Early Cancer Detection in Li-Fraumeni Syndrome with Cell-Free DNA.

People with Li-Fraumeni syndrome (LFS) harbor a germline pathogenic variant in the TP53 tumor suppressor gene, face a near 100% lifetime risk of cancer, and routinely undergo intensive surveillance protocols. Liquid biopsy has become an attractive tool for a range of clinical applications, including early cancer detection. Here, we provide a proof-of-principle for a multimodal liquid biopsy assay that integrates a targeted gene panel, shallow whole-genome, and cell-free methylated DNA immunoprecipitation sequencing for the early detection of cancer in a longitudinal cohort of 89 LFS patients. Multimodal analysis increased our detection rate in patients with an active cancer diagnosis over uni-modal analysis and was able to detect cancer-associated signal(s) in carriers prior to diagnosis with conventional screening (positive predictive value = 67.6%, negative predictive value = 96.5%). Although adoption of liquid biopsy into current surveillance will require further clinical validation, this study provides a framework for individuals with LFS. SIGNIFICANCE: By utilizing an integrated cell-free DNA approach, liquid biopsy shows earlier detection of cancer in patients with LFS compared with current clinical surveillance methods such as imaging. Liquid biopsy provides improved accessibility and sensitivity, complementing current clinical surveillance methods to provide better care for these patients. See related commentary by Latham et al., p. 23. This article is featured in Selected Articles from This Issue, p. 5.

Unbiased curriculum learning enhanced global-local graph neural network for protein thermodynamic stability prediction.

MOTIVATION: Proteins play crucial roles in biological processes, with their functions being closely tied to thermodynamic stability. However, measuring stability changes upon point mutations of amino acid residues using physical methods can be time-consuming. In recent years, several computational methods for protein thermodynamic stability prediction (PTSP) based on deep learning have emerged. Nevertheless, these approaches either overlook the natural topology of protein structures or neglect the inherent noisy samples resulting from theoretical calculation or experimental errors. RESULTS: We propose a novel Global-Local Graph Neural Network powered by Unbiased Curriculum Learning for the PTSP task. Our method first builds a Siamese graph neural network to extract protein features before and after mutation. Since the graph's topological changes stem from local node mutations, we design a local feature transformation module to make the model focus on the mutated site. To address model bias caused by noisy samples, which represent unavoidable errors from physical experiments, we introduce an unbiased curriculum learning method. This approach effectively identifies and re-weights noisy samples during the training process. Extensive experiments demonstrate that our proposed method outperforms advanced protein stability prediction methods, and surpasses state-of-the-art learning methods for regression prediction tasks. AVAILABILITY AND IMPLEMENTATION: All code and data is available at https://github.com/haifangong/UCL-GLGNN.

Bile acid-dependent transcription factors and chromatin accessibility determine regional heterogeneity of intestinal antimicrobial peptides.

Antimicrobial peptides (AMPs) are important mediators of intestinal immune surveillance. However, the regional heterogeneity of AMPs and its regulatory mechanisms remain obscure. Here, we clarified the regional heterogeneity of intestinal AMPs at the single-cell level, and revealed a cross-lineages AMP regulation mechanism that bile acid dependent transcription factors (BATFs), NR1H4, NR1H3 and VDR, regulate AMPs through a ligand-independent manner. Bile acids regulate AMPs by perturbing cell differentiation rather than activating BATFs signaling. Chromatin accessibility determines the potential of BATFs to regulate AMPs at the pre-transcriptional level, thus shaping the regional heterogeneity of AMPs. The BATFs-AMPs axis also participates in the establishment of intestinal antimicrobial barriers of fetuses and the defects of antibacterial ability during Crohn's disease. Overall, BATFs and chromatin accessibility play essential roles in shaping the regional heterogeneity of AMPs at pre- and postnatal stages, as well as in maintenance of antimicrobial immunity during homeostasis and disease.

Thyroid region prior guided attention for ultrasound segmentation of thyroid nodules.

Ultrasound segmentation of thyroid nodules is a challenging task, which plays an vital role in the diagnosis of thyroid cancer. However, the following two factors limit the development of automatic thyroid nodule segmentation algorithms: (1) existing automatic nodule segmentation algorithms that directly apply semantic segmentation techniques can easily mistake non-thyroid areas as nodules, because of the lack of the thyroid gland region perception, the large number of similar areas in the ultrasonic images, and the inherently low contrast images; (2) the currently available dataset (i.e., DDTI) is small and collected from a single center, which violates the fact that thyroid ultrasound images are acquired from various devices in real-world situations. To overcome the lack of thyroid gland region prior knowledge, we design a thyroid region prior guided feature enhancement network (TRFE+) for accurate thyroid nodule segmentation. Specifically, (1) a novel multi-task learning framework that simultaneously learns the nodule size, gland position, and the nodule position is designed; (2) an adaptive gland region feature enhancement module is proposed to make full use of the thyroid gland prior knowledge; (3) a normalization approach with respect to the channel dimension is applied to alleviate the domain gap during the training process. To facilitate the development of thyroid nodule segmentation, we have contributed TN3K: an open-access dataset containing 3493 thyroid nodule images with high-quality nodule masks labeling from various devices and views. We perform a thorough evaluation based on the TN3K test set and DDTI to demonstrate the effectiveness of the proposed method. Code and data are available at https://github.com/haifangong/TRFE-Net-for-thyroid-nodule-segmentation.

Risk stratification and pathway analysis based on graph neural network and interpretable algorithm.

BACKGROUND: Pathway-based analysis of transcriptomic data has shown greater stability and better performance than traditional gene-based analysis. Until now, some pathway-based deep learning models have been developed for bioinformatic analysis, but these models have not fully considered the topological features of pathways, which limits the performance of the final prediction result. RESULTS: To address this issue, we propose a novel model, called PathGNN, which constructs a Graph Neural Networks (GNNs) model that can capture topological features of pathways. As a case, PathGNN was applied to predict long-term survival of four types of cancer and achieved promising predictive performance when compared to other common methods. Furthermore, the adoption of an interpretation algorithm enabled the identification of plausible pathways associated with survival. CONCLUSION: PathGNN demonstrates that GNN can be effectively applied to build a pathway-based model, resulting in promising predictive power.

VQAMix: Conditional Triplet Mixup for Medical Visual Question Answering.

Medical visual question answering (VQA) aims to correctly answer a clinical question related to a given medical image. Nevertheless, owing to the expensive manual annotations of medical data, the lack of labeled data limits the development of medical VQA. In this paper, we propose a simple yet effective data augmentation method, VQAMix, to mitigate the data limitation problem. Specifically, VQAMix generates more labeled training samples by linearly combining a pair of VQA samples, which can be easily embedded into any visual-language model to boost performance. However, mixing two VQA samples would construct new connections between images and questions from different samples, which will cause the answers for those new fabricated image-question pairs to be missing or meaningless. To solve the missing answer problem, we first develop the Learning with Missing Labels (LML) strategy, which roughly excludes the missing answers. To alleviate the meaningless answer issue, we design the Learning with Conditional-mixed Labels (LCL) strategy, which further utilizes language-type prior to forcing the mixed pairs to have reasonable answers that belong to the same category. Experimental results on the VQA-RAD and PathVQA benchmarks show that our proposed method significantly improves the performance of the baseline by about 7% and 5% on the averaging result of two backbones, respectively. More importantly, VQAMix could improve confidence calibration and model interpretability, which is significant for medical VQA models in practical applications. All code and models are available at https://github.com/haifangong/VQAMix.

Xyloketal B: A marine compound with medicinal potential.

In recent decades, technological advantages have allowed scientists to isolate medicinal compounds from marine organisms that exhibit unique structure and bioactivity. The mangrove fungus Xylaria sp. from the South China Sea is rich in metabolites and produces a potent therapeutic compound, xyloketal B. Since its isolation in 2001, xyloketal B has been extensively studied in a wide variety of cell types and in vitro and in vivo disease models. Xyloketal B and its derivatives exhibit cytoprotective effects in cardiovascular and neurodegenerative diseases by reducing oxidative stress, regulating the apoptosis pathway, maintaining ionic balance, mitigating inflammatory responses, and preventing protein aggregation. Xyloketal B has also shown to alleviate lipid accumulation in a non-alcoholic fatty liver disease model. Moreover, xyloketal B treatment induces glioblastoma cell death. This review summarizes our current understanding of xyloketal B in various disease models.

Inhibition of TRPM7 with waixenicin A reduces glioblastoma cellular functions.

Glioblastoma (GBM) is the most common malignant primary brain tumour originating in the CNS. Median patient survival is <15 months with standard treatment which consists of surgery alongside radiation therapy and temozolomide chemotherapy. However, because of the aggressive nature of GBM, and the significant toxicity of these adjuvant therapies, long-term therapeutic effects are unsatisfactory. Thus, there is urgency to identify new drug targets for GBM. Recent evidence shows that the transient receptor potential melastatin 7 (TRPM7) cation channel is aberrantly upregulated in GBM and its inhibition leads to reduction of GBM cellular functions. This suggests that TRPM7 may be a potential drug target for GBM treatment. In this study, we assessed the effects of the specific TRPM7 antagonist waixenicin A on human GBM cell lines U87 or U251 both in vitro and in vivo. First, we demonstrated in vitro that application of waixenicin A reduced TRPM7 protein expression and inhibited the TRPM7-like currents in GBM cells. We also observed reduction of GBM cell viability, migration, and invasion. Using an intracranial xenograft GBM mouse model, we showed that with treatment of waixenicin A, there was increased cleaved caspase 3 activity, alongside reduction in Ki-67, cofilin, and Akt activity in vivo. Together, these data demonstrate higher GBM cell apoptosis, and lower proliferation, migration, invasion and survivability following treatment with waixenicin A.

Ginsenoside Rg1 protects against ischemic/reperfusion-induced neuronal injury through miR-144/Nrf2/ARE pathway.

Ginsenoside Rg1 (Rg1), a saponin extracted from Panax ginseng, has been well documented to be effective against ischemic/reperfusion (I/R) neuronal injury. However, the underlying mechanisms remain obscure. In the present study, we investigated the roles of Nrf2 and miR-144 in the protective effects of Rg1 against I/R-induced neuronal injury. In OGD/R-treated PC12 cells, Rg1 (0.01-1 µmol/L) dose-dependently attenuated the cell injury accompanied by prolonging nuclear accumulation of Nrf2, enhancing the transcriptional activity of Nrf2, as well as promoting the expression of ARE-target genes. The activation of the Nrf2/ARE pathway by Rg1 was independent of disassociation with Keap1, but resulted from post-translational regulations. Knockdown of Nrf2 abolished all the protective changes of Rg1 in OGD/R-treated PC12 cells. Furthermore, Rg1 treatment significantly decreased the expression of miR-144, which downregulated Nrf2 production by targeting its 3'-untranlated region after OGD/R. Knockdown of Nrf2 had no effect on the expression of miR-144, suggesting that miR-144 was an upstream regulator of Nrf2. We revealed that there was a direct binding between Nrf2 and miR-144 in PC12 cells. Application of anti-miR-144 occluded the activation of the Nrf2/ARE pathway by Rg1 in OGD/R-treated PC12 cells. In tMCAO rats, administration of Rg1 (20 mg/kg) significantly alleviated ischemic injury, and activated Nrf2/ARE pathway. The protective effects of Rg1 were abolished by injecting of AAV-HIF-miR-144-shRNA into the predicted ischemic penumbra. In conclusion, our results demonstrate that Rg1 alleviates oxidative stress after I/R through inhibiting miR-144 activity and subsequently promoting the Nrf2/ARE pathway at the post-translational level.

Marine Compound Xyloketal B as a Potential Drug Development Target for Neuroprotection.

Xyloketal B is a natural compound isolated from the mangrove fungus, Xylaria sp. in the South China Sea. In the past decade, studies have shown that xyloketal B exhibits anti-oxidative, anti-inflammatory, and anti-apoptotic abilities and may serve as a treatment for ischemic stroke. Xyloketal B has been shown to interact with both neurons and residential microglial cells and regulate a number of proteins involved in the apoptotic events during ischemia. Such mechanisms include inhibition of specific NADPH oxidase subunits, upregulation of HO-1, increase of Bcl-1/Bax ratio, and downregulation of TLR4 receptor. Both in vitro and in vivo stroke models have validated its potential in preventing ischemia-induced neuronal cell death. This review summarizes our current understanding of the effects of xyloketal B in ischemic conditions. As stroke ranks second in the causes of mortality worldwide and still lacks effective treatment, it is necessary to seek novel therapeutic options. Understanding the role of xyloketal B in ischemic stroke could reveal a new aspect of stroke treatment.