More than 300 million people worldwide experience depression; annually, ~800,000 people die by suicide. Unfortunately, conventional interview-based diagnosis is insufficient to accurately predict a psychiatric status. We developed machine learning models to predict depression and suicide risk using blood methylome and transcriptome data from 56 suicide attempters (SAs), 39 patients with major depressive disorder (MDD), and 87 healthy controls. Our random forest classifiers showed accuracies of 92.6% in distinguishing SAs from MDD patients, 87.3% in distinguishing MDD patients from controls, and 86.7% in distinguishing SAs from controls. We also developed regression models for predicting psychiatric scales with R2 values of 0.961 and 0.943 for Hamilton Rating Scale for Depression–17 and Scale for Suicide Ideation, respectively. Multi-omics data were used to construct psychiatric status prediction models for improved mental health treatment.

Coxsackievirus B3 (CVB3), a member of Picornaviridae family, is an important human pathogen that causes a wide range of diseases, including myocarditis, pancreatitis, and meningitis. Although CVB3 has been well demonstrated to target murine neural progenitor cells (NPCs), gene expression profiles of CVB3-infected human NPCs (hNPCs) has not been fully explored. To characterize the molecular signatures and complexity of CVB3-mediated host cellular responses in hNPCs, we performed QuantSeq 3′ mRNA sequencing. Increased expression levels of viral RNA sensors (RIG-I, MDA5) and interferon-stimulated genes, such as IFN-β, IP-10, ISG15, OAS1, OAS2, Mx2, were detected in response to CVB3 infection, while IFN-γ expression level was significantly downregulated in hNPCs. Consistent with the gene expression profile, CVB3 infection led to enhanced secretion of inflammatory cytokines and chemokines, such as interleukin-6 (IL-6), interleukin-8 (IL-8), and monocyte chemoattractant protein-1 (MCP-1). Furthermore, we show that type I interferon (IFN) treatment in hNPCs leads to significant attenuation of CVB3 RNA copy numbers, whereas, type II IFN (IFN-γ) treatment enhances CVB3 replication and upregulates suppressor of cytokine signaling 1/3 (SOCS) expression levels. Taken together, our results demonstrate the distinct molecular patterns of cellular responses to CVB3 infection in hNPCs and the pro-viral function of IFN-γ via the modulation of SOCS expression.

PurposeThe linkage between the genetic and phenotypic heterogeneity of the tumor has not been thoroughly evaluated. Herein, we investigated how the genetic and metabolic heterogeneity features of the tumor are associated with each other in head and neck squamous cell carcinoma (HNSC). We further assessed the prognostic significance of those features.MethodsThe mutant-allele tumor heterogeneity (MATH) score (n = 508), a genetic heterogeneity feature, and tumor glycolysis feature (GlycoS) (n = 503) were obtained from the HNSC dataset in the cancer genome atlas (TCGA). We identified matching patients (n = 33) who underwent 18F-fluorodeoxyglucose positron emission tomography (FDG PET) from the cancer imaging archive (TCIA) and obtained the following information from the primary tumor: metabolic, metabolic-volumetric, and metabolic heterogeneity features. The association between the genetic and metabolic features and their prognostic values were assessed.ResultsTumor metabolic heterogeneity and metabolic-volumetric features showed a mild degree of association with MATH (n = 25, ρ = 0.4~0.5, P < 0.05 for all features). The patients with higher FDG PET features and MATH died sooner. Combination of MATH and tumor metabolic heterogeneity features showed a better stratification of prognosis than MATH. Also, higher MATH and GlycoS were associated with significantly worse overall survival (n = 499, P = 0.002 and 0.0001 for MATH and GlycoS, respectively). Furthermore, both MATH and GlycoS independently predicted overall survival after adjusting for clinicopathologic features and the other (P = 0.015 and 0.006, respectively).ConclusionBoth tumor metabolic heterogeneity and metabolic-volumetric features assessed by FDG PET showed a mild degree of association with genetic heterogeneity in HNSC. Both metabolic and genetic heterogeneity features were predictive of survival and there was an additive prognostic value when the metabolic and genetic heterogeneity features were combined. Also, MATH and GlycoS were independent prognostic factors in HNSC; they can be used for precise prognostication once validated.

Differentiating the aetiology of thrombocytosis is limited yet crucial in patients with essential thrombocythaemia (ET). MicroRNAs (miRNAs) regulate haematopoiesis and lineage commitment; aberrant expression of miRNAs plays an important role in myeloproliferative neoplasms. However, the miRNA profile has been poorly explored in ET patients compared to patients with reactive thrombocytosis (RT). A total of 9 samples, including 5 ET patient samples, 2 RT patient samples, and 2 healthy control samples, were analysed in this study. We produced 81.43 million reads from transcripts and 59.60 million reads from small RNAs. We generated a comprehensive miRNA-mRNA regulatory network and identified unique 14 miRNA expression patterns associated with ET. Among the 14 miRNAs, miR-1268a was downregulated in ET and showed an inverse correlation with its 8 putative target genes, including genes associated with thrombus formation and platelet activation (<i>CDH6, EHD2, FUT1, KIF26A, LINC00346, PTPRN, SERF1A,</i> and <i>SLC6A9</i>). Principal component analysis (PCA) showed ET and non-ET groups well clustered in space, suggesting each group had a distinctive expression pattern of mRNAs and miRNAs. These results suggest that the significant dysregulation of miR-1268a and its 8 target genes could be a unique expression of platelet mi­RNAs and miRNA/mRNA regulatory network in ET patients.