Biology subjects

Powell, E.

Publications and source records attributed to Powell, E..

17 recordsLinked to original sources

bioRxiv2025

Myeloid HDAC7 drives liver inflammation and systemic glucose dysregulation during diet-induced obesity

ObjectivesHistone deacetylase 7 (HDAC7), a classical HDAC family member, promotes LPS-inducible glycolysis and inflammatory mediator production in macrophages, innate immune cells that contribute to pathology in metabolic diseases. Here, we investigated myeloid HDAC7 functions in obesity-driven metabolic disease. MethodsWe used gain- and loss-of-function genetic approaches in mice to investigate myeloid HDAC7 functions in hepatic inflammation and metabolic disease, as well as associations with hepatic gene signatures characteristic of advanced chronic liver disease (CLD). ResultsTransgenic expression of Hdac7 in myeloid cells increased liver inflammation and liver mRNA levels of Ccl2 and Il1b, key inflammatory mediators linked to CLD. Liver glycogen levels were also decreased, another feature of CLD. Transgenic expression of Hdac7 in myeloid cells mimicked the hepatic inflammatory phenotype that was observed in mice fed a high fat, high cholesterol, and high sucrose (HFHCHS) diet, an obesity model that mimics some features of metabolic dysfunction-associated steatotic liver disease. In myeloid Hdac7 transgenic mice fed a HFHCHS diet, relative weight gain was increased, fasted glucose levels were elevated and glucose tolerance was dysregulated by comparison to control mice. Conversely, fasted blood glucose levels were reduced and glucose tolerance was improved in myeloid Hdac7-deleted mice on a HFHCHS diet. HDAC7 mRNA levels were also elevated in the livers of people with advanced CLD and spatial transcriptomics revealed that myeloid HDAC7 directs hepatic gene signatures characteristic of advanced CLD. ConclusionsMyeloid HDAC7 contributes to hepatic inflammation and systemic glucose dysregulation in a mouse model of obesity and liver inflammation.

immunology↗

bioRxiv2025

VASAL: A Vascular Substrate Algorithm to Generate Microvascular Network Phantoms

To date, few methods have been proposed for synthesising realistic microvascular networks, particularly with compatibility for diffusion MRI (dMRI) simulations. This work presents a generative algorithm for building computational brain microvascular network phantoms, together with a Monte Carlo framework for simulating spin flow dynamics and corresponding dMRI signals. Phantom morphology can be user-tuned according to the: number of generations (branches); total number of segments; segment diameter and length; angle between connecting segments, and; maximum extent in space. The geometric fidelity of resulting VASAL phantoms was validated against an exemplar rat hippocampal network. Comparable distributions of segment lengths, diameters and orientations, and branching characteristics were obtained, demonstrating VASAL replicated realistic microvascular structures. Implementation of spin flow dynamics was validated by simulating dMRI signals in the ballistic regime, which has a well-defined analytical solution; agreement was excellent between simulated signals and analytical solutions (average root mean square error of 0.005). Following this validation of network geometry and spin flow dynamics, VASAL phantoms with different morphologies were generated to evaluate key IVIM model assumptions. Phantom geometry substantially affected dMRI signals and caused deviations from the IVIM model: for example, complex branching phantoms displayed non-Gaussian signal attenuation and diffusion time dependence. While the IVIM model was exemplified here, the presented VASAL algorithm holds potential for advancing other dMRI acquisitions and models: by enabling simulation of complex blood flow within physiologically accurate vascular networks, a route to disentangling how vascular structure and function influence dMRI signals is initiated.

biophysics↗

bioRxiv2025

Hierarchical Bayesian Modelling Improves Microstructural Parameter Mapping in Diffusion and Exchange MRI Data

Microstructure modelling quantifies subvoxel tissue features by combining an MRI acquisition with a mathematical model, which is typically fitted voxel-by-voxel with least-squares (LSQ) minimisation to give voxelwise maps of microstructural quantities such as diffusivity and compartmental fractions. Such approaches are susceptible to voxelwise noise, which can lead to erroneous values in parameter maps. Hierarchical Bayesian modelling (HBM) can address this limitation, but has only been demonstrated for simple models. We previously derived an HBM approach for an arbitrary microstructure model with flexible parameter constraints, utilising a Markov chain Monte Carlo algorithm for parameter estimation; here the method is demonstrated and evaluated using simulated and human data for two previously unexplored diffusion MRI techniques, namely diffusion kurtosis imaging and blood-brain barrier filter exchange imaging. When compared with LSQ minimisation, HBM increased the accuracy, precision, contrast-to-noise ratio, and parameter map quality in both simulated and human data. HBM was also able to resolve local parameter variations associated with white matter lesions in a small sample of cerebral small vessel disease subjects, which were obscured by high noise levels in the LSQ-derived parameter maps. Finally, a noise sensitivity assessment in simulations showed that HBM improved the contrast-to-noise ratio and parameter map quality even at low signal-to-noise ratios. This generalised HBM framework can improve parameter estimation for more complex diffusion MRI microstructural models that extend beyond linear combinations of exponentials.

biophysics↗

bioRxiv2025

Directed evolution of L-DOPA sensing and production enables efficient incorporation of an expanded genetic code into proteins

3,4-dihydroxyphenylalanine (L-DOPA) is a catechol-containing amino acid derived from L-tyrosine that can be introduced into proteins primarily as a post-translational modification. It can also be introduced in a site-specific manner into proteins via orthogonal tRNA synthetase:tRNA pairs. However, despite the interest in expanded genetic codes containing L-DOPA, efforts to improve metabolism relating to its production have been limited. Here, we report and engineer a LysR-family transcription factor, PP_2251, that can serve as a biosensor for L-DOPA, thereby allowing the optimization of genetic circuits for vastly improved L-DOPA production via compartmentalized partnered replication (CPR), an emulsion PCR-based directed evolution scheme. Using this biosensor, a promiscuous E. coli flavin-dependent monooxygenase (HpaB) was optimized via CPR for enhanced L-DOPA biosynthesis. Engineered HpaB variants could support efficient translational incorporation of L-DOPA into proteins with 80% incorporation efficiency and yields in excess of 250 mg.L-1. Improvements in L-DOPA production are not only of great interest for protein engineering and genetic code expansion but should impact the production of DOPA-derived natural products including catecholamine neurotransmitters and benzylisoquinoline alkaloids (BIAs), molecules of intense pharmaceutical interest.

synthetic biology↗

bioRxiv2025

Fractionated radiation alters the extracellular matrix produced by muscle-invasive bladder cancer cells

Muscle-invasive bladder cancer (MIBC) is a prevalent disease that can be treated with radiotherapy, but has a poor prognosis. Radiation-induced extracellular matrix (ECM) remodelling and fibrosis can induce tumour resistance and recurrence, but has not been studied in MIBC. Here we aimed to characterise the impact of radiation on the ECM composition of MIBC. Materials and MethodsThree MIBC cell lines (T24, UMUC3, J82) were treated with fractionated radiation. We used proteomics to analyse the ECM composition produced by surviving cancer cells and immunofluorescence to investigate changes in the morphology and number of ECM fibres. We evaluated the RNA expression of identified ECM proteins (FN1, COL5A1, COL1A1, TNF6AIP6, FLG) in one cystectomy (TCGA-BLCA, n=397) and two radiotherapy (BC2001, n=313; BCON, n=151) cohorts. ResultsThere were 613 proteins affected by radiation (padj<0.05, fold change >2 or <-2), 68 of which were ECM-associated proteins. There was a general increase in proteases and protease regulators but heterogeneity across cell lines. Enrichment analysis showed ECM organisation was the primary pathway affected. Immunofluorescence confirmed radiation affected ECM structure, generally, reducing the number, length and width of fibres. High FN1, COL5A1, COL1A1, TNF6AIP6 and FLG mRNA levels were adverse prognostic markers in clinical cohorts. ConclusionRadiation alters the composition and structure of the ECM produced by MIBC. As a proof-of-concept, we showed the expression of radiation-affected ECM genes are prognostic markers in MIBC. Future studies should validate these radiation-induced ECM changes in clinical samples.

cancer biology↗

bioRxiv2025

Spatial Transcriptomic Signature of Progressive Fibrosis in Human MASLD: Role of Senescence and Metabolic Reprogramming.

Granular detail about the location and nature of liver cell interactions and the metabolic, inflammatory and fibrogenic pathways driving progressive fibrosis in metabolic dysfunction-associated steatotic liver disease (MASLD) is needed to deliver novel therapeutic targets. Here we used spatial transcriptomic data from human MASLD liver biopsies to identify the major cell types and their potential interconnected activities within specific tissue regions across the spectrum of MASLD. Gene expression data were generated using 10X Genomics Visium technology from 33 formalin-fixed paraffin-embedded liver biopsy samples and overlaid with annotated anatomical regions. Differential gene expression (DEG) and pathway analyses, cellular deconvolution and ligand-receptor interactions were conducted for each annotated anatomical category, with specific protein expression validated using CODEX spatial proteomics and immunohistochemistry staining. Unsupervised gene expression data grouped the annotated spots into 2 main clusters enriched for early/intermediate vs late fibrosis and transcriptome-based cellular deconvolution was well aligned with annotated histopathological features. In addition to extracellular matrix/receptor interactions and immune cell recruitment and trafficking, several genes encoding immunoglobulins were highly upregulated in late-stage fibrosis and were spatially associated with a senescence signature. Upregulated DEGs for early/intermediate-stage fibrosis were significantly enriched for metabolic pathways, oxidative phosphorylation and fatty acid metabolism. In contrast glycolysis genes were strongly co-expressed with late stage fibrosis. MASLD progression is accompanied by a decline in normal liver metabolic function and significant reprogramming of metabolic fuel utilisation. The spatial association of a senescence signature with expression of genes encoding immunoglobulins and complement has been linked to aging and is associated with progressive fibrosis. This work provides a valuable discovery dataset spanning different stages of human liver fibrosis and highlights the complex crosstalk between metabolic perturbations and inflammation underpinning fibrosis progression.

genomics↗

bioRxiv2024

Controlled by disorder: phosphorylation modulates SRSF1 domain availability for spliceosome maturation

Serine/arginine-rich splicing factor 1 (SRSF1) is key in the mRNA lifecycle including transcription, splicing, nonsense-mediated decay, and nuclear export. Consequently, its dysfunction is linked to cancers, viral evasion, and developmental disorders. The functionality of SRSF1 relies on its interactions with other proteins and RNA molecules. These processes are regulated by phosphorylation of its unstructured arginine/serine-rich tail (RS). Here, we characterize how phosphorylation affects SRSF1s protein and RNA interaction and phase separation. Using NMR paramagnetic relaxation enhancement and chemical shift perturbation, we find that when unphosphorylated, SRSF1s RS interacts with its first RNA-recognition motif (RRM1). Phosphorylation of RS decreases its interactions with RRM1 and increases its interactions with the RNA-binding site. This change in SRSF1s intramolecular interactions increases the availability of protein-interacting sites on RRM1 and weakens RNA binding of SRSF1. Phosphorylation alters the phase separation of SRSF1 by diminishing the role of arginine in intermolecular interactions. These findings provide an unprecedented view of how SRSF1 influences the early-stage spliceosome assembly. SUMMARYPhosphorylation of SRSF1 is pivotal in pre-mRNA processing and is dysregulated in various pathologies. Modeling of SRSF1 based on NMR restraints reveals phosphorylation alters the accessibility of protein-protein and protein-RNA interaction sites on SRSF1s RRM1 domain, altering its binding preferences

biochemistry↗

bioRxiv2024

An annotated biobank of triple negative breast cancer patient-derived xenografts featuring treatment-naive and longitudinal samples throughout neoadjuvant chemotherapy

Triple negative breast cancer (TNBC) that fails to respond to neoadjuvant chemotherapy (NACT) can be lethal. Developing effective strategies to eradicate chemoresistant disease requires experimental models that recapitulate the heterogeneity characteristic of TNBC. To that end, we established a biobank of 92 orthotopic patient-derived xenograft (PDX) models of TNBC from the tumors of 75 patients enrolled in the ARTEMIS clinical trial (NCT02276443) at MD Anderson Cancer Center, including 12 longitudinal sets generated from serial patient biopsies collected throughout NACT and from metastatic disease. Models were established from both chemosensitive and chemoresistant tumors, and nearly 30% of PDX models were capable of lung metastasis. Comprehensive molecular profiling demonstrated conservation of genomes and transcriptomes between patient and corresponding PDX tumors, with representation of all major transcriptional subtypes. Transcriptional changes observed in the longitudinal PDX models highlight dysregulation in pathways associated with DNA integrity, extracellular matrix interactions, the ubiquitin-proteasome system, epigenetics, and inflammatory signaling. These alterations reveal a complex network of adaptations associated with chemoresistance. This PDX biobank provides a valuable resource for tackling the most pressing issues facing the clinical management of TNBC, namely chemoresistance and metastasis.

cancer biology↗

bioRxiv2024

Development and validation of a computational tool to predict treatment outcomes in cells from High-Grade Serous Ovarian Cancer patients

Treatment of High-Grade Serous Ovarian Cancer (HGSOC) is often ineffective due to frequent late-stage diagnosis and development of resistance to therapy. Timely selection of the most effective (combination of) drug(s) for each patient would improve outcomes, however the tools currently available to clinicians are poorly suited to the task. We here present a computational simulator capable of recapitulating cell response to treatment in ovarian cancer. The technical development of the in silico framework is described, together with its validation on both cell lines and patient-derived laboratory models. A calibration procedure to identify the parameters that best recapitulate each patients response is also presented. Our results support the use of this tool in preclinical research, to provide relevant insights into HGSOC behaviour and progression. They also provide a proof of concept for its use as a personalised medicine tool and support disease monitoring and treatment selection.

bioengineering↗

bioRxiv2023

Quantification of water exchange across the blood-brain barrier using non-contrast MR fingerprinting

PurposeA method is proposed to quantify cerebral blood volume (vb) and intravascular water residence time ({tau}b) using magnetic resonance fingerprinting (MRF), applied using a spoiled gradient echo sequence, without the need for contrast agent. MethodsAn in silico study optimised an acquisition protocol to maximise the sensitivity of the measurement to vb and{tau} b changes. Its accuracy in the presence of variations in T1,t, T1,b, and B1 was evaluated. The optimised protocol (scan time of 19 minutes) was then tested in a exploratory healthy volunteer study (10 volunteers, mean age 24 {+/-} 3, 6 male) at 3 T with a repeat scan taken after repositioning to allow estimation of repeatability. ResultsSimulations show that assuming literature values for T1,b and T1,t, no variation in B1, while fitting only vb and{tau} b, leads to large errors in quantification of vb and{tau} b, regardless of noise levels. However, simulations also show that matching [Formula] and{tau} b, simultaneously is feasible at clinically achievable noise levels. Across the healthy volunteers, all parameter quantifications fell within the expected literature range. In addition, the maps show good agreement between hemispheres suggesting physiologically relevant information is being extracted. Expected differences between white and grey matter T1,t (p<0.0001) and vb (p<0.0001) are observed, T1,b and{tau} b show no significant differences, p=0.4 and p=0.6 repectively. Good repeatability was seen between repeat scans: mean ICC of T1,t : 0.91, T1,b : 0.58, vb : 0.90, and v{tau}b : 0.96. ConclusionWe demonstrate that regional simultaneous quantification of vb,{tau} b, T1,b,T1,t, and B1 using MRF is feasible in vivo.

neuroscience↗

bioRxiv2023

Accurate identification of cancer cells in complex pre-clinical models using deep-learning: a transfection free approach.

3D co-cultures are key tools for in vitro biomedical research as they recapitulate more closely the in vivo environment, while allowing control of the density and type of cells included in the analysis, as well as the experimental conditions in which they are maintained. More widespread application of these models is hampered however by the limited technologies available for their analysis. The separation of the contribution of the different cell types, in particular, is a fundamental challenge. In this work, we present ORACLE, a deep neural network trained to distinguish between ovarian cancer and healthy cells based on the shape of their nucleus. The extensive validation that we have conducted includes multiple cell lines and patient derived cultures to characterise the effect of all the major potential confounding factors. High accuracy and reliability were maintained throughout the analysis demonstrating ORACLE effectiveness with this detection and classification task. ORACLE is freely available (https://github.com/MarilisaCortesi/ORACLE/tree/main) and can be used to recognise both ovarian cancer cell lines and primary patient-derived cells. This feature sets ORACLE apart from currently available analysis methods and opens the possibility of analysing in vitro co-cultures comprised solely of patient-derived cells.

bioengineering↗

bioRxiv2023

Blood-brain barrier water exchange measurements using contrast-enhanced ASL

A technique for quantifying regional blood-brain barrier (BBB) water exchange rates using contrast-enhanced arterial spin labelling (CE-ASL) is presented and evaluated in simulations and in vivo. The two-compartment ASL model describes the water exchange rate from blood to tissue, kb, but to estimate kb in practice it is necessary to separate the intra- and extravascular signals. This is challenging in standard ASL data owing to the small difference in T1 values. Here, a gadolinium-based contrast agent is used to increase this T1 difference and enable the signal components to be disentangled. The optimal post-contrast blood T1 [Formula] at 3T was determined in a sensitivity analysis, and the accuracy and precision of the method quantified using Monte Carlo simulations. Proof-of-concept data were acquired in six healthy volunteers (five female, age range 24 - 46 years). The sensitivity analysis identified the optimal [Formula] at 3T as 0.8 s. Simulations showed kb could be estimated in individual cortical regions with a relative error{epsilon} < 1% and coefficient of variation CoV = 30 %; however, a high dependence on blood T1 was also observed. In volunteer data, mean parameter values in grey matter were: arterial transit time tA = 1.15{+/-}0.49 s, cerebral blood flow f = 58.0{+/-}14.3 ml blood / min / 100 ml tissue, water exchange rate kb = 2.32 {+/-} 2.49 s-1. CE-ASL can provide regional BBB water exchange rate estimates; however, the clinical utility of the technique is dependent on the achievable accuracy of measured T1 values.

biophysics↗

bioRxiv2022

Development of the gut microbiota during early life in premature and term infants

BackgroundThe gastrointestinal (GI) microbiota has been linked to health consequences throughout life, from early life illnesses (e.g. sepsis and necrotising enterocolitis) to lifelong chronic conditions such as obesity and inflammatory bowel disease. It has also been observed that events in early life can lead to shifts in the microbiota, with some of these changes having been documented to persist into adulthood. A particularly extreme example of a divergent early GI microbiota occurs in premature neonates, who display a very different GI community to term infants. Certain characteristic patterns have been associated with negative health outcomes during the neonatal period, and these patterns may prove to have continual damaging effects if not resolved. ResultsIn this study we contrast a set of premature infants with a paired set of term infants (n=37 pairs) at six weeks of life and at two years. In the samples taken at six weeks we find microbial communities differing in both diversity and specific bacterial groups between the two infant cohorts. We identify clinical factors associated with over-abundance of potentially pathogenic organisms (e.g. Enterobacteriaceae) and reduced abundances of some beneficial organisms (e.g. Bifidobacterium). We contrast these findings with samples taken at two years of age, which show that despite a very different initial microbiota, the two infant groups converge to a similar, more adult-like state. We identify clinical factors, including both prematurity and delivery method, that remain associated with components of the microbiota. Both clinical factors and microbial characteristics are compared to the occurrence of childhood wheeze and eczema, revealing associations between components of the GI microbiota and the development of these allergic conditions. ConclusionsThe faecal microbiota differs greatly between infants born at term and those born prematurely during early life, yet it converges over time. Despite this, early clinical factors remain significantly associated with the abundance of some bacterial groups at two years of age. Given the associations made between health conditions and the microbiota, factors that alter the makeup of the gut microbiota, and potentially its trajectory through life, could have important lifelong consequences.

genomics↗

bioRxiv2021

Pixelwise H-score: a novel digital image analysis based-metric to quantify membrane biomarker expression from immunohistochemistry images

Immunohistochemistry (IHC) assays play a central role in evaluating biomarker expression in tissue sections for diagnostic and research applications. Manual scoring of IHC images, which is the current standard of practice, is known to have several shortcomings in terms of reproducibility and scalability to large scale studies. Here, by using a digital image analysis-based approach, we introduce a new metric called the pixelwise H-score (pix H-score) that quantifies biomarker expression from whole-slide scanned IHC images. The pix H-score is an unsupervised algorithm that only requires the specification of intensity thresholds for the biomarker and the nuclear-counterstain channels. We present the detailed implementation of the pix H-score in two different whole-slide image analysis software packages Visiopharm and HALO. We consider three biomarkers P-cadherin, PD-L1, and 5T4, and show how the pix H-score exhibits tight concordance to multiple orthogonal measurements of biomarker abundance such as the biomarker mRNA transcript and the pathologist H-score. We also compare the pix H-score to existing automated image analysis algorithms and demonstrate that the pix H-score provides either comparable or significantly better performance over these methodologies. We also present results of an empirical resampling approach to assess the performance of the pix H-score in estimating biomarker abundance from select regions within the tumor tissue relative to the whole tumor resection. We anticipate that the new metric will be broadly applicable to quantify biomarker expression from a wide variety of IHC images. Moreover, these results underscore the benefit of digital image analysis-based approaches which offer an objective, reproducible, and highly scalable strategy to quantitatively analyze IHC images.

pathology↗

bioRxiv2020

Comparative analysis of macrophage post-translational modifications during intracellular bacterial pathogen infection

Macrophages activate robust antimicrobial functions upon engulfing virulent bacteria, yet a wide array of pathogens paradoxically thrive within these innate immune cells. To probe the pathogen-macrophage interface, we used proteomics to comprehensively quantify changes in post-translational modifications (PTMs) of host proteins during infection with three evolutionarily diverse intracellular pathogens: Mycobacterium tuberculosis, Salmonella enterica serovar Typhimurium, and Listeria monocytogenes. Comparing global phosphorylation and ubiquitylation patterns identified extensive reprogramming of cellular pathways during infection, with ubiquitylation patterns revealing unique pathogen-specific molecular response signatures undetectable by transcriptional profiling. Differential PTM changes during infection with attenuated M. tuberculosis cells lacking the ESX-1 virulence determinant revealed extensive modification of phagosome dynamics and antiviral type I interferon activation. We found that M. tuberculosis-mediated activation of the antiviral OASL1-IRF7 pathway promotes bacterial replication, uncovering a new mechanism of virus-bacterial synergy. Our data reveals remarkable specificity in innate cellular responses to complex stimuli and provides a resource for deeper understanding of host-pathogen interactions.

microbiology↗

bioRxiv2019

Novel engraftment and T cell differentiation of human hematopoietic cells in Art-/- IL2RG-/ SCID pigs

Pigs with severe combined immunodeficiency (SCID) are an emerging biomedical animal model. Swine are anatomically and physiologically more similar to humans than mice, making them an invaluable tool for preclinical regenerative medicine and cancer research. One essential step in further developing this model is the immunological humanization of SCID pigs. In this work we have generated T- B- NK- SCID pigs through site directed CRISPR/Cas9 mutagenesis of IL2RG within a naturally occurring DCLRE1C (Artemis)-/- genetic background. We confirmed Art-/- IL2RG-/Y pigs lacked T, B, and NK cells in both peripheral blood and lymphoid tissues. Additionally, we and successfully performed a bone marrow transplant on one Art-/- IL2RG-/Y male SCID pig with a bone marrow from a complete swine leukocyte antigen (SLA) matched donor without conditioning to reconstitute porcine T and NK cells. Next, we performed in utero injections of cultured human CD34+ selected cord blood cells into the fetal Art-/- IL2RG-/Y SCID pigs. At birth, human CD45+ CD3{varepsilon}+ cells were detected in peripheral blood of in utero injected SCID piglets. Human leukocytes were also detected within the bone marrow, spleen, liver, thymus, and mesenteric lymph nodes of these animals. Taken together, we describe critical steps forwards the development of an immunologically humanized SCID pig model.\n\nOne sentence summaryWe have generated a T- B- NK- SCID pig model through site directed mutagenesis of IL2RG in a naturally occurring Artemis null background and show successful engraftment of human T and B cells in blood and lymphoid organs after in utero injection of human hematopoietic stem cells.

immunology↗

bioRxiv2019

Atg8a interacts with transcription factor Sequoia to control the expression of autophagy genes in Drosophila

Autophagy is a fundamental, evolutionarily conserved, process in which cytoplasmic material is degraded through the lysosomal pathway [1-7]. One of the most important and well-studied autophagy-related proteins is LC3 [Microtubule-associated protein 1 light chain 3, (called Atg8 in yeast and Drosophila)], which participates in autophagosome formation and autophagy cargo selection in the cytoplasm, and is one of the most widely utilized markers of autophagy [8, 9]. Despite growing evidence that LC3 is enriched in the nucleus, little is known about the mechanisms involved in targeting LC3 to the nucleus and the nuclear components it interacts with [10-13]. Here we show that Drosophila Atg8a protein, homologous to mammalian LC3 and yeast Atg8, interacts with the transcription factor Sequoia in a LIR-motif dependent manner. We show that Sequoia depletion induces autophagy in nutrient rich conditions through enhanced expression of autophagy genes. We also show that Atg8a interacts with YL-1, a component of a nuclear acetyltransferase complex, and is acetylated at position K46. Additionally, we show that Atg8a interacts with the deacetylase Sir2, which deacetylates Atg8a during starvation in order to activate autophagy. Our results suggest a mechanism of regulation of expression of autophagy genes by Atg8a, which is linked to its acetylation status and its interaction with Sequoia, YL-1 and Sir2.

cell biology↗