Asmann, Yan W

Glioblastoma (GBM) is the most prevalent and aggressive malignant primary brain tumor. GBM proximal to the lateral ventricles (LVs) is more aggressive, potentially because of subventricular zone contact. Despite this, cross-talk between GBM and neural stem/progenitor cells (NSC/NPCs) is not well understood. Using cell-specific proteomics, we show that LV-proximal GBM prevents neuronal maturation of NSCs through induction of senescence. In addition, GBM brain tumor–initiating cells (BTICs) increase expression of cathepsin B (CTSB) upon interaction with NPCs. Lentiviral knockdown and recombinant protein experiments reveal that both cell-intrinsic and soluble CTSB promote malignancy-associated phenotypes in BTICs. Soluble CTSB stalls neuronal maturation in NPCs while promoting senescence, providing a link between LV-tumor proximity and neurogenesis disruption. Last, we show LV-proximal CTSB up-regulation in patients, showing the relevance of this cross-talk in human GBM biology. These results demonstrate the value of proteomic analysis in tumor microenvironment research and provide direction for new therapeutic strategies in GBM.

The immune system substantially influences age-related cognitive decline and Alzheimer’s disease (AD) progression, affected by genetic and environmental factors. In a Mayo Clinic Study of Aging cohort, we examined how risk factors like APOE genotype, age, and sex affect inflammatory molecules and AD biomarkers in cerebrospinal fluid (CSF). Among cognitively unimpaired individuals over 65 ( N = 298), we measured 365 CSF inflammatory molecules, finding age, sex, and diabetes status predominantly influencing their levels. We observed age-related correlations with AD biomarkers such as total tau, phosphorylated tau-181, neurofilament light chain (NfL), and YKL40. APOE4 was associated with lower Aβ42 and higher SNAP25 in CSF. We explored baseline variables predicting cognitive decline risk, finding age, CSF Aβ42, NfL, and REG4 to be independently correlated. Subjects with older age, lower Aβ42, higher NfL, and higher REG4 at baseline had increased cognitive impairment risk during follow-up. This suggests that assessing CSF inflammatory molecules and AD biomarkers could predict cognitive impairment risk in the elderly.

TREM2 is exclusively expressed by microglia in the brain and is strongly linked to the risk for Alzheimer’s disease (AD). As microglial responses modulated by TREM2 are central to AD pathogenesis, enhancing TREM2 signaling has been explored as an AD therapeutic strategy. However, the effective therapeutic window targeting TREM2 is unclear. Here, by using microglia-specific inducible mouse models overexpressing human wild-type TREM2 (TREM2-WT) or R47H risk variant (TREM2-R47H), we show that TREM2-WT expression reduces amyloid deposition and neuritic dystrophy only during the early amyloid seeding stage, whereas TREM2-R47H exacerbates amyloid burden during the middle amyloid rapid growth stage. Single-cell RNA sequencing reveals suppressed disease-associated microglia (DAM) signature and reduced DAM population upon TREM2-WT expression in the early stage, whereas upregulated antigen presentation pathway is detected with TREM2-R47H expression in the middle stage. Together, our findings highlight the dynamic effects of TREM2 in modulating AD pathogenesis and emphasize the beneficial effect of enhancing TREM2 function in the early stage of AD development.

Abstract Explainable artificial intelligence aims to interpret how machine learning models make decisions, and many model explainers have been developed in the computer vision field. However, understanding of the applicability of these model explainers to biological data is still lacking. In this study, we comprehensively evaluated multiple explainers by interpreting pre-trained models for predicting tissue types from transcriptomic data and by identifying the top contributing genes from each sample with the greatest impacts on model prediction. To improve the reproducibility and interpretability of results generated by model explainers, we proposed a series of optimization strategies for each explainer on two different model architectures of multilayer perceptron (MLP) and convolutional neural network (CNN). We observed three groups of explainer and model architecture combinations with high reproducibility. Group II, which contains three model explainers on aggregated MLP models, identified top contributing genes in different tissues that exhibited tissue-specific manifestation and were potential cancer biomarkers. In summary, our work provides novel insights and guidance for exploring biological mechanisms using explainable machine learning models.

Abstract Background In the United States, cancer disproportionately impacts Black and African American individuals. Identifying genetic factors underlying cancer disparities has been an important research focus and requires data that are equitable in both quantity and quality across racial groups. It is widely recognized that DNA databases quantitatively underrepresent minorities. However, the differences in data quality between racial groups have not been well studied. Methods We compared the qualities of germline and tumor exomes between ancestrally African and European patients in The Cancer Genome Atlas of 7 cancers with at least 50 self-reported Black patients in the context of sequencing depth, tumor purity, and qualities of germline variants and somatic mutations. Results Germline and tumor exomes from ancestrally African patients were sequenced at statistically significantly lower depth in 6 out of the 7 cancers. For 3 cancers, most ancestrally European exomes were sequenced in early sample batches at higher depth, whereas ancestrally African exomes were concentrated in later batches and sequenced at much lower depth. For the other 3 cancers, the reasons of lower sequencing coverage of ancestrally African exomes remain unknown. Furthermore, even when the sequencing depths were comparable, African exomes had disproportionally higher percentages of positions with insufficient coverage, likely because of the known European bias in the human reference genome that impacted exome capture kit design. Conclusions Overall and positional lower sequencing depths of ancestrally African exomes in The Cancer Genome Atlas led to underdetection and lower quality of variants, highlighting the need to consider epidemiological factors for future genomics studies.