Corrigendum: Urokinase-type plasminogen activator and plasminogen activator inhibitor-1 complex as a serum biomarker for COVID-19.

[This corrects the article DOI: 10.3389/fimmu.2023.1299792.].

Full-scale network analysis reveals properties of the FV protein structure organization.

Blood coagulation is a vital process for humans and other species. Following an injury to a blood vessel, a cascade of molecular signals is transmitted, inhibiting and activating more than a dozen coagulation factors and resulting in the formation of a fibrin clot that ceases the bleeding. In this process, the Coagulation factor V (FV) is a master regulator, coordinating critical steps of this process. Mutations to this factor result in spontaneous bleeding episodes and prolonged hemorrhage after trauma or surgery. Although the role of FV is well characterized, it is unclear how single-point mutations affect its structure. In this study, to understand the effect of mutations, we created a detailed network map of this protein, where each node is a residue, and two residues are connected if they are in close proximity in the three-dimensional structure. Overall, we analyzed 63 point-mutations from patients and identified common patterns underlying FV deficient phenotypes. We used structural and evolutionary patterns as input to machine learning algorithms to anticipate the effects of mutations and anticipated FV-deficiency with fair accuracy. Together, our results demonstrate how clinical features, genetic data and in silico analysis are converging to enhance treatment and diagnosis of coagulation disorders.

A graph-based machine learning framework identifies critical properties of FVIII that lead to hemophilia A.

Introduction: Blood coagulation is an essential process to cease bleeding in humans and other species. This mechanism is characterized by a molecular cascade of more than a dozen components activated after an injury to a blood vessel. In this process, the coagulation factor VIII (FVIII) is a master regulator, enhancing the activity of other components by thousands of times. In this sense, it is unsurprising that even single amino acid substitutions result in hemophilia A (HA)-a disease marked by uncontrolled bleeding and that leaves patients at permanent risk of hemorrhagic complications. Methods: Despite recent advances in the diagnosis and treatment of HA, the precise role of each residue of the FVIII protein remains unclear. In this study, we developed a graph-based machine learning framework that explores in detail the network formed by the residues of the FVIII protein, where each residue is a node, and two nodes are connected if they are in close proximity on the FVIII 3D structure. Results: Using this system, we identified the properties that lead to severe and mild forms of the disease. Finally, in an effort to advance the development of novel recombinant therapeutic FVIII proteins, we adapted our framework to predict the activity and expression of more than 300 in vitro alanine mutations, once more observing a close agreement between the in silico and the in vitro results. Discussion: Together, the results derived from this study demonstrate how graph-based classifiers can leverage the diagnostic and treatment of a rare disease.

Urokinase-type plasminogen activator and plasminogen activator inhibitor-1 complex as a serum biomarker for COVID-19.

Patients with coronavirus disease-2019 (COVID-19) have an increased risk of thrombosis and acute respiratory distress syndrome (ARDS). Thrombosis is often attributed to increases in plasminogen activator inhibitor-1 (PAI-1) and a shut-down of fibrinolysis (blood clot dissolution). Decreased urokinase-type plasminogen activator (uPA), a protease necessary for cell-associated plasmin generation, and increased tissue-type plasminogen activator (tPA) and PAI-1 levels have been reported in COVID-19 patients. Because these factors can occur in free and complexed forms with differences in their biological functions, we examined the predictive impact of uPA, tPA, and PAI-1 in their free forms and complexes as a biomarker for COVID-19 severity and the development of ARDS. In this retrospective study of 69 Japanese adults hospitalized with COVID-19 and 20 healthy donors, we found elevated free, non-complexed PAI-1 antigen, low circulating uPA, and uPA/PAI-1 but not tPA/PAI-1 complex levels to be associated with COVID-19 severity and ARDS development. This biomarker profile was typical for patients in the complicated phase. Lack of PAI-1 activity in circulation despite free, non-complexed PAI-1 protein and plasmin/α2anti-plasmin complex correlated with suPAR and sVCAM levels, markers indicating endothelial dysfunction. Furthermore, uPA/PAI-1 complex levels positively correlated with TNFα, a cytokine reported to trigger inflammatory cell death and tissue damage. Those levels also positively correlated with lymphopenia and the pro-inflammatory factors interleukin1ß (IL1ß), IL6, and C-reactive protein, markers associated with the anti-viral inflammatory response. These findings argue for using uPA and uPA/PAI-1 as novel biomarkers to detect patients at risk of developing severe COVID-19, including ARDS.

COVID-19 Severity and Thrombo-Inflammatory Response Linked to Ethnicity.

Although there is strong evidence that SARS-CoV-2 infection is associated with adverse outcomes in certain ethnic groups, the association of disease severity and risk factors such as comorbidities and biomarkers with racial disparities remains undefined. This retrospective study between March 2020 and February 2021 explores COVID-19 risk factors as predictors for patients' disease progression through country comparison. Disease severity predictors in Germany and Japan were cardiovascular-associated comorbidities, dementia, and age. We adjusted age, sex, body mass index, and history of cardiovascular disease comorbidity in the country cohorts using a propensity score matching (PSM) technique to reduce the influence of differences in sample size and the surprisingly young, lean Japanese cohort. Analysis of the 170 PSM pairs confirmed that 65.29% of German and 85.29% of Japanese patients were in the uncomplicated phase. More German than Japanese patients were admitted in the complicated and critical phase. Ethnic differences were identified in patients without cardiovascular comorbidities. Japanese patients in the uncomplicated phase presented a suppressed inflammatory response and coagulopathy with hypocoagulation. In contrast, German patients exhibited a hyperactive inflammatory response and coagulopathy with hypercoagulation. These differences were less pronounced in patients in the complicated phase or with cardiovascular diseases. Coagulation/fibrinolysis-associated biomarkers rather than inflammatory-related biomarkers predicted disease severity in patients with cardiovascular comorbidities: platelet counts were associated with severe illness in German patients. In contrast, high D-dimer and fibrinogen levels predicted disease severity in Japanese patients. Our comparative study indicates that ethnicity influences COVID-19-associated biomarker expression linked to the inflammatory and coagulation (thrombo-inflammatory) response. Future studies will be necessary to determine whether these differences contributed to the less severe disease progression observed in Japanese COVID-19 patients compared with those in Germany.

A Machine Learning Framework Predicts the Clinical Severity of Hemophilia B Caused by Point-Mutations.

Blood coagulation is a vital physiological mechanism to stop blood loss following an injury to a blood vessel. This process starts immediately upon damage to the endothelium lining a blood vessel, and results in the formation of a platelet plug that closes the site of injury. In this repair operation, an essential component is the coagulation factor IX (FIX), a serine protease encoded by the F9 gene and whose deficiency causes hemophilia B. If not treated by prophylaxis or gene therapy, patients with this condition are at risk of life-threatening bleeding episodes. In this sense, a deep understanding of the FIX protein and its activated form (FIXa) is essential to develop efficient therapeutics. In this study, we used well-studied structural analysis techniques to create a residue interaction network of the FIXa protein. Here, the nodes are the amino acids of FIXa, and two nodes are connected by an edge if the two residues are in close proximity in the FIXa 3D structure. This representation accurately captured fundamental properties of each amino acid of the FIXa structure, as we found by validating our findings against hundreds of clinical reports about the severity of HB. Finally, we established a machine learning framework named HemB-Class to predict the effect of mutations of all FIXa residues to all other amino acids and used it to disambiguate several conflicting medical reports. Together, these methods provide a comprehensive map of the FIXa protein architecture and establish a robust platform for the rational design of FIX therapeutics.

Computational Analyses Reveal Fundamental Properties of the Hemophilia Literature in the Last 6 Decades.

Hemophilia is an inherited blood coagulation disorder caused by mutations on the coagulation factors VIII or IX genes. Although it is a relatively rare disease, the research community is actively working on this topic, producing almost 6000 manuscripts in the last 5 years. Given that the scientific literature is increasing so rapidly, even the most avid reader will find it difficult to follow it closely. In this study, we used sophisticated computational techniques to map the hemophilia literature of the last 60 years. We created a network structure to represent authorship collaborations, where the nodes are the researchers and 2 nodes are connected if they co-authored a manuscript. We accurately identified author clusters, namely, researchers who have collaborated systematically for several years, and used text mining techniques to automatically synthesize their research specialties. Overall, this study serves as a historical appreciation of the effort of thousands of hemophilia researchers and demonstrates that a computational framework is able to automatically identify collaboration networks and their research specialties. Importantly, we made all datasets and source code available for the community, and we anticipate that the methods introduced here will pave the way for the development of systems that generate compelling hypothesis based on patterns that are imperceptible to human researchers.

Country transition index based on hierarchical clustering to predict next COVID-19 waves.

COVID-19 has widely spread around the world, impacting the health systems of several countries in addition to the collateral damage that societies will face in the next years. Although the comparison between countries is essential for controlling this disease, the main challenge is the fact of countries are not simultaneously affected by the virus. Therefore, from the COVID-19 dataset by the Johns Hopkins University Center for Systems Science and Engineering, we present a temporal analysis on the number of new cases and deaths among countries using artificial intelligence. Our approach incrementally models the cases using a hierarchical clustering that emphasizes country transitions between infection groups over time. Then, one can compare the current situation of a country against others that have already faced previous waves. By using our approach, we designed a transition index to estimate the most probable countries' movements between infectious groups to predict next wave trends. We draw two important conclusions: (1) we show the historical infection path taken by specific countries and emphasize changing points that occur when countries move between clusters with small, medium, or large number of cases; (2) we estimate new waves for specific countries using the transition index.

Protein residue network analysis reveals fundamental properties of the human coagulation factor VIII.

Hemophilia A is an X-linked inherited blood coagulation disorder caused by the production and circulation of defective coagulation factor VIII protein. People living with this condition receive either prophylaxis or on-demand treatment, and approximately 30% of patients develop inhibitor antibodies, a serious complication that limits treatment options. Although previous studies performed targeted mutations to identify important residues of FVIII, a detailed understanding of the role of each amino acid and their neighboring residues is still lacking. Here, we addressed this issue by creating a residue interaction network (RIN) where the nodes are the FVIII residues, and two nodes are connected if their corresponding residues are in close proximity in the FVIII protein structure. We studied the characteristics of all residues in this network and found important properties related to disease severity, interaction to other proteins and structural stability. Importantly, we found that the RIN-derived properties were in close agreement with in vitro and clinical reports, corroborating the observation that the patterns derived from this detailed map of the FVIII protein architecture accurately capture the biological properties of FVIII.

Prediction of hemophilia A severity using a small-input machine-learning framework.

Hemophilia A is a relatively rare hereditary coagulation disorder caused by a defective F8 gene resulting in a dysfunctional Factor VIII protein (FVIII). This condition impairs the coagulation cascade, and if left untreated, it causes permanent joint damage and poses a risk of fatal intracranial hemorrhage in case of traumatic events. To develop prophylactic therapies with longer half-lives and that do not trigger the development of inhibitory antibodies, it is essential to have a deep understanding of the structure of the FVIII protein. In this study, we explored alternative ways of representing the FVIII protein structure and designed a machine-learning framework to improve the understanding of the relationship between the protein structure and the disease severity. We verified a close agreement between in silico, in vitro and clinical data. Finally, we predicted the severity of all possible mutations in the FVIII structure - including those not yet reported in the medical literature. We identified several hotspots in the FVIII structure where mutations are likely to induce detrimental effects to its activity. The combination of protein structure analysis and machine learning is a powerful approach to predict and understand the effects of mutations on the disease outcome.

A robust human norovirus replication model in zebrafish larvae.

Human noroviruses (HuNoVs) are the most common cause of foodborne illness, with a societal cost of $60 billion and 219,000 deaths/year. The lack of robust small animal models has significantly hindered the understanding of norovirus biology and the development of effective therapeutics. Here we report that HuNoV GI and GII replicate to high titers in zebrafish (Danio rerio) larvae; replication peaks at day 2 post infection and is detectable for at least 6 days. The virus (HuNoV GII.4) could be passaged from larva to larva two consecutive times. HuNoV is detected in cells of the hematopoietic lineage and the intestine, supporting the notion of a dual tropism. Antiviral treatment reduces HuNoV replication by >2 log10, showing that this model is suited for antiviral studies. Zebrafish larvae constitute a simple and robust replication model that will largely facilitate studies of HuNoV biology and the development of antiviral strategies.

Investigation of tumor-tumor interactions in a double human cervical carcinoma xenograft model in nude mice.

Tumor-tumor distant interactions within one organism are of major clinical relevance determining clinical outcome. To investigate this poorly understood phenomenon, a double human cervical xenograft model in nude mice was developed. A first tumor was induced subcutaneously by injection of human papillomavirus positive cervical carcinoma cells into the mouse lower right flank and 3 weeks later, animals were challenged with the same tumor cell line injected subcutaneously into the upper left flank. These tumors had no direct physical contact and we found no systemic changes induced by the primary tumor affecting the growth of a secondary tumor. However, ablation of the primary tumor by local treatment with cidofovir, a nucleotide analogue with known antiviral and antiproliferative properties, resulted not only in a local antitumor effect but also in a temporary far-reaching effect leading to retarded growth of the challenged tumor. Cidofovir far-reaching effects were linked to a reduced tumor-driven inflammation, to increased anti-tumor immune responses, and could not be enhanced by co-administration with immune stimulating adjuvants. Our findings point to the potential use of cidofovir in novel therapeutic strategies aiming to kill tumor cells as well as to influence the immune system to fight cancer.

Effects of myogenic precursor cells (C2C12) transplantation and low-level laser therapy on muscle repair.

OBJECTIVE: The aim of the present study was to evaluate the effects of myoblast inoculation in combination with photobiomodulation therapy (PBMT) on skeletal muscle tissue following injury. MATERIALS AND METHODS: Sixty-five Wistar rats were divided into five groups: Control-animals not submitted to any procedure; Injury-cryoinjury of the tibialis anterior muscle; HBSS-animals submitted to cryoinjury and intramuscular Hank's Balanced Salt Solution; Injury + Cells-animals submitted to cryoinjury, followed by myogenic precursor cells (C2C12) transplantation; Injury + Cells + LLLT-animals submitted to cryoinjury, followed by myogenic precursor cells (C2C12) transplantation and PBMT (780 nm, 40 mW, 3.2 J in 8 points). The periods analyzed were 1, 3, and 7 days. The tibialis anterior muscle was harvest for histological analysis, collagen analysis, and immunolabeling of macrophages. RESULTS: No differences were found between the HBSS group and injury group. The Injury + Cells group exhibited an increase of inflammatory cells and immature fibers as well as a decrease in the number of macrophages on Day 1. The Injury + Cells + LLLT group exhibited a decrease in myonecrosis and inflammatory infiltrate at 7 days, but an increase in inflammatory infiltrate at 1 and 3 days as well as an increase in blood vessels at 3 and 7 days, an increase in macrophages at 3 days and better collagen organization at 7 days. CONCLUSION: Cell transplantation combined with PBMT led to an increase in the number of blood vessels, a reduction in myonecrosis and total inflammatory cells as well as better organization of collagen fibers during the skeletal muscle repair process. Lasers Surg. Med. © 2018 Wiley Periodicals, Inc.

Molecular genetics of the transcription factor GLIS3 identifies its dual function in beta cells and neurons.

The GLIS family zinc finger 3 isoform (GLIS3) is a risk gene for Type 1 and Type 2 diabetes, glaucoma and Alzheimer's disease endophenotype. We identified GLIS3 binding sites in insulin secreting cells (INS1) (FDR q<0.05; enrichment range 1.40-9.11 fold) sharing the motif wrGTTCCCArTAGs, which were enriched in genes involved in neuronal function and autophagy and in risk genes for metabolic and neuro-behavioural diseases. We confirmed experimentally Glis3-mediated regulation of the expression of genes involved in autophagy and neuron function in INS1 and neuronal PC12 cells. Naturally-occurring coding polymorphisms in Glis3 in the Goto-Kakizaki rat model of type 2 diabetes were associated with increased insulin production in vitro and in vivo, suggestive alteration of autophagy in PC12 and INS1 and abnormal neurogenesis in hippocampus neurons. Our results support biological pleiotropy of GLIS3 in pathologies affecting ß-cells and neurons and underline the existence of trans­nosology pathways in diabetes and its co-morbidities.

Effect of glide path on transportation promoted by NiTi and M-Wire instruments / Efeito do glide path no transporte promovido por instrumentos de NiTi e M-Wire

Objective: To evaluate the effect of glide path creation on transportation promoted by NiTi and M-Wire instruments. Material and Methods: Sixty polyester resin blocks containing a simulated root canal were distributed into four groups (n=15), according to the protocols/systems used for root canal preparation: GPR group - glide path + Revo-S system; R group - no glide path + Revo-S system; GPPN group - glide path + ProTaper Next system and PN group - no glide path + ProTaper Next system. Root canals were photographed before and after preparation, and the images were superimposed to evaluate the transportation at the apical, middle and coronal thirds. The time spent to perform preparation was also measured (seconds). Data were submitted to the non-parametric Kruskal-Wallis test (p0.05). GPR and R groups were similar in the middle third (p>0.05). However, the transportation value in GPR group was statistically higher in comparison with GPPN and PN groups (p0.05). Only R group presented significant difference in comparison with GPPN and PN groups (p0.05). Conclusion: None of the systems were capable of maintaining the original trajectory of the simulated root canal, and the glide path had no effect on the transportation promoted by instruments. (AU)

Objetivo: Avaliar o efeito do glide path no transporte promovido por instrumentos fabricados em NiTi e M-Wire. Material e Métodos: Sessenta blocos de resina poliéster contendo um canal simulado foram separados em quatro grupos (n=15), de acordo com os protocolos/ sistemas utilizados para realização do preparo: Grupo GPR - glide path + sistema Revo-S; Grupo R - sem glide path + sistema Revo-S; Grupo GPPN - glide path + sistema ProTaper Next e Grupo PN - sem glide path + sistema ProTaper Next. Os canais foram fotografados antes e após o preparo, e as imagens sobrepostas para avaliação do transporte nos terços apical, médio e cervical. O tempo para realização do preparo também foi mensurado (segundos). Os dados foram submetidos ao teste não-paramétrico de Kruskal-Wallis (p0,05). Os grupos GPR e R foram semelhantes no terço médio (p>0,05), entretanto, o transporte no grupo GPR foi estatisticamente maior em comparação aos grupos GPPN e P (p0,05). Somente o grupo R apresentou diferença em comparação aos grupos GPPN e PN (p0,05). Conclusão: Nenhum sistema foi capaz de manter a trajetória original do canal simulado, e o glide path não teve efeito sobre o transporte promovido pelos instrumentos.(AU)

Neuron-enriched RNA-binding Proteins Regulate Pancreatic Beta Cell Function and Survival.

Pancreatic beta cell failure is the central event leading to diabetes. Beta cells share many phenotypic traits with neurons, and proper beta cell function relies on the activation of several neuron-like transcription programs. Regulation of gene expression by alternative splicing plays a pivotal role in brain, where it affects neuronal development, function, and disease. The role of alternative splicing in beta cells remains unclear, but recent data indicate that splicing alterations modulated by both inflammation and susceptibility genes for diabetes contribute to beta cell dysfunction and death. Here we used RNA sequencing to compare the expression of splicing-regulatory RNA-binding proteins in human islets, brain, and other human tissues, and we identified a cluster of splicing regulators that are expressed in both beta cells and brain. Four of them, namely Elavl4, Nova2, Rbox1, and Rbfox2, were selected for subsequent functional studies in insulin-producing rat INS-1E, human EndoC-ßH1 cells, and in primary rat beta cells. Silencing of Elavl4 and Nova2 increased beta cell apoptosis, whereas silencing of Rbfox1 and Rbfox2 increased insulin content and secretion. Interestingly, Rbfox1 silencing modulates the splicing of the actin-remodeling protein gelsolin, increasing gelsolin expression and leading to faster glucose-induced actin depolymerization and increased insulin release. Taken together, these findings indicate that beta cells share common splicing regulators and programs with neurons. These splicing regulators play key roles in insulin release and beta cell survival, and their dysfunction may contribute to the loss of functional beta cell mass in diabetes.

Parâmetros microbiológicos em diferentes cortes de carne bovina resfriada / Microbiological parameters in different cuts of cooled beef

O objetivo deste estudo foi verificar a contagem de mesófilos, coliformes totais, Staphylococcus aureus, Escherichia coli e presença de Salmonella sp. em cortes de traseiro de bovinos obtidos em um abatedouro frigorífico sob Inspeção Federal. Os resultados indicaram que todos os cortes mostraram-se ausentes para Salmonella sp. e a maior média de contagem para mesófilos foi no Filé de costela, seguido pela Picanha, Coxão mole, Fralda, Pera (recorte do Coxão Mole) e Maminha. Destes, a Pera e a Picanha apresentaram os maiores valores para Staphylococcus aureus. As maiores médias de contagens em UFC/g para coliformes totais foram nos cortes: Coxão mole, Alcatra, Pera e Filé-Mignon. De forma geral, os níveis de coliformes totais foram baixos, porém revelaram a presença de Escherichia coli principalmente no Coxão Mole. A Alcatra apresentou resultados elevados para coliformes totais, porém, sem a presença de E. coli. Os resultados dos cortes coletados mostraram que não houve desvio aparente nos processos quanto às operações do abate, a desossa e refile.

The aim of this study was to verify the mesophilic count, total coliforms, Staphylococcus aureus, Escherichia coli and the presence of Salmonella sp. in cattle rear cuts obtained in a slaughterhouse governed under Federal Inspection. The results indicated that all the cuts proved to be absent for Salmonella sp. and the highest average count for mesophilic was in rib steak, followed by Top Sirloin Cap, Topside, Flank Steak, Cut of Topside and Tenderloin. Of these, Cut of Topside and Top Sirloin Cap showed the highest values for S. aureus. The highest average scores in CFU/g for total coliforms were in cuts: Topside, Rump Steak, Cut of Topside and Tenderloin. The levels of total coliforms were low and showed the presence of Escherichia coli in the Topside. The results showed high thighs of coliforms, but without the presence of E. coli. The results of the collected cuttings showed no apparent shift in processes and operations during slaughter and boning and trimming.

Cidofovir is active against human papillomavirus positive and negative head and neck and cervical tumor cells by causing DNA damage as one of its working mechanisms.

Human papillomavirus (HPV) causes cervical cancer and a large fraction of head and neck squamous cell carcinomas (HNSCC). Cidofovir (CDV) proved efficacious in the treatment of several HPV-induced benign and malignant hyper proliferations. To provide a better insight into how CDV selectively eradicates transformed cells, HPV+ and HPV- cervical carcinoma and HNSCC cell lines were compared to normal cells for antiproliferative effects, CDV metabolism, drug incorporation into cellular DNA, and DNA damage. Incorporation of CDV into cellular DNA was higher in tumor cells than in normal cells and correlated with CDV antiproliferative effects, which were independent of HPV status. Increase in phospho-ATM levels was detected following CDV exposure and higher levels of γ-H2AX (a quantitative marker of double-strand breaks) were measured in tumor cells compared to normal cells. A correlation between DNA damage and CDV incorporation into DNA was found but not between DNA damage and CDV antiproliferative effects. These data indicate that CDV antiproliferative effects result from incorporation of the drug into DNA causing DNA damage. However, the anti-tumor effects of CDV cannot be exclusively ascribed to DNA damage. Furthermore, CDV can be considered a promising broad spectrum anti-cancer agent, not restricted to HPV+ lesions.

Resistance to the nucleotide analogue cidofovir in HPV(+) cells: a multifactorial process involving UMP/CMP kinase 1.

Human papillomavirus (HPV) is responsible for cervical cancer, and its role in head and neck carcinoma has been reported. No drug is approved for the treatment of HPV-related diseases but cidofovir (CDV) exhibits selective antiproliferative activity. In this study, we analyzed the effects of CDV-resistance (CDVR) in two HPV(+) (SiHaCDV and HeLaCDV) and one HPV(-) (HaCaTCDV) tumor cell lines. Quantification of CDV metabolites and analysis of the sensitivity profile to chemotherapeutics was performed. Transporters expression related to multidrug-resistance (MRP2, P-gp, BCRP) was also investigated. Alterations of CDV metabolism in SiHaCDV and HeLaCDV, but not in HaCaTCDV, emerged via impairment of UMP/CMPK1 activity. Mutations (P64T and R134M) as well as down-regulation of UMP/CMPK1 expression were observed in SiHaCDV and HeLaCDV, respectively. Altered transporters expression in SiHaCDV and/or HeLaCDV, but not in HaCaTCDV, was also noted. Taken together, these results indicate that CDVR in HPV(+) tumor cells is a multifactorial process.