Insulin-positive (+) cells (IPCs), detected in multiple organs, are of great interest as a probable alternative to ameliorate pancreatic beta-cells dysfunction and insulin deficiency in diabetes. Liver is a potential source of IPCs due to it common embryological origin with pancreas. We previously demonstrated the presence of IPCs in the liver of healthy and diabetic rats, but detailed description and analysis of the factors, which potentially can induced ectopic hepatic expression of insulin in type 1 (T1D) and type 2 diabetes (T2D), were not performed. In present study we evaluate mass of hepatic IPCs in the rat models of T1D and T2D and discuss factors, which may stimulate it generation: glycaemia, organ injury, involving of hepatic stem/progenitor cell compartment, expression of transcription factors and inflammation. Quantity of IPCs in the liver was up by 1.7-fold in rats with T1D and 10-fold in T2D compared to non-diabetic (ND) rats. We concluded that ectopic hepatic expression of insulin gene is activated by combined action of a number of factors, with inflammation playing a decision role. Currently diabetes mellitus (DM) is one of the highly prevalent socially significant chronic diseases, with a high disability of patients. Prevalence of diabetes is growing every year, and it is classified as one of the leading causes of high mortality in developed countries . DiabeteMany investigators had reported that pancreatic islet beta-cells are not the single source of insulin in the body. Cells in different tissues beside pancreatic islets are also capable to synthesize insulin; insulin-positive cells (IPCs) were found in the exocrine pancreas, liver, thymus, brain, bone marrow, spleen and adipose tissue –8. ExtraWhat stimulates ectopic insulin expression in non-pancreatic tissues? Research data testify that mature cells may be reprogrammed and change their phenotype , 10. LivGlucose is the main physiological regulator of insulin gene expression. Produced by glucose injections hyperglycemia in non-diabetic or diabetic mice led to the appearance of proinsulin- and insulin-positive cells in liver, adipose tissue and bone marrow within 3 days . HoweverMaturation of beta-cells and synthesis of insulin are regulated by sequential expression of transcription factors. The most essential factors for development of beta-cells are pancreas and duodenal homeobox protein 1 (Pdx1), Neurogenin-3 (Ngn3) and V-maf musculoaponeurotic fibrosarcoma oncogene homolog A (MafA) . Pdx1 isDue to the high phenotypic and functional heterogeneity of liver cells and peculiarities of liver zonal regeneration , the preBoth T1D and T2D are chronically inflammatory diseases, so all events in the body are developed in the pro-inflammatory background, which is known as associated with metabolic process. Inflammation is able to induce cell transition during regeneration . Early wIn this study we evaluate the mass of hepatic IPCs in rat models of T1D and T2D with the equal hyperglycemia, but different expression of pancreatic transcription factors, number of oval cells and severity of inflammation and tissue damage.Experiments were performed on 30 12-weeks old male Wistar rats of weight 303.0 ± 25.3 g breeding in the animal facility of the Institute of Immunology and Physiology, Ural Branch Russian Academy of Science  and showing no disease symptoms. Rats were housed 5 per polypropylene cage and kept under equal laboratory conditions (12h light/12h dark суcle with temperature 22 ± 2°C above zero). Animals were fed according to customary schedule with soy protein-free extruded rodent diets  and allowed free access to filtered tap water.All experimental procedures involving animals were in compliance with the applicable laws and regulations as well as the principles expressed in Directive 2010/63/EU of the European Parliament and of the Council of 22 September 2010 on protection of animals used for scientific purposes. The use of animals was approved by Ethical Committee of Institute of Immunology and Physiology of Russian Academy of Science (protocol number 04/19 from 18 Dec 2019).T1D was modeled by 3 i.p. injections of alloxan monohydrate  dissolved in 0.85% NaCl, with total dose of 170 mg/kg b.w. . T2D wasFasting blood glucose (FBG) level was measured 72 h after injection of diabetic agents and rats with FBG level of ≥ 7.1 mmol/L were selected as successfully prepared diabetic models and used in the study.Animals were randomly divided into three groups, each group comprised ten rats (n = 10). Group 1 –non-diabetic (ND) age-matched intact control rats, group 2 –rats with alloxan-induced T1D, group 3 –rats with Stz/NA-induced T2D. In 30 days after the first injections of diabetogenic agents the rats were deeply anesthetized with an i.m. injection of 0.1 mg/kg b.w. xylazine  and euthanized by i.m. injection of 5 mg/kg b.w. Zoletil-100 .Blood samples were collected before euthanasia from the rats via the tail vein and centrifuged at 500x g for 10 min at 4°C above zero. The plasma supernatant was using for biochemical tests using a DU-800 spectrophotometer  at specified wavelength according to manufacturers. FBG level was determined by the glucose oxidase method using diagnostic kit . Level of HbA1c was measured using kit for affinity chromatography . To evaluate glycemic control in animals with T2D OGTT was done at the 4th week of the experiment (n = 6). Assessment was based on FBG level at 0 min, and postprandial glucose (PG) level measured after the oral administration of glucose (1 g/kg b.w.) at 30, 60, and 120 min. Administration of glucose was conducted using oral dosing needles . The area under the curve (AUC) was calculated according to Sakaguchi et al. (2016) using the trapezoidal approximation of PG levels .Serum insulin levels were measured using Rat Insulin ELISA kit . Insulin resistance was estimated by homeostasis model assessment of insulin resistance (HOMA IR), according to method described by Matthews et al. , 40. It HOMA IR = fasting insulin (μU/mL) × fasting glucose (mmol/L) / 22.5.Plasma levels of aspartate aminotransferase (AST), alkaline phosphatase (ALT), alanine aminotransferase (ALP) and total protein were determined using ready to use reagents kits .The number of leukocytes  and lymphocytes in heparinized blood samples was estimated using an automated hematology analyzer Celly 70 . Plasma level of total protein was measured using standard reagent kit .Samples of liver tissue were excised and after 24 h fixation in 10% formalin were embedded in paraffin for histological assessment. Tissues were processed using tissue processor Leica TP 1020. Paraffin blocks were made on paraffin embedding station Leica EG 1160. 4–5 μm sections of liver tissue were cut, using sliding microtome Leica SM 2000R. The periodic acid-Schiff staining (PAS) was performed using standard kit for PAS 07-010/S .Liver paraffin sections were studied using immunohistochemistry (IHC) and fluorescent IHC. Insulin+, F4/80+ cells (macrophages), white blood cells (CD45+ cells), lymphocytes (CD3+ cells), KRT19+ and OV6+ cells  were detected by IHC, while Pdx1+, MafA+ and Ngn3+ cells were detected using fluorescent IHC. Tissues were labeled with primary antibodies  overnighThe immunohistochemical procedure was performed using the avidin-biotin peroxidase complex method. Staining was performed according to the manufacturer’s standard protocols. To check the protocol and exclude nonspecific staining, negative and positive controls were set. Sections of the pancreas of non-diabetic rats were used as positive control –43. As t2 of liver parenchyma (N/mm2).Number of insulin+, F4/80+ cells (macrophages), white blood cells (CD45+ cells), lymphocytes (CD3+ cells), KRT19+ and OV6+ cells  and sinusoidal cells were counted under the light microscope (Leica DM 2500) using ×400 and х1000 magnification with Las 4.9 Software (Leica Microsystems GmbH). Immunofluorescence images were obtained using confocal laser scanning microscope Carl Zeiss LSM710 at magnification ×400 with ZEN 2.0 software . All morphometric evaluations were based on 20 microscopy fields per tissue section of each rat and were converted to the 1 mmStatistical analysis was performed using Statistica 6.0 . Data were presented as mean ± standard error of the mean (SEM). SEM was calculated by dividing the standard deviation (SD) of the sample by the square root of the sample size (n) (SEM = SD/√n). Further data were analyzed by nonparametric Mann-Whitney U-test and Kruskal-Wallis test. P < 0.05 was regarded as statistically significant.As illustrated in Rats with T2D showed poor glucose tolerance, as evident by higher AUC value observed in T2D group compared to ND group (p < 0.05) С 38]. B. B38]. BInsulin-positive cells (IPCs) were found in the liver tissue of both ND and diabetic rats , they weTo evaluate functional state of hepatocytes in the models of T1D and T2D, we analyzed activity of aminotransferases (ALT and AST), alkaline phosphatase (AP) and protein content in blood plasma . ALT is Taking into account inflammatory component in pathogenesis of diabetes, particularly in T2D, the leukocyte blood count was calculated. Relative to ND rats, the total number of white blood cells and lymphocytes in the peripheral blood of the T1D and T2D rats was elevated, especially in T2D. Granulocytes elevated only in T1D, demonstrating a high-scatter meaning in T2D .Diffuse-focal glycogen accumulation without signs of the toxic damage and rearrangement of liver histological architecture and vascular network was noted in both T1D and T2D .Regeneration rates may reflect organ damage. Morphometric analysis showed an increase in the number of signs of both cellular and intracellular regeneration in the liver of diabetic animals, but differently in T1D and T2D. Thus, compared to ND animals, number of binucleated hepatocytes elevated in animals with T1D and T2D by 1.7 and 1.4-fold, and mitotic index by 1.7 and 2.0-fold respectively .It is known that sinusoids cells, first of all, macrophages and leukocytes, infiltrate damaged liver, are able to produce proinflammatory cytokines and growth factors and participate in regulation of destructive and compensatory processes as well as regenerative growth. Presumably, they can influence the formation of IPCs. As shown in Development of experimental T1D and T2D was accompanied by the rise in the number of CD45+ cells, located both in parenchyma and in perivascular spaces. It was noticed that in both T1D and T2D, most of the CD45+ cells are localized in liver parenchyma, while in ND animals their number in parenchyma and perivascular spaces was approximately the same . The numGrowth of the number of IPCs in the liver can be affected by transcription factors Pdx, MafA and Ngn3, which participate in reprogramming of cells in an adult body into IPCs and contributes to expression of insulin gene. In present study, Pdx1+, MafA+ and Ngn3+ cells were detected in liver of both ND and diabetic animals Figs  and 11A.Reparative regeneration of damaged organs in adult body involves stem/progenitor cell, so we aimed to assess hepatic stem cell compartment. Quantitative analysis of the oval cell markers KRT19 and OV6 revealed, that number of KRT19+ and OV6+ cells increased in the liver of diabetic animals. Data presented in Insulin+ cells found in various organs are attracting more and more attention of researchers, since they can partially compensate for damage of islet beta-cells in DM , 7. PresIn present study we evaluate the mass of hepatic IPCs in rat models of T1D and T2D and discuss the role of glycaemia level, expression of pancreatic transcription factors Pdx1, MafA and Ngn3, number of oval cells, inflammation and tissue damage in emerging of hepatic IPCs in vivo. We demonstrated that the mass of hepatic IPCs may be differ at equal glycemia level. The main findings are: 1) Hepatic insulin synthesis increased in rat models of T1D and T2D with the similar level of glycemia differently, the number of insulin+ cells in the liver in T2D is in several times more than in T1D. 2) Probably, generation of insulin+ cells in liver is associated with inflammatory agents.The feasibility of controlling hyperglycemia in diabetes through hepatic synthesis of insulin is attractive for researchers. Glucose stimulates insulin gene transcription –50. It wIt is known, that organ injury trigger regeneration process to maintain tissue integrity and function . We measSince the liver and pancreas have a common origin, we hypothesized that the generation of IPCs in liver can be stimulated by the same factors as the formation of IPCs in pancreas. Restoring of pancreatic beta-cells mass can occur through stimulation of existing beta-cell replication, activated by transcription factors transdifferentiation of specialized mature cells into beta-cells and from stem/progenitor cells , 59.Regenerative processes in liver may proceed in different ways, depending on the severity and type of damage, including via progenitor cells . In healExpression of the transcription factors Pdx1, Ngn3 and MafA (PNM) is able to reprogram not-beta cells to beta-like insulin-producing cells , 61–63. Pdx1 regulates expression of various genes during early development of pancreas, including formation of beta-cells. Pdx1 is required for proliferation and maturation of beta-cells and is also important for regulation of insulin gene expression in them. MafA is required for functional activity of beta-cells, regulating synthesis of insulin and GLUT2. The importance of MafA for the activation of insulin genes and it expression in pancreatic beta-cells increases the likelihood that MafA is a major activator of pancreatic beta-cell formation and function . In thisT2D showed lower expression of Pdx1 and MafA in comparison with T1D. Less expression of Pdx1 and MafA in liver in T2D is associated with higher serum insulin vs T1D. Al-Adsani et al. reported, that expression of PNM transcription factors in the liver of recovering streptozotocin-induced diabetic rats decreased as blood insulin levels increased .In  it was dIncreasing in the number of IPCs occurs in the terms of different expression of PNM transcription factors which do not contradict with the findings of Al-Adsani et al. . The greChronic inflammation is a hallmark of both T1D and T2D , 69. SigPro-inflammatory stress is associate with epigenetic changes  and can So, it was suggested, that proinflammatory microenvironment via multiple contact- and paracrine- mediated interactions shape the cell differentiations in different tissues–pancreatic cells , adiposeEarlier we supposed, that inflammation in the pancreas caused by diabetic agent streptozotocin first triggers processes aimed at restoring the functional integrity of the pancreatic tissue, and, combined with hyperglycemia, affect the generation of insulin+ cells in exocrine pancreas. It manifest itself in increased mass and square of extra-islet pancreatic cells in 30 days of experimental T2D in rats. We demonstrated, that progress of inflammation in diabetes develops is associated with the dysfunction of extra-islet insulin+ cells, but decreasing the degree of macrophage infiltration and inflammatory level in pancreatic exocrine tissue in experimental model of T2D lead to the increasing of the mass of extra-islet insulin+ cells .In present study we note that increased number of hepatic IPCs in T2D vs T1D is associated with higher severity of inflammation in T2D. At the same time, all other factors traditionally considered as affecting the ectopic expression of insulin gene  in T2D compare to T1D is similar or even less. We speculate, that hepatic insulin synthesis is stimulated at a certain inflammatory level, decrease in both insufficiency and excessiveness. In this case, increased hepatic insulin expression in experimental T1D and T2D may be the part of the manifestation of cellular stress, a typical cellular response to any form of macromolecular damage aimed at restoring cellular and tissue homeostasis , 80. If We have demonstrated that mass of hepatic insulin+ cells in rat models of T1D and T2D with the similar glycemia is differ. We concluded, that hepatic expression of insulin activated by the combined action of a number of factors, among which the most important seems to be inflammation, and occur differently in T1D and T2D. Since extra-pancreatic insulin+ cells can partially compensate for lack of insulin in diabetes, it is very important to clear metabolic regulation of insulin expression. The results suggested that development of anti-inflammatory-focused therapy for diabetes is reasonable and promising.S1 File(DOCX)Click here for additional data file. 6 Aug 2023 <div> PONE-D-23-22087 </div>  <div> Hepatic Insulin Synthesis Increases in Rat Models of Diabetes Mellitus Type 1 and 2 Differently </div>  <div> PLOS ONEDear Dr. Aydin Ozgur, </div>  <div>   </div>  <div> Please submit your revised manuscript by Sep 20 2023 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. 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Reviewer #1: The manuscript "Hepatic Insulin Synthesis Increases in Rat Models of Diabetes Mellitus Type 1 and 2 Differently” investigates insulin positive cells in the liver by different methods in both types of diabetes mellitus which is interesting; however, I think this manuscript and its figures need major revision.General comments:• Manuscript needs English language revising• Paragraph should not start with abbreviation, so please write the full name, and here are some examples: IPS “third paragraph in introduction”, T1D “second paragraph in material and methods”, …ect.Abstract:• Line 6; Replace were not (done) with were not (performed).Introduction:• Correct the following words: per-manent to permanent, idi-opathic, proinsulin-, ex-pression, in-sulin, vec-tor, peculiar-ities, insu-lin, transdif-ferentiate, insu-lin, phago-, immunolog-ical, pit-cells, inflammation – in,• Introduction is too long, there is no clear coherence and sequence in the writing, and most of the paragraphs require reconstruction and here are some examples for improvement:Old one: In any case, the number of the functioning β-cells is insufficient in both T1D and T2D, and β-cells loss and dysfunction are the main reasons of hyperglycemia and associated with it complications [5].New one: Loss and dysfunction of β-cells are the main reasons of hyperglycemia and its associated complications in both types of DM [5].Old one: first, they are an additional source of insulin, the increase in the significance of which for the organism is logical to assume when pancreatic islets β-cells are damaged. Earlier we demonstrated [8] that in experimental T2D mass and functional activity of islet β-cells decrease faster, than of IPCs in pancreatic acini and ducts. Understanding the mechanisms and conditions, leading to the generation of IPCs in organs, are able to alleviate the problem of lack of β-cells. Second, since knowledge about ectopic insulin expression and, more, developmental plasticity of adult somatic cells and its reprogramming to committed lineages, unsufficient for complete and clear picture of this phenomenon, studying of extra-pancreatic insulin expression is also of theoretical interest.New one: first, they are additional sources of insulin, as they showed significant increase within organism that are able to alleviate the problem when pancreatic islets β-cells are damaged as recorded in our previous study [8]. Second, to disclose the extra pancreatic insulin expression from adult somatic cells and reprogramming it to committed lineages.• Write the full name of the Pdx1 abbreviation within the text.• Rewrite the aim of the study in a simple and clear form.Material and methods:• Correct the following words: ac-cording, fluo-rescent.• Insulin+, macrophages (F4/80+ cells), oval cells (OV6+ and KRT19+ cells), CD3+ and CD45+ cells were detected by IHC, (replace and by while) Pdx1+, MafA+, Ngn3+ cells were detected using fluorescent IHC.• Add data about the color and site of immune-expression (cytoplasmic or nuclear) in the stained cells.• Morphometric analysis: specify the number of captured field from each slide/group.RESULTSResult section needs SUBSTANTIAL revision.• Correct the following words: ob-served, insu-lin, De-pending, ana-lyzed, pro-tein, particu-larly, rear-rangement, regenera-tive,• Write the full name of the following abbreviations within the text .• Figure (1 C) compares ND and T2D, while figure legend compares T2D from T1D, revise it.• Results of  are not mentioned in figure legend, need to be added.• Results of  are not mentioned in the text, need to be added.• Add scale bar in all photomicrographs in figure 2.• Reorganize the writing of result section (text and legend) according to figures distribution. For example authors write about figure 1 A-D, then fig 2B, then figure 6, then return to figure 1F-J and so on…...• For scientific benefit, it is better to insert each bar charts immediately below their corresponding histological or IHC figures.• Figures 5 and 6 are the same figure???? Need to insert figure 5 again.• Figure 6 formed of only five bar charts (A-E), while in result section authors had mention results about (fig 6 G&F)???• Hepatic Stem Cells in the Models of T1D and T2D:  NOT  showed that the greatest number of OV6+ and KRT19+ cells in liver. Correct.Discussion:• Discussion section needs to be re-written in a better way and most of paragraphs need to be reconstructed.• Correct the following words: an-imals, insu-lin, ex-pression, gen-eration, in-flammation, dia-betes, an-imals, gen-eration, insu-lin, in-crease, con-trol, re-storing, ex-tra).https://doi.org/10.1016/j.scr.2020.101958• Remove the sentence: , since there is a previous study investigated the same points of the current research • Remove this repeated sentence in the 2nd paragraph in discussion (Glucose is the major physiologic regulator of insulin gene expression.) as it was mentioned before in the 4th paragraph in introduction section.• Write the full name of the following abbreviations within the text .Reviewer #2: The authors examined the quantity of IPCs in the liver could increase in rats both with T1D and T2D compared to non-diabetic controls. The ectopic hepatic insulin positive cells could be a new therapeutic window in T1D and T2D.Major: E,F,G in Figure.2 was too hard to see or calculate the positive cells. The authors should replace the new or visuable pictures.Minor:There were some mistake letter such as "insu-lin-producing cells" and "immunolog-ical reactions". The authors should caefully revise these letter in the manuscript. Also, English grammer should be revised.********** <font color="black"> 6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.If you choose “no”, your identity will remain anonymous but your review may still be made public.Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy. </font> Reviewer #1: NoReviewer #2: No**********https://pacev2.apexcovantage.com/. 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When you resubmit, please ensure that you provide the correct grant numbers for the awards you received for your study in the ‘Funding Information’ section.Response 3:Done. Point 4: Thank you for stating the following financial disclosure: "122020900136-4 IIP UB RAS" Please state what role the funders took in the study. If the funders had no role, please state: "The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript." If this statement is not correct you must amend it as needed. 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For more information about our data policy, please see http://journals.plos.org/plosone/s/data-availability#loc-recommended-repositories. Any potentially identifying patient information must be fully anonymized."Upon re-submitting your revised manuscript, please upload your study’s minimal underlying data set as either Supporting Information files or to a stable, public repository and include the relevant URLs, DOIs, or accession numbers within your revised cover letter. For a list of acceptable repositories, please see http://journals.plos.org/plosone/s/data-availability#loc-unacceptable-data-access-restrictions. Note that it is not acceptable for the authors to be the sole named individuals responsible for ensuring data access.Important: If there are ethical or legal restrictions to sharing your data publicly, please explain these restrictions in detail. Please see our guidelines for more information on what we consider unacceptable restrictions to publicly sharing data: We will update your Data Availability statement to reflect the information you provide in your cover letter.Response 6:Done. Minimal underlying data set was upload as a separate Supporting Information file.Point 7: Your ethics statement should only appear in the Methods section of your manuscript. If your ethics statement is written in any section besides the Methods, please move it to the Methods section and delete it from any other section. Please ensure that your ethics statement is included in your manuscript, as the ethics statement entered into the online submission form will not be published alongside your manuscript. Response 7:Done.Point 8: Please include a copy of Table 2 which you refer to in your text on page 24.Response 8:Done. We inserted Tables 1 and 2 immediately after the first paragraph in which they are cited.Point 9: Please upload a copy of Supporting Information Table 2 which you refer to in your text on page 14.Done. We inserted Tables 1 and 2 immediately after the first paragraph in which they are cited.Response to Reviewer 1 Comments:Point 1: General comments: Manuscript needs English language revising.Response 1:Done.Point 2:Paragraph should not start with abbreviation, so please write the full name, and here are some examples: IPS “third paragraph in introduction”, T1D “second paragraph in material and methods”, …ect.Response 2:Done.Point 3:Abstract: Line 6; Replace were not (done) with were not (performed).Response 3:Done. Point 4:Introduction: Correct the following words: per-manent to permanent, idi-opathic, proinsulin-, ex-pression, in-sulin, vec-tor, peculiar-ities, insu-lin, transdif-ferentiate, insu-lin, phago-, immunolog-ical, pit-cells, inflammation – inResponse 4:Done.Point 5:Introduction is too long, there is no clear coherence and sequence in the writing, and most of the paragraphs require reconstruction and here are some examples for improvement:Old one: In any case, the number of the functioning β-cells is insufficient in both T1D and T2D, and β-cells loss and dysfunction are the main reasons of hyperglycemia and associated with it complications [5].New one: Loss and dysfunction of β-cells are the main reasons of hyperglycemia and its associated complications in both types of DM [5].Old one: first, they are an additional source of insulin, the increase in the significance of which for the organism is logical to assume when pancreatic islets β-cells are damaged. Earlier we demonstrated [8] that in experimental T2D mass and functional activity of islet β-cells decrease faster, than of IPCs in pancreatic acini and ducts. Understanding the mechanisms and conditions, leading to the generation of IPCs in organs, are able to alleviate the problem of lack of β-cells. Second, since knowledge about ectopic insulin expression and, more, developmental plasticity of adult somatic cells and its reprogramming to committed lineages, unsufficient for complete and clear picture of this phenomenon, studying of extra-pancreatic insulin expression is also of theoretical interest.New one: first, they are additional sources of insulin, as they showed significant increase within organism that are able to alleviate the problem when pancreatic islets β-cells are damaged as recorded in our previous study [8]. Second, to disclose the extra pancreatic insulin expression from adult somatic cells and reprogramming it to committed lineages.Response 5:We shortened and reconstructed the Introduction. Point 6:Introduction: Write the full name of the Pdx1 abbreviation within the text.Response 6:Done.Point 7:Introduction: Rewrite the aim of the study in a simple and clear form.Response 7:Done. Point 8:Material and methods: Correct the following words: ac-cording, fluo-rescent.Response 8:Done.Point 9:Material and methods: Insulin+, macrophages (F4/80+ cells), oval cells (OV6+ and KRT19+ cells), CD3+ and CD45+ cells were detected by IHC, (replace and by while) Pdx1+, MafA+, Ngn3+ cells were detected using fluorescent IHC.Response 9:Done.Point 10:Material and methods: Add data about the color and site of immune-expression (cytoplasmic or nuclear) in the stained cells.Response 10:Done. We specify the color and site of immune expression in the Figure Captions. Point 11:Material and methods: Morphometric analysis: specify the number of captured field from each slide/group.Response 11:The number of captured field was specified in the block “Morphometric analysis”: All morphometric evaluations were based on 20 microscopy fields per tissue section of each rat and were converted to the 1 mm2 of liver parenchyma (N/mm2).Point 12:RESULTS: Result section needs SUBSTANTIAL revision. Correct the following words: ob-served, insu-lin, De-pending, ana-lyzed, pro-tein, particu-larly, rear-rangement, regenera-tiveResponse 12:Done.Point 13:RESULTS: Write the full name of the following abbreviations within the text .Response 13: HOMA-IR – Done.The full name of the abbreviation “AUC” there is in line # 313: The area under the curve (AUC) was calculated according to Sakaguchi et al. (2016) using the trapezoidal approximation of PG levels [38].Point 14:RESULTS: Figure (1 C) compares ND and T2D, while figure legend compares T2D from T1D, revise it.Response 14:Done.Point 15:RESULTS: Results of  are not mentioned in figure legend, need to be added.Response 15:Done. Point 16:RESULTS: Results of  are not mentioned in the text, need to be added.Response 16:Done. Point 17:RESULTS: Add scale bar in all photomicrographs in figure 2.Response 17:Done.Point 18:RESULTS: Reorganize the writing of result section (text and legend) according to figures distribution. For example authors write about figure 1 A-D, then fig 2B, then figure 6, then return to figure 1F-J and so on…...Response 18:Done.Point 19:RESULTS: For scientific benefit, it is better to insert each bar charts immediately below their corresponding histological or IHC figures.Response 19:Done. Point 20:RESULTS: Figures 5 and 6 are the same figure???? Need to insert figure 5 again.Response 20:Done.Point 21:RESULTS: Figure 6 formed of only five bar charts (A-E), while in result section authors had mention results about (fig 6 G&F)???Response 21:Done. All figures were remaked.Point 22:RESULTS: Hepatic Stem Cells in the Models of T1D and T2D:  NOT  showed that the greatest number of OV6+ and KRT19+ cells in liver. Correct.Response 22:We fixed in Figure Caption.Point 23:Discussion: Discussion section needs to be re-written in a better way and most of paragraphs need to be reconstructed.Response 23:Done.Point 24:Discussion: Correct the following words: an-imals, insu-lin, ex-pression, gen-eration, in-flammation, dia-betes, an-imals, gen-eration, insu-lin, in-crease, con-trol, re-storing, ex-tra).Response 24:Done.Point 25:https://doi.org/10.1016/j.scr.2020.101958Discussion: Remove the sentence: , since there is a previous study investigated the same points of the current research Response 25:https://doi.org/10.1016/j.scr.2020.101958 research is the first study have demonstrated that functional insulin-producing cells (IPCs) can be generated by using induced pluripotent stem cells (iPSCs) derived from as well non-diabetic (ND) as T1D and T2D individuals in vitro. The In our research we evaluate the mass of IPCs in the liver in the rat models of T1D and T2D with the comparable glycemic level in vivo and try to evaluate factors impact emerging of IPCS in the liver. Point 26:Discussion: Remove this repeated sentence in the 2nd paragraph in discussion (Glucose is the major physiologic regulator of insulin gene expression.) as it was mentioned before in the 4th paragraph in introduction section.Response 26:Done.Point 27:Discussion: Write the full name of the following abbreviations within the text .Response 27:Done.Point 28:Discussion: Correct the following sentence: So, hyperglycemia is a necessary, but insufficient agent, in stimulation of hepatic insulin synthesis, instead of .Response 28:Done.Response to Reviewer 2 Comments:Point 1 (Major): E,F,G in Figure.2 was too hard to see or calculate the positive cells. The authors should replace the new or visuable pictures.Response 1:Done.Point 2 (Minor):There were some mistake letter such as "insu-lin-producing cells" and "immunolog-ical reactions". The authors should caefully revise these letter in the manuscript. Also, English grammer should be revised.Response 2:Done.List of changes 1) We revised the numbering of affiliations. The right variant is:Musa Abidov 1, Ksenia Sokolova 2, Irina Danilova 2, Madina Baykenova 3, Irina Gette 2, Elena Mychlynina 2, Burcin Aydin Ozgur 4,5* , Ali Osman Gurol 6,7, M. Temel Yilmaz 8 1 Institute of Immunopathology and Preventive Medicine, Ljubljana, Slovenia2 Institute of Immunology and Physiology, Ural Branch of the Russian Academy of Sciences, Yekaterinburg, Russian Federation3 Kostanay Oblast Tuberculosis Dispensary, Kostanay, Republic of Kazakhstan 4 Department of Medical Biology and Genetics, Faculty of Medicine, Demiroglu Bilim University, Istanbul, Turkey 5 Diabetes Application and Research Center, Demiroglu Bilim University, Istanbul, Turkey6 Department of Immunology, Aziz Sancar Institute of Experimental Medicine, Istanbul University, Istanbul, Turkey7 Diabetes Application and Research Center, Istanbul University, Istanbul, Turkey8 International Diabetes Center, Acibadem University, Istanbul, Turkey2) We remove any funding-related text from the manuscript. We expand the acronym “IIP UB RAS”.In the Funding Statement section of the online submission form one’s should to note the follow:“This research was carried out within the state assignment of the Institute Immunology and Physiology Ural Branch Russian Academy of Science (IIP UB RAS) № 122020900136-4. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The work was performed using the equipment of the Shared Research Center of Scientific Equipment of the IIP UB RAS.”AttachmentResponse to Reviewers.docxSubmitted filename: Click here for additional data file. 5 Oct 2023 <div> PONE-D-23-22087R1 </div>  <div> Hepatic Insulin Synthesis Increases in Rat Models of Diabetes Mellitus Type 1 and 2 Differently </div>  <div> PLOS ONEDear Dr. Aydin Ozgur,Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.Please submit your revised manuscript by Nov 19 2023 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at  </div> A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.A marked-up copy of your manuscript that highlights changes made to the original version. 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Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: Yes********** <font color="black"> 3. Has the statistical analysis been performed appropriately and rigorously?  </font> Reviewer #1: Yes********** <font color="black"> 4. Have the authors made all data underlying the findings in their manuscript fully available?PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. 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Reviewer #1: Reviewer RecommendationsI would like to thank the authors of manuscript entitled "Hepatic Insulin Synthesis Increases in Rat Models of Diabetes Mellitus Type 1 and 2 Differently” for their effort in making the suggested modifications, Now the manuscript showed great improvement, yet some minor modifications are recommended.Introduction:   Add reference to this paragraph .�RESULTS   Remove number 38 from this paragraph:   [38]).�   Figure 2 & 5 legends: Cite the group according to figure letters :�(A) ND – non-diabetic group, (B) T1D – type 1 diabetes, (C) T2D – type 2 diabetes.   Reorganize the text to be matching with figure order:�Markers of liver injury and inflammation in bloodTo evaluate functional state of hepatocytes in the models of T1D and T2D, we analyzedactivity of aminotransferases (AST and ALT) instead of (ALT and AST), alkaline phosphatase (ALP) instead of (AP) and protein content in blood plasma . There was an increase in serum AST level in T1D, compared to ND. In contrast, AST level in T2D did not change. ALT is a rather specific marker of hepatocellular injury [45]. Increase in ALT level compared to normal was higher in T2D group than in T1D. A high ALT level led to a decrease in AST/ALT ratio (De Ritis coefficient) in T2D. ))) REMOVE the sentence.   Add letter (E) to figure 3�********** <font color="black"> 7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.If you choose “no”, your identity will remain anonymous but your review may still be made public.Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy. </font> Reviewer #1: No**********https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, AttachmentReviwer recommendations.pdfSubmitted filename: Click here for additional data file. 31 Oct 2023Dear colleagues, We would like to thank you for your helpful comments. Please finf below a point-by-point response to your comments and questions.Response to Reviewer #1 Comments:Point 1: Introduction:Add reference to this paragraph -«Extra-islet and extra-pancreatic IPCs are the spotlight of researchers for at least two reasons: first, they are additional sources of insulin and probably are able to alleviate hyperglycemia and prevent complications of diabetes. Secondly, ectopic insulin expression in functional mature cell in different tissues is of fundamental theoretical interest and expand the understanding of tissue adaptation in pathology condition» (Reference???).Response 1: Done.Point 2: RESULTSRemove number 38 from this paragraph: «Rats with T2D showed poor glucose tolerance, as evident by higher AUC value observed in T2D group compared to ND group (p < 0.05)  [38])».Response 2:Done.Removed reference was the article of Sakaguchi, K. et al. Glucose area under the curve during oral glucose tolerance test as an index of glucose intolerance. Diabetol Int 2016, 7(1), 53–58, which discuss the usage of an oral glucose tolerance test (OGTT) for clear detection of glucose intolerance in the early stage of diabetes.Point 3: Figure 2 & 5 legends: Cite the group according to figure letters :(A) ND – non-diabetic group, (B) T1D – type 1 diabetes, (C) T2D – type 2 diabetes.Response 3:Done.Point 4: Reorganize the text to be matching with figure order:Markers of liver injury and inflammation in bloodTo evaluate functional state of hepatocytes in the models of T1D and T2D, we analyzedactivity of aminotransferases (AST and ALT) instead of (ALT and AST), alkaline phosphatase (ALP) instead of (AP) and protein content in blood plasma . There was an increase in serum AST level in T1D, compared to ND. In contrast, AST level in T2D did not change. ALT is a rather specific marker of hepatocellular injury [45]. Increase in ALT level compared to normal was higher in T2D group than in T1D. A high ALT level led to a decrease in AST/ALT ratio (De Ritis coefficient) in T2D. ))) REMOVE the sentence.Response 4:Done.Point 5: Add letter (E) to figure 3Response 5:Done.AttachmentResponse to Reviewers.docxSubmitted filename: Click here for additional data file. 2 Nov 2023Hepatic Insulin Synthesis Increases in Rat Models of Diabetes Mellitus Type 1 and 2 DifferentlyPONE-D-23-22087R2Dear Dr. Aydin Ozgur,We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. 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AlyAcademic EditorPLOS ONEAdditional Editor Comments :Reviewers' comments: 17 Nov 2023PONE-D-23-22087R2 Hepatic Insulin Synthesis Increases in Rat Models of Diabetes Mellitus Type 1 and 2 Differently Dear Dr. Aydin Ozgur:I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department. onepress@plos.org.If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact customercare@plos.org.If we can help with anything else, please email us at Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staffon behalf ofDr. Riham M. Aly Academic EditorPLOS ONE


Melospiza melodia) subspecies that vary ~300% in body mass (17 − 50 g). Comparing large- and smaller-bodied subspecies revealed 9 candidate genes in three genomic regions associated with body mass. Further comparisons to the five smallest subspecies endemic to California revealed eight SNPs within four of the candidate genes  associated with body mass and varying as predicted by Bergmann’s Rule. Our results support the hypothesis that co-variation in environment, body mass and genotype reflect the influence of natural selection on local adaptation and a capacity for contemporary evolution in this diverse species.Ecogeographic rules denote spatial patterns in phenotype and environment that may reflect local adaptation as well as a species’ capacity to adapt to change. To identify genes underlying Bergmann’s Rule, which posits that spatial correlations of body mass and temperature reflect natural selection and local adaptation in endotherms, we compare 79 genomes from nine song sparrow ( Ecogeographic rules link spatial patterns in phenotype and environment, potentially reflecting adaptation. This study identifies nine genes associated with body mass variation in song sparrow populations, supporting Bergmann’s Rule and highlighting the role of natural selection in local adaptation. More recently, questions about local adaptation and the adaptive capacity of species to persist given environmental change have reinvigorated efforts to understand the micro-evolutionary processes involved. For example, correlations between ambient temperature, phenotype, and life-history traits affecting fitness and subject to natural selection are widely reported in vertebrates8 and summarized as ecogeographical rules9. Of particular relevance to climate adaptation, Bergmann’s rule posits an inverse correlation between ambient temperature and body size in endotherms10 and has garnered substantial empirical support . If such patterns that follow Bergman’s rule reflect local adaptation in traits affecting fitness, such as thermal tolerance, we expect genes underlying such trait variation to show signs of historical or ongoing selection and to vary predictably with phenotype.Explicating correlations among phenotypes and environmental variables is a historic focus of theoretical and empirical research on natural selection, local adaptation, and speciation16. We focused on song sparrows (Melospiza melodia), which are among the world’s most variable species18 as indexed by the number of recognized subspecies (25), and which accordingly exhibit a stunning range of co-variation in phenotype, life history, and environment . Song sparrows inhabit highly heterogeneous environments, spanning about 36 degrees of latitude (26 °N to 62 °N) and 15 °C in mean annual temperatures (ca. 1 °C to 16 °C). Because many phenotypic traits of song sparrows are known to have an additive genetic basis, respond to selection, and affect individual fitness , we hypothesized that the phenotypic clines in body mass exemplified in song sparrows at least partly reflect local adaptation to environment. If so, we further suggest that identifying the genomic mechanisms underlying such patterns can inform us about the adaptive capacity of other species displaying local adaptation to heterogeneous environments.Species with large ranges that encompass steep environmental gradients are excellent candidates in which to test for a genomic basis of local adaptationM. m. maxima, M. m. sanaka; mean mass [range]: 45.9 [41.9–50.0 g]) and two smaller-bodied subspecies  that breed in northern British Columbia and Alaska in order to evaluate the genomic landscape of divergence and identify candidate genes associated with body mass. Maxima and sanaka reside year-round from the Alaskan Peninsula to the Aleutian Islands, experience cold winters and are nearly twice the mass of rufina and merrilli, which experience warmer winters24  and smaller-bodied (merrilli and rufina) subspecies  indicated substantial divergence, with pairwise genome-wide values from 0.035 ± 0.026 in the control comparisons (merrilli-rufina) to 0.247 ± 0.112  was lowest between the control comparisons  and highest between maxima-merrilli (0.425 ± 0.046). Overall, the strongest signals of genetic differentiation were best explained by body size.Body mass (g) and mean winter and summer temperatures (°C) were strongly negatively related in the subspecies we studied Fig. , consistFST that were shared by at least two pairwise comparisons of large- and smaller-bodied subspecies . Overall, the mean FST values in the 50 kb outlier windows were elevated for each of the four pairwise comparisons , relative to low genome-wide FST averages , and two shared by three comparisons , with six candidate genes occurring on the same scaffold  and smaller (merrilli and rufina) subspecies pairs, as expected if these differentiated regions arose via divergent selection on body size . Six genes were shared by two subspecies comparisons , whereas the genome-wide average was an order of magnitude higher  , sanaka (0.229), and merrilli (−0.789), as compared to genome-wide means , but not in rufina . Dxy estimated in candidate regions was also elevated in size-related pairwise comparisons  compared to controls of within-size comparisons  test statistic to test for the presence of selective sweeps on the 3 contigs containing the 9 candidate genes , 1 SNP in GARNL3, and 3 SNPs in COL15A1  falls within the coding region of the gene, while the remaining SNPs are located within non-coding regions of genes. Notably, non-reference allele frequencies of the 8 candidate SNPs were also highly negatively correlated with average winter and summer temperature , for which we had prior genomic dataR2: 0.820–0.953; p < 0.001; Supplementary Table R2: 0.619–0.722; p < 0.05; Supplementary Table r = 0.183; p = 0.004).We observed consistent positive regressions of mass on non-reference allele frequency at each of the 8 focal SNPs  = 31.96–142.66; range FST estimates for the 8 focal SNPs noted ranged from 0.63 to 0.96 among all large- and smaller-bodied subspecies , two large Aleutian endemics , and five small California endemics . Whilst these findings suggest compelling support for our hypothesis that historical and/or ongoing natural selection has contributed to genetic differentiation of body size between subspecies, lower coverage for the San Francisco Bay populations may limit our ability to fully resolve heterozygotes and should be validated using higher coverage, phased sequence data.A phylogenetic tree reconstructed from individual genotypes of the 8 focal SNPs and 1–3 additional SNPs immediately up and downstream  that predicted allele frequency of small-bodied subspecies in California  and COL15A1  similarly associate with percent and distribution of body fat, size and BMI in humans32. ANGPTL2 contained several highly differentiated SNPs correlated with body mass and temperature . ANGPTL2 codes for angiopoietin-like protein 2 which has important roles in lipid, glucose and energy metabolism across taxa, and has also been found to be associated with human height and obesity34. In contrast, GARNL3  was informative in differentiating subspecies by body size and predicting the genotype of smaller-bodied song sparrows, but it has not yet been linked to phenotype in other species. Notably, there were high proportions of fixed or nearly fixed SNPs (FST > 0.95) within RALGPS1, GARNL3, and ANGTPL2 . These results support further our supposition that some or all of the genes we identified are directly or indirectly associated with thermoregulatory capacity and influential in their contributions to the roughly 300% increase in the body mass of song sparrows observed from California to Alaska.Having identified candidate genes capable of differentiating large- and smaller-bodied subspecies in Alaska, we then validated these patterns by predicting the genotypes of 39 birds from the five smallest subspecies endemic to California. Of the 9 candidate genes in regions shared by all large- and smaller-bodied pairwise comparisons, four contained SNPs  and M. m. sanaka , and two smaller-bodied subspecies, M. m. merrilli  and M. m. rufina . Tissue samples for these individuals were provided by the University of Alaska Museum. All birds were collected between 1997 and 2000 ; M. m. heermanni ; M. m. samuelis ; M. m. pusillula ; and M. m. maxillaris . Because extreme cold and heat are both expected to influence body size we used mean winter  and summer  temperature data, which were acquired from ClimateNA v.6.0049 for each of the sample locations to visualize the relationship between body mass and temperature . Whole-genome sequencing yielded a mean of 48,428,940 reads per individual in our British Columbia and Alaska populations.Genomic DNA was extracted using the DNeasy Blood and Tissue Kit . DNA concentrations were quantified using the Qubit BR dsDNA Assay Kit (Life Technologies). Using 150 ng of DNA from each sample, we prepared individually barcoded libraries with a 300 bp insert size following the protocol for the NEBNext Ultra II FS DNA Library Prep Kit . Libraries for http://www.bioinformatics.babraham.ac.uk/projects/fastqc). We used AdapterRemoval v.2.1.1 for sequence trimming, adapter removal, and quality filtering , and we merged overlapping paired-end reads. We aligned filtered reads to the song sparrow reference genome based on a small-bodied subspecies (M. m. morphna) from Southern British Columbia50 using the default settings in Bowtie2 v.2.4.251 and obtained alignment statistics from Qualimap v.2.2.152. Mean alignment rates for the four subspecies comparisons were 97.98%. Mean coverage and mapping quality were 7X (range: 4X–16X), and 18.62  to add index groups and mark duplicates. We used mpileup module in Bcftools v.1.12 for SNP variant discovery and genotyping for all song sparrows and filtered out variants that were not biallelic, had minor allele frequencies less than 5%, mean coverage less than 2X or more than 50X, and more than 20% missing data. This resulted in a total of 13,089,663 SNPs across the four subspecies. The mean missing data across individuals was 16.9%  of our subspecies were sampled from islands and are likely to have higher levels of inbreeding54, this signal is likely biologically relevant. To assess whether retaining related individuals had any impact on population structure, exploratory PCA plots were run both with and without these individuals. This comparison produced similar results  for all individuals. We then calculated Tajima’s D, nucleotide diversity (π), and absolute genetic divergence (Dxy) using the popgenWindows.py script  to further provide insight into the evolutionary processes that have shaped population-level genetic variation. Putative chromosomal locations of different scaffolds were obtained by aligning them to the zebra finch (Taeniopygia guttata) assembly  using the pseudo-chromosome scaffolding command in SatsumaSynteny60. To analyze patterns of genetic structure among the four subspecies, Admixture v.1.2361 analyses were run using a filtered dataset  that contained no missing data and was pruned to avoid linkage using the script ldPruning.sh (https://github.com/speciationgenomics/scripts/blob/master/ldPruning.sh). We investigated one to four population clusters with 200 bootstrap resampling iterations.We visualized genetic clustering in the SNP dataset by performing a PCA using snpgdsPCA function in the SNPRelate packageFST estimates. Elevated values of FST averaged over non-overlapping 50 kb windows were considered elevated if exceeding the 99.9th percentile of genome-wide mean value .We identified regions of elevated divergence among the four pairwise (or between size) comparisons including large- and smaller-bodied northern populations using the sliding window M. m. merrilli and M. m. rufina, that also differ in migratory behavior as a measure of ensuring that the candidate genes are correlated with our trait of interest. We also compared the two large-bodied populations, M. m. sanaka and M. m. maxima as an additional control for our smaller- vs large-bodied comparisons. There were no overlaps in elevated peaks between our large-small bodied (between size) comparisons with those identified within our smaller-bodied or large-bodied (within size) control comparisons, increasing our confidence that we have identified genes related to body size variation as opposed to migratory behavior or other traits differing between subspecies.To control for phenotypic differences between subspecies in traits that are not body size , we compared the two smaller-bodied populations, Among our large- and smaller-bodied (between size) comparisons, we classified outlier regions as shared if two or more paired comparisons identified the same gene within 50 kb of an elevated window  from a different geographic region, the San Francisco Bay area of California. Whole-genome sequences for the 39 San Francisco Bay individuals were generated in a separate study25 using library preparation and sequencing methods as detailed above. For the whole-genome sequencing summary statistics of the 39 San Francisco Bay individuals see Mikles and colleagues25.To test the hypothesis that our narrowed set of 9 candidate genes is related to body size, we predicted the genotypes of more distantly related small-bodied subspecies  distance.We first performed a partial Mantel test on the 3 contigs containing the 9 candidate genes across all 9 subspecies to assess whether patterns of divergence were due to neutral, including isolation by distance, or selective processes. Using the veganr) to quantify the relationship between the non-reference allele frequency and body mass (g), and between non-reference allele frequency and average summer and winter temperature (°C) at each of the SNPs. We selected the top 8 SNPs with a r > 0.90 between non-reference allele frequency and mass: contig 391 (chr 17) positions: 19152, 57980, 61835, 86656, 122228; contig 3361 (chr 2) positions: 255216, 255218, 262569. We also performed linear regression analyses to further explore the relationships between allele frequency and mass and temperature at each of the focal SNPs. For each analysis, allele frequency served as the independent variable, while mass and temperature were the dependent variables. Model parameters, including regression coefficients, intercepts, confidence intervals, effect sizes, R-squared values, p-values, and leverage and influence statistics can be found in Supplementary Tables 69  for the 467 SNPs. We used Pearson correlation coefficient . Less than three SNPs were used in cases where the SNP was located at the end of a contig or adjacent to another focal SNP. We then used the Ward algorithm as a grouping method to express the relationships between sites, which was implemented using the hclust function in vegan. We plotted the output using the plot.phylo function in the R package ape70 and visualized genotypes by constructing a modified heatmap in which the base pairs of each individual at a locus are represented in the two halves of a diagonally split pixel . To do this, we used the R package veganFurther information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Reporting SummarySource Data


In women with hyperglycemia in pregnancy living in France, psychosocial deprivation is associated with both earlier and greater exposure to the condition, as well as poorer maternofetal prognosis. We explored the impact of this and two other socioeconomic vulnerability indicators—food insecurity and poor language proficiency—on adherence to prenatal care and maternal and fetal outcomes.In a socially deprived suburb of Paris, we selected women who delivered between 01/01/2012 and 31/12/2018 and received care  for hyperglycemia in pregnancy. We analyzed the associations between individual psychosocial deprivation, food insecurity, French language proficiency  and fetal growth (main outcome), as well as other core maternal and fetal outcomes.Among the 1,168 women included , 56%, 17.9%, and 27.5% had psychosocial deprivation, food insecurity, and poor French language proficiency, respectively. Forty-three percent were prescribed insulin therapy. Women with more than one vulnerability had more consultations for diabetes. The rates for small (SGA), appropriate (AGA), and large-for-gestational-age (LGA) infant were 11.4%, 76.5% and 12.2%, respectively. These rates were similar in women with and without psychosocial deprivation, and in those with and without food insecurity. Interestingly, women with poor French language proficiency had a higher odds ratio of delivering a small- or large-for-gestational age infant than those with good proficiency.We found similar pregnancy outcomes for women with hyperglycemia in pregnancy living in France, irrespective of whether or not they had psychosocial deprivation or food insecurity. Optimized single-center care with specialized follow-up could contribute to reduce inequalities in maternal and fetal outcomes in women with hyperglycemia in pregnancy.The online version contains supplementary material available at 10.1186/s12884-023-06048-9. The pooled global standardized prevalence of gestational diabetes mellitus (GDM) was estimated at 14.0% by Wang et al. using data from studies conducted between 1990–2020 . Fo. Fo24]. Second, food insecurity was assessed using a single question with four possible response options as follows: “Which of these statements best describes the food eaten in your household? i) Enough of the kinds of food you want to eat; ii) Enough but not always the kinds of food you want to eat; iii) Sometimes not enough to eat; iv) Often not enough to eat”. This question is used in the U.S. Household Food Security Survey Module by the USA Department of Agriculture to measure food insecurity . ParticiThird, participants were asked how well they spoke French. There were three propositions: poor, moderate, or good proficiency.th percentile and lower than the 10th percentile for the standard French population, respectively [The primary outcome was neonatal weight at birth according to gestational age, classified into three categories: SGA, appropriate-for-gestational-age (AGA), and LGA infant. LGA and SGA infant were defined as a birth weight greater than the 90ectively .The secondary outcomes were HIP-related events, categorized into maternal and neonatal complications . For matFor neonatal outcomes, we considered shoulder dystocia, prematurity, neonatal hypoglycemia, neonatal death and stillbirth . ShouldeFor the present study, we collected data on two habits (smoking status and fruit and vegetable consumption), and on healthcare during pregnancy. During the dietary survey at HIP care initiation, all women answered the following question “Do you eat fruit and/or vegetables every day?”. Data collected on healthcare included gestational weight gain (GWG), insulin therapy (yes or no), the time between their most recent HIP outpatient visit and their first related prescription of insulin (days), the number of consultations with a diabetologist between HIP diagnosis and delivery (number), whether hospitalization was required for uncontrolled diabetes or for pregnancy complications , and hospitalization duration (days). GWG was calculated as the weight just before delivery minus the self-reported pre-pregnancy weight. At Jean Verdier hospital, women receive insulin therapy (no other treatment is used in France for HIP) when pre-prandial and/or 2-h post-prandial glucose levels are greater than 5.0 and/or 6.7 mmol/L, respectively .We collected all the study data with the help of other physicians, and a research study technician created the database. EC and EV ensured the accuracy of the database contents. Descriptive statistics were used to describe the entire population in terms of sociodemographic parameters, medical history, and glycemic figures. Baseline continuous variables were expressed as mean ± standard deviation. Categorical variables were expressed as frequencies (percentages).Missing data were not replaced. With regard to French language proficiency level, we created a binary variable: poor combined ‘poor’ and ‘moderate’ proficiency, and good reflected ‘good’ proficiency.Student’s t-test was used to compare continuous variables. The Chi-squared or Fisher’s-exact test was used for categorical variables, as appropriate. To explore the possible presence of selection bias, the baseline characteristics of the women included were compared with those not included.Patient characteristics were compared for the three vulnerability indicators .The three categories of neonatal weight at birth according to gestational age  were compared between the three secondary outcomes), as well as vulnerability indicators. We then analyzed maternal and neonatal outcomes  and care during pregnancy  according to the same three indicators. To evaluate HIP-related events, we performed an exploratory analysis where we created a composite criterion that included pre-eclampsia, LGA infant, and shoulder dystocia. However, as pre-eclampsia and shoulder dystocia were rare, this analysis was not retained as it did not add any new information.p-value < 0.10 in the univariate analysis were included in the multinomial logistic regression. We did not include tobacco as the prevalence of smoking was very low or inexistent.We analyzed patient characteristics associated with the three categories of neonatal weight at birth and performed a multinomial logistic regression with AGA as the reference category. Variables associated with the outcomes SGA or LGA infant  with a p-value < 0.05 was considered statistically significant. All data analyses were performed using R software, version 3.6.3.A p = 0.01)  .Of those included, 31.2% had eGDM, 62.3% GDM and 6.5% DIP. Mean age was 33 years, 27% had obesity and 34.2% reported a family history of diabetes , LGA infant (12.2%) and/or an infant with shoulder dystocia (0.1%). Irrespective of the socioeconomic vulnerability indicator chosen, the rates of HIP-related outcomes were similar between each indicator category .Women with poor French language proficiency were more likely to be non-smokers, and less likely to have an unhealthy diet than those reporting good proficiency. Weight gain was lower in women who had food insecurity than in those who did not . This percentage was similar for persons with and without each studied vulnerability. No difference was observed in the time before initiating insulin after the first HIP outpatient visit.p = 0.04; 3.9 ± 2.7 in women with poor French language proficiency vs. 3.4 ± 2.4 in those with good proficiency, p = 0.01). The mean number of hospitalizations was lower among women who had psychosocial deprivation compared to those who did not Table Having food insecurity or poor French language proficiency was associated with a higher number of consultations .p = 0.04), South-Asian ethnicity (OR 2.97 (1.16–7.58), p = 0.02), and poor French language proficiency (OR 0.36 (0.15–0.84), p = 0.02) were independent predictors of SGA infant. Weight gain (OR 1.04 (1.00–1.08), p = 0.045), macrosomia (OR 5.33 (2.47–11.54), p < 0.01), and complementary universal health protection (OR 2.35 (1.21–4.55), p = 0.01) were independently associated with LGA infant.Multinomial logistic regression variables included BMI, parity, gestational weight gain, HIP status, macrosomia history, ethnicity, health insurance cover, French language proficiency, and newborn’s sex. Of these, parity (odds ratio (OR) 0.76 , In this monocentric cohort, neither the maternal nor neonatal prognosis of HIP was related to psychosocial deprivation or to food insecurity. The high proportion of participants receiving insulin therapy and the large number of follow-up consultations are two factors that may explain these reassuring results. However, poor French language proficiency was associated with SGA and LGA infant. The latter result suggests the importance of a good understanding of dietary messages in HIP populations. Poor nutrition could impact fetal growth and induce placental dysfunction.Unlike the present work, previous studies reported poorer maternal and neonatal outcomes for the most psychosocially deprived pregnant women. The inverse care law supports the notion that “the availability of good medical care tends to vary inversely with the need for it in the population served” . This coWe found a relationship between poor French language proficiency and abnormal fetal growth. This is not surprising given that women with a low socioeconomic status and with HIP have a higher risk of LGA , 34 or S. demonstrated a 35% higher odds ratio of LGA infant (OR 1.35 (1.09–1.70)) in women with a middle-school level of education compared to college-educated participants [Furthermore, in a previous study by our group, the risk of LGA infant was 50% higher among women with a low socioeconomic status in a cohort comprising 996 women  . In Chinicipants .th quartile compared to the 1st) [In contrast, in another Chinese cohort, the most psychosocially deprived participants had a higher odds ratio of SGA infant than the least deprived (OR 1.63 (95% CI 1.29–2.08) for the 4the 1st) . In the the 1st) . Recentlthe 1st) .Elsewhere, Wentz et al. highlighted that area-based deprivation was more strongly associated with LGA infant than with SGA infant . The sam2 [Here we discuss some hypotheses as to why we found similar maternal and neonatal outcomes, irrespective of psychosocial deprivation or food insecurity status. First, the monocentric characteristic of our study may have reduced disparities. Second, the rate of insulin therapy was higher in the present study (43.3%) than in a previous study by our team on psychosocial deprivation in women with HIP from the same Paris suburb (29.4%) . It was 2 ). In oth2 , 37. ThiWe observed a higher rate of SGA infant in women with poor French language proficiency. A previous study in Canada highlighted the consequences of language barriers on pregnancy outcomes. Specifically, it found that non-English-speaking Asian women had a lower risk of LGA infant than English-speaking women . We hypoHowever, in the multivariable analysis (data not presented), the main factor associated with SGA infant was South Asian ethnicity, while poor proficiency was an independent protective factor. This finding mitigates our initial hypothesis that women restrict their consumption of certain food types. Furthermore, Venkatesh et al. published data on maternal and fetal outcomes for women with HIP in the USA over a period of six years. They reported a higher risk of SGA infant in persons of Asian/Pacific island origin than in white women (OR 1.84 (1.82–1.87)), after multiple adjustment .There was a relatively high rate of pre-eclampsia (3.3% (range 1.3–5%)) in our study population, especially in women with food insecurity (5%). The high SGA infant rate in this group (13.2%) would suggest overrepresentation of fetal growth restriction, due to a possible combination of endogenic and exogenic oxidative stress with placental dysfunction, reflected in the higher rate of pre-eclampsia observed . Women wThe strengths of our study include a large study sample with three socioeconomic vulnerability indicators to evaluate socioeconomic deprivation (French language proficiency level being investigated for the first time), a multi-ethnic cohort with adjustments being made for confounders, and similar prenatal care provided by specialists, diabetologists and obstetricians to all those included.The study also has several limitations. First, as only women who provided complete data  were included, they differed somewhat from those not included . Second,Socioeconomic vulnerabilities are established risk factors of poorer maternal and neonatal outcomes during pregnancy, and of higher rates of HIP. However, in our study of women with HIP, we found similar outcomes irrespective of psychosocial deprivation and food security status. Only poor French language proficiency was associated with a lower rate of AGA infant; the reasons for this are unclear. We believe that optimized comprehensive care in a single center could reduce health inequalities in women with HIP experiencing one or more socioeconomic vulnerabilities.Additional file 1.Additional file 2.