Post-COVID symptoms are associated with endotypes reflecting poor inflammatory and hemostatic modulation.

Introduction: Persistent symptoms after COVID-19 infection ("long COVID") negatively affects almost half of COVID-19 survivors. Despite its prevalence, its pathophysiology is poorly understood, with multiple host systems likely affected. Here, we followed patients from hospital to discharge and used a systems-biology approach to identify mechanisms of long COVID. Methods: RNA-seq was performed on whole blood collected early in hospital and 4-12 weeks after discharge from 24 adult COVID-19 patients (10 reported post-COVID symptoms after discharge). Differential gene expression analysis, pathway enrichment, and machine learning methods were used to identify underlying mechanisms for post-COVID symptom development. Results: Compared to patients with post-COVID symptoms, patients without post-COVID symptoms had larger temporal gene expression changes associated with downregulation of inflammatory and coagulation genes over time. Patients could also be separated into three patient endotypes with differing mechanistic trajectories, which was validated in another published patient cohort. The "Resolved" endotype (lowest rate of post-COVID symptoms) had robust inflammatory and hemostatic responses in hospital that resolved after discharge. Conversely, the inflammatory/hemostatic responses of "Suppressive" and "Unresolved" endotypes (higher rates of patients with post-COVID symptoms) were persistently dampened and activated, respectively. These endotypes were accurately defined by specific blood gene expression signatures (6-7 genes) for potential clinical stratification. Discussion: This study allowed analysis of long COVID whole blood transcriptomics trajectories while accounting for the issue of patient heterogeneity. Two of the three identified and externally validated endotypes ("Unresolved" and "Suppressive") were associated with higher rates of post-COVID symptoms and either persistently activated or suppressed inflammation and coagulation processes. Gene biomarkers in blood could potentially be used clinically to stratify patients into different endotypes, paving the way for personalized long COVID treatment.

Characterization of the immune cell landscape in CRC: Clinical implications of tumour-infiltrating leukocytes in early- and late-stage CRC.

Introduction: Colorectal cancer (CRC) is the third leading cause of cancer-related deaths globally. Tumour-infiltrating leukocytes play an important role in cancers, including CRC. We therefore sought to characterize the impact of tumour-infiltrating leukocytes on CRC prognosis. Methods: To determine whether the immune cell profile within CRC tissue could influence prognosis, we employed three computational methodologies (CIBERSORT, xCell and MCPcounter) to predict abundance of immune cell types, based on gene expression. This was done using two patient cohorts, TCGA and BC Cancer Personalized OncoGenomics (POG). Results: We observed significant differences in immune cell composition between CRC and normal adjacent colon tissue, as well as differences in based on method of analysis. Evaluation of survival based on immune cell types revealed dendritic cells as a positive prognostic marker, consistently across methodologies. Mast cells were also found to be a positive prognostic marker, but in a stage-dependent manner. Unsupervised cluster analysis demonstrated that significant differences in immune cell composition has a more pronounced effect on prognosis in early-stage CRC, compared to late-stage CRC. This analysis revealed a distinct group of individuals with early-stage CRC which have an immune infiltration signature that indicates better survival probability. Conclusions: Taken together, characterization of the immune landscape in CRC has provided a powerful tool to assess prognosis. We anticipate that further characterization of the immune landscape will facilitate use of immunotherapies in CRC.

Identification of novel cerebellar developmental transcriptional regulators with motif activity analysis.

BACKGROUND: The work of the FANTOM5 Consortium has brought forth a new level of understanding of the regulation of gene transcription and the cellular processes involved in creating diversity of cell types. In this study, we extended the analysis of the FANTOM5 Cap Analysis of Gene Expression (CAGE) transcriptome data to focus on understanding the genetic regulators involved in mouse cerebellar development. RESULTS: We used the HeliScopeCAGE library sequencing on cerebellar samples over 8 embryonic and 4 early postnatal times. This study showcases temporal expression pattern changes during cerebellar development. Through a bioinformatics analysis that focused on transcription factors, their promoters and binding sites, we identified genes that appear as strong candidates for involvement in cerebellar development. We selected several candidate transcriptional regulators for validation experiments including qRT-PCR and shRNA transcript knockdown. We observed marked and reproducible developmental defects in Atf4, Rfx3, and Scrt2 knockdown embryos, which support the role of these genes in cerebellar development. CONCLUSIONS: The successful identification of these novel gene regulators in cerebellar development demonstrates that the FANTOM5 cerebellum time series is a high-quality transcriptome database for functional investigation of gene regulatory networks in cerebellar development.

Discovery of Transcription Factors Novel to Mouse Cerebellar Granule Cell Development Through Laser-Capture Microdissection.

Laser-capture microdissection was used to isolate external germinal layer tissue from three developmental periods of mouse cerebellar development: embryonic days 13, 15, and 18. The cerebellar granule cell-enriched mRNA library was generated with next-generation sequencing using the Helicos technology. Our objective was to discover transcriptional regulators that could be important for the development of cerebellar granule cells-the most numerous neuron in the central nervous system. Through differential expression analysis, we have identified 82 differentially expressed transcription factors (TFs) from a total of 1311 differentially expressed genes. In addition, with TF-binding sequence analysis, we have identified 46 TF candidates that could be key regulators responsible for the variation in the granule cell transcriptome between developmental stages. Altogether, we identified 125 potential TFs (82 from differential expression analysis, 46 from motif analysis with 3 overlaps in the two sets). From this gene set, 37 TFs are considered novel due to the lack of previous knowledge about their roles in cerebellar development. The results from transcriptome-wide analyses were validated with existing online databases, qRT-PCR, and in situ hybridization. This study provides an initial insight into the TFs of cerebellar granule cells that might be important for development and provide valuable information for further functional studies on these transcriptional regulators.