Cell-specific housekeeping role of lncRNAs in COVID-19-infected and recovered patients.

A plethora of studies have demonstrated the roles of lncRNAs in modulating disease severity and outcomes during infection. However, the spatio-temporal expression of these lncRNAs is poorly understood. In this study, we used single-cell RNA-seq to understand the spatio-temporal expression dynamics of lncRNAs across healthy, SARS-CoV-2-infected, and recovered individuals and their functional role in modulating the disease and recovery. We identified 203 differentially expressed lncRNAs, including cell type-specific ones like MALAT1, NEAT1, ZFAS1, SNHG7, SNHG8, and SNHG25 modulating immune function in classical monocyte, NK T, proliferating NK, plasmablast, naive, and activated B/T cells. Interestingly, we found invariant lncRNAs (no significant change in expression across conditions) regulating essential housekeeping functions (for example, HOTAIR, NRAV, SNHG27, SNHG28, and UCA1) in infected and recovered individuals. Despite similar repeat element abundance, variant lncRNAs displayed higher Alu content, suggesting increased interactions with proximal and distal genes, crucial for immune response modulation. The comparable repeat abundance but distinct expression levels of variant and invariant lncRNAs highlight the significance of investigating the regulatory mechanisms of invariant lncRNAs. Overall, this study offers new insights into the spatio-temporal expression patterns and functional roles of lncRNAs in SARS-CoV-2-infected and recovered individuals while highlighting the importance of invariant lncRNAs in the disease context.

Dynamics of Whole Transcriptome Analysis (WTA) and Surface markers expression (AbSeq) in Immune Cells of COVID-19 Patients and Recovered captured through Single Cell Genomics.

Introduction: Single-cell multi-omics studies, such as multidimensional transcriptomics (whole transcriptomic analysis, WTA), and surface marker analysis (antibody sequencing, AbSeq), have turned out to be valuable techniques that offer inaccessible possibilities for single-cell profiling of mRNA, lncRNA, and proteins. Methods: We used this technique to understand the dynamics of mRNA and protein-level differences in healthy, COVID-19-infected and recovered individuals using peripheral blood mononuclear cells (PBMCs). Our results demonstrate that compared to mRNA expression, protein abundance is a better indicator of the disease state. Results: We demonstrate that compared to mRNA expression, protein abundance is a better indicator of the disease state. We observed high levels of cell identity and regulatory markers, CD3E, CD4, CD8A, CD5, CD7, GITR, and KLRB1 in healthy individuals, whereas markers related to cell activation, CD38, CD28, CD69, CD62L, CD14, and CD16 elevated in the SARS-CoV-2 infected patients at both WTA and AbSeq levels. Curiously, in recovered individuals, there was a high expression of cytokine and chemokine receptors (CCR5, CCR7, CCR4, CXCR3, and PTGRD2). We also observed variations in the expression of markers within cell populations under different states. Discussion: Furthermore, our study emphasizes the significance of employing an oligo-based method (AbSeq) that can help in diagnosis, prognosis, and protection from disease/s by identifying cell surface markers that are unique to different cell types or states. It also allows simultaneous study of a vast array of markers, surpassing the constraints of techniques like FACS to query the vast repertoire of proteins.

Suppressed transcript diversity and immune response in COVID-19 ICU patients: a longitudinal study.

Understanding the dynamic changes in gene expression during Acute Respiratory Distress Syndrome (ARDS) progression in post-acute infection patients is crucial for unraveling the underlying mechanisms. Study investigates the longitudinal changes in gene/transcript expression patterns in hospital-admitted severe COVID-19 patients with ARDS post-acute SARS-CoV-2 infection. Blood samples were collected at three time points and patients were stratified into severe and mild ARDS, based on their oxygenation saturation (SpO2/FiO2) kinetics over 7 d. Decline in transcript diversity was observed over time, particularly in patients with higher severity, indicating dysregulated transcriptional landscape. Comparing gene/transcript-level analyses highlighted a rather limited overlap. With disease progression, a transition towards an inflammatory state was evident. Strong association was found between antibody response and disease severity, characterized by decreased antibody response and activated B cell population in severe cases. Bayesian network analysis identified various factors associated with disease progression and severity, viz. humoral response, TLR signaling, inflammatory response, interferon response, and effector T cell abundance. The findings highlight dynamic gene/transcript expression changes during ARDS progression, impact on tissue oxygenation and elucidate disease pathogenesis.

Single-cell RNA-Seq reveals intracellular microbial diversity within immune cells during SARS-CoV-2 infection and recovery.

Intracellular microorganisms, like viruses, bacteria, and fungi, pose challenges in detection due to their non-culturable forms. Transcriptomic analysis at cellular level enables exploration of distributions and the impact of these microorganisms on host cells, a domain that remains underexplored because of methodological limitations. Single-cell technology shows promise in addressing this by capturing polyadenine-tailed transcripts, because recent studies confirmed polyadenylation in microbial transcriptomes. We utilized single-cell RNA-seq from PBMCs to probe intracellular microbes in healthy, SARS-CoV-2-positive, and recovered individuals. Among 76 bacterial species detected, 16 showed significant abundance differences. Buchnera aphidicola, Streptomyces clavuligerus, and Ehrlichia canis emerged significantly in memory-B, Naïve-T, and Treg cells. Staphylococcus aureus, Mycoplasma mycoides, Leptospira interrogans, and others displayed elevated levels in SARS-CoV-2-positive patients, suggesting possible disease association. This highlights the strength of single-cell technology in revealing potential microorganism's cell-specific functions. Further research is essential for functional understanding of their cell-specific abundance across physiological states.

Dysregulated metal ion homeostasis underscores non-canonical function of CD8+ T cell during COVID-19.

Introduction: Several efforts have been made to describe the complexity of T cell heterogeneity during the COVID-19 disease; however, there remain gaps in our understanding in terms of the granularity within. Methods: For this attempt, we performed a single-cell transcriptomic analysis of 33 individuals (4 healthy, 16 COVID-19 positive patients, and 13 COVID-19 recovered individuals). Results: We found CD8+ T cell-biased lymphopenia in COVID-19 patients compared to healthy and recovered individuals. We also found an optimal Th1/Th2 ratio, indicating an effective immune response during COVID-19. Expansion of activated CD4+ T and NK T was detected in the COVID-19-positive individuals. Surprisingly, we found cellular and metal ion homeostasis pathways enriched in the COVID-19-positive individuals compared to the healthy and recovered in the CD8+ T cell populations (CD8+ TCM and CD8+ TEM) as well as activated CD4+ T cells. Discussion: In summary, the COVID-19-positive individuals exhibit a dynamic T cell mediated response. This response may have a possible association with the dysregulation of non-canonical pathways, including housekeeping functions in addition to the conventional antiviral immune response mediated by the T cell subpopulation. These findings considerably extend our insights into the heterogeneity of T cell response during and post-SARS-CoV-2 infection.

SARS-CoV-2 infection severity and mortality is modulated by repeat-mediated regulation of alternative splicing.

Like single-stranded RNA viruses, SARS-CoV-2 hijacks the host transcriptional machinery for its own replication. Numerous traditional differential gene expression-based investigations have examined the diverse clinical symptoms caused by SARS-CoV-2 infection. The virus, on the other hand, also affects the host splicing machinery, causing host transcriptional dysregulation, which can lead to diverse clinical outcomes. Hence, in this study, we performed host transcriptome sequencing of 125 hospital-admitted COVID-19 patients to understand the transcriptomic differences between the severity sub-phenotypes of mild, moderate, severe, and mortality. We performed transcript-level differential expression analysis, investigated differential isoform usage, looked at the splicing patterns within the differentially expressed transcripts (DET), and elucidated the possible genome regulatory features. Our DTE analysis showed evidence of diminished transcript length and diversity as well as altered promoter site usage in the differentially expressed protein-coding transcripts in the COVID-19 mortality patients. We also investigated the potential mechanisms driving the alternate splicing and discovered a compelling differential enrichment of repeats in the promoter region and a specific enrichment of SINE (Alu) near the splicing sites of differentially expressed transcripts. These findings suggested a repeat-mediated plausible regulation of alternative splicing as a potential modulator of COVID-19 disease severity. In this work, we emphasize the role of scarcely elucidated functional role of alternative splicing in influencing COVID-19 disease severity sub-phenotypes, clinical outcomes, and its putative mechanism. IMPORTANCE The wide range of clinical symptoms reported during the COVID-19 pandemic inherently highlights the numerous factors that influence the progression and prognosis of SARS-CoV-2 infection. While several studies have investigated the host response and discovered immunological dysregulation during severe infection, most of them have the common theme of focusing only up to the gene level. Viruses, especially RNA viruses, are renowned for hijacking the host splicing machinery for their own proliferation, which inadvertently puts pressure on the host transcriptome, exposing another side of the host response to the pathogen challenge. Therefore, in this study, we examine host response at the transcript-level to discover a transcriptional difference that culminates in differential gene-level expression. Importantly, this study highlights diminished transcript diversity and possible regulation of transcription by differentially abundant repeat elements near the promoter region and splicing sites in COVID-19 mortality patients, which together with differentially expressed isoforms hold the potential to elaborate disease severity and outcome.

Effect of short-term use of FFP2 (N95) masks on the salivary metabolome of young healthy volunteers: a pilot study.

The use of face masks has become an integral part of public life in the post-pandemic era. However, the understanding of the effect of wearing masks on physiology remains incomplete and is required for informing public health policies. For the first time, we report the effects of wearing FFP2 masks on the metabolic composition of saliva, a proximal matrix to breath, along with cardiopulmonary parameters. Un-induced saliva was collected from young (31.2 ± 6.3 years) healthy volunteers (n = 10) before and after wearing FFP2 (N95) masks for 30 minutes and analyzed using GCMS. The results showed that such short-term mask use did not cause any significant change in heart rate, pulse rate or SpO2. Three independent data normalization approaches were used to analyze the changes in metabolomic signature. The individuality of the overall salivary metabotype was found to be unaffected by mask use. However, a trend of an increase in the salivary abundance of L-fucose, 5-aminovaleric acid, putrescine and phloretic acid was indicated irrespective of the method of data normalization. Quantitative analysis confirmed increases in concentrations of these metabolites in saliva of paired samples amid high inter-individual variability. The results showed that while there was no significant change in measured physiological parameters and individual salivary metabotypes, mask use was associated with correlated changes in these metabolites plausibly originating from altered microbial metabolic activity. These results might also explain the change in odour perception reported to be associated with mask use. Potential implications of these changes on mucosal health and immunity warrants further investigation to evolve more prudent mask use policies.

BA.1, BA.2 and BA.2.75 variants show comparable replication kinetics, reduced impact on epithelial barrier and elicit cross-neutralizing antibodies.

The Omicron variant of SARS-CoV-2 is capable of infecting unvaccinated, vaccinated and previously-infected individuals due to its ability to evade neutralization by antibodies. With multiple sub-lineages of Omicron emerging in the last 12 months, there is inadequate information on the quantitative antibody response generated upon natural infection with Omicron variant and whether these antibodies offer cross-protection against other sub-lineages of Omicron variant. In this study, we characterized the growth kinetics of Kappa, Delta and Omicron variants of SARS-CoV-2 in Calu-3 cells. Relatively higher amounts infectious virus titers, cytopathic effect and disruption of epithelial barrier functions was observed with Delta variant whereas infection with Omicron sub-lineages led to a more robust induction of interferon pathway, lower level of virus replication and mild effect on epithelial barrier. The replication kinetics of BA.1, BA.2 and BA.2.75 sub-lineages of the Omicron variant were comparable in cell culture and natural infection in a subset of individuals led to a significant increase in binding and neutralizing antibodies to the Delta variant and all the three sub-lineages of Omicron but the level of neutralizing antibodies were lowest against the BA.2.75 variant. Finally, we show that Cu2+, Zn2+ and Fe2+ salts inhibited in vitro RdRp activity but only Cu2+ and Fe2+ inhibited both the Delta and Omicron variants in cell culture. Thus, our results suggest that high levels of interferons induced upon infection with Omicron variant may counter virus replication and spread. Waning neutralizing antibody titers rendered subjects susceptible to infection by Omicron variants and natural Omicron infection elicits neutralizing antibodies that can cross-react with other sub-lineages of Omicron and other variants of concern.

Transcriptomic study reveals lncRNA-mediated downregulation of innate immune and inflammatory response in the SARS-CoV-2 vaccination breakthrough infections.

Introduction: The emergence of multiple variants of concerns (VOCs) with higher number of Spike mutations have led to enhanced immune escape by the SARS-CoV-2. With the increasing number of vaccination breakthrough (VBT) infections, it is important to understand the possible reason/s of the breakthrough infections. Methods: We performed transcriptome sequencing of 57 VBT and unvaccinated COVID-19 patients, followed by differential expression and co-expression analysis of the lncRNAs and the mRNAs. The regulatory mechanism was highlighted by analysis towards repeat element distribution within the co-expressed lncRNAs, followed by repeats driven homologous interaction between the lncRNAs and the promoter regions of genes from the same topologically associated domains (TAD). Results: We identified 727 differentially expressed lncRNAs (153 upregulated and 574 downregulated) and 338 mRNAs (34 up- and 334 downregulated) in the VBT patients. This includes LUCAT1, MALAT1, ROR1-AS1, UGDH-AS1 and LINC00273 mediated modulation of immune response, whereas MALAT1, NEAT1 and GAS5 regulated inflammatory response in the VBT. LncRNA-mRNA co-expression analysis highlighted 34 lncRNAs interacting with 267 mRNAs. We also observed a higher abundance of Alu, LINE1 and LTRs within the interacting lncRNAs of the VBT patients. These interacting lncRNAs have higher interaction with the promoter region of the genes from the same TAD, compared to the non-interacting lncRNAs with the enrichment of Alu and LINE1 in the gene promoter. Discussion: Significant downregulation and GSEA of the TAD gene suggest Alu and LINE1 driven homologous interaction between the lncRNAs and the TAD genes as a possible mechanism of lncRNA-mediated suppression of innate immune/inflammatory responses and activation of adaptive immune response. The lncRNA-mediated suppression of innate immune/inflammatory responses and activation of adaptive immune response might explain the SARS-CoV-2 breakthrough infections with milder symptoms in the VBT. Besides, the study also highlights repeat element mediated regulation of genes in 3D as another possible way of lncRNA-mediated immune-regulation modulating vaccination breakthroughs milder disease phenotype and shorter hospital stay.

Single-cell multiomics revealed the dynamics of antigen presentation, immune response and T cell activation in the COVID-19 positive and recovered individuals.

Introduction: Despite numerous efforts to describe COVID-19's immunological landscape, there is still a gap in our understanding of the virus's infections after-effects, especially in the recovered patients. This would be important to understand as we now have huge number of global populations infected by the SARS-CoV-2 as well as variables inclusive of VOCs, reinfections, and vaccination breakthroughs. Furthermore, single-cell transcriptome alone is often insufficient to understand the complex human host immune landscape underlying differential disease severity and clinical outcome. Methods: By combining single-cell multi-omics (Whole Transcriptome Analysis plus Antibody-seq) and machine learning-based analysis, we aim to better understand the functional aspects of cellular and immunological heterogeneity in the COVID-19 positive, recovered and the healthy individuals. Results: Based on single-cell transcriptome and surface marker study of 163,197 cells (124,726 cells after data QC) from the 33 individuals (healthy=4, COVID-19 positive=16, and COVID-19 recovered=13), we observed a reduced MHC Class-I-mediated antigen presentation and dysregulated MHC Class-II-mediated antigen presentation in the COVID-19 patients, with restoration of the process in the recovered individuals. B-cell maturation process was also impaired in the positive and the recovered individuals. Importantly, we discovered that a subset of the naive T-cells from the healthy individuals were absent from the recovered individuals, suggesting a post-infection inflammatory stage. Both COVID-19 positive patients and the recovered individuals exhibited a CD40-CD40LG-mediated inflammatory response in the monocytes and T-cell subsets. T-cells, NK-cells, and monocyte-mediated elevation of immunological, stress and antiviral responses were also seen in the COVID-19 positive and the recovered individuals, along with an abnormal T-cell activation, inflammatory response, and faster cellular transition of T cell subtypes in the COVID-19 patients. Importantly, above immune findings were used for a Bayesian network model, which significantly revealed FOS, CXCL8, IL1ß, CST3, PSAP, CD45 and CD74 as COVID-19 severity predictors. Discussion: In conclusion, COVID-19 recovered individuals exhibited a hyper-activated inflammatory response with the loss of B cell maturation, suggesting an impeded post-infection stage, necessitating further research to delineate the dynamic immune response associated with the COVID-19. To our knowledge this is first multi-omic study trying to understand the differential and dynamic immune response underlying the sample subtypes.

Integrative genomics important to understand host-pathogen interactions.

Infectious diseases are the leading cause of morbidity and mortality worldwide. Causative pathogenic microbes readily mutate their genome and lead to outbreaks, challenging the healthcare and the medical support. Understanding how certain symptoms manifest clinically is integral for therapeutic decisions and vaccination efficacy/protection. Notably, the interaction between infecting pathogens, host response and co-presence of microbes influence the trajectories of disease progression and clinical outcome. The spectrum of observed symptomatic patients (mild, moderate and severe) and the asymptomatic infections highlight the challenges and the potential for understanding the factors driving protection/susceptibility. With the increasing repertoire of high-throughput tools, such as cutting-edge multi-omics profiling and next-generation sequencing, genetic drivers of factors linked to heterogeneous disease presentations can be investigated in tandem. However, such strategies are not without limits in terms of effectively integrating host-pathogen interactions. Nonetheless, an integrative genomics method (for example, RNA sequencing data) for exploring multiple layers of complexity in host-pathogen interactions could be another way to incorporate findings from high-throughput data. We further propose that a Holo-transcriptome-based technique to capture transcriptionally active microbial units can be used to elucidate functional microbiomes. Thus, we provide holistic perspective on investigative methodologies that can harness the same genomic data to investigate multiple seemingly independent but deeply interconnected functional domains of host-pathogen interaction that modulate disease severity and clinical outcomes.

Mutational dynamics across VOCs in International travellers and Community transmission underscores importance of Spike-ACE2 interaction.

BACKGROUND: Emergence of SARS-CoV-2 VOCs at different time points through COVID-19 pandemic raised concern for increased transmissibility, infectivity and vaccination breakthroughs. METHODS: 1567 international travellers plus community transmission COVID-19 cases were analysed for mutational profile of VOCS, that led to notable waves in India, namely Alpha, Delta, and Omicron. Spike mutations in Linkage Disequilibrium were investigated for potential impact on structural and functional changes of Spike-ACE2. RESULTS: ORF1ab and spike harboured diverse mutational signatures for each lineage. B.1.617.2 and AY. * demonstrated comparable profile, yet non-clade defining mutations were majorly unique between international vs community samples. Contrarily, Omicron lineages showed substantial overlap in non-clade defining mutations, signifying early phase of transmission and evolution within Indian community. Mutations in LD for Alpha [N501Y, A570D, D1118H, S982A], Delta [P681R, L452R, EFR:156-158 G, D950N, G142D] and Omicron [P681H, D796Y, N764K, N969K, N501Y, S375F] resulted in decreased binding affinity of Spike-ACE2 for Alpha and BA.1 whereas Delta, Omicron and BA.2 demonstrated strong binding. CONCLUSION: Genomic surveillance tracked spread of VOCs in international travellers' vs community transmission. Behavioural transmission patterns of variants, based on selective advantage incurred by spike mutations, led us to predict sudden takeover of Delta over Alpha and BA.2 over BA.1 in India.

Increased Abundance of Achromobacter xylosoxidans and Bacillus cereus in Upper Airway Transcriptionally Active Microbiome of COVID-19 Mortality Patients Indicates Role of Co-Infections in Disease Severity and Outcome.

The modulators of severe COVID-19 have emerged as the most intriguing features of SARS-CoV-2 pathogenesis. This is especially true as we are encountering variants of concern (VOC) with increased transmissibility and vaccination breakthroughs. Microbial co-infections are being investigated as one of the crucial factors for exacerbation of disease severity and complications of COVID-19. A key question remains whether early transcriptionally active microbial signature/s in COVID-19 patients can provide a window for future disease severity susceptibility and outcome? Using complementary metagenomics sequencing approaches, respiratory virus oligo panel (RVOP) and Holo-seq, our study highlights the possible functional role of nasopharyngeal early resident transcriptionally active microbes in modulating disease severity, within recovered patients with sub-phenotypes (mild, moderate, severe) and mortality. The integrative analysis combines patients' clinical parameters, SARS-CoV-2 phylogenetic analysis, microbial differential composition, and their functional role. The clinical sub-phenotypes analysis led to the identification of transcriptionally active bacterial species associated with disease severity. We found significant transcript abundance of Achromobacter xylosoxidans and Bacillus cereus in the mortality, Leptotrichia buccalis in the severe, Veillonella parvula in the moderate, and Actinomyces meyeri and Halomonas sp. in the mild COVID-19 patients. Additionally, the metabolic pathways, distinguishing the microbial functional signatures between the clinical sub-phenotypes, were also identified. We report a plausible mechanism wherein the increased transcriptionally active bacterial isolates might contribute to enhanced inflammatory response and co-infections that could modulate the disease severity in these groups. Current study provides an opportunity for potentially using these bacterial species for screening and identifying COVID-19 patient sub-groups with severe disease outcome and priority medical care. IMPORTANCE COVID-19 is invariably a disease of diverse clinical manifestation, with multiple facets involved in modulating the progression and outcome. In this regard, we investigated the role of transcriptionally active microbial co-infections as possible modulators of disease pathology in hospital admitted SARS-CoV-2 infected patients. Specifically, can there be early nasopharyngeal microbial signatures indicative of prospective disease severity? Based on disease severity symptoms, the patients were segregated into clinical sub-phenotypes: mild, moderate, severe (recovered), and mortality. We identified significant presence of transcriptionally active isolates, Achromobacter xylosoxidans and Bacillus cereus in the mortality patients. Importantly, the bacterial species might contribute toward enhancing the inflammatory responses as well as reported to be resistant to common antibiotic therapy, which together hold potential to alter the disease severity and outcome.

Clinico-Genomic Analysis Reiterates Mild Symptoms Post-vaccination Breakthrough: Should We Focus on Low-Frequency Mutations?

Vaccine development against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been of primary importance to contain the ongoing global pandemic. However, studies have demonstrated that vaccine effectiveness is reduced and the immune response is evaded by variants of concern (VOCs), which include Alpha, Beta, Delta, and, the most recent, Omicron. Subsequently, several vaccine breakthrough (VBT) infections have been reported among healthcare workers (HCWs) due to their prolonged exposure to viruses at healthcare facilities. We conducted a clinico-genomic study of ChAdOx1 (Covishield) VBT cases in HCWs after complete vaccination. Based on the clinical data analysis, most of the cases were categorized as mild, with minimal healthcare support requirements. These patients were divided into two sub-phenotypes based on symptoms: mild and mild plus. Statistical analysis showed a significant correlation of specific clinical parameters with VBT sub-phenotypes. Viral genomic sequence analysis of VBT cases revealed a spectrum of high- and low-frequency mutations. More in-depth analysis revealed the presence of low-frequency mutations within the functionally important regions of SARS-CoV-2 genomes. Emphasizing the potential benefits of surveillance, low-frequency mutations, D144H in the N gene and D138Y in the S gene, were observed to potentially alter the protein secondary structure with possible influence on viral characteristics. Substantiated by the literature, our study highlights the importance of integrative analysis of pathogen genomic and clinical data to offer insights into low-frequency mutations that could be a modulator of VBT infections.

COVID-19 Risk Stratification and Mortality Prediction in Hospitalized Indian Patients: Harnessing clinical data for public health benefits.

The variability of clinical course and prognosis of COVID-19 highlights the necessity of patient sub-group risk stratification based on clinical data. In this study, clinical data from a cohort of Indian COVID-19 hospitalized patients is used to develop risk stratification and mortality prediction models. We analyzed a set of 70 clinical parameters including physiological and hematological for developing machine learning models to identify biomarkers. We also compared the Indian and Wuhan cohort, and analyzed the role of steroids. A bootstrap averaged ensemble of Bayesian networks was also learned to construct an explainable model for discovering actionable influences on mortality and days to outcome. We discovered blood parameters, diabetes, co-morbidity and SpO2 levels as important risk stratification features, whereas mortality prediction is dependent only on blood parameters. XGboost and logistic regression model yielded the best performance on risk stratification and mortality prediction, respectively (AUC score 0.83, AUC score 0.92). Blood coagulation parameters (ferritin, D-Dimer and INR), immune and inflammation parameters IL6, LDH and Neutrophil (%) are common features for both risk and mortality prediction. Compared with Wuhan patients, Indian patients with extreme blood parameters indicated higher survival rate. Analyses of medications suggest that a higher proportion of survivors and mild patients who were administered steroids had extreme neutrophil and lymphocyte percentages. The ensemble averaged Bayesian network structure revealed serum ferritin to be the most important predictor for mortality and Vitamin D to influence severity independent of days to outcome. The findings are important for effective triage during strains on healthcare infrastructure.

Infection outcome needs two to tango: human host and the pathogen.

Infectious diseases are potential drivers for human evolution, through a complex, continuous and dynamic interaction between the host and the pathogen/s. It is this dynamic interaction that contributes toward the clinical outcome of a pathogenic disease. These are modulated by contributions from the human genetic variants, transcriptional response (including noncoding RNA) and the pathogen's genome architecture. Modern genomic tools and techniques have been crucial for the detection and genomic characterization of pathogens with respect to the emerging infectious diseases. Aided by next-generation sequencing (NGS), risk stratification of host population/s allows for the identification of susceptible subgroups and better disease management. Nevertheless, many challenges to a general understanding of host-pathogen interactions remain. In this review, we elucidate how a better understanding of the human host-pathogen interplay can substantially enhance, and in turn benefit from, current and future applications of multi-omics based approaches in infectious and rare diseases. This includes the RNA-level response, which modulates the disease severity and outcome. The need to understand the role of human genetic variants in disease severity and clinical outcome has been further highlighted during the Coronavirus disease 2019 (COVID-19) pandemic. This would enhance and contribute toward our future pandemic preparedness.

Single-Cell Genomics: Enabling the Functional Elucidation of Infectious Diseases in Multi-Cell Genomes.

Since the time when detection of gene expression in single cells by microarrays to the Next Generation Sequencing (NGS) enabled Single Cell Genomics (SCG), it has played a pivotal role to understand and elucidate the functional role of cellular heterogeneity. Along this journey to becoming a key player in the capture of the individuality of cells, SCG overcame many milestones, including scale, speed, sensitivity and sample costs (4S). There have been many important experimental and computational innovations in the efficient analysis and interpretation of SCG data. The increasing role of AI in SCG data analysis has further enhanced its applicability in building models for clinical intervention. Furthermore, SCG has been instrumental in the delineation of the role of cellular heterogeneity in specific diseases, including cancer and infectious diseases. The understanding of the role of differential immune responses in driving coronavirus disease-2019 (COVID-19) disease severity and clinical outcomes has been greatly aided by SCG. With many variants of concern (VOC) in sight, it would be of great importance to further understand the immune response specificity vis-a-vis the immune cell repertoire, the identification of novel cell types, and antibody response. Given the potential of SCG to play an integral part in the multi-omics approach to the study of the host-pathogen interaction and its outcomes, our review attempts to highlight its strengths, its implications for infectious disease biology, and its current limitations. We conclude that the application of SCG would be a critical step towards future pandemic preparedness.

Plasma Gradient of Soluble Urokinase-Type Plasminogen Activator Receptor Is Linked to Pathogenic Plasma Proteome and Immune Transcriptome and Stratifies Outcomes in Severe COVID-19.

Disease caused by SARS-CoV-2 coronavirus (COVID-19) led to significant morbidity and mortality worldwide. A systemic hyper-inflammation characterizes severe COVID-19 disease, often associated with acute respiratory distress syndrome (ARDS). Blood biomarkers capable of risk stratification are of great importance in effective triage and critical care of severe COVID-19 patients. Flow cytometry and next-generation sequencing were done on peripheral blood cells and urokinase-type plasminogen activator receptor (suPAR), and cytokines were measured from and mass spectrometry-based proteomics was done on plasma samples from an Indian cohort of COVID-19 patients. Publicly available single-cell RNA sequencing data were analyzed for validation of primary data. Statistical analyses were performed to validate risk stratification. We report here higher plasma abundance of suPAR, expressed by an abnormally expanded myeloid cell population, in severe COVID-19 patients with ARDS. The plasma suPAR level was found to be linked to a characteristic plasma proteome, associated with coagulation disorders and complement activation. Receiver operator characteristic curve analysis to predict mortality identified a cutoff value of suPAR at 1,996.809 pg/ml (odds ratio: 2.9286, 95% confidence interval 1.0427-8.2257). Lower-than-cutoff suPAR levels were associated with a differential expression of the immune transcriptome as well as favorable clinical outcomes, in terms of both survival benefit (hazard ratio: 0.3615, 95% confidence interval 0.1433-0.912) and faster disease remission in our patient cohort. Thus, we identified suPAR as a key pathogenic circulating molecule linking systemic hyperinflammation to the hypercoagulable state and stratifying clinical outcomes in severe COVID-19 patients with ARDS.