The SARS-CoV-2 coronavirus has led to a pandemic with millions of people affected. The present study finds that risk-factors for severe COVID-19 disease courses, i.e. male sex, older age and sedentary life style are associated with higher prostaglandin E2 (PGE2) serum levels in blood samples from unaffected subjects. In COVID-19 patients, PGE2 blood levels are markedly elevated and correlate positively with disease severity. SARS-CoV-2 induces PGE2 generation and secretion in infected lung epithelial cells by upregulating cyclo-oxygenase (COX)-2 and reducing the PG-degrading enzyme 15-hydroxyprostaglandin-dehydrogenase. Also living human precision cut lung slices (PCLS) infected with SARS-CoV-2 display upregulated COX-2. Regular exercise in aged individuals lowers PGE2 serum levels, which leads to increased Paired-Box-Protein-Pax-5 (PAX5) expression, a master regulator of B-cell survival, proliferation and differentiation also towards long lived memory B-cells, in human pre-B-cell lines. Moreover, PGE2 levels in serum of COVID-19 patients lowers the expression of PAX5 in human pre-B-cell lines. The PGE2 inhibitor Taxifolin reduces SARS-CoV-2-induced PGE2 production. In conclusion, SARS-CoV-2, male sex, old age, and sedentary life style increase PGE2 levels, which may reduce the early anti-viral defense as well as the development of immunity promoting severe disease courses and multiple infections. Regular exercise and Taxifolin treatment may reduce these risks and prevent severe disease courses. The 2019 strain of coronavirus (severe acute respiratory syndrome coronavirus-2 SARS-CoV-2) caused a pandemic with COVID-19 disease affecting millions of people worldwide. Patients with serious disease courses frequently present with severe acute respiratory syndrome that can progress to pneumonia and acute respiratory distress syndrome and shock . SystemiAn increased risk for infection and severe disease courses have been found in association with older age, male sex, cardiovascular comorbidities and air pollution , 13–15. Prostaglandin (PG) E2, a metabolite of arachidonic acid, is a well-known modulator of viral infection . As suchWe hypothesized that PGE2 modulates the immune response in individuals at risk for severe COVID-19 disease. To test this, we first measured serum PGE2 levels in COVID-19 patients with different levels of disease severity, as well as in subjects with putative risk factors  for a severe disease course. To analyze the direct effects of SARS-CoV-2 on PGE2 production, we infected human lung epithelial cells and human precision-cut-lung-slices (PCLS) with SARS-CoV-2. Additionally, we further dissected the mechanisms of PGE2 modulation of immune defense, e.g. through B-cell maturation and the formation of memory cells, and correlated disease severity with lung B-cell content in patient samples. We further tested strategies to reduce PGE2 production or the effect on the above parameters as preventive or therapeutic modalities against severe COVID-19.Unless otherwise stated, chemicals and reagents were all purchased from Sigma-Aldrich.In this study of 89 patients diagnosed with COVID-19, 41 presented with mild/moderate symptoms and 48 were hospitalized with severe disease. Blood samples were also obtained from male (n = 18) and female subjects (n = 28) (age 18–50 years) from a healthy population established by Hannover Unified Biobank (HUB).At the time of blood sampling, for 29 patients it was known whether they obtained corticoids or not. Among those n = 14 obtained no corticoids and n = 15 COVID-19 patients with mild and severe disease course received corticoids (Dexamethasone n = 11 or Medrol n = 4). Information on the use of NSAIDs or leukotriene modifiers were not available. None of the healthy controls were under corticoids or nonsteroidal anti-inflammatory drugs (NSAIDs) treatment.The local ethics committees at Hannover Medical School, Comité d’Ethique Hospitalo-Facultaire of UCLouvain, and the Ethical Committee of IEO has been obtained (IEO1271) approved this study. All patients and healthy control subjects provided written informed consent. The study conforms to the principles outlined in the Declaration of Helsinki.All subjects in the Rebirth 60plus cohort (DRKS00013885) were initially tested for maximum power output on a cycle ergometer with graded exercise test (GXT). Based on their activities, physical fitness and pathologies, each subject was given an aerobic exercise training program. Once a month, the subjects were contacted by phone to assess training progress and adjust the exercise program, if necessary. All subjects of the Rebirth 60plus study were informed about benefits and risks regarding all study procedures. Height and weight were measured using a scale . Body fat was measured with a medical Body Composition Analyzer mBCA . The physical activity was tracked using a GPS watch Forerunner 30  and a daily diary where all physical activities were additionally documented. All study procedures were approved by the local ethics committee of Hannover Medical School (Vote #7617) and all subjects provided informed written consent prior to the commencement of the study procedures.Blood samples were collected in S-Monovette® tubes containing ethylenediaminetetraacetic acid  or clot activator (for serum) at the time of hospital admission or at study inclusion  and at the follow-up (FU) visits after 12 months for the Rebirth 60Plus male and female subjects (age >60 years). Blood samples were also obtained from young male and female subjects (age 18–50 years) from a healthy population established by Hannover Unified Biobank (HUB). Plasma or serum was separated by centrifugation at 1500 rpm for 10 min and aliquots were stored at -80°C. Laboratory workup was performed as part of routine analysis by hospital laboratories for leukocytes, neutrophils, lymphocytes, platelets, CRP and LDH. PGE2 serum and plasma levels were measured using the prostaglandin E2 ELISA kit (abcam ab133021) according to the manufacturer’s protocol.2. Both media were supplemented with 10% fetal bovine serum, 2 mM glutamine, 0.1 mM non-essential amino acids and 1% Penicillin/Streptomycin. Calu-3 cells (4.5x105 cells/well) were seeded in collagen-coated 24-well plates. For infection, the SARS-CoV-2  ..37].+ B-cell acute lymphoblastic leukemia who developed high AB titers against COVID-19 after an initial recovery. However, the patient experienced a viral reactivation after she lost her COVID-19 AB following the administration of rituximab, cytarabine, and dasatinib for her leukemia, and experienced severe COVID‑19 pneumonia with lymphopenia and high inflammatory markers [As reported above, high PGE2 in COVID-19 serum impairs the B-cell mediated immune response at least in part by reducing PAX5. PAX5 expression is also necessary for the development of memory B-cells after follicular B-cells have encountered antigens , 47. In  markers . PGE2 no markers  and immu markers .in silico identified Taxifolin as a direct inhibitor of the SARS-CoV-2 main protease [Larix sibirica, [Based on the suspected crucial role of PGE2 for COVID-19 disease courses, we tested the potential of the PGE2 inhibitor Taxifolin, also known as dihydroquercetin, to limit SARS-CoV-2-induced PGE2 production in human lung cells . In agreprotease . Taxifolibirica, , 36. It ibirica,  and in tibirica, , 56. Moribirica, . TreatmeThus, known risk factors for severe COVID-19 disease such as age, sex and physical inactivity are associated with elevated PGE2 levels prior infection and may thereby contribute to a reduced immune response at the time of SARS-CoV-2 infection. In addition, the SARS-CoV-2 infection may further compromise the immune response by further upregulating PGE2 in those individuals with pre-existing higher PGE2 levels. Furthermore, it is known that also the exposure to high levels SARS-CoV-2 virus particles contribute to severe COVID-19 disease also in individuals with otherwise low risk factors (for example severe disease cases in nurses and physicians) . As we cIn conclusion, our data suggest that PGE2 production, either induced by SARS-CoV-2 infection or determined by endogenous and exogenous risk factors critically influences COVID-19 disease severity, . MechaniLimitations of our study include the limited numbers of blood samples from COVID-19 patients and that clinical data on COVID-19 patients, i.e. as C-reactive protein (CRP), lactate dehydrogenase (LDH), leukocytes normal count, neutrophils normal count, and lymphocytes were not available for all patients.PGE2 synthesis can be blocked by corticosteroids that inhibit the phospholipases or by NSAIDs that inhibit the cyclooxygenase. In this study, at the time of blood sampling a part of the COVID-19 patients with mild or severe disease were treated with corticosteroids or NSAIDs. Information on the use of NSAIDs or leukotriene modifiers were not available. PGE2 levels in those patients might be underestimated, since both medications may reduce PGE2 biosynthesis.Most individuals in the healthy elderly collective displayed age-related normal BMI and numbers in subgroup with increased or reduced BMI were too low to perform conclusive correlation analyses with PGE2 levels.Serum and plasma samples have to be stored at -80°C immediately to avoid degradation of PGE2 and to avoid further prostaglandin synthesis by COX-2. For the present study serum and plasma was immediately being processed, frozen and stored at -80°C.ex vivo platelet activation may alter plasma prostanoid concentrations, a feature that cannot be completely excluded.Venipuncture and S1 FigOzone correlation analysis of serum PGE2 levels with (A-E) BMI ((A) males: n = 40, Spearman r: -0.1485, P value: 0.3604; (B) males in normal range BMI 25–30: n = 24, Spearman r: -0.1231, P value: 0.5667; (C) males with a BMI >30: n = 9, Spearman r: 0.3167, P value: 0.4101 (D) males with a BMI <25: n = 7, Spearman r: -0.2143, P value: 0.6615 (E) females: n = 45, Pearson r: 0.03956, P value: 0.7964),  BW  and  body fat content  in  males and  females. (A-I) Ozone correlation, Spearman or Pearson correlation coefficients, two-tailed P value. Underlying data can be found in (TIFF)Click here for additional data file.S2 Figt-test. (B) The bar graph summarizes TNFa mRNA expression of SARS-CoV-2 infection in Calu-3 cells in cell culture lysates (n = 3 independent cell culture experiments). Data are presented as mean±SD, mock was set at 100%, *P<0.05 vs mock, #P<0.05 vs h.i., one-way ANOVA, Dunnett post hoc test. Underlying data can be found in (A) Heat-inactivation (h.i.) of PGE2 for 30 min at 70°C compared to untreated PGE2 (ctrl) from the same sample (n = 4). Data are presented as mean±SD, ctrl was set at 100%, one-sample (TIFF)Click here for additional data file.S3 Figt-test, n.s. (D) Mann-Whitney-U test, n.s. (E) mock was set at 100%, **P<0.01 vs mock, #P<0.05 vs h.i., one-way ANOVA, Bonferroni’s post hoc test. Underlying data can be found in The bar graph summarizes PGD2 content in supernatants of Calu-3 cells infected with SARS-CoV-2 and treated for 48 h with Taxifolin  compared with DMSO control (n = 10). The bar graphs summarize the mRNA expression of the proliferation markers (B) Ki67, (C) TOP2A and (D) TPX2 of Calu-3 cells treated with Taxifolin (100 μM) for 48 h (n = 7 for ctrl and PGE2 treated cells). (E) The bar graph summarizes PGD2 content in supernatants of Calu-3 cells infected with SARS-CoV-2 (n = 6) compared with untreated mock (n = 6) and heat-inactivated (h.i.) SARS-CoV-2 (n = 6) normalized to total RNA. (A-E) Data are presented as mean±SD,  unpaired two-tailed (TIFF)Click here for additional data file.S4 Figt-test. Underlying data can be found in Representative gel images of PTGER4 and B2M mRNA expression in pre-B-cell lines (A) 697 and (B) SUP-B15. (C) The bar graph summarizes the percentage of live cells of control treated and PGE2 (10 μM) treated 697 cells after 48h stimulation. Total cell number was set at 100%. (D-F) The bar graphs summarize the mRNA expression of the proliferation markers (C) Ki67, (D) TOP2A and (E) TPX2 of pre-B-cells 697 treated with PGE2 (10 μM) for 48 h (n = 5 for ctrl and PGE2 treated cells). (C-E) Data are presented as mean±SD, (C-E) n. s., **P<0.01 vs ctrl, unpaired two-tailed (TIFF)Click here for additional data file.S5 Figt-test and (B) **P<0.01, Mann-Whitney-U test. Underlying data can be found in The bar graph summarizes TNFa mRNA expression of (A) 697 or (B) SUP-B18 pre-B-cells treated with PGE2 (10 μM) after 48 h (n = 5). Control cells were treated with the solvent ETHO (n = 5). The bar graph summarizes IFNg mRNA expression of (C) 697 or (D) SUP-B18 pre-B-cells treated with PGE2 (10 μM) after 48 h (n = 5). Control cells were treated with the solvent ETHO (n = 4). (A-D) Data are presented as mean±SD,  n. s., **P<0.01 vs ctrl, unpaired two-tailed (TIFF)Click here for additional data file.S6 Fig(TIFF)Click here for additional data file.S7 Fig(TIFF)Click here for additional data file.S8 Fig(TIFF)Click here for additional data file.S1 DataAll numerical raw data are combined in a single excel file, “S1_Data.xlsx,” this file consists of several spreadsheets and each contains the data of 1 figure or table.(XLSX)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 TableBody mass index (BMI) was determined with BMI = bodyweight (BW) / squared height. Body weight, body height, BMI, body fat and activity were analyzed at BL and after 12M FU controlled exercise. Comparison between the groups BL vs 12M FU was performed using Student’s t-test for Gaussian distributed data (presented as mean ± SD) and the Mann-Whitney-U test where at least one column was not normally distributed (presented as median and interquartile range (IQR)). ***P<0.001, ****P<0.00001 BL vs 12M FU. Underlying data can be found in (DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.


Reversible phosphorylation is a major mechanism for regulating protein function and controls a wide range of cellular functions including responses to external stimuli. The plant-specific SNF1-related protein kinase 2s (SnRK2s) function as central regulators of plant growth and development, as well as tolerance to multiple abiotic stresses. Although the activity of SnRK2s is tightly regulated in a phytohormone abscisic acid (ABA)-dependent manner, recent investigations have revealed that SnRK2s can be activated by group B Raf-like protein kinases independently of ABA. Furthermore, evidence is accumulating that SnRK2s modulate plant growth through regulation of target of rapamycin (TOR) signaling. Here, we summarize recent advances in knowledge of how SnRK2s mediate plant growth and osmotic stress signaling and discuss future challenges in this research field. Arabidopsis thaliana  and [-(pS/pT)-x-x-x-x-(D/E)-] as the preferred motifs that SnRK2s phosphorylate within their substrates in Arabidopsis [Progress has been made in identifying proteins phosphorylated by subclass III SnRK2s. For example, bZIP-type transcription factors AREB/ABFs (ABA-RESPONSIVE ELEMENT-BINDING PROTEINS/ABRE-BINDING FACTORS) are phosphorylated by SnRK2s to induce ABA- or stress-responsive gene expression ,46 Figu. In guarL (GORK)  and K+ r+ influx . Additio+ influx . The acc+ influx ,15. Amon+ influx , whereas+ influx . The globidopsis .Physcomitrella patens genome encodes only four SnRK2 genes (PpSnRK2A/2B/2C/2D), all of which are categorized into subclass III [Ppsnrk2a/b/c/d quadruple knockout mutant displayed drastic ABA insensitivity and reduced osmotic stress tolerance, indicating that the roles of subclass III SnRK2s in abiotic stress signaling are evolutionarily conserved between bryophytes and angiosperms [There are several notable differences in the composition of SnRK2 in Arabidopsis compared to other plants. The subclass III-type SnRK2s are the most ancestral SnRK2 kinases that can be found in semiterrestrial algae ,54. The lass III . The PpsSRK2C confers drought tolerance in Arabidopsis transgenic plants [srk2dei), the Arabidopsis double mutant (srk2cf) did not showed remarkable phenotypic changes even under osmotic stress conditions [Selaginella tamariscina), it could be an intermediate molecule during the transition from subclass III SnRK2 in algae to subclass I SnRK2 in seed plants [In Arabidopsis, there are two subclass II SnRK2s (SRK2C/SnRK2.8 and SRK2F/SnRK2.7), both of which are strongly activated in response to osmotic stress but weakly to ABA  Figure . The firc plants . Howevernditions , indicatnditions . On the nditions . Given td plants .7G-cap, leading to exonuclease-mediated mRNA decay [srk2abgh) and artificial micro RNA (amiRNA)-mediated VCS-knockdown plants shows similar growth retardation under osmotic stress conditions, suggesting the common phenotype is due to misregulation of mRNA metabolism in response to osmotic stress [P. patens subclass III SnRK2s quadruple mutant (Ppsnrk2a/b/c/d), indicating that the functions of subclass I SnRK2s are not compatible with subclass III SnRK2s [Subclass I-type SnRK2s are found in most angiosperms, but not in bryophytes or algae. There are five members in the Arabidopsis genome , all of which except for SRK2J are rapidly activated by osmotic stress perception prior to ABA accumulation ,12 Figu. Unlike NA decay . Both thc stress . Given tc stress , subclasI SnRK2s . In conc2O2-mediated stomatal closure [2O2-mediated stomatal closure [SLAC1 transgene with mutations that produce a single substitution of Ser-59 to Ala (S59A), or the OST1-target site Ser-120 to Ala (S120A), rescued the slac1 knockout mutant phenotype. However, a transgenic line carrying a SLAC1 transgene with mutations that produce both Ser to Ala substitutions did not, suggesting that both CPK6- and OST1-mediated phosphorylation is essential for SLAC1 activation [In addition to the SnRK2 family, other protein kinases such as RECEPTOR-LIKE KINASES (RLKs), MITOGEN-ACTIVATED PROTEIN KINASES (MAPKs), CALCIUM-DEPENDENT PROTEIN KINASES (CDPKs/CPKs) contribute to plant responses to various abiotic stresses. For example, a plasma membrane localized receptor-like kinase GUARD CELL HYDROGEN PEROXIDE-RESISTANT 1 (GHR1) phosphorylates SLAC1 and is involved in both ABA- and H closure . Another closure . RALF1-F closure . MPK9 an closure . ABA-ind closure . MAPKKK2 closure . CPKs al closure . Notablytivation . In additivation ,71.As described above, a subset of subclass III SnRK2s, with C-terminal regions rich in aspartic acid, are activated in an ABA-dependent manner ,12,13. SThe ABA signaling “core components” PYL, PP2C and SnRK2 also associate with additional regulatory proteins. For example, ARABIDOPSIS EL1-LIKE (AEL) casein kinases phosphorylate PYLs to promote their ubiquitination and degradation, resulting in suppressed ABA responses . CYTOSOLZea mays CASEIN KINASE 2 (CK2) phosphorylates the ABA-box of ZmOST1, increasing its binding to PP2C and triggering ZmOST1 degradation [In addition, to indirect regulation of SnRK2 activity by controlling PYL-PP2C module, SnRK2s are also directly regulated by several mechanisms. GLYCOGEN-SYNTHASE KINASE-3 (GSK3)-like kinase BRASSINOSTEROID INSENSITIVE 2 (BIN2), a central negative regulator in brassinosteroid signaling, activates SRK2D and SRK2I through phosphorylation on Thr-181 in SRK2D and Thr-180 in SRK2I to enhance ABA signaling . Leucineradation . E3 ubiqradation  and HIGHradation  attenuatradation ,94. A plradation . SRK2E cradation  but actiradation  in guardNCED3 promoter to enhance its expression [NGA1 that result in phospho-mimetic substitutions of NGA1 caused altered transcriptional activity [NCED3 expression to induce stomatal closure [Another important issue is how plants induce ABA biosynthesis after osmotic stress perception  and recpression . Such osactivity . Howeveractivity . Root-de closure . In addi closure . FurtherOryza sativa [Nicotiana tabacum [Glycine max [Physcomitrella patens [pp2c mutants under optimal growth conditions [in planta [All SnRK2 protein kinases in Arabidopsis, except for SRK2J, are activated in response to osmotic stress  and thisa sativa , Nicotia tabacum , Glycinecine max  and Physa patens . Althouga patens ,17,18,19a patens ,40, india patens ,74 and ia patens . Howevera patens . Second,nditions ,95, indiP. patens. In this research, ABA-insensitive mutants were isolated from a UV-irradiated mutant population and one of these mutants named AR7 showed drastically reduced ABA sensitivity, osmotic and freezing tolerance [ABA AND ABIOTIC STRESS-RESPONSIVE RAF-LIKE KINASE (ARK)” [ABA NON-RESPONSIVE (ANR)” [CONSTITUTIVE TRIPLE RESPONSE 1-LIKE (CTR1L)” [A breakthrough in understanding of SnRK2 activation was made in the moss olerance . The mutE (ARK)” , also knE (ANR)”  or “CONS(CTR1L)” . SnRK2 a(CTR1L)” , suggest(CTR1L)” . Large-s(CTR1L)”  and ABA-(CTR1L)”  regulateAtark TKO) showed reduced ABA-sensitivity at the cotyledon greening stage and impaired responses to osmotic stress, including stress-responsive gene expression and stomatal closure. Among SRK2E, SRK2C and SRK2G, AtARK1 prefers subclass III SRK2E as a substrate and directly phosphorylated the two serine residues (Ser-171 and Ser-175) in SRK2E in vitro. Intriguingly, in Atark TKO, osmotic stress-induced activation of subclass III SnRK2s was clearly reduced, whereas ABA-induced SnRK2 activation was still observed, indicating that AtARKs are required specifically for osmotic stress-induced SnRK2 activation. In addition, 60–70% of osmotic stress- and AREB/ABF-regulated genes [amiRNA lines, both T-DNA insertions and CRISPR/Cas9 mediated-mutations were used to generate mutations in genes encoding Raf3/M3Kδ1, Raf4/M3Kδ7/AtARK1 and Raf5/M3Kδ6/AtARK2. The resulting triple mutant showed reduced ABA sensitivity and osmotic stress responses and less SnRK2 activity. Notably, phosphorylation of Ser-171 in SRK2E by B3-Raf kinases is necessary for autophosphorylation-mediated SRK2E re-activation after dephosphorylation by PP2Cs [raf18/20/24) displayed the same growth retardation phenotype as a quadruple srk2abgh mutant that lacks all subclass I SnRK2s. Additionally, Raf20 directly phosphorylated the specific serine residue within the activation loop of SRK2G (Ser-154) and the osmostress-induced subclass I SnRK2 activation was significantly reduced in raf18/20/24, suggesting the essential roles of B4 Raf-like protein kinases in the activation of the subclass I SnRK2–VARICOSE pathway [100 and OK130. Using a phosphoproteomic approach and CRISPR/Cas9 to generate high-order raf mutants, the authors revealed that B2/B3-Raf kinases correspond to OK100 and are responsible for the activation of subclass III SnRK2s, while B4-Raf kinases correspond to OK130 and are responsible for the activation of subclass I SnRK2s under osmotic-stress conditions  are less sensitive to ABA at seed germination and cotyledon greening and in ABA-responsive gene expression [In 2020, four groups independently reported that group B Raf-like protein kinases regulate SnRK2s in Arabidopsis ,22,23,24ed genes  overlapped genes , demonstby PP2Cs , were id pathway   exists in the Arabidopsis genome and the double mutant of AtRAPTORs (raptor1a/b) shows normal embryonic development but is unable to maintain post-embryonic seedling growth [LST8 (LST8-1 and LST8-2), but only LST8-1 is significantly expressed [lst8-1 mutant displays modest dwarf and early senescence phenotypes [In addition to the induction of stress tolerance under unfavorable conditions, it is important to understand how plants maintain a balance between growth and stress responses as well. Under abiotic stress conditions such as drought, plants spontaneously suppress growth to cope with limited energy-availability conditions caused by reduced photosynthetic rate . That iso lethal . Two-copg growth . Arabidoxpressed . The lstenotypes . Recent enotypes  , are more sensitive to exogenous ABA treatment compared to wild-type plants [Recently, a reciprocal negative feedback loop between TOR signaling and ABA signaling has been shown to regulate a balance between growth and protective stress responses in Arabidopsis. Under optimal growth conditions, TOR phosphorylates ABA receptor PYLs at a conserved serine residue in PYL family (corresponding to Ser-114 in PYL4 and Ser-119 in PYL1) . This phe plants ,127. On e plants . Therefoe plants . In thate plants . Under se plants  -induced activation of SRK2E/OST1 in guard cells was observed, a basal level of OST1/SRK2E activity is required for the both CO2- and MeJA-induced stomatal closure [In addition to the regulation of TOR complex, recent studies have reported that SnRK2 can modulate some cellular processes under non-stressed conditions. For example, subclass III SnRK2s modulate metabolism and leaf growth under optimal growth conditions through regulation of the tricarboxylic acid (TCA) cycle . In addi closure ,130. Int closure . FurtherP. patens, Arabidopsis group B Raf-like protein kinases (group B Raf) were identified as essential components that integrate early osmotic stress signaling into osmotic stress-induced SnRK2 activation. The next challenge is to identify upstream components that activate group B Raf during early osmotic stress responses. Notably, P. patens ARK/CTR1L [P. patens ARK is rapidly activated after ABA treatment [PYLs mutant, indicating that PYL-mediated ABA signaling antagonizes ABA-independent SnRK2 activity [Under osmotic stress conditions, SnRK2-type protein kinases are activated in the both ABA-dependent and -independent manner and play essential roles in the induction of multiple osmotic stress responses. In addition to the mechanisms of PYL-PP2C complex-mediated ABA-dependent activation, additional mechanisms of osmotic stress-induced or ABA-independent SnRK2 activation has been unveiled recently. Following the identification and characterization of ARK in RK/CTR1L  and ArabRK/CTR1L  physicalreatment , while nreatment ,117. Thiactivity . Additioactivity . Such a activity , BAK1 [8activity  and RPK1activity , may alsraptor1b and lst8-1, as well as wild-type seedlings treated with the TOR inhibitor AZD-8055 [In addition to the roles of SnRK2 during osmotic stress responses, recent studies also have highlighted the roles of SnRK2 in the plant growth regulation under optimal conditions ,26. ThesAZD-8055 , accumul


Genomic studies of this cell line showed focal amplification of the TBX3 locus at 12q24 which may underlie its aberrant expression. In addition, promoter analysis and comparative expression profiling of HL cell lines followed by knockdown experiments revealed overexpressed transcription factors E2F4 and FOXC1 and chromatin modulator KDM2B as functional activators. Furthermore, we identified repressed target genes of TBX3 in HL including CDKN2A, NFKBIB and CD19, indicating its respective oncogenic function in proliferation, NFkB-signaling and B-cell differentiation. Taken together, we have revealed a lymphoid TBX-code and used it to identify an aberrant network around deregulated T-box gene TBX3 in HL which promotes hallmark aberrations of this disease. These findings provide a framework for future studies to evaluate deregulated T-box genes in lymphoid malignancies.T-box genes encode transcription factors which control basic processes in development of several tissues including cell differentiation in the hematopoietic system. Here, we analyzed the physiological activities of all 17 human T-box genes in early hematopoiesis and in lymphopoiesis including developing and mature B-cells, T-cells, natural killer (NK)-cells and innate lymphoid cells. The resultant expression pattern comprised six genes, namely EOMES, MGA, TBX1, TBX10, TBX19 and TBX21. We termed this gene signature TBX-code which enables discrimination of normal and aberrant activities of T-box genes in lymphoid malignancies. Accordingly, expression analysis of T-box genes in Hodgkin lymphoma (HL) patients using a public profiling dataset revealed overexpression of EOMES, TBX1, TBX2, TBX3, TBX10, TBX19, TBX21 and TBXT while MGA showed aberrant downregulation. Analysis of T-cell acute lymphoid leukemia patients indicated aberrant overexpression of six T-box genes while no deregulated T-box genes were detected in anaplastic large cell lymphoma patients. As a paradigm we focused on TBX3 which was ectopically activated in about 6% of HL patients analyzed. Normally, TBX3 is expressed in tissues like lung, adrenal gland and retina but not in hematopoiesis. HL cell line KM-H2 expressed enhanced TBX3 levels and was used as an  Lymphopoiesis encompasses the generation of all types of lymphocytes and starts with hematopoietic stem cell (HSC)-derived common lymphoid progenitors (CLP) in the bone marrow. According developmental processes are regulated mainly at the transcriptional level and specific transcription factors (TFs) control the development of lymphoid lineages –4: for eTFs are classified systematically according to similarities in sequence and structure . HomeoboThe clustered homeobox genes belong to the HOXL subclass and are expressed within the head and branchial region of the embryo in a particular pattern termed HOX-code . ThroughHere, we followed the same approach of expression analysis used for the generation of the lymphoid NKL- and TALE-codes for the study of T-box genes in early hematopoiesis and in lymphopoiesis. The human genome contains 17 T-box genes which are arranged in five subfamilies: namely T (containing TBXT and TBX19), Tbx1 , Tbx2 , Tbx6  and Tbr1 . This classification reflects the evolutionary history of these genes in metazoa . Some meT-ALL cells are derived from developing thymic T-cells while the origin of the malignant ALCL cells is less clear but they may derive from innate lymphoid cell type 3 (ILC3) ,36. Hodgwww.ebi.ac.uk/arrayexpress) via E-MTAB-7721 [https://github.com/pinin4fjords/shinyngs).For our screening approach of normal cell types we exploited both expression profiling and RNA-sequencing data which were obtained from Gene Expression Omnibus  as described previously ,20–23. TTAB-7721 . Corresp6 cells by electroporation using the EPI-2500 impulse generator  at 350 V for 10 ms. Electroporated cells were harvested after 20 h cultivation. Trichostatin A (TSA) was obtained from Sigma  and added for 20 h at a final concentration of 10 μg/ml.Cell lines were held by the DSMZ  and cultivated as described previously . All celProliferation and apoptosis were analyzed using the IncuCyte S3 Live-Cell Analysis System . For detection of apoptotic cells we used the IncuCyte Caspase-3/7 Green Apoptosis Assay diluted at 1:2000 (Essen Bioscience). Live-cell imaging experiments were performed twice with fourfold parallel tests. Statistical significance was calculated for the last timepoint by T-Test.Total RNA was extracted from cultivated cell lines using TRIzol reagent . Primary human total RNA derived from spleen, lung and retina was purchased from Biochain/BioCat , and RNA from peripheral CD19-positive B-cells and CD3-positive T-cells from Miltenyi Biotec . cDNA was synthesized using 5 μg RNA, random priming and Superscript II (Invitrogen). Real time quantitative (RQ)-PCR analysis was performed using the 7500 Real-time System and commercial buffer and primer sets . For normalization of expression levels we quantified the transcripts of TATA box binding protein (TBP).GAGCCTCTCCATGAGAGATCCG-3´, TBX3-rev 5´-TGATCCATGATCGGCTTGGCCAGG-3´, TBX5-for 5`-AGCGCACAGCAGAGGAGGTGTG-3´, TBX5-rev 5´-GCTGCTCCTAGCAGGGAAGCCG-3´, MEF2C-for 5´-GCAGGAATTTGGGAACTGAG-3´, MEF2C-rev 5´-CCCATAGTCCCCGTTTTTCT-3´.For copy number quantification we extracted genomic DNA using the High Pure PCR Template Preparation Kit . Analyses of TBX3 and TBX5 gene copy numbers were performed with reference to the MEF2C control. The following oligonucleotides  were used: TBX3-for 5`-Quantitative analyses were performed as biological replicates and measured in triplicate. Standard deviations are presented in the figures as error bars. Statistical significance was assessed by Student´s T-Test (two-tailed) and the calculated p-values indicated by asterisks .Western blots were generated by the semi-dry method. Protein lysates from cell lines were prepared using SIGMAFast protease inhibitor cocktail (Sigma). Proteins were transferred onto nitrocellulose membranes  and blocked with 5% dry milk powder dissolved in phosphate-buffered-saline buffer (PBS). The following antibodies were used: alpha-Tubulin , TBX3 , E2F4 , BCL2L1 , and CDKN2A . For loading control blots were reversibly stained with Poinceau (Sigma) and detection of alpha-Tubulin (TUBA) performed thereafter. Secondary antibodies were linked to peroxidase for detection by Western-Lightning-ECL . Documentation was performed using the digital system ChemoStar Imager .Immuno-cytology was performed as follows: cells were spun onto slides and subsequently air-dried and fixed with methanol/acetic acid for 90 s. Antibodies were diluted 1:20 in PBS containing 5% BSA and incubated for 30 min. Washing was performed 3 times with PBS. Preparations were incubated with secondary antibody (diluted 1:100) for 20 min. After final washing cells were mounted for nuclear in Vectashield , containing DAPI. Documentation of subcellular protein localization was performed using an Axio-Imager microscope  configured to a dual Spectral Imaging system .Flow cytometry was performed for quantification of CD19 protein on the cell surface. Cells were washed and incubated with mouse anti-human CD19 mAB  or with the isotope-matched control mouse immunoglobulin (BD Biosciences) for 30 min at 4°C. Subsequently, cells were treated with FITC conjugated anti-mouse secondary antibody  and Propidium Iodide (Sigma). Labeled cells were analyzed on a FACS Calibur (BD Biosciences) using CellQuest Pro software.For genomic profiling genomic DNA of HL cell lines was prepared by the Qiagen Gentra Puregene Kit (Qiagen). Labelling, hybridization and scanning of Cytoscan HD arrays was performed by the Genome Analytics Facility located at the Helmholtz Centre for Infection Research , according to the manufacturer´s protocols . Data were interpreted using the Chromosome Analysis Suite software version 3.1.0.15 (Affymetrix) and copy number alterations determined accordingly.HindIII/BamHI and EcoRI sites of expression vector pcDNA3 downstream of the CMV enhancer. Oligonucleotides used for the amplification of the regulator were obtained from Eurofins MWG. Their sequences were as follows: CDKN2A-for 5´-CTAAGCTTCCTGAGTAGCTGGAATTACACACG-3´, CDKN2A-rev 5´-TAGGATCCGTGCCACACATCCTAAGCTAATAC-3´. Introduced restriction sites used for cloning are underlined. Constructs were validated by sequence analysis (Eurofins MWG). Transfections of plasmid-DNA into NIH-3T3 cells (DSMZ) were performed using SuperFect Transfection Reagent (Qiagen). Commercial HOXA9 and TBP assays were used for RQ-PCR to quantify the spliced reporter-transcript, corresponding to the regulator activity (Thermo Fisher Scientific).For creation of reporter gene constructs we combined a reporter with a regulatory genomic fragment derived from the upstream region of CDKN2A. PCR products of the corresponding genomic region (regulator) and of the HOXA9 gene, comprising exon1-intron1-exon2 (reporter), were cloned into respective S1 Fig and summarized in Fig 1. We detected expression of six T-box genes in the hematopoietic entities analyzed, comprising EOMES, MGA, TBX1, TBX10, TBX19 and TBX21, and termed the combined T-box gene signature TBX-code.To identify normal expression patterns for all 17 human T-box genes in early hematopoiesis and lymphopoiesis several public datasets were analyzed. Dataset GSE69239 contains RNA-seq data for HSCs, lymphoid and myeloid primed progenitors (LMPP), CLP, B-cell progenitors (BCP) and T-cell progenitors of the double negative (DN) and double positive (DP) stages in addition to mature single positive CD4+ and CD8+ T-cells . Stages We found T-box gene activities in all lymphoid lineages. The TBX-code contains genes from all T-box subfamilies except Tbx2. The numbers of expressed genes in single entities ranged from zero to five. Stem and early progentor cells expressed MGA only. The B-cell lineage showed MGA and TBX21 activities. The greatest number of expressed T-box genes was detected in the T-cell lineage and included all six members of the TBX-code. Mature NK-cells and ILCPs expressed TBX21 and EOMES, ILC1 only EOMES while no T-box gene activities were detected in ILC2 and ILC3. Thus, T-box genes show a specific expression pattern which may underlie regulation of normal cell differentiation in lymphopoiesis. By the same token, aberrations of this TBX-code may promote generation of leukemia and lymphoma.Table 1). Furthermore, we analyzed T-box gene deregulation in 117 T-ALL and 16 ALCL patients using public datasets GSE26713 and GSE14879, respectively [Table 1). Thus, our T-box gene expression results show significant differences between lymphoid malignancies, indicating that deregulated T-box genes may play a role in the pathogenesis of HL and T-ALL but not of ALCL.Next, we proceeded to analyze expression of all T-box genes in patients with diverse lymphoid malignancies for comparison with the newly established TBX-code. Public dataset GSE12453 contains gene expression profiling data of 12 classical and 5 nodular lymphocyte predominant HL patients and of normal developing B-cells used as controls . This coectively ,44. In TTable 1). Thus, the most aberrantly expressed T-box genes detected in HL patients were also conspicuously overexpressed in HL cell lines. Of these, TBX3 belongs to the subfamily Tbx2, whose members are hitherto unrepresented in the TBX-code. TBX3 is an oncogene reported in solid tumors, including breast cancer, fibrosarcoma, melanoma and lung cancer but not as yet in hematopoietic neoplasms [Accordingly, we then focused on the role of deregulated T-box genes in HL. Expression analysis of six HL cell lines employing LL-100 RNA-seq data (E-MTAB-7721) demonstrated overexpression of EOMES in L-428 and L-1236, TBX3 and TBX5 in KM-H2, TBX18 in SUP-HD1, TBX19 in HDLM-2 and L-1236, and TBXT in L-1236 . Comparison of TBX3 transcript levels from KM-H2 with representative primary samples demonstrated a similar gene activity in lung and lower activities in spleen and retina. As shown by our screening data, no TBX3 expression was detected by RQ-PCR in primary peripheral B- and T-cells from healthy donors . Western blot and immuno-fluorescence-analysis confirmed TBX3 expression in KM-H2 at the protein level . At the subcellular level TBX3 was detected in nuclear speckles and uniform in the cytoplasma, suggesting that transcriptional activity of TBX3 is restricted to specific genes and may be controlled by nuclear import.KM-H2 expressed elevated levels of TBX3 as indicated by RNA-seq data and confirmed by RQ-PCR . TBX3 is located together with its gene neighbor TBX5 at chromosomal position 12q24 in a tandem arrangement, reflecting their evolutionary history [Fig 3A and 3B). Genomic Q-PCR analysis confirmed this gain which was significantly higher for TBX3 than TBX5 . Thus, the aberrant expression levels observed for TBX3 and TBX5 reflected their copy number gains which, thus, represent a major oncogenic mechanism of activation in KM-H2.KM-H2 cells overexpressed both TBX3 and TBX5, indicating the possible mechanistic connection of their activation although the expression level of TBX3 strongly exceeded TBX5 . The TBX3 gene in addition to its 10 kb upstream region contain several TF binding sites including those for STAT5, GATA3 and E2F. These TFs have been shown to play an oncogenic role in HL and were, therefore, selected for more detailed analysis [Fig 4B). E2F4 showed elevated expression levels in KM-H2 as demonstrated by RNA-seq data and confirmed by RQ-PCR and Western blot analysis . Moreover, although the locus of E2F4 at 16q22 escaped chromosomal rearrangement (see above) it was also amplified in KM-H2 , indicating its likely activation by genomic aberration. Finally, public chromatin immuno-precipitation (ChIP) data from the ENCODE project show binding of E2F4 at TBX3 , supporting its direct impact in transcriptional regulation.To identify TFs involved in TBX3 activation we screened its promoter region for potential binding sites using the UCSC genome browser . Among the top-1000 overexpressed genes in KM-H2 we detected in addition to TBX3 and TBX5 the genes OTX2, FOXC1 and KDM2B respectively encoding two oncogenic TFs and a chromatin modulator. RNA-seq data confirmed their elevated transcript levels in KM-H2 . Afterwards, we performed siRNA-mediated knockdowns of these regulatory candidates, demonstrating their downregulation and their effects on TBX3 expression by RQ-PCR analysis . The results showed that FOXC1 activated TBX3 strongly, KDM2B weakly while OTX2 showed no regulatory impact. FOXC1 has been reported as an aberrantly expressed TF in KM-H2 which is activated via genomic amplification at 6p25 [Fig 5C). Thus, both TBX3 itself and three identified activators, namely E2F4, FOXC1 and KDM2B, were rearranged by genomic amplification in KM-H2. Finally, analysis of expression profiling data from HL patients (GSE12453) indicated significantly elevated transcript levels of E2F4 and FOXC1 but not of KDM2B . These results show clinical relevance for increased expression of E2F4 and FOXC1 in this malignancy.To identify additional activators of TBX3 we performed comparative expression profiling analysis of HL cell lines. We compared KM-H2 versus DEV, HDLM-2, L-428, L-540, L-1236, SUP-HD1 and U-HO1, to reveal differentially expressed genes ( at 6p25 . InteresS1 Table). Because TBX3 has been described as repressor we looked for genes conspicuously downregulated in KM-H2 [Fig 6A). SiRNA-mediated knockdown of TBX3 in KM-H2 raised CDKN2A expression, showing its suppressive role in HL . Furthermore, an established reporter gene assay using an isolated genomic fragment of the CDKN2A promoter region from KM-H2 which contains a TBX3 consensus site at -1293 bp (AGGTGTGA) confirmed the repressive impact of TBX3 on this gene . In addition, functional analysis by live-cell-imaging of TBX3 performed after its knockdown in KM-H2 cells showed significantly reduced proliferation . Thus, TBX3 promoted proliferation in HL cell line KM-H2 probably via CDKN2A suppression. Additional live-cell imaging analysis of KM-H2 cells treated for siRNA-mediated knockdown indicated that TBX3 also inhibited apoptosis . The same experimental setting was performed after knockdown of E2F4, demonstrating similar effects in proliferation and apoptosis which may be operated by its target TBX3 .To identify potential target genes of TBX3 we reanalyzed our comparative expression profiling data . Activated NFkB-signaling represents a hallmark of HL. Many NFkB factors and their regulators are aberrantly expressed in HL and mediate survival of the malignant cells [Fig 6E). Thus, repression of NFkB-inhibitor NFKBIB by TBX3 forms an additional mechanism of aberrant NFkB-pathway activation in HL which may operate anti-apoptotically in KM-H2.To examine the underlying mechanism of TBX3 in mediation of apoptosis we focused on BCL2L1 and the NFkB-pathway. BCL2L1  encodes an anti-apoptotic protein of the BCL2-family which is regulated by alternative splicing, a process reportedly influenced by TBX3 . Howevernt cells . Here, wnt cells . KnockdoFig 6F). However, flow cytometry data showed no alteration of CD19 expression at the protein level in knockdown-treated KM-H2 cells . In contrast, forced expression of TBX3 in the CD19-positive normal B-cell line NC-NC resulted in slightly elevated expression of CD19 at the RNA level which was probably too weak for detection at the protein level . These results show that the transcriptional regulation of CD19 differs between HL and normal B-cells. Therefore, the presence of particular cofactors may influence the activity of TBX3 in abnormal B-cells. Consistently, treatment of KM-H2 with histone deacetylase inhibitor TSA resulted in increased transcription of CD19 , demonstrating an inhibitory impact of these chromatin-modifying cofactors. TF binding site analysis of the upstream regions of NFKBIB and CD19 revealed TBX consensus sequences at -3441 bp and -1758 bp, respectively, suggesting that TBX3 regulates the transcription these genes directly.Suppression of B-cell differentiation via deregulation of developmental TFs is an additional hallmark of HL –40. To e), supporting the above findings of oncogenic TBX3 activity in HL. Taken together, our functional data indicate that TBX3 activated proliferation and NFkB-signaling and inhibited differentiation, and is thus to be regarded as a potent oncogene in HL subsets.Finally, gene set annotation analysis of the top-1000 downregulated genes in KM-H2 versus control HL cell lines revealed several GO-terms, including regulation of growth, regulation of apoptotic signaling pathway, and regulation of B-cell differentiation . This code covers physiological activities for six T-box genes and serves to illuminate regulatory processes in hematopoiesis. Furthermore, the TBX-code allows the identification of novel T-box oncogenes which may prove useful for diagnostics and tenable identification of therapeutic targets in lymphoid malignancies.Comprehensive expression analysis of T-box genes in early hematopoiesis and lymphopoiesis revealed a specific gene signature therein which we have termed TBX-code 2 and HDACs to inhibit its gene targets ,79. PRCsFig 7. Furthermore, our data indicate that TBX3 performs these repressive impacts by recruitment of histone deacetylases, as demonstrated in breast cancer cells [According to its documented role as transcriptional repressor, we report for TBX3 in HL cell line KM-H2 three suppressed genes bearing consensus T-box binding sites in their upstream regions. The identified target genes CDKN2A, NFKBIB and CD19 are displayed in summarizing er cells . Functioer cells ,85,86. Ter cells ,87,88. TTaken together, we have established the TBX-code for early hematopoiesis and lymphopoiesis and used it to evaluate aberrant T-box gene activities in the lymphoid malignancies HL, ALCL and T-ALL. We have shown TBX3 to be ectopically expressed in HL and documented its role as a potent oncogene in subsets of patients.S1 FigAnalyses of five public datasets revealed activities of particular T-box genes in early lymphopoiesis, T-cell and B-cell development, mature lymphocytes, and mature and progenitor ILCs, using (A) RNA-seq dataset GSE69239 for HSC, LMPP, CLP, BCP, DN T-cells, DP T-cells, CD4+ T-cells and CD8+ T-cells, (B) gene expression profiling dataset GSE56315 for naïve, germinal centre, memory and plasma B-cells, (C) gene expression profiling dataset GSE72642 for NK-cells, CD4+ T-cells, CD8+ T-cells, and B-cells, and (D) RNA-seq datasets GSE124474 and E-MTAB-8494 for ILC1, ILC2, ILC3 and ILCP.(TIF)Click here for additional data file.S2 Fig(A) Analysis of T-box gene activities in HL patients using expression profiling datasets GSE12453 revealed six genes overexpressed in subsets of HL patients (red). Normal B-cells serve as controls and are indicated. (B) Analysis of T-box gene activities in T-ALL patients using expression profiling datasets GSE26713 revealed six genes overexpressed in subsets of T-ALL patients (red). (C) Analysis of T-box gene activities in ALCL patients using expression profiling datasets GSE14879 showed absence of deregulated genes in ALCL patients.(TIF)Click here for additional data file.S3 FigTranscript levels of 16 T-box genes in 100 leukemia/lymphoma cell lines using RNA-seq dataset E-MTAB-7721. Note, TBX22 is not expressed and therefore omitted. HL cell lines are indicated.(TIF)Click here for additional data file.S4 FigTranscript levels of TBX3-activators E2F4, FOXC1 and KDM2B in HL patients were compared to normal B-cell entities as controls and are visualized as boxplots. Statistical significance was calculated by T-test. The according p-values are indicated, showing significant differences for E2F4 and FOXC1.(TIF)Click here for additional data file.S5 FigLive-cell-imaging of KM-H2 cells treated for siRNA-mediated knockdown of E2F4 showed that E2F4 promoted proliferation  and inhibited apoptosis .(TIF)Click here for additional data file.S6 Fig(A) Flow cytometry analysis of CD19 in KM-H2 treated for TBX3 knockdown. (B) Flow cytometry analysis of CD19 in NC-NC treated for TBX3 overexpression.(PDF)Click here for additional data file.S7 Fig(TIF)Click here for additional data file.S8 Fig(TIF)Click here for additional data file.S9 Fig(TIF)Click here for additional data file.S10 Fig(TIF)Click here for additional data file.S11 Fig(TIF)Click here for additional data file.S12 Fig(TIF)Click here for additional data file.S13 Fig(TIF)Click here for additional data file.S14 Fig(TIF)Click here for additional data file.S1 TableGene expression profiling data of eight HL cell lines are indicated, highlighting KM-H2 by a gray background. Line DH451SU gives medium expression levels for control cell lines DEV, HDLM-2, L-428, L-540, L-1236, SUP-HD1 and U-HO1. Line K-DH451SU gives the calculated differential gene expression levels between KM-H2 and the controls.(XLS)Click here for additional data file.S2 TableExpression profiling analysis of HL cell line KM-H2 in comparison to seven control cell lines revealed differentially expressed genes see . The top(TIF)Click here for additional data file.


Biochemical analyses demonstrated that RACK1 interacts with POE and ERK to promote ERK degradation. These results suggest that RACK1 plays a key role in polyQ pathogenesis by promoting POE-dependent degradation of ERK, and implicate RACK1/POE/ERK as potent drug targets for treatment of polyQ diseases.Polyglutamine diseases are neurodegenerative diseases caused by the expansion of polyglutamine (polyQ) tracts within different proteins. Although multiple pathways have been found to modulate aggregation of the expanded polyQ proteins, the mechanisms by which polyQ tracts induced neuronal cell death remain unknown. We conducted a genome-wide genetic screen to identify genes that suppress polyQ-induced neurodegeneration when mutated. Loss of the scaffold protein RACK1 alleviated cell death associated with the expression of polyQ tracts alone, as well as in models of Machado-Joseph disease (MJD) and Huntington’s disease (HD), without affecting proteostasis of polyQ proteins. A genome-wide RNAi screen for modifiers of this  Drosophila for mutations that alleviate polyQ cytotoxicity. We found that a mutation in the gene rack1, which encodes a scaffold protein, suppressed polyQ-induced neurodegeneration when polyQ tracts were expresses, and in models of HD and MJD. Importantly, this effect was seen without clearance of the polyQ proteins. Further, we found that RACK1 binds both the E3 ubiquitin ligase POE and ERK, thereby promoting ERK degradation. These data suggest that the RACK1/POE/ERK pathway is involved in pathogenesis of polyQ diseases, and identify RACK1/POE/ERK as potential therapeutic targets for treating polyQ diseases.Polyglutamine (polyQ) diseases are neurodegenerative diseases caused by the expansion of glutamine repeats in specific proteins. After >100 years of effort trying to decipher the pathogenesis of polyQ diseases, the mechanisms by which these mutant proteins induced neuronal death are still unknown. Here, we performed a genome-wide genetic screen in  A class of neurodegenerative disorders are cause by the expansion of CAG repeats within associated genes, resulting in polyglutamine (polyQ) insertions. There are currently nine types of polyQ diseases, including spinocerebellar ataxias (SCAs) and Huntington’s disease (HD) , 2. One huntingtin (htt) , 49ufd4 poe or kcmf1 (or overexpressing USP47) alleviated MJD-Q78-induced photoreceptor cells loss. Knocking down ufd4 had no effect  increased ERG amplitude for MJD-Q78 flies, whereas co-expression of rack1RNAi with poeRNAi, kcmf1RNAi, or USP47 completely rescued ERG amplitude and transients for MJD-Q78 flies  induced extra wing vein material within the posterior compartment, a phenotype consistent with erk activation  weakened the interaction between POE and ERK  UAS-mCD8-GFP attP2 (dcp-1RNAi), (13) P[TRiP.HMC06381]attP40 (znf598RNAi), (14) P[TRiP. HMS01224]attP2 (p38aRNAi), (15) M(vas-int.Dm)ZH-2A;M(3xP3-RFP.attP)ZH-86Fb, (16) M(vas-int.Dm)ZH-2A;M{3xP3-RFP.attP}ZH-51C, (17) w1118, (18) rack11.8FRT40A/CyO. The following fly RNAi strains were obtain from the Tsinghua Fly Center: (1) P[TRiP. HMS00739]attP2 (poeRNAi), (2) P[TRiP. HMS00511]attP2 (kcmf1RNAi), (3) P[TRiP. JF01366]attP2 (erkRNAi), (4) P[TRiP. SH05382.N]attP40 (rprRNAi), (5) P[TRiP. SH05271.N]attP2 (decayRNAi), (6) P[TRiP. JF02641]attP2 (pkc53eRNAi), (7) P[TRiP. GL00140]attP2 (p38bRNAi), (8) P[TRiP. SH05212.N]attP2 (jnkRNAi), (9) P[TRiP. HMS00758]attP2 (droncRNAi). The P[KK109073]VIE-260B (rack1RNAi) and P[GD12135]v27858 (rack1RNAi) flies were obtained from Vienna Drosophila Resource Center ). S2 cells were pre-treated with dsRNA against GFP or rack1 for four days, and then transfected with plasmids for two days. To inhibit proteasome activity, cells were treated with 5 μM MG132 for two days. Immunoprecipitations were performed with mCherry beads (Chromotek) and GFP beads (Chromotek). The bound proteins were analyzed by western blotting against Rabbit GFP antibodies , Mouse FLAG antibodies , Rat HA antibodies , Rabbit Myc antibodies , and Rabbit mCherry antibodies .S2 cells were grown at 26°C in Schneider’s Drosophila medium (Sigma- Aldrich) with 10% fetal bovine serum (Gibco BRL), dsRNA was synthesized reported . PlasmidQuantitative RT-PCR was performed as described . Total RThe following primers were used for this study: ERK: forward, 5′-TGCACATCCCTATTTAGAGCAA-3′; reverse, 5′-AATGGCACTTCAGCGACAGCC-3′; Rpl32: forward, 5′-GCCGCTTCAAGGGACAGTATCTG-3′; reverse, 5′-AAACGCGGTTCTGCATGAG-3′; RACK1: forward, 5′-TCAGGTGGCAAGGACTCCAAG-3′; reverse, 5′-GCACAGGGCGTTGATGATGTCGTT-3′; POE: forward, 5′-TCAGCAAGCTACATGACCTTAGA-3′; reverse, 5′-TGCACAAGTCGAATGTTCCT-3′; KCMF1: forward, 5′-AATACAAACAGAACGCTAGTGCAG-3′; reverse, 5′-TGTCGGGAATTTGATTTGTG-3′; UFD4: forward, 5′-AATTACCAGGGACCGTAGAGG-3′; reverse, 5′-TTCGCGTCCTCCATAGGAT-3′.8 cells were pelleted and washed once with cold PBS containing 0.1 mg/ml CHX. The cells were then lysed in polysome lysis buffer , and 1 mM PMSF). The lysates were centrifuged at 12,000 x g for 10 min at 4°C and 1/10 of the supernatant was removed as input sample. The remainder of each supernatant was loaded on a 20%–50% sucrose gradient prepared in PLB (without Triton X-100) and resolved by centrifugation in an SW41 rotor (Beckman) at 38,000 rpm for 3 h at 4°C. Fractions were collected while monitoring the absorbance at 254 nm. RNA was prepared from each fraction and assayed by quantitative RT-PCR.Polysome fractionation was performed as described . S2 celln numbers and the statistical test performed, can be found in the corresponding figure legend. Data are presented as mean ± standard error of the mean (SEM). Statistical analysis was performed using Prism 6 software. Student’s t test was used for datasets with a normal distribution and a single intervention. One-way ANOVA was performed with Tukey’s post hoc test for multiple comparisons, and two-way ANOVA with Sidak’s post hoc test was used to compare ribosome loading of erk mRNA. p < 0.05 was considered statistically significant. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.Statistical details for each experiment, including S1 FigGMR-63Q (ey-flp GMR-63Q-HA) flies. Scale bar: 50 μm. (B) Transmission electron microscopy (TEM) images of 1-day-old flies from wild-type and GMR-63Q eyes. Scale bar: 2 μm. (C) Eye imaginal discs (left panels) and pupa eye (right panels) of GMR-63Q and GMR-31Q flies were stained against anti-HA antibody and anti-Myc antibody, respectively. A: anterior area, P: posterior area. Scale bar: 10 μm. (D) EMS screening strategy to identify suppressors of 63Q induced cell death. Take the screening of the second chromosome as an example. The second chromosome of ey-flp;FRT40A flies was isogenized. Flies by feeding 25 mM EMS (Sigma) in 2% sucrose for 8 h, followed by mating to ey-flp GMR-63Q-HA;GMR-hid CL FRT40A/CyO hs-hid flies. Flies were heat shocked at 37°C for 1 h to avoid having heterozygous flies among the F1 progeny (the pro-apoptosis gene hid was induced by heat-shock (hs-hid) in heterozygous flies). Suppression of retinal cell death in 1-day-old male flies were accessed. For other chromosomal arms, the isogenized ey-flp;FRT42D, ey-flp;FRT2A and ey-flp;FRT82B flies were mutagenized, followed by crossing with ey-flp GMR-63Q-HA;FRT42D GMR-hid CL /CyO hs-hid, ey-flp GMR-63Q-HA;GMR-hid CL FRT2A/TM3 hs-hid flies and ey-flp GMR-63Q-HA;FRT82B GMR-hid CL/TM3 hs-hid flies, respectively.(A) Light microscope images of eyes of 1-day-old wild-type and (TIF)Click here for additional data file.S2 Figrack1s12 (rack1s12FRT40A/GMR-hid CL FRT40A) fly eyes with antibody against RACK1. β-Actin was used as loading control. (B) TEM images of 1-day-old flies from wild type, MJD-Q78 , and MJD-Q78 rack1RNAi  eyes. Scale bar: 2 μm. (C) TEM images of wild type, GMR>Htt96Q  and GMR>Htt96Q, rack1RNAi  fly eyes at indicated days. Scale bar: 2 μm. (D) Light microscope images of eyes of 1-day-old wild-type (GMR-GAL4/+), Rh1G69D (GMR-GAL4 UAS-Rh1G69D/+), Rh1G69Drack1RNAi (GMR-GAL4 UAS-Rh1G69D/rack1KK109073), TauV337M (GMR-TauV337M/+), and TauV337M, rack1RNAi  flies. Scale bar: 50 μm. (E) Western blot analysis of Htt25Q proteins extracted from fly eyes with antibody against GFP. β-Actin was used as a loading control. (F) Fold-change of Htt25Q level in GMR>Htt25Q, rack1RNAi  flies compared to GMR>Htt25Q (GMR-GAL4/UAS-Htt25Q-eGFP) flies (n = 3). ns: not significant (Student’s unpaired t test). Data are presented as mean ±SEM.(A) Western blot analysis of proteins extracted from wild type and (TIF)Click here for additional data file.S3 FigGFP, pkc53eRNAi, pkc53e, and znf598RNAi or with deletion of pkc53e (pkc53eKO) in either GMR-63Q;GMR-GAL4 (top panels), GMR-63Q;GMR>rack1RNAi (middle panels), or GMR-GAL4 (bottom panels) background. (B) RACK1 associated JNK/p38 pathways are not involved in suppression of polyQ induced cell death by rack1 mutations. Light microscope images of eyes of 1-day-old flies expressing p38bRNAi, p38b, jnkRNAi, JNK, p38aRNAi, and p38a in either GMR-63Q;GMR-GAL4 (top panels), GMR-63Q;GMR>rack1RNAi (middle panels), or GMR-GAL4 (bottom panels) background. (C) Apoptosis pathways are not involved in regulation of polyQ cytotoxicity by rack1. Light microscope images of eyes of 1-day-old flies of Df(3L)H99 , UAS-rprRNAi, UAS-diap1, UAS-droncRNAi, UAS-dcp-1RNAi, and UAS-decayRNAi in combination with GMR-63Q;GMR-GAL4 (top panels), GMR-63Q;GMR>rack1RNAi (middle panels), or GMR-GAL4 (bottom panels). Scale bar: 50 μm. Significant differences were determined by Two-way ANOVA with Sidak’s post hoc test (n = 3).(A) RACK1 associated translational pathways are not involved in suppression of polyQ cytotoxicity. Light microscope images of eyes of 1-day-old flies expressing (TIF)Click here for additional data file.S4 FigmCD8-GFP , dnaJ-1 , poeRNAi , kcmf1RNAi , ufd4RNAi , USP47 , erkRNAi  and erk  were generated by heat-shock. GFP positive clones are cells expressing indicated RNAi/genes. The mCD8-GFP and dnaJ-1 expressing flies were served as negative and positive controls, respectively. Scale bar: 10 μm. (B) Quantification of 63Q relative fluorescence intensity (upper panel) and relative quantity of 63Q aggregates (lower panel) between mutant cells and control cells from indicated genotypes. Data are presented as mean ± standard error of the mean (SEM). Significant differences were determined by one-way ANOVA with Tukey’s post hoc test (n = 3). (C-D) Expression of ERK is unaffected by loss of rack1.(A) Pupa eyes of indicated genotypes were stained for 63Q-Myc using anti-Myc antibodies. The “flip-out” clones expressing (TIF)Click here for additional data file.S5 FigGFP , rack1RNAi , poeRNAi , kcmf1RNAi , and ufd4RNAi  were generated by heat-shock. GFP positive clones are cells expressing indicated RNAis. The GFP expressing flies were served as negative control. Scale bar: 10 μm. (B) Quantification of ERK relative fluorescence intensity between mutant cells and control cells from indicated genotypes. Data are presented as mean ± standard error of the mean (SEM). Significant differences were determined by one-way ANOVA with Tukey’s post hoc test (n = 3).(A) Pupa eyes of indicated genotypes were stained for ERK and 63Q-Myc using anti-ERK and anti-Myc antibodies, respectively. The “flip-out” clones expressing (TIF)Click here for additional data file.S6 Figerk mRNA levels are measured by qPCR in indicated fly eyes . Data are presented as mean ± standard error of the mean (SEM). (B) Polysomal loading of erk transcripts is not altered by rack1 depletion. S2 cells were treated with GFP or rack1 dsRNAs, and the polysome and monosome fractions were separated on a sucrose gradient. qPCR was used to assay erk mRNA transcript levels in both fractions. Data are presented as mean ± standard error of the mean (SEM). (C) Western blot analysis of proteins extracted from pupa eyes of wild-type (GMR-GAL4/+), GMR-63Q (GMR-63Q-HA), MJD-78Q  flies with antibody against ERK. β-Actin was used as an internal control. (D) Quantification of ERK levels in (C). Data are represented as mean ± SEM, and one-way ANOVA with Tukey’s post hoc test is used (n = 4).(A) The (TIF)Click here for additional data file.S7 FigGFP , rack1RNAikcmf1OE  and rack1RNAipoeOE  were generated by heat-shock. GFP positive clones are cells expressing indicated RNAi/genes. The GFP expressing flies were served as negative control. Scale bar: 10 μm. (E) Quantification of ERK relative fluorescence intensity between mutant cells and control cells from indicated genotypes. Data are presented as mean ± standard error of the mean (SEM). Significant differences were determined by one-way ANOVA with Tukey’s post hoc test (n = 3).(A) Schematic representations of POE protein with the position of identifiable domains and regions. UBR: ubiquitin protein ligase E3 component n-recognin domain; DZR: double zinc ribbon domain; UBR4: E3 ubiquitin ligase domain. Different mCherry-tagged portions of POE are presented below with their respective amino acids (a.a.) positions. (B) RACK1 interacts with 1864–2374 and 3377–4492 mCherry-tagged POE. Truncate POE-mCherry was co-expressed in S2 cells with GFP or RACK1-GFP, followed by immunoprecipitation with anti-GFP antibody. (C) S2 cells were transfected with GFP or RACK1-GFP for two days, followed by co-expression of different mCherry-tagged portions of POE and FLAG-tagged ERK. Cells were lysed and immunoprecipitated with anti-mCherry antibody. (D) Pupa eyes of indicated genotypes were stained for ERK and 63Q-Myc using anti-ERK and anti-Myc antibodies, respectively. The “flip-out” clones expressing (TIF)Click here for additional data file.S8 Fig(A) quantification of eye degeneration of flies in (TIF)Click here for additional data file.S1 TableUAS-RNAi lines were crossed with GMR-GAL4, and adult eyes of F1 flies were dissected for RNA extraction and subsequent qPCR. Samples were prepared in triplicate. All values were normalized to GMR-GAL4 samples. RpL32 was used as the reference gene for standardization. The p-values were obtained using a Student’s T-tests against the GMR-GAL4 samples.(XLSX)Click here for additional data file.S1 Data(XLSX)Click here for additional data file.


Scientific Reportshttps://doi.org/10.1038/s41598-020-79878-8, published online 29 December 2020Correction to: The Supplementary Table The Gene Bank Accessions numbers for genes Histone 3 (HIS), partial β-tubulin (TUB) and translation elongation factor 1-α (TEF) were omitted.The original Supplementary Table This error has now been corrected in the Supplementary Information file that accompanies the original Article.Supplementary Table 2.


We describe a March 2020 co-occurrence of Legionnaires’ disease (LD) and coronavirus disease in France. Severe acute respiratory syndrome coronavirus 2 co-infections were identified in 7 of 49 patients from LD case notifications. Most were elderly men with underlying conditions who had contracted severe pneumonia, illustrating the relevance of co-infection screening.  Legionella have been reported (The coronavirus disease (COVID-19) pandemic spread to France in mid-February 2020  results. There were 65 LD case notifications in March 2020 compared with 79 in March 2019. To evaluate the number of UATs, which are performed in 96% of LD cases . This finding may be an overestimate because COVID-19 incidence was <5 cases/100,000 persons in the region of residence of the 16 patients not tested at the time of symptom onset; actual co-infection frequency could be from 10.8% (7/65) to 14.3% (7/49). Most patients (4/7) with co-infection lived in the Grand Est region, the area in France with the most COVID-19 cases during the study period and a region that usually reports a high number of LD cases. Median patient age was 72 years (range 37–83 years); male-to-female ratio was 6:1 , higher At hospital admission, co-infected patients had more underlying conditions; 6 (86%) of 7 patients had ≥1, compared with 25 (60%) of 42 LD-positive/COVID-19–negative patients . For carCommunity-acquired LD and COVID-19 were diagnosed at hospital admission in 5 of 7 patients with both infections. For patient 2 , whose sAt admission, all 7 co-infected patients had hyperthermia, 6 had cough or dyspnea, and 2 had neurologic symptoms. Five patients needed orotracheal intubation for a median of 13 days (range 3–30 days); acute respiratory distress syndrome developed in 4 patients, and 1 required extracorporeal membrane oxygenation . The medLegionella (10 RNA copies/100 cells), followed by a decrease to 1.3 log10 RNA copies/100 cells within 21 days (Legionella DNA load increased and remained high (cycle threshold 21.9) until day 31. To identify potential bacterial co-infections, we performed a lung microbiota analysis on a D19 bronchoalveolar lavage using 16S MinION long-read sequencing technology . Similar to another study of LD microbiomes (Legionella (61%) and the presence of commensal lung bacteria  but no additional bacterial co-infection. On day 26, while high lung Legionella DNA load persisted, a third chest scan found pseudocavitation. Persistence of culture or PCR positivity in respiratory samples, or both, has been described in patients with Legionella lung abscesses, especially if immunocompromised  and productive cough, he required ICU admission on day 9 because of acute respiratory distress syndrome. A thoracic computed tomography scan found left lobar atelectasis, multiple ground-glass opacities compatible with COVID-19, and pleural effusion suggesting possible bacterial infection. Results of UAT and nasopharyngeal SARS-CoV-2 reverse transcription PCR were both positive. On day 10, a serum sample was PCR positive for both SARS-CoV-2 and gionella , panel A 21 days , panel BOur study found a substantial proportion of patients in LD notifications in France during March 2020, mostly elderly men with underlying conditions, also had COVID-19. They required ICU admission more frequently and had a higher case-fatality rate than patients without SARS-CoV-2 co-infection, but these rates were similar to that for all ICU-admitted LD patients (Legionella and worsening of respiratory symptoms while SARS-CoV-2 decreased. Initial viral infection could establish pulmonary damage suitable for the bacteria to develop, similar to how bacterial superinfections develop in influenza-infected patients (Legionella and other pulmonary microorganisms share clinical and radiological features with SARS-CoV-2 infection, they should be included in COVID-19 differential diagnoses (Longitudinal monitoring of a single co-infected patient found a first phase of predominant SARS-CoV-2 replication followed by a resurgence of Additional information about study of co-infection with Legionnaires’ disease and coronavirus disease.


ACE2), the gene coding for the SARS-CoV2 host receptor, harbors tight co-expression links with dopa-decarboxylase (DDC), an enzyme involved in the metabolism of dopamine. Here, we mined and integrated data from distinct human expression atlases and found that, among a wide range of tissues and cells, enterocytes of the small intestine express the highest expression levels of ACE2, DDC and several key genes supporting the metabolism of neurotransmitters. Based on these results, we performed co-expression analyses on a recently published set of RNA-seq data obtained from SARS-CoV2-infected human intestinal organoids. We observed that in SARS-CoV2-infected enterocytes, ACE2 co-regulates not only with DDC but also with a specific group of genes involved in (i) the dopamine/trace amines metabolic pathway, (ii) the absorption of microbiota-derived L-DOPA and (iii) the absorption of neutral amino acids serving as precursors to neurotransmitters. We conclude that in patients with long COVID, a chronic infection and inflammation of small intestine enterocytes might be indirectly responsible for prolonged brain alterations.For a yet unknown reason, a substantial share of patients suffering from COVID-19 develop long-lasting neuropsychiatric symptoms ranging from cognitive deficits to mood disorders and/or an extreme fatigue. We previously reported that in non-neural cells, angiotensin-1 converting enzyme 2 ( DDC) is the human gene exhibiting the closest co-expression link with ACE2 . In. InACE2 athways) . Raw datsubjects . Interessubjects . Of noteOverall, since SARS-CoV2 was shown to chronically infect human enterocytes in vivo, our findings raise the possibility that in patients with long COVID, a dysfunction of ACE2 in human enterocytes might alter the blood levels of molecules that shape or impact brain functions. These notably include neutral amino acids L-DOPA, tryptamine and β-PEA.∗DDC): an enzyme allowing the synthesis of dopamine from L-DOPA, the synthesis of tyramine from tyrosine [Dopa-decarboxylase : an enzyme allowing the synthesis of dopamine from tyramine [Cytochrome P450 family 2 subfamily D member 6  and solute carrier family 3 member 1 (SLC3A1): transporters allowing the cellular influx of L-DOPA [Solute carrier family 7 member 9 , solute carrier family 7 member 8 (SLC7A8) and solute carrier family 3 member 2 (SLC3A2): transporters allowing the cellular efflux of L-DOPA [Solute carrier family 6 member 10 , sulfotransferase family 1A member 2 (SULT1A2), sulfotransferase family 1A member 3 (SULT1A3): enzymes allowing the sulfation of dopamine, leading to the generation of dopamine 3-O-sulfate and dopamine 4-O-sulfate [Sulfotransferase family 1A member 1 : an enzyme essentially allowing the catabolism of dopamine and tyramine [Monoamine oxidase A : an enzyme allowing the catabolism of dopamine, tryptamine and β-PEA [Monoamine oxidase B  and SLC6A19 . As a negative control, we also extracted data regarding the enterocytic expression of tyrosine hydroxylase (TH), a gene supporting the generation of L-DOPA from tyrosine and whose expression pattern is limited to catecholaminergic cells of the adrenal glands and to dopaminergic neurons [https://www.proteinatlas.org/ (accessed on 24 September 2021)) [https://www.proteinatlas.org/ (accessed on 24 September 2021)), the FANTOM5 dataset (https://fantom.gsc.riken.jp/5/ (accessed on 24 September 2021)) [https://gtexportal.org/home/ (accessed on 24 September 2021)) [https://www.proteinatlas.org/ (accessed on 24 September 2021)). In brief, for each of the 3 transcriptomics datasets , the average transcripts per kilobase million (TPM) value of all individual samples for each human tissue or human cell type was extracted. All TPM values of all the samples within each data source were normalized using the trimmed mean of M values (TMM) method, followed by Pareto scaling of each gene within each data source. Tissue data from the three transcriptomics datasets were subsequently integrated using batch correction through the “removeBatchEffect” function of R package Limma, using the data source as a batch parameter. The resulting transcript expression values, denoted normalized expression (NX), were then calculated for each gene in every sample. Mining the HPA consensus dataset for each queried gene thus allowed us to rank mRNA levels in the human small intestine as compared with 60 other human tissues. As a confirmatory investigation, we explored a recently published expression atlas of the human intestine obtained by single-cell RNA-seq analyses of human gut cells [In parallel, the same transcriptomic datasets were mined to assess in human small intestine enterocytes the basal expression of  neurons . To explr 2021)) . In partr 2021))  and the r 2021)) . The biout cells ,36. Finahttps://www.proteinatlas.org/ENSG00000130234-ACE2/tissue/small+intestine (accessed on 24 September 2021); SLC6A19: https://www.proteinatlas.org/ENSG00000174358-SLC6A19/tissue/small+intestine (accessed on 24 September 2021); SLC7A9: https://www.proteinatlas.org/ENSG00000021488-SLC7A9/tissue/small+intestine (accessed on 24 September 2021); SLC3A1: https://www.proteinatlas.org/ENSG00000138079-SLC3A1/tissue/small+intestine (accessed on 24 September 2021); SLC3A2: https://www.proteinatlas.org/ENSG00000168003-SLC3A2/tissue/small+intestine (accessed on 24 September 2021); SLC7A8: https://www.proteinatlas.org/ENSG00000092068-SLC7A8/tissue/small+intestine (accessed on 24 September 2021); SLC16A10: https://www.proteinatlas.org/ENSG00000112394-SLC16A10/tissue/small+intestine (accessed on 24 September 2021); DDC: https://www.proteinatlas.org/ENSG00000132437-DDC/tissue/small+intestine (accessed on 24 September 2021); MAOA: https://www.proteinatlas.org/ENSG00000189221-MAOA/tissue/small+intestine (accessed on 24 September 2021); MAOB: https://www.proteinatlas.org/ENSG00000069535-MAOB/tissue/small+intestine (accessed on 24 September 2021); CYP2D6: https://www.proteinatlas.org/ENSG00000100197-CYP2D6/tissue/small+intestine (accessed on 24 September 2021); SULT1A1: https://www.proteinatlas.org/ENSG00000196502-SULT1A1/tissue/small+intestine (accessed on 24 September 2021); SULT1A2: https://www.proteinatlas.org/ENSG00000197165-SULT1A2/tissue/small+intestine (accessed on 24 September 2021); SULT1A3: https://www.proteinatlas.org/ENSG00000261052-SULT1A3/tissue/small+intestine (accessed on 24 September 2021); TH: https://www.proteinatlas.org/ENSG00000180176-TH/tissue/small+intestine (accessed on 24 September 2021).ACE2: ACE2, DDC and key genes of the dopamine/trace amines synthetic pathways, we re-assessed a recently published dataset in which RNA-seq analyses were performed on control vs. SARS-CoV2-infected human intestinal organoids [n = 2), differentiated human intestinal organoids at 24 h following infection with SARS-CoV2 (n = 2), differentiated human intestinal organoids at 60 h following infection with SARS-CoV2 (n = 2). We then assessed across samples from these distinct experimental conditions the co-expression of ACE2 with DDC and with key genes involved in the metabolism of dopamine and/or trace amines. Two analytical approaches were followed concurrently: (i) the calculation of Pearson’s correlation coefficients between ACE2 and genes of interest, (ii) the unsupervised identification of the 25 genes being the most closely co-expressed with ACE2 among a total of 18,011 genes with reported values. To this aim, we used the network visualization software Cytoscape [To evaluate the impact of SARS-CoV2 on the intestinal expression of rganoids . We extrytoscape  and the ytoscape , as prevytoscape .Altogether our observations indicate that the chronic infection of intestinal enterocytes by SARS-CoV2 might be indirectly responsible for the neuropsychiatric symptoms reported in patients with long COVID. A clinical support to this view is provided by a recent work showing that the occurrence of gastrointestinal symptoms during the acute phase of the disease is a clinical predictor of cognitive alterations during the so-called post-COVID phase . We sugg


This recombinant vector was used to generate mutations in the rice cultivar Nipponbare via Agrobacterium-mediated transformation. This approach produced heritable homozygous mutations in the transgene-free T1 generation. Sequence analysis of the eIF4G target region from T1 transgenic plants identified 3 bp deletion mutants, and analysis of the predicted amino acid sequence identified one amino acid deletion in mutants that possess near full-length eIF4G. Furthermore, our data suggest that eIF4G may plays an important role in rice normal development, as there were no eIF4G knock-out homozygous mutants in T1 generation plants. When homozygous mutant lines were inoculated with RBSDV, they exhibited enhanced tolerance to virus infection, without visibly affecting plant growth and development. However, the eif4g mutant plants showed the same sensitivity to rice stripe virus (RSV) infection as wild-type plants. Notably, the wild-type and mutant N-termini of eIF4G interacted directly with RBSDV P8 in yeast and in planta. Additionally, compared to wild-type plants, the eIF4G transcript level was reduced twofold in the mutant plants. These results indicate that site-specific mutation of rice eIF4G successfully conferred partial resistance specific to RBSDV associated with less transcription of eIF4G in mutants. Therefore, this study demonstrates that the novel eIF4G alleles generated by CRISPR/Cas9 represent valuable disease-resistant resources that can be used to develop RBSDV-resistant varieties.Rice black-streaked dwarf disease, caused by rice black-streaked dwarf virus (RBSDV), is a serious constraint in Chinese rice production. Breeding disease-resistant varieties through multigene aggregation is considered an effective way to control diseases, but few disease-resistant resources have been characterized thus far. To develop novel resources for resistance to RBSDV through CRISPR/Cas9-mediated genome editing, a guide RNA sequence targeting exon 1 of  Laodelphax striatellus Fallén), mainly causes rice black-streaked dwarf disease and maize rough dwarf diseases, which lead to serious cereal crop yield losses in China [Rice black-streaked dwarf virus (RBSDV), transmitted by the small brown planthopper  and facilitates viral accumulation in maize [RBSDV is a double-stranded RNA virus and belongs to the genus Fijivirus within the family Reoviridae . RBSDV vin maize . All theeIF4E) and eukaryotic translation initiation factor 4G (eIF4G), and their isoforms are required for RNA viruses to translate viral RNAs [eIF(iso)4E confer complete resistance to turnip mosaic virus (TuMV) in Arabidopsis plants [eIF4E gene in cucumbers resulted in resistance to cucumber vein yellowing virus (CYVY), zucchini yellow mosaic virus (ZYMV) and papaya ring spot mosaic virus-W (PRSV-W) under greenhouse conditions [eIF4G alleles produced through the CRISPR/Cas9 system confer resistance to rice tungro spherical virus (RTSV) [eIF4E/eIF4G alleles generated by CRISPR/Cas9-mediated genome editing are expected to be used in the development of virus-resistant crop plants.To manage rice black-streaked dwarf disease caused by RBSDV, insecticides are frequently employed to control SBPH vectors, accompanied by increasing environmental and economic concerns . Recentlral RNAs ,21. Accos plants . Similarnditions . In rices (RTSV) . Thus, teIF4G loci, we mutated eIF4G in Oryza sativa var. japonica cv. Nipponbare using CRISPR/Cas9-mediated genome editing technology and generated the T0 transgenic rice plant that carried eIF4G mutants. The selected T2 rice plants that carried an edited eIF4G gene but not the introduced transgene were tested for resistance to RBSDV. Compared with wild-type plants, homozygous rice plants with edited eIF4G genes had significantly increased tolerance to RBSDV, without affecting rice stripe virus (RSV) infection and normal plant growth. Furthermore, the wild-type and mutant N-termini of eIF4G interacted directly with RBSDV P8 in yeast and in planta. Altogether, this study provides important information on the mechanism of interaction between RBSDV and rice plants, and novel eIF4G alleles represent valuable materials for the development of RBSDV-resistant varieties.In this study, to introduce novel alleles of rice Oryza sativa L. cv. Wuyujing No. 3) in glass incubators in an insect-rearing room at 25 °C. The rice cultivar Nipponbare  was used as a background to generate eIF4G CRISPR/Cas9 mutant (eif4g) lines. All rice plants were grown in the greenhouse at 28–30 °C with a 12 h light/12 h dark photoperiod.RBSDV or RSV-infected rice plants were obtained from fields in Jiangsu Province, China, and the virus was confirmed by RT-PCR assay . The nonNonviruliferous instar nymphs of SBPHs were fed on RBSDV(or RSV)-infected rice plants for 7 days to acquire the virus. Then, the nymphs were transferred to healthy rice seedlings and allowed to feed for 10 days. The percentage of viruliferous SBPHs was determined by dot enzyme-linked immunosorbent assay (Dot-ELISA) using the monoclonal antiRBSDV or antiRSV antibody . TransgeThe RBSDV or RSV inoculated rice seedlings were transplanted into an experimental plot at the Jiangsu Academy of Agricultural Sciences, Nanjing, China. The seedlings were grown under a standard agricultural practice without the using of pesticides during growth. Four weeks after transplanting (28 dpi), the RBSDV or RSV incidence was recorded for the wild-type and its mutant lines, respectively. Three replicates were used for each treatment, and each replicate contained at least 30 rice seedlings, and the disease incidence of each rice line (%) was determined use the following formula: number of RBSDV (or RSV)-infected plants/total number of plants counted × 100. In addition, 10 rice seedlings were pooled at 28 dpi for qRT-PCR and Western blot analysis.mu) and full-length RBSDV P8 sequences were amplified by PCR from RBSDV-infected rice plants, and the PCR products were cloned individually into pDONR-zero vectors using BP Clonase Enzyme Mix . The eIF4G-N or eIF4G-Nmu and P8 sequences were then transferred to the appropriate destination vectors using LR Clonase (Invitrogen) for yeast two-hybrid and luciferase complementary imaging assays.The N-terminus of wild-type eIF4G (eIF4G-N) or mutant eIF4G . These sgRNA oligos were synthesized  and cloned into the pHUE401 vector by Golden Gate cloning, as previously described [Agrobacterium tumefaciens strain GV3101 by electroporation and transformed into Nipponbare by Agrobacterium-mediated transformation. The primers used in this study are listed in For gene editing, rice eIF4G-specific sgRNA was designed by the CRISPR PLANT website (escribed . CRISPR/Western blotting was performed according to the protocol described previously . BrieflyUBQ10) using the 2−ΔΔCt method reported previously [Total RNA was extracted from 100 mg rice plants with RNAiso Plus reagent  according to the manufacturer’s instructions. cDNA was synthesized with the iScript™ cDNA Synthesis Kit  using random hexamers as primers. qPCR was performed using SsoFast EvaGreen Supermix  on a Bio-Rad iQ5 qRT-PCR system with gene-specific primers . The reseviously .mu) and RBSDV P8. The CDSs of eIF4G-N (1–1053) (or eIF4G-Nmu) and RBSDV P8 were cloned into pGBK-DC and pGAD-DC gateway destination vectors , respectively, by the LR reaction . The plasmids pGBK-eIF4G-N (or pGBK-eIF4G-Nmu) and pGAD-P8 were cotransformed into the Y2H Gold (Clontech) yeast strain and selected on medium lacking leucine, tryptophan, histidine and adenine. The plasmids pGBK-53 and pGAD-T7 were cotransformed into the Y2H Gold yeast strain as a positive control.The Matchmaker Gold Y2H system  was used to identify interactions between eIF4G-N (or eIF4G-Nmu) and RBSDV P8 were cloned into pCAMBIA-DC-NLuc and pCAMBIA-CLuc-DC , respectively, by the LR reaction. Then, the constructs were transformed into A. tumefaciens strain GV3101. Agrobacteria harboring the pCAMBIA-eIF4G-N-Nluc and pCAMBIA-CLuc-P8 plasmids co-infiltrated into 4-week-old N. benthamiana leaves. After 2 days, the infiltrated leaves were sprayed with 10 mM luciferin  and photographed using the Tanon 5200 Luminescent Imaging Workstation .The CDSs of eIF4G-N (1–1053)  to develop RBSDV-resistant rice plants. To design sgRNAs, we identified eIF4G target sgRNA sequences using CRISPR PLANT tools . sgRNAs targeting the first exon with the Bsg I restriction site were used for genome editing . The results showed that, compared with wild-type (WT) plants (Nipponbare), the expression of eIF4G RNA transcript was significantly reduced in the mutant plants eif4g #2 and eif4g #5 , and 3 did not   were all homozygous mutants.Among the T0 generation of 4, 7, 8) A. Sequeneif4g #4 mutants containing eIF4G small fragments (A2), we analyzed these eIF4G small fragments in the T1 population plants with PCR. Our results showed that there were no eIF4G small fragment homozygous mutants in the T1 generation of eif4g #4  and wild-type plants were inoculated with RBSDV. The wild-type plants showed typical RBSDV symptoms at 21 days postinoculation (DPI), including severe dwarfing and dark green leaves. Both eif4g mutant rice lines showed mild symptoms (eif4g mutant lines (eif4g #2 and eif4g #5) was significantly lower than the RBSDV disease incidence of the wild-type plants , which is type species in the genus Tenuivirus and transmitted by small brown planthopper (SBPH). The RSV infection assay showed that the eif4g mutant rice plants developed severe stunting symptom similar to wild-type plants (eif4g mutant lines (eif4g #2 and eif4g #5) was also similar to wild-type plants  generated by CRISPR/Cas9, we conducted yeast two-hybrid and LCI assays. The results of To understand the molecular mechanism of eIF4G-mediated RBSDV resistance, we used a yeast two-hybrid assay to screen nine RBSDV proteins  for eIF4G interactions using the N-terminus of eIF4G (eIF4G-N) fused to the GAL4 DNA binding domain (BK-eIF4G-N) as bait. The yeast two-hybrid results indicated that cotransformation with BK-eIF4G-N and AD-P8 vectors allowed yeast to grow on selective media without histidine and adenine, in contrast to the negative controls A. The in in vivo B. To fureif4g mutants through Agrobacterium-mediated transformation. Sequencing of the eIF4G target region revealed that indels occurred 3 bp upstream of the PAM site, creating a 3-bp deletion or 1 amino acid deletion 4E protein interrupt the interaction between viral genome-linked protein (VPg) and eIF4E/(iso)4E and confer plant resistance to virus infection ,40. The t plants E. Additi


Bacillus species, namely, TM-R and SY1-1, have been identified previously. In this study, we report the draft genome sequences of these strains and demonstrate the presence of 12 and 14 gene clusters for secondary metabolite biosynthesis in TM-R and SY1-1, respectively.Two antimicrobial compound-producing strains of  Bacillus species have been characterized  by selecting “perform taxonomy check,” which calculates average nucleotide identity  according to the manufacturer’s protocol. Sequencing libraries were prepared using the NEBNext Ultra DNA library prep kit for Illumina . The libraries were sequenced with 150-bp paired-end reads by Novogene  using a NovaSeq 6000 instrument . Adapter sequences and low-quality bases were trimmed using fastp v 0.20.1 with default parameters  , 2 RRE-element-containing clusters, 2 beta-lactones (beta-lactone containing protease inhibitor), 1 terpene, 1 terpene/siderophore (siderophore cluster), 1 type III polyketide synthase (T3PKS), and 1 unspecified ribosomally synthesized and posttranslationally modified peptide product (RiPP)-like cluster. Furthermore, the analysis showed that SY1-1 has 14 gene clusters for secondary metabolite biosynthesis—2 NRPS, 2 trans-AT polyketide synthases (transAT-PKS), 2 terpenes, 1 T3PKS, 1 PKS-like (other types of PKS cluster), 1 transAT-PKS/T3PKS/NRPS, 1 NRPS/transAT-PKS/beta-lactone, 1 NRPS/RiPP-like, 1 lantipeptide-class-II (class II lanthipeptide clusters like mutacin II), 1 ladderane (ladderane cluster), and 1 other (cluster containing a secondary metabolite-related protein that does not fit into any other category). Most of these metabolites may be responsible for the production of antimicrobial compounds.In conclusion, the draft genome sequences provide fundamental information for the identification of antimicrobial compounds.The draft genome sequences and corresponding read data are available in DDBJ/GenBank. The DDBJ/GenBank and SRA accession numbers for TM-R and SY1-1 are listed in


To understand primary care providers’ (PCPs) experiences with referring patients to home-based palliative care (HBPC), we conducted individual, key-informant interviews with 31 PCPs. About half participants were male (54.8%), White (42.5%), US-born (58.1%), and were 57 years old (SD=9.17), on average. About one-third of participants (32.3%) indicated they refer 10+ patients annually to HBPC, while most (80.7%) reported “strong” comfort discussing palliative care with patients. Qualitative analysis revealed three prominent thematic categories, each related to barriers PCP experienced when referring patients to palliative care: (1) PCP-level (lack of knowledge and comfort); (2) perceived patient-level ; and (3) HBPC program-level . PCP recommendations for overcoming identified barriers will be discussed. Findings hold important implications for timely patient-referrals to palliative care by PCPs and for sustaining palliative programs that rely on these referrals.


To develop COVID-19 countermeasures, powerful research tools are essential. We produced a SARS-COV-2 reverse genetic (RG) infectious clone toolkit that will benefit a variety of investigations. in vivo, the RG-rescued SARS-CoV-2 viruses will be valuable resources to advance numerous areas of SARS-CoV-2 basic research and COVID-19 vaccine development.Newly emerged severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of the ongoing coronavirus disease 2019 (COVID-19) pandemic, which has caused extensive mortality and morbidity and wreaked havoc on socioeconomic structures. The urgent need to better understand SARS-CoV-2 biology and enable continued development of effective countermeasures is aided by the production of laboratory tools that facilitate SARS-CoV-2 research. We previously created a directly accessible SARS-CoV-2 toolkit containing user-friendly reverse genetic (RG) infectious clones of SARS-CoV-2. Here, using K18-human ACE2 (hACE2) mice, we confirmed the validity of RG-rescued SARS-CoV-2 viruses to reproduce the infection profile, clinical disease, and pathogenesis already established in mice infected with natural SARS-CoV-2 isolates, often patient derived. RG-rescued SARS-CoV-2-infected K18-hACE2 mice developed substantial clinical disease and weight loss by day 6 postinfection. RG-rescued SARS-CoV-2 was recovered from the lungs and brains of infected K18-hACE2 mice, and infection resulted in viral pneumonia with considerable changes in lung pathology, as seen previously with natural SARS-CoV-2 infection. In mice infected with RG-rescued SARS-CoV-2-mCherry, mCherry was detected in areas of lung consolidation and colocalized with clinically relevant SARS-CoV-2-assocated immunopathology. RG-rescued SARS-CoV-2 viruses successfully recapitulated many of the features of severe COVID-19 associated with the K18-hACE2 model of SARS-CoV-2 infection. With utility  Notably, the emergence and rapid spread of new pathogenic variants of SARS-COV-2 (1Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) emerged from Wuhan, China, in late 2019 and rapidly developed into a global pandemic. As of 14 February 2021, the World Health Organization has reported over 108 million confirmed clinical cases of COVID-19 and over 2.3 million deaths worldwide (S-COV-2 15 and theS-COV-2 1 are makiin vitro or in vivo transcription (–https://mrcppu-covid.bio/). The infectious clones will be of benefit to a variety of studies, including viral functional genomics, protein spatiotemporal dynamics, and virus-host interactions.A number of SARS-COV-2 reverse genetic (RG) systems based on the yeast artificial chromosome (YAC) or bacterial artificial chromosome (BAC) have been used to generate live virus following either cription –9. We reription –. In addiin vivo and the immunopathogenesis associated with infection, we used the human angiotensin-converting enzyme 2 (hACE2)-K18 transgenic mouse model, which expresses hACE2 driven by the keratin 18 (K18) promoter. Other mouse models of SARS-CoV-2 infection, including transient expression of hACE2 using adenovirus vectors, are unable to replicate severe disease  of SARS-CoV-2  or SARS-CoV-2-mCherry . Mock-infected mice  received 20 μl sterile Dulbecco’s modified Eagle’s medium (DMEM) with 2% fetal bovine serum (FBS) intranasally. Beginning at day 6 postinfection, both SARS-CoV-2- and SARS-CoV-2-mCherry-infected mice showed significant weight loss compared to mock-infected mice  and Ly6G+ neutrophils in the lungs of infected mice. AMs have been shown to play a critical role in the initiation of a cytokine storm in severe COVID-19, and reports have shown that they are susceptible to infection with SARS-CoV-2  and pCC1-4K-SARS-CoV-2-mCherry (GenBank accession no. MT926411) infectious clones were assembled from synthetic DNA fragments (GenScript) using restriction endonuclease-based cloning procedures as described in detail previously  in DMEM with 2% FCS. The plates were incubated for 72 h. Cells were fixed with 4% paraformaldehyde (PFA) and stained with 0.1% crystal violet. Viral titers were calculated using the following formula: PFU/ml = (average number of plaques/volume [ml] of virus added) × the dilution factor.Infectious titers were enumerated by plaque assay . Mouse tAustralian Code for the Care and Use of Animals for Scientific Purposes (4 PFU of SARS-CoV-2 (Wuhan-Hu-1) or SARS-CoV-2-mCherry derived from infectious clones in a volume of 20 μl. Mock-infected mice received 20 μl of sterile DMEM with 2% FBS. Mice were weighed and scored for disease signs daily. Mice were scored using a cumulative and progressive clinical disease matrix. Mice were given a score between 0 and 3 for each of the following five health indicators: eating habit, locomotion, behavior, appearance, and weight loss. A score of 0 was normal. After receiving an overall score of >3, mice were monitored twice daily. A score of 3 in any one of the 5 categories  or an overall score of >10 led to the mouse being euthanized.All animal experiments were approved by the Animal Ethics Committee of Griffith University (protocol no. MHIQ/07/20/AEC). All procedures conformed to the Purposes . K18-hACTissues were fixed in 4% paraformaldehyde overnight at 4°C. Fixed tissues were subsequently washed in PBS and dehydrated in 30% (wt/vol) sucrose (in PBS). Tissues were embedded in optimal-cutting-temperature (OCT) compound (Sakura Finetek) and frozen prior to cryosectioning. Eighteen to twenty-micrometer-thick sections were prepared and permeabilized in cold acetone. Sections were blocked  and immunolabeled with rat anti-mouse CD169 (AbD Serotec), rabbit anti-mouse cytokeratin 14 (Abcam), Alexa Fluor 647-conjugated rat anti-mouse Ly6G (BD Biosciences) antibodies, rabbit anti-mouse collagen type IV (Abcam), or rat monoclonal F4/80 (Abcam), and secondary antibody detection was performed using anti-rat Alexa Fluor 488 and anti-rabbit Alexa Fluor 546 antibodies (Thermo Fisher). Sections were washed and nuclei were labeled using Hoechst 33258 dye and mounted using ProLong Gold antifade (Thermo Fisher). Images were acquired using a spectral laser scanning confocal microscope (Olympus FV3000) using a 10×  or a 20× (0.95-NA) objective. Sequential acquisition was performed using single 405-nm, 488-nm, 561-nm, 594-nm, and 640-nm laser lines to excite Hoechst 33258 dye, Alexa Fluor 488, Alexa Fluor 546, mCherry, and Alexa Fluor 647. z-stacks with a 1-μm interval were acquired, channels were merged using Olympus CellSens software, and images were processed using Imaris . Spectral overlap was resolved using the channel arithmetics function of the Imaris XT plugin, and the noniterative deconvolution algorithm was applied to z-stack images. Images were exported as 300-dpi TIFF files and assembled using Adobe Illustrator 2020.2) of 10, a maximum nuclear size (μm2) of 100, a minimum roundness of 0.4, a minimum compactness of 0.4, and a minimum elongation of 0.2.Tissues were dissected and fixed in 4% PFA, followed by paraffin embedding. Samples were cut into 5-μm-thick sections and stained with hematoxylin and eosin (H&E). Images were taken using an Aperio AT2 digital whole-slide scanner (Leica). Tissue cellular infiltrates were quantified using ImageScope software (Algorithm Nuclear v9) using the following threshold settings: image zoom of 0.5, a minimum nuclear size (μmGTGAAATGGTCATGTGTGGCGG-3′ (forward), 5′-CAAATGTTAAAAACACTATTAGCATA-3′ (reverse), and FAM-CAGGTGGAACCTCATCAGGAGATGC-BHQ (probe sequence), where FAM is 6-carboxyfluorescein and BHQ is black hole quencher (MT926410).Tissues were collected in TRIzol (Invitrogen) and homogenized using the Bead Ruptor 24 Elite homogenizer according to manufacturer’s instructions. Total RNA was extracted from tissue homogenates using TRIzol (Invitrogen) according to the manufacturer’s instructions. RNA was reverse transcribed into cDNA using reverse transcriptase (Sigma-Aldrich) with random primers. SYBR green and QuantiTect probe real-time PCR was performed on a CFX96 touch real-time PCR system (Bio-Rad) to measure the expression of inflammatory mediators and viral genome copy numbers, respectively. Primers for GAPDH , TNF-α, CXCL10, IL-1β, CCL5, IFN-γ, IL-6, and IFN-β were purchased from Qiagen. SYBR green real-time PCR conditions were set as follows: (i) 1 cycle of 95°C for 15 min and (ii) 40 cycles of 94°C for 15 s, followed by 55°C for 30 s and 72°C for 30 s. SARS-COV-2 RdRP gene-targeting primer and probe sequences were 5′-post hoc test. Cytokine production and infiltrated cell counts were analyzed using an unpaired t test. All statistical analyses were performed with GraphPad Prism 6.0 software.All data were tested for normality using the D’Agostino-Pearson normality test prior to analysis. Data for mouse weight was analyzed using two-way analysis of variance (ANOVA) with the Bonferroni