For the 13 novel loci, 9 regions have been reported of association with obesity related traits by previous GWA studies. Three loci encoding long intergenic non-protein coding RNAs (lncRNA), and 2 loci involved in N-linked glycosylation are also highlighted in this study.With polygenic risk score (PRS) for autoimmune type 1 diabetes (T1D), this study identified T1D cases with low T1D PRS and searched for susceptibility loci in these cases. Our hypothesis is that genetic effects (likely mediated by relatively rare genetic variants) of non-mainstream (or non-autoimmune) T1D might have been diluted in the previous studies on T1D cases in general. Two cohorts for the PRS modeling and testing respectively were included. The first cohort consisted of 3302 T1D cases and 6181 controls, and the independent second cohort consisted of 3297 T1D cases and 6169 controls. Cases with low T1D PRS were identified using PRSice-2 and compared to controls with low T1D PRS by genome-wide association (GWA) test. Thirteen novel genetic loci with high imputation quality (Quality Score r There is no cure for T1D to date. The molecular mechanisms underlying T1D are complex and not completely understood. Human genetic studies have uncovered multiple T1D genes that contribute to our understanding of the pathogenesis ofT1D7. With the rapid advances in human genomics technology in recent years, over 70 T1D loci have been identified8 (https://www.ebi.ac.uk/gwas/). While these discoveries of T1D-associated genes have greatly increased our knowledge of T1D, our current genetic knowledge on T1D is far from complete, and a large number of T1D genes remain uncovered9. A key bottleneck for the GWAS approach is limitation of sample size even with the presence of collaborative international consortia10. The phenotype of type 1 diabetes has been regarded as heterogeneous. While the majority of T1D patients have autoimmune disease, 5–10% of Caucasian diabetic subjects with recent-onset T1D do not have islet cell antibodies, often referred to as T1bD11. Due to different pathogenesis, T1bD cases may be associated with different genetic loci from autoimmune T1D, or T1aD. However, the smaller proportion of T1bD cases suggests that T1bD-related genetic effects have been diluted in the previous studies with T1D cases studied in general. Besides T1bD, the non-autoimmune and monogenic form of pediatric diabetes, maturity-onset diabetes of the young (MODY) cases, may be misdiagnosed as T1D12, which further contributes to the heterogeneity of the T1D phenotype.Type 1 diabetes (T1D) is caused by T-cell mediated autoimmune destruction of pancreatic β-cells13. The T1D loci identified by the GWAS studies to date are mainly associated with the genetic susceptibility of the major component of the heterogeneous T1D phenotype, i.e. T1aD, while the genetic susceptibility of the minor non-autoimmune components (e.g. T1bD and misdiagnosed MODY) are under-represented in those results likely as a result of being diluted. In this study, we propose that a high T1D PRS score predicts or suggests a T1aD case, whereas a low T1D PRS score in a T1D case suggests the opposite and represents our major interest in this study. Our aim in this study is to identify low PRS T1D cases and to run a separate GWAS in an attempt to uncover genetic loci associated with T1bD patients. Our approach effectively concentrates the dilution of non-mainstream T1D by excluding high PRS T1D cases, to uncover novel genetic loci associated with non-mainstream T1D. Therefore, the dilution of low PRS T1D by misdiagnosed MODY is not a concern. On the other hand, although the low PRS cases may include MODY patients, there are no MODY mutation identified with genome-wide significance in this GWAS study, which is as expected while next generation sequencing, e.g. whole exome sequencing, is the more proper approach.With numerous genetic loci for many human complex diseases identified to date, polygenic risk scores (PRS) aggregate the effects of many genetic variants across the human genome into a single score, an approach that has been shown of improve disease prediction and differential diagnosis14, The Montreal Children's Hospital14, The Diabetes Control and Complications Trial—Epidemiology of Diabetes Interventions and Complications (DCCT-EDIC) cohort (http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000086.v2.p1), the Type 1 Diabetes Genetics Consortium , and later recruited subjects at CHOP, respectively. The T1D cases were mainly recruited by clinical diagnosis, i.e. insulin dependent for at least 6 months, and diagnosed under the age of 18 for the subjects recruited at CHOP and Montreal. The non-mainstream T1D cases in this study were defined by low T1D PRS scores, with the cut-off value of PRC determined by Receiver Operating Characteristic (ROC) curve analysis. The included cases were all confirmed of European ancestry by principal component analysis (PCA) with genome-wide SNP markers, with individuals from other populations or with admixed ancestries excluded. The genotyping was done by the Illumina Human Hap550 Genotyping BeadChip or a newer version of Illumina Genotyping BeadChip. Other demographic, phenotypic and genotypic details about these individuals were described in our previous publication15. Imputation of single nucleotide polymorphisms (SNP) on auto-chromosomes was done using the TOPMed Imputation Server  with the TOPMed (Version R2 on GRC38) Reference Panel, with the quality filters of R2 ≥ 0.3. Altogether, 104,689,647 autosomal single nucleotide variants (SNV) with quality R2 ≥ 0.3 were included in this study. Population stratification was assessed by PCA analysis, and genetic association tests conditioned on sex were corrected by the first 10 principal components (PC). The association test was done using PLINK1.9 software16.6599 European T1D cases and 12,350 European controls were included in this study. The T1D cases were from the Children's Hospital of Philadelphia (CHOP)17, based on the statistics of the training group. The performance of a series of cutoff of T1D association P values  for selection of SNP markers was assessed by the Area Under the ROC Curve (AUC). The P value cutoff with the largest AUC was adopted.To avoid the issue of overfitting for PRS scoring, the subjects were randomly split into two independent cohorts without duplication, i.e. the PRS training cohort (Cohort A) including 3302 T1D cases  and 6181 controls, and the PRS testing cohort (Cohort B) including 3297 T1D cases  and 6169 controls . PRSs of the test cohort were calculated using the Polygenic Risk Score software (PRSice-2)18. Genetic association signals within each locus were plotted by LocusZoom19.The flow chart of the study approach is shown in Fig. Consequently, we switched the two cohorts, i.e. using Cohort B for the statistics of PRS modelling, then we tested the PRS models in Cohort A. GWAS of T1D patients with low PRS was done using the same approach as described above.The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.All methods were carried out in accordance with relevant guidelines and regulations. The study was approved by the Institutional Review Boards of Children’s Hospital of Philadelphia (CHOP). Written informed consent was obtained from each participating subject or, if subjects are under 18, their parent/guardian.Type 1 diabetes (T1D) is a highly heterogeneous genetic disease. Human genetic and genomic study on T1D has gained us significant knowledge on the molecular basis of autoimmunity in T1D. However, it has been recognized for long that a small number of T1D cases present without autoantibodies and are considered non-autoimmune. Human genetic approach has not been helpful for the study of these patients, as genetic effects of these non-mainstream (or non-autoimmune) T1D have been diluted in the previous studies on T1D cases in general. For the first, we identified non-mainstream T1D cases represented by low T1D polygenic risk score (PRS), and identified 13 novel loci represented by rare SNVs. This study presents a brand-new genomic landscape of pediatric T1D.P values for selection of SNP sets are shown in Table P value ≤ 1E−05, which suggests that stricter cutoff may cause the missing of informative SNPs, while looser may introduce noise by including SNPs with spurious T1D association. Based on the SNP markers with T1D association P value ≤ 1E−05, a PRS score was acquired for each individual in the independent test cohort. By the maximum MCC (Supplementary Table The AUCs of different cutoffs of T1D association P ≤ 5.0 × E−08), from 10 genetic loci  and PTPN22 (P = 0.052) were smaller in the low PRS cases. Besides these 3 established T1D loci, 7 loci associated with low PRS T1D were identified (Table ANK3) locus, related to neural control of the endocrine pancreas20, has been identified of genome-wide significance in our study on low T1D genetic risk scores (GRS) patients22.The GWAS of T1D patients with low T1D PRS compared to controls with low T1D PRS identified a large number of SNPs associated with T1D with genome-wide significance  as well . Besides these established T1D loci, 18 loci associated with low PRS T1D were identified in this cohort (Table DLL1) locus, with the gene function essential for pancreatic islet homeostasis23, have been identified by our gene-based association study on low PRS T1D24. The other locus, containing the UDP-GlcNAc:betaGal beta-1,3-N-acetylglucosaminyltransferase 2 gene (B3GNT2) and transmembrane protein 17 gene (TMEM17), is ~ 200 kb from the Eps15 homology domain binding protein 1 locus (EHBP1) that has been identified of genome-wide significance in our study on low T1D GRS patients21.As expected from the above results, in the switched cohort, the GWAS of T1D patients with low T1D PRS compared to controls with low T1D PRS identified a large number of SNPs associated with T1D with genome-wide significance (P < 5.00E−08), in addition to the 4 established T1D loci, 2 reported loci in the low GRS patients, and 1 locus by our gene-based study. The genome-wide significant association signals of these loci are only seen in low PRS T1D cases, but not in the T1D cases overall, thus were missed previously due to rare allele frequencies and diluted genetic effects in the general T1D cohort. A number of genetic associations with body mass index (BMI), obesity, and autoimmunity, have been reported in the flanking regions of 300 kb on each side of these new loci according to the GWAS Catalog . Further discussion on these novel loci is focused on 13 loci with high imputation quality . Among these loci, 9 loci are related to obesity traits (T1bD mechanism), 2 loci are related to glucose homeostasis (T1bD mechanism), and 2 loci are related to autoimmunity (T1aD mechanism).Altogether, rare variants (MAF < 5%) from 22 novel loci were identified in the low PRS T1D cases with genome-wide significance (CYRIA) and the RAD51 associated protein 2 gene (RAD51AP2). CYRIA is highly expressed in brain and thyroid, while RAD51AP2 has restricted expression toward testis25. Previous GWAS has identified association of this locus with subcutaneous adipose tissue26.Two coding genes in this locus are the CYFIP related Rac1 interactor A gene (NFIB) encodes a transcription factor in the FOXA1 transcription factor network. NFIB has been shown to play critical roles in lung and brain development. A previous study has shown that NFIB can bind with FoxA1 and modulate the transcriptional activity of FoxA127, while the later has been suggested to play a role in pancreatic and ß-cell function and non-autoimmune diabetes as discussed above. The nuclear factor I B gene (NFIB) has ubiquitous expression in fat, brain, and other tissues25. This locus has been reported of association with BMI by several GWA studies30.The nuclear factor I B gene . While the function of these two genes remain unknown, this locus has been reported of association with obesity-related traits31.The two genes at this locus, FAM136A) encodes a mitochondrially localized protein32. The transforming growth factor alpha gene (TGFA) mediates cell–cell adhesion and activates cell proliferation, differentiation and development33. This region has been reported of association with obesity-related traits31.The family with sequence similarity 136 member A gene (CALN1), encoding a protein with high similarity to the calcium-binding proteins of calmodulin, is highly expressed in brain and adrenal25. This genetic region has established association with BMI by previous studies30.The calneuron 1 gene (EPHB4) has ubiquitous expression in multiple tissues, and is involved in numerous developmental processes34. EPHB4 plays critical roles in vascular development35 and lymphatic valve development36. Previous GWAS has identified association of this locus with BMI37 and waist circumference adjusted for BMI30.The EPH receptor B4 gene (25. Thioredoxin plays a protective role against oxidative stresses38. Thioredoxin interacting protein (TXNIP) has been implicated in β cells death in diabetes and is a novel potential therapeutic target of diabetes39. Previous GWAS has identified association of this locus with BMI40 and waist-to-hip ratio adjusted for BMI30.The thioredoxin gene (TXN) has ubiquitous expression in multiple tissues, while the thioredoxin domain containing 8 gene (TXNDC8) has restricted expression toward testisSYT10) encodes a membrane protein of secretory vesicles expressed in pancreas, lung and kidney41. The ALG10 alpha-1,2-glucosyltransferase gene (ALG10) encodes a membrane-associated protein that adds the third glucose residue to the lipid-linked oligosaccharide precursor for N-glycosylation in endoplasmic reticulum (ER)42. As discussed above in the ZNF804B locus, N-glycosylation of IgG, cytokines and proteases is also a regulatory mechanism in inflammation and autoimmunity44 associated with different autoimmune diseases. Several previous GWASs have identified association of this locus with waist-to-hip ratio and waist-to-hip ratio adjusted for BMI45.The synaptotagmin 10 gene (CHFR) encodes an E3 ubiquitin-protein ligase and is involved in the DNA damage response and checkpoint regulation46. The structure and function of the gene LOC101928530 is still uncharacterized. The function of the zinc finger protein 605 gene (ZNF605) may be related to Herpes Simplex Virus 1 infection (https://pathcards.genecards.org/card/herpes_simplex_virus_1_infection). This region has been reported of association with BMI by previous study28.The checkpoint with forkhead and ring finger domains gene (LOC730100 encodes a long non-coding RNA (ncRNA), a competing endogenous RNA for human microRNA 760 (miR-760)47. The latter inhibits the expression of the Forkhead Box A1 gene (FOXA1). As a hepatocyte nuclear factor, FOXA1, also known as HNF3A or TCF3A, regulates tissue-specific gene expression in liver and many other tissues48. FoxA1 is essential for normal pancreatic and ß-cell function and a negative regulator of the hepatocyte nuclear factor-1 (HNF1) homeobox A gene (HNF1A) and the hepatocyte nuclear factor 4, alpha gene (HNF4A)50. HNF1A and HNF4A are established genes causing maturity-onset diabetes of the young (MODY). The FOXA1 mutation Ser448Asn has been suggested of association with impaired glucose homeostasis50.LINC01695) is still uncharacterized. The long intergenic non-protein coding RNA 161 gene (LINC00161) encodes a functional RNA that regulates Mitogen-activated protein kinase 1 (MAPK1) expression51. The MAPK1/STAT3 pathway has been proposed as a novel diabetes target for its critical role in glucose homeostasis52.Function of the long intergenic non-protein coding RNA 1695 gene  has higher expression in kidney and pancreas25. Interleukin 15 (IL-15) encoded by the gene IL15 is essential for regulating activation and proliferation of T and natural killer cells, and supporting lymphoid homeostasis54. IL-15 and interleukin 2 (IL-2) share many biological activities and receptor components with IL-255. IL-2 is a powerful growth factor for both T and B lymphocytes56. Both IL2 and the α chain of the IL2 receptor complex gene (IL2RA) has been established of genetic association with T1D by previous studies59.The long intergenic non-protein coding RNA 2432 gene (ZNF804B) has been reported of association with N-linked glycosylation of human immunoglobulin G (IgG), which modulates its binding to Fc receptors43. N-glycosylation of cytokines and proteases is also a regulatory mechanism in inflammation and autoimmunity44. Changes in N-glycosylation have been associated with different autoimmune diseases, including rheumatoid arthritis60, type 1 diabetes61, Crohn's disease62.The zinc finger protein 804B gene  identified in this study emphasize the importance of lncRNAs in these diabetes patients. However, we admit that this study has limitations related to the bottleneck of sample size and data resources. The novel loci reported in this study still need replication in independent samples. In addition, the functional mechanisms of these genetic loci in diabetes warrant experimental investigation. Due to the lack of data of T1D autoantibodies in the subjects, the mixture of rare forms of autoimmune diabetes (e.g. monogenic autoimmunity53) in addition to non-autoimmune diabetes may exist as suggested by the identification of rare variants in autoimmune-related genes.In summary, in the genetic regions containing the 13 novel loci with high imputation quality disclosed by this study, 9 of these regions have been reported of association with obesity-related traits, BMI, or waist circumference. The correlation with obesity related traits or impaired glucose homeostasis is in keeping with non-autoimmune roles in the diabetes patients with low T1D PRS. Interestingly, the genes Supplementary Figures.Supplementary Table 1.Supplementary Table 2.Supplementary Table 3.Supplementary Table 4.Supplementary Information 1.


Although there are structural similarities between each member, they are not functionally interchangeable. The S100 proteins function both as intracellular Ca2+ sensors and as extracellular factors. Dysregulated responses of multiple members of the S100 family are observed in several diseases, including the lungs . To this degree, extensive research was undertaken to identify their roles in pulmonary disease pathogenesis and the identification of inhibitors for several S100 family members that have progressed to clinical trials in patients for nonpulmonary conditions. This review outlines the potential role of each S100 protein in pulmonary diseases, details the possible mechanisms observed in diseases, and outlines potential therapeutic strategies for treatment.The S100 protein family consists of over 20 members in humans that are involved in many intracellular and extracellular processes, including proliferation, differentiation, apoptosis, Ca In multiple diseases, numerous exogenous and endogenous factors trigger inflammatory cascades. A family of endogenous proinflammatory mediators named damage-associated molecular patterns  have become of special interest in the past few decades. During stress reactions or cell injury, DAMPs are released and activate inflammatory pathways . DAMPs f2+, S100 proteins also bind many other transition metal ions  that result in a conformational change allowing interaction with target proteins [κB (NF-κB), which results in upregulation of the proinflammatory gene expression [S100 proteins are involved in both intracellular and extracellular processes including cell apoptosis, migration, protein phosphorylation, calcium balance, differentiation, proliferation, and inflammation –6. The tproteins , 12. S10proteins . They doproteins . By bindpression , 16. In 2+, posttranslational modifications, receptor-mediated transduction, and direct responses S100 proteins mediate many processes.S100 proteins interact with a variety of target proteins including enzymes, cytoskeletal subunits, receptors, transcription factors, and nucleic acids. Therefore, through structural changes, interactions with Ca2+ binding [α-helices [in vitro, but only the S100A8-S100A9 heterodimer is believed to play a significant role in vivo [2+-binding domains, a carboxy-terminal canonical domain composed of 12 amino acids and an amino-terminal domain composed of 14 amino acids [2+-binding affinity drastically increases when a target engages [2+, S100 proteins undergo conformational rearrangement exposing a hydrophobic cleft that is required for target binding. Therefore, S100 target binding is mostly Ca2+-dependent. Individual S100 family members exhibit differences in surface geometries, hydrophobic residue distribution, and charge density [Pending on the cellular and extracellular location of S100 proteins, they can interact with many proteins and form dimers. There are two critical steps for S100 protein activation: Ca binding  and dime binding . Typical-helices . Several in vivo . S100 su engages –22. Upon density . Several density –29. The  density . RegardiEach S100 family protein is encoded by a separate gene, mostly but not limited to chromosome 1q21 . When exβ [2+ and K+ levels [Little is known about the mechanisms that mediate the secretion of S100 proteins. The S100 proteins lack a leader sequence and are not secreted via the classical Golgi pathway. The secretion of S100 proteins appears to occur either passively upon cell necrosis or actively following cell activation. Several S100 proteins undergo secretion following stimulation by particular cell activators, such as serotonin-receptor agonists , antidepβ , lysopho+ levels . Some S1+ levels . Equally+ levels , to indu+ levels . OxidatiκB, resulting in the production of proinflammatory cytokines [β [Once released to the extracellular space, S100 proteins trigger immune cell activation through binding to different cell surface receptors. S100 proteins primarily trigger inflammation responses through their interactions with toll-like receptor (TLR) 4 and receptor for advanced glycation end products (RAGE) . S100A8 ytokines , 51. S10ytokines . Howeverytokines . S100 prytokines , FGFR1 [ytokines , CD166 aytokines , IL-10 rytokines , neuroplkines [β , CD68 [5kines [β , and Erbkines [β   protein that predominantly functions to modulate the Camyocytes . It is amyocytes , 63. Teimyocytes . Pigs emmyocytes .S100A2 is linked to both suppressions of tumor progression as well as being a promoter of carcinogenesis –69. Its α, a transcription factor that plays a role in the antiproliferative effects of All-trans retinoic acid (ATRA) [Mutation of the S100A3 gene leads to the replacement of arginine with cysteine at the 77th position of the S100A3 protein along with a mutation of the S100A13 gene leading to decreased expression of both proteins. This is implicated in an atypical genetic variant of early onset interstitial pulmonary fibrosis . The decd (ATRA) , by protd (ATRA) . Tumor md (ATRA) . Similard (ATRA) .α-SMA, type I collagen, and sphingosine-1-phosphate (S1P) [β-catenin pathway in a RAGE-dependent manner [S100a4−/− mice develop spontaneous tumors that are p54 positive, including bronchioalveolar carcinomas [S100a4 in mice coincided with elevated S100a3 and S100a5 expression in several tissues [S100A4 was originally labeled as a specific marker of fibroblasts in different tissues and was termed as FSP-1 (fibroblast-specific-protein-1), but further studies showed that S100A4 is also involved in the fibrogenic of mesenchymal progenitor cells in the lung thus contributing to the pathogenesis of IPF . S100A4 te (S1P) . S100A4 te (S1P) , a procete (S1P) , 81. S10t manner . S100A4 t manner . Interesrcinomas . This lo tissues .The prognostic value of high levels of expression of S100A5 and S100G mRNA is associated with worse overall survival in the nonsmoking NSCLC patients . FurtherS100a6−/− mice have impaired self-renewal and regenerative capacity [Cftr−/− mice have elevated lung S100a8 and S100a9, while demonstrating reduced S100a6 and S100a13 [S100A6 plays a role in the proliferation, apoptosis, cytoskeleton modulation, and stress-induced responses of normal adult cell types as well as several tumorous cells . It is ocapacity . Chroniccapacity . Mice de S100a13 . A recen S100a13 . Finally S100a13 .The majority of early work looking at S100A7, also called psoriasin, focused on psoriasis , 97. Howβ signaling pathway in the progression of NSCLC demonstrated that the TGFβ-induced long noncoding RNA (TBILA) binds to S100A7 thus activating the S100A7/JAB1 pathway leading to NSCLC proliferation and metastasis [S100A7 also appears to play a role in the pathogenesis of NSCLC. A study investigating the TGFtastasis . The S10tastasis . S100A7 tastasis . Knockdotastasis .γ and IL-22. The expression of S100A7 is induced by IL-22, and these effects are in turn inhibited by IFN-γ [S. aureus is found in the respiratory tract thus stressing the role of this molecule in the epithelial barrier of the lower respiratory tract [In asthma, S100A7 plays a role in the interplay between the proinflammatory cytokine IFN-by IFN-γ . The expry tract . S100A7 ry tract .Cftr−/− animals, thus suggesting a role of these proteins in the disease process of CF [As mentioned above, S100A8 and S100A9 proteins are upregulated in ss of CF . The antss of CF . Interesss of CF . Similarss of CF . Our gross of CF . Levels ss of CF . The uniss of CF  while S1ss of CF .κB pathway [+ Treg cells [The S100A8/A9 complex is expressed in tuberculosis (TB) –115, as  pathway . S100A9  pathway . S100A9 eg cells .Saito et al. elucidated the role of alarmins in chronic lung allograft dysfunction (CLAD), a major contributor to morbidity and mortality in long-term survivors of lung transplantation . The twoS100A10 is believed to be a signature gene involved in blood pressure regulation and maybe a potential target for the detection of risk, prevention, and treatment of hypertension . S100A10S100A11 also called S100C or calgizzarin, overexpression is linked with alterations in K-RAS mutated lung adenocarcinomas as well as poorly differentiated lung adenocarcinomas . It is aS100A12 is strongly expressed in the early stages of ALI and early acute respiratory distress syndrome (ARDS) . PossiblThe S100A14 expression is associated with a subset of lung adenocarcinoma and has a strong correlation with the invasive and migratory nature of lung adenocarcinoma cells . Its expS100A7A gene) and A16 expression are altered in lung cancers. Increased S100A15 expression and decreased DNA methylation of its gene promoter region are associated with poor outcomes in lung adenocarcinoma cases and potentially greater metastasis [Both S100A15  are noted to induce multiple pathways associated with pulmonary disease phenotypes. Here, we will outline the known signaling transduction of these S100 proteins in noncancerous pulmonary diseases. The primary pathways investigated are TLR4 and RAGE-mediated .S100a1 results in pulmonary hypertension in mice due to enhanced eNOS activity and nitric oxide levels, via Akt/ERK1/2 pathways, and reduced endothelial cell survival [β-estradiol increases the S100A4 expression and proliferation in human pulmonary artery smooth muscle cells, and this proliferation is prevented by blocking RAGE signaling [Deficiency of survival . Alternasurvival . Stimulaignaling . S100A4 ignaling .β) and collagen type III, via ERK and NF-κB signaling [α interacts with S100A8 in lung tissue obtained from smokers [S100a8 and S100a9 are both elevated in the lungs of CF transmembrane conductance regulator (Cftr) knockout mice, while S100a6 and S100a13 are reduced [S100A9−/− mice displayed significantly enhanced allergic airway inflammation upon exposure to Alternaria alternata, including IL-13, CCL11, CCL24, serum IgE, lung eosinophils, IL-13+IL-5+CD4+ T-helper type 2 cells, airway resistance, and elastance [Similarly, S100A9 promotes proliferation of lung fibroblasts and upregulated expression of proinflammatory cytokines  cells following exposure to Alternaria alternata [Aspergillus fumigatus infection, S100B can itself resolve inflammation via transcriptional inhibition of itself [Despite S100 proteins being associated with cellular and tissue damage, several members  are reported to also play a critical role in tissue repair . These Slternata , leadingf itself . TherefoDue to the involvement of S100 proteins in the pathogenesis of numerous diseases as highlighted in this review and mouse models suggest that genetic deletion has minimal effects on normal physiology, there is increasing interest in therapeutic targeting these proteins. Clinical trials to date have primarily focused on nonpulmonary diseases such as systemic lupus erythematosus (SLE), ischemic heart failure, and rheumatoid arthritis. We will discuss S100 inhibitors and clinical trials undertaken with these therapeutics to date , tasquinimod (ABR-215050), and laquinimod (ABR-215062). They are quinoline-3-carboxamide derivatives that primarily block the interaction of S100A8 and S100A9 with RAGE and TLR4 , 166. LaPhase II randomized, double-blind, placebo-controlled study in men with minimally symptomatic metastatic castrate-resistant prostate cancer (CRPC), tasquinimod improves progression-free survival in patients with metastatic castration-resistant, possibly by reducing the recruitment of MDSCs and inhibiting metastasis , 169. HoThere are multiple published findings from clinical trials with laquinimod, most notably in multiple sclerosis populations. In a large, multicenter phase III clinical trial (ALLEGRO), laquinimod was well tolerated . The mosPaquinimod is reported to be beneficial in several animal models including systemic sclerosis , COPD 1, type 1 Here, we will briefly outline some alternative approaches to target S100 proteins that could be applied to lung diseases. These include gene delivery approaches, inhibitory antibodies and peptides, vaccines, and chemical inhibitors see . Severalin vitro and in vivo by attenuation of epithelial-to-mesenchymal transition, matrix proteins, and Wnt/β-catenin signaling [Equally several compounds are known to inhibit the S100A4 expression, with calcimycin , niclosamide (an antihelminth drug), and sulindac  are all reported as inhibitors of S100A4 transcription , 186. Inignaling . Sulindaignaling . Whetherignaling . By blocignaling . Alternaignaling .Other approaches to modulate S100 protein activity include neutralizing antibodies , 196 andFinally, we want to briefly discuss the importance of S100 proteins and neutrophils in pulmonary diseases. Neutrophils are the first responders to the site of inflammation and are essential for microbial containment, eradication, and host survival. Dysregulated neutrophil responses are central to the pathophysiology of many inflammatory lung diseases. Approximately 45% of the cytosolic proteins in neutrophils are constituted with S100A8, S100A9, and S100A12 and are released upon injury or infection , 204. Fo+ exchanges through the ATP-sensitive K+ channel [β-2 integrin Mac-1 (CD11b/CD18) on neutrophils [Rammes et al. have suggested that in monocytes, the nonclassical S100A8/A9 secretion constitutes an intact microtubule network and PKC activation . However channel . Schente channel , 207. Eqtrophils  and coultrophils . HoweverWith the new human viral disease severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), there is emerging data identifying the roles of S100 proteins to coronavirus disease 2019 (COVID-19). In preclinical models of SARS-CoV-2 infected animals and patients, there are increased levels of immature neutrophils and dramatically upregulated S100A8 levels . In addiIn the past two decades, our knowledge of the roles of S100 proteins in pulmonary diseases has grown, and many novel approaches were identified to target members of this protein family as therapeutic options. Despite considerable progress in S100 protein biology, we still know little about posttranslational modifications or heterodimer formation impact on S100 signaling, and the COVID-19 pandemic highlights the potential role of S100 proteins in tissue damage.


SPINK1 gene (encoding pancreatic secretory trypsin inhibitor) has been identified in patients with chronic pancreatitis (CP). The haplotype harboring the SPINK1 c.101A>G (p.Asn34Ser or N34S) variant (rs17107315:T>C) is one of the most important heritable risk factors for CP as a consequence of its relatively high prevalence worldwide  and its considerable effect size (odds ratio ≈ 11). The causal variant responsible for this haplotype has been intensively investigated over the past two decades. The different hypotheses tested addressed whether the N34S missense variant has a direct impact on enzyme structure and function, whether c.101A>G could affect pre-mRNA splicing or mRNA stability, and whether another variant in linkage disequilibrium with c.101A>G might be responsible for the observed association with CP. Having reviewed the currently available genetic and experimental data, we conclude that c.-4141G>T (rs142703147:C>A), which disrupts a PTF1L-binding site within an evolutionarily conserved HNF1A-PTF1L cis-regulatory module located ∼4 kb upstream of the SPINK1 promoter, can be designated as the causal variant beyond reasonable doubt. This case illustrates the difficulties inherent in determining the identity of the causal variant underlying an initially identified disease association.A diverse range of loss-of-function variants in the  PRSS1  and loss-of-function variants in SPINK1  and CTRC (encoding chymotrypsin C (MIM# 601405), which specifically degrades all human trypsinogen/trypsin isoforms) in patients with the disease ([Chronic pancreatitis (CP) is a complex disease that can be caused by genetic and/or environmental factors  [A diverse range of variants in the terature ,4. Whils315:T>C) , represe315:T>C) ) worldwiSaccharomyces cerevisiae [cis-linked intronic variants may affect pre-mRNA splicing or mRNA stability [SPINK1 genomic sequence extending from the translation initiation codon to the translation termination codon that was inserted into the expression vector. In other words, a possible effect of c.101A>G and its four cis-linked intronic variants on pre-mRNA splicing and/or mRNA stability was simultaneously analyzed in the gene’s natural genomic sequence context as far as the entire coding and intronic sequences are concerned. Employing both qualitative and quantitative reverse transcription-PCR (RT-PCR) analyses, we did not observe any measurable effect of the c.101A>G or its four cis-linked intronic variants on pre-mRNA splicing and/or mRNA stability [SPINK1 mRNA were not subjected to quantitative analysis in this latter study. Additionally, no significant single-tissue expression quantitative trait loci (eQTLs) for SPINK1 in human pancreatic tissue are listed in the Genotype-Tissue Expression (GTEx) database [Witt and colleagues initially postulated that N34S, which is located near the reactive site of SPINK1 (Lys41-Ile42), might impair the enzyme’s inhibitory activity on prematurely activated trypsin within the pancreas, thereby conferring predisposition to CP . Howeverrevisiae , Chineserevisiae , and humrevisiae  failed ttability  also faitability  and fulltability ,15 splictability ,15, it itability ,15. Moretability . Howeverdatabase .r2 ≥ 0.40 and querying the 1000 Genomes Project Phase 1 data in the context of the European population by means of HaploReg v4.1, we identified a total of 25 single nucleotide polymorphisms in strong LD  with c.101A>G in the region spanning 20 kb 3’ of SPINK1 to 18 kb 5’ of SPINK1  with c.101A>G (N34S). Employing an LD threshold of f SPINK1 .SPINK1 demarcated as +1 [cis-regulatory module (CRM) , was fong genes ). Both Hng genes ,22,23. Ing genes  have dem variant .SPINK1 N34S haplotype with CP. This notwithstanding, three recent studies have claimed that the pathogenic mechanism underlying the SPINK1 N34S haplotype is still unknown and have therefore continued to search for a possible direct effect of the N34S missense variant on SPINK1 structure and function. Two of these studies involved purely hypothetical simulation or modeling [Taken together, the findings from the aforementioned studies, and particularly the two from 2017 ,24, suggmodeling ,26. The modeling . Herein,modeling  that poimodeling ,11. Firsmodeling ,30. Usinmodeling .r2 = 1; 548 chronic patients and 562 controls genotyped) as well as in the European population of the 1000 Genomes Project [r2 = 0.80; 1104 patients and 1196 controls genotyped) or the Indian population  [r2 was observed. Only in the Indian population was c.101A>G (N34S) found to have a higher odds ratio (OR) for CP than c.-4141G>T (15.12 versus 14.82). By contrast, in the Chinese population, c.-4141G>T had a higher OR than c.101A>G (6.13 versus 5.47) whilst the two variants had an equal OR in the French population where they are in complete LD. It should also be pointed out that (i) of the three populations studied, the Indian population had the smallest sample size [Finally, it should be noted that the c.-4141G>T functional enhancer variant is in perfect LD with c.101A>G (N34S) in the French population ( Project  but not notyped) . Conditinotyped) . With hiple size  and (ii)ple size . The conSPINK1 N34S haplotype with CP. In other words, the true culprit underlying the SPINK1 N34S haplotype-disease association should no longer be thought to be at large [SPINK1 mRNA expression in vivo. Alternatively, given the availability of 2 cell lines heterozygous for both the c.101A>G (N34S) and c.-4141G>T variants, CRISPR/cas9 technology could be employed to generate isogenic cell lines carrying only the c.101A>G (N34S) variant or the c.-4141G>T variant. Subsequent comparison of SPINK1 mRNA expression levels in these cell lines could then serve to strengthen the evidence for causality. Indeed, it is always better to provide additional experimental evidence if resources are available. However, in this particular case, the evidence supporting our contention that the c.-4141G>T variant is the true pathogenic variant is, we believe, beyond reasonable doubt.In summary, based upon the currently available genetic and experimental data, we conclude that c.-4141G>T represents the true culprit underlying the association of the at large . This noSPINK1 N34S-containing haplotype has one immediate clinical implication. Genetic correction of the c.-4141G>T variant in the corresponding carriers could be explored as a patient-tailored therapy.The conclusion that c.-4141G>T is the true pathogenic culprit within the


Pancreatic cancer (PC) is one of the most common cancers worldwide, with high mortality. The UGT1A gene family plays important roles in pharmacology and toxicology, contributing to interindividual differences in drug disposition. However, mRNA expression and prognostic value of the UGT1A gene family in PC have not been identified.  Oncomine, GEPIA2, DAVID 6.8, Metascape, Kaplan-Meier plotter, cBioPortal, GeneMANIA, TRRUST v2, TIMER, and R software were used in our study.  The transcriptional levels of UGT1A1/3/6/8/9/10 in PC tissues were significantly higher than those in normal tissues. These results were further validated using five pairs of PC tumor tissues and adjacent nontumor tissues. A significant correlation was found between the expression of UGT1A1/6/10 and the pathological stage of PC. PC patients with lower transcriptional levels of UGT1A1/4/5/6/10 were associated with a better prognosis. The differentially expressed UGT1A gene family functions were primarily related to the glucuronidation pathway, cytokine-cytokine receptor interactions, and the ILK signaling pathway. Our data suggest that HNF1A, AHR, and CDX2 are key transcription factors for the UGT1A gene family. Furthermore, the expression levels of UGT1A1/3/8/9/10 were positively correlated with the activities of tumor-infiltrating immune cells, especially B cells. The expression levels of UGT1A6/9 were negatively correlated with macrophage infiltration levels.  These results suggest that the UGT1A gene family could serve as a potential prognostic biomarker and target for PC. However, future studies are required to validate our findings and promote the clinical utility of the UGT1A gene family in PC. Pancreatic cancer (PC) is characterized by poor prognosis, primarily associated with genetic conditions, diabetes, smoking, and obesity –3. PancrUDP-glycosyltransferases (UGTs) are a superfamily of enzymes found in diverse species . UGTs catalyze the covalent addition of sugars from nucleotide UDP-sugar donors to functional groups in a broad range of lipophilic molecules . In mammRecent studies have reported the general expression profile and function of some UGT1A gene family members in PC  and otheIn this study, we used several large public databases to perform a comprehensive analysis of the relationships between the UGT1A gene family members and the pathogenesis, tumor-infiltrating immune cells, and progression of PC. Consequently, we hope to aid clinicians in selecting appropriate therapeutic drugs and more accurately predicting long-term outcomes in patients with PC.The main methods in this study followed the methods of previous studies , 15.http://www.oncomine.org) is a publicly accessible online cancer microarray database with a powerful set of analysis functions that compute gene expression signatures, clusters, and gene set modules, automatically extracting biological insights from the data [P value = 0.05, fold change (FC) = 2, and data type = mRNA.The Oncomine platform . The human specimens used for validation in this study were collected between May and June 2021. The local ethics committee approved the use of these specimens. The adjacent samples were taken at a distance of at least 5 cm from the tumor.Total RNA was extracted from the tissue specimens using TRIzol (Invitrogen), and qRT-PCR was performed using SYBR Green Dye  according to the manufacturer's instructions. The primer sequences of the UGT1A gene family are listed in Supplementary Table http://www.gepia2.cancer-pku.cn) is a newly developed interactive web server for analyzing RNA sequencing expression data of 9736 tumors and 8587 normal samples from the Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) dataset projects, which provide customizable functions, such as tumor/normal differential expression analysis, profiling according to cancer type or pathological stage, patient survival analysis, similar gene detection, correlation analysis, and dimensionality reduction analysis [2FC = 1, P value = 0.05, use = log2(TPM + 1) for log scale, jitter size = 0.04, and match = TCGA normal and GTEx data.GEPIA2  is a comprehensive, functional annotation website that helps investigators better clarify the biological function of submitted genes [DAVID 6.8 (ed genes . In thishttp://metascape.org) is a free, well-maintained, user-friendly gene list analysis tool for gene annotation and analysis [Metascape  is an online database containing microarray gene expression data and survival information of cancer patients derived from Gene Expression Omnibus, TCGA, and the Cancer Biomedical Informatics Grid [P value.The Kaplan-Meier plotter  is affiliated with the Memorial Sloan Kettering Cancer Center and provides information regarding the integrative analysis of complex cancer genomics and clinical profiles from 105 cancer studies in the TCGA pipeline [The cBioPortal for cancer genomics (pipeline . The frehttp://www.genemania.org) is a website that provides information on physical interactions, pathways, coexpression, colocalization, and shared protein domain similarity of submitted genes [GeneMANIA (ed genes . In thishttps://www.grnpedia.org/trrust/) is a manually curated database of human and mouse transcriptional regulatory networks, which includes 8444 and 6552 transcription factor- (TF-) target regulatory relationships of 800 human TFs and 828 mouse TFs, respectively [TRRUST v2 (ectively . In thishttp://timer.cistrome.org/) is a comprehensive resource to systematically analyze immune infiltrates across diverse cancer types [TIMER 2.0 , UGT1A3 , UGT1A6 , UGT1A8 , and UGT1A9  in PC. Logsdon et al. [P = 8.41E − 6, FC = 6.999) in PC was significantly upregulated.The UGT1A gene family members  were retrieved from the Oncomine database. We first explored the transcriptional levels of the UGT1A gene family in PC and normal pancreatic tissues in the Oncomine database. The results shown in Supplementary n et al.  also repP < 0.05) and UGT1A10 (P < 0.05) in PC tissues were significantly higher than those in normal tissues , UGT1A6 (P = 0.00378), UGT1A10 (P = 0.00244), and pathological stage  in PC tissues than in the corresponding nontumor tissues , UGT1A4 (P = 0.0017), UGT1A5 (P = 0.02), UGT1A6 (P = 0.0036), and UGT1A10 (P = 0.00046) were significantly associated with shorter OS (P = 0.015), UGT1A4 (P = 0.025), and UGT1A10 (P = 0.0083) were significantly associated with shorter RFS , UGT1A3 , UGT1A8 , UGT1A9 , and UGT1A10  expression and infiltrating B cells. A positive correlation was observed between UGT1A8  and infiltrating CD8+ T cells. Moreover, UGT1A6  and UGT1A9  expression was negatively correlated with infiltrating macrophages.Finally, because the prognosis of PC may be affected by inflammatory response and infiltrating immune cells, we further evaluated the association between differentially expressed UGT1A gene family members and immune cell infiltration using the TIMER 2.0 database. As shown in The UGT1A gene family encodes pivotal enzymes that play an important role in pharmacology and toxicology, contributing to interindividual differences in drug disposition and cancer risk . AccumulFirst, we explored the mRNA expression of the UGT1A gene family members and their correlation with the pathological stage in PC. We found that the transcriptional levels of UGT1A1/3/6/8/9/10 in PC tissues were significantly higher than those in normal tissues. These results were then validated using five pairs of PC tumor tissues and adjacent nontumor tissues. A significant correlation was found between the expression of UGT1A1/6/10 and the pathological stage of PC. The expression of UGT1A1, UGT1A6, and UGT1A10 increased as the tumor progressed, suggesting that the UGT1A gene family plays an important role in the tumorigenesis and progression of PC.Second, we explored the function of differentially expressed UGT1A gene family members using GO enrichment analysis and KEGG pathway enrichment analysis. The results showed that the UGT1A gene family members were mainly enriched in glucuronidation and metabolic regulation functions, and the functions of these genes were primarily related to ascorbate and aldarate metabolism, pentose and glucuronate interconversions, porphyrin and chlorophyll metabolism, drug metabolism-other enzymes, steroid hormone biosynthesis, drug metabolism-cytochrome P450, metabolism of xenobiotics by cytochrome P450, chemical carcinogenesis, and metabolic pathways. Glucuronidation of drugs via elevation of UGT1As correlates with clinical resistance . GlucuroThird, to evaluate the value of differentially expressed UGT1A gene family members in the progression of PC, we assessed the correlation between the differentially expressed UGT1A gene family and clinical outcomes. The results showed that patients with lower transcriptional levels of UGT1A1/4/5/6/10 were associated with a significantly better prognosis, whereas patients with high transcriptional levels of UGT1A1, UGT1A4, and UGT1A10 were significantly associated with shorter RFS. A study showed that the UGT1A gene family plays an important role in the treatment of modified FOLFIRINOX, improving the long-term survival of patients with PC . These dBecause multiple UGT1A gene family members were significantly differentially expressed in PC, we explored the genetic alteration and carcinogenic mechanism of the UGT1A gene family members. The genetic alterations in UGT1A gene family members in PC patients varied from 0.4 to 1.1% for individual genes. Studies have shown that the genetic alteration of UGT1A gene family members reduces the ability of UGT to use UDP-GlcUA and CYP3A4-mediated enhancement of catalytic activity . FurtherWe also sought to characterize the TF targets of the differentially expressed UGT1A gene family members and found that HNF1A, AHR, and CDX2 are the three key TFs associated with the regulation of the UGT1A gene family.Recently, an increasing number of studies have shown that the tumor immune microenvironment is of great importance in predicting clinical outcomes and developing immunotherapy in pancreatic cancer . Xu et aOur study has some limitations. Analysis at the transcriptional level can reflect some aspects of immune status, but not global changes. Moreover, another independent cohort and in vitro or in vivo studies should be performed to validate our results.Overall, our results suggest that the UGT1A gene family may serve as a potential target and prognostic biomarker for patients with PC. However, future studies are required to validate our findings and thus promote the clinical utility of the UGT1A gene family in PC.


APOBEC3 (A3) cytidine deaminases inhibit hepatitis B virus (HBV) infection and play vital roles in maintaining a variety of biochemical processes, including the regulation of protein expression and innate immunity. Emerging evidence indicates that the deaminated deoxycytidine biochemical activity of A3 proteins in single-stranded DNA makes them a double-edged sword. These enzymes can cause cellular genetic mutations at replication forks or within transcription bubbles, depending on the physiological state of the cell and the phase of the cell cycle. Under pathological conditions, aberrant expression of A3 genes with improper deaminase activity regulation may threaten genomic stability and eventually lead to cancer development. This review attempted to summarize the antiviral activities and underlying mechanisms of A3 editing enzymes in HBV infections. Moreover, the correlations between A3 genes and hepatocarcinogenesis were also elucidated.  As a suAll the members of the A3 family are located at 22q13.1. A3B localizes to the nucleus. A3A, A3C and A3H are distributed cell-wide and are able to enter the nucleus via passive diffusion. A3D, A3F and A3G are cytoplastic and can enter the nucleus through an active import mechanism . In addiHence, understanding the antiviral roles and cancer-promotion functions of A3 genes in the HBV-infected population is highly important . The aim2.2.1The mRNAs of A3B, A3C, A3F, and A3G are detectable in normal human liver tissues . A3-foot2.2Multiple approaches confirmed that the content of A3G in liver tissues was the highest in CHB patients, slightly lower in patients with liver cirrhosis and the lowest in liver cancer . No signSince the G1896A pre-core (PC) and A1762T/G1764A basal core promoter (BCP) mutations are established risk factors for HCC , a recen3.IFN can induce A3 gene activation ,48. In p+ T cells [A3F mRNA was expressed in HepG2 and Hep3B cell lines and could be induced by IFNα in a dose- and time-dependent fashion . In prim T cells .However, Turelli et al. revealed that A3 enzymes are not essential effectors of the anti-HBV activities of type I and II IFN. They found that murine A3 (muA3) can also inhibit HBV replication. Although the baseline expression of muA3 in mouse liver was low, it was evaluated under IFN induction. In contrast, in HBV-transgenic muA3 knockout mice, IFN-induced blockade of HBV DNA production was as effective as that in control HBV-transgenic muA3-competent animals . A study4.More evidence has demonstrated that HBV DNA is vulnerable to A3 cytidine deaminase genetic editing ,58,59. AIn Huh-7 and HepG2 cells, as well as the avian hepatoma cell line LMH, A3G blocked replicative HBV intermediates in a dose-dependent manner. Even a single N-terminal or C-terminal cytosine deaminase domain could inhibit HBV replication in HepG2 and Huh7 cells . HoweverHigher anti-HBV activity of the A3C I188 variant for HBV DNA was observed compared to that of the A3C S188 variant. Among A3H haplotypes and splicing variants, hap II SV-183 caused the most significant decrease in HBV DNA levels, while only a small amount of decrease was detected in hap III, hap IV, and SV-154 (with hap II) transfectants. No significance of HBV DNA was found between A3G H186 and R186 . The preSingle-nucleotide polymorphism (SNP) analysis revealed that both the C and G alleles of rs3077 and rs9277535 significantly increased the risk for advanced liver disease in HBV carriers without the A3G H186R variant . In a co−2 to 10−5 levels in vitro, which proved that A3A was the most effective editor [In a quail cell line, a 3D PCR approach showed that all genes in the A3 family except A3D could deaminate HBV DNA at 10e editor . In conte editor . A3F coue editor .5.A3 genes influence HBV replication through various modes including mediation of G-to-A hypermutation, suppress HBV gene transcription, and inhibition of HBV viral particles. The HBV life cycle and potential mechanisms of A3 gene in anti-HBV process were illustrated in 5.1 G-to-A hypermutation exists in HBV isolates and influences HBV pathogenesis ,75. Geno5.2The interaction of the HBV core protein with nuclear cccDNA results in cytidine deamination, purine/pyrimidine site formation, and cccDNA degradation, which finally prevents HBV reactivation ,54. A3B Early steps of HBV viral morphogenesis, including RNA and protein synthesis, binding of pregenomic RNA to core proteins, and self-assembly of viral core proteins, were unaffected by A3G. The total HBV RNA production was not reduced by A3G, while pgRNA coprecipitated with cytoplasmic core proteins independently of the absence of A3G. In addition, viral DNA products were similar in the absence and presence of A3G. Notably, the presence of A3G was linked to the reduction in full-length HBV DNA derived from pgRNA. The anti-HBV effect of A3G was not altered by abolishing or enhancing the expression of the accessory HBV X protein . This stIn HepG2 cells, approximately 85.2% of cells coexpressed A3G and HBV core protein (HBc), and more extensive colocalization of A3G and HBc was found in a number of coexpressing cells. An interaction between A3G and HBc was also detected in living cells by a fluorescence resonance energy transfer approach. A surface plasmon resonance assay tested a direct interaction between A3G and HBc, and RNA had no impact on stabilizing the interaction . In anotA study by Koh et al. indicated that HBV-speciﬁc TCR-reprogrammed resting T cell treatment for 24 hours or 48 hours could induce a 20% to 40% increase in A3A and A3B mRNA, respectively, and a 450% increase in A3G mRNA in HepG2 cells. These data suggest that HBV-speciﬁc TCR-reprogrammed resting T cells might be capable of rapidly activating the A3 innate antiviral pathway by interacting with the lymphotoxin-β receptor on HBV-infected targets. Lymphotoxin-β increased by 9-fold and 19-fold with 0.5 million and 1 million resting T cells, respectively. A3B mRNA increased by 45-fold in mice treated with 1 million resting T cells. The authors concluded that resting T cells activate lymphotoxin-β and A3B in HBV-infected primary hepatocytes and inhibit HBV replication without inducing liver inflammation .6.A3 cytidine deaminase activation boosts the immune response by mutating immune or HBV viral genomes. Because of their genome-mutating activities, A3 genes are also drivers of tumorigenesis ,82–84. UIn a study including 35 matched tumor and nontumor tissues from HCC patients, sequence analysis showed that G-to-A hypermutation was significantly higher in HBV cccDNA in tumor samples than in nontumor samples. The frequency of G-to-A mutations was 4.85 per 1000 sites in tumor samples, while it was only 0.16 in nontumor samples. Among the A3 subbranches, A3B was significantly upregulated at both the transcriptional and protein levels in tumor samples compared to nontumor samples. These findings suggest that upregulation of A3B contributes to editing and degradation of cccDNA in HCC patients . Xu et aA study by He et al. enrolled 104 CHB patients, 265 HBV-related cirrhosis patients and 285 HBV-related HCC patients. single-nucleotide polymorphisms (SNPs) including two A3A rs7286317 and rs7290153, A3B rs2267398, rs2267401 and rs2076109, and A3H rs56695217, rs139302, rs139297, rs139316 and rs139292 were genotyped. No significant correlations between these SNPs and the development of HCC were found. However, mutant haplotypes C-T-A, C-T-G, T-G-G and T-T-G from the A3B SNPs rs2267398-rs2267401-rs2076109 carried a lower risk of HCC compared to the reference haplotype [Previously, we compared A3 gene expression between tumor and nontumor tissues in HCC patients in a public database from the Gene Expression Omnibus (GEO). In GSE36376, A3B, A3D, A3F and A3H were upregulated in HCC tumor tissues compared to nontumor tissues. High levels of A3G and A3F contributed to poor overall survival (OS) and disease-free survival (DFS), respectively. In contrast, A3C and A3H overexpression was associated with favorable OS and DFS, respectively. In addition, A3F upregulation might increase the risk of vascular invasion, intrahepatic metastasis, and alpha-fetoprotein elevation, while high A3H decreased these risks . In anot7.A3 family members exhibit effective antiviral activities against HBV infection. In HBV infections, A3 genes exert anti-HBV roles by inducing G-to-A hypermutations and interactions with HBV proteins and are active molecules induced by interferon and other cytokines. On the other hand, A3 enzymes were shown to induce tumor mutations through aberrant DNA editing mechanisms. In HCC tumorigenesis, A3-mutated HBV X gene mutants and A3-induced HBV genetic polymorphisms lead to the development of HBV-related liver cancer. That is, A3 genes are the underlying molecules in the pathogenesis of HCC. However, the expression of A3 genes in liver tissues of different stages of liver diseases is still controversial. The anti-HBV mechanisms and activities of A3 family members need further investigation in detail. Moreover, as a viral repressor and tumor inducer, dual roles of A3 genes in the development of HBV-related HCC are questionable. As we know, suppressing of HBV transcription and replication leading to lower risk of cancer occurrence and tumor aggressiveness, and HBV gene mutations contribute to higher risk of cancer development. Since members of A3 family own functional roles in both sides, the underlying links between anti-HBV and tumor-promotion of A3 genes should be addressed. Which function of A3 is dominant is still a question in the development of HBV-related liver cancer. Conclusively, the potential mechanisms need to be investigated in different populations to understand their true association with cancer risk, viral restriction, and mutagenesis.


SARS-CoV-2 attaches to angiotensin-converting enzyme 2 (ACE2) to gain entry into cells after which the spike protein is cleaved by the transmembrane serine protease 2 (TMPRSS2) to facilitate viral-host membrane fusion. ACE2 and TMPRSS2 expression profiles have been analyzed at the genomic, transcriptomic, and single-cell RNAseq levels. However, transcriptomic data and actual protein validation convey conflicting information regarding the distribution of the biologically relevant protein receptor in whole tissues. To describe the organ-level architecture of receptor expression, related to the ability of ACE2 and TMPRSS2 to mediate infectivity, we performed a volumetric analysis of whole Syrian hamster lung lobes. Lung tissue of infected and control animals was stained using antibodies against ACE2 and TMPRSS2, combined with SARS-CoV-2 nucleoprotein staining. This was followed by light-sheet microscopy imaging to visualize their expression and related infection patterns. The data demonstrate that infection is restricted to sites containing both ACE2 and TMPRSS2, the latter is expressed in the primary and secondary bronchi whereas ACE2 is predominantly observed in the bronchioles and alveoli. Conversely, infection completely overlaps where ACE2 and TMPRSS2 co-localize in the tertiary bronchi, bronchioles, and alveoli. The ongoing COVID19 pandemic necessitates additional tools to study SARS-CoV infection dynamics. This increases our understanding of where these viruses cause infections in the lung, and how essential different receptor molecules are. Here we applied tissue optical clearing and 3D immunofluorescence of whole organs from our best animal model, the Syrian hamster. We describe the organ-level architecture of receptor expression profiles, to visualize how their concerted action mediates SARS-CoV-2 infection patterns. SARS-CoV-2 has sparked a pandemic and additional means to understand the infection dynamics of this virus will facilitate counter-measures. SARS coronaviruses carry a single protruding envelope protein, called spike, that is essential for binding to and subsequent infection of host cells. The trimeric spike protein binds to angiotensin-converting enzyme 2 (ACE2), which functions as an entry receptor for SARS-CoV ,2. AfterACE2 and TMPRSS2 are expressed in a wide variety of tissues and have been analyzed using different genetic techniques –9. High dataset [Beyond transcript concentration other factors contribute to protein expression, hence a direct correlation between mRNA and protein abundance for the same location/cell type may not reflect the actual situation . Importadataset . InitialHigh-resolution mapping of three-dimensional (3D) structures in intact tissues is indispensable in many biological studies, yet rarely employed to study viral receptors in their host organs concerning viral infection . The conHere, we examined receptor distribution in Syrian hamster lungs and correlated this distribution pattern of ACE2 and TMPRSS2 with the location of infection. Whole lung lobes of SARS-CoV-2 infected Syrian hamsters and control animals were stained using a variety of antibodies for ACE2, TMPRSS2, and the viral nucleoprotein to detect the functional receptor, cellular protease, and location of infection. We observed for ACE2 and TMPRSS2 a variation in protein expression levels within different regions of the lung lobes, with also a variation in protein expression between lung lobes from different Syrian hamsters. The results demonstrate that ACE2 and TMPRSS2 are unequally distributed in the Syrian hamster lung and that mainly overlapping regions were infected by SARS-CoV-2.Previously, we examined receptor binding of SARS-CoV trimeric receptor-binding domain (RBD) proteins to detect ACE2 in serial tissue slices of Syrian hamster lungs . Here, wVolumetric analysis was performed on Syrian hamster lung lobes to assess the spatial expression of ACE2 and TMPRSS2. The workflow of volumetric imaging and data analysis is described in To assess where potential virus binding and membrane fusion can occur, co-localization of ACE2 and TMPRSS2 was assessed in Syrian hamster lung lobes. Substantial staining was observed using the anti-TMPRSS2 antibody in the secondary and several tertiary bronchi Figs  and S4. To determine in which specific lung regions SARS-CoV-2 infection took place, we used hamster lung lobes isolated four days post-infection and stained these with anti-NP antibodies. High signal intensity was observed in the bronchi and bronchioles, however, several tertiary bronchi remained unstained Figs , and S5ABeing able to detect ACE2, TMPRSS2, and infected cells in a 3D format, we focused on examining overlapping regions of ACE2 and SARS-CoV-2 infection to assess if an infection is restricted to specific regions . We obseAfter assessing ACE2 and NP overlap , with alP = 0.0033), whereby the signal is highest in the “bottom”  and lowest in the “upper” region (i.e. primary/secondary branches)  . The sigranches)  and the ranches) , and 5. The Syrian hamster is considered the most relevant animal model in studying SARS-CoV-2 infection since this model reflects infection and pathology in humans. In our study, we elaborated on the expression patterns of ACE2 and TMPRSS2 to assess the dependency of the virus for infection in a spatial context. In the literature, SARS-CoV-2 infection in ferrets is predominantly in the upper respiratory tract (URT). Indeed we observed significant ACE2 and TMPRSS2 levels in the ferret trachea, however, expression in the ferrets lung for both ACE2 and TMPRSS2 was minimal . Here weTranscriptomic data of ACE2 and TMPRSS2 expression has been informative, although a direct correlation between mRNA transcripts and protein abundance does not necessarily convey actual biochemical expression as described with the deep proteome analyses, also demonstrated by The Human Cell/Protein Atlas consortium  S1 Fig)S1 Fig). In this study, we demonstrate distinct spatial distribution, or gradient, of ACE2 and TMPRSS2 proteins in the lung lobes of Syrian hamsters, which is considered to be the most relevant small animal model for studying SARS-CoV-2 infection ,21–23. ILight-sheet microscopy enables imaging of whole organs or large intact tissues in three dimensions. Previous implementations have focused on studying developmental biology or neuroscience , whilst Finally, we envisage our approach can be applied to circulating variant viruses that are more infectious ,41. It cResearch was conducted in compliance with the Dutch legislation for the protection of animals used for scientific purposes  and other relevant regulations. The licensed establishment where this research was conducted (Erasmus MC) has an approved OLAW Assurance # A5051-01. The research was conducted under a project license from the Dutch competent authority and the study protocol (#17–4312) was approved by the institutional Animal Welfare Body.Animals were handled in a BSL3 biocontainment laboratory. Animals were housed in groups of 2 animals in filter top cages , in Class III isolators allowing social interactions, under controlled conditions of humidity, temperature, and light (12-hour light/12-hour dark cycles). Food and water were available ad libitum. Animals were cared for and monitored (pre-and post-infection) by qualified personnel. The animals were sedated/anesthetized for all invasive procedures.2/min and 3 to 5% isoflurane). Animals were inoculated with 105 TCID50 of SARS-CoV-2 or PBS (mock controls) in a 100 μl volume via the intranasal route. Animals were monitored for general health status and behavior daily and were weighed regularly for the duration of the study . Animals were euthanized on day 4 after inoculation, and lung samples were removed and stored in 10% formalin for histopathology.Female Syrian golden hamsters  were anesthetized by chamber induction . Tissues were rehydrated with 100% methanol for 1 hour, followed by 100% methanol for 1 hour, 80% methanol, 50% methanol, and 1x PBS for 1 hour on a tilting shaker. Syrian hamster lungs were subsequently blocked for 24 hours at room temperature (22°C) in 1x PBS with 0.2% gelatin, 0.5% triton-x-100, and 0.01% sodium azide (PBSGT) on a tilting shaker. Hamster lungs were stained with primary/secondary antibodies possessing an alexa555, alexa647, or alexa750 dye. After blocking samples were incubated for 120 hours with the primary antibody in PBSGT with 0.1% saponin  on a shaking incubator at 200 rpm, following 120 hours incubation, hamster lungs were washed with PBSGT six times for 1 hour on a tilting shaker. Post-PBSGT wash, samples stained with primary antibodies were incubated with secondary antibodies that possess alexa555, alexa647 or alexa750 dyes, the antibodies were diluted in PBSGT with 0.1% saponin and filtered with 0.45 μm filter for potential antibody aggregates. Hereafter samples were incubated for 120 hours at room temperature on a shaking incubator at 200 rpm. After staining washing was performed with PBSGT six times for 1 hour on a tilting shaker. Immunostained samples were subsequently treated with 50% methanol for 24 hours, 80% methanol for 24 hours, 100% methanol for 24 hours followed by 100% methanol for 24 hours on a tilting shaker for dehydration of the lungs. Hereafter lipid solubilization was performed with dichloromethane  for 40 minutes after which refractive index matching and optical clearing were performed overnight with dibenzyl ether .SARS-CoV-2 infected and non-infected Syrian hamster lungs were provided in 10% formalin, for longer storage hamster lungs were kept in PBS and 0.01% sodium azide. Dehydration of the lungs was performed by washing with PBS for 1.5 hours, followed by 50% methanol for 1.5 hours, 80% methanol for 1.5 hours, and as of last 100% methanol for 1.5 hours on a tilting laboratory shaker. Bleaching was performed overnight at 4°C in 90% methanol (100% v/v) and 10% H2). A corrected dipping cap (CDC) (1.26x – 12.6x) was utilized with an Olympus MVPLAPO 2x objective lens. Imaging was performed with 0.63 zoom and step size of 5 μm , also with 6.3 zoom and step size of 2 μm . The exposure time was set to 200 ms and illumination for the 0.63 zoom was bidirectional and 6.3 zoom unidirectional. The sheet numerical aperture was 0.033 with a sheet thickness of 7.09 μm and a sheet width of 40%. The imaging chamber was filled with dibenzyl ether and the microscope software is Inspector version 7.1. Obtained image stacks were analyzed with ImageJ v1.54f and Imaris 9.7. Stills of image slices (orthoslices) were generated in ImageJ/Imaris, whilst 3D renders were generated in Imaris. TIFF files generated by the light-sheet microscope were imported to ImageJ with “File -> Import -> Bio-formats”, the stack is viewed as “Hyperstack” color mode was set to “Composite” and “Display metadata” with “Display OME-XML metadata” was enabled to obtain voxel information. Brightness adjustments were performed for each channel with setting “Image -> Adjust -> Brightness/Contrast”. The 488 channel color was set to hex code #FFFFFF, the 647 channel was set to hex code #FF0000 and the 730 channel was set to hex code #8080FF. Snapshots of the Z-stack were saved as JPEGs and the orthoslice animations with “Save As -> Avi -> Compression JPEG and Frame Rate 30 FPS”. For the 3D renders obtained imaging data was imported into Imaris, display adjustments were made for each channel. Snapshots and animations were made with 3D renders (3D View) within Imaris. Volume mode was set to maximum intensity projection (MIP) and the rendering quality was set to highest. Frame settings were used for the outer bounding box of the 3D render, “Box”, “Tickmarks”, and “Axis labels” were enabled. The spacing of the tickmarks was set to μm 500 for the X, Y, and Z-axis. The 488 channel color was set to hex code #FFFFFF, the 647 channel was set to hex code #FF0000 and the 730 channel was set to hex code #8080FF. For the triple stains, the 488 channel color was set to hex code #FFFFFF, the 561 channel color was set to hex code #0000FF, the 647 channel was set to hex code #FF0000 and the 730 channel was set to #00FF00. The 3D animations were made with 1920 x 1080 resolution (16:9) and 360 frames, 360° horizontal turn, and exported using Adobe Media Encoder 2020 or Adobe Premiere Pro 2020.Light-sheet imaging was performed using the Ultramicroscope II (LaVision BioTec) equipped with an MVX-10 Zoom Body. The laser lines 488 nm (Coherent OBIS 488–50 LX Laser 50mW), 561 nm (Coherent OBIS 561–100 LS Laser (100mW), 647 nm (Coherent OBIS 647–120 LX Laser 120mW), and 730 nm (Coherent OBIS 730–30 LX Laser 30mW) were used with 70% laser power for 488 channel, 70% laser power for the 555 channel, 70% laser power for the 647 channel and 100% laser power for the 730 nm channel. Emission filters ET525/50 (488 channel), ET615/40m (561 channel), ET676/29 (647 channel) and 716/40 (730 channel) filter was used with a Neo 5.5 sCMOS detector . In this surface/mask regions of interest were selected, i.e. the branches and alveoli in the hamster lung. Regions of interest were selected in the surface tool with the corresponding source channel, whereby the 647 channel with TMPRSS2 signal was used for TMPRSS2 and ACE2 double-stained samples and 730 source channel for ACE2 and anti-NP double-stained samples. For the TMPRSS2 single stained sample, the 647 channel was used as the source channel. Thresholding (Absolute Intensity) was performed arbitrarily to obtain complete coverage of the alveoli and branches. The automatically provided value for “Sphere Diameter” was used. For the separation of two or more objects that are identified as one, the “Split touching Objects (Region Growing)” setting was utilized. The “Seed Points Diameter” value was set to 35 μm. The generated surfaces were subsequently used to mask the TMPRSS2 and ACE2 channels. The surfaces are split into multiple smaller regions within the surface, due to the setting “Seed Points Diameter”, hereafter all statistical values from these multiple smaller regions can be obtained and exported to GraphPad Prism 9 after which a grouped analysis was performed for statistical significance.S1 Fig(A) EIF4A3 gene has no correlation (rho = -0.03) with p = 0.88. (B) Occasional correlation for ACTB (rho = 0.52) with p = 0.0068. (C) Almost perfect correlation for SYK (rho = 0.92) with p < 0.0001. (D) ACE2 expression occasionally correlates (rho 0.45) with p = 0.0143. (E) For TMPRSS2 almost perfect correlation (rho = 0.98) with P < 0.0001Spearman correlation coefficient (rho) calculated  to assess whether the concentration of transcripts (FPKM) and protein abundance (IBAQ) vary together. P value provided, small p value rejects that correlation is due to random sampling. Large p value gives no compelling evidence that correlation is actually real and not due to chance. Intensity-based absolute quantification (IBAQ). Fragments Per Kilobase Million (FPKM)) (TIF)Click here for additional data file.S2 Fig(A) No SARS-CoV-2 infection present in non-infected Syrian hamster lung (1–3), SARS-CoV-2 infection detected in the bronchioles and alveoli of infected Syrian hamster lungs (4–6). (B) ACE2 expression observed in Syrian hamster lungs. (C) TMPRSS2 expression is characterized, with reduced TMPRSS2 stain in the alveoli of infected Syrian hamsters (4–6). (D) K8/K18 stains with predominant bronchiolar staining. (E) With the secondary antibodies only, no staining is observed. (F) Overlap of ACE2 and TMPRSS2 is assessed in non-infected Syrian hamster lungs (1–3), with overlap of NP, ACE2 and TMPRSS2 in infected Syrian hamster lungs (4–6). Red arrow indicates no overlap while blue arrow indicates overlap.(TIF)Click here for additional data file.S3 Fig(A) ACE2 distribution over Syrian hamster lung lobe. (B) TMPRSS2 signal present in larger branches. (C) K8/K18 staining in the primary, secondar and in tertiary bronchi, with bronchiolar and alveolar staining. (D) Autofluorescence in the antibody control.(TIF)Click here for additional data file.S4 Fig(A) TMPRSS2 expression in primary, secondary and tertiarty bronchi. Signal spreading to bronchioles. ACE2 staining in tertiary bronchi and bronchioles. In the tertiary bronchi overlap visualised of the receptor and co-factor 0.63 zoom, voxel resolution X,Y,Z: 4.79 μm, 4.79 μm, 5 μm. (B) Bronchiolar and alveolar TMPRSS2 and ACE2 expression visualized and superimposed. 6.3 zoom, voxel resolution X,Y,Z: 0.48 μm, 0.48 μm, 2 μm. (Row 1) 3D render 647 channel . (Row 2) 3D render 730 channel . (Row 3) Merge 3D render 647 channel  and 730 channel . (Row 4) Orthoslice merge 488 and 647 channel. (Row 5) Orthoslice 647 channel . (Row 6) Orthoslice merge 488 and 730 channel. Row 7) orthoslice 730 channel . (Row 8) Merge orthoslice 647 channel  and 730 channel . (Row 9) Orthoslice 488 channel (autofluorescence).(TIF)Click here for additional data file.S5 Fig(A) NP staining in the primary/secondary bronchi. (B) Anti-NP antibody displays hardly non-specific binding in non-infected Syrian hamster lung lobes. (C) Autofluorescence in the antibody control.Light-sheet staining in red (647 channel) and confocal staining in red (488 channel). (TIF)Click here for additional data file.S6 Fig(A) Antibody 40143-MM05 on non-infected Syrian hamster lungs shows non-specific interaction towards the outer regions of the lung lobe. (B) Intense staining with antibody 40143-R001 in a Syrian hamster lung lobe, severe non-specific binding is observed. (C)Non-specific interaction of antibody MA1-7401 in a lung lobe whereby similar bronchiolar structures as infected hamster lungs are stained.Autofluorescence in grey (488 channel) and staining in red (647 channel). (TIF)Click here for additional data file.S7 Fig(A) Top-to-bottom NP stain, signal in the secondary bronchi extending towards the tertiary bronchi. A bottom-to-top gradient for ACE2 stain, with signal in the secondary and various tertiary bronchi. Overlap of NP and ACE2 in tertiary bronchi and bronchioles. 0.63 zoom, voxel resolution X,Y,Z: 4.79 μm, 4.79 μm, 5 μm. (B) NP and ACE2 foci overlayed, severe overlap is visualized in the alveoli. 6.3 zoom, voxel resolution X,Y,Z: 0.48 μm, 0.48 μm, 2 μm. (C) TMPRSS2 expression profile, predominant staining in the primary, secondary and tertiary bronchi with signal spreading towards the bronchioles. ACE2 and TMPRSS2 superimpose in the primarily in the tertiary bronchi and bronchioles 0.63 zoom, voxel resolution X,Y,Z: 4.79 μm, 4.79 μm, 5 μm. (D) Overlap of TMPRSS2 and ACE2 only in the bronchioles, whilst ACE2 expression is also present in the alveoli. 6.3 zoom, voxel resolution X,Y,Z: 0.48 μm, 0.48 μm, 2 μm. (Row 1) 3D render 647 channel . (Row 2) 3D render 730 channel . (Row 3) Merge 3D render 647 channel  and 730 channel . (Row 4) Orthoslice merge 488 and 647 channel. (Row 5) Orthoslice 647 channel . (Row 6) Orthoslice merge 488 and 730 channel. (Row 7) Orthoslice 730 channel . (Row 8) Merge orthoslice 647 channel  and 730 channel . (Row 9) Orthoslice 488 channel (autofluorescence).(TIF)Click here for additional data file.S8 Fig(A/B) ACE2 and TMPRSS2 signal in the nasal cavity with SARS-CoV-2 infection overlap. 0.63 zoom, voxel resolution X,Y,Z: 4.79 μm, 4.79 μm, 5 μm. (C/D) In the left lobe TMPRSS2 signal is seen in the upper portions of the lung lobe. ACE2 signal mostly lower in the lung lobe. SARS-CoV-2 infection is transposing in regions where both ACE2 and TMPRSS2 signal is present. (E/F) In the accessory lobe fluorescence signal of TMPRSS2, NP and ACE2 overlap. (G/H) The right upper/mid lobe contains TMPRSS2, ACE2 and NP signal superimpose. Regions with SARS-CoV-2 infection either show high TMPRSS2 and low ACE2 signal or low TMPRSS2 and high ACE2.TMPRSS2 imaged in the 555 channel, NP in the 647 channel and ACE2 in the 730 channel. Merges of all channels shown in red (ACE2), green (NP) and blue (TMPRSS2). (TIF)Click here for additional data file.S1 TableLung lobes from multiple Syrian hamsters have been stained with various primary and secondary antibodies. For co-staining experiments two primary and two secondary antibodies were utilized. For triple stain three primary and three secondary antibodies were used.(XLSX)Click here for additional data file.S1 VideoACE2 in purple and TMPRSS2 in red with overlap in pink.(MP4)Click here for additional data file.S2 VideoACE2 in purple and TMPRSS2 in red with overlap in pink.(MP4)Click here for additional data file.S3 VideoACE2 in purple and SARS-CoV-2 infection in red with overlap in pink.(MP4)Click here for additional data file.S4 VideoACE2 in purple and SARS-CoV-2 infection in red with overlap in pink.(MP4)Click here for additional data file.S5 VideoACE2 in purple and TMPRSS2 in red with overlap in pink.(MP4)Click here for additional data file.S6 VideoACE2 in purple and TMPRSS2 in red with overlap in pink.(MP4)Click here for additional data file.S7 VideoIn red ACE2, in green SARS-CoV-2 and in blue TMPRSS2 with overlapping regions in white.(MP4)Click here for additional data file.S8 VideoIn red ACE2, in green SARS-CoV-2 and in blue TMPRSS2 with overlapping regions in white.(MP4)Click here for additional data file.S9 VideoIn red ACE2, in green SARS-CoV-2 and in blue TMPRSS2 with overlapping regions in white. Yellow arrow indicates regions with low ACE2 and high TMPRSS2 expression. Red arrow indicates regions with high ACE2 and low TMPRSS2 expression.(MP4)Click here for additional data file.S10 VideoIn red ACE2, in green SARS-CoV-2 and in blue TMPRSS2 with overlapping regions in white. Yellow arrow indicates regions with low ACE2 and high TMPRSS2 expression. Red arrow indicates regions with high ACE2 and low TMPRSS2 expression.(MP4)Click here for additional data file.S11 VideoIn red ACE2, in green SARS-CoV-2 and in blue TMPRSS2 with overlapping regions in white. Yellow arrow indicates regions with low ACE2 and high TMPRSS2 expression. Red arrow indicates regions with high ACE2 and low TMPRSS2 expression.(MP4)Click here for additional data file.S12 VideoIn red ACE2, in green SARS-CoV-2 and in blue TMPRSS2 with overlapping regions in white. Yellow arrow indicates regions with low ACE2 and high TMPRSS2 expression. Red arrow indicates regions with high ACE2 and low TMPRSS2 expression.(MP4)Click here for additional data file.S13 VideoIn red ACE2, in green SARS-CoV-2 and in blue TMPRSS2 with overlapping regions in white. Yellow arrow indicates regions with low ACE2 and high TMPRSS2 expression. Red arrow indicates regions with high ACE2 and low TMPRSS2 expression.(MP4)Click here for additional data file.S14 VideoIn red ACE2, in green SARS-CoV-2 and in blue TMPRSS2 with overlapping regions in white. Yellow arrow indicates regions with low ACE2 and high TMPRSS2 expression. Red arrow indicates regions with high ACE2 and low TMPRSS2 expression.(MP4)Click here for additional data file.


More effective treatments are needed for human papilloma virus (HPV)-induced cancers despite HPV virus vaccination. The oncogenic HPV protein targets are currently undruggable and intracellular and therefore there are no antibodies to these targets. Here we report the discovery of TCR mimic monoclonal antibodies (TCRm mAb) specific for the HPV E7 protein p11-19, YMLDLQPET, when presented on the cell surface in the context of HLA-A*02:01 by use of human phage display libraries. One of the mAbs, 3F8, was able to specifically mediate T cell- redirected cytotoxicity, in a bispecific T cell engager (BiTE) form. While further studies are required to assess the therapeutic potential of this approach, the study provided the proof of concept that TCRm mAb could be a therapeutic strategy for HPV-induced human cancers. High risk HPV causes malignant transformation following persistent infection and is the cause of virtually all cases of cervical cancer and represents nearly 30% of all infection-related cancers . The virHPV type 16 E6 and E7 proteins are consistently expressed in HPV-associated cancers and are thus ideal targets for vaccine design. Therefore, therapeutic vaccines have mostly focused on E6 and E7 as target antigens to induce HPV-specific T cell responses. Various formats of vaccines including naked DNA, short and overlapping long peptides, fusion constructs with Toll-like receptor agonists, have been studied. However, significant clinical benefit over historical controls remains to be observed . PrimingWe hypothesize that a mAb that mimics TCR recognition of a HPV-derived epitopes presented by HLA class I molecules could be an effective immunotherapeutic approach targeting HPV-induced malignancies. The advantages of mAb therapy are well known and include their high target specificity, high efficacy, limited side effects, prolonged half-life, availability, and infrequent dosing. These features make a mAb therapy particularly useful and practical in undeveloped countries. In addition to its inherent properties, mAb can be engineered into many formats to enhance its potency and not patient-specific as adoptive T cell therapy.We selected a well-defined HPV-E7-derived CD8 T cell epitope , as the target for the discovery of a TCRm mAb. This epitope has been reproducibly detected in most cervical cancer biopsies and HPV positive cancer cell lines in the context of HLA-A*02:01 molecule by mass spectrometry –14. The After written and witnessed informed consent on Memorial Sloan-Kettering Cancer Center [MSKCC] Institutional Review Board–approved protocols, peripheral blood mononuclear cells [PBMCs] from HLA-typed healthy donors were obtained by Ficoll density centrifugation. Tumor cell lines used in this study were obtained from American Tissue Culture Collection [ATCC]. mAbs against human HLA-A2 (clone BB7.2) conjugated to fluorescein isothiocyanate [FITC] or allophycocyanin (APC), and its isotype control mouse IgG2b/FITC or APC were purchased from Biolegend. Goat F(ab′)2 anti-hIgG conjugated with phycoerythrin [PE] or FITC, mouse anti-human CD3 mAb, and 6x-His Tag mAb/FITC were purchased from Invitrogen. APC conjugation kit-lighting link (ab201817) was purchased from Abcam and was used to label 3F8, 2A5 and 1B1 mAbs according to manufacturer’s instruction. Human isotype control hIgG1 antibody was purchased from Bingo Biotech [catalog number ET901].All peptides were purchased and synthesized by Genemed Synthesis Inc . Peptides were >95% pure . The pepFor cell surface staining, cells were incubated with appropriate mAbs for 30 min on ice, washed, and incubated with secondary antibody reagents when necessary. Flow cytometry data were collected on a LSR Fortessa (BD Biosciences) and analyzed with FlowJoV10.6.1 software.E. coli and subsequent in vitro refolding and assembly in the presence of high concentrations of HPV-E7p11, WT1-RMF or HPV-AAAA peptide.The method used follows directly the original established protocol –17. BrieA proprietary naïve, semi-synthetic human single chain fragment variable (scFv) phage display library  was usedThe full-length IgG for 1B1, 2A5 and 3F8 was constructed by cloning variable heavy into human IgG1 constant domain and variable light into human kappa light chain backbone. The gene was cloned into pcDNA3.4 vector for expression in mammalian cells. For BiTE construction, 3F8 scFv in vL-vH was linked via G4S linker to an anti-CD3 L2K scFv  in vH-vL6/mL with >95% viability at linear growth. Enhancers were added on the following day and culture was harvested on day 5. Media was cleared by centrifugation and filtering through a 0.22μm membrane. Cleared supernatants were processed for protein purification using either MabSelect SuRe [IgG] or His-trap excel resin (BiTE) .Full length human IgG1 and 3F8 BiTE were expressed in Expi293F cells  as per manufacturer guidelines. Briefly, cells were transfected at 3x10The specificities of the fully human IgG1 mAbs for the HPV-E7/A2 complex were determined by staining T2 cells pulsed with or without HPV-E7p11-19, WT1-RMF or HPV-E7AAAA peptides, after directly conjugating mAbs to APC. Binding of the BiTE was determined by staining cells with the 3F8 BiTE, followed by anti-His/PE secondary antibody. The fluorescence intensity was measured by flow cytometry. The same method was used to determine the binding of the mAbs to peptide un-pulsed tumor cell lines with endogenous expression.51Cr-release assay. In brief, target cells were labeled with Na251CrO4 [50 uCi/million cells] for one hour, washed and incubated with effector cells for 5 hours. The supernatant fluid was collected and radioactivity was measured in a gamma counter. Percentage specific lysis was determined from the following formula: 100 × [/(maximum release − spontaneous release)]. Maximum release was determined by lysis of radiolabeled targets in 2.5% Triton X-100.The 3F8-BiTE or its control BiTE at indicated concentrations were incubated with target cells and PBMCs at different effector: target [E/T] ratios for 5 hours. The cytotoxicity was measured by standard Binding affinities of candidate antibodies in solution were determined using surface plasmon resonance (SPR) MASS-2 instrument [Bruker]. Biotinylated HLA-E7 peptide complex ligand (46 kDa) was immobilized on Biotin-Tag Capture Sensor (Bruker). Candidate antibodies (150 kDa) serially diluted in running buffer  starting at 1μM concentration were injected over the chip at 25°C. The complex was allowed to associate and dissociate for 180 and 300s, respectively. The data were processed using steady state 1:1 interaction model with double referencing.g at 4°C, and supernatant fluids were isolated. For immunopurification of HLA class I ligands, 0.5 mg of W6/32 antibody (BioXCell) were bound to 40 mg CN-Br activated sepharose and incubated with the protein lysate overnight. HLA complexes and binding peptides were eluted five times using 1% TFA. Peptides and HLA complexes were separated using C18 columns . C18 columns were preconditioned with 80% ACN (MilliporeSigma) in 0.1% TFA and equilibrated with 2 washes of 0.1% TFA. Samples were loaded, washed again twice with 0.1% TFA, and eluted in 300 μL of 30%, 40%, and 50% acetonitrile in 0.1% TFA. All three fractions were pooled, dried down using vacuum centrifugation and stored at -80°C until further processing. HLA ligands were isolated by solid-phase extractions using in-house C18 minicolumns. Samples were analyzed by high-resolution/high-accuracy liquid chromatography-tandem mass spectrometry (LC-MS/MS]) . MS and MS/MS were operated at resolutions of 60,000 and 30,000, respectively. Only charge states 1, 2, and 3 were allowed. The isolation window was chosen as 1.6 thomson, and collision energy was set at 30%. For MS/MS, maximum injection time was 100 ms with an automatic gain control of 50,000. MS data were processed using Byonic software  through a custom-built computer server equipped with 4 Intel Xeon E5-4620 8-core CPUs operating at 2.2 GHz and 512 GB physical memory (Exxact Corporation). Protein FDR was disabled to allow complete assessment of potential peptide identifications. Oxidization of methionine; phosphorylation of serine, threonine, and tyrosine; as well as N-terminal acetylation were set as variable modifications for all samples. Samples were searched against a database comprising UniProt human reviewed proteins supplemented with the HPV proteome as well as common contaminants. For mirror plots ion intensities were exported and re-plotted with Graphpad prism.Cell lines were cultured until 10M to 30 million cells were available and then harvested. Harvested cells were pelleted and washed 3 times in ice-cold sterile PBS  Immunoprecipitation, HLA ligand separation and LC-MS/MS were performed as previously described . BrieflyTo select phage clones that mimic TCR recognition, e.g. recognizing amino acids in the middle of the peptide/HLA complex, we performed the following screening strategies: first, counter screening against a peptide/MHC complex with a peptide with a non-similar sequence to HPV using peptide RMFPNAPYL (from the WT1 protein) that binds to HLA-A2 with high affinity. WT1-RMF/HLA-A2 complex to remove all clones that bound to the HLA-A2 molecule. Second: positive screening for HPV-E7p11 (YMLDLQPET) /HLA-A2 complex to broadly select clones that bound to the desired HPV complex. Third: screening against a mutant peptide E7-AAAA [YMAAAAPET]/HLA-A2 complex to remove phage clones that bind to either end of the peptide. In addition, specificity to middle amino acids LDLQ should reduce binding to many potential human proteomic off-target peptides. Positive clones specific for the HPV E7p11/HLA-A2 complex were selected by 3–4 rounds of panning, confirmed by ELISA and live cell binding to T2 cells pulsed with HPV-E7p11 or other control peptides. Three out of total 19 phage clones were selected and engineered into full length human IgG1 format for further characterization.The specificity of full length human IgG1 mAbs was evaluated using T2 cells, pulsed with or without HPV-E7p11, HPV-AAA or control peptide RMF, and tested for their binding capacity. All three hIgG mAbs, named 1B1, 2A5 and 3F8, bound to T2 pulsed with HPV-E7p11, but not to T2 cells alone, or pulsed with irrelevant RMF peptide. When T2 cells were pulsed with HPV-AAAA peptide, the 1B1 and 2A5 mAbs retained binding at a lower level, but 3F8 completely lost binding . These dThe binding positions of the mAbs were further investigated by pulsing T2 cells with HPV-E7p11 analog peptides that were substituted with alanine at each amino acid position . 1B1 andTo further assess the specificity of the mAbs, a panel of HLA-A2-positive or negative cell lines  that areThe low affinity of the mAb 3F8 suggested it might not have useful functional activity in an IgG format, especially for ultra-low density antigens such as HPV E7 PV HOpeptide/MHC complexes; therefore, we proceeded to engineer 3F8 to a bispecific T cell engager (BiTE) format to enhance its potency. Binding specificity of 3F8 BiTE for HPV-E7p11 was tested using T2 cells. There was no binding to either T2 alone or T2 when pulsed with RMF peptide, but the 3F8 BiTE bound well to T2 pulsed with the HPV target peptide . For theAlthough BiTE recognition is monovalent for both the target and the T cell, this level of binding should be sufficient to elicit functional cytotoxicity. We next assessed the efficacy and potency of 3F8 BiTE-directed T cell cytotoxicity in 5 hr 51Cr- release assays against T2 cells pulsed with HPV or control RMF peptide. Compare to control BiTE, 3F8 BiTE showed cytotoxicity against T2 pulsed with HPV E7-p11-19, down to levels below 10ng/ml. These data demonstrated that 3F8, while a low affinity mAb, was able to kill the target with specificity.To test if 3F8 mAb was able to recognize the naturally processed HPV-E7p11-20 epitope presented by HLA- A*02:01 molecules, multiple tumor cell lines that are HLA-A*02:01 and HPV-E7 positive derived from human cervical cancer were tested for the binding of the 3F8 hIgG1 and cytotoxicity of the 3F8-BiTE . The BiTThe development of TCRm mAbs is emerging as a powerful strategy to effectively target intracellular tumor antigens and expands the therapeutic opportunities for currently undruggable oncogenic and tumor-associated proteins. Virus-derived tumor antigens such as from HPV remain an attractive target because of their lack of normal tissue expression. The challenge for T cell-based therapies including TCRm mAbs remains the identification of suitable epitopes to ensure on-target specificity. While various HPV16-derived peptides have been tested as vaccines, the HPV-E7p11-19 was an epitope that was reproducibly detected by mass spectrometry analysis of HPV 16-E7-expressing cervical cancer cell lines and biopsies from patients with cervical cancer –14. TherHere, we show the discovery of the first TCRm mAb specific for the HPV16-E7p11 epitope when presented by HLA-A2 on the cell surface. Our initial characterization has demonstrated the specificity and the functional activity of a mAb clone, 3F8. Alanine scanning showed a broad range of amino acids from positions 3–8 that were important for the recognition by the mAb 3F8. This predicts a low risk for broad cross-reactivity to similar off-target amino acid sequences that may be in the proteome. A BiTE form of 3F8 was also able to kill the T2 cells pulsed with the target peptide, but not irrelevant peptide, despite the low affinity of the mAb. This could be due to an ability of the BiTE engaging a serial killing of the target cells once T cells are activated . However