A single-cell atlas reveals shared and distinct immune responses and metabolism during SARS-CoV-2 and HIV-1 infections
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Abstract
SARS-CoV-2 and HIV-1 are RNA viruses that have killed millions of people worldwide. Understanding the similarities and differences between these two infections is critical for understanding disease progression and for developing effective vaccines and therapies, particularly for 38 million HIV-1 + individuals who are vulnerable to SARS-CoV-2 co-infection. Here, we utilized single-cell transcriptomics to perform a systematic comparison of 94,442 PBMCs from 7 COVID-19 and 9 HIV-1 + patients in an integrated immune atlas, in which 27 different cell types were identified using an accurate consensus single-cell annotation method. While immune cells in both cohorts show shared inflammation and disrupted mitochondrial function, COVID-19 patients exhibit stronger humoral immunity, broader IFN-I signaling, elevated Rho GTPase and mTOR pathway activities, and downregulated mitophagy. Our results elucidate transcriptional signatures associated with COVID-19 and HIV-1 that may reveal insights into fundamental disease biology and potential therapeutic targets to treat these viral infections.
Highlights
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COVID-19 and HIV-1 + patients show disease-specific inflammatory immune signatures
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COVID-19 patients show more productive humoral responses than HIV-1 + patients
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SARS-CoV-2 elicits more enriched IFN-I signaling relative to HIV-I
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Divergent, impaired metabolic programs distinguish SARS-CoV-2 and HIV-1 infections
Article activity feed
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SciScore for 10.1101/2022.01.10.475725: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources We performed quality control and downstream analysis using the Seurat package (v4.0.4) (Stuart et al., 2019). Seuratsuggested: (SEURAT, RRID:SCR_007322)We performed GSEA on DGEs using the clusterProfiler package (v3.18.1) (Yu et al., 2012) with the “GSEA” function using default parameters using pathways from the MSigDB database (Subramanian et al., 2005) clusterProfilersuggested: (clusterProfiler, RRID:SCR_016884)Receptor-ligand analysis: To infer the putative receptor-ligand interactions between pairs of cell types, we utilized CellPhoneDB (Efremova et al., 2020). CellPhoneDBsuggested: …SciScore for 10.1101/2022.01.10.475725: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources We performed quality control and downstream analysis using the Seurat package (v4.0.4) (Stuart et al., 2019). Seuratsuggested: (SEURAT, RRID:SCR_007322)We performed GSEA on DGEs using the clusterProfiler package (v3.18.1) (Yu et al., 2012) with the “GSEA” function using default parameters using pathways from the MSigDB database (Subramanian et al., 2005) clusterProfilersuggested: (clusterProfiler, RRID:SCR_016884)Receptor-ligand analysis: To infer the putative receptor-ligand interactions between pairs of cell types, we utilized CellPhoneDB (Efremova et al., 2020). CellPhoneDBsuggested: (CellPhoneDB, RRID:SCR_017054)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:This limitation can be well addressed with deep-learning based classification methods such as scANVI (Xu et al., 2021). The deep generative neural network can learn highly non-linear representations of each immune subset from the most finely-manually-annotated disease-specific scRNA-seq atlas and can leverage on the knowledge to classify new cells from the same disease in the same representation space. Nevertheless, overfitting can be a common issue for deep learning models, and expert knowledge is still required to keep the classification results in check. Thus, our strategy effectively consolidates the advantages of all three methods to annotate scRNA-seq data by 1) overcoming the subjectivity of manual annotation, 2) leveraging existing knowledge of cell phenotypes to quickly and accurately assign labels with a trained model, and 3) validating classified labels with biologically relevant markers. Besides integration of COVID-19 and HIV-1 PBMC data, we anticipate our integration strategy can be easily adapted for integration of scRNA-seq data from different tissues, organs, or diseases. While we highlighted the integration of manual, correlation-based, and deep-learning-based annotation methods, there is flexibility for the specific software used in each method. The software we used in this study, namely Seurat, SingleR and scANVI, are all publicly available and highly rated across multiple benchmarking studies (Abdelaal et al., 2019; Huang et al., 2021; Krzak et al., 2019)...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
Results from scite Reference Check: We found no unreliable references.
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