Biology subjects

Kannan, K.

Publications and source records attributed to Kannan, K..

17 recordsLinked to original sources

bioRxiv2026

Assessing the defensive behaviour of Apis mellifera scutellata in its native range reveals its variability, organization and persistence

Colony defence is critical for the survival of wild honeybees but presents a challenge for local populations and beekeepers. Apis mellifera scutellata is an important pollinator in its native range, a large area of Southern and Eastern Africa 1, but is reputed for its explosive temperament. Here, we present data demonstrating that this behaviour is only exhibited by a small fraction of colonies, while others are relatively docile. This sheds new light on this subspecies and on its likely evolutionary trajectory. We further characterize the different elements of this defensive response and compare them to that of A. m. carnica and A. m. mellifera. We show that each subspecies invests differently in the two steps of the behavioural sequence (alert and attack). Furthermore, our results suggest that the task of attacking is carried out by specialized workers in carnica, but not in the other subspecies tested, highlighting subtle differences in colony organization across subspecies. Finally, we discovered that defenders from A. m. scutellata and scutellata-hybrid colonies can locate a target that was stung up to 2 h and 3 h earlier, respectively, whereas European bees could only do so for 30 min. This long-term marking could confer an advantage against returning predators with few weak points, such as honey badgers and humans wearing protective clothing. Overall, we offer a fresh perspective on this controversial but important subspecies of honeybees, with implications for both future studies and daily management.

ecology↗

bioRxiv2026

Graph-based modeling of multiparametric MRI deciphers molecular states of high-grade glioma invasion with prognostic implications

AbstractThe infiltrative, non-enhancing margin of IDH wildtype high grade glioma (IDHwt HGG) harbors distinct molecular programs that drive invasion and therapeutic resistance, yet remains largely unevaluable by conventional tissue sampling approaches and by conventional imaging. Here we show that this invasive architecture is encoded within multiparametric MRI (mpMRI) feature relationships and can be decoded using a graph-based framework trained on multiregional image-localized biopsies. Across 134 spatially matched biopsy-imaging pairs from 35 patients with primary IDHwt HGG (29 glioblastomas (GBM) and 6 non-glioblastoma HGGs), unsupervised graph community detection identifies two imaging-defined clusters that localize to invasive tumor regions without molecular supervision. Transcriptomic profiling associates these clusters with neuronal (NEU) and glycolytic-plurimetabolic (GPM) molecular programs. Building on this framework, a graph convolutional network (GCN) accurately predicts NEU and GPM transcriptional states in independent training and validation cohorts and significantly outperforms conventional convolutional neural networks. Applied to whole-tumor mpMRI volumes, the trained GCN generates spatially resolved probability maps that quantify the distribution and relative burden of NEU and GPM programs across both MRI contrast-enhancing and non-enhancing invasive regions. These imaging-derived molecular maps stratify patients by overall survival. Increased GPM burden is associated with poorer survival, consistent with the aggressive behavior associated with mesenchymal-like transcriptional programs in IDHwt HGG. In contrast, increased NEU burden is associated with improved survival, identifying a previously unrecognized imaging-derived prognostic biomarker that was not detected by biopsy-based molecular classification alone. Together, these findings establish a graph-based imaging framework for spatially resolved molecular classification of invasive IDHwt HGG and demonstrate that whole-tumor molecular state architecture carries prognostic information beyond conventional tissue sampling.

cancer biology↗

bioRxiv2026

Microbiome contribution to Indy longevity in Drosophila

Reduction in the Indy (Im not dead yet) gene, a plasma membrane citrate transporter, in Drosophila and its homolog in worms extends lifespan by promoting metabolic homeostasis. Indy reduction delays the onset of aging-associated pathology in the fly midgut, including preservation of intestinal barrier integrity and intestinal stem cell homeostasis. Gut microbiota has broad impacts on host metabolism, health, and aging. Age-related dysbiosis impairs intestinal barrier function and drives mortality. However, the underlying mechanisms that link increased microbial load to frailty and negative effects on health remain mostly unclear. Here we show that Indy heterozygote flies have significantly lower bacterial load and increased diversity during aging compared to controls. However, the presence of the microbiome was not required for Indy lifespan extension, though removal of microbes did enhance the effects of Indy reduction on longevity, suggesting potential interactions between the microbiome and Indy. Indy down-regulation was linked to reduced expression of the JAK/STAT signaling ligands Upd3 and Upd2 in the midgut of young flies, which likely contributes to preserved intestinal stem cell homeostasis. Altogether, our results suggest that Indy reduction impacts microbiome load and composition, which preserves gut homeostasis and extends lifespan through impacts on JAK/STAT signaling pathway. Significance StatementIndy is a fly homologue of mammalian SLC13A5 (mSLC13A5) plasma membrane citrate transporter, a central metabolic regulator involved in health, longevity, and disease. Reduction of fly Indy gene activity preserves metabolic and intestinal stem cell homeostasis and extends longevity. Gut microbiota impacts host metabolism, health, and aging. Here we show that Indy reduction prevents age-associated increases in bacterial load and expression of the JAK/STAT signaling ligands Upd3, and Upd2, while maintaining microbiome diversity. These changes likely slow activation of epithelial cell turnover in the gut and contribute to downstream lifespan effects. As the role of INDY and microbiome are conserved across organisms, our study provides a framework to study underlying mechanisms of the effects of reduced Indy and the microbiome on health and longevity.

genetics↗

bioRxiv2026

The Herbicide Glyphosate Promotes Hypertension via Gut Microbiota-Mediated Mechanisms

Glyphosate, the active ingredient in herbicide Roundup, is the most widely used environmental contaminant that has been extensively studied for its potential carcinogenic effects. In the US alone, a staggering 81% of the US population [≥]6 years of age is exposed to glyphosate. Notably, this coincides with the alarming rise in the incidence of hypertension, the single largest risk factor for global mortality through cardiovascular diseases. Here we asked if there is a link between glyphosate exposure and hypertension, the premise being that glyphosate targets the shikimate pathway present in gut microbiota coupled with more recent knowledge that gut microbiota causally regulate blood pressure. We hypothesized that glyphosate elevates hypertension through microbiota-mediated mechanisms and document a highly concerning detrimental effect of glyphosate by demonstrating a causal link between oral glyphosate exposure and significant elevation in blood pressure. Gut microbiota was identified as the central mediator of this effect. Mechanistically, glyphosate-mediated elevation in blood pressure was through disruption of both gut-liver and gut-vascular homeostasis via FXR-signaling and accumulation of the microbial metabolite shikimic acid, respectively. Together, these findings underscore the need to reconsider the unabated use of this herbicide, which adversely affects a cardinal sign of health.

physiology↗

bioRxiv2025

The Placental Transcriptome Serves as a Mechanistic Link between Prenatal Phthalate Exposure and Placental Efficiency

Prenatal exposure to phthalates, pervasive endocrine-disrupting chemicals, has been linked to child health outcomes, including prematurity and low birthweight. Placental transcriptomics data can reveal mechanisms by which environmental toxicants alter placental and fetal growth. This study aims to investigate the placental transcriptome as a mediator between prenatal maternal urinary phthalate metabolites and placental efficiency. We identified significant associations between maternal urinary concentrations of two phthalate metabolites and the placental transcriptome (132 genes and 27 gene modules). 7 genes and 3 gene modules exhibited significant consistent mediation of the relationship between phthalates and placental efficiency measures. These genes were involved in syncytialization, metabolism, DNA damage and cellular senescence, and steroid biosynthesis--processes essential to fetal growth and development because of the placentas role in nutrient supply, hormone production, and detoxification. These findings suggest a key mediating role of the placental transcriptome in toxicological mechanisms by which phthalates may disrupt fetal growth. TeaserPlacental gene expression mediates the relationship between prenatal phthalate exposure and fetal growth measures.

developmental biology↗

bioRxiv2025

A Network-Based Discovery of Prognostic Markers in Recurrent IDH wild-type Gliomas

BackgroundIsocitrate dehydrogenase wild-type (IDH wild-type) gliomas represents the most aggressive subtype of diffuse gliomas, characterized by therapeutic resistance and dismal prognosis. Despite advances in molecular classification, reliable prognostic biomarkers for these tumors remain limited, particularly for recurrent disease. This study aims to identify gene expression signatures associated with survival outcomes in recurrent IDH wild-type gliomas, with the goal of improving patient stratification and potential therapeutic targeting. MethodsWe analyzed gene expression data from 180 recurrent IDH wild-type glioma samples from the Glioma Longitudinal AnalySiS (GLASS) Consortium. Using multiple computational approaches including a novel network-based method (netSurvival) and various survival analysis techniques, we identified genes associated with patient survival outcomes. ResultsOur comprehensive analysis identify several gene expression markers that are significantly associated with survival outcomes in recurrent IDH wildtype gliomas. The AFT log normal model revealed that FN1, HIF3A, and EIF4B are associated with poorer survival (hazard ratios of 1.40, 1.49, and 1.54, respectively; p {inverted exclamation} 0.05), while PTK2, CCND2, RAD51L3 RFFL, and MAX demonstrated protective effects (hazard ratios of 0.76, 0.78, 0.79, and 0.79, respectively; p {inverted exclamation} 0.05). Five genes (KIF5C, LINC00632, B4GALNT3, HIF3A, and RAD51L3 RFFL) show significant differential expression between primary and recurrent tumors, with four having established functional roles in glioma pathobiology. ConclusionThis study identifies a panel of gene expression markers with significant prognostic value in recurrent IDH wild-type gliomas. The differential impacts of these genes on survival outcomes provide insights into the biological heterogeneity underlying clinical behavior in these aggressive tumors. Particularly significant are the biomarkers associated with both survival outcomes and recurrence patterns, which may represent key drivers of disease progression. These findings represent an important step toward improved prognostic stratification and therapeutic targeting in IDH wild-type gliomas, addressing a critical unmet need in neuro-oncology.

bioinformatics↗

bioRxiv2025

NETWORK REGULARIZED ACCELERATED FAILURE TIME MODELS FOR ROBUST BIOMARKER IDENTIFICATION

BackgroundHigh-dimensional genomic studies in cancer research face significant challenges when analyzing survival outcomes, particularly in p >> n scenarios where thousands of genes are measured across relatively small patient cohorts. Traditional penalized regression methods like lasso and ridge regression apply uniform shrinkage without considering underlying biological relationships between genes, often resulting in unstable feature selection and reduced interpretability. Network-regularized methods that incorporate prior biological knowledge have emerged as promising alternatives, yet systematic comparisons of different network penalties in accelerated failure time (AFT) models remain limited. MethodsWe developed and evaluated Weibull AFT models incorporating two network-regularized approaches: inverse-degree penalties that apply stronger shrinkage to highly connected hub genes, and graph Laplacian penalties that promote smoothness across connected nodes. We conducted comprehensive simulations across varying signal strengths, censoring rates (30%, 50%, 80%), and noise levels, followed by real-data applications on three cancer datasets (TCGA-KIRC, TCGA-COAD, and IDH-wildtype gliomas) using both STRING protein-protein interaction (PPI) networks and co-expression networks. Performance was assessed using estimation and prediction mean squared error, false positive/negative rates, concordance index, and pathway enrichment analysis. ResultsThe inverse-degree penalty demonstrated consistently more conservative feature selection behavior, achieving substantially lower false positive rates while maintaining competitive predictive performance compared to Laplacian penalties. In simulation studies, the inverse-degree method achieved false positive rates of 0.126 versus 0.299 for Laplacian penalties under strong signal conditions. Real-data applications showed comparable predictive accuracy (C-index: 0.61-0.73) between methods across cancer types. Both methods successfully identified biologically relevant pathways including mTORC1 regulation and fatty acid metabolism in kidney renal clear cell carcinoma (KIRC), and IL-17/chemokine signaling in IDH-wildtype gliomas. Notably, in colorectal adenocarcinoma (COAD) analysis, the inverse-degree method identified ten coherent pathways centered on inflammation, cell-cycle regulation, and proteasome function, while the Laplacian approach introduced potentially spurious neurotransmitter-release associations, demonstrating the inverse-degree penaltys superior biological interpretability. ConclusionsNetwork-regularized AFT models with inverse-degree penalties offer a valuable tool for biomarker discovery in high-dimensional genomic survival analysis, providing conservative feature selection and superior biological interpretability while maintaining competitive predictive performance. The accompanying DegreeAFT R package makes these methods accessible to the broader research community, facilitating adoption in precision oncology applications where identifying reliable prognostic signatures is critical for treatment decision-making.

bioinformatics↗

bioRxiv2025

GNC is a regulator of metabolic and productivity responses to elevated CO2 in Arabidopsis thaliana

Despite established understanding of plant physiological responses to elevated [CO2], the underlying genes are poorly understood. Soybean transcriptomics previously identified a GATA transcription factor, involved in carbon and nitrogen metabolism, as responsive to elevated [CO2]. Supported by in silico modeling, we therefore hypothesized that this gene plays a previously unrecognized role in responding to elevated [CO2]. Wildtype and a T-DNA insertion line of Arabidopsis thaliana for GNC (GATA, Nitrate Inducible, Carbon Metabolism Involved) were grown under three treatments: sustained ambient [CO2], sustained elevated [CO2], and transfer from ambient to elevated [CO2], to assess changes in their physiology, biochemistry, and transcriptome. Photosynthetic and biomass responses to elevated [CO2] and transfer [CO2] in plants lacking GNC were significantly weaker than WT. A lag of 25-73 hrs in transcriptomic responses after transfer to elevated [CO2] was consistent with indirect sensing, presumably via sugar signals. The breakdown of the gene expression network around GNC was most pronounced in the transfer treatment and suggests targets for further study of interactions between elevated [CO2] and sulfur and nitrogen metabolism. This work provides a case study of a CO2-responsive transcription factor that may be a compelling target for adapting crops to future growing conditions after further characterization. Summary StatementA GATA transcription factor modulates plant metabolic and productivity responses to elevated CO2.

plant biology↗

bioRxiv2025

EXPRESSION GRAPH NETWORK FRAMEWORK FOR BIOMARKERDISCOVERY

AO_SCPLOWBSTRACTC_SCPLOWBiomarker discovery for complex diseases like cancer hinges on uncovering molecular signatures that capture intricate, interconnected relationships within biological data, a challenge that traditional statistical and machine learning methods often fail to meet due to the complexity of high-dimensional gene expression profiles. To overcome this, we introduce the Expression Graph Network Framework (EGNF), a cutting-edge graph-based approach that integrates graph neural networks (GNNs) with network-based feature engineering to enhance predictive biomarker identification. EGNF constructs biologically informed networks by combining gene expression data and clinical attributes within a graph database, utilizing hierarchical clustering to generate dynamic, patient-specific representations of molecular interactions. Leveraging graph learning techniques, including Graph Convolutional Networks (GCNs) and Graph Attention Networks (GATs), our framework identifies statistically significant and biologically relevant gene modules for classification. Validated across three independent datasets consisting of contrasting tumor types and clinical scenarios, EGNF consistently outperforms traditional machine learning models, achieving superior classification accuracy and interpretability. Notably, it delivers perfect separation between normal and tumor samples while excelling in nuanced tasks such as classifying disease progression and treatment outcome. This scalable, interpretable, and robust framework provides a powerful tool for biomarker discovery, with wide-ranging applications in precision medicine and the elucidation of disease mechanisms across diverse clinical contexts.

bioinformatics↗

bioRxiv2022

Parallel motion vision pathways in the brain of a tropical bee

Spatial orientation is a prerequisite for most behaviors. In insects, the underlying neural computations take place in the central complex (CX), the brains navigational center. In this region different streams of sensory information converge to enable context-dependent navigational decisions. Accordingly, a variety of CX input neurons deliver information about different navigation-relevant cues. In bees, direction encoding polarized light signals converge with translational optic flow signals that are suited to encode the flight speed of the animals. The continuous integration of speed and directions in the CX can be used to generate a vector memory of the bees current position in space in relation to its nest, i.e. perform path integration. This process depends on specific, complex features of the optic flow encoding CX input neurons, but it is unknown how this information is derived from the visual periphery. Here, we thus aimed at gaining insight into how simple motion signals are reshaped upstream of the speed encoding CX input neurons to generate their complex features. Using electrophysiology and anatomical analyses of the halictic bees Megalopta genalis and Megalopta centralis, we identified a wide range of motion-sensitive neurons connecting the optic lobes with the central brain. While most neurons formed pathways with characteristics incompatible with CX speed neurons, we showed that one group of lobula projection neurons possess some physiological and anatomical features required to generate the visual responses of CX optic-flow encoding neurons. However, as these neurons cannot explain all features of CX speed cells, local interneurons of the central brain or alternative input cells from the optic lobe are additionally required to construct inputs with sufficient complexity to deliver speed signals suited for path integration in bees.

neuroscience↗

bioRxiv2021

A single-cell based precision medicine approach using glioblastoma patient-specific models

Glioblastoma (GBM) is a heterogeneous tumor made up of cell states that evolve over time. Here, we modeled tumor evolutionary trajectories during standard-of-care treatment using multimodal single-cell analysis of a primary tumor sample, corresponding mouse xenografts subjected to standard of care therapy, and recurrent tumor at autopsy. We mined the multimodal data with single cell SYstems Genetics Network AnaLysis (scSYGNAL) to identify a network of 52 regulators that mediate treatment-induced shifts in xenograft tumor-cell states that were also reflected in recurrence. By integrating scSYGNAL-derived regulatory network information with transcription factor accessibility deviations derived from single-cell ATAC-seq data, we developed consensus networks that regulate subpopulations of primary and recurrent tumor cells. Finally, by matching targeted therapies to active regulatory networks underlying tumor evolutionary trajectories, we provide a framework for applying single-cell-based precision medicine approaches in a concurrent, neo-adjuvant, or recurrent setting. SummaryInference of mechanistic drivers of therapy-induced evolution of glioblastoma at single cell resolution using RNA-seq and ATAC-seq from patient samples and model systems undergoing standard-of-care treatment informs strategy for identification of tumor evolutionary trajectories and possible cell state-directed therapeutics.

systems biology↗

bioRxiv2020

Detection of M-Protein in Acetonitrile Precipitates of Serum using MALDI-TOF Mass Spectrometry

Purpose of the researchMultiple myeloma and plasmacytomas belong to a group of disorders, namely plasma cell dyscrasias and are identified by the presence of a monoclonal protein (M-protein). MALDI-TOF-mass spectrometry (MS) has demonstrated superior analytical sensitivity for the detection of M-protein and is now used for screening of M-protein at some centres. We present the results of an alternative methodology for M-protein analysis by MALDI-TOF MS. MethodsSerum samples from patients with newly diagnosed multiple myeloma or plasmacytoma with positive M-protein detected by serum protein electrophoresis, immunofixation electrophoresis and serum free light chain analysis, underwent direct reagent-based extraction process using Acetonitrile (ACN) precipitation. Serum{kappa} and{lambda} light chains were validated using immunoenrichment by anti-{kappa} and anti-{lambda} biotin-labelled antibodies immobilised on streptavidin magnetic beads. MALDI-TOF MS measurements were obtained for intact proteins using alpha-cyano-4-hydroxycinnamic acid as matrix. The images obtained were overlaid on apparently healthy donor serum samples to confirm the presence of M-protein. Principle resultsCharacteristic M-protein peaks were observed in the ACN precipitates of serum in the predicted mass ranges for{kappa} and{lambda} . The{kappa} and{lambda} peaks were confirmed by immunoenrichment analysis. Twenty-seven patient samples with either newly diagnosed multiple myeloma or plasmacytoma with monoclonal gammopathy detected by the standard methods were chosen for Acetonitrile precipitation and analysed by MALDI-TOF MS. All 27 patient samples demonstrated a peak suggestive of M-protein with mass/charge (m/z) falling within the{kappa} and{lambda} range. The concordance rate with serum immunofixation electrophoresis and free light chain analysis was above 90%. Major conclusionsWe report the results of a low-cost reagent-based extraction process using Acetonitrile precipitation to enrich for{kappa} and{lambda} light chains, which can be used for the screening and qualitative analysis of M-protein.

biochemistry↗

bioRxiv2019

Lyophilized B. subtilis ZB183 Spores: 90-Day Repeat Dose Oral (Gavage) Toxicity Study In Wistar Rats

A 90-day repeated-dose oral toxicological evaluation was conducted according to GLP and OECD guidelines on lyophilized spores of the novel genetically modified strain B. subtilis ZB183. Lyophilized spores at doses of 109, 1010, and 1011 CFU/kg body weight/day were administered by oral gavage to Wistar rats for a period of 90 consecutive days. B.Subtilis ZB183 had no effects on clinical signs, mortality, ophthalmological examinations, functional observational battery, body weights, body weight gains and food consumption in both sexes. There were no test item-related changes observed in haematology, coagulation, urinalysis, thyroid hormonal analysis, terminal fasting body weights, organ weights, gross pathology and histopathology. A minimal increase in the plasma albumin level was observed at 1010 and 1011 CFU/kg/day doses without an increase in total protein in males or females and was considered a non-adverse effect. The \"No Observed Adverse Effect Level (NOAEL)\" is defined at the highest dose of 1011 CFU/kg body weight/day for lyophilized B.Subtilis ZB183 Spores under the test conditions employed.

microbiology↗

bioRxiv2019

Gibbs Process Determines Survival and Reveals Contact-Inhibition Genes in Glioblastoma Multiforme

Tumor growth is a spatiotemporal birth-and-death process with loss of heterotypic contact-inhibition of locomotion (CIL) of tumor cells promoting invasion and metastasis. Therefore, representing tumor cells as two-dimensional points, we can expect the tumor tissues in histology slides to reflect realizations of spatial birth-and-death process which can be mathematically modeled to reveal molecular mechanisms of CIL, provided the mathematics models the inhibitory interactions. Gibbs process as an inhibitory point process is a natural choice since it is an equilibrium process of the spatial birth-and-death process. That is if the tumor cells maintain homotypic contact inhibition, the spatial distributions of tumor cells will result in Gibbs hard core process over long time scales. In order to verify if this is the case, we applied the Gibbs process to 411 TCGA Glioblastoma multiforme patient images. Our imaging dataset included all cases for which diagnostic slide images were available. The model revealed two clusters, one of which - the "Gibbs cluster," showed the convergence of the Gibbs process with significant survival difference. Further smoothing the discretized (and noisy) inhibition metric, for both increasing and randomized survival time, we found a significant association of the patients in the Gibbs cluster with increasing survival time. The mean inhibition metric also revealed the point at which the homotypic CIL establishes in tumor cells. Besides, RNAseq analysis between patients with loss of heterotypic CIL and intact homotypic CIL in the Gibbs cluster unveiled cell movement gene signatures and differences in Actin cytoskeleton and RhoA signaling pathways as key molecular alterations. These genes and pathways have established roles in CIL. Taken together, our integrated analysis of patient images and RNAseq data provides for the first time a mathematical basis for CIL in tumors, explains survival as well as uncovers the underlying molecular landscape for this key tumor invasion and metastatic phenomenon.

cancer biology↗

bioRxiv2019

Combining gene network, metabolic, and leaf-level models show means to future-proof soybean photosynthesis under rising CO2.

Global population increase coupled with rising urbanization underlies the predicted need for 60% more food by 2050, but produced on the same amount of land as today. Improving photosynthetic efficiency is a largely untapped approach to addressing this problem. Here, we scale modeling processes from gene expression through photosynthetic metabolism to predict leaf physiology in evaluating acclimation of photosynthesis to rising [CO2]. Model integration with the yggdrasil interface enabled asynchronous message passing between models. The multiscale model of soybean photosynthesis calibrated to physiological measures at ambient [CO2] successfully predicted the acclimatory changes in the photosynthetic apparatus that were observed at 550 ppm [CO2] in the field. We hypothesized that genetic alteration is necessary to achieve optimal photosynthetic efficiency under global change. Flux control analysis in the metabolic system under elevated [CO2] identified enzymes requiring the greatest change to adapt optimally to the new conditions. This predicted that Rubisco was less limiting under elevated [CO2] and should be down-regulated allowing re-allocation of resource to enzymes controlling the rate of regeneration of ribulose-1:5 bisphosphate (RubP). By linking the GRN through protein concentration to the metabolic model it was possible to identify transcription factors (TF) that matched the up- and down-regulation of genes needed to improve photosynthesis. Most striking was TF GmGATA2, which down-regulated genes for Rubisco synthesis while up-regulating key genes controlling RubP regeneration and starch synthesis. The changes predicted for this TF most closely matched the physiological ideotype that the modeling predicted as optimal for the future elevated [CO2] world.

plant biology↗

bioRxiv2018

G-quadruplex DNA drives genomic instability and represents a targetable molecular abnormality in ATRX-deficient malignant glioma

Mutational inactivation of ATRX (-thalassemia mental retardation X-linked) represents a defining molecular alteration in large subsets of malignant glioma. Yet the pathogenic consequences of ATRX deficiency remain unclear, as do tractable mechanisms for its therapeutic targeting. Here we report that ATRX loss in isogenic glioma model systems induces replication stress and DNA damage by way of G-quadruplex (G4) DNA secondary structure. Moreover, these effects are associated with the acquisition of disease-relevant copy number alterations over time. We then demonstrate, both in vitro and in vivo, that ATRX deficiency selectively enhances DNA damage and cell death following chemical G4 stabilization. Finally, we show that G4 stabilization synergizes with other DNA-damaging therapies, including ionizing radiation, in the ATRX-deficient context. Our findings reveal novel pathogenic mechanisms driven by ATRX deficiency in glioma, while also pointing to tangible strategies for drug development.

cancer biology↗

bioRxiv2017

Toward Capturing the Exposome: Exposure Biomarker Variability and Co-Exposure Patterns in the Shared Environment

BACKGROUNDAlong with time, variation in the exposome is dependent on the location and sex of study participants. One specific factor that may influence exposure co-variations is a shared household environment.\n\nOBJECTIVESTo examine the influence of shared household and partners sex in relation to the variation in 128 endocrine disrupting chemical (EDC) exposures among couples.\n\nMETHODSIn a cohort comprising 501 couples trying for pregnancy, we measured 128 (13 chemical classes) persistent and non-persistent EDCs and estimated 1) sex-specific differences; 2) variance explained by shared household; and 3) Spearmans rank correlation coefficients (rs) for females, males, and couples exposures.\n\nRESULTSSex was correlated with 8 EDCs including polyfluoroalkyl substances (PFASs) (p < 0.05). Shared household explained 43% and 41% of the total variance for PFASs and blood metals, respectively, but less than 20% for the remaining 11 EDC classes. Co-exposure patterns of the exposome were similar between females and males, with within-class rs higher for persistent and lower for non-persistent chemicals. Median rss of polybrominated compounds and urine metalloids were 0.45 and 0.09, respectively, for females (0.41 and 0.08 for males), whereas lower rss for these 2 classes were found for couples (0.21 and 0.04).\n\nCONCLUSIONSOverall, sex did not significantly affect EDC levels in couples. Individual, rather than shared environment, could be a major factor influencing the co-variation of 128 markers of the exposome. Correlations between exposures are lower in couples than in individual partners and have important analytical and sampling implications for epidemiological study.

epidemiology↗