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Comparative analysis of dynamic transcriptomes reveals specific COVID-19 features and pathogenesis of immunocompromised populations - PubMed

  • ️Mon Jan 01 2024

Comparative Study

. 2024 Jun 18;9(6):e0138523.

doi: 10.1128/msystems.01385-23. Epub 2024 May 16.

Affiliations

Comparative Study

Comparative analysis of dynamic transcriptomes reveals specific COVID-19 features and pathogenesis of immunocompromised populations

Xiaodi Yang et al. mSystems. 2024.

Abstract

A dysfunction of human host genes and proteins in coronavirus infectious disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a key factor impacting clinical symptoms and outcomes. Yet, a detailed understanding of human host immune responses is still incomplete. Here, we applied RNA sequencing to 94 samples of COVID-19 patients with and without hematological tumors as well as COVID-19 uninfected non-tumor individuals to obtain a comprehensive transcriptome landscape of both hematological tumor patients and non-tumor individuals. In our analysis, we further accounted for the human-SARS-CoV-2 protein interactome, human protein interactome, and human protein complex subnetworks to understand the mechanisms of SARS-CoV-2 infection and host immune responses. Our data sets enabled us to identify important SARS-CoV-2 (non-)targeted differentially expressed genes and complexes post-SARS-CoV-2 infection in both hematological tumor and non-tumor individuals. We found several unique differentially expressed genes, complexes, and functions/pathways such as blood coagulation (APOE, SERPINE1, SERPINE2, and TFPI), lipoprotein particle remodeling (APOC2, APOE, and CETP), and pro-B cell differentiation (IGHM, VPREB1, and IGLL1) during COVID-19 infection in patients with hematological tumors. In particular, APOE, a gene that is associated with both blood coagulation and lipoprotein particle remodeling, is not only upregulated in hematological tumor patients post-SARS-CoV-2 infection but also significantly expressed in acute dead patients with hematological tumors, providing clues for the design of future therapeutic strategies specifically targeting COVID-19 in patients with hematological tumors. Our data provide a rich resource for understanding the specific pathogenesis of COVID-19 in immunocompromised patients, such as those with hematological malignancies, and developing effective therapeutics for COVID-19.

Importance: A majority of previous studies focused on the characterization of coronavirus infectious disease 2019 (COVID-19) disease severity in people with normal immunity, while the characterization of COVID-19 in immunocompromised populations is still limited. Our study profiles changes in the transcriptome landscape post-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in hematological tumor patients and non-tumor individuals. Furthermore, our integrative and comparative systems biology analysis of the interactome, complexome, and transcriptome provides new insights into the tumor-specific pathogenesis of COVID-19. Our findings confirm that SARS-CoV-2 potentially tends to target more non-functional host proteins to indirectly affect host immune responses in hematological tumor patients. The identified unique genes, complexes, functions/pathways, and expression patterns post-SARS-CoV-2 infection in patients with hematological tumors increase our understanding of how SARS-CoV-2 manipulates the host molecular mechanism. Our observed differential genes/complexes and clinical indicators of normal/long infection and deceased COVID-19 patients provide clues for understanding the mechanism of COVID-19 progression in hematological tumors. Finally, our study provides an important data resource that supports the increasing value of the application of publicly accessible data sets to public health.

Keywords: SARS-CoV-2; hematological malignancies; infection; protein complex; protein-protein interaction; transcriptome.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Fig 1
Fig 1

Multi-stage transcriptome atlas of COVID-19 patients with and without hematological tumors. (A) Flowchart of the overall experimental design. (B) Distribution of differentially expressed viral targets and nontargets of COVID-19 patients with (hematological tumors patients) and without (non-tumor individuals) hematological tumors post-SARS-CoV-2 infection. (C) Distribution of differentially expressed targets and nontargets of COVID-19 patients with hematological tumors (normal infection, long infection, and acute death) and uninfected non-tumor individuals (ever infected vs ever uninfected). (D) Distribution of targets and nontargets in differentially expressed complexes of COVID-19 patients with (hematological tumors patients) and without (non-tumor individuals) hematological tumors post-SARS-CoV-2 infection. (E) Distribution of targets and nontargets in differentially expressed complexes of COVID-19 patients with hematological tumors (normal infection, long infection, and acute death) and distribution of targets and nontargets in differentially expressed complexes of uninfected non-tumor individuals (ever infected vs ever uninfected).

Fig 2
Fig 2

Transcriptome changes in COVID-19 patients with and without hematological tumors. (A) Overlapping and unique genes comparing DEGs of hematological tumor patients and non-tumor individuals post-SARS-CoV-2 infection and corresponding distribution of SARS-CoV-2 targets/nontargets. The gene lists in the left and right panels indicate the top DEGs (ordered by |log2FoldChange| from largest to smallest) of hematological tumor patients and non-tumor individuals, respectively, where yellow fonts represent SARS-CoV-2 targets. DEGs of non-tumor individuals significantly overlap with DEGs of hematological tumor patients (***P-value < 0.001; two-sided Fisher’s exact test) and SARS-CoV-2 targets (*P-value < 0.05; two-sided Fisher’s exact test). A total of 13 targets in panel B and 38 nontargets in panel C were shared DEGs between hematological tumor patients and non-tumor individuals, while a total of 25 targets in panel B and 169 targets of DEGs were unique for hematological tumor patients and non-tumor individuals, respectively. Most targeted genes in panel B were membrane-localized, essential, innate immune-related, host factors, or scaffolding proteins compared to non-targeted genes in panel C.

Fig 3
Fig 3

Comparison of network and functional characteristics between SARS-CoV-2 targets and nontargets of hematological tumor patients/non-tumor individuals within DEGs and DECs. (A) Degree distributions of DEGs. (B) Within-complex degree distributions of DEGs. (C) Proportions of essential genes. (D) Proportions of complexes containing host factors. (E) Specific complex distribution and complex cases of targets/nontargets in DEGs of hematological tumor patients/non-tumor individuals post-SARS-CoV-2 infection.

Fig 4
Fig 4

Functional enrichments of DEGs and SARS-CoV-2-targeted DEGs of COVID-19 patients with (hematological tumor patients) and without hematological tumors (non-tumor individuals) post-SARS-CoV-2 infection. (A) Distribution of DEGs and their corresponding enriched GO terms. (B) Significantly enriched GO terms that were shared between DEGs of hematological tumor patients and non-tumor individuals. (C) Significantly enriched GO terms that were unique for DEGs of hematological tumor patients. (D) Significantly enriched GO terms that were unique for DEGs of non-tumor individuals. (E) Distribution of SARS-CoV-2-targeted DEGs and their corresponding enriched GO terms. (F) Protein interaction network between SARS-CoV-2 proteins and human DEGs post-SARS-CoV-2 infection of hematological tumor patients and an upregulated co-complex case. (G) Significantly enriched GO terms that were unique for SARS-CoV-2-targeted DEGs of hematological tumor patients.

Fig 5
Fig 5

DEGs and DECs between multiple infection statuses of hematological tumor patients. (A) Distribution of DEGs between multiple infection statuses, including normal infection, long infection, and acute death of hematological tumor patients and their overlaps with SARS-CoV-2 targets. (B–D) Expression atlas of shared targeted DEGs between any two treatment-control groups. (E) Specific DECs of DEGs in panels B–D.

Fig 6
Fig 6

Significantly different clinical indicators between death and normal/long infection cases. (A) Three blood indicators: HGB, PLT, and LY. (B) Four coagulation indicators: PT, INR, and TT. (C) Three immune-related indicators: IL6, PCT, and ALB. *, **, and *** indicate P-values < 0.05, 0.01, and 0.001 (Wilcoxon rank sum test), respectively.

Fig 7
Fig 7

DEGs and DECs between non-tumor individuals ever SARS-CoV-2 infected and uninfected in the 2022 COVID-19 pandemic. (A) A total of 40 genes including 10 SARS-CoV-2 targets were identified as DEGs between non-tumor individuals ever SARS-CoV-2 infected and uninfected. (B) Significantly enriched functional terms and pathways of these upregulated and downregulated DEGs, respectively. (C) Up- and downregulated DECs of these DEGs including targets and nontargets.

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