Complete Genome Sequencing, Annotation, and Mutational Profiling of the Novel Clade I Human Mpox Virus, Kamituga Strain.

INTRODUCTION: Human Mpox (formerly monkeypox) infection is an emerging zoonotic disease caused by the Mpox virus (MPXV). We describe the complete genome annotation, phylogeny, and mutational profile of a novel, sustained Clade I Mpox outbreak in the city of Kamituga in Eastern Democratic Republic of the Congo (DRC). METHODOLOGY: A cross-sectional, observational, cohort study was performed among patients of all ages admitted to the Kamituga Hospital with Mpox infection symptoms between late September 2023 and late January 2024. DNA was isolated from Mpox swabbed lesions and sequenced followed by phylogenetic analysis, genome annotation, and mutational profiling. RESULTS: We describe an ongoing Clade I Mpox outbreak in the city of Kamituga, South Kivu Province, Democratic Republic of Congo. Whole-genome sequencing of the viral RNA samples revealed, on average, 201.5 snps, 28 insertions, 81 deletions, 2 indels, 312.5 total variants, 158.3 amino acid changes, 81.66 intergenic variants, 72.16 synonymous mutations, 106 missense variants, 41.16 frameshift variants, and 3.33 inframe deletions across six samples. By assigning mutations at the proteome level for Kamituga MPXV sequences, we observed that seven proteins, namely, C9L (OPG047), I4L (OPG080), L6R (OPG105), A17L (OPG143), A25R (OPG151), A28L (OPG153), and B21R (OPG210) have emerged as hot spot mutations based on the consensuses inframe deletions, frameshift variants, synonymous variants, and amino acids substitutions. Based on the outcome of the annotation, we found a deletion of the D14L (OPG032) gene in all six samples. Following phylogenetic analysis and whole genome assembly, we determined that this cluster of Mpox infections is genetically distinct from previously reported Clade I outbreaks, and thus propose that the Kamituga Mpox outbreak represents a novel subgroup (subgroup VI) of Clade I MPXV. CONCLUSIONS: Here we report the complete viral genome for the ongoing Clade I Mpox Kamituga outbreak for the first time. This outbreak presents a distinct mutational profile from previously sequenced Clade I MPXV oubtreaks, suggesting that this cluster of infections is a novel subgroup (we term this subgroup VI). These findings underscore the need for ongoing vigilance and continued sequencing of novel Mpox threats in endemic regions.

PASC (Post Acute Sequelae of COVID-19) is associated with decreased neutralizing antibody titers in both biological sexes and increased ANG-2 and GM-CSF in females.

Post-acute sequelae of COVID-19 (PASC) or the continuation of COVID-19 (Coronavirus disease 2019) symptoms past 12 weeks may affect as many as 30% of people recovering from a SARS-CoV-2 (severe acute respiratory coronavirus 2) infection. The mechanisms regulating the development of PASC are currently not known; however, hypotheses include virus reservoirs, pre-existing conditions, microblood clots, immune dysregulation, as well as poor antibody responses. Importantly, virus neutralizing antibodies are essential for COVID-19 recovery and protection from reinfection but there is currently limited information on these immune regulators and associated cytokines in PASC patients. Understanding the key drivers of general and specific symptoms associated with Long COVID and the presence of virus neutralizing antibodies in PASC will aid in the development of therapeutics, diagnostics, and vaccines which currently do not exist. We designed a cross-sectional study to investigate systemic antibody and cytokine responses during COVID-19 recovery and PASC. In total, 195 participants were recruited in one of four groups: (1) Those who never had COVID-19 (No COVID); (2) Those in acute COVID-19 recovery (Acute Recovery) (4-12 weeks post infection); (3) Those who recovered from COVID-19 (Recovered) (+ 12 weeks from infection); and (4) those who had PASC (PASC) (+ 12 weeks from infection). Participants completed a questionnaire on health history, sex, gender, demographics, experiences with COVID-19 acute and COVID-19 recovery/continuing symptoms. Serum samples collected were evaluated for antibody binding to viral proteins, virus neutralizing antibody titers, and serum cytokine levels using Ella SimplePlex Immunoassay™ panels. We found participants with PASC reported more pre-existing conditions (e.g. such as hypertension, asthma, and obesity), and PASC symptoms (e.g. fatigue, brain fog, headaches, and shortness of breath) following COVID-19 than COVID-19 Recovered individuals. Importantly, we found PASC individuals to have significantly decreased levels of neutralizing antibodies toward both SARS-CoV-2 and the Omicron BA.1 variant. Sex analysis indicated that female PASC study participants had sustained antibody levels as well as levels of the inflammatory cytokines GM-CSF and ANG-2 over time following COVID-19. Our study reports people experiencing PASC had lower levels of virus neutralizing antibodies; however, the results are limited by the collection time post-COVID-19 and post-vaccination. Moreover, we found females experiencing PASC had sustained levels of GM-CSF and ANG-2. With lower levels of virus neutralizing antibodies, this data suggests that PASC individuals not only have had a suboptimal antibody response during acute SARS-CoV-2 infection but may also have increased susceptibility to subsequent infections which may exacerbate or prolong current PASC illnesses. We also provide evidence suggesting GM-CSF and ANG-2 to play a role in the sex-bias of PASC. Taken together, our findings maybe important for understanding immune molecular drivers of PASC and PASC subgroups.

Model-interpreted outcomes of artificial neural networks classifying immune biomarkers associated with severe infections in ICU.

Introduction: Millions of deaths worldwide are a result of sepsis (viral and bacterial) and septic shock syndromes which originate from microbial infections and cause a dysregulated host immune response. These diseases share both clinical and immunological patterns that involve a plethora of biomarkers that can be quantified and used to explain the severity level of the disease. Therefore, we hypothesize that the severity of sepsis and septic shock in patients is a function of the concentration of biomarkers of patients. Methods: In our work, we quantified data from 30 biomarkers with direct immune function. We used distinct Feature Selection algorithms to isolate biomarkers to be fed into machine learning algorithms, whose mapping of the decision process would allow us to propose an early diagnostic tool. Results: We isolated two biomarkers, i.e., Programmed Death Ligand-1 and Myeloperoxidase, that were flagged by the interpretation of an Artificial Neural Network. The upregulation of both biomarkers was indicated as contributing to increase the severity level in sepsis (viral and bacterial induced) and septic shock patients. Discussion: In conclusion, we built a function considering biomarker concentrations to explain severity among sepsis, sepsis COVID, and septic shock patients. The rules of this function include biomarkers with known medical, biological, and immunological activity, favoring the development of an early diagnosis system based in knowledge extracted from artificial intelligence.

Parallel Dysregulated Immune Response in Severe Forms of COVID-19 and Bacterial Sepsis via Single-Cell Transcriptome Sequencing.

Critically ill COVID-19 patients start developing single respiratory organ failure that often evolves into multiorgan failure. Understanding the immune mechanisms in severe forms of an infectious disease (either critical COVID-19 or bacterial septic shock) would help to achieve a better understanding of the patient's clinical trajectories and the success of potential therapies. We hypothesized that a dysregulated immune response manifested by the abnormal activation of innate and adaptive immunity might be present depending on the severity of the clinical presentation in both COVID-19 and bacterial sepsis. We found that critically ill COVID-19 patients demonstrated a different clinical endotype that resulted in an inflammatory dysregulation in mild forms of the disease. Mild cases (COVID-19 and bacterial non severe sepsis) showed significant differences in the expression levels of CD8 naïve T cells, CD4 naïve T cells, and CD4 memory T cells. On the other hand, in the severe forms of infection (critical COVID-19 and bacterial septic shock), patients shared immune patterns with upregulated single-cell transcriptome sequencing at the following levels: B cells, monocyte classical, CD4 and CD8 naïve T cells, and natural killers. In conclusion, we identified significant gene expression differences according to the etiology of the infection (COVID-19 or bacterial sepsis) in the mild forms; however, in the severe forms (critical COVID-19 and bacterial septic shock), patients tended to share some of the same immune profiles related to adaptive and innate immune response. Severe forms of the infections were similar independent of the etiology. Our findings might promote the implementation of co-adjuvant therapies and interventions to avoid the development of severe forms of disease that are associated with high mortality rates worldwide.

An artificial neural network classification method employing longitudinally monitored immune biomarkers to predict the clinical outcome of critically ill COVID-19 patients.

Background: The severe form of COVID-19 can cause a dysregulated host immune syndrome that might lead patients to death. To understand the underlying immune mechanisms that contribute to COVID-19 disease we have examined 28 different biomarkers in two cohorts of COVID-19 patients, aiming to systematically capture, quantify, and algorithmize how immune signals might be associated to the clinical outcome of COVID-19 patients. Methods: The longitudinal concentration of 28 biomarkers of 95 COVID-19 patients was measured. We performed a dimensionality reduction analysis to determine meaningful biomarkers for explaining the data variability. The biomarkers were used as input of artificial neural network, random forest, classification and regression trees, k-nearest neighbors and support vector machines. Two different clinical cohorts were used to grant validity to the findings. Results: We benchmarked the classification capacity of two COVID-19 clinicals studies with different models and found that artificial neural networks was the best classifier. From it, we could employ different sets of biomarkers to predict the clinical outcome of COVID-19 patients. First, all the biomarkers available yielded a satisfactory classification. Next, we assessed the prediction capacity of each protein separated. With a reduced set of biomarkers, our model presented 94% accuracy, 96.6% precision, 91.6% recall, and 95% of specificity upon the testing data. We used the same model to predict 83% and 87% (recovered and deceased) of unseen data, granting validity to the results obtained. Conclusions: In this work, using state-of-the-art computational techniques, we systematically identified an optimal set of biomarkers that are related to a prediction capacity of COVID-19 patients. The screening of such biomarkers might assist in understanding the underlying immune response towards inflammatory diseases.

Heterologous immunization with Covishield and Pfizer vaccines against SARS-CoV-2 elicits a robust humoral immune response.

Understanding the efficacy and durability of heterologous immunization schedules against SARS-CoV-2 is critical, as supply demands and vaccine choices become significant issues in the global vaccination strategy. Here we characterize the neutralizing antibodies produced in two subjects who received combination immunizations against SARS-CoV-2, first with Covishield (Oxford-AstraZeneca) vaccine, followed 33 days later with a second dose (booster) shot of the Pfizer-BioNTech vaccine. Serum samples were collected 25 days following the primary vaccination and 13 days after the secondary Pfizer vaccination. Both subjects exhibited increased levels of isotype IgG and IgM antibodies directed against the entire spike protein following immunizations. These antibodies also exhibited increased reactivity with the receptor binding domain (RBD) in the spike protein and neutralized the infectivity of replicating vesicular stomatitis virus (VSV) that contains the COVID-19 coronavirus S protein gene in place of its normal G glycoprotein. This VSV pseudovirus also contains the reporter gene for enhanced green fluorescent protein (eGFP). Antibody titers against the spike protein and serum neutralization titers against the reporter virus are reported for the 2 heterologous vaccinated individuals and compared to a positive control derived from a convalescent patient and a negative control from an unexposed individual. The Pfizer-BioNTech vaccine increased antibody binding to the spike protein and RBD, and approached levels found in the convalescent positive control. Neutralizing antibodies against the VSV-S pseudovirus in the 2 subjects also approached levels in the convalescent sera. These results firmly validate the value of the Pfizer-BioNTech vaccine in boosting immunity following initial Covishield inoculation.

Comparison of real-time and droplet digital PCR to detect and quantify SARS-CoV-2 RNA in plasma.

BACKGROUND: The presence of SARS-CoV-2 RNA in plasma has been linked to disease severity and mortality. We compared RT-qPCR to droplet digital PCR (ddPCR) to detect SARS-CoV-2 RNA in plasma from COVID-19 patients (mild, moderate, and critical disease). METHODS: The presence/concentration of SARS-CoV-2 RNA in plasma was compared in three groups of COVID-19 patients (30 outpatients, 30 ward patients and 30 ICU patients) using both RT-qPCR and ddPCR. Plasma was obtained in the first 24h following admission, and RNA was extracted using eMAG. ddPCR was performed using Bio-Rad SARS-CoV-2 detection kit, and RT-qPCR was performed using GeneFinder™ COVID-19 Plus RealAmp Kit. Statistical analysis was performed using Statistical Package for the Social Science. RESULTS: SARS-CoV-2 RNA was detected, using ddPCR and RT-qPCR, in 91% and 87% of ICU patients, 27% and 23% of ward patients and 3% and 3% of outpatients. The concordance of the results obtained by both methods was excellent (Cohen's kappa index = 0.953). RT-qPCR was able to detect 34/36 (94.4%) patients positive for viral RNA in plasma by ddPCR. Viral RNA load was higher in ICU patients compared with the other groups (P < .001), by both ddPCR and RT-qPCR. AUC analysis revealed Ct values (RT-qPCR) and viral RNA load values (ddPCR) can similarly differentiate between patients admitted to wards and to the ICU (AUC of 0.90 and 0.89, respectively). CONCLUSION: Both methods yielded similar prevalence of RNAemia between groups, with ICU patients showing the highest (>85%). RT-qPCR was as useful as ddPCR to detect and quantify SARS-CoV-2 RNAemia in plasma.

Viral RNA load in plasma is associated with critical illness and a dysregulated host response in COVID-19.

BACKGROUND: COVID-19 can course with respiratory and extrapulmonary disease. SARS-CoV-2 RNA is detected in respiratory samples but also in blood, stool and urine. Severe COVID-19 is characterized by a dysregulated host response to this virus. We studied whether viral RNAemia or viral RNA load in plasma is associated with severe COVID-19 and also to this dysregulated response. METHODS: A total of 250 patients with COVID-19 were recruited (50 outpatients, 100 hospitalized ward patients and 100 critically ill). Viral RNA detection and quantification in plasma was performed using droplet digital PCR, targeting the N1 and N2 regions of the SARS-CoV-2 nucleoprotein gene. The association between SARS-CoV-2 RNAemia and viral RNA load in plasma with severity was evaluated by multivariate logistic regression. Correlations between viral RNA load and biomarkers evidencing dysregulation of host response were evaluated by calculating the Spearman correlation coefficients. RESULTS: The frequency of viral RNAemia was higher in the critically ill patients (78%) compared to ward patients (27%) and outpatients (2%) (p < 0.001). Critical patients had higher viral RNA loads in plasma than non-critically ill patients, with non-survivors showing the highest values. When outpatients and ward patients were compared, viral RNAemia did not show significant associations in the multivariate analysis. In contrast, when ward patients were compared with ICU patients, both viral RNAemia and viral RNA load in plasma were associated with critical illness (OR [CI 95%], p): RNAemia (3.92 [1.183-12.968], 0.025), viral RNA load (N1) (1.962 [1.244-3.096], 0.004); viral RNA load (N2) (2.229 [1.382-3.595], 0.001). Viral RNA load in plasma correlated with higher levels of chemokines (CXCL10, CCL2), biomarkers indicative of a systemic inflammatory response (IL-6, CRP, ferritin), activation of NK cells (IL-15), endothelial dysfunction (VCAM-1, angiopoietin-2, ICAM-1), coagulation activation (D-Dimer and INR), tissue damage (LDH, GPT), neutrophil response (neutrophils counts, myeloperoxidase, GM-CSF) and immunodepression (PD-L1, IL-10, lymphopenia and monocytopenia). CONCLUSIONS: SARS-CoV-2 RNAemia and viral RNA load in plasma are associated with critical illness in COVID-19. Viral RNA load in plasma correlates with key signatures of dysregulated host responses, suggesting a major role of uncontrolled viral replication in the pathogenesis of this disease.

Detection, Virulence Gene Assessment and Antibiotic Resistance Pattern of O157 Enterohemorrhagic Escherichia coli in Tabriz, Iran.

BACKGROUND: Shiga toxin-producing Escherichia coli (STEC) is a food-borne pathogen and infection with this organism causes illnesses such as bloody diarrhea, hemorrhagic colitis and hemolytic-uremic syndrome. OBJECTIVES: Considering the lack of any information about the prevalence rate and the antibiotic resistance pattern of O157:H7 serotype in Tabriz, finding answers to the above mentioned subjects was among the goals of this study. MATERIALS AND METHODS: Two hundred E. coli strains from diarrheal or non-diarrheal stools of outpatients and hospitalized cases in Tabriz Imam Reza hospital were isolated between September and December 2014 using MacConkey agar and standard biochemical tests and then cultured on sorbitol MacConkey agar. The sorbitol-negative isolates were confirmed as the O157 serotype using O157 antisera. A multiplex polymerase chain reaction (PCR) method was used for the detection of stx-1, stx-2, eae, and mdh genes and the antibiotic resistance pattern of these isolates was determined using Kirby-Bauer method and clinical and laboratory standards institute (CLSI) standards. RESULTS: Of the isolates 11 (5.5%) were sorbitol-negative, which were later analyzed by multiplex PCR and the results revealed that 2 (18.18%) isolates contained the stx-1 gene, 10 (90.91%) contained the stx-2 gene, and 5 (45.45%) contained the eae gene. The stx-2 and eae genes were the most commonly encountered virulence factors. All or most of the isolates were susceptible to ceftazidime (100%), gentamicin (100%), ciprofloxacin (100%), nalidixic acid (90.9%), trimetoprim sulfamethoxazole (90.9%), chloramphenicol (90.9%), ampicillin (81.8%), and cephalothin (72.7%). On the contrary, moderate susceptibility of the isolates to doxycycline (54.5%) was observed. CONCLUSIONS: Due to the low frequency of STEC O157 and the high susceptibility rates of the isolates to the tested antibiotics in this study, STEC O157 has not become a major problem in Tabriz yet, but comprehensive microbiological surveillance programs that provide early warning and limit the scale of possible outbreaks would be essential.