Mycobacterium tuberculosis (Mtb) infection. Pathogenic mycobacteria can inhibit and/or regulate host cell TNF-α production in a variety of ways to evade antituberculosis (anti-TB) immunity as well as facilitate immune escape. However, the mechanisms by which TNF-α expression in host cells is modulated to the benefit of mycobacteria is still an interesting topic and needs further study. Here, we report that macrophages infected with Mycobacterium marinum (Mm)—a close relative of Mtb—upregulated the expression of E3 ubiquitin ligase FBXW7. Specific silencing FBXW7 with small interfering RNA (siRNA) significantly elevates TNF-α expression and eventually promotes the elimination of intracellular bacteria. In turn, overexpression of FBXW7 in Raw264.7 macrophages markedly decreased TNF-α production. Furthermore, partial inhibition of FBXW7 in an Mm-infected murine model significantly reduced TNF-α tissue content, alleviated tissue damage as well as reduced the bacterial load of mouse tails. Finally, FBXW7 could decrease TNF-α in a K63-linked ubiquitin signaling dependent manner. Taken together, our study uncovered a previously unknown role of FBXW7 in regulating TNF-α dynamics during mycobacterial infection, which provides new insights into understanding the role of FBXW7 in anti-tuberculosis immunity and its related clinical significance.Tumor necrosis factor alpha (TNF-α) is a crucial factor in the control of  Mycobacterium tuberculosis (Mtb), is still a serious disease that results in significant morbidity and mortality worldwide in humans and animals , caused by  animals . Over thnfection . Macrophnfection . They prnfection . As a plreaction  and maind tissue . It has d tissue , suggestsue  is a subunit of an E3 ubiquitin ligase and is considered to have a tumor suppressor function. FBXW7 recognizes numerous oncoproteins like c-Myc, Notch, c-Jun, Cyclin E, and MCL1 as substrates in order to promote their degradation  pathway . FBXW7 m pathway . Recentl pathway , respira pathway , human p pathway , and pri pathway  aberrant pathway . In cont pathway . Chen re pathway . Furthersurvival . Recentlnfection . Moreovenfection . These fMycobacterium marinum (Mm), a close relative of Mtb that induces caseating granulomas in the mouse tail, similar to those formed in human TB  M strain (ATCC BAA-535) was originally obtained from Dr. L. Ramakrishnan  and preserved by Dr. Gao (Fudan University); it was used as the wild-type (WT) parental strain in this study. Mm induces granulomas in the tail rather than the organs of the mouse because the tail has a lower temperature that is more permissive to Mm growth than its body (Mm M strain (fadD26:Tn) was used as the attenuated strain (tdTomato fluorescence according to the published protocols for △RD-1 strain (Mm strains were grown at 32°C in Middlebrook 7H9 broth (Difco) supplemented with 10% oleic acid albumin dextrose-catalase (OADC enrichment), 0.5% glycerol, and 0.05% Tween 80 or on Middlebrook 7H10 agar supplemented with 10% OADC and 0.5% glycerol. For fluorescence-labeled strains, 100 μL of 50 mg/mL hygromycin B was added to 7H9 OADC per 100 mL.The its body . A previd strain . The WT 1 strain . All Mm For the detection of bacterial load in tail tissues, tail tissues with lengths of 5 mm were weighed and homogenized in 0.5 mL of DMED medium containing 0.1% Triton-X. The homogenized tissue suspension was diluted and plated on Middlebrook 7H10 agar with 10% OADC and 0.5% glycerol at 32°C for 8 to 10 days. Then, the number of bacteria colonies was counted, and the bacteria load was expressed as colony forming units (CFU)/g.C57BL/6 mice were obtained from the Center of Experimental Animals of China Three Gorge University (CTGU). All animal experiments were reviewed and approved by the Animal Care and Use Committee of CTGU. Raw264.7 macrophages  were maintained in DMEM medium, Thioglycolate GIBCO BRL  with 10% FCS.Antibodies to the haemagglutinin tag , β-actin  and FBXW7  were from Santa Cruz, Inc. Myc tag  and Flag tag  were from Proteintech Group, Inc. Alexa Fluor 488-labeled anti-rabbit , Alexa Fluor 647-labeled anti-mouse IgG  and Alexa Fluor Plus 405-labeled anti-mouse  were from Thermo Fisher Scientific. Antibodies specific for FBXW7 , TNF-α , TGF-β  and IL-10  were from Abcam. Antibody specific for NF-κBp65  was from Cell Signaling Technology. MG132 (M8699) was from Sigma-Aldrich.Recombinant vector encoding mouse TNF-α  was created with PCR-based amplification of Raw264.7 complementary DNA, and then subcloned into the pcDNA3.1 eukaryotic expression vector (Invitrogen). The vectors were constructed by OBiO Technology (Shanghai) Corp., Ltd and were confirmed by DNA sequencing. The Myc-FBXW7, HA-Ub, HA-K48R Ub, and HA-K63R Ub constructs were kindly provided by Prof. Qingqing Wang . All constructs were also confirmed by DNA sequencing. The plasmids were transfected into Raw264.7 with ExFect Transfection Reagent  according to the standard protocol.To confirm whether the degradation of TNF-α was mediated by FBXW7 with ubiquitination, Raw264.7 cells were transfected with Flag-TNF-α, Flag-TNF-α, and Ha-Ub, or Flag-TNF-α, Ha-Ub and Myc-FBXW7 plasmids for 24 h. The cells were then treated with MG132 (25 mM) for 6 h before cell harvest and applied with immunoblotting as described in section 2.6.To determine the type of FBXW7-mediated ubiquitination, we transfected Flag-TNF-α, cotransfected with Flag-TNF-α and Ha-Ub plasmids, co-transfected with Flag-TNF-α plasmids, Ha-Ub and different doses of Myc-FBXW7 plasmids, and co-transfected with Flag-TNF-α, Myc-FBXW7, and different Ha-Ub mutants (K48R or K63R) plasmids into Raw264.7 cells, respectively.5 cells per well into six-well plates. The cells were allowed to adhere to the plate overnight and infected with Mm strains  or treated with the same volume of DMEM medium as if infected (MOCK). The cells were then incubated at 37°C in 5% CO2 for 1 to 12 h. The cells with different treatments were harvested at the specified times and were used for qRT-PCR or Western blot Assays. Three replicate wells were used for each time point (n=3).Macrophages were seeded with a density of 6*107 CFU Mm via tail intravenous injection (n=22/group). On the seventh day post infection, half mice from each infection were intraperitoneally injected with SB-216763  with a dosage of 20 mg/kg (PBS with 5% DMSO) in a volume of 150 μL once every other day, until the 21st day when the observation period was ended. The dosage was according to the instruction of the manufacturer  analysis was applied with SYBR Green Master Mix  according to the manufacturer’s instructions. Delta Ct value (ΔCt = Ct target genes – Ct house keeping genes) was estimated, then ΔΔCt value was calculated as (ΔCt infected group - ΔCt mock-infected group), and the relative expression was expressed as 2(-ΔΔCt). The sequences for primers are listed in Cells or tissues were immediately treated with Trizol reagent (Invitrogen) and total RNA was isolated from the cells or tissues according to the manufacturer’s instructions. Then, single-strand cDNA was generated from total RNA by using reverse transcriptase (Takara). Quantitative real-time reverse transcriptase PCR  containing a protease inhibitor “cocktail” ; The harvested cells were lysed with cell lysis buffer with a protease inhibitor “cocktail”. After centrifugation at 12,000 g for 5 min at 4°C, the protein concentrations were measured with a BCA assay . Then, the extracts were boiled for 10 min in SDS-PAGE Sample Loading Buffer . Equal amounts of protein extract were separated by SDS–PAGE, then transferred onto PVDF blotting membrane , blocked with 5% BSA or 5% defatted milk in TBST buffer , probed with the antibody for immunoblot analysis, and incubated with secondary antibody. The secondary antibodies used were either HRP-conjugated anti-mouse or anti-rabbit IgG depending on the primary antibodies. Immunoreactive bands were visualized by detection of chemiluminescence with ChemiDocT XRS+ Molecular Imager , and quantified by analysis with the Image Lab software (Bio-Rad).Mtb experimentally-infected rabbits (Mtb strain HN-878) and mice (Mtb strain H37Rv) paraffin embedded lung sections were kindly provided by Clifton E. Barry, Tuberculosis Research Section, Laboratory of Clinical Immunology and Microbiology, NIAID, NIH. All the tissues were sectioned, stained with hematoxylin and eosin (H & E) solution, and examined by light microscopy.Tails sections from control or infected mice were fixed in 10% phosphate-buffered formalin and embedded into paraffin. Paraffin embedded lung tissues from patients were collected by the Third People’s Hospital of Yichang and provided by the hospital. The patients were diagnosed as clinical TB according to a previous report . Mtb expSections of lungs and tails were analyzed by immunohistochemistry assay as previously described . In thisMm for 4 h or treated as if infected (MOCK), as the method mentioned in section 2.4. Cells were fixed with 2% paraformaldehyde for 30 min at 4°C, permeabilized with 0.1% Triton X-100 for 15 min, blocked with 5% BSA in TBST for 30 min, and stained with rabbit anti-FBXW7 antibody and mouse anti-NF-κBp65. Following the primary antibody, the samples were stained with FITC-labeled anti-rabbit secondary antibody and Cy3-labeled anti-mouse secondary antibody. DAPI  was used to stain the nuclei. The co-localization of FBXW7 and NF-κBp65 was detected by imaging with a Nikon AIR+ confocal laser microscope. The signal for nuclear p65 was determined by counting the number of nuclei (DAPI) merged with p65, dividing by the total number of nuclei scored. At least 200 total nuclei per well were counted in each of the triplicate wells.Macrophages were cultured on sterilized glass coverslips in 24-well plates and infected with P-value less than 0.05.Differences between the groups in the ordinal data were analyzed by the Mann-Whitney U test to compare two data sets or Kruskal-Wallis one-way analysis of variance (ANOVA) to compare more than two data sets. The statistical analyses were performed using GraphPad Prism 4.0 for Windows . Statistical significance was considered at Mm-infected Raw264.7 macrophages, we infected Raw264.7 macrophages with WT_Mm, harvested the macrophages at various times post infection, and analyzed FBXW7 mRNA and protein levels. FBXW7 mRNA was found to be increased in WT_Mm infected macrophages at 4 h and 8 h post infection (hpi), compared to MOCK and △PDIM cells, P < 0.01  and △PDIM cells  in Raw264.7 cells  via tail vein injection. Seven days post infection (dpi), intraperitoneal injection with SB-216763 to inhibit the FBXW7 was initiated. At 21 dpi, mice with WT Mm infection displayed severe tail damage , lower TNF-α expression at 14th dpi (P < 0.01) and lower TGF-β expression at the 21st dpi (P < 0.01) compared to the △PDIM group  in the tail tissues than untreated ones at the 14th and 21st dpi (Mm group at 14th (P < 0.01) but not at 21st dpi  and alternatively activated macrophages (anti-inflammatory or M1 macrophages)  macrophage activation program. Our data corroborated the previous reports that Mtb-infected macrophages are predisposed to differentiate into M2 phenotype which exhibit the properties of enhanced protease-dependent motility, pathogen survival, and immuno-modulation  to restrain TLR-mediated NF-κB activation by limiting the activation of TNF˗α receptor associated factor (TRAF), and this mechanism pushed macrophage polarity toward an M2 phenotype to facilitate the organisms’ intracellular survival (Mtb directly inhibited the activation of NF-κB and IFN regulatory factors (IRFs) downstream of TLR2 signaling via Akt-dependent mechanisms  . Macrophes)  regulating the activation of NF-κB signaling pathway besides of P100 during the infection. Another possibility is that the NF-κB signaling pathway was insufficiently activated under the microenvironment of lower production of TNF-α.Many cytokines are involved in the development of granulomas during the e to Mtb , and a csistence . In the bacteria . Howeverlike PGL  and PtpAlike PGL . It was d ANAPC2 . This isd ANAPC2 . TherefoIt was further shown that FBXW7 changed the ubiquitination level of proteins in macrophages, mainly K63-linked ubiquitination. This indicated that the degradation of TNF-α induced by FBXW7 might be associated with K63-linked ubiquitination. Further exploration is needed to determine whether FBXW7 regulates TNF-α directly or by interacting with another substrate(s). Moreover, there is a seemingly contradictory point between the negative regulation of FBXW7 on TNF-α vs FBXW7-mediated granulomas formation in this study.Mtb on the host immune responses are multidirectional and complex. It has been identified that the virulent strain Mtb induced fewer TNF-α than the attenuated strains such as M. smegmatis and M. phle. Reduced TNF-α contributes to a more persistent intracellular infection in the hosts , the National Key Research and Development Program of China (2021YFC2301500 to LZ) a research project of the Science and Technology Bureau of Yichang (A21-2-049 to JC), the new faculty startup research funds of China Three Gorges University , and the Scientific Research Project of Education Department of Hubei Province (B2020025 to SH). BY is the recipient of NSFC (81801977). LV and JG are supported by the intramural research program of NIAID, NIH.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


The two members of the UBASH3/TULA/STS-protein family have been shown to critically regulate cellular processes in multiple biological systems. The regulatory function of TULA-2  in platelets is one of the best examples of the involvement of UBASH3/TULA/STS proteins in cellular regulation. TULA-2 negatively regulates platelet signaling mediated by ITAM- and hemITAM-containing membrane receptors that are dependent on the protein tyrosine kinase Syk, which currently represents the best-known dephosphorylation target of TULA-2. The biological responses of platelets to collagen and other physiological agonists are significantly downregulated as a result. The protein structure, enzymatic activity and regulatory functions of UBASH3/TULA/STS proteins in the context of platelet responses and their regulation are discussed in this review. The family discussed in this review is currently termed UBASH3 for the ubiquitin-associated (UBA) and Src-homology 3 (SH3) domain-containing gene. The protein product of UBASH3A is also called STS-1 for the suppressor of TCR signaling, CLIP4 for the Cbl-interacting protein and TULA (or TULA-1) for the T-cell ubiquitin ligand, while the protein product of UBASH3B, originally designated p70, is also called STS-1 and TULA-2 ,6,7. TheThe structure of TULA proteins is conserved within the family and sports an unusual combination of functional domains ,2,3,4 F. One of p-nitrophenyl phosphate (pNPP) is ~6000-fold, but for 3-O-methyl-fluorescein phosphate (OMFP), it is ~200-fold [The structures of TULA-1 and TULA-2 are very similar, especially within the functional domains; this notion is apparent from sequence comparisons and structural studies ,24,25,26200-fold . FurtherThese results are consistent with the finding that multiple pY-peptide substrates of TULA-2 have been identified in the course of random peptide library screening, while no substrates have been identified for TULA-1 in these experiments . The subOne may hypothesize that UBA-mediated interactions are important for the binding of TULA PTPs to their substrates, since many pY-containing proteins are ubiquitylated ,33,34,35It should be noted that most studies with TULA-family proteins were conducted with mouse proteins. However, human TULA-1 and TULA-2 have been described as structurally similar and behaving comparably to their mouse counterparts . As showFinally, despite its relatively low phosphatase activity, TULA-1 is capable of dephosphorylating ZAP-70, a protein tyrosine kinase (PTK) critical for TCR/CD3 signaling ,28,37, aAlthough this review is focused on platelets, it should be noted that TULA-family proteins play a regulatory role in several different cell types. Originally, it was shown that both TULA-1 and TULA-2 downregulate signaling through TCR/CD3 and the resulting T-cell proliferation and cytokine secretion ,28. The Candida albicans, a yeast pathogen, as well as in signaling mediated by Dectin-1, a receptor for β-glucan, a major component of the fungal cell wall [Francisella tularensis [TULA-1 and TULA-2 also downregulate the signaling, activation and responses of monocytes and dendritic cells (DCs), important immune cells belonging to the myeloid lineage. Thus, dKO bone marrow (BM) monocytes and BM-derived DCs show an increase in antifungal activity toward ell wall . The effell wall . Furtherlarensis .Consistent with the effects of TULA proteins on myeloid immune cells, the differentiation and physiological functions of osteoclasts, which are specialized bone-resorbing cells of the macrophage lineage, are likewise downregulated by TULA-2 . This stFor systems in which the molecular basis of these effects was examined ,40,42,43It has been shown that ubiquitously expressed TULA-2 is substantially overexpressed in platelets as compared to its expression in peripheral blood mononuclear cells . The hig2+ mobilization was enhanced. Physiological responses in vitro, such as aggregation and dense granule secretion, were also enhanced in dKO platelets. In contrast, signaling through protease-activated receptor 4 (PAR4), a G-protein-coupled receptor of thrombin, was not altered in dKO platelets -2-mediated platelet activation . The CLE3-injury-induced thrombosis [Importantly, the studies discussed above definitively demonstrate the significant effects of TULA-2 in vivo. A reduced level of TULA-2 is associated with a shortened tail bleeding time ,62, enharombosis  and an erombosis ,62 in mirombosis , these rrombosis  are linkThe specific physiological consequence of the downregulation of ITAM-mediated signaling by TULA-2 remains to be fully understood. It is likely that this regulation acts primarily as a biological brake preventing platelet responses to sub-optimal stimuli. This notion is consistent with several observations: (i) TULA-2 inhibits platelet responses to a much greater extent at low than at high agonist concentrations ,61,62,63The correlation of the downregulatory effects of TULA-2 on physiological platelet responses with the TULA-2-dependent dephosphorylation of Syk, together with the specificity of these effects for Syk-dependent platelet responses when ITAM-mediated signaling is affected, while G-protein-mediated signaling is not, strongly suggests that Syk is the main regulatory target of TULA-2 ,61,62. IThe effects of TULA-2 on Syk activity may be complex, because not only Syk pY346 but also, to some extent, Syk pY342 has been shown to be targets of TULA-2 , while bDespite the established importance of the TULA-2-dependent dephosphorylation of Syk for platelet regulation, it cannot be ruled out that TULA-2 may dephosphorylate other protein substrates, for example, Src-family kinases (SFKs). The dephosphorylation of Src by TULA-2 has been shown in reaction mixes and in 293T cells overexpressing both Src and TULA-2 . LikewisAnother potentially important molecular element of the TULA-2-mediated regulation of platelet activation is ubiquitylation, since (i) UBA of TULA-family proteins is capable of binding ubiquitin ,5, (ii) Considering the importance of TULA-2 as a regulator of platelet functions, the mechanisms controlling the effects of TULA are of great interest. It should be noted that the function of TULA-2 as a PTP can be regulated simply by the availability of substrates; fully active TULA-2 may be present in the cell but exert no substantial effect until a pY-site with the characteristic TULA-2 specificity determinants is formed on a protein accessible to TULA-2, such as the pY346 site of Syk, as a result of specific receptor-mediated signaling. However, the existence of mechanisms regulating the TULA-2 amount and/or specific activity could substantially enrich the flexibility of this regulatory circuit. One such mechanism is mediated by microRNA; it has been shown that miR-148a targets the 3′ untranslated region of TULA-2 mRNA and downregulates the level of TULA-2 in platelets and erythroleukemia cells, thus facilitating ITAM- and Syk-dependent signaling through the FcγRIIA receptor . The treThe functions of TULA-2 may be affected by its posttranslational modifications, as well. The ability of TULA-family proteins to bind to ubiquitin appears to be critical for the TULA-dependent inhibition of epithelial growth factor receptor degradation ,5,17, anFinally, the phosphorylation of TULA-2 on a tyrosine residue in the N-terminal region in stimulated platelets has been reported . This pYThe available data allow us to conclude that TULA-2 is a critically important regulator of platelet signaling mediated by ITAM- or hemITAM-bearing receptors and the platelet responses induced by this signaling. Furthermore, it has been convincingly demonstrated that TULA-2 exerts significant effects on platelet functions in vivo. The molecular basis of this effect is provided by a high level of TULA-2 expression in platelets, far exceeding that in other blood/immune cells, together with the substrate specificity of TULA-2 PTP activity rendering Syk, a key PTK of platelet ITAM/hemITAM-mediated signaling, a prime target of TULA-2. The molecular mechanisms of TULA-2′s effects on Syk-mediated events clearly involve the dephosphorylation of specific regulatory pY-sites of Syk, including Syk pY346, although the dephosphorylation of pY-sites on other proteins might also participate in the events of TULA-2-mediated regulation. The effects of TULA-2 appear to be regulated at the TULA-2 transcript level, although the phosphorylation-mediated regulation of TULA-2 cannot be ruled out.


Currently the calculator uses primarily data for antibodies elicited by Wuhan-Hu-1-like vaccination or infection and so is expected to work best for calculating escape from such immunity for mutations relative to early SARS-CoV-2 strains.A key goal of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) surveillance is to rapidly identify viral variants with mutations that reduce neutralization by polyclonal antibodies elicited by vaccination or infection. Unfortunately, direct experimental characterization of new viral variants lags their sequence-based identification. Here we help address this challenge by aggregating deep mutational scanning data into an ‘escape estimator’ that estimates the antigenic effects of arbitrary combinations of mutations to the virus’s spike receptor-binding domain. The estimator can be used to intuitively visualize how mutations impact polyclonal antibody recognition and score the expected antigenic effect of combinations of mutations. These scores correlate with neutralization assays performed on SARS-CoV-2 variants and emphasize the ominous antigenic properties of the recently described Omicron variant. An interactive version of the estimator is at  Human coronaviruses undergo antigenic evolution that erodes antibody-based neutralization . This anprospectively measure how viral mutations impact antibody binding or neutralization. Deep mutational scanning can systematically measure the antigenic impacts of all possible amino-acid mutations in the key regions of spike on monoclonal antibodies , which is the primary target of neutralizing antibodies to SARS-CoV-2 . The res1.1.1.A deep mutational scanning experiment can measure how all single amino-acid mutations to the SARS-CoV-2 RBD affect binding by a monoclonal antibody . This muAs a small example to illustrate the principle behind our approach, Now consider a hypothetical polyclonal antibody mix of these three antibodies combined at equal potencies. We can generate an escape map for this hypothetical antibody mix simply by averaging the experimentally measured escape maps for the three individual antibodies, yielding the thick black line in https://jbloomlab.github.io/SARS2_RBD_Ab_escape_maps/mini-example-escape-calc/ (last accessed 11 March 2022).Next consider removing one antibody from the hypothetical mix by mutating its epitope. 1.2.https://jbloomlab.github.io/SARS2_RBD_Ab_escape_maps/ (last accessed 11 March 2022).The illustrative example in the previous section illustrates how experimental data for individual antibodies can be combined to yield an escape map for a hypothetical polyclonal antibody mix. To create an escape map for an antibody mix that more realistically represents the actual human sera, we aggregated previously generated deep mutational scanning data for thirty-three neutralizing antibodies elicited by SARS-CoV-2. These antibodies were isolated from a variety of patient cohorts within the first year of the pandemic (see Methods for details). An assumption of the analysis that follows is that an equipotent mixture of these thirty-three antibodies represents the neutralizing activity of human sera; we emphasize that this assumption is imperfect since in reality the antibodies were chosen for prior study for a variety of ad hoc reasons. The escape maps for all the individual antibodies can be interactively interrogated at The overall polyclonal escape map generated by averaging the experimental data for all thirty-three antibodies is in after mutating sites in the RBD. Specifically, we reduce the contribution of each antibody by an amount that scales with how strongly that antibody targets each mutated site (see Methods for details). For instance, the blue lines in We can follow the principle outlined in the illustrative example in the previous section to estimate the expected polyclonal escape map We can also calculate the expected effects of compound mutations. https://jbloomlab.github.io/SARS2_RBD_Ab_escape_maps/escape-calc/ (last accessed 11 March 2022), to perform calculations like those in We recommend the reader explore the interactive escape estimator at 1.3.https://jbloomlab.github.io/SARS2_RBD_Ab_escape_maps/escape-calc/ (last accessed 11 March 2022) and returns a score that ranges from one (no mutations affect binding of any antibodies) to zero .For each set of mutated RBD sites, we can define a quantitative score that represents the polyclonal antibody binding that remains after mutating these sites. This score is defined using the same principle as the site-wise escape estimator in the previous section: we reduce the contribution of each antibody by an amount that scales with how strongly it is escaped by each mutated site and define the overall score as the fraction of all antibody contributions that remain (see Methods for details). This calculation is implemented in the interactive estimator at To test how these escape-estimator scores compare to experimentally measured neutralization titers, we collated neutralization data from three previously published studies , each of1.4.We applied the escape estimator to the recently reported Omicron variant, which has fifteen mutated sites in its RBD . The calhttps://jbloomlab.github.io/SARS2_RBD_Ab_escape_maps/escape-calc/ (last accessed 11 March 2022) indicates that mutations at Sites 484, 446, and 417 are the largest drivers of this antigenic change, although other mutations also contribute. The residual peaks in the map suggest that the remaining antibody activity against the Omicron variant RBD could be further eroded by mutations at sites like 346, 378, 444, and 504.The site-level escape map for the Omicron variant’s RBD is shown in 2.We have described an escape estimator that uses experimental data for thirty-three monoclonal antibodies to estimate the antigenic effects of arbitrary combinations of mutations to the SARS-CoV-2 RBD. The key insight is to aggregate data for individual antibodies to define both which RBD sites are antigenically important and which combinations of mutations have redundant versus additive effects on antibody binding. For instance, Sites 417, 484, and 490 are all peaks of antibody escape. But mutations at 484 and 490 have redundant effects since they generally escape the same antibodies, whereas mutations at 484 and 417 have additive effects since they generally escape different antibodies.https://jbloomlab.github.io/SARS2_RBD_Ab_escape_maps/escape-calc/ (last accessed 11 March 2022), we also provide a Python module for batch processing of large numbers of sequences at https://github.com/jbloomlab/SARS2_RBD_Ab_escape_maps/ (last accessed 11 March 2022).Another key aspect of our approach is the interactive visual implementation of the escape estimator, so the user can interrogate the effects of mutations (or combinations of mutations) simply by clicking (or shift-clicking) on sites. This interactivity provides an intuitive understanding of the antigenic structure of the RBD and shows how the estimator is performing simple transformations directly on experimental data. We encourage interactive use of the estimator so it acts as a visual aid to augment human interpretation. However, we note that in addition to the interactive estimator at There are caveats that should be kept in mind when using the escape estimator. First, the estimator only considers sites in the RBD and ignores mutations to other regions of spike. Second, the estimator assumes that the neutralizing activity of human polyclonal serum is represented by an equipotent mix of the monoclonal antibodies that happen to have been previously characterized by deep mutational scanning. Third, the estimator simply averages site-level escape measurements across antibodies and ignores differences in the effects of different amino-acid mutations at the same site. Fourth, the estimator does not yet implement a real biophysical model of the combined activity of multiple antibodies . FinallyDespite all these caveats, the escape estimator yields binding scores that correlate with experimentally measured neutralization titers. In addition, the actual antigenic evolution of SARS-CoV-2 seems to follow the principles captured by the escape estimator: variants of concern generally have combinations of mutations calculated to have additive effects on antibody escape  rather than combinations calculated to have redundant effects . We suspect the estimator works well because the RBD is the dominant target of neutralizing activity  and the However, the situation will become more complex over time. Currently, most humans with antibodies to SARS-CoV-2 have been exposed to an RBD antigen that is identical or very similar to that of the early Wuhan-Hu-1 strain. Therefore, antigenic studies can reasonably define mutations in reference to that RBD, since it is what the antibodies target. But as humans are exposed to more diverged RBD variants, it will become difficult to determine what reference to use to define antigenic mutations, since different individuals will have antibodies targeting different RBDs. Additionally, differing exposure histories can leave individuals with different antibody specificities , a proce3.3.1.https://github.com/jbloomlab/SARS2_RBD_Ab_escape_maps (last accessed 11 March 2022), and the version described in this paper is at https://github.com/jbloomlab/SARS2_RBD_Ab_escape_maps/tree/bioRxiv_v1 (last accessed 11 March 2022).The most up-to-date code and data used to implement the escape estimator are at https://github.com/jbloomlab/SARS2_RBD_Ab_escape_maps/blob/main/processed_data/escape_calculator_data.csv (last accessed 11 March 2022), and the version used for this paper is at https://github.com/jbloomlab/SARS2_RBD_Ab_escape_maps/blob/bioRxiv_v1/processed_data/escape_calculator_data.csv (last accessed 11 March 2022).The data used by the escape estimator are at https://github.com/jbloomlab/SARS2_RBD_Ab_escape_maps/raw/main/docs/_includes/escape_calc_chart.html (last accessed 11 March 2022), with the version described in this paper at https://github.com/jbloomlab/SARS2_RBD_Ab_escape_maps/blob/bioRxiv_v1/docs/_includes/escape_calc_chart.html (last accessed 11 March 2022).A downloadable HTML version of the escape estimator is at 3.2.https://jbloomlab.github.io/RBD_escape_calculator_paper/ (last accessed 11 March 2022). These figures allow mousing over points to see details, etc.Interactive versions of all of the figures in this paper are at 3.3.https://github.com/jbloomlab/SARS-CoV-2-RBD_MAP_COV2-2955 (last accessed 11 March 2022). In total, these studies contain data for thirty-six monoclonal antibodies. Three of these antibodies  were elicited by infection with SARS-CoV-1 and so are excluded from the datasets used for the calculations in this paper, although the estimator has an option (eliciting_virus) that allows optional inclusion of these antibodies. The majority of the antibodies were originally isolated from cohorts of individuals infected with SARS-CoV-2 in the first half of 2020 and were initially characterized by the Crowe lab (https://jbloomlab.github.io/SARS2_RBD_Ab_escape_maps/ (last accessed 11 March 2022) and available in raw form at https://raw.githubusercontent.com/jbloomlab/SARS2_RBD_Ab_escape_maps/main/processed_data/escape_data.csv (see https://github.com/jbloomlab/SARS2_RBD_Ab_escape_maps/blob/bioRxiv_v1/processed_data/escape_data.csv for a stable version of the raw data corresponding to that used in this paper) (last accessed 11 March 2022).The experimental data used by the escape estimator are drawn from seven previously published deep mutational scanning studies  and one rowe lab , Nussenzrowe lab , or Vir rowe lab , with a rowe lab . The fulescape_metric) to use the mean instead  with min_upperlim=1 and max_from_quantile=: essentially this corresponds to scaling the site-escape values for each antibody so that a value of one corresponds to the larger of the maximum escape at a site or twenty times the median value across sites. Specifically, the normalization is done so that for each antibody a, the maximum escape r is set so that r for antibody a. The rationale for this normalization is that it usually scales the site-level escape metric so that the maximum value at a site for each antibody is one, but for very ‘flat’ escape profiles where no site has more escape than twenty times the median, then the maximum value is smaller corresponding to no real peaks of escape for the antibody.We normalize the site-level escape metrics for each antibody to account for different strengths of antibody selection in different experiments using the approach described in 3.4.The escape estimator determines the impact of mutating sites by calculating how much each antibody is escaped by mutations at each site and adjusting its contribution to the overall polyclonal mix accordingly.a we have a deep mutational scanning measurement r escapes that antibody. In the absence of any mutations, the overall escape map shown for the polyclonal mix is simply the mean over all antibodies, A is the number of antibodies.Specifically, for each Antibody a, much of the binding is lost . The variable s represents how dramatically binding is lost for mutations at sites of escape that are not the strongest: larger values of s means mutations at even moderate sites of escape reduce binding a lot. In this paper we report calculations with s = 2, although the estimator has an option (mutation_escape_strength) to choose other values. We then define the escape map after the mutations Let The overall antibody binding scores represent the fraction of antibodies that still bind and are calculated simply as 3.5.https://jbloomlab.github.io/SARS2_RBD_Ab_escape_maps/escape-calc/ (last accessed 11 March 2022) is implemented using Altair  and has all the same options as the interactive estimator.A Python module that implements the calculations is at 3.7.https://github.com/jbloomlab/RBD_escape_calculator_paper/tree/main/results/neut_studies (last accessed 11 March 2022).For 3.8.For veac021_SuppClick here for additional data file.


Aldose reductase, classified within the aldo-keto reductase family as AKR1B1, is an NADPH dependent enzyme that catalyzes the reduction of hydrophilic as well as hydrophobic aldehydes. AKR1B1 is the first enzyme of the so-called polyol pathway that allows the conversion of glucose into sorbitol, which in turn is oxidized to fructose by sorbitol dehydrogenase. The activation of the polyol pathway in hyperglycemic conditions is generally accepted as the event that is responsible for a series of long-term complications of diabetes such as retinopathy, cataract, nephropathy and neuropathy. The role of AKR1B1 in the onset of diabetic complications has made this enzyme the target for the development of molecules capable of inhibiting its activity. Virtually all synthesized compounds have so far failed as drugs for the treatment of diabetic complications. This failure may be partly due to the ability of AKR1B1 to reduce alkenals and alkanals, produced in oxidative stress conditions, thus acting as a detoxifying agent. In recent years we have proposed an alternative approach to the inhibition of AKR1B1, suggesting the possibility of a differential inhibition of the enzyme through molecules able to preferentially inhibit the reduction of either hydrophilic or hydrophobic substrates. The rationale and examples of this new generation of aldose reductase differential inhibitors (ARDIs) are presented. Aldose reductase E.C. 1.1.1.21) is a cytosolic enzyme which belongs to the aldo-keto reductase superfamily that includes about 190 enzymes capable of reducing different carbonyl substrates including sugars and products deriving from lipid peroxidation .1.1.21 i. The enz+ dependent reaction , acting as a cofactor of antioxidant enzymes such as glutathione reductase, the consumption of NADPH causes an increase of cell oxidative stress  pyrimidine compounds  . Some otic site . To concIn any case, the search for AKR1B1 differential inhibitors appears to be a new and promising research field. Understanding the structural requirements that allow the differential inhibition of AKR1B1 can first of all contribute to the development of new molecules able to counteract the secondary complications of diabetes without affecting the detoxifying capacity of the enzyme, but it can also contribute to the development of new inhibition strategies for all those multifunctional enzymes capable of recognizing different substrates through different interaction pathways.


Lactobacillus rhamnosus GG (LGG) treatment. Furthermore, an increased proliferation and decreased apoptosis in colon epithelial cells were observed in the LGG-treated septic mice. In vitro, we found that a LGG supernatant was effective in maintaining the colonoid morphology and proliferation under the damage of TNF-α. Both in the mice colon and the colonoid, the LGG-induced barrier repair process was accompanied by an increased expression of Lgr5+ and lysozyme+ cells. This may be attributed to the upregulation of the IL-17, retinol metabolism, NF-kappa B and the MAPK signaling pathways, among which, Tnfaip3 and Nfkbia could be used as two potential biomarkers for LGG in intestinal inflammation therapy. In conclusion, our finding suggests that LGG protects a sepsis-injured intestinal barrier by promoting ISCs regeneration, highlighting the protective mechanism of oral probiotic consumption in sepsis.Disruption of the intestinal barrier is both the cause and result of sepsis. The proliferation and differentiation of intestinal stem cells (ISCs) promote the regenerative nature of intestinal epithelial cells, repairing the injured intestinal mucosal barrier; however, it is uncertain whether the recovery effects mediated by the ISCs are related to the gut microbiota. This research found that the survival rate of septic mice was improved with a  Lactobacillus rhamnosus GG (LGG) has been one of the most widely used probiotic genera to remold the intestinal microbiota; however, the effects of LGG on modulating the intestinal barrier function remain largely uncertain, and lacking an appropriate in vitro model has been one of the reasons.Intestinal epithelium dysfunction induced by sepsis results in an increased translocation of bacteria to the blood, contributing to the adverse outcome of sepsis . The disLGG [LGG acts on traditional 2D intestinal epithelial cell cultures may be limited. Therefore, a 3D colonoid model composed of enterocyte cells, goblet cells, intestinal stem cells (ISCs), Paneth cells, and enteroendocrine cells [LGG on the intestinal barrier function. Colonoids are characterized by self-differentiation, organization, and forming spatial structures such as the intestine in vivo, making them an ideal model for the research of colon disease pathogenesis [Due to a series of host biological reactions, including cell proliferation and apoptosis, anti-oxidative stress, metabolism, and immune activity that can be affected by LGG , explorine cells  was usedogenesis  and drugogenesis .LGG on the intestinal barrier function in sepsis. Colonoids were applied to study whether a pretreatment with the supernatant of LGG can prevent TNF-α-induced changes in ISCs, Paneth cells, and tight junction protein expression. An RNA-seq analysis revealed the transcriptome profiling of colonoids with different treatments and this might help to obtain further insights about the beneficial effects of LGG [LGG treatment. In summary, we established a new platform and explored the stimulatory effect of LGG on ISCs regeneration, which explains the protective effects of LGG on the colon barrier in another way, except for competing with pathogenic bacteria and producing antimicrobial peptides [This study combined colonoid and RNA-sequencing (RNA-seq) to investigate the effects of s of LGG . Therefopeptides .Four-weeks-old male C57BL6 mice were purchased from the Experimental Animal Center of Zhejiang (license number: SCXK (J) 2019-0002). The animal research was approved by the Animal Experimental Ethical Inspection of the First Affiliated Hospital, College of Medicine, Zhejiang University and followed the guidelines of the American Association for Laboratory Animal Science.Lactobacillus rhamnosus GG (ATCC 53103) (2 × 109 CFU/mL) once a day for four weeks before a cecum ligation and puncture (CLP) operation), a control septic group, and a sham group , respectively. The detailed steps of the CLP operation refer to the research of Rittirsch D [The mice were split randomly into a LGG + septic group , and maintained on MRS agar plates  at 37 °C for 24 h. Bacterial 16S ribosomal RNA was sequenced to confirm the LGG strain. The confirmed LGGs were cultured in the MRS broth at 37 °C overnight until the logarithmic phase. The medium was centrifuged , and the supernatant was filtered with a 0.2 μm filter to remove the live bacteria. Escherichia coli MG1655 (ATCC 700926) were cultured in a brain heart infusion (BHI) broth as appropriate at 37 °C under aerobic conditions overnight until reaching the logarithmic phase. The supernatant was then obtained just as in the steps for the LGG.The 2+ and Mg2+ (DPBS)  three times. Then, a vertical incision was made in the colon fractions, gently rewashed using DPBS several times, and cut into 2 mm pieces. To isolate the crypts from the basal membrane, the colon pieces were incubated in a Gentle Cell Dissociation Reagent (GCDR)  for 20 min at room temperature, centrifuged at 20 rpm, then resuspended with cold DPBS containing 0.1% bovine serum albumin (BSA) , before being stood for 30 s until most of the colon fractions were sinking to the bottom. The supernatants were filtered through a 70 μm cell mesh  and collected in a 50 mL centrifuge tube. The resuspension and filtration steps were repeated three times to obtain the other three supernatants, choosing one that contained the most crypts for the following colonoids culture. After counting under light microscopy, the colon crypts were resuspended using a mixture of IntestiCult™ Organoid Growth Medium  and Matrigel Matrix  in a ratio of 1:1. Then, 50 µL of the colon crypt suspension (500 crypts) in each well was planted on a prewarmed 24-well plate to form a dome, and 750 μL of room-temperature Organoid Growth Medium was added to each well and kept at 37 °C. The medium was changed three times per week after the enteroids were established, and the colonoids were passaged every 7–10 days in a ratio of 1:2.Stem cell-enriched proximal colon fractions from four-week-old C57BL6 male mice were used to culture the colonoids. Briefly, the proximal 3–6 cm of the colon was obtained and washed with cold Dulbecco’s Phosphate buffered-saline without CaE. coli + TNF-α group (where the mice colonoids were treated with TNF-α (100  ng/mL) for 24 h, but before that, the colonoids were co-cultured with a supernatant of E. coli (5 μL per well) for 12 h). Here, both live and heat-killed LGG decreased LPS-induced proinflammatory mediators and increased anti-inflammatory mediators [The colonoids were divided into four groups. The first, was a TNF-α group (where the mice colonoids were treated with TNF-α (100  ng/mL) for 24 h). Second, was a LGG + TNF-α group (where the mice colonoids were treated with TNF-α (100  ng/mL) for 24 h, but before that, the colonoids were co-cultured with the supernatant of LGG (5 μL per well) for 12 h). The supernatant of the LGG addition promoted the growth of colonoids in a dose-dependent manner; therefore, 5 μL per well of the LGG supernatant was chosen as the test dose in subsequent experiments. Third, was an ediators . MoreoveFor mice tissue staining, the proximal colon tissues of the C57BL6 mice were fixed in 4% paraformaldehyde  for 24 h, then embedded in paraffin, and sectioned at 4 μm. For the colonoids staining, the colonoids were obtained and fixed in 4% paraformaldehyde at 4 °C for 6 h, then dehydrated with graded ethanol, embedded in paraffin and cut at 0.5 μm for immunostaining. The sections of the colon tissue and colonoids were deparaffinized and hydrated, and then incubated with anti-mouse occludin antibody , anti-mouse lysozyme antibody , anti-mouse Lgr5 antibody , or anti-mouse Ki67 antibody  overnight. Subsequently, the sections were incubated with secondary antibodies  for 1 h at room temperature. Moreover, the samples were treated with a terminal deoxynucleotidyl-transferase-mediated dUTP-biotin nick end-labeling (TUNEL)  assay to evaluate the apoptosis of both the colon epithelial cells and colonoids.n = 5; LGG, n = 5). The DNA was extracted from frozen fecal samples using a QiaAMP DNA stool Minikit  according to the manufacturer’s instructions. The LGG quantification was performed using a quantitative PCR (QT-PCR) with the following primers: LactoF, 5′-AGCAGTAGGGAATCTTCCA-3′; and LactoR, 5′-ATTYCACCGCTACACATG-3′. The PCR reaction was performed as follows: at 95 °C for 5 min, followed by 35 cycles at 94 °C for 15 s, then at 53 °C for 30 s, and at 72 °C for 45 s before a final extension at 72 °C for 10 min. The quantification data from the PCR analysis were expressed as log qPCR copy/fecal (g).Fecal samples were obtained from a separate set of mice before the CLP operation , and then subjected to a reverse-transcribed reaction with a miScript Reverse Transcription kit . The synthesized cDNA from the above steps was applied for the quantitative PCR (QT-PCR) using SYBR-Green . LGG + TNF-α group of murine colonoids (with three replicates of each group). They were then submitted for a subsequent library construction. The RNA-seq libraries were established applying a NEBNext Ultra™ RNA Library Prep Kit for Illumina  according to the operation manual of the manufacturer, and the index codes were introduced to identify the different sequencing samples. After constructing the libraries, a Qubit2.0 Fluorometer was used for the initial quantification, and then an Agilent 2100 bioanalyzer  was applied to test the insert size of the library; when the latter met expectations, a qRT-PCR was next adopted to quantify the effective concentration of the library. An effective concentration ≥ 2 nM was considered as a qualified library. After the quality test, the libraries were pooled according to the effective concentration and demand for targeting offline data volume, and then sequenced on an Illumina Hiseq platform. Raw data was presented in the form of a fastq format, and was filtered by removing reads with an adapter, namely, reads containing N , and low-quality bases to obtain clean reads. Hisat2 v2.0.5  was used to compare the clean reads with the reference genome to obtain the location information of the reads in the reference genome. The read numbers mapped to each gene were calculated using the Htseq-count package. Then, the fragments per kilobase million (FPKM) of each gene was obtained based on the depth and gene length of the sequencing. DEGs between the TNF-α group and the LGG + TNF-α group were determined using the DESeq2 R package. A significance analysis of the microarray data was performed, with the selection criteria as follows: (1) an adjusted p-value (Padj) ≤ 0.05; and (2) |log2 (fold change)| >  1. The genes were classified into up-regulated and down-regulated genes based on the |log2 (fold change)| value. The selected DEGs were further used for the GO (Gene Ontology) database and a KEGG (Kyoto Encyclopedia of Genes and Genomes) analysis. The GO is a database developed to describe gene functions. The KEGG is a database that integrates information on genomic, chemical and system functions. The R packages of the “top GO” and “Cluster Profiler” were used for the GO and KEGG analyses, respectively. Padj ≤ 0.05 was a statistical difference in the GO and KEGG analyses. Finally, Cytoscape (version 3.9.1) was applied to score the effect of different genes on the protein–protein interaction (PPI) network and to generate an LGG pathway target interaction network to identify the hub genes.Cellular RNA was extracted from the colonoids and 4 μg of RNA was extracted from the control, the TNF-α group and the t test was applied to compare the abundance of LGG in the fecal segment of the colon. A one-way analysis of variance (ANOVA) with a post hoc Bonferroni test was used to compare the variables of the gene expression, tissues, and colonoids among the three groups. The data were presented as means ± SD, and a p ≤ 0.05 was considered statistically significant.GraphPad Prism 6.0  was used for the data analysis. The Student’s LGG for four weeks, the LGG colonized in the mucosa of the colon under an electron microscope, which was essential for the LGG to interact with the colon epithelium  could protect the colonoids from the damage caused by the TNF-inflammatory factor TNF-α damage. First, we established a mice colonoid model  in the TNF-α-treated colonoids ; however, the declining trend was reversed by the LGG instead of the reatment A–B. Furtolonoids C–D. The olonoids E–H. Unliolonoids E. Panetholonoids G.LGG, we built a colonoid model to mimic IEC-microbe interactions in vitro. An RNA-seq analysis of the total RNA extracted from the colonoid stimulated with TNF-α, LGG + TNF-α, or just a colonoid growth medium was performed. We found 2000 DEGs between the TNF-α treated and control colonoid groups, with 868 upregulated, and 1132 downregulated in the TNF-α group compared to the control group. With the LGG treatment, the TNF-α only upregulated the expression of 407 genes of 1015 DEGs while decreasing 608 gene expressions compared to the control group (LGG + TNF-α and the control group (0.922) was markedly higher than that between the TNF-α and the control groups (0.86), indicating that the TNF-α induced a special gene transcriptional profile in the colonoids, which could be remolded by the LGG treatment   C. To furactivity D.LGG were mainly enriched in the IL-17-signaling pathway, retinol metabolism-signaling pathway, NF-kappa B-signaling pathway, and the MAPK-signaling pathway (red boxes). All of these signaling pathways were associated with the proliferation and regeneration of ISCs. Furthermore, the KEGG pathways involved in reconstructing the colon barrier, such as cell adhesion molecules, focal adhesion, ECM receptor interaction, cytokine–cytokine receptor interaction (blue boxes), and pathways related to cell apoptosis and inflammation—including the TNF-signaling pathway, arachidonic acid metabolism (black boxes)—were included in the top 20 pathways  zone to experience a proliferation boom. Afterward, they continue to move to the top of the crypt and differentiate into all types of colon cells . FollowiThe Wnt- and Notch-signaling pathways are critical for the proliferation and self-renewal of ISCs ,27. HoweIn the PPI analysis, using the MCODE in Cytoscape, Tnfaip3 and Nfkbia were chosen as two hub genes for a high degree of connectivity. Tnfaip3, known as A20, maintains the stability of the intestinal barrier from IEC apoptosis caused by cytokines . Mice wiHowever, this study has several limitations. First, the 3D spheroidal architecture of a colonoid is an obstacle for a microbe to access the apical side of colon epithelial cells. The Toll-like receptor (TLR) and other pattern recognition receptors vary, when comparing the apical and basal surfaces of cells. Therefore, the measurement of the barrier function is indirect in a 3D colonoid model. To solve the problem, the conversion of 3D colonoids into 2D monolayer colonoids is required in further studies. Second, although a prophylactic consumption of LGG decreased the sepsis mortality observed in this research, few studies have indicated the potential risk of bacteremia after a probiotic administration . ConsequLGG promoted the recovery of the colon epithelial structure under sepsis and a TNF-α treatment. Based on a 3D colonoid model and RNA-seq, multi-pathways and multi-targets that play a role in ISC regeneration illustrated the therapeutic effect of LGG in sepsis. Moreover, Tnfaip3 and Nfkbia may be used as two potential biomarkers for LGG in treating intestinal inflammation, and our research provides a new perspective on the therapeutic effect of LGG.Our results revealed that


Heart failure with preserved ejection fraction (HFpEF) is an increasing public health concern. Currently, data regarding the clinical application value of plasma Galectin-3  in HFpEF are contradictory. Therefore, we performed the following meta-analysis to appraise the clinical implications of serum Gal-3 in HFpEF, including its capacity to predict new-onset disease, long-term unfavorable endpoints, and the degree of cardiac structural abnormality and left ventricular diastolic dysfunction (LVDD).PubMed, Embase, Scopus, and Web of Science were retrieved exhaustively from their inception until November 30, 2021, to obtain studies assessing the correlation between plasma Gal-3 and the clinical features of HFpEF .p = 0.910, I2 = 0%; P = 0.002); (ii) the high risk of adverse outcomes of HFpEF patients [all-cause death  and the composite events [all-cause death and HF hospitalization  or cardiovascular (CV) death and HF hospitalization ]; (iii) echocardiographic indices [E/e ratio  and DT ].A total of 24 papers containing 27 studies were ultimately included in the present research. The results of the meta-analysis revealed that high plasma Gal-3 levels are strongly associated with the following clinical characteristics of HFpEF: (i) the increased risk of new-onset HFpEF , and the severity of LVDD in HFpEF populations. Heart failure (HF) with preserved ejection fraction (HFpEF) is a heterogeneous medical condition not only characterized by a normal left ventricular ejection fraction (LVEF) and a slew of symptoms and signs due to either cardiac remodeling or left ventricular diastolic dysfunction (LVDD) , but alsCardiac biomarkers are positively associated with the pathophysiology of HF, allowing them to be applied to track disease severity and progression and determine appropriate therapy . CurrentA meta-analysis was undertaken based on Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) . Two selpost hoc analysis studies); (ii) endpoints: The connection between plasma Gal-3 concentrations and the risk of new-onset HFpEF or adverse endpoints of HFpEF patients , with the results presented as hazard ratios (HRs) and 95% confidence intervals (CIs). Moreover, the relevance of plasma Gal-3 concentrations and echocardiographic parameters , and the findings were expressed as correlation coefficient (r). Articles will be eliminated if they satisfy at least one of the following exclusion criteria: (i) irrelevant articles or duplicated studies; (ii) the papers were case reports, reviews, letters, commentaries, editorials, or non-human studies; (iii) the articles reported irrelevant endpoints; (iv) the articles lacked full text or the sufficient crude data.During the screening process, candidate research must fulfill the following inclusion criteria: (i) study design: observational studies were included  using the Newcastle–Ottawa Quality Assessment Scale (NOS) system, a “star-based” grading system which is comprised of three parts . Specifically, we assessed the following characteristics of each study: (i) representativeness of the exposed cohort, (ii) selection of the non-exposed cohort, (iii) ascertainment of exposure, (iv) demonstration that outcome of interest was not present at the start of the study, (v) comparability of cohorts based on study design or analysis, (vi) assessment of outcomes, (vii) was follow-up long enough for outcomes to occur, and (viii) adequacy of follow-up of cohorts.The required data from the included research were extracted and tabulated in specifically constructed Microsoft Excel spreadsheets for analysis. The extracted contents were as follows: (i) publication details: last name of the first author, year of publication, and the country setting; (ii) demographic characteristics: sample size, proportions of males, and mean age; (iii) study details: study design, mean or median concentrations of plasma Gal-3, variables adjusted, follow-up duration, and endpoints; (iv) NOS quality score. Two independent researchers (Wang and Qiao) conducted data extraction, and disagreements were resolved by mutual coordination or third-party adjudication (Dong and Liu).2 statistic. The fixed-effect model was employed when the Q-test  revealed no significant heterogeneity across studies. When the Q-test (I2 > 50% or p < 0.05) found prominent heterogeneity among studies, the random-effect model was utilized, followed by a Galbraith plot to explore the source of heterogeneity if over 10 trials were involved. Furthermore, the Funnel plot, Begg's test, and Egger's test were conducted to estimate publication bias, and sensitivity analysis was employed to evaluate the impact of single research on the overall estimate by omitting one study each time.STATA (Version 16.0) was used to analyze the association of plasma Gal-3 and the risk of new-onset HFpEF or adverse outcomes. The connection between plasma Gal-3 and echocardiographic data was analyzed using Comprehensive Meta-Analysis Software (Version 3.0). The heterogeneity among the included studies was appraised by the Cochran Q statistics (chi-square test) and quantified with the IA flowchart of database search and text screening procedure is demonstrated in post hoc analysis studies. A total of 24 articles published from 2011 to 2021 were investigated. These papers contained 27 studies, including 15 prospective cohort studies, 10 case-control studies, and two p = 0.910, I2 = 0%; P = 0.002)  .p = 0.001, I2 = 61%; P = 0.000)     or CV de= 0.000) ]. Moreov= 0.000) , 32, 35 = 0.000) .p = 0.000, I2 = 93%; P = 0.001)       and DT (= 0.027) , but not= 0.078) , LAVI (r= 0.160) , and LVM= 0.218) .p-values of Begg's test (p = 0.029) (p = 0.002)   and Egge= 0.002)  were low= 0.002) . OverallHF represents the end-stage manifestation of numerous cardiovascular illnesses. The 2021 European Society of Cardiology (ESC) guidelines advocated categorizing HF into three groups based upon the measurement of LVEF: HFrEF (LVEF ≤ 40%), HF with mildly reduced ejection fraction , and HFpEF (LVEF ≥ 50%) , 43. MorGal-3, a soluble β-galactoside-binding lectin implicated in the process of cell proliferation, adhesion, migration, and apoptosis, appeared to promote inflammation or tissue fibrosis in HF , atherotp = 0.910, I2 = 0%; P = 0.002), implying that plasma Gal-3 can be applied as a biomarker to predict new-onset HFpEF. Second, increased plasma Gal-3 levels were correlated with the high risk of long-term adverse outcomes in HFpEF patients  and the composite events [all-cause death and HF hospitalization  or CV death and HF hospitalization ], indicating that plasma Gal-3 can be utilized as a predictor of undesirable long-term prognosis in HFpEF patients. Finally, elevated Gal-3 levels were connected to E/e ratio  and DT , revealing that plasma Gal-3 can be applied as a predictor of LVDD in HFpEF populations.The findings of the current meta-analysis illustrated a strong association between plasma Gal-3 and HFpEF clinical characteristics. First, high plasma Gal-3 levels were significantly associated with the increased risk of new-onset HFpEF , and the severity of LVDD in HFpEF populations.The original contributions presented in the study are included in the article/GD and JL designed this meta-analysis. GD reviewed the manuscript. YS performed the meta-analysis and wrote the manuscript. YS and XS developed the search strategy and performed literature searches and screening. Data extraction was conducted by WQin and WQia, while CL and CY evaluated the quality of the enrolled studies. All authors contributed to the article and approved the submitted version.This study was supported by grants from the National Natural Science Foundation of China (8207153216) and the Major Innovation Project of the China Academy of Traditional Chinese Medicine (CI2021A00903).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


The main management strategy of heart failure with preserved ejection fraction (HFpEF) is prevention since HFpEF is associated with many cardiovascular (CV) risk factors, especially since HFpEF is linked to a high risk for both mortality and recurrent heart failure (HF) hospitalizations. Therefore, there is a need for new tools to identify patients with a high risk profile early. Regional strain assessment by CMR seems to be superior in describing deformation impairment in HF. The MyoHealth score is a promising tool to identify cardiac changes early.n = 19) was divided into asymptomatic subjects without CV co-morbidities or evidence of cardiac abnormalities and (n = 12) and asymptomatic subjects with CV co-morbidities or evidence of cardiac abnormalities (n = 7) as well as patients with HFpEF (n = 19). We performed CMR scans at rest and during a stress test using isometric handgrip exercise (HG). Assessing the MyoHealth score at rest revealed preserved regional strain in 85 ± 9% of LV segments in controls, 73 ± 11% in at Risk subjects and 73 ± 8% in HFpEF patients. During stress the MyoHealth score was 84 ± 7% in controls, 83 ± 7 in at risk subjects and 74 ± 11 in HFpEF patients.Heart failure patients irrespective of LVEF and asymptomatic controls were recruited, and CMR based measures were obtained. For this analysis the asymptomatic control group (In summary, we show for the first time that asymptomatic subjects with increased CV risk present with HFpEF like impaired myocardial deformation at rest, while they show results like controls under HG stress. The potential of preventive treatment in this group of patients merits further investigation in future.https://drks.de/search/de/trial/DRKS00015615], identifier [DRKS00015615].[ Heart failure with preserved ejection fraction (HFpEF) is defined as symptomatic heart failure (HF), a left ventricular ejection fraction (LVEF) ≥ 50% and evidence of diastolic dysfunction and/or raised LV filling pressures . HFpEF iWith very limited and only recently introduced treatment options, prevention including the early identification of vulnerable patients with CV risk factors, remains the focus of HFpEF management , 5. ThisOnce, HFpEF is suspected, the introduced algorithm to diagnose HFpEF is a comprehensive approach requiring multiple steps . The morHence, there is an urgent need to screen for patients at risk in clinical routine to prevent HFpEF.Cardiovascular magnetic resonance imaging (CMR) provides anatomical and functional cardiac parameters. Myocardial strain analysis may detect impaired myocardial contractility in patients despite a preserved LVEF . RegionaWe hypothesize that asymptomatic healthy subjects with CV co-morbidities may demonstrate detectable impairments in regional strain compatible with pre-clinical HFpEF.This study was a prospective study conducted in Berlin, Germany, approved by the local Ethics Committee . Its rationale and design have been described previously –13.Briefly, HF patients irrespective of LVEF and controls without HF were recruited, and CMR based measures of cardiac structure and function, including assessment of cardiac contractility were obtained. For this analysis, we included the control subjects and the HFpEF patients.nota bene patients with atrial fibrillation were excluded to maintain better CMR image quality.We divided the control group into (a) subjects without HF and no CV co-morbidities or evidence of cardiac dysfunction and (b) those without HF but CV co-morbidities or evidence of cardiac dysfunction. CV co-morbidities or evidence of cardiac dysfunction were defined as the presence of diabetes, suboptimal managed arterial hypertension , increased NT-proBNP levels (>120 pg/dL), or LV hypertrophy  on CMR (minimum 1 criterion). They were compared to (c) patients with HFpEF . We perf® Suite MR , strain analysis by MyoStrain   , 11.t-test, comparisons across three groups were conducted using one-way analysis of variance (one-way ANOVA). A P-value < 0.05 was considered statistically significant.Pairwise comparisons were conducted using a student’s The endpoint was the MyoHealth score at rest and under HG.n = 12; controls with CV risk factors: n = 7 and HFpEF: n = 19) are shown in 1–Q3]: control: 63.00 [59.22–64.70]%; at risk: 61.52 [57.88–64.58]%; HFpEF: 61.12 [58.17–64.17]%.The baseline characteristics of the three groups , at the same level as the HFpEF cohort (p = 0.45). This is in line with our recent finding that demonstrated the potential diagnostic window across different heart-failure stages using CMR-strain-analysis (p = 0.32) and higher than the HFpEF values (p = 0.07). The “at risk” group improved significantly between rest and stress  or HFpEF (p = 0.35). Like the HFpEF pattern, the impaired segments were mainly septal. During stress the impaired septal segments improved primarily in terms of circumferential strain (At rest, the MyoHealth score in at the risk cohort was reduced compared to the healthy controls (analysis . Howeverl strain . It has l strain .In summary, we show for the first time that asymptomatic subjects with evidence of CV risk present with HFpEF like impaired myocardial deformation at rest. The absence of HF symptoms in these subjects is well explained by the compensation capacities during stress when their deformation capacities are similar to healthy subjects.Performing a quick medication-free CMR-stress-test as HG in asymptomatic patients provides the chance to assess cardiac manifestations of their individual risk-profile. In patients with pathological changes, a stricter management of co-morbidities and shorter follow-up intervals may be adequate to prevent the transition to HFpEF. The potential of preventive treatment in this group of patients merits further investigation in future studies. The feasibility of its use in clinical practice is underlined by the quick acquisition of the exam as it added only up to 10 min. to the regular scan protocol during our study. This time included the more extensive informed consent process regarding the HG application and the additional image acquisition during the HG test.However, the limited number of subjects included in this study restrains the generalizability of the results. Nonetheless, the reasoning that changes in cardiac function do not develop at a certain tipping point but are present to some degree even at a preclinical state is both shown and intuitive – we propose an emerging tool promising to detect changes early.Therefore, CMR scans including HG are a promising tool in future preventive cardiology trials for better risk stratification and phenotyping.djawid.hashemi@charite.de.The datasets presented in this article are not readily available because data safety regulations of the informed consent process limit open access. Requests to access the datasets should be directed to DH, The studies involving human participants were reviewed and approved by the Charité – Universitätsmedizin Berlin Ethics Committee. The patients/participants provided their written informed consent to participate in this study.DH, FE, and SK: conception and design of the study and literature review. DH, MB, and SK: analysis and interpretation of the data. DH: drafting of the manuscript. DH, PD, and MB: data collection. All authors contributed to revising and editing the manuscript.


Notably, simultaneous immunization with a mixture of six VLPs revealed no immunological interference among the component antigens. These results demonstrate that Hexa-VLPs are promising broad-spectrum vaccines to provide immunoprotection against major GI/GII epidemic strains in the future.Norovirus is a major cause of acute gastroenteritis worldwide, and no vaccine is currently available. The genetic and antigenic diversity of Norovirus presents challenges for providing broad immune protection, which calls for a multivalent vaccine application. In this study, we investigated the possibility of developing a virus-like particle (VLP)-based 6-valent Norovirus vaccine candidate (Hexa-VLPs) that covers GI.1, GII.2, GII.3, GII.4, GII.6, and GII.17 genotypes. Hexa-VLPs (30 µg) adjuvanted with 500 µg of aluminum hydroxide  were selected as the optimal immunization dose after a dose-escalation study. Potent and long-lasting blockade antibody responses were induced by 2-or 3-shot Hexa-VLPs, especially for the emerging GII.P16-GII.2 and GII.17 (Kawasaki 2014) genotypes. Hexa-VLPs plus alum elicited Th1/Th2 mixed yet Th2-skewed immune responses, characterized by an IgG1-biased subclass profile and significant IL-4 Norovirus (NoV) is a major cause of acute gastroenteritis (AGE), causing 685 million infections and 210,000 deaths worldwide each year .Humans are mainly infected by GI, GII, and GIV genogroups . HoweverEscherichia coli (E. coli), Pichia pastoris (yeast), or plants , we additionally chose GII.P16-GII.2, GII.3 (Hong Kong 2014 strain), GII.6 (Guangdong 2016 strain), GII.17 (Hong Kong 2014 strain) as constituents. Our results have demonstrated that no immunological interference exists among the component antigens. Strain-specific humoral and T-cell responses without mutual immunogenicity inhibition indicate a viable strategy to combine six heterogeneous NoV VLPs together, which sheds light on the development of broad-spectrum NoV vaccines.Vaccines against NoVs are likely to face obstacles similar to those faced by seasonal influenza vaccines: new influenza epidemic strains arise every 1 to 2 years due to the general antigenic drift and shift . DespitePichia pastoris strains  using an electroporator  under a condition of 1.5 kV, 5 ms. Positive colonies were selected from the PAD plates and confirmed via PCR and Western blot.VP1 genes of GI.1 Norwalk strain (GenBank ID: NP_056821.2) , Env/CHNvia centrifugation and resuspended in 0.15 M PBS containing 1 mM PMSF, homogenized at 4°C, 1500 bar, and supernatants were then collected via centrifugation at 12,000 rpm for 30 min. 7% PEG6000 and 0.2 M NaCl were added to concentrate the supernatants overnight. Concentrated supernatants were centrifuged, the precipitate was resuspended in 0.15 M PBS and dissolved at 4°C overnight. The next day, the pellets were centrifuged at 12,000 rpm for 30 min, and the supernatants were retained.Positive clones were selected from PAD plates and cultured in 5 ml BMGY at 220 rpm, 28°C for approximately 30 h, then 1-2 ml solution was transferred to 500 ml BMGY and inoculated under the same conditions until OD600 reached 2-6. Fungi were harvested and added to 100-150 ml BMMY medium, methanol was supplemented every 24 h to reach a final concentration of 1%. After 3-5 days, fungi were collected Yeast extract supernatant was preliminarily subjected to 20% sucrose cushion ultra-centrifugation at 27,000 rpm for 4 h. Pellets were resuspended in 0.15 M PBS, dissolved at 4°C overnight, layered onto 10-50% sucrose gradients for ultracentrifugation at 39,000 rpm for 3 h . Twelve fractions were taken from top to bottom and analyzed for VLPs content using SDS-PAGE. The original pellet buffer was replaced with PBS through Amicon Ultra-15  centrifugation. Purified VLPs were quantified using the Pierce™ BCA Protein Assay Kit .The VLP samples (5-10 µg) were mixed with 5 × loading buffer, boiled for 10 min, and then were subjected to 12% SDS-PAGE. The gel was stained with Coomassie brilliant blue G250 . The purity of VLP protein was analyzed by Image Lab  software. VLPs with purity greater than 95% were used for mice immunization.For western blot analysis, the VLP samples were fractionated by SDS-PAGE and then were transferred to PVDF membrane. The membranes were blocked with 5% skim milk for 2 h at room temperature and then were incubated with mouse anti-VP1 monoclonal antibody at 1:5000 dilution overnight at 4°C. Monoclonal antibodies of GII.17 and GII.4 were purchased , while GI.1, GII.2, GII.3, and GII.6 antibodies were customized . Membranes were then incubated with horseradish peroxidase (HRP)-conjugated goat anti-mouse antibody at 1:10000 dilution  for 1 h at room temperature. Bands were developed using Chemiluminescent Assay Kit .The VLPs were subjected to negative staining with 2% phosphotungstic acid, transmission electron microscopy was performed using a Hitachi HT7700 transmission electron microscope  operated at 80 kV to analyze the shape and integrity of the VLPs.Female BALB/c mice (6-8 weeks old) were purchased from Vital River Laboratory Animal Technology Co., Ltd (Beijing China). For the dose-escalation study, mice were divided into six groups (five mice/group) as follows: (1) PBS + Alum; (2) 6 µg Hexa-VLPs + Alum; (3) 15 µg Hexa-VLPs + Alum; (4) 30 µg Hexa-VLPs + Alum; (5) 60 µg Hexa-VLPs + Alum; (6) 60 µg Hexa-VLPs. The dose of alum  was 500 µg per mouse. Mice were immunized intramuscularly (IM) on days 0, 14 and 28.st Hexa-VLP group: 30 µg Hexa-VLPs + Alum; (9) 2nd Hexa-VLPs group: 30 µg Hexa-VLPs + Alum; (10) 3rd Hexa-VLP group: 30 µg Hexa-VLPs + Alum. Mice in 1st Hexa-VLP group were immunized on day 0. Mice in 2nd Hexa-VLPs group were immunized on days 0 and 28. Mice in monovalent groups, PBS group and 3rd Hexa-VLPs groups were immunized on days 0, 14 and 28. The dose of alum  was 500 µg per mouse. The expression of 2 w, 4 w, 6 w, etc. there-in-after refer to the time point corresponding to the time axis in To compare Hexa-VLPs and monovalent vaccine, mice were divided into ten groups (five mice/group) as follows: (1) PBS + Alum; (2) 5 µg GI.1 + Alum; (3) 5 µg GII.2 + Alum; (4) 5 µg GII.3 + Alum; (5) 5 µg GII.4 + Alum; (6) 5 µg GII.6 + Alum; (7) 5 µg GII.17 + Alum; (8) 1Blood samples were collected 2 days before each immunization and every 2 weeks after the last inoculation. Blood samples were then placed under 37°C for 15 min, and sera were separated by centrifugation at 4,000 rpm for 10 min and then 12,000 rpm for 15 min. Then sera were stored at -80°C till tested.VLPs-specific total IgG and IgG1/IgG2a subclass antibodies were detected using ELISA as described elsewhere in detail . In brierd monovalent VLP immunized mice were assayed for cross-blocking antibody response. Sera were diluted 100-fold and incubated with the other 5 heterologous VLPs that were included in Hexa-VLPs, the following procedures were performed as described above.The ability of antisera to block VLPs interaction with histo-blood group antigens (HBGAs) was assessed using a well-established blockade assay, in which pig gastric mucin (PGM) type III  was used as the source of HBGAs , 22. Briex vivo IFN-γ, TNF-α, IL-2, and IL-4 production using the ELISPOT assay. The BALB/c mice were euthanized after 12 w. The spleens were isolated, mashed, and treated with erythrocyte lysis solution. Multiscreen HTS-IP filter plates  were coated with anti-mouse IFN-γ, TNF-α, IL-2, and IL-4 antibodies, according to the manufacturer’s instructions . The resuspended splenocytes were then filtered through 70 µm tissue screen and plated at a density of 6×105 cells/well. The VLPs were diluted in RPMI 1640 medium to a final concentration of 10 µg/ml and added to the plates. A negative control (1640 medium) and positive control  were added to each assay. The plates were incubated for 48 h at 37°C and 5% CO2, after which the spots were developed by adding biotinylated anti mouse IFN-γ, TNF-α, IL-2, IL-4 antibodies, HRP-conjugated streptavidin, and TMB substrate. Spots were counted using an Immuno-spot® analyzer . The results are expressed as mean spot-forming cells (SFC)/6 х105 cells for each VLP or control.NoV-specific T-cell responses were determined by the ability of VLPs to induce All animal studies were approved by the Institutional Animal Care and Use Committee at Tianjin Medical University (ID: TMUaMEC 2021001), and the animals were cared for in accordance with the institutional guidelines.The Prism 8.4.2 software  was used to draw graphs and perform statistical analyses. Differences between groups were analyzed using one-way analysis of variance (ANOVA) with Tukey’s adjustment. For every test, statistical significance was indicated as *p < 0.05, **p < 0.01, ***p < 0.001, and ****p < 0.0001.Pichia pastoris expression system and identified using Western blot and electron microscopy  genotypes. 30 µg Hexa-VLPs + Alum elicited a peak GII.P16-GII.2-specific GMBT50 of 4,834 at 4 weeks after the third inoculation, and also triggered a potent GII.17-blocking GMBT50 of 5,818 at 2 weeks after the third vaccination. These data confirmed the dominance of Hexa-VLPs in blocking these two emerging genotypes, which hold the potential to cause major epidemics in the future.via ELISPOT assays at the end of the experiment   was able to induce ideal immunity and thus chosen for further studies. We compared Hexa-VLPs with six monovalent VLPs Figure 4 < 0.05) , whereasrd Hexa-VLPs group .Owing to the homology of the six chosen VP1 sequences, we found ubiquitous cross-reactivity among the six VLP strains (nd and 3rd Hexa-VLPs groups triggered robust blocking responses against six VLPs when compared with the PBS group (p < 0.001). At 12 w post-vaccination, antisera from the 3rd Hexa-VLPs group showed higher BT50 against GII.2 than the GII.2 monovalent group (rd Hexa-VLPs decreased (rd GI.1 group (p < 0.05), whereas all the other antisera from the 3rd Hexa-VLPs group elicited comparable genotype-specific blocking responses with the corresponding 3rd monovalent vaccine (p > 0.05). Overall, peak blockade antibodies against GII.2, GII.4 and GII.17, which were elicited by either 2nd or 3rd Hexa-VLPs, were more advantageous than those against GI.1, GII.3 and GII.6. nd and 3rd Hexa-VLPs remained steady throughout the entire test period. Hence, sequential immunization with two or three shots of Hexa-VLPs presented a more advantageous immune effect than the single-dose regimen. There were no detectable cross-blocking antibodies induced by 3rd monovalent VLP immunized antisera, which further confirms the necessity of our novel multivalent vaccine .We further delineated the magnitude of the IgG subclass profile following NoV VLPs vaccination. Each formulation generated high levels of IgG1 and IgG2a antibodies, indicating a mixed Th1/Th2 response  (® (human papillomavirus) (® (hepatitis B virus) (® (human papillomavirus)  , Cervarimavirus) , Merck aB virus)  and Gardmavirus) . Two VLPIn our preclinical study, both the 2-shot and 3-shot regimens induced potent binding antibody responses in mice, which were comparable to those of a VLP-based bivalent vaccine (covering NoV GII.4 and Enterovirus 71)  and a teHigher levels of serum antibodies that block viral binding to HBGAs are associated with a lower risk of illness and infection . The firThe epidemic of GII.17 Kawasaki  and recoMalm et al. confirmed that sequential immunization with genetically distant genotype  was successful in inducing high GII.17-specific immune response, while sequential immunization with closely related VLP  failed to induce strong GII.4 SYD-blocking responses compared to simultaneous immunization. These results indicate that there might be an original antigenic sin (OAS) for closely related antigens, and simultaneous immunization (a cocktail) could avert the problem of immunological interference among component antigens. Malm’s multivalent VLP vaccine showed no immunological inhibition when delivered simultaneously, as binding and blocking antibodies were at an equal level to the responses induced by monovalent VLPs . Hexa-VL+ T cells and induce the differentiation of Th0 cells toward Th1 and Th2 phenotypes  would provide insight into the usefulness of Hexa-VLPs. In conclusion, by combining an ancestor Norwalk GI.1 strain with five other candidate strains, we have developed a novel 6-valent NoV vaccine to guard against multiple NoV genotypes. Hexa-VLPs broadened the breadth of antibody repertoire on the basis of improving their magnitude and elicited long-term memory T-cell responses. Together, Hexa-VLPs hold the potential to become a broad-spectrum NoV vaccine candidate.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/The animal study was reviewed and approved by Institutional Animal Care and Use Committee at Tianjin Medical University(ID: TMUaMEC 2021001).DZ, HL, and WH conceived, designed, and supervised the entire study. LL, JR constructed the vaccines and carried out all immunogenicity evaluations in mice. LL, YW, MX, and JR performed data analysis. YW and DZ wrote the manuscript draft. YG and DZ were responsible for manuscript reviewing and editing. WH, HL, DX, XW, and XZ provided experimental resources and assisted all immunology experiments. All authors contributed to the article and approved the submitted version.This work was chiefly supported by funds from the National Natural Science Foundation of China , and partially supported by the key project of the Natural Science Foundation of Tianjin (20JCZDJC00090).Authors DX, XW, XZ, and HL are employed by Chengdu Kanghua Biological Products Co., Ltd.The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.