Transcriptomic and functional profiling reveal autophagy inhibition and persistent bioenergetic collapse following parallel photodamage to lysosomes and mitochondria

Transcriptomic and functional profiling reveal autophagy inhibition and persistent bioenergetic collapse following parallel photodamage to lysosomes and mitochondria | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Transcriptomic and functional profiling reveal autophagy inhibition and persistent bioenergetic collapse following parallel photodamage to lysosomes and mitochondria View ORCID Profile Márcia Silvana Freire Franco , Felipe Gustavo Ravagnani , View ORCID Profile Suely Kazue Nagahashi Marie , Sueli Mieko Oba-Shinjo , View ORCID Profile Leonardo Vinicius Monteiro de Assis , View ORCID Profile Maurício S. Baptista doi: https://doi.org/10.1101/2025.08.21.671577 Márcia Silvana Freire Franco 1 Department of Biochemistry, Institute of Chemistry, University of Sao Paulo , Sao Paulo, Brazil Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Márcia Silvana Freire Franco Felipe Gustavo Ravagnani 1 Department of Biochemistry, Institute of Chemistry, University of Sao Paulo , Sao Paulo, Brazil Find this author on Google Scholar Find this author on PubMed Search for this author on this site Suely Kazue Nagahashi Marie 2 Department of Neurology, Laboratory of Molecular and Cellular Biology , LIM15, Faculdade de Medicina FMUSP, Universidade de São Paulo , Brazil Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Suely Kazue Nagahashi Marie Sueli Mieko Oba-Shinjo 2 Department of Neurology, Laboratory of Molecular and Cellular Biology , LIM15, Faculdade de Medicina FMUSP, Universidade de São Paulo , Brazil Find this author on Google Scholar Find this author on PubMed Search for this author on this site Leonardo Vinicius Monteiro de Assis 3 Institute of Neurobiology, Center of Brain Behavior & Metabolism, University of Lübeck , Germany 4 University Hospital Schleswig-Holstein , Campus Lübeck, Lübeck, Germany 5 Department of Chemistry and Molecular Biology, University of Gothenburg , Gothenburg, Sweden 6 Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg , Gothenburg, Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Leonardo Vinicius Monteiro de Assis For correspondence: leonardo.deassis{at}cmb.gu.se baptista{at}iq.usp.br Maurício S. Baptista 1 Department of Biochemistry, Institute of Chemistry, University of Sao Paulo , Sao Paulo, Brazil Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Maurício S. Baptista For correspondence: leonardo.deassis{at}cmb.gu.se baptista{at}iq.usp.br Abstract Full Text Info/History Metrics Supplementary material Preview PDF ABSTRACT Photodynamic therapy (PDT) using 1,9-dimethyl methylene blue (DMMB) induces coordinated mitochondrial and lysosomal damage and results in strong cellular death induction. However, the underlying transcriptional regulation in response to DMMB remains elusive. We compared the transcriptome response of photoactivated DMMB (paDMMB) to the gene signature triggered by autophagy-modulating agents: rapamycin (an autophagy activator) and bafilomycin A1 (a lysosomal acidification inhibitor). Transcriptome analysis revealed a pronounced transcriptomic response to paDMMB, with 884 differentially expressed genes (DEGs), compared to 291 for bafilomycin and 154 for rapamycin. paDMMB treatment upregulated genes associated with autophagy, mitochondrial stress responses, and proteostasis, while downregulating genes involved in miRNA processing and lipid catabolism. Rapamycin treatment downregulated amino acid biosynthesis pathways, while upregulating processes associated with nutrient starvation. Conversely, bafilomycin treatment upregulated genes related to lipid metabolism, while suppressing cytoskeletal programs. Transcriptomic comparisons revealed a striking overlap (95%) between paDMMB and bafilomycin signatures. Among the several biological processes affected by paDMMB, mitochondrial-related processes were strongly enriched. To determine whether the acute transcriptome changes caused by paDMMB led to persistent functional effects, we stimulated cells with DMMB and assessed mitochondrial respiration after a recovery period. paDMMB reduced basal respiration, ATP production, proton leak, and maximal respiration. These effects were not further altered by bafilomycin co-treatment but were markedly exacerbated by rapamycin. Collectively, we show that paDMMB leads to a transcriptome rewiring, closely resembling autophagy inhibition with a sustained mitochondrial dysfunction. These findings provide a valuable resource to understand the interplay between DMMB-induced lysosomal stress, transcriptional regulation, and PDT. INTRODUCTION Macroautophagy is a lysosome-dependent degradation process that starts with the generation of phagophores and then evolves into autophagosomes. The fusion of autophagosomes with lysosomes produces autolysosomes, where cellular components are degraded and recycled to support energy and nutrient balance 1 . While autophagy typically serves a protective role, it can also contribute to cell death 2 . A selective form of autophagy, mitophagy, eliminates damaged mitochondria and can prevent apoptosis by preserving mitochondrial integrity 3 . Photodynamic therapy (PDT) employs light-sensitive photosensitizers to produce reactive oxygen species (ROS) that lead to oxidative stress across various organelles. Such responses result in the upregulation of antioxidant defenses like glutathione and glutathione peroxidase 4 (GPX4) 4 , as well as heat shock proteins (HSPs) that stabilize misfolded proteins 5 . PDT has been demonstrated to initiate autophagy by activating transcription factors like HIF-1α, NRF2, p53, and FoxO3 6 , 7 . Clearance of damaged peroxisomes and mitochondria, via pexophagy and mitophagy, respectively, plays a critical role in maintaining cellular health during oxidative stress 8 . We recently demonstrated that 1,9-dimethyl methylene blue (DMMB), a phenothiazinium-based PDT photosensitizer, achieves exceptional cytotoxicity under remarkably mild conditions (IC50 ≈ 10 nM in 10 5 cells with only 5 min irradiation). This potency exceeds conventional PDT photosensitizers by two orders of magnitude, as typical agents require micromolar concentrations to induce comparable damage in vitro. We attribute this dramatic enhancement in cell death efficiency to DMMB’s unique capacity to simultaneously target mitochondria and lysosomes, activating regulated cell death pathways through selective organelle photodamage 9 . Towards a better understanding of the role of autophagy in DMMB-mediated phototoxicity, we analyzed the transcriptome signature of photoactivated DMMB and compared it with the signatures of cells undergoing autophagy or autophagy inhibition. Specifically, we employed bafilomycin A1 (a V-ATPase inhibitor that blocks autophagic flux by impairing lysosomal acidification, causing lysosomal swelling, ER stress, and mitochondrial dysfunction; 10 , 11 , 11 and rapamycin, which induces autophagy through mTORC1 inhibition, promoting metabolic adaptation and stress resilience 12 . In this study, comparative transcriptomic profiling revealed distinct molecular programs and pathways linked to autophagy regulation, lysosomal stress, and mitochondrial dysfunction. Functional analyses focusing on mitochondrial respiration revealed that DMMB effects lead to sustained mitochondrial respiration impairment, following DMMB exposure. Our findings integrate molecular and functional assessment to uncover the molecular mechanisms driving DMMB-induced phototoxicity. Overview of the transcriptomic response to photoactivated DMMB compared to autophagy modulators To gain a thorough understanding of the molecular mechanisms involved in DMMB treatment, we loaded human nonmalignant immortalized keratinocytes (HaCaT) cells with DMMB (10 nM) and subsequently stimulated them with red light to photoactivate DMMB (paDMMB). We also exposed HaCaT cells to Rapamycin (100 nM) and Bafilomycin (5 nM), which are known to activate and inhibit autophagy, respectively 13 , 14 to gain molecular insights into paDMMB-induced autophagy modulation. This framework served as a basis to investigate the possible effects of paDMMB in autophagy modulation ( Figure 1A ). Download figure Open in new tab Figure 1. Overview of the transcriptome alteration in HaCat cells in response to different treatments. A) The experimental protocol is illustrated. B) The UpSet plot shows the count of differentially expressed genes (DEGs) detected under each condition using the Likelihood Method (LRT) in DESeq2 (padj < 0.05) compared to the control group. C) The UpSet plot displays the number of enriched biological processes associated with the DEGs identified in panel B. The image depicted in A was obtained from Bioicons. In our analysis, we accounted for the individual effects of each factor (e.g., photoactivation, DMMB, rapamycin and bafilomycin) using the Likelihood Ratio Test (LRT) method in DESeq2 15 . The LRT method evaluates a complete model encompassing all experimental factors against a simplified model that includes only the intercept, which assumes no impact from any factors. Compared to the unexposed control group (padj < 0.05), the treatments resulted in distinct sets of differentially expressed genes (DEGs): 99 for rapamycin, 291 for bafilomycin, and 884 for paDMMB. The effect of DMMB in the absence or in the presence of photoactivation protocols led to a marginal response of 39 and 57 DEGs, respectively. However, only two genes (e.g., TBL1X and S100A8) were exclusively identified as being influenced by the photoactivation protocol (Table S1). Given the minimal transcriptional effect of DMMB alone and light exposure per se, we focused subsequent analyses on the main treatment groups: rapamycin, bafilomycin, and paDMMB. Most DEGs were unique to each condition, with limited overlap across treatments ( Figure 1B ). Gene ontology enrichment analysis of these DEGs revealed distinct biological processes associated with each treatment, showing the condition-specific nature of the transcriptional responses ( Figure 1C ). Overview of the transcriptome signature evoked by rapamycin In order to investigate the transcriptomic changes induced by rapamycin, we focused on genes associated with rapamycin treatment ( Figure 2A ), a well-known autophagy inducer 13 , 16 – 18 . A total of 154 DEGs were identified in the rapamycin-treated group (82 downregulated and 72 upregulated genes, padj < 0.05 and log2FC ± 0.58). Enrichment analysis of these DEGs confirmed the metabolic effects caused by rapamycin. Downregulated genes were enriched for several metabolic pathways, including amino acid metabolism, monocarboxylic acid and fatty acid metabolism, carbohydrate metabolism, and nutrient sensing. Conversely, enrichment analysis of the upregulated genes identified processes related to lipid and triglyceride homeostasis, response to starvation, and inflammatory signaling. Additionally, pathways related to angiogenesis, endothelial cell migration, NF-κB signaling, and interleukin-6 production were overrepresented (Figure S1; Table 1). Download figure Open in new tab Figure 2. Transcriptome signatures in response to rapamycin treatment. A) The experimental setup is illustrated. B) The volcano plot displays the unique DEGs (identified solely in the Rapamycin-treated group). C) Representative unique Rapamycin DEGs are displayed. D) Enrichment analyses conducted for downregulated genes (in blue). E) Predictive transcription factors based on the unique DEGs (shown in B) are presented. The mean score is represented on the x-axis and the log2 fold change on the y-axis. The Image described in A was obtained from Bioicons. We focused on identifying specific genes associated with Rapamycin by filtering our dataset to find exclusive DEGs. A total of 40 DEGs (17 downregulated and 23 upregulated genes, padj < 0.05 and log2FC ± 0.58) were exclusively identified in response to rapamycin treatment ( Figure 2B ; Table S1). Representative DEGs are shown in Figure 2C . No enriched pathway was identified for the upregulated DEGs. In contrast, the downregulated genes were significantly enriched in multiple amino acid metabolic and biosynthetic processes, reflecting the metabolic impact of the treatment 17 ( Figure 2D ; Table S1). Specifically, processes such as proteinogenic amino acid metabolism, L-amino acid metabolism, and alpha-amino acid metabolism were highly represented (e.g., ASNS , ATF4 , PSAT1 , SLC7A11 , and PYCR1 ). Additionally, genes involved in the glutamine family amino acid metabolic process and amino acid biosynthetic process also showed a downregulation ( ASNS , SLC7A11 , and PYCR1 ), further suggesting suppression of essential components of protein and nitrogen metabolism. A total of three predicted transcriptional factors, such as ATF4 , NR2E3 , and ZNF784 , were identified ( Figure 2C and E ). Overall, our annotated metabolic processes showed a broad suppression of amino acid turnover and biosynthesis, reflecting reduced anabolic activity, in accordance with well-known effect of rapamycin in the inhibition of the mTORC1 pathway, a key nutrient sensor regulating protein synthesis and metabolism 17 . Furthermore, these processes highlight rapamycin’s role in mimicking nutrient deprivation by inhibiting mTORC1, effectively triggering adaptive cellular responses that promote autophagy 16 . Overview of the transcriptome signature evoked by bafilomycin In order to investigate the transcriptomic changes induced by bafilomycin, we focused on genes associated with bafilomycin treatment ( Figure 3A ). Bafilomycin A1 inhibits autophagic flux in vitro by preventing lysosomal acidification. This macrolide targets the V-ATPase ATP6V0C, inhibiting protons from entering the lysosomal lumen. This disruption alters the acidic environment needed for effective lysosomal function and autophagic degradation 14 , 19 . A total of 291 DEGs were identified in the bafilomycin-treated group (113 downregulated and 178 upregulated genes, padj < 0.05 and log2FC ± 0.58, Table S1). Enrichment analysis of these DEGs confirmed the metabolic effects caused by bafilomycin. Downregulated genes enriched for development, cytoskeleton, migration as well as metabolic processes (e.g., lipid and steroid metabolism). Whereas upregulated genes were enriched for processes associated with lipids, cholesterol, and carbohydrate metabolism, inflammatory and wound healing response, hormone secretion, apoptosis and extracellular matrix organization (Figure S2; Table S1). Download figure Open in new tab Figure 3. Transcriptome signatures in response to bafilomycin treatment. A) The experimental setup is illustrated. B) The volcano plot displays the unique DEGs (identified solely in the bafilomycin-treated group). C) Representative unique bafilomycin DEGs are displayed. D) Enrichment analyses conducted for upregulated genes (in red). E) Predictive transcription factors based on the unique DEGs (shown in B) are presented. The mean score is represented on the x-axis and the log2 fold change on the y-axis. The Image described in A was obtained from Bioicons. Focusing on identifying specific genes associated with bafilomycin, we filtered our dataset to find exclusive DEGs. A total of 46 DEGs (10 downregulated and 36 upregulated genes, padj < 0.05 and log2FC ± 0.58) were exclusively identified in response to bafilomycin treatment ( Figure 3B ; Table S1). Representative DEGs are shown in Figure 3C . While no pathways were enriched among downregulated DEGs, upregulated genes were significantly associated with lipid and cholesterol metabolism, alcohol and isoprenoid pathways, acyl-CoA and pyruvate metabolism, and small molecule biosynthetic processes ( Figure 3D ; Table S1). A total of three predicted transcriptional factors (TFs), such as ETV5 , ZNF248 , and CASZ1 , were identified ( Figure 3C and 3E ). Overall, the pathways triggered by bafilomycin treatment indicate a metabolic shift affecting both carbohydrate and lipid metabolism because of bafilomycin’s role as a lysosomal acidification inhibitor and autophagic influx. These effects on autophagic influx led to a compensatory, transcriptional response in several energy-related pathways. Overview of the transcriptome signature evoked by paDMMB Previously, we identified that at low concentrations (10 nM), paDMMB induced mitochondrial damage and mitophagy, but lysosomal damage prevented its completion, leading to enhanced cell death. This parallel damage strategy led to a significant improvement in photoinduced cell death 9 . To gain a more comprehensive understanding of the impacted transcriptional programs, we examined the transcriptome of HaCaT cells treated with paDMMB alone ( Figure 4A ). A total of 884 DEGs were identified in the paDMMB-treated group (343 downregulated and 541 upregulated genes, padj < 0.05 and log2FC ± 0.58, Table S1). Enrichment analysis of these DEGs showed a strong impact on the transcriptome. For example, downregulated DEGs were linked to processes involving development and differentiation, miRNA metabolism, lipid breakdown, and signal transduction (Notch, SMAD, and MAPK signaling). Conversely, upregulated DEGs were associated with inflammation and immune response, cell migration, apoptosis, protein folding, lipid/cholesterol metabolism, autophagy, and proteostasis (Figure S3; Table S1). Download figure Open in new tab Figure 4. Transcriptome signatures of photoactivated DMMB. The experimental setup is illustrated. B) The volcano plot displays the unique DEGs (identified solely in the DMMB). C) Representative unique DMMB DEGs are displayed, with the first and second rows showing upregulated and downregulated DEGs, respectively. D) Enrichment analyses conducted for upregulated genes (in red) and downregulated genes (in blue). E) Predictive transcription factors based on the unique DEGs (shown in B) are presented. The mean score is represented on the x-axis and the log2 fold change on the y-axis. The Image described in A was obtained from Bioicons. Focusing on identifying specific genes associated with paDMMB, we filtered our dataset to find exclusive DEGs. A total of 579 DEGs (215 downregulated and 364 upregulated genes, padj < 0.05 and log2FC ± 0.58) were exclusively identified in response to paDMMB treatment ( Figure 3B ; Table S1). Representative DEGs are shown in Figure 4C . Exclusively downregulated genes in response to paDMMB were significantly enriched for processes related to development, differentiation, and stem cell fate determination, together with pathways involved in post-transcriptional and post-translational regulation (e.g., miRNA transcription). In contrast, exclusively upregulated genes were strongly associated with cellular stress response and homeostasis, including apoptosis regulation, autophagy, and proteostasis. Key pathways included the regulation of mitochondrial dynamics (e.g., fission, membrane potential, and organelle organization) and immune-related signaling (e.g., cytokine production and inflammation). Additional enrichment was observed in genes associated with proteolysis and ubiquitin-mediated protein degradation, as well as responses to unfolded or misfolded proteins ( Figure 4D ; Table S1). TF prediction analyses identified a total of 49 genes, such as TP63 , PPARD , GRHL2 , BARX2 , MAFG , CEBPB , and GATA3 ( Figure 4C and E ). Overall, the transcriptional response to paDMMB is characterized by a strong upregulation of stress-related programs, including autophagy, mitochondrial regulation, proteolysis, and apoptosis, accompanied by a marked downregulation of developmental pathways linked to cell differentiation, stem cell fate, and post-transcriptional regulation. Transcriptome signature comparisons reveal a strong link between the paDMMB-induced and the bafilomycin-induced signatures Given the pronounced transcriptional changes induced by paDMMB treatment, we analyzed the paDMMB-induced transcriptional signature and compared it to the modifications elicited by autophagy modulation, specifically through either the induction with rapamycin or the inhibition with bafilomycin. We hypothesized that if the transcriptional profiles of paDMMB-treated cells are further shaped in a direction similar to known drug-specific signatures, the resulting overlap in gene expression could suggest how autophagy contributes to the effects of paDMMB. We found that only a limited number of genes (46 out of 99 DEGs) showed consistent direction of change (e.g., UP in paDMMB and UP in the rapamycin group) between the transcriptomes of paDMMB and rapamycin-treated cells ( Figure 5A – B; Table S1). In contrast, we observed a significant overlap of 95% (221 out of 231 DEGs) with the same direction of change between the paDMMB and bafilomycin groups, reflecting comparable enriched processes identified between these conditions ( Figure 5 C – D; Table S1). Download figure Open in new tab Figure 5. Comparison of transcriptome signatures between DMMB and Rapamycin or Bafilomycin treated groups. A – C) Venn diagram depicts the shared differentially expressed genes (DEGs) between DMMB and Rapamycin (A) or Bafilomycin (C). B – D) Enrichment analyses of the shared DEGs with the same regulation identified for each comparison. Overall, our analysis suggests that the transcriptional effects of paDMMB resemble an autophagy inhibition profile, as evidenced by the high DEGs overlap and similar enriched biological processes observed with bafilomycin treatment. Conversely, the activation of autophagy with rapamycin via rapamycin does not yield a similar impact, indicating that the molecular outcomes of paDMMB are more closely associated with pathways disrupted by lysosomal inhibition than by mTOR suppression. Mitochondrial Respiration in response to DMMB treatment One of the most impacted processes in paDMMB was those related to mitochondria ( Figure 4D ). While transcriptome profiling offered a detailed view of the immediate effects following paDMMB within 6 hours, it only captured short-term changes. To assess whether these alterations led to lasting mitochondrial dysfunction, we conducted a Seahorse assay to evaluate mitochondrial function. Right after irradiation, cells were cultured in a mild nutrient– and growth-factor–restricted medium (1% FBS) for 12 hours, to enhance the cellular response during early recovery. Then, they were switched back to a nutrient-rich medium (10% FBS) for 36 hours before Seahorse analysis. This longer period helped us determine if mitochondrial impairments persisted beyond the initial injury, allowing us to distinguish between transient phototoxic effects and sustained deficits. Additionally, we tested the impact of paDMMB in the presence or absence of autophagy modulators like rapamycin and Bafilomycin. Mitochondrial function was assessed by real-time monitoring of the oxygen consumption rate (OCR; primarily consumed by glucose metabolism through the citric acid cycle). Figure 6A shows a typical experimental scheme in which specific inhibitors were added to respiratory complexes, allowing for the evaluation of various respiratory chain components, including basal respiration, ATP-linked respiration, proton leak, and maximal respiration. Download figure Open in new tab Figure 6. Mitochondrial response in response to DMMB in the presence of rapamycin or bafilomycin. Oxygen consumption rate (OCR) from HaCat cells exposed to DMMB (10 nM), Bafilomycin (BAFILO), Rapamycin (RAPA), DMMB+BAFILO, and DMMB+RAPA for 24 h was determined by the mitochondrial stress test. OCR was measured before and after sequential injection of Oligomycin (0.1 µg/mL), FCCP (1 µM), and Antimycin A (10 µM). A) mitochondrial respiration by Seahorse, following the mitochondrial stress analysis. The oxygen consumption rate (OCR) curves along the time interval up to 60 min are presented according to applied drugs. B ) Bar chart of basal respiration; C) Proton Leak; D) ATP production; E) Non-Mitochondrial Oxygen Consumption; F) Maximal respiration; G) Spare Respiration Capacity; H) Coupling Efficiency; I) Spare Respiratory Capacity. * p< 0.05, ** p< 0.01, *** p< 0.001, **** p< 0.0001. Cells treated with Bafilomycin or DMMB in the dark group (not exposed to red light) had no significant differences in basal respiration ( Figure 6B ), ATP production ( Figure 6D ), and maximal respiration ( Figure 6F ) when compared to the control. The exception was a slight increase in spare respiratory capacity in the group that received DMMB without photoactivation ( Figure 6I ). The effects of bafilomycin in the absence of paDMMB resulted in higher non-mitochondrial oxygen consumption ( Figure 6E ). However, rapamycin-treated cells in the absence of paDMMB showed a decrease in basal respiration ( Figure 6B ), proton leak ( Figure 6C ), ATP production ( Figure 6D ), maximal respiration ( Figure 6F ), and spare capacity ( Figure 6I ). These results clearly show a strong effect of rapamycin on mitochondrial respiration. In the presence of paDMMB, the cells showed reduced basal respiratory ( Figure 6B ), proton leak ( Figure 6C ), ATP production ( Figure 6D ), and maximal respiration ( Figure 6F ) when compared to the control group. Importantly, the effects of DMMB were similar to those evoked in the BAFILO-DMMB group, demonstrating no additional effects. However, the effects of DMMB were maximized in the presence of rapamycin, leading to further decreases in basal respiration ( Figure 6B ), proton leak ( Figure 6C ), ATP production ( Figure 6D ), and non-mitochondrial oxygen consumption ( Figure 6E ). Overall, paDMMB caused widespread and lasting impairments in mitochondrial respiration, an effect that was significantly worsened by rapamycin but not further changed by bafilomycin. DISCUSSION Rapamycin effects The downregulation of several metabolic processes associated with amino acid, lipid, and fatty acid metabolism and biosynthesis reflects a shift away from anabolic processes, aligning with autophagy activation. In contrast, the upregulation of pathways involving acylglycerol and triglyceride, starvation, and chemokine (e.g., IL-6) suggests an adaptive mechanism to cope with nutrient and energy stress, reinforcing its impact on autophagic and metabolic regulation 20 . This shift is orchestrated by central metabolic regulators such as the mammalian target of rapamycin (mTOR) and AMP-activated protein kinase (AMPK), which integrate signals from amino acid availability, energy status, hormone and growth factor signaling, and various stress conditions (e.g., oxidative stress, hypoxia, DNA damage). AMPK and mTORC1 have an antagonistic relationship: whereas mTOR stimulates anabolic processes, its inhibition promotes autophagy and enhances metabolic flexibility, supporting cellular survival and maintaining energy balance under balanced stress 21 – 23 . We observed a significant downregulation of genes related to protein synthesis and extracellular signaling, consistent with mTORC1 inhibition, and possibly AMPK activation 20 , 24 . Glucose deprivation typically reduces intracellular ATP levels, decreasing the ATP/ADP and ATP/AMP ratios, which can activate AMPK. Cells treated with rapamycin had increased expression of ZBTB20 (Zinc finger and BTB domain-containing protein 20), a transcriptional repressor involved in cellular development, metabolism, differentiation, and innate immunity 25 . However, this gene was not exclusively associated with rapamycin effects. The role of lipid mobilization to meet energy demands is evident through the upregulation of several genes, such as RORA , FGF1 , and PNPLA2 . For instance, PNPLA2 catalyzes triglyceride hydrolysis and releases free fatty acids for mitochondrial β-oxidation during energy depletion. In the context of autophagy, PNPLA2 activity can complement lipophagy, enhancing the supply of fatty acids from lipid droplets 26 , 27 . Oxidative stress is likely a key factor in the rapamycin-induced effects. For instance, under physiological conditions, H₂O₂ is detoxified by enzymes such as catalase, peroxiredoxins, or GSH-dependent enzymes like GPX1, which convert GSH into GSSG. However, when in excessive amounts, hydrogen peroxide (H₂O₂) can produce harmful hydroxyl radicals (·OH). The enzyme GSR regenerates GSH from GSSG using NADPH as an electron donor. This antioxidant defense system is regulated by NRF2, a redox-sensitive transcription factor that coordinates cellular responses to oxidative stress 28 . In rapamycin-treated cells, several genes associated with oxidative stress were identified, including HMOX1 , SLC7A11 , BNIP3 , and PYCR1 . Under amino acid deprivation, ATF4 initiates transcription of genes involved in amino acid transport and biosynthesis, including asparagine synthetase (ASNS), which increases de novo asparagine synthesis when asparagine levels drop 29 , 30 . This upregulation promotes the expression of glutamine synthetase ( GLUL ) and lysis of glutamine, thereby facilitating the continuous use of intracellular glutamine 31 . Through this mechanism, cells can restore global protein translation disrupted by glutamine scarcity 31 , 32 . In our dataset, ATF4 was downregulated in rapamycin-treated cells, a finding that diverges from the typical nutrient-stress signature where ATF4 is upregulated. Previous studies identified that rapamycin’s effects are mediated by NRF2 33 , 34 . We identified two putative novel TFs ( NR2E3 and ZNF784 ) that can mediate rapamycin effects. Seahorse metabolic profiling showed that rapamycin alone significantly diminished basal respiration, ATP production, proton leak, and spare respiratory capacity, indicating reduced mitochondrial activity. In cells with paDMMB, mitochondrial functions were severely compromised; however, the additional treatment with rapamycin worsened this dysfunction, leading to a further decline in all essential respiratory parameters beyond what paDMMB alone caused. These results suggest that although rapamycin triggers stress-adaptive transcriptional programs, it also restricts mitochondrial plasticity, which may make cells more susceptible to oxidative stress and energy depletion when combined with paDMMB. Bafilomycin effects Bafilomycin-treated cells adjust to metabolic stress in ways that maintain self-renewal, proliferation, and survival. Under nutrient-limiting conditions, such as glucose and oxygen deprivation, cells undergo the “Warburg effect,” a shift from oxidative phosphorylation to aerobic glycolysis in the pentose phosphate pathway, resulting in lactate accumulation while maintaining ATP production 35 . Bafilomicyn sustains this uncoupled metabolism and elevated respiration without compromising viability, a response attributed to increased cytosolic Ca 2+ level due to V-ATPase inhibition, while the mitochondrial Ca 2+ and mitochondrial pH gradient remain moderately affected, supporting continued ATP production via oxidative phosphorylation 11 . Consistent with hypoxia-like adaptation, we observed a strong upregulation of PDK1 , whose protein inhibits the pyruvate dehydrogenase complex (PDH), limiting pyruvate entry into the TCA cycle and enhancing glycolytic flux. This is a common adaptive response to hypoxia and mitochondrial stress 36 . These pathways are transcriptionally regulated by HIF1α, which activates PDK1 , FUT11 , and BNIP3L under low-oxygen conditions 37 , 38 . In our conditions, the previous genes ( PDK1 , FUT11 , and BNIP3L ) were upregulated exclusively in the bafilomycin-treated group. For instance, FUT11 has been associated with hypoxic pancreatic cancer progression through PDK1 stabilization and AKT/mTOR activation, promoting cell survival 37 . We also observed a coordinated upregulation of genes involved in acetyl-CoA and sterol biosynthesis, including ACLY , MVD , MVK , FDFT1 , and DHCR7 , suggesting a metabolic rerouting to support lipid synthesis. Importantly, these genes were exclusively upregulated in the bafilomycin-treated group. Acetyl-CoA serves as a crucial metabolic hub, feeding into the TCA cycle and biosynthetic pathways 39 . In the cytosol, acetyl-CoA is produced from citrate via ATP-citrate lyase (ACLY), while pantothenate kinase (PANK) initiates Coenzyme A biosynthesis, supplying the CoA necessary for acetyl-CoA formation. In mitochondria, acetyl-CoA combines with oxaloacetate to form citrate via citrate synthase; citrate is then transported to the cytosol through the citrate transporter SLC25A1 and converted back into acetyl-CoA by ACLY, supporting lipid biosynthesis 40 . This acetyl-CoA pool fuels the mevalonate pathway to produce isoprenoids, with squalene synthase (FDFT1) and lanosterol synthase (LSS) catalyzing key steps in cholesterol biosynthesis. Cholesterol and its intermediates contribute not only to membrane structure but also to intracellular signaling, including modulation of AKT and NF-κB pathways, thereby promoting pro-survival and stress-response gene expression 41 . Notably, FDFT1 activity also elevates intracellular squalene, a lipid antioxidant that protects against ferroptosis 42 . Further downstream, LSS catalyzes lanosterol formation, which supports proteostasis by promoting clearance of misfolded proteins through heat shock factor 1 (HSF1) activation 43 . Similarly, DHCR7 expression is linked to developmental processes, cellular differentiation, and apoptosis, suggesting the physiological relevance of sterol metabolism in stress response 44 , 45 . We identified three potential TFs ( ETV5 , ZNF248 , and CASZ1 ), which are novel to the effects of bafilomycin. Previous studies suggest that ETV5 is involved in lipid metabolism by interacting with PPAR signaling 46 and as a driver for cell proliferation in different cancer types 47 . The role of ZNF248 is still being explored, but it has been implicated in cancer progression 48 . Lastly, CASZ1 was previously shown to be induced by DNA damage 49 and as an essential activator of epidermal differentiation 50 . Seahorse metabolic analysis showed that bafilomycin alone did not significantly alter mitochondrial respiration, with basal respiration, ATP production, and maximal respiration comparable to control cells. A modest increase in non-mitochondrial oxygen consumption was observed, possibly reflecting minor redox changes. In contrast, photoactivated DMMB led to a marked suppression of mitochondrial functions, and this effect was not further enhanced or mitigated by bafilomycin co-treatment. Despite transcriptomic evidence of metabolic adaptation, including upregulation of glucose metabolism, sterol biosynthesis, and hypoxia-responsive genes, these changes were insufficient to rescue mitochondrial activity. Together, these findings suggest that bafilomycin-treated cells interact in compensatory pathways to maintain metabolic flexibility but fail to counteract the dominant mitochondrial dysfunction induced by paDMMB. Effects of photoactivated DMMB The photodynamic effect in the presence of DMMB triggered major pathways related to macroautophagy, mitophagy, protein folding, and oxidative stress, which is consistent with previous observations by our group showing the formation of acidic vacuoles and damage to mitochondria and lysosomes as central events in DMMB-induced cell death 9 . Among the autophagy-related genes upregulated, we identified C9ORF72 , ARL8B , ATP6V1G1 , STAM, and CHMP1B , supporting activation of autophagic and endolysosomal compartments. Of these genes, only C9ORF72 and CHMP1B were not exclusively identified in the group that received paDMMB. C9ORF72 positively regulates autophagosome formation by activating RAB proteins, essential for membrane trafficking and autophagosome maturation 51 . ATP6V1G1 contributes to autophagosome acidification and degradation of damaged organelles 52 , whereas ARL8B and STAM may recruit the autophagic machinery to damaged mitochondria. The upregulation of CHMP1B , a component of the ESCRT-III complex, is consistent with roles in autophagosome closure and endosomal sorting 53 , 54 . We also observed activation of SMAD-specific E3 ligase SMURF1, a regulator of TFEB-dependent lysosomal biogenesis through PPP3CB ubiquitination 55 . The phospholipase PLAA and VCP/p97 were also induced, which may contribute to lysosomal damage resolution and autophagic clearance 56 . This lysosomal-autophagy axis is central to stress resolution, yet its disruption likely underlies the enhanced DMMB-induced cytotoxicity that we previously described 9 . Moreover, the genes HSPA1A , HSPA1B , DNAJB6 , and BAG3 , which are involved in heat shock responses and chaperone-assisted selective autophagy, were exclusively identified in cells receiving paDMMB. Notably, BAG3 interacts with p62/SQSTM1 to facilitate the degradation of damaged proteins and organelles. Our transcriptome data revealed overexpression of DNAJBa in the paDMMB group, and the upregulation of this gene has been implicated in both autophagy modulation and oxidative stress responses, promoting ferroptosis and altering mitochondrial dynamics 57 , 58 . The ROS stress system activation was also enriched in cells that received paDMMB, as genes involved in oxidative stress, such as RAC2 59 , GCLC/GCLM 60 , and PINK1 61 were identified. This reflects the high oxidative load induced by paDMMB and the cell’s attempt to counteract it through redox balancing mechanisms. We also noted metabolic rewiring in response to paDMMB. For example, genes related to insulin signaling (e.g., GRB10 , SIRT1 , SOCS3 , INSIG1 , LPIN1 ) were upregulated in cells treated with paDMMB. Several regulators of lipid metabolism (e.g., SREBF1 , EGR1 , CROT , SLC27A2 , FABP5 , PLD1 , and ABCG1 ) were upregulated, which suggests a strong modulation of lipid metabolism in response to DMMB activation and is likely associated with autophagosome membrane dynamics 62 , 63 . Interestingly, PPARD was upregulated, which is known to promote fatty acid oxidation and autophagy via AMPK-mTOR signaling 64 . Importantly, enrichment for insulin and lipid metabolism was not observed in genes exclusively identified in the DMMB group. Additionally, we observed the enrichment of circadian-related genes ( CRY2 , BMAL1 , RORA , BHLHE40 , CSNK1E ) in response to paDMMB, indicating a change in circadian rhythms following DMMB activation, a common feature of stress response 65 – 67 . Finally, mitochondrial stress was evidenced by a regulation of several genes (e.g., PINK1 , BNIP3 , PPIF , GCLC, PLD6 ), which regulate mitophagy and antioxidant responses 68 . For instance, PPIF, associated with mitochondrial permeability transition pore (mPTP), promotes membrane depolarization and interacts with PINK1 to initiate mitophagy 69 . GCLC supports glutathione synthesis and mitochondrial redox buffering 70 . The solute carrier family members SLC25A33 and SLC25A39, which mediate nucleotide and glutathione transport across the mitochondrial membrane, were also upregulated, suggesting sustained mitochondrial metabolism under stress 71 , 72 . These transcriptional changes were functionally supported by Seahorse metabolic flux analysis, designed to evaluate the long-term consequences of paDMMB treatment on mitochondrial function. This experimental approach allowed us to assess whether the short-term transcriptomic changes observed within 6 hours translated into sustained mitochondrial dysfunction. Specifically, DMMB treatment led to significant decreases in basal respiration, proton leak, ATP production, and maximal respiration, consistent with mitochondrial dysfunction. DMMB without photoactivation caused only minor changes, with a slight increase in spare respiratory capacity. These findings confirm that photoactivation is essential for DMMB toxicity and suggest that DMMB impairs mitochondrial function through oxidative and lysosomal stress. Importantly, blue and visible light may act as a chronic environmental stressor that gradually disrupts cellular homeostasis, triggering oxidative stress 73 . We have recently shown that chronic exposure to blue light leads to widespread effects on nuclear morphology, chromatin organization, and transcriptional rewiring, ultimately promoting a pro-survival and potentially pre-malignant phenotype 74 . In contrast, paDMMB induces acute, targeted cellular stress (e.g., mitochondria and lysosomes), leading to enhanced cellular death 9 and disrupting mitochondrial function (our current findings). While blue light induces sustained global damage over time 74 , DMMB causes rapid, organelle-specific dysfunction upon activation. These findings highlight the distinct nature of chronic versus targeted photo-induced stress and their differential impact on cellular fate. LIMITATIONS Our study has limitations. First, transcriptome profiling only captured an early response following DMMB photoactivation, whereas mitochondrial function was assessed after a recovery period, with the goal of evaluating long-term, rather than acute effects on cellular respiration. Second, a transcriptional signature comparison between the treatments suggests a strong overlap between paDMMB and bafilomycin. However, these comparisons are based on transcriptional similarities rather than direct comparative experiments. Furthermore, we can have similar gene expression profiles and biological effects while activating different mechanisms/systems for cell signaling. CONCLUSION Building on our previous study showing that DMMB induces mitochondrial and lysosomal damage to impair autophagy and promote cell death 9 , our current transcriptional and metabolic analyses reveal that this dual organelle stress triggers a coordinated stress response. Photoactivated DMMB upregulates mitophagy and lysosome-related genes while impairing mitochondrial respiration and ATP production, supporting mitochondrial failure as a key outcome. Transcriptome comparisons revealed strong pathway-level overlap with bafilomycin, but not rapamycin, indicating lysosomal dysfunction, rather than mTOR inhibition, as the dominant mechanism impairing autophagic flux. Together, these findings show that targeted disruption of autophagy via combined mitochondrial and lysosomal damage reprograms cellular metabolism and potentiates DMMB-based photodynamic therapy. MATERIAL AND METHODS Cell Culture HaCaT cells were maintained in Dulbecco Modified Eagle Medium, DMEM (Sigma-Aldrich, D5648) supplemented with 10% (v:v) fetal bovine serum (FBS; Gibco™, 12657029), 100 units/mL of penicillin, 100 μg/mL of streptomycin and 250 ng/mL of amphotericin B in a 37°C incubator under a moist atmosphere of 5% carbon dioxide. Experimental protocol for RNAseq Twenty-four hours after sending, HaCaT cells were incubated with DMMB (10 nM) for one hour, then washed with PBS twice. Then, irradiation was carried out with specialized equipment (Ethik, Biolambda, Brazil) with a maximum emission wavelength at 630 nm. To provide 12 J.cm −2 , cells were irradiated for 9 min (23,5 mW). In parallel, a different batch of cells was treated with 5 nM Bafilomycin-A1 (Bafilomycin) or 100 nM Rapamycin. All samples were collected 6h later. Total RNA was extracted using the PureLink RNA Mini kit (ThermoFisher Scientific, USA) with digestion by DNAse (ThermoFisher Scientific, USA) into the columns. RNA sequencing RNA integrity was performed using Bioanalyzer and libraries were prepared using a QuantSeq 3’ mRNA-Seq Library Prep Kit-FWD (Lexogen, Vienna, Austria) with 1μg RNA. The library concentration was measured by Qubit Fluorometer and Qubit dsDNA HS Assay Kit (Applied Biosystems), and the size distribution was determined using an Agilent D1000 ScreenTape System (Agilent Technologies). Sequencing was performed on the NextSeq 500 platform at the NGS facility core SELA. The sequencing data were aligned to the GRCh38 version of the human genome using STAR and the bamsort tool from biobambam2, for downstream processing of the BAM file, including merging, sorting, and marking of duplicates. We used featureCounts to count the number of reads that overlap each gene. Differential Expression Analysis Raw RNA-seq counts were normalized using the median-of-ratios method implemented in DESeq2 15 . Technical replicates were collapsed into biological replicates using collapseReplicates and genes with fewer than 25 total counts across all samples were removed to eliminate low-expression noise. Normalized expression values were obtained using variance stabilizing transformation (VST) with blind = FALSE. PCA was performed on the transformed data to assess sample clustering. The Likelihood Ratio Test (LRT) was employed to evaluate the effects of treatments and experimental factors. The full model included all experimental factors, while the reduced model included only the intercept. DEGs were identified using a false discovery rate (FDR) 0.58, unless otherwise mentioned. Exclusive DEGs for each treatment condition were determined by filtering genes uniquely responsive to a specific treatment while meeting these criteria. All steps were done in RStudio (v. 4.2.1). Graphs were generated using ggplot2. A total of 64 RNA-seq libraries were generated across six experimental conditions: control without light/photoactivation (n = 6), control with photoactivation (n = 6), rapamycin (n = 4), bafilomycin (n = 4), DMMB without photoactivation (n = 6), and DMMB with photoactivation (n = 6). All samples were included in downstream analyses, with no exclusions. Functional Enrichment Analysis Significant DEGs were analyzed for enrichment in biological pathways and processes using clusterProfiler (GO terms) and KEGG pathway analysis. For clusterProfiler, pathways were considered significant if p-value < 0.01, with a minimum of two genes per pathway. Transcription Factor Prediction To identify upstream transcriptional regulators, the CHEA3 method 75 was utilized to rank transcription factors related to DEGs according to the mean rank scoring system and log2FC change. Transcription factors exclusively associated with a single treatment were filtered and represented based on mean rank scoring and log2FC. Photosensitization protocol for Mitochondrial Respiration Analysis Considering photoactivation response of the DMMB compound in cells, all experiments were carried out in four conditions: ( 1 ) cells without exposure to red light and in the absence of photosensitizer ( dark control ); ( 2 ) cells exposed only to red light, in the absence of photosensitizer ( Irradiated Control ); (3) cells incubated with photosensitizer and photoactivated with red light ( DMMB Irradiated ) and (4) cells incubated with photosensitizer in the absence of irradiation ( DMMB Dark ). Twenty-four hours after sending, HaCaT cells were incubated with DMMB (10 nM) for one hour, then washed with PBS twice. Irradiation was carried out with specialized equipment (Ethik, Biolambda, Brazil) with a maximum emission wavelength at 630 nm. To provide 12 J cm−2, cells were irradiated for 9 min (23,5 mW). Immediately after the irradiation, 5 nM Bafilomycin-A1 (Bafilomycin) or 100 nM Rapamycin was added to the DMMB-irradiated groups in DMEM containing 1% FBS for twelve hours. Then the media was exchanged to DMEM containing 10% FBS. Cells were prepared for mitochondrial respiration 36h later. Then the media was exchanged to DMEM containing 10% FBS. Cells were prepared for mitochondrial respiration 36h later. Mitochondrial Respiration Analysis The Seahorse XFe24 Analyzer (Agilent Technology, Santa Clara, CA, USA) was used for mitochondrial respiration analysis. The cells were washed three times with 500 μL DMEM/F-12 containing 1% P/S and 5 mM HEPES. The media did not contain bicarbonate or FBS. After incubation, plates were placed in the equipment and OCRs were measured under basal conditions, followed by different injections: 1) oligomycin, an ATP synthase inhibitor (complex V), and acting as OCR reducing agent; 2) carbonyl cyanide 3-chlorophenylhydrazone (CCCP), to induce maximum electron transport; 3) antimycin and rotenone (R/AA, final concentration, 1 μM each) complex III and complex I inhibitor, respectively. Basal mitochondrial respiration was determined by monitoring OCR in the absence of any inhibitors and was calculated by reducing extracellular OCR through the inhibition of ATP synthase by oligomycin. The injection of oligomycin inhibits the complex V (F1–Fo ATP synthase), decreasing proton flux through complex V, and increasing the proton concentration, which reduces electron transport and oxygen consumption. The mitochondrial OCR remaining after oligomycin treatment is a measure of proton leak. The uncoupling agent, Carbonyl cyanide-4 (trifluoromethoxy) phenylhydrazone (FCCP), collapses the proton gradient and interferes with the mitochondrial membrane potential. As a consequence, electron flow through the electron transport chain is uninhibited, and it is possible to observe the maximal oxygen consumption by complex IV. The spare capacity was calculated by the difference between maximal and basal OCR. To conclude, all mitochondrial-associated respiration is eliminated by the addition of rotenone (a complex I inhibitor) and antimycin A (an inhibitor of cytochrome C reductase). A One-Way ANOVA followed by a Tukey post-test was used for each condition (Dark and irradiated). A p-value < 0.05 was considered significant (GraphPad Prism, v. 10). Each experimental group consisted of 4 to 5 independent biological replicates. The exception was the photoactivated DMMB group, which included only two replicates due to a technical error that prevented proper data acquisition from the remaining wells. DATA AVAILABILITY Data is being deposited in the Gene Expression Omnibus (GEO) database. The processed data derived from the RNA sequencing are presented in Table S1. Additional data that support the findings in this manuscript can be obtained from the corresponding author upon request. ARTIFICIAL INTELLIGENCE STATEMENT The authors used ChatGPT and Grammarly to improve readability and language, then reviewed and edited the content, taking full responsibility for the publication content. AUTHOR’S CONTRIBUTION Márcia Silvana Freire Franco: conceptualization, methodology, data curation, formal analysis, investigation, manuscript writing, and review. Felipe Gustavo Ravagnani: conceptualization, investigation, manuscript review. Suely Kazue Nagahashi Marie: methodology, data curation, formal analysis, manuscript review. Sueli Mieko Oba-Shinjo: methodology, data curation, manuscript review. Leonardo Vinicius Monteiro de Assis: conceptualization, methodology, data curation, formal analysis, supervision, manuscript writing and review. Maurício S. Baptista: conceptualization, methodology, funding, supervision, manuscript writing and review. Download figure Open in new tab Figure S1: Enriched pathways for Rapamycin. A – B) Enrichment analyses conducted for upregulated (red) and downregulated genes (in blue) for Rapamycin-treated cells. Additional processes are shown in Table S1. Download figure Open in new tab Figure S2: Enriched pathways for Bafilomycin. A – B) Enrichment analyses conducted for upregulated (red) and downregulated genes (in blue) for Bafilomycin-treated cells. Additional processes are shown in Table S1. Download figure Open in new tab Figure S3: Enriched pathways for photoactivated DMMB. A – B) Enrichment analyses conducted for upregulated (red) and downregulated genes (in blue) for DMMB-treated cells. Additional processes are shown in Table S1. ACKNOWLEDGMENTS AND FUNDING Baptista, M received grants from the São Paulo Research Foundation (2022/13066-9, 2021/08521-6, and 2013/07937-8). de Assis is supported by the Knut and Alice Wallenberg Foundation as a Wallenberg Molecular Medicine Fellow. Funder Information Declared FundaÇão de Amparo à Pesquisa do Estado de São Paulo, https://ror.org/02ddkpn78 , 2022/13066-9, 2021/08521-6, and 2013/07937-8 to Baptista, MS Knut and Alice Wallenberg Foundation , Wallenberg Molecular Medicine Fellow to de Assis, LVM Footnotes ↵ $ shared contribution ↵ * shared contribution REFERENCES 1. ↵ Feng Y , He D , Yao Z , Klionsky DJ . The machinery of macroautophagy . Cell Res 2014 ; 24 : 24 – 41 . OpenUrl CrossRef PubMed Web of Science 2. ↵ Kroemer G , Levine B . Autophagic cell death: the story of a misnomer . 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