Immune cell proteomes of Long COVID patients have functional changes similar to those in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome

doi:10.21203/rs.3.rs-3335919/v1

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Immune cell proteomes of Long COVID patients have functional changes similar to those in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome

https://doi.org/10.21203/rs.3.rs-3335919/v1

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published 12 Dec, 2023

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Of those infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), ~ 10% develop the chronic post-viral debilitating condition, Long COVID (LC). Although LC is a heterogeneous condition, about half of cases have a typical post-viral fatigue condition with onset and symptoms that are very similar to Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS). A key question is whether these conditions are closely related. ME/CFS is a post-stressor fatigue condition that arises from multiple triggers. To investigate the pathophysiology of LC, a pilot study of patients and healthy controls has used quantitative proteomics to discover changes in peripheral blood mononuclear cell (PBMC) proteins. A principal component analysis separated all Long COVID patients from healthy controls. Analysis of 3131 proteins identified 162 proteins differentially regulated, of which 37 were related to immune functions, and 21 to mitochondrial functions. Markov cluster analysis identified clusters involved in immune system processes, and two aspects of gene expression-spliceosome and transcription. These results were compared with an earlier dataset of 346 differentially regulated proteins in PBMC’s from ME/CFS patients analysed by the same methodology. There were overlapping protein clusters and enriched molecular pathways particularly in immune functions, suggesting the two conditions have similar immune pathophysiology as a prominent feature, and mitochondrial functions involved in energy production were affected in both conditions.

Biological sciences/Biochemistry

Biological sciences/Immunology

Biological sciences/Molecular biology

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Health sciences/Medical research

Infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) results in a post-viral condition, Long COVID (LC), in a significant proportion (~ 10%) of those affected ¹. With now 770 million cases of SARS-CoV-2 infection worldwide as at August 16 2023 ² there will be an estimated ~ 80 million cases of LC. Long COVID is a chronic debilitating disease arising from this unique viral trigger. The diagnosis of LC is made three months from initial infection when the condition persists for more than eight weeks ³. Although LC is recognized as heterogeneous, with some patients suffering from ongoing organ damage, at least 50% have a post-viral fatigue condition ^4,5 with onset and symptoms very similar to Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS), the collective term for the post-viral or post-stressor fatigue condition arising from such multiple triggers ⁶. A recent paper has defined four clinical phenotypes amongst LC patients with the ME/CFS-like fatigue condition the dominant phenotype ⁵. Children, adolescents, and adults can all be affected by ME/CFS and LC with impacts including the inability to pursue education, work or normal life activities ^7,8. In contrast to LC, ME/CFS is diagnosed formally with a clinical case definition if symptoms persist for at least six months ⁹ while LC is diagnosed after three months³, but for most ME/CFS patients diagnosis has been made much later, when their condition has become well established and other illnesses have been eliminated ¹⁰. For those patients with ongoing ME/CFS, the debilitating condition is lifelong ¹¹ with a resulting heavy burden on families and with severe economic and social impacts. For both conditions, symptoms reported are numerous (estimated to be > 100) and the vast majority of those of LC overlay with those of ME/CFS, with only a small number of differences, perhaps relating to the unique effects from the triggering SARS-CoV-2 virus. Nevertheless, the key commonest symptoms identified by the WHO when deriving a clinical case definition for LC: fatigue, brain fog (cognitive defects), activity related post exertional malaise, unrefreshing sleep, pain, and other neurological symptoms are shared with ME/CFS in the most widely accepted clinical case definitions for this condition.

Onset of several diseases have also been attributed to infection with SARS-CoV-2, such as type 2 diabetes, and dysautonomia, especially postural orthostatic tachycardia syndrome (POTS) ^4,12. Increasingly, with focus on LC within the first few months of onset some commentators have proposed that if the post-viral fatigue condition of LC patients lasts beyond one year it ‘becomes’ ME/CFS. Most likely, the persistent subgroup with LC fatigue symptoms simply represents a specific example of ME/CFS that, while being facilitated from the single triggering virus in susceptible people, has the same dysfunctional physiology generally as ME/CFS. If there were fewer cases of COVID-19 worldwide, as with the first SARS CoV-1 outbreak in 2003, it would most likely have been classified as ME/CFS like the other 75 boutique outbreaks of probable viral infections reported since about 1930 ¹³.

We have proposed a model to explain the complex dysfunctional physiology for both ME/CFS and LC ¹⁴. A key feature is that in susceptible people the normal transitory immune/inflammatory response of the peripheral system to infection or stress does not resolve quickly as in most people but becomes chronic and that leads to a cascade effect, with involvement of the brain and its immune system, and other components of the central nervous system. The disturbed functions of the CNS result in multiple neurological symptoms, and in poor brain regulation of body physiology. A number of studies have documented chronic dysregulation of the immune cell function involving multiple cell types, and cytokines in ME/CFS ^15–21,and now more recently in LC ^22–25. Multiple disturbances have been reported in molecular homeostasis of the transcriptomes^26–28, proteomes ^29–31, and DNA methylomes ^32–36 of peripheral blood mononuclear cells (PBMC) that include lymphocytes (T cells, B cells, NK cells) and monocytes ³⁷, but not as yet in LC.

This current study analysed the PBMC proteomes of post-viral fatigue LC patients whose illness had lasted for one year, compared with age/sex matched healthy controls. It aimed to identify specific areas of cellular physiology that were still dysfunctional a year on from the patients’ initial SARS-CoV-2 infection. Groups of proteins involved with the immune response, and gene expression (spliceosome and transcription) and mitochondrial function were shown to be differentially regulated in LC patients. We have previously published data on the differentially regulated proteins in a ME/CFS cohort with an average disease duration of 16 years, using the same mass spectrometry strategy ²⁹, and here we compared the two datasets to identify common or distinct features. We hypothesised that there would be very similar molecular pathways affected that would contribute to explain the closely overlapping symptoms and pathophysiology of both Long COVID and ME/CFS. Despite the significant difference in the disease durations of the two groups of patients (1 year vs an average of 16 years) many common effects were identified. Therapeutic targeting of the immune response/inflammatory pathways may be beneficial for treatment of both diseases.

The proteomes of LC patients and healthy controls (HC) were analysed by Sequential Window Acquisition of all Theoretical Fragment Ion Spectra-Mass Spectrometry (SWATH-MS) to identify proteins differentially regulated in LC patients.

The Spectral Library

A comprehensive spectral library containing the peptide spectra of all of the identified proteins in a pool of the samples from all subjects was generated through data-dependent acquisition (DDA) mass spectrometry. The pooled sample contained an equal amount of sample from each patient and control subject. The DDA (Data Dependent Acquisition) of the pre-fractionated pooled sample identified 3566 protein groups at a confidence interval of ≥ 99 and an FDR of q ≤ 0.01, which were integrated into the spectral library. After aligning the Data Independent Acquisition (DIA) SWATH-MS data to the spectral library, 3131 proteins and 11127 individual peptides were used for subsequent quantification between the patient and control groups based on an FDR for a peak matching of q ≤ 0.01 in at least one sample.

Differentially regulated proteins in Long COVID patients

A principal component analysis (PCA) on all quantitative protein data and all samples detected sufficient differences to segregate all LC patients from the HC group in principal component 3 (Fig. 1A). Using the average of all the peptide intensities of each protein for quantification, 162 of the 3131 proteins in the spectral library were identified as being differentially regulated in the LC samples (minimum 1.5-fold change (log2(FC) ≤ − 0.58 or ≥ 0.58) and a significance threshold of p ≤ 0.05 (− log10(p-value) ≥ 1.3). There were 79 proteins down-regulated (green) and 83 up-regulated (red), as illustrated in the volcano plot (Fig. 1B). A list of all the proteins that were significantly differentially regulated is found in Supplementary Table S1.

A STRING (v12) functional network analysis ³⁸ on the 162 differentially regulated proteins highlighted enrichment of proteins associated with a number of Gene Ontology (GO) terms (see Table 1, and supplementary Table S2). Markov Cluster Algorithm (MCL) analysis ³⁹ with an inflation parameter of 1.5 identified three major clusters containing 10 or more proteins within the full network (Fig. 2A and supplementary Table S3). One cluster (Fig. 2B) containing 36 proteins is broadly associated with immune response, and the other two with gene expression (spliceosome - Fig. 2C), and (transcription - Fig. 2D). These clusters have 15 proteins mostly linked to RNA splicing and 13 proteins mostly linked to RNA Polymerase II Transcription respectively. All the clusters are shown in supplementary Table S3.

The full network of the 162 differentially regulated proteins (Fig. 2A) comprised 16 protein nodes with first level connections to ten or more differentially regulated proteins. These 16 protein nodes and their numbers of first level interactions shown in brackets are B2M (16), BCL2 (20), CD4 (22), GFM1 (14), HLA-DRB1 (10), HNRNPM (10), LCK (12), NMP1 (16), NRAS (12), PLCG2 (10), PSMB9 (12), SNRPB (10), SNRPD1 (12), SYK (14), TLR2 (10) and UBQLN2 (11) (see Supplementary Table S1 for protein names). This demonstrates a high level of functional association and intra connectivity (betweenness centrality) among the regulated proteins. An enrichment analysis shown in Table 1 highlights a selection of the Reactome and KEGG pathways that suggest the correlations of subsets of these proteins in immune system functions. DAP12 is a DNAX-activating protein of 12 kDa that acts as a signaling adapter protein expressed in Natural Killer (NK) cells and myleoid cells participating in innate immune responses ⁴⁰. Terms related to responses to SARS-COV-2, Epstein-Barr virus and HIV infections were prominent.

Table 1

**Reactome and KEGG pathways most significantly represented by protein nodes with a high level of connectivity (10 or more first level edges to other nodes).** Abbreviations: OGC - observed gene count, BGC - total background gene count in that category, FDR-false discovery rate. Strength measures the confidence score of interactions between proteins or genes. It quantifies the reliability of associations, reflecting the likelihood of true functional connections > 0.4 is considered significant.
Reactome ID	Term description	OGC	BGC	strength	FDR
HSA-168256	Immune System	37	1979	0.36	4.50E-04
HSA-168249	Innate Immune System	27	1041	0.50	6.81E-05
HSA-1280215	Cytokine Signaling in Immune system	19	706	0.51	1.60E-03
HSA-2172127	DAP12 interactions	7	39	1.34	6.37E-05
HSA-5663205	Infectious disease	31	917	0.61	6.86E-08
HSA-9679506	SARS-CoV Infections	17	411	0.70	6.37E-05
KEGG ID
hsa04650	Natural killer cell mediated cytotoxicity	8	120	0.91	1.70E-03
hsa04664	Fc epsilon RI signaling pathway	6	65	1.05	2.80E-03
hsa05169	Epstein-Barr virus infection	9	192	0.76	3.50E-03
hsa05170	Human immunodeficiency virus 1 infection	12	203	0.86	6.93E-05

This Table 1 indicates that changes in immune system-related proteins feature significantly in the data from LC patients. The broad generalised Reactome (HSA-168256) category 'Immune System' was significantly enriched with 37 or 23% of the differentially regulated proteins (strength 0.36, FDR 0.00045). The more specific term 'cytokine signaling in immune system' (Reactome HSA-1280215) included 19 proteins (strength 0.51, FDR 0.001) listed here: CD4, BCL2, B2M, HLA-DRB1, LCK, NRAS, PSMB9, PLCG2 and SYK (all first level interactors for ≥ 10 other differentially regulated proteins), while TIMP1 (6), KPNA3 (4), NUP93 (4), PSMF1 (1), UBA3 (3), HLA-E (9), TRIM22 (2), MAPK9 (5), HLA-B (5) and IRF5 (4) also interact with multiple proteins (numbers of interacting proteins shown in brackets), giving weight to the association of immune system dysregulation in LC.

The immune system’s 37 differentially regulated proteins are listed here: AP2S1, ATP6V1G1, B2M, BCL2, CD300LF, CD4, DOK3, DYNC1I2, FGR, GMFG, HLA-B, HLA-DRB1, HLA-E, IRF5, KPNA3, LCK, MAPK9, MNDA, NDUFC2, NRAS, NUP93, PAFAH1B2, PLCG2, POLR2H, PSMB9, PSMF1, PTGES2, RAB6A, RAF1, RIPK1, SERPINB10, SYK, TIMP1, TLR2, TRIM22, TRIP12 and UBA3.

Interestingly, 17 proteins (out of a possible 411 in the category) in Table 1 are enriched from the Reactome (HSA-9679506) category 'SARS-CoV Infections' (Strength 0.7, FDR 6.37E-05); RIPK1, TLR2, MAN2A1, CSNK1A1, NPM1, SNRPD1, NUP93, SYK, HLA-E, RBBP7, HLA-B, SNRPB, CHMP2A, AP2S1, PTGES3, PLCG2 and B2M. The changed regulation of some of the proteins discussed above that are associated with ‘Immune System’ in general may have originated first in response to infection, in this case infection with the 'SARS-CoV-2 virus’, but persisted with the onset of LC.

To identify potential specific pathways involved in LC, a search was made for any GO terms where at least 25% of proteins from the total background gene counts (BGC) were differentially regulated in the LC dataset. More highly specialised GO terms describe specific molecular functions that involve fewer proteins i.e. these terms have lower BGC. All GO terms where there are ≥ 25% proteins (the observed gene count-OGC) of the BGC (differentially regulated), have been included along with the relevant protein symbols in Table 2.

Table 2

**Functions associated with differentially regulated proteins related to specific immune cell functions.** After GO term enrichment analysis GO terms where the OGC was ≥ 25% of the BGC were identified. Abbreviations: OGC - observed gene count, BGC - background gene count.
Category	Term ID	Term description	OGC	BGC	Protein symbols
GO Process	GO:0002477	Antigen processing and presentation of exogenous peptide antigen via MHC class Ib	2	4	HLA-E,B2M
InterPro	IPR010579	MHC class I, alpha chain, C-terminal	2	4	HLA-E,HLA-B
COMPARTMENTS	GOCC:0043384	pre-T cell receptor complex	2	5	CD4,LCK
GO Process	GO:0042270	Protection from NKC mediated cytotoxicity	2	6	HLA-E,HLA-B
Reactome	HSA-9706374	FLT3 signaling through SRC family kinases	2	6	SYK,LCK
DISEASES	DOID:12894	Sjogrens syndrome	2	7	HLA-DRB1,IRF5
Reactome	HSA-9637628	Modulation by Mtb of host immune system	2	7	TLR2,B2M
COMPARTMENTS	GOCC:0042612	MHC class I protein complex	3	8	HLA-E,HLA-B,B2M
DISEASES	DOID:3275	Thymoma	2	8	CD4,CD5
GO Function	GO:0042609	CD4 receptor binding	2	8	HLA-DRB1,LCK

Comparison with an earlier study of ME/CFS patients

We reported a similar proteome study with a well characterized group of ME/CFS patients in 2020 compared with age/sex matched healthy controls ²⁹. By contrast this patient cohort had been affected by ME/CFS on average for 16 years compared with each of the LC cohort for only 1 year. In this ME/CFS study, there were 346 differentially regulated proteins compared with the 162 proteins identified in the current LC study that met the same criteria (minimum 1.5-fold change and p-value ≤ 0.05) used for the selection in the LC study (Supplementary Table S4).

A MCL cluster analysis of regulated proteins detected in the previous study on ME/CFS patients, using the most recently updated STRING functional network analysis version 12 for consistency, revealed five clusters with 12 or more proteins (Fig. 3); two clusters were broadly associated with the immune system - antigen presentation and cytokine signalling (63 proteins - Fig. 3B) and immune system process - platelet activation, signalling and aggregation (20 proteins Fig. 3C), one with gene expression and metabolism - translation, RNA metabolism, protein metabolism and cellular response to stress (119 proteins-Figure 3D), and smaller clusters associated with the mitochondria - oxidative phosphorylation (13 proteins - Fig. 3E and vesicle-mediated transport (12 proteins - Fig. 3F).

Annotating each protein with functional information (gene ontology terms) allowed for enrichment analysis to identify both common and distinct functional categories or pathways that are enriched in the two datasets. A comparison of the STRING functional network analysis outputs for the LC data against the ME/CFS dataset (FC ≥ 1.5 with p ≤ 0.05) revealed that out of the Reactome and KEGG pathways, 22 and five GO terms were common to both LC and ME GO term enrichment analyses, respectively (Supplementary Table S4). Figure 4 shows the Reactome pathways and KEGG pathways that were common to the two datasets, with the categories listed in the box in each case.

An important outcome of the original ME/CFS study was the number of differentially regulated mitochondrial proteins involved in both general functions, metabolism, electron transport complexes, and the reactive oxygen species stress response. The MCL cluster analysis of the LC differentially regulated proteins in the current study identified a 6 protein cluster associated with the mitochondria (Cluster 7, Supplementary Table S3) that was small in contrast to the ME/CFS analysis but after the differentially regulated proteins from the LC data were searched against the Human MitoCarta3.0 ⁴¹ database of human mitochondrial associated proteins, 21 were identified (Table 3).

Table 3

**Differentially regulated mitochondrial proteins in the LC data.** The human MitoCarta3.0 database was accessed to search for proteins associated with the mitochondria within the LC data set. Abbreviation: FC - fold change.
Peak Name	Symbol	Protein name	FC	p-value	Activity (MitoPathways)
Mitochondrial Metabolism
gi\|373251164	GLS	Glutaminase	1.53	0.028	Glutamate metabolism
gi\|937827788	LDHB	L-lactate dihydrog. B chain	1.57	0.011	Glyoxylate metabolism
gi\|23618867	SFXN1	sideroflexin-1	1.70	0.002	Serine and Vitamin metabolism
gi\|578829057	PDPR	pyruvate dehydrogenase (PDH) phosphatase regulatory subunit	2.85	0.013	Pyruvate metabolism
gi\|767969704	DLAT	dihydrolipoyllysine-residue acetyltransferase (PDH complex)	1.64	0.042	Pyruvate metabolism
gi\|32189392	PRDX2	peroxiredoxin-2	1.70	0.024	ROS and GSH metabolism
gi\|8923001	ABHD10	mycophenolic acid acyl-glucuronide esterase	1.60	0.035	Xenobiotic metabolism
gi\|395394071	TST	thiosulfate sulfurtransferase	0.64	0.010	Sulfur metabolism
gi\|13376617	PTGES2	prostaglandin E synthase 2	1.75	0.027	Eicosanoid metabolism
gi\|15277342	HSD17B8	estradiol 17-beta-dehydrogenase 8	2.43	0.018	Type II fatty acid Cholesterol, bile acid, steroid synthesis
gi\|37594464	NUDT5	ADP-sugar pyrophosphatase	0.64	0.028	Nucleotide synthesis and processing
Mitochondrial Translation
gi\|38683855	PTCD3	pentatricopeptide repeat domain-containing protein 3, mitochondrial precursor	1.84	0.029	Mitochondrial ribosome;
gi\|8923421	SARS2	seryl-tRNA synthetase 2, mitochondrial	2.07	0.008	mt-tRNA synthetases
gi\|46852147	IARS2	isoleucyl-tRNA synthetase 2, mitochondrial precursor	1.51	0.040	mt-tRNA synthetases
gi\|815890954	GFM1	elongation factor G, mitochondrial	0.45	0.044	Translation factors
Mitochondrial dynamics and surveillance
gi\|767999127	BCL2	apoptosis regulator Bcl-2	2.20	0.044	Apoptosis
gi\|151108473	FIS1	mitochondrial fission 1 protein	1.54	0.025	Fission
Oxidative Phosphorylation - Complex I
gi\|7661786	NDUFAF4	NADH dehydrogenase [ubiquinone] 1 alpha subcomplex assembly factor 4	1.97	0.008	OXPHOS assembly factors
gi\|4758784	NDUFC2	NADH dehydrogenase [ubiquinone] 1 subunit C2	0.48	0.015	OXPHOS subunits
gi\|7706351	PTRH2	peptidyl-tRNA hydrolase 2, mitochondrial	0.46	0.001	none
gi\|767902514	CRYZ	quinone oxidoreductase	1.56	0.010	none

This indicated the prominence of the disturbed mitochondrial functions seen in the original ME/CFS study was also seen in LC patients, though at a less well established state.

The changes represented in the differential regulation of the proteins in the immune cells of PBMCs from the LC patients determined in this study were significant as indicated from the principal component analysis and were predominately focused in clusters whose functions were related to immune function, and gene expression. Not surprisingly, changes in proteins related to gene expression were highlighted as they would facilitate changes in expression that led to higher or lower amounts of proteins being synthesised. The results indicated that the immune system activity of LC patients one year after the onset of their COVID infectious illness was dramatically different from that of the healthy controls. It reflected a chronic dysfunctional state that had not been restored from the expected transient immune/inflammatory response mounted to cope with the original infection from the SARS-CoV-2.

The conclusions described here complement emerging studies with LC patients of changes in cellular immunology ^22,42. Immune profiling of 215 patients in conjunction with machine learning identified key features that differed from controls ⁴². A study using multi-omics and serology compared LC patients with those who did not develop LC after their SARS-CoV-2 infection demonstrating systemic inflammation and immune dysregulation in the LC group. It was concluded the normal crosstalk between the humoral and cellular arms of adaptive immunity had broken down in the LC patients ⁴³. A further study up to 24 weeks post COVID revealed differences in innate immune cells natural killer cells, neutrophils, CXCR3 + monocytes, and in adaptive T cell populations ⁴⁴. LC patients have been shown to have reduced CD4 + and CD8 + effector memory cells ⁴⁵. Somewhat conflicting results have been found also in separate cohort studies of cytokines in LC ^23,24,46. Interleukin-1β, IL6 and TNα were elevated in those who did not recover from their viral infection indicating these three cytokines may have a significant role ²³.

Of the immune system proteins identified in the current study several human leukocyte antigen (HLA) proteins were identified as differentially regulated in both LC and ME/CFS datasets. HLA proteins are transmembrane proteins that bind and present peptide antigens for detection and appropriate immune response by T cells, macrophages and natural killer cells (NK) ⁴⁷ and their role is to aid distinguishing between self and non-self. HLA genes are highly polymorphic and some immunological diseases arise from genetic variation ⁴⁸. Class I and Class II HLA proteins are expressed on antigen presenting cells (B cells, macrophages, dendritic cells). Differential regulation of HLA-B (Class I) and HLA-E (Non-classical Class I) were common to LC and ME/CFS, but HLA-DRB1 (Class II) was differentially regulated in the LC data alone, and HLA-A (Class I), HLA-DQB1 and HLA-DPB1 (both class II) were also differentially regulated in the ME/CFS study. HLA-B was down-regulated in both LC (6-fold) and ME (1.7-fold) suggesting altered T cell recognition and reduced inhibitory signaling in Natural Killer (NK) cells in both disorders. By contrast HLA-E was down-regulated in ME (1.6-fold) but up-regulated in LC (1.7-fold). HLA-E specifically recognises CD94 expressed on Natural Killer (NK) cells ⁴⁹. HLA-E overexpression has been shown to negatively interfere with innate immune responses protecting cells from susceptibility to lysis by NK cell-mediated cytotoxicity ⁵⁰.

Cluster of Differentiation markers (with the prefix CD) are a plethora of antigens on the cell surface of leucocytes. For example, CD5 is a T-cell surface glycoprotein that negatively regulates T cell receptor signaling from the onset of T-cell activation. CD4, 5, 84 and 300LF were differentially regulated in the LC dataset. Upregulated in both LC and ME/CFS datasets, CD4 is involved in the early phase of T cell activation, CD5 regulates T cell/B cell interactions and inhibits T cell receptor mediated signaling from the onset of T cell activation. Downregulated in both datasets, CD84 enhances T cell and NK cell activation and cytokine activation, and CD300LF is an inhibitory receptor of myeloid cells.

Since LC is a specific example of a post-viral fatigue syndrome that develops from a specific triggering virus and has a symptom profile and clinical case definition very similar to ME/CFS, the collective term that has been given to post-viral fatigue syndromes from multiple triggers, we compared the LC dataset with an ME/CFS dataset we had derived earlier using the same mass spectrometry approach, re-analysing the dataset with the same statistical parameters used in the current study. Data from supplementary Table S4 in the ME/CFS proteomic study ²⁹ that listed all identified differentially regulated proteins was filtered to give the same stringency (p-value and fold change) as was used in this LC study. With these parameters there were ~ 2-fold more differentially regulated proteins (346) compared with the LC data set (162) and thereby a somewhat more complex pattern in their clustering patterns (compare Fig. 3 to Fig. 2). Cluster analysis from the ME/CFS data set (Fig. 3) identified two separate clusters related to the immune system and its processes, a large cluster related to gene expression/metabolism, and two smaller clusters related to mitochondrial energy production, and vesicle mediated transport as the LC data set.

Of the 34 Reactome categories enriched in the LC data, 22 were also enriched in our earlier ME/CFS data, whereas of the 32 enriched KEGG pathways in the LC data, five were also enriched in ME/CFS (Fig. 4). This highlights pathways that are similarly affected while showing there are distinctions.

A feature of the original ME/CFS study had been the number of differentially regulated mitochondrial proteins. A search of the LC dataset for significantly differentially regulated mitochondrial proteins identified 21 that were involved in mitochondrial metabolism, translation, dynamics, and oxidative phosphorylation. Of the 54 differentially regulated mitochondrial proteins identified in the ME/CFS data set, 14 formed a cluster in the string analysis here (Fig. 3E), whereas there was no cluster with at least 10 proteins in the LC string analysis from the 21 mitochondrial proteins identified. Thirty-five of the differentially regulated mitochondrial proteins in the ME/CFS dataset were detected in the 3131-protein spectral library used to analyse the LC data set. Despite the fewer differentially regulated proteins in the LC dataset 17 of these had very similar fold changes in both LC and ME/CFS datasets (supplementary Table S6), despite only some meeting the statistical criteria for significance, with the others trending towards significance. This may reflect a wider variation among the LC patient in the levels of differential regulation among some of the mitochondrial proteins.

Peroxiredoxin-2 (PRDX2) is a mitochondrial antioxidant enzyme upregulated in both LC and ME/CFS datasets. PRDX2 reduces reactive oxygen species (ROS) by hydrolysing H₂O₂. Elevated levels of PRDX2 in our studies suggests there is increased ROS generation in both LC and ME/CFS immune cells. PRDX2 has been implicated in neurological disease due to aberrant management of ROS ⁵¹. Peroxisomes are an independent organelle that metabolically interact with mitochondria but also play a role in ROS production and scavenging, and dysfunctional effects on mitochondria can affect peroxisomal physiology⁵². Peroxisomal dysfunction is linked with decreased levels of plasmalogens (a class of glycerophospholipids in cell membranes) that have been reported in ME/CFS ⁵³.

Limitations of the study:

It should be noted the average duration time of the condition in the ME/CFS group was 16 years (Table 4), whereas the Long COVID patients had a much shorter duration of their post-viral condition (1 year) so the two groups are at different stages of their ongoing conditions. Now, with so many LC cases synchronized in time as a result of the pandemic longitudinal studies will be possible to follow the course of the dysfunctional pathology of the conditions with time. This has not occurred with ME/CFS as there has been a continuous ‘drip feed of cases from endemic viruses like Epstein-Barr virus, and stressor triggers affecting individuals, as well as cases from boutique infectious disease outbreaks. It may be, despite the many similarities of the result from the two cohorts indicated in this study, that differences reflect the different timepoint since onset of the conditions in the respective cohorts.

While this is a pilot study with only a small number of LC cases, we have found with multi-omic studies of well characterized ME/CFS patients that significant and meaningful results are forthcoming from such small cohorts when compared with age/sex matched controls, and the observed molecular changes are consistent across different molecular classes to detect common and expected pathophysiology ^26,29,32. Subgroups have been suggested for ME/CFS patients in multiple publications ^54–57. Indeed, we have examined individual patients by a precision medicine approach within a well characterized homogeneous small subgroup (age/sex, length of illness, relative functional activity levels) and have found individual patient differences in the molecular changes that still results in a very similar pathophysiology ^6,21. We believe this reflects the individual’s health history and genetic background that makes them susceptible to mount an inappropriate but immune response with individual variations to an external stressor that becomes chronic and spreads to involve the brain and CNS ¹⁴, resulting in the ongoing neurological symptoms that characterise the condition. LC is known to be heterogeneous in that it contains those with ongoing organ damage from their SARS-CoV-2 infection as a well as a significant cohort of a classic post-viral fatigue syndrome like ME/CFS. Recently, four clinical phenotypes have been defined in a large LC cohort analysis, the major chronic fatigue-like condition (42%), a respiratory syndrome (~ 23%), a chronic pain syndrome (22%), and a neurosensorial syndrome (~ 11%) ⁵.

This may help to explain conflicting results from the study of different cohorts of ME/CFS in the literature if there are different proportions with these phenotypes. It complicates understanding the holistic pattern of changes in the chronic immune system in LC (and in ME/CFS if there are indeed clinical phenotypes).

How does the occurrence of clinical phenotypes interfere with obtaining an accurate understanding of how closely related LC and ME/CFS are and on developing therapeutic strategies to improve the health of patients? Multiple papers have documented changes in both peripheral immune cell numbers and their activities, and the up or down regulation of cytokines ²¹. While there may be individual differences dependent upon the viral trigger in LC or heterogeneous triggers in ME/CFS, this is likely also to reflect variations among the individual patients themselves according to their genetic background. The complexity is compounded as regulation can be in either direction according to the state and severity of the illness. Further coherence and integration to the myriad of changes is still required to provide more informed insight into what opportunities there are for therapeutic immune modulation in patients aimed at reversing the cascade of events that have led many to an ongoing severely debilitated state of health with LC, and for many years with ME/CFS. Potential therapeutic strategies to target immune mediated inflammatory diseases like LC and ME/CFS have progressed from broad specificity approaches to highly specific targeting of cytokines and their receptors and to small molecule drugs targeting the inflammatory pathways. Improving the quality of life for LC and ME/CFS patients generally might still be possible if the patient-to-patient variability in immune dysfunction is not too diverse.

Cohort recruitment

New Zealand LC patients who contracted the SARS-Cov-2 virus with the first wave of infection in March/April of 2020, and who had an ongoing fatigue illness with classical symptoms that were consistent with the clinical case definitions of ME/CFS ^58,59 were recruited with the help of a social media group of LC- affected and blood was collected between 8/3/21 and 12/4/21 together with age and sex matched healthy controls. The LC patients had contracted the virus ~ 12 months prior to sampling and been diagnosed with Long COVID by an expert ME/CFS clinician (Dr. Rosamund Vallings ) or their own doctor. The ME/CFS cohort from the previously published study ²⁹ were a well characterized group clinically who had also been diagnosed also by Dr Vallings. Characteristics of the two groups are shown below.

Table 4

Cohort characteristics
Clinical characteristics	Long COVID cohort	ME/CFS cohort
Number	6	9
Median age	39	49
Sex	M = 1, F = 5	M = 4, F = 5
Median illness duration	1 year	16 years
Initial trigger	SARS-Cov-2	Infection (6), other (3)

The study conformed to the ethics approval 17/STH/188 for patient studies from the Southern Health and Disability Ethics Committee of New Zealand. Patient information sheet was provided and informed consent was obtained from all participants. Consultation with Ngai Tahu Research Committee of the University of Otago was carried out before the beginning of this research to ensure our research gives effect to Te Tiriti o Waitangi. Research involving human research participants was performed in accordance with the Declaration of Helsinki.

Blood collection and PMBC isolation

Blood (20 mL) was collected in BD Vacutainer™ blood collection tubes containing K2 EDTA 2. In a class 2 biological safety cabinet, blood was diluted 2-fold in PBS and 4 mL was added to tubes containing 3ml Ficoll-Paque Plus. After centrifugation at 400 x g in a swinging bucket centrifuge without braking, the plasma layer was removed, followed by the PBMC layer which was transferred to a tube, mixed gently with 3 volumes of PBS and centrifuged at 100 x g for 10 min. The pellet was washed by gentle resuspension in 6 ml sterile PBS and centrifugated at 100 x g for 10 min. Finally, the pellet was resuspended in PBS or 6 ml FBS containing 10% DMSO when stored in cryovials in liquid nitrogen until required. Then extensive washing of the cells in PBS to remove FBS proteins was carried out before subsequent protein analysis.

Sample preparation

Proteins were extracted from PBMCs and digested with trypsin using the S-Trap-mini kit (ProtiFi, Fairport NY) according to the manufacturer’s instructions. In brief PBMCs were thawed in 50 µL of lysis buffer (100 mM triethylammonium bicarbonate (TEAB) pH 7.5, and 5% (w/v) sodium dodecyl sulphate (SDS) in water). Cells were lysed and homogenised by three consecutive vortex (10 s) and sonication (1 min in a sonication bath) steps. The homogenate was then supplemented with 100 U of benzonase. After incubation at 37°C for 30 min, the cell lysates were centrifuged at 30,000 × g for 30 min at 20°C to remove insoluble material and cell debris. The supernatant containing the soluble protein fraction was recovered and an aliquot of each sample was used for the estimation of protein amounts using the BCA Protein Assay Kits (Thermo Scientific). A volume containing 100 µg of protein was taken from each sample, and subjected to reduction and alkylation of cysteines using 5 mM tris(2-carboxyethyl) phosphine (TCEP) and 10 mM iodoacetamide (IAM) before loading the samples on individual S-Trap mini units following the recommended protocol.

Protein Identification and Quantification by SWATH-MS

Data-dependent acquisition (DDA) mass spectrometry of a pooled sample containing an equal amount of every sample was used to generate a comprehensive spectral library containing the peptide spectra of all identified proteins from all of the samples. To achieve greater protein identification, the pooled sample was subjected to peptide pre-fractionation using a high pH reversed-phase peptide fractionation kit according to the manufacturer’s instructions (Thermo Scientific). Each of the 11 fractions was then analysed in two technical replicates using DDA mass spectrometry on a 5600 + Triple Time-Of-Flight (TOF) mass spectrometer coupled to an Eksigent “ekspert nanoLC 415” uHPLC system (AB Sciex), as previously described ²⁹. For peptide/protein identification, the raw data files of all fractions and technical replicates were searched against the human reference sequence database (downloaded from https://www.ncbi.nlm.nih.gov/on29/03/2019), which contains 87,570 sequence entries, using the Protein Piolet software (version 4.5). Trypsin, carboxymethylcysteine and biological modifications were selected for a thorough search setup.

Each sample (from five healthy controls and six long COVID patients) was analysed in four technical replicates by data-independent acquisition (DIA) mass spectrometry using the SWATH-MS workflow according to the details described previously ²⁹.

Data Analysis and Statistics

The spectral library was built using the SWATH application (version 2.0) embedded into PeakView software (version 2.2, AB Sciex), applying the criteria outlined in ²⁹. DIA spectral data were aligned to the library spectra at a false discovery threshold for peak picking of q = 0.01 in at least one sample. The peak area under the curve (AUC) was then exported to MarkerView software (version 1.2, AB Sciex) for further statistical analysis. The median value of the AUC of the four technical replicates was used to calculate the mean between the biological replicates of LC or HC samples for each peptide and protein. The mean peptide/protein values were then compared between disease and control groups using a 2-tailed Student’s t-test. The criteria for being considered significantly differentially regulated were ≥ 1.5-fold (Log2 fold change ≥ 0.58 or ≤ − 0.58) with a p-value of ≤ 0.05 (− log10(p-value) ≤ 1.3).

Regulated proteins were analysed by functional association network analysis using the STRING database tool version 12 (https://string-db.org/). MCL cluster analysis with a 1.5 inflation parameter returned clusters of functionally proteins and enrichment analysis of Gene Ontology (GO) terms enabled identification of any overrepresented biological processes or pathways within the data. The comparison of disease and control samples was performed to find further potentially regulated proteins that are functionally associated.

Re-analysis of the ME/CFS date from Sweetman et al 2020

A comparison of the LC data with a previously published ME/CFS data set generated using the same methods was attempted to ascertain if the two diseases shared any similarity. The list of significantly differentially regulated proteins from Sweetman et al supplementary Table 4 ²⁹ were filtered to retain only those with a fold change minimum of 1.5. These proteins were subjected to a STRING functional network analysis as described for the LC data set.

Acknowledgements: We thank the patients who participated in this study, and acknowledge affected families, and the New Zealand ME/CFS Advisory association, ANZMES, and Brain Research NZ Centre of Research Excellence for financial support. We acknowledge Dr Eiren Sweetman who conducted the original ME/CFS study.

Author contributions: KP analysed the Long COVID proteomic data and compared it with the original ME/CFS study data. She prepared the manuscript with WPT. CDE processed the blood samples and prepared the PBMCs for protein analysis, and critiqued the manuscript, TK ran the samples through SWATH MS analyses, analysed the data and critiqued the manuscript, WT conceived and supervised the project, recruited the patients and obtained financial support for the project, and prepared the manuscript with KP.

Data availability statement: The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the data set identifier PXD045508

Additional Information: The authors have no conflicting interests in the report of this study

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Immune cell proteomes of Long COVID patients have functional changes similar to those in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome

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Abstract

Figures

Introduction

Results

The Spectral Library

Differentially regulated proteins in Long COVID patients

Comparison with an earlier study of ME/CFS patients

Discussion

Limitations of the study:

Conclusion

Methods

Cohort recruitment

Blood collection and PMBC isolation

Sample preparation

Protein Identification and Quantification by SWATH-MS

Data Analysis and Statistics

Re-analysis of the ME/CFS date from Sweetman et al 2020

Declarations

References

Additional Declarations

Supplementary Files

Status:

Journal Publication

Version 1