Liver macrophages regulate systemic metabolism through non-inflammatory factors
Authors: Cecilia Morgantini
André Sulen
Gabriela Krejčová
Myriam Aouadi
Sara Straniero
Laura Levi
Valerio Azzimato
Mikael Rydén
Connie Xu
Xidan Li
Kjell Hultenby
Emelie Barreby
Erik Näslund
Volker M Lauschke
Aleš Tomčala
Jennifer Jager
Chanchal Kumar
Tracey Hurrell
Jeremie Boucher
Michaela Tencerova
Francisco Verdeguer
Claudia Kutter
Niklas K Björkström
Adam Bajgar
Ewa Ellis
Journal: Nature Metabolism (2019)
Discipline: BIOLOGY & BIOCHEMISTRY
Keywords: Liver macrophages; metabolic disease; inflammatory cytokines; insulin resistance; insulin-like growth factor-binding protein 7 (IGFBP7)
Affiliations: Integrated Cardio Metabolic Center (ICMC), Department of Medicine, Karolinska Institutet, Huddinge, Sweden., Université Nice Côte d'Azur, INSERM U1065, C3M, Team Cellular and Molecular Physiopathology of Obesity, Nice, France., Department of Molecular Endocrinology, KMEB, University of Southern Denmark, Odense University Hospital and Danish Diabetes Academy, Odense, Denmark., Division of Surgery, Department of Clinical Sciences, Danderyd Hospital, Karolinska Institutet, Stockholm, Sweden., Translational Sciences, Cardiovascular, Renal and Metabolic Diseases, IMED Biotech Unit, AstraZeneca, Gothenburg, Sweden., Department of Molecular Mechanisms of Disease, University of Zurich, Zurich, Switzerland., Metabolism Unit C2:94, Department of Medicine, and Center for Innovative Medicine, Department of Biosciences and Nutrition, Karolinska Institutet, Huddinge, Stockholm, Sweden., Department of Laboratory Medicine, Clinical Research Center, Karolinska Institutet, Huddinge, Sweden., Center for Infectious Medicine, Department of Medicine Huddinge, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden., Division of Transplantation Surgery, CLINTEC, Karolinska Institutet, Huddinge, Sweden., Unit of Endocrinology, Department of Medicine, Karolinska Institutet, Huddinge, Sweden., Department of Microbiology, Tumor and Cell Biology, Science for Life Laboratory, Karolinska Institutet, Stockholm, Sweden., Section of Pharmacogenetics, Department of Physiology and Pharmacology, Karolinska Institutet, Solna, Sweden., Bioscience, Cardiovascular, Renal and Metabolism, IMED Biotech Unit, AstraZeneca, Gothenburg, Sweden., Wallenberg Centre for Molecular and Translational Medicine, Lundberg Laboratory for Diabetes Research, University of Gothenburg, Gothenburg, Sweden., Laboratory of Evolutionary Protistology, Institute of Parasitology, Biology Centre, Czech Academy of Sciences, Ceske Budejovice, Czech Republic., Faculty of Science, University of South Bohemia, and Institute of Entomology, Biology Centre, Czech Academy of Sciences, Ceske Budejovice, Czech Republic., Integrated Cardio Metabolic Center (ICMC), Department of Medicine, Karolinska Institutet, Huddinge, Sweden. .

Abstract:  Liver macrophages (LMs) have been proposed to contribute to metabolic disease through secretion of inflammatory cytokines. However, anti-inflammatory drugs lead to only modest improvements in systemic metabolism. Here we show that LMs do not undergo a proinflammatory phenotypic switch in obesity-induced insulin resistance in flies, mice and humans. Instead, we find that LMs produce non-inflammatory factors, such as insulin-like growth factor-binding protein 7 (IGFBP7), that directly regulate liver metabolism. IGFBP7 binds to the insulin receptor and induces lipogenesis and gluconeogenesis via activation of extracellular-signal-regulated kinase (ERK) signalling. We further show that IGFBP7 is subject to RNA editing at a higher frequency in insulin-resistant than in insulin-sensitive obese patients (90% versus 30%, respectively), resulting in an IGFBP7 isoform with potentially higher capacity to bind to the insulin receptor. Our study demonstrates that LMs can contribute to insulin resistance independently of their inflammatory status and indicates that non-inflammatory factors produced by macrophages might represent new drug targets for the treatment of metabolic diseases. 

Retracted: No

PubPeer comments:
 Why do these Ob Ob mice show substantial differences in basal blood glucose levels (Time 0) in different experiments? (particularly figure 4a 4c) 200 mg dl vs 100 mg dl is au00a0lot foru00a0inter-animal differences within the same conditions.u00a0Are these due to intra-personnel variations? ![file] The observed differences in glucose levels between GeRP-Scr and GeRP-Igfbp7 treated groups are smaller than deviations introduced by their technical procedures. Do the authors consider these variations as negligible differences, how does this affect their conclusions?u00a0 , 
 Actually these are mice fed a high fat diet not ob ob. The difference in basal or fasted glucose between the values in the gtt and the graph are due to the timing of fasting. The gtt is done after 6 hours of fasting, which is pretty common to study glucose level regulation by liver. Fasting glycaemia on the other hand is commonly measured after an overnight fast. Hence lower values. The graphs are presented + -s.e.m not standard deviation. Please note that all these details of experimental and statistical methods are available in the paper. , 
 #2 Thank you to the author for the quick reply, although it did not answer my question.u00a0 My inquiryu00a0was about figure 4A (Glucose tolerance tests, GTT) and figure 4C (insulin tolerance tests, ITT). **Not**u00a0figure 4B (overnight fasting glycaemia).u00a0 ![file] Basal glucose levels for** both** GTT and ITTu00a0is often measured **after a 4 u2013 6 hours fast**, not overnight, as also the author described it in the method section in the paper *u00a8Metabolic analyses in mice. The glucose tolerance test and insulin toleranceu00a0test (IP-GTT and IP-ITT) were performed on the day of the last GeRP injectionu00a0and following a 6-h fast. A dose of 1 g kg glucose or insulin(0.25 IU kg) wasu00a0intraperitoneally injected, and blood glucose levels were measured with au00a0glucometer at defined time points from the tail vein. u00a8*u00a0 Hence, the basal glucoseu00a0levels should not differ much betweenu00a0GTT(figure 4A) or ITTu00a0 (figure 4C).u00a0Are these due to intra-personnel variations? Do the authors consider these variations as negligible differences, how does this affect their conclusions?u00a0u00a0 , 
 We realised that we had an editing error and we have contacted the editor, with the following: The Y-axis in graph in 4 C should be labelled with u201cBlood Glucose (percent base line)u201d and not u201cBlood Glucose (mg dl)u201d. Therefore the 100 you are referring to is actually 100%. However, this does not change anything to our conclusion. , 
 Thank you to the author for the reply. Could theu00a0authors please post all original values for figures 4a,4b 4c, as well as raw mitochondrial quantification valuesu00a0for individual biological replicates in figureu00a0 4g? The original data may undoubtedly resolve my remaining concerns.u00a0 , 
 A major concern has arisen while I was going through original figures in the supplementary files, which may also explain my doubts regarding figure 4.u00a0 The main proteinu00a0discussed in this paper is Igfbp7. According to UniProt, Igfbp7 protein has two isoforms withu00a0281u00a0 and 313 amino acids, corresponding to a molecular weightu00a0of **~29 KDa**. However, the author reported a completely differentu00a0protein at **~70 KDa** as a band for Igfbp7 in all figures of western blot and IP (figure 2c, 5c,5f,5l, S2a S5c). How could this be possible?u00a0 https: www.uniprot.org uniprot Q61581u00a0u00a0 According to Ensembl, *Igfbp7* has two splicing mRNAs 1116 and 1200nt, which encodeu00a0281u00a0 and 313 amino acids respectively, and it is impossible that they encode for a 70 KDa protein. https: www.ensembl.org Mus_musculus Gene Splice?db=core;g=ENSMUSG00000036256;r=5:77349240-77408041u00a0u00a0 This can not come from an editing error as the protein ladder is shown in original plots.u00a0 This raises a major concern about the relevance of conclusions obtained from figures 2c, 5c,5f,5l, S2a S5c, and probably ELISA results, and hence the main conclusion of the paper. Can the authors please comment on this concern?.u00a0 Figure 5C ![file] Figure 5i ![file] , 
 Moderator: we have received the following comment regarding #6  The person writing this further comment is posting our figures partly cut and this is misleading the reader of pubpeer. Indeed, in our original figures , it is clearly written IP (immunoprecipitation). The technical procedure is cited in the figure legends and explained in the method section. Hence, the comment is not relevant. In the previous comment, not only he she is expressing general concerns (already reported) but also asking for raw data of mitochondrial quantification which is not even something shown in our paper (the figure cited is showing cellular lipid droplet). It seems that the person has not read the paper carefully. I believe that this kind of comments are not making any improvement to the scientific discussion, are a wasting of time for the authors and discredit pubpeer. I hope that you would consider deleting both comments. , 
 #7 The authors objections in post #7 do not make sense. Instead, the concern raised in post #6 is entirely valid: namely, IGFBP7 is expected to run at roughly 30 kDa, but all of the blots in the Supp material show it running between 70 and 100 kDa. Do the authors have an explanation for this? (E.g., is it known that glycosylation of IGFBP7 makes it run much slower than expected?) Their comments in #7 do not address this point at all. Moreover, I did not find any controls in the paper showing that the signal detected at 70-100 kDa actually depends on the presence of IGFBP7, and that it is not a non-specific cross-reacting protein. Do such data exist? Without it, there seems to be legitimate basis for skepticism that the 70-100 kDa species is actually IGFBP7. (It is notable that in the Supplemental blots for Fig 5f, the banding pattern of the putative IGFBP7 species is extremely similar to that in the p-tyr blot, raising the possibility of a mis-assignment.) Here is the text from the spec sheet from the source of the anti-IGFBP7 antibodies used in this paper; it confirms the concern posted in #6: * Insulin-u00adlike growth factor (IGF) binding proteinu00ad-related protein 1 (IGFBP-u00adrp1), also known as IGF binding proteinu00ad-7 (IGFBPu00ad7) and Mac25 is a 31u00ad-37 kDa secreted glycoprotein that belongs to the IGFBP superfamily of molecules. * Regarding the authors specific comments: * * The person writing this further comment is posting our figures partly cut and this is misleading the reader of pubpeer. * No, the portions posted in #6 are not in any way misleading; they accurately represent what is in the Supp material. * * Indeed, in our original figures , it is clearly written IP (immunoprecipitation). The technical procedure is cited in the figure legends and explained in the method section. Hence, the comment is not relevant. * The fact that it is an IP is not relevant to the concern raised. The apparent MW should not change upon IP, so it seems it is the authors comment that is irrelevant. Bottom line: if the authors understand why this small protein runs so much larger than expected, they should provide a clear explanation. , 
 #7 I agree with theu00a0facts provided in posts #6 #8 that the lack of any data explaining the misassignment in molecular weightu00a0ofu00a0Igfbp7 between allu00a0publicu00a0available databases (i.e.u00a030 KDa) and the one reported by the authorsu00a0 (70u00a0KDa) raisesu00a0a major concern about this paper conclusions. Additionally, the images in figures 5C and 5i posted byu00a0Oncaea Borealis reveal another concern. The heavy chain s IgG bands seem to appear in figure 5G, but not in figure 5i, despite both are western blots analysis for u00a8the questionableu00a0 Igfbp7 u00a8 co-immunoprecipitatedu00a0with insulin receptor (IR). Bands corresponding to the IgG heavy and light chains should also appear in figure 5i,u00a0because the same antibody was used for precipitationu00a0and the same antibody was used for detection in both experimentsu00a0according to the described methods.u00a0 , 
 #7 Thanks to the moderator for posting the author s comment here.u00a0 Yes, indeed, my request was regarding raw data ofu00a0electronu00a0microscopy images, i.eu00a0cellular lipid droplet size (It was mixed up with mitochondria in my comment #5). There appears tou00a0be an inconsistencyu00a0in figures 4f,g,h between the displayed images (Figures 4f **h (lower panel)** ) and the corresponding quantification s plot (figure 4g).u00a0 The authors seemed to be unwilling to provide these published data for figure 4, which also raises many concerns. However, the reason became very well understood after spottingu00a0the major issues with the MW misalignment ofu00a0Igfbp7 from 30 KDa to 70 KDau00a0(without providing any supportive data or scientific explanation), which confirmed my doubts, and hence it wouldn t change much whether they provide them or not.u00a0 Theu00a0confusion about mitochondria in my previous comment #5 cameu00a0while I was checking the values ofu00a0IP-GTT plots in another publishedu00a0paper by overlapping authors to this oneu00a0u00a0u00a8[Azzimato et al 2020](http: pubpeer.com publications 475E4B6DA93FB22E5C090D2F8E52A9)u00a8 . That paper appears to have many similar concernsu00a0tou00a0the ones raised here. I have provided now some comments under this Pubpeer thread:u00a0 u00a0 https: pubpeer.com publications 475E4B6DA93FB22E5C090D2F8E52A9 All other points raised by the author in comment #7 wereu00a0concisely and nicely addresses byu00a0Leucanella Acutissimau00a0in comment #8 (Please accept my gratitude)u00a0 , 
 In figure 2g (IGFBP7 RNA editing in human LMs.), multiple major issues can be highlighted:: ![file] * Surprisingly, all numbers in the figure 2g for RNA editing ratios are the exact multiples of 10 (i.e 10,30,40,70 90). It is hard to imagineu00a0that all these numbers can beu00a0integeru00a0numbers,u00a0but it su00a0even harderu00a0tou00a0believeu00a0that all can be theu00a0exact multiples of 10 (Editing ratio is the number of reads with editedu00a0nucleotide to the total number of reads covering that site). * The numbers in the text and in theu00a0figure do not match: It is stated in the text u00a8u00a0*IGFBP7 was subject to adenosine-to-inosine RNA editing, which changed codon 95 fromu00a0AAG to AIG in 90% of insulin-resistant obese patients ( **83%** u00b1 9%, mean transcripts edited u00b1 s.e.m.) in comparison to 30% of insulin-sensitive obese patients and lean individuals (Fig. 2g).* u00a8 Inu00a0Fig 2g, the RNA edited ratio in resistant obese patientsu00a0(IR) is **90%** , while in the text, it is **83%** .u00a0 * The ratio of patients doesn t match with the number of patients: The total numberu00a0insulin-resistant obese patients are 7, and in the above text it is stated u00a890% of insulin-resistant obese patientsu00a8, thus 90% of them will be 6.3 patients (It is not possible to have 0.3 of a patient with RNA editing, and the rest (0.7) of the same patient without RNA editing!). If there are 6 out of 7 patients who have RNA editing, then the ratio would be **85,7%** , not 90%. The same also applies for u00a830% of insulin-sensitive obese patientsu00a8 where there are only 3 patients, thus 30% is not a possible ratio with only 3 patients. * u00a0Which cut-off of RNA editing ratio did the author consider to classifyu00a0a patient as having RNA editing or not? 1%? 50?u00a0 ... Nothing is mentioned. * Nothing is even describedu00a0for RNA editing in the method part. No other data is shown for the edited ratios.u00a0u00a0 These issues raise a major concern about the reliability of these numbers and the drawn conclusions.u00a0 u00a0u00a0u00a0 ,