MALAT1 is one of the most highly expressed long non-coding RNAs in both the small intestine and colon epithelium (Figure 1A). Previous studies suggest that MALAT1 can regulate gene expression at the transcriptional and post-transcriptional levels (Tripathi et al., 2010; Miyagawa et al., 2012). Based on these reports, we hypothesized that MALAT1 may contribute to intestine functions by regulating gene expressions in intestine epithelial cells. To test this possibility, we crossed the Malat1 ^+/− mice to generate gender-matched and cohoused control (CTL, Malat1 ^+/+ and Malat1 ^+/−) and Malat1 knockout (Malat1 ^−/−) littermates for our study. CTL and Malat1 ^−/− littermates were born in Mendelian ratios and all survive to adulthood without notable spontaneous diseases, which is consistent with a previous report (Zhang et al., 2012). CTL and Malat1 ^−/− littermates showed similar weights for the duration of our experiments between days 50 and 130 (Figure 1B). H&E staining of the CTL and Malat1 ^−/− colonic sections confirmed similar intestine epithelium morphology (Figures 1C, D). To assess intestine barrier function, mice were orally gavaged with 4 kDa Fluorescein isothiocyanate-dextran (FITC-dextran) together with 70 kDa Rhodamine B isothiocyanate-dextran (RITC-dextran). In the CTL and Malat1 ^−/− bloodstream, we found similar levels of RITC-dextran, indicating Malat1 ^−/− mice have intact barrier activity against bacteria-size macromolecules. Interestingly, Malat1 ^−/− mice showed reduced levels of serum FITC-dextran, suggesting that MALAT1 promotes protein-size macromolecule passage in the intestine (Figure 1E).

To elucidate the mechanism(s) underlying MALAT1 function in the intestine, we isolated small intestine and colon IECs from two pairs of 8-week-old wildtype and MALAT1 knockout female cohoused littermates for differential transcriptome and splicing analyses. Differential gene expression analysis identified 67 and 143 MALAT1-dependent small intestine and colon IEC transcripts, respectively (Figure 2A, Supplementary Table S1). Splicing analysis was performed using rMATS (Shen et al., 2014) and revealed a larger MALAT1-regulatory footprint (Figure 2B, Supplementary Table S2 and Supplementary Table S3). The majority of the MALAT1-dependent alternative splicing events in both small intestine and colon are skipped exons. These results indicate MALAT1 regulates RNA abundance and processing in both the small intestine and colon and that its higher expression levels in the colonic epithelium are associated with a larger set of MALAT1-dependent targets identified in that tissue. We selected two genes that displayed MALAT1-dependent splicing patterns for validation by flow cytometry and confirmed a reduced proportion of TGF-β receptor 1-positive IECs in the Malat1 ^−/− small intestine (Supplementary Figure S1A,B) and an increased proportion of IL-27 receptor alpha-positive IECs in the Malat1 ^−/− colon (Supplementary Figure S1C,D).

Gene Ontology analysis of the IEC genes relying on MALAT1 at the expression and/or splicing levels revealed enrichment for pathways implicated in anti-microbial responses in both the small intestine and colon (Figure 3A and Supplementary Figure S2A). Therefore, we performed meta-genomic analysis to test whether alterations in anti-microbial response programs in the Malat1-deficient epithelium may be associated with changes to the mucosal-associated microbial communities. Interestingly, we identified an increase of Neisseria meningitidis, Escherichia coli, Mycobacterium tuberculosis, Mycobacterium kansasii, and Nakamurella panacisegetis in the MALAT1-deficient small intestine (Figure 3B). In the MALAT1-deficient colon, however, there was a decrease of Acinetobacter, Rhodobacteraceae, Varucomicrobia, Micrococcales and Sulfolobaceae. These results suggest that the impact of MALAT1 on the intestine microbial communities is region-specific.

Previous studies reported that dysregulated intestine microbiota and altered host anti-microbial responses can modulate individual risks for developing intestine inflammation and cancer (Louis et al., 2014). In human colorectal cancers, MALAT1 transcripts were downregulated relative to normal tissue (Kwok et al., 2018). Compared to primary Stage I lesions, MALAT1 levels were elevated in Stage IV and metastatic lesions (Ji et al., 2019; Zhang et al., 2020). These results suggest that MALAT1 is dynamically regulated during tumorigenesis and may contribute to multiple aspects of colorectal cancer pathogenesis in a stage-specific manner. We then tested whether MALAT1-deficiency influences disease susceptibility in a tumorigenesis setting. To address this question, we employed a mouse model of human familial adenomatous polyposis known as the APC mutant line (Kwong and Dove, 2009). In this model, haploinsufficiency of the tumor suppressor APC results in hyperactivation of WNT and early onset of epithelial dysplasia (Fodde et al., 1994; Hinoi et al., 2007; Kwong and Dove, 2009; Grivennikov et al., 2012; Wang et al., 2014). Similar to human colorectal cancers (Kwok et al., 2018), MALAT1 is slightly downregulated in the murine colonic polyps compared to healthy tissues, a pattern also observed in genes encoding known colorectal cancer tumor suppressor molecules, such as Mbd1 and Tmigd1 (Qi and Ding, 2017; Mu et al., 2022) (Figure 4A). To assess the role of MALAT1 in intestine tumorigenesis, we crossed the MALAT1 knockout mice to the Apc ^fl/+ Vil1Cre⁺ (APC^ΔIEC) line (Figure 4B). In the small intestine and colon, APC^ΔIEC Malat1 ^−/− mice harbored more polyps than MALAT1-expressing mice (Figures 4C, D). Interestingly, the average sizes of the small intestine polyps in the APC^ΔIEC Malat1 ^−/− mice were smaller than those found in the MALAT1-expressing mice. These results revealed a surprising bivalent role of MALAT1 in restricting intestine polyp generation and later promoting aberrant polyp growth.

RNAs from intestine polyps and adjacent normal tissues were harvested and assessed for the expression of Ctnnb1 encoding β-Catenin and Ki67 as an index of cell proliferation. In the small intestine polyps and adjacent normal tissues, Ctnnb1 and Ki67 levels negatively correlate with the Malat1 gene dosage (Supplementary Figure S3 and Supplementary Figure S4). In contrast, expression of the tumor stem cell marker Cd44 was MALAT1-independent. To determine the epithelial cell-intrinsic role of MALAT1 in polyposis, we purified and cultured colonic crypt stem cells from APC^ΔIEC CTL and APC^ΔIEC Malat1 ^−/− mice and assessed their capacity to establish colonies on Matrigel in vitro. Overall, APC^ΔIEC Malat1 ^−/− colonies were more abundant than those derived from APC^ΔIEC CTL cells (Figure 5A). Flow cytometry analysis revealed that the APC^ΔIEC Malat1 ^−/− cultures harbored a larger fraction of Ki67-positive actively proliferating population (Figures 5B, C). These results suggest that MALAT1 in epithelial cells is a negative regulator of colony establishment in vitro.

We hypothesized that MALAT1 suppresses polyposis in the small intestine and colon by regulating a common set of targets in both intestine regions that are involved in epithelial cell transformation. To identify these targets, we overlaid the small intestine and colon MALAT1-dependent transcripts identified from the DESeq and rMATS analysis and found 30 targets that were dependent on MALAT1 at the overall RNA expression or alternative splicing levels (Figure 6A), including those encoding an acetylglucosaminyltransferase MGAT4C, an aldehyde dehydrogenase ALDH1A1, and an adenylate kinase AK4 that have been previously implicated in other types of cancers. In addition, two of the MALAT1 targets shared across the small intestine and colon, ZNF638 and SENP8, are associated with a change in hazards ratio for overall survival and disease-free survival in human colon adenocarcinoma patients (Figure 6B). These results suggest that MALAT1 downstream targets may contribute to cancer pathogenesis in humans.

To determine whether these targets were regulated by MALAT1 at the chromatin level and/or through more complex mechanisms, we employed the GRID-seq assay to characterize the chromatin occupancy of MALAT1 in small intestine epithelium as previously described (Li et al., 2017b; Zhou et al., 2019). In the small intestine epithelium, 12 of the 30 MALAT1 target genes identified earlier had MALAT1 binding near the gene locus (Figure 7A). For example, MALAT1 occupied the promoter/5′UTR region on Mgat4c, Slc10a2, and Sorbs2, intragenic regions of Mapt, Tcf7l2, and Crem, and distal elements on Zfp638, Rps6ka3, Timm23, Shoc2, Smtn, and Herc3 (Figure 7B). Chromatin accessibility ATAC-seq assays further indicate that MALAT1 occupied regions in the small intestine epithelium lied within both regions of open and closed chromatin in a gene-specific manner. These results suggest that MALAT1 regulates intestine epithelial cell gene programs through both direct and indirect mechanisms.

C57BL/6 wild-type were obtained from the Jackson Laboratory. Malat1^−/− mice were obtained from Dr. David Spector’s laboratory and have been previously described (Zhang et al., 2012). Heterozygous mice were bred to yield 8–12-week old cohoused littermates for transcriptomic and genomic studies. Apc ^flox mice were obtained from Dr. Eric Fearon’s Laboratory and previously described in reference (Grivennikov et al., 2012). Intestine tissues were harvested from 120 to 130-day-old CTL and Malat1 ^−/− in the Apc ^flox background to assess tumor burden. Tumor measurements were determined by double-blinded analyses using ImageJ. Both male and female mice were used in the experiments described. All animal studies were approved and followed the Institutional Animal Care and Use Guidelines of the University of California San Diego (Protocol #S16156). Our vivarium at the University of California San Diego is kept under specific pathogen-free conditions. Regular serology and PCR tests are used to monitor and ensure the absence of epizootic diarrhea of infant mouse virus (EDIM), mouse hepatitis virus (MHV), mouse parvovirus (MPV), minute virus of mice (MVM), Theiler’s murine encephalomyelitis virus (TMEV), fur mites and pinworms. Colonic sections from 8-week-old littermates were stained with H&E and scored for changes in the inflammatory infiltrate, submucosal inflammation, crypt morphology, and muscle thickening in a double-blind fashion as described in (Abbasi et al., 2020).

Mice were deprived of food and bedding for 4 h prior to oral gavage with 4 kDa Fluorescein isothiocyanate–dextran (Millipore-Sigma, FD4, 100 mg/kg) together with 70 kDa Rhodamine B isothiocyanate-dextran (Millipore-Sigma, R9379, 50 mg/kg). Blood samples were taken 4 h post-gavage by submandibular bleed. The FD4 and Rhodamine signals were measured using a TECAN fluorescent plate reader at the excitation/emission wavelengths of 485/535 and 540 nm/585 nm, respectively.

Colonic crypts were isolated according to the manufacturer’s recommendation (STEMCELL, technical bulletin #28223). Briefly, intestine tissues were harvested from 6–8-week-old CTL and Malat1 ^−/− in the Apc ^flox background and cut into 2 mm pieces. After 20 washes in cold PBS, tissues were resuspended in 25 mL room temperature Gentle Cell Dissociation Reagent (STEMCELL, #07174) and incubated at room temperature for 15 min on a rocking platform at 20 rpm. The pellets enriched with intestinal crypts were resuspended in cold PBS containing 0.1% BSA. Isolated colonic crypts were embedded in Corning^® Matrigel^® Matrix (Corning™ 356231) and seeded onto pre-warmed, non-treated 24-well plates (CytoOne^® by StarLab) and overlaid with conditioned media (STEMCELL, #6005) as described previously (Miyoshi and Stappenbeck, 2013). Organoid pictures were imaged using a Keyence bz-x800 microscope at ×20 magnification with image stacks capturing the entire organoid volume.

Intestinal epithelial cells were surface stained with LIVE/DEAD Fixable Cell stain (ThermoFisher, L34957), and fluorescent conjugated antibodies against EpCAM, TGFBR1, and IL27RA (see Supplementary Table S4 for detailed information, 1:400 in PBS) for 30 min. For intracellular staining of Ki67, cells were first fixed/permeabilized (ThermoFisher Cat: 00-5521-00) and then incubated with the anti-Ki67 antibody for 1 h at room temperature. Intestine epithelial cells were defined as live Epcam⁺. Flow cytometry data was analyzed with FlowJo (version 10.8.1).

Total RNA was extracted with the RNeasy Plus kit (QIAGEN) and reverse transcribed using iScript™ Select cDNA Synthesis Kit (Bio-Rad). Real time RT-PCR was performed using iTaq™ Universal SYBR^® Green Supermix (Bio-Rad). Results were normalized to mouse Hprt. Primers were designed using Primer-BLAST to span across splice junctions, resulting in PCR amplicons that span at least one intron. Primer sequences are listed in Supplementary Table S4.

Small intestine and colonic epithelial cells from two pairs of Malat1 ^−/− and CTL cohoused littermates were enriched as previously described in (Abbasi et al., 2020). Ribosome-depleted RNAs were used to prepare sequencing libraries. 100 bp paired-end sequencing was performed on an Illumina HiSeq4000 by the Institute of Genomic Medicine (IGM) at the University of California San Diego. Each sample yielded approximately 30–40 million reads. Paired-end reads were aligned to the mouse mm10 genome with the STAR aligner version 2.6.1a (Dobin et al., 2013) using the parameters: “--outFilterMultimapNmax 20 --alignSJoverhangMin 8 --alignSJDBoverhangMin 1 --outFilterMismatchNmax 999 --outFilterMismatchNoverReadLmax 0.04 --alignIntronMin 20 --alignIntronMax 1000000 --alignMatesGapMax 1000000”. Uniquely mapped reads overlapping with exons were counted using featureCounts (Liao et al., 2014) for each gene in the GENCODE. vM19 annotation. Differential expression analysis was performed using DESeq2 (v1.18.1 package) (Love et al., 2014), including a covariate in the design matrix to account for differences in harvest batch/time points. Regularized logarithm (rlog) transformation of the read counts of each gene was carried out using DESeq2. Pathway analysis was performed on differentially expressed protein coding genes with minimal counts of 10, log₂ fold change cutoffs of ≥0.5 or ≤ −0.5, and p-values < 0.05 using Gene Ontology (http://www.geneontology.org/) where all expressed genes in the specific cell type were set as background.

Alternative splicing events were analyzed by Multivariate analysis of transcript splicing (rMATS (Shen et al., 2014)) using the parameters “python rmats. py --b1/path/to/b1. txt --b2/path/to/b2. txt–gtf/path/to/the.gtf -t paired --readLength 150 --nthread 4 --od/path/to/output --tmp/path/to/tmp_output”. Using a 0.01 FDR cutoff, MALAT1-dependent splicing events were identified.

For metatranscriptomic analysis of ileal associated microbial populations, reads from the CTL and Malat1^−/− IEC RNA-seq dataset that were not mapped to the mouse genome were assigned with taxonomic labels using Kraken V.1. The standard Kraken database encompassing annotated bacterial, archaeal, and viral genomes was used for classification of sequences with the command: “kraken --classified-out/path/to/classified_fq --unclassified-out/path/to/unclassified. fq --db $DBNAME --paired --fastq-input pair1. fa pair2. fa >/path/to/results”. A Kraken report was generated with the command: “kraken-report --$DBNAME kraken. output” (Wood and Salzberg, 2014). Differential microbial counts were assessed by DEseq2 cut-off of p < 0.05 with the Wald test and Log2 fold change (Malat1 ^−/−/CTL) > 1.5 or ≤1.5.

GRID-seq of small intestine epithelial cells were performed as described (Li et al., 2017b; Zhou et al., 2019). Briefly, two independent biological replicates (5–10 × 10⁶) were crosslinked with disuccinimidyl glutarate (DSG) and formaldehyde. DNA in isolated nuclei were digested with AluI. A biotinylated bivalent linker was ligated to chromatin-associated RNA (with the ssRNA stretch on the linker) and nearby fragmented genomic DNA and captured by streptavidin microbeads for library construction. Single-end sequencing was performed on HiSeq400 (Illumina, 200 million reads/sample). Raw sequencing reads were evaluated by FastQC (Gu et al., 2014). Reads below 85bp were filtered out and those above 90bp with high quality were trimmed by Cutadapt (Martin, 2011) as suggested according to previous report (Li et al., 2017b; Zhou et al., 2019). The GRID-seq linker position at each read and the paired reads originated from RNA or genomic DNA were identified by matefq in GridTools (Zhou et al., 2019). Paired RNA and DNA reads were then mapped to the mouse genome (GRCm38/mm10) by BWA respectively. Uniquely paired reads were used to generate a set of RNA-DNA interaction matrix for downstream analyses in the GridTools pipeline. Read counts from the two repeats were summarized into two 1 kb genomic bins. Chromatin enriched with MALAT1 RNAs (GRID-seq peak call) were defined as 2 kb regions with clustered MALAT1 signals above the background signal expected from random interactions (>5-fold changes).

ATAC-seq libraries were generated as described in (Buenrostro et al., 2015). ATAC-seq processing followed the ENCODE guideline with some modifications (Landt et al., 2012). Specifically, single-end raw reads were mapped to the mouse genome (GENCODE assembly mm10) by bowtie2 (Version 2.3.4.1) in the local mapping mode with parameter “--local”, followed by PCR deduplication by SAMTools (Version 1.9) with the utility markedup (Li et al., 2009). Mapped reads from each sample repeats were merged into a single BAM file by SAMTools, and peaks were called using MACS2 (Version 2.2.6) (Zhang et al., 2008) with “callpeak --nomodel --extsize 100”. Regions with peak-score below 30 were filtered out and the remaining reliable peak profiles were transformed into bigwig format and visualized on the Integrative Genomics Viewer (IGV Version 2.8.2) (Thorvaldsdottir et al., 2013).

The values are presented as the mean ± standard deviation (SD). Statistical significance was evaluated using GraphPad Prism V.8 software (GraphPad). The t-test was used to determine significant differences between groups. A p-value of less than 0.05 was considered statistically significant in all experiments.