Tissue specific expression of SERPINA1 mRNA

We begin our investigation of post-transcriptional regulation by visualizing median expression of the SERPINA1 mRNA across individuals sequenced in the Gene Tissue Expression (GTEx) Atlas (Fig 1A). We identified six tissues (Liver, Blood, Lung, Small Intestine, Spleen and Kidney) where the median expression was above 1000 TPM and therefore sufficient to obtain individual estimates of exon specific expression. In Fig 1B, we illustrate the standard deviation of expression across individuals in the GTEx data and map them to the human body [6,21]. Analysis of mean short-read coverage across individuals for these six tissues (Fig 1C) reveals well defined exons in the coding region of the gene (exons 4–7); the hg38 reference annotation of exons is shown in gray (untranslated) and black (protein coding) in Fig 1C. We observe differential coverage in exons 1–3 consistent with the tissue specific expression of SERPINA1 mRNA transcript isoforms previously described [3,4,22,23].

The premise for our study is illustrated in Fig 1D where we have expanded exon 7, which includes the 3′ UTR of the mRNA. We observe a significant drop off in coverage approximately ~60 nucleotides downstream of the stop codon, which suggests the presence of distal and proximal alternative polyadenylation (APA) sites. The steep drop is also observed in coverage in RNA-seq data from Hep-G2 cell lines (Fig 1C, turquoise tracks). We therefore performed 3′-seq sequencing on these cells to quantitatively map the alternative poly-adenylation sites with single nucleotide resolution (Fig 1D). We observe two peaks in this signal (labeled proximal and distal) with sharp 3′ cliffs confirming that SERPINA1 mRNA is alternatively poly adenylated. These peaks are consistent with the drop off in coverage in RNA-seq. Upon analysis of the sequence directly upstream of the distal drop off site, we observe a canonical AAUAAA motif consistent with a poly-A signal, while a weaker signal (AUUAAA) is present at the proximal site (S1A Fig). Furthermore our 3′-seq data allow us to map the cleavage site and identify the G/U-rich region downstream (shown in S1A Fig). These sequence motifs are hallmarks of APA in 3′ UTRs [11,24]. Finally, IGV analysis of PacBio long read RNA sequencing from primary human liver tissue confirms the existence of both long and short isoforms in the 3′ UTR as well as expression of multiple 5’ UTR splice isoforms (Fig 1E) [25].

Our results so far establish that the 3′ UTR of SERPINA1 is alternatively polyadenylated, and that in all tissues (and HepG2 cell lines) the shorter isoform is preferentially expressed. Importantly, however, in all tissues and cell lines we observe coverage over the entire long isoform, suggesting that both isoforms are constitutively present, albeit at different levels. From the RNA-seq coverage data we compute the distal ratio for the six tissues by dividing the mean distal coverage depth by the proximal depth normalized by length. When we compute the distal ratio in human primary tissues, we observe the highest distal site usage in the liver (Fig 2A). Furthermore, we also observe significant variability in the distal ratio among individuals, especially in the lung and liver.

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Fig 2. Distal ratio across tissues and in COPD.

The distal ratio is measured as the relative depth of SERPINA1 3′ UTR distal vs. proximal reads in RNA-seq data. A) Distal ratio measured in the six tissues expressing SERPINA1 mRNA above 1000 TPM in Gtex RNA-seq consortium data [10,11]. We observe the highest distal ratio in liver, but important variation in the lung. B) The distal ratio measured in a publicly available [26] short-read lung tissue RNA-seq data set from n = 82 (red) COPD subjects and n = 64 (blue) controls. C) Distal ratio measured from lung tissue RNA-seq data in the Lung Tissue Research Consortium for n = 376 COPD (red) subjects and n = 267 (blue) normal participants. In both studies we observe a significant increase of the distal ratio in COPD subjects. D) The characteristic drop at the proximal APA site indicative of alternative polyadenylation for mean COPD (red) and Normal (blue) lung tissue for n = 376 COPD subjects and n = 267 normal patients. E) Distal ratio analysis of LTRC subjects broken down by SERPINA1 M (normal), S (mild disease) and Z (severe disease) alleles showing that the S and Z alleles exacerbate use of the distal alternative poly-adenylation site in the lungs of individuals with COPD.

https://doi.org/10.1371/journal.pgen.1009912.g002

APA in the lung and COPD

To investigate the role of APA in the SERPINA1 3′ UTR and its relationship with COPD we computed distal ratio in lung tissue from two independent population studies. In the first we analyzed a publicly available [26] short-read lung tissue RNA-seq data set from n = 82 subjects and n = 64 controls and observed a 2.1 (p = 2.5x10^-6) increase of the median distal ratio in individuals with COPD (Fig 2B). We therefore sought to replicate this finding in the larger Lung Tissue Research Consortium (LTRC) study where we obtained RNA-seq from 376 COPD cases and 267 controls (Fig 2C). There we observed a similar 1.5-fold (p < 7.6 x 10⁻¹⁰) increase in the distal ratio, suggesting higher use of the distal polyadenylation site in individuals with COPD. This is also visualized as the mean normalized read depth for the LTRC data in Fig 2D. We observe in the LTRC data the characteristic drop-off at the proximal APA site consistent with our previous analysis of primary human tissues (Fig 1D).

The most common genetic variation leading to A1AT deficiency is the SERPINA1 Z allele [7–9]. Individuals homozygous for the S allele (SS) and heterozygous for the Z allele (MZ) may also have reduced A1AT [23,27]. Although there are many other SERPINA1 variant alleles, most are less common or do not significantly associate with A1AT levels [6]. In addition to this genetic effect, the likelihood of an individual to have lung-related A1AT deficiency-associated diseases such as COPD is strongly influenced by environmental exposures, mainly cigarette smoke [28–30]. To determine if protease inhibitor (PI) type M, S or Z alleles influenced the SERPINA1 distal ratio, we separately analyzed the distal ratio in individuals with MM, MS, MZ, and ZZ genotypes based on whole genome sequencing in our LTRC cohort data. This dataset includes genotyped MM individuals with no disease (n = 210) and with COPD (n = 284). Rare genotypes have proportionally lower representation in our data (MS: n = 14 and n = 20), (MZ: n = 8 and n = 16) and (ZZ: n = 1 and n = 7). As can be seen in Fig 2E, we observe a higher distal ratio amongst all genotypes with COPD (red box plots, Fig 2E). The distal ratio is significantly higher for individuals with COPD and an MS (p = 0.005) or MZ (p = 0.013) genotype compared to individuals with COPD and an MM genotype (Fig 2E). In the LTRC data set there were only n = 7 ZZ homozygous individuals, limiting the statistical power of this analysis (p = 0.15), but it is clear from Fig 2E that most ZZ individuals with COPD also have a higher distal. This indicates that COPD likely exacerbates use of the longer 3′ UTR. We further analyzed the distal ratio as a function of the Global Initiative for Chronic Obstructive Lung Disease (GOLD) spirometric stage [31] which shows a statistically significant increase in the distal ratio correlated with GOLD stage (S2 Fig). Together these data indicate that the distal ratio in lungs increases in individuals with COPD and increases with higher disease spirometric severity, raising the question of what the post-transcriptional functional effects of this lengthening are on A1AT expression.

Role of long and short 3′ UTRs in SERPINA1 translation and stability

To understand the functional consequences of APA site usage in the SERPINA1 mRNA 3′ UTR we performed a series of assays using nanoluciferase reporter constructs in both HepG2 (liver hepatocyte) and A549 (adenocarcinomic human alveolar basal epithelial) cell lines. In these experiments we aim to measure the amount of protein produced for the short (proximal APA site used) and long (distal APA site used) isoforms (Fig 3A). In our construct design, the upstream APA site (proximal) is mutated (indicated by an X in Fig 3A) so that only the correct 3’UTR isoform can be expressed. Both constructs have an additional strong SV40 polyadenylation signal to ensure that the transcripts end as expected. Using 3’ end specific sequencing we confirmed that we can accurately detect 3’ ends (S3A and S3B Fig) and that short and long 3’UTR constructs have the expected sequence (S3C and S3D Fig). As can be seen in Fig 3B the proximal (or short) isoform yields 50-fold more luminescence relative to the distal (long) isoform. This is the case both in HepG2 and A549 cell lines.

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Fig 3. Luciferase reporter and mRNA stability assays to measure the effect of long vs. short 3′ UTRs.

To measure effect on translation efficiency and mRNA stability of the long and short 3′ UTR sequences we performed a series of luciferase reporter assays. A) Schematic of the luciferase reporter assay, combining a nanoluciferase reporter, PEST domain and the SERPINA1 exon 7 coding sequence upstream of the long and short 3′ UTRs. The proximal APA site is mutated to inhibit use of this site (indicated with an x on the long construct). These constructs are co-transfected in HepG2 and A549 cell lines with a control firefly reporter and the ratio of nanoluciferase protein to firefly protein measured. B) Log normalized luminescence, which indicates gene expression, measured for short (blue) and long (tan) SERPINA1 isoforms. The expression is significantly higher for the short isoform by close to two orders of magnitude in both lung derived A540 cells and liver derived HepG2 cells. C) Pulse chase experiments in A549 cells using ethylene uridine (EU) and click-it chemistry for labeling with biotin-azide to measure relative mRNA stability by qRT-PCR [63]. We confirmed that GAPDH was consistently stable over the time period and similar declines in both the long and short SERPINA1 RNAs, indicating similar stability for both long and short 3′ UTRs. D) Deletion construct design to identify regions controlling gene expression in SERPINA1 3′ UTR. Six constructs were designed to selectively delete regions 1–4. E) Relative luminescence which indicates expression for the six deletion constructs compared to short (blue) and long (tan) SERPINA1 3′ UTRs. F) Predicted vs. measured expression of deletion constructs using regression model described by Eq 1. This model yields the translation coefficients of the four regions reported in Table 1.

https://doi.org/10.1371/journal.pgen.1009912.g003

To distinguish between changes in RNA stability and translational efficiency, we also measured the relative stability of the long and short isoforms in A549 cell lines. We observed a decline over time in both the long and short SERPINA1 constructs (Fig 3C), consistent with high ΔCT values at 24 hours (S4A Fig). This finding indicates that the relative stability of the long and short isoforms (distal and proximal, respectively) is similar (Fig 3C). Longer 3′ UTRs are thought to destabilize mRNAs as they include more micro-RNA and RNA binding protein sites which affect mRNA stability and translation efficiency [12,32]. However, for SERPINA1, we do not detect significant stability differences between the short and long isoforms at early or late timepoints (Figs 3C and S4A). We find that steady state levels of the long isoform are lower than steady state levels of the short isoform (S4B Fig). However, this difference does not account for the large difference in translation between the short and long isoforms (Fig 3B). Thus, these experiments support the long isoform primarily inhibiting translation of the protein product.

Mechanism of translation suppression by long SERPINA1 3′ UTR

To identify regions of the long SERPINA1 3′ UTR that control translation we designed a series of six deletion constructs based on the long 3’UTR construct that uses only a distal APA site (Figs 3D and S3). We measured the relative luminescence of these constructs in A549 cells and observed that in general shorter constructs resulted in higher protein production. However, the relationship between construct length and expression is not linear, rather each deleted region appears to affect translation to a different degree (Fig 3E). To understand the role of each region in controlling translation we developed a model to predict the relative contribution to translation efficiency of each specific region (Eq 1). When we fit this model to our data (Fig 3F) we obtained high correlation between predicted and experimental values (R² = 0.96). Furthermore, an analysis of the relative weight of each region’s translation (Table 1) reveals that no single region uniquely controls translation efficiency. Nonetheless, regions 1, 3 and 4 together account for over 90% of the variation in translation efficiency between the long and short 3′ UTRs. Together these experiments suggest that the primary function of the long (distal) 3′ UTR in SERPINA1 is to inhibit translation, and that regions 1, 3, and 4 contribute the most to the translation repression. To dissect the mechanisms controlling translation repression and distal vs. proximal APA site usage we now investigate the effects of trans factors on the SERPINA1 3′ UTR.

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Table 1. Translation Coefficient of SERPINA1 3′ UTR Regions.

https://doi.org/10.1371/journal.pgen.1009912.t001

Role of RNA Binding Proteins and structure in controlling translation efficiency

The fact that the long isoform of the 3′ UTR does not destabilize the message (Fig 3C) but decreases translation efficiency by several orders of magnitude is uncommon, but not unprecedented [33]. To reveal a mechanism that can account for this we begin our investigation by mining eCLIP data (enhanced CrossLinking and ImmunoPrecipitation) from recent ENCODE (Encyclopedia of DNA Elements) experiments on RNA binding proteins carried out in HepG2 cell lines [34–36]. In Fig 4A, we show high-confidence experimental eCLIP binding sites on the long SERPINA1 3′ UTR isoform. Several interesting features emerge from this analysis. First, multiple proteins bind near the proximal APA site, but further downstream fewer binding sites are present. The most distal binding site identified by eCLIP is for the RNA binding protein QKI (Quaking homolog) which spans regions 3 and 4. No other RBPs were identified by eCLIP binding downstream of this site. As a reminder, Deletion 1 (which removes Regions 2, 3 and 4) restores the translation efficiency of SERPINA1 3′ UTR to near proximal levels (Fig 3E). Also, deletions 5 and 6 (which maintain regions 3 and 4) both suppress translation to near full-length levels (Fig 3E). While other RNA binding proteins may have a role in regulating SERPINA1 mRNA or A1AT expression [37–39] (S1 Table), QKI’s distal binding position in Regions 3 and 4 in the 3’UTR is unique.

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Fig 4. Role of RNA binding proteins and structure in modulating translation efficiency.

Mapping of eCLIP (enhanced CrossLinking and ImmunoPrecipitation) from recent ENCODE (Encyclopedia of DNA Elements) experiments on RNA binding proteins carried out in HepG2 cell lines [34–36] onto the long 3′ UTR isoform of SERPINA1 mRNA. Each rectangle indicates a protein binding site and sites are colored by RBP. B) SHAPE (Selective 2’ Hydroxyl Acylation by Primer Extension) structure probing long SERPINA1 3′ UTR. SHAPE data identifies flexible (unpaired) nucleotides in the RNA structure revealing regions of high accessibility for protein binding. Red indicates highly reactive nucleotides, yellow intermediate and black low. C) Using SHAPE data, we compute the entropy of the SERPINA1 mRNA structure. Low entropy regions adopt single, well-defined structures, while high entropy regions are more disordered [64,65]. D) SHAPE derived secondary structure model for short (top) and long SERPINA1 3′ UTR indicated as an arc diagram. Only highly probable base-pairs are shown (green). As can be seen QKI (Quaking Homolog) binds to a low-entropy region in the 3′ UTR with extensive local base-pairing. E) SHAPE reactivity for the nucleotides for the eCLIP QKI binding site including the putative binding motif of the RNA binding protein 5’-NACUAAY-N(1,20)-UAAY-3′ [46]. F) We found the QKI binding site to impact gene expression from the SERPINA1 3’UTR using our nanoluciferase assay. Mutating the QKI binding site increases expression from the long 3’UTR isoform (QKImutant) while adding the QKI binding site and flanking nucleotides decreases expression of the short 3’UTR isoform (Short+QKI) in A549 cells. G) Secondary structure model derived from experimental SHAPE data for QKI binding region, both regions of the motif are accessible for binding. H) QKI is known to retain mRNAs in the nucleus. We measured the SERPINA1 distal ratio in Hepatocellular carcinoma (●) and HepG2 (Δ) nuclear and cytoplasmic RNA-seq fractions and find the highest ratio in the nucleus. This suggests QKI binds and retains the long SERPINA1 isoform in the nucleus thereby inhibiting translation.

https://doi.org/10.1371/journal.pgen.1009912.g004

Our previous work on the SERPINA1 5’ UTR established that RNA structure plays an important role in controlling translation efficiency of the A1AT protein [4]. To determine if RNA structure could play a role in controlling translation by mediating QKI binding, we performed SHAPE-MaP (Selective 2’ Hydroxyl Acylation by Primer Extension and Mutational Profiling) to determine the secondary structure of SERPINA1 3′ UTR [40–43]. The SHAPE data is shown in Fig 4B, where red bars indicate high, yellow intermediate, and black low nucleotide reactivity. High SHAPE reactivities indicate the nucleotides are flexible and therefore likely unpaired [4,41,42]. From the SHAPE data we compute the Shannon Entropy of the RNA, which measures the degree of structuredness (Fig 4C) [42–45], as well as the structure indicated as an arc-plot in Fig 4D. We observe low Shannon Entropy near the distal APA site and in the short isoform, suggesting a high level of RNA structuredness in the short isoform but high entropy (low structuredness) in Regions 2, 3 and 4. Interestingly, the QKI site has low Shannon Entropy indicative of higher structure near the binding site. Previously published analysis of QKI in vitro RNA binding specificity suggests that the protein binds to the consensus motif 5’-NACUAAY-N(1,20)-UAAY-3′ [46]. The motif is present in the SERPINA1 3′ UTR within the eCLIP QKI binding site, and multiple nucleotides have high SHAPE reactivity in the motif suggesting it will be accessible for binding (Fig 4E).

The eCLIP data, the presence of the QKI consensus motif in the SERPINA1 3′ UTR, SHAPE structural analysis, and our luciferase data suggest QKI is likely playing a central role in translation repression of A1AT expression. Using our nanoluciferase translation assay, we therefore mutated the bipartite QKI motif in the long isoform, which significantly restores translational efficiency (Fig 4F, QKImutant). Addition of the QKI bipartite motif to the short isoform (Short+QKI) significantly represses translation (Fig 4F). These data therefore further support that QKI is directly affecting translation of SERPINA1 3′ UTR.

Finally, we modeled the RNA secondary structure of the SERPINA1 3′ UTR (Fig 4G) using the SHAPE data as a pseudo-free energy as previously carried out in [42,47,48]. This structure has potential ramifications as a modulator of translation repression and would also be a logical RNA target for small molecule or anti-sense therapeutic development [49]. We observed significant base-pairing in Region 1 and several hairpins spanning Regions 3 and 4 (indicated as green arcs, Fig 4D), which are consistent with low Shannon Entropy observed for the QKI binding site. Our structural model shows the proximal APA site is accessible (S1B Fig).

One known function of QKI is to retain transcripts in the nucleus, which has the effect of repressing translation [50]. Thus, a majority of the SERPINA1 long 3′ UTR should be retained in the nucleus if this is the mechanism of translation suppression. When we analyze the distal ratio from cytoplasmic and nuclear poly-A selected fractions of HepG2 and HCC (Hepatocellular carcinoma) RNA-seq data [51,52] we observe a much higher proportion of distal reads in the nuclear fraction (Fig 4H). Based on the increased ratio of distal reads in the nucleus, we posit that one mechanism by which QKI could be altering translational efficiency is by retaining SERPINA1 mRNA in the nucleus. This would effectively inhibit translation since mRNAs must be exported to the cytoplasm to be translated by the ribosome. Additional mechanisms may be involved in translational inhibition in other regions of the 3’UTR that we did not detect since the ENCODE RNA binding protein data is not a comprehensive atlas of all known RNA binding proteins. Nonetheless, our findings demonstrate the value of integrating large scale genomic data sets in investigating post-transcriptional regulation.

Differential expression of RBPs in COPD and the distal ratio

Having analyzed eCLIP RNA binding protein data, we were interested in identifying RBPs that alter the distal ratio of the SERPINA1 3′ UTR in COPD that may not have been detected by RNA binding protein crosslinking. We therefore measured differential expression in the LTRC data for the 224 RBPs for which shRNA ENCODE knock-down data is available [34–36]. For these 224 RBPs we also measured the distal ratio upon shRNA knock-down and plot this on the y-axis of Fig 5A. The plot is centered on the control, empty vector shRNA distal ratio, indicated by a horizontal line. On the x-axis we plot the log₂ differential expression fold change log₂(DE_FC) for these 224 RBPs when comparing the 376 COPD cases and 267 controls in the LTRC primary lung tissue data. In Fig 5A, the further the RBP is from the center of this two-dimensional volcano plot the more likely it modulates the distal ratio in COPD lungs.

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Fig 5. Role of RNA binding proteins and relative single-cell populations in modulating SERPINA1 APA.

A) Two-dimensional volcano plot of endogenous shRNA distal ratio in SERPINA1 (y-axis) as a function of log₂ differential expression fold change (log₂(DE_FC)) in LTRC primary lung tissue from 376 COPD cases and 267 controls for the 224 corresponding RNA binding proteins. The horizontal line represents the mean distal ratio in the corresponding empty vector shRNA controls, while the vertical line is centered on zero. Blue points (bottom right quadrant and top left quadrant) will decrease the distal ratio in COPD, while orange points (top left and bottom right quadrant) will increase the distal ratio. Filled circles indicate RNA binding proteins for which the distal ratio changes from shRNA control, and log₂(DE_FC) are both significant in both data sets with p_adj<0.05. B.) log₂ computed Euclidean distance from center of two-dimensional volcano plot for significant (p_adj<0.05) RNA binding proteins affecting COPD. Again, those RNA binding proteins expected to lower the distal ratio are colored blue and negative, while those expected to increase the distal ratio are orange and positive.

https://doi.org/10.1371/journal.pgen.1009912.g005

When we measure the distal ratio change in shRNA knockdown experiments (y-axis, Fig 5A), we are reporting the change in distal ratio upon down-regulation of that RBP. These same RBPs may be up, down, or not regulated in the lung tissue of COPD vs. control individuals. As we consider the representation developed in Fig 5A, there are two types of proteins: 1.) RBPs that increase the distal ratio (e.g. because their distal ratio is above the control ratio and log₂(DE_FC)<0 in COPD), or 2.) RBPs that decrease the distal ratio in COPD (e.g. because their measured distal ratio after shRNA is below that of the control and log₂(DE_FC)<0). Thus, RBPs in the top left and bottom right corners of Fig 5E contribute to increasing the distal ratio in COPD (orange), and those in the bottom left and top right corners contribute to decreasing the distal ratio (blue). In Fig 5A, filled circles meet the p_adj<0.05 for both distal ratio difference from control and DE_FC. Numerical data used to create Fig 5A is also provided in S2 Table. We notice that QKI is in the bottom left quadrant as it is significantly down regulated in COPD (log₂(DE_FC) = -0.13, p_adj = 5.4x10^-16) and significantly decreases the distal ratio in shRNA knockdown experiments (distal ratio = 0.036, p = 1.54x10^-32). QKI decreased expression in COPD therefore decreases the long isoform of the SERPINA1 mRNA, which would lead to higher translation efficiency of the A1AT protein.

One clear result in Fig 5A is that no single protein can account for the increase in usage of the distal 3′ UTR site we report in Fig 2. To quantify the effect of each protein we compute the log₂ Euclidean distance from the center of the graph (Fig 5B) which quantifies the potential effect of each RNA binding protein on the distal ratio in COPD. A higher distance indicates a higher effect in COPD of the RBP on the distal ratio. We plot this distance as negative if the RBP lowers the distal ratio in COPD (blue) and positive (orange) if it increases. If we consider all RBPs where the absolute value of the log₂ Euclidean distance > 1 and p_adj<0.05 for shRNA distal ratio change and log₂(DE_FC) then we identify 6 RBPs that decrease the distal ratio and only 2 that increase it (Fig 5B blue and orange, respectively, S2 Table). CSTF2 (cleavage stimulation factor 2), an important modulator of the cleavage stimulation factor complex which regulates APA is in the bottom left-hand corner and has the most negative Euclidean distance in Fig 2B, such that its differential expression in COPD will also contribute to less distal SERPINA1 mRNA expression.

This analysis reveals that in COPD lung tissue, RNA binding protein expression levels are adjusted in a way that favors the short SERPINA1 isoform, thereby resulting in higher A1AT translation efficiency. This is contrary to what we observed in Fig 2, where the distal ratio is increased in COPD individuals. Furthermore, SERPINA1 mRNA is not differentially expressed in COPD LTRC data (log₂(FC) = 0.07, p_adj = 0.16), and that these changes only affect the ratio of long to short isoform, and therefore the translation of the A1AT protein in the lung. An important caveat in interpreting the data in Fig 5 is that it is not a comprehensive analysis of all known RNA binding proteins, since we are limited on the y-axis by existing ENCODE shRNA experiments. Furthermore, the shRNA data were collected in HepG2 cells, whereas our differential expression analysis was measured in primary lung tissue. There are therefore likely additional RBPs controlling the distal ratio in the lung that this analysis cannot reveal. It is possible that a different molecular mechanism this analysis cannot reveal explains the observed lengthening in COPD lungs. However, our analysis in Fig 5A and 5B of expression changes in the LTRC lung cohort reveal that in COPD lungs, multiple RNA binding proteins (including QKI and CSTF2) have altered expression that, in the context of corresponding shRNA knock-down experiments, will lead to a decreased distal ratio. This may act as a molecular buffer, effectively keeping the distal ratio from increasing more.

We also analyzed single-cell RNA-seq data to investigate if another mechanism could explain the observed higher distal ratio observed in Fig 2B and 2C. Single-cell RNA-seq from five healthy human liver biopsies previously identified 20 clusters of cells, including six different hepatocyte clusters, shown in S5A Fig [53]. SERPINA1 mRNA is most predominantly expressed (green) in the six hepatocyte clusters (cluster numbers 1, 3, 5, 6, 24 and 15) (S5C Fig). In comparison lung UMAP (Uniform Manifold Approximation and Projection) single cell RNA-seq analysis tissue from five healthy donors previously revealed 25 cell type clusters (S5B Fig) [54]. In these data SERPINA1 mRNA is predominantly expressed in Alveolar Type 1 and 2 cells as well as Macrophages (S5D Fig).

Read coverage in single-cell RNA-seq data is significantly sparser and has higher variability than bulk RNA-seq data. We were able to visualize the distribution of distal ratios for each cell type in clusters with sufficient cells expressing SERPINA1 mRNA. For the liver data these include three hepatocyte clusters (1, 3, and 6) as well as Macrophages, αβ-Tcells and Plasma cells (S5E Fig). In the lung, these are Alveolar Type 1 and 2, Dendritic cells and Macrophages (S5F Fig). We observe that the distal ratio distributions are different in each cell type, but these differences are not statistically significant in these data, mostly due to very large variations observed. Recent single-cell analysis of lung tissue in COPD individuals observed important changes in AT2 cell populations in COPD individuals [55], however, the methods used for cell dissociation do cause substantial losses in AT1 cells. This makes it difficult to quantitatively relate these results with our observations in the LTRC data. Nonetheless, any changes in relative cell-type abundance in COPD lungs will affect the observed distal ratio in bulk-RNA seq data, and we hypothesize this is the mechanism that causes the observed increase in distal ratio observed in the LTRC COPD data (Fig 2C). Although we cannot quantitatively establish that RBPs or changes in cell type population alone are responsible for the observed difference in the distal ratio of SERPINA1 mRNA in COPD lungs (Fig 2B, 2C and 2D), our data reveal the complex network of factors that ultimately control A1AT protein production at the molecular and cellular level in the lung.

Cell culture

Liver derived hepatocellular carcinoma (HepG2) cells and lung derived epithelial carcinoma (A549) cells were obtained from ATCC through UNC Tissue Culture Facility. HepG2 cells were cultured in Eagle’s Minimum Essential Media supplemented with 10% fetal bovine serum. A549 cells were cultured in RPMI 1640 media supplemented with 10% fetal bovine serum. Cells were split regularly using Tryple (Fisher). All cells were grown in 5% CO2 and 37°C.

3′ sequencing

Total RNA was extracted from HepG2 cells using trizol (ThermoFisher) followed by RNA column purification (Purelink Invitrogen) and on-column DNA digestion (Purelink, Invitrogen). RNA was used to create 3′ end specific libraries with the Quant Seq kit (Lexogen). The same sample of HepG2 RNA was depleted of ribosomal RNA (Ribominus Eukaryotic v2, Fisher) and used to generate standard Illumina short-read libraries using the Nextera Flex kit (Illumina). Both libraries were sequenced (Illumina MiSeq, 300 kit) and the fastq sequence files used in downstream analysis. The standardized Bluebee Lexogen Quant Seq FWD protocol was used for trimming, read alignment, and quality control steps of the Quant Seq library. The standard HepG2 transcriptome sequencing was analyzed in the same manner as other fastq datasets (see—Calculation of SERPINA1 distal ratio from sequencing).

Calculation of SERPINA1 distal ratio from RNA sequencing

We followed the GTEx Consortium and TopMed guidelines for sequence alignment to the human genome (https://github.com/broadinstitute/gtex-pipeline/commits/master/TOPMed_RNAseq_pipeline.md). Briefly, we used STAR [66] to align fastq files to hg38 (containing no alternative, no decoy and no HLA chromosomes). After alignment, or with pre-aligned samples, we used samtools to determine the read depth at each nucleotide in the 3′UTR, summed up the depth within the distal and proximal specific regions and normalized by the gene region length. We calculated the ratio of distal 3′UTR reads by taking the length normalized read counts in the distal region and dividing by the sum of the length normalized distal and proximal reads. The same approach was used on individual clusters for single-cell RNA-seq analysis (S5 Fig).

Construction of nanoluciferase SERPINA1 3′UTR reporter system

We gene synthesized the full 3′UTR sequence of SERPINA1 (Twist biosciences) with a mutated proximal polyA site (AUUAAA to AUCAAG) and 60 nucleotides of coding sequence. This coding region and full 3′UTR were cloned into pNL3.2.CMV (Promega) using sequence homology (NEB Builder HiFi). A short 3′UTR isoform was amplified from the gene synthesized construct using PCR with the same coding sequence portion. This short isoform was cloned into the same backbone with sequence homology. The nanoluciferase expressed from this construct contains a PEST domain to prevent protein accumulation. All additional deletion and mutation constructs were created from verified long 3’UTR plasmids. Primers for cloning are listed in S3 Table. The mutated polyA site was reverted back to wildtype for both the long and short 3′UTR constructs (NEB Q5 Site directed mutagenesis kit) for structural studies. All constructs were verified by Sanger sequencing and restriction digest.

Luciferase experiments

For deletion series analysis, A549 cells were split into 96 well opaque, clear bottom plates (~150,000 cells/mL) and transfected the next day with 10 ng of control firefly luciferase and 10 ng of nanoluciferase constructs using standard quantities of Lipofectamine 3000 and P3000 in serum-free Optimem (Fisher). HepG2 cells were treated similarly, but 50 ng of firefly and nanoluciferase constructs were used. Cells were transfected in triplicate. After 30 hours plates were used to measure luminescence using the Promega Nano-Glo Dual-Luciferase Reporter Assay System. Luminescence was quantified at 480 nm on the BMG Labtech Clariostar plate reader. Four biological replicates from separate days were combined for analysis. R was used to normalize samples (0 to 1) and we created a linear model with no y-intercept. Coefficients for each region are listed in Table 1. For QKI mutation analysis, A549 cells were split into 12 well plates and transfected the next day with 0.5 ug of control firefly luciferase and 0.5 ug of nanoluciferase constructs using standard quantities of Lipofectamine 3000 and P3000 in serum-free Optimem (Fisher). Cells were transfected in triplicate. After 48 hours plates were used to measure luminescence using the Promega Nano-Glo Dual-Luciferase Reporter Assay. Luminescence was quantified at 480 nm on the SpectraMax iD5 plate reader. Three replicates from separate days were combined for analysis.

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RNA stability quantification

We transfected A549 cells (same conditions as Luciferase assays) with equimolar amounts of either the long or short 3′UTR nanoluciferase reporter and performed a pulse-chase experiment by incubating transfected cells with ethylene uridine (EU) and harvesting either immediately, two hours or six hours later (Click-iT Nascent RNA Capture Kit, Invitrogen). Incorporation of EU into RNA does not alter cellular biology and allows for click-it chemistry and labeling with biotin-azide [63]. RNA was extracted using Trizol (Invitrogen) chloroform extraction and Phaselock Tubes (Invitrogen). RNA was purified using Purelink RNA Mini Kit (Invitrogen) and treated with DNAse I (Invitrogen) prior to being converted to cDNA using VILO reverse transcriptase according to manufacturer’s instructions (Invitrogen). qPCR was carried out by using PowerUp Sybr Green Master Mix (Thermo Fisher Scientific) on cDNA isolated from 0, 2 and 6 hour time points and primers specific for nanoluciferase, the short or long isoform of SERPINA1. Primers are listed in S3 Table. The qPCR reactions were performed on an Applied Biosystems QuantStudio 6. We confirmed that GAPDH was consistently stable over the entire time period and then determined SERPINA1 3′UTR RNA stability by normalizing expression values of triplicate samples to GAPDH (ΔCT = CT_SERPINA1—CT_GAPDH).

RNA structure experiments

A T7 promoter was added to the wild-type nanoluciferase SERPINA1 3′UTR during PCR amplification. In vitro RNA was produced off the purified PCR product (NEB HiScribe T7 High yield RNA synthesis kit), treated with Turbo DNase, purified and incubated at 37°C for 10 min in folding buffer (final concentration, 200 mM Bicine, 100 mM NaCl and 10 mM MgCl2). SHAPE treatment was performed with an addition of DMSO (control, 10% final concentration) or 5NIA (25 mM in DMSO final concentration) for 5 min at 37°C. The RNA was purified (AmpureXP RNA beads). We generated sequencing libraries (Swift RNA library kit), bioanalyzed and quantified the libraries and then sequenced the samples (Illumina MiSeq 300 kit). Reactivity profiles were generated using SHAPEMapper [40]. Base-pairing probabilities were generated with SuperFold and RNA structure models with RNAstructure [67,68].

RBP shRNA knockdown effects and eCLIP analysis of SERPINA1 3′UTR isoforms

The RBP eCLIP binding data was downloaded from the ENCODE data portal on Nov. 18, 2019. This download was restricted to assays in K562 and HepG2 cell lines and for RBPs with a “Target Category” in ENCODE that matched “RNA binding protein” and mapped to GRCh38. This resulted in eCLIP data for 122 RBPs. All downloaded eCLIP sites were originally identified through the described ENCODE 3 pipeline, a stringent process that takes into account experimental reproducibility and fraction of reads in peaks (FRiP) over background [34]. We then mapped peaks using the narrowPeak bed files with “1, 2" listed in the biological replicates column in the downloaded ENCODE metadata file. We also downloaded sequencing files for RBP shRNA knockdown for control shRNA treatment and shRNAs against SERPINA1 RBPs in HepG2 cells from the ENCODE portal. All shRNA experiments had a knockdown efficiency of at least 50% compared to the scrambled control for protein and RNA. We aligned these fastq files and calculated the ratio of distal 3′UTR reads (see—Calculation of SERPINA1 distal ratio from sequencing).

RBP differential expression analysis in LTRC data

COPD was defined as forced expiratory volume in one second (FEV₁) to forced vital capacity (FVC) of less than 0.7 and FEV₁ less than 80% predicted (Global Initiative for Chronic Obstructive Lung Disease spirometric stages 2–4). No individual with alternative pathologic lung diagnosis other than emphysema was included as a subject or control. Control subjects were defined by FEV₁/FVC ratio > 0.70. In pre-analysis filtering, we excluded genes with fewer than 1 count per million reads in at least 50% of subjects. We performed differential gene expression using the limma [69] and voom [70] packages from R/Bioconductor. We tested the associations of gene expression levels and COPD. We adjusted the models for age, race, sex, current smoking status, smoking pack-years, and library preparation batch. Surrogate variables were used to estimate other latent effects and were computed using the sva package. We controlled for multiple testing with a false discovery rate (FDR) of 1%.

Datasets

We obtained pre-aligned hg38 GTEX tissue specific sequencing through dbGAP (dbGaP Accession phs000424.v8.p2) and focused on the top tissues where SERPINA1 is expressed (Liver, Lung, Blood, Spleen, Kidney and Small Intestine). We re-analyzed publicly available lung RNA-seq data from 82 COPD individuals and 64 individuals with normal spirometry (Bioproject PRJNA245811). Sequencing runs without coverage in the long 3′UTR region of SERPINA1 were excluded from analysis. We repeated our analysis with a larger set of lung RNA-Seq data from 376 individuals exhibiting COPD and 277 individuals with normal lung function (Lung Tissue Research Consortium generated by the NHLBI TOPMed Project (https://www.nhlbiwgs.org/). This larger RNA-Seq dataset is available with authorization (dbGaP Accession phs001662.v2.p1). Individuals without clear phenotype were excluded. We used the ENCODE HepG2 fractionation experiments (Bioproject PRJNA30709:GSE30567—SRR307915, SRR307916, SRR307928, SRR307929) and the patient-derived hepatocellular carcinoma cell line fractionation experiments (Bioproject PRJNA543441) to investigate cellular localization of SERPINA1 isoforms. To analyze SERPINA1 long read sequencing, we used publicly available data from PacBio human liver aligned using IsoSeq. Links to this data and human blood and heart sequencing are available on https://s3.amazonaws.com/datasets.pacb.com/downloadtools.html.