Abstract

   HIV-1 associated colorectal cancer (HA-CRC) is one of the most
   understudied non-AIDS-defining cancers. In this study, we analyzed the
   proteome of HA-CRC and the paired remote tissues (HA-RT) through
   data-independent acquisition mass spectrometry (MS). The quantified
   proteins could differentiate the HA-CRC and HA-RT groups per PCA or
   cluster analyses. As a background comparison, we reanalyzed the MS data
   of non-HIV-1 infected CRC (non-HA-CRC) published by CPTAC. According to
   the GSEA results, we found that HA-CRC and non-HA-CRC shared similarly
   over-represented KEGG pathways. Hallmark analysis suggested that terms
   of antiviral response were only significantly enriched in HA-CRC. The
   network and molecular system analysis centered the crosstalk of
   IFN-associated antiviral response and cancerous pathways, which was
   favored by significant up-regulation of ISGylated proteins as detected
   in the HA-CRC tissues. We further proved that defective HIV-1 reservoir
   cells as represented by the 8E5 cells could activate the IFN pathway in
   human macrophages via horizonal transfer of cell-associated HIV-1 RNA
   (CA-HIV RNA) carried by extracellular vesicles (EVs). In conclusion,
   HIV-1 reservoir cells secreted and CA-HIV RNA-containing EVs can induce
   IFN pathway activation in macrophages that contributes to one of the
   mechanistic explanations of the systems crosstalk between antiviral
   response and cancerous pathways in HA-CRC.

   Keywords: HIV-1-associated colorectal cancer, Proteomics, HIV-1
   reservoir, Extracellular vesicles, IFN pathway, Cell-associated HIV-1
   RNA, Antiviral response

Graphical Abstract

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1. Introduction

   According to the World Health Organization, there were approximately
   38.4 million people living with HIV (PLWH) globally in 2021. Since the
   first HIV-1-infected case was reported in 1981, numerous cancer types
   had been found to predominantly occur in PLWH, including Kaposi’s
   sarcoma, primary central nervous system lymphoma, non-Hodgkin lymphoma,
   and invasive cervical cancer [41][1], [42][2]. These cancer types were
   classified as AIDS-defining cancers (ADC). Since the highly active
   antiretroviral therapy (HAART) was introduced, ADC incidence had
   swiftly declined; however, non-AIDS-defining cancers (NADC) were
   becoming a major cause of morbidity and mortality [43][3], [44][4]. For
   example, Yuan et al. found that as compared with the general
   population, 30 out of the 40 NADC types, such as lung, brain and small
   intestine cancers, showed increased standardized incidence rates (SIR)
   in PLWH [45][5].

   Multiple theories and hypotheses have been proposed to explain the
   mechanisms of developing NADC. In general, immunosuppression, oncogenic
   viral infection, chronic inflammation, pro-oncogenic effect of HIV, and
   drug influence were deemed potentially primary factors [46][6],
   [47][7], [48][8]. In this context, the -omics strategy has shown its
   significance. For example, regarding HIV-1 associated lung cancer
   (HALC), Wu et al. [49][9] and Zheng et al. [50][10] independently
   performed transcriptomic analyses on paired tumor/adjacent tissues from
   5 and 17 subjects, respectively. They revealed over-represented
   pathways of mitosis and drug response in HALC. Bao et al. performed
   RNA-seq analysis on 7 paired tissues of HIV-1-infected kidney cancer,
   in which they highlighted vascular endothelial growth factor-activated
   receptor, IgG binding, and lipopolysaccharide receptor activities in
   this NADC [51][11].

   HIV-1-associated colorectal cancer (HA-CRC) is one of the most
   understudied NADCs. It should be mentioned that differential
   conclusions had been made regarding the risk of CRC in PLWH. For
   example, in a British Columbia Hepatitis Testers Cohort study on
   658,697 individuals, those with HIV-1 mono-infection were found to have
   significantly higher risk of CRC, with the hazard ratio (HR) of 2.30
   [52][12]. Numerous other studies favored the increased risk of CRC in
   PLWH, including a meta-analysis on publications of 1985–1998 (SIR,
   1.41) [53][13], a prospective study in US (SIR, 2.3) [54][14], and an
   Asian cohort follow-up study (SIR, 2.1) [55][15]. In contrast, there
   were reports showing that CRC had less or similar incidence in PLWH,
   including meta-analyses on publications of 1985–1999 (SIR, 0.45)
   [56][16], 1981–2004 (SIR, 0.81) [57][17] and 1992–2022 (SIR, 1.09)
   [58][5], as well as a US cohort study (1996–2012, SIRs, 0.51–0.69)
   [59][18]. But CRC screening was found to be significantly
   under-utilized in PLWH, serving as a possible factor for reaching the
   above-mentioned inconsistent conclusions on HA-CRC [60][19], [61][20].

   Clinical Proteomics Tumor Assessment Consortium (CPTAC
   https://pdc.cancer.gov/pdc/study/PDC000116) published proteogenomics
   analysis on tissues from 96 subjects with CRC, in which the critical
   role of Rb phosphorylation and glycolysis pathways were revealed
   [62][21]. HA-CRC could be classified as a special CRC subtype.
   Therefore, in this study, we reported the first proteomic analysis on
   HA-CRC in comparison with the non-HA-CRC (CPTAC dataset) to explore
   their differential mechanisms in terms of systems biology and
   biological verifications.

2. Materials and methods

2.1. Participants and tissue sample acquisition

   This study was approved by the ethics review committees of The First
   Hospital of Changsha and written informed consents were obtained from
   all participants. In total, seven patients were enrolled in this study.
   Tissue samples from Donors 1–4 were subjected to the proteomics study,
   while samples from Donors 5–7 were freshly obtained in the year of 2022
   for biological verifications. All donors were diagnosed with primary
   and treatment-naïve HA-CRC, and their CRC staging was determined based
   on the 2021 version of Chinese Society of Clinical Oncology (CSCO)
   guidelines for CRC [63][22]. All donors had received HAART for 1–10
   years upon enrollment, and their HIV-1 RNA viral load were all
   continuously suppressed to< 50 copies/mL. Detailed clinical
   characteristics of participants can be found in [64]Table 1.

Table 1.

   Clinical characteristics of HA-CRC donors.
   Donor Gender Age Year (s) since HIV-1 infection Histopathologic type
   TNM[65]^a Stage Tumor site Size[66]^b
   1 Male 65 10 Moderately differentiated adenocarcinoma T3N1cM0 IIIB
   Sigmoid colon 3.4
   2 Female 55 5 Moderately differentiated adenocarcinoma T3N2aM0 IIIB
   Transverse colon 5.0
   3 Male 51 1 Moderately differentiated adenocarcinoma T3N1cM0 IIIB
   Rectum 2.5
   4 Male 45 5 Poorly differentiated adenocarcinoma T4bN1aM0 IIIC
   Descending colon 4.5
   5 Female 55 0.3 Moderately differentiated adenocarcinoma T3N0M0 IIA
   Sigmoid colon 9.0
   6 Male 68 0.7 Moderately differentiated adenocarcinoma T2N0M0 I Rectum
   1.7
   7 Female 70 1 Moderately differentiated adenocarcinoma T3N1aM0 IIIB
   Rectum 1.0
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   ^a

   TNM staging was performed per the Chinese Society of Clinical Oncology
   (CSCO) guideline (version 2021) for colorectal cancer [68][22].
   ^b

   Size indicates the tumor’s maximum diameter (cm).

   HA-CRC tissues and HIV-1 associated remote tissues (HA-RT,>2-cm-away
   from the CRC tumor edge) were obtained during surgical resection.
   HA-CRC samples were subjected to postoperative pathological examination
   by professional pathologists. As we previously reported [69][23], the
   tumor cellularity, which was estimated as the ratio of tumor cells to
   all intertumoral cells, should be>85% for subsequent mass spectrometry
   (MS) analyses. After surgical resection, tissue samples were freshly
   frozen in liquid nitrogen, followed by storage at −80 ℃ prior to
   analyses.

2.2. Protein extraction

   As we previously described (Luo et al., 2017; Zhang et al., 2017),
   tissues were washed for 3 times with 4 ℃ PBS (phosphate buffered
   saline, pH 7.4, ThermoFisher Scientific, Shanghai, China) and treated
   with ice-cold SDS lysis buffer (Beyotime, Nanjing, China) supplemented
   with 1 mM phenylmethanesulfonyl fluoride (PMSF, Sigma-Aldrich,
   Shanghai, China), protease inhibitor cocktail and phosphatase inhibitor
   (Roche, Shanghai, China). Tissues were then cut into small pieces and
   homogenized with a FastPrep-24 instrument (MP Biomedicals, Shanghai,
   China) for 30 s at the speed of 6.0 m/s; after an interval incubation
   at − 20 ℃ for 10 min, a repeated homogenization was performed. To
   disrupt DNA, supernatants were subjected to sonication (1 s on and 2 s
   off, 720 W, 10 min on ice) with a non-contact ultrasonic cell
   pulverizer (Scientz08-III, Ningbo, Zhejiang, China). To remove debris,
   the lysate was centrifuged for 30 min at 15,500g, 4 °C and the
   supernatant was collected in a new tube. Supernatant protein
   concentrations were determined by a BCA protein assay kit following the
   manufacturer’s instructions (Thermo).

2.3. Protein digestion

   Protein digestion was performed as we previously described [70][24],
   [71][25], [72][26]. In brief, the extracted proteins were reduced (8 M
   Urea and 50 mM DTT at 37 °C, 1 h) and alkylated (100 mM IAA, at room
   temperature, 30 min) in 1.5 mL tubes. In-solution tryptic digestion was
   then performed by using the filter-aided sample preparation (FASP)
   method, exactly as the procedure in our previous reports [73][24],
   [74][25], [75][26].

2.4. High-pH RPLC fractionation

   Peptides were rehydrated with deionized water containing 0.1% (v:v)
   formic acid and the peptide concentration was measured by the Pierce™
   Quantitative Peptide Assays (Thermo). Peptides were then fractionated
   by a Waters XBridge® C18 5 µm column (4.6 × 250 mm) on LC-10 HPLC
   System (Wufeng, Shanghai, China) with mobile phases A (20 mM Ammonium
   formate, pH = 10) and phases B (phases A: acetonitrile = 1:5 (v:v)).
   Peptides were fractionated with a 44-min gradient elution at a speed of
   0.8 mL/min with 5–30% phases B for 22 min, 30–42% phases B for 17 min
   and 42–65% phases B for 5 min. The eluate was collected every 2 min and
   22 fractions were collected that were further pooled into 8 fractions.
   These pooled fractions were dried with a speed-vac, rehydrated with
   300 μL 0.5% trifluoroacetic acid and desalted with Mono Tip™ C18
   Pipette Tips (GL Sciences, Shanghai, China).

2.5. Mass spectrometry analysis

   MS analysis was performed as we previously described with minor
   modifications [76][27], [77][28]. Peptides were analyzed in the
   data-dependent acquisition (DDA) to build spectral library, followed by
   the data-independent acquisition (DIA) MS analysis on individual
   samples. LC-MS/MS instrument used in this study was an Orbitrap Fusion
   Lumos Tribrid mass spectrometer equipped with an EASY-nanoLC 1200 HPLC
   system (Thermo). To allow retention time calibration, the iRT-standard
   (Biognosys, Schlieren, Switzerland) was added into the peptide solution
   at the volume ratio of 1:10. The DDA MS parameters were set as follows.
   (1) MS parameters: detector type, orbitrap; orbitrap resolution,
   120000; scan range, 350–1350 m/z; RF lens, 40%; automatic gain control
   (AGC) target, 400000; maximum injection time, 50 ms. (2) MS/MS
   parameters: isolation mode, quadrupole; activation type, HCD; collision
   energy, 32%; detector type, orbitrap; orbitrap resolution, 15000; mass
   range, normal; AGC target, 50000; maximum injection time, 22 ms. The
   DIA MS parameters were set as follows. (1) MS parameters: detector
   type, orbitrap; orbitrap resolution, 120000; scan range, 350–1250 m/z;
   RF lens, 40%; AGC target, 400000; maximum injection time, 50 ms. (2)
   MS/MS parameters: isolation mode, quadrupole; activation type, HCD;
   collision energy, 32%; detector type, orbitrap; orbitrap resolution,
   30000; mass range, normal; scan range: 200–2000 m/z; AGC target,
   500000; maximum injection time, 50 ms.

2.6. Database searches

   To generate DDA-based spectral library for DIA, we employed the
   Spectronaut Pulsar (version 14.10, Biognosys, Switzerland) to generate
   library by searching DDA raw files against a combined fasta database
   containing human (downloaded from Swiss-Prot on Aug 1, 2022, 20386
   entries) and HIV-1 (downloaded from Swiss-Prot on Aug 1, 2022, 381
   entries) proteins, as well as the iRT standard peptide sequences.
   Searching parameters included: enzyme, trypsin; max peptide length, 52;
   min peptide length, 9; missed cleavages, 2; fixed modifications,
   carbamidomethyl (C); variable modifications, acetyl (N-term),
   deamidation (NQ), oxidation (M); data filtering, Qvalue; precursor
   Qvalue cutoff, 0.01; protein Qvalue cutoff, 0.01; quantity MS-level,
   MS2; cross run normalization, true; normalization strategy, global
   normalization.

   DIA raw files were then imported into Spectronaut to match the
   DDA-based spectral library. In the report section of Spectronaut,
   protein identification and quantitation information was exported for
   further analyses.

2.7. Gene set enrichment analysis (GSEA)

   The GSEA software (version 4.2.3) was used to analyze the whole
   proteome against the hallmark gene sets (version 2022.1)[78][29],
   [79][30]. The R package clusterProfiler (version 4.4.4) [80][31] was
   employed to process Kyoto Encyclopedia of Genes and Genomes (KEGG)
   pathway enrichment analysis.

2.8. Active-subnetwork-oriented pathway enrichment analyses

   The R package pathfindR (version 1.6.4) was applied to process
   active-subnetwork-oriented pathway enrichment analyses [81][32]. The
   gene names, log[2] (Fold change) and P values of differentially
   expressed proteins (DEPs, P < 0.05) were imported into pathfindR. The
   parameters for pathfindR processing were: PIN, KEGG database; Gene set,
   gene ontology-biological process (GO-BP); and enrichment threshold
   = 0.05.

2.9. Immunoblotting

   Immunoblotting (IB) was performed as we previously described [82][25],
   [83][26], [84][27], [85][28]. Briefly, the Blot Mini Gel Tank and Blot
   Module Set (Thermo) with precast polyacrylamide gels of NuPAGE 10%
   Bis−Tris protein gels (1.5 mm × 10 wells, Thermo) were used. Primary
   antibodies included rabbit anti-ISG15 mAb (Sinobiological, Beijing,
   China; 1:2000), rabbit anti-syntenin mAb (Abcam, Shanghai, China;
   1:1000), rabbit anti-TSG101 mAb (Merck, Guangzhou, China; 1:2000),
   rabbit anti-CD9 pAb (Abcam; 1:2000), and rabbit anti-GRP78/BIP pAb
   (Proteintech, Wuhan, China; 1:2000). HRP-conjugated anti-rabbit IgG mAb
   (CST; 1:20000) was used as the secondary antibody.

2.10. Extracellular vesicle isolation and characterization

   The 8E5 cells (CRL-8993, ATCC, Virginia, USA) and CCRF-CEM cells (CEM,
   GDC0219, CCTCC, Wuhan, China) were cultured in the complete RPMI 1640
   medium (Thermo), containing 1% penicillin/streptomycin (Thermo), and
   10% fetal bovine serum (FBS, VivaCell, Denzlingen, Germany) at 37 °C,
   5% CO[2]. To isolate extracellular vesicles (EVs), cells were seeded in
   75 cm^2 - flasks at 1 × 10^6 cells/mL culturing in RPMI without FBS for
   24 h. Cell supernatant was then collected and sequentially centrifuged
   at 220g for 10 min, and 15000g for 30 min at room temperature to remove
   cells and debris, respectively. Next, supernatant was filtered by a
   0.22 µm Filters (Sartorius, Guangzhou, China), followed by EV
   harvesting through Amicon Ultrafilters with 30 kDa molecular weight
   cut-off (Merck Millipore, Guangzhou, China).

   As we previously described, the NanoSight NS300 analyzer (Malvern,
   Shanghai, China) was employed to determine the size distribution and
   concentration of EVs [86][24], [87][27]. Specifically, EVs were diluted
   with PBS to a concentration among 1–10 × 10^8 particles/mL and the
   camera level was set to 12. EVs were also subjected to transmission
   electron microscopy (TEM) observation and protein marker purity
   examination as we previously described [88][24], [89][27].

2.11. Quantification of cell-associated HIV-1 RNA

   We employed an HIV-1 Cell-Associated RNA (CA-HIV RNA) Detection Kit
   (SUPBIO, Guangzhou, China) to quantify HIV-1 RNA carried by EVs
   secreted by 8E5 cells (8E5 EVs). This kit targets the 3′UTR region of
   HIV-1 RNA that quantifies various spliced and unspliced HIV-1 RNA forms
   [90][33], [91][34], [92][35]. In each PCR analysis, a strong positive
   control, a weak positive control, and a water negative control provided
   by this kit were used. RNA was extracted from 3 × 10^9 8E5 EVs and
   analyzed by this qPCR kit according to the manufacturer’s instructions
   with a SLAN 96-P Real Time PCR System (Hongshitech, Shanghai, China).

2.12. Preparation and characterization of SC-macrophages (SCM)

   The SC cell is a non-cancerous human monocytic cell line that can be
   differentiated into resting macrophages (SC-macrophages, SCM) [93][36].
   To prepare SCM, 2 × 10^5 SC cells (CRL-9855, ATCC, Virginia, USA) per
   well were plated in 6-well plates and cultured with complete RPMI
   supplemented with 10% FBS at 37 °C, 5% CO[2]. SC cells were exposed to
   200 nM phorbol-12-myristate-13-acetate (PMA, Sigma-Aldrich) for 3 d to
   allow monocytic differentiation, followed by the removal of PMA and
   continued 5-day culture to obtain resting SCM.

   Flow cytometry (FCM) was performed to measure the CD14 expression on SC
   cells and SCM. Cells were washed twice with PBS, and treated with Human
   Trustain FcX (BioLegend, Beijing, China) for 15 min to block the Fc
   receptors. Cells were then stained with FITC conjugated anti-human CD14
   antibody or anti-mouse IgG isotype control antibody (Biolegend) for
   20 min in dark. For phagocytosis analysis, cells were treated with 2 mL
   0.5 µm FluoSpheres carboxylate-modified microspheres (505/515, 1.68 ×
   10^8 particles/mL, Thermo). After PBS washes, cells were resuspended
   and filtered by 70 µm sieve meshes. A FACSCelesta™ Cell Analyzer (BD
   Biosciences, Shanghai, China) was used for all FCM analyses.

2.13. RNA extraction and qPCR

   SCM cells were treated with 8E5 or CEM EVs for 24 h, followed by total
   RNA extraction using the Eastep® Super Total RNA Extraction Kit
   (Promega, Shanghai, China). RNA was reversely transcribed to cDNA with
   the Hifair® III 1st Strand cDNA Synthesis SuperMix for qPCR (YEASEN,
   Shanghai, China). The qPCR was performed with the SsoAdvanced Universal
   SYBR Green Supermix (Bio-Rad, Guangzhou, China) on a CFX96™ System
   (Bio-Rad). All gene-specific primers used in this study were identical
   to Nasr et al. [94][37].

2.14. Statistics

   For DEPs determination, Student’s t test or Kolmogorov-Smirnov test
   (KS-test) were used based on the Jarque-Bera normal test (JB-test) with
   MATLAB software (version 2016b, The MathWorks, Beijing, China). MATLAB
   software was employed for principal component analysis (PCA) and
   hierarchical clustering (distance = Euclidean, linkage = ward).
   GraphPad Prism version 6.01 (GraphPad Software, La Jolla, CA, USA) was
   used to perform Student’s t test. The density plot, violin plot and
   volcano plot analyses were performed by the ggplot2 R package (version
   3.3.6, https://ggplot2.tidyverse.org).

3. Results

3.1. Proteome of HIV-1-associated colorectal cancer

   In this study, we obtained HA-CRC and HA-RT tissue samples from four
   donors, followed by MS analysis ([95]Fig. 1A). The HA-CRC samples were
   of high quality with confirmative CRC morphology for diagnosis. The
   cancer cellularity of the four donors was between 86% and 96% ([96]Fig.
   1B). We established a DDA proteome spectral library, consisting of
   96,630 precursors, 66,453 peptides and 8615 protein groups. For each
   HA-CRC sample, approximately 4900 proteins were quantified. There was
   no significant difference in terms of the number of quantified proteins
   when comparing HA-CRC with HA-RT ([97]Fig. 1C). As shown in density
   distribution plots, we observed normal distribution for protein
   quantifications in each sample ([98]Fig. 1D). There were 3335 proteins
   that could be commonly quantified in all HA-RT and HA-CRC samples
   without missing values ([99]Fig. 1E). The abundance distribution of
   these 3335 proteins was normal-like in each sample ([100]Fig. 1F).
   Detailed information of the 3335 proteins can be found in
   [101]Supplemental Table S1. The above data suggested that the quality
   of MS data was acceptable for subsequent analyses. All MS raw data can
   be downloaded from iProX database [102][38] (Accession number:
   [103]IPX0005207000).

Fig. 1.

   [104]Fig. 1
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   Summary of the mass spectrometry analysis on HIV-associated colorectal
   cancer and HIV-associated remote tissues. (A) Experimental design. (B)
   Image of HA-CRC tissue. Sections were stained with hematoxylin and
   eosin. Scale-bar = 200 µm. The cellularity is indicated in the left
   corner of each image. (C) Comparison of quantified protein numbers.
   Data are shown with mean± s.e.m., paired Student’s t test, n = 4. (D)
   Protein abundance distribution. The density plots of protein abundances
   are shown. (E) Venn diagram comparison of quantified proteins. (F)
   Abundance distribution of the commonly quantified proteins across all
   samples. The codes started with HA-RT and HA-CRC represent samples from
   remote tissue and HA-CRC primary tumor tissue, respectively.

3.2. Clinical relevance of the proteome of HA-CRC and HA-RT

   We used all the 3335 commonly quantified proteins to perform PCA
   analysis. HA-CRC and HA-RT tissue samples could be separated in a
   two-dimensional space formed by PC1 and PC2, with the variances of
   30.2% and 26.9%, respectively ([106]Fig. 2A). To be noted, PCA could
   not clearly separate the two groups, potentially due to the sample size
   limitation. Through the Student’s t test or KS-test, as compared with
   the paired HA-RT tissue, we identified 363 (10.9%) and 104 (3.1%)
   significantly up- and down- regulated DEPs in HA-CRC among the 3335
   commonly quantified proteins (P < 0.05; [107]Fig. 2B). Detailed
   information of these DEPs can be found in [108]Supplemental Table S2.
   Using unsupervised clustering analysis, we noted that these 467 DEPs
   could well differentiate the HA-CRC and the HA-RT tissues ([109]Fig.
   2C). These analyses suggested that the proteome of HA-CRC and HA-RT
   tissues were of systematical in-group similarity and inter-group
   difference.

Fig. 2.

   [110]Fig. 2
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   Pathological association of tissue proteomes. (A) Principal component
   analysis (PCA) with all quantified proteins. (B) Determination of DEPs
   comparing the abundance fold changes of HA-CRC vs. HA-RT. The p-values
   were obtained from Student t test or Kolmogorov-Smirnov test per normal
   tests. DEPs threshold was set as P = 0.05, and significantly up- and
   down- regulated DEPs in HA-CRC are shown in red and blue, respectively.
   (C) Hierarchical clustering with Euclidean Distance of DEPs, linkage
   = ward.

3.3. HA-CRC and non-HA-CRC share similar metabolic features per proteome

   To discern the potential systems difference between HA-CRC and
   non-HA-CRC, we downloaded and re-analyzed the published data from CPTAC
   for comparison [112][21]. This CPTAC dataset contains isobaric tandem
   mass tag (TMT) labeling-based MS analysis results of 96 CRC tissues and
   their paired normal adjacent tissues (NAT). From this dataset, we
   extracted 4590 proteins that were quantified across all of the samples
   for further comparative analyses with all the 3335 quantified proteins
   in our current study.

   By clusterProfiler analysis with the KEGG gene set, we found that
   spliceosome, nucleocytoplasmic transport and mRNA surveillance pathways
   were commonly over-represented in HA-CRC and non-HA-CRC ([113]Fig. 3A
   and B). These key pathways are known to feature CRC in numerous studies
   that will be discussed in the following sections. Although multiple
   pathways were found to be enriched in HA-RT and NAT tissues, the fatty
   acid degradation, pyruvate metabolism, oxidative phosphorylation,
   protein digestion and absorption pathways suggested the relatively
   normal status of these non-cancerous colon tissues ([114]Fig. 3A and
   [115]Fig. 3B).

Fig. 3.

   [116]Fig. 3
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   KEGG gene set enrichment analyses for all proteins quantified in both
   (A) HA-CRC and (B) non-HA-CRC by clusterProfiler. The proteome datasets
   of non-HA-CRC were downloaded from CPTAC [118][21].

3.4. Antiviral response differentiated HA-CRC from non-HA-CRC

   The hallmark gene set is one of the Human Molecular Signatures Database
   (MSigDB) collections in GSEA [119][39]. While using the hallmark gene
   set as reference, typically physiological terms were significantly
   enriched in the HA-RT group, including oxidative-phosphorylation,
   myogenesis, metabolism for fatty and bile acids, and estrogen response
   ([120]Fig. 4A). In contrast, significantly enriched terms for the NAT
   group (CPTAC dataset) combined both physiological ones similar to the
   HA-RT group and the cancer-associated ones, including K-ras signaling,
   epithelial-mesenchymal transition (EMT) and complement activation
   ([121]Fig. 4B).

Fig. 4.

   [122]Fig. 4
   [123]Open in a new tab

   Hallmark gene set enrichment analyses for (A) HA-CRC and in (B)
   non-HA-CRC by GSEA. P < 0.05 was deemed significant enrichment, and the
   node size indicates the number of genes involved in a certain term.

   Interestingly, the enriched terms for HA-CRC indicated collective
   features of cancerous phenotypes and anti-viral response ([124]Fig.
   4A). The cancerous terms included transforming growth factor beta
   (TGF-β), Myc and E2F targets, G2M checkpoint, angiogenesis, and DNA
   repair ([125]Fig. 4A). In addition, the interferon gamma (IFN-γ) and
   inflammation responses were among the top significantly enriched terms
   in HA-CRC ([126]Fig. 4A). The detailed enrichment results can be found
   in [127]Supplemental Table S3.

   In comparison, the analysis on the non-HA-CRC (CPTAC dataset) featured
   commonly known cancerous hallmarks, but with no anti-viral response
   indications ([128]Fig. 4B). To be noted, the unfolded protein response
   (UPR) was enriched as the top term, and the aberrant translation
   associated mTOR pathway was also over-represented ([129]Fig. 4B).
   Similar to HA-CRC, the terms of G2M checkpoint, DNA repair, and Myc and
   E2F targets were commonly enriched in the non-HA-CRC.

3.5. Cancerous features in non-HA-CRC extracted from sub-network analysis on
DEPs

   We next performed active-subnetwork-oriented pathway enrichment
   analysis on DEPs of the CPTAC dataset ([130]Fig. 5). The purpose of
   this analysis was to serve as a background of the systems feature of
   non-HA-CRC for the subsequent comparison with HA-CRC. As compared with
   NAT tissues, 2095 and 1907 DEPs were found up- and down- regulated in
   the non-HA-CRC tissues. Following the pathfindR workflow, 1627 DEPs
   (40.7%) were found to have protein-protein interactions recorded in the
   KEGG Pathway database, which resulted in identifications of 46 clusters
   of enrichment terms and 69 active sub-networks ([131]Supplemental Table
   S4). With respect to cluster rankings, we found that pathways
   associated with cancer hallmarks were among the top 5 clusters
   ([132]Fig. 5A). Specifically, these pathways were regulating oxidative
   phosphorylation, differentiation, cell adhesion and proliferation
   ([133]Fig. 5 A).

Fig. 5.

   [134]Fig. 5
   [135]Open in a new tab

   Active-subnetwork-oriented pathway enrichment of DEPs in non-HA-CRC.
   (A) Bubble chart of enrichment results of non-HA-CRC, grouped by
   clusters. The x axis denotes the fold enrichment value, while the y
   axis indicates the significantly enriched terms (enrichment threshold
   =0.05). The bubble size represents the number of DEPs in a certain
   term. The color scale corresponds to the -log[10] (lowest-P) value. (B)
   Consolidated active subnetworks of non-HA-CRC. The up- and down-
   regulated DEPs were indicated in red and green, respectively. The Top 5
   clusters per p-value ranking and their corresponding sub-networks are
   shown.

   By consolidating the sub-networks, we could further extract the key
   DEPs and discriminate their collective roles in non-HA-CRC ([136]Fig.
   5B). This analysis suggested the top alteration of the mitochondria
   function, featured by the down-regulation of 28 proteins in
   mitochondrial respiratory chain complex I assembly that implicated the
   hypoxia status ([137]Fig. 5B). Five proteins inducing the
   phosphatidylinositol dephosphorylation were significantly
   down-regulated suggesting the activation of PI3K pathway and promoted
   proliferation ([138]Fig. 5B). Endodermal cell differentiation was
   significantly enriched based on 7 up-regulated DEPs including MMP14,
   MMP2 and ITGB5, and 7 down-regulated DEPs, such as COL4A4 and COL6A1
   ([139]Fig. 5B). Six significantly enriched terms focused on regulating
   cell-adhesion and integrin-mediated signaling pathway, based on 47 up-
   and down-regulated DEPs, including ITGA6 and RhoA ([140]Fig. 5B).

3.6. Crosstalk of antiviral response and cancerous pathways in HA-CRC
discerned

   Using similarly analytical procedure to [141]Section 3.5, we ran
   pathfindR with 467 DEPs of HA-CRC, among which 185 DEPs had KEGG
   recorded protein-protein interactions. Seven clusters with 18
   significantly enriched terms were found ([142]Fig. 6). Comparing with
   the non-HA-CRC group, antiviral response and its associated
   inflammation were found to be significantly enriched ([143]Fig. 6A),
   which involved in the up-regulation of EIF2AK2, ADAR, MX2, DDX58, MYD88
   and IFI16, as well as the down-regulation of HSPB1 ([144]Fig. 6B). The
   reactive oxygen species (ROS) pathway and mitochondria dysfunction were
   connected via MAPK14. Notably, four proteins of mitochondrial
   respiratory chain complex V were down-regulated ([145]Fig. 6B). Similar
   to non-HA-CRC, the endodermal cell differentiation pathway was
   significantly enriched based on MMP2, MMP14 and ITGB5 ([146]Fig. 6B).
   In addition, actin nucleation regulating pathways such as Arp2/3 was
   significantly activated per up-regulated ARPC5, ARPC1B, ACTR3 and ACTR2
   ([147]Fig. 6B). Finally, the epigenetic regulation tended to be
   over-represented in HA-CRC, as implicated the upregulation of SMARCC1,
   SMARCD2, ARID1A and NNMT ([148]Fig. 6B).

Fig. 6.

   [149]Fig. 6
   [150]Open in a new tab

   Active-subnetwork-oriented pathway enrichment of DEPs in HA-CRC and
   biological verification. Bubble chart of enrichment results of HA-CRC
   (A), and consolidated active subnetworks of HA-CRC (B) are shown.
   Please refer to the legend of [151]Fig. 5 for other analytical settings
   and specifications. All significantly enriched terms and their
   corresponding sub-networks are shown. (C,D) IB analysis on ISGylated
   proteins comparing the HA-CRC with the HA-RT tissues (C), and the gray
   scale statistics (D). Huh-7 cells treated with and without 500 U/mL
   IFN-α were used as positive and negative controls, respectively.

   To prove whether antiviral response was over-represented in HA-CRC, we
   further enrolled Donors 5–7 to perform biological verifications. It is
   known that protein ISGylation is a critical marker of the activation of
   IFN signaling pathway [152][40]. With reference to Rueschenbaum et al.
   [153][41], we used IFN-α (500 U/mL) to induce the protein ISGylation in
   a human liver cell line Huh-7 cells that serves as a positive control
   ([154]Fig. 6C), in which Coomassie Brilliant Blue R-250 staining was
   performed to control the sample loading amount ([155]Supplemental Fig.
   S1). Post IFN-α treatment, Huh-7 cells showed remarkable up-regulation
   of ISGylated proteins ([156]Fig. 6C). We found that as compared with
   HA-RT tissues, the HA-CRC tissues showed significantly up-regulated
   protein ISGylation ([157]Fig. 6C and 6D).

3.7. EVs from 8E5 cells are HIV-1 RNA positive and induce antiviral response
in macrophages

   All HA-CRC Donors of this study had achieved viral suppression, it is
   interesting to trace the sources of the antiviral response. In PLWH,
   the majority of HIV-1 DNA is defective proviral DNA [158][42],
   [159][43]. Defective HIV-1 DNA is not silenced, rather they are
   transcriptional and/or translational active [160][44]. In addition, EVs
   have been recognized as an important compartment that carries and
   transfers cell-derived biomolecules, including RNA and
   proteins[161][45]. As such, we posit that defective HIV-1 reservoir can
   release viral RNA through EVs to induce antiviral response in target
   cells. Furthermore, macrophages are known to be the primary immune
   cells that recognize viral RNAs followed by the activation of IFN
   pathways to defend virial infections [162][37], [163][46]. To address
   this hypothesis, we employed 8E5 cells to represent defective HIV-1
   reservoir cells. 8E5 cells are known to be derived from an acute
   lymphoblastic leukemia cell line CEM cells. An 8E5 cell is integrated
   with a single-copy HIV-1 genome that is defective in the pol region.

   First, we isolated EVs from 8E5 and CEM cells, followed by the
   examination of EV purity according to the MISEV2018 recommendations
   published by International Society of Extracellular Vesicles [164][47].
   Specifically, both 8E5 EVs ([165]Fig. 7A) and CEM EVs ([166]Fig. 7B)
   showed positive for EV markers of CD9, TSG101 and syntenin, while the
   EV co-isolating contaminant marker BIP (HSPA5) was largely removed. In
   addition, the diameter ranges of 8E5 and CEM EVs were 60–200 nm, and
   small cup-shaped particles could be observed via TEM ([167]Fig. 7A and
   7B). The particle protein ratio of 8E5 and CEM EVs were (3.0 ± 0.3)
   × 10^8 and (3.8 ± 0.2) × 10^8 particles/μg protein (mean± s.e.m,
   n = 3), respectively. The above results indicated that EVs had been
   isolated with high purity per MISEV2018.

Fig. 7.

   [168]Fig. 7
   [169]Open in a new tab

   Defective HIV-1 reservoir cells elicit anti-viral response in
   macrophages via extracellular vesicles carrying cell-associated HIV-1
   RNA. (A,B) Characterization of extracellular vesicles (EVs) isolated
   from 8E5 cells (A) and CEM cells (B). EVs were subjected to IB, NTA and
   TEM analyses. Scale bar = 100 nm. (C) qPCR analysis of CA-HIV RNA
   carried by 8E5 EVs. (D) Preparation of SC-macrophages. Scale bar
   = 100 µm. (E∼K) qPCR analysis on antiviral response - associated genes
   in EV-stimulated SCM. SCM were exposed to different amounts of EVs
   isolated from 8E5 or CEM cells. The expression of IRF1 (E), IRF7 (F),
   IFN-α (G), IFN-β (H), IFIT1 (I), IFIT2 (J) and IFIT3 (K) were measured
   by qPCR, in which ΔCt = Ct (target gene) - Ct (GAPDH). All results were
   acquired from three independent biological replications. Data are shown
   with mean± s.e.m. Statistical comparison was performed by the unpaired
   Student’s t test.

   Second, for CA-HIV RNA measurement, the strong and weak positive
   controls, and the water negative controls were all properly worked
   ([170]Fig. 7 C), while the R (correlation coefficient) of the standard
   curve was> 0.999 ([171]Supplemental Fig. S2). We found that 8E5 EVs
   carried 71 ± 21 copies CA-HIV RNA/1 × 10^6 EVs (mean± s.e.m, n = 3,
   [172]Fig. 7 C), while CEM EVs were CA-HIV RNA negative.

   Next, we successfully prepared SCM according to the following results.
   Post PMA treatment for 3 d, suspended and round-shaped SC cells became
   attached and some cells formed protrusions ([173]Fig. 7D). After
   removal of PMA and continued 5-day culture, typical spread morphology
   could be observed in most SCM ([174]Fig. 7D). In addition, SCM showed
   significantly up-regulated CD14 expression ([175]Supplemental Fig.
   S3A), and significantly more phagocytotic cells ([176]Supplementary
   Fig. S3A) as compared with SC cells.

   Finally, we treated SCM with different amounts of 8E5 or CEM EVs for
   24 h, followed by the determination of the antiviral response
   associated gene alterations. We found that as compared with CEM EVs,
   8E5 EVs (6 × 10^10 and/or 9 × 10^10 EVs) significantly increased the
   expression of IFN transcription factors IRF1 ([177]Fig. 7E) and IRF7
   ([178]Fig. 7F) in SCM. Although no significant alteration was observed
   for the IFN-α expression comparing all experimental groups ([179]Fig.
   7G), IFN-β was found to be significantly up-regulated in the 6 × 10^10
   and the 9 × 10^10 8E5 EVs treated SCM groups ([180]Fig. 7H). In
   addition, we found that the interferon-stimulated genes (ISGs) of IFIT1
   ([181]Fig. 7I), IFIT2 ([182]Fig. 7J) and IFIT3 ([183]Fig. 7K) were all
   significantly up-regulated in the 8E5 EVs treated SCM groups (6 × 10^10
   and/or 9 × 10^10 EVs) as compared with the corresponding CEM EVs
   treated SCM groups.

4. Discussion

   In the field of NADC, there are major obstacles for molecular mechanism
   exploration, especially due to the lack of in vitro and/or animal
   models to adequately reproduce the interactive functions of viral and
   host factors. Despite this, numerous groups have made promising
   advances to address this question via simplified models focusing on
   certain cell types and viral proteins/nucleic acids. While in this
   study, we tried to employ a holistic strategy with a high-quality
   proteomics analysis that discerned the crosstalk between antiviral
   response and cancerous pathways in HA-CRC, leading us to biologically
   reveal a critical role of EVs released from defective HIV-1 reservoir
   cells (8E5 cells) in activating IFN pathways in macrophages.

   We performed restricted quality control in both sample acquisition and
   MS analysis. It is generally accepted that tissues with tumor
   cellularity of ≥50% is sufficient for clinical proteomics [184][48],
   [185][49]. In our previous report, we set a criteria of cancer
   cellularity of>85% that was proven to be important for subsequent
   proteome subtyping for liver cancer [186][23]. Accordingly, HA-CRC
   samples with the cellularity of>85% that were used in this study served
   as a stringent baseline control. Furthermore, we followed the
   recommendations of Human Proteome Project (HPP) MS Data Interpretation
   Guidelines 3.0, specifically employing peptide and protein FDR<0.01,
   and peptide length ≥9 aa for database search [187][50]. This tends to
   improve the evidence level for both protein identification and
   quantification that facilitates data sharing with the scientific
   community.

   The subsequent whole proteome analysis allowed us to discern the
   general similarity and difference between HA-CRC and non-HA-CRC. First,
   the GSEA based on KEGG pathway implicated that the two types of CRC
   shared similarly over-represented pathways, including spliceosome,
   nucleocytoplasmic transport and mRNA surveillance that were known in
   CRC progression [188][51], [189][52]. The nucleocytoplasmic transport
   is a basic requirement for transcription and translation. We have
   previously shown that cancer cells are highly active in translation
   (Wang et al., 2013; Guo et al., 2015), which echoes the significantly
   enriched ribosome-associated terms in the non-HA-CRC group. Second,
   Hanahan has proposed fourteen hallmarks of cancer [190][53], and among
   them, three hallmarks are connected to the enriched terms in this
   study. These hallmarks included: activating invasion and metastasis
   (enriched terms in this study, TGF-β signaling, and EMT), sustained
   proliferation (c-Myc, E2F targets, G2M checkpoint, mTOR), genome
   instability and mutation (DNA repair), and tumor-promoting inflammation
   (inflammatory response, unfolded protein response).

   The active sub-network analysis helped us to discern the systems
   mechanism and molecular-based network crosstalk implicated by the
   HA-CRC and non-HA-CRC proteome. Through reanalysis of the CPTAC
   dataset, we found massive downregulation of proteins that constitute
   mitochondria complex I, suggesting the hypoxia status of non-HA-CRC.
   This is comparable with Vasaikar et al. [191][21] showing that
   glycolysis pathway activation is important in the subtyping of advanced
   CRC. Phosphorylation of phosphatidylinositol by PI3K is a critical
   event for oncogenesis and cancer progression [192][54]. We noted that
   multiple proteins regulating dephosphorylation of phosphatidylinositol
   were down-regulated, suggesting their collective role for maintaining
   the PI3K pathway. In addition, numerous down-regulating proteins
   constituted the cell adhesion associated sub-networks, implicating the
   gain of migratory ability of CRC cells [193][55], [194][56]. Moreover,
   MMP2 [195][57], MMP14 [196][58] and ITGB5 [197][59] were
   differentiation and metastasis regulating proteins in CRC, while they
   were enriched in the sub-networks in both non-HA-CRC and HA-CRC.

   However, we found very different molecular systems in HA-CRC as
   compared with non-HA-CRC. The antiviral response sub-network centered
   the IFN-mediated immune response in HA-CRC. This sub-network consisted
   of EIF2AK2, ADAR1, MX2, MYD88, DDX58 and IFI16, and all of these genes
   were interferon-stimulated genes (ISGs) that were critical to inhibit
   virus replication [198][60]. Such activated innate immunity sub-network
   tended to bridge the crosstalk of antiviral response and cancerous
   pathways. For example, MyD88 has been shown to promote to the
   proliferation, migration and invasion of cancer cells via
   MyD88/NF-κB/AP-1 signaling [199][61]. In addition, the mitochondria
   impairment in HA-CRC was also linked to the antiviral response
   associated subnetwork suggesting the hypoxia micro-environment (TME)
   conversion in HA-CRC [200][62].

   We biologically verified the significant up-regulation of antiviral
   response in HA-CRC, as indicated by the protein ISGylation analysis.
   Indeed, chronic and weak activation of IFN pathways have beneficial
   effects to numerous cancer cell types [201][63]. On one hand, most
   cancer tissues are hypoxic, while necrotic cells can release DNA
   fragments into TME. These DNA can be recognized by cGMP-AMP synthase to
   induce IFN expression that contributes to the chronic IFN responses in
   cancer cells [202][63]. On the other hand, cancer cells can aberrantly
   express endogenous retroviral elements (ERE) that mimic pathogenic
   viral infection to maintain chronic IFN expression by immune cells in
   TME [203][64], [204][65].

   Our findings implicated that defective HIV-1 reservoir derived EVs can
   transfer CA-HIV RNA to macrophages and induce the activation of IFN
   pathways. It should be noted that the all donors of this study had
   achieved HIV-1 RNA suppression prior to CRC surgery. Although plasma
   HIV-1 RNA is below the limits of detection, it is known that HIV-1 DNA
   intergraded in human genome can still actively transcribe CA-HIV RNA
   [205][66]. Through the mechanism revealed from our current study,
   HA-CRC does not need to solely use ERE, while the CA-HIV RNA contained
   in EVs released from HIV-1 reservoirs is of significance in maintaining
   the chronic activation of IFN pathways. Comparably, Fan et al. have
   found that IFN-induced protein ISGylation can intensify intestinal
   inflammation and colitis-associated CRC in mouse models [206][67].
   Similar mechanisms in other infectious diseases are comparable to our
   study. For example, EVs released from HBV-infected hepatocytes have
   been found to contain viral nucleic acids that are associated with
   increased NKG2D ligand expression in macrophages by stimulating MyD88,
   TICAM-1, and MAVS-dependent pathways [207][68]. In addition, Dreux et
   al. have found the HCV-RNA containing EVs can induce innate immunity in
   plasmacytoid dendritic cells [208][69].

   In conclusion, we have reported the first functional proteomic analysis
   on HA-CRC that systematically and biologically discerns the converged
   antiviral response and cancerous pathways. Defective HIV-1 reservoir
   cells as represented by 8E5 cells can induce the IFN pathway activation
   via horizonal transfer of CA-HIV RNA via EVs.

CRediT authorship contribution statement

   T.W. and M.W.： Conceived the idea and supervised the entire study. Z.C.
   and K.Y.：Designed and performed most of the experiments. Z.C., J.Z.,
   S.R.: Contributed to the data analysis. H.C.: Performed pathological
   experiments and data analysis. J.G. and Y.C.: Contributed to study
   supervision, and data analysis. T.W. Z.C., K.Y., J.Z., S.R., M.W.:
   Contributed to manuscript preparation and paper writing. All authors
   reviewed and approved the manuscript.

Declaration of Competing Interest

   The authors declare that they have no known competing financial
   interests or personal relationships that could have appeared to
   influence the work reported in this paper.

Acknowledgements