COVID-19: A complex disease with a unique metabolic signature

Abstract

Plasma of COVID-19 patients contains a strong metabolomic/lipoproteomic signature, revealed by the NMR analysis of a cohort of >500 patients sampled during various waves of COVID-19 infection, corresponding to the spread of different variants, and having different vaccination status. This composite signature highlights common traits of the SARS-CoV-2 infection. The most dysregulated molecules display concentration trends that scale with disease severity and might serve as prognostic markers for fatal events. Metabolomics evidence is then used as input data for a sex-specific multi-organ metabolic model. This reconstruction provides a comprehensive view of the impact of COVID-19 on the entire human metabolism. The human (male and female) metabolic network is strongly impacted by the disease to an extent dictated by its severity. A marked metabolic reprogramming at the level of many organs indicates an increase in the generic energetic demand of the organism following infection. Sex-specific modulation of immune response is also suggested.

Fig 1. Metabolomic profiling of the main COVID-19 variants.

(A) Main demographic and clinical characteristics of the enrolled subjects (right panel); for each group of subjects, the sample collection period was reported (left panel). (B) List of the metabolites quantified in plasma samples. The p-values and Cliff’s Delta effects size are reported for the comparison between each of the three groups of variants with respect to the COVID-19-R group; p-values <0.05 are highlighted. (C) Values of Log₂ fold change (FC) of quantified metabolites. Positive/negative values have higher/lower concentration in plasma samples from each group of variants with respect to the COVID-19-R group. Colour coding: wt-α-β group (cyan); δ group (red); o group (orange).

Fig 2. Lipoproteomic profiling of the main COVID-19 variants.

A) List of lipoprotein parameters (main parameters, calculated features and main fractions) quantified in plasma samples. The p-values and Cliff’s Delta effects size are reported for the comparison between each of the three groups of variants with respect to the COVID-19-R group; p-values <0.05 are highlighted. B) Values of Log₂ fold change (FC) of quantified lipoprotein parameters (main parameters, calculated features and main fractions). Positive/negative values have higher/lower concentration in plasma samples from each group of variants with respect to COVID-19-R group. Colour coding: wt-α-β group (cyan); δ group (red); o group (orange).

Fig 3. Metabolomic and lipoproteomic profiling of the VAX and NO-VAX groups.

(A) Main demographic and clinical characteristics of the subjects. (B-C) Values of Log₂ fold change (FC) of quantified metabolites (B) and lipoprotein parameters (main parameters, calculated features and main fractions) (C). Positive/negative values have higher/lower concentration in plasma samples from the VAX or NO-VAX groups with respect to COVID-19-R group; p-values <0.05 are highlighted with coloured triangles. Colour coding: VAX group (green); NO-VAX (grey).

Fig 4. Sex-related metabolomic and lipoproteomic profiling of COVID-19 subjects.

(A) Number of samples used for the analysis. (B-C) Values of Log₂ fold change (FC) of quantified metabolites (B) and lipoprotein parameters (main parameters, calculated features and main fractions) (C). Positive/negative values have higher/lower concentration in plasma samples from the male (M) or female (F) groups with respect to the M or F COVID-19-R group, respectively; p-values <0.05 are highlighted with coloured triangles. Colour coding: male group (blue); female group (pink).

Fig 5. Metabolomic alterations in COVID-19 patients associated with clinical severity.

(A) Proximity plots of the RF model discriminating COVID-19 patients (mild light red dots, severe red dots), and COVID-19-R subjects (yellow dots) using bucketed NOESY spectra. The confusion matrix with accuracy, specificity and sensitivity values. Notably, the misclassified patients all belong to the mild group. (B) List of the metabolites quantified in plasma samples. The p-values and Cliff’s Delta effect-size are reported for the comparison between the COVID-19 group and the COVID-19-R group and for the comparison between mild and severe COVID-19 subjects; p-values <0.05 are highlighted. (C) Values of Log₂ fold change (FC) of quantified metabolites. Positive/negative values have higher/lower concentration in plasma samples from mild or severe subjects with respect to the COVID-19-R group. Colour coding: mild (light red); severe (red).

Fig 6. Lipoproteomic alterations in COVID-19 patients associated with clinical severity.

(A) List of lipoprotein parameters (main parameters, calculated figures and main fractions) quantified in plasma samples. The p-values and Cliff’s Delta effect size are reported for the comparison between the COVID-19 group with respect to the COVID-19-R group and for the comparison between mild and severe COVID-19 subjects; p-values <0.05 are highlighted. (B-C) Values of Log₂ fold change (FC) of quantified lipoprotein main parameters, calculated features and main fractions (B) and lipoprotein subfractions (C). Positive/negative values have higher/lower concentration in plasma samples from the COVID-19 group with respect to the COVID-19-R group. Colour coding: mild (light red); severe (red).

Fig 7. Markers of fatal events.

Box plots of the concentration levels for (A) metabolites and (B) lipoprotein parameters (main parameters, calculated figures and main fractions) that have a p-value < 0.05 and a large Cliff’s delta effect size in the comparison between mild, sever and fatal COVID-19 groups. The concentration levels in COVID-19-R subjects are also reported as control values. In each plot, the grey stripe embraces the concentration range in the reference “healthy” population. Colour coding: mild (light red); severe (red); fatal (dark red) COVID-19-R (yellow). * indicates p-value < 0.05: the upper line indicates the statistical significance between all the COVID-19 subjects and the COVID-19-R group; the lower lines indicate statistical significance between pairs of severity groups.

Fig 8. Genome-scale metabolic modelling of male subjects.

A) Clustering of context-specific models. Each column represents a reaction in the human metabolic network and its color accounts for the (normalized) activity of that specific reaction in the corresponding context-specific model. Models are clustered according to their profile of active/inactive reactions. B) Heatmap that includes those metabolic processes (subsystems) showing an overall flux increase that paralleled that of disease severity (from mild to severe). Their activity in the healthy-model is also included in the figure for clarity. C) Fold change of average metabolic activity for each subsystem in the healthy vs. fatal condition. D) Heatmaps showing the clustering of the different organs according to the activity of each reaction (left healthy state, right fatal state). As in in the panel A, each column represents a reaction in the human metabolic network and its color accounts for the (normalized) activity of that specific reaction in the corresponding organ. In the heatmap on the left, three main clusters are labelled with a red, green and orange dot and the organs belonging to each cluster are labelled accordingly and this color code is maintained in the heatmap on the right. Organs changing clusters in the two heatmaps are connected with a line.