetta-2 cells with an N-terminal 6x-His tag as described previously

(

Figure

1.C

)

. Consistent with previous reports, the hp-GGT propeptide underwent autocatalytic

cleavage, and we observed two distinct bands of 40 and 20 kDa

33,35

(

Figure 1.B

). To confirm that

this fragmentation was due to autoproteolysis, we cloned, expressed, and purified the hp-GGT

100

T380A mutant which lacks the autocatalytic cleavage capability because of the absence of

101

nucleophilic threonine

. The T380A mutant appeared as a single 60 kDa band on SDS-PAGE gel

102

(

Figure 1.C

). Since our motivation was to explore hp-GGT as a glutaminase

in vivo

, we conjugated

103

polyethylene glycol (NHS-PEG-5000) to lysine residues of hp-GGT to prevent renal clearance,

104

increase circulation persistence, and mask immunogenicity

(

Figure 1.D

). We used the standard

105

NHS ester conjugation chemistry and confirmed the conjugation of PEG chains to hp-GGT using

106

a standard SDS-PAGE gel (

Figure 1.E

107

To verify that PEGylation did not significantly alter the catalytic efficiency of the enzyme

108

for glutamine hydrolysis, we characterized kinetics using the chromogenic substrate, L-



109

glutamyl-para-nitroanilide (



GPNA). The V

max

[2.9



0.2



mol/(min.mg)] and K

(21





M) of hp-

110

GGT at 37 °C were consistent with the published values

33,35

. PEG-GGT hydrolyzed



GPNA with

111

similar kinetics with V

max

and K

values of 2.4



0.2



mol/(min.mg) and 23





M respectively

112

(

Figure 1.F

). As glutamine is the most abundant physiologically relevant substrate for hp-GGT,

113

the kinetics of glutamine hydrolysis by hp-GGT is well established and has been reported to be

114

the same as



GPNA

33,35

. To confirm that PEG-GGT showed similar kinetics for glutamine and

115

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

GPNA, we set up a competitive assay in which we added equimolar amounts of glutamine and

116



GPNA and monitored the release of the chromophore p-nitroaniline (PNA). At concentrations

117

from 500



M to 60



M, the rate of release of PNA was approximately half when an equimolar

118

amount of glutamine was present compared to no glutamine control (

Figure 1.G

). Collectively,

119

these results demonstrate that PEG-GGT has comparable kinetics to the unmodified GGT for the

120

hydrolysis of glutamine. Furthermore, the glutaminase catalytic efficiency of PEG-GGT was

121

comparable to published low K

glutaminases

(

Table S1

122

We next tested whether PEG-GGT could hydrolyze asparagine. In the competitive assay

123

with



GPNA, the PNA release rate was not reduced when asparagine was added at equimolar

124

concentrations implying no competition for substrate binding (

Figure 1.G

). To further bolster our

125

finding that PEG-GGT had no asparaginase activity, we incubated 2.5 mM asparagine with 0.1 and

126

0.2 mg/ml PEG-GGT at 37



C and measured asparagine concentration after 8 hours (

Figure 1.H

127

There was no reduction in asparagine concentration even at remarkably high concentrations of

128

PEG-GGT and hence we concluded that PEG-GGT has the requisite enzymatic selectivity with no

129

asparaginase activity.

130

PEG-GGT inhibits the growth of multiple cell lines

in vitro

131

As glutamine is an anabolic substrate, and its absence in culture has been shown to halt

132

the growth of multiple cell lines

in vitro

, we wanted to investigate the effect of glutamine

133

depletion via PEG-GGT on cell growth kinetics. First, we tested the serum stability of PEG-GGT in

134

culture media with 10% FBS. PEG-GGT in culture media retained 60% of its glutaminase activity

135

after 48 hours of incubation at 37 °C (

Figure S1.A

). We measured the growth rate of CT26

136

(colorectal carcinoma), 4T1 (mammary carcinoma), and MC38 (colon adenocarcinoma) cell lines

137

in vitro

in the presence and absence of glutamine using MTT assay (

Figure 2.A

). The lack of

138

glutamine abolished the growth of all three cell lines tested

in vitro.

At 72 hours after media

139

exchange, the relative number of cells in media supplemented with glutamine were significantly

140

higher (CT26:

-value=0.02,

4T1:

-value=0.002, MC38:

-value=0.001,

Figure 2.B-D

). Adding

141

PEG-GGT at 10



g/ml to culture media containing 2mM glutamine also inhibited the growth of all

142

the tested cell lines (CT26:

-value=0.02,

4T1:

-value=0.003, MC38:

-value=0.006,

Figure 2.B-

143

). To confirm that this arrest in growth could be ascribed to the enzymatic activity of PEG-GGT,

144

we heat-inactivated the enzyme by incubating it at 70 °C for 20 min. When heat-inactivated PEG-

145

GGT was added at the same concentration to culture media containing 2mM glutamine, we

146

observed no impact on cell growth after 24 hours confirming that enzymatic activity is important

147

for the observed cell growth arrest upon addition of PEG-GGT (

Figure S1.B

). Therefore, PEG-GGT

148

abolishes the growth of multiple cell lines

in vitro

and its enzymatic activity is essential for this

149

effect.

150

Previous reports have suggested that cell lines overexpressing Myc are particularly

151

sensitive to glutamine deprivation and hence we tested the CT26 cell line in which Myc is

152

overexpressed

. To assess the impact of extracellular glutamine hydrolysis on intracellular

153

glutamine and glutamate pools, we cultured CT26 cells with 2 mM glutamine, treated the cells

154

with PEG-GGT for 48 hours and extracted the metabolites (

Figure S1.C

). When we measured the

155

total glutamate and glutamine concentrations using an enzymatic assay, PEG-GGT treated cells

156

had 3-fold lower glutamate pool (No treatment: 3.1±0.3, PEG-GGT: 1.0±0.1

-value<0.0001)

157

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(

Figure 2.E

) and 4.7-fold lower glutamine pool (No treatment: 4.7±1.7, PEG-GGT: 1.0±0.8

158

value=0.002) (

Figure 2.F

) compared to non-treated cells. Glutathione (GSH), which is synthesized

159

intracellularly from glutamate, was also significantly reduced upon treatment with PEG-GGT (No

160

treatment: 1.3±0.1, PEG-GGT: 1.0±0.2

-value=0.03) (

Figure 2.G

). In summary, intracellular

161

glutamate, glutamine, and total glutathione pools were reduced in CT26 cells upon PEG-GGT

162

treatment.

163

Although PEG-GGT arrested the growth of cell lines, we wanted to investigate if it was

164

cytotoxic. Upon depletion of glutamine, cells can adapt to low extracellular glutamine by

165

synthesizing glutamine intracellularly via the enzyme glutamine synthetase (Glul) which is

166

irreversibly inhibited by L-Methionine sulfoximine (MSO)

. To test whether PEG-GGT was

167

cytotoxic and if this cytotoxicity was synergistic with Glul inhibition, we treated cells

in vitro

with

168

either MSO, PEG-GGT, or both, and monitored cell death as a function of time using microscopy.

169

MSO as a single agent (500



M) was not toxic and did not inhibit cell growth (

Figure 2.H

). PEG-

170

GGT by itself at 10



g/ml inhibited cell growth (

Figure 2.H

) and was cytotoxic to 6



1 % (

-value

171

= 0.005) of cells after 24 hours. Remarkably, the combination of PEG-GGT and MSO was cytotoxic

172

to more than 90



9% (

-value = 0.0002) of cells after 24 hours (

Figure 2.H, I

). These combined

173

results suggest that while glutamine depletion via PEG-GGT inhibits the growth of cell lines,

174

simultaneous glutamine depletion and inhibition of Glul is cytotoxic to cells.

175

Enzymatic depletion of circulating glutamine does not inhibit tumor growth

in vivo

176

We wanted to investigate whether PEG-GGT can be utilized to facilitate the enzymatic

177

depletion of glutamine

in vivo

. First, we established the pharmacokinetics (PK) and

178

pharmacodynamics (PD) of PEG-GGT in mice. We injected a single dose of 20 mg/kg PEG-GGT

179

into mice intraperitoneally (ip) and measured glutaminase activity in serum collected at different

180

time points for 72 hours by directly monitoring the hydrolysis rate of the substrate



GPNA at

181

saturating concentrations (500



M) (

Figure 3.A

). We detected glutaminase activity corresponding

182

to 180





g/ml PEG-GGT in serum at 72 hours (

Figure 3.B

). Assuming first-order elimination

183

kinetics, PEG-GGT has a half-life of 67



8 hours in serum. We also measured glutamine and

184

glutamate concentration in serum and while glutamine was not detectable at 24/48 hours, we

185

detected 25





M glutamine at 72 hours. As expected by the hydrolysis of glutamine to

186

glutamate by PEG-GGT, we observed an increase of glutamate reaching up to 2.6



0.1 mM in

187

serum at 72 hours (

Figure 3.C

188

As PEG-GGT persisted in circulation and efficiently depleted glutamine for at least 48 hours

189

in mice, we set out to test the effect of PEG-GGT on the growth of tumors in syngeneic mouse

190

models. Since we wanted to study the integrated impact of glutamine depletion on cancer cells

191

and immune cells in the TME, we chose the CT26 tumor model which is well characterized for high

192

tumor immune infiltrates and T cell responses

. We implanted CT26 subcutaneously in BALB/c

193

mice, and after the tumor was palpable, initiated treatment with PEG-GGT on a twice weekly

194

schedule (

Figure 3.D

). PEG-GGT was well tolerated with no evident signs of toxicity and the

195

kinetics of weight change were the same as the vehicle treated group during the treatment (

Figure

196

S2.A

). There was no difference in the tumor growth rate of the PEG-GGT treated and vehicle

197

treated group (

Figure 3.E

) even though we did not detect any circulating glutamine (<50 nM) in

198

serum during treatment (

Figure 3.F

). To ensure that these results are generalizable across multiple

199

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tumor models, we implanted 4T1 breast cancer cells in mice and treated the animals with PEG-

200

GGT using the same treatment schedule (

Figure 3.G

). Consistent with the CT26 model, treatment

201

of 4T1 tumors with PEG-GGT did not inhibit tumor growth in mice (

Figure 3.H

) and changes in

202

body weight were similar in treated and control groups (

Figure S2.B

). We also confirmed that

203

there was no circulating glutamine in the serum of PEG-GGT treated tumor bearing mice (

Figure

204

3.I

). Collectively, these results demonstrate that while PEG-GGT treatment inhibits growth of

205

cancer cells

in vitro

, it does not significantly impact tumor growth

in vivo

206

To map the impact of PEG-GGT on circulating metabolites and tumor metabolism, we

207

measured the relative amounts of amino acids in serum and tumors of CT26 tumor bearing mice

208

treated either with PEG-GGT or vehicle using liquid chromatography - mass spectrometry (LC-MS)

209

(

Figure S2.D

). Consistent with our hypothesis that PEG-GGT does not hydrolyze asparagine, there

210

was no significant difference in asparagine levels in serum of PEG-GGT and vehicle treated mice

211

(fold-change [FC]: 0.9,

-value = 0.45) (

Figure 3.J

). We observed a significantly decreased

212

concentration of GSH (FC: 0.03,

-value = 0.0008); and an increased concentration of cysteine (FC:

213

1.2,

-value = 0.01), cystine (FC: 3.9,

-value = 0.0007) and glycine (FC: 1.8,

-value = 0.005) in

214

serum (

Figure 3.J

). This observation is expected since PEG-GGT hydrolyses the GSH (reported

215

concentration in serum ~25



M) to cysteinyl-glycine which can be further hydrolyzed to cysteine

216

and glycine by membrane bound dipeptidases

41,42

. Mass-spectrometry based approaches for

217

measuring amino acids unfortunately, do not always resolve glutamine and lysine, which have the

218

same molecular weight, especially when they have same retention time on LC

. Thus, while mass-

219

spectrometry reported only one-third reduction in glutamine/lysine concentration (

Figure S2.E

220

the direct enzymatic assay for quantifying glutamate/glutamine concentrations demonstrated a

221

direct reduction in glutamine and a concomitant increase in glutamate (

Figure 3.F

). In summary,

222

PEG-GGT depletes circulating glutamine and glutathione resulting in increased glutamate,

223

cysteine/cystine and glycine.

224

To map the metabolomic changes in the tumor tissue (which includes both

225

interstitial/vascular and intracellular metabolites), we homogenized snap frozen CT26 tumor tissue

226

and performed LC-MS (

Figure S2.D

). In the tumor tissue, we did not see a significant change in

227

glutamate levels upon PEG-GGT treatment (FC: 0.9,

-value = 0.26) (

Figure 3.K

). Since the

228

dominant contributor to the total glutamate is likely the intracellular glutamate

, our results

229

suggest that unlike

in vitro

treatment, PEG-GGT treatment did not significantly reduce intracellular

230

glutamate. One hypothesis that can explain these results is that the other non-tumor cells in the

231

tumor function as reservoirs of glutamate

. Consistent with the serum and

in vitro

profiling

232

however, we observed a significant reduction in glutathione (FC: 0.5,

-value = 0.05), and increase

233

in cysteine (FC: 2.2,

-value = 0.001), and cystine (FC: 5.3,

-value = 0.05) levels in PEG-GGT treated

234

tumors (

Figure 3.K

). Unexpectedly proline concentrations were also decreased (FC: 0.7,

-value =

235

0.02) in PEG-GGT treated tumors (

Figure 3.K

). Both GSH and proline are present at millimolar

236

concentrations (GSH 5-10 mM, Proline: 1-2 mM) inside the cell and need glutamate for their

237

synthesis

. Recent reports have shown that the inhibition of proline synthesis allowed

238

proliferation of cancer cells in glutamine limiting conditions

. Because LC-MS could not

239

distinguish lysine and glutamine, we used the enzymatic assay to measure glutamine in PEG-GGT

240

and vehicle treated tumors but while there was an overall reduction in the glutamine

241

concentrations this was not significant in our small sample size (FC=0.3,

-value = 0.32) (

Figure

242

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S2.C

). In summary, despite the extracellular increase in glutamate concentrations, intracellular

243

concentrations of glutamate were not altered in the tumor wherein cells maintain their glutamate

244

pool in glutamine limited environments marked with downregulation of proline and GSH.

245

Since we observed an increase in cysteine/cystine concentrations in serum and tumor of

246

PEG-GGT treated mice, we wanted to test whether increased cysteine/cystine generated from

247

hydrolysis of GSH could be responsible for difference in tumor cell proliferation

in vitro

and

248

vivo

. We treated cells cultured in RPMI-1640 media containing 2 mM glutamine with 10



g/ml

249

PEG-GGT and supplemented the media with either 100



M GSH or 100



M N-acetyl cysteine

250

(NAC). Supplementing the media with either GSH or NAC did not rescue the cell proliferation

251

(

Figure S1.D,E

). These results confirmed that unlike increase in cysteine/cystine from GSH/GSSG

252

hydrolysis, the primary impact of PEG-GGT is glutamine depletion leading to arrest in cell

253

proliferation.

254

Transcriptomics reveals adaptation pathways upon glutamine depletion

255

To understand how tumors adapt to low extracellular glutamine

in vivo

, we sequenced

256

mRNA isolated from CT26 tumors treated either with PEG-GGT or vehicle (

Figure 4.A

). 223 genes

257

were differentially upregulated while 262 were downregulated (Log

FC>0.3, FDR<0.1). As

258

glutamine is indispensable for nucleotide synthesis, and nucleotide availability has a profound

259

impact on the cell cycle

, it is not surprising that several candidate genes associated with the cell

260

cycle like

Brca1, Polq, Cdc6, Mcm

family proteins were significantly upregulated in the PEG-GGT

261

treated tumors in comparison to the vehicle treated tumors (

Figure 4.B

). Since glutamine is a

262

major substrate for amino acid synthesis, consistent with several reports of translation

263

downregulation upon glutamine deprivation, we observed significant downregulation of

264

eukaryotic translation initiation factors

Eif4b, Eif2a

and multiple ribosomal protein genes like

265

Rpl28, Rps28, Rps12.

48,49

266

To analyze the differentially expressed pathways, we performed gene set enrichment

267

analysis (GSEA)

. Since glutamine depletion mediated reduction of nucleotide pool leads to

268

accumulation of single-stranded DNA which activates ATR kinase

47,51

, we observed an

269

upregulation of genes related to ATR kinase activation upon replication stress (

Figure S3.A

270

Consistent with the activation of the G2M checkpoint downstream of ATR signaling

, we observed

271

a strong upregulation of G2M checkpoint signaling upon glutamine depletion (

Figure 4.C

). To

272

adapt to increased replication stress, cells must synthesize DNA at a higher rate and since E2F

273

transcription factors directly regulate the synthesis of S phase proteins for DNA synthesis, we

274

observed an increase in E2F target transcripts including genes involved in DNA replication

275

(

Figure S3.B, 4.D

). Finally, since glutamine deficiency induces DNA alkylation damage via

276

inhibition of ALKBH enzymes, GSEA analyses identified several significantly enriched gene clusters

277

related to DNA damage response

(

Figure 4.D

). To validate if these pathways were also relevant

278

upon glutamine depletion

in vitro

, we used RT-qPCR to quantify transcript abundances of some

279

of the genes that were most significantly upregulated in our RNA-Seq data. Transcripts of

Brca1

280

which is associated with DNA repair

, Polq

which has DNA polymerase activity, and

Cdc6

which

281

assembles pre-replicative complex were all significantly upregulated

in CT26 cells treated with

282

PEG-GGT for five days

in vitro

(

Figure S3.G

). In aggregate, these results demonstrate that

283

glutamine makes an indispensable contribution to the nucleotide pool, and upon depletion of

284

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glutamine, cells upregulate pathways associated with DNA replication and DNA repair pathways

285

to compensate for the replication stress.

286

Many reports support that Myc expression reinforces glutamine dependency in diverse

287

cancer models

. Since CT26 tumors overexpress Myc

, we used the RNA-Seq data to test the

288

impact of glutamine depletion on Myc overexpressing tumors. Unexpectedly, Myc target genes

289

were upregulated upon glutamine depletion via PEG-GGT, suggesting a potential adaptive

290

mechanism to maintain the nucleotide pool

(

Figure 4.B,C, S3.D

). Specifically, genes like

Cad

291

Ppat

, and

Pfas

; which directly use glutamine

’



-nitrogen for nucleotide synthesis and have

292

previously been demonstrated to be under the control of Myc, exhibited a significant upregulation

293

in the PEG-GGT treated group. Furthermore, there was a pronounced enrichment of gene sets

294

pertaining to nucleotide biosynthesis and one-carbon metabolism (

Figure 4.D

). Collectively, our

295

RNA-Seq data suggests that

in vivo

glutamine depletion leads to an elevation in the expression

296

of downstream targets of Myc, including genes responsible for nucleotide synthesis.

297

To explore the mechanisms underlying cellular adaptation to the depletion of extracellular

298

glutamine, we investigated if cells synthesize glutamine intracellularly. Accordingly, we focused

299

on Glul which is the only enzyme capable of intracellular glutamine synthesis

. Since the

300

abundance of Glul protein is determined by post-translational modification via glutamine

301

dependent acetylation and subsequent proteasomal degradation

, we did not observe an

302

upregulation in

Glul

transcripts in PEG-GGT treated tumors in comparison to the untreated tumors

303

(

Figure 4.B

). The mammalian target of rapamycin complex 1 (mTORC1) inhibits proteasomal

304

degradation of Glul

. We observed an increase in mTORC1 target transcripts (

Figure 4.C, S3.C

)

305

suggesting that mTORC1 might function to stabilize Glul. To verify if mTORC1 activity is essential

306

for cell growth under glutamine depletion, we treated CT26 cells either with PEG-GGT, mTORC1

307

inhibitor temsirolimus,

or both

. While 20



g/ml temsirolimus in culture media containing

308

glutamine did not affect cell growth, the combination of 10



g/ml PEG-GGT and 20



g/ml

309

temsirolimus inhibited cell growth to a much greater extent than PEG-GGT alone (

-value = 0.003,

310

Figure S3.H

). Thus, upon glutamine depletion, there is an increase in mTORC1 signaling which

311

when inhibited inhibits cell proliferation.

312

To investigate the impact of glutamine depletion on the immune system, we focused on

313

immune related pathways within our RNA-Seq data. We observed a significant downregulation of

314

interferon-alpha, interferon-gamma, and TNF-



responses upon treatment with PEG-GGT (

Figure

315

5.A,B, S3.E

). To investigate the changes in the immune cell composition upon glutamine

316

depletion

in vivo

, we performed immune cell deconvolution using mouse immune cell gene

317

signatures published in the Immgen database

61,62

. We observed that tumors treated with PEG-

318

GGT were significantly enriched in macrophages (F4/80

ICAM2

) and deprived of dendritic cells

319

(Cd11c

MHCII

Flt3

) (

Figure 5.C

). As macrophages can either have a pro-inflammatory (M1) or

320

anti-inflammatory (M2) phenotype, we investigated the M1 and M2 markers in our RNA-Seq data

321

and observed enrichment of

bona

fide

M2 markers

Mrc1

(Cd206

)

Cd163 and F13a1

. We also

322

observed a reduction in

Cd86, H2-Ab1

(MHC-II)

and

Ido1

which are known markers of M1

323

macrophages

63,64

(

Figure 5.D

). Collectively, our RNA-Seq data suggests that extracellular

324

glutamine depletion compromises anti-tumor immunity which is marked by downregulation of

325

IFN-



, IFN-



and TNF-



responses, and an increased frequency of anti-inflammatory

326

macrophages.

327

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Enzymatic depletion of circulating glutamine is immunosuppressive

328

To validate our finding that depletion of extracellular glutamine is immunosuppressive, we

329

used flow cytometry to phenotype the tumor-infiltrating myeloid and lymphoid immune

330

populations (

Figure S4.A

). We implanted CT26 tumors subcutaneously and utilized the same PEG-

331

GGT treatment schedule as before (

Figure 3.D

). To ensure that we harvested enough cells for

332

myeloid and lymphoid phenotyping, we harvested the tumor when the average volume reached

333

300-400 mm

day after treatment). In accordance with our transcriptomic data, macrophages

334

(Cd45

Cd11b

Ly6C

F4/80

) in PEG-GGT treated tumors were skewed towards M2 phenotype as

335

Cd206

high

macrophages were significantly enriched in PEG-GGT (40



13%) vs vehicle treated mice

336

(25



8%,

-value = 0.03) (

Figure 5.E

). Furthermore, we observed a significant increase in the

337

polymorphonuclear myeloid derived suppressive cell (PMN-MDSC) (Cd45

Cd11b

Ly6C

Ly6G

)

338

population upon PEG-GGT treatment (7



3%) compared to vehicle (1



1%,

-value = 0.0003)

339

(

Figure 5.F

As PMN-MDSC accumulation in tumors is associated with G-CSF and GM-CSF

340

secretion, we observed 15



3 (

-value = 0.003) and 18



2 (

-value = 0.0003) fold increase in

341

transcript levels of these genes respectively in CT26 cells treated with PEG-GGT

in vitro

65,66

(

Figure

342

5.G

). Cd11b

GR-1

cells which form a major population of MDSCs were also enriched in PEG-GGT

343

treated tumors (PEG-GGT: 16



2% vs Vehicle: 6



1%,

-value = 0.0008) (

Figure 5.H

)

. Unlike

344

PMN-MDSCs, the frequency of monocytic MDSCs (Mo-MDSC) (Cd45

Cd11b

Ly6C

high

Ly6G

) did

345

not differ between PEG-GGT (5.3



1.3%) and vehicle treated (3.7



0.6%,

-value = 0.3) group

346

(

Figure S4.F).

The frequency of infiltrating CD8 T cells (PEG-GGT: 2.5



0.8% vs Vehicle: 3.2



0.7%,

347

-value = 0.50) and NK cells (PEG-GGT: 0.7



0.6% vs Vehicle: 2.4



1.0%,

-value = 0.09) did not

348

change significantly after PEG-GGT treatment (

Figure 5.I,J

). In summary, our flow cytometry data

349

demonstrates that depletion of glutamine through PEG-GGT induces a state of

350

immunosuppression, marked by a notable enrichment in populations of PMN-MDSCs and M2

351

macrophages.

352

Glutamine depletion is not associated with favorable outcomes in human cancers

353

To determine whether glutamine depletion could be of therapeutic benefit in human

354

cancers and whether it was associated with immunosuppressive phenotype as seen in our tumor

355

model, we derived a gene signature associated with glutamine depletion (

Figure 6.A

). To ensure

356

that our gene signature is broadly reflective of glutamine depletion, we used the differentially

357

expressed genes (FDR<0.1) from our study and a recent study in which two pancreatic cancer cell

358

lines (SUIT2 and 89388T) were adapted to grow in low glutamine media

(

Figure 6.A

). Based on

359

the core set of genes common to all three datasets, we derived the glutamine depletion signature

360

[Supplementary file 1,

Table

361

To investigate if our glutamine depletion signature had translational relevance, we took

362

advantage of human tumors within the TCGA. We investigated combined transcriptomic and

363

clinical/pathological annotations in nine human cancers. To quantify the enrichment of our

364

glutamine depletion gene signature in patient transcriptomic data, we performed single sample

365

GSEA (ssGSEA)

. Samples in the first quartile with the highest expression of glutamine depletion

366

signature were

labeled “Gln depletion high” and those in the last quartile w

ere

labeled “Gln

367

depletion low” (

Figure 6.A

). A high glutamine depletion ssGSEA score was not associated with

368

significantly favorable prognostic outcome in most cancers (

Figure 6.E,H,J, S5

). Consistent with

369

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our data showing no therapeutic benefit of glutamine depletion in 4T1 tumor model, the five-year

370

overall survival (OS) did not differ for both groups in breast cancer (HR (Hazard Ratio): 1.1, 95%

371

CI:0.7 to 1.8). The five-

year OS for patients in the “Gln depletion high” group was significantly

372

lower in liver hepatocellular carcinoma (HR: 2.9, 95% CI:1.8 to 4.9), and pancreatic adenocarcinoma

373

(HR: 3.1, 95% CI:1.7 to 5.6). The five-

year OS for the “Gln depletion high” group was significantly

374

better only in stomach adenocarcinoma (HR: 0.6, 95% CI: 0.4 to 1) (

Figure S5.F

). Thus, our data

375

suggests that glutamine deprivation is not associated with better outcomes in multiple human

376

cancers.

377

We next evaluated if glutamine depletion score differed in different pathological

378

subcategories of breast cancer like tumor size, molecular subtype, and stage. We observed that

379

the glutamine depletion ssGSEA score was significantly higher in larger tumors (T2) than smaller

380

tumors (T1) (0.71



0.15 vs 0.64



0.14,

-value = 10

-9

) (

Figure 6.B

). Among the different molecular

381

subtypes of breast cancers, glutamine depletion ssGSEA score was significantly enriched in more

382

aggressive cancers like luminal B, HER2+, and basal compared to normal and luminal A breast

383

cancers (0.81



0.11 vs 0.60



0.10,

-value < 10

-16

) (

Figure 6.C

). Furthermore, glutamine depletion

384

ssGSEA score was higher in advanced stage II and stage III tumors compared to stage I tumors

385

(0.70



0.15 vs 0.65



0.15,

-value = 10

-4

) (

Figure 6.D

). Taken together, glutamine deprivation is

386

associated with increased tumor size and more aggressive phenotypes in human breast cancers.

387

As our data showed that low glutamine in TME was associated with increased infiltration

388

of PMN-MDSCs, we wanted to test whether this was also consistent in human cancers. Because

389

of their low transcriptional activity and close resemblance to neutrophils, we still lack a unifying

390

gene signature for PMN-MDSCs

. However, there have been several reports of the differences in

391

the expression of PMN-MDSCs compared to other MDSCs and neutrophils

70,71

. We derived a

392

seven-gene signature (

S100A8

S100A9,

LYZ1, MMP8, MMP9

SLC27A2,

and

IL4R

) for PMN-MDSCs

393

based on several recent reports including single-cell RNA sequencing from 33 glioma patients

70-

394

. We validated this signature with our mouse RNA-Seq data and confirmed that these genes

395

were indeed upregulated in PEG-GGT treated tumors, consistent with our flow-cytometry data on

396

the same tumors (

Figure 6.F

). To restrict our analysis to immune cells and remove bias from tumor

397

cells in the TCGA RNA-Seq data, we calculated the fraction of immune cells in the tumor using

398

EPIC

. We then normalized the expression of the signature genes based on immune cell

399

infiltration and calculated

“PMN

MDSC score” as the sum of

Z-scores of all the genes in the

400

signature

. When we tested this signature in the TCGA dataset, we found that the

“

Gln depletion

401

high” group had significantly higher

PMN-MDSC scores in breast (0.4



0.2 vs -0.4



0.3,

402

value=0.04), pancreatic (0.5



0.7 vs -1.8



0.9,

-value=0.04) and liver cancer (1.0



0.5 vs -0.4



403

0.4,

-value=0.02) (

Figure 6.G,I,K

). Taken together these results demonstrate that glutamine

404

depletion is likely associated with the accumulation of PMN-MDSCs in the TME and is not

405

associated with a therapeutic benefit in human cancers.

406

407

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Discussion

408

Glutamine contributes to major anabolic pathways and is consumed at a high rate during

409

cell growth which often leads to its depletion in the tumor core

. To investigate how depletion of

410

glutamine alters the tumor and immunological landscape

in vivo

, we leveraged the high affinity

411

glutaminase activity (low K

) of

H. pylori

GGT (hp-GGT) and repurposed it as a potent

in vivo

412

glutaminase (with no asparaginase activity). We showed that PEGylated hp-GGT (PEG-GGT) had a

413

half-life of 67 ± 8 hours in serum, and a dose of 20 mg/kg could efficiently eliminate circulating

414

glutamine to undetectable levels without affecting asparagine for at least 48 hours in BALB/c mice.

415

Our approach is different from other studies that have utilized highly efficient glutaminases-

416

asparaginases of bacterial origin to systemically deplete glutamine. As

de novo

asparagine

417

synthesis is dependent on glutamine, asparagine becomes an essential amino acid when

418

glutamine is absent

. Furthermore, asparagine directly binds to LCK to promote its activity and

419

enhance T cell activation

. Since the bacterial glutaminase-asparaginases deplete glutamine and

420

asparagine simultaneously, we hypothesized that depleting glutamine while preserving

421

asparagine provides a specific tool to investigate the selective impact of glutamine depletion. But

422

to our knowledge, there have been no reports of high affinity glutaminases (K

in the micromolar

423

range) with no concomitant asparaginase activity.

424

When extracellular glutamine is abundant, intracellular glutamine triggers acetylation

425

dependent proteasomal degradation of glutamine synthetase (Glul), the only protein capable of

426

intracellular glutamine synthesis

. As extracellular glutamine becomes limiting, Glul degradation

427

is inhibited, and it synthesizes glutamine intracellularly by ligating ammonia and glutamate

57,58

428

Since Glul is degraded upon glutamine accumulation, intracellular glutamine concentration can

429

reach only a particular threshold via Glul. Hence, glutamine flux is redirected towards nucleotide

430

and DNA synthesis while flux towards protein synthesis is reduced by global downregulation of

431

translation. In our metabolomic studies, we observed decreased levels of proline and GSH upon

432

depletion of extracellular glutamine. In multiple human cancer cell lines, reducing proline

433

biosynthesis conserved glutamate and allowed cells to proliferate in glutamine limiting conditions

434

providing a potential mechanism to adapt to glutamine depletion

. In pancreatic cancer models,

435

cells adapted to grow in low glutamine concentration (100



M) had significantly increased Glul

436

protein although changes in mRNA levels were not different

. Metabolomic and transcriptomic

437

analysis showed that adapted clones indeed had a low concentration of intracellular glutamine, a

438

significant flux of glutamine towards nucleotide synthesis, and increased mTORC1 signaling to

439

avoid Glul degradation. Our metabolomics and transcriptomic results align well with this model.

440

As glutamine is indispensable for hexosamine biosynthesis, depletion of glutamine

441

induces Endoplasmic reticulum (ER) stress which increases G-CSF and GM-CSF secretion from

442

tumors via Ire1



-Jnk pathway

. Consistent with increased ER stress, genes related to unfolded

443

protein response were upregulated in tumors upon PEG-GGT treatment. Treatment with PEG-GGT

444

also significantly increased

G-CSF

and

GM-CSF

transcripts in CT26 cells

in vitro

. In mice, G-CSF and

445

GM-CSF mobilize myeloid precursors from the bone marrow to the tumor, where they

446

differentiate into PMN-MDSCs

65,66

. Our flow cytometry data on PEG-GGT treated tumors showed

447

pronounced infiltration of PMN-MDSCs into the tumors.

448

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Polarization of M0 macrophages to M1 or M2 macrophages is influenced by the ratio of

449



-ketoglutarate (



-KG) to succinate

. Increased



-KG demethylates repressive H3K27Me3 M2

450

promoters via Jmjd3 while succinate inhibits Jmjd3 and promotes M1 phenotype by stabilizing

451

HIF1

. In cell culture, contradictory results have been reported. While one study reported that

452

glutamine deprivation impaired the expression of M2 markers which was restored by treatment

453

with cell-permeable dimethyl-



-KG

, another study reported that depriving M0 macrophages of

454

glutamine promoted M2 phenotype without any cytokines by increased expression of Glul, which

455

reduced the GABA shunt mediated flux of glutamate towards succinate

. We observed an increase

456

in M2 macrophages in PEG-GGT treated tumors. While our data supports the latter observation,

457

we cannot rule out the possibility of the former. Since macrophages have been shown to uptake

458

glutamate via SLC1A2

81,82

, which is abundant in circulation upon glutamine hydrolysis, these cells

459

might be able to maintain their



-KG pool.

460

To investigate whether glutamine depletion could be a viable strategy in human cancers,

461

we developed a gene signature associated with glutamine depletion in cancers. We discovered

462

that high glutamine depletion signature was not associated with favorable outcomes in most

463

cancers, rather it was significantly unfavorable for pancreatic, liver, and skin cancers. Glutamine

464

depletion signature was positively correlated with tumor size, stage, and aggressiveness in breast

465

cancers. Moreover, a high glutamine depletion score was associated with increased infiltration of

466

PMN-MDSC in breast, pancreatic, and liver cancers. Since glutamine is often depleted in the core

467

of the TME

, attempts to reinstate glutamine levels in the core might lift the immunological

468

barriers created by tumor cells in the TME.

469

Multiple studies have tried to target the glutamine metabolism of tumors for therapeutic

470

benefit. Enzymatic depletion of circulating glutamine via

Pseudomonas 7A

and

Acinetobacter

471

asparaginase glutaminase had no significant effect on the growth of syngeneic solid tumor

472

models including non-metastatic breast cancer (EO771) and metastatic melanoma (B16). These

473

studies however documented severe lymphodepletion and splenic weight loss in treated

474

animals

. It is to be noted that both these enzymes deplete both circulating glutamine and

475

asparagine. In our studies utilizing PEG-GGT, we did not observe splenic weight loss compared to

476

untreated mice and hence these differences in toxicity might be attributable to the simultaneous

477

depletion (or lack thereof) of both amino acids. Nonetheless, consistent with the other two

478

enzymes, PEG-GGT did not have an anti-tumor effect in the models we tested. Taken together,

479

these observations suggest that enzymatic extracellular depletion of glutamine does not have an

480

anti-tumor effect. Contrary to the immunosuppression induced by extracellular glutamine

481

depletion, pan-inhibition of glutamine utilizing enzymes in TME via a prodrug of DON promoted

482

anti-tumor immunity with decreased infiltration of MDSCs, polarization of macrophages to M1

483

phenotype and reduced GM-CSF secretion in the TME

16,17

. While a complete understanding of this

484

complete opposite shift in immunological landscape upon DON treatment compared to PEG-GGT

485

treatment is lacking, we acknowledge that these two modalities of targeting glutamine

486

metabolism are fundamentally different as DON treatment inhibits glutamine metabolism and

487

leads to accumulation of glutamine in the TME while PEG-GGT depletes glutamine in the TME but

488

does not inhibit intracellular glutamine metabolism. Moreover, effects of DON are concentration

489

dependent which further complicates direct comparison. For example, treatment of 4T1 cells with

490

1 µM DON leads to reduced secretion of G-CSF and GM-CSF while treatment with 50 µM DON

491

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leads to significantly increased secretion of G-CSF and GM-CSF

17,65

. More studies are required on

492

the role of different glutamine utilizing pathways in different cell types in the TME to further

493

understand the integrated impact of glutamine metabolism in tumors.

494

Since we have shown that PEGylated

H. pylori

GGT is a potent glutaminase with no

495

asparaginase activity, we envision other applications of PEG-GGT as a molecular tool to deplete

496

glutamine in live animals. For example, bacterial asparaginases are standard treatment for acute

497

lymphoblastic leukemia, and there is considerable debate on the importance of glutaminase

498

activity for therapeutic efficacy

83,84

, PEG-GGT could be used to independently study the roles of

499

glutamine and asparagine metabolism in these cancers. PEG-GGT can thus be used as a selective

500

and efficient tool for glutamine depletion in diverse disease contexts including rheumatoid

501

arthritis

, acute respiratory distress syndrome (ARDS)

, cancer cachexia

and cystic fibrosis

502

In conclusion, we show that enzymatic depletion of glutamine is immunosuppressive and

503

does not inhibit tumor growth

in vivo

. We developed a glutamine depletion gene signature and

504

showed that glutamine depletion is not associated with favorable clinical outcomes in human

505

cancers. Our data suggests that glutamine replete TME is required for optimal anti-tumor immune

506

responses.

507

Limitations of the study

508

Our objective in this report was to document the impact of depleting extracellular glutamine in

509

tumor models and understanding the impact of glutamine depletion on tumor growth. For this

510

purpose, we engineered an enzyme designed to selectively deplete circulating glutamine while

511

preserving asparagine. While our enzyme did not deplete circulating asparagine, it resulted in an

512

elevation of plasma cysteine/cystine and glycine levels, the implications of which require further

513

exploration. While we have demonstrated that glutamine depletion is not anti-tumor in the

514

models we have studied, a more detailed investigation of the impact of glutamine depletion and

515

metabolic fluxes upon glutamine depletion can be undertaken using metabolomics/mass

516

spectrometry. Second, it would be important to extend our findings on immune cell infiltration

517

with the CT26 model to other tumor models in mice. We ensure the generalizability of our results

518

by deriving a glutamine depletion signature that we used to test the impact of glutamine

519

depletion in human tumors. Our results illustrate that glutamine depletion is not associated with

520

clinical benefit in diverse human tumors. Although we derived a signature for PMN-MDSC to

521

investigate if these immunosuppressive cells are increased in glutamine depleted tumors, we

522

acknowledge that this signature is not ideal and that a better molecular signature for PMN-MDSC

523

will be needed. Nonetheless, our work presents a tool for systemic depletion of glutamine without

524

depleting asparagine in live animals and proposes that extracellular glutamine depletion will not

525

be an effective anti-cancer therapeutic strategy.

526

527

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AUTHOR CONTRIBUTIONS

528

NV and MK designed the study, analyzed the data, and prepared the manuscript. MK, AL, RK,

529

MMP, SRS, and SN performed the experiments. SN helped in protein purification. AL, RK, MMP

530

and SRS helped in mice experiments. MK did the bioinformatic analyses. All authors edited and

531

approved the manuscript.

532

ACKNOWLEDGMENTS

533

This publication was supported by the NIH (R01GM143243). We thank Dr. Patrick Cirino for

534

providing us with access to their lab for bacterial culture and Dr. Yuri Tanno from Dr. George

535

Georgiou’s lab at UT Austin for their inputs in protein synthesis. The Baylor College of Medicine

536

metabolomics core was supported by the CPRIT Core Facility Support Award RP210227

537

“Proteomic and Metabolomic Core Facility,” NCI Cancer Center Support Grant P30CA125123,

538

NIH/NCI R01CA220297, NIH/NCI R01CA216426 intramural funds from the Dan L. Duncan Cancer

539

Center (DLDCC). Parts of the figures were created using BioRender (BioRender.com).

540

DECLARATIONS OF INTERESTS

541

NV is a co-founder of CellChorus and AuraVax. None of these conflicts of interest influenced any

542

part of the study design or results. UH has filed a disclosure based on some of the findings in this

543

study.

544

FIGURE TITLE AND LEGENDS

545

Figure 1.

H. pylori

GGT is a high affinity glutaminase with no asparaginase activity.

546

(A)

The chemical reactions catalyzed by GGTs.

547

(B)

Crystal structure of wild-type

H. pylori

gamma-glutamyl transferase (hp-GGT). hp-GGT

548

exists as

αββα heterodimer.

hp-GGT undergoes autocatalytic cleavage into two subunits

549

and the N terminus threonine of the smaller subunit acts as the nucleophile. The blue

550

surface represents the catalytic center, and the yellow surface depicts the lid-loop

551

structure. The hp-GGT crystal structure was obtained from PDB entry 2NQO

, and

552

visualized in PyMOL.

553

(C)

SDS-PAGE of hp-GGT (lane 2) and hp-GGT T380A mutant (lane 3) lacking autocatalytic

554

cleavage.

555

(D)

Schematic showing the conjugation of NHS-PEG-5000 chains to lysine residues of hp-GGT

556

to produce PEG-GGT.

557

(E)

SDS-PAGE of PEG-GGT (lane 2) and hp-GGT (lane 3).

558

(F)

Michaelis-Menten kinetic characterization of hydrolysis reaction for hp-GGT and PEG-GGT

559

using



-glutamyl analog



GPNA.

560

(G)

Plot showing relative hydrolysis rate of



GPNA by PEG-GGT in presence of equimolar

561

amounts of glutamine (black) and asparagine (blue).

562

(H)

Plot of final asparagine concentration after incubation of 2.5 mM asparagine with 0.1 and

563

0.2 mg/ml of PEG-GGT for 8 hours.

564

565

Journal Pre-proof

In panel

analysis was performed using a two-tailed

student’s

t-test

with Welsch’s correction.

566

panel F, G and H, vertical bars show mean values with error bars representing SD (Standard

567

Deviation)

. Student’s t test: ***p < 0.001; **p < 0.01; *p < 0.05; ns: not significant.

568

569

Figure 2. PEG-GGT inhibits the growth of multiple cell lines

in vitro.

570

(A)

Schematic of MTT assay to assess growth kinetics of cell lines

in vitro

upon PEG-

571

GGT treatment.

572

(B, C, D)

Relative growth of CT26 (B), 4T1 (C), and MC38 (D) cells in presence of glutamine

573

(red), presence of glutamine and PEG-GGT (blue) and absence of glutamine (black)

574

in vitro

575

(E, F, G)

Relative levels of intracellular glutamate (E), glutamine (F) and total glutathione (G)

576

in CT26 cells upon PEG-GGT treatment for 48 hours.

577

(H, I)

Merged brightfield and GFP channel image of CT26 cells at 8, 16, and 24h after

578

treatment with MSO, PEG-GGT, and combination of PEG-GGT and MSO

in vitro

(H).

579

Plot representing average number of sytox positive cells per field of view (2 mm x

580

2 mm) at different time points (I). Scale bar, 300 µM.

581

See also Figure S5

652

In panel B, C, D, G, I and K, analysis was performed using two-

tailed student’s t

test with Welsch’s

653

correction. In panel F, the scale of the heatmap represents a Z-score

Student’s t test: ***p < 0.001;

654

**p < 0.01; *p < 0.05; ns: not significant. In panel E, H and J, survival analysis was performed using

655

the Log-Rank test.

656

657

STAR Methods

658

RESOURCE AVAILABILITY

659

Lead contact:

660

Further information and requests about this study should be directed to and will be fulfilled by

661

the lead contact, Navin Varadarajan (nvaradar@central.uh.edu).

662

Materials availability:

663

This study did not generate new unique reagents.

664

Data and code availability:

665

RNA-seq data has been deposited at GEO (GSE247472) and are publicly available as of the date

666

of publication. Accession numbers are listed in the key resources table.

667

This paper does not report original code.

668

Any additional information required to reanalyze the data reported in this paper is available from

669

the lead contact upon request.

670

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EXPERIMENTAL MODEL AND STUDY PARTICIPANT DETAILS:

671

Cell lines:

All studies using animal experiments were reviewed and approved by the University of

672

Houston (UH) IACUC (Institutional Animal Care and Use Committee). We bought CT26 and 4T1

673

cell lines from ATCC. MC38 cell line was provided by Dr. Wei Peng (UH). All cell lines were cultured

674

in RPMI-1640 media supplemented with 10% fetal bovine serum (FBS), 2 mM L-Glutamine, 10 mM

675

HEPES buffer and 1% penicillin-streptomycin maintained at 5% CO2, 80% relative humidity and

676

37°C. We routinely tested all cell lines for mycoplasma using RT-PCR.

677

Bacterial strains:

678

We used E. coli MC1061 strain for plasmid transformation and propagation. For protein

679

expression, we used E. coli Rosetta-2 strain. We used 2xYT media to culture bacterial strains.

680

Animals:

681

All studies using animal experiments were reviewed and approved by the University of Houston

682

(UH) IACUC (Institutional Animal Care and Use Committee) under protocol PROTO202000039. We

683

purchased the female, 6

–

8-week-old BALB/c mice from Charles River Laboratories. Sex/gender as

684

a variable was not tested.

685

686

METHOD DETAILS

687

Generation of hp-GGT Expression Constructs:

688

We expressed hp-GGT as described in a previous study

. The gene encoding GGT in

H. pylori

has

689

been sequenced and is available on the KEGG database (entry HP1118). Codon optimized 1.7 kb

690

fragment corresponding to hp-GGT sequence excluding the 26 amino acid periplasmic signal

691

peptide sequence was provided by Integrated DNA Technologies and was PCR amplified with

692

overhang primers containing NdeI and XhoI sites. We cloned this fragment in the pET28a

693

(Novagen) vector using NdeI and XhoI restriction sites by Gibson Assembly. The use of the NdeI

694

site in pET-28a allowed us to include a thrombin cleavable His-tag at the N-terminal of the

695

expressed hp-GGT. We sequenced the construct between the T7 promoter and stop codon

696

through Sanger sequencing (Genewiz) and protein sequence was identical to the HP1118 protein

697

sequence excluding the signal peptide. To introduce the T380A mutation in the construct

698

expressing hp-GGT, we generated two overlapping PCR fragments using appropriate primers to

699

introduce the desired mutation in the overlapping region and assembled the fragments using

700

Gibson Assembly. We propagated the plasmid in

E. coli

MC1061 strain.

701

Expression and purification of hp-GGT and synthesis of PEG-GGT:

702

We transformed hp-GGT containing pET28a plasmid in

Escherichia. coli

strain Rosetta

703

2(DE3) (Novagen) and allowed the bacteria to grow on kanamycin and chloramphenicol-resistant

704

lb-agar plates. We picked a single colony and inoculated it in 2xYT medium containing 34 µg/ml

705

chloramphenicol (Sigma, #C0738)

and 50 µg/ml kanamycin (Sigma, #K1377) and grew the culture

706

overnight at 37°C and 250 rotations per minute (rpm). We used this starter culture to inoculate

707

fresh 2xYT media with kanamycin and chloramphenicol concentrations same as for the starter

708

culture. We kept the OD (Culture absorbance at 600 nm) at the start of the culture at 0.01. We

709

Journal Pre-proof

allowed this culture to grow at

37°C with shaking at 250 rpm till the OD reached 0.5

–

0.6. At this

710

point, we induced GGT expression by adding isopropyl

-D-thiogalactopyranoside (IPTG)

711

(FisherScientific, #BP1755-10) to a final concentration of 500 µM and grew the culture further for

712

8 hours. We then centrifuged the culture at 6000 ×g for 20 minutes, decanted the supernatant,

713

and harvested the cells. We resuspended the bacterial pellet in lysis buffer containing (50 mM

714

NaH

, 300 mM NaCl, 10 mM Imidazole (Sigma, #I0250), 0.05% Tween-20, pH = 8), universal

715

nuclease (Thermofisher, #88700) and protease inhibitor cocktail (Sigma, #P8849). We then lysed

716

the cells by sonication and centrifuged the lysate at 21000 ×g for 10 minutes at 4°C. We extracted

717

GGT from the supernatant by affinity chromatography using Nickel chelating columns. We first

718

equilibrated the column with binding buffer (20 mM NaH

, 500 mM NaCl, 10 mM imidazole,

719

pH= 7.4). We then passed the bacterial supernatant through the column followed by a wash with

720

the binding buffer. To remove the endotoxins, we washed the column with 200 column volumes

721

of sterile ice-cold 0.1% Triton X-114 (Sigma, #X114) in PBS overnight. We washed the column

722

further with 20 column volumes of sterile PBS to wash the Triton X-114. Finally, we eluted the

723

protein in the elution buffer (20 mM NaH

, 500 mM NaCl, 500 mM imidazole, pH= 7.4). We

724

then buffer exchanged the eluted recombinant hp-GGT with PBS using 10 kDa NMWCO

725

centrifugal filters (Sartorius) and stored the protein at 4°C. We then incubated the purified enzyme

726

at 37°C for 6 hours for complete maturation of the enzyme. We determined the protein

727

concentration by bicinchoninic acid (BCA) assay.

728

To make PEG-GGT, we concentrated hp-GGT in PBS to 5 mg/ml using 10 kDa NMWCO

729

centrifugal filters and added Methoxy-PEG-5000 (methoxy-PEG-CH

COO-NHS, Mw 5,000)

730

(Sunbright, #ME-050AS)

powder to the solution at a molar ratio of 100:1 (100 molecules of PEG

731

per molecule of hp-GGT) and mixed gently for 1 hour at 4°C. We removed excess PEG by buffer

732

exchange with PBS using 10 kDa NMWCO centrifugal filters. We concentrated PEG-GGT to 5

733

mg/ml in 10% glycerol, sterile filtered using 0.22 µM filter, and stored PEG-GGT at -80°C. We

734

tested PEG-GGT for endotoxins using a chromogenic LAL-based detection assay (ThermoFisher,

735

#A39552S).

736

Kinetic characterization of hp-GGT and PEG-GGT:

737

We investigated the kinetic parameters for hydrolysis of



-glutamyl compounds using

738

substrate analog L-Glutamic acid



(p-nitroanilide) (



GPNA) (Sigma, #G1135). The hydrolysis of

739

GNA produces 4-nitroaniline whose release can be continuously measured at 412 nm. We made

740

serial dilutions of



GPNA in 100 mM Tris-HCl (pH = 6.5) to obtain concentrations ranging from

741

1000 µM to 3.9 µM. We added 10 µL of GGT (40 µg/ml, 100 mM Tris, pH= 6.5) to 190 µL substrate

742



GPNA in a flat transparent 96-well plate to get a final concentration of 2 µg/ml GGT. For each

743



GPNA substrate concentration, the corresponding control with no enzyme was also set up to

744

determine the non-enzymatic rate of hydrolysis. We continuously recorded the absorbance of the

745

samples at 412 nm and 37°C immediately after the addition of the enzyme using a plate reader

746

(Infinite 200 Pro-Tecan Life Sciences). We obtained a standard curve relating to 4-nitroanaline

747

concentration and absorbance and calculated the extinction coefficient. We determined the

748

kinetic constants V

max

and K

from the Lineweaver Burk plot and direct fit to Michaelis Menten

749

equation. For determining the asparaginase activity of PEG-GGT, we added 0.1 and 0.2 mg/ml

750

PEG-GGT to 2.5 mM asparagine in 100 mM Tris-HCl, pH=8, and measured asparagine

751

concentration after 8 hours. For determining asparagine concentration after PEG-GGT incubation,

752

Journal Pre-proof

we used a colorimetric method as described previously using ninhydrin reagent (Sigma,

753

#151173)

754

Cell culture and cell viability assays:

755

We studied the effects of glutamine depletion on 4T1, CT26, and MC38 cell lines. MC38

756

cell line was provided by Dr. Wei Peng (UH). We bought 4T1 and CT26 cells from ATCC. For each

757

cell line, we seeded 5x10

cells in 500 µl RPMI-1640 (Corning) media supplemented with L-

758

glutamine (2 mM) and 10% FBS (RnD Biosystems, S11150) in a 24-well plate. We incubated the

759

cells at 37°C and 5% CO

After

24 hours, we removed the media and washed the cells with 500 µl

760

PBS. In the first group, we added 750 µl RPMI-1640 medium without L-glutamine supplemented

761

with 10% dialyzed FBS (dFBS) (Sigma, #F0392) and 2 mM L-glutamine to each well. In the second

762

group, we added PEG-hp-GGT at 10 µg/ml in addition to the media added in group 1. In the third

763

group, we added 750 µl RPMI-1640 media without L

–

glutamine but supplemented with 10%

764

dialyzed FBS (dFBS). After every 24 hours, we measured the cell viability of 3 wells from each group

765

using MTT assay. For studying the combined effect of PEG-GGT and mTORC1 inhibition on cell

766

growth, we performed a similar assay with regular RPMI-1640 media, and either PEG-GGT (10

767

µg/ml) or Temsirolimus (20 µg/ml) (Selleckchem, #S1044) or both were added to the wells.

768

We used a standard MTT assay to determine the viability of cells under different media

769

conditions. We dissolved MTT powder (VWR, #97062-376) in PBS (pH =7.4) to 5 mg/ml. We

770

prepared a solubilization solution by dissolving 40% v/v dimethylformamide (DMF) in 2% v/v

771

glacial acetic acid and further dissolving 16% w/v sodium dodecyl sulfate (SDS) into this solution

772

(pH = 4.7). For measuring the cell viability, we added 75 µl MTT solution to 750 µl media to achieve

773

a final concentration of 0.45 mg/ml of MTT. We incubated the culture at 37°C and 5% CO

. After

774

3 hours, we removed the media with MTT and added 500 µl solubilization solution to dissolve the

775

formazan crystals formed. The absorbance of this solution at 570 nm is directly proportional to

776

the number of viable cells.

777

For the fluorescence-based cytotoxicity assay, we seeded 10

cells in a transparent 96-well

778

plate and grew them in RPMI-1640 media. After 24 hours, we replenished the media with RPMI-

779

1640 media containing L-glutamine and 100 nM Sytox green (ThermoFisher, #S7020) and added

780

either PEG-GGT (10 µg/ml), MSO (Sigma, #M5379) (0.5 mM) or both to the wells. We then

781

incubated the cells at 37°C and 5% CO

and imaged 4 fields of view under brightfield and GFP

782

channel (488 nm) in each well every hour for 24 hours at 10x magnification (BioTek Cytation). We

783

labeled the dead cells as fluorescence-positive cells in the 488 nm channel and counted them

784

using ImageJ. For studying the impact of GSH, GSSG and NAC on proliferation of cells in presence

785

of PEG-GGT, we made 10 mM GSH, GSSG and NAC solution in ultrapure water and adjusted the

786

pH to 7.4. We added GSH, GSSG and NAC at final concentration of 100 µM.

787

Cell culture metabolite assays

788

We cultured CT26 cells in 24 well plates to 60% confluency in RPMI-1640 media with 10% FBS. We

789

then washed the cells and replaced the media with RPMI-1640 media without glutamine and 10%

790

dialyzed FBS (dFBS) supplemented with either 2 mM glutamine or 2 mM glutamine and 10 µg/ml

791

PEG-GGT. After 48 hours, we washed the cells twice with PBS. We added 200 µl of 80% methanol

792

solution stored at -80°C to the wells and kept the plate in -80°C for 10 minutes. We then scraped

793

Journal Pre-proof

off the cells and vortexed the solution at 4°C for 5 minutes. We then sonicated (40 kHz) the cells

794

at 4°C for 15 minutes. Finally, we centrifuged the mixture at 20000g at 40°C for 10 minutes. We

795

vacuum evaporated the methanol-water solution and reconstituted the metabolites in 20 µl PBS.

796

We determined the glutamine and glutamate levels in serum by enzymatic assay detection kit

797

according to the manufacturer’s protocol (Promega Glutamine/Glutamate Glo kit). We assayed

798

the total glutathione by an enzymatic assay kit according to manufactures protocol (Cayman total

799

glutathione assay kit: #703002).

800

PEG-GGT in-vivo pharmacokinetics and pharmacodynamics:

801

All studies using animal experiments were reviewed and approved by the University of Houston

802

(UH) IACUC (Institutional Animal Care and Use Committee). We purchased the female 6 to 8-

803

week-old BALB/c mice from Charles River Laboratories. 8 mice were administered PEG-GGT at a

804

dose of 20 mg/kg body weight (b.w). We collected mouse serum at 8, 24, 48, and 72 hours. To

805

determine the PEG-GGT concentration in serum, we first prepared a standard curve in which we

806

compared the rate of



GPNA hydrolysis by adding 5 µL of PEG-GGT at different concentrations to

807

200 µL of 0.5 mM



GPNA in PBS spiked with 5 µl of serum from untreated tumor-bearing mice.

808

We also confirmed that serum from non-treated tumor-bearing mice had no



GPNA hydrolysis

809

activity. To find the concentration of PEG-GGT in the serum of treated mice, we incubated 5 µl of

810

serum in 200 µL of 0.5 mM GPNA in PBS and measured the rate of



GPNA hydrolysis. We

811

determined the concentration of PEG-GGT from the standard curve generated as mentioned

812

above. We determined the circulating glutamine and glutamate levels in serum by enzymatic assay

813

detection kit according to the manufacturer’s protocol (Promega Glut

amine/Glutamate Glo kit).

814

In vivo

tumor models:

815

For studying tumor growth kinetics

in vivo

, we injected 6

–

8-week-old BALB/c mice subcutaneously

816

either with a single cell suspension of 100K CT26 (right flank) cells or 50K 4T1 (fourth mammary

817

fat pad) cells suspended in RPMI-1640 media. We tested and confirmed that all cell lines were

818

negative for mycoplasma contamination by qPCR. After the average tumor volumes reached 100

819

, we administered either 20 mg/kg PEG-GGT IP to the treatment group every three days or

820

the vehicle (PBS,10% glycerol) to the control group. We measured tumor dimensions with Vernier

821

calipers and tumor volume was approximated as L*H*H/2. We measured the tumor volume and

822

mice weight every three days.

823

824

In vivo

metabolite measurements

825

We injected 6

–

8-week-old BALB/c mice subcutaneously with a single cell suspension of 100,000

826

CT26 cells in the right flank. Once the tumor volume reached 100 mm

, we started treating the

827

mice either with 20 mg/kg PEG-GGT or vehicle on a biweekly basis. On the 7

day after treatment

828

initiation (3 PEG-GGT doses) we euthanized the mice. We isolated and washed small sections of

829

tumors (20 mg) in phosphate buffer saline (PBS) and then splash-froze them in liquid nitrogen.

830

We collected the mouse blood from cardiac puncture and left the blood to coagulate at room

831

temperature for 10 minutes. We then centrifuged the blood at 2000g for 10 minutes, isolated the

832

serum and froze it in -80 °C. The amino acids were extracted from mouse tissue and serum using

833

the liquid-liquid extraction method as explained earlier

90,91

. Pooled samples were used as quality

834

control samples during MS acquisition. Agilent 6495 triple quadrupole MS coupled to Infinity 1290

835

Journal Pre-proof

LC was used for data acquisition via multiple reaction monitoring (MRM) mode through Agilent

836

Mass Hunter Data Acquisition Software (ver. 10.1) as described previously

. Peak integration and

837

data analysis were performed using Agilent Mass Hunter Quantitative Analysis Software. Peak

838

areas were normalized with Tryptophan-

spike internal standard.

839

Flow cytometry of tumor-infiltrating immune cells:

840

We cut the tumors from PEG-GGT treated or control groups into 1-10 mm

pieces. To dissociate

841

the tumor, we incubated the cut pieces with 0.1% w/v collagenase D (Roche #11088858001) and

842

0.01% w/v DNAse (Sigma #DN25) at 37°C for 1 hour in RPMI-1640. We obtained a single-cell

843

suspension by passing through a 70 µm cell strainer. After obtaining a single cell suspension, we

844

washed the cells twice with PBS and labeled them with Live/dead aqua die (ThermoFisher,

845

#L34957) in PBS for 20 minutes at room temperature (RT). The live/dead staining was stopped by

846

adding 5 volumes of 4% FACS buffer. We then washed the cells twice with a 4% FACS buffer. We

847

blocked the FC receptors by CD16/CD32 (clone 2.4G2; BD Biosciences) antibody in 4% FACS buffer

848

for 20 minutes. For myeloid panel, we incubated the cells with an antibody cocktail of CD45-

849

BUV395 (BD, #564279), CD11b-BV421 (Biolegend, #101235), CD11c-BV785 (Biolegend, #117335),

850

F4/80-APC/Cy7 (Biolegend, #123117), Ly6C-PE (Biolegend, #128007), Ly6G-PerCP/Cy5.5

851

(Biolegend, #127615), CD206-AF488 (Biolegend, #141709) and CD86-AF700 (Biolegend,

852

#105023). For lymphoid panel, we incubated the cells with an antibody cocktail of CD45-BUV395

853

(BD, #564279), CD3-APC (Biolegend, #100235), CD4-AF594 (Biolegend, #100446), CD8-PerCP/Cy5

854

(Biolegend, #100731), NKp45-e450 (Invitrogen, #48-3351-82), and CD19-APC-Cy7 (Biolegend,

855

#115519). We fixed the cells with BD CytoFix/Perm and washed them with BD Perm/Wash solution.

856

For FoxP3 staining in the lymphoid panel, we labeled the cells with anti-mouse FOXP3-AF488

857

(Biolegend; #126405) prepared in BD Perm/Wash solution and then washed with FACS buffer. We

858

acquired the data on a BD LSRFortessa X-20 flow cytometer apparatus and analyzed it using

859

FlowJo software (BD Biosciences).

860

mRNA isolation, sequencing, and analysis

861

We injected 6

–

8-week-old BALB/c mice subcutaneously with a single cell suspension of 100K CT26

862

cells in the right flank. Once the tumor volume reached 100 mm

, we started treating the mice

863

either with 20 mg/kg PEG-GGT or vehicle on a biweekly basis. On the 7

day after treatment

864

initiation (3 PEG-GGT doses) we euthanized the mice.

We isolated and washed small sections of

865

tumors (20 mg) in PBS and then splash-froze them in liquid nitrogen. We lysed the tissue in

866

RNeasy lysis buffer (RLT) and a single stainless steel bead using tissue lyser (Qiagen, Hilden,

867

Germany). We extracted total RNA using the RNeasy kit (Qiagen, #74104), DNAse treated the RNA

868

(Invitrogen, #AM1906), and sent the RNA for sequencing to Novogene. Novogene processed the

869

RNA to enrich mRNA and prepared the cDNA library. They sequenced the cDNA on Illumina HiSeq

870

2500 in paired-end mode. We checked the quality of sequencing data by FastQC, and the results

871

showed good quality of reads and no further need for trimming. We aligned the sequencing data

872

to the BALB/c reference genome (Ensembl v1.108) using STAR aligner and quantified the transcript

873

abundance

. We performed differential expression between treated and non-treated groups

874

using the package DESeq2

. The raw RNA-Seq data and normalized gene-count data has been

875

submitted to Gene Expression Omnibus (GEO) (Accession code: GSE247472). We performed Gene

876

Journal Pre-proof

set enrichment analysis (GSEA) using GSEA software and visualized the enriched clusters in

877

Cytoscape

. To perform infiltrating immune cell deconvolution from RNA-Seq data, we first

878

obtained the TPM counts for our RNA-Seq data using RSEM

. We then made the signature matrix

879

of immune infiltrates using immgen database

in CibersortX

. We calculated the percentage of

880

infiltrating immune cells using CibersortX.

881

In vitro

RT-qPCR:

882

We treated CT26 cells with either PEG-GGT or left them untreated for 120 hours in RPMI-1640

883

media in 24 well plates. We aspirated the media and lyzed the cells with RLT buffer. We extracted

884

the RNA using the RNeasy kit (Qiagen, #74104). We further treated the extracted RNA with DNAse

885

treatment kit to remove genomic DNA (Invitrogen, #AM1906) and synthesized cDNA using cDNA

886

reverse transcription kit (Invitrogen, #4368813). We performed RT-qPCR reaction using SsoFastTM

887

EvaGreen® Supermix with Low ROX (Bio-Rad, # 1725211) on AriaMx Real-time PCR System

888

(Agilent Technologies, Santa Clara, CA). We normalized the results to Actin or GAPDH

889

(glyceraldehyde-3- phosphate dehydrogenase). To determine the fold change, we used the 2-

890

ΔΔCt method by comparing PEG

-GGT treated cells to non-treated controls. See Table S3 for the

891

list of primer sequences used in this study.

892

Glutamine depletion signature and TCGA Analysis:

893

To construct the glutamine depletion signature, we downloaded the raw counts for cells adapted

894

to grow in low glutamine from GEO series accession number GSE144883

. We performed

895

differential analysis on the adapted cell lines and filtered the genes with FDR<0.1. We chose the

896

coordinatively upregulated or downregulated gene from our dataset and this data set to build the

897

glutamine depletion signature. To perform ssGSEA

of our signature on human cancers, we

898

downloaded the TCGA data for different cancers. We performed the ssGSEA on different human

899

cancers and stratified the top 25% ssGSEA scores as “Gln depletion high” and bottom 25% as “Gln

900

depletion low”. We then performed overall survival analysis using clinical data in the TCGA

901

database. For the PMN-MDSC score calculation for each patient, we first calculated the percentage

902

of immune infiltrates from bulk RNA-Seq data using EPIC to remove the bias from non-immune

903

cells in the data

. We then divided the raw RSEM counts of genes in the PMN-MDSC signature

904

with the fraction of immune cells predicted by EPIC. We then log transformed the RSEM counts,

905

and Z-normalized the expression of PMN-MDSC related genes across the TCGA dataset. PMN-

906

MDSC score was the sum of Z-scores for all the genes in the PMN-MDSC signature

907

QUANTIFICATION AND STATISTICAL ANALYSIS

908

Statistical significance was assigned when p values were <0.05 using GraphPad Prism (v6.07 and

909

v9.0). Tests, number of animals (n), mean values, and statistical comparison groups are indicated

910

in the Figure legends.

911

Journal Pre-proof

912

References

913

914

Pavlova, Natalya N., and Thompson, Craig B. (2016). The Emerging Hallmarks of Cancer

915

Metabolism. Cell Metabolism

, 27-47. 10.1016/j.cmet.2015.12.006.

916

Altman, B.J., Stine, Z.E., and Dang, C.V. (2016). From Krebs to clinic: glutamine metabolism

917

to cancer therapy. Nature Reviews Cancer

, 619-634. 10.1038/nrc.2016.71.

918

Ahluwalia, G.S., Grem, J.L., Hao, Z., and Cooney, D.A. (1990). Metabolism and action of

919

amino acid analog anti-cancer agents. Pharmacology & Therapeutics

, 243-271.

920

10.1016/0163-7258(90)90094-I.

921

Moreadith, R.W., and Lehninger, A.L. (1984). The pathways of glutamate and glutamine

922

oxidation by tumor cell mitochondria. Role of mitochondrial NAD(P)+-dependent malic

923

enzyme. Journal of Biological Chemistry

259

, 6215-6221. 10.1016/S0021-9258(20)82128-

924

925

Young, V.R., and Ajami, A.M. (2001). Glutamine: The Emperor or His Clothes? The Journal

926

of Nutrition

131

, 2449S-2459S. 10.1093/jn/131.9.2449S.

927

Ovejera, A.A., Houchens, D.P., Catane, R., Sheridan, M.A., and Muggia, F.M. (1979). Efficacy

928

of 6-Diazo-5-oxo-l-norleucine and N-[N-

-Glutamyl-6-diazo-5-oxo-norleucinyl]-6-diazo-

929

5-oxo-norleucine against Experimental Tumors in Conventional and Nude Mice1. Cancer

930

Research

, 3220-3224.

931

Esslinger, C.S., Cybulski, K.A., and Rhoderick, J.F. (2005). Nγ

-Aryl glutamine analogues as

932

probes of the ASCT2 neutral amino acid transporter binding site. Bioorganic & Medicinal

933

Chemistry

, 1111-1118. 10.1016/j.bmc.2004.11.028.

934

Gross, M.I., Demo, S.D., Dennison, J.B., Chen, L., Chernov-Rogan, T., Goyal, B., Janes, J.R.,

935

Laidig, G.J., Lewis, E.R., Li, J., et al. (2014). Antitumor Activity of the Glutaminase Inhibitor

936

CB-839 in Triple-Negative Breast Cancer. Molecular Cancer Therapeutics

, 890-901.

937

10.1158/1535-7163.MCT-13-0870.

938

Poster, D.S., Bruno, S., Penta, J., Neil, G.L., and McGovren, J.P. (1981). Acivicin. An antitumor

939

antibiotic. Cancer Clinical Trials

, 327-330.

940

10.

Ma, H., Wu, J., Zhou, M., Wu, J., Wu, Z., Lin, L., Huang, N., Liao, W., and Sun, L. (2021).

941

Inhibition of Glutamine Uptake Improves the Efficacy of Cetuximab on Gastric Cancer.

942

Integrative Cancer Therapies

, 15347354211045349. 10.1177/15347354211045349.

943

11.

Van Geldermalsen, M., Quek, L.-E., Turner, N., Freidman, N., Pang, A., Guan, Y.F., Krycer, J.R.,

944

Ryan, R., Wang, Q., and Holst, J. (2018). Benzylserine inhibits breast cancer cell growth by

945

disrupting intracellular amino acid homeostasis and triggering amino acid response

946

pathways. BMC Cancer

, 689. 10.1186/s12885-018-4599-8.

947

Journal Pre-proof

12.

Hassanein, M., Hoeksema, M.D., Shiota, M., Qian, J., Harris, B.K., Chen, H., Clark, J.E., Alborn,

948

W.E., Eisenberg, R., and Massion, P.P. (2013). SLC1A5 mediates glutamine transport

949

required for lung cancer cell growth and survival. Clinical Cancer Research: An Official

950

Journal of the American Association for Cancer Research

, 560-570. 10.1158/1078-

951

0432.CCR-12-2334.

952

13.

Schulte, M.L., Fu, A., Zhao, P., Li, J., Geng, L., Smith, S.T., Kondo, J., Coffey, R.J., Johnson,

953

M.O., Rathmell, J.C., et al. (2018). Pharmacological blockade of ASCT2-dependent

954

glutamine transport leads to antitumor efficacy in preclinical models. Nature Medicine

955

194-202. 10.1038/nm.4464.

956

14.

Bröer, A., Fairweather, S., and Bröer, S. (2018). Disruption of Amino Acid Homeostasis by

957

Novel ASCT2 Inhibitors Involves Multiple Targets. Frontiers in Pharmacology

, 785.

958

10.3389/fphar.2018.00785.

959

15.

Magill, G.B., Myers, W.P., Reilly, H.C., Putnam, R.C., Magill, J.W., Sykes, M.P., Escher, G.C.,

960

Karnofsky, D.A., and Burchenal, J.H. (1957). Pharmacological and initial therapeutic

961

observations on 6-diazo-5-oxo-1-norleucine (DON) in human neoplastic disease. Cancer

962

, 1138-1150. 10.1002/1097-0142(195711/12)10:6<1138::aid-cncr2820100608>3.0.co;2-

963

964

16.

Leone, R.D., Zhao, L., Englert, J.M., Sun, I.-M., Oh, M.-H., Sun, I.-H., Arwood, M.L.,

965

Bettencourt, I.A., Patel, C.H., Wen, J., et al. (2019). Glutamine blockade induces divergent

966

metabolic programs to overcome tumor immune evasion. Science (New York, N.Y.)

366

967

1013-1021. 10.1126/science.aav2588.

968

17.

Oh, M.-H., Sun, I.-H., Zhao, L., Leone, R.D., Sun, I.-M., Xu, W., Collins, S.L., Tam, A.J., Blosser,

969

R.L., Patel, C.H., et al. (2020). Targeting glutamine metabolism enhances tumor-specific

970

immunity by modulating suppressive myeloid cells. Journal of Clinical Investigation

130

971

3865-3884. 10.1172/JCI131859.

972

18.

Ishida, Y., Agata, Y., Shibahara, K., and Honjo, T. (1992). Induced expression of PD-1, a novel

973

member of the immunoglobulin gene superfamily, upon programmed cell death. The

974

EMBO journal

, 3887-3895. 10.1002/j.1460-2075.1992.tb05481.x.

975

19.

Leach, D.R., Krummel, M.F., and Allison, J.P. (1996). Enhancement of antitumor immunity

976

CTLA-4

blockade.

Science

(New

York,

N.Y.)

271

1734-1736.

977

10.1126/science.271.5256.1734.

978

20.

Reinfeld, B.I., Madden, M.Z., Wolf, M.M., Chytil, A., Bader, J.E., Patterson, A.R., Sugiura, A.,

979

Cohen, A.S., Ali, A., Do, B.T., et al. (2021). Cell-programmed nutrient partitioning in the

980

tumour microenvironment. Nature

593

, 282-288. 10.1038/s41586-021-03442-1.

981

21.

Ma, G., Zhang, Z., Li, P., Zhang, Z., Zeng, M., Liang, Z., Li, D., Wang, L., Chen, Y., Liang, Y.,

982

and Niu, H. (2022). Reprogramming of glutamine metabolism and its impact on immune

983

response in the tumor microenvironment. Cell Communication and Signaling

, 114.

984

10.1186/s12964-022-00909-0.

985

22.

Varghese, S., Pramanik, S., Williams, L.J., Hodges, H.R., Hudgens, C.W., Fischer, G.M., Luo,

986

C.K., Knighton, B., Tan, L., Lorenzi, P.L., et al. (2021). The Glutaminase Inhibitor CB-839

987

Journal Pre-proof

(Telaglenastat) Enhances the Antimelanoma Activity of T-Cell-Mediated Immunotherapies.

988

Molecular Cancer Therapeutics

, 500-511. 10.1158/1535-7163.MCT-20-0430.

989

23.

Nabe, S., Yamada, T., Suzuki, J., Toriyama, K., Yasuoka, T., Kuwahara, M., Shiraishi, A.,

990

Takenaka, K., Yasukawa, M., and Yamashita, M. (2018). Reinforce the antitumor activity of

991

CD8+ T cells via glutamine restriction. Cancer Science

109

, 3737-3750. 10.1111/cas.13827.

992

24.

Carr, E.L., Kelman, A., Wu, G.S., Gopaul, R., Senkevitch, E., Aghvanyan, A., Turay, A.M., and

993

Frauwirth, K.A. (2010). Glutamine Uptake and Metabolism Are Coordinately Regulated by

994

ERK/MAPK during T Lymphocyte Activation. The Journal of Immunology

185

, 1037-1044.

995

10.4049/jimmunol.0903586.

996

25.

Byun, J.-K., Park, M., Lee, S., Yun, J.W., Lee, J., Kim, J.S., Cho, S.J., Jeon, H.-J., Lee, I.-K., Choi,

997

Y.-K., and Park, K.-G. (2020). Inhibition of Glutamine Utilization Synergizes with Immune

998

Checkpoint Inhibitor to Promote Antitumor Immunity. Molecular Cell

, 592-606.e598.

999

10.1016/j.molcel.2020.10.015.

1000

26.

Guo, C., You, Z., Shi, H., Sun, Y., Du, X., Palacios, G., Guy, C., Yuan, S., Chapman, N.M., Lim,

1001

S.A., et al. (2023). SLC38A2 and glutamine signalling in cDC1s dictate anti-tumour

1002

immunity. Nature

620

, 200-208. 10.1038/s41586-023-06299-8.

1003

27.

Schmid, F.A., and Roberts, J. (1974). Antineoplastic and toxic effects of Acinetobacter and

1004

Pseudomonas glutaminase-asparaginases. Cancer Chemotherapy Reports

, 829-840.

1005

28.

Covini, D., Tardito, S., Bussolati, O., R. Chiarelli, L., V. Pasquetto, M., Digilio, R., Valentini, G.,

1006

and Scotti, C. (2012). Expanding Targets for a Metabolic Therapy of Cancer: L-

1007

Asparaginase. Recent Patents on Anti-Cancer Drug Discovery

, 4-13.

1008

10.2174/157489212798358001.

1009

29.

Brown, G., Singer, A., Proudfoot, M., Skarina, T., Kim, Y., Chang, C., Dementieva, I.,

1010

Kuznetsova, E., Gonzalez, C.F., Joachimiak, A., et al. (2008). Functional and Structural

1011

Characterization of Four Glutaminases from Escherichia coli and Bacillus subtilis.

1012

Biochemistry

, 5724-5735. 10.1021/bi800097h.

1013

30.

Hartman, S.C. (1968). Glutaminase of Escherichia coli. Journal of Biological Chemistry

243

1014

853-863. 10.1016/S0021-9258(18)93595-7.

1015

31.

Okada, T., Suzuki, H., Wada, K., Kumagai, H., and Fukuyama, K. (2006). Crystal structures of

1016

-glutamyltranspeptidase from Escherichia coli , a key enzyme in glutathione

1017

metabolism, and its reaction intermediate. Proceedings of the National Academy of

1018

Sciences

103

, 6471-6476. 10.1073/pnas.0511020103.

1019

32.

Orlowski, M., and Meister, A. (1970). The gamma-glutamyl cycle: a possible transport

1020

system for amino acids. Proc Natl Acad Sci U S A

, 1248-1255. 10.1073/pnas.67.3.1248.

1021

33.

Boanca, G., Sand, A., and Barycki, J.J. (2006). Uncoupling the Enzymatic and Autoprocessing

1022

Activities of Helicobacter pylori γ

-Glutamyltranspeptidase. Journal of Biological Chemistry

1023

281

, 19029-19037. 10.1074/jbc.M603381200.

1024

34.

West, M.B., Chen, Y., Wickham, S., Heroux, A., Cahill, K., Hanigan, M.H., and Mooers, B.H.M.

1025

(2013). Nove

l Insights into Eukaryotic γ

-Glutamyltranspeptidase 1 from the Crystal

1026

Journal Pre-proof

Structure of the Glutamate-bound Human Enzyme. Journal of Biological Chemistry

288

1027

31902-31913. 10.1074/jbc.M113.498139.

1028

35.

Shibayama, K., Wachino, J.i., Arakawa, Y., Saidijam, M., Rutherford, N.G., and Henderson,

1029

P.J.F. (2007). Metabolism of glutamine and glutathione via γ‐glutamyltranspeptidase and

1030

glutamate transport in Helicobacter pylori : possible significance in the

1031

pathophysiology of the organism. Molecular Microbiology

, 396-406. 10.1111/j.1365-

1032

2958.2007.05661.x.

1033

36.

Boanca, G., Sand, A., Okada, T., Suzuki, H., Kumagai, H., Fukuyama, K., and Barycki, J.J.

1034

(2007). Autoprocessing of Helicobacter pylori γ

-Glutamyltranspeptidase Leads to the

1035

Formation of a Threonine-Threonine Catalytic Dyad. Journal of Biological Chemistry

282

1036

534-541. 10.1074/jbc.M607694200.

1037

37.

Gupta, V., Bhavanasi, S., Quadir, M., Singh, K., Ghosh, G., Vasamreddy, K., Ghosh, A.,

1038

Siahaan, T.J., Banerjee, S., and Banerjee, S.K. (2019). Protein PEGylation for cancer therapy:

1039

bench to bedside. Journal of Cell Communication and Signaling

, 319-330.

1040

10.1007/s12079-018-0492-0.

1041

38.

Eagle, H., Oyama, V.I., Levy, M., Horton, C.L., and Fleischman, R. (1956). The growth

1042

response of mammalian cells in tissue culture to L-glutamine and L-glutamic acid. The

1043

Journal of Biological Chemistry

218

, 607-616.

1044

39.

Rowe, W.B., Ronzio, R.A., and Meister, A. (1969). Inhibition of glutamine synthetase by

1045

methionine sulfoximine. Studies on methionine sulfoximine phosphate. Biochemistry

1046

2674-2680. 10.1021/bi00834a065.

1047

40.

Castle, J.C., Loewer, M., Boegel, S., de Graaf, J., Bender, C., Tadmor, A.D., Boisguerin, V.,

1048

Bukur, T., Sorn, P., Paret, C., et al. (2014). Immunomic, genomic and transcriptomic

1049

characterization of CT26 colorectal carcinoma. BMC genomics

, 190. 10.1186/1471-

1050

2164-15-190.

1051

41.

OLSON, C.K., and BINKLEY, F. (1950). Metabolism of glutathione. III. Enzymatic hydrolysis

1052

of cysteinylglycine. J Biol Chem

186

, 731-735.

1053

42.

Anderson, M.E., and Meister, A. (1980). Dynamic state of glutathione in blood plasma. J

1054

Biol Chem

255

, 9530-9533.

1055

43.

Zhang, P., Chan, W., Ang, I.L., Wei, R., Lam, M.M.T., Lei, K.M.K., and Poon, T.C.W. (2019).

1056

Revisiting Fragmentation Reactions of Protonated α

-Amino Acids by High-Resolution

1057

Electrospray Ionization Tandem Mass Spectrometry with Collision-Induced Dissociation.

1058

Sci Rep

, 6453. 10.1038/s41598-019-42777-8.

1059

44.

Chen, W.W., Freinkman, E., Wang, T., Birsoy, K., and Sabatini, D.M. (2016). Absolute

1060

Quantification of Matrix Metabolites Reveals the Dynamics of Mitochondrial Metabolism.

1061

Cell

166

, 1324-1337.e1311. 10.1016/j.cell.2016.07.040.

1062

45.

Yang, L., Achreja, A., Yeung, T.L., Mangala, L.S., Jiang, D., Han, C., Baddour, J., Marini, J.C.,

1063

Ni, J., Nakahara, R., et al. (2016). Targeting Stromal Glutamine Synthetase in Tumors

1064

Journal Pre-proof

Disrupts Tumor Microenvironment-Regulated Cancer Cell Growth. Cell Metab

, 685-700.

1065

10.1016/j.cmet.2016.10.011.

1066

46.

Linder, S.J., Bernasocchi, T., Martínez-Pastor, B., Sullivan, K.D., Galbraith, M.D., Lewis, C.A.,

1067

Ferrer, C.M., Boon, R., Silveira, G.G., Cho, H.M., et al. (2023). Inhibition of the proline

1068

metabolism rate-limiting enzyme P5CS allows proliferation of glutamine-restricted cancer

1069

cells. Nat Metab

, 2131-2147. 10.1038/s42255-023-00919-3.

1070

47.

Diehl, F.F., Miettinen, T.P., Elbashir, R., Nabel, C.S., Darnell, A.M., Do, B.T., Manalis, S.R., Lewis,

1071

C.A., and Vander Heiden, M.G. (2022). Nucleotide imbalance decouples cell growth from

1072

cell proliferation. Nature Cell Biology

, 1252-1264. 10.1038/s41556-022-00965-1.

1073

48.

Papež, M., Jiménez Lancho, V., Eisenhut, P., Motheramgari, K., and Borth, N. (2023). SLAM‐

1074

seq reveals early transcriptomic response mechanisms upon glutamine deprivation in

1075

Chinese hamster ovary cells. Biotechnology and Bioengineering

120

, 970-986.

1076

10.1002/bit.28320.

1077

49.

Gameiro, P.A., and Struhl, K. (2018). Nutrient Deprivation Elicits a Transcriptional and

1078

Translational Inflammatory Response Coupled to Decreased Protein Synthesis. Cell

1079

Reports

, 1415-1424. 10.1016/j.celrep.2018.07.021.

1080

50.

Subramanian, A., Tamayo, P., Mootha, V.K., Mukherjee, S., Ebert, B.L., Gillette, M.A.,

1081

Paulovich, A., Pomeroy, S.L., Golub, T.R., Lander, E.S., and Mesirov, J.P. (2005). Gene set

1082

enrichment analysis: a knowledge-based approach for interpreting genome-wide

1083

expression profiles. Proceedings of the National Academy of Sciences of the United States

1084

of America

102

, 15545-15550. 10.1073/pnas.0506580102.

1085

51.

Bester, Assaf C., Roniger, M., Oren, Yifat S., Im, Michael M., Sarni, D., Chaoat, M., Bensimon,

1086

A., Zamir, G., Shewach, Donna S., and Kerem, B. (2011). Nucleotide Deficiency Promotes

1087

Genomic Instability in Early Stages of Cancer Development. Cell

145

, 435-446.

1088

10.1016/j.cell.2011.03.044.

1089

52.

Lin, J.J., and Dutta, A. (2007). ATR Pathway Is the Primary Pathway for Activating G2/M

1090

Checkpoint Induction After Re-replication. Journal of Biological Chemistry

282

, 30357-

1091

30362. 10.1074/jbc.M705178200.

1092

53.

Gaglio, D., Soldati, C., Vanoni, M., Alberghina, L., and Chiaradonna, F. (2009). Glutamine

1093

Deprivation Induces Abortive S-Phase Rescued by Deoxyribonucleotides in K-Ras

1094

Transformed Fibroblasts. PLoS ONE

, e4715. 10.1371/journal.pone.0004715.

1095

54.

Tran, T.Q., Ishak Gabra, M.B., Lowman, X.H., Yang, Y., Reid, M.A., Pan, M., O’Connor, T.R.,

1096

and Kong, M. (2017). Glutamine deficiency induces DNA alkylation damage and sensitizes

1097

cancer cells to alkylating agents through inhibition of ALKBH enzymes. PLOS Biology

1098

e2002810. 10.1371/journal.pbio.2002810.

1099

55.

Yuneva, M., Zamboni, N., Oefner, P., Sachidanandam, R., and Lazebnik, Y. (2007). Deficiency

1100

in glutamine but not glucose induces MYC-dependent apoptosis in human cells. The

1101

Journal of Cell Biology

178

, 93-105. 10.1083/jcb.200703099.

1102

Journal Pre-proof

56.

Liu, Y.-C., Li, F., Handler, J., Huang, C.R.L., Xiang, Y., Neretti, N., Sedivy, J.M., Zeller, K.I., and

1103

Dang, C.V. (2008). Global Regulation of Nucleotide Biosynthetic Genes by c-Myc. PLoS ONE

1104

, e2722. 10.1371/journal.pone.0002722.

1105

57.

Tardito, S., Oudin, A., Ahmed, S.U., Fack, F., Keunen, O., Zheng, L., Miletic, H., Sakariassen,

1106

P.Ø., Weinstock, A., Wagner, A., et al. (2015). Glutamine synthetase activity fuels nucleotide

1107

biosynthesis and supports growth of glutamine-restricted glioblastoma. Nature Cell

1108

Biology

, 1556-1568. 10.1038/ncb3272.

1109

58.

Nguyen, T.V., Lee, J.E., Sweredoski, M.J., Yang, S.-J., Jeon, S.-J., Harrison, J.S., Yim, J.-H., Lee,

1110

S.G., Handa, H., Kuhlman, B., et al. (2016). Glutamine Triggers Acetylation-Dependent

1111

Degradation of Glutamine Synthetase via the Thalidomide Receptor Cereblon. Molecular

1112

Cell

, 809-820. 10.1016/j.molcel.2016.02.032.

1113

59.

Tsai, P.-Y., Lee, M.-S., Jadhav, U., Naqvi, I., Madha, S., Adler, A., Mistry, M., Naumenko, S.,

1114

Lewis, C.A., Hitchcock, D.S., et al. (2021). Adaptation of pancreatic cancer cells to nutrient

1115

deprivation is reversible and requires glutamine synthetase stabilization by mTORC1.

1116

Proceedings of the National Academy of Sciences

118

, e2003014118.

1117

10.1073/pnas.2003014118.

1118

60.

Shor, B., Zhang, W.-G., Toral-Barza, L., Lucas, J., Abraham, R.T., Gibbons, J.J., and Yu, K.

1119

(2008). A New Pharmacologic Action of CCI-779 Involves FKBP12-Independent Inhibition

1120

of mTOR Kinase Activity and Profound Repression of Global Protein Synthesis. Cancer

1121

Research

, 2934-2943. 10.1158/0008-5472.CAN-07-6487.

1122

61.

Yoshida, H., Lareau, C.A., Ramirez, R.N., Rose, S.A., Maier, B., Wroblewska, A., Desland, F.,

1123

Chudnovskiy, A., Mortha, A., Dominguez, C., et al. (2019). The cis-Regulatory Atlas of the

1124

Mouse Immune System. Cell

176

, 897-912.e820. 10.1016/j.cell.2018.12.036.

1125

62.

Newman, A.M., Steen, C.B., Liu, C.L., Gentles, A.J., Chaudhuri, A.A., Scherer, F., Khodadoust,

1126

M.S., Esfahani, M.S., Luca, B.A., Steiner, D., et al. (2019). Determining cell type abundance

1127

and expression from bulk tissues with digital cytometry. Nature Biotechnology

, 773-

1128

782. 10.1038/s41587-019-0114-2.

1129

63.

Mosser, D.M., and Edwards, J.P. (2008). Exploring the full spectrum of macrophage

1130

activation. Nature Reviews Immunology

, 958-969. 10.1038/nri2448.

1131

64.

Biswas, S.K., and Mantovani, A. (2010). Macrophage plasticity and interaction with

1132

lymphocyte subsets: cancer as a paradigm. Nature Immunology

, 889-896.

1133

10.1038/ni.1937.

1134

65.

Sun, H.-W., Wu, W.-C., Chen, H.-T., Xu, Y.-T., Yang, Y.-Y., Chen, J., Yu, X.-J., Wang, Z., Shuang,

1135

Z.-Y., and Zheng, L. (2021). Glutamine Deprivation Promotes the Generation and

1136

Mobilization of MDSCs by Enhancing Expression of G-CSF and GM-CSF. Frontiers in

1137

Immunology

, 616367. 10.3389/fimmu.2020.616367.

1138

66.

Ruffolo, L.I., Jackson, K.M., Kuhlers, P.C., Dale, B.S., Figueroa Guilliani, N.M., Ullman, N.A.,

1139

Burchard, P.R., Qin, S.S., Juviler, P.G., Keilson, J.M., et al. (2022). GM-CSF drives

1140

myelopoiesis, recruitment and polarisation of tumour-associated macrophages in

1141

Journal Pre-proof

cholangiocarcinoma and systemic blockade facilitates antitumour immunity. Gut

, 1386-

1142

1398. 10.1136/gutjnl-2021-324109.

1143

67.

Gabrilovich, D.I., Velders, M.P., Sotomayor, E.M., and Kast, W.M. (2001). Mechanism of

1144

immune dysfunction in cancer mediated by immature Gr-1+ myeloid cells. J Immunol

166

1145

5398-5406. 10.4049/jimmunol.166.9.5398.

1146

68.

Barbie, D.A., Tamayo, P., Boehm, J.S., Kim, S.Y., Moody, S.E., Dunn, I.F., Schinzel, A.C., Sandy,

1147

P., Meylan, E., Scholl, C., et al. (2009). Systematic RNA interference reveals that oncogenic

1148

KRAS-driven cancers require TBK1. Nature

462

, 108-112. 10.1038/nature08460.

1149

69.

Veglia, F., Sanseviero, E., and Gabrilovich, D.I. (2021). Myeloid-derived suppressor cells in

1150

the era of increasing myeloid cell diversity. Nature Reviews Immunology

, 485-498.

1151

10.1038/s41577-020-00490-y.

1152

70.

Perez, C., Botta, C., Zabaleta, A., Puig, N., Cedena, M.-T., Goicoechea, I., Alameda, D., San

1153

José-Eneriz, E., Merino, J., Rodríguez-Otero, P., et al. (2020). Immunogenomic identification

1154

and characterization of granulocytic myeloid-derived suppressor cells in multiple

1155

myeloma. Blood

136

, 199-209. 10.1182/blood.2019004537.

1156

71.

Ouzounova, M., Lee, E., Piranlioglu, R., El Andaloussi, A., Kolhe, R., Demirci, M.F., Marasco,

1157

D., Asm, I., Chadli, A., Hassan, K.A., et al. (2017). Monocytic and granulocytic myeloid

1158

derived suppressor cells differentially regulate spatiotemporal tumour plasticity during

1159

metastatic cascade. Nature Communications

, 14979. 10.1038/ncomms14979.

1160

72.

Jackson, C., Cherry, C., Bom, S., Dykema, A.G., Thompson, E., Zheng, M., Ji, Z., Hou, W., Li,

1161

R., Zhang, H., et al. (2023). Distinct Myeloid Derived Suppressor Cell Populations Promote

1162

Tumor

Aggression

Glioblastoma.

preprint

Immunology.

2023/03/27/.

1163

http://biorxiv.org/lookup/doi/10.1101/2023.03.26.534192.

1164

73.

Spiegel, A., Brooks, M.W., Houshyar, S., Reinhardt, F., Ardolino, M., Fessler, E., Chen, M.B.,

1165

Krall, J.A., DeCock, J., Zervantonakis, I.K., et al. (2016). Neutrophils Suppress Intraluminal

1166

NK Cell

–

Mediated Tumor Cell Clearance and Enhance Extravasation of Disseminated

1167

Carcinoma Cells. Cancer Discovery

, 630-649. 10.1158/2159-8290.CD-15-1157.

1168

74.

Veglia, F., Tyurin, V.A., Blasi, M., De Leo, A., Kossenkov, A.V., Donthireddy, L., To, T.K.J.,

1169

Schug, Z., Basu, S., Wang, F., et al. (2019). Fatty acid transport protein 2 reprograms

1170

neutrophils in cancer. Nature

569

, 73-78. 10.1038/s41586-019-1118-2.

1171

75.

Racle, J., De Jonge, K., Baumgaertner, P., Speiser, D.E., and Gfeller, D. (2017). Simultaneous

1172

enumeration of cancer and immune cell types from bulk tumor gene expression data. eLife

1173

, e26476. 10.7554/eLife.26476.

1174

76.

Ebi, H., Tomida, S., Takeuchi, T., Arima, C., Sato, T., Mitsudomi, T., Yatabe, Y., Osada, H., and

1175

Takahashi, T. (2009). Relationship of Deregulated Signaling Converging onto mTOR with

1176

Prognosis and Classification of Lung Adenocarcinoma Shown by Two Independent In

1177

silico Analyses. Cancer Research

, 4027-4035. 10.1158/0008-5472.CAN-08-3403.

1178

77.

Pavlova, N.N., Hui, S., Ghergurovich, J.M., Fan, J., Intlekofer, A.M., White, R.M., Rabinowitz,

1179

J.D., Thompson, C.B., and Zhang, J. (2018). As Extracellular Glutamine Levels Decline,

1180

Journal Pre-proof

Asparagine Becomes an Essential Amino Acid. Cell Metabolism

, 428-438.e425.

1181

10.1016/j.cmet.2017.12.006.

1182

78.

Wu, J., Li, G., Li, L., Li, D., Dong, Z., and Jiang, P. (2021). Asparagine enhances LCK signalling

1183

to potentiate CD8+ T-cell activation and anti-tumour responses. Nature Cell Biology

1184

75-86. 10.1038/s41556-020-00615-4.

1185

79.

Liu, P.-S., Wang, H., Li, X., Chao, T., Teav, T., Christen, S., Di Conza, G., Cheng, W.-C., Chou,

1186

C.-

H., Vavakova, M., et al. (2017). α

-ketoglutarate orchestrates macrophage activation

1187

through metabolic and epigenetic reprogramming. Nature Immunology

, 985-994.

1188

10.1038/ni.3796.

1189

80.

Tannahill, G.M., Curtis, A.M., Adamik, J., Palsson-McDermott, E.M., McGettrick, A.F., Goel,

1190

G., Frezza, C., Bernard, N.J., Kelly, B., Foley, N.H., et al. (2013). Succinate is an inflammatory

1191

signal that induces IL-

1β through HIF

1α. Nature

496

, 238-242. 10.1038/nature11986.

1192

81.

Palmieri, E.M., Menga, A., Martín-Pérez, R., Quinto, A., Riera-Domingo, C., De Tullio, G.,

1193

Hooper, D.C., Lamers, W.H., Ghesquière, B., McVicar, D.W., et al. (2017). Pharmacologic or

1194

Genetic Targeting of Glutamine Synthetase Skews Macrophages toward an M1-like

1195

Phenotype and Inhibits Tumor Metastasis. Cell Reports

, 1654-1666.

1196

10.1016/j.celrep.2017.07.054.

1197

82.

Berger, U.V., and Hediger, M.A. (2006). Distribution of the glutamate transporters GLT-1

1198

(SLC1A2) and GLAST (SLC1A3) in peripheral organs. Anatomy and Embryology

211

, 595-

1199

606. 10.1007/s00429-006-0109-x.

1200

83.

Chan, W.K., Lorenzi, P.L., Anishkin, A., Purwaha, P., Rogers, D.M., Sukharev, S., Rempe, S.B.,

1201

and Weinstein, J.N. (2014). The glutaminase activity of l-asparaginase is not required for

1202

anticancer activity against ASNS-negative cells. Blood

123

, 3596-3606. 10.1182/blood-

1203

2013-10-535112.

1204

84.

Chan, W.-K., Horvath, T.D., Tan, L., Link, T., Harutyunyan, K.G., Pontikos, M.A., Anishkin, A.,

1205

Du, D., Martin, L.A., Yin, E., et al. (2019). Glutaminase Activity of <span style="font-

1206

variant:small-caps;">L -Asparaginase Contributes to Durable Preclinical Activity

1207

against Acute Lymphoblastic Leukemia. Molecular Cancer Therapeutics

, 1587-1592.

1208

10.1158/1535-7163.MCT-18-1329.

1209

85.

Chimenti, M.S., Triggianese, P., Conigliaro, P., Candi, E., Melino, G., and Perricone, R. (2015).

1210

The interplay between inflammation and metabolism in rheumatoid arthritis. Cell Death &

1211

Disease

, e1887-e1887. 10.1038/cddis.2015.246.

1212

86.

Oliveira, G., De Abreu, M., Pelosi, P., and Rocco, P. (2016). Exogenous Glutamine in

1213

Respiratory Diseases: Myth or Reality? Nutrients

, 76. 10.3390/nu8020076.

1214

87.

Baazim, H., Antonio-Herrera, L., and Bergthaler, A. (2022). The interplay of immunology

1215

and cachexia in infection and cancer. Nature Reviews Immunology

, 309-321.

1216

10.1038/s41577-021-00624-w.

1217

88.

Hamanaka, R.B., O'Leary, E.M., Witt, L.J., Tian, Y., Gökalp, G.A., Meliton, A.Y., Dulin, N.O., and

1218

Mutlu, G.M. (2019). Glutamine Metabolism Is Required for Collagen Protein Synthesis in

1219

Journal Pre-proof

Lung Fibroblasts. American Journal of Respiratory Cell and Molecular Biology

, 597-606.

1220

10.1165/rcmb.2019-0008OC.

1221

89.

Sheng, S.J., Kraft, J.J., and Schuster, S.M. (1993). A Specific Quantitative Colorimetric Assay

1222

for L-Asparagine. Analytical Biochemistry

211

, 242-249. 10.1006/abio.1993.1264.

1223

90.

Putluri, N., Shojaie, A., Vasu, V.T., Vareed, S.K., Nalluri, S., Putluri, V., Thangjam, G.S., Panzitt,

1224

K., Tallman, C.T., Butler, C., et al. (2011). Metabolomic profiling reveals potential markers

1225

and bioprocesses altered in bladder cancer progression. Cancer Res

, 7376-7386.

1226

10.1158/0008-5472.CAN-11-1154.

1227

91.

Vantaku, V., Donepudi, S.R., Piyarathna, D.W.B., Amara, C.S., Ambati, C.R., Tang, W., Putluri,

1228

V., Chandrashekar, D.S., Varambally, S., Terris, M.K., et al. (2019). Large-scale profiling of

1229

serum metabolites in African American and European American patients with bladder

1230

cancer reveals metabolic pathways associated with patient survival. Cancer

125

, 921-932.

1231

10.1002/cncr.31890.

1232

92.

Arunachalam, A.R., Samuel, S.S., Mani, A., Maynard, J.P., Stayer, K.M., Dybbro, E., Narayanan,

1233

S., Biswas, A., Pathan, S., Soni, K., et al. (2023). P2Y2 purinergic receptor gene deletion

1234

protects mice from bacterial endotoxin and sepsis-associated liver injury and mortality.

1235

Am J Physiol Gastrointest Liver Physiol

325

, G471-G491. 10.1152/ajpgi.00090.2023.

1236

93.

Dobin, A., Davis, C.A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M.,

1237

and Gingeras, T.R. (2013). STAR: ultrafast universal RNA-seq aligner. Bioinformatics

1238

(Oxford, England)

, 15-21. 10.1093/bioinformatics/bts635.

1239

94.

Love, M.I., Huber, W., and Anders, S. (2014). Moderated estimation of fold change and

1240

dispersion for RNA-seq data with DESeq2. Genome Biology

, 550. 10.1186/s13059-014-

1241

0550-8.

1242

95.

Shannon, P., Markiel, A., Ozier, O., Baliga, N.S., Wang, J.T., Ramage, D., Amin, N.,

1243

Schwikowski, B., and Ideker, T. (2003). Cytoscape: a software environment for integrated

1244

models of biomolecular interaction networks. Genome Research

, 2498-2504.

1245

10.1101/gr.1239303.

1246

96.

Li, B., and Dewey, C.N. (2011). RSEM: accurate transcript quantification from RNA-Seq data

1247

with or without a reference genome. BMC Bioinformatics

, 323. 10.1186/1471-2105-12-

1248

323.

1249

1250

Journal Pre-proof

KEY RESOURCES TABLE

The table highlights the reagents, genetically modified organisms and strains, cell lines, software,
instrumentation, and source data

essential

to reproduce results presented in the manuscript. Depending

on the nature of the study, this may include standard laboratory materials (i.e., food chow for metabolism
studies, support material for catalysis studies), but the table is

not

meant to be a comprehensive list of all

materials and resources used (e.g., essential chemicals such as standard solvents, SDS, sucrose, or
standard culture media do not need to be listed in the table).

However, please note that items in the

table must also be reported in the method details section within the context of their use.

ALL references cited in the key resources table must be included in the main references list.

Citations should be formatted as “Author name et al.

” (e.g., Smith et al.

), with the citation number

matching that in the main references list.

Please report the information as follows:

•

REAGENT or RESOURCE:

Provide the full descriptive name of the item so that it can be identified

and linked with its description in the manuscript (e.g., provide version number for software, host
source for antibody, strain name). See the sample tables

at the end of this document for examples of

how to report reagents.

In the

experimental models sections

(applicable only to experimental life science

studies), please include all models used in the paper and describe each line/strain as
model organism: name used for strain/line in paper: genotype (e.g.,
Mouse: OXTR

fl/fl

: B6.129(SJL)-Oxtr

tm1.1Wsy/J

The

Biological samples section

(applicable only to experimental life science studies)

should list all samples obtained in this study or from commercial sources or biological
repositories.

You may list a maximum of 10 oligonucleotides or RNA sequences

in the table. If

there are more than 10 to report, please provide this information as a supplemental
document and reference the file (e.g., See Table S1 for XX) in the key resources table.

Deposited data

should include both newly deposited data from this manuscript and

existing datasets that were used in the manuscript.

Please include software and code mentioned in the method details or data and code

availability section under

software and algorithms

Any item that does not fit the existing subheadings should be added to the

“other”

section.

Please do not add your own subheadings.

•

SOURCE:

Report the company, manufacturer, or individual that provided the item or where the item

can be obtained (e.g., stock center or repository).

For materials distributed by Addgene, please cite the article describing the plasmid and

include “Addgene” as part of the identifier.

If an item is from another lab, please include the name of the principal investigator and a

citation if it has been previously published.

If the material is being reported for the first time in the current paper, please indicate as

“this paper.”

For software, please provide the company name if it is commercially available or cite the

paper in which it has been initially described.

•

IDENTIFIER:

Include catalog numbers

(entered in the column as “Cat#” followed by the number,

e.g., Cat#3879S). Where available, please include unique entities such as RRIDs, Model Organism
Database numbers, and accession numbers preceded by database abbreviations such as PDB or

Journal Pre-proof

CCDC). Please ensure the accuracy of the identifiers, as they are essential for generation of
hyperlinks to external sources when available. For more information about data sharing policies and
a list of recommended data repositories for abbreviations, please see the Cell Press

Author’s guide

to data sharing.

For antibodies, if applicable and available, please also include the lot number or clone

identity.

For software or data resources, please include the URL where the resource can be

downloaded.

When listing more than one identifier for the same item, use semicolons to separate them

(e.g., Cat#3879S; RRID: AB_2255011).

If an identifier is not available,

please enter “N/A” in the column.

A NOTE ABOUT RRIDs:

we highly recommend using RRIDs as the identifier (in

particular for antibodies and organisms but also for software tools and databases). For
more details on how to obtain or generate an RRID for existing or newly generated
resources, please visit the RII or search for RRIDs.

Please use the empty table that follows to organize the information under the provided subheadings and
skip sections that are not relevant to your study. To add a row, place the cursor at the end of the row
above where you would like to add the row, just outside the right border of the table. Then press the
ENTER key to add the row. Alternatively, you can right-click on your mouse and choose Insert > Insert
rows above or Insert rows below. Please delete empty rows. Each entry must be on a separate row; do
not list multiple items in a single table cell. Please see the sample tables at the end of this document for
relevant examples in the life and physical sciences of how reagents and instrumentation should be cited.

Journal Pre-proof

TABLE FOR AUTHOR TO COMPLETE

Please do not add custom subheadings. If you wish to make an entry that does not fall into one of the
subheadings below, please contact your handling editor or add it under

the “other

” subheading

. Any subheadings

not relevant to your study can be skipped. (NOTE: references should be in numbered style, e.g., Smith et al.

)

Key resources table

REAGENT or RESOURCE

SOURCE

IDENTIFIER

Antibodies

Anti-Mouse CD16/CD52

BD Biosciences

#553142

Anti-Mouse CD45-BUV395

BD Biosciences

#564279

Anti-Mouse CD11b-BV421

Biolegend

#101235

Anti-Mouse CD11c-BV785

Biolegend

#117335

Anti-Mouse F4/80-APC/Cy7

Biolegend

#123117

Anti-Mouse Ly6C-PE

Biolegend

#128007

Anti-Mouse Ly6G-PerCP/Cy5.5

Biolegend

#127615

Anti-Mouse CD206-AF488

Biolegend

#141709

Anti-Mouse CD86-AF700

Biolegend

#105023

Anti-Mouse CD3-APC

Biolegend

#100235

Anti-Mouse CD4-AF594

Biolegend

#100446

Anti-Mouse CD8-PerCP/Cy5

Biolegend

#100731

Anti-Mouse NKp45-e450

Invitrogen

#48-3351-82

Anti-Mouse CD19-APC-Cy7

Biolegend

#115519

Anti-Mouse FOXP3-AF488

Biolegend

#126405

Bacterial and virus strains

E. coli

Rosetta-2

Novagen

#71402

E. coli

MC1061

ThermoFisher

#C66303

Biological samples

Chemicals, peptides, and recombinant proteins

Chloramphenicol

Sigma

#C0738

Kanamycin

Sigma

#K1377

isopropyl

-D-thiogalactopyranoside (IPTG)

FisherScientific

#BP1755-10

Imidazole

Sigma

#I0250

Universal nuclease

Thermofisher

#88700

Protease inhibitor cocktail

Sigma

#P8849

Triton X-114

Sigma

#X114

Methoxy-PEG-CH

COO-NHS, Mw 5,000

Sunbright

#ME-050AS

𝛄

(p-nitroanilide) (

𝛄

GPNA)

Sigma

#G1135

Ninhydrin reagent

Sigma

#151173

RPMI-1640

Corning

#10-040-CV

Dialyzed Fetal bovine serum (dFBS)

Sigma

#F0392

Fetal bovine serum (FBS)

RnD Biosystems

#S11150

3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium
bromide (MTT)

VWR

#97062-376

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Temsirolimus

Selleckchem

#S1044

Sytox green

ThermoFisher

#S7020

Collagenase D

Roche

#11088858001

DNAse

Sigma

#DN25

Live/dead aqua die

ThermoFisher

#L34957)

L-Methionine-sulfoximine (MSO)

Sigma

#M5379

Critical commercial assays

bicinchoninic acid (BCA) assay

ThermoFisher

#23225

chromogenic endotoxin detection kit

ThermoFisher

#A39552S

Promega Glutamine/Glutamate Glo kit

Promega

#J8021

Total glutathione assay kit

Cayman

#703002

RNeasy kit

Qiagen

#74104)

cDNA reverse transcription kit

Invitrogen

#4368813

SsoFastTM EvaGreen® Supermix with Low ROX

Bio-Rad

# 1725211

Deposited data

Raw and analyzed RNA-seq data

This Paper

GEO: GSE247472

Raw data

Tsai-P-Y et al.

GEO: GSE144833

The Cancer Genome Atlas

National Cancer
Institute (NCI) and
National Broad
Institute Human
Genome Research
Institute (NHGRI)

www.firebrowse.org

Experimental models: Cell lines

CT26

ATCC

#CRL-2638

4T1

ATCC

#CRL-2539

MC38

Sigma-Aldrich

#SCC172

Experimental models: Organisms/strains

Mouse: BALB/c

Charles River
Laboratories

BALB/cAnNCrl

Oligonucleotides

Recombinant DNA

hpGGT gene sequence

IDT

N/A

pET-28a

Novagen

# 69864

Software and algorithms

GraphPad Prism

GraphPad

V9.0

STAR alignment

Dobin, A et al.

https://github.com/al
exdobin/STAR

R (v 4.0.1)

r-project.org

Journal Pre-proof

DESeq2

Love M.I. et al.

https://bioconductor.
org/packages/releas
e/bioc/html/DESeq2.
html

GSEA

UC San Diego and
Broad Institute

gsea-msigdb.org

ssGSEA

Barbie, D.A. et al.

https://github.com/br
oadinstitute/ssGSEA
2.0

Cytoscape

Shannon. P et al.

https://cytoscape.org

RSEM

Li, B. et al.

https://github.com/de
weylab/RSEM

CIBERSORTx

Newman, A.M. et al.

https://cibersortx.sta
nford.edu

AriaMX

Agilent

Version 2.1

EPIC

Racle, J. et al.

https://github.com/Gf
ellerLab/EPIC

Biorender

www.biorender.com

FlowJo

BD Biosciences

FlowJo v10

Other

10 kDa NMWCO centrifugal filters

Sartorius

#VS2002

Journal Pre-proof

SAMPLE TABLES FOR AUTHOR REFERENCE

LIFE SCIENCES

REAGENT or RESOURCE

SOURCE

IDENTIFIER

Antibodies

Rabbit monoclonal anti-Snail

Cell Signaling Technology

Cat#3879S; RRID:
AB_2255011

Mouse monoclonal anti-Tubulin (clone DM1A)

Sigma-Aldrich

Cat#T9026; RRID:
AB_477593

Rabbit polyclonal anti-BMAL1

This paper

N/A

Bacterial and virus strains

pAAV-hSyn-DIO-hM3D(Gq)-mCherry

Krashes et al.

Addgene AAV5;
44361-AAV5

AAV5-EF1a-DIO-hChR2(H134R)-EYFP

Hope Center Viral Vectors
Core

N/A

Cowpox virus Brighton Red

BEI Resources

NR-88

Zika-SMGC-1, GENBANK: KX266255

Isolated from patient
(Wang et al.

)

N/A

Staphylococcus aureus

ATCC

ATCC 29213

Streptococcus pyogenes

: M1 serotype strain: strain

SF370; M1 GAS

ATCC

ATCC 700294

Biological samples

Healthy adult BA9 brain tissue

University of Maryland
Brain & Tissue Bank;
http://medschool.umarylan
d.edu/btbank/

Cat#UMB1455

Human hippocampal brain blocks

New York Brain Bank

http://nybb.hs.colum
bia.edu/

Patient-derived xenografts (PDX)

Children's Oncology
Group Cell Culture and
Xenograft Repository

http://cogcell.org/

Chemicals, peptides, and recombinant proteins

MK-2206 AKT inhibitor

Selleck Chemicals

S1078; CAS:
1032350-13-2

SB-505124

Sigma-Aldrich

S4696; CAS:
694433-59-5 (free
base)

Picrotoxin

Sigma-Aldrich

P1675; CAS: 124-
87-8

Human TGF-

R&D

240-B; GenPept:
P01137

Activated S6K1

Millipore

Cat#14-486

GST-BMAL1

Novus

Cat#H00000406-
P01

Critical commercial assays

EasyTag EXPRESS 35S Protein Labeling Kit

PerkinElmer

NEG772014MC

CaspaseGlo 3/7

Promega

G8090

TruSeq ChIP Sample Prep Kit

Illumina

IP-202-1012

Deposited data

Journal Pre-proof

Raw and analyzed data

This paper

GEO: GSE63473

B-RAF RBD (apo) structure

This paper

PDB: 5J17

Human reference genome NCBI build 37, GRCh37

Genome Reference
Consortium

http://www.ncbi.nlm.
nih.gov/projects/gen
ome/assembly/grc/h
uman/

Nanog STILT inference

This paper; Mendeley
Data

http://dx.doi.org/10.1
7632/wx6s4mj7s8.2

Affinity-based mass spectrometry performed with 57
genes

This paper; Mendeley
Data

Table S8;
http://dx.doi.org/10.1
7632/5hvpvspw82.1

Experimental models: Cell lines

Hamster: CHO cells

ATCC

CRL-11268

D. melanogaster

: Cell line S2: S2-DRSC

Laboratory of Norbert
Perrimon

FlyBase:
FBtc0000181

Human: Passage 40 H9 ES cells

MSKCC stem cell core
facility

N/A

Human: HUES 8 hESC line (NIH approval number
NIHhESC-09-0021)

HSCI iPS Core

hES Cell Line:
HUES-8

Experimental models: Organisms/strains

C. elegans

: Strain BC4011: srl-1(s2500) II; dpy-

18(e364) III; unc-46(e177)rol-3(s1040) V.

Caenorhabditis Genetics
Center

WB Strain: BC4011;
WormBase:
WBVar00241916

D. melanogaster

: RNAi of Sxl: y[1] sc[*] v[1];

P{TRiP.HMS00609}attP2

Bloomington Drosophila
Stock Center

BDSC:34393;
FlyBase:
FBtp0064874

S. cerevisiae

: Strain background: W303

ATCC

ATTC: 208353

Mouse: R6/2: B6CBA-Tg(HDexon1)62Gpb/3J

The Jackson Laboratory

JAX: 006494

Mouse: OXTRfl/fl: B6.129(SJL)-Oxtr

tm1.1Wsy

The Jackson Laboratory

RRID:
IMSR_JAX:008471

Zebrafish: Tg(Shha:GFP)t10: t10Tg

Neumann and Nuesslein-
Volhard

ZFIN: ZDB-GENO-
060207-1

Arabidopsis

: 35S::PIF4-YFP, BZR1-CFP

Wang et al.

N/A

Arabidopsis

: JYB1021.2:

pS24(AT5G58010)::cS24:GFP(-G):NOS #1

NASC

NASC ID: N70450

Oligonucleotides

siRNA targeting sequence: PIP5K I alpha #1:
ACACAGUACUCAGUUGAUA

This paper

N/A

Primers for XX, see Table SX

This paper

N/A

Primer: GFP/YFP/CFP Forward:
GCACGACTTCTTCAAGTCCGCCATGCC

This paper

N/A

Morpholino: MO-pax2a
GGTCTGCTTTGCAGTGAATATCCAT

Gene Tools

ZFIN: ZDB-
MRPHLNO-061106-
5

ACTB (hs01060665_g1)

Life Technologies

Cat#4331182

RNA sequence: hnRNPA1_ligand:
UAGGGACUUAGGGUUCUCUCUAGGGACUUAG
GGUUCUCUCUAGGGA

This paper

N/A

Recombinant DNA

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pLVX-Tight-Puro (TetOn)

Clonetech

Cat#632162

Plasmid: GFP-Nito

This paper

N/A

cDNA GH111110

Drosophila Genomics
Resource Center

DGRC:5666;
FlyBase:FBcl013041
5

AAV2/1-hsyn-GCaMP6- WPRE

Chen et al.

N/A

Mouse raptor: pLKO mouse shRNA 1 raptor

Thoreen et al.

Addgene Plasmid
#21339

Software and algorithms

ImageJ

Schneider et al.

https://imagej.nih.go
v/ij/

Bowtie2

Langmead and Salzberg

http://bowtie-
bio.sourceforge.net/
bowtie2/index.shtml

Samtools

Li et al.

http://samtools.sourc
eforge.net/

Weighted Maximal Information Component Analysis
v0.9

Rau et al.

https://github.com/C
hristophRau/wMICA

ICS algorithm

This paper; Mendeley
Data

http://dx.doi.org/10.1
7632/5hvpvspw82.1

Other

Sequence data, analyses, and resources related to
the ultra-deep sequencing of the AML31 tumor,
relapse, and matched normal

This paper

http://aml31.genome
.wustl.edu

Resource website for the AML31 publication

This paper

https://github.com/ch
risamiller/aml31Supp
Site

Journal Pre-proof

PHYSICAL SCIENCES

REAGENT or RESOURCE

SOURCE

IDENTIFIER

Chemicals, peptides, and recombinant proteins

QD605 streptavidin conjugated quantum dot

Thermo Fisher Scientific

Cat#Q10101MP

Platinum black

Sigma-Aldrich

Cat#205915

Sodium formate BioUltra, ≥99.0% (NT)

Sigma-Aldrich

Cat#71359

Chloramphenicol

Sigma-Aldrich

Cat#C0378

Carbon dioxide (

C, 99%) (<2%

Cambridge Isotope
Laboratories

CLM-185-5

Poly(vinylidene fluoride-co-hexafluoropropylene)

Sigma-Aldrich

427179

PTFE Hydrophilic Membrane Filters, 0.22



m, 90

Scientificfilters.com/Tisch
Scientific

SF13842

Critical commercial assays

Folic Acid (FA) ELISA kit

Alpha Diagnostic
International

Cat# 0365-0B9

TMT10plex Isobaric Label Reagent Set

Thermo Fisher

A37725

Surface Plasmon Resonance CM5 kit

GE Healthcare

Cat#29104988

NanoBRET Target Engagement K-5 kit

Promega

Cat#N2500

Deposited data

B-RAF RBD (apo) structure

This paper

PDB: 5J17

Structure of compound 5

This paper; Cambridge
Crystallographic Data
Center

CCDC: 2016466

Code for constraints-based modeling and analysis
of autotrophic

E. coli

This paper

https://gitlab.com/ela
d.noor/sloppy/tree/ma
ster/rubisco

Software and algorithms

Gaussian09

Frish et al.

https://gaussian.com

Python version 2.7

Python Software
Foundation

https://www.python.or
g

ChemDraw Professional 18.0

PerkinElmer

https://www.perkinel
mer.com/category/ch
emdraw

Weighted Maximal Information Component Analysis
v0.9

Rau et al.

https://github.com/Ch
ristophRau/wMICA

Other

DASGIP MX4/4 Gas Mixing Module for 4 Vessels
with a Mass Flow Controller

Eppendorf

Cat#76DGMX44

Agilent 1200 series HPLC

Agilent Technologies

https://www.agilent.c
om/en/products/liquid
-chromatography

PHI Quantera II XPS

ULVAC-PHI, Inc.

https://www.ulvac-
phi.com/en/products/
xps/phi-quantera-ii/

Journal Pre-proof