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Further comparative studies on the biological and biochemical features of virus-derived transplantable and chemically induced hepatomas may contribute to the knowledge of human hepatomas. Evidence for the reprogramming of gene expression found in chemically induced transplantable hepatomas [22] was also found in this virus-derived hepatoma.
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Cathepsin D, an enzyme consistently found to be lysosomal in many cells, has an unusual localization in rat thoracic duct lymphocytes (TDL). After fractionation of homogenates of rat TDL, most of the enzyme activity, as measured at pH 3.6 on denatured bovine hemoglobin, is distributed differently from the other lysosomal enzymes. The enzyme also has some unique properties: it is not inhibited by an antiserum inhibitory for rat liver cathepsin D; it exists in two molecular weight forms (approximately 45,000 and approximately 95,000) both of which have a higher specific activity than rat liver cathepsin D, as determined by studies using the irreversible inhibitor, sodium pepstatin; the high molecular weight form converts to the low molecular weight form after treatment with beta-mercaptoethanol without any loss in activity. These enzymes appear to be restricted to rodent lymphoid tissues. Reasons for considering them to be a type of cathepsin D are given in the text.
Congenitally athymic BALB/cA nu/nu mice were employed to elucidate the role of the thymus in experimental Mycoplasma pulmonis strain m53 infection, and nu/+ mice were used for comparison. Chronic polyarthritis was frequently produced in both of nu/nu and nu/+ mice by intravenous injection of the organisms. Macroscopically, nu/nu mice developed severer arthritis and a much lower grade of resolution than nu/+ mice. Periarticular abscess, conjunctivitis, and emaciation were observed in some of the nu/nu mice, but not in the nu/+ mice. Mycoplasmas were isolated from joints and other tissues (including periarticular abscesses and eyelids) of infected nu/nu mice at higher frequencies as well as in greater quantities, and did not show any elimination trends for at least 20 weeks after inoculation. However, nu/+ mice, mycoplasmas were almost exclusively located in joints, and distribution of organisms to the other organs disappeared soon after the infection. Increases in complement-fixing antibody titers were not related to the inhibition of mycoplasmal spread. Thymus-dependent functions that may in some way prevent growth and spread of mycoplasmas in mice are discussed.
A female Macaca mulatta was observed for 31 months after the initial surgical removal of an ovarian tumor. Solitary metastatic lesions were surgically removed 26 and 28 months after excision of the primary tumor. The animal was killed after 31 months because of additional metastatic lesions. Histological evaluation by light microscopy was not conclusive in determining the origin of neoplasm. Transmission electron microscopy, lymphocyte marker studies, and hormone assays were utilized to confirm the diagnosis of dysgerminoma.
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Consensus molecular subtype 4 (CMS4) colorectal cancer (CRC) is associated with an aggressive clinical course and poor survival, yet the biological basis of heterogeneity within this subtype remains incompletely understood. DNA methylation is an epigenetic mechanism involved in transcriptional regulation, cellular differentiation, and colorectal tumorigenesis. Here, we integrated single-cell RNA sequencing (scRNA-seq), bulk data, and promoter DNA methylation data to characterize CMS4-associated cancer cell states and methylation-related features. Using the scAB algorithm, we integrated scRNA-seq with bulk CMS4 data and identified CMS4-related cells distributed across multiple patients. Single-cell analyses of cell-cell communication and transcriptional regulation revealed a CMS4-related cancer cell population characterized by macrophage migration inhibitory factor (MIF)-centered intercellular communication, enhanced caudal type homeobox 1 (CDX1) and Kruppel-like factor 5 (KLF5) regulon activity, and gene modules enriched in differentiation-related pathways. CytoTRACE analysis further stratified CMS4 cancer cells into poorly and well-differentiated states, yielding 802 differentially expressed genes (DEGs). Linking these differentiation-associated DEGs with bulk expression and promoter methylation data identified 218 methylation-associated DEGs showing significant inverse methylation expression correlations, suggesting a link between differentiation-related heterogeneity and promoter methylation. Univariable Cox regression followed by LASSO regression further prioritized eight genes for construction of the methylation and differentiation-related prognostic model (MeDiff-PM). MeDiff-PM consistently stratified overall survival in the TCGA CMS4 cohort and two independent validation cohorts, with cutoff-independent continuous Cox analyses further supporting its prognostic association across cohorts. And MeDiff-PM remained prognostically significant after adjustment for available clinical variables. High MeDiff-PM risk scores were associated with activation of P53, WNT, and ubiquitin-mediated proteolysis pathways and with consistent predicted drug response differences for compounds across three CMS4 cohorts. While individual in silico knockout analysis suggested links between MeDiff-PM genes and metallothionein-related and immune-associated transcriptional responses. Collectively, these findings indicate that methylation-associated differentiation features represent a molecular dimension of intra-CMS4 heterogeneity and provide a biologically informed framework for prognostic stratification within CMS4 CRC.
It is suggested that the amount of energy extracted by a dissipative biological system from macroergic compounds depends on the energetic state of the system. The non-linear character of this relationship with the maximum in intermediate phases and presence of the upper limit of energetic states in the system with an energy increase cause a decrease of population of intermediate levels and an increase of the upper and lower levels. The antientropic distribution is presented as a principal thermodynamic peculiarity of biological systems.
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BACKGROUND: Extramammary Paget's disease (EMPD) is a rare malignancy without established systemic therapy. EMPD shares molecular features with breast cancer, such as human epidermal growth factor receptor 2 (HER2) and hormone receptor (HR) expression, but their clinical relevance remains unclear. MATERIALS AND METHODS: Tumors from 20 metastatic invasive EMPD cases were analyzed for molecular and biological features. Genomic features, transcriptomic profiles, and HER2 and HR expression status were investigated using immunohistochemistry, fluorescence in situ hybridization, and targeted-genome next-generation sequencing and nCounter BC360 panels. Metastatic breast cancer samples were used as a comparison to clarify metastatic EMPD's clinical relevance. RESULTS: Estrogen receptor expression was observed in 45% of EMPD tumors, while only 10% expressed progesterone receptor. HER2 was overexpressed in 30% of cases, and HER2-directed therapies were durably effective. Among 8 patients with NGS data, 63% (5/8) harbored oncogenic ERBB2 alterations independent of HER2 expression. BC360 profiling revealed biological differences between EMPD and breast cancer, particularly poor biological compatibility for HR-positive tumors. Immune profiling showed that a subset of EMPD tumors exhibited CD8+ T-cell signatures and PD-1/PD-L1 gene expression comparable to triple-negative breast cancer. The median overall survival was 22.1 months (95% CI, 12.0-42.2), with 16 patients (80%) treated with systemic therapy, including anti-HER2 therapy, hormonal therapy, or cytotoxic therapies based on their molecular features. CONCLUSIONS: This study highlights the unique molecular and biological features of metastatic EMPD, emphasizing the need for tailored treatment approaches. This information should be used to guide future clinical strategies for metastatic EMPD.
BACKGROUND: Accurate prediction of postoperative recurrence in lung adenocarcinoma (LUAD) is essential for guiding clinical decision-making and improving patient outcomes. Although various predictive models have been developed, most rely on complex genomic analyses and high-dimensional clinical data. The complexity of these approaches substantially limits their feasibility for routine clinical use. To address this clinical challenge, this study aims to predict postoperative recurrence using routinely available hematoxylin and eosin (H&E)-stained images and characterize the associated biological features. METHODS: A total of 329 patients who underwent curative resection at the First Affiliated Hospital of Wenzhou Medical University (FHWMU) were retrospectively enrolled and randomly assigned to training and internal validation cohorts in a 7:3 ratio. An independent external validation cohort comprising 70 patients from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) was included. Three patch-level feature extractors (Inception_V3, ResNet18, and DenseNet121) were evaluated within a weakly supervised multiple-instance learning (MIL) framework incorporating automated region-of-interest (ROI) detection on segmented whole-slide images (WSIs). Model performance was assessed using the area under the receiver operating characteristic curve (AUC), Kaplan-Meier (KM) survival analysis, and multivariable Cox proportional hazards regression. Transcriptomic profiling and gene set enrichment analysis (GSEA) were conducted to investigate biological differences between risk groups. RESULTS: The model achieved AUCs of 0.923 in the training cohort, 0.891 in the internal validation cohort, and 0.847 in the external validation cohort. The model effectively stratified patients into high- and low-risk groups with significantly different recurrence-free survival (RFS) across all cohorts (all P < 0.001) and retained prognostic value within AJCC stages I-III. Transcriptomic analyses revealed consistent enrichment of cell cycle-related pathways and neutrophil extracellular trap (NET) formation in high-risk patients across both institutional and CPTAC cohorts, aligning with distinct biological profiles of the model-derived risk stratification. CONCLUSIONS: This weakly supervised deep learning framework enables accurate and externally validated prediction of postoperative recurrence in LUAD using routinely available histopathological images, and integration of histopathological features with molecular analyses enhances biological interpretability. This work provides a clinically accessible and cost-effective tool for postoperative risk assessment in LUAD patients.
MOTIVATION: Genome stability is shaped by DNA sequence and chromatin context, but their relative contributions to double-strand break (DSB) sensitivity remain unclear. RESULTS: We show that the DNA language model, GROVER, can infer DSB location based on sequence. DSB hotspots tend to contain GC-rich sequences that belong to promoters, genes and short interspersed nuclear elements (SINEs). Additionally, we identified several specific short sequences (tokens) that are associated with modulating DSB sensitivity. Another model using chromatin and genome regulatory features outperforms the sequence-only model, highlighting complementary and cell-type specific information. Integrating sequence and genome biological features yields the best performance, demonstrating their synergy. Analyzing this model revealed that, dependent on the sample, genome stability information encoded in H3K36me3 and DNase-seq can be learned from the sequence, but not H3K27ac or H3K9me3. Embedding chromatin data directly into the GROVER architecture enabled cell-type specific modeling with performance matching the full chromatin feature model. Our results suggest that while chromatin and regulatory context provides important information, such as cell-type specificity, much of the information shaping DSB patterns is already encoded in the DNA sequence itself. Our integrative modeling approach not only reveals DSB patterns but also provides a generalizable strategy for tracing predictions in genomic data. AVAILABILITY: Data, models, and a tutorial are available on Zenodo.
The ability to measure gene expression at single-cell resolution has elevated our understanding of how biological features emerge from complex and interdependent networks at molecular, cellular, and tissue scales. As technologies have evolved that complement scRNAseq measurements with things like single-cell proteomic, epigenomic, and genomic information, it becomes increasingly apparent how much biology exists as a product of multimodal regulation. Biological processes such as transcription, translation, and post-translational or epigenetic modification impose both energetic and specific molecular demands on a cell and are therefore implicitly constrained by the metabolic state of the cell. While metabolomics is crucial for defining a holistic model of any biological process, the chemical heterogeneity of the metabolome makes it particularly difficult to measure, and technologies capable of doing this at single-cell resolution are far behind other multiomics modalities. To address these challenges, we present GEFMAP (Gene Expression-based Flux Mapping and Metabolic Pathway Prediction), a method based on geometric deep learning for predicting flux through reactions in a global metabolic network using transcriptomics data, which we ultimately apply to scRNAseq. GEFMAP leverages the natural graph structure of metabolic networks to learn both a biological objective for each cell and estimate a mass-balanced relative flux rate for each reaction in each cell using novel deep learning models.
The estimation of risks from exposure to carcinogens is an important problem from the viewpoint of protection of human health. It also poses some very difficult dose-response problems. Two dose-response models may fit experimental data about equally well and yet predict responses that differ by many orders of magnitude at low doses. Mechanisms of carcinogenesis are not sufficiently understood so that the shape of the dose-response curve at low doses can be satisfactorily predicted. Mathematical theories of carcinogenesis and statistical procedures can be of use with dose-reponse problems such as this and, in addition, can lead to a better understanding of the mechanisms of carcinogenesis. In this paper, mathematical dose-response models of carcinogenesis are considered as well as various proposed dose-response procedures for estimating carcinogenic risks at low doses. Areas are suggested in which further work may be useful. These areas include experimental design problems, statistical procedures for use with time-to-occurrence data, and mathematical models that incorporate such biological features as pharmacokinetics of carcinogens, synergistic effects, DNA repair, susceptible subpopulations, and immune reactions.
Five children from 3 different families presented with severe dwarfism and the morphological and biological features described by Laron: familial occurrence, small stature, peculiar facies, high levels of plasma HGH and resistance to treatment by GH. This therapeutic inefficiency is expressed by an absence of physical growth and unchanged nitrogen balance, during a prolonged treatment. The plasma levels of somatomedine were very low (K. Hall's biological method) and not influenced by administration of exogenous HGH. These findings suggest that the fundamental disorder is not an abnormal structure of the molecules of GH. This hypothesis seems further confirmed by the structural analysis of plasma HGH, which gave the same results as those of the reference GH.
Recent studies on the mechanism by which the virion-associated RNA polymerase of vesicular stomatitis virus transcribes RNA have revealed several new biological features of general interest. The mode of synthesis of the 5'-terminal cap structure of the mRNAs, the sequential transcription of the genes and the presence of a transcribed "leader" RNA segment are properties which are either not shown by other viruses, or have not yet been described. These features are probably inter-related with the primary transcription process, which itself may be a useful model for future studies on mRNA biosynthesis in eukaryotic systems.