Изменения иммунитета после ковида

[chuka_lis]

Работа австралийцев по тому, какие характерные девиации иммунитета произошли в течение полугода  после ковида легкого, умеренного, тяжелого и критического течения, по сравнению с контролем.
Выборка была не сильно большая, зато исследованы многие параметры, и  по времени - тоже (3, 4, 6 мес. после болезни)
У всех переболевших сформировался гуморальный иммунитет, который сохранялся до 6 месяцев на приличном уровне, и положительно  коррелировал с тяжестью симптомов во время болезни.
IgG держались дольше, уврони их были выше, и снижались медленнее, чем IgА и IgМ. Так же, в РСЧ иммунитет затухал быстрее, чем к шипику в целом.
Но при этом у выздоровевших обнаружился разлад в иммунной системе после выздоровления, даже если они болели легко и умеренно. Авторы считают, что  отклонения в субпопуляциях клеток и их активности (по сравнению с "нормой", не болевшими ковидом, конролем) могут быть ответственны за постковидные симптомы, вызванные как  изменениями иммунитета после ковида (даже не требовавшего госпитализации), так и пониженной эффективностью работы не восстановившегося "до нормы" иммунитета при встрече с новыми патогенами (или при имеющихся хронических болезнях).
У всех переболевших сохранялась лимфопения и "разлад" в клеточных линиях, после ковида, до 3 месяцев. Имеются ввиду именно не только специфичные к антигенам коронавируса лимфоциты, а вообще те, что есть в периферической крови.
Восстановление шло разными темпами, неравномерно, и у некоторых не пришло в норму и  за 24 недели.
Среди клеточных популяций более всего пострадали Т-хелперы и Т-киллеры: и взрослые клетки, и клетки памяти.
Примерно к 3 месяцу Т-лимфоциты в целом начинали количественно и качественно восстанавливаться (но до контроля не дотягивали). Тогда же, к 3-4 мес, нарастало количество и молодых В-лимофцитов, часть из которых созрела в клетки памяти к 4 месяцу. Но вот сопутствующие - клетки памяти формировались меньше и медленее
При этом, у переболевших, к 3 месяцу и далее, было больше натуральных киллеров, неклассических натуральных Т-киллеров, и лейкоцитов (любых), чем у контроля, и эта тенденция сохранялась и дальше.
Вероятно, это связано с активацией звеньев иммунитета, используемых для  очистки и "ремонта" поврежденных вирусом тканей.
При этом, уменьшалось количество "секреторных" Th9 лимфоцитов, а Th2/22 "выравнивались" только к 4 месяцу - авторы думают, что тк страдала слизистая легких, а эти лимфоциты обычно отвечают за  ее нормальную работу (в том числе)- то они были рекрутированы в ткани, что может подтверждать и повышенное количество в крови Th2/17/22 клеток (и памяти в том числе)  после 3 мес, сохранившееся до 6 мес.
Среди регуляторных Т-клеток не было достоверных оличий в количестве- но у переболевших была выше доля "молодых", и так же больше Т-фолликулярных -клеток-помощников формирования этих молодых лимфоцитов,  и меньше субполуляция регуляторных, сдерживающих "созревание" лимфоцитов.
Среди лейкоцитов только CD14+CD16+ нейтрофилов было меньше чем в контроле, в период 3-4 месяца после болезни. А так  гранулоцитов любых линий было больше, во все периоды измерений, по сравнению с контролем (те  они были простимулированы на размножение и долгую жизнь).
Количественно, по моноцитам переболевшие от контроля - достоверно не отличались. Но у переболевших было больше про-воспалительных и ткане-формирующих молодых моноцитов через 3 месяца после болезни. Так что это звено определенно пострадало во время ковида.
У авторов при анализе обнаружились корреляции между изменениями в клеточных линиях и уровнями антител к коронавирусу, для всего периода времени. Что  может свидетельствовать о том, что именно иммунный ответ на этого возбудителя, и тяжесть течения ковида проинициировали все эти изменения  в клеточных линиях, и формирование "нового поколения" специфического клеточного иммунитета.
Примерно то же показал и анализ транскриптома крови. Болезнь поспосбствовала тому, что активность генов значительно изменилась по сравнению с контролем. Сильно увеличилась активность рибосомальных генов, вероятно, последствия вирусной стимуляции. Так же выросла активность генов "врожденного иммунного ответа" в целом, выработки антимикробных белков, противовирусных факторов вроде интерферона, дегрануляции нейтрофилов, итд. Была снижена активность сектора, ответственного за окислительное фософрилирование (считай: энергетический балланс),  и ответственного за сигнальные пути, аггрегацию и активацию тромбоцитов.
У переболевших все это "приходило в норму" долгое время (и не у всех пришло через полгода).
Так что после ковида оставались признаки активации-истощения имумнной системы, и "несогласованности" между звеньями, которые, по мнению авторов, показывают что люди все еще "нездоровы", хотя могли болеть не обязательно тяжело, и прошло много времени с момента болезни.
Все укаызвает, на мой взгляд, на то, что организм старается  восстановиться,  "нарастить" армию клеток чтобы компенсировать повреждения и сформировать специфический иммунитет, но это отнимает силы и время.

Here, we have performed anti-receptor binding domain (RBD) and anti-Spike serology, comprehensive multi-parameter immunophenotyping, and transcriptome-wide RNA sequencing on blood collected from individuals recovering from mild/moderate or severe/critical COVID-19 at 12, 16, and 24 weeks after their first positive SARS-CoV-2 PCR test, as well as age-matched healthy controls (HCs). Our analyses reveal robust but heterogenous humoral immunity in convalescents until at least 6 months post-infection. Deep immunophenotyping highlighted profound changes in immune cell populations in COVID-19 convalescents compared with HCs, particularly at 12 and 16 weeks post-infection (wpi). Furthermore, RNA sequencing revealed significant changes in whole blood gene expression for up to 24 wpi, even in individuals that had mild disease without hospitalisation. These data suggest that SARS-CoV-2 infection leads to persistent changes to the peripheral immune system long after the infection is cleared which has important potential implications for understanding symptoms associated with long COVID. These changes to the peripheral immune system could have implications for how individuals recovering from infection respond to other challenges encountered in this period and persistent immune activation may also exacerbate other chronic conditions. We used a multi-parameter flow cytometry approach to identify and enumerate ~130 different immune cell sub-populations in samples collected from COVID-19 convalescent individuals at 12, 16 and 24 wpi and from HCs (Table S2; Document S1). Our analysis included deep immunophenotyping of the CD4 and CD8 compartments, interrogating their maturation status, and in the CD4 compartment, interrogation of T helper (Th) lineage subsets, T regulatory (Treg) subsets, and T follicular helper (Tfh) subsets using a combination of chemokine receptor expression patterns to resolve Th lineages (Th1, 2, 17,1/7, 9, 22, 2/22). Immune cell populations were first categorised into 10 major lineages. Differences in these major lineages, compared with HCs, were most strongly evident at 12 wpi but some populations were still significantly different at 24 wpi. CD3+ T cells were significantly increased at 12 wpi . CD19+ B cells were also significantly increased at 12 and 16 wpi. We also observed significantly increased CD38+CD27+ memory B cells at 16 wpi. When interrogating CD4+ T cell maturation, we observed a significant reduction in both the CD4+ and CD8+ compartments at 12 and 16 wpi. CD4+effector memory (EM) pools were significantly reduced (Fig. 2H) and we also observed a significant reduction in migratory central memory (CM) CD4+ T cells, defined as CCR7+CD62L-, at all timepoints). he NK cell compartment was also altered in convalescents at 12 and 16 wpi with CD56++ NK cells significantly elevated at 12 wpi whether enumerated as total (Fig. 2J)or tissue migratory (CXCR3+) . We also observed a significant increase in total granulocytes at all 3 timepoints post-infection, and this was also observed for low density (LD) neutrophils at 12 and 16 wpi. CXCR3+ LD neutrophils, which are actively recruited to sites of tissue damage (26), were elevated in convalescents at 12 wpi but returned to baseline by 16 wpi. Interestingly, CD14+CD16+ neutrophils were significantly decreased at 12 and 16 wpi. While total monocyte proportions were not significantly altered, two subsets of tissue-homing CXCR3+ monocytes (HLA-DR+, activated antigen-presenting proinflammatory monocytes and HLA-DR-, regulatory monocytes) were significantly increased in convalescent individuals at 12 wpi Next, we assessed correlations between immune cell populations (at 12, 16 or 24 wpi) and both anti-Spike and anti-RBD IgG, IgM, and IgA responses at 24 wpi. Significant positive correlations were observed between the frequency of granulocytes, CD16+ NK and NKT-like cells at 12 wpi and anti-Spike IgG1 and anti-RBD IgG titres at 24wpi. These data may be reflective of the correlation between disease severity and antibody responses. For example, there was a positive correlation between the proportion of CD4+ cells in transition from naïve to CM, CM to EM CD4+ T cells, and activated (HLA-DR+ or CD38+) CD4+ T cells and anti-Spike and anti-RBD IgG/G1 titres at 24 wpi, suggesting each of these CD4 populations might contribute to robust T cell help. Significant correlations between immune cell populations at 16 and 24 wpi and anti-Spike or anti-RBD antibody responses were also observed. We observed a significant decrease in Th9 cells at all timepoints. There was also a significant increase in Th2/22 cells at 16 wpi . This may reflect a failure to mount a normal tissue repair response in the mucosa in the lung, as the Th9 and Th22 family are predicted to home to epithelial mucosa (30, 31). Alternatively, these subsets may be underrepresented in the circulation as they have transmigrated to sites of damage. In examining the formation of Th cell memory, we observed that while the proportion of Th17 and Th22 cells was not significantly different between groups, there was an increased proportion of Th17 and Th22 CM cells at all timepoints n addition, there was evidence of increased formation of Th2/22 memory at 12 wpi (Fig. 3H), suggesting establishment of memory focused on tissue repair (32). In the Tfh compartment, we observed significant differences in Tfh1, 9, 22 and 2/22 cells at different timepoints post-infection , with Tfh1 cells significantly elevated in convalescents at 12 and 16 wpi As with CD8+ and CD4+ effector T cells, Tregs segregate into naïve and mature populations depending on antigen exposure. While we found no difference in total Tregs, we observed a significant increase in naïve Tregs at all timepoints post-infection, accompanied by a significant decrease in CM and EM Tregs at 12 and 16 wpi, and a significant increase in TEMRA Tregs (effector memory with acquired CD45RA) at 12 and 16 wpi. These data suggest either a block in maturation, or an increase in formation of naïve Treg cells in convalescents. We observed a significant decrease in the proportion of ThR2 Tregs at 12 and 16 wpi, and a significant decrease in ThR22 and ThR2/22 Tregs at all timepoints suggesting a block in commitment of theses lineages. Finally, we also examined the follicular regulatory T cell lineages (TfhR), as they serve a similar regulatory role in germinal centres, controlling Tfh function and B cell help. We observed a significant decrease in total TfhR at 12 and 16 wpi suggesting that follicular help is less restrained by TfhR in individuals recovering from COVID-19. Specifically, TfhR2, 22 and 2/22 subsets were all significantly reduced at 12 and 16 wpi, but returned to baseline by 24 wpi. This is consistent with the regulatory follicular arm licencing a Tfhl response early in infection, but later, shaped by the tissue location of the virus, skewing to Tfh2/22 driven B cell help in germinal centres, both of which are required to drive an effective anti-viral B cell response. we performed a correlation analysis between CD4+ T cell subsets at 12, 16 and 24 wpi and antibody responses at 24 wpi. We observed a number of interesting statistically significant correlations. For example, we observed a significant positive correlation between anti-Spike IgG1 levels and both ThR2/22 and TfhR2/22 subsets, suggesting that the effector function of this epithelial tissue homing lineage may regulate antibody responses. Similar correlations between these subsets and anti-RBD IgG responses were also evident. RNA sequencing was also performed on blood collected from age-matched HCs (n=14) with negative serology for the SARS-CoV-2 Spike and RBD proteins. After adjusting for sex and batch effects, MDS analysis of the gene expression data revealed a clear separation between HCs and convalescent individuals at each timepoint (Fig. 4A-C). Consistent with these data, differential gene expression analysis identified >950 genes that were significantly (FDR < 0.05, fold change >1.25) differentially expressed (738 up-regulated genes; 230 down-regulated) in convalescent individuals at 12 wpi compared to HCs Fewer differentially expressed genes (DEGs) were identified at 16 and 24 wpi, but there were still >250 DEGs identified at 24 wpi Unsupervised hierarchical clustering analysis of DEGs did not reveal an obvious clustering by disease severity, suggesting that even individuals with mild COVID-19 have long-lasting changes to their blood transcriptome There was a tendency for samples from the earlier timepoints to cluster together, consistent with a decrease in the number of DEGs over time, but clearly there was a spectrum in the recovery in gene expression among convalescent individuals, with some recovering more quickly (clustering with HCs) In many cases these signatures were predominantly driven by the up-regulation of ribosomal RNA (rRNA) genes. Viral polypeptide synthesis is reliant upon host ribosomes and many viruses have been reported to stimulate rRNA synthesis upon infection (33, 34), although the SARS-CoV-2 Nsp1 protein has been shown to act a strong inhibitor of translation (35). Interestingly, a recent study has surprisingly shown that rRNA accumulation positively regulates antiviral innate immune responses against human cytomegalovirus infection (36), raising the possibility that the continued up-regulation of rRNAs in individuals recovering from COVID-19 is a cellular defence mechanism. Consistent with this, the Reactome pathway “innate immune system” was significantly enriched among genes up-regulated in convalescents Other statistically enriched pathways among up-regulated genes included neutrophil degranulation, antimicrobial peptides, immune system, pathways related to other viral infections, cell cycle related pathways, and pathways related to the citric acid (TCA) cycle and respiratory electron transport/oxidative phosphorylation Among down-regulated genes at 12 and 16 wpi there was a strong enrichment for metabolic related pathways such as oxidative phosphorylation as well as pathways related to platelet activation, signaling and aggregation (Fig. 4G, Table S4). Platelet aggregation has previously been identified as a marker of severe SARS-CoV-2 infection (37), so it is interesting that genes involved in this process appear to be down-regulated in recovering individuals (Fig. S2A, Table S4). Interestingly, we identified oxidative phosphorylation to be enriched among up-regulated genes as well as down-regulated genes. Increased expression of genes involved in oxidative phosphorylation has recently been reported in another study assessing COVID-19 convalescents (20). Further examination of our data revealed that down-regulated oxidative phosphorylation genes were encoded by the mitochondria, whereas up-regulated ones were nuclear encoded Many of the most strongly up-regulated genes in COVID-19 convalescents encoded known biomarkers of inflammation and innate immunity including S100 calcium-binding protein A8 (S100A8), and high-mobility group protein 1 (HMGB1), 5-azacytidine induced 2 (AZI2), and granzyme A (GZMA). As we performed total RNA sequencing we were also able to identify many differentially expressed long-non-coding RNAs  including metastasis-associated lung adenocarcinoma transcript 1 (MALAT1), which has been found up-regulated in response to flavivirus and SARS-CoV-2 infection (39, 40) and is an important regulator of immunity and the cell cycle As detailed above, flow cytometry analysis revealed significant changes in the proportion of multiple immune cell populations in convalescent individuals compared with HCs Blood transcriptional module analysis highlights variable rates of recovery in the transcriptome of COVID-19 convalescents and correlations with antibody responses. Finally, we undertook a systems-level integration of blood transcriptional modules (BTM) activity scores, anti-Spike and anti-RBD antibody data, and flow cytometry data at 12, 16 and 24 wpi Using limma we identified 80 of these BTMs that were differentially active in convalescents. The annotation of these BTMs was broadly consistent with our pathway analysis identifying multiple modules related to transcription/translation, the cell cycle and specific immune cell populations and pathways as being significantly enriched in convalescents. Interestingly, this analysis highlighted that while the proportion of recovering COVID-19 convalescents with ‘healthy-like’ BTM activity increased over time (consistent with a recovery to baseline over time), there were still a subset of convalescents with persistent transcriptional dysregulation at 24 wpi. Many BTMs, including those differentially active in convalescents, were strongly correlated with each other. For example, monocyte, DC, neutrophil, and inflammasome related BTMs were strongly correlated with each other and, interestingly, with metabolism related BTMs. DC related BTMs also correlated with antiviral and interferon related BTMs. At 12 wpi there were also significant correlations identified between BTMs and >30 different immune cell populations. Anti-Spike and/or anti-RBD antibody titres were also significantly correlated with BTMs at 16 and 24 We also identified that multiple different immune cell populations that correlated with antibody titres at each timepoint. These relationships were particularly evident at 24 wpi. For example, the proportion of LD granulocytes, CD16+ NK cells and CCR7-CD62L+ transitional memory T cells were significantly positively correlated with anti-Spike and anti-RBD IgG titres at 24 wpi. In addition to our serological analysis of COVID-19 convalescents, we extensively and longitudinally profiled immune cell populations in the same individuals using a multi-panel approach that enabled the identification and enumeration of ~130 different sub-populations including deep phenotyping of the CD4 and CD8 compartments. Differences in immune cell populations compared with HCs were most strongly evident at 12 wpi, but some populations were still significantly different at 24 wpi. CD56++ NK cells, granulocytes, LD neutrophils and tissue-homing CXCR3+ monocytes were significantly increased in convalescents at 12 wpi. Many of these changes persisted until at least 16 or 24 weeks. Consistent with our data, increased NK cells (46) and granulocytes (49) have been reported in other cohorts of convalescents and scRNAseq has revealed that increased non-classical monocytes are associated with more severe disease during active infection (60). In contrast to our study, a study of 109 Austrian convalescents at 10 weeks post-infection, did not find neutrophils, monocytes, CD3+ T cells, CD56+ NK cells or CD19+ B cells to be significantly different in convalescents (49). Other studies have also reported significant decreases in the frequencies of invariant NKT and NKT-like cells (47), which we and others (20) did not observe. Several previous studies have reported that T and B cell activation/exhaustion markers remain elevated following SARS-CoV-2 infection (15). Furthermore, CD4+ and CD8+ EM T cells have been reported to be significantly higher in convalescents at 10 wpi (49). Consistent with reports in active infection and convalescence (15), convalescent individuals in our study had lymphopenia until at least 16 wpi, however, CD3+ T cells were significantly increased at 12 wpi. We also observed significantly increased CD19+ B cells at 12 and 16 wpi and CD38+CD27+ memory B cells at 16 wpi in convalescents. Recent studies have shown that increased activation and exhaustion of memory B cells observed during COVID-19 correlates with CD4+ T cell functions (61), and consistent with this we observed reduced CD4+ EM cell proportions in COVID-19 convalescents at 12 wpi. We were particularly interested in the role of regulatory T cells (Tregs) in COVID-19, as there have been conflicting reports of Tregs being either increased or decreased in convalescents. Significantly increased Foxp3+ Tregs were observed in 49 convalescents from Wuhan at ~112 days post-recovery (47), however, another study observed that CD25+Foxp3+ Tregs were significantly reduced 10 weeks after COVID-19 (49). We observed no significant difference in the total (CD4+CD25+CD127low) Treg pool at any timepoint, but when we interrogated Tregs for their memory/maturation status, we observed that the naïve and TEMRA Treg compartment was significantly expanded at 12 and 16 wpi, while EM and CM Tregs were significantly reduced, mirroring a similar reduction in the proportion of CD4+ EM and CM pools at 12 and 16 wpi. Interestingly, a number of the Th lineage subsets including Th2, Th22, Th2/22, and Th17 had an increased proportions of CM vs EM, revealing subtle skewing of the Th memory formation. The expansion of naïve Tregs could be an attempt to restore the balance in the Treg pool in the face of both inflammation and tissue damage,which is supported by emerging evidence of a dual role for Tregs in supressing immune responses and promoting tissue repair (62). Increased TEMRA Tregs, which are often associated with exhaustion, but are in fact a poly-functional effector Treg population with characteristics of cytotoxic cells, migratory T cells and tissue repair cells (63, 64), further suggest a competition between classical immune suppression and tissue repair by these cells in response to tissue damage in COVID-19 convalescents. Each Th subset has a paired regulatory subset (28), and this includes Tfh subsets, as B cell help in germinal centres also requires regulation in the steady state (65). In a stereotypical antiviral immune response, Th1 cells migrate to sites of viral infection to establish an adaptive response, and regulatory cells co-migrate to limit chronic inflammation once the pathogen levels decline, however, there is an emerging function of tissue resident Treg cells in tissue repair (62, 66). We did not observe increased Th1 cells, but we did observe a reduction of Th9 cells, which are believed to home to the gut mucosa (67), potentially suggesting a diversion of Th cells to other sites. We also observed that the maturation of Th pools was enhanced in both Th17 and Th22 subsets, where CM marker proportions were increased at all timepoints post-infection. This may suggest that epithelial homing and tissue damage trigger activation and form part of the COVID-19 T cell recall response. It is intriguing that the Treg partners of these lineages, including ThR2, ThR22 and ThR2/22 were all significantly reduced over the same time course post-infection, suggesting that the signal recruiting Th cells to tissue locations are persistent long after COVID infection. There was a very strong enrichment for pathways and BTMs related to transcription, translation, and ribosome biosynthesis among genes up-regulated in recovering individuals, at all 3 timepoints. Many viruses upregulate rRNA synthesis during infection [42, 43], but why rRNA gene expression remains up-regulated months after infection is currently unknown. Other statistically enriched pathways among up-regulated genes included neutrophil degranulation, antimicrobial peptides, immune system and pathways related to other viral infections. These data suggest ongoing inflammatory responses and immune dysregulation in COVID-19 convalescents weeks-to-months after infection. Consistent with these data, neutrophil degranulation has reported to be significantly up-regulated in active infection (69, 70), suggesting that certain signatures of active infection persist well into convalescence. We also found evidence for dysregulated expression of genes involved in oxidative phosphorylation, a signature which has also been identified in one other recent study of convalescents to occur irrespective of whether elevated inflammatory markers persist or not (20), but whose functional significance is currently unknown. While some changes in gene expression were associated with variation in specific immune cell populations between individuals, differences in gene expression were not solely explained by changes in the frequency of any single immune cell population. A patient-specific analysis of the gene expression activity of pre-annotated BTMs enabled a more thorough assessment of the variation in gene expression responses. There was a broad spectrum in the recovery of gene expression responses in both mild/moderate and severe/critical convalescents. Variation in the rate of recovery from infection at a cellular and transcriptional level may explain the persistence of symptoms, such as fatigue, associated with long COVID in some convalescent individuals, although data related to ongoing symptoms was unfortunately not collected for this cohort. Interestingly, a link between gene expression in peripheral blood and fatigue following infectious mononucleosis has been previously reported (71), with at least some of the same genes differentially expressed in COVID-19 convalescents. These data may point towards common mechanisms regulating ‘long COVID’ and post-viral infection fatigue more generally. Finally, we also uncovered significant inverse correlations between dysregulated BTMs and anti-Spike and anti-RBD antibody responses suggesting that prolonged transcriptional dysregulation may be associated with reduced antibody responses with potential consequences for the durability of protective immunity.