Non-SARS-CoV-2 genome sequences identified in clinical samples from COVID-19 infected patients: Evidence for co-infections

Prediction of BMI traits in the Chinese population based on the gut metagenome

BACKGROUND

Identifying individual characteristics based on trace evidence left at a crime scene is crucial in forensic identification. Microbial communities found in fecal traces have high individual specificity and could serve as potential markers for forensic characterization. Previous research has established that predicting body type based on the relative abundance of the gut microbiome is relatively accurate. However, the long-term stability and high individual specificity of the gut microbiome are closely linked to changes at the genome level of the microbiome. No studies have been conducted to deduce body shape from genetic traits. Therefore, in this study, the vital role of gut bacterial community characteristics and genetic traits in predicting body mass index (BMI) was investigated using gut metagenomic data from a healthy Chinese population.

RESULTS

Regarding the gut microbial community, the underweight group displayed increased α-diversity in comparison to the other BMI groups. There were significant differences in the relative abundances of 19 species among these three BMI groups. The BMI prediction model, based on the 31 most significant species, showed a goodness of fit (R2) of 0.56 and a mean absolute error (MAE) of 2.09 kg/m2. The overweight group exhibited significantly higher α-diversity than the other BMI groups at the level of gut microbial genes. Furthermore, there were significant variations observed in the single-nucleotide polymorphism (SNP) density of 732 contigs between these three BMI groups. The BMI prediction model, reliant on the 62 most contributing contigs, exhibited a model R2 of 0.72 and an MAE of 1.56 kg/m2. The model predicting body type from 44 contigs correctly identified the body type of 93.55% of the study participants.

CONCLUSION

Based on metagenomic data from a healthy Chinese population, we demonstrated the potential of genetic traits of gut bacteria to predict an individual's BMI. The findings of this study suggest the effectiveness of a novel method for determining the body type of suspects in forensic applications using the genetic traits of the gut microbiome and holds great promise for forensic individual identification.

Identification of hub genes and potential ceRNA networks of diabetic cardiomyopathy

Diabetic cardiomyopathy (DCM), a common complication of diabetes, is defined as ventricular dysfunction in the absence of underlying heart disease. Noncoding RNAs (ncRNAs), including long noncoding RNAs (lncRNAs) and microRNAs (miRNAs), play a crucial role in the development of DCM. Weighted Gene Co-Expression Network Analysis (WGCNA) was used to identify key modules in DCM-related pathways. DCM-related miRNA-mRNA network and DCM-related ceRNA network were constructed by miRNA-seq to identify hub genes in these modules. We identified five hub genes that are associated with the onset of DCM, including Troponin C1 (Tnnc1), Phospholamban (Pln), Fatty acid binding proteins 3 (Fabp3), Popeye domain containing 2 (Popdc2), and Tripartite Motif-containing Protein 63 (Trim63). miRNAs that target the hub genes were mainly involved in TGF-β and Wnt signaling pathways. GO BP enrichment analysis found these miRNAs were involved in the signaling of TGF-β and glucose homeostasis. Q-PCR results found the gene expressions of Pln, Fabp3, Trim63, Tnnc1, and Popdc2 were significantly increased in DCM. Our study identified five hub genes (Tnnc1, Pln, Fabp3, Popdc2, Trim63) whose associated ceRNA networks are responsible for the onset of DCM.

Analysis of bronchoalveolar lavage fluid metatranscriptomes among patients with COVID-19 disease

To better understand the potential relationship between COVID-19 disease and hologenome microbial community dynamics and functional profiles, we conducted a multivariate taxonomic and functional microbiome comparison of publicly available human bronchoalveolar lavage fluid (BALF) metatranscriptome samples amongst COVID-19 (n = 32), community acquired pneumonia (CAP) (n = 25), and uninfected samples (n = 29). We then performed a stratified analysis based on mortality amongst the COVID-19 cohort with known outcomes of deceased (n = 10) versus survived (n = 15). Our overarching hypothesis was that there are detectable and functionally significant relationships between BALF microbial metatranscriptomes and the severity of COVID-19 disease onset and progression. We observed 34 functionally discriminant gene ontology (GO) terms in COVID-19 disease compared to the CAP and uninfected cohorts, and 21 GO terms functionally discriminant to COVID-19 mortality (q < 0.05). GO terms enriched in the COVID-19 disease cohort included hydrolase activity, and significant GO terms under the parental terms of biological regulation, viral process, and interspecies interaction between organisms. Notable GO terms associated with COVID-19 mortality included nucleobase-containing compound biosynthetic process, organonitrogen compound catabolic process, pyrimidine-containing compound biosynthetic process, and DNA recombination, RNA binding, magnesium and zinc ion binding, oxidoreductase activity, and endopeptidase activity. A Dirichlet multinomial mixtures clustering analysis resulted in a best model fit using three distinct clusters that were significantly associated with COVID-19 disease and mortality. We additionally observed discriminant taxonomic differences associated with COVID-19 disease and mortality in the genus Sphingomonas, belonging to the Sphingomonadacae family, Variovorax, belonging to the Comamonadaceae family, and in the class Bacteroidia, belonging to the order Bacteroidales. To our knowledge, this is the first study to evaluate significant differences in taxonomic and functional signatures between BALF metatranscriptomes from COVID-19, CAP, and uninfected cohorts, as well as associating these taxa and microbial gene functions with COVID-19 mortality. Collectively, while this data does not speak to causality nor directionality of the association, it does demonstrate a significant relationship between the human microbiome and COVID-19. The results from this study have rendered testable hypotheses that warrant further investigation to better understand the causality and directionality of host-microbiome-pathogen interactions.

Vetinformatics from functional genomics to drug discovery: Insights into decoding complex molecular mechanisms of livestock systems in veterinary science

Having played important roles in human growth and development, livestock animals are regarded as integral parts of society. However, industrialization has depleted natural resources and exacerbated climate change worldwide, spurring the emergence of various diseases that reduce livestock productivity. Meanwhile, a growing human population demands sufficient food to meet their needs, necessitating innovations in veterinary sciences that increase productivity both quantitatively and qualitatively. We have been able to address various challenges facing veterinary and farm systems with new scientific and technological advances, which might open new opportunities for research. Recent breakthroughs in multi-omics platforms have produced a wealth of genetic and genomic data for livestock that must be converted into knowledge for breeding, disease prevention and management, productivity, and sustainability. Vetinformatics is regarded as a new bioinformatics research concept or approach that is revolutionizing the field of veterinary science. It employs an interdisciplinary approach to understand the complex molecular mechanisms of animal systems in order to expedite veterinary research, ensuring food and nutritional security. This review article highlights the background, recent advances, challenges, opportunities, and application of vetinformatics for quality veterinary services.

Angiotensin II induces reactive oxygen species, DNA damage, and T cell apoptosis in severe COVID-19

Background

Lymphopenia is predictive of survival in patients with coronavirus disease 2019 (COVID-19).

Objective

The aim of this study was to understand the cause of the lymphocyte count drop in severe forms of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection.

Methods

Monocytic production of reactive oxygen species (ROSs) and T-cell apoptosis were measured by flow cytometry, DNA damage in PBMCs was measured by immunofluorescence, and angiotensin II (AngII) was measured by ELISA in patients infected with SARS-CoV-2 at admission to an intensive care unit (ICU) (n = 29) or not admitted to an ICU (n = 29) and in age- and sex-matched healthy controls.

Results

We showed that the monocytes of certain patients with COVID-19 spontaneously released ROSs able to induce DNA damage and apoptosis in neighboring cells. Of note, high ROS production was predictive of death in ICU patients. Accordingly, in most patients, we observed the presence of DNA damage in up to 50% of their PBMCs and T-cell apoptosis. Moreover, the intensity of this DNA damage was linked to lymphopenia. SARS-CoV-2 is known to induce the internalization of its receptor, angiotensin-converting enzyme 2, which is a protease capable of catabolizing AngII. Accordingly, in certain patients with COVID-19 we observed high plasma levels of AngII. When looking for the stimulus responsible for their monocytic ROS production, we revealed that AngII triggers ROS production by monocytes via angiotensin receptor I. ROSs released by AngII-activated monocytes induced DNA damage and apoptosis in neighboring lymphocytes.

Conclusion

We conclude that T-cell apoptosis provoked via DNA damage due to the release of monocytic ROSs could play a major role in COVID-19 pathogenesis.

Warm Autoimmune Hemolytic Anemia and Pure Red Cell Aplasia during a Severe COVID-19 B.1.1.7 Infection

Warm autoimmune hemolytic anemia (AIHA) is a rare complication of COVID-19 infection. We report a case of warm AIHA in a patient with COVID-19 pneumonia treated with methylprednisolone and several red blood cell transfusions. Despite treatment of the warm AIHA, the patient’s reticulocyte count remained low, and his biochemical markers were suggestive of pure red cell aplasia, which was later attributed to a concurrent parvovirus B19 infection. This case highlights an unusual situation of two separate hematological processes caused by two separate and simultaneous viral infections.

Viral coinfections in COVID-19

The most consequential challenge raised by coinfection is perhaps the inappropriate generation of recombinant viruses through the exchange of genetic material among different strains. These genetically similar viruses can interfere with the replication process of each other and even compete for the metabolites required for the maintenance of the replication cycle. Due to the similarity in clinical symptoms of most viral respiratory tract infections, and their coincidence with COVID-19, caused by SARS-CoV-2, it is recommended to develop a comprehensive diagnostic panel for detection of respiratory and nonrespiratory viruses through the evaluation of patient samples. Given the resulting changes in blood markers, such as coagulation factors and white blood cell count following virus infection, these markers can be of diagnostic value in the detection of mixed infection in individuals already diagnosed with a certain viral illness. In this review, we seek to investigate the coinfection of SARS-CoV-2 with other respiratory and nonrespiratory viruses to provide novel insights into the development of highly sensitive diagnostics and effective treatment modalities.

Using RNA-Sequencing Data to Examine Tissue-Specific Garlic Microbiomes

Garlic (Allium sativum) is a perennial bulbous plant. Due to its clonal propagation, various diseases threaten the yield and quality of garlic. In this study, we conducted in silico analysis to identify microorganisms, bacteria, fungi, and viruses in six different tissues using garlic RNA-sequencing data. The number of identified microbial species was the highest in inflorescences, followed by flowers and bulb cloves. With the Kraken2 tool, 57% of identified microbial reads were assigned to bacteria and 41% were assigned to viruses. Fungi only made up 1% of microbial reads. At the species level, Streptomyces lividans was the most dominant bacteria while Fusarium pseudograminearum was the most abundant fungi. Several allexiviruses were identified. Of them, the most abundant virus was garlic virus C followed by shallot virus X. We obtained a total of 14 viral genome sequences for four allexiviruses. As we expected, the microbial community varied depending on the tissue types, although there was a dominant microorganism in each tissue. In addition, we found that Kraken2 was a very powerful and efficient tool for the bacteria using RNA-sequencing data with some limitations for virome study.