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The principal component analysis of 21 chlorella cell survival curves, adjusted by one-hit and two-hit target models, lead to quite similar projections on the principal plan: the homologous parameters of these models are linearly correlated; the reason for the statistical equivalence of these two models, in the present state of experimental inaccuracy, is revealed.
The survival curves obtained when cellular recovery follows various first radiation dose deliveries DI seem, when semi-logarithmically plotted, to be translated from the part of the curve corresponding to an unfractionated irradiation beyond a dose DR. A possible assumption consistent with such experimental observations is proposed which allows the generalization of any survival model S = f (D). The derived equation S = f (DR + D - DI) f (DI)/f (DR) is convenient for the whole family of experimental survival curves involving cellular damage repairs when the first radiation doses vary. All the parameters of the family equation can be simultaneously fitted so that their reliability is increased. The generalized equations are given for the four following models: two-hits targets, Chadwick and Leenhouts, Green and Burki, Wideröe. As an example, the Chadwick and Leenhouts generalized model parameters are fitted to a family of experimental survival curves concerning Chlorella cells exposed to fractionated and continuous gamma irradiation. The fittings are presented with their confidence limits and are briefly discussed.
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Candida albicans exhibits substantial phenotypic and ecological diversity; however, the exact relationship between its population structure, adaptation to specific niches, and antifungal resistance remains incompletely understood. To investigate these evolutionary dynamics, we analyzed the whole-genome sequences from 591 publicly available isolates, integrating nuclear and mitochondrial phylogenomics with ecological and resistance-associated genomic analyses. Phylogenomic analyses resolved 18 core nuclear clusters together with multiple admixed lineages. Strong cytonuclear concordance was noted in the majority of the central lineages, contrasting with a higher discordance among the admixed groups, consistent with recurrent genetic exchange. The analysis revealed that geographic origin explains a larger fraction of genetic variance than anatomical niche, supporting a predominantly generalist population structure. A notable exception was Cluster N16 (Candida africana), which presented a strict genital origin in our dataset (n = 34). Additionally, although the mitochondrial genome exhibits strong purifying selection, candidate residues under diversifying selection correlated with specific niches (e.g., bloodstream) have been identified. Analysis of five resistance-associated genes (ERG11, UPC2, FKS1, TAC1 and FUR1) revealed that resistance-associated variants were generally rare but exhibited distinct gene-specific patterns. In case of ERG11 and FUR1 they were concentrated in a specific clade (N11, N17, and their admixed Group A) and exhibit gene-dependent zygosity patterns. In summary, the evolution of C. albicans appears to be driven by a predominantly clonal model punctuated by episodic genetic exchange, where both ecological adaptation and antifungal resistance mutations exhibit genomic signatures marked by lineage specificity.
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A "recptor unit" for gamma-aminobutyric acid (GABA), which includes brainlike receptor binding sites for tritium-labeled GABA and benzodiazepines (diazepam, clonazepam, and flunitrazepam) and a thermostable endogenous protein (GABA modulin) that inhibits both GABA and benzodiazepine binding, has been demonstrated in membranes prepared from NB2a neuroblastoma and C6 glioma clonal cell lines. In these cells, as in brain, diazepam (1 micromolar) prevents the effect of GABA modulin, and in turn GABA (0.oma and, to a lesser extent, the glioma cells represent a suitable model to study the interactions and the sequence of membrane and intracellular events triggered by the stimulation of benzodiazepine and GABA receptors.
BACKGROUND: Gene therapy (GT) using retroviral vectors (RVs) is efficacious in treating monogenic diseases. However, there is an inherent risk for severe adverse effects due to insertional mutagenesis. Preclinical safety assessment and patient monitoring are inevitable in GT. To assess the genotoxic risk of novel RV vectors, mainly murine hematopoietic stem and progenitor cells (HPSCs) are routinely used, because human HSPCs cannot be immortalized in vitro using mutagenic vectors. In this study, we aim to identify early signs of clonal outgrowth by performing integration site analyses (ISA). METHODS: The small molecules A83-01, pomalidomide, and UM171 (APU) were used for the ex vivo expansion, lentiviral transduction, and long-term cultivation of umbilical cord blood-derived HSPCs. We determined the influence of APU on the stemness of HSPCs and their differentiation capacity via single-cell RNA sequencing (scRNA seq) and in xenotransplantation studies. To track vector insertion site dynamics, we transduced 7-day expanded HSPCs with a mutagenic or a safer RV. ISA was conducted in human HSPCs over a 5-week cultivation in vitro and compared to the bone marrow of xenotransplanted mice to assess clonal skewings. RESULTS: APU supported the expansion of CD34+CD38-CD45RA-CD90+EPCR+ HSPCs. scRNA seq confirmed the enrichment of HSC signature genes in APU-expanded HSPCs compared to the clinically used medium SFT3 (SCF, FLT3-L, TPO, IL-3). After RV transduction, APU still maintained around 30% of CD34+ cells for 5 more weeks. Without the compounds, already 2 weeks post-transduction, less than 10% of cells were CD34+. The long-term culture allowed the detection of high-risk integrations of the mutagenic SIN-LV.SF in MEIS1 or SUSD6 due to their increasing abundance over time. Bone marrow of xenotransplanted mice was less clonal but did not support the outgrowth of insertional mutants. Overall, APU increased clonal diversity. CONCLUSIONS: Our findings propose that long-term cultivation of transduced HSPC in APU allows for outgrowth of clonal integration sites. The decrease of clonality has been observed in gene therapy patient's years after treatment. Thus, the in vitro model could be used to develop novel human HSPC-based genotoxicity assays that predict insertional mutagenesis, in addition to existing preclinical biosafety assays.
Partitioning of genetic variance into additive and non-additive components using the pedigree-based best linear unbiased prediction (P-BLUP) model is possible because of the family structure and replicated clones in clonally propagated crops, but this model may overestimate these components. However, the genomic best linear unbiased prediction (G-BLUP) method, which integrates the genetic relationship through molecular marker information reduces the overestimation. Alternatively, a combination of the P-BLUP and G-BLUP, sourcing to create a hybrid matrix that estimates hybrid best linear unbiased prediction (H-BLUP), is proposed. We investigated if integrating molecular information into the clonal model could improve the partitioning of the variance components leading to more accurate estimates of genetic parameters and prediction accuracy of breeding values of 14 key traits in diploid banana. In this study, we used clones of 14 full-sib families from a factorial mating design of four female and five diploid male banana (Musa acuminata) parents, generated at the International Institute of Tropical Agriculture in Arusha. The genomic-based relationship matrices were constructed using a set of 2792 filtered single-nucleotide polymorphism markers. Additive variance and heritability derived from G-BLUP and H-BLUP models reduced bias compared to the P-BLUP model. The H-BLUP estimated the highest prediction accuracies for yield-related and cycling traits, while the P-BLUP model had the highest prediction accuracy estimates for agronomic traits. The use of marker-based models enhances the accuracy of predicting breeding values, contributing to accurate estimates of genetic gain while paving a way for further genomic exploration in diploid banana breeding programs.
1. Clonal theories of disease, particularly progressive clonal growth and selection in tumorogenesis, were briefly cited. 2. Evidence for the clonal nature of PNH was presented. Correlation of red cell hemolysis with (a) G-6-PD type in two female G-6-PD mosaics with PNH and with (b) erythrocyte acetylcholinesterase deficiency, provides strong evidence for the clonal theory of PNH. 3. Possible pitfalls in defining "hidden PNH clones" in other diseases by the use of PNH hemolytic tests were discussed. 4. The potential of PNH as a study model for clonal evolution in human disease was emphasized.
Understanding the genetic factors facilitating emergence of infectious diseases is critical, however, mechanisms underlying expansion of pathogenic bacterial variants remain unclear. Here we performed a large-scale genomic analysis of 44,597 Salmonella Typhimurium genomes and observe that sequential gene loss in monophasic Salmonella Typhimurium (mSTM) ST34 explains its clonal expansion as an increasingly prevalent zoonotic lineage. Functional and in vivo competition experiments show that a frameshift mutation in dinB, a polymerase for translesion DNA synthesis, leads to transcriptional changes affecting flagellin gene expression and subsequent loss of the flagellin-encoding fljB, altering the requirements for gut infection. Temporal evolutionary modelling supports a role for gene loss events in a specific chronological order for mSTM ST34 expansion. Our findings reveal a stepwise pathoadaptation model underpinning clonal global spread, providing mechanistic insights relevant to forecasting future pandemics.
Self-reactive B cells arise during development and can increase pathogenicity through activation-induced cytidine deaminase (AID)-mediated diversification. Clonal deletion is thought to eliminate these cells, yet how deletion is distributed across developmental and activation stages to prevent autoimmune disease remains unclear. Here, we show that self-tolerance is enforced through temporally distinct mitochondrial outer membrane permeabilization (MOMP) checkpoints. Using conditional Bcl-2 expression to inhibit MOMP either from B cell development or activation, we find that early inhibition permits survival of autoreactive B cells after peripheral egress, expanding the pool available for activation and AID-dependent diversification. This results in broadened class-switched IgG autoreactivity, complement activation, kidney pathology, and drives lethal autoimmune disease. In contrast, post-activation MOMP inhibition promotes autoreactive cell accumulation and autoantibody production but causes limited tissue damage and normal survival. Together, these findings support a Distributed Clonal Deletion Model in which temporally distinct checkpoints cooperate to constrain autoimmune disease progression.
Metastasis to the liver drives mortality in pancreatic ductal adenocarcinoma (PDAC), yet mechanisms of colonization remain unclear. Using genomic barcoding, we developed a clonal competition model under immune surveillance, isolating murine PDAC subclones with high or low liver-colonization potential. Combined transcriptome and chromatin-accessibility analyses revealed a distinct "metastatic-potential axis," separate from the normal-to-PDAC and classical-basal axes. We established "MetScore" as a biomarker of this axis. MetScore distinguishes metastases from primary PDAC tumors in patients, predicts outcomes beyond classical-basal classifications, and generalizes across carcinoma subtypes, suggesting conserved colonization mechanisms. High-MetScore PDAC cells preferentially occupy immune cell-enriched niches, suggesting they remodel the metastatic microenvironment. Functional screening identified c-Fos as a positive mediator of colonization and a candidate anti-metastatic target. Collectively, we identify a cell-state axis underpinning PDAC liver colonization, introduce MetScore as a broadly applicable biomarker, and nominate actionable targets for peri-operative therapeutic intervention.
Disruptions to regulatory signals governing stem cell fate open the pathway to tumorigenesis. To determine how these programs become destabilized, we fate-map thousands of murine wild-type and KrasG12D-mutant alveolar type II (AT2) stem cells in vivo and find evidence for two independent AT2 subpopulations marked by distinct tumorigenic capacities. By combining clonal analyses with single-cell transcriptomics, we unveil striking parallels between lung regeneration and tumorigenesis that implicate Il1r1 as a common activator of AT2 reprogramming. We show that tumor evolution proceeds through the acquisition of lineage infidelity and reversible transitions between mutant states, which, in turn, modulate wild-type AT2 dynamics. Finally, we discover how sustained nuclear factor κB (NF-κB) activation sets tumorigenesis apart from regeneration, allowing mutant cells to subvert differentiation in favor of tumor growth.
Self-reactive B cells are generated during normal development and can acquire increased pathogenicity through activation-induced cytidine deaminase (AID)-mediated diversification following activation. Clonal deletion is thought to eliminate these cells, yet how deletion is distributed across developmental and activation stages to prevent autoimmune disease remains unclear. Here, we show that clonal deletion is enforced through temporally distinct mitochondrial apoptosis (MOMP) checkpoints that differentially regulate autoreactive B cell fate and disease progression. Using conditional Bcl-2 expression to inhibit MOMP either before or after B cell activation, we find that early inhibition permits the survival and maturation of autoreactive B cells after peripheral egress, expanding the pool of cells available for activation. These cells subsequently undergo AID-dependent diversification, producing class-switched IgG autoantibodies with expanded antigen breadth that target a wider range of self-antigens and drive lethal, female-biased autoimmune disease characterized by complement activation and kidney pathology. In contrast, inhibition of MOMP only after activation allows the accumulation of germinal center, switched memory, and plasma cells and promotes autoantibody production, but results in more restricted IgG autoreactivity, limited complement activation and limited tissue damage, and normal survival. Notably, early MOMP inhibition does not expand immature bone marrow B cells, indicating that a major clonal deletion checkpoint operates in the periphery rather than during initial B cell generation. Together, these findings support a Distributed Clonal Deletion Model in which early checkpoints restrict the entry of autoreactive B cells into diversification pathways, while later checkpoints limit the persistence of diversified autoreactive clones, thereby constraining autoimmune disease progression.
This paper offers an explanation for the higher female incidence found in many of the autoimmune and rheumatic diseases. A list of these diseases (Table 1) shows that half of them occur in three females for each male affected. Females are genetic and hence antigenic mosaics, half their somatic cells expressing antigens derived from the paternal X, half from the maternal X (female heterochromatinization). The Burnet-Jerne theory of somatic generation of antibody diversity and forbidden clone elimination states that lymphocytes under maturation in the thymus are killed or suppressed if they recognize and hence react to a histocompatibility antigen. If this were to hold for other self antigens as well, as recent models of clonal generation and selection mechanisms predict, then lymphocytes happening to pass the crucial stage in the thymus meeting only cells expressing one of the parental X's could be released still able to react to self i.e. those somatic cells expressing the other parental X with which the lymphocyte had not been in contact. Thus, self-tolerance would be more easily broken in females than in males.
Through natural or synthetic lineage barcodes, single-cell technologies now enable the joint measurement of molecular states and clonal identities, providing an unprecedented opportunity to study cell fate and dynamics. Yet, most computational methods for inferring cell development and differentiation rely exclusively on transcriptional similarity, overlooking the lineage information encoded by lineage barcodes. This limitation is exemplified by T cells, where subtle transcriptional differences mark divergent fates with distinct biological activity. Single-cell RNA and matched TCR sequencing is now ubiquitous in the analysis of clinical samples, where the TCR sequence provides an endogenous clonal barcode and could reveal clonal T cell responses. We present Clonotrace, a computational framework that jointly models gene expression and clonotype information to infer cell state transitions and fate biases with higher fidelity. While motivated by challenges in analyzing T cell populations, especially in the tumor microenvironment and immunotherapy settings, Clonotrace is broadly applicable to any lineage-barcoded single-cell dataset. Across diverse systems including T cells, hematopoietic differentiation, and cancer therapy resistance models, Clonotrace reveals differentiation hierarchies, distinguishes unipotent from multipotent states, and identifies candidate fate-determining genes driving lineage commitment.
Through natural or synthetic lineage barcodes, single-cell technologies now enable the joint measurement of molecular states and clonal identities, providing an unprecedented opportunity to study cell fate and dynamics. Yet, most computational methods for inferring cell development and differentiation rely exclusively on transcriptional similarity, overlooking the lineage information encoded by lineage barcodes. This limitation is exemplified by T cells, where subtle transcriptional differences mark divergent fates with distinct biological activity. Single-cell RNA and matched TCR sequencing is now ubiquitous in the analysis of clinical samples, where the TCR sequence provides an endogenous clonal barcode and could reveal clonal T cell responses. We present Clonotrace, a computational framework that jointly models gene expression and clonotype information to infer cell state transitions and fate biases with higher fidelity. While motivated by challenges in analyzing T cell populations, especially in the tumor microenvironment and immunotherapy settings, Clonotrace is broadly applicable to any lineage-barcoded single-cell dataset. Across diverse systems including T cells, hematopoietic differentiation, and cancer therapy resistance models, Clonotrace reveals differentiation hierarchies, distinguishes unipotent from multipotent states, and identifies candidate fate-determining genes driving lineage commitment.
UNLABELLED: Recent studies have reported increases in early-onset cancer cases (diagnosed less than 50 years of age) and raised questions about whether the increase is related to earlier diagnosis from nonspecific medical tests as reflected by decreasing tumor-size-at-diagnosis (apparent effects) or actual increases in underlying cancer risk (true effects), or both. The classic Multistage Clonal Expansion (MSCE) model assumes cancer detection at the first malignant cell's emergence, although later modifications have included lag-times or stochasticity in detection to represent the delay in tumor detection. In this study, we introduced an approach to explicitly incorporate tumor-size-at-diagnosis in the MSCE framework accounting for improvements in cancer detection over time to distinguish between apparent and true increases in early-onset cancer incidence. The model was structurally identifiable and provided better parameter estimation than the classic model. The model was applied to colorectal, breast, and thyroid cancers to examine changes in cancer risk while accounting for detection improvements over time in three representative birth cohorts (1950-1954, 1965-1969, and 1980-1984). The analyses suggested accelerated carcinogenic events and shorter mean sojourn times (the average time from the first malignant cell emergence to cancer detection) in more recent cohorts. Furthermore, using this model to examine the screening impact on the incidence of breast and colorectal cancers, for which both have established screening protocols, provided results that align with well-documented differences in screening effects between these cancers. These findings underscore the importance of incorporating tumor-size-at-diagnosis in cancer modeling and support true increases in early-onset cancer risk in recent years for breast, colorectal, and thyroid cancers. SIGNIFICANCE: A model of early-onset cancer trends that distinguishes true risk from detection effects accurately captures cancer kinetics, trends in cancer progression, and the impact of screening, which could inform cancer prevention strategies. This article is part of a special series: Driving Cancer Discoveries with Computational Research, Data Science, and Machine Learning/AI .