Molecular consequences of SARS-CoV-2 liver tropism

In this study, we examined three patient cohorts to characterize severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-associated liver injury and infection. First, a clinical cohort (Hamburg; cohort 1, n = 99) was used to examine changes in liver function tests (LFTs) in patients admitted to hospital due to coronavirus disease 2019 (COVID-19) and those who were diagnosed with COVID-19 during hospitalization (Fig. 1a ); 42% of these patients were at least 65 years old, 70% were males, only 23% had at least 3 coexisting conditions (Source Data Fig. 1 ). Among patients admitted due to COVID-19 ( n = 72), raised aspartate aminotransferase (AST) and alanine aminotransferase (ALT) were shown in 63 and 39% of cases (Fig. 1b ), respectively despite the low frequency of liver disease (1.4%), confirming previous studies 4 – 11 . Next, we analysed a subgroup of patients that were admitted to hospital due to alternative diagnoses ( n = 27) and suffered from nosocomial SARS-CoV-2 infection. Both AST and ALT were significantly increased after the diagnosis of COVID-19 (Fig. 1c ). While the percentage of patients with raised ALT almost doubled before and after COVID-19 diagnosis (33 versus 59%), the percentage of patients with elevated AST almost tripled (22 versus 67%) (Fig. 1d ). Next, we evaluated a large validation cohort of patients admitted to hospital due to COVID-19 (Michigan; cohort 2, n = 1,219) (Fig. 1e ); 44% of patients were at least 65 years old, 57% were males and only 16% had at least 3 coexisting conditions (Supplementary Table 1 ). Importantly, 57% ( n = 699) of patients showed elevations of AST and 37% ( n = 452) of ALT at admission (Fig. 1e ). Furthermore, LFT elevations at admission and during the second week of hospitalization were associated with mortality (Fig. 1f ), raising questions about their role in disease severity. Together, these observations clearly highlight hepatic injury as an important clinical feature of patients with COVID-19 requiring hospitalization. Fig. 1 Elevated LFTs among patients with COVID-19. a , Overview of cohort 1. n = 99 patients required hospitalization due to COVID-19 (Germany). n = 72 were admitted due to moderate/severe COVID-19 and n = 27 acquired COVID-19 during their hospital stay. b , Only 1.4% of patients admitted due to COVID-19 from cohort 1 had a history of liver disease, yet LFTs at admission showed elevated AST in 63% and ALT in 39% of patients. c , d , AST and ALT levels in patients with COVID-19 acquired during hospitalization worsened after COVID-19 diagnosis in 81% and 67% of patients, respectively. e , Demographic overview of cohort 2. n = 1,219 patients required hospitalization due to COVID-19 (Michigan). Only 2.4% of admitted patients from cohort 2 had a history of liver disease, yet LFTs at admission show elevated AST in 57% and ALT in 37% of patients with COVID-19. f , Variation of mean AST and ALT over time in patients with COVID-19, showing elevations in LFTs associated with mortality. Source data It has been postulated that LFT elevations in hospitalized patients with COVID-19 may result from systemic inflammation or severe cellular stress (for example, hypoxia), as generally observed in critically ill patients 9 . However, an autopsy study reported ultrastructural evidence of SARS-CoV-2 (ref. 12 ), and a second study reported histopathological findings in a liver biopsy of a patient with abnormal liver enzymes, including no obvious inflammation in the portal area, with normal description of the interlobular bile duct, interlobular vein, interlobular artery and hepatocytes with minimal inflammatory cell infiltration 11 . Given that liver biopsies are not routinely performed in patients with COVID-19 with altered LFTs, we evaluated a third cohort (cohort 3, n = 45 autopsy cases) in search of direct evidence of liver infection (Supplementary Table 2 ); 73% ( n = 33) were older than 65, 69% ( n = 31) had at least 3 coexisting conditions and 62% ( n = 28) were males, matching the demographic characteristics linked to severe COVID-19. SARS-CoV-2 RNA was detected using quantitative PCR with reverse transcription (RT–qPCR) targeting the E gene in 69% of cases ( n = 31) (Fig. 2a ), which was more frequently associated with older age, male sex and multiple coexisting conditions, as shown in previous reports 2 , 13 , 14 . Fig. 2 SARS-CoV-2 liver tropism is associated with transcriptional regulation of IFN responses. a , Clinical heatmap of 45 patients indicating age ≥65 years, sex, 3 or more coexisting conditions and SARS-CoV-2 liver tropism (PCR + liver). b , Immunofluorescence images show the presence of the SARS-CoV-2 receptor ACE2 in hepatic cells (that is, Kupffer cells). Staining was performed in samples from five different patients (data shown in Extended Data Fig. 2). c , SARS-CoV-2 spike protein detection in autopsy liver tissues (that is, Kupffer cells and hepatocytes). d , Successful SARS-CoV-2 isolation in postmortem livers and respec

Tissue samples were systematically sampled during the autopsy procedure in 45 individuals. Automated nucleic acid extraction was performed according to the manufacturer’s recommendations with whole process control (control kit; Roche), with a final elution volume of 100 μl. For virus quantification, a previously published assay was adopted with modifications using chimeric 2′-O-methyl RNA bases at the penultimate base of both primers (mG and mC) to reduce primer dimer formation 28 . The forward primer 5′-ACAGGTACGTTAATAGTTAATAGCmGT-3′ (400 nM end concentration), 5′-TATTGCAGCAGTACGCACAmCA-3′ (400 nM end concentration) and probe 5′-Fam-ACACTAGCC/ZEN/ATCCTTACTGCGCTTCG-Iowa Black FQ-3′ (100 nM end concentration) were used. Primer and probes were obtained from Integrated DNA Technologies. One-step RT–PCR (25 μl volume) was run on the LightCycler 480 system (Roche) using the one-step RNA control kit as master mix (Roche) and 5 μl of eluate. The Ct value for the target SARS-CoV-2 RNA (FAM) was determined using the second derivative maximum method. To quantify the standard in vitro-transcribed RNA, the E gene of SARS-CoV-2 was used. The standard was obtained via the European Virus Archive 4 . The linear range of the assay was between 1 × 10 3 and 1 × 10 9 copies ml −1 . β-Globin qPCR was performed with the commercial TaqMan primer set (catalogue no. 401846; Thermo Fisher Scientific) and Roche DNA control kit. The PCR was run on the LightCycler 480 system. The amount of DNA was normalized using a human DNA standard (KR0454). SARS-CoV-2 RNA levels in tissues were normalized to β-globin DNA.

In situ hybridization was carried out to detect virus RNA of SARS-CoV-2 on paraffin sections utilizing the RNAscope 2.5 HD detection kit (catalogue no. 322310; Advanced Cell Diagnostics) according to the manufacturer’s instructions 31 . Briefly, tissue sections were deparaffinized in xylene followed by target retrieval at 95 °C for 10 min. Subsequently, internal peroxidase activity was quenched by hydrogen peroxide incubation for 10 min followed by permeabilization using protease plus treatment at 40 °C for 30 min. The SARS-CoV-2-specific RNAscope probe V-nCoV2019-S (catalogue no. 848561) was hybridized at 40 °C for 2 h. RNAscope probes specific for either the human ubiquitin C mRNA (catalogue no. 310041) or the bacterial dihydrodipicolinate reductase mRNA (catalogue no. 310043) were used as positive or negative Ctrl, respectively. The RNAscope signal was developed with 3,3′-diaminobenzidine and nuclei were counterstained with haematoxylin.

Sample selection was based on collection date and RNA quality. RNA was extracted with the QIAGEN RNeasy Micro Kit according to the manufacturer’s instructions followed by Agilent Bioanalyzer sample quality control, library preparation with Lexogen CORALL Total RNA and ribosomal RNA depletion. Single-end RNA-seq was done using 75-base pair NextSeq v.2.5 with >30 million reads. Raw reads were trimmed with Trim Galore! v.0.4.3 ( http://www.bioinformatics.babraham.ac.uk/projects/trim_galore/ ) and aligned with RNA STAR: Galaxy Version 2.7.2b 33 . Mapped reads were counted with HTSeq: Galaxy Version 0.9.1. 34 . GTEX data Raw counts from several healthy tissues were downloaded directly from the GTEX portal ( www.gtexportal.org ) on 4 March 2020 35 .

GSEA was performed using the GAGE R package v.2.36.0 (ref. 37 ) with the Molecular Signatures Database (MSigDB) gene set v.7.1, comparing PCR-positive to PCR-negative groups. Both groups were also compared with GTEX liver. A Benjamini–Hochberg-adjusted P < 0.05 was considered significant.

The relative fraction of single-guide RNAs (sgRNAs) was determined by identifying the leader sgRNA fusion containing sequencing reads normalized to read counts containing the genomic RNA leader sequence 42 . Pharyngeal swab data served as a positive control for the detection of sgRNA due to the high amount of replicating virus in this specimen. Swab sample data were reproduced from our own data 42 .

Raw MS data were processed with the MaxQuant software v.1.6.17 using the built-in Andromeda search engine to search against the human proteome (UniprotKB, release 2019_10) containing forward and reverse sequences concatenated with the SARS-CoV-2 polyprotein with the individual viral open reading frames manually annotated, and the label-free quantitation algorithm 44 . Additionally, the intensity-based absolute quantification (iBAQ) algorithm and match between runs option were used. In MaxQuant, carbamidomethylation was set as fixed and methionine oxidation and N-acetylation as variable modifications. Search peptide tolerance was set at 70 p.p.m. and the main search was set at 30 p.p.m. (other settings left as default). Experiment type was set as TIMS-DDA with no modification to the default settings. Search results were filtered with a false discovery rate of 0.01 for peptide and protein identification. The Perseus software v.1.6.10.4 was used to process the data further. Protein tables were filtered to eliminate the identifications from the reverse database and common contaminants. When analysing the MS data, only proteins identified on the basis of at least one peptide and a minimum of three quantitation events in at least one experimental group were considered. The iBAQ protein intensity values were normalized against the median intensity of each sample (using only peptides with recorded intensity values across all samples and biological replicates) and log-transformed; missing values were filled by imputation with random numbers drawn from a normal distribution calculated for each sample 45 . Differential analysis and GSEA were performed as described above on mRNA.

For every single gene, we calculated the average fold change between positive and negative. The gene set-specific correlation was assessed by first selecting the genes that belonged to a given gene set, then by calculating the mRNA fold change versus the protein fold change Spearman’s correlation of these genes. To estimate the significance of the correlation for one gene set, correlation values were also calculated from 10,000 random sets of genes, using the same number of genes as the given gene set. Finally, the ECDF was used to return lower- and upper-tail P values.

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Extended Data Fig. 1 Histopathology in autopsy samples. a, b , Histological sections from COVID-19 autopsy tissue without signs of overt cytopathic changes. Examples of other pathological alterations. c , Overview of a case with focal fatty changes and centrilobular necrosis, likely as a consequence of shock. d , Zoom in of a case with moderate fatty liver and marked centrilobular necrosis, also likely due to shock. These images represent the main findings after careful examination of 18 cases. Scale bar represents 350um. Extended Data Fig. 2 ACE2 expression. ACE expression (orange) and DNA (grey) in the human liver. Based on anatomical location, the staining pattern suggests expression by Kupffer cells. All images were taken at the same magnification. Scale bar represents 10um. This experiment was repeated 3 times in 3 samples with identical results. Source data Extended Data Fig. 3 SARS-CoV-2 spike protein in autopsy samples. a , Expression of SARS-CoV-2 spike protein (orange) and DNA (grey) in autopsy tissue from 3 patients with confirmed RT-qPCR + for SARS-CoV-2 in the respiratory tract (during clinical course) and in the liver (at autopsy). Based on anatomical locations, these findings suggest expression of SARS-CoV-2 spike protein in Kupffer cells, immune cells, and hepatocytes. Scale bar represents 10um. Source data Extended Data Fig. 4 Expression of known SARS-CoV-2 entry receptors and facilitators in liver autopsy samples. a , Gene expression of ACE2 in different GTEX tissues shows log2 transcript per million (TPM) expression levels in the liver after intestine, kidney and lung. ACE2 expression levels in the liver samples of COVID-19 patients and controls are comparable with liver and lung expression in GTEX data set. b , Gene expression of CTSL and c , TMPRSS2 and d , RAB7A in different GTEX tissues shows log2 TPM expression levels in the liver after intestine, kidney and lung. Box plots showing boxes from the 25th to the 75th percentile with the median shown as a line in the middle and whiskers indicating 1.5 times the interquartile range. Extended Data Fig. 5 Detection of SARS-CoV-2 subgenomic RNA. a , Relative fraction of sgRNA in SARS-CoV-2-positive liver samples is comparable to pharyngeal swab sample data (medRxiv 2020.06.11.20127332; doi: 10.1101/2020.06.11.20127332). b , Total number of SARS-CoV-2-positive reads divided by total number of human reads (*1000) shows a higher number of reads aligning to the SARS-CoV-2 genome in SARS-CoV-2 liver POS samples than in SARS-CoV-2 liver NEG samples and controls. *, p-value = 0.0122 (Mann-Whitney test, two-tailed). Box plot: box extending from the 25th to the 75th percentile with the median shown as a line in the middle and whiskers indicating smallest and largest values. Extended Data Fig. 6 Transcriptional changes associated with SARS-CoV-2 hepatic tropism. a , Principal Component Analysis performed on the normalized intensity showing PCR positive (blue), PCR negative (red) Covid-19 liver samples and Control liver (green) samples. PCR- samples are well embedded with healthy liver tissue. b , Volcano plot shows the differentially regulated genes (red dots, adjusted p-value < 0.05) in SARS-CoV-2 PCR positive vs. negative liver samples. Top 25 regulated genes, based on p-value, are labeled. c , Row-wise scaled intensity (mat, z -score) heatmap showing genes from Notch signaling pathway and d , Interferon gamma response gene-set. Genes are ranked according to the log2 fold change, indicated on the right side. Adjusted p-value < 0.05 is indicated with ‘*’. Within each group, samples are clustered based on Euclidean distance. Source data Extended Data Fig. 7 Gene ontology analysis. Barplots showing the top 10 UP- and DOWN-regulated gene-sets from Gene Ontology Biological processes between PCR positive and negative samples. Color code represent the number of genes within each gene set. Extended Data Fig. 8 Canonical pathway analysis. a , Transcription factor analysis reveals differential transcription factor usage in CTL, COVID-19 liver NEG and liver POS samples. Summary of mean pathway activity in each infection status. b , Canonical pathways up (+) or down (-) regulated. Extended Data Fig. 9 Gene expression of SCARB1. Gene expression of SCARB1 in different GTEX tissues shows log2 transcript per million (TPM). Our own datasets are also used to provide context, including Liver (Controls) and Liver (COVID-19). Box plots showing boxes from the 25th to the 75th percentile with the median shown as a line in the middle and whiskers indicating 1.5 times the interquartile range. Extended Data Fig. 10 Protein expression of SR-B1 and SARs-CoV-2 spike in autopsy tissue. Protein expression of SR-B1 (cyan) in one post-mortem liver sample – and co-expression with SARS-CoV-2 spike protein (orange) within the same cell. In A, we show an overview of a large liver region, showcasing widespread expression of SR-B1 among hepatocytes with a zoom-in to a cell expressin

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Nature Metabolism thanks Eleanor Barnes, Bing Zhang and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Isabella Samuelson, in collaboration with the Nature Metabolism team.