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.

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.

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.

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.

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.

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.

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.

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.

SMDB: a Spatial Multimodal Data Browser.

Understanding the relationship between fine-scale spatial organization and biological function necessitates a tool that effectively combines spatial positions, morphological information, and spatial transcriptomics (ST) data. We introduce the Spatial Multimodal Data Browser (SMDB, https://www.biosino.org/smdb), a robust visualization web service for interactively exploring ST data. By integrating multimodal data, such as hematoxylin and eosin (H&E) images, gene expression-based molecular clusters, and more, SMDB facilitates the analysis of tissue composition through the dissociation of two-dimensional (2D) sections and the identification of gene expression-profiled boundaries. In a digital three-dimensional (3D) space, SMDB allows researchers to reconstruct morphology visualizations based on manually filtered spots or expand anatomical structures using high-resolution molecular subtypes. To enhance user experience, it offers customizable workspaces for interactive exploration of ST spots in tissues, providing features like smooth zooming, panning, 360-degree rotation in 3D and adjustable spot scaling. SMDB is particularly valuable in neuroscience and spatial histology studies, as it incorporates Allen's mouse brain anatomy atlas for reference in morphological research. This powerful tool provides a comprehensive and efficient solution for examining the intricate relationships between spatial morphology, and biological function in various tissues.

Base editing-mediated one-step inactivation of the Dnmt gene family reveals critical roles of DNA methylation during mouse gastrulation.

During embryo development, DNA methylation is established by DNMT3A/3B and subsequently maintained by DNMT1. While much research has been done in this field, the functional significance of DNA methylation in embryogenesis remains unknown. Here, we establish a system of simultaneous inactivation of multiple endogenous genes in zygotes through screening for base editors that can efficiently introduce a stop codon. Embryos with mutations in Dnmts and/or Tets can be generated in one step with IMGZ. Dnmt-null embryos display gastrulation failure at E7.5. Interestingly, although DNA methylation is absent, gastrulation-related pathways are down-regulated in Dnmt-null embryos. Moreover, DNMT1, DNMT3A, and DNMT3B are critical for gastrulation, and their functions are independent of TET proteins. Hypermethylation can be sustained by either DNMT1 or DNMT3A/3B at some promoters, which are related to the suppression of miRNAs. The introduction of a single mutant allele of six miRNAs and paternal IG-DMR partially restores primitive streak elongation in Dnmt-null embryos. Thus, our results unveil an epigenetic correlation between promoter methylation and suppression of miRNA expression for gastrulation and demonstrate that IMGZ can accelerate deciphering the functions of multiple genes in vivo.

Multi-omics of extracellular vesicles: An integrative representation of functional mediators and perspectives on lung disease study.

Extracellular vesicles are secreted by almost all cell types. EVs include a broader component known as exosomes that participate in cell-cell and tissue-tissue communication via carrying diverse biological signals from one cell type or tissue to another. EVs play roles as communication messengers of the intercellular network to mediate different physiological activities or pathological changes. In particular, most EVs are natural carriers of functional cargo such as DNA, RNA, and proteins, and thus they are relevant to advancing personalized targeted therapies in clinical practice. For the application of EVs, novel bioinformatic models and methods based on high-throughput technologies and multi-omics data are required to provide a deeper understanding of their biological and biomedical characteristics. These include qualitative and quantitative representation for identifying cargo markers, local cellular communication inference for tracing the origin and production of EVs, and distant organ communication reconstruction for targeting the influential microenvironment and transferable activators. Thus, this perspective paper introduces EVs in the context of multi-omics and provides an integrative bioinformatic viewpoint of the state of current research on EVs and their applications.

Kupffer-cell-derived IL-6 is repurposed for hepatocyte dedifferentiation via activating progenitor genes from injury-specific enhancers.

Stem cell-independent reprogramming of differentiated cells has recently been identified as an important paradigm for repairing injured tissues. Following periportal injury, mature hepatocytes re-activate reprogramming/progenitor-related genes (RRGs) and dedifferentiate into liver progenitor-like cells (LPLCs) in both mice and humans, which contribute remarkably to regeneration. However, it remains unknown which and how external factors trigger hepatocyte reprogramming. Here, by employing single-cell transcriptional profiling and lineage-specific deletion tools, we uncovered that periportal-specific LPLC formation was initiated by regionally activated Kupffer cells but not peripheral monocyte-derived macrophages. Unexpectedly, using in vivo screening, the proinflammatory factor IL-6 was identified as the niche signal repurposed for RRG induction via STAT3 activation, which drove RRG expression through binding to their pre-accessible enhancers. Notably, RRGs were activated through injury-specific rather than liver embryogenesis-related enhancers. Collectively, these findings depict an injury-specific niche signal and the inflammation-mediated transcription in driving the conversion of hepatocytes into a progenitor phenotype.

Towards precision medicine: Omics approach for COVID-19.

The coronavirus disease 2019 (COVID-19) pandemic had a devastating impact on human society. Beginning with genome surveillance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the development of omics technologies brought a clearer understanding of the complex SARS-CoV-2 and COVID-19. Here, we reviewed how omics, including genomics, proteomics, single-cell multi-omics, and clinical phenomics, play roles in answering biological and clinical questions about COVID-19. Large-scale sequencing and advanced analysis methods facilitate COVID-19 discovery from virus evolution and severity risk prediction to potential treatment identification. Omics would indicate precise and globalized prevention and medicine for the COVID-19 pandemic under the utilization of big data capability and phenotypes refinement. Furthermore, decoding the evolution rule of SARS-CoV-2 by deep learning models is promising to forecast new variants and achieve more precise data to predict future pandemics and prevent them on time.