Livers of hyperglycemic mice with Hnf1a-deficient pancreatic beta-cells show unexpected hepatic steatosis signs further exacerbated by high fat diet

Livers of hyperglycemic mice with Hnf1a-deficient pancreatic beta-cells show unexpected hepatic steatosis signs further exacerbated by high fat diet | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (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],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Livers of hyperglycemic mice with Hnf1a-deficient pancreatic beta-cells show unexpected hepatic steatosis signs further exacerbated by high fat diet Shayla Sharmine, Lucas Unger, Altanchimeg Altankhuyag, Thomas Aga Legøy, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8013440/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background In the past decades tissue/cell targeted single gene modifications using transgenic systems became a main-stream practice aimed at demultiplexing tissue- and cell-specific gene function. Yet, targeting of many genes and cell types can cause systemic effects, impacting the functionality of other off-target organs. This can further generate a discrete dysfunction loop fueling back to the targeted cell altering their profile readout, effect demultiplexing and results interpretation. Despite the high impact of such scenario especially in the study of endocrine organs, most research is focused on targeted mutation-bearing cell population, while the other organs bearing intact candidate gene activity, receive no or limited attention. Results To assess the potential readout bias caused by off-target organs, we performed here a focused pilot transcriptomics study to map the effects on liver of a monogenic diabetes gene mutation restricted to insulin-expressing beta-cells. Mice with beta-cell restricted disfunction were mildly hyperglycemic and presented normal target gene levels in the liver. Despite normal expression, pathway analyses identified profound transcriptional prolife changes in the liver. These involved the dysregulation of lipid metabolism and extracellular matrix organization, cholesterol biosynthesis being further exacerbated by HFD, consistent with a systemic factor effect such as chronically elevated blood sugar levels. Furthermore, key markers of hepatic steatosis were highly increased, with the livers’ histopathology reflecting lipid droplet accumulation. As hepatic steatosis is an important cause of hepatic insulin resistance that can further alter beta-cell function, the interpretation of the transcriptional background in the targeted beta-cell population must be performed with care. Conclusions Based on this pilot we conclude that multi-organ dysfunction loops can drastically change the read-out in the mutated cell complicating the effect separation. Thus investigating off-target organs is crucial, especially when characterizing genes and cell populations involved in endocrine regulation. Figures Figure 1 Figure 2 Figure 3 1. Background The cellular and molecular bases of complex diseases are notoriously difficult to study due to their multifactorial and polygenic nature. Consequently, in many complex diseases, like Parkinson’s or diabetes, the monogenic versions of the disease represent a unique tool for demultiplexing the key contributors to disease onset and progression [ 1 – 4 ]. Alongside with this strategy, in the past 30 years the use of transgenic models with tissue- or cell-specific drivers allowing gene modulations in the tissue or cell of interest was paramount for elucidating disease mechanisms of action. In mice the Cre/loxP system and its variations [ 5 – 7 ], is the most popular approach used for targeting distinct genes in specific tissues [ 8 , 9 ], resulting usually in an irreversible genomic alteration at the desired locus. The most classical and arguable the widest used approach for tissue/cell-targeted gene inactivation in vivo consists of a specific promoter-driven constitutive Cre -tagger, which recognizes and “defloxes” the loxP sites flanking a region of the gene intended for inactivation. As expected, in these contexts, the focus of most research studies is the targeted mutation-bearing cell population, while much less attention is given to the other non-carrier organs, which present intact target gene activity. Yet, this is an issue if the targeted tissue/cell type dysfunctionality causes systemic effects, impacting other organs functionality, which in turn could fuel back and further deteriorate the target population molecular landscape or function. The occurrence of such unexpected dysfunctionality loops can strongly bias the target cell/tissue characterization and thus requires consideration. This is especially important for the study of endocrine diseases, such as diabetes, where complex regulatory organ axes are involved in tuning critical metabolic processes, like blood glucose regulation. Consequently, we performed here a transcriptomics pilot study to map the effects on liver of a pancreatic insulin-producing beta-cells restricted monogenic diabetes gene mutation. For this we employed a previously generated mouse model [ 10 ], allowing the cell-specific targeting and defloxing of Hnf1a gene exon4 (Hnf1a fl[exon4] ). Heterozygous mutations in Hnf1a are causing the most prevalent monogenic diabetes type, HNF1A-MODY in humans [ 11 – 16 ], with symptoms similar to the ones described in type 1 diabetes (T1D), the patients ultimately requiring insulin administration to survive [ 17 ]. Mouse models of whole body Hnf1a deficiency exhibit hyperglycemia, although in homozygous state[ 10 , 18 – 26 ]. Similarly, in the Hnf1a fl[exon4] model, the homozygous Hnf1a defloxing specifically in beta-cells, leads to mild, yet significant, hyperglycemia, which we recently showed as further aggravated by additional stressors, such as high fat diet (HFD)[ 27 ]. To map the in vivo effects of Hnf1a-deficient beta-cells on livers with normal Hnf1a expression, in this study we performed pathway analysis on liver transcriptomics data with or without additional systemic stressors. 2. Materials and Methods Murine models The Hnf1a flox/flox mice were generated in our laboratory, and previously described by us [ 10 ], while the RIPcre (Tg(Ins2-cre) 23Herr ) [ 28 ] was kindly provided by Prof. Pedro Herrera. Both strains were previously bred onto C57BL6/J mixed background. The experimental RIPcre x Hnf1a flox/flox mice were obtained by crossing the Hnf1a flox/flox mice with the RIPcre (Tg(Ins2-cre) 23Herr ), having the exon4 of Hnf1A deleted upon Cre-recombination, thus removing the IPR001356 homeobox domain and therefore producing a non-functional downstream sequence. Genotyping was used to identify the experimental mice using the following primers for the Hnf1a flox allele: 5’-AAC CAC CCT CTC TCC CAG TAA G-3’(forward) and 5’-GTG TGT GTA ACC GGA GTA GAA G-3’(reverse); and RIP-cre allele: 5’TAA GGC TAA GTA GAG GTG T-3’ (forward) and 5’- TCC ATG GTG ATA CAA GGG AC-3’ (reverse). All animals used in this study were housed locally at Vivarium (Faculty of Medicine, University of Bergen) in a temperature-controlled room at 22 °C under a 12-hour light/dark cycle. Experimental mice were housed in groups of 2–5 in individual ventilated cages (IVC systems) enriched in wooden bedding and nesting material. Eight weeks old male and female mice were randomly allocated to diet groups but taking into consideration the experimental genotype, with follow-up until 20 weeks old. All the mice were given ad libitum access to sterile water and standard diet RM1A (Special Diets Services, SDS UK), V1534-703 Mouse maintenance feed (Ssniff Spezialdiäten GmbH, Germany), synthetic diet (SD; cat. no. 824050–10% AFE Fat, SDS) or high fat diet (HFD; cat. no. 824054–60% AFE Fat, SDS) as described below. At the end of the experiments, mice were sacrificed by cervical dislocation, without anesthesia, by trained experienced personnel, death confirmed by separation of the spinal cord from the skull. The study is reported in accordance with ARRIVE guidelines. Feeding experiments Eight weeks old littermates were separated into groups of standard diet (SD; cat. no. 824050–10% AFE Fat, SDS), and high fat diet (HFD; cat. no. 824054–60% AFE Fat, SDS) and allowed ad-libitum access to food which was refilled weekly. Glycemia measurements Conscious animals were restrained using modified 50 mL falcon tube, and the lateral tail vein was nicked by a sterile scalpel blade or by pricking the vein using a sterile needle. One clean droplet of blood was placed on a glucose test strip (Contour Next glucose strips, Bayer) and glucose level was read using a commercial glucometer (Contour XT glucometer, Bayer). The collection of physiological data (blood glucose or weight measurements) could not be blinded due to noticeable difference in glycaemia. Organ collection and processing Mice were euthanized by cervical dislocation, and livers and pancreases were dissected, briefly washed in phosphate buffered saline (VWR, A9162.01000) and fixed in 4% PFA. Tissues were prepared for fresh freezing, and paraffin embedding. Fresh Frozen sections The collected liver was fixed in 4% PFA overnight at 4°C, then dehydrated in sucrose gradient of 10, 20 and 30% and embedded in Tissue Tek OCT compound (Sakura JP) as previously described [ 29 ]. The blocks were then transferred to -20 o C for freezing. Frozen tissues were cut into 10 µm cryosections using Leica CM 1950 cryotome and used for staining. Paraffin embedding Collected tissue samples were dehydrated through a graded series of ethanol solutions (70%, 80%, 95% and 100%) for 1 hour each. Dehydrated tissues were cleared in xylene for 2 hours following infiltration with molten paraffin wax at 60°C changing twice for 1 hour each. Infiltrated tissues were embedded in paraffin blocks using embedding molds. Tissue orientation was maintained, and blocks were sectioned at 5–7 µm slices using RM2155 microtome. Preparation of FFPE tissue for staining Paraffin-embedded tissue slides were deparaffinized by immersion in xylene twice. Following deparaffinization the slides were rehydrated through graded ethanol series (twice in 100%, once in 95%, 70% and 50%) and washed with deionized water. The tissue sections were then used for staining. Hematoxilin eosin staining A Hematoxylin and Eosin staining kit (Abcam, ab245880) was used by following the provider’s instructions and as previously described [ 30 ]. Slides were mounted in Entallan mounting medium. The stained sections were imaged using Olympus VS120 slide scanner. Nile red staining Fresh frozen liver sections were used for lipid droplets staining as previously described [ 30 ], combining Nile Red (Thermo Fisher Scientific, N1142), Phalloidin A488 (Thermo Fisher Scientific, A12379) and DAPI (Invitrogen, P36931). Slides were mounted in ProLong Diamond anti-fade mounting media (Invitrogen, P36931) and images were acquired by using Leica SP8-STED confocal microscope (Leica Microsystems). Immunofluorescence staining Paraffin sections were processed as previously described [ 31 ]. In short, sections were deparaffinized, rehydrated and heat-induced retrieval was carried out for 3 minutes at high pressure using pressure cooker (Ninja). The primary antibody used for immunofluorescence was rabbit anti-ChREBP (1/100, Novus biologicals, NB400-135), while secondary antibody and dyes were donkey anti-rabbit A546 (1:500, Molecular Probes, A10040), Phalloidin A488 (Thermo Fisher Scientific, A12379) and DAPI (Invitrogen, P36931). Slides were mounted in ProLong Gold Antifade (Molecular Probes) and imaged with a Leica SP8-STED confocal microscope. Liver Processing for RNA extraction Liver was partitioned in small pieces, placed into 2 mL Eppendorf tubes and snap frozen in liquid nitrogen. Frozen livers were pulverized into fine powder using mortar and pastel pre chilled in liquid N2. Aliquots were rapidly transferred into prechilled Eppendorf tubes and immediately transferred to -80°C for subsequent RNA extraction. Total RNA extraction Total RNA was extracted from fresh frozen liver pieces from 4 WT mice, 5 RIPcre x Hnf1a flox/flox mice fed with standard diet and 4 RIPcre x Hnf1a flox/flox mice after 10 weeks on HFD by using Qiazol reagent (Qiagen). Briefly, liver powder was thawed and homogenized using Qiazol lysis Reagent followed by addition of Chloroform for phase separation of RNA-containing aqueous phase from DNA and protein by centrifugation. RNA was precipitated with isopropanol, followed by washing and finally eluting in RNase free water in 30 µl volume. Isolated RNA was quantified and measured for integrity using Tapestation 4150 (Agilent G2992AA), and samples were stored at -80 o C. RNA samples with minimum RIN value of 8 were shipped to Novogene GmBH facility for sequencing. Bulk Sequencing Liver RNA samples were processed by Novogene (Cambridge, UK), where a second quality control and library preparation (polyA enrichment) were performed. The sequencing was performed on NovaSeq X Plus Series (PE150) 30 million reads each end. Data and pathway analysis FastQ files from sequencing were analyzed using the CLC Genomics Workbench 25.0 (Qiagen, Aarhus, Denmark). Pre-processing included adapter and quality score-based trimming, using the default setting provided by the trimming tool in the CLC software. Alignment and quantification were carried out using the RNAseq Analysis tool, following the default settings provided by the CLC Workbench. To generate the DEG lists, groups were compared using the “Empirical Analysis of DGE” algorithm of the CLC software. The DEG lists were subsequently uploaded to Ingenuity Pathway Analysis for further analysis [ 32 ] as previously described [ 33 , 34 ]. Only DEGs with a fold change (FC) ≥ 1.5 and p-value < 0.05 were included for pathway prediction, further Network settings were kept at default setting. 3. Results Mice with Hnf1a-deficient beta-cells are hyperglycemic and display normal Hnf1a expression in the liver The RIP-Cre, Hnf1a fl[exon4] (HMZ) allows the ablation of Hnf1a exon4 specifically in the insulin-secreting beta-cells of the pancreatic islet (RIP - rat insulin promoter), the resulted Hnf1a-deficient beta-cells residing in an otherwise normal Hnf1a genetic context. As in our previous studies, to avoid for any potential driver transgene interference (e.g. RIP-Cre), we used RIP-Cre positive mice without Hnf1a fl[exon4] as controls for the experiments (WT). In these HMZ mice, we observed the statistically significant downregulation of Hnf1a transcripts in the beta-cells (Fig. 1 A, upper graph p = 2.11E-03), but not in the liver (Fig. 1 A, lower graph), indicating that, as expected, Hnf1a was not mutated in the liver. The mice were hyperglycemic, displaying increased glycemia values in both non-fasted (Fig. 1 B, p < 0.0001) and fasted conditions (Fig. 1 C, p < 0.0001), with previous studies demonstrating also impaired glucose tolerance [ 19 , 35 ]. Liver of mice with Hnf1a-deficient beta-cells exhibit a robust deregulation of lipid metabolism The transcriptomics analysis filtered 1891 differentially expressed genes (DEGs, FC > 1.5x, p-value < 0.05) between livers from Hnf1a-deficient beta-cells (HMZ) and their WT counterparts, indicating a massive signature shift in these animals, despite the absence of a specific Hnf1a mutation in the liver (Fig. 1 D). Pathway analysis using the Ingenuity Pathway Analysis (IPA) pipeline identified mainly signaling involved in extracellular matrix regulation in the top inhibited signaling pathways (Fig. 1 E, blue), suggesting possible structural changes. In contrast, the top activated signaling pathways was dominated by signaling involved in adipogenesis and lipid metabolism (Fig. 1 E, orange). The focused analysis of the metabolic pathways inferred the robust activation of diverse pathways involved in biosynthesis, while most pathways involved in degradation presented an inhibition activity pattern (Fig. 1 F). As before, signaling involved in cholesterol biosynthesis was the most represented group amongst the metabolic signaling, with other pathways with positive activity pattern being involved in glucose (such as Gluconeogenesis) or energy (such as Pentose phosphate pathway) metabolism. In line with these observations, the analysis of the upstream regulators predicted in the top the activation of key transcription factors involved fatty acids, phospholipids, and triglycerides synthesis, Srebf1 and Mlxipl (Fig. 1 G). In contrast, Tgfb1 and Egr1, two regulators of cell differentiation, proliferation and growth, were inferred in the top inhibited upstream regulators based on the observed differential transcriptional landscape (Fig. 1 G, blue). Of note, these factors also followed an observed regulation pattern in line with the above predictions (Fig. 1 H). The diseases and functions analysis further confirmed the elevation of lipid metabolism in the liver of diabetic mice with Hnf1a-deficient beta-cells, with increased Synthesis of sterol, Synthesis of cholesterol and Dyslipidemia function, while Lipolysis, a degradation-related process, was inferred as decreased. The focused analysis of the DEGs driving biosynthesis functions, such as Dyslipidemia (p-value 1.54E-08, z-score 2.084), revealed the observed deregulation of key genes involved in energy metabolism integration and regulation, such as Slc2a4 (Glut4, important glucose transporter), Acly (ATP citrate lyase), Gckr (glucokinase regulator) and Glp2r (Glucagon-like peptide-2 receptors), amongst many others (Fig. 1 J). In addition to the connection with several of the above genes (such as the Mlxipl and Acly ), the reconstruction of the top upstream regulator Srebf1 immediate molecular network indicated the robust upregulation of the hepatic steatosis markers Pnpla3 and Cidea (Fig. 1 K, first network). Of note, Pnpla3 is a target of both top upstream regulator Srebf1 and Mlxipl (Fig. 1 K, second network), while Cidea upregulation can be driven by Srebf1 upregulation and Foxa3 (key regulator of regeneration, lipid and glucose metabolism in the liver) downregulation (Fig. 1 K, third network). Overall, these data indicate the deregulation of the lipid metabolism landscape in the liver of diabetic mice with Hnf1a-deficient beta cells, characterized by increased cholesterol biosynthesis and markers of hepatic steatosis. This observation combined with the deregulation of extracellular matrix signatures suggests the occurrence of liver architecture remodeling. High fat diet mainly promotes further cholesterol biosynthesis We previously showed that high fat diet (HFD) exacerbates the glucose regulation phenotypes in mice with Hnf1a-deficient beta-cells [ 27 ]. To investigate if the liver phenotype exhibits a similar trend, we exposed the mice to HFD for 10 weeks. As expected, HFD-fed HMZ mice (HMZ-HFD) became overtly hyperglycemic, with blood sugar values statistically significantly higher than its standard diet (SD) fed counterpart (Fig. 2 A, p = 0.0268). The analysis of the differential transcriptional landscape revealed 478 DEGs between the livers of HMZ mice fed with HFD and the ones receiving SD (Fig. 2 B). The unbiased overview of the transcriptional landscape revealed a further activation of pathways involved in cholesterol biosynthesis, centered around the Srebf2, a regulator primarily involved in cholesterol metabolism and synthesis (Fig. 2 C). Of note, the focused analysis of the top metabolic pathways inferred the exclusive activation of biosynthesis-related pathways, with signaling involved in cholesterol biosynthesis at the top (Fig. 2 D). Indeed, the comparison analysis between this differential transcriptional landscape (HMZ-HFD vs HMZ) and the previously analyzed one (HMZ vs WT), confirmed this signaling is promoted by HFD, while pathways involved in Gluconeogenesis or Degradation exhibit no change in their activity pattern in response to the dietary stressor (Fig. 2 E). The assessment of top canonical pathways identified a limited number of pathways with a negative activity pattern score with the Calcium Signaling being the top inactivated pathway (Fig. 2 F, blue). In contrast, besides the metabolic pathways, the analysis inferred in the top 6 the activation of signaling involved in cell cycle regulation (Fig. 2 F, orange). Indeed, the specific analysis of the cell cycle signature, identified a range of signaling involved in DNA synthesis and cell cycle progression (Fig. 2 G). Srebf2 (p-value:2.05E-20, z:2.713) was predicted as the top upstream regulator with observed upregulation responsible for the observed differential transcriptional landscape (Fig. 2 H). Moreover, the diseases and functions analysis indicated the increase of a large number of processes related to cholesterol, steroid, sterol and terpenoid synthesis and metabolism. (Fig. 1 I). Taken together, our data show a dysregulation of the extracellular matrix and lipid signatures in the livers of the mice with Hnf1a-deficient beta-cells, with increased key markers of hepatic steatosis. Exposure to high fat diet showed a further exacerbation of lipid synthesis, especially cholesterol, while also activating cell cycle signatures. We thus first assessed the livers of these mice for signs of hepatic steatosis by performing hematoxylin-eosin staining on liver sections. In line with the molecular observations, the staining revealed disrupted liver architecture with glycogen accumulation (arrowheads) and fat buildup indicated by increased lipid droplets (arrows) in diabetic mice with Hnf1a-deficient beta-cells (Fig. 3 A). These observations were further confirmed by the specific lipid droplet Nile Red staining (Fig. 3 B), which identified the accumulation of lipid droplets in HFD-treated WT mice as well as in HMZ animals, regardless of diet regimen. Of note, the size of the lipid droplets was enlarged in mice with Hnf1a-defective beta-cells, supporting the molecular observations of defective lipid metabolism in this context, even before HFD administration. In addition, immunofluorescence identified ChREBP ( Mlxipl ) potent translocation into the nucleus in HMZ mice regardless of diet (Fig. 3 C). In contrast, in the WT mice ChREBP pattern was cytoplasmic, with some nuclear translocation following HFD, once more confirming the observation of a strong deleterious phenotype in the liver of the HMZ mice, despite the strict beta-cell localization of the Hnf1a-deficiency and even in the absence of additional stressors, such as HFD. 4. Discussions Briefly, in this focused pilot study, we used transcriptomics to assess for potential signature changes in the liver of mice bearing the Hnf1a mutation exclusively in the insulin-secreting beta-cells. We report that despite the normal level of liver Hnf1a expression, the hepatic transcriptional landscape drastically changes towards increased lipid metabolism and overexpression of liver steatosis markers, while exposure to HFD further exacerbated cholesterol biosynthesis. Besides lipid metabolism, changes in extracellular matrix organization were identified in the livers of HMZ mice, while high HFD promoted proliferation signatures. The normal levels of Hnf1a in the livers of mice bearing the Hnf1a mutation exclusively in the insulin-secreting beta-cells suggests that the reported hepatic signature dysregulation is triggered by a systemic factor. Due to their beta-cell dysfunctionality, the HMZ mice are mildly hyperglycemic, and thus the chronically elevated blood glucose level is likely the systemic cue eliciting the observed hepatic effects. The connection between hyperglycemia and liver steatosis is not new, with previous studies indicating that elevated blood sugar is a high-risk factor for Non-Alcoholic Fatty Liver Disease (NAFLD) [ 36 ]. In this context, it was shown that excess glucose activates Mlxipl (Chrebp), which in turn promotes genes involved in lipogenesis and triglyceride synthesis, leading to increased fatty acid synthesis accumulation and NAFLD development [ 37 ]. This is in line with the observations in this pilot where Mlxipl was upregulated and inferred as top second upstream regulator defining the liver transcriptional landscape of the HMZ mice. Of relevance, Srebf1, the top upstream regulator, is a known target of Mlxipl/Chrebp and a key factor involved in regulating fatty acids and triglycerides synthesis. Increased Srebf1 expression in the liver was strongly connected with NAFLD [ 38 ]. Here we report its upregulation in HMZ mice, however not following HFD administration. Of note, after HFD, we observe the upregulation of Srebf2 , another sterol regulator binding protein that is mainly involved in cholesterol biosynthesis and which regulation was not driven in the absence of the diet stressor. Interestingly, although HFD exacerbated HMZ mice glycemia, the number of deregulated genes was ~ 4x lower than the ones observed in the livers of mice with Hnf1a-deficient beta cells. In line with this observation, there were less gene signatures changes, involved in either further promotion of cholesterol biosynthesis or cell proliferation regulation. This is an interesting finding as it suggests that even low chronic increase in blood glucose can lead to a powerful molecular domino in the liver, with further stressors such as HFD eliciting just a confined augmentation effect. In line with the link between hyperglycemia and NAFLD, we observed a massive upregulation of key markers of hepatic steatosis, such as Pnpla3 (~ 130x fold), in the livers of mice with Hnf1a-deficient beta-cells even before HFD administration. Pnpla3 is a triglyceride lipase that regulates the lipid droplet metabolism and a target of both Mlxipl [ 39 ] and Srebf [ 40 ] and its accumulation on lipid droplets is considered a direct cause for hepatic steatosis [ 41 ]. Cidea (Cell Death Inducing DFFA Like Effector A) was a second hepatic steatosis marker that showed robust upregulation (~ 65x fold). Interestingly, a recent study indicated an opposite coupling between Cidea and Egr1 at transcriptional level, which we also observed in this pilot [ 42 ]. Egr1 is a regulator of cell proliferation, lipid metabolism and hepatic circadian clock [ 43 ], which acts as a molecular break to prevent excessive stimulation and controls blood glucose levels’ fluctuation in physiological conditions [ 42 ]. Egr1 deletion leads to triglyceride accumulation and large lipid droplet accumulations [ 42 ]. In line with these findings, we report the downregulation of Egr1 and upregulation of Cidea in the liver of mice with Hnf1a-deficient beta-cells. In addition, we observed activity in signaling regulating extracellular matrix organization in the HMZ mice fed on standard diet, suggesting tissue-remodeling events. Previous studies indicated that hyperglycemia disrupts extracellular matrix by collagen crosslinking in a variety of organs and systems [ 44 – 47 ], thus it is tempting to connect also these modifications to increased blood glucose in the HMZ mice. Furthermore, HFD exposure impacted on cell cycle regulation signatures, promoting RNA synthesis and cell cycle progression. This potential increase in proliferation might be connected to potential regenerative events aimed at compensating tissue loss due to steatosis or it can be triggered by exacerbated glycemia. Indeed, hepatic stellate cells were shown to boost proliferation in response to high glucose [ 48 ]. Moreover, recent studies indicated that HFD can promote liver proliferation [ 49 , 50 ], probably via cellular stress [ 51 ]. A potential proposed mechanisms is represented by hepatocyte fat accumulation causing lipid overload, nucleotide pool imbalance and replication stress damage, stimulating proliferation [ 51 ]. Of relevance, previous research indicated that hepatic steatosis leads to hepatic insulin resistance thus fueling back on beta-cell functionality, which are consequently required to augment insulin secretion despite pre-existing elevated insulin levels [ 37 , 52 , 53 ]. This is expected to interfere with the beta-cells functionality signature and transcriptional landscape, thus interfering with the readout. 5. Conclusions In conclusion, the observed robust hepatic signature changes in the absence of a directly targeted liver mutation reinforce the importance of assessing the indirect involvement of other organs following targeted gene inactivation, especially when it involves endocrine organs. In these cases, the restriction of the mutation to the desired cellular compartment might not completely demultiplex the defective gene effect on the targeted cell biology but also reflect the feed-back of other organs dysfunction, caused by mutation-induced changes in systemic parameters. This multi-organ dysfunction loop can drastically change the read-out in the mutated cells complicating the effect separation. Although this is an unavoidable limitation of such directed gene inactivation strategies, it is important to acknowledge the existence of a potential confounding effect for data interpretation. One limitation of the current pilot is the exclusive focus on insulin-secreting beta-cells mutations on a single organ, the liver. Further complexity will be reached by studying diverse cell populations with targeted mutations combined to assessing multi-organs systemic involvement. Moreover, the pilot was limited to transcriptional landscape analysis, while the analysis of the proteomic or epigenetic level will help understanding the network of interactions. Along the same line a cellular compartment characterization of the molecular observations will help mapping organ-specific cell-population crosstalk. Declarations Ethics approval The animal experiments in this study were approved by the Norwegian Food Safety Authority Mattilsynet (FOTS number 10785, 12105 and 19800) in accordance with the European Union (EU) Directive 2010/63/EU, and performed according to the guidelines and regulations on the use of animals in the research at the Vivarium Laboratory Animal Facility at the University of Bergen. Consent for participation and publication Not applicable. Availability of data and materials The datasets used in this work have been deposited in NCBI's Gene Expression Omnibus [54] and will be accessible through GEO Series accession number GSE311980 after manuscript publication: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE311980 The differentially expressed gene lists are presented as Supplemental Table 1 – comparison of the HNF1A HMZ islets over WT islets, and Supplemental Table 2 – comparison of HFD HNF1A HMZ islets over HNF1A HMZ islets. Should any raw data files be needed they are available from the corresponding authors upon reasonable request. Competing interests The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Funding This work was supported by funds from the Research Council of Norway (NFR 304615 and 314397), Novo Nordic Foundation (NNF21OC0067325), Stiftelsen Trond Mohn Foundation (Mohn Center of Diabetes Precision Medicine) to S.C.; Diabetesforbundets forskningsfond and University of Bergen to S.C. and L.G; Clinical Science Department doctoral fund to S.S. L.U. and S.S. are supported by doctoral fellowships from Faculty of Medicine, University of Bergen. The funding sources had no role in the study design, its execution, analyses, interpretation of the data, nor the decision to publish these results. Author Contributions S.S. collected the physiological data, performed the diet experiments, collected samples, RNA preparations, performed immunostaining, confocal imaging, processing, counting and participated in experimental design; L.U. performed data analyses, including the generation of DEGs for transcriptomics comparisons; A.A. performed mouse work and liver sample processing; T.A.L performed the RIPcre; HNF1A mouse characterization and collected physiological data; L.U. and S.C. performed the pathway analysis for the transcriptomics datasets; L.G. and S.C. acquired funding, conceived the experiments, supervised the work, interpreted the observations and wrote the manuscript. All authors edited and approved the final version of the manuscript. Acknowledgments We thank June Helen Gudmestad and Birgitte Feginn Berle for technical help. The preparation of FFPE tissue blocks and sections, part of hematoxylin and eosin staining, and confocal imaging was performed at the Molecular Imaging Center (MIC) and was thus supported by the Department of Biomedicine and the Faculty of Medicine at the University of Bergen, and its partners. The RIP-Cre mice were kindly provided by Pedro Herrera. References Horowitz MP, Greenamyre JT: Gene-environment interactions in Parkinson's disease: the importance of animal modeling . Clinical pharmacology and therapeutics 2010, 88 (4):467-474. Bird TD: Genetic factors in Alzheimer's disease . The New England journal of medicine 2005, 352 (9):862-864. Ghosh U, Samanta A: Monogenic inflammatory bowel disease: An unfolding enigma . World J Clin Pediatr 2025, 14 (3):107165. Hattersley AT, Patel KA: Precision diabetes: learning from monogenic diabetes . Diabetologia 2017, 60 (5):769-777. Yarmolinsky M, Hoess R: The Legacy of Nat Sternberg: The Genesis of Cre-lox Technology . 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BMC Public Health 2024, 24 (1):1865. Bellini MI, Urciuoli I, Del Gaudio G, Polti G, Iannetti G, Gangitano E, Lori E, Lubrano C, Cantisani V, Sorrenti S et al : Nonalcoholic fatty liver disease and diabetes . World J Diabetes 2022, 13 (9):668-682. Edgar R, Domrachev M, Lash AE: Gene Expression Omnibus: NCBI gene expression and hybridization array data repository . Nucleic Acids Res 2002, 30 (1):207-210. Additional Declarations No competing interests reported. Supplementary Files SupplementalTable1HMZvsWTSTD.xls SupplementalTable2HMZHFDvsHMZSDHFD.xls Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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with Hnf1a-deficient beta-cells (HMZ) as compared to their respective control (Mann-Whitney); \u003cstrong\u003ec)\u003c/strong\u003e Increased fasted glycemia values in mice with Hnf1a-deficient beta-cells (HMZ) as compared to their respective control (Mann-Whitney); \u003cstrong\u003ed)\u003c/strong\u003e Scheme of the RNAseq experimental design and identified number of differentially expressed genes (DEG) between the two analyzed conditions; \u003cstrong\u003ee)\u003c/strong\u003e Top signaling pathways characterizing the analyzed differential landscape (blue – inactivated, orange – activated, -2≤z-value≥2); \u003cstrong\u003ef)\u003c/strong\u003e Top metabolic pathways characterizing the analyzed differential landscape (blue – inactivated, orange – activated, -2≤z-value≥2); \u003cstrong\u003eg)\u003c/strong\u003e Top predicted upstream regulators characterizing the analyzed differential landscape; \u003cstrong\u003eh) \u003c/strong\u003eThe observed regulation of the top upstream regulators in the analyzed differential transcriptional landscape; \u003cstrong\u003ei)\u003c/strong\u003e Top metabolic functions and unbiased map of the dyslipidemia network including relevant genes for the process; \u003cstrong\u003ej)\u003c/strong\u003e Graphs displaying the observed deregulation of important targets involved in dyslipidemia; \u003cstrong\u003ek)\u003c/strong\u003e Immediate interaction partner regulatory networks for \u003cem\u003eSrebf1\u003c/em\u003e, \u003cem\u003ePnlpa3\u003c/em\u003e and \u003cem\u003eCidea\u003c/em\u003e as well as graphs displaying the observed \u003cem\u003ePnpla3 \u003c/em\u003eand \u003cem\u003eCidea\u003c/em\u003e upregulation; p-values range: *p\u0026lt;0.05, **p\u0026lt;0.01, ***p\u0026lt;0.001, p\u0026lt;0.0001\u003c/p\u003e","description":"","filename":"Figure1ShaylaLiverv2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8013440/v1/bb6619e3e9fa5ba0e6003699.jpg"},{"id":98513989,"identity":"e920fe12-7ea2-43c6-8d02-8a9809138fce","added_by":"auto","created_at":"2025-12-18 12:16:24","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2027952,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe differential transcriptomic landscape in high fat diet (HFD) exposed mice with Hnf1a-deficient beta-cells.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea)\u003c/strong\u003e Increased non-fasted glycemia values in HMZ mice receiving high fat or standard diet (Mann-Whitney); \u003cstrong\u003eb)\u003c/strong\u003e Scheme of the RNAseq experimental design and identified number of differentially expressed genes (DEG) between the two analyzed conditions; \u003cstrong\u003ec)\u003c/strong\u003e Graphical summary overview of the analyzed differential transcriptional landscape; \u003cstrong\u003ed)\u003c/strong\u003e Top metabolic pathways characterizing the analyzed differential landscape (blue – inactivated, orange – activated, -2≤z-value≥2); \u003cstrong\u003ee)\u003c/strong\u003e Comparison analysis of the top metabolic pathways characterizing the two (HMZ vs WT \u0026amp; HMZ-HFD vs HMZ) analyzed differential landscape (blue – inactivated, orange – activated, -2≤z-value≥2); \u003cstrong\u003ef)\u003c/strong\u003e Top signaling pathways characterizing the analyzed differential landscape (blue – inactivated, orange – activated, -2≤z-value≥2); \u003cstrong\u003eg)\u003c/strong\u003e \u0026nbsp;Activation of the cell cycle regulation signature in the analyzed differential landscape; \u003cstrong\u003eh)\u003c/strong\u003e Top predicted upstream regulator characterizing the analyzed differential landscape, graph displaying the observed \u003cem\u003eSrebf2 \u003c/em\u003eupregulation and the Srebf2 upstream regulator network; \u003cstrong\u003ei)\u003c/strong\u003e Top activated metabolic functions characterizing the analyzed differential landscape; p-values range: *p\u0026lt;0.05, **p\u0026lt;0.01, ***p\u0026lt;0.001, p\u0026lt;0.0001\u003c/p\u003e","description":"","filename":"Figure2ShaylaLiverv4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8013440/v1/edd741f334a1594fdaa33c2c.jpg"},{"id":98513994,"identity":"30eb0e81-e408-4095-a83a-7c75d15aa38d","added_by":"auto","created_at":"2025-12-18 12:16:24","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1669491,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLiver architecture remodeling in mice with Hnf1a-deficient beta cells with or without HFD and their respective WT controls\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea)\u003c/strong\u003e Hematoxylin-eosin staining of slide scan liver sections (scale bar: 100µm, arrowhead – glycogen accumulation, arrow – lipid droplets);\u003cstrong\u003e b)\u003c/strong\u003e Lipid droplets staining in WT and HMZ mice in the absence or presence of HFD (scale bar: 100µm, Nile Red – orange, phalloidin – green, DAPI – blue, arrow – lipid droplets); \u003cstrong\u003ec)\u003c/strong\u003e ChREBP immunofluorescence in WT and HMZ mice in the absence or presence of HFD (scale bar: 100µm, ChREBP – red, DAPI – blue arrow – nuclear ChREBP).\u003c/p\u003e","description":"","filename":"Figure3ShaylaLiverv2stainings.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8013440/v1/0fdc0387d722ebc5b12bbdf1.jpg"},{"id":103404064,"identity":"f48cd580-53be-4a62-bb28-6209fb5c7465","added_by":"auto","created_at":"2026-02-25 09:44:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9258161,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8013440/v1/fd584422-ff3f-472c-8d5c-2e57ca55261d.pdf"},{"id":98625198,"identity":"d3163791-7317-4539-a4aa-963c39e1f7a8","added_by":"auto","created_at":"2025-12-19 17:08:59","extension":"xls","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":392704,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTable1HMZvsWTSTD.xls","url":"https://assets-eu.researchsquare.com/files/rs-8013440/v1/917f9b0ad9e5bc105cee5c6a.xls"},{"id":98624166,"identity":"69dbb879-9799-4933-92b1-954bd6cc92f7","added_by":"auto","created_at":"2025-12-19 17:08:07","extension":"xls","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":117760,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTable2HMZHFDvsHMZSDHFD.xls","url":"https://assets-eu.researchsquare.com/files/rs-8013440/v1/74ee1447110b67a31f888275.xls"}],"financialInterests":"No competing interests reported.","formattedTitle":"Livers of hyperglycemic mice with Hnf1a-deficient pancreatic beta-cells show unexpected hepatic steatosis signs further exacerbated by high fat diet","fulltext":[{"header":"1. Background","content":"\u003cp\u003eThe cellular and molecular bases of complex diseases are notoriously difficult to study due to their multifactorial and polygenic nature. Consequently, in many complex diseases, like Parkinson\u0026rsquo;s or diabetes, the monogenic versions of the disease represent a unique tool for demultiplexing the key contributors to disease onset and progression [\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Alongside with this strategy, in the past 30 years the use of transgenic models with tissue- or cell-specific drivers allowing gene modulations in the tissue or cell of interest was paramount for elucidating disease mechanisms of action.\u003c/p\u003e \u003cp\u003eIn mice the Cre/loxP system and its variations [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], is the most popular approach used for targeting distinct genes in specific tissues [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], resulting usually in an irreversible genomic alteration at the desired locus. The most classical and arguable the widest used approach for tissue/cell-targeted gene inactivation \u003cem\u003ein vivo\u003c/em\u003e consists of a specific promoter-driven constitutive \u003cem\u003eCre\u003c/em\u003e-tagger, which recognizes and \u0026ldquo;defloxes\u0026rdquo; the \u003cem\u003eloxP\u003c/em\u003e sites flanking a region of the gene intended for inactivation.\u003c/p\u003e \u003cp\u003eAs expected, in these contexts, the focus of most research studies is the targeted mutation-bearing cell population, while much less attention is given to the other non-carrier organs, which present intact target gene activity. Yet, this is an issue if the targeted tissue/cell type dysfunctionality causes systemic effects, impacting other organs functionality, which in turn could fuel back and further deteriorate the target population molecular landscape or function. The occurrence of such unexpected dysfunctionality loops can strongly bias the target cell/tissue characterization and thus requires consideration. This is especially important for the study of endocrine diseases, such as diabetes, where complex regulatory organ axes are involved in tuning critical metabolic processes, like blood glucose regulation.\u003c/p\u003e \u003cp\u003eConsequently, we performed here a transcriptomics pilot study to map the effects on liver of a pancreatic insulin-producing beta-cells restricted monogenic diabetes gene mutation. For this we employed a previously generated mouse model [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], allowing the cell-specific targeting and defloxing of \u003cem\u003eHnf1a\u003c/em\u003e gene exon4 (Hnf1a\u003csup\u003efl[exon4]\u003c/sup\u003e). Heterozygous mutations in \u003cem\u003eHnf1a\u003c/em\u003e are causing the most prevalent monogenic diabetes type, HNF1A-MODY in humans [\u003cspan additionalcitationids=\"CR12 CR13 CR14 CR15\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], with symptoms similar to the ones described in type 1 diabetes (T1D), the patients ultimately requiring insulin administration to survive [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMouse models of whole body Hnf1a deficiency exhibit hyperglycemia, although in homozygous state[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan additionalcitationids=\"CR19 CR20 CR21 CR22 CR23 CR24 CR25\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Similarly, in the Hnf1a\u003csup\u003efl[exon4]\u003c/sup\u003e model, the homozygous \u003cem\u003eHnf1a\u003c/em\u003e defloxing specifically in beta-cells, leads to mild, yet significant, hyperglycemia, which we recently showed as further aggravated by additional stressors, such as high fat diet (HFD)[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. To map the \u003cem\u003ein vivo\u003c/em\u003e effects of Hnf1a-deficient beta-cells on livers with normal Hnf1a expression, in this study we performed pathway analysis on liver transcriptomics data with or without additional systemic stressors.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cp\u003e \u003cb\u003eMurine models\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe \u003cem\u003eHnf1a\u003c/em\u003e\u003csup\u003e\u003cem\u003eflox/flox\u003c/em\u003e\u003c/sup\u003e mice were generated in our laboratory, and previously described by us [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], while the \u003cem\u003eRIPcre\u003c/em\u003e (Tg(Ins2-cre)\u003csup\u003e23Herr\u003c/sup\u003e) [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] was kindly provided by Prof. Pedro Herrera. Both strains were previously bred onto C57BL6/J mixed background. The experimental \u003cem\u003eRIPcre\u003c/em\u003e x \u003cem\u003eHnf1a\u003c/em\u003e\u003csup\u003e\u003cem\u003eflox/flox\u003c/em\u003e\u003c/sup\u003e mice were obtained by crossing the \u003cem\u003eHnf1a\u003c/em\u003e\u003csup\u003e\u003cem\u003eflox/flox\u003c/em\u003e\u003c/sup\u003e mice with the \u003cem\u003eRIPcre\u003c/em\u003e (Tg(Ins2-cre)\u003csup\u003e23Herr\u003c/sup\u003e), having the exon4 of \u003cem\u003eHnf1A\u003c/em\u003e deleted upon Cre-recombination, thus removing the IPR001356 homeobox domain and therefore producing a non-functional downstream sequence. Genotyping was used to identify the experimental mice using the following primers for the \u003cem\u003eHnf1a flox\u003c/em\u003e allele: 5\u0026rsquo;-AAC CAC CCT CTC TCC CAG TAA G-3\u0026rsquo;(forward) and 5\u0026rsquo;-GTG TGT GTA ACC GGA GTA GAA G-3\u0026rsquo;(reverse); and \u003cem\u003eRIP-cre\u003c/em\u003e allele: 5\u0026rsquo;TAA GGC TAA GTA GAG GTG T-3\u0026rsquo; (forward) and 5\u0026rsquo;- TCC ATG GTG ATA CAA GGG AC-3\u0026rsquo; (reverse).\u003c/p\u003e \u003cp\u003eAll animals used in this study were housed locally at Vivarium (Faculty of Medicine, University of Bergen) in a temperature-controlled room at 22 \u0026deg;C under a 12-hour light/dark cycle. Experimental mice were housed in groups of 2\u0026ndash;5 in individual ventilated cages (IVC systems) enriched in wooden bedding and nesting material. Eight weeks old male and female mice were randomly allocated to diet groups but taking into consideration the experimental genotype, with follow-up until 20 weeks old. All the mice were given \u003cem\u003ead libitum\u003c/em\u003e access to sterile water and standard diet RM1A (Special Diets Services, SDS UK), V1534-703 Mouse maintenance feed (Ssniff Spezialdi\u0026auml;ten GmbH, Germany), synthetic diet (SD; cat. no. 824050\u0026ndash;10% AFE Fat, SDS) or high fat diet (HFD; cat. no. 824054\u0026ndash;60% AFE Fat, SDS) as described below. At the end of the experiments, mice were sacrificed by cervical dislocation, without anesthesia, by trained experienced personnel, death confirmed by separation of the spinal cord from the skull. The study is reported in accordance with ARRIVE guidelines.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFeeding experiments\u003c/b\u003e \u003c/p\u003e \u003cp\u003eEight weeks old littermates were separated into groups of standard diet (SD; cat. no. 824050\u0026ndash;10% AFE Fat, SDS), and high fat diet (HFD; cat. no. 824054\u0026ndash;60% AFE Fat, SDS) and allowed \u003cem\u003ead-libitum\u003c/em\u003e access to food which was refilled weekly.\u003c/p\u003e \u003cp\u003e \u003cb\u003eGlycemia measurements\u003c/b\u003e \u003c/p\u003e \u003cp\u003eConscious animals were restrained using modified 50 mL falcon tube, and the lateral tail vein was nicked by a sterile scalpel blade or by pricking the vein using a sterile needle. One clean droplet of blood was placed on a glucose test strip (Contour Next glucose strips, Bayer) and glucose level was read using a commercial glucometer (Contour XT glucometer, Bayer). The collection of physiological data (blood glucose or weight measurements) could not be blinded due to noticeable difference in glycaemia.\u003c/p\u003e \u003cp\u003e \u003cb\u003eOrgan collection and processing\u003c/b\u003e \u003c/p\u003e \u003cp\u003eMice were euthanized by cervical dislocation, and livers and pancreases were dissected, briefly washed in phosphate buffered saline (VWR, A9162.01000) and fixed in 4% PFA. Tissues were prepared for fresh freezing, and paraffin embedding.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFresh Frozen sections\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe collected liver was fixed in 4% PFA overnight at 4\u0026deg;C, then dehydrated in sucrose gradient of 10, 20 and 30% and embedded in Tissue Tek OCT compound (Sakura JP) as previously described [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The blocks were then transferred to -20\u003csup\u003eo\u003c/sup\u003eC for freezing. Frozen tissues were cut into 10 \u0026micro;m cryosections using Leica CM 1950 cryotome and used for staining.\u003c/p\u003e \u003cp\u003e \u003cb\u003eParaffin embedding\u003c/b\u003e \u003c/p\u003e \u003cp\u003eCollected tissue samples were dehydrated through a graded series of ethanol solutions (70%, 80%, 95% and 100%) for 1 hour each. Dehydrated tissues were cleared in xylene for 2 hours following infiltration with molten paraffin wax at 60\u0026deg;C changing twice for 1 hour each. Infiltrated tissues were embedded in paraffin blocks using embedding molds. Tissue orientation was maintained, and blocks were sectioned at 5\u0026ndash;7 \u0026micro;m slices using RM2155 microtome.\u003c/p\u003e \u003cp\u003e \u003cb\u003ePreparation of FFPE tissue for staining\u003c/b\u003e \u003c/p\u003e \u003cp\u003eParaffin-embedded tissue slides were deparaffinized by immersion in xylene twice. Following deparaffinization the slides were rehydrated through graded ethanol series (twice in 100%, once in 95%, 70% and 50%) and washed with deionized water. The tissue sections were then used for staining.\u003c/p\u003e \u003cp\u003e \u003cb\u003eHematoxilin eosin staining\u003c/b\u003e \u003c/p\u003e \u003cp\u003eA Hematoxylin and Eosin staining kit (Abcam, ab245880) was used by following the provider\u0026rsquo;s instructions and as previously described [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Slides were mounted in Entallan mounting medium. The stained sections were imaged using Olympus VS120 slide scanner.\u003c/p\u003e \u003cp\u003e \u003cb\u003eNile red staining\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFresh frozen liver sections were used for lipid droplets staining as previously described [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], combining Nile Red (Thermo Fisher Scientific, N1142), Phalloidin A488 (Thermo Fisher Scientific, A12379) and DAPI (Invitrogen, P36931). Slides were mounted in ProLong Diamond anti-fade mounting media (Invitrogen, P36931) and images were acquired by using Leica SP8-STED confocal microscope (Leica Microsystems).\u003c/p\u003e \u003cp\u003e \u003cb\u003eImmunofluorescence staining\u003c/b\u003e \u003c/p\u003e \u003cp\u003eParaffin sections were processed as previously described [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. In short, sections were deparaffinized, rehydrated and heat-induced retrieval was carried out for 3 minutes at high pressure using pressure cooker (Ninja). The primary antibody used for immunofluorescence was rabbit anti-ChREBP (1/100, Novus biologicals, NB400-135), while secondary antibody and dyes were donkey anti-rabbit A546 (1:500, Molecular Probes, A10040), Phalloidin A488 (Thermo Fisher Scientific, A12379) and DAPI (Invitrogen, P36931). Slides were mounted in ProLong Gold Antifade (Molecular Probes) and imaged with a Leica SP8-STED confocal microscope.\u003c/p\u003e \u003cp\u003e \u003cb\u003eLiver Processing for RNA extraction\u003c/b\u003e \u003c/p\u003e \u003cp\u003eLiver was partitioned in small pieces, placed into 2 mL Eppendorf tubes and snap frozen in liquid nitrogen. Frozen livers were pulverized into fine powder using mortar and pastel pre chilled in liquid N2. Aliquots were rapidly transferred into prechilled Eppendorf tubes and immediately transferred to -80\u0026deg;C for subsequent RNA extraction.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTotal RNA extraction\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTotal RNA was extracted from fresh frozen liver pieces from 4 WT mice, 5 \u003cem\u003eRIPcre\u003c/em\u003e x \u003cem\u003eHnf1a\u003c/em\u003e\u003csup\u003e\u003cem\u003eflox/flox\u003c/em\u003e\u003c/sup\u003e mice fed with standard diet and 4 \u003cem\u003eRIPcre\u003c/em\u003e x \u003cem\u003eHnf1a\u003c/em\u003e\u003csup\u003e\u003cem\u003eflox/flox\u003c/em\u003e\u003c/sup\u003e mice after 10 weeks on HFD by using Qiazol reagent (Qiagen). Briefly, liver powder was thawed and homogenized using Qiazol lysis Reagent followed by addition of Chloroform for phase separation of RNA-containing aqueous phase from DNA and protein by centrifugation. RNA was precipitated with isopropanol, followed by washing and finally eluting in RNase free water in 30 \u0026micro;l volume. Isolated RNA was quantified and measured for integrity using Tapestation 4150 (Agilent G2992AA), and samples were stored at -80\u003csup\u003eo\u003c/sup\u003eC. RNA samples with minimum RIN value of 8 were shipped to Novogene GmBH facility for sequencing.\u003c/p\u003e \u003cp\u003e \u003cb\u003eBulk Sequencing\u003c/b\u003e \u003c/p\u003e \u003cp\u003eLiver RNA samples were processed by Novogene (Cambridge, UK), where a second quality control and library preparation (polyA enrichment) were performed. The sequencing was performed on NovaSeq X Plus Series (PE150) 30\u0026nbsp;million reads each end.\u003c/p\u003e \u003cp\u003e \u003cb\u003eData and pathway analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFastQ files from sequencing were analyzed using the CLC Genomics Workbench 25.0 (Qiagen, Aarhus, Denmark). Pre-processing included adapter and quality score-based trimming, using the default setting provided by the trimming tool in the CLC software. Alignment and quantification were carried out using the RNAseq Analysis tool, following the default settings provided by the CLC Workbench. To generate the DEG lists, groups were compared using the \u0026ldquo;Empirical Analysis of DGE\u0026rdquo; algorithm of the CLC software. The DEG lists were subsequently uploaded to Ingenuity Pathway Analysis for further analysis [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] as previously described [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Only DEGs with a fold change (FC)\u0026thinsp;\u0026ge;\u0026thinsp;1.5 and p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were included for pathway prediction, further Network settings were kept at default setting.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003eMice with Hnf1a-deficient beta-cells are hyperglycemic and display normal \u003cem\u003eHnf1a\u003c/em\u003e expression in the liver\u003c/p\u003e \u003cp\u003eThe RIP-Cre, Hnf1a\u003csup\u003efl[exon4]\u003c/sup\u003e (HMZ) allows the ablation of Hnf1a exon4 specifically in the insulin-secreting beta-cells of the pancreatic islet (RIP - rat insulin promoter), the resulted Hnf1a-deficient beta-cells residing in an otherwise normal Hnf1a genetic context. As in our previous studies, to avoid for any potential driver transgene interference (e.g. RIP-Cre), we used RIP-Cre positive mice without Hnf1a\u003csup\u003efl[exon4]\u003c/sup\u003e as controls for the experiments (WT).\u003c/p\u003e \u003cp\u003eIn these HMZ mice, we observed the statistically significant downregulation of \u003cem\u003eHnf1a\u003c/em\u003e transcripts in the beta-cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, upper graph p\u0026thinsp;=\u0026thinsp;2.11E-03), but not in the liver (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, lower graph), indicating that, as expected, \u003cem\u003eHnf1a\u003c/em\u003e was not mutated in the liver. The mice were hyperglycemic, displaying increased glycemia values in both non-fasted (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and fasted conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), with previous studies demonstrating also impaired glucose tolerance [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eLiver of mice with Hnf1a-deficient beta-cells exhibit a robust deregulation of lipid metabolism\u003c/p\u003e \u003cp\u003eThe transcriptomics analysis filtered 1891 differentially expressed genes (DEGs, FC\u0026thinsp;\u0026gt;\u0026thinsp;1.5x, p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05) between livers from Hnf1a-deficient beta-cells (HMZ) and their WT counterparts, indicating a massive signature shift in these animals, despite the absence of a specific \u003cem\u003eHnf1a\u003c/em\u003e mutation in the liver (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). Pathway analysis using the Ingenuity Pathway Analysis (IPA) pipeline identified mainly signaling involved in extracellular matrix regulation in the top inhibited signaling pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE, blue), suggesting possible structural changes. In contrast, the top activated signaling pathways was dominated by signaling involved in adipogenesis and lipid metabolism (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE, orange).\u003c/p\u003e \u003cp\u003eThe focused analysis of the metabolic pathways inferred the robust activation of diverse pathways involved in biosynthesis, while most pathways involved in degradation presented an inhibition activity pattern (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF). As before, signaling involved in cholesterol biosynthesis was the most represented group amongst the metabolic signaling, with other pathways with positive activity pattern being involved in glucose (such as Gluconeogenesis) or energy (such as Pentose phosphate pathway) metabolism.\u003c/p\u003e \u003cp\u003eIn line with these observations, the analysis of the upstream regulators predicted in the top the activation of key transcription factors involved fatty acids, phospholipids, and triglycerides synthesis, Srebf1 and Mlxipl (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eG). In contrast, Tgfb1 and Egr1, two regulators of cell differentiation, proliferation and growth, were inferred in the top inhibited upstream regulators based on the observed differential transcriptional landscape (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eG, blue). Of note, these factors also followed an observed regulation pattern in line with the above predictions (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eH).\u003c/p\u003e \u003cp\u003eThe diseases and functions analysis further confirmed the elevation of lipid metabolism in the liver of diabetic mice with Hnf1a-deficient beta-cells, with increased Synthesis of sterol, Synthesis of cholesterol and Dyslipidemia function, while Lipolysis, a degradation-related process, was inferred as decreased. The focused analysis of the DEGs driving biosynthesis functions, such as Dyslipidemia (p-value 1.54E-08, z-score 2.084), revealed the observed deregulation of key genes involved in energy metabolism integration and regulation, such as \u003cem\u003eSlc2a4\u003c/em\u003e (Glut4, important glucose transporter), \u003cem\u003eAcly\u003c/em\u003e (ATP citrate lyase), \u003cem\u003eGckr\u003c/em\u003e (glucokinase regulator) and \u003cem\u003eGlp2r\u003c/em\u003e (Glucagon-like peptide-2 receptors), amongst many others (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eJ).\u003c/p\u003e \u003cp\u003eIn addition to the connection with several of the above genes (such as the \u003cem\u003eMlxipl\u003c/em\u003e and \u003cem\u003eAcly\u003c/em\u003e), the reconstruction of the top upstream regulator Srebf1 immediate molecular network indicated the robust upregulation of the hepatic steatosis markers \u003cem\u003ePnpla3\u003c/em\u003e and \u003cem\u003eCidea\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eK, first network). Of note, \u003cem\u003ePnpla3\u003c/em\u003e is a target of both top upstream regulator \u003cem\u003eSrebf1\u003c/em\u003e and \u003cem\u003eMlxipl\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eK, second network), while \u003cem\u003eCidea\u003c/em\u003e upregulation can be driven by \u003cem\u003eSrebf1\u003c/em\u003e upregulation and \u003cem\u003eFoxa3\u003c/em\u003e (key regulator of regeneration, lipid and glucose metabolism in the liver) downregulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eK, third network).\u003c/p\u003e \u003cp\u003eOverall, these data indicate the deregulation of the lipid metabolism landscape in the liver of diabetic mice with Hnf1a-deficient beta cells, characterized by increased cholesterol biosynthesis and markers of hepatic steatosis. This observation combined with the deregulation of extracellular matrix signatures suggests the occurrence of liver architecture remodeling.\u003c/p\u003e \u003cp\u003eHigh fat diet mainly promotes further cholesterol biosynthesis\u003c/p\u003e \u003cp\u003eWe previously showed that high fat diet (HFD) exacerbates the glucose regulation phenotypes in mice with Hnf1a-deficient beta-cells [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. To investigate if the liver phenotype exhibits a similar trend, we exposed the mice to HFD for 10 weeks. As expected, HFD-fed HMZ mice (HMZ-HFD) became overtly hyperglycemic, with blood sugar values statistically significantly higher than its standard diet (SD) fed counterpart (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, p\u0026thinsp;=\u0026thinsp;0.0268).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe analysis of the differential transcriptional landscape revealed 478 DEGs between the livers of HMZ mice fed with HFD and the ones receiving SD (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). The unbiased overview of the transcriptional landscape revealed a further activation of pathways involved in cholesterol biosynthesis, centered around the Srebf2, a regulator primarily involved in cholesterol metabolism and synthesis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003eOf note, the focused analysis of the top metabolic pathways inferred the exclusive activation of biosynthesis-related pathways, with signaling involved in cholesterol biosynthesis at the top (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). Indeed, the comparison analysis between this differential transcriptional landscape (HMZ-HFD vs HMZ) and the previously analyzed one (HMZ vs WT), confirmed this signaling is promoted by HFD, while pathways involved in Gluconeogenesis or Degradation exhibit no change in their activity pattern in response to the dietary stressor (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003eThe assessment of top canonical pathways identified a limited number of pathways with a negative activity pattern score with the Calcium Signaling being the top inactivated pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF, blue). In contrast, besides the metabolic pathways, the analysis inferred in the top 6 the activation of signaling involved in cell cycle regulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF, orange). Indeed, the specific analysis of the cell cycle signature, identified a range of signaling involved in DNA synthesis and cell cycle progression (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG).\u003c/p\u003e \u003cp\u003e \u003cem\u003eSrebf2\u003c/em\u003e (p-value:2.05E-20, z:2.713) was predicted as the top upstream regulator with observed upregulation responsible for the observed differential transcriptional landscape (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eH). Moreover, the diseases and functions analysis indicated the increase of a large number of processes related to cholesterol, steroid, sterol and terpenoid synthesis and metabolism. (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eI).\u003c/p\u003e \u003cp\u003eTaken together, our data show a dysregulation of the extracellular matrix and lipid signatures in the livers of the mice with Hnf1a-deficient beta-cells, with increased key markers of hepatic steatosis. Exposure to high fat diet showed a further exacerbation of lipid synthesis, especially cholesterol, while also activating cell cycle signatures. We thus first assessed the livers of these mice for signs of hepatic steatosis by performing hematoxylin-eosin staining on liver sections. In line with the molecular observations, the staining revealed disrupted liver architecture with glycogen accumulation (arrowheads) and fat buildup indicated by increased lipid droplets (arrows) in diabetic mice with Hnf1a-deficient beta-cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). These observations were further confirmed by the specific lipid droplet Nile Red staining (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB), which identified the accumulation of lipid droplets in HFD-treated WT mice as well as in HMZ animals, regardless of diet regimen. Of note, the size of the lipid droplets was enlarged in mice with Hnf1a-defective beta-cells, supporting the molecular observations of defective lipid metabolism in this context, even before HFD administration. In addition, immunofluorescence identified ChREBP (\u003cem\u003eMlxipl\u003c/em\u003e) potent translocation into the nucleus in HMZ mice regardless of diet (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). In contrast, in the WT mice ChREBP pattern was cytoplasmic, with some nuclear translocation following HFD, once more confirming the observation of a strong deleterious phenotype in the liver of the HMZ mice, despite the strict beta-cell localization of the Hnf1a-deficiency and even in the absence of additional stressors, such as HFD.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"4. Discussions","content":"\u003cp\u003eBriefly, in this focused pilot study, we used transcriptomics to assess for potential signature changes in the liver of mice bearing the \u003cem\u003eHnf1a\u003c/em\u003e mutation exclusively in the insulin-secreting beta-cells. We report that despite the normal level of liver \u003cem\u003eHnf1a\u003c/em\u003e expression, the hepatic transcriptional landscape drastically changes towards increased lipid metabolism and overexpression of liver steatosis markers, while exposure to HFD further exacerbated cholesterol biosynthesis. Besides lipid metabolism, changes in extracellular matrix organization were identified in the livers of HMZ mice, while high HFD promoted proliferation signatures.\u003c/p\u003e \u003cp\u003eThe normal levels of Hnf1a in the livers of mice bearing the \u003cem\u003eHnf1a\u003c/em\u003e mutation exclusively in the insulin-secreting beta-cells suggests that the reported hepatic signature dysregulation is triggered by a systemic factor. Due to their beta-cell dysfunctionality, the HMZ mice are mildly hyperglycemic, and thus the chronically elevated blood glucose level is likely the systemic cue eliciting the observed hepatic effects.\u003c/p\u003e \u003cp\u003eThe connection between hyperglycemia and liver steatosis is not new, with previous studies indicating that elevated blood sugar is a high-risk factor for Non-Alcoholic Fatty Liver Disease (NAFLD) [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. In this context, it was shown that excess glucose activates Mlxipl (Chrebp), which in turn promotes genes involved in lipogenesis and triglyceride synthesis, leading to increased fatty acid synthesis accumulation and NAFLD development [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. This is in line with the observations in this pilot where \u003cem\u003eMlxipl\u003c/em\u003e was upregulated and inferred as top second upstream regulator defining the liver transcriptional landscape of the HMZ mice. Of relevance, Srebf1, the top upstream regulator, is a known target of Mlxipl/Chrebp and a key factor involved in regulating fatty acids and triglycerides synthesis. Increased \u003cem\u003eSrebf1\u003c/em\u003e expression in the liver was strongly connected with NAFLD [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Here we report its upregulation in HMZ mice, however not following HFD administration. Of note, after HFD, we observe the upregulation of \u003cem\u003eSrebf2\u003c/em\u003e, another sterol regulator binding protein that is mainly involved in cholesterol biosynthesis and which regulation was not driven in the absence of the diet stressor.\u003c/p\u003e \u003cp\u003eInterestingly, although HFD exacerbated HMZ mice glycemia, the number of deregulated genes was ~\u0026thinsp;4x lower than the ones observed in the livers of mice with Hnf1a-deficient beta cells. In line with this observation, there were less gene signatures changes, involved in either further promotion of cholesterol biosynthesis or cell proliferation regulation. This is an interesting finding as it suggests that even low chronic increase in blood glucose can lead to a powerful molecular domino in the liver, with further stressors such as HFD eliciting just a confined augmentation effect.\u003c/p\u003e \u003cp\u003eIn line with the link between hyperglycemia and NAFLD, we observed a massive upregulation of key markers of hepatic steatosis, such as \u003cem\u003ePnpla3\u003c/em\u003e (~\u0026thinsp;130x fold), in the livers of mice with Hnf1a-deficient beta-cells even before HFD administration. Pnpla3 is a triglyceride lipase that regulates the lipid droplet metabolism and a target of both Mlxipl [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] and Srebf [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] and its accumulation on lipid droplets is considered a direct cause for hepatic steatosis [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCidea (Cell Death Inducing DFFA Like Effector A) was a second hepatic steatosis marker that showed robust upregulation (~\u0026thinsp;65x fold). Interestingly, a recent study indicated an opposite coupling between Cidea and Egr1 at transcriptional level, which we also observed in this pilot [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Egr1 is a regulator of cell proliferation, lipid metabolism and hepatic circadian clock [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], which acts as a molecular break to prevent excessive stimulation and controls blood glucose levels\u0026rsquo; fluctuation in physiological conditions [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Egr1 deletion leads to triglyceride accumulation and large lipid droplet accumulations [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. In line with these findings, we report the downregulation of Egr1 and upregulation of Cidea in the liver of mice with Hnf1a-deficient beta-cells.\u003c/p\u003e \u003cp\u003eIn addition, we observed activity in signaling regulating extracellular matrix organization in the HMZ mice fed on standard diet, suggesting tissue-remodeling events. Previous studies indicated that hyperglycemia disrupts extracellular matrix by collagen crosslinking in a variety of organs and systems [\u003cspan additionalcitationids=\"CR45 CR46\" citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e], thus it is tempting to connect also these modifications to increased blood glucose in the HMZ mice. Furthermore, HFD exposure impacted on cell cycle regulation signatures, promoting RNA synthesis and cell cycle progression. This potential increase in proliferation might be connected to potential regenerative events aimed at compensating tissue loss due to steatosis or it can be triggered by exacerbated glycemia. Indeed, hepatic stellate cells were shown to boost proliferation in response to high glucose [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Moreover, recent studies indicated that HFD can promote liver proliferation [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e], probably via cellular stress [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. A potential proposed mechanisms is represented by hepatocyte fat accumulation causing lipid overload, nucleotide pool imbalance and replication stress damage, stimulating proliferation [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOf relevance, previous research indicated that hepatic steatosis leads to hepatic insulin resistance thus fueling back on beta-cell functionality, which are consequently required to augment insulin secretion despite pre-existing elevated insulin levels [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. This is expected to interfere with the beta-cells functionality signature and transcriptional landscape, thus interfering with the readout.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eIn conclusion, the observed robust hepatic signature changes in the absence of a directly targeted liver mutation reinforce the importance of assessing the indirect involvement of other organs following targeted gene inactivation, especially when it involves endocrine organs. In these cases, the restriction of the mutation to the desired cellular compartment might not completely demultiplex the defective gene effect on the targeted cell biology but also reflect the feed-back of other organs dysfunction, caused by mutation-induced changes in systemic parameters. This multi-organ dysfunction loop can drastically change the read-out in the mutated cells complicating the effect separation. Although this is an unavoidable limitation of such directed gene inactivation strategies, it is important to acknowledge the existence of a potential confounding effect for data interpretation.\u003c/p\u003e\n\u003cp\u003eOne limitation of the current pilot is the exclusive focus on insulin-secreting beta-cells mutations on a single organ, the liver. Further complexity will be reached by studying diverse cell populations with targeted mutations combined to assessing multi-organs systemic involvement. Moreover, the pilot was limited to transcriptional landscape analysis, while the analysis of the proteomic or epigenetic level will help understanding the network of interactions. Along the same line a cellular compartment characterization of the molecular observations will help mapping organ-specific cell-population crosstalk.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe animal experiments in this study were approved by the Norwegian Food Safety Authority Mattilsynet (FOTS number 10785, 12105 and 19800) in accordance with the European Union (EU) Directive 2010/63/EU, and performed according to the guidelines and regulations on the use of animals in the research at the Vivarium Laboratory Animal Facility at the University of Bergen.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConsent for participation and publication\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eThe datasets used in this work have been deposited in NCBI\u0026apos;s Gene Expression Omnibus [54] and will be accessible through GEO Series accession number GSE311980 after manuscript publication: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE311980 \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe differentially expressed gene lists are presented as Supplemental Table 1 \u0026ndash; comparison of the HNF1A HMZ islets over WT islets, and Supplemental Table 2 \u0026ndash; comparison of HFD HNF1A HMZ islets over HNF1A HMZ islets. Should any raw data files be needed they are available from the corresponding authors upon reasonable request.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis work was supported by funds from the Research Council of Norway (NFR 304615 and 314397), Novo Nordic Foundation (NNF21OC0067325), Stiftelsen Trond Mohn Foundation (Mohn Center of Diabetes Precision Medicine) to S.C.; Diabetesforbundets forskningsfond and University of Bergen to S.C. and L.G; Clinical Science Department doctoral fund to S.S.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eL.U. and S.S. are supported by doctoral fellowships from Faculty of Medicine, University of Bergen.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe funding sources had no role in the study design, its execution, analyses, interpretation of the data, nor the decision to publish these results.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAuthor Contributions\u003c/p\u003e\n\u003cp\u003eS.S. collected the physiological data, performed the diet experiments, collected samples, RNA preparations, performed immunostaining, confocal imaging, processing, counting and participated in experimental design; L.U. performed data analyses, including the generation of DEGs for transcriptomics comparisons; A.A. performed mouse work and liver sample processing; T.A.L performed the RIPcre; HNF1A mouse characterization and collected physiological data; L.U. and S.C. performed the pathway analysis for the transcriptomics datasets; L.G. and S.C. acquired funding, conceived the experiments, supervised the work, interpreted the observations and wrote the manuscript. All authors edited and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003eAcknowledgments\u003c/p\u003e\n\u003cp\u003eWe thank June Helen Gudmestad and Birgitte Feginn Berle for technical help. The preparation of FFPE tissue blocks and sections, part of hematoxylin and eosin staining, and confocal imaging was performed at the Molecular Imaging Center (MIC) and was thus supported by the Department of Biomedicine and the Faculty of Medicine at the University of Bergen, and its partners. The RIP-Cre mice were kindly provided by Pedro Herrera.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHorowitz MP, Greenamyre JT: \u003cstrong\u003eGene-environment interactions in Parkinson\u0026apos;s disease: the importance of animal modeling\u003c/strong\u003e. \u003cem\u003eClinical pharmacology and therapeutics \u003c/em\u003e2010, \u003cstrong\u003e88\u003c/strong\u003e(4):467-474.\u003c/li\u003e\n\u003cli\u003eBird TD: \u003cstrong\u003eGenetic factors in Alzheimer\u0026apos;s disease\u003c/strong\u003e. \u003cem\u003eThe New England journal of medicine \u003c/em\u003e2005, \u003cstrong\u003e352\u003c/strong\u003e(9):862-864.\u003c/li\u003e\n\u003cli\u003eGhosh U, Samanta A: \u003cstrong\u003eMonogenic inflammatory bowel disease: An unfolding enigma\u003c/strong\u003e. \u003cem\u003eWorld J Clin Pediatr \u003c/em\u003e2025, \u003cstrong\u003e14\u003c/strong\u003e(3):107165.\u003c/li\u003e\n\u003cli\u003eHattersley AT, Patel KA: 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Yet, targeting of many genes and cell types can cause systemic effects, impacting the functionality of other off-target organs. This can further generate a discrete dysfunction loop fueling back to the targeted cell altering their profile readout, effect demultiplexing and results interpretation. Despite the high impact of such scenario especially in the study of endocrine organs, most research is focused on targeted mutation-bearing cell population, while the other organs bearing intact candidate gene activity, receive no or limited attention.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eTo assess the potential readout bias caused by off-target organs, we performed here a focused pilot transcriptomics study to map the effects on liver of a monogenic diabetes gene mutation restricted to insulin-expressing beta-cells. Mice with beta-cell restricted disfunction were mildly hyperglycemic and presented normal target gene levels in the liver. Despite normal expression, pathway analyses identified profound transcriptional prolife changes in the liver. These involved the dysregulation of lipid metabolism and extracellular matrix organization, cholesterol biosynthesis being further exacerbated by HFD, consistent with a systemic factor effect such as chronically elevated blood sugar levels. Furthermore, key markers of hepatic steatosis were highly increased, with the livers\u0026rsquo; histopathology reflecting lipid droplet accumulation. As hepatic steatosis is an important cause of hepatic insulin resistance that can further alter beta-cell function, the interpretation of the transcriptional background in the targeted beta-cell population must be performed with care.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eBased on this pilot we conclude that multi-organ dysfunction loops can drastically change the read-out in the mutated cell complicating the effect separation. Thus investigating off-target organs is crucial, especially when characterizing genes and cell populations involved in endocrine regulation.\u003c/p\u003e","manuscriptTitle":"Livers of hyperglycemic mice with Hnf1a-deficient pancreatic beta-cells show unexpected hepatic steatosis signs further exacerbated by high fat diet","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-18 12:16:19","doi":"10.21203/rs.3.rs-8013440/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ca7b846a-08cb-404f-bef2-309682ed2643","owner":[],"postedDate":"December 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-25T09:44:23+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-18 12:16:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8013440","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8013440","identity":"rs-8013440","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}