Supplemental materials and methods

Patient selection

Bone marrow aspirates and peripheral blood were taken from 57 newly diagnosed MM patients before receiving bortezomib based treatment between July 2013 and Dec 2016.

Patients were divided into resistance group (45) and sensitive group (12) according to the response to bortezomib. Patients who were refractory to bortezomib were defined as bortezomib-resistant patients according to the International Myeloma Working

Group (IMWG) definition of refractory disease (progressive on or within 60 days of stopping bortezomib). The other patients were defined as sensitive ones.

Purification of primary CD138+ plasma cells from MM patients and quality assurance

Isolation of primary CD138+ plasma cells was performed as described previously (1,

2). CD138+ plasma cells were enriched using immuno-magnetic beads (Miltenyi Biotec,

Germany). To determine the purity of the CD138 sorts, samples were evaluated by flow cytometry. The CD138+ plasma cell purity was more than 90%.

Isolation and characterization of BM-derived MSCs

BM-derived MSCs were performed as described previously (1-6). Bone marrow aspirates obtained from patients with MM were diluted with phosphate buffered saline

(PBS) and mononuclear cells were separated by a Ficoll-Paque gradient centrifugation

(specific gravity 1.077 g/mL, GE Healthcare Bio-sciences AB, Uppsala, Sweden). The collected mononuclear cells were cultured in Dulbecco’smodified Eagle’s medium/Ham’s F-12 medium (DF12, Gibco Life Technologies, Paisley, UK)

1 containing 10% fetal calf serum (BioInd, Israel). Non-adherent cells were removed, and the adherent cells were collected as the MSCs. MSCs were characterized by their differentiation ability towards adipocytes, osteoblasts, myocytes and chondrocytes in specific induction media as described previously (1-6).

Cell lines

Myeloma cell lines were obtained from ATCC and maintained as previously described

(2). Cell lines were authenticated by cell line characterization core using short tandem repeat profiling (Genetic Testing Biotechnology Corporation, Suzhou). Cells were tested for mycoplasma contamination using mycoALERT Mycoplasm Detection Kit

(Lonza/Rockland, ME).

Exosomes purification

Exosomes secreted by cultured cell lines and MSCs were isolated using ExoQuick™ solution according to the manufacturer's recommendations (System Biosciences).

Briefly, culture medium was collected and cellular debris were removed by centrifugation at 3,000g for 15min. Medium was transferred to a fresh tube and exosomes were precipitated using ExoQuick™ exosomes precipitation solution (1:4 ratio) at 4°C overnight on rotations. The mixture was centrifuged at 1500g for 30min at 4°C to pellet exosomes. Supernatant was removed by aspiration and pelleted exosomes were washed, filtered and re-suspended in PBS and used for further analyses.

Exosomes in the plasma were isolated using ExoQuick-LP™ solution according to the manufacturer’s instructions. (System Biosciences). Briefly, plasma sample were centrifuged at 3,000g for 15min to remove cells and cell debris. The collected

2 supernatant was passed through a 0.22 μm filter. Then add 100 μl of filtered plasma into the bead mixture and incubate the tube with rotation for 3 hours at 4°C. Next,

Transfer the supernatant (which contains lipoprotein-depleted plasma) into a new tube and add ExoQuick (using 1/5 of the volume of the supernatant) and incubate at 4°C overnight. Centrifuge the tube at 14000 rpm for 10 minutes next day. Aspirate the supernatant, and the remaining pellet contains lipoprotein-depleted exosomes.

Antibodies

Primary antibodies against β-actin (Abcam, ab6276), PSMA3 (Abcam, ab180784),

HSP70 (Abcam, ab182844) and Flotillin-1 (Abcam, ab41927) were used in western blot assay.

U266-luc cell line generation.

To obtain cell lines stably expressing luciferase and GFP, recombinant lentiviruses containing luciferase and GFP (PHBLV-CMV-MCS-EF1-ZsGreen-T2A-fLUC) were purchased from HANBIO (Shanghai, China). U266 were transfected lentiviruses according to the instruction, 24h after transfection, cells were collected and Dual-

Luciferase reporter assays were preformed using the Dual Luciferase Reporter Assay

System (Promega).

Development of inhibitors resistant cells

U266, 8226 and MM.1S drug-naive cell lines, and their proteasome inhibitors resistant- counterparts (bortezomib resistant, BR; carfilzomib resistant, CR; ixazomib resistant,

IR) were developed and maintained as described previously (2). (BR, CR and IR cells were developed by exposing parental cells to serially increased drug concentrations.

3

Cell line authentication was performed by cell line characterization core using short tandem repeat profiling (Genetic Testing Biotechnology Corporation, Suzhou).

Exosome labeling, confocal microscope and Flow cytometry assay

For the exosome-tracking experiments, purified exosomes from MSCs were

fluorescently labeled using PKH67 membrane dye (Sigma-Aldrich) following the manufacturer’s instructions, labeled exosomes were washed twice with RPMI 1640.

Human MM cells were seeded into 24-well chamber culture slides and labeled exosomes were added and incubated 24 h.

For confocal microscope, samples were fixed with 4% methanol-free paraformaldehyde for 10 min. After fixation, cells were washed thrice with PBS, and then blocked in 0.1%

Triton-X 100 in horse Serum albumin for 1 h, and incubated with Actin antibody overnight. Next day, cells were stained with a solution containing 20 μg /mL Alexa

Fluor 594 donkey anti-mouse IgG (H+L) (Molecular Probes, Life Technologies, Grand

Island, NY) for 10 min at room temperature. Cellular nuclei were stained using DAPI.

Imaging of exosome uptake was performed using confocal microscope (LSM 710, Carl

Zeiss).

For flow cytometry assay, labeled exosomes were washed twice with RPMI 1640 and incubated with human MM cells 24 h. then the populations and mean fluorescence intensities were measured using FC500 (Beckman) and Flowjo software (TreeStar),

MM cells didn't incubate with labeled exosome was used as negative control.

Transmission electron microscope (TEM)

Exosomes purified as described above were re-suspended in 100μl phosphate buffer

4

(pH 7.4) and then fixed in 50μl glutaraldehyde. Fixed exosomes were dropped onto a formvar-carbon coated grid and left to dry at room temperature for 2-5 min and stained with phosphotungstic acidoxalate for 1min. The excess liquid was removed. The grid was dried at room temperature for 10 min and visualized on Tecnai G2 F20 transmission electron microscope (FEI, United States) at 185 kV.

GEO dataset analysis

Analysis of expression profiles was obtained from the Expression Omnibus (GEO)

(https://www.ncbi.nlm.nih.gov/geo/; accession no. GSE9782) (7). The dataset consists of 169 patients evaluable for bortezomib response. In this dataset, patients were classified as achieving complete response (CR), partial response (PR), minimal response (MR), no change (NC), or progressive disease PD.

Gene set enrichment analysis (GSEA) (8) was performed using the Java implementation of GSEA software (version 3.0) and the BIOCARTA_PROTEASOME_PATHWAY,

WONG_PROTEASOME_GENE_MODULE and KEGG_ PROTEASOME gene sets from the Molecular Signature Database (v6.0).The Broad Institute Gene Set

Enrichment Analysis website (www.broad.mit.edu/gsea) provides detailed information about the computational method. GSEA was carried out to determine whether defined sets of were differentially expressed between bortezomib-sensitive and -resistant patients-derived CD138+ cells. Gene sets satisfying the default multiple hypothesis testing threshold (FDR q value < 0.25) and having nominal P values no more than 0.05 were identified (84 associated with bortezomib resistance (NC/PD) and 85 patients associated with bortezomib sensitive (CR/PR/MR) ). Gene sets were then ranked by

5

FDR q value.

Gene set enrichment analysis (GSEA).

GSEA (8) was performed using the Java implementation of GSEA software (version

3.0) and the C5 GO gene sets from the Molecular Signature Database (v6.0).The Broad

Institute Gene Set Enrichment Analysis website (www.broad.mit.edu/gsea) provides detailed information about the computational method. GSEA was carried out to determine whether defined sets of genes were differentially expressed in bortezomib resistance (NC/PD) versus bortezomib sensitive (CR/PR/MR) MM patient.

Oncomine database analysis

An analysis on online Oncomine Expression Array database (www.oncomine.org) was conducted to compare the different expression of PSMA3 in primary CD138+ cells from monoclonal gammopathy of undetermined significance (MGUS), smoldering myeloma

(SM), multiple myeloma (MM) or plasma cell leukemia (PCL) using the following terms: “PSMA3”, “ vs. Normal Analysis” and “Multiple Myeloma”. The dataset

Zhan myeloma (9), Zhan myeloma (10) and Agnelli myeloma (11) were extracted and analyzed by R.

RNA extraction and qPCR analysis

Total RNA of cells was extracted by TRIZOL (Invitrogen, USA) according to the manufacturer’s instructions. RNA in plasma was extracted from fixed volume (250 μl) by TRIZOL LS (Invitrogen, USA). The mRNA and lncRNA levels were normalized against GAPDH in cells. The RNA levels in plasma and exosomes were normalized against a synthesized exogenous reference λ polyA RNA (Takara, China).

6

RNA fluorescence in situ hybridization (FISH) and immunofluorescence microscope

Fluorescence-conjugated PSMA3-AS1 probes for RNA FISH were generated according to protocols of RIBO BIO (China). MSCs were treated by 10% paraformaldehyde for 10 minutes to be fixed, and then followed by hybridization with

RNA probe sets. All the experiments were performed according to the manuals of RIBO

BIO. DAPI (1:5000 diluent in PBS) were used to label nucleus. The treated samples were visualized by confocal microscopy (LSM 710, Carl Zeiss).

Cell nucleus/cytoplasm fraction isolation

Cytoplasmic and nuclear Fraction were isolated and purified using the Nuclear/Cytosol

Fractionation Kit (BioVision, the USA) according to the manufacturer’s instructions.

Cytoplasmic and nuclear RNA were isolated as described previously.

Rapid amplification of cDNA ends (RACE) assay

We used the 5'-RACE and 3'-RACE analyses to determine the transcriptional initiation and termination sites of PSMA3-AS1 using a 5´ RACE System for Rapid Amplification of cDNA Ends (Invitrogen)according to the manufacturer’s instructions. The gene- specific primers used for the PCR of the RACE analysis were listed in the supplementary table 2.

RNase protection assay (RPA)

The total RNA was purified by TRIZOL reagent (Invitrogen, USA). Each RNA sample was treated with RNase A (TIANGEN, China), which digests single stranded RNAs but not RNA duplexes. The RNA samples were incubated at 37˚C for 30 min after addition

7 of the RNase A. Following the RPA assay, we used RT-PCR to detect duplex formation by primers within the overlapping region or non-overlapping regions. The sequences of primers used for qPCR in this study were listed in the supplementary table 2.

Cell viability assay

U266 or MM.1S cells were planted in 96-well plates with or without different amounts of exosomes and bortezomib or carfilzomib for 72h. The cell vitality was measured using a cell proliferation assay kit (CCK-8).

Small interfering RNA for PSMA3-AS1, PSMA3 and scrambled control non-targeting siRNA were synthesized by GenePharma (Shanghai, China). MM cells were transfected with 20 µM of siPSMA3-AS1 and siPSMA3 or scrambled siRNA using the NeonTM

Transfection system (Invitrogen, USA) as previously described. Then cells were treated with carfilzomib at IC50 (MM.1S 5 nM, U266 13.6 nM) for 48 h. The cell vitality was measured as described above.

Cell Transfection

Electroporation of siRNA into cells was performed using the NeonTM Transfection system (Invitrogen, USA) according to the manufacturer’s instructions. Briefly, 20 µM siRNA and appropriate amount of cells were added into a sterile, 1.5 mL microcentrifuge tube and gently mix. Fill the NeonTM Tube with 3 mL Electrolytic

Buffer and insert it into the NeonTM Pipette Station, then aspirate the cell-siRNA mixture into NeonTM Tip electroporated at appropriate parameters. Sequences of siRNA against specific target in this study were listed in the supplementary table 3.

Measurement of proteasome activity

8

Chymotrypsin-like proteasome activity was assayed in a total volume of 50 µl using

384-well plates performed according to the manufacturer’s instructions (Promega,

Madison, WI). Briefly, Proteasome-GloTM cell based reagent was prepared by reconstituting the luciferin detection reagent, Proteasome-GloTM cell-based buffer, and the Suc-LLVY-GloTM substrate was then added to an equal volume of samples containing 10,000 cells and incubated for a minimum of 5–10 min before luminescence measurements.

Stability and α-amanitin treatment

U266 or MM.1S cells were plated in regular growth medium, and then transfected with

20 µM siRNA using Neon electroporation system (Invitrogen, USA) according to the manufacturer’s instructions. 20 µM siRNA were mixed in 100 µl of R buffer and electroporated at 1400V, 20 ms, 2 pulse (MM1S), 1500V, 20 ms, 1 pulse (U266). Cells were subsequently treated with 20 nM of α-amanitin and harvested for RNA purification and qPCR at 6, 12 and 24 h post treatment. 18s RNA was used as internal control. We used three independent samples for each data point.

In vivo xenograft studies

Four week old female NOD-Prkdcscid Il2rgtm1/Bcgen (NSG) mice were purchased from biocytogen company (Beijing, China), and were acclimated one week prior to tumor cell inoculation. A total of 1×106 luciferase-labeled U266 cells were injected via the lateral tail vein. When the sizes of xenografts were matched, five mice were intravenously injected with carfilzomib (8 mg/kg) and vivo-grade PSMA3-AS1 siRNA

(1.5 mg/kg), and five mice with carfilzomib and vivo-grade scrambled siRNA, twice a

9 week for 4 weeks. Cholesterol-conjugated siRNAs have been shown to be effective in delivering siRNA-mediated silencing in vivo (12). In this study, vivo-grade siRNAs were modified by cholesterol and obtained from GenePharma (Shanghai, China).

GenePharma cholesterol modified siRNAs have been demonstrated great longevity and stability, and enable effective siRNA uptake into cells and gene silencing in vivo (13-

16).

In vitro data showed that PSMA3-AS1 could regulate PSMA3 expression, and thus reduced proteasome inhibitors sensitivity in MM cells. The upregulated PSMA3 level in myeloma cells originated from the upregulated precursor PSMA3-AS1. Moreover, compared to knockdown of PSMA3, knockdown of PSMA3-AS1 caused a more increased carfilzomib sensitivity in U266 (p = 0.0317, Figure 3D). Based on the results,

PSMA3-AS1 played more vital role in regulating carfilzomib sensitivity in U266 cells.

Considering MM xenograft models were established by U266-luc, therefore, PSMA3-

AS1, not PSMA3 was chosen for siRNA targeting in vivo.

Mice were imaged after injection of 75 mg/kg of D-luciferin (Promega, Madison, WI) using IVIS Lumina II optical imaging system (Caliper Life Sciences, Hopkinton, MA).

Tumor burden was assessed by serial bioluminescence imaging. Bioluminescence was quantified using the Living Images software.

10

Supplementary Table 1. Characterization of myeloma patients

Patient Sex Age Clinical Stage M- Myeloma cells Serum Creatinine Serum β2-MG LDH PFS OS Group number (yr) (ISS) in BM (%) (μmol/L) Albumin (g/L) (ng/mL) (U/L) (m) (m) 1 F 59 Ⅲ IgG-κ 79 199 23.8 17808 404 7 20 SP

2 F 44 Ⅰ IgA-κ 28 39 36.4 2134 189 50 >55 SP

3 M 75 Ⅱ IgG-κ 58 74 32 4713 126 36 >53 SP

4 F 59 Ⅰ IgG-κ 19 38 42.7 2005 150 48 >53 SP

5 M 71 Ⅱ IgG-λ 29 59 27.6 1726 240 49 >52 SP

6 M 59 Ⅰ IgG-λ 31 81 38.2 2539 113 >51 >51 SP

7 F 59 Ⅱ IgA-λ 25 59 32.8 2284 148 29 >51 SP

8 M 50 Ⅱ κ-light 16.5 90 44.1 3843 133 48 >50 SP chain 9 F 61 Ⅱ IgG-λ 42 50 28.8 2772 183 18 >50 SP

10 F 58 Ⅱ IgG-κ 16 48 34.5 2361 166 28 >49 SP

11 F 46 Ⅱ IgG-λ 21 46 33.3 2536 144 >48 >48 SP

12 M 62 Ⅱ IgG-κ 14 56 31.8 2678 143 19 43 SP

13 M 64 Ⅰ IgG-κ 16 75 38.3 2780 122 >46 >46 SP

11

14 F 84 Ⅰ IgA-λ 10.5 68 38.2 2964 152 >44 >44 SP

15 M 75 Ⅰ IgG-λ 40 66 36.2 2852 143 24 42 SP

16 F 68 Ⅱ IgG-κ 26 31 27.2 2975 107 >42 >42 SP

17 F 57 Ⅰ IgG-κ 14 50 38.6 2513 124 30 >41 SP

18 M 77 Ⅲ IgA-λ 53 299 30.1 20000 103 7 25 SP

19 M 78 Ⅲ κ-light 81 96 37.7 6737 171 >41 >41 SP chain 20 F 51 Ⅱ κ-light 21 87 27.4 3493 325 >39 >39 SP chain 21 M 70 Ⅰ IgA-λ 21 77 40.2 2051 144 36 >39 SP

22 M 60 Ⅲ λ-light 44 86 36.1 6599 204 4 16 SP chain 23 F 50 Ⅲ IgG-λ 21 252 32.1 8729 210 36 >37 SP

24 F 65 Ⅲ IgG-κ 59 64 33.8 5525 158 >37 >37 SP

25 M 45 Ⅱ IgA-λ 77 65 29.6 2104 81 28 >36 SP

26 F 58 Ⅰ IgG-κ 19 55 37 2937 131 >35 >35 SP

27 M 73 Ⅱ IgG-λ 17.5 73 31.5 4828 159 19 >34 SP

28 M 61 Ⅰ IgG-κ 9 73 36 3342 225 >34 >34 SP

29 F 46 Ⅰ IgG-λ 42 54 35.3 1974 87 >33 >33 SP

12

30 M 60 Ⅲ IgA-κ 23.5 60 27.7 9024 144 12 24 SP

31 M 74 Ⅲ λ-light 7 558 39.9 13403 219 25 32 SP chain 32 M 67 Ⅱ IgD-λ 36 135 37.7 4922 242 >32 >32 SP

33 M 79 Ⅱ IgD-λ 76 78 38.9 3847 230 4 11 SP

34 F 63 Ⅱ λ-light 60 298 44.2 3800 198 17 20 SP chain 35 F 67 Ⅱ IgG-λ 86 67 26.8 3322 171 4 16 SP

36 F 70 Ⅱ IgA-λ 14 53 26.5 3079 167 5 18 SP

37 M 70 Ⅱ IgD-λ 13 95 31.3 3178 198 18 18 SP

38 M 53 Ⅲ λ-light 13 807 39.6 5843 305 15 >15 SP chain 39 M 73 Ⅰ κ-light 3 53 37.3 2379 185 >21 >21 SP chain 40 M 69 Ⅲ κ-light 48 146 40.8 9698 339 >18 >18 SP chain 41 M 75 Ⅱ IgG-λ 30 72 29.6 2791 131 >17 >17 SP

42 F 54 Ⅲ λ-light 18 416 40.5 19356 279 >17 >17 SP chain 43 F 69 Ⅱ IgA-κ 14 85 43 3500 186 >15 >15 SP

44 M 85 Ⅱ IgG-λ 15 119 26.3 5319 150 >14 >14 SP

13

45 M 67 Ⅱ IgG-λ 90 71 31.2 2756 398 8 >14 SP

46 F 66 Ⅱ non- 90 27 42.9 3555 157 7 13 RP secretary 47 M 86 Ⅰ IgA-κ 3 111 41.5 2766 137 11 48 RP

48 F 62 Ⅰ λ-light 35 53 36.4 3411 122 22 42 RP chain 49 M 67 Ⅲ IgG-λ 5 79 22.2 5824 233 5 8 RP

50 M 68 Ⅲ IgA-λ 88 75 39.2 14800 1280 1 6 RP

51 F 72 Ⅰ IgG-κ 9 34 38.9 2319 212 15 18 RP

52 M 68 Ⅱ IgA-к 15 63 30.2 4109 176 6 6 RP

53 M 70 Ⅰ IgG-λ 21 51 42.1 1928 167 15 20 RP

54 M 62 Ⅲ IgG-λ 12 170 29.8 8001 240 15 33 RP

55 F 69 Ⅱ IgG-κ 78 65 27.0 4076 192 8 10 RP

56 M 68 Ⅰ IgG-λ 5 62 35.6 1396 151 14 >30 RP

57 F 57 Ⅱ IgG-λ 13 132 39.4 4084 147 16 >33 RP

M, male; F, female; BM: bone marrow, ISS: international staging system, β2-MG: β2-microglobulin, LDH: lactate dehydrogenase, PFS: progressive free survival, OS: overall survival, SP: bortezomib sensitive patients, RP: bortezomib resistance patients.

14

Supplementary Table 2. Primers used in this study

Forward primers (5'-3') Reverse primers (5'-3')

Pre-PSMA3 primers CCTCTACATTCTCTCCTGAC CCAAAGTAACATAACAAAGC

PSMA3-AS1 primers AACAGACCATCAGAAGAGAACA GAACAGAAACCAGAGCCATACA

PSMA3 primers CTGTTACTAGTTTGCGGCATC TTCCACAGCCTTCATAGCAT

GAPDH primers AAGGTGAAGGTCGGAGTCAA GGAAGATGGTGATGGGATTT

18sRNA primers GTAACCCGTTGAACCCCATT AACAGACCATCAGAAGAGAACA

For RNase protection assays

Non-overlap region in PSMA3-AS1 CGGAGTTTTCATCAGGTA ATCGCAGATCCAGGTTTC

Non-overlap region in PSMA3 CCTCTACATTCTCTCCTGAC CCAAAGTAACATAACAAAGC

Overlap region GCACCATCTCTGCTCACT GAAAACCCGTCTCTGTATTA

For RACE

5' RACE-outer GACGGCCGCACAAAAACCAATCT

5' RACE-inner CACGACTAGGTGCTGTCCTGGAGGAAA

3' RACE-outer GCAGGGGTCAGCAGATGGATTTTGTA

3' RACE-inner CTGTATGGCTCTGGTTTCTGTTCCCTGTT

15

Supplementary Table 3. Sequences of siRNA against specific target in this study siRNA Control Sense(5’-3’) UUCUCCGAACGUGUCACGUTT

Antisense(5’-3’) ACGUGACACGUUCGGAGAATT

PSMA3-AS1(siRNA-1) Sense(5’-3’) CCAGCAUCAAGAUGAUUUATT

Antisense(5’-3’) UAAAUCAUCUUGAUGCUGGTT

PSMA3-AS1(siRNA-2) Sense(5’-3’) GGCCUAAGAAUCUCUUGAATT

Antisense(5’-3’) UUCAAGAGAUUCUUAGGCCTT

PSMA3 (siRNA-1) Sense(5’-3’) GCAAGCUGCAAAGACGGAATT

Antisense(5’-3’) UUCCGUCUUUGCAGCUUGCTT

PSMA3 (siRNA-2) Sense(5’-3’) GGGUUGGUGAAUUAACUAATT

Antisense(5’-3’) UUAGUUAAUUCACCAACCCTT

16

Supplementary Table 4, 5. In silico analysis of PSMA3-AS1 coding potential. Two splice variants of PSMA3-AS1 were analyzed regarding their protein coding probabilities using Coding Potential Calculator (CPC, http://cpc.cbi.pku.edu.cn/) and the Coding Potential Assessment Tool

(CPAT, http://lilab.research.bcm.edu/cpat/).

Supplementary Table 4. Prediction of putative encoded by PSMA3-AS1 using CPC. FrameFinder's ORF coverage: A large coveragre of the predicted ORF is an indicator of good ORF quality. Log-odds score is an indicator of the quality of a predicted ORF and the higher score, the higher the quality. The integrity of the predicted ORF indicates whether an ORF begins with a start codon and ends with an in-frame stop codon.

Transcript ORF length Coverage Log-odds score

PSMA3-AS1-01 214 8.53% 28.67

PSMA3-AS1-02 145 5.86% 32.55

XIST 612 3.17% 60.77

GAPDH 1006 66.42% 240.84

β-actin 1126 60.75% 270.85

17

Supplementary Table 5. Prediction of putative proteins encoded by PSMA3-AS1 using CPAT. Analysis was done using default settings for human sequences and coding probabilities below 0.364 were considered non-coding. CPAT determines the coding probability of transcript sequences using a logistic regression model built from ORF size, Fickett TESTCODE statistic, and hexamer usage bias.

Transcript ORF size Ficket Score Hexamer Score Coding Probability Coding Label

PSMA3-AS1-01 252 0.7379 -0.212 0.0099 No

PSMA3-AS1-02 171 0.8809 -0.226 0.0059 No

XIST 411 0.6629 -0.128 0.0268 No

GAPDH 1008 1.2926 0.512 0.9999 Yes

β-actin 1128 1.35 0.698 0.9999 Yes

18

Supplementary Table 6. Summary for the results of univariate multivariable analysis.

PFS OS Univariate Multivariate Univariate Multivariate Factor HR 95%CI p-Value HR 95%CI p-Value HR 95%CI p-Value HR 95%CI p-Value ISS2 1.9644 0.8848- 0.0971 1.7121 0.6334- 0.2892 1.5247 0.5093-4.5643 0.4509 1.4963 0.3708-6.0383 0.5713 4.3615 4.6275 ISS3 2.6734 1.0659- 0.0361 1.3545 0.2265- 0.7395 3.8787 1.2123- 0.0223 3.0805 0.3704- 0.2979 6.7051 8.0986 12.4092 25.6209 Age 1.2695 0.9390- 0.1209 1.2828 0.9143- 0.1495 1.5407 1.0422-2.2777 0.0302 1.6458 1.0036-2.6992 0.0484 1.7163 1.799 M-protein 0.9126 0.6443- 0.6064 0.6374 0.3802- 0.0876 1.0641 0.6918-1.6368 0.7773 1.0689 0.5843-1.9556 0.8286 1.2925 1.0686 Myeloma cells 1.4354 1.0039- 0.0476 1.5528 0.9782- 0.0619 1.7098 1.0971-2.6646 0.0178 1.5086 0.8103-2.8086 0.1948 in BM 2.0524 2.4647 Serum 1.1984 0.9031- 0.2098 1.7579 1.1118- 0.0158 1.2523 0.8364-1.8749 0.2746 1.7219 0.8506-3.4856 0.1309 Creatinine 1.5900 2.7797 Serum 0.7869 0.5502- 0.1894 1.1012 0.6984- 0.6782 0.7612 0.4786-1.2107 0.2492 0.8119 0.4573-1.4414 0.4768 Albumin 1.1255 1.7362 β2 MG 1.4124 1.0328- 0.0306 0.9871 0.5636- 0.9638 1.5575 1.1264-2.1534 0.0073 0.4807 0.2171-1.0648 0.0710 1.9315 1.7287 LDH 2.1508 1.3075- 0.0026 1.6478 0.9553- 0.0726 1.9544 1.3252-2.8823 0.0007 1.6777 1.0754-2.6172 0.0226 3.5379 2.8427 PSMA3-AS1 2.1396 1.4414- 0.00016 2.3684 1.5532- <0.0001 2.9087 1.7374-4.8696 <0.0001 3.4751 1.7012-7.0965 0.0006 3.1761 3.6114

19

PFS OS Univariate Multivariate Univariate Multivariate Factor HR 95%CI p-Value HR 95%CI p-Value HR 95%CI p-Value HR 95%CI p-Value

ISS2 1.9644 0.8848- 0.0971 1.3385 0.4726- 0.5831 1.5247 0.5093- 0.4509 1.8856 0.3949- 0.4265 4.3615 3.7905 4.5643 9.0040 ISS3 2.6734 1.0659- 0.0361 1.6388 0.2956- 0.5719 3.8787 1.2123- 0.0223 8.8213 0.7228- 0.0881 6.7051 9.0849 12.4092 107.662 Age 1.2695 0.9390- 0.1209 1.3266 0.9165- 0.1342 1.5407 1.0422- 0.0302 4.0353 1.7314- 0.0012 1.7163 1.9201 2.2777 9.4046 M-protein 0.9126 0.6443- 0.6064 0.8323 0.4869- 0.5022 1.0641 0.6918- 0.7773 2.7973 0.9736- 0.0561 1.2925 1.4227 1.6368 8.0373 Myeloma cells 1.4354 1.0039- 0.0476 1.3572 0.8768- 0.1706 1.7098 1.0971- 0.9420 0.9896 0.4818- 0.8614 in BM 2.0524 2.1006 2.6646 1.8419 Serum 1.1984 0.9031- 0.2098 1.2083 0.7343- 0.4565 1.2523 0.8364- 0.5832 1.0664 0.1761- 0.3775 Creatinine 1.5900 1.988 1.8749 1.9314 Serum 0.7869 0.5502- 0.1894 1.1311 0.7287- 0.5828 0.7612 0.4786- 0.2492 1.1299 0.6219- 0.6885 Albumin 1.1255 1.7557 1.2107 2.0527 β2 MG 1.4124 1.0328- 0.0306 1.0255 0.5585- 0.9352 1.5575 1.1264- 0.0073 0.6333 0.2165- 0.4042 1.9315 1.8831 2.1534 1.8527 LDH 2.1508 1.3075- 0.0026 1.3971 0.7933- 0.2468 1.9544 1.3252- 0.0007 1.1162 0.6713- 0.6718 3.5379 2.4604 2.8823 1.8559 PSMA3 2.6827 1.8537- <0.0001 2.5171 1.6533- <0.0001 3.8057 2.3063- <0.0001 12.2854 3.7391- <0.0001 3.8825 3.8320 6.2799 40.365

20

Supplementary Table 7. C-index of ISS alone, ISS with PSMA3, and ISS with PSMA3-AS1

PFS OS C-index C-index (Cross-validated) C-index C-index (Cross-validated) ISS 0.6188 0.4996 0.6473 0.5315 ISS+PSMA3 0.7869 0.7041 0.8851 0.8220 ISS+PSMA3-AS1 0.7576 0.6905 0.8399 0.7553

21

Figure S1. MSCs derived exosomes were transferred to myeloma cells and conferred proteasome inhibitors resistance to myeloma cells. (A) Primary myeloma cells were cultured in the presence of MSCs–derived PKH67-labeled exosomes for 24 hours. Exosome uptakes by myeloma cells were shown using a confocal microscope

(original magnification, ×400). Myeloma cells were stained using DAPI (nuclei) and

594 conjugated anti-actin antibody. Scale bars, 5 μm. (B) Flow cytometric analysis of

MM.1S or primary myeloma cells after incubation with fluorescently labeled exosomes.

FL1 fluorescence indicates exosome uptake. (C) U266 or MM1.S cells were planted in

96-well plates with or without different amounts of exosomes and bortezomib or carfilzomib for 72h. The cell viability was measured using CCK-8. Error bars represent the mean ± SD of 3 independent experiments, *, P <0.05, **, P < 0.01 and ***, P <

0.001. 22

Figure S2. Identifying mRNAs involved in bortezomib-resistant patients. PSMA3 and PSMA3-AS1 expression were determined in MM.1S drug-naïve, bortezomib- resistant (B20R), carfilzomib-resistant (C40R), ixazomib-resistant (I20R) cells using qPCR. Proteasome inhibitor resistant cells had increased expression of PSMA3 and

PSMA3-AS1.

23

24

Figure S3. Exosome-mediated transfer of PSMA3 and PSMA3-AS1 from MSCs to

MM cells contributed to proteasome inhibitors resistance. (A) QPCR analysis of

PSMA3 and PSMA3-AS1 expression in MM.1S after incubation with indicated exosomes. R-exo: R-MSCs-derived exosomes; S-exo: S-MSCs-derived exosomes. (B)

(C) (D) U266 or MM.1S cells were treated with indicated exosomes. The cell numbers were counted and a proteasome activity assay was done (B). The cell viability was measured using CCK-8 (C). The was measured by Annexin V /7-AAD apoptosis detection kit (D). (E) QPCR analysis of PSMA3-AS1in MM.1S after incubation with indicated exosomes. oePSMA3: PSMA3-overexpressing; oePSMA3-

AS1: PSMA3-AS1-overexpressing; oeVec: empty vector. (F) MSCs were transfected with indicated siRNAs or overexpressing vectors for 24 hours, and exosomes were isolated. These exosomes were then treated with RNase to remove unincorporated

RNAs. Cell viability of U266 or MM.1S was assessed 48 hours after incubation with or without indicated exosomes and bortezomib or carfilzomib using CCK-8 assay. (G)

U266 or MM.1S cells were transfected with PSMA3 siRNA, PSMA3-AS1 siRNA, control siRNA, PSMA3-overexpressing vector, PSMA3-AS1-overexpressing vector or empty vector and treated without or with bortezomib. The cell viability was measured using CCK-8. (H) MM.1S cells were transfected with PSMA3 siRNA, PSMA3-AS1 siRNA, control siRNA, PSMA3-overexpressing vector, PSMA3-AS1-overexpressing vector or empty vector. The proteasome activity assay was done. Fold changes of activity against no-treatment control was calculated. Error bars represent the mean ±

SD of 3 independent experiments, *, P <0.05, **, P < 0.01 and ***, P < 0.001.

25

Figure S4. PSMA3-AS1 reduced proteasome inhibitors sensitivity by regulating

PSMA3 expression in MM cells. (A) U266 or MM.1S were transiently transfected with PSMA3-AS1siRNA or a control siRNA. QPCR was performed to determine gene levels, with GAPDH used as an internal normalization control. (B) U266 or MM.1S cells were treated without or with bortezomib and transfected with PSMA3-AS1 siRNA or control siRNA, with or without PSMA3-overexpressing vector or empty vector. The cell viability was measured using CCK-8. (C) MM.1S cells were transfected with

PSMA3-AS1 siRNA, control siRNA with or without PSMA3-overexpressing vector or empty vector. The cell numbers were counted and a proteasome activity assay was done.

Fold changes of activity against no-treatment control was calculated. Error bars represent the mean ± SD of 3 independent experiments, *, P <0.05, **, P < 0.01 and

***, P < 0.001.

26

Figure S5. PSMA3-AS1 formed an RNA duplex with PSMA3-AS1 pre-mRNA and increased its stability. (A) Up panel: Genomic organization of human PSMA3 and

PSMA3-AS1. PSMA3 and PSMA3-AS1 genes are located at 14p23.1.

They are overlapped partially and transcribed in opposite directions. Down panel: The schematic is also showing PSMA3-AS1 interacting region relative to PSMA3 pre-

27 mRNA, along with the RNA: RNA interaction. Boxes represent exons. PSMA3 exons are in grey; PSMA3-AS1 exons are green. Introns are indicated as lines. Yellow highlighted segments are the overlapping region of PSMA3 and PSMA3-AS1. PSMA3 pre-mRNA and PSMA3-AS1 interacting locus are indicated as red dotted line. The long grey arrow and the green arrow indicate the orientation of the sense RNA (PSMA3 pre- mRNA) and the antisense RNA (PSMA3-AS1), respectively. PrPr: proximal promoter;

TSS: the transcriptional start site. (B) RPA was performed on RNA samples from U266 and MM.1S cells. Depicted here were RT-PCR results from three sets of primers covering overlapping and nonoverlapping regions of pre-PSMA3 and PSMA3-AS1.

The overlapping region of pre-PSMA3 and PSMA3-AS1 is protected from degradation by RNase A, suggesting RNA duplex formation. 1: DL2000 DNA marker; 2, 5: Non- overlapping region in PSMA3-AS1; 3, 6: Non-overlapping region in pre-PSMA3; 4, 7:

Overlapping region. Right panel: the arrows showed three sets of primers covering overlapping or nonoverlapping regions of pre-PSMA3 or PSMA3-AS1. (C) Stability of

PSMA3 transcript over time was measured by qPCR relative to time 0 after blocking new RNA synthesis with α-amanitin in U266. Half-life of PSMA3, GAPDH and 18s ribosomal RNA was showed. 18s ribosomal RNA, which is a product of RNA polymerase I, was not affected by α-amanitin treatment. (D) U266 or MM.1S cells was transiently transfected with siRNA PSMA3-AS1, siRNA control, vector control, overexpression of PSMA3-AS1. The stability of PSMA3 was measured.

28

References

1.Li B, Shi M, Li J, Zhang H, Chen B, Chen L, et al. Elevated -alpha suppresses TAZ expression and impairs osteogenic potential of Flk-1+ mesenchymal stem cells in patients with multiple myeloma. Stem cells and development 2007;16: 921-930. 2.Li B, Fu J, Chen P, Ge X, Li Y, Kuiatse I, et al. The Nuclear Factor (Erythroid-derived 2)-like 2 and Proteasome Maturation Protein Axis Mediate Bortezomib Resistance in Multiple Myeloma. The Journal of biological chemistry 2015;290: 29854-29868. 3.Li B, Fu J, Chen P, Zhuang W Impairment in immunomodulatory function of mesenchymal stem cells from multiple myeloma patients. Archives of medical research 2010;41: 623-633. 4.Zhuang W, Ge X, Yang S, Huang M, Zhuang W, Chen P, et al. Upregulation of lncRNA MEG3 Promotes Osteogenic Differentiation of Mesenchymal Stem Cells From Multiple Myeloma Patients By Targeting BMP4 Transcription. Stem cells 2015;33: 1985-1997. 5.Shi M, Li J, Liao L, Chen B, Li B, Chen L, et al. Regulation of CXCR4 expression in human mesenchymal stem cells by treatment: role in homing efficiency in NOD/SCID mice. Haematologica 2007;92: 897-904. 6.Ma J, Shi M, Li J, Chen B, Wang H, Li B, et al. Senescence-unrelated impediment of osteogenesis from Flk1+ bone marrow mesenchymal stem cells induced by total body irradiation and its contribution to long-term bone and hematopoietic injury. Haematologica 2007;92: 889-896. 7.Mulligan G, Mitsiades C, Bryant B, Zhan F, Chng WJ, Roels S, et al. profiling and correlation with outcome in clinical trials of the proteasome inhibitor bortezomib. Blood 2007;109: 3177-3188. 8.Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences of the United States of America 2005;102: 15545-15550. 9.Zhan F, Barlogie B, Arzoumanian V, Huang Y, Williams DR, Hollmig K, et al. Gene-expression signature of benign monoclonal gammopathy evident in multiple myeloma is linked to good prognosis. Blood 2007;109: 1692-1700. 10.Zhan F, Hardin J, Kordsmeier B, Bumm K, Zheng M, Tian E, et al. Global gene expression profiling of multiple myeloma, monoclonal gammopathy of undetermined significance, and normal bone marrow plasma cells. Blood 2002;99: 1745-1757. 11.Agnelli L, Mosca L, Fabris S, Lionetti M, Andronache A, Kwee I, et al. A SNP microarray and FISH-based procedure to detect allelic imbalances in multiple myeloma: an integrated genomics approach reveals a wide gene dosage effect. Genes Cancer 2009;48: 603-614. 12.Wolfrum C, Shi S, Jayaprakash KN, Jayaraman M, Wang G, Pandey RK, et al. Mechanisms and optimization of in vivo delivery of lipophilic siRNAs. Nature biotechnology 2007;25: 1149-1157. 13.An S, Jiang X, Shi J, He X, Li J, Guo Y, et al. Single-component self-assembled RNAi nanoparticles functionalized with tumor-targeting iNGR delivering abundant siRNA for efficient glioma therapy. Biomaterials 2015;53: 330-340. 14.Chu S, Tang C, Yin C Effects of mannose density on in vitro and in vivo cellular uptake and RNAi efficiency of polymeric nanoparticles. Biomaterials 2015;52: 229-239. 15.Gao LY, Liu XY, Chen CJ, Wang JC, Feng Q, Yu MZ, et al. Core-shell type lipid/rPAA-Chol polymer hybrid nanoparticles for in vivo siRNA delivery. Biomaterials 2014;35: 2066-2078.

29

16.Xia W, Wang P, Lin C, Li Z, Gao X, Wang G, et al. Bioreducible polyethylenimine-delivered siRNA targeting human telomerase reverse transcriptase inhibits HepG2 in vitro and in vivo. J Control Release 2012;157: 427-436.

30