Neurotransmitter classification from electron microscopy images at synaptic sites in Drosophila melanogaster.

High-resolution electron microscopy of nervous systems has enabled the reconstruction of synaptic connectomes. However, we do not know the synaptic sign for each connection (i.e., whether a connection is excitatory or inhibitory), which is implied by the released transmitter. We demonstrate that artificial neural networks can predict transmitter types for presynapses from electron micrographs: a network trained to predict six transmitters (acetylcholine, glutamate, GABA, serotonin, dopamine, octopamine) achieves an accuracy of 87% for individual synapses, 94% for neurons, and 91% for known cell types across a D. melanogaster whole brain. We visualize the ultrastructural features used for prediction, discovering subtle but significant differences between transmitter phenotypes. We also analyze transmitter distributions across the brain and find that neurons that develop together largely express only one fast-acting transmitter (acetylcholine, glutamate, or GABA). We hope that our publicly available predictions act as an accelerant for neuroscientific hypothesis generation for the fly.

Integration of Parallel Opposing Memories Underlies Memory Extinction.

Accurately predicting an outcome requires that animals learn supporting and conflicting evidence from sequential experience. In mammals and invertebrates, learned fear responses can be suppressed by experiencing predictive cues without punishment, a process called memory extinction. Here, we show that extinction of aversive memories in Drosophila requires specific dopaminergic neurons, which indicate that omission of punishment is remembered as a positive experience. Functional imaging revealed co-existence of intracellular calcium traces in different places in the mushroom body output neuron network for both the original aversive memory and a new appetitive extinction memory. Light and ultrastructural anatomy are consistent with parallel competing memories being combined within mushroom body output neurons that direct avoidance. Indeed, extinction-evoked plasticity in a pair of these neurons neutralizes the potentiated odor response imposed in the network by aversive learning. Therefore, flies track the accuracy of learned expectations by accumulating and integrating memories of conflicting events.

Deterministic Evolutionary Trajectories Influence Primary Tumor Growth: TRACERx Renal.

The evolutionary features of clear-cell renal cell carcinoma (ccRCC) have not been systematically studied to date. We analyzed 1,206 primary tumor regions from 101 patients recruited into the multi-center prospective study, TRACERx Renal. We observe up to 30 driver events per tumor and show that subclonal diversification is associated with known prognostic parameters. By resolving the patterns of driver event ordering, co-occurrence, and mutual exclusivity at clone level, we show the deterministic nature of clonal evolution. ccRCC can be grouped into seven evolutionary subtypes, ranging from tumors characterized by early fixation of multiple mutational and copy number drivers and rapid metastases to highly branched tumors with >10 subclonal drivers and extensive parallel evolution associated with attenuated progression. We identify genetic diversity and chromosomal complexity as determinants of patient outcome. Our insights reconcile the variable clinical behavior of ccRCC and suggest evolutionary potential as a biomarker for both intervention and surveillance.

The fly connectome reveals a path to the effectome.

A goal of neuroscience is to obtain a causal model of the nervous system. The recently reported whole-brain fly connectome1-3 specifies the synaptic paths by which neurons can affect each other, but not how strongly they do affect each other in vivo. To overcome this limitation, we introduce a combined experimental and statistical strategy for efficiently learning a causal model of the fly brain, which we refer to as the 'effectome'. Specifically, we propose an estimator for a linear dynamical model of the fly brain that uses stochastic optogenetic perturbation data to estimate causal effects and the connectome as a prior to greatly improve estimation efficiency. We validate our estimator in connectome-based linear simulations and show that it recovers a linear approximation to the nonlinear dynamics of more biophysically realistic simulations. We then analyse the connectome to propose circuits that dominate the dynamics of the fly nervous system. We discover that the dominant circuits involve only relatively small populations of neurons-thus, neuron-level imaging, stimulation and identification are feasible. This approach also re-discovers known circuits and generates testable hypotheses about their dynamics. Overall, we provide evidence that fly whole-brain dynamics are generated by a large collection of small circuits that operate largely independently of each other. This implies that a causal model of a brain can be feasibly obtained in the fly.

Network statistics of the whole-brain connectome of Drosophila.

Brains comprise complex networks of neurons and connections, similar to the nodes and edges of artificial networks. Network analysis applied to the wiring diagrams of brains can offer insights into how they support computations and regulate the flow of information underlying perception and behaviour. The completion of the first whole-brain connectome of an adult fly, containing over 130,000 neurons and millions of synaptic connections1-3, offers an opportunity to analyse the statistical properties and topological features of a complete brain. Here we computed the prevalence of two- and three-node motifs, examined their strengths, related this information to both neurotransmitter composition and cell type annotations4,5, and compared these metrics with wiring diagrams of other animals. We found that the network of the fly brain displays rich-club organization, with a large population (30% of the connectome) of highly connected neurons. We identified subsets of rich-club neurons that may serve as integrators or broadcasters of signals. Finally, we examined subnetworks based on 78 anatomically defined brain regions or neuropils. These data products are shared within the FlyWire Codex ( https://codex.flywire.ai ) and should serve as a foundation for models and experiments exploring the relationship between neural activity and anatomical structure.

A Drosophila computational brain model reveals sensorimotor processing.

The recent assembly of the adult Drosophila melanogaster central brain connectome, containing more than 125,000 neurons and 50 million synaptic connections, provides a template for examining sensory processing throughout the brain1,2. Here we create a leaky integrate-and-fire computational model of the entire Drosophila brain, on the basis of neural connectivity and neurotransmitter identity3, to study circuit properties of feeding and grooming behaviours. We show that activation of sugar-sensing or water-sensing gustatory neurons in the computational model accurately predicts neurons that respond to tastes and are required for feeding initiation4. In addition, using the model to activate neurons in the feeding region of the Drosophila brain predicts those that elicit motor neuron firing5-a testable hypothesis that we validate by optogenetic activation and behavioural studies. Activating different classes of gustatory neurons in the model makes accurate predictions of how several taste modalities interact, providing circuit-level insight into aversive and appetitive taste processing. Additionally, we applied this model to mechanosensory circuits and found that computational activation of mechanosensory neurons predicts activation of a small set of neurons comprising the antennal grooming circuit, and accurately describes the circuit response upon activation of different mechanosensory subtypes6-10. Our results demonstrate that modelling brain circuits using only synapse-level connectivity and predicted neurotransmitter identity generates experimentally testable hypotheses and can describe complete sensorimotor transformations.

Whole-brain annotation and multi-connectome cell typing of Drosophila.

The fruit fly Drosophila melanogaster has emerged as a key model organism in neuroscience, in large part due to the concentration of collaboratively generated molecular, genetic and digital resources available for it. Here we complement the approximately 140,000 neuron FlyWire whole-brain connectome1 with a systematic and hierarchical annotation of neuronal classes, cell types and developmental units (hemilineages). Of 8,453 annotated cell types, 3,643 were previously proposed in the partial hemibrain connectome2, and 4,581 are new types, mostly from brain regions outside the hemibrain subvolume. Although nearly all hemibrain neurons could be matched morphologically in FlyWire, about one-third of cell types proposed for the hemibrain could not be reliably reidentified. We therefore propose a new definition of cell type as groups of cells that are each quantitatively more similar to cells in a different brain than to any other cell in the same brain, and we validate this definition through joint analysis of FlyWire and hemibrain connectomes. Further analysis defined simple heuristics for the reliability of connections between brains, revealed broad stereotypy and occasional variability in neuron count and connectivity, and provided evidence for functional homeostasis in the mushroom body through adjustments of the absolute amount of excitatory input while maintaining the excitation/inhibition ratio. Our work defines a consensus cell type atlas for the fly brain and provides both an intellectual framework and open-source toolchain for brain-scale comparative connectomics.

Neuronal wiring diagram of an adult brain.

Connections between neurons can be mapped by acquiring and analysing electron microscopic brain images. In recent years, this approach has been applied to chunks of brains to reconstruct local connectivity maps that are highly informative1-6, but nevertheless inadequate for understanding brain function more globally. Here we present a neuronal wiring diagram of a whole brain containing 5 × 107 chemical synapses7 between 139,255 neurons reconstructed from an adult female Drosophila melanogaster8,9. The resource also incorporates annotations of cell classes and types, nerves, hemilineages and predictions of neurotransmitter identities10-12. Data products are available for download, programmatic access and interactive browsing and have been made interoperable with other fly data resources. We derive a projectome-a map of projections between regions-from the connectome and report on tracing of synaptic pathways and the analysis of information flow from inputs (sensory and ascending neurons) to outputs (motor, endocrine and descending neurons) across both hemispheres and between the central brain and the optic lobes. Tracing from a subset of photoreceptors to descending motor pathways illustrates how structure can uncover putative circuit mechanisms underlying sensorimotor behaviours. The technologies and open ecosystem reported here set the stage for future large-scale connectome projects in other species.

Socioeconomic and household framework influences in school-aged children's eating habits: Understanding the parental roles.

Maternal influences on children's diet have been widely studied, while paternal and household frameworks require further research. This study aimed to evaluate how individual, socioeconomic and household characteristics at birth and 7 years (y) impact children's fruit and vegetable (FV) and energy-dense foods (EDF) intake at 10y, distinguishing maternal and paternal effects. The sample included 2750 children evaluated at 7 and 10y in the Portuguese birth cohort Generation XXI. The children's food intake was assessed through a Food Frequency Questionnaire at both ages, where 4 food groups were defined: FV, 'Sweet foods', 'Salty snacks', and 'Soft drinks'. The associations between food intake at 7y, parents' and children's characteristics, and food intake at 10y were evaluated via binary logistic regression models. Offspring of older mothers with higher age and education, who live with siblings and had higher family income were likelier to have 5 servings of FV daily at 10y. Children eating 5 portions/day of FV at 7y had higher odds of keeping this pattern at 10y. Higher maternal age and education, and father's education decreased the odds of having soft drinks daily at 10y. Higher family income was linked with lower odds of weekly salty snacks and daily sweet foods at 10y. In conclusion, parental education, maternal age, living with siblings, and higher family income influenced children's FV and EDF intake at 10y. Mothers' effects appear to impact children's food intake more than fathers'.

Longitudinal tracking of diet quality from childhood to adolescence: The Interplay of individual and sociodemographic factors.

This study aimed to examine diet tracking from childhood to adolescence, using 4 time-points, and the influence of socioeconomic and individual characteristics in this transition. The sample included 6893 children from the Portuguese birth cohort Generation XXI with complete information on Food Frequency Questionnaire in at least one of the considered follow-ups. A Healthy Eating Index (HEI), previously developed to assess adherence to WHO's dietary recommendations, was applied at all ages (4, 7, 10 and 13y). The intraclass correlation coefficient (ICC) was used to analyse the tracking of diet quality. Linear mixed-effect models were performed to estimate the association of the child's socioeconomic and individual characteristics with the HEI score and respective trajectories over time. The overall diet quality decreased from childhood (22.2 ± 3.6 at 4y) to adolescence (18.2 ± 3.9 at 13y), with moderate tracking (ICC = 0.53), showing that children who start a healthy diet earlier might have a better diet quality as time goes by. Children of older mothers (ß = 0.079, 95%CI = 0.061-0.097) and with higher education (ß = 0.203, 95%CI = 0.178-0.229) and a higher household monthly income (ß = 0.024,95%CI = 0.007-0.041) had a higher diet quality over time. Besides family characteristics, the child's sedentary activities (ß = -0.009, 95%CI = -0.014--0.003) negatively influence diet quality throughout life. In contrast, being a girl (ß = -0.094, 95%CI = -0.132--0.056) and having higher sleep duration (ß = 0.039, 95%CI = 0.015-0.064) are associated with a higher diet quality over time. The presence of dietary tracking from childhood to adolescence implies that promoting healthy eating habits during the first years of life is crucial for a healthier diet quality during late childhood and early adolescence, focusing on maternal and individual child characteristics.

Smooth muscle tumours of the uterus: MR imaging malignant predictive features-a 12-year analysis in a referral hospital in Portugal.

PURPOSE: To evaluate the magnetic resonance imaging (MRI) features that may help distinguish leiomyosarcomas from atypical leiomyomas (those presenting hyperintensity on T2-W images equal or superior to 50% compared to the myometrium). MATERIALS AND METHODS: The authors conducted a retrospective single-centre study that included a total of 57 women diagnosed with smooth muscle tumour of the uterus, who were evaluated with pelvic MRI, between January 2009 and March 2020. All cases had a histologically proven diagnosis (31 Atypical Leiomyomas-ALM; 26 Leiomyosarcomas-LMS). The MRI features evaluated in this study included: age at presentation, dimension, contours, intra-tumoral haemorrhagic areas, T2-WI heterogeneity, T2-WI dark areas, flow voids, cyst areas, necrosis, restriction on diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC) values, signal intensity and heterogeneity after contrast administration in T1-WI, presence and location of unenhanced areas. The association between the MRI characteristics and the histological subtype was evaluated using Chi-Square and ANOVA tests. RESULTS: The MRI parameters that showed a statistically significance correlation with malignant histology and thus most strongly associated with LMS were found to be: irregular contours (p < 0.001), intra-tumoral haemorrhagic areas (p = 0.028), T2-WI dark areas (p = 0.016), high signal intensity after contrast administration (p = 0.005), necrosis (p = 0.001), central location for unenhanced areas (p = 0.026), and ADC value lower than 0.88 × 10-3 mm2/s (p = 0.002). CONCLUSION: With our work, we demonstrate the presence of seven MRI features that are statistically significant in differentiating between LMS and ALM.

The Chromenopyridine Scaffold: A Privileged Platform in Drug Design.

The chromenopyridine scaffold represents an important class of heterocyclic compounds exhibiting a broad spectrum of biological properties. This review describes novel and efficient procedures for the synthesis of this scaffold. Herein, several methods were detailed and grouped according to their starting material (e.g., salicylaldehydes, chromones, chromanones and coumarins) and respective biological activity, when reported. This review highlights the potential of the reported synthetic strategies for preparing chromenopyridine derivatives with promising biological activity, paving the way for further developments in drug discovery.

Enhanced bioprocess control to advance the manufacture of mesenchymal stromal cell-derived extracellular vesicles in stirred-tank bioreactors.

Extracellular vesicles (EVs) derived from mesenchymal stromal cells (MSCs) act as signaling mediators of cellular responses. However, despite representing a promising alternative to cell-based therapies, clinical translation of EVs is currently limited by their lack of scalability and standardized bioprocessing. Herein, we integrated scalable downstream processing protocols with standardized expansion of large numbers of viable cells in stirred-tank bioreactors to improve EV production. Higher EV yields were linked to EV isolation by tangential flow filtration followed by size exclusion chromatography, rendering 5 times higher number of EVs comparatively to density gradient ultracentrifugation protocols. Additionally, when compared to static culture, EV manufacture in bioreactors resulted in 2.2 higher yields. Highlighting the role of operating under optimal cell culture conditions to maximize the number of EVs secreted per cell, MSCs cultured at lower glucose concentration favored EV secretion. While offline measurements of metabolites concentration can be performed, in this work, Raman spectroscopy was also applied to continuously track glucose levels in stirred-tank bioreactors, contributing to streamline the selection of optimal EV collection timepoints. Importantly, MSC-derived EVs retained their quality attributes and were able to stimulate angiogenesis in vitro, therefore highlighting their promising therapeutic potential.

Modifier pathways in polyglutamine (PolyQ) diseases: from genetic screens to drug targets.

Polyglutamine (PolyQ) diseases include a group of inherited neurodegenerative disorders caused by unstable expansions of CAG trinucleotide repeats in the coding region of specific genes. Such genetic alterations produce abnormal proteins containing an unusually long PolyQ tract that renders them more prone to aggregate and cause toxicity. Although research in the field in the last years has contributed significantly to the knowledge of the biological mechanisms implicated in these diseases, effective treatments are still lacking. In this review, we revisit work performed in models of PolyQ diseases, namely the yeast Saccharomyces cerevisiae, the nematode worm Caenorhabditis elegans and the fruit fly Drosophila melanogaster, and provide a critical overview of the high-throughput unbiased genetic screens that have been performed using these systems to identify novel genetic modifiers of PolyQ diseases. These approaches have revealed a wide variety of cellular processes that modulate the toxicity and aggregation of mutant PolyQ proteins, reflecting the complexity of these disorders and demonstrating how challenging the development of therapeutic strategies can be. In addition to the unbiased large-scale genetic screenings in non-vertebrate models, complementary studies in mammalian systems, closer to humans, have contributed with novel genetic modifiers of PolyQ diseases, revealing neuronal function and inflammation as key disease modulators. A pathway enrichment analysis, using the human orthologues of genetic modifiers of PolyQ diseases clustered modifier genes into major themes translatable to the human disease context, such as protein folding and transport as well as transcription regulation. Innovative genetic strategies of genetic manipulation, together with significant advances in genomics and bioinformatics, are taking modifier genetic studies to more realistic disease contexts. The characterization of PolyQ disease modifier pathways is of extreme relevance to reveal novel therapeutic possibilities to delay disease onset and progression in patients.

Prognostic Value of Monocarboxylate Transporter 1 Overexpression in Cancer: A Systematic Review.

Energy production by cancer is driven by accelerated glycolysis, independently of oxygen levels, which results in increased lactate production. Lactate is shuttled to and from cancer cells via monocarboxylate transporters (MCTs). MCT1 works both as an importer and an extruder of lactate, being widely studied in recent years and generally associated with a cancer aggressiveness phenotype. The aim of this systematic review was to assess the prognostic value of MCT1 immunoexpression in different malignancies. Study collection was performed by searching nine different databases (PubMed, EMBASE, ScienceDirect, Scopus, Cochrane Library, Web of Science, OVID, TRIP and PsycINFO), using the keywords "cancer", "Monocarboxylate transporter 1", "SLC16A1" and "prognosis". Results showed that MCT1 is an indicator of poor prognosis and decreased survival for cancer patients in sixteen types of malignancies; associations between the transporter's overexpression and larger tumour sizes, higher disease stage/grade and metastasis occurrence were also frequently observed. Yet, MCT1 overexpression correlated with better outcomes in colorectal cancer, pancreatic ductal adenocarcinoma and non-small cell lung cancer patients. These results support the applicability of MCT1 as a biomarker of prognosis, although larger cohorts would be necessary to validate the overall role of MCT1 as an outcome predictor.

Reproductive seasonality of hair rams under tropical conditions: an alternative for non-seasonal lamb production?

Reproductive seasonality limits the periods of breeding on the year and, therefore, productive output. However, some breeds appear as probably non-seasonal. The aim of the study was to characterize the seasonal pattern of Santa Inês rams, including an ultrasound characterization of the reproductive tract, testosterone concentrations, and semen characteristics. Fifteen Santa Inês rams remained in a grazing system with concentrate supplementation, and measurements of the reproductive tract and ultrasound evaluation (biometrics and pixel intensity) of the testicles and accessory sex glands were monthly recorded. Computerized seminal evaluations were also performed monthly, and serum testosterone concentration was measured every 15 days. Body weight and condition remained stable throughout the year. In general, reproductive traits varied along the year and reached maximum values during autumn and minimum in spring. Despite that, as fresh semen remained with enough quality to breed all along the year, seasonality does not appear as a limiting factor to breed along the year. Therefore, Santa Inês rams can be used for all-year-round breeding or for crossbreeding when rams from other breeds decrease their fertilizing ability.

Novel Machado-Joseph disease-modifying genes and pathways identified by whole-exome sequencing.

Machado-Joseph disease (MJD/SCA3) is a neurodegenerative polyglutamine disorder exhibiting a wide spectrum of phenotypes. The abnormal size of the (CAG)n at ATXN3 explains ~55% of the age at onset variance, suggesting the involvement of other factors, namely genetic modifiers, whose identification remains limited. Our aim was to find novel genetic modifiers, analyse their epistatic effects and identify disease-modifying pathways contributing to MJD variable expressivity. We performed whole-exome sequencing in a discovery sample of four age at onset concordant and four discordant first-degree relative pairs of Azorean patients, to identify candidate variants which genotypes differed for each discordant pair but were shared in each concordant pair. Variants identified by this approach were then tested in an independent multi-origin cohort of 282 MJD patients. Whole-exome sequencing identified 233 candidate variants, from which 82 variants in 53 genes were prioritized for downstream analysis. Eighteen disease-modifying pathways were identified; two of the most enriched pathways were relevant for the nervous system, namely the neuregulin signaling and the agrin interactions at neuromuscular junction. Variants at PARD3, NFKB1, CHD5, ACTG1, CFAP57, DLGAP2, ITGB1, DIDO1 and CERS4 modulate age at onset in MJD, with those identified in CFAP57, ACTG1 and DIDO1 showing consistent effects across cohorts of different geographical origins. Network analyses of the nine novel MJD modifiers highlighted several important molecular interactions, including genes/proteins previously related with MJD pathogenesis, namely between ACTG1/APOE and VCP/ITGB1. We describe novel pathways, modifiers, and their interaction partners, providing a broad molecular portrait of age at onset modulation to be further exploited as new disease-modifying targets for MJD and related diseases.

Longitudinal bidirectional relationship between children's appetite and diet quality: A prospective cohort study.

This study explored the relationship between eating behaviours and diet quality, from 7 to 10y. The sample included 3879 children evaluated, from the Portuguese birth cohort Generation XXI, with complete information on Food Frequency Questionnaire (FFQ) and the Children Eating Behaviour Questionnaire (CEBQ). A healthy eating index (HEI) (range: 9-31) was developed to evaluate the child's diet quality. The eight CEBQ's eating behaviours were included in the analysis. Generalised linear models were used to estimate the associations. Adjusted structural equation modelling was performed to study the direction of the associations. Eating behaviours measured at age 7y tracked to age 10y. A bidirectional inverse association between the food fussiness trait and diet quality was found, with a similar magnitude (p < 0.001), thus a higher score on food fussiness predicted a lower quality of the diet. A higher HEI score at 7y was associated with lower satiety responsiveness at 10y (ßstandardized = -0.039), and a higher HEI score at age 10y was associated with higher enjoyment of food at 7y (ßstandardized = 0.046). Eating behaviours were linked to diet quality in children with high enjoyment of food and low food fussiness at age 7y linked to a high-quality diet at 10y. In addition, children with high-quality diets at 7y were more likely to have lower food fussiness and satiety responsiveness at 10y. Given the stability of eating behaviours and the bidirectional nature of effects between eating behaviours and dietary quality in the long term, tailored interventions may be needed to encourage the acquisition of healthy eating behaviours and habits in early life.

Associations between serum biomarkers of cartilage metabolism and serum hyaluronic acid, with risk factors, pain categories, and disease severity in knee osteoarthritis: a pilot study.

BACKGROUND: Specific serum biomarkers of cartilage metabolism such as cartilage oligomeric matrix protein (sCOMP) and procollagen type II C-terminal propeptide (sPIICP) as well as hyaluronan (sHA), a biomarker of synovitis, have been implicated in the pathophysiology of knee osteoarthritis (OA). However, the associations of these biomarkers with the severity of the disease and OA risk factors, including age and obesity remain inconclusive. This analysis examines the associations between these serum biomarkers and the radiographic severity of OA and knee pain, as wells as obesity, the age and gender of the participants, and other OA risk factors. METHODS: From 44 patients with early knee OA and 130 patients with late knee OA we analyzed the radiographic severity of the disease using the Kellgren and Lawrence (KL) grading system. Moreover, 38 overweight healthy individuals were used as a control group. Specific information was collected from all participants during their recruitment. The levels of the three serum biomarkers were quantified using commercially available ELISA kits. Serum biomarkers were analyzed for associations with the average KL scores and pain in both knees, as well as with specific OA risk factors. RESULTS: The levels of sCOMP were elevated in patients with severe late OA and knee pain and correlated weakly with OA severity. A weakly correlation of sHA levels and OA severity OA was observed. We demonstrated that only sPIICP levels were markedly decreased in patients with late knee OA suggesting the alterations of cartilage metabolism in this arthritic disease. Moreover, we found that sPIICP has the strongest correlation with obesity and the severity of OA, as well as with the knee pain at rest and during walking regardless of the severity of the disease. ROC analysis showed that the area under the ROC curve (AUC) was 0.980 (95% CI: 0.945-0.995; p < 0.0001), suggesting high diagnostic accuracy of sPIICP. Interestingly, gender and age had also an effect on the levels of sPIICP. CONCLUSION: This study revealed the potential of serum PIICP to be used as a biomarker to monitor the progression of knee OA, however, further studies are warranted to elucidate its clinical implication.

Selective Cytotoxicity of Portuguese Propolis Ethyl Acetate Fraction towards Renal Cancer Cells.

Renal cell carcinoma is the most lethal cancer of the urological system due to late diagnosis and treatment resistance. Propolis, a beehive product, is a valuable natural source of compounds with bioactivities and may be a beneficial addition to current anticancer treatments. A Portuguese propolis sample, its fractions (n-hexane, ethyl acetate, n-butanol and water) and three subfractions (P1-P3), were tested for their toxicity on A498, 786-O and Caki-2 renal cell carcinoma cell lines and the non-neoplastic HK2 kidney cells. The ethyl acetate fraction showed the strongest toxicity against A498 (IC50 = 0.162 µg mL-1) and 786-O (IC50 = 0.271 µg mL-1) cells. With similar toxicity against 786-O, P1 (IC50 = 3.8 µg mL-1) and P3 (IC50 = 3.1 µg mL-1) exhibited greater effect when combined (IC50 = 2.5 µg mL-1). Results support the potential of propolis and its constituents as promising coadjuvants in renal cell carcinoma treatment.