Integrating multi-omics data of childhood asthma using a deep association model.

Childhood asthma is one of the most common respiratory diseases with rising mortality and morbidity. The multi-omics data is providing a new chance to explore collaborative biomarkers and corresponding diagnostic models of childhood asthma. To capture the nonlinear association of multi-omics data and improve interpretability of diagnostic model, we proposed a novel deep association model (DAM) and corresponding efficient analysis framework. First, the Deep Subspace Reconstruction was used to fuse the omics data and diagnostic information, thereby correcting the distribution of the original omics data and reducing the influence of unnecessary data noises. Second, the Joint Deep Semi-Negative Matrix Factorization was applied to identify different latent sample patterns and extract biomarkers from different omics data levels. Third, our newly proposed Deep Orthogonal Canonical Correlation Analysis can rank features in the collaborative module, which are able to construct the diagnostic model considering nonlinear correlation between different omics data levels. Using DAM, we deeply analyzed the transcriptome and methylation data of childhood asthma. The effectiveness of DAM is verified from the perspectives of algorithm performance and biological significance on the independent test dataset, by ablation experiment and comparison with many baseline methods from clinical and biological studies. The DAM-induced diagnostic model can achieve a prediction AUC of 0.912, which is higher than that of many other alternative methods. Meanwhile, relevant pathways and biomarkers of childhood asthma are also recognized to be collectively altered on the gene expression and methylation levels. As an interpretable machine learning approach, DAM simultaneously considers the non-linear associations among samples and those among biological features, which should help explore interpretative biomarker candidates and efficient diagnostic models from multi-omics data analysis for human complex diseases.

Deep Learning of radiology-genomics integration for computational oncology: A mini review.

In the field of computational oncology, patient status is often assessed using radiology-genomics, which includes two key technologies and data, such as radiology and genomics. Recent advances in deep learning have facilitated the integration of radiology-genomics data, and even new omics data, significantly improving the robustness and accuracy of clinical predictions. These factors are driving artificial intelligence (AI) closer to practical clinical applications. In particular, deep learning models are crucial in identifying new radiology-genomics biomarkers and therapeutic targets, supported by explainable AI (xAI) methods. This review focuses on recent developments in deep learning for radiology-genomics integration, highlights current challenges, and outlines some research directions for multimodal integration and biomarker discovery of radiology-genomics or radiology-omics that are urgently needed in computational oncology.

Ketogenic diet-induced bile acids protect against obesity through reduced calorie absorption.

The low-carbohydrate ketogenic diet (KD) has long been practiced for weight loss, but the underlying mechanisms remain elusive. Gut microbiota and metabolites have been suggested to mediate the metabolic changes caused by KD consumption, although the particular gut microbes or metabolites involved are unclear. Here, we show that KD consumption enhances serum levels of taurodeoxycholic acid (TDCA) and tauroursodeoxycholic acid (TUDCA) in mice to decrease body weight and fasting glucose levels. Mechanistically, KD feeding decreases the abundance of a bile salt hydrolase (BSH)-coding gut bacterium, Lactobacillus murinus ASF361. The reduction of L. murinus ASF361 or inhibition of BSH activity increases the circulating levels of TDCA and TUDCA, thereby reducing energy absorption by inhibiting intestinal carbonic anhydrase 1 expression, which leads to weight loss. TDCA and TUDCA treatments have been found to protect against obesity and its complications in multiple mouse models. Additionally, the associations among the abovementioned bile acids, microbial BSH and metabolic traits were consistently observed both in an observational study of healthy human participants (n = 416) and in a low-carbohydrate KD interventional study of participants who were either overweight or with obesity (n = 25). In summary, we uncover a unique host-gut microbiota metabolic interaction mechanism for KD consumption to decrease body weight and fasting glucose levels. Our findings support TDCA and TUDCA as two promising drug candidates for obesity and its complications in addition to a KD.

TMO-Net: an explainable pretrained multi-omics model for multi-task learning in oncology.

Cancer is a complex disease composing systemic alterations in multiple scales. In this study, we develop the Tumor Multi-Omics pre-trained Network (TMO-Net) that integrates multi-omics pan-cancer datasets for model pre-training, facilitating cross-omics interactions and enabling joint representation learning and incomplete omics inference. This model enhances multi-omics sample representation and empowers various downstream oncology tasks with incomplete multi-omics datasets. By employing interpretable learning, we characterize the contributions of distinct omics features to clinical outcomes. The TMO-Net model serves as a versatile framework for cross-modal multi-omics learning in oncology, paving the way for tumor omics-specific foundation models.

Membrane-Anchored Aggregation-Induced Emission Luminogens: Accurate Labeling and Efficient Photodynamic Inactivation of Streptococcus mutans in Anticaries Therapy.

Dental caries is a widespread bacterial infectious disease that imposes a significant public health burden globally. The primary culprits in caries development are cariogenic bacteria, notably Streptococcus mutans (S. mutans), due to their robust biofilm-forming capabilities. To address this issue, a series of cationic pyridinium-substituted photosensitizers with aggregation-induced emission have been designed. All of these aggregation-induced emission luminogens (AIEgens) exhibit outstanding microbial visualization and photodynamic killing of S. mutans, thanks to their luminous fluorescence and efficient singlet oxygen generation ability. Notably, one of the membrane-anchored AIEgens (TDTPY) can inactivate planktic S. mutans and its biofilm without causing significant cytotoxicity. Importantly, application of TDTPY-mediated photodynamic treatment on in vivo rodent models has yielded commendable imaging results and effectively slowed down caries progression with assured biosafety. Unlike traditional single-mode anticaries materials, AIEgens integrate the dual functions of detecting and removing S. mutans and are expected to build a new caries management diagnosis and treatment platform. To the best of our knowledge, this is also the first report on the use of AIEgens for anticaries studies both in vitro and in vivo.

Identification of DNA methylation characteristics associated with metastasis and prognosis in colorectal cancer.

Colorectal cancer (CRC) is prone to metastasis and recurrence after surgery, which is one of the main causes for its poor treatment and prognosis. Therefore, it is essential to identify biomarkers associated with metastasis and recurrence in CRC. DNA methylation has a regulatory role in cancer metastasis, tumor immune microenvironment (TME), and prognosis and may be one of the most valuable biomarkers for predicting CRC metastasis and prognosis. We constructed a diagnostic model and nomogram that can effectively predict CRC metastasis based on the differential methylation CpG sites (DMCs) between metastatic and non-metastatic CRC patients. Then, we identified 17 DMCs associated with progression free survival (PFS) of CRC and constructed a prognostic model. The prognosis model based on 17 DMCs can predict the PFS of CRC with medium to high accuracy. The results of immunohistochemical analysis indicated that the protein expression levels of the genes involved in prognostic DMCs were different between normal and colorectal cancer tissues. According to the results of immune-related analysis, we found that the low-risk patients had better immunotherapy response. In addition, high risk scores were negatively correlated with high tumor mutation burden (TMB) levels, and patients with low TMB levels in the high-risk group had the worst PFS. Our work shows the clinical value of DNA methylation in predicting CRC metastasis and PFS, as well as their correlation with TME, immunotherapy, and TMB, which helps understand the changes of DNA methylation in CRC metastasis and improving the treatment and prognosis of CRC.

Genotypic-phenotypic landscape computation based on first principle and deep learning.

The relationship between genotype and fitness is fundamental to evolution, but quantitatively mapping genotypes to fitness has remained challenging. We propose the Phenotypic-Embedding theorem (P-E theorem) that bridges genotype-phenotype through an encoder-decoder deep learning framework. Inspired by this, we proposed a more general first principle for correlating genotype-phenotype, and the P-E theorem provides a computable basis for the application of first principle. As an application example of the P-E theorem, we developed the Co-attention based Transformer model to bridge Genotype and Fitness model, a Transformer-based pre-train foundation model with downstream supervised fine-tuning that can accurately simulate the neutral evolution of viruses and predict immune escape mutations. Accordingly, following the calculation path of the P-E theorem, we accurately obtained the basic reproduction number (${R}_0$) of SARS-CoV-2 from first principles, quantitatively linked immune escape to viral fitness and plotted the genotype-fitness landscape. The theoretical system we established provides a general and interpretable method to construct genotype-phenotype landscapes, providing a new paradigm for studying theoretical and computational biology.

Effects of fermentation with different probiotics on the quality, isoflavone content, and flavor of okara beverages.

The present study aimed to prepare and evaluate a new probiotic functional beverage, using single-probiotic and compound probiotic fermentation on okara. Four different forms of fermentation microorganisms used were Lacticaseibacillus rhamnosus S24 (Lr), Lacticaseibacillus paracasei 6244 (Lp), Lactobacillus acidophilus 11,073 (La), and mixed fermentation (Lr + Lp + La). The physicochemical properties, antioxidant activity, flavor change, and storage period of fermented okara beverages with probiotics were investigated. The results showed that different fermentation schemes could significantly improve the physicochemical properties, antioxidant activity, and sensory quality of the okara beverages. The number of viable bacteria in the Lp group (3.53 × 108 CFU/mL), isoflavone content (0.514 µg/mL) were the highest; total phenol and flavonoid content were 3.32 and 5.68 times higher than in the CK group, respectively. DPPH and ABTS+ free radical scavenging rates were increased by 11.32% and 20%, respectively (p < .05). Through SPME/GC-MS analysis, 44 volatile compounds were identified in the Lr + Lp + La groups, mainly as a result of changes in alcohols and aldehydes produced by fermentation metabolism. It enhances the floral and fruity aroma of the okara beverage. All probiotic-fermented okara beverages can be stored at 4°C for 15 days, with probiotic activity greater than 107 CFU/mL. This study can obtain a probiotic okara beverage rich in soybean isoflavones and with good flavor. Overall, okara can be used to develop functional beverages containing probiotics and contribute to a zero-waste approach in the food industry.

Donor-derived stem cell infusion for sustained pancytopenia after CD19 CAR-T therapy for relapsed patients post allogeneic stem cell transplantation.

OBJECTIVES: To investigate the effectiveness of donor-derived chimeric antigen receptor T (CAR-T) cells in the treatment of relapsed cases after allogeneic hematopoietic stem cell transplantation (allo-HSCT), and whether donor-derived peripheral blood stem cells (PBSCs) have a therapeutic effect on pancytopenia after CAR-T cell therapy. METHODS: We analyzed data from five adults with B-cell acute lymphoblastic leukemia (ALL) who had relapse after allo-HSCT and received donor-derived CAR-T cell therapy and donor-derived PBSCs to promote hematopoietic recovery. RESULTS: All patients had negative minimal residual disease after CAR-T therapy, grade 1-2 cytokine release syndrome, and developed grade 4 hematologic toxicity. During the pancytopenia stage after CAR-T cell therapy, donor-derived PBSCs were transfused without graft-versus-host disease (GVHD) prophylaxis. Four patients had grade I-II acute GVHD (aGVHD). After corticosteroid treatment, aGVHD resolved and hematopoiesis was restored. Although steroids in combination with etanercept and ruxolitinib relieved symptoms in one patient with grade IV aGVHD, complete hematopoietic recovery was not achieved, and the patient died due to severe infection. CONCLUSIONS: Donor-derived CAR-T cell therapy is safe and effective in patients with relapsed/refractory ALL after allo-HSCT. Donor-derived PBSCs infusion could achieve hematopoietic recovery with controllable aGVHD in patients with persistent pancytopenia.

Anomaly Detection Models for SARS-CoV-2 Surveillance Based on Genome k-mers.

Since COVID-19 has brought great challenges to global public health governance, developing methods that track the evolution of the virus over the course of an epidemic or pandemic is useful for public health. This paper uses anomaly detection models to analyze SARS-CoV-2 virus genome k-mers to predict possible new critical variants in the collected samples. We used the sample data from Argentina, China and Portugal obtained from the Global Initiative on Sharing All Influenza Data (GISAID) to conduct multiple rounds of evaluation on several anomaly detection models, to verify the feasibility of this virus early warning and surveillance idea and find appropriate anomaly detection models for actual epidemic surveillance. Through multiple rounds of model testing, we found that the LUNAR (learnable unified neighborhood-based anomaly ranking) and LUNAR+LUNAR stacking model performed well in new critical variants detection. The results of simulated dynamic detection validate the feasibility of this approach, which can help efficiently monitor samples in local areas.

Review of Predicting Synergistic Drug Combinations.

The prediction of drug combinations is of great clinical significance. In many diseases, such as high blood pressure, diabetes, and stomach ulcers, the simultaneous use of two or more drugs has shown clear efficacy. It has greatly reduced the progression of drug resistance. This review presents the latest applications of methods for predicting the effects of drug combinations and the bioactivity databases commonly used in drug combination prediction. These studies have played a significant role in developing precision therapy. We first describe the concept of synergy. we study various publicly available databases for drug combination prediction tasks. Next, we introduce five algorithms applied to drug combinatorial prediction, which include traditional machine learning methods, deep learning methods, mathematical methods, systems biology methods and search algorithms. In the end, we sum up the difficulties encountered in prediction models.

DNMT3A governs tyrosine kinase inhibitors responses through IAPs and in a cell senescence-dependent manner in non-small cell lung cancer.

Patients with non-small cell lung cancer (NSCLC) treated with tyrosine kinase inhibitors (TKIs) inevitably exhibit drug resistance, which diminishes therapeutic effects. Nonetheless, the molecular mechanisms of TKI resistance in NSCLC remain obscure. In this study, data from clinical and TCGA databases revealed an increase in DNMT3A expression, which was correlated with a poor prognosis. Using NSCLC organoid models, we observed that high DNMT3A levels reduced TKI susceptibility of NSCLC cells via upregulating inhibitor of apoptosis proteins (IAPs). Simultaneously, the DNMT3Ahigh subset, which escaped apoptosis, underwent an early senescent-like state in a CDKN1A-dependent manner. Furthermore, the cellular senescence induced by TKIs was observed to be reversible, whereas DNMT3Ahigh cells reacquired their proliferative characteristics in the absence of TKIs, resulting in subsequent tumour recurrence and growth. Notably, the blockade of DNMT3A/IAPs signals enhanced the efficacy of TKIs in DNMT3Ahigh tumour-bearing mice, which represented a promising strategy for the effective treatment of NSCLC.

Differential effects of social isolation on oligodendrocyte development in different brain regions: insights from a canine model.

Social isolation (SI) exerts diverse adverse effects on brain structure and function in humans. To gain an insight into the mechanisms underlying these effects, we conducted a systematic analysis of multiple brain regions from socially isolated and group-housed dogs, whose brain and behavior are similar to humans. Our transcriptomic analysis revealed reduced expression of myelin-related genes specifically in the white matter of prefrontal cortex (PFC) after SI during the juvenile stage. Despite these gene expression changes, myelin fiber organization in PFC remained unchanged. Surprisingly, we observed more mature oligodendrocytes and thicker myelin bundles in the somatosensory parietal cortex in socially isolated dogs, which may be linked to an increased expression of ADORA2A, a gene known to promote oligodendrocyte maturation. Additionally, we found a reduced expression of blood-brain barrier (BBB) structural components Aquaporin-4, Occludin, and Claudin1 in both PFC and parietal cortices, indicating BBB disruption after SI. In agreement with BBB disruption, myelin-related sphingolipids were increased in cerebrospinal fluid in the socially isolated group. These unexpected findings show that SI induces distinct alterations in oligodendrocyte development and shared disruption in BBB integrity in different cortices, demonstrating the value of dogs as a complementary animal model to uncover molecular mechanisms underlying SI-induced brain dysfunction.

Temporal genomic heterogeneity guiding individualized therapy in recurrent non-small cell lung cancer.

Introduction: Despite the benefit of adjuvant systemic therapy for patients with resected non-small cell lung cancer (NSCLC), the risk of postoperative recurrence remains high. Our objective was to characterize temporal genetic heterogeneity between primary resected and recurrent tumors, and its impact on treatment outcomes. Methods: In this study, next-generation sequencing (NGS) testing was performed on tissue specimens and circulating tumor DNA (ctDNA) collected at postoperative recurrence, and results were compared to the genotypes of initial surgical specimens. Results: Of forty-five patients with matched primary and post-operative recurrent tumors, EGFR status switched in 17 patients (37.8%) at post-operative recurrence and 28 patients (62.2%) had no genotype change (17 mutant, 11 wild-type). Based on the changes of EGFR status, patients were divided into 4 groups. Following subsequent treatment with EGFR TKI o chemotherapy: In group A, with sustained sensitive mutation, the percentage achieving partial response (PR) was the highest, at 72.2%, the median progression-free survival (PFS) was 17 months, and the median overall survival (OS) was 44.0 months respectively; In group B, with genotype changed from wild-type to mutant, 50% achieved PR, PFS was 10 months, and OS was 35 months; In group C, in which mutant status shifted to wild-type or new co-mutation emerged, the percentage achieving PR was 30%, PFS was 9 months, and OS was 35 months. In group D, with sustained wild type, the percentage achieving PR was 27.3%, PFS was 8 months, and OS was 22 months. Discussion: Genotypic shift between paired primary and post-operative recurrent tumors was not infrequent, and this temporal genomic heterogeneity substantially impacted subsequent treatment outcomes.

Using Machine Learning Methods to Study Colorectal Cancer Tumor Micro-Environment and Its Biomarkers.

Colorectal cancer (CRC) is a leading cause of cancer deaths worldwide, and the identification of biomarkers can improve early detection and personalized treatment. In this study, RNA-seq data and gene chip data from TCGA and GEO were used to explore potential biomarkers for CRC. The SMOTE method was used to address class imbalance, and four feature selection algorithms (MCFS, Borota, mRMR, and LightGBM) were used to select genes from the gene expression matrix. Four machine learning algorithms (SVM, XGBoost, RF, and kNN) were then employed to obtain the optimal number of genes for model construction. Through interpretable machine learning (IML), co-predictive networks were generated to identify rules and uncover underlying relationships among the selected genes. Survival analysis revealed that INHBA, FNBP1, PDE9A, HIST1H2BG, and CADM3 were significantly correlated with prognosis in CRC patients. In addition, the CIBERSORT algorithm was used to investigate the proportion of immune cells in CRC tissues, and gene mutation rates for the five selected biomarkers were explored. The biomarkers identified in this study have significant implications for the development of personalized therapies and could ultimately lead to improved clinical outcomes for CRC patients.

Current progress, challenges, and future perspectives of language models for protein representation and protein design.

The sequence-structure-function paradigm of protein is the basis of molecular biology. What is the underlying mechanism of such sequence and structure/function corresponding relationship? We reviewed the methods for protein representation and protein design. With these protein representation models, we can accurately predict many properties of proteins, such as stability and binding affinity. Progen, Chroma, RF Diffusion, SCUBA, and other protein design models have demonstrated how human-designed artificial proteins can have desired biological functions. The protein design will revolutionize drug development. And more efficient artificial enzymes that break down industrial waste or plastics will contribute to carbon neutrality. We also discussed the three greatest challenges of protein design in future and possible solutions.

Early warning of emerging infectious diseases based on multimodal data.

The coronavirus disease 2019 (COVID-19) pandemic has dramatically increased the awareness of emerging infectious diseases. The advancement of multiomics analysis technology has resulted in the development of several databases containing virus information. Several scientists have integrated existing data on viruses to construct phylogenetic trees and predict virus mutation and transmission in different ways, providing prospective technical support for epidemic prevention and control. This review summarized the databases of known emerging infectious viruses and techniques focusing on virus variant forecasting and early warning. It focuses on the multi-dimensional information integration and database construction of emerging infectious viruses, virus mutation spectrum construction and variant forecast model, analysis of the affinity between mutation antigen and the receptor, propagation model of virus dynamic evolution, and monitoring and early warning for variants. As people have suffered from COVID-19 and repeated flu outbreaks, we focused on the research results of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and influenza viruses. This review comprehensively viewed the latest virus research and provided a reference for future virus prevention and control research.

A Noncoding A-to-U Kozak Site Change Related to the High Transmissibility of Alpha, Delta, and Omicron VOCs.

Three prevalent SARS-CoV-2 variants of concern (VOCs) emerged and caused epidemic waves. It is essential to uncover advantageous mutations that cause the high transmissibility of VOCs. However, viral mutations are tightly linked, so traditional population genetic methods, including machine learning-based methods, cannot reliably detect mutations conferring a fitness advantage. In this study, we developed an approach based on the sequential occurrence order of mutations and the accelerated furcation rate in the pandemic-scale phylogenomic tree. We analyzed 3,777,753 high-quality SARS-CoV-2 genomic sequences and the epidemiology metadata using the Coronavirus GenBrowser. We found that two noncoding mutations at the same position (g.a28271-/u) may be crucial to the high transmissibility of Alpha, Delta, and Omicron VOCs although the noncoding mutations alone cannot increase viral transmissibility. Both mutations cause an A-to-U change at the core position -3 of the Kozak sequence of the N gene and significantly reduce the protein expression ratio of ORF9b to N. Using a convergent evolutionary analysis, we found that g.a28271-/u, S:p.P681H/R, and N:p.R203K/M occur independently on three VOC lineages, suggesting that coordinated changes of S, N, and ORF9b proteins are crucial to high viral transmissibility. Our results provide new insights into high viral transmissibility co-modulated by advantageous noncoding and nonsynonymous changes.