RNA-seq of Human T Cells after Hematopoietic Stem Cell Transplantation Identifies Linc00402 as a Regulator of T-Cell Alloimmunity

Daniel Peltier; Molly Radosevich; Visweswaran Ravikumar; Sethuramasundaram Pitchiaya; Thomas Decoville; Sherri C Wood; Guoqing Hou; Cynthia Zajac; Katherine Oravecz-Wilson; David Sokol; Israel Henig; Julia Wu; Stephanie Kim; Austin Taylor; Hideaki Fujiwara; Yaping Sun; Arvind Rao; Arul M Chinnaiyan; Daniel R Goldstein; Pavan Reddy

doi:10.1126/scitranslmed.aaz0316

. Author manuscript; available in PMC: 2021 Nov 12.

Published in final edited form as: Sci Transl Med. 2021 Mar 17;13(585):eaaz0316. doi: 10.1126/scitranslmed.aaz0316

RNA-seq of Human T Cells after Hematopoietic Stem Cell Transplantation Identifies Linc00402 as a Regulator of T-Cell Alloimmunity^{^**}

Daniel Peltier ¹, Molly Radosevich ¹, Visweswaran Ravikumar ², Sethuramasundaram Pitchiaya ³, Thomas Decoville ¹, Sherri C Wood ⁴, Guoqing Hou ⁵, Cynthia Zajac ⁵, Katherine Oravecz-Wilson ⁵, David Sokol ⁵, Israel Henig ⁵, Julia Wu ⁵, Stephanie Kim ⁵, Austin Taylor ⁵, Hideaki Fujiwara ⁵, Yaping Sun ⁵, Arvind Rao ², Arul M Chinnaiyan ⁶, Daniel R Goldstein ⁷, Pavan Reddy ^5,^*

PMCID: PMC8589011 NIHMSID: NIHMS1751603 PMID: 33731431

Abstract

Mechanisms governing allogeneic T-cell responses after allogeneic hematopoietic stem cell (HSC) and solid organ transplantation are incompletely understood. To identify lncRNAs involved in regulation of human donor T cells after clinical HSCT, we performed RNA-seq on T cells from healthy subjects as well as on donor T cells from three different groups of HSCT recipients that differed in their degree of histocompatibility mismatch. We found that lncRNA differential expression was greatest in T cells following major histocompatibility complex (MHC)–mismatched HSCT relative to T cells following either MHC-matched or autologous HSCT. The differential expression was validated in an independent patient cohort and in mixed lymphocyte reactions using ex vivo healthy human T-cells. Linc00402, an uncharacterized lncRNA, was identified among the differentially expressed lncRNAs between the mismatched unrelated and matched unrelated donor T cells. We nominated it for further characterization because it was classified as conserved using genomic evolutionary rate profiling and exhibited an 88-fold increase in human T cells relative to all other samples in the FANTOM5 Consortium database. Linc00402 was also increased in donor T cells from patients who underwent allogeneic cardiac transplantation, and in murine T cells. Lower amounts of Linc00402 were found in patients who subsequently developed acute graft-versus-host disease (GVHD). Linc00402 enhanced the activity of the kinases ERK1 and ERK2, increased FOS nuclear accumulation, and augmented the expression of interleukin-2 (IL-2) and Egr-1 after T cell receptor engagement. Functionally, Linc00402 augmented the T cell proliferative response to an allogeneic stimulus but not to a nominal ovalbumin peptide antigen or polyclonal anti-CD3/CD28 stimulus. Thus, our studies identified Linc00402 as a regulator of allogeneic T cell function.

One sentence summary:

LncRNAs are differentially expressed by allogeneic antigen-stimulated T cells, and Linc00402 regulates allogeneic T cells.

INTRODUCTION

Allogeneic solid organ and hematopoietic stem cell transplantation (HSCT) are life-saving treatments for organ failure and high-risk malignancies, non-malignant hematologic disorders, and inherited metabolic disorders. Their efficacy is limited by T cell–mediated alloimmune reactions resulting in graft-versus-host disease (GVHD) and allograft organ rejection, which are mitigated by nonspecific immunosuppressive therapies that have serious side effects. Better understanding of T-cell alloimmunity could lead to more specific and better tolerated therapies for mitigating the undesirable effects caused by allogeneic T cells.

Non-coding RNAs (ncRNAs) fine tune gene expression and control cellular processes. Long non-coding RNAs (lncRNAs) are a subset of ncRNAs that are 200 nucleotides or longer and are devoid of functional open reading frames (1, 2). LncRNAs both positively and negatively regulate gene expression in multiple ways, including by modulating transcription in cis or trans, by competing with mRNAs for translation to limit protein production, or by both positively and negatively regulating the stability of specific mRNAs (3, 4). In contrast to other ncRNA subsets such as microRNAs, lncRNAs are produced in a tissue- and context-specific manner (5, 6), which could make them attractive targets for antisense oligonucleotide–based medicines.

The abundance of lncRNAs in T cells varies among T cell subsets and changes upon T cell activation (5–7), but the functions of most remain unknown (8). Furthermore, lncRNA abundance and function in T cells following allogeneic HSCT and solid organ transplantation have not been explored. Here, we used RNA sequencing of donor T cells from healthy donors and well-controlled patient samples following HSCT to investigate the role of lncRNAs in human allogeneic T cells.

RESULTS

Allogeneic stimulation causes differential expression of lncRNAs in human T cells

To determine whether lncRNAs are differentially expressed by human alloimmune T cells in a relevant in vivo context, we performed RNA-seq of human donor T cells following HSCT from subjects that varied in the degree of major histocompatibility complex (MHC) matching with their HSCT recipient. Specifically, T cells were isolated from healthy subjects and 3 groups of HSCT recipients; autologous recipients, in whom T cells received proliferation signals due to lymphopenia; matched unrelated donor recipients (MUD), in whom T cells received proliferation signals due to minor histocompatibility–driven allo-stimulation in the context of lymphopenia and immune suppression; and mis-matched unrelated donor (MMUD) recipients, in whom T cells received proliferation signals driven by both minor and major histocompatibility allo-stimulation in the context of lymphopenia and immune suppression (Figure 1A). All patients were controlled for absence of clinically important acute GVHD, absence of infections, absence of corticosteroid therapy, type of immunosuppression (T-cell suppressive regimen with tacrolimus), similar myeloablative conditioning, peripheral blood monocytes (PBMCs) as the source of stem cells without in vivo or ex vivo T-cell depletion, and time after HSCT (mean of 30 days post-HSCT with a range of 26–38) (Table 1). The transplants occurred from August 2006 through October 2012. Ages ranged from 28 to 68 with similar distributions among the patient groups. The sex distribution among the groups was also similar. The most common primary diagnosis in the autologous group was multiple myeloma, whereas for the MUD and MMUD groups indications for transplant skewed more towards myeloid malignancies and myelodysplasia. Fludarabine and busulphan (Flu Bu4) were primarily used as the preparative regimen for the MUD and MMUD patients. Tacrolimus with low dose methotrexate was the predominant GVHD prophylaxis used in both the MUD and MMUD groups. Percent CD3 donor chimerism on day +30 was not significantly different between the allogeneic groups (MUD mean 90%, MMUD mean 77%, p = 0.17 by unpaired t-test). The MMUD patients were mismatched at HLA-DQ or HLA-A.

Figure 1: — Total RNA sequencing was performed on healthy control CD3⁺ cells or CD3⁺ cells 30 days after either autologous (Auto), MUD, or MMUD HSCT. (A) Experimental and analysis schema. (B) Principal component analysis. (C) Differentially expressed (p_adj < 0.1) protein-encoding (top) and lncRNA-encoding (bottom) genes relative to healthy controls. (D) The top 10 enriched gene ontology (GO) terms for the differentially expressed (p_adj < 0.05) protein-encoding genes in the autologous (Auto), MUD, and MMUD groups relative to healthy controls. A full list of enriched GO terms is shown in tables S4 – S6. (E) Hierarchical clustering heatmaps of differentially expressed genes (p_adj < 0.05) identified by comparing MMUD and Auto groups and MMUD and MUD groups. Only the groups that underwent HSCT are shown. (F) Differentially expressed protein-coding (top) and lncRNA-coding (bottom) genes (p_adj < 0.1) relative to Auto are shown. (**G, H**) Enriched GO terms for differentially expressed protein-encoding genes (p_adj < 0.05) in the MMUD group relative to the Auto (G) and MUD groups (H). A full list of enriched GO terms is shown in tables S9 and S10. (I) Volcano plot of differentially expressed (p_adj < 0.1) lncRNAs in the MMUD group relative to either Auto or MUD controls. (J) Heat maps showing FPKMs for differentially expressed lncRNAs (p_adj < 0.1) in the MMUD group with Auto or MUD HSCT recipients are shown. Underlined lncRNAs were differentially expressed relative to both Auto and MUD HSCT recipients.

Table 1.

RNA-seq cohort patient characteristics.

	Transplant Group	Age	Sex	Primary Diagnosis	Conditioning	GVHD Prophylaxis	Stem cell Source	HSCT Year	Day Post BMT	Donor CD3% Day 30	HLA Mismatch	Recipient Mismatched Allele	Donor Mismatched Allele
HC 1	Healthy Control	28	Male	None
HC 2	Healthy Control	32	Female	None
Auto 1	Autologous	52	Female	Multiple Myeloma	Melphalan	None	PBSC	2007	30
Auto 2	Autologous	54	Female	Multiple Myeloma	Melphalan	None	PBSC	2006	30
Auto 3	Autologous	68	Female	Plasma Cell Leukemia	Melphalan	None	PBSC	2006	32
Auto 4	Autologous	47	Male	Diffuse Large B-Cell Lymphoma	R-BEAM	None	PBSC	2007	35
Auto 5	Autologous	53	Male	Mantle Cell Lymphoma	R-CVP	None	PBSC	2007	26
MUD 1	Matched Unrelated	56	Male	Multiple Myeloma	Flu Bu4	Tacro/MTX	PBSC	2012	29	93	None (10/10)
MUD 2	Matched Unrelated	55	Male	CLL	R-Flu Bu4	Tacro/MTX	PBSC	2012	38	95	None (8/8)
MUD 3	Matched Unrelated	67	Female	MDS	Flu Bu4	Tacro/MMF/Enbrel/ECP	PBSC	2012	30	95	None (10/10)
MUD4	Matched Unrelated	42	Male	AML	Flu Bu4	Tacro/MTX	PBSC	2012	31	77	None (8/8)
MMUD 1	Mis-Matched Unrelated	29	Male	MDS	Flu Bu4	Tacro/MTX/Enbrel	PBSC	2009	30	91	A (9/10)	HLA-A*02:01/03:07	HLA-A*02:01/03:01
MMUD 2	Mis-Matched Unrelated	62	Female	Myelofibrosis	Flu Bu4	Tacro/MTX	PBSC	2012	34	65	DQ (9/10)	HLA-DQB1*03:01/05:03	HLA-DQB1*03:02/05:03
MMUD 3	Mis-Matched Unrelated	43	Male	AML	Flu Bu4	Tacro/MTX	PBSC	2012	26	75	DQ (9/10)	HLA-DQ1*06:03/06:04	HLA-DQ1*05:01/06:04

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AML: acute myeloid leukemia. BEAM: Rituxan, carmustine, etoposide, cytarabine, melphalan. CLL: chronic lymphocytic leukemia. ECP: extracorporeal photopheresis. Flu Bu 4: fludarabine, busulphan. MDS: myelodysplastic syndrome. MMF: mycophenolic acid. MTX: methotrexate. PBSC: peripheral blood stem cells. R-Flu Bu 4: Rituxan, fludarabine, busulphan R-CVP: Rituxan, cyclophosphamide, vincristine, prednisone. Tacro: tacrolimus.

To assess global differences in protein- and lncRNA-encoding transcripts among patient groups, we performed RNA-seq on purified CD3⁺ T cells followed by principal component analysis (PCA). PCA showed clustering of healthy controls, and intermingling of the autologous, MUD, and MMUD samples (Figure 1B). These data indicated that the transcriptomic profile of T cells changes after any type of HSCT. Consistent with this, multiple differentially expressed (p_adj < 0.1) protein-encoding and lncRNA-encoding genes were shared among the autologous, MUD, and MMUD T cells relative to healthy control T cells (Figure 1C, see tables S1–S3 for complete gene lists). The most enriched gene ontology (GO) terms for differentially expressed protein-encoding genes (p_adj < 0.05) in the autologous, MUD, and MMUD samples relative to the healthy controls overlapped and included terms related to transcriptional and translational regulation (Figure 1D and tables S4–S6). Further, multiple terms pertaining to T cell functions were enriched in all groups relative to healthy controls (tables S4–S6), indicating the cells were proliferative and activated following any HSCT. This increased proliferative and activated state of T cells after HSCT was consistent with that observed in a rhesus macaque GVHD model that used microarrays to profile the transcriptome of CD3⁺ T cells after HSCT (9).

We observed large differences in the transcriptomes of autologous, MUD, or MMUD T cells relative to healthy control T cells. To assess for changes due to allogeneic stimulation among transplanted T cells, we compared the MUD and MMUD groups relative to the autologous group (p_adj < 0.05) and performed hierarchical clustering. There were too few differentially expressed transcripts between the MUD and autologous groups to perform meaningful hierarchical clustering. By contrast, hierarchical clustering based on differentially expressed transcripts between the MMUD and autologous groups showed that the MUD and autologous transcriptomes were similar, whereas the MMUD samples separated into a distinct cluster (Figure 1E). To control for effects of immunosuppressive GVHD prophylaxis on differential expression associated with allogeneic stimulation of T cells after HSCT, we hierarchically clustered transcripts differentially expressed (p_adj < 0.05) between the MMUD and MUD groups. This showed a distinct MMUD cluster and slightly improved the clustering of the MUD and autologous samples (Figure 1E). These data demonstrated that in the context of immunoprophylaxis, the transcriptomes of transplanted donor T cells with an MHC mismatch relative to their recipient (MMUD samples), which typically confers a greater allogeneic T-cell response (10), differed the most relative to the autologous or MUD groups.

We determined the number of differentially expressed protein-encoding genes (p_adj < 0.1) in the MUD and MMUD groups relative to the Auto group. In agreement with the hierarchical clustering analysis, 92% (70/76) of the differentially expressed protein-encoding genes were exclusively differentially expressed in the MMUD group relative to the Auto group (Figure 1F, see tables S7 and S8 for complete gene lists). These data further indicated that the protein-encoding Auto and MUD transcriptomes were similar and that MHC mismatching between the donor and recipient, as in the MMUD group, enhanced differential expression of protein-encoding transcripts.

We next performed gene ontology (GO) analysis on differentially expressed protein-encoding genes (p_adj < 0.05) in the MMUD group relative to the MUD group or the autologous controls. Because expression increased in the MMUD group for nearly all of the protein-encoding genes (Figure 1F), we included any differentially expressed protein coding gene, regardless of its direction, in the GO analysis (Figures 1G and H, and tables S9 and S10). Several terms consistent with T cell activation were enriched, including interleukin-2 (IL-2) regulation, IL-23 signaling, and chemotaxis. Terms associated with immunologic regulation, such as IL-10 and transforming growth factor β (TGF-β) signaling, were also enriched. Extending previous data from macaque CD3⁺ T cells post-allogeneic HSCT, we additionally observed the enrichment of GO terms for Wnt signaling and for phenylpropanoid and stilbene metabolic processes in human T cells (9).

Previous work demonstrated that microRNAs, a type of ncRNA, are important regulators of allogeneic T-cell responses following HSCT (11). To verify that ncRNAs were differentially regulated in our RNA-seq data set, we assessed the differential expression of microRNAs. We detected a number of differentially expressed microRNAs when we compared any of the post-HSCT groups with the healthy control group (tables S11–S13).

Because lncRNA differential expression has never been assessed in allogeneic T cells, we also examined the expression patterns of the differentially expressed genes encoding lncRNAs among transplanted T cells. We assessed the number of differentially expressed (p_adj < 0.1) lncRNAs in the MUD and MMUD groups relative to the Auto group. Similar to the protein-encoding genes, their expression generally increased when MHC mismatches were present (in the MMUD recipient group) (Figures 1F, I, and J). Strikingly, 100% (13/13) of the differentially expressed lncRNAs were exclusively differentially expressed in the MMUD group relative to the Auto group (Figure 1F). By contrast, just one lncRNA was decreased (p_adj < 0.1) when we compared the MMUD and MUD groups (Figures 1I and J, see table S14 for a complete gene list). However, no lncRNAs were differentially expressed in the Auto group compared to the MUD group. These data suggested that MHC disparity, as in the MMUD group, influenced differential expression of lncRNAs when compared with either the MHC-matched but minor histocompatibility disparate allogeneic MUD group or the Auto group. Notably, fewer differentially expressed genes encoding lncRNAs were identified than were protein-encoding genes (Figures 1C and F and tables S1–S3, S7, S8, and S14 for complete gene lists), which may reflect the lower number of annotated lncRNAs (15,767) than protein-encoding genes (19,950) in Genecode 25 used as the reference annotation.

Validation of lncRNA differential expression in an independent HSCT patient cohort

To confirm the sequencing data, we performed quantitative reverse transcriptase (qRT)-PCR with the RNA that was used for sequencing. We randomly tested 11 of the 14 lncRNAs identified by RNA-seq as differentially expressed in MMUD cells compared to either MUD or autologous donor T cells (p_adj < 0.1). The three that we did not test from this set of samples were Linc00824, RP11-357H14.17, and RP11-489E7.4. Eight of the 11 (73%) were validated using qRT-PCR with ANOVA p < 0.1 (Figure 2A, table S15). The three lncRNAs not validated were RP11-730K11.1, Linc01422, and Linc00402. However, all three trended towards statistical significance, and their statistical significance was likely limited by our inability to validate each lncRNA with all of the RNA-seq samples due to sample limitations. Similar results were obtained for protein-encoding genes (figure S1, table S16). Of the 17 protein-coding genes tested, 7 (41%) showed a similar change in abundance in the MMUD samples as we found by RNA-seq. Overall, these data showed that MHC disparity between HSCT donors and recipients predominantly increases the abundance of transcripts for protein-encoding genes and lncRNAs in donor T cells after HSCT.

Figure 2: — A. qRT-PCR was performed on total RNA derived from CD3⁺ peripheral blood T cells from the RNA-seq patient cohort (Table 1) and the expression of the indicated transcripts was measured relative to β-actin. B. qRT-PCR was performed on cryogenically preserved CD3⁺ peripheral blood T cells from the independent validation patient cohort (Table 2). Relative expression of the indicated transcripts is shown relative to β-actin. Values are mean +/− SEM. *p < 0.05. p-values were calculated using original FDR method of Benjamini and Hochberg following a one-way ANOVA in which p < 0.1 (See tables S15 and S16 for details).

To further validate and enhance the generalizability of lncRNA differential expression from the RNA-seq data, we performed qRT-PCR on T cells isolated from a larger independent cohort of HSCT patients with similar patient characteristics to the cohort used for the sequencing analysis (Table 2). To control for sample quality, we monitored the percentage of CD3⁺ live lymphocytes and the RNA integrity of each sample, neither of which exhibited differences among the groups (figures S2A and B). We then validated a similar change in abundance in the MMUD samples as we found by RNA-seq for 4 of the differentially regulated lncRNAs from the RNA-seq data: Linc00402, RP11-348F1.3, RP5-1185I7.1, and Linc01395 (Figure 2B, and table S17). We also validated the differential transcript abundance of 7 protein-encoding genes from our RNA-seq patient cohort (comparing MMUD or MUD to Auto) in the independent validation patient cohort (figure S3 and table S18).

Table 2.

Validation cohort characteristics.

	Transplant Group	Age	Sex	Primary Diagnosis	Conditioning	GVHD Prophylaxis	Stem cell Source	HSCT Year	Day Post BMT	Donor CD3% Day 30	HLA Mismatch	Recipient Mismatched Allele	Donor Mismatched Allele
Auto 1	Autologous	51	Female	MM	Mel		PBSC	2006	36
Auto 2	Autologous	63	Male	MM	Mel		PBSC	2008	35
Auto 3	Autologous	50	Female	MM	Mel		PBSC	2006	34
Auto 4	Autologous	52	Male	MM	Mel		PBSC	2008	28
Auto 5	Autologous	44	Female	MM	Mel		PBSC	2008	28
Auto 6	Autologous	68	Male	MM	Mel		PBSC	2007	31
Auto 7	Autologous	48	Male	MM	Mel		PBSC	2006	32
Auto 8	Autologous	54	Male	MM	Mel		PBSC	2008	28
Auto 9	Autologous	61	Female	MM	Mel		PBSC	2008	32
Auto 10	Autologous	72	Male	MM	Mel		PBSC	2006	25
Auto 11	Autologous	61	Male	MM	Mel		PBSC	2006	34
Auto 12	Autologous	62	Male	MM	Mel		PBSC	2008	33
Auto 12	Autologous	62	Male	MM	Mel		PBSC	2008	33
Auto 13	Autologous	68	Male	MM	Mel		PBSC	2008	26
Auto 14	Autologous	61	Male	MM	Mel		PBSC	2008	31
MUD 1	Matched Unrelated	30	Male	MDS-T	Flu Bu4	Tacro/MTX	PBSC	2012	35	76%	None (10/10)
MUD 2	Matched Unrelated	50	Female	TR B-ALL	Flu Mel	Tacro/MMF	PBSC	2012	34	95%	None (8/8)
MUD 3	Matched Unrelated	66	Male	MDS	Flu Bu4	Tacro/MTX	PBSC	2012	36	90%	None (8/8)
MUD 4	Matched Unrelated	55	Male	MF	Flu Bu4	Tacor/MTX	PBSC	2012	32	57%	None (10/10)
MUD 5	Matched Unrelated	53	Male	MF	Flu Bu4	Tacro/MTX	PBSC	2012	33	30%	None (10/10)
MUD 6	Matched Unrelated	45	Male	FL	R-Flu Bu4	Tacro/MTX	BM	2012	33	90%	None (10/10)
MUD 7	Matched Unrelated	29	Female	T-ALL/HLH	Cy TBI	Tacro/MTX	PBSC	2012	31	84%	None (10/10)
MUD 8	Matched Unrelated	51	Male	AML	Flu Bu4	Tacro/MTX	PBSC	2012	30	95%	None (10/10)
MUD 9	Matched Unrelated	58	Male	AML	Flu Bu4	Tacro/MTX	PBSC	2012	39	64%	None (8/8)
MUD 10	Matched Unrelated	48	Male	DLBCL	R-Flu Bu4	Tacro/MTX	PBSC	2011	27/30	95%	None (10/10)
MUD 11	Matched Unrelated	61	Female	AML	Flu Bu4	Tacro/MTX	PBSC	2012	27	95%	None (10/10)
MMUD 1	Mis-matched Unrelated Donor	64	Male	AML	Flu Bu4	Tacro/MTX	PBSC	2010	28	92%	A (7/8)	Not Available	Not Available
MMUD 2	Mis-matched Unrelated Donor	69	Male	ALL	Flu Bu4	Tacro/MTX	PBSC	2013	34	81%	B (7/8)	HLA-B*27:09/57:01	HLA-B*27:05/57:01
MMUD 3	Mis-matched Unrelated Donor	52	Male	ALL	Cy/TBI	Tacro/MTX	PBSC	2012	32	91%	DQ (9/10)	HLA-DQ1*03:02/03:02	HLA-DQ1*03:02/03:01
MMUD 4	Mis-matched Unrelated Donor	63	Female	ALL	Flu Bu4	Tacro/MTX	PBSC	2013	35	93%	A (9/10)	HLA-A*03:01/32:01	HLA-A*03:01/02:01
MMUD 5	Mis-matched Unrelated Donor	59	Male	CTCL	FluMel	Tacro/MTX	PBSC	2013	21	100%	C (9/10)	HLA-C*03:04/15:02	HLA-C*03:04/06:02
MMUD 6	Mis-matched Unrelated Donor	63	Male	MDS	Flu Bu4	Tacro/MTX/Vorinostat	PBSC	2013	23	74%	DQ (9/10)	HLA-DQB1*03:01/03:01	HLA-DQB1*03:01/03:02
MMUD 7	Mis-matched Unrelated Donor	61	Male	DLBCL	R-Flu Bu2 TBI	Tacro/MMF/Enbrel	PBSC	2009	30	96%	C (9/10)	Not Available	Not Available
MMUD 8	Mis-matched Unrelated Donor	55	Female	MCL	Flu Bu2 TBI	Tacro/MTX/Vorinostat	PBSC	2009	38	76%	DRB1/DQ1 (8/10)	HLA-DRB103:01/03:01 HLA-DQ102:01/02:01	HLA-DRB103:01/13:03 HLA-DQ102:01/03:01
MMUD 9	Mis-matched Unrelated Donor	52	Female	AML	Flu Bu4	Tacro/MTX	PBSC	2013	30	51%	C (9/10)	HLA-C*07:02/07:02	HLA-C*03:03/07:02
MMUD 10	Mis-matched Unrelated Donor	54	Male	AML	Flu Bu4	Tacro/MTX	PBSC	2010	35	95%	A (9/10)	HLA-A*11:01/11:01	HLA-A*11:01/Not Available
MMUD 11	Mis-matched Unrelated Donor	56	Female	AML	Flu Bu4	Tacro/MMF/Enbrel/ECP	PBSC	2012	29	95%	A (9/10)	HLA-A*11:01/32:01	HLA-A*11:01/33:01
MMUD 12	Mis-matched Unrelated Donor	10	Female	HgSS	Campath Flu Mel	Tacro/MMF/Methylprednisone	BM	2012	23/32	95%	DQ (9/10)	HLA-DQ1*05:02/06:02	HLA-DQ1*05:02/06:03
MMUD 13	Mis-matched Unrelated Donor	23	Male	SAA	Thymo Flu Cy TBI	Cyclosporin/MTX	BM	2012	31/36	95%	C (7/8)	HLA-C*03:03/12:Novel	HLA-C*03:03/12:02
MMUD 14	Mis-matched Unrelated Donor	66	Male	MDS	Flu Bu4	Tacro/MMF/Enbrel/ECP	PBSC	2010	25	72%	C (7/8)	HLA-C*05:01/16:02	HLA-C*05:01/05:01
MMUD 15	Mis-matched Unrelated Donor	61	Male	DLBCL	R-Flu Bu4 TBI	Tacro/MMF	PBSC	2010	32/35	94%	A (7/8)	Not Available	Not Available
MMUD 16	Mis-matched Unrelated Donor	61	Male	AML	Flu Bu4	Tacro/MTX	PBSC	2012	29	67%	DRB1 (9/10)	HLA-DRB1*04:01/15:01	HLA-DRB1*13:19/15:01
MMUD 17	Mis-matched Unrelated Donor	66	Female	AML	Flu Bu2 TBI	Tacro/MMF/Enbrel/ECP	PBSC	2009	33	95%	A (9/10)	HLA-A*01:01/25:01	HLA-A*01:01/02:01

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AML: acute myeloid leukemia. BM: Bone marrow. CTCL: Cutaneous T-cell lymphoma. Cy: Cyclophosphamide. DLBCL: Diffuse large B-cell lymphoma. ECP: extracorporeal photopheresis. Flu Bu 4: fludarabine, busulphan. HgSS: Sickle cell disease. MCL: Mantle cell lymphoma. MDS: myelodysplastic syndrome. MDS-T: Therapy related myelodysplastic syndrome. Mel: Melphalan. MF: Myelofibrosis. MM: multiple myeloma. MMF: mycophenolic acid. MTX: methotrexate. PBSC: peripheral blood stem cells. R: Rituxan. SAA: Severe aplastic anemia. Tacro: tacrolimus. TBI: Total body irradiation. T-ALL/HLH: Treatment-related T-cell acute lymphoblastic leukemia/hemophagocytic lymphohistiocytosis. TR B-ALL: Treatment related B-cell acute lymphoblastic leukemia. Thymo: Thymoglobuli

Linc00402 is present in the nucleus and cytoplasm in human T cells

To characterize the tissue-specific expression of our identified lncRNAs, we queried the FANTOM CAT database (12). Of the 4 lncRNAs that were differentially abundant and validated in the independent cohort, Linc00402 and RP11-348F1.3 were enriched in primary human T cells 88.3- and 17.8-fold compared to all other tissue and cell types in the FANTOM CAT database, respectively (Figure 3A). By contrast, RP5-1185I7.1 was not enriched in T cells, and Linc01395 was not annotated in the database (Figure 3A). Using 4 different published algorithms, the FANTOM CAT database predicted that Linc00402 and RP11-348F1.3 did not possess protein-coding potential (Figure 3B). Given these results, we characterized and analyzed the function of these T-cell specific lncRNAs that we found are differentially expressed in allogeneic human T cells after HSCT.

Because subcellular localization of lncRNAs often correlates with their molecular function (13), we determined the subcellular localization of the two T cell specific lncRNAs, Linc00402 and RP11-348F1.3. We fractionated unstimulated healthy human T cells and cells stimulated with antibodies against CD3 and CD28 (anti-CD3/CD28) and extracted RNA for qRT-PCR. Actin-encoding transcripts and the small nuclear RNA U2 (snRNA-U2) were included as cytoplasmic and nuclear controls, respectively, both of which shifted more towards their expected subcellular compartments following anti-CD3/CD28 stimulation (Figure 3C). By contrast, most Linc00402 and RP11-348F1.3 transcripts localized to the cytoplasm, and their distributions were unaffected by anti-CD3/CD28 stimulation (Figure 3C). These results are consistent with Malat1, another lncRNA present in T cells (6, 14), and with two independent in silico lncRNA subcellular localization prediction algorithms (13, 15). To further confirm these results, we performed RNA fluorescence in situ hybridization (RNA-FISH) on primary human peripheral blood T cells from healthy individuals. The RNA-FISH analysis suggested that each human T cell contained an average of 21 molecules of Linc00402 and that ~52% localized in the nucleus (Figures 3D–F). Although the amount of GAPDH transcripts was similar (~24 molecules/cell), only ~22% localized in the nucleus (Figures 3D–F). By comparing to a standard curve generated by qRT-PCR using a human Linc00402 cDNA expression plasmid, we found that bulk human T cells have ~0.5 copies of Linc00402 per cell (figure S4), likely an underestimation due to our plasmid-based standard curve not undergoing reverse-transcription. Overall, our data indicated that Linc00402 is a nucleocytoplasmic lncRNA, suggesting that it may function in both of these compartments of the cell.

RNA-FISH showed heterogeneity for the number of Linc00402 spots/cell in primary human T cells (Figure 3E). We hypothesized that this is due to differences in the abundance of Linc00402 in T cell subsets. To test this, we assessed Linc00402 in enriched human CD4⁺CD8⁻ and CD4⁻CD8⁺ T cells (figure S5). Linc00402 was slightly more abundant in the CD4⁺CD8⁻–enriched fraction (Figure 3G). In addition, the FANTOM CAT RNA-seq dataset reported greater expression of Linc00402 in naïve regulatory T cells (Tregs) relative to naïve conventional CD4⁺ cells (Figure 3H). These results suggested that Linc00402 abundance varies in T cell subsets.

In vitro allogeneic stimulation differentially regulates Linc00402 abundance

Because greater differences in lncRNA abundance correlated with a greater degree of MHC disparity in donors and recipients of HSCT, we tested the hypothesis that allogeneic stimulation regulated Linc00402 abundance in vitro using mixed lymphocyte reactions (MLRs). Negatively selected T cells from healthy donors were mixed with either positively selected, lethally-irradiated (30 Gy) autologous monocytes or a pool of 2 – 3 unrelated (allogeneic) donor peripheral blood monocytes. In addition, T cells were incubated with anti-CD3/CD28–coated beads as a positive control for T cell activation. Following incubation, the T cells were separated from the remaining irradiated monocytes by negative selection and analyzed for lncRNAs by qRT-PCR. As a control for upregulated genes, we included the confirmed protein-encoding gene SV2A and, for downregulated genes, the lncRNA MIAT. As expected, the abundance of SV2A transcripts increased following allogeneic stimulation or T cell activation with anti-CD3/CD28 and the abundance of MIAT decreased (Figure 4A). Surprisingly and in contrast to the in vivo data following HSCT, in vitro allogeneic stimulation resulted in decreased amounts of all 4 of these lncRNAs. Downregulation of Linc00402 and RP11-348F1.3 also occurred rapidly following anti-CD3/CD28 stimulation in vitro (Figures 4B), suggesting that their abundance was directly regulated by T-cell activation. Thus, Linc00402 and RP11-348F1.3 exhibited opposite regulation in vivo versus in vitro. We therefore explored the reason for this difference.

Figure 4: — (A) Human T cells were incubated with irradiated (30 Gy) autologous or allogeneic monocytes for 6 days or anti-CD3/CD28 DynaBeads for 2 days. The T cells were then re-isolated with negative magnetic selection and subject to qRT-PCR (*p < 0.05, one-way ANOVA with original FDR method of Benjamini and Hochberg, n = 3 – 6 independent trials per group). B. Human T cells from healthy donors were activated with anti-CD3/CD28 DynaBeads for the indicated times and then the expression of the indicated transcripts was assessed by qRT-PCR ( n = 2 – 4 independent trials per group). Values are mean +/− SEM. **C-D.** Human T cells were pre-incubated with tacrolimus (30 ng/mL) or placebo for 1 hour prior to mixing them with irradiated (30 Gy) autologous or allogeneic monocytes or placebo. Seven days later, T cells were negatively magnetically isolated and subjected to qRT-PCR analysis of *Linc00402* with fold changes set relative to autologous placebo-treated controls (n = 2 – 3 independent trials per condition, *p < 0.05, one-sample t-test). E. Proliferation was assessed by uptake of tritiated thymidine relative to autologous placebo control uptake (n = 3 independent trials per condition; values are mean +/− SEM). F. Human T cells were pre-incubated with the indicated concentrations of rapamycin or DMSO for 1 hour prior to mixing them with irradiated autologous or allogeneic monocytes or PBS. Seven days later, T cells were negatively magnetically isolated and subjected to qRT-PCR analysis of Linc00402 with fold changes set relative to autologous DMSO-treated controls. (n = 3 independent trials per condition; values are mean +/− SEM). G. The correlation between *Linc00402* abundance (log₂ fold change from autologous controls) in MUD and MMUD HSCT recipient T cells and their day plus 30 tacrolimus trough concentration (p-value calculated using simple linear regression). H. Average day +30 tacrolimus trough concentrations from MUD and MMUD HSCT recipients (values are mean +/− SEM; two-tailed t-test). I. Average day +30 *Linc00402* abundance (fold change from autologous controls) in T cells from MUD and MMUD HSCT recipients who did or did not develop grade II-IV GVHD within 3 weeks of their day +30 *Linc00402* measurement (Welch’s t-test). J. CD3⁺ T cells from a third cohort of age- and sex-matched patients approximately 30 days post HSCT (**table S19**) were subjected to qRT-PCR analysis for the fold change of *Linc00402* relative to matched autologous HSCT control patients (* p<0.05, ANOVA with Tukey’s multiple comparisons test). K. A representative fluorescence activated cell sorting (FACS) plot (gated on live CD8⁺CD4⁻ cells) shows CMV-specific and non-specific cytotoxic T cell fractions. L. The absolute number of sorted CMV-specific CTLs (pp65 dextramer⁺ Live CD8⁺ CD4⁻) is shown for the four patients described in table S20 (values are mean +/− SEM). M. qRT-PCR analysis measuring the fold change of *Linc00402* in CMV-specific and non-specific CTL fractions (values are mean +/− SEM). N. *Linc00402* fold change assessed via qRT-PCR in CD3⁺ T cells isolated from peripheral blood from patients awaiting cardiac transplantation or patients 4 weeks post-cardiac transplantation (values are mean +/− SEM; Welch’s t-test).

Inhibition of T cell activation preserves Linc00402 abundance

A key difference between the allogeneic (MMUD and MUD) T cell patient samples and the allogeneic T cells tested in vitro was that the patient samples were exposed to tacrolimus for GVHD prophylaxis. Tacrolimus is a calcineurin inhibitor that blocks T cell activation by inhibiting the nuclear translocation of nuclear factor of activated T cells (NFAT) (16). Therefore, we hypothesized that its presence may affect the regulation of Linc00402 in T cells. To test this, we performed human in vitro MLRs in the absence and presence of tacrolimus. Consistent with this hypothesis, tacrolimus-treated allo-stimulated T cells had more Linc00402 than placebo-treated autologous-stimulated T cells (Figure 4C), consistent with the in vivo results between the MMUD and autologous patient groups (Figure 2B). Resting, placebo-treated T cells had the greatest amount of Linc00402, and the amount declined with increasing amounts of stimulation provided by irradiated autologous or allogeneic monocytes (Figures 4D), in accordance with what we observed in our RNA-seq data set. In agreement with tacrolimus influencing the expression of Linc00402 by modulating T-cell activation, T-cell proliferation inversely correlated with Linc00402 expression in MLRs (Figure 4E). Tacrolimus also preserved the amount of RP11-348F1.3 in allogeneic-stimulated T cells relative to placebo-treated autologous-stimulated human T cells (figures S6A and B). Similar preservation of Linc00402 abundance was also observed in a dose-dependent manner when we performed MLRs using the T cell activation inhibitors rapamycin [an inhibitor of the kinase mechanistic target of rapamycin (mTOR) (Figure 4F), cyclosporine A (CSA, a calcineurin inhibitor), or mycophenolic acid (MPA, an inosine-5’-monophosphate dehydrogenase inhibitor) (figures S7A–E). Of note, we did not observe any substantive T cell toxicity with rapamycin, MPA, or CSA (figure S7F).

Because two calcineurin inhibitors preserved Linc00402 abundance, we hypothesized that NFAT transcription factors may regulate Linc00402 expression. This was tested in human allogeneic-stimulated T cells following depletion of NFATc1 and NFATc2 (17) with CRISPR/Cas9 RNP nucleoporation (figure S8A). Depletion of NFATc1 and NFATc2 reduced tritiated thymidine uptake and IFNγ secretion following an allogeneic stimulus (figures S8B and C) but did not alter Linc00402 or IL2 transcript abundance (figures S8D and E) (18). Consistent with tacrolimus limiting T cell activation and thereby preserving Linc00402 abundance, there was a trend towards a positive correlation between day +30 tacrolimus troughs and the amount of Linc00402 in cells from MUD and MMUD HSCT recipients (Figure 4G).

These data suggested that inhibition of T cell activation by tacrolimus resulted in higher amounts of Linc00402 in allogeneic antigen-stimulated T cells both in vivo (MMUD patients) and in vitro (MLR). However, this still did not account for the lack of increased Linc00402 in the MUD patient samples, which were also exposed to tacrolimus and presumably received allogeneic stimulation from disparate minor histocompatibility antigens, albeit likely a milder allogeneic stimulus than that received by the MMUD samples (10). To address this conundrum, we reasoned that physicians kept tacrolimus concentrations higher in MMUD patients due to their increased risk of GVHD relative to the risk in MUD patients (19). To test this, we analyzed whether higher tacrolimus concentrations in the MMUD patients that limited T cell activation account for the higher amount of Linc00402 in MMUD patient T cells relative to the amount in MUD patient T cells. Consistent with this notion, we observed a trend towards higher concentrations of the day +30 tacrolimus troughs in the MMUD patients relative to the MUD patients (Figure 4H).

These data collectively suggested that Linc00402 abundance was inversely proportional to the activation status of T cells. We further correlated the clinical consequence of this by determining whether Linc00402 abundance inversely correlated with the development of clinically significant acute GVHD (grade II-IV) within 3 weeks of the day +30 Linc00402 measurement in MUD and MMUD patients. We restricted the analysis to acute GVHD onset within the subsequent 3 weeks after the day +30 Linc00402 measurement because low calcineurin inhibitor troughs increase the risk of developing acute GVHD in the following week (20). Consistent with this, we observed a significantly higher Linc00402 abundance in those who did not go on to develop GVHD in the subsequent 3 weeks (Figure 4I).

To confirm these results, we assembled from our biobank a third independent HSCT cohort of autologous, MUD, and MMUD HSCT recipients with similar characteristics as the RNA-seq and independent validation patient cohorts (Tables 1 and 2), except we performed a pairwise analysis with age- and sex-matched samples (table S19). As before, we analyzed Linc00402 by qRT-PCR relative to matched autologous HSCT recipient samples from peripheral blood T cells approximately 30 days post HSCT (figure S9A). Relative to matched autologous controls, the amount of Linc00402 increased in both MUD and MMUD recipients (Figure 4J). This cohort of patients showed no difference in the mean tacrolimus trough concentrations between the MUD and MMUD recipients; however, the tacrolimus trough standard deviation was greater in the MMUD group (figure S9B). There was no correlation between the tacrolimus trough and Linc00402 abundance in our third cohort when both MUD and MMUD recipients were analyzed together (figure S9C). Perhaps due to the greater tacrolimus trough deviation in the MMUD group, a correlation trend was observed when just the MMUD recipients were analyzed (figure S9D). Possibly due to just two patients developing acute grade II-IV GVHD within 3 weeks of Linc00402 measurement, there was no significant difference in Linc00402 abundance between allogeneic recipients who did and did not go on to develop GVHD in this third cohort (Figure S9E). We also examined whether Linc00402 abundance in MMUD recipients differed between MHC class I versus class II mismatches. For this analysis, we pooled MMUD recipients from all three cohorts to increase our sample size; however, there was no statistical difference in Linc00402 abundance between class I- or class II-mismatched recipients (figure S9F). Overall, this third cohort confirmed that Linc00402 was higher in T cells from allogeneic recipients (disparate for either minor histocompatibility or major histocompatibility antigens and exposed to T cell suppressive GVHD prophylactic therapy) relative to T cells from autologous HSCT recipients.

In vitro, Linc00402 decreased following stimulation with autologous or allogeneic monocytes, as well as by polyclonal activation with anti-CD3/CD28 antibodies. To assess if Linc00402 abundance was affected by viral antigens or a specific minor histocompatibility antigen, we attempted to isolate cytomegalovirus (CMV) and female H-Y-specific T cells from HLA-A*02:01 haplotype CMV sero-positive male recipients of matched related CMV sero-positive female donors from cryopreserved PBMCs (table S20). We focused on approximate day 100 post-HSCT samples because prior reports indicated H-Y and CMV-specific cytotoxic T lymphocytes (CTLs) were often abundant at this time (21, 22). None of the patients had a history of GVHD or active uncontrolled infections or systemic steroid use at the time of the sample collection. With these criteria, we identified CMV-specific CTLs from four patients, of which only two patients had a sufficient quantity of CMV-specific CTLs for qRT-PCR analysis (Figures 4K and L). Notably, no H-Y-specific T cells were seen. Linc00402 abundance was measured by qRT-PCR in the sorted CMV-specific and non-specific CTL fractions from these remaining two patients. There was no difference in Linc00402 abundance between matched CMV specific and non-specific CTL fractions (Figure 4M); however, this analysis was limited by the number of patients examined and the inability to control the amount or quality of allogeneic and CMV antigen presentation in these complex clinical samples.

Linc00402 abundance in T cells decreases following allogeneic cardiac transplantation

We tested whether Linc00402 abundance changed following allogenic solid organ transplantation. T cells were isolated from cryopreserved PBMCs from patients awaiting cardiac transplantation and patients approximately four weeks post-cardiac transplantation (table S21 and figures S10A–C). Linc00402 abundance decreased following cardiac transplantation (Figure 4N), consistent with greater T cell activation following cardiac transplantation, likely from allograft-derived antigens. Similar results were obtained when Linc00402 abundance was compared to healthy control T cells (figure S10E). Graft rejection prophylaxis consisted of tacrolimus for all of the cardiac transplantation patients. There was no correlation between tacrolimus trough concentrations and Linc00402 abundance (figure 10D); although, the sample size was small.

Collectively, these results indicated that transplant conditions that stimulate allogeneic T-cell activation impact Linc00402 abundance with conditions producing high T cell activation limiting the amount of Linc00402. Nonetheless, given the complexity of the in vivo clinical context and limitations imposed by multiple variables and patient numbers, we analyzed the role of Linc00402 in murine T cells.

Linc00402 is conserved in mouse T cells and is regulated by T cell activation both in vitro and in vivo

Sequence conservation is generally considered a hallmark of functionally important genes; however, only about 40 – 70% of lncRNAs are conserved between humans and mice (12, 23, 24). To assess for murine orthologues, we queried the FANTOM CAT database, which indicated the transcription initiation region and exons of Linc00402 were conserved (Figure 5A), and an orthologous region on mouse chromosome 14 was identified using TransMap Ensembl Mappings Version 4. By contrast, RP11-348F1.3 was not conserved. This Linc00402 orthologous region maintained synteny with the gene encoding transcription factor Kruppel like factor 12 (KLF12) (Figure 5B). Maintenance of synteny and sequence conservation are characteristics associated with functional lncRNAs (25). Therefore, we next assessed whether Linc00042 and RP11-348F1.3 were present in mouse T cells. Primers were designed for highly conserved regions of each and used to conduct qRT-PCR on mouse T cells. A significant signal above a non-template control was obtained for Linc00402 but not for RP11-348F1.3 (Figure 5C). A 2.5 kb band, which is slightly larger than the 3.3 kb human transcript for Linc00402 (figure S11), was detected by northern blotting in murine resting T cells but not in negative-control human HeLa cells (Figure 5D). In addition, this 2.5 kb band was downregulated in cells stimulated with anti-CD3/CD28 antibodies (Figure 5D).

Figure 5: — **(A)** LncRNA conservation was queried using the FANTCOM-CAT database. B. Depiction of the *Linc00402* loci on chromosome 13 (chr13) in human and 14 (chr14) in mouse. Downward arrows indicate the approximate primer positions used to detect mouse *Linc00402* in panel C. C. qRT-PCR was performed on murine T cells using primers for regions predicted by the Ensembl genome browser and Transmap as orthologous to the indicated human lncRNA exonic region. Data depict the subtraction of qRT-PCR threshold cycles from threshold cycles obtained from negative reverse transcriptase controls (*p < 0.05, two-tailed t-test, data are representative of 2 – 4 independent trials per group). D. Ten μg of total RNA from resting mouse T cells or mouse T cells stimulated for 48 hours with anti-CD3/CD28 Dynabeads was analyzed by northern blot using probes specific for murine *Linc00402* and mRNA encoding β-actin. Total RNA from HeLa cells was used as a negative control. Triplicate biologic replicates are shown. The arrow points to a 2.5 kb band that is similar in size to human *Linc00402*, absent in HeLa cells, and decreases in mouse T cells following anti-CD3/CD28 stimulation. E. The relative abundance of *Linc00402* compared to non-template controls was assessed in C57BL/6 primary cells (DC: dendritic cell, LSK: Lin⁻ Sca-I⁺ c-Kit⁺ bone marrow cells, *p <0.05,two-tailed one sample t-test with a null hypothesis of 0; 2 – 7 independent trials were performed per group). F.C57BL/6 splenic T cells were incubated with irradiated (30 Gy) syngeneic or allogeneic (BALB/c) splenocytes or anti-CD3/CD28 Dynabeads. T cells were then re-isolated and subjected to qRT-PCR (*p < 0.05, one-way ANOVA, original FDR method of Benjamini and Hochberg, data are representative of 2–7 independent trials per group). G. Mouse T cells were activated with anti-CD3/CD28 Dynabeads for the indicated times and then the expression of the indicated transcripts was assessed by qRT-PCR (data are representative of 2 – 4 independent trials per group, error bars represent the mean +/− the SEM). H. The proliferation (measured as H3 thymidine incorporation) of OT-I T cells following mock stimulation or stimulation with placebo or SIINFEKL-pulsed dendritic cells for 48 hours is shown (*p <0.05 via a one-way ANOVA with Tukey’s multiple comparison test, values are mean +/− SEM, data are from 3 independent trials). I. *Linc00402* fold change (relative to resting T cells) from OT-I T cells harvested following the incubation described in H are shown (*p < 0.05, ratio paired t-test, values are mean +/− SEM, data are from 3 independent trials). **J and K.** Mouse T cells were pre-incubated with tacrolimus (90 ng/mL) or placebo for 1 hour prior to mixing them with irradiated (30 Gy) syngeneic or allogeneic splenic monocytes or placebo. Seven days later, T cells were negatively magnetically isolated and subjected to qRT-PCR analysis of *Linc00402* with fold changes set relative to syngeneic placebo-treated controls (data are from 3 independent trials per condition, *p<0.05 using a two-tailed t-test). **L and M.** The differential expression of selected transcripts from allogeneic splenic donor T cells was assessed by qRT-PCR on days +7 and +14 from mHA-disparate (L) and multiple miHA-disparate (M) recipients (*p < 0.05, one-way ANOVA, original FDR method of Benjamini and Hochberg, data are from 3–4 independent trials per group). Values are mean +/− SEM.

We also assessed whether murine Linc00402 was restricted to certain cell types. We isolated stem cell enriched lineage negative/Sca-I⁺/c-Kit⁺ (LSK) bone marrow cells, T cells, B cells, natural killer (NK) cells, dendritic cells, neutrophils, and macrophages, in addition to culturing primary mouse hepatocytes, colonic epithelial cells, dermal fibroblasts, skeletal muscle cells, cortical neurons, and coronary endothelial cells. A positive signal for murine Linc00402 by qRT-PCR above a non-template control was found only in primary mouse cortical neurons and T cells, suggesting that Linc00402 expression is also restricted in mice (Figure 5E). These data demonstrated that Linc00402 is present, enriched, and conserved in T cells from both humans and mice.

We next assessed whether Linc00402 in murine T cells was also regulated by allo-stimulation as in human T cells. We first tested this in vitro with murine MLRs with T cells and irradiated monocytes derived from mouse spleens. As in the human MLRs, the abundance of the control protein-encoding transcript SV2A increased upon allo-stimulation or non-specific T cell activation. By contrast, lncRNAs Miat and Malat1 decreased in response to an allogeneic or anti-CD3/CD28 stimulus (Figure 5F). Similar to that observed in human T cells (Figure 4A and B), murine Linc00402 abundance also decreased with allo-stimulation and T-cell activation (Figure 5F). Furthermore, just as with human Linc00402, murine Linc00402 abundance in T cells rapidly declined following stimulation with anti-CD3/CD28 (Figure 5G), and its abundance declined following activation of antigen-specific OT-I T cells with dendritic cells presenting the OT-I specific antigen SIINFEKL (Figures 5H and I).

We explored whether, as in human allogeneic T cells, incubation of murine allogeneic T cells with tacrolimus also increased Linc00402 abundance. Consistently, the abundance of murine Linc00402 was higher in mouse T cells treated with tacrolimus and stimulated with allogeneic monocytes relative to the abundance observed in placebo-treated mouse T cells stimulated with syngeneic monocytes (Figures 5J and K). These data demonstrated that the in vitro regulation of Linc00402 in allogeneic T cells was conserved in humans and mice.

We hypothesized that regulation of Linc00402 in allogeneic T cells following allogenic HSCT in mice is the same as that observed following in vitro experiments with allogeneic stimulation. To determine this, we measured the amount of Linc00402 in donor T cells at 7 and 14 days after HSCT in two murine models of GVHD (B6→Balb/c and B6→C3H.SW). Specifically, the amount of murine Linc00402 decreased to varying extents depending on the time point and model (Figures 5L and M). The decreased amount of murine Linc00402 in allo-antigen–stimulated T cells is consistent with the lack of tacrolimus in these models and demonstrated that Linc00402 abundance is downregulated upon murine T-cell activation both in vitro and in vivo.

Antisense-mediated knockdown of Linc00402 impairs human and murine T cell proliferation in response to allogeneic stimulation

Having established the differential abundance of Linc00402 in human and mouse allogeneic T cells both in vitro and in vivo, we explored the functional relevance of Linc00402 in T cells. We utilized GapmeR antisense oligonucleotides, which are effective in knocking down transcripts in primary T cells both in vitro and in vivo (26, 27). To assess the efficiency of GapmeR uptake in resting T cells, varying concentrations of a non-targeting fluorescently-labeled GapmeRs were incubated with human or mouse T cells for 72 or 48 hours, respectively (figure S12A). Cryogenically preserved peripheral blood human T cells and freshly isolated mouse splenic T cells efficiently took up the GapmeRs within 72 and 48 hours, respectively (figure S12A). GapmeRs targeting human and mouse Linc00402 or human RP11-348F1.3 and mouse Malat1 (controls) did not cause any cytotoxicity (figures S12B and C). Relative to a non-targeting control, these GapmeRs reduced the expression of their target transcripts in human and mouse T cells (figures S12D and E). In MLR assays, knockdown of Linc00402 in either human or murine T cells inhibited proliferation in response to an allogeneic stimulus but had no effect on proliferation in response to an anti-CD3/CD28 stimulus (Figures 6A and B). Knockdown of the ubiquitously expressed, pro-proliferative lncRNA murine Malat1 (28), inhibited T cell proliferation following either allogeneic or anti-CD3/CD28 stimulation (Figure 6B). Similar to Linc00402, knockdown of RP11-348F1.3 inhibited human allogeneic T cell proliferation but had no effect on T cell proliferation following anti-CD3/CD28 stimulation (Figure 6A). Because there is no mouse orthologue for RP11-348F1.3, we could not test its influence on murine allogeneic T cell proliferation. Together, these data demonstrated a conserved pro-proliferative function of Linc00402 following an allogeneic stimulus in both human and murine T cells.

Figure 6: — **(A, B)** Human or mouse T cells were incubated with the indicated GapmeRs (see figures S12D and S12E). MLRs were then performed as described in Figures 4 and 5, respectively. T cell proliferation was then assessed by tritiated thymidine incorporation (*p < 0.05, one-way ANOVA with original FDR method of Benjamini and Hochberg, data are from 3 – 7 independent trials per group). Error bars represent the mean +/− the SEM. C. Transcript abundance for *Linc00402*, *RP11-348F1.3*, and *KLF12* was assessed by qRT-PCR in human T cells 72 hours after electroporation of *Linc00402*-targeting or non-targeting control CRISPR-Cas9 RNPs (*p < 0.05, 2-way ANOVA with Sidak’s multiple comparison test, data are from 3 – 4 independent trials per group). Values are mean +/− the SEM. D. Human T cells were electroporated with non-targeting control or *Linc00402*-targeting CRISPR-Cas9 RNPs and then stimulated with Dynabeads or the indicated concentrations of soluble anti-CD3/CD28 antibodies for 48 hours. Alternatively, electroporated cells were stimulated with irradiated autologous or allogeneic monocytes and incubated for 14 days. Proliferation was measured by tritiated thymidine incorporation (*p < 0.05, one-way ANOVA and original FDR method of Benjamini and Hochberg, data are from 2 – 3 independent trials per group). Values are mean +/− SEM. E. OT-I cells were transduced with vector control or human *Linc000402*-overexpression lentiviruses. Transduced sorted OT-I T cells were then rested for 3 – 4 days followed by incubation alone, with negative control dendritic cells, or with SIINFEKL-presenting dendritic cells for 48 hours. Proliferation was measured by tritiated thymidine uptake (data are from 2 independent experiments with 3 biologic replicates in total; error bars show the mean +/− SEM). **F-H.** Stable, polyclonal vector control or *Linc00402*-overexpressing Jurkat cells were stimulated with 1 μg/mL of anti-CD3/CD28 antibodies for the indicated times after which *IL2* mRNA change was measured by qRT-PCR and presented as fold change over unstimulated vector control **(F),** secreted IL-2 was measured by ELISA **(G)**, and percent of cells positive for CD25 was determined by flow cytometry **(H)** (*p < 0.05, paired t-test for F and G and a 2-way ANOVA with Tukey’s multiple comparison test for H, values are mean +/− SEM, data are from 3 independent trials). **I-K.** Stable, polyclonal vector control or *Linc00402*-overexpressing Jurkat cells were either unstimulated or stimulated with α-CD3/CD28 (1 μg/mL) for 30 **(I)** or 15 minutes **(J and K)**. The cells were then stained for activated ERK1/2 (phosphorylation of T202/Y204) **(I)**, p38 (phosphorylation ofT180/Y182) **(J)**, or p65 NF-κB (phosphorylation of S529) **(K)** (*p < 0.05 using a t-test, error bars show the mean +/− the SEM, data are from 2 – 3 independent trials). **L and M.** Abundance of *FOS* **(L)** and *EGR1* **(M)** mRNA in stable, polyclonal vector control or Linc00402-overexpressing Jurkat cells was measured by qRT-PCR (*p < 0.05, paired t-test, values are mean +/− SEM, data are from 3 independent trials). **N, O.** Stable, polyclonal vector control or *Linc00402*-overexpressing Jurkat cells were either unstimulated or stimulated with α-CD3/CD28 (1 μg/mL) for the times indicated followed by isolation of nuclear lysates. Western blots were then performed, and the abundance of FOS, JUN, and NFAT1 was quantified. (*p < 0.05, one-sample t-test. Values are mean +/− SEM, data are from 3 – 6 independent trials per group).

CRISPR-Cas9-mediated genomic deletion of Linc00402 impairs human T cell proliferation in response to allogeneic stimulation

We confirmed the knockdown results by deleting Linc00402 in human T cells by nucleoporation of CRISPR-Cas9 ribonucleoprotein complex (RNP) pools (29). Using this approach, we achieved high nucleoporation efficiencies (figure S12F) and robust depletion of Linc00402 without effecting the expression of its neighboring gene KLF12 (Figure 6C). CRISPR-Cas9–mediated depletion of Linc00402 inhibited the ability of T cells to proliferate in response to an allogeneic stimulus but had no effect on their ability to proliferate following stimulation with anti-CD3/CD28 DynaBeads or soluble anti-CD3/CD28 antibodies (Figure 6D). These data demonstrated that Linc00402 specifically regulated allogeneic T-cell proliferation in mice and humans.

Linc00402 overexpression does not augment nominal antigen-specific T cell proliferation

To assess whether Linc00402 enhanced T cell proliferation in response to a specific non-allogeneic antigen being presented by APCs, we transduced OT-I cells with blank or Linc00402 overexpressing lentiviruses (figures S13A and B). Transduced GFP⁺ cells were then sorted and rested for 3–4 days (figure S13C) prior to stimulating them with control dendritic cells or dendritic cells presenting the OT-I–specific ovalbumin peptide SIINFEKL. Linc00402 overexpression did not enhance OT-I proliferation in response to stimulation with its specific antigen (Figure 6E), nor did it alter production of IL-2 or IFNγ (figures S13D and E).

Linc00402 promotes signaling through the ERK pathway in stimulated T cells

Having confirmed that Linc00402 specifically augmented allogeneic T cell proliferation, we evaluated the cellular processes that this lncRNA affects in T cells that could produce this phenotype. Because we found that the abundance of Linc00402 was rapidly downregulated following T cell activation, we hypothesized that Linc00402 itself functions as a positive regulator of T cell activation pathways. This hypothesis would be consistent with the reduction in allogeneic T cell proliferation observed following depletion of Linc00402. To limit variation due to effects related to differences among T cell subsets, we tested this hypothesis by stably overexpressing Linc00402 in the CD4⁺ T-cell acute lymphoblastic leukemia Jurkat cell line (figures S14A and B), which expresses a negligible amount of Linc00402 at baseline (12) (figure S11). Overexpression of Linc00402 resulted in a similar number of RNA-FISH spots/cell and distribution as we observed in primary human T cells (figures 14C-G; Figure 3D–F). Linc00402 overexpression had no effect on the baseline proliferation or viability of these cells (figures S14H and I). Polyclonal stimulation with varying concentrations of anti-CD3/CD28 antibodies or anti-CD3/CD28-coated DynaBeads also had little effect on the proliferation of transient vector control or Linc000402 overexpressing Jurkat cells (figure S14E). To assess the impact of Linc00402 on T cell activation, we stimulated Jurkat cells with anti-CD3/CD28 antibodies and measured IL2 mRNA, IL-2 secretion, and percent of cells positive for CD25 as readouts of the T cell activation signal transduction cascade (30). Relative to stable blank vector-controls, Linc00402 overexpressing Jurkat cells produced more IL2 mRNA, secreted more IL-2, and a greater proportion possessed CD25 on their surface (Figures 6F–H). These data suggested that Linc00402 augments T cell receptor (TCR) signal transduction.

We asked what specific signaling pathway(s) following T cell activation were enhanced by Linc00402. TCR signal transduction converges on the activation of the canonical transcription factors AP-1, NFAT, and NF-κB (31). AP-1 consists of a heterodimer of JUN and FOS, which are activated downstream of the mitogen-activated protein kinases (MAPKs). JUN is activated by p38 or JNK and JUN transcription is induced by these kinases as well; FOS and the encoding gene are activated by ERK1 or ERK2 (ERK1/2) (31), and NFAT is activated by the calcium influx that promotes NFAT dephosphorylation enabling translocation into the nucleus (31). NF-κB consists of a dimer of p65 and p50 (32), which is retained in the cytoplasm and translocates to the nucleus following a cascade of phosphorylation events (31). We assessed whether Linc00402 increased activity through the MAPK to AP-1 or NF-κB pathways, or both, using phosphorylation-specific flow cytometry for activated ERK1/2 (T202/Y204), p38 (T180/Y182), and the p65 subunit of NF-κB (S529) (33, 34) in the stable vector control or Linc00402-overexpressing Jurkat cells. Overexpression of Linc00402 increased the phosphorylation of ERK1/2 but had no effect on the phosphorylation of p38 or p65 in cells stimulated with anti-CD3/CD28 (Figures 6I–K). Linc00402 overexpression also increased the transcripts encoded by the ERK-responsive genes FOS and EGR1, both of which enhance T cell activation and IL-2 production (Figures 6L and M) (31, 35–44). In addition, overexpression of Linc00402 increased the nuclear accumulation of FOS after anti-CD3/CD28 stimulation, had no effect on NFAT1 nuclear accumulation, and transiently decreased the nuclear accumulation of JUN (Figures 6N and O). Together, these data indicated that Linc00402 augments an ERK/FOS pathway following T cell activation.

In silico analysis predicts Linc00402-interacting partners

LncRNAs often achieve their molecular function by interacting with other RNA molecules and/or proteins in the cell (8). To identify potential Linc00402-interacting RNAs and proteins, we used the MechRNA program (63), which predicts a molecular mechanism for an input lncRNA sequence by predicting RNA-RNA interactions and RNA-protein interactions, the latter using high-throughput sequencing of RNA isolated by crosslinking immunoprecipitation (CLIP-seq) data from 22 RNA-binding proteins, and by determining expression correlation from RNA-seq data. For this analysis, we used human T cell expression data from FANTOM CAT. When we input all of the RNA-seq data sets across multiple T cell subsets in the FANTOM CAT database and ran MechRNA in the screening mode, the top RNA-binding proteins predicted to interact with Linc00402 included HNRNPK, TIA1, MOV10 and FUS (table S22). These protein interactions varied when we restricted the input RNA-seq data by T cell subset. For instance, when we limited the RNA-seq data sets to bulk CD4+ cells, the top RNA-binding proteins predicted to interact with Linc00402 included FUS, PUM2, and HNRNPK (table S23). When we limited the RNA-seq data sets to bulk CD8+ cells, the top RNA-binding proteins predicted to interact with Linc00402 included HuR, IGF2BP2, and FMR1 (table S24).

DISCUSSION

The mechanisms governing human allogeneic T cell–mediated toxicities remain incompletely understood, which limits the wider application of allogeneic HSCT and solid organ transplantation. Here, we identified a previously uncharacterized lncRNA, Linc00402, that regulates allogeneic T cells. Specifically, we found that, relative to healthy control T cells, transplanted T cells altered the abundance of thousands of lncRNAs. Among transplanted T cells, MHC-disparate donor T cells demonstrated the largest amount of differentially expressed lncRNAs compared to T cells that were MHC-matched but disparate for minor histocompatibility antigens or T cells proliferating in lymphopenic autologous hosts. These results were confirmed in an independent patient cohort. Changes in the abundance of several of these lncRNAs also occurred in both human and murine MLRs, as well as in multiple murine models of HSCT. Because of its specific expression in T cells, sequence conservation, and synteny in human and mouse, we focused on Linc00402 and validated its functional relevance in both human and murine T cells.

LncRNAs possess remarkable species-, tissue-, and context-specific expression relative to mRNAs (5, 23, 24, 45–47). We observed mainly increased Linc00402 abundance in minor or major histocompatibility disparate donor T cells relative to T cells from autologous HSCT recipients 30 days after HSCT. Linc00402 abundance was also altered in response to an allogeneic stimulus in ex vivo human and murine T cells as well as in vivo murine T cells following multiple models of experimental HSCT. However, we observed that the regulation of Linc00402 in allo-stimulated T cells from clinical samples exposed to immunosuppression was opposite that observed in our immunosuppression-free ex vivo and in vivo models. In T cells from HSCT patients, Linc00402 increased in cells from MMUD or both MMUD and MUD recipients, depending on the cohort. We demonstrated that these differences were due to tacrolimus exposure in the allogeneic patient T cells, which inhibited T cell activation and enhanced the abundance of Linc00402. We further demonstrated that the amount of Linc00402 was directly proportional to the trough concentration of tacrolimus in patients. This was consistent with higher Linc00402 abundance in the validation cohort of MMUD patient T cells in which tacrolimus concentrations were kept higher than in MUD patients. In the third HSCT cohort, we observed increased abundance of Linc00402 in MUD recipients as well as MMUD recipients. We propose that this discrepancy may be related to differences in the amount of T cell inhibition by tacrolimus in the MUD recipients between cohorts. Specifically, the mean tacrolimus concentrations were similar in the MUD and MMUD groups in the third cohort; therefore, T cell activation was presumably similar between these groups. However, tacrolimus concentrations trended lower in the MUD recipients from our validation cohort (second cohort depicted in Figure 2 and Table 2). Thus, in cohorts with similar amounts of T cell inhibition (concentrations of tacrolimus), Linc00402 increased in T cells from both MUD and MMUD recipients, whereas in cohorts with greater inhibition in the MMUD recipients, only T cells from those recipients showed increased Linc00402.

The correlation between tacrolimus concentration and Linc00402 abundance was likely related to suppression of T cell activation, which we confirmed using multiple small molecule inhibitors of T cell activation. Although two of these small molecules inhibited NFAT, depleting NFATc1/c2 using CRISPR/Cas9 RNP electroporation did not affect Linc00402 abundance. These data suggested that NFAT was dispensable for calcineurin inhibitor-mediated preservation of Linc00402; however, we cannot exclude insufficient depletion of NFAT or compensation by other NFAT isoforms not targeted by our gRNAs as reasons for a lack of effect on Linc00402 abundance. The exact molecular pathway(s) regulating Linc00402 require further detailed studies. One promising possibility is that MAPK pathways regulate Linc00402 expression. This possibility is supported by the presence of binding sites for the MAPK pathway-responsive transcription factors, JUN and FOS, in regulatory elements within the intronic sequence of Linc00402 (48–50) and by inhibition of MAPK pathways by tacrolimus, CSA, MPA, and rapamycin (51–53). In this scenario, we specifically postulate that T cell activation induces repressive AP-1 complexes (54, 55) that are recruited to these binding sites within the regulatory elements of Linc00402. The mechanism(s) responsible for the negative feedback loop decreasing Linc00402 expression following T-cell activation require future studies.

In contrast to the patients receiving allogeneic HSCT, Linc00402 decreased in T cells following allogeneic cardiac transplantation, which is consistent with higher T cell activation following cardiac transplantation. Due to small sample numbers, these data will need to be verified in future studies. The downregulation of Linc0402 was not unique to an allogeneic stimulus, because we also observed this following activation with autologous or syngeneic monocytes, anti-CD3/CD28 antibodies, and with specific activation of OT-I T cells. In contrast to these data, Linc00402 abundance was similar between CMV-specific and non-specific CTLs from allogeneic HSCT recipients. By extrapolation of Linc00402 down-regulation following T cell activation, the similar amounts of Linc00402 in CMV-specific and non-specific CTLs following allogeneic HSCT likely indicated that Linc00402 was downregulated to a similar extent in these CTL fractions relative to resting healthy control T cells. However, this analysis is limited by the number of patients examined and the inability to control the amount or quality of allogeneic and CMV antigen presentation in these complex clinical samples.

T cells are required for the development of GVHD, and the agents that form the backbone of GVHD prophylaxis inhibit T cell activation (56). Previous studies demonstrated that sub-optimal inhibition of T cell activation increased the risk of subsequent GVHD (20, 57, 58). Here, we showed that T cell activation downregulated Linc00402; therefore, we wondered if low Linc00402 expression was associated with the subsequent development of acute GVHD. While acknowledging our small sample size and the retrospective nature of this analysis, in our confirmation cohort, we observed significantly less Linc00402 in those patients who developed grade II-IV acute GVHD in the next 3 weeks. These studies need to be repeated in a prospective manner with larger sample sizes, but nonetheless, they highlight the potential of lncRNAs as biomarkers for outcomes of HSCT. However, given its conservation and expression in murine and human T cells, its differential expression in response to allogeneic stimulation both in vitro and in vivo, and its influence on T cell proliferation following allogeneic antigen stimulation, our data demonstrated that Linc00402 is a previously unknown regulator of T cell allo-immunity following both HSC and solid organ allogeneic transplantation.

Functionally, Linc00402 promoted the proliferation of bulk human and mouse T cells following an allogeneic stimulus. Strikingly, Linc00402 had no effect on T cell proliferation in response to autologous or syngeneic monocytes, polyclonal stimulation with anti-CD3/CD28 antibodies, or to a specific nominal antigen (OT-I/SIINFEKL). The biologic basis for this selectivity is not known but may relate to differences in antigen strengths, the presence of co-receptors, or differences in cytokines produced following each stimulation. In addition to its effect on allogeneic Tcell proliferation, Linc00402 also augmented ERK/FOS pathways in response to anti-CD3/CD28 stimulation in Jurkat cells. This suggested that Linc00402 may also be functionally important for T cell responses beyond allo-immune responses (35, 59, 60). Future studies will need to carefully explore the role of Linc00402 on the regulation of T cell responses in different contexts.

The molecular mechanisms mediating lncRNA regulation of gene expression vary (4). Here, we identified characteristics of Linc00402 that likely relate to its molecular function, but further work is required. For instance, we found that Linc00402 localized to both the cytoplasm and nucleus in human T cells. This is an important clue, because lncRNAs that primarily localize to the nucleus tend to regulate transcription, whereas those that primarily localize to the cytoplasm tend to regulate gene expression through various posttranscriptional mechanisms (4). We also used an agnostic in silico approach to identify potential Linc00402-interacting RNAs and proteins to help focus future experimental efforts. In addition, we found that overexpression of Linc00402 in Jurkat T cells increased the proportion of cells positive for CD25 at the cell surface and IL-2 production following anti-CD3/CD28 stimulation. We further determined that Linc00402 increased an ERK/FOS pathway and had no effect on the NFAT or NK-κB pathways. Cytoplasmic lncRNAs modulate TCR signal transduction. In particular, the lncRNAs NRON and NKILA inhibit NFAT and NF-κB nuclear translocation, respectively (61, 62). To our knowledge, Linc00402 is the only lncRNA so far reported to enhance ERK/FOS signaling following T cell activation. The mechanisms of lncRNAs can be complex and multipronged and are likely context dependent. Thus Linc00402’s effect on ERK/FOS signaling does not preclude additional regulatory and context-dependent mechanisms. Our in silico analysis identified putative protein partners of Linc00402 with some specificity for different subsets of T cells. Many of these potential RNA-binding protein partners may regulate or may be regulated by the ERK/FOS pathway (64–68). These predictions provide a hypothesis-driven guide for future experiments exploring these potential interactions and their functional consequences in T cells. A working model of the role of Linc00402 role in allogeneic T cells is shown in figure S15. Due to its influence on T cell function, we propose re-naming Linc00402 to “regulatory long non-coding RNA of T cells” (ReLoT).

Although not exhaustive, we and others (12) found evidence that Linc00402 abundance likely varies among T cell subsets, as did its predicted RNA-binding protein interactions, suggesting the need for detailed comprehensive characterization of its expression and function within individual T cell subsets. Although Linc00402 expression was enriched in T cells, we also observed its expression in cortical neurons. ERK/FOS signaling is important for neuronal activation; therefore, we speculate that Linc00402 may influence this pathway in neurons as well (38).

In addition to providing evidence that lncRNAs regulate allogeneic T cells, our RNA-seq data also established an untargeted human T cell transcriptome post-HSCT, which will likely prove a valuable hypothesis-generating resource. For instance, in addition to differentially expressed lncRNAs, we also found many differentially expressed protein-coding genes for which their function in allogeneic T cells is incompletely described. Further study of these differentially expressed genes may provide new insights into allogeneic T cell biology. One limitation of this post-HSCT T cell transcriptome is that it did not contain lncRNAs not yet included in Genecode 25, which was the reference annotation.

Several previous observations supported the hypothesis that lncRNAs influenced allogeneic T-cell function, (5–7, 14, 69–73). Here, we showed that lncRNAs, such as Linc00402, are differentially expressed by allogeneic T cells and regulate their function. Importantly, the tissue- and context-specific expression of lncRNAs, such as Linc00402, may make them potentially attractive therapeutic targets for improving outcomes after allogeneic HSC and solid organ transplantation.

MATERIALS AND METHODS

Study Design

To identify potential lncRNAs that regulate allogeneic T cells, we first used RNA-seq to identify differentially expressed lncRNAs in donor human alloimmune T cells. These donor T cells were isolated following clinical hematopoietic stem cell transplantation (HSCT) that varied in the degree of MHC matching with their stringently controlled HSCT recipient. These cryopreserved samples were identified retrospectively. No randomization or blinding was used in these studies. Clinical samples were selected based on the clinical and biochemical characteristics described above in the results section. Please refer to the figure legends for details on sample numbers and experimental replicates.

Human T cell isolation

Human peripheral blood was obtained by routine venipuncture according to IRB protocol UMCC 2001.0234 (HUM00043287). PBMCs from patients awaiting cardiac transplantation or post-cardiac transplantation were isolated by density centrifugation in BD Vacutainer CPT sodium heparin tubes as per manufacturer’s protocol and cryopreserved in AIM-V media + 10% DMSO per IRB protocol HUM 146650. Alternatively, heparinized whole blood was purchased from Innovative Research. Peripheral blood mononuclear cells (PBMC) were purified from heparinized whole blood by density centrifugation with Ficoll-Paque Premium (GE Healthcare) per manufacturer’s recommendations. PBMCs were then treated with red blood cell (RBC) lysis buffer (Sigma) per manufacturer’s instructions, and cryopreserved in heat-shocked fetal calf serum (Gibco) containing 10% DMSO (Sigma). PBMCs were thawed in a 37° C water bath and immediately mixed with complete cell media (RPMI 1640, 10% HS-FBS, penicillin, streptomycin, L-glutamine) supplemented with 50 U/mL of Benzonase (EMD Millipore). Cells were rinsed once with medium and then CD3⁺ T cells were isolated by positive magnetic selection or pan T cells were isolated by negative selection. CD4⁺ T cells were isolated by negative magnetic selection (Miltenyi Biotec) following a negative pan T cell isolation, and a CD8+ T cell–enriched fraction was eluted off of the CD4+ negative selection column.

Donor and recipient transplant mice

Eight to 12 week-old female C57BL/6 (B6, H-2b, CD45.2) and BALB/c (H-2d, CD45.2) recipient mice were purchased from Charles River Laboratories. Eight to twelve week-old female C3H.SW (CD45.2) recipient and B6.SJL-Ptprc^a Pepc^b/BoyJ (B6, CD45.1) donor mice were purchased from Jackson Laboratories. Animals were housed under specific pathogen-free conditions. All animal studies were approved by the University Committee on Use and Care of Animals of the University of Michigan, based on University Laboratory Animal Medicine guidelines.

Murine allogeneic bone marrow transplant

Murine bone marrow transplantation was performed as previous on 8 – 12 week-old animals (27). Briefly, splenic T cells from donor mice were enriched using the Pan T-cell Isolation Kit II (Miltenyi Biotec), while bone marrow (BM) cells were depleted of T cells by using CD90.2 magnetic beads (Miltenyi Biotec). C57BL/6 (syngeneic), BALB/c (allogeneic) and C3H.SW (allogeneic) recipient mice were irradiated from a ¹³⁷Cs source with 9.5 Gy for C57BL/6 recipients, 8 Gy for BALB/c recipients (dose split equally 3 – 4 hours apart), or 10.5 Gy for C3H.SW recipients on day −1. On day 0, recipient C57BL/6, BALB/c, and C3H.SW mice received by tail vein injection 5 × 10⁶ T cell depleted (TCD) BM as well as the following doses of splenic T cells from B6.SJL-Ptprc^a Pepc^b/BoyJ mice: 2.5 × 10⁶ T cells for C57BL/6 recipients, 1.0 × 10⁶ T cells for BALB/c recipients, or 3.0 × 10⁶ T cells for C3H.SW recipients. All recipients received pH 3.0 water starting day −1.

OT-1 isolation and stimulation

Eight to ten week old C57BL/6-Tg(TcraTcrb)1100Mjb/J (OT-I) female mice were purchased from Jackson. OT-I T cells were isolated from the spleens of these animals (see Mixed Lymphocyte Reactions in Supplementary Materials and Methods). Dendritic cells were isolated from the spleens of WT C57BL/6 female mice using CD11c Ultrapure MicroBead kits from Miltenyi Biotec, as previously described (84). Isolated dendritic cells were incubated with 1 ng/mL of the SIINFEKL ovalbumin peptide (Sigma) or placebo for 2.5 hours at 37° C. The dendritic cells were then rinsed twice and mixed with effector OT-I T cells at a ratio of 8 T cells (2 × 10⁶) to 1 dendritic cell (2.5 × 10⁵) per flat-bottom 24-well in 1 mL of media with 10 ng/mL of IL-7. Cells were incubated for 48 hours prior to RNA isolation for qRT-PCR. H³-thymidine (1 μCi/96 well) was incorporated on 100 uL aliquots of the original cultures during the last 6 hours. C57BL/6 splenic dendritic cells were then incubated with the SIINFEKL peptide (0.1 ng/mL) for 2.5 hours at 37° C, rinsed as above and incubated at a ratio of 8 T cells (2 × 10⁵/flat bottom 96 well) to 1 dendritic cell in 200 μL of media with IL-7 for 48 hours. ³H-thymidine (1 μCi/96 well) was incorporated during the last 3 hours.

C57BL/6 primary cell isolation and culture

E17 Primary cortical neurons were purchased from Gibco and cultured on poly-D-lysine (Gibco) coated plates in Neurobasal (Gibco) supplemented with B27 (Gibco), GlutaMax (Gibco), and penicillin/streptomycin (Gibco) for 11 days prior to RNA isolation per manufacturer instruction. Primary colonic epithelial cells, hepatocytes, skeletal muscle cells, dermal fibroblasts, and coronary endothelial cells were purchased from Cell Biologics along with their respective media kits and cultured according to manufacturer instructions. Splenic B cells, NK cells, and dendritic cells were isolated using the Mouse Pan-B Cell Isolation Kit, NK Cell Isolation Kit, and CD11c Ultrapure MicroBead kits from Miltenyi Biotec, respectively. Prior to CD11c MicroBead purification, mouse spleens were digested with Collagenase D (Sigma) as previously described (84). Neutrophils were isolated from mouse femur and tibia bone marrow using the Neutrophil Isolation Kit from Miltenyi Biotec. Macrophages, isolated from mouse spleens and peritoneal lavage fluid, were purified using Mouse anti-F4/80 UltraPure MicroBeads from Miltenyi Biotec. Lin⁻/Sca⁺/c-Kit⁺ bone marrow cells were isolated from mouse femur and tibia using the Lineage Cell Depletion Kit (Miltenyi Biotec) followed by sorting Lin⁻ (FITC, BioLegend Catalog Number 78022), Sca⁺ (APC, E13.161.7, BioLegend) and c-Kit⁺ (PE, ACK2, BioLegend) cells.

Statistical analysis

GraphPad Prism 7 and 8 were used to calculate all statistics unless otherwise noted. P-values were calculated using an ANOVA or two-tailed t-test where appropriate. Data were not tested for normality. P-values less than 0.05 were considered significant unless noted elsewhere. To mitigate the effects of small sample size and heterogeneity in our confirmation cohort, samples with fold changes greater than +/−1.3 standard deviations from the mean (that is the approximate top and bottom 10% of the normal distribution) were eliminated from the analysis. At least three independent replicates of experiments were performed unless noted otherwise.

Supplementary Material

Supplemental Material

figure S1. Confirmatory expression of protein-encoding genes identified in the RNA-seq data from the patient cohorts

figure S2. Validation patient cohort CD3⁺ purity and RNA integrity values

figure S3. Expression of protein-encoding genes in cells from the independent validation patient cohort

figure S4. Absolute qRT-PCR of Linc00402 in human T cells

figure S5. Purity of human CD8-single positive and CD4-single positive subsets

figure S6. Treatment of activated T cells with tacrolimus preserves RP11-348F1.3 abundance

figure S7. Small molecule T cell activation inhibitors preserve Linc00402 abundance in human T cells

figure S8. Depletion of NFATc1 and NFATc2 in human T cells

figure S9. Additional details pertaining to HSCT patient cohort 3

figure S10. Additional details pertaining to the cardiac transplantation patient cohort

figure S11. Human Linc00402 northern blot

figure S12. Validation of gene expression modulation techniques in T cells

figure S13. Lentiviral transduction of OT-I cells

figure S14. Characterization of Linc00402 overexpression in Jurkat cells

figure S15. Working model of Linc00402 in allogeneic T cells

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Table S26. Single Molecule RNA-FISH Probes

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Table S25. Primer Sequences

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Table S24. Linc00402 MechRNA results using RNA-seq data from bulk CD8⁺ T cells in FANTOM CAT database

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Table S23. Linc00402 MechRNA results using RNA-seq data from bulk CD4⁺ T cells in FANTOM CAT database

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Table S22. Linc00402 MechRNA results using RNA-seq data from all T-cell subsets in FANTOM CAT database

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Table S21. Allogeneic heart transplant cohort characteristics

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Table S20. CMV-Specific T Cell Isolation HSCT cohort characteristics

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Table S19. HSCT cohort 3 characteristics

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Table S18. Validation cohort patients tested for expression of each protein coding gene

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Table S17. Validation cohort patients tested for expression of each lncRNA gene

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Table S16. RNA-seq cohort patients tested for expression of each protein-coding gene via qRT-PCR

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Table S15. RNA-seq cohort patients tested for expression of each lncRNA gene via qRT-PCR

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Table S14. Differentially expressed protein-coding and lncRNA genes between the MMUD and MUD HSCT recipient groups

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Table S12. Differentially expressed microRNAs between the MUD HSCT recipient and healthy control groups

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Table S13. Differentially expressed microRNAs between the MMUD HSCT recipient and healthy control groups

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Table S11. Differentially expressed microRNAs between the autologous HSCT recipient and healthy control groups

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Table S10. Enriched GO terms for differentially express protein coding genes between the MMUD and MUD HSCT recipient groups

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Table S9. Enriched GO terms for differentially express protein coding genes between the MMUD and autologous HSCT recipient groups

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Table S8. Differentially expressed protein-coding and lncRNA genes between the MUD and Auto HSCT recipient groups

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Table S7. Differentially expressed protein-coding and lncRNA genes between the MMUD and Auto HSCT recipient groups

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Table S6. RNA-seq GO terms for MMUD HSCT recipients versus healthy controls

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Table S5. RNA-seq GO terms for MUD HSCT recipients versus healthy controls

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Table S4. RNA-seq GO terms for autologous HSCT recipients versus healthy controls

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Table S3. Differentially expressed protein-coding and lncRNA genes between the Auto HSCT recipient and healthy control groups

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Table S2. Differentially expressed protein-coding and lncRNA genes between the MUD HSCT recipient and healthy control groups

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Table S1. Differentially expressed protein-coding and lncRNA genes between the MMUD HSCT recipient and healthy control groups

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Data File S2. Tabular data for figures S1 – 15

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Data File S1. Tabular data for Figures 2 – 6

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ACKNOWLEDGEMENTS

We would like to thank the NY Genome Institute for their assistance with the human T-cell RNA-sequencing and analysis, Lofstrand Labs for their assistance with northern blot analysis, and the University of Michigan Bioinformatics Core for their assistance with executing the MechRNA program. We wish to thank Xia Jiang for technical assistance with RNA-FISH. Editorial services were provided by Nancy R. Gough (BioSerendipity, LLC).

Funding

DP was supported by the NIH T32 Training Grant in Molecular and Translational Hematology (4T32HL007622-30), NIH K12 Children’s Health Research Center Development Award (5K12HD028820-27), the Hope from Harper St. Baldrick’s Foundation Fellowship, and a Hyundai Hope on Wheels Young Investigator Grant. MR, TD, GH, CZ, KOW, DS, IH, JW, SK, AT, HF, and YS were supported by National Institutes of Health RO1 grants HL128046, CA203542, and CA217156 of which PR is the PI. SP was supported by a NCI-SPORE Career Enhancement Award, a PCF Young Investigator Award and a Department of Defense Idea Development Award. SW and DRG were supported by the NIH grants UL1TR002240 and R01-AI138347. VR and AR were supported through NCI R37CA214955-01A1, and a Research Scholar Grant from the American Cancer Society (RSG-16-005-01). A. R was also supported from CCSG P30 CA046592, and Institutional Research Grants (MCubed, O’Brien Kidney Center) from The University of Michigan.

Footnotes

SUPPLEMENTARY MATERIALS

Materials and Methods

Competing Interests

A.R. is member of Voxel Analytics, LLC. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

^**

This manuscript has been accepted for publication in Science Translational Medicine. This version has not undergone final editing. Please refer to the complete version of record at www.sciencetranslationalmedicine.org/. The manuscript may not be reproduced or used in any manner that does not fall within the fair use provisions of the Copyright Act without the prior written permission of AAAS.

Data and materials availability

All data associated with this study are available in the main text or the supplementary materials. All SAM sequencing files were deposited in the sequence read archive (SRA) at the NCBI (accession number PRJNA550422).

REFERENCES and notes

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