Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2–4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

Selection and Evaluation of Reference Genes for Reverse Transcription-Quantitative PCR Expression Studies in a Thermophilic Bacterium Grown under Different Culture Conditions

<div><p>The phylum Deinococcus-Thermus is a deeply-branching lineage of bacteria widely recognized as one of the most extremophilic. Members of the <i>Thermus</i> genus are of major interest due to both their bioremediation and biotechnology potentials. However, the molecular mechanisms associated with these key metabolic pathways remain unknown. Reverse-transcription quantitative PCR (RT-qPCR) is a high-throughput means of studying the expression of a large suite of genes over time and under different conditions. The selection of a stably-expressed reference gene is critical when using relative quantification methods, as target gene expression is normalized to expression of the reference gene. However, little information exists as to reference gene selection in extremophiles. This study evaluated 11 candidate reference genes for use with the thermophile <i>Thermus scotoductus</i> when grown under different culture conditions. Based on the combined stability values from BestKeeper and NormFinder software packages, the following are the most appropriate reference genes when comparing: (1) aerobic and anaerobic growth: TSC_c19900, <i>polA2</i>, <i>gyrA</i>, <i>gyrB</i>; (2) anaerobic growth with varied electron acceptors: TSC_c19900, <i>infA</i>, <i>pfk</i>, <i>gyrA</i>, <i>gyrB</i>; (3) aerobic growth with different heating methods: <i>gyrA</i>, <i>gap</i>, <i>gyrB</i>; (4) all conditions mentioned above: <i>gap</i>, <i>gyrA</i>, <i>gyrB</i>. The commonly-employed <i>rpoC</i> does not serve as a reliable reference gene in thermophiles, due to its expression instability across all culture conditions tested here. As extremophiles exhibit a tendency for polyploidy, absolute quantification was employed to determine the ratio of transcript to gene copy number in a subset of the genes. A strong negative correlation was found to exist between ratio and threshold cycle (C_T) values, demonstrating that C_T changes reflect transcript copy number, and not gene copy number, fluctuations. Even with the potential for polyploidy in extremophiles, the results obtained via absolute quantification indicate that relative quantification is appropriate for RT-qPCR studies with this thermophile.</p></div

Correlation between <i>gyrB</i> transcript C_T values and gene expression as derived from the transcript-to-gene copy number ratios.

<p>A standard curve was constructed and used to calculate the transcript copies per mL of sample and gene copies per mL of sample (“transcript-to-gene ratio”) under the different culture conditions described in the text. Ratio and C_<b>T</b> values obtained for <i>gyrB</i> under: anaerobic growth with (A) glucose or (B) lactate as carbon source compared to (C) small-volume aerobic growth in TYG; (D) anaerobic growth with glucose as carbon source and nitrate or iron as terminal electron acceptor; and aerobic growth in TYG using (E) oven or (F) microwave heating. Glu = glucose, Lac = lactate, MW = microwave heating. a = indicates statistically significant difference (p < 0.05) among different culture conditions at specific time points; b = indicates statistically significant difference (p < 0.05) across time under one culture condition.</p

Descriptive statistics and stability values of candidate reference genes across all conditions and time points (<i>n</i> = 45).

<p>SD = standard deviation, NF = NormFinder</p><p>Descriptive statistics and stability values of candidate reference genes across all conditions and time points (<i>n</i> = 45).</p

Correlation between <i>polA2</i> transcript C_T values and gene expression as derived from the transcript-to-gene copy number ratios.

<p>A standard curve was constructed and used to calculate the transcript copies per mL of sample and gene copies per mL of sample (“transcript-to-gene ratio”) under the different culture conditions described in the text. Ratio and C_<b>T</b> values obtained for <i>polA2</i> under: anaerobic growth with (A) glucose or (B) lactate as carbon source compared to (C) small-volume aerobic growth in TYG; (D) anaerobic growth with glucose as carbon source and nitrate or iron as terminal electron acceptor; and aerobic growth in TYG using (E) oven or (F) microwave heating. Glu = glucose, Lac = lactate, MW = microwave heating. a = indicates statistically significant difference (p < 0.05) among different culture conditions at specific time points; b = indicates statistically significant difference (p < 0.05) across time under one culture condition.</p

Pearson Product Correlation values obtained for ratio and C_T of <i>gyrB</i>, <i>polA2</i>, and <i>rpoC</i> under each condition.

<p>* indicates Spearman rank value</p><p>e- = terminal electron acceptor experiments</p><p>O/MW = oven v microwave heating experiments</p><p>Pearson Product Correlation values obtained for ratio and C_T of <i>gyrB</i>, <i>polA2</i>, and <i>rpoC</i> under each condition.</p

Descriptive statistics and stability values of candidate reference genes under anaerobic (glucose, lactate) and aerobic (TYG) growth over time (<i>n</i> = 24).

<p>SD = standard deviation, NF = NormFinder</p><p>Descriptive statistics and stability values of candidate reference genes under anaerobic (glucose, lactate) and aerobic (TYG) growth over time (<i>n</i> = 24).</p

Correlation between <i>rpoC</i> transcript C_T values and gene expression as derived from the transcript-to-gene copy number ratios.

<p>A standard curve was constructed and used to calculate the transcript copies per mL of sample and gene copies per mL of sample (“transcript-to-gene ratio”) under the different culture conditions described in the text. Ratio and C_<b>T</b> values obtained for <i>rpoC</i> under: anaerobic growth with (A) glucose or (B) lactate as carbon source compared to (C) small-volume aerobic growth in TYG; (D) anaerobic growth with glucose as carbon source and nitrate or iron as terminal electron acceptor; and aerobic growth in TYG using (E) oven or (F) microwave heating. Glu = glucose, Lac = lactate, MW = microwave heating. a = indicates statistically significant difference (p < 0.05) among different culture conditions at specific time points; b = indicates statistically significant difference (p < 0.05) across time under one culture condition.</p

Descriptive statistics and stability values of candidate reference genes under different heating methods (oven or microwave) (<i>n</i> = 16).

<p>SD = standard deviation, NF = NormFinder</p><p>Descriptive statistics and stability values of candidate reference genes under different heating methods (oven or microwave) (<i>n</i> = 16).</p