Biology subjects

Lozano, A.

Publications and source records attributed to Lozano, A..

At least 19 recordsLinked to original sources

bioRxiv2026

B cells sustain tumor-specific CD4 T cells to promote response to PD-1 targeted therapy

Immunotherapy transformed cancer treatment, yet precise correlates of response remain to be defined. While intratumoral follicular helper like (Tfhl) CD4 T cells and IgG1+ B cells have been associated with improved outcomes to PD-1 blockade for hepatocellular carcinoma (HCC), their mechanisms contributing to response are unclear. To address this question, we developed a murine model of HCC and examined CD8, CD4 and B cell responses. Increasing CD4-help sensitizes mice to PD-1 blockade, in a CD4- and CD8-dependent manner. Tumor-specific CD4 T cells contained Tfhl and Th1 populations, and both Bcl6 and T-bet were required for efficacy. Antigen-specific B cells were essential for CD4-helper expansion, yet secretion of antibodies was not required for long-term survival. Thus, B cells are critical for effective immunotherapy, but secreted antibodies are not.

immunology↗

bioRxiv2026

Cell-type-specific coupling of single-unit spikes and cortical ripples in macaque and human V1

Cortical ripples are brief high-frequency (80-150 Hz) oscillatory bursts in the extracellular potential, associated with memory and attention. So far, it is unclear whether they are brain-state specific and which neuron types participate in their generation. We report cortical ripples in macaque V1 at rest, present with both eyes open and closed. Using massively parallel Utah array recordings, we identify six single-unit classes and describe their relation to ripple-band oscillations. The classes differ in firing-rate modulation, phase-locking strength, and preferred ripple-band phase. One class of putative deep-layer neurons exhibits non-sinusoidal phase locking, explained by a simple thresholding mechanism in a computational model. During ripples, most units fire irregularly with transient regular bursts. The ripple-associated spiking organisation is largely preserved during natural-image viewing and confirmed in V1 of a blind human. Together, these findings suggest cortical ripples arise from a shared circuit conserved across species and conditions.

neuroscience↗

bioRxiv2026

CellFluxV2: An Image Generative Foundation Model for Virtual Cell Modeling

Building a virtual cell that simulates cellular behavior in silico is a central goal of computational biology. We introduce CellFluxV2, an image-generative model that predicts how cell morphologies change in response to chemical and genetic perturbations. A core innovation of CellFluxV2 is to learn distribution-level transformations from unperturbed to perturbed cells within the same experimental batch using flow matching, enabling it to disentangle true perturbation effects from confounding batch effects. Incorporating three methodological advances, CellFluxV2 achieves up to a 77% improvement in image fidelity over diffusion- and GAN-based baselines, while maintaining biological fidelity comparable to ground-truth images. Scaling up CellFluxV2, we establish the first scaling laws in image-based virtual cell modeling, demonstrating that performance improves consistently with both dataset size and model capacity. Furthermore, the scaled-up model generalizes well to out-of-distribution perturbations and exhibits two novel capabilities: batch-effect correction and cell-state interpolation. Together, these results position CellFluxV2 as a powerful foundation model advancing the vision of a virtual cell, unlocking novel opportunities for in silico drug screening.

bioinformatics↗

bioRxiv2026

Focal brain stimulation sites that modify autonomic arousal map to a convergent brain circuit and potential therapeutic target

Causal modulation of autonomic outflow could yield new therapeutic targets for autonomic hyperactivation. We employed three natural experiments in which different brain regions were targeted using transcranial magnetic stimulation (TMS) (n=139 sites, n=14 individuals), deep brain stimulation (n=392 sites, n=58 individuals), or low-intensity focused ultrasound (n=46 sites, n=23 individuals) with subsequent autonomic measurements. Using a human connectome database (n=1000) as a wiring diagram, we identified a convergent brain circuit that, when focally modulated, transiently reduces autonomic arousal. This circuit significantly resembled previously reported causal circuits for posttraumatic stress disorder (PTSD) and anxiety. In independent datasets, TMS to the autonomic arousal circuit reduced laboratory startle in healthy volunteers (n=28), lesions to this circuit reduced exaggerated startle in PTSD (n=193), and TMS to this circuit reduced anxiety-related autonomic symptoms in patients with clinically significant anxiety (n=30). Thus, the convergent circuit may serve as a potential neuromodulation target for autonomic hyperactivation.

neuroscience↗

bioRxiv2025

Bacteroidetes promote hepatocellular carcinoma progression and resistance to immunotherapy

Background and AimsGrowing evidence highlight the critical role of the gut microbiome in tumorigenesis and response to immunotherapies. However, the impact of gut microbes on hepatocellular carcinoma (HCC) progression and response to immune-checkpoint blockade (ICB) remains unclear due to the lack of combined preclinical and clinical studies. Approach & ResultsWe performed 16S rRNA of cross-cohort stool samples from 10 HCC responders (R) and 40 non-responders (NR) to ICB at baseline and on-treatment time-points. We identified an enrichment of Bacteroidetes in NR. To study the role of the microbiome in the cancer immune response, we generated an immunogenic mouse model of HCC via hydrodynamic tail-vein injection (HDTVI) of DNA plasmids mimicking common HCC alterations and immunogenicity by expressing model antigens (MYC-lucOS;CTNNB1 tumors). We found that antibiotic (ABX)-induced dysbiosis promoted a pro-tumorigenic effect in the MYC-lucOS;CTNNB1 HCC model by the expansion of a specific Bacteroidetes, Parabacteroides distasonis. Colonization of mice carrying MYC-lucOS;CTNNB1 HCCs with Parabacteroides distasonis confirmed its pro-tumorigenic effect in vivo. Furthermore, we explored the effects of colonizing with microbiotas from patients and showed that microbiota from a NR donor enriched in Bacteroidetes promoted faster tumorigenesis than microbiota from a R donor with reduced Bacteroidetes. We isolated 6 Bacteroidetes species from the NR donor, cultured them, and used them as a cocktail to colonize mice; similarly, mice transplanted with this cocktail showed increased tumorigenesis and reduced survival. ConclusionsThis study identified Bacteroidetes enrichment as a potential biomarker of ICB resistance in HCC and, by using immunogenic mouse models, established that Bacteroidetes abundance influences tumor development.

cancer biology↗

bioRxiv2025

Deep Learning-Based Control of Electrically Evoked Activity in Human Visual Cortex

Visual cortical prostheses offer a promising path to sight restoration, but current systems elicit crude, variable percepts and rely on manual electrode-by-electrode calibration that does not scale. This work introduces an automated data-driven neural control method for a visual neuroprosthesis using a deep learning framework to generate optimal multi-electrode stimulation patterns that evoke targeted neural responses. Using a 96-channel Utah electrode array implanted in the occipital cortex of a blind participant, we trained a deep neural network to predict single-trial evoked responses. The network was used in two complementary control strategies: a learned inverse network for real-time stimulation synthesis and a gradient-based optimizer for precise targeting of desired neural responses. Both approaches significantly outperformed conventional methods in controlling neural activity, required lower stimulation currents, and adapted stimulation parameters to resting state data, reliably evoking more stable percepts. Crucially, recorded neural responses better predicted perceptual outcomes than stimulation parameters alone, underscoring the value of our neural population control framework. This work demonstrates the feasibility of data-driven neural control in a human implant and offers a foundation for next-generation, model-driven neuroprosthetic systems, capable of enhancing sensory restoration across a range of clinical applications.

neuroscience↗

bioRxiv2025

Coping strategy dynamics and resilience profiles after early life stress revealed by behavioral sequencing

Animal models can provide valuable insights into the mechanisms underlying stress-related disorders. Yet, significant translational challenges persist, as laboratory behavioral assays are often reductionistic, with limited attention to ethologically relevant behavioral diversity. Recent advances in high-throughput pose-estimation tools and computational ethology methods are addressing this limitation by enhancing the resolution and validity of behavioral phenotyping. In this context, it is known that early life stress (ELS) reshapes how animals handle subsequent threats later in life, but the fine-scale dynamics and ethological details of this shift remain elusive. To overcome this, we combined a deep-learning pose-estimation pipeline (DeepLabCut) with a supervised freezing classifier (SimBA) and an unsupervised behavioral motifs identification platform (keypoint MoSeq) to study in detail the diversity and dynamics of behavior in an auditory fear-conditioning (FC) paradigm in two independent cohorts of adult male mice that were exposed to ELS through the limited bedding and nesting (LBN) paradigm. We first validated the blunted freezing response after ELS in a supervised manner using SimBA. Next, keypoint MoSeq segmented the same pose-estimation data into ethologically meaningful motifs over time. When compared to control animals, ELS offspring showed an altered FC response, reduced behavioral entropy and limited diversity in their behavioral repertoire. Such response was characterized by longer active-behavior bouts and more recurrent transitions between states, indicating a more stereotyped and predictable response. Multidimensional scaling of time-binned behavioral vectors and distance metrics identified a resilient subpopulation within the ELS group that displayed a control-like behavioral profile, characterized by a steeper increase in freezing behavior during the FC task and a more diverse behavioral repertoire with reduced recurrence of stereotyped actions, less frequent and shorter active bouts and prolonged passive responses. Overall, our findings suggest that ELS shifts the balance between passive and active coping strategies and that resilience is marked by a less stereotypical yet more diverse and flexible behavioral response to a subsequent stressful demand. Finally, we further validated the unsupervised behavioral motifs with a predictive model that identified distinctive kinematic features of these responses, which could be used to build new behavioral classifiers that can be applied in other behavioral paradigms. These results demonstrate the potential of computational ethology to dissect complex behavioral patterns and improve our understanding of individual stress responses. By combining supervised and unsupervised behavioral analysis tools, we can deepen our understanding of the latent structure of stress behavior and identify objective markers of vulnerability and resilience.

neuroscience↗

bioRxiv2025

A machine-learning-guided hydrogen-bonded organic framework for long-term, ultrasound-triggered pain therapy

Effective treatment of chronic pain remains hindered by the lack of drug delivery systems that simultaneously achieve long-term stability, high spatial precision, and non-invasiveness1,2,3. Here, we utilize a programmable, ultrasound-responsive drug delivery platform based on hydrogen-bonded organic framework (HOF) nanoparticles, enabling on-demand anesthetic release with long-term and durable analgesic efficacy. A machine learning (ML)-guided screening pipeline was developed to evaluate about 250 FDA-approved drugs, spanning both hydrophilic and lipophilic agents, and identified bupivacaine (lipophilic) and lidocaine hydrochloride (hydrophilic) as optimal candidates. These agents were efficiently encapsulated into HOF nanoparticles via diffusion and double-solvent methods. Ultrasound-triggered drug release in vitro transiently suppressed calcium signaling in adeno-associated virus (AAV)-transfected neurons. In vivo, ultrasound-activated release of bupivacaine or lidocaine HCl at the sciatic nerve site significantly elevated mechanical nociceptive thresholds for up to seven days, reduced autotomy (self-mutilaion) behavior, and improved motor function in a rat model of chronic pain. Notably, the machine learning-identified top candidates not only exhibited high loading efficiency, but also demonstrated superior therapeutic outcomes in vivo, establishing a direct link between computational prediction and biological efficacy. This non-invasive, programmable HOF-based system provides a clinically translatable platform for on-demand, spatiotemporally precise pain management.

bioengineering↗

bioRxiv2025

Targeting ZNRF3 and RNF43 to Restore Regeneration and Reverse Metabolic Dysfunction-Associated Steatotic Liver Disease

Liver regeneration and hepatocyte metabolic identity are disrupted in metabolic dysfunction-associated steatotic liver disease (MASLD) and its advanced form, metabolic dysfunction-associated steatohepatitis (MASH), yet the mechanisms of restore liver regeneration and reprogram metabolism for disease reversal remains poorly understood. Here, we show that {beta}-Catenin activity progressively declines in hepatocytes during MASH in both humans and mice, coinciding with impaired regeneration and defective lipid clearance. Targeted deletion of the endogenous WNT pathway inhibitors ZNRF3 and RNF43 in hepatocytes after MASH onset reactivates {beta}-Catenin signaling, leading to robust regression of steatosis, inflammation, and fibrosis, and restoring regenerative capacity across multiple fatty liver disease models. Mechanistically, this therapeutic effect is driven by {beta}-Catenin-dependent induction of the alternative bile acid synthesis pathway without disrupting systemic lipid homeostasis. Importantly, both short-and long-term deletion of ZNRF3/RNF43 restores liver function without triggering tumorigenesis or hepatotoxicity, indicating a safe therapeutic window. These findings reveal that physiological activation of WNT/{beta}-catenin signaling via ZNRF3 and RNF43 offers a viable regenerative and metabolic strategy for reversing fatty liver disease.

cell biology↗

bioRxiv2024

NOTCH1 drives tumor plasticity and metastasis in hepatocellular carcinoma

Background & AimsLiver cancer, the third leading cause of cancer-related mortality worldwide, has two main subtypes: hepatocellular carcinoma (HCC), accounting the majority of the cases, and cholangiocarcinoma (CAA). Notch pathway primarily regulates the intrahepatic development of bile ducts, which are lined with cholangiocytes, but it can also be upregulated in 1/3 of HCCs. To better understand the role of NOTCH1 in HCC, we developed a novel mouse model driven by activated Notch1 intracellular domain (NICD1) and MYC overexpression in hepatocytes. MethodsUsing the hydrodynamic tail-vein injection method for establishing primary liver tumors, we generated a novel murine model of liver cancer harboring MYC overexpression and NOTCH1 activation. We characterized this model histopathologically as well as transcriptomically, utilizing both bulk and single cell RNA-sequencing. We also performed functional experiments using monoclonal antibodies. ResultsMYC;NICD1 tumors displayed a combined HCC-CCA phenotype with temporal plasticity. At early time-points, histology was predominantly "cholangiocellular", which then progressed to mainly "hepatocellular". The "hepatocellular" component was enriched in mesenchymal genes and gave rise to lung metastasis. Metastatic cells were enriched in the TGFB and VEGF pathways and their inhibition significantly reduced the metastatic burden. ConclusionsOur novel mouse model uncovered NOTCH1 as a driver of temporal plasticity and metastasis in HCC, the latter of which is, in part, mediated by angiogenesis and TGF{beta} pathways. Impact and ImplicationsThis study develops a novel murine model of NOTCH1-driven liver cancer, an understudied oncogene in HCC. Using this model, we show that NOTCH1 drives plasticity in HCC and metastasis to the lungs that can be therapeutically targeted through inhibition of VEGF and TGF{beta} pathways. HighlightsO_LINOTCH1 activation in combination with MYC overexpression drives combined HCC-CCA. C_LIO_LINOTCH1 activation in hepatocytes drives temporal plasticity. C_LIO_LINOTCH1 activation drives metastasis of HCC cells to the lungs, but not of CCA cells. C_LIO_LIAngiogenesis and TGF{beta} pathways mediate NOTCH1-induced lung metastasis. C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=93 SRC="FIGDIR/small/619856v2_ufig1.gif" ALT="Figure 1"> View larger version (39K): org.highwire.dtl.DTLVardef@143c866org.highwire.dtl.DTLVardef@119ddb6org.highwire.dtl.DTLVardef@12ad951org.highwire.dtl.DTLVardef@2160b8_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2024

Optimal placement of high-channel visual prostheses in human retinotopic visual cortex

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSObjectiveC_ST_ABSRecent strides in neurotechnology show potential to restore vision in individuals afflicted with blindness due to early visual pathway damage. As neuroprostheses mature and become available to a larger population, manual placement and evaluation of electrode designs becomes costly and impractical. An automatic method to optimize the implantation process of electrode arrays at large-scale is currently lacking. ApproachHere, we present a comprehensive method to automatically optimize electrode placement for visual prostheses, with the objective of matching pre-defined phosphene distributions. Our approach makes use of retinotopic predictions combined with individual anatomy data to minimize discrepancies between simulated and target phosphene patterns. While demonstrated with a 1000-channel 3D electrode array in V1, our pipeline is versatile, potentially accommodating any electrode design and allowing for design evaluation. Main resultsNotably, our results show that individually optimized placements in 362 brain hemi-spheres outperform average brain solutions, underscoring the significance of anatomical specificity. We further show how virtual implantation of multiple individual brains highlights the challenges of achieving full visual field coverage owing to single electrode constraints, which may be overcome by introducing multiple arrays of electrodes. Including additional surgical considerations, such as intracranial vasculature, in future iterations could refine the optimization process. SignificanceOur open-source software streamlines the refinement of surgical procedures and facilitates simulation studies, offering a realistic exploration of electrode configuration possibilities.

neuroscience↗

bioRxiv2023

Brain2GAN: Feature-disentangled neural coding of visual perception in the primate brain

A challenging goal of neural coding is to characterize the neural representations underlying visual perception. To this end, multi-unit activity (MUA) of macaque visual cortex was recorded in a passive fixation task upon presentation of faces and natural images. We analyzed the relationship between MUA and latent representations of state-of-the-art deep generative models, including the conventional and feature-disentangled representations of generative adversarial networks (GANs) (i.e., z- and w-latents of StyleGAN, respectively) and language-contrastive representations of latent diffusion networks (i.e., CLIP-latents of Stable Diffusion). A mass univariate neural encoding analysis of the latent representations showed that feature-disentangled w representations outperform both z and CLIP representations in explaining neural responses. Further, w-latent features were found to be positioned at the higher end of the complexity gradient which indicates that they capture visual information relevant to high-level neural activity. Subsequently, a multivariate neural decoding analysis of the feature-disentangled representations resulted in state-of-the-art spatiotemporal reconstructions of visual perception. Taken together, our results not only highlight the important role of feature-disentanglement in shaping high-level neural representations underlying visual perception but also serve as an important benchmark for the future of neural coding. Author summaryNeural coding seeks to understand how the brain represents the world by modeling the relationship between stimuli and internal neural representations thereof. This field focuses on predicting brain responses to stimuli (neural encoding) and deciphering information about stimuli from brain activity (neural decoding). Recent advances in generative adversarial networks (GANs; a type of machine learning model) have enabled the creation of photorealistic images. Like the brain, GANs also have internal representations of the images they create, referred to as "latents". More recently, a new type of feature-disentangled "w-latent" of GANs has been developed that more effectively separates different image features (e.g., color; shape; texture). In our study, we presented such GAN-generated pictures to a macaque with cortical implants and found that the underlying w-latents were accurate predictors of high-level brain activity. We then used these w-latents to reconstruct the perceived images with high fidelity. The remarkable similarities between our predictions and the actual targets indicate alignment in how w-latents and neural representations represent the same stimulus, even though GANs have never been optimized on neural data. This implies a general principle of shared encoding of visual phenomena, emphasizing the importance of feature disentanglement in deeper visual areas.

neuroscience↗

bioRxiv2022

Biologically plausible phosphene simulation forthe differentiable optimization of visual corticalprostheses

Blindness affects millions of people around the world, and is expected to become increasingly prevalent in the years to come. For some blind individuals, a promising solution to restore a form of vision are cortical visual prostheses, which convert camera input to electrical stimulation of the cortex to bypass part of the impaired visual system. Due to the constrained number of electrodes that can be implanted, the artificially induced visual percept (a pattern of localized light flashes, or phosphenes) is of limited resolution, and a great portion of the fields research attention is devoted to optimizing the efficacy, efficiency, and practical usefulness of the encoding of visual information. A commonly exploited method is the non-invasive functional evaluation in sighted subjects or with computational models by making use of simulated prosthetic vision (SPV) pipelines. Although the SPV literature has provided us with some fundamental insights, an important drawback that researchers and clinicians may encounter is the lack of realism in the simulation of cortical prosthetic vision, which limits the validity for real-life applications. Moreover, none of the existing simulators address the specific practical requirements for the electrical stimulation parameters. In this study, we developed a PyTorch-based, fast and fully differentiable phosphene simulator. Our simulator transforms specific electrode stimulation patterns into biologically plausible representations of the artificial visual percepts that the prosthesis wearer is expected to see. The simulator integrates a wide range of both classical and recent clinical results with neurophysiological evidence in humans and non-human primates. The implemented pipeline includes a model of the retinotopic organisation and cortical magnification of the visual cortex. Moreover, the quantitative effect of stimulation strength, duration, and frequency on phosphene size and brightness as well as the temporal characteristics of phosphenes are incorporated in the simulator. Our results demonstrate the suitability of the simulator for both computational applications such as end-to-end deep learning-based prosthetic vision optimization as well as behavioural experiments. The modular approach of our work makes it ideal for further integrating new insights in artificial vision as well as for hypothesis testing. In summary, we present an open-source, fully differentiable, biologically plausible phosphene simulator as a tool for computational, clinical and behavioural neuroscientists working on visual neuroprosthetics.

neuroscience↗

bioRxiv2022

Modelling Instantaneous Firing Rate of DBS Target Neuronal Ensembles in Basal Ganglia and Thalamus

ObjectiveDeep Brain Stimulation (DBS) is an effective treatment for movement disorders, including Parkinsons disease and essential tremor. However, the underlying mechanisms of DBS remain elusive. Despite the capability of existing models in interpreting experimental data qualitatively, there are very few unified computational models that quantitatively capture the dynamics of the neuronal activity of varying stimulated nuclei--including subthalamic nucleus (STN), substantia nigra pars reticulata (SNr) and ventral intermediate nucleus (Vim)--across different DBS frequencies. Materials and MethodsBoth synthetic and experimental data were utilized in the model fitting; the synthetic data were generated by an established spiking neuron model that was reported in our previous work 1, and the experimental data were provided using single-unit microelectrode recordings (MER) during DBS (microelectrode stimulation). Based on these data, we developed a novel mathematical model to represent the firing rate of neurons receiving DBS, including neurons in STN, SNr and Vim--across different DBS frequencies. In our model, the DBS pulses were filtered through a synapse model and a nonlinear transfer function to formulate the firing rate variability. For each DBS-targeted nucleus, we fitted a single set of optimal model parameters consistent across varying DBS frequencies. ResultsOur model accurately reproduced the firing rates observed and calculated from both synthetic and experimental data. The optimal model parameters were consistent across different DBS frequencies. ConclusionThe result of our model fitting were in agreement with experimental single-unit microelectrode recording data during DBS. Reproducing neuronal firing rates of different nuclei of the basal ganglia and thalamus during DBS can be helpful to further understand the mechanisms of DBS, and to potentially optimize stimulation parameters based on their actual effects on neuronal activity.

neuroscience↗

bioRxiv2021

Persistent synaptic inhibition of the subthalamic nucleus by high frequency stimulation

BackgroundDeep brain stimulation (DBS) provides symptomatic relief in a growing number of neurological indications, but local synaptic dynamics in response to electrical stimulation that may relate to its mechanism of action have not been fully characterized. ObjectiveThe objectives of this study were to (1) study local synaptic dynamics during high frequency extracellular stimulation of the subthalamic nucleus (STN), and (2) compare STN synaptic dynamics with those of the neighboring substantia nigra pars reticulata (SNr). MethodsTwo microelectrodes were advanced into the STN and SNr of patients undergoing DBS surgery for PD. Neuronal firing and evoked field potentials (fEPs) were recorded with one microelectrode during stimulation from an adjacent microelectrode. ResultsExcitatory and inhibitory fEPs could be discerned within the STN and their amplitudes predicted bidirectional effects on neuronal firing (p = .007). There were no differences between STN and SNr inhibitory fEP dynamics at low stimulation frequencies (p > .999). However, inhibitory neuronal responses were sustained over time in STN during high frequency stimulation, but not SNr (p < .001) where depression of inhibitory input was coupled with a return of neuronal firing (p = .003). InterpretationPersistent inhibitory input to the STN suggests a local synaptic mechanism for the suppression of subthalamic firing during high frequency stimulation. Moreover, differences in the resiliency versus vulnerability of inhibitory inputs to the STN and SNr suggest a projection source- and frequency-specificity for this mechanism. The feasibility of targeting electrophysiologically-identified neural structures may provide insight into how DBS achieves frequency-specific modulation of neuronal projections.

neuroscience↗

bioRxiv2021

Ras/MAPK signalling intensity defines subclonal fitness in a mouse model of primary and metastatic hepatocellular carcinoma

Quantitative differences in signal transduction are to date an understudied feature of tumour heterogeneity. The MAPK Erk pathway, which is activated in a large proportion of human tumours, is a prototypic example of distinct cell fates being driven by signal intensity. We have used primary hepatocyte precursors transformed with different dosages of an oncogenic form of Ras to model subclonal variations in MAPK signalling. Orthotopic allografts of Ras-transformed cells in immunocompromised mice gave rise to fast-growing aggressive tumours, both at the primary location and in the peritoneal cavity. Fluorescent labelling of cells expressing different oncogene levels, and consequently varying levels of MAPK Erk activation, highlighted the selection processes operating at the two sites of tumour growth. Indeed, significantly higher Ras expression was observed in primary as compared to metastatic tumours, despite the evolutionary trade-off of increased apoptotic death in the liver that correlated with high Ras dosage. Analysis of the immune tumoral microenvironment at the two locations suggests that fast metastatic growth in the immunocompromised setting is abrogated in immunocompetent animals due to efficient antigen presentation by peritoneal dendritic cells. Furthermore, our data indicate that, in contrast to the metastatic outgrowth, strong MAPK signalling is required in the primary liver tumours to resist elimination by NK cells. Overall, this study describes a quantitative aspect of tumour heterogeneity and highlights potential vulnerability of a subtype of hepatocellular carcinoma as a function of MAPK Erk signalling intensity.

cancer biology↗

bioRxiv2019

Investigation of DPPC liposomes reveals their capability to entrap Aroclor 1260, an emerging environmental pollutant

Persistent organic pollutants (POPs) are a class of organic compounds that can accumulate in biological and ecological environments due to their resistive nature to chemical, thermal and photo degradation. Polychlorinated biphenyls (PCBs) are a class of man-made POPs that saw wide-spread use in commercial and industrial infrastructure as both an insulator and coolant in electrical transformers and capacitors. 2,2,3,3,4,4-hexachlorobiphenyl (HCBP) was one of the most widely produced PCBs. As these mechanical structures fail or are decommissioned, PCBs are released into the soil, migrate to the water table, and eventually spread to nearby ecosystems by rain and wind. The stability of POPs and specifically PCBs leave few options for environmental waste removal. Conventionally, liposomes have been used for their drug delivery capabilities, but here we have chosen to investigate their capability in removing this class of emerging environmental pollutants. Liposomes are small, nonpolar lipid bi-layered aggregates capable of capturing a wide variety of both polar and nonpolar compounds. Dipalmitoylphosphatidylcholine (DPPC) is a well-characterized lipid that can be derived from natural sources. It is a phospholipid typically found as a major component of pulmonary surfactant mixtures. Liposomes were prepared using probe-tip sonication for both direct and passive incorporation of the HCBP compound. Assimilation was assessed using both differential scanning calorimetry and UV-Vis spectroscopy. After direct incorporation of HCBP the phase transition temperature, Tm, decreased from 40.8 {degrees}C to 37.4 {degrees}C. A subsequent UV-Vis analysis of HCBP by both direct and passive incorporation showed an increase in HCBP incorporation proportionate to the length of exposure time up to 24 hours and relative to the initial quantity present during the direct incorporation. Together the decrease in Tm and increase in absorbance are indicative of HCBP incorporation and further demonstrate the potential for their use as a method of sustainable environmental cleanup.

biophysics↗

bioRxiv2019

CDK8 and CDK19 kinases have non-redundant oncogenic functions in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a common cancer with high mortality. The limited therapeutic options for advanced disease include treatment with Sorafenib, a multi-kinase inhibitor whose targets include the Mediator kinase CDK8. Since CDK8 has reported oncogenic activity in Wnt-dependent colorectal cancer, we investigated whether it is also involved in HCC. We find that CDK8 and its paralogue CDK19 are significantly overexpressed in HCC patients, where high levels correlate with poor prognosis. Liver-specific genetic deletion of CDK8 in mice is well supported and protects against chemical carcinogenesis. Deletion of either CDK8 or CDK19 in hepatic precursors had little effect on gene expression in exponential cell growth but prevented oncogene-induced transformation. This phenotype was reversed by concomitant deletion of TP53. These data support important and non-redundant roles for mediator kinases in liver carcinogenesis, where they genetically interact with the TP53 tumor suppressor.

cancer biology↗