The global efforts to control COVID-19 are threatened by the rapid emergence of novel SARS-CoV-2 variants that may display undesirable characteristics such as immune escape, increased transmissibility or pathogenicity. Early prediction for emergence of new strains with these features is critical for pandemic preparedness. We present Strainflow, a supervised and causally predictive model using unsupervised latent space features of SARS-CoV-2 genome sequences. Strainflow was trained and validated on 0.9 million sequences for the period December, 2019 to June, 2021 and the frozen model was prospectively validated from July, 2021 to December, 2021. Strainflow captured the rise in cases 2 months ahead of the Delta and Omicron surges in most countries including the prediction of a surge in India as early as beginning of November, 2021. Entropy analysis of Strainflow unsupervised embeddings clearly reveals the explore-exploit cycles in genomic feature-space, thus adding interpretability to the deep learning based model. We also conducted codon-level analysis of our model for interpretability and biological validity of our unsupervised features. Strainflow application is openly available as an interactive web-application for prospective genomic surveillance of COVID-19 across the globe.
Background: Osteoporosis is a common orthopedic disease with high prevalence in patients older than 50 years. Osteoporosis is often detected only after the fracture and is hard to treat. Therefore, it is of great significance to explore the molecular mechanism of the occurrence of osteoporosis.
Methods: The expression of Heme oxygenase-1 (HO-1) in people with different bone mineral density (BMD) was analyzed based on public databases. GenHacncer and JASPAR databases were adopted to search and verify the upstream transcription factor of HO-1. qRT-PCR, western blot and tartrate-resistant acid phosphatase assays were performed to explore the impact of HO-1 and Kruppel-like factor 7 (KLF7) on osteoclast differentiation. Chromatin immunoprecipitation (ChIP) assay confirmed the binding relationship between KLF7 and HO-1. Finally, Hemin, the agonist of HO-1, was applied in rescue assays, thereby verifying the mechanism of KLF7 modulating osteoclast differentiation by HO-1.
Results: Bioinformatics analysis revealed that HO-1 was highly-expressed while KLF7 lowly-expressed in people with high BMD. Besides, a potential binding site of KLF7 was found on the promoter region of HO-1. ChIP assay further manifested the targeting relationship between HO-1 and KLF7. Western blot and TRAP staining unveiled that osteoclast differentiation was suppressed by HO-1, while facilitated by KLF7. Rescue experiments indicated that over-expressed HO-1 could reverse of the promoting effect of KLF7 on osteoclast differentiation.
Conclusion: The study confirmed that osteoclast differentiation was promoted by KLF7 constraining HO-1, thereby facilitating osteoporosis. The cognation of the pathogenesis of osteoporosis was further enriched. New treatment could be developed on this basis.
Cell–cell interaction event (CCEs) dysregulation may relate to the heterogeneity of the tumor microenvironment (TME) and would affect therapeutic responses and clinical outcomes. To reveal the alteration of the immune microenvironment in bone marrow from a healthy state to multiple myeloma (MM), scRNA-seq data of the four states, including healthy state normal bone marrow (NBM) and three disease states (MGUS, SMM, and MM), were collected for analysis. With immune microenvironment reconstruction, the cell types, including NK cells, CD8+ T cells, and CD4+ T cells, with a higher percentage in disease states were associated with prognosis of MM patients. Furthermore, CCEs were annotated and dysregulated CCEs were identified. The number of CCEs were significantly changed between disease states and NBM. The dysregulated CCEs participated in regulation of immune cell proliferation and immune response, such as MIF-TNFRSF14 interacted between early B cells and CD8+ T cells. Moreover, CCE genes related to drug response, including bortezomib and melphalan, provide candidate therapeutic markers for MM treatment. Furthermore, MM patients were separated into three risk groups based on the CCE prognostic signature. Immunoregulation-related differentiation and activation of CD4+ T cells corresponded to the progression status with moderate risk. These results provide a comprehensive understanding of the critical role of intercellular communication in the immune microenvironment over the evolution of premalignant MM, which is related to the tumorigenesis and progression of MM, which moreover, suggests a way of potential target selection for clinical intervention.
Metagenomic studies unravel details about the taxonomic composition and the functions performed by microbial communities. As a complete metagenomic analysis requires different tools for different purposes, the selection and setup of these tools remain challenging. Furthermore, the chosen toolset will affect the accuracy, the formatting, and the functional identifiers reported in the results, impacting the results interpretation and the biological answer obtained. Thus, we surveyed state-of-the-art tools available in the literature, created simulated datasets, and performed benchmarks to design a sensitive and flexible metagenomic analysis pipeline. Here we present MEDUSA, an efficient pipeline to conduct comprehensive metagenomic analyses. It performs preprocessing, assembly, alignment, taxonomic classification, and functional annotation on shotgun data, supporting user-built dictionaries to transfer annotations to any functional identifier. MEDUSA includes several tools, as fastp, Bowtie2, DIAMOND, Kaiju, MEGAHIT, and a novel tool implemented in Python to transfer annotations to BLAST/DIAMOND alignment results. These tools are installed via Conda, and the workflow is managed by Snakemake, easing the setup and execution. Compared with MEGAN 6 Community Edition, MEDUSA correctly identifies more species, especially the less abundant, and is more suited for functional analysis using Gene Ontology identifiers.
Postmenopausal osteoporosis (PMOP) is a systemic metabolic bone disease in postmenopausal women. It has been known that long non-coding RNAs (lncRNAs) play a regulatory role in the progression of osteoporosis. However, the mechanism underlying the effects of exosome-derived lncRNA on regulating the occurrence and development of PMOP remains unclear. Exosomes in the serum of patients PMOP were collected and identified. RNA sequencing was performed to obtain the expression profile of exosome-derived lncRNAs in the serum of PMOP patients. RNA sequencing identified 26 differentially expressed lncRNAs from the exosomes between healthy people and PMOP patients. Among them, the expression of TCONS_00072128 was dramatically down-regulated. A co-location method was employed and searched its potential target gene caspase 8. TCONS_00072128 knockdown notably decreased the expression of caspase 8, while the osteogenic differentiation of BMSCs was also reduced. Reversely, TCONS_00072128 overexpression enhanced caspase 8 expression and osteogenic differentiation of BMSCs. Moreover, the continuous expression of caspase 8 regulated by TCONS_00072128 significantly activated inflammation pathways including NLRP3 signaling and NF-κB signaling. Simultaneously, RIPK1 which has emerged as a promising therapeutic target for the treatment of a wide range of human neurodegenerative, autoimmune, and inflammatory diseases, was also phosphorylated. The results of the present study suggested that exosome-derived lncRNA TCONS_00072128 could promote the progression of PMOP by regulating caspase 8. In addition, caspase 8 expression in BMSCs was possible to be a key regulator that balanced cell differentiation and inflammation activation.
Frontiers in Genetics
Computational Learning Models and Methods Driven by Omics for Biology for “The Fifth China Computer Society Bioinformatics Conference”