Severe Dysbiosis and Specific Haemophilus and Neisseria Signatures as Hallmarks of the Oropharyngeal Microbiome in Critically Ill Coronavirus Disease 2019 (COVID-19) Patients.

BACKGROUND: At the entry site of respiratory virus infections, the oropharyngeal microbiome has been proposed as a major hub integrating viral and host immune signals. Early studies suggested that infections with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are associated with changes of the upper and lower airway microbiome, and that specific microbial signatures may predict coronavirus disease 2019 (COVID-19) illness. However, the results are not conclusive, as critical illness can drastically alter a patient's microbiome through multiple confounders. METHODS: To study oropharyngeal microbiome profiles in SARS-CoV-2 infection, clinical confounders, and prediction models in COVID-19, we performed a multicenter, cross-sectional clinical study analyzing oropharyngeal microbial metagenomes in healthy adults, patients with non-SARS-CoV-2 infections, or with mild, moderate, and severe COVID-19 (n = 322 participants). RESULTS: In contrast to mild infections, patients admitted to a hospital with moderate or severe COVID-19 showed dysbiotic microbial configurations, which were significantly pronounced in patients treated with broad-spectrum antibiotics, receiving invasive mechanical ventilation, or when sampling was performed during prolonged hospitalization. In contrast, specimens collected early after admission allowed us to segregate microbiome features predictive of hospital COVID-19 mortality utilizing machine learning models. Taxonomic signatures were found to perform better than models utilizing clinical variables with Neisseria and Haemophilus species abundances as most important features. CONCLUSIONS: In addition to the infection per se, several factors shape the oropharyngeal microbiome of severely affected COVID-19 patients and deserve consideration in the interpretation of the role of the microbiome in severe COVID-19. Nevertheless, we were able to extract microbial features that can help to predict clinical outcomes.

Presence of gustatory and olfactory dysfunction in the time of the COVID-19 pandemic.

BACKGROUND: The unexpected outbreak of the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused more than 49 million cases and an estimated 2,000,000 associated deaths worldwide. In Germany, there are currently more than 2,000,000 laboratory-confirmed coronavirus disease 2019 (COVID-19) cases including 51,800 deaths. However, regional differences also became apparent and with the second wave of infections, the detailed characterization of COVID-19 patients is crucial to early diagnosis and disruption of chains of infections. METHODS: Handing out detailed questionnaires to all individuals tested for COVID-19, we evaluated the clinical characteristics of negative and positive tested individuals. Expression of symptoms, symptom duration and association between predictor variables (i.e. age, gender) and a binary outcome (olfactory and gustatory dysfunction) were assessed. RESULTS: Overall, the most common symptoms among individuals who tested positive for SARS-CoV-2 were fatigue, headache, and cough. Olfactory and gustatory dysfunction were also reported by many SARS-CoV-2 negative individuals, more than 20% of SARS-CoV-2 negative tested individuals in our study reported olfactory and gustatory dysfunction. Independent of SARS-CoV-2 status, more females displayed symptoms of gustatory (29.8%, p = 0.0041) and olfactory dysfunction (22.9%, p = 0.0174) compared to men. CONCLUSIONS: Bringing early SARS-CoV-2 tests to the populations at risk must be a main focus for the upcoming months. The reliability of olfactory and gustatory dysfunction in COVID-19 negative tested individuals requires deeper investigation in the future.

A mechanistic PK/PD model of AZD0171 (anti-LIF) to support Phase II dose selection.

AZD0171 (INN: Falbikitug) is being developed as a humanized monoclonal antibody (mAb), immunoglobulin G subclass 1 (IgG1), which binds specifically to the immunosuppressive human cytokine leukemia inhibitory factor (LIF) and inhibits downstream signaling by blocking recruitment of glycoprotein 130 (gp130) to the LIF receptor (LIFR) subunit (gp190) and the phosphorylation of signal transducer and activator of transcription 3 (STAT3) and is intended to treat adult participants with advanced solid tumors. LIF is a pleiotropic cytokine (and a member of the IL-6 family of cytokines) involved in many physiological and pathological processes and is highly expressed in a subset of solid tumors, including non-small cell lung cancer (NSCLC), colon, ovarian, prostate, and pancreatic cancer. The aim of this work was to develop a mechanistic PK/PD model to investigate the effect of AZD0171 on tumor LIF levels, predict the level of downstream signaling complex (LIF:LIFR:gp130) inhibition, and examine the dose-response relationship to support dose selection for a Phase II clinical study. Modeling results show that tumor LIF is inhibited in a dose-dependent manner with >90% inhibition for 95% of patients at the Phase II clinical dose of 1500 mg Q2W.

Hepamine - A Liver Disease Microarray Database, Visualization Platform and Data-Mining Resource.

Numerous gene expression profiling data on liver diseases were generated and stored in public databases. Only few were used for additional analyses by the hepatology research community. This may mostly be due to limited bioinformatics knowledge of most biomedical research personnel. In order to support an easy translation of bioinformatics data into translational hepatology research, we created Hepamine, a liver disease gene expression, visualization platform and data-mining resource. Microarray data were obtained from the NCBI GEO database. Pre-analysis of expression data was performed using R statistical software and the limma microarray analysis package from the Bioconductor repository. We generated Hepamine, a web-based repository of pre-analyzed microarray data for various liver diseases. At its initial release Hepamine contains 13 gene expression datasets, 20 microarray experiments and approximately 400 000 gene expression measurements. A self-explanatory website offers open and easy access to gene expression profiles. Results are furthermore visualized in simple three-color tables indicating differential expression. All data were linked to common functional and genetic databases particularly through the DAVID bioinformatics suite. Hepamine provides comprehensive data and easy access to hepatologic gene expression data even without in depth bioinformatics or microarray profiling experience. http://www.hepamine.de.