COV2Var, a function annotation database of SARS-CoV-2 genetic variation.

The COVID-19 pandemic, caused by the coronavirus SARS-CoV-2, has resulted in the loss of millions of lives and severe global economic consequences. Every time SARS-CoV-2 replicates, the viruses acquire new mutations in their genomes. Mutations in SARS-CoV-2 genomes led to increased transmissibility, severe disease outcomes, evasion of the immune response, changes in clinical manifestations and reducing the efficacy of vaccines or treatments. To date, the multiple resources provide lists of detected mutations without key functional annotations. There is a lack of research examining the relationship between mutations and various factors such as disease severity, pathogenicity, patient age, patient gender, cross-species transmission, viral immune escape, immune response level, viral transmission capability, viral evolution, host adaptability, viral protein structure, viral protein function, viral protein stability and concurrent mutations. Deep understanding the relationship between mutation sites and these factors is crucial for advancing our knowledge of SARS-CoV-2 and for developing effective responses. To fill this gap, we built COV2Var, a function annotation database of SARS-CoV-2 genetic variation, available at http://biomedbdc.wchscu.cn/COV2Var/. COV2Var aims to identify common mutations in SARS-CoV-2 variants and assess their effects, providing a valuable resource for intensive functional annotations of common mutations among SARS-CoV-2 variants.

StemDriver: a knowledgebase of gene functions for hematopoietic stem cell fate determination.

StemDriver is a comprehensive knowledgebase dedicated to the functional annotation of genes participating in the determination of hematopoietic stem cell fate, available at http://biomedbdc.wchscu.cn/StemDriver/. By utilizing single-cell RNA sequencing data, StemDriver has successfully assembled a comprehensive lineage map of hematopoiesis, capturing the entire continuum from the initial formation of hematopoietic stem cells to the fully developed mature cells. Extensive exploration and characterization were conducted on gene expression features corresponding to each lineage commitment. At the current version, StemDriver integrates data from 42 studies, encompassing a diverse range of 14 tissue types spanning from the embryonic phase to adulthood. In order to ensure uniformity and reliability, all data undergo a standardized pipeline, which includes quality data pre-processing, cell type annotation, differential gene expression analysis, identification of gene categories correlated with differentiation, analysis of highly variable genes along pseudo-time, and exploration of gene expression regulatory networks. In total, StemDriver assessed the function of 23 839 genes for human samples and 29 533 genes for mouse samples. Simultaneously, StemDriver also provided users with reference datasets and models for cell annotation. We believe that StemDriver will offer valuable assistance to research focused on cellular development and hematopoiesis.

SPASCER: spatial transcriptomics annotation at single-cell resolution.

In recent years, the explosive growth of spatial technologies has enabled the characterization of spatial heterogeneity of tissue architectures. Compared to traditional sequencing, spatial transcriptomics reserves the spatial information of each captured location and provides novel insights into diverse spatially related biological contexts. Even though two spatial transcriptomics databases exist, they provide limited analytical information. Information such as spatial heterogeneity of genes and cells, cell-cell communication activities in space, and the cell type compositions in the microenvironment are critical clues to unveil the mechanism of tumorigenesis and embryo differentiation. Therefore, we constructed a new spatial transcriptomics database, named SPASCER (https://ccsm.uth.edu/SPASCER), designed to help understand the heterogeneity of tissue organizations, region-specific microenvironment, and intercellular interactions across tissue architectures at multiple levels. SPASCER contains datasets from 43 studies, including 1082 sub-datasets from 16 organ types across four species. scRNA-seq was integrated to deconvolve/map spatial transcriptomics, and processed with spatial cell-cell interaction, gene pattern and pathway enrichment analysis. Cell-cell interactions and gene regulation network of scRNA-seq from matched spatial transcriptomics were performed as well. The application of SPASCER will provide new insights into tissue architecture and a solid foundation for the mechanistic understanding of many biological processes in healthy and diseased tissues.

AgeAnno: a knowledgebase of single-cell annotation of aging in human.

Aging is a complex process that accompanied by molecular and cellular alterations. The identification of tissue-/cell type-specific biomarkers of aging and elucidation of the detailed biological mechanisms of aging-related genes at the single-cell level can help to understand the heterogeneous aging process and design targeted anti-aging therapeutics. Here, we built AgeAnno (https://relab.xidian.edu.cn/AgeAnno/#/), a knowledgebase of single cell annotation of aging in human, aiming to provide comprehensive characterizations for aging-related genes across diverse tissue-cell types in human by using single-cell RNA and ATAC sequencing data (scRNA and scATAC). The current version of AgeAnno houses 1 678 610 cells from 28 healthy tissue samples with ages ranging from 0 to 110 years. We collected 5580 aging-related genes from previous resources and performed dynamic functional annotations of the cellular context. For the scRNA data, we performed analyses include differential gene expression, gene variation coefficient, cell communication network, transcription factor (TF) regulatory network, and immune cell proportionc. AgeAnno also provides differential chromatin accessibility analysis, motif/TF enrichment and footprint analysis, and co-accessibility peak analysis for scATAC data. AgeAnno will be a unique resource to systematically characterize aging-related genes across diverse tissue-cell types in human, and it could facilitate antiaging and aging-related disease research.

Chelator boosted tumor-retention and pharmacokinetic properties: development of 64Cu labeled radiopharmaceuticals targeting neurotensin receptor.

PURPOSE: Accumulating evidence suggests that neurotensin (NTS) and neurotensin receptors (NTSRs) play key roles in lung cancer progression by triggering multiple oncogenic signaling pathways. This study aims to develop Cu-labeled neurotensin receptor 1 (NTSR1)-targeting agents with the potential for both imaging and therapeutic applications. METHOD: A series of neurotensin receptor antagonists (NRAs) with variable propylamine (PA) linker length and different chelators were synthesized, including [64Cu]Cu-CB-TE2A-iPA-NRA ([64Cu]Cu-4a-c, i = 1, 2, 3), [64Cu]Cu-NOTA-2PA-NRA ([64Cu]Cu-4d), [64Cu]Cu-DOTA-2PA-NRA ([64Cu]Cu-4e, also known as [64Cu]Cu-3BP-227), and [64Cu]Cu-DOTA-VS-2PA-NRA ([64Cu]Cu-4f). The series of small animal PET/CT were conducted in H1299 lung cancer model. The expression profile of NTSR1 was also confirmed by IHC using patient tissue samples. RESULTS: For most of the compounds studied, PET/CT showed prominent tumor uptake and high tumor-to-background contrast, but the tumor retention was strongly influenced by the chelators used. For previously reported 4e, [64Cu]Cu-labeled derivative showed initial high tumor uptake accompanied by rapid tumor washout at 24 h. The newly developed [64Cu]Cu-4d and [64Cu]Cu-4f demonstrated good tumor uptake and tumor-to-background contrast at early time points, but were less promising in tumor retention. In contrast, our lead compound [64Cu]Cu-4b demonstrated 9.57 ± 1.35, 9.44 ± 2.38 and 9.72 ± 4.89%ID/g tumor uptake at 4, 24, and 48 h p.i., respectively. Moderate liver uptake (11.97 ± 3.85, 9.80 ± 3.63, and 7.72 ± 4.68%ID/g at 4, 24, and 48 h p.i.) was observed with low uptake in most other organs. The PA linker was found to have a significant effect on drug distribution. Compared to [64Cu]Cu-4b, [64Cu]Cu-4a had a lower background, including a greatly reduced liver uptake, while the tumor uptake was only moderately reduced. Meanwhile, [64Cu]Cu-4c showed increased uptake in both the tumor and the liver. The clinical relevance of NTSR1 was also demonstrated by the elevated tumor expression in patient tissue samples. CONCLUSIONS: Through the side-by-side comparison, [64Cu]Cu-4b was identified as the lead agent for further evaluation based on its high and sustained tumor uptake and moderate liver uptake. It can not only be used to efficiently detect NTSR1 expression in lung cancer (for diagnosis, patient screening, and treatment monitoring), but also has the great potential to treat NTSR-positive lesions once chelating to the beta emitter 67Cu.

Inferring evolutionary trajectories from cross-sectional transcriptomic data to mirror lung adenocarcinoma progression.

Lung adenocarcinoma (LUAD) is a deadly tumor with dynamic evolutionary process. Although much endeavors have been made in identifying the temporal patterns of cancer progression, it remains challenging to infer and interpret the molecular alterations associated with cancer development and progression. To this end, we developed a computational approach to infer the progression trajectory based on cross-sectional transcriptomic data. Analysis of the LUAD data using our approach revealed a linear trajectory with three different branches for malignant progression, and the results showed consistency in three independent cohorts. We used the progression model to elucidate the potential molecular events in LUAD progression. Further analysis showed that overexpression of BUB1B, BUB1 and BUB3 promoted tumor cell proliferation and metastases by disturbing the spindle assembly checkpoint (SAC) in the mitosis. Aberrant mitotic spindle checkpoint signaling appeared to be one of the key factors promoting LUAD progression. We found the inferred cancer trajectory allows to identify LUAD susceptibility genetic variations using genome-wide association analysis. This result shows the opportunity for combining analysis of candidate genetic factors with disease progression. Furthermore, the trajectory showed clear evident mutation accumulation and clonal expansion along with the LUAD progression. Understanding how tumors evolve and identifying mutated genes will help guide cancer management. We investigated the clonal architectures and identified distinct clones and subclones in different LUAD branches. Validation of the model in multiple independent data sets and correlation analysis with clinical results demonstrate that our method is effective and unbiased.

CCPE: cell cycle pseudotime estimation for single cell RNA-seq data.

Pseudotime analysis from scRNA-seq data enables to characterize the continuous progression of various biological processes, such as the cell cycle. Cell cycle plays an important role in cell fate decisions and differentiation and is often regarded as a confounder in scRNA-seq data analysis when analyzing the role of other factors. Therefore, accurate prediction of cell cycle pseudotime and identification of cell cycle stages are important steps for characterizing the development-related biological processes. Here, we develop CCPE, a novel cell cycle pseudotime estimation method to characterize cell cycle timing and identify cell cycle phases from scRNA-seq data. CCPE uses a discriminative helix to characterize the circular process of the cell cycle and estimates each cell's pseudotime along the cell cycle. We evaluated the performance of CCPE based on a variety of simulated and real scRNA-seq datasets. Our results indicate that CCPE is an effective method for cell cycle estimation and competitive in various applications compared with other existing methods. CCPE successfully identified cell cycle marker genes and is robust to dropout events in scRNA-seq data. Accurate prediction of the cell cycle using CCPE can also effectively facilitate the removal of cell cycle effects across cell types or conditions.

Real-time prediction of organ failures in patients with acute pancreatitis using longitudinal irregular data.

It is extremely important to identify patients with acute pancreatitis who are at high risk for developing persistent organ failures early in the course of the disease. Due to the irregularity of longitudinal data and the poor interpretability of complex models, many models used to identify acute pancreatitis patients with a high risk of organ failure tended to rely on simple statistical models and limited their application to the early stages of patient admission. With the success of recurrent neural networks in modeling longitudinal medical data and the development of interpretable algorithms, these problems can be well addressed. In this study, we developed a novel model named Multi-task and Time-aware Gated Recurrent Unit RNN (MT-GRU) to directly predict organ failure in patients with acute pancreatitis based on irregular medical EMR data. Our proposed end-to-end multi-task model achieved significantly better performance compared to two-stage models. In addition, our model not only provided an accurate early warning of organ failure for patients throughout their hospital stay, but also demonstrated individual and population-level important variables, allowing physicians to understand the scientific basis of the model for decision-making. By providing early warning of the risk of organ failure, our proposed model is expected to assist physicians in improving outcomes for patients with acute pancreatitis.

ExonSkipDB: functional annotation of exon skipping event in human.

Exon skipping (ES) is reported to be the most common alternative splicing event due to loss of functional domains/sites or shifting of the open reading frame (ORF), leading to a variety of human diseases and considered therapeutic targets. To date, systematic and intensive annotations of ES events based on the skipped exon units in cancer and normal tissues are not available. Here, we built ExonSkipDB, the ES annotation database available at https://ccsm.uth.edu/ExonSkipDB/, aiming to provide a resource and reference for functional annotation of ES events in multiple cancer and tissues to identify therapeutically targetable genes in individual exon units. We collected 14 272 genes that have 90 616 and 89 845 ES events across 33 cancer types and 31 normal tissues from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx). For the ES events, we performed multiple functional annotations. These include ORF assignment of exon skipped transcript, studies of lost protein functional features due to ES events, and studies of exon skipping events associated with mutations and methylations based on multi-omics evidence. ExonSkipDB will be a unique resource for cancer and drug research communities to identify therapeutically targetable exon skipping events.

Systematically understanding the immunity leading to CRPC progression.

Prostate cancer (PCa) is the most commonly diagnosed malignancy and the second leading cause of cancer-related death in American men. Androgen deprivation therapy (ADT) has become a standard treatment strategy for advanced PCa. Although a majority of patients initially respond to ADT well, most of them will eventually develop castration-resistant PCa (CRPC). Previous studies suggest that ADT-induced changes in the immune microenvironment (mE) in PCa might be responsible for the failures of various therapies. However, the role of the immune system in CRPC development remains unclear. To systematically understand the immunity leading to CRPC progression and predict the optimal treatment strategy in silico, we developed a 3D Hybrid Multi-scale Model (HMSM), consisting of an ODE system and an agent-based model (ABM), to manipulate the tumor growth in a defined immune system. Based on our analysis, we revealed that the key factors (e.g. WNT5A, TRAIL, CSF1, etc.) mediated the activation of PC-Treg and PC-TAM interaction pathways, which induced the immunosuppression during CRPC progression. Our HMSM model also provided an optimal therapeutic strategy for improving the outcomes of PCa treatment.

Development of a radiomics nomogram based on the 2D and 3D CT features to predict the survival of non-small cell lung cancer patients.

OBJECTIVES: The aim of this study was to develop a radiomics nomogram by combining the optimized radiomics signatures extracted from 2D and/or 3D CT images and clinical predictors to assess the overall survival of patients with non-small cell lung cancer (NSCLC). METHODS: One training cohort of 239 and two validation datasets of 80 and 52 NSCLC patients were enrolled in this study. Nine hundred seventy-five radiomics features were extracted from each patient's 2D and 3D CT images. Least absolute shrinkage and selection operator (LASSO) regression was used to select features and generate a radiomics signature. Cox hazard survival analysis and Kaplan-Meier were performed in both cohorts. The radiomics nomogram was developed by integrating the optimized radiomics signature and clinical predictors, its calibration and discrimination were evaluated. RESULTS: The radiomics signatures were significantly associated with NSCLC patients' survival time. The signature derived from the combined 2D and 3D features showed a better prognostic performance than those from 2D or 3D alone. Our radiomics nomogram integrated the optimal radiomics signature with clinical predictors showed a significant improvement in the prediction of patients' survival compared with clinical predictors alone in the validation cohort. The calibration curve showed predicted survival time was very close to the actual one. CONCLUSIONS: The radiomics signature from the combined 2D and 3D features further improved the predicted accuracy of survival prognosis for the patients with NSCLC. Combination of the optimal radiomics signature and clinical predictors performed better for individualied survival prognosis estimation in patients with NSCLC. These findings might affect trearment strategies and enable a step forward for precise medicine. KEY POINTS: • We found both 2D and 3D radiomics signature have favorable prognosis, but 3D signature had a better performance. • The radiomics signature generated from the combined 2D and 3D features had a better predictive performance than those from 2D or 3D features. • Integrating the optimal radiomics signature with clinical predictors significantly improved the predictive power in patients' survival compared with clinical TNM staging alone.

Deep learning of the splicing (epi)genetic code reveals a novel candidate mechanism linking histone modifications to ESC fate decision.

Alternative splicing (AS) is a genetically and epigenetically regulated pre-mRNA processing to increase transcriptome and proteome diversity. Comprehensively decoding these regulatory mechanisms holds promise in getting deeper insights into a variety of biological contexts involving in AS, such as development and diseases. We assembled splicing (epi)genetic code, DeepCode, for human embryonic stem cell (hESC) differentiation by integrating heterogeneous features of genomic sequences, 16 histone modifications with a multi-label deep neural network. With the advantages of epigenetic features, DeepCode significantly improves the performance in predicting the splicing patterns and their changes during hESC differentiation. Meanwhile, DeepCode reveals the superiority of epigenomic features and their dominant roles in decoding AS patterns, highlighting the necessity of including the epigenetic properties when assembling a more comprehensive splicing code. Moreover, DeepCode allows the robust predictions across cell lineages and datasets. Especially, we identified a putative H3K36me3-regulated AS event leading to a nonsense-mediated mRNA decay of BARD1. Reduced BARD1 expression results in the attenuation of ATM/ATR signalling activities and further the hESC differentiation. These results suggest a novel candidate mechanism linking histone modifications to hESC fate decision. In addition, when trained in different contexts, DeepCode can be expanded to a variety of biological and biomedical fields.

Radiation-induced lung injury: impact on macrophage dysregulation and lipid alteration - a review.

Lung cancer continues to be the leading cause of cancer deaths and more than one million lung cancer patients will die every year worldwide. Radiotherapy (RT) plays an important role in lung cancer treatment, but the side effects of RT are pneumonitis and pulmonary fibrosis. RT-induced lung injury causes damage to alveolar-epithelial cells and vascular endothelial cells. Macrophages play an important role in the development of pulmonary fibrosis despite its role in immune response. These injury activated macrophages develop into classically activated M1 macrophage or alternative activated M2 macrophage. It secretes cytokines, interleukins, interferons, and nitric oxide. Several pro-inflammatory lipids and pro-apoptotic proteins cause lipotoxicity such as LDL, FC, DAG, and FFA. The overall findings in this review conclude the importance of macrophages in inducing toxic/inflammatory effects during RT of lung cancer, which is clinically vital to treat the radiation-induced fibrosis.

Annotating function to differentially expressed LincRNAs in myelodysplastic syndrome using a network-based method.

MOTIVATION: Long non-coding RNAs (lncRNAs) have been implicated in the regulation of diverse biological functions. The number of newly identified lncRNAs has increased dramatically in recent years but their expression and function have not yet been described from most diseases. To elucidate lncRNA function in human disease, we have developed a novel network based method (NLCFA) integrating correlations between lncRNA, protein coding genes and noncoding miRNAs. We have also integrated target gene associations and protein-protein interactions and designed our model to provide information on the combined influence of mRNAs, lncRNAs and miRNAs on cellular signal transduction networks. RESULTS: We have generated lncRNA expression profiles from the CD34+ haematopoietic stem and progenitor cells (HSPCs) from patients with Myelodysplastic syndromes (MDS) and healthy donors. We report, for the first time, aberrantly expressed lncRNAs in MDS and further prioritize biologically relevant lncRNAs using the NLCFA. Taken together, our data suggests that aberrant levels of specific lncRNAs are intimately involved in network modules that control multiple cancer-associated signalling pathways and cellular processes. Importantly, our method can be applied to prioritize aberrantly expressed lncRNAs for functional validation in other diseases and biological contexts. AVAILABILITY AND IMPLEMENTATION: The method is implemented in R language and Matlab. CONTACT: xizhou@wakehealth.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Modeling co-occupancy of transcription factors using chromatin features.

Regulation of gene expression requires both transcription factor (TFs) and epigenetic modifications, and interplays between the two types of factors have been discovered. However study of relationships between chromatin features and TF-TF co-occupancy remains limited. Here, we revealed the relationship by first illustrating distinct profile patterns of chromatin features related to different binding events, including single TF binding and TF-TF co-occupancy of 71 TFs from five human cell lines. We further implemented statistical analyses to demonstrate the relationship by accurately predicting co-occupancy genome-widely using chromatin features including DNase I hypersensitivity, 11 histone modifications (HMs) and GC content. Remarkably, our results showed that the combination of chromatin features enables accurate predictions across the five cells. For individual chromatin features, DNase I enables high and consistent predictions. H3K27ac, H3K4me 2, H3K4me3 and H3K9ac are more reliable predictors than other HMs. Although the combination of 11 HMs achieves accurate predictions, their predictive ability varies considerably when a model obtained from one cell is applied to others, indicating relationship between HMs and TF-TF co-occupancy is cell type dependent. GC content is not a reliable predictor, but the addition of GC content to any other features enhances their predictive ability. Together, our results elucidate a strong relationship between TF-TF co-occupancy and chromatin features.

OCIAD2 suppressed tumor growth and invasion via AKT pathway in Hepatocelluar carcinoma.

Hepatocellular carcinoma (HCC) is an aggressive tumor and the third leading cause of cancer-related death worldwide. Ovarian carcinoma immunoreactive antigen-like protein 2 (OCIAD2) has been found frequently methylated in various cancers, including HCC. The aim of the present study was to investigate the role of OCIAD2 in HCC progression. We analyzed liver hepatocellular carcinoma patients' data from the Cancer Genome Atlas (TCGA), including data extracted from 371 HCC tissues and 50 adjacent normal liver tissues. The RNA sequencing and DNA methylation data revealed that OCIAD2 were significantly hypermethylated and its expression level in the tumor tissues was much lower than that in the corresponding adjacent normal tissues. The methylation level in the promoter was negatively correlated with the expression level of OCAID2. Treatment of HCC cell lines with the DNA methylation inhibitor 5-aza-2'-deoxycitydine (5-Aza) induced a significant increase in the OCIAD2 mRNA and protein. Knocking-down OCIAD2 led to an increased colony formation, migration and invasion dramatically, accompanying with an enhanced expression of MMP9 and activation of AKT and FAK. Inhibition of AKT signaling restored OCIAD2-mediated changes in HCC cell clonogenic growth, migration and invasion. Survival analysis of HCC patient's data indicated patients with a higher expression ratio of OCIAD2/MMP9 had a shorter overall survival than those with a lower expression ratio of OCIAD2/MMP9. Overall, our data indicate that reduced expression of OCIAD2 by DNA hypermethylation plays an important role in HCC tumor growth and invasion. Hypermethylation of OCIAD2 may contribute to HCC treatment development.

Immune signaling-based Cascade Propagation approach re-stratifies HNSCC patients.

The availability of high-throughput genomic assays and rich electronic medical records allows us to identify cancer subtypes with greater accuracy and resolution. The integration of multiplatform, heterogenous, and high dimensional data remains an enormous challenge in using big data in bioinformatics research. Previous methods have been developed for patient stratification, however, these approaches did not incorporate prior knowledge and offer limited biology insight. New computational methods are needed to better utilize multiple types of information to identify clinically meaningful subtypes. Recent studies have shown that many immune functional genes are associated with cancer progression, recurrence and prognosis in head and neck squamous cell carcinoma (HNSCC). Therefore, we developed a novel immune signaling based Cascade Propagation (CasP) subtyping approach to stratify HNSCC patients. Unlike previous stratification methods that use only patient genomic data, our approach makes use of prior biological information such as immune signaling and protein-protein interactions, as well as patient survival information. CasP is a multi-step stratification procedure, composed of a dynamic network tree cutting step followed by a mutational stratification step. Using this approach, HNSCC patients were first stratified into clinically relative subgroups with different survival outcomes and distinct immunogenic features. We found that the good outcome of a subgroup of HNSCC patients was due to an enhanced immune response. The gene sets were characterized by a significant activation of T cell receptor signaling pathways, in addition to other important cancer related pathways such as PI3K and JAK/STAT signaling pathways. Further stratification of patients based on somatic mutation profiles detected three survival-distinct subnetworks. Our newly developed CasP subtyping approach allowed us to integrate multiple data types and identify clinically relevant subtypes of HNSCC patients.

CD138-negative myeloma cells regulate mechanical properties of bone marrow stromal cells through SDF-1/CXCR4/AKT signaling pathway.

As the second most prevalent hematologic malignancy, multiple myeloma (MM) remains incurable and relapses due to intrinsic or acquired drug resistance. Therefore, new therapeutic strategies that target molecular mechanisms responsible for drug resistance are attractive. Interactions of tumor cells with their surrounding microenvironment impact tumor initiation, progression and metastasis, as well as patient prognosis. This cross-talk is bidirectional. Tumor cells can also attract or activate tumor-associated stromal cells by releasing cytokines to facilitate their growth, invasion and metastasis. The effect of myeloma cells on bone marrow stromal cells (BMSCs) has not been well studied. In our study, we found that higher stiffness of BMSCs was not a unique characteristic of BMSCs from MM patients (M-BMSCs). BMSCs from MGUS (monoclonal gammopathy of undetermined significance) patients were also stiffer than the BMSCs from healthy volunteers (N-BMSCs). The stiffness of M-BMSCs was enhanced when cocultured with myeloma cells. In contrast, no changes were seen in myeloma cell-primed MGUS- and N-BMSCs. Interestingly, our data indicated that CD138⁻ myeloma cells, but not CD138⁺ cells, regulated M-BMSC stiffness. SDF-1 was highly expressed in the CD138⁻ myeloma subpopulation compared with that in CD138⁺ cells. Inhibition of SDF-1 using AMD3100 or knocking-down CXCR4 in M-BMSCs blocked CD138⁻ myeloma cells-induced increase in M-BMSC stiffness, suggesting a crucial role of SDF-1/CXCR4. AKT inhibition attenuated SDF-1-induced increases in M-BMSC stiffness. These findings demonstrate, for the first time, CD138⁻ myeloma cell-directed cross-talk with BMSCs and reveal that CD138⁻ myeloma cells regulate M-BMSC stiffness through SDF-1/CXCR4/AKT signaling.

Strained cycloalkynes as new protein sulfenic acid traps.

Protein sulfenic acids are formed by the reaction of biologically relevant reactive oxygen species with protein thiols. Sulfenic acid formation modulates the function of enzymes and transcription factors either directly or through the subsequent formation of protein disulfide bonds. Identifying the site, timing, and conditions of protein sulfenic acid formation remains crucial to understanding cellular redox regulation. Current methods for trapping and analyzing sulfenic acids involve the use of dimedone and other nucleophilic 1,3-dicarbonyl probes that form covalent adducts with cysteine-derived protein sulfenic acids. As a mechanistic alternative, the present study describes highly strained bicyclo[6.1.0]nonyne (BCN) derivatives as concerted traps of sulfenic acids. These strained cycloalkynes react efficiently with sulfenic acids in proteins and small molecules yielding stable alkenyl sulfoxide products at rates more than 100× greater than 1,3-dicarbonyl reagents enabling kinetic competition with physiological sulfur chemistry. Similar to the 1,3-dicarbonyl reagents, the BCN compounds distinguish the sulfenic acid oxoform from the thiol, disulfide, sulfinic acid, and S-nitrosated forms of cysteine while displaying an acceptable cell toxicity profile. The enhanced rates demonstrated by these strained alkynes identify them as new bioorthogonal probes that should facilitate the discovery of previously unknown sulfenic acid sites and their parent proteins.

Revealing chronic disease progression patterns using Gaussian process for stage inference.

OBJECTIVE: The early stages of chronic disease typically progress slowly, so symptoms are usually only noticed until the disease is advanced. Slow progression and heterogeneous manifestations make it challenging to model the transition from normal to disease status. As patient conditions are only observed at discrete timestamps with varying intervals, an incomplete understanding of disease progression and heterogeneity affects clinical practice and drug development. MATERIALS AND METHODS: We developed the Gaussian Process for Stage Inference (GPSI) approach to uncover chronic disease progression patterns and assess the dynamic contribution of clinical features. We tested the ability of the GPSI to reliably stratify synthetic and real-world data for osteoarthritis (OA) in the Osteoarthritis Initiative (OAI), bipolar disorder (BP) in the Adolescent Brain Cognitive Development Study (ABCD), and hepatocellular carcinoma (HCC) in the UTHealth and The Cancer Genome Atlas (TCGA). RESULTS: First, GPSI identified two subgroups of OA based on image features, where these subgroups corresponded to different genotypes, indicating the bone-remodeling and overweight-related pathways. Second, GPSI differentiated BP into two distinct developmental patterns and defined the contribution of specific brain region atrophy from early to advanced disease stages, demonstrating the ability of the GPSI to identify diagnostic subgroups. Third, HCC progression patterns were well reproduced in the two independent UTHealth and TCGA datasets. CONCLUSION: Our study demonstrated that an unsupervised approach can disentangle temporal and phenotypic heterogeneity and identify population subgroups with common patterns of disease progression. Based on the differences in these features across stages, physicians can better tailor treatment plans and medications to individual patients.