ABSTRACT Microbes are extensively present among various cancer tissues and play a vital role in cancer prevention and treatment responses. However, the underlying relationships between intratumoral microbes and tumors are still not well understood. Here, we developed a MIcrobial Cancer-association Analysis using a Heterogeneous graph transformer (MICAH) to identify intratumoral cancer-associated microbial communities. MICAH integrates metabolic and phylogenetic relationships among microbes into a heterogeneous graph representation. It uses a graph attention transformer to holistically capture the relationships between intratumoral microbes and cancer tissues, which improves the explainability of the association between identified microbial communities and cancer. We applied MICAH to intratumoral microbiome data across five cancer types and demonstrated its good generalizability and reproducibility. We believe this graph neural network framework can provide novel insights into cancer pathogenesis associated with the intratumoral microbiome.
5-Hydroxymethylcytosine (5hmC) is an oxidation product of 5-methylcytosine (5mC), and adjacent CpG sites in mammalian genome can be co-methylated and co-hydroxymethylated due to the processivity of DNMT and TET enzymes. We applied TAB-seq and oxBS-seq to selectively detect 5hmC and 5mC at base resolution in the mouse cortex, olfactory bulb and cerebellum tissues. We found that majority of the called 5hmC CpG sites frequently have 5mC modification simultaneously and are enriched in gene body regions of neuron development-related genes in brain tissues. Strikingly, by a systematic search of regions that show highly coordinated methylation and hydroxymethylation (MHBs and hMHBs), we found that MHBs significantly overlapped with hMHBs in gene body regions. Moreover, using a metric called methylation haplotype load, we defined a subset of 1361 tissue-specific MHBs and 3818 shared MHBs. Shared MHBs with low MHL correspond with developmental enhancers, and tissue-specific MHBs resemble the regulatory elements for tissue identity. Our results provide new insights into the role of coordinately oxidized 5mC to 5hmC as distal regulatory elements may involve in regulating tissue identity.
Abstract Advanced engineering materials such as fiber-reinforced composites are gradually dominating the modern industry and becoming one of the leading engineering materials on the market. Before manufacturing any structure made of these advanced materials, it is often critical to first obtain their elastic properties. Traditional methods for quantifying elastic properties include analytical and experimental methods. Analytical methods may be used to obtain closed-form solutions, but these methods are limited only to specific types of simple problems. Experimental methods may be derived using simple protocols, but the tests can be expensive and time-consuming. Therefore, numerical analysis can be used to obtain the necessary elastic properties of these advanced engineering materials. Starting at release 19.2 in late 2018, ANSYS introduced Material Designer (MD). The MD essentially allows the elastic properties of advanced materials to be estimated more affordably by using numerical simulation. In this study, ANSYS MD is applied to predict the elastic constants for randomly oriented fiber-reinforced composites, including tensile and shear moduli and Poisson’s ratios. The results were compared with existing models in literature and with available experimental data.
It has been reported multiple times that exercise can prevent cancer development, help anti-tumor therapies, and lower relapse rate after successful cancer treatment. However, an underlying molecular mechanism for these beneficial effects remained elusive. Recent studies revealed that cytotoxic immune cell populations contribute to exercise mediated effects, and exercise induced hormones including insulin, cortisol, testosterone, and epinephrine have been studied in this context, especially related to CD8+ T cell function. Exercise also induces secretion of another myokine irisin, which was originally discovered in 2012. Role of irisin has been primarily studied in metabolic disease including obesity and diabetes. However, the role of irisin in tumor immunology has not been studied.
Methods
To address this point, multiple pre-clinical tumor models including colon, skin, bladder, and lung cancer were utilized. After tumor implantation, irisin was intraperitoneally injected for every 2 days starting at day 7. Tumor growth was monitored to check anti-tumoral effect of irisin. To address immunological changes within the tumor microenvironment (TME), high-dimensional flow cytometry panels were applied and analyzed. To confirm our findings, publicly available scRNA-seq data sets were re-analyzed and web-based TCGA database was utilized for further validation.
Results
Irisin displayed anti-tumoral activity when tested in multiple pre-clinical models. High-dimensional flow cytometry analysis revealed that irisin treatment reduced accumulation of regulatory T cells (Tregs) in TME. Suppressive marker expressions in Tregs were also decreased and led to better CD8+ T cell function with evidence of less dysfunctional signature. Integrin αv-TGF-β axis was identified for responsible mechanism, and myeloid cell specific knockout of integrin αv further confirmed importance of this pathway. Our findings were validated using publicly available scRNA-seq, spatial-transcriptomics, and TCGA data sets. Integrin αv was specifically expressed in myeloid cells, and TGF-β signaling pathway was higher in integrin av adjacent spots. Also, overall survival rate and immunotherapy response rate was lower in patients with high expression of integrin complex and TGF-β. This was experimentally validated in pre-clinical model by combining irisin with PD-1 blocking antibody; the combination group exhibited better tumor control.
Conclusions
We conclude that irisin treatment reduced tumor growth in multiple tumor models and this outcome was mediated by T cells. Integrin αv-TGF-β axis in the TME was responsible for this effect, and blocking this pathway exhibited better tumor control. Re-analysis of publicly available data sets further validated importance of this signaling pathway, implying therapeutic potential of irisin in treating cancer.
Abstract Accurately predicting peptide secondary structures remains a challenging task due to the lack of discriminative information in short peptides. In this study, we propose PHAT, a deep graph learning framework for the prediction of peptide secondary structures. The framework includes a novel interpretable deep hypergraph multi-head attention network that uses residue-based reasoning for structure prediction. Our algorithm can incorporate sequential semantic information from large-scale biological corpus and structural semantic information from multi-scale structural segmentation, leading to better accuracy and interpretability even with extremely short peptides. Our interpretable models are able to highlight the reasoning of structural feature representations and the classification of secondary substructures. We further demonstrate the importance of secondary structures in peptide tertiary structure reconstruction and downstream functional analysis, highlighting the versatility of our models. To facilitate the use of our model, we establish an online server which is accessible via http://inner.wei-group.net/PHAT/ . We expect our work to assist in the design of functional peptides and contribute to the advancement of structural biology research.
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