Male obesity causes adipose mitochondrial dysfunction in F1progeny via a let-7-DICER axis

Male obesity causes adipose mitochondrial dysfunction in F1 progeny via a let-7-DICER axis | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Male obesity causes adipose mitochondrial dysfunction in F 1 progeny via a let-7-DICER axis View ORCID Profile Chien Huang , View ORCID Profile Joo Hyun Park , View ORCID Profile Ali Altıntaş , Natasa Stanic , View ORCID Profile Kristine Kyle de Leon , View ORCID Profile Signe Isacson , View ORCID Profile Panagiotis Kalogeropoulos , View ORCID Profile Hande Topel Batarlar , View ORCID Profile Rocio Valdebenito Malmros , View ORCID Profile Jesper Havelund , View ORCID Profile Bjørk Ditlev Marcher Larsen , Yen-Ting Chien , Wen-Chi Huang , View ORCID Profile Karolina Szczepanowska , View ORCID Profile Jan-Wilm Lackmann , View ORCID Profile Aleksandra Trifunovic , View ORCID Profile Eva Kildall Hejbøl , View ORCID Profile Sönke Detlefsen , View ORCID Profile Ida Engberg Jepsen , Stefanie Hansborg Kolstrup , Ricardo Laguna-Barraza , View ORCID Profile Javier Martin-Gonzalez , View ORCID Profile Konstantin Khodosevich , View ORCID Profile Nils J. Færgeman , View ORCID Profile Marcelo A. Mori , View ORCID Profile Marc R. Friedländer , View ORCID Profile Anita Oest , View ORCID Profile Romain Barrès , View ORCID Profile Jan-Wilhelm Kornfeld doi: https://doi.org/10.1101/2024.10.03.615866 Chien Huang 1 Department of Biochemistry and Molecular Biology (BMB), University of Southern Denmark (SDU) , Odense, Denmark 2 Laboratory of Animal Physiology, Department of Animal Science and Technology, National Taiwan University , Taipei, Taiwan Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Chien Huang Joo Hyun Park 1 Department of Biochemistry and Molecular Biology (BMB), University of Southern Denmark (SDU) , Odense, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Joo Hyun Park Ali Altıntaş 3 Novo Nordisk Foundation Center for Basic Metabolic Research (CBMR), Faculty of Health and Medical Sciences, University of Copenhagen , Copenhagen, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ali Altıntaş Natasa Stanic 1 Department of Biochemistry and Molecular Biology (BMB), University of Southern Denmark (SDU) , Odense, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kristine Kyle de Leon 1 Department of Biochemistry and Molecular Biology (BMB), University of Southern Denmark (SDU) , Odense, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Kristine Kyle de Leon Signe Isacson 4 Division of Cell and Neurobiology, Department of Biomedical and Clinical Sciences, Linköping University , Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Signe Isacson Panagiotis Kalogeropoulos 5 Science for Life Laboratory, Department of Molecular Biosciences, The Wenner-Gren-Institute, Stockholm University , Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Panagiotis Kalogeropoulos Hande Topel Batarlar 1 Department of Biochemistry and Molecular Biology (BMB), University of Southern Denmark (SDU) , Odense, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Hande Topel Batarlar Rocio Valdebenito Malmros 1 Department of Biochemistry and Molecular Biology (BMB), University of Southern Denmark (SDU) , Odense, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Rocio Valdebenito Malmros Jesper Havelund 1 Department of Biochemistry and Molecular Biology (BMB), University of Southern Denmark (SDU) , Odense, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jesper Havelund Bjørk Ditlev Marcher Larsen 1 Department of Biochemistry and Molecular Biology (BMB), University of Southern Denmark (SDU) , Odense, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Bjørk Ditlev Marcher Larsen Yen-Ting Chien 2 Laboratory of Animal Physiology, Department of Animal Science and Technology, National Taiwan University , Taipei, Taiwan Find this author on Google Scholar Find this author on PubMed Search for this author on this site Wen-Chi Huang 2 Laboratory of Animal Physiology, Department of Animal Science and Technology, National Taiwan University , Taipei, Taiwan Find this author on Google Scholar Find this author on PubMed Search for this author on this site Karolina Szczepanowska 6 Cologne Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD) & Center for Molecular Medicine (CMMC), University of Cologne , Cologne, Germany 7 IMol Polish Academy of Sciences , 02-247 Warsaw, Poland. Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Karolina Szczepanowska Jan-Wilm Lackmann 6 Cologne Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD) & Center for Molecular Medicine (CMMC), University of Cologne , Cologne, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jan-Wilm Lackmann Aleksandra Trifunovic 6 Cologne Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD) & Center for Molecular Medicine (CMMC), University of Cologne , Cologne, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Aleksandra Trifunovic Eva Kildall Hejbøl 8 Department of Pathology, Odense University Hospital , Odense, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Eva Kildall Hejbøl Sönke Detlefsen 8 Department of Pathology, Odense University Hospital , Odense, Denmark 9 Department of Clinical Research, Faculty of Health Science, University of Southern Denmark , Odense, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Sönke Detlefsen Ida Engberg Jepsen 10 The Fertility Clinic, Department of Obstetrics and Gynaecology, Zealand University Hospital , Koege, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ida Engberg Jepsen Stefanie Hansborg Kolstrup 11 Biomedical Laboratory, Institute of Molecular Medicine, University of Southern Denmark , Odense, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ricardo Laguna-Barraza 12 Core Facility for Transgenic Mice, Department of Experimental Medicine, Faculty of Health and Medical Sciences, University of Copenhagen , Copenhagen, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site Javier Martin-Gonzalez 12 Core Facility for Transgenic Mice, Department of Experimental Medicine, Faculty of Health and Medical Sciences, University of Copenhagen , Copenhagen, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Javier Martin-Gonzalez Konstantin Khodosevich 13 Biotech Research & Innovation Centre (BRIC), University of Copenhagen , Copenhagen, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Konstantin Khodosevich Nils J. Færgeman 1 Department of Biochemistry and Molecular Biology (BMB), University of Southern Denmark (SDU) , Odense, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Nils J. Færgeman Marcelo A. Mori 14 Department of Biochemistry and Tissue Biology, Institute of Biology, Universidade Estadual de Campinas , Campinas, São Paulo, Brazil 15 Program in Genetics and Molecular Biology, Institute of Biology, Universidade Estadual de Campinas , Campinas, São Paulo, Brazil 16 Obesity and Comorbidities Research Center (OCRC), Universidade Estadual de Campinas , Campinas, São Paulo, Brazil 17 Experimental Medicine Research Cluster (EMRC), Universidade Estadual de Campinas , Campinas, São Paulo, Brazil Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Marcelo A. Mori Marc R. Friedländer 5 Science for Life Laboratory, Department of Molecular Biosciences, The Wenner-Gren-Institute, Stockholm University , Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Marc R. Friedländer Anita Oest 4 Division of Cell and Neurobiology, Department of Biomedical and Clinical Sciences, Linköping University , Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Anita Oest Romain Barrès 3 Novo Nordisk Foundation Center for Basic Metabolic Research (CBMR), Faculty of Health and Medical Sciences, University of Copenhagen , Copenhagen, Denmark 18 Institut de Pharmacologie Moléculaire et Cellulaire, Université Côte d’Azur & Centre National pour la Recherche Scientifique (CNRS) , Valbonne, 06560, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Romain Barrès Jan-Wilhelm Kornfeld 1 Department of Biochemistry and Molecular Biology (BMB), University of Southern Denmark (SDU) , Odense, Denmark 19 Novo Nordisk Foundation Center for Adipocyte Signalling, University of Southern Denmark , Odense, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jan-Wilhelm Kornfeld For correspondence: janwilhelmkornfeld{at}bmb.sdu.dk Abstract Full Text Info/History Metrics Preview PDF Abstract We here describe that obesity and weight loss in male mice cause reversible abnormalities in glucose and lipid metabolism, serum metabolomes and lipidomes as well as expression of microRNAs, mRNAs and proteins controlling mitochondrial function in epididymal white adipose tissue. When mating obese male mice with lean females, we observed reductions in expression and translation of genes encoding mitochondrial respiratory components in (F 1 ) offspring that closely resemble those observed in the paternal (F 0 ) generation. When mapping miRNA regulation across somatic organs (i.e., liver, adipose) and sperm and F 0/1 generations, we found that obesity and weight loss reversibly affected miRNA levels, and that let-7 isoforms were induced in obese F 0 and F 1 adipose tissue and sperm of obese F 0 mice, eliciting qualitatively similar responses in two adjacent tissues. Overexpressing let-7 in adipocytes silenced DICER1, a miRNA processing enzyme crucial for adipose adaptation to obesity as evidenced by deficiencies in mitochondrial function following DICER1 loss in primary adipocytes. Also, microinjection of synthetic let-7 mimetics at physiological levels found in obese sperm into zygotes from lean mice elicited glucose intolerance and impediments in adipose mitochondrial gene expression in mice sired from let-7 microinjected zygotes, phenocopying hereditary aspects of paternal obesity. When performing single-cell RNA-Seq of miRNA-injected embryos, let-7 impaired mitochondrial gene expression, suggesting altered oxidative metabolism following zygotic let-7 delivery. When studying miRNA alterations in human semen, lifestyle-induced weight loss downregulated hsa-let-7 , suggesting similar roles for human let-7 in gametic epigenomes and embryogenesis. Introduction Intergenerational epigenetic inheritance of metabolic dysfunction Genome-wide association studies have demonstrated that genetic (i.e., DNA) variants only explain 20% of obesity heritability 1 . Given pandemic rises in obesity in evolutionarily relatively short timescales, it was proposed that non-genetic or ‘ epi-genetic ’ processes, that are spurred by obesogenic lifestyles and other changes in environment exposures, might increase obesity susceptibility and contribute to disease incidences. This form of epigenetic inheritance, if convincingly demonstrated 2 , is particularly intriguing for cases of paternal programming where affected fathers contribute only sperm as biological material to embryos (intergenerational inheritance) or no genetic/biological material at all (transgenerational inheritance). Epidemiological support for the existence of hereditary, paternally-acquired traits comes from historical records demonstrating that ample ancestral food supplies affects cardiometabolic traits up to two generations 3 , 4 , showing that individuals carry increased risks of cardiometabolic dysfunction if their parents or grandparents were obese or were malnourished 5 . In line with correlative observations in human, mechanistic studies in isogenic (genetically identical) mouse strains like C57BL76 demonstrated that paternal obesity can impair metabolism in offsprings (F 1 generation and beyond) and, specific diet like low-protein 6 or high-fat diet feeding 7 – 9 and stress-related cues such as depression-like behavior 10 , fear-conditioning 11 and psychological traumata 12 impart specific epigenetic behavioral and metabolic effects in offsprings. Recently, beneficial types of epigenetic programming in adipose tissue were also demonstrated where offsprings of mice exposed to cold temperatures display increased amounts of metabolically favourable brown adipose tissue 13 , suggesting an unexpectedly high plasticity of the molecular and cellular mechanisms underlying intergenerational epigenetic responses. The molecular basis for epigenetic inheritance of paternal obesity in male gametes The molecular and cellular basis for epigenetic heredity remains largely enigmatic: Although changes in epigenetic marks like methylation of genomic DNA, histone post-translational modifications and alterations in small Noncoding RNAs (sncRNAs) correlate with hereditary processes in male gametes 14 . Although lifestyle/diet-induced changes to DNA methylation and histones modifications were reported, the evidence for causatively implicating these in eliciting paternal response in progeny is rare for higher mammalian species like rodents and humans. Other emerging instigators of intergenerational response are found amongst the different types of gametic sncRNAs such as microRNAs (miRNAs): miRNAs in gametes were initially identified and functionally dissected in nematodes and fruit flies 15 – 18 , thus species where sncRNAs-evoked gametic gene silencing processes are well-understood 19 . In contrast to DNA-linked marks, sncRNAs are very mobile in nature and are subject to microvesicular trafficking between cells and organs and secreted by somatic cell types in testes 20 , 21 . Thus, due to their high abundance of miRNAs in sperm, their dynamic changes following diet exposures 22 , 23 , their exchange between sperm and oocytes 23 sperm-born sncRNAs have the unique potential for exchanging information about past lifestyle choices to zygotes and F 1 progeny. Consistent with this, metabolic trait/disease-associated miRNAs can affect intergenerational phenotypes and phenocopy F 0 lifestyles if microinjected 24 , 25 or removed from zygotes 26 . Beyond controlling gametic epigenomes, miRNAs are also essential for germ cell development processes such as spermatogenesis 27 and embryogenesis 28 , and miRNA are exchanged between zygotes and other cell types 12 , 25 , 29 – 31 . Taken together, it is plausible to hypothesize that obesity-evoked changes in sperm miRNAs alter zygotic miRNA pools and embryonic development, instigating epigenetic phenotypic traits and somatic gene expression in offsprings. As diverse paternal lifestyle exposures ranging from inflammation 15 , nutrition 29 , 31 to stress 12 , 30 alter gametic miRNA repertoires and F 1 phenotypes in non-overlapping ways, the intriguing possibility exists that environmental experiences are encoded in individual gametic sncRNA signature and that these sperm sncRNAs have the potential to transmit complex lifestyle exposures given the high bandwidth of RNA-mediated effects 32 . Regulation of adipose tissue (mitochondrial) function by miRNAs Adipose tissue is a multifunctional endocrine organ that coordinates tissue-level (autocrine, juxtacrine, paracrine) and systemic (endocrine) responses by release of adipokines, lipids and inflammatory factors and, shown recently, miRNA-containing microvesicles (exosomes) 33 – 35 . It is believed that adipose tissue can safely store ingested nutrients as lipids, although chronic calorie excess causes insulin resistance 36 , aberrant gene expression in adipocytes 37 , vascular remodelling and immune cell infiltration 33 . Mitochondria in adipose tissue exert important functions for tissue/energy homeostasis, particularly during nutrient deprivation/excess 35 : Whereas brown adipose tissue (BAT) contains large numbers of mitochondria to generate heat by dissipation of the electron transport chain (ETC) proton gradients 38 , inguinal (beige, iWAT) and epididymal (white, eWAT) adipose tissue protects from calorie overload by removing nutrients from circulation and storage as energetic, indolent macromolecules such as triglycerides. Mitochondrial function in WAT is impaired in obesity 39 and reduced mitochondrial function in fat causes metabolic imbalance 40 – 43 . Mitochondrial DNA content, oxidative phosphorylation (OxPhos), abundance and composition of ETC complexes are all functionally impaired in obesity and morphological aberrations in mitochondrial structures are found in obesity 39 , 43 and aging 44 . Conversely, treatment with the AMP-activated protein kinase (AMPK) agonist Metformin improves mitochondrial function in adipose tissue, as do exercise or calorie restriction, and all these interventions correlate with metabolic improvement 45 . Recently, miRNAs were identified as important regulators of mitochondrial formation and function and known ‘mito-miRs’ like miR-143, miR-181a, miR-378 instigate, whereas miR-532 which impairs mitochondrial function by (post-transcriptionally) silencing mitochondrial translocation and mt-RNA homeostasis 46 – 48 . We and others showed that removing miRNAs from adipocytes by conditionally ablating DICER1, a central component of the conserved miRNA processing machinery, impairs adipogenesis, triggering lipodystrophy and metabolic dysfunction in mice 49 – 51 . Collectively, we report that obesity in male mice is linked to reversible miRNA, mRNA and proteins alterations in adipose mitochondrial gene regulation that match those in adipose tissue of lean isogenic F 1 progeny. Using integrative analyses of adipose, liver and sperm miRNA:mRNA gene networks, we identify let-7 as induced by obesity across cell types and generations and mechanistically implicate let-7 driven suppression of DICER1 and concomitant declines in mitochondrial function in adipocytes in inherited mouse phenotypes, thereby establishing sperm-born let-7 as intergenerational driver of mitochondrial dysfunction and glucose impairments in F 1 offspring. Results Paternal obesity elicits intergenerational impairments in adipose mitochondrial function Whilst intergenerational effects of paternal (F 0 ) obesity on offspring (F 1 ) phenotypes and metabolic health have been reported, the specific organ-level manifestations of this dysfunction in progeny, and the precise somatic and germ cell-intrinsic molecular mechanisms in obese mice and their descendants, remain surprisingly elusive. To investigate this using isogenic rodents ( Mus musculus ), we used diet-induced obese (DIO) and weight-regressed C57BL/6N male mice, and fed 6-week-old animals with 1) low-fat diet (LF) for 18 weeks (F 0 LF), 2) high-fat diet (HF) for 18 weeks (F 0 HF) or 3) HF for 9 weeks, followed by 9 weeks of LF-driven weight loss (F 0 HF-LF, Fig. 1a ) to allow at least three cycles of spermatogenesis. For each F 0 group, we mated eight randomly selected mice with unexposed lean virgin, female C57BL/6N mice, and exposed both male and female F 1 progeny of each group, i.e., F 1 (F 0 LF, n=44 pups), F 1 (F 0 HF, n=52 pups) and F 1 (F 0 HF-LF, n=38 pups) mice to LF after weaning to test for metabolic and metabolic sequelæ of F 0 diet exposure and removal. We did not additionally challenge F 1 mice with HF given that effects of HF and epigenetic F 0 effects were reported as cumulative with regards to F 1 phenotypes 52 ( Fig. 1b ). Noteworthy, we detected no alterations in fecundity, male-to-female pup ratio and litters sizes ( Fig.S1a,b ), suggesting that F 0 obesity did not affect development and embryonic implantation rates nor indirectly affected maternal provisioning after birth due to different litter sizes, two potential confounders. Download figure Open in new tab Figure 1: Paternal obesity elicits heritable impairments in glucose metabolism and adipose mitochondrial function. ( a,b ) Illustration of intergenerational in vivo cohorts depicting F 0 groups of (a) paternal (F 0 ) obesity and weight-regression, male and female F 1 offspring mice (b) and analytical workflows incl. RNA-seq for mRNA and sRNA, mass spectrometry for protein, and ultra-high pressure liquid chromatography (UPLC) for metabolite and lipid differential regulation in F 0 /F 1 epididymal white adipose tissue (eWAT), liver, sperm and serum. (c) Body weights of F 0 LF (n=6), F 0 HF (n=13; n=6 for 15-24 weeks of age), F 0 HF-LF (n=7, left ) and F 1 (F 0 LF, n=18), F 1 (F 0 HF, n=25) and F 1 (F 0 HF-LF, n=21, right ) male C57BL/6N mice. (d) Blood glucose during intraperitoneal glucose tolerance test in F 0 LF (n=6), F 0 HF (n=6) and F 0 HF-LF (n=7, left ) and F 1 (F 0 LF, n=10), F 1 (F 0 HF, n=10) and F 1 (F 0 HF-LF, n=10, right ) male C57BL/6N mice. (e) Blood glucose during intraperitoneal insulin tolerance test in F 0 LF (n=6), F 0 HF (n=6) and F 0 HF-LF (n=7, left ) and F 1 (F 0 LF, n=9), F 1 (F 0 HF, n=10) and F 1 (F 0 HF-LF, n=10, right ) male C57BL/6N mice. (f) Relative eWAT weights in F 0 LF (n=6), F 0 HF (n=6) and F 0 HF-LF (n=7, left ) and F 1 (F 0 LF, n=10), F 1 (F 0 HF, n=10) and F 1 (F 0 HF-LF, n=7, right ) male C57BL/6N mice. (g) Serum leptin levels measured by ELISA in F 0 LF (n=6), F 0 HF (n=6) and F 0 HF-LF (n=7, left ) and F 1 (F 0 LF, n=8), F 1 (F 0 HF, n=8), and F 1 (F 0 HF-LF, n=8, right ) male C57BL/6N mice. (h) Fractional area size distribution of eWAT adipocytes in (i) F 0 LF (n=6), F 0 HF (n=6) and F 0 HF-LF (n=6, left ) and F 1 (F 0 LF, n=6), F 1 (F 0 HF, n=6) and F 1 (F 0 HF-LF, n=6, right ) male C57BL/6N mice. (i,j) Representative haematoxylin and eosin staining of eWAT of ( i ) F 0 LF, F 0 HF and F 0 HF-LF and (j) F 1 (F 0 LF), F 1 (F 0 HF), and F 1 (F 0 HF-LF) male C57BL/6N mice. (k,l) Visualisation of high-confidence STRING interaction networks of proteins downregulated in ( k ) F 0 LF (n=6) vs F 0 HF (n=6) and ( l ) F 1 (F 0 LF, n=6), F 1 (F 0 HF, n=6) eWAT ( top ) and GOBP enrichment ( bottom ). Two-tailed Student’s t-test (c-e,h) for each timepoint and adipocyte sizes or one-way ANOVA followed by Tukey’s multiple comparisons (f-h) were used for statistical analysis. Data are presented as mean ± standard error with individual values shown for n≤10. P-Values are indicated in the panel or represented as letters ( a , p <0.05, F 0 LF versus F 0 HF or F 1 (F 0 LF) versus F 1 (F 0 HF); b , p <0.05, F 0 HF versus F 0 HF-LF or F 1 (F 0 HF) versus F 1 (F 0 HF-LF); c , p <0.05, F 0 LF versus F 0 HF-LF or F 1 (F 0 LF) versus F 1 (F 0 HF-LF)). When phenotyping F 0 and male F 1 animals for metabolic traits commonly associated with obesity and type 2 diabetes (T2D), we found that HF increased and ensuing HF-LF feeding reduced body weight to levels seen in F0 LF mice, whilst F 1 progeny were indistinguishable in terms of body weight ( Fig. 1c ). Intriguingly, male obesity impaired glucose tolerance ( Fig. 1d , Fig.S2a ) and insulin sensitivity ( Fig. 1e ) not only in F 0 HF mice, but also in their F 1 (F 0 HF) offsprings without changes in circulating F 1 insulin ( Fig.S2b ) and no alterations in liver and brown adipose tissue mass (not shown). In contrast, we found that obesity increased iWAT ( Fig.S2c ) and eWAT ( Fig. 1f ) masses and elevated serum levels of leptin, an important adipokine reflecting adipocyte size and numbers, in F 0 and F 1 mice ( Fig. 1g ). Adipocyte size determination using haematoxylin/eosine (H/E) staining of formalin-fixated, paraffine-embedded adipose sections demonstrated comparable shifts towards increased eWAT adipocyte sizes in F 0 and F 1 ( Fig. 1h -j ), suggesting that paternal obesity might promote lipid accrual/lipolysis as well as metabolic processes governing adipocyte health by similar molecular mechanisms in both generations and via this mechanism contribute to glucose intolerance in F 0 and male F 1 . When phenotyping female offsprings of lean, obese and weight-regressed F 0 fathers, we observed no changes in F 1 weight ( Fig. S2d ), mild glucose intolerance ( Fig.S2e,f ) and only trends towards changes in insulin sensitivity ( Fig.S2g ). In support of overall mild impairments in female offsprings, random fed glycemia ( Fig.S2h ) and organ weights ( Fig.S2i ) were also unchanged in female progeny. Thus, male obesity and weight loss reversibly affect glucose tolerance in F 1 , with metabolic effects predominantly observed in male progeny . For comprehensive insights into molecular processes affected by paternal obesity and weight loss, and those governing intergenerational effects, we performed bulk tissue mass spectrometry to determine differentially abundant proteins in F 0 and F 1 eWAT. We observed 980/431 proteins increased in F 0 HF compared to F 0 LF mice and in F 1 (F 0 HF) compared to F 1 (F 0 LF) mice. Conversely, 632/305 proteins were less abundant comparing F 0 HF to F 0 LF mice ( Fig.S3a ) and F 1 (F 0 HF) compared to F 1 (F 0 LF) mice ( Fig.S3b ). Using the STRING database ( https://string-db.org/ ) followed by Gene Ontology (GO) analysis of overrepresented biological processes (GOBP), we intriguingly saw that F 0 HF mice ( Fig. 1k ) and F 1 (F 0 HF) progeny ( Fig. 1l ) shared the same downregulated, mitochondria-associated GOBPs such as Oxphos, adenosine triphosphate (ATP) synthesis, and regulation of ETC subunits. Examples of proteins similarly repressed in F 0 HF and F 1 (F 0 HF) eWAT included components of ETC Complex I like NADH dehydrogenase 1 alpha subcomplex subunit 8 (NDUFA8) and NADH dehydrogenase iron-sulfur protein 6 (NDUFS6), of Complex IV like Cytochrome C Oxidase Subunit 6B1 (COX6B1), of mitochondrial membrane-intrinsic lipid carriers such as Carnitine Palmitoyltransferase 1A (CPT1A) and of mitochondrial protein translocation such as Translocase Of Outer Mitochondrial Membrane 20 (TOMM20). In contrast, upregulated GOBP terms in F 0 HF mice ( Fig.S3a ) and their F 1 (F 0 HF) progeny ( Fig.S3b ) included unrelated GOBP terms like protein translation, Golgi-mediated transport and secretion as well as protein localisation. Importantly, abundance of metabolite– ( Fig.S4a-c ) and lipid-based ( Fig.S4d,e ) biomarkers that can reflect systemic mitochondrial dysfunction, for instance tricarbon cycle acid (TCA) intermediates like citrate, malate, fumarate, alpha-ketoglutarate and citrate, were reduced by paternal obesity and restored by weight loss ( Fig.S4f ) and mitochondrial lipid carriers like (acetylated) carnitine ( Fig.S4g ) were reduced. Furthermore, saturated, short-chain C:16:0/C:18:0 ceramides, i.e., lipid species reported to disrupt mitochondrial function and elicit mitochondrial ER stress in obese adipocytes 53 , 54 were induced, whilst mito-protective long-chain C18:1/C24:1 ceramides were reduced in F 0 HF mice ( Fig.S4h ). Thus, male obesity downregulates proteins associated with mitochondrial processes in obese F 0 and F 1 offsprings that correlate with reduced levels of mitochondrial function and lipid catabolism, likely reflecting a system-wide hereditary form of mitochondrial dysfunction, also in adipose tissue . Paternal obesity negatively affects expression of mitochondria-associated miRNA-mRNA networks We next turned our attention to in detail study gene-regulatory processes at the messenger and miRNA level that might underly the observed repression of mitochondria-associated proteins in F 0 and F 1 adipose tissue, and to this end performed mRNA-seq of eWAT and liver of F 0 and F 1 groups and performed miRNA and mRNA differential expression analysis, specifically focussing on mitochondrial processes. Comparing F 0 HF to F 0 LF eWAT using adjusted p-Values (pV adj ) ≤0.05 we detected yielded 3,265 up-regulated and 2,890 down-regulated genes ( Fig. 2a ). When comparing F 1 (F 0 HF) to F 1 (F 0 LF) mice we detected no significantly regulated genes when accounting for multiple testing (pV adj ). Yet when specifically interrogating differential expression to reflect the more variable transcriptional effects in F 1 mice, we found 766 up-regulated and 893 down-regulated genes using unadjusted p-Values (pV) ≤ 0.05 ( Fig. 2b ). When performing gene set enrichment analysis (GSEA) of affected GOBP we found upregulated processes linked to cell proliferation and immune activation that likely reflect adipocyte hyperplasia and inflammation, thus cellular processes commonly associated with obesity, and GOBP associated with germ cell and cilial function in F 1 . Importantly, we confirmed our proteomic findings and detected overrepresented GOBP related to lipid catabolism, ATP synthesis, OxPhos and ETC downregulated in adipose tissue of F 0 HF mice ( Fig. 2c ) and their F 1 offsprings ( Fig. 2d ) and, when interrogating expression of mRNAs encoding mitochondrial ETC I-V, we confirmed the repression of nuclear-expressed mitochondrial constituents at the mRNA level ( Fig. 2a ,b ). Importantly, aberrant mitochondrial effects were to large extends reversible by weight loss, as comparing F 0 HF to F 0 HF-LF eWAT yielded 2,687 up-regulated and 2,237 down-regulated using pV adj ( Fig.S5a ), whilst comparing F 1 (F 0 HF) to F 1 (F 0 HF-LF) eWAT yielded 547 up-regulated and 732 down-regulated genes without adjusting for multiple testing, ( Fig.S5b ) and GOBP restored by weight loss also fell into genes linked to mitochondrial function ( Fig.S5c,d ). Download figure Open in new tab Figure 2: Paternal obesity dysregulates miRNA:mRNA gene networks spurring intergenerational mitochondrial dysfunction. ( a,b ) Volcano plot of significantly up– (grey) and down-regulated (white) mRNAs in eWAT from (a) F 0 HF versus F 0 LF with 3,495 upregulated and 1,639 downregulated mRNAs and ( b ) F 1 (F 0 HF) versus F 1 (F 0 LF) with 1,284 up– and 702 down-regulated mRNAs. Plots show log10 transformed fold-changes (fc) and log10-transformed (a) adjusted or (b) non-adjusted p-Values of mRNA changes. Mitochondrial complex I-V genes are annotated in the plot. (c,d) Gene Set Enrichment Analysis and gene ontology enrichment of biological processes (GOBP) of differentially expressed mRNAs from (a,b). GOBP linked to mitochondrial respiration, ATP synthesis and Complex I assembly are marked with blue bars. (e,f) Volcano plot of significantly regulated miRNAs in eWAT from (e) F 0 HF versus F 0 LF with 154 up– and 193 down-regulated miRNAs and ( f ) F 1 (F 0 HF) versus F 1 (F 0 LF) with 63 up– and 87 downregulated miRNAs. Plots depict log10 transformed fc and log10-transformed p-Values of expression changes. Mitochondria-activating miRNAs are annotated in pink. (g,h) STRING interaction networks of (g) miR-378/miR-143 and (h) miR-532-5p targets predicted by AGO2-CLIP ( left ) and network subset for eWAT proteins downregulated when comparing F 0 LF (n=6) versus F 0 HF (n=6) male C57BL/6N mice. Submodules containing mitochondrial proteins are marked by a red square and proteins marked by red dots are AGO2-CLIP predicted let-7 targets and downregulated in F0 HF versus F 0 LF eWAT. Given that miRNAs are important (rheostatic) components in post-transcriptional gene network and have previously been linked to mitochondrial maturation and function 55 , albeit not in adipocytes, we next wondered whether concerted down-regulation of mitochondrial programs in obese eWAT might (partially) be instigated by altered expression of mito-miRs 1 ’ like let-7 2 , miR-378 3 , miR-181 4 , miR-143 5 that functionally support mitochondrial formation and oxidative metabolism. Thus, when conducting small RNA (sRNA)-seq to determine differential miRNA expression, we found that F 0 obesity repressed mito-miRs that positively affect mitochondrial biogenesis and function, for instance miR-143-3p 56 , miR-181a-2-3p 57 , 58 , miR-182 59 , miR-200 60 and miR-378a-3p 61 in eWAT of F 0 HF ( Fig. 2e ) and F 1 (F 0 HF, Fig. 2f ) mice, whereas mitochondria-inactivating miR-532 62 was induced in F 0 HF ( Fig. 6c ) and F 1 (F 0 HF, Fig. 6d ) eWAT, suggesting that miRNA dysregulation contributed to mitochondrial impediments observed in obese F 0 eWAT. Integration of miRNA-mRNA NGS datasets with adipocyte-specific miRNA-mRNA-Argonaute 2 interactomes using AGO-HITS-CLIP 6 revealed that miR-378 and miR-143 , despite being predicted, did not coinidce with downregulation of their mitochondrial protein targets ( Fig. 2g ), whilst miR-532-5p was both predicted to target and observed to coincide with mitochondrial proteins repressed in F0 obesity, arguing for roles of miR-532, but not miR-143/378 in driving mitochondria dysfunction in F 0 HF eWAT ( Fig. 2h ). In line with body weight loss-driven mRNA transcriptional responses ( Fig.S6a,b ), miRNA alterations were highly dynamic, and weight loss restored expression of mitochondria-promoting in F 0 HF ( Fig.S5e ) and F 1 (F 0 HF, Fig.S5f ) mice. Thus, F 0 obesity causes reversible repression of mitochondrial and lipid catabolic genes, and miRNA-mRNA network analysis implicates obesity-regulated mito-miRs in this process . The specificity of obesity-induced heritable epigenetic mitochondrial alterations to adipose tissue We were in the following interested to address how confined the observed heritability of mitochondrial dysfunction was i. as a cellular and metabolic process and to ii. adipose tissue and performed mRNA-seq to assess the heredity of other obesity-associated processes such as chronic, low-grade inflammation (meta-inflammation). Focussing on expression of gene markers of classically (M1) and alternatively activated (M2) monocytes/macrophages, cell types that are linked to meta-inflammation-driven adipose tissue dysfunction 33 , we found that, as expected, mRNAs encoding M1 markers such as Interleukin 1b (IL1b) , Monocyte chemoattractant protein-1 (Mcp1/Ccl2) and Tumor Necrosis Factor Alpha (TNF) ; but also M2 markers like Arginase 1 (Arg1) , Mannose Receptor C-Type 1 (Mrc1) , C-Type Lectin Domain Containing 10A (Clec10a) and Resistin Like Beta (Retnla) were induced in F 0 HF, but not F 1 eWAT ( Fig.S6e,f ), emphasising that a meta-inflammatory tone is not passed on from F 0 HF mice. Given the dysfunction in a specialised cellular process like mitochondrial respiration in eWAT, we next aimed to juxtapose adipose tissue with transcriptional effects in liver, an organ essential for glucose and lipid homeostasis: Analysis of mRNAseq data using multidimensional scaling (MDS) plots showed that F 0 LF segregated from F 0 HF and F 0 HF-LF, arguing for mostly irreversible effects of obesity in F 0 liver ( Fig.S7a ) and modest effects of F 0 obesity on F 1 liver transcriptomes ( Fig.S7b ). When comparing F 0 HF to F 0 LF livers, we found 2,474 up– and 2,612 down-regulated genes with pV adj ≤0.05 ( Fig.S7c ), whereas comparing F 1 (F 0 HF) to F 1 (F 0 LF) without adjusting for multiple testing (pV ≤0.05) yielded 716up– and 546 down-regulated genes ( Fig.S7d ). Gene Set Enrichment analysis (GSEA) 63 of differentially regulated mRNAs identified lipid metabolism and organelle formation as induced, whereas repressed terms included lipoprotein trafficking, endoplasmic reticulum and ribosome, yet no transcriptional evidence for altered mitochondrial processes in liver of obese mice and their F 1 progeny ( Fig.S7e,f ), emphasising the specificity of mitochondrial alterations in obese fat. The non-hereditary effects of paternal obesity were supported by histological evidence, and pathologist assessment of H/E (morphology), Sirius Red (collagen/fibrosis) and anti-CD45 (leukocytes) staining revealed important hallmarks of metabolic dysfunction-associated steatosis liver disease (MASLD) like micro-/macro-vesicular steatosis, fibrosis and locular inflammation in F 0 HF mice, but not lean F 0 or F 1 descendants ( Fig.S8a-h ). Targeted analysis of expression changes in genes linked to hepatic lipid metabolism and meta-inflammation such as Adhesion G Protein-Coupled Receptor E1 (Adgre, F4/80) , Apolipoprotein A4 (Apoa4), Apolipoprotein E (Apoe), Low Density Lipoprotein Receptor (Ldlr), Peroxisome Proliferator Activated Receptor Gamma (Pparg), Cluster of Differentiation 36 (Cd36) and Sterol Regulatory Element Binding Transcription Factor 1 (Srebf/Srebp1) confirmed that induction was confined to F 0 HF livers ( Fig.S8i ). Furthermore, mitochondria-associated genes that were activated in obesity, presumably counteracting lipid accumulation in MASLD, for instance Carnithine Palmitoyltransferase 1A (Cpt1a) , Carnithine O-Acetyltransferase (Crat), Acyl-Coa-Oxidase (Acox1) and Acetyl-CoA-Acetyltransferase (Acat1) were induced in livers from F 0 HF, but not F 1 , mice. Thus, combinatorial analyses of transcriptional and histological changes argue against prominent effects of paternal obesity onto F 1 gene expression and mitochondrial processes in hepatic cell types . Paternal obesity causes qualitatively similar alterations in eWAT and sperm of obese male mice Spermic sncRNA were reported to be subjected to dietary cues such as high-fat and –sugar diets in mice and humans 22 ; and it is therefore conceivable that HF feeding and weight loss alter spermic sncRNA levels that can then, following oocyte fertilisation, be passed on to fertilised oocytes (zygotes) as shown recently 23 . By changing sRNA composition in zygotes, sncRNAs thus have the unique ability to post-transcriptionally affect decision points in embryogenesis, thereby contributing to the observed glucose intolerance and mitochondrial protein repression observed in F 1 offsprings sired by F 0 HF bucks. A particular aspect of sperm transcriptomes is the complex profile of sncRNAs involving miRNAs, ribosomal RNAs (rRNAs), PIWI-interacting RNAs (piRNAs), (mitochondrial) transfer RNAs ((mt)-tRNAs) and (mt)-tRNA fragments 26 that, as described by Tomar et al. 23 , are affected by mitochondrial dysfunction in obesity and, particularly relevant in this setting, are physically exchanged between sperm and oocytes during fertilisation, a scenario also conceivable for other types of mitochondria-associated sncRNAs, for example mito-miRs. To the map the effects of paternal obesity and weight loss on spermic sncRNAs and to gain insights into sRNA-driven metabolic adaptations in F 0 HF sperm, we next isolated motile spermatozoa using swim-up assays and charted differential spermic sncRNAs responses as consequence of body weight gain and losses and found sperm harboured complex sncRNA pools of thousands of (mt)-tRNAs, piRNAs, rRNAs and miRNAs ( Fig. 3a -c ). In addition to effects elicited by obesity and/or LF feeding at the single sncRNA level, we observed that (mt)-tRNAs and miRNAs as entire RNA biotype were repressed by HF feeding. As a prior study from Tomar et. al 23 had shown effects on mt-RNA in sperm after HF feeding, this was investigated further: Mt-tsRNA fragments were found for nine mt-tRNAs ( mt-Tl1 , mt-Tq , mt-Tm , mt-Ts1 , mt-Tr , mt-Th , mt-Ts2 , mt-Te , mt-Tp ), where mean cpm above 10 was observed in mt-Tm , mt-Ts1 , mt-Ts2 and mt-Th ( Fig. 9a ), but only 5’ fragments of mt-Ts-1 were significantly changed in sperm from F 0 HF and F 0 HF-LF diet ( Fig. 9b ). As miRNAs are abundant in somatic cell types and sperm, harbour the possibility of horizontal transfer between them, and exert gene-regulatory functions within both cellular compartments, we turned our attention to miRNA regulation in sperm. We found that F 0 obesity and weight loss caused a coordinated and inverse (i.e., downregulation and upregulation, respectively) effects on miRNome regulation ( Fig. 3d ,e ), a finding previously observed by our labs 51 and others 49 , 50 , 64 for obese adipose tissues; in the latter case at least partially due reduced abundance of DICER1. Intrigued by the repression of miRNAs in two functionally distinct, yet anatomically adjacent tissues and cell types like eWAT and sperm, we asked if differential miRNA responses were qualitative similar in both organ which, if present, would support the possibility of physical miRNA exchange between eWAT adipocytes and sperm as shown for other testicular cell types and sperm 21 : Indeed, upon comparison of lean, obese and weight-regressed sperm and eWAT miRNAs, we found highly congruent responses across both compartments with activating mito-miRNAs like miR-378 and miR-143 being down-regulated, whilst inactivating mito-miRs of the let-7 family up-regulated in obese F 0 eWAT and sperm ( Fig. 3f ) . Importantly, when broadly mapping the regulation of let-7 family isoforms, we found mild, but synchronous inductions of let-7 in F 0 eWAT and sperm and F 1 eWAT ( Fig. 3g -3i ). As increases of let-7c were reported in intergenerational rat studies by us 8 and others 65 and because let-7 impairs embryo implantation and glucose homeostasis when overexpressed in mice 66 , 67 , we focussed on let-7 isoforms for functional follow-up studies in germ cells and adipocytes. Thus, paternal obesity triggers similar alterations in adipose tissue and sperm miRNAs and let-7 isoforms are consistently induced across cell types and generations . Download figure Open in new tab Figure 3: Obesity reversibly affects sperm sncRNomes and induces let-7 in eWAT and sperm. (a) Size distribution on nucleotide length of biotypes present in F 0 HF-LF sperm. Data presented is mean cpm per diet. (b) Size distribution on nucleotide length of biotypes present in F 0 HF sperm. Data presented is mean cpm per diet. (c) Size distribution on nucleotide length of biotypes present in F 0 LF sperm. Data presented is mean cpm per diet. (d) Fold change of F 0 HF vs F 0 LF sncRNA in sperm. Each points represents one sequence. (e) Fold change of F 0 HF-LF vs F 0 HF sncRNA in sperm. Each points represents one sequence. Dark blue= Mitochondrial tRNA, dark green=piRNA, green=ribosomal RNA, light green =long non-coding RNA, yellow=mitochondrial ribosomal RNA, orange= miRNA, dark orange=protein coding, red= miscellaneous RNA, dark red= tRNA, grey=unannotated. (f) Heatmap depicting scaled abundances of miRNAs in F 0 LF, F 0 HF and F 0 HF-LF (n=6/group) eWAT and sperm as determined by sRNA-seq. (g-i) Abundance of miR-143-3p , let-7d-5p and in F 0 LF (n=6), F 0 HF (n=6) and F 0 HF-LF (n=6) eWAT and sperm. One-way ANOVA followed by Tukey’s multiple comparisons (g-i) were used for statistical analysis. Data are presented as mean ± standard error with individual values shown for n≤10. P-Values are indicated in the panel. Zygotic delivery of let-7 causes glucose intolerance and mitochondrial impairment in sired mice Prompted by profound and reversible effects of male obesity and weight loss on sperm miRNomes and let-7 in particular, as environmentally and artificially induced (e.g., elicited by miRNA microinjection) alteration in zygote miRNA (pools) trigger abnormalities in progeny 25 , 68 , and because embryonic trajectories require maturation of pre-into mature miRNAs 69 , we tested the functional relevance of let-7 gain-of-function in one-cell embryos for F 1 phenotypes. We focussed on let-7d-5p and let-7e-5p given that these exhibit the most concordant increases amongst let-7 family members in F 0 and F 1 eWAT and sperm ( Fig. 3g -i ) and because let-7 has before been connected to embryonic cell fate decisions 70 , 71 . To test if zygotic delivery of let-7 corresponding at levels seen in few obese spermatozoa triggers phenotypical and metabolic changes in mice sired from let-7 microinjected embryos (F MI generation) and whether these would resemble mice sired by F 0 HF fathers, we injected physiological amounts (50 fmol, i.e., let-7 found in 5-10 spermatozoa) of cel-mir-67 mimic as non-mammalian ( C.elegans ) control, and mimics for let-7d-5p and let-7e-5p by microinjection into zygotes conceived from lean males and females C57BL/6N mice using standard protocols 29 ( Fig. 4a ). We injected 126/138/106 zygotes with cel-mir-67 , let-7d-5p and let-7e-5p , but observed no discernible difference in implantation success, litter sizes and number of pregnancies in F MI mice ( Fig.S10 ), arguing against roles of let-7 for zygote viability and implantation. Next, we analysed at least 19 pups per group from 3-4 pregnancies but observed no overt differences in postnatal body weight trajectories ( Fig. 4b ). Despite similar weight, we detected impaired glucose tolerance ( Fig. 4c ,d ), insulin resistance ( Fig. 4e ) and elevated random-fed glycemia ( Fig. 4f ) in mice sired by let-7 injected F MI embryos, whilst eWAT and liver weights were indistinguishable between F MI groups ( Fig. 4g ). Importantly, when performing mRNA-seq in eWAT of F MI mice, despite mild changes, we found that let-7 down-regulated mitochondrial processes in F MI mice ( Fig. 4h ). Thus, zygotic delivery of let-7 at levels corresponding to few F 0 HF sperm cells, causes glucose intolerance and aberrant mitochondrial gene regulation in mice derived from these embryos . Download figure Open in new tab Figure 4: Zygotic delivery of let-7 causes glucose intolerance in sired F MI mice and impairs embryonic mitochondria. ( a ) Illustration of experimental workflow for zygotic miRNA injection and phenotypic characterisation of sired offsprings (F MI mice). (b) Body weights of male F MI mice sired from with cel-mir-67 (n=9), let-7d-5p (n=14) and let-7e-5p (n=14) injected zygotes. (c,d) Blood glucose levels (c) and glucose AUC (d) during intraperitoneal glucose tolerance test in male F MI mice sired from cel-mir-67cel-mir-67 (n=7), let-7d-5p (n=10) and let-7e-5p (n=10) injected zygotes. (e) Blood glucose levels during intraperitoneal insulin tolerance test in male F MI mice sired from cel-mir-67 (n=7), let-7d-5p (n=9) and let-7e-5p (n=11) injected zygotes. (f) Random-fed blood glucose at indicated in male F MI mice sired from cel-mir-67 (n=6), let-7d-5p (n=6) and let-7e-5p (n=6) injected zygotes (g) Relative weights of indicated organs from male F MI mice sired from cel-mir-67 (n=7), let-7d-5p (n=6) and let-7e-5p (n=6) injected zygotes. (h) GSEA-GOBP analysis of eWAT of male F MI mice sired from cel-mir-67 (n=5), let-7d-5p (n=6) and let-7e-5p (n=6) treated zygotes. GOBP linked to mitochondrial respiration, ATP synthesis and Complex I assembly are marked with blue bars. (i) Illustration of workflow to obtain single 8-cell stage blastomeres derived from zygotes injected with cel-mir-67 , let-7d-5p and let-7e-5p subjected to smart-seq2 single-cell sequencing. (j) Multidimensional scaling plots of single-cell RNAseq profiles in 8-cell stage blastomeres obtained from cel-mir-67 (n=22 blastomeres derived from n=3 zygotes), let-7d-5p (n=19 cells blastomeres from n=3 zygotes) and let-7e-5p (n=20 blastomeres derived from n=4 zygotes). (l,m) Correlation plot and regression analysis between differential BMI loss (ΔBMI) and expression changes (ΔCt) in human semen ( l ) LET-7D-5P and ( m ) LET-7E-5P. Two-tailed Student’s t-test (c,e) per timepoint, one-way ANOVA followed by Tukey’s multiple comparisons (d,g) or Pearson’s regression analysis (l,m) were used for statistical analysis. Data are presented as mean ± standard error with individual values shown for n≤10. P-Values are indicated in the panel or represented as letters ( a , p <0.05, cel-mir-67 versus let-7d-5p , b , p <0.05, cel-mir-67 versus let-7e-5p ). Obesity-associated let-7 rewires mitochondrial metabolism during embryogenesis As transfer of miRNA perturbation in sperm to oocytes can have deleterious consequences and give rise to incompletely developed embryos 20 , 72 , 73 , and as zygotes and blastocysts have the capacity to differentiate into virtually all cell types, (early) embryogenesis represents an important window of developmental plasticity that might affect F MI phenotypes if perturbed by miRNA delivery, despite the physiological levels of miRNA delivery and absence of embryonic lethality. Nonetheless, it is conceivable that alterations in zygotic miRNAs still entails far-reaching consequences in sired mice due to a combination of altered developmental, epigenetic and metabolic processes 73 . Based on the specific mitochondrial alterations, but absence of increased let-7 levels in F MI eWAT, we next hypothesized that let-7d-5p and let-7e-5p could carry out important roles in cell fate decisions during embryogenesis which will indirectly contribute to the glucose intolerance in F MI pups via altered embryonic development. To test this experimentally, we repeated zygotic let-7 injections but allowed miRNA-injected embryos to divide thrice until the 8-cell blastomere stage. We dissociated embryos into single blastomeres and performed single-cell RNA-Seq (scRNA-seq) using Smart-seq2 74 ( Fig. 4i ). Remarkably, we found that after three cell divisions, miRNA-injected blastomeres were still transcriptionally distinct, reflecting the profound transcriptional and developmental consequences of inducing let-7 in zygotes ( Fig. 4j ). When performing GSEA of let-7 -dependent gene sets, we corroborated that let-7d-5p repressed terms linked to mitochondrial processes like ATP synthesis, TCA cycle and ETC ( Fig. 4k ). Intergenerational programming in isogenic rodents has been described before, yet it is elusive if these effects are translatable to species with highly different reproductive apparatuses, genetic backgrounds, and timescales of obesity development like humans. Although epidemiological evidence 4 , 75 suggests heritable effects of overfeeding and malnutrition in humans (e.g., the famous ‘Dutch Hunger Winter’ 76 ), causal evidence for epigenetic inheritance is hard to separate from cultural and ecological inheritance in humans 2 . As it is becoming appreciated that nutritional and metabolic cues shape human sncRNA expression 22 , we tested effects of weight change on semen let-7 in a small cohort of n=15 obese men (BMI, 39.49 ± 1.80) undergoing voluntary weight loss 77 and quantified hsa-let-7 using TaqMan qPCR. Intriguingly, we found that hsa-let-7d-5p and hsa-let-73-5p followed the trends observed for weight regulation, suggesting similar correlations between metabolic improvement and let-7 reductions in human semen ( Fig. 4l ,m ). Thus, obesity-associated let-7d affects murine embryogenesis by impairing mitochondrial gene regulation and similar processes might be occurring following weight loss in obese humans . Adipocyte let-7 impairs mitochondrial function via post-transcriptional silencing of DICER1 Beyond F 0 HF sperm ( Fig. 3g -i ), we found let-7d-5p and let-7e-5p upregulated in F 0 /F 1 eWAT, and AGO2-CLIP interactome analyses confirmed that let-7 gene targets are enriched for genes encoding mitochondrial proteins, that are downregulated by obesity in F 0 /F 1 eWAT ( Fig. 5a ). For this, and to add to their previously ascribed role in fertilized oocytes, we aimed to dissect the transcriptional and functional consequences of elevated let-7 in adipocytes, with a specific focus on mitochondrial gene regulation as highly sequence-related let-7i can impair commitment of mitochondria-rich adipocytes 78 . Yet, if roles for let-7 in mitochondrial formation and function exist in mature adipocytes is unknown. To test this, we delivered by reverse lipofection mimetics for let-7d-5p and let-7e-5p and cel-mir-67 into stromal vascular fraction-derived mature epididymal white adipocytes (1°eWA) to mimic obesity-associated increased in let-7 in eWAT. When interrogating the effects of let-7 overexpression, we observed that let-7d-5p elicited more pronounced effects (1,768 up-regulated, 1,387 down-regulated genes, Fig. 5b ) than let-7e-5p (557 up-regulated, 893 down-regulated genes, Fig. 5c ) compared to cel-mir-67 , yet both let-7 isoforms downregulated processes related to lipid metabolism, Insulin-AKT (Protein Kinase B) signalling and carbohydrate metabolism ( Fig.S11 ). Although not overrepresented at the GOBP level, we found rate-limiting genes in glycolysis, TCA, and mitochondrial lipid import like Carnithine Palmitoyltransferase 2 ( Cpt2 ), Carnithine O-Acetyltransferase ( Crat ), Hexokinase 2 ( Hk2 ), Isocitrate Dehydrogenase 2 ( Idh2 ), Pyruvate Carboxylase ( Pcx ), Pyruvate Dehydrogenase Kinase 2 ( Pdk2 ) and mitochondrial ATP-magnesium/phosphate antiporter Solute Carrier Family 25 Member 24 ( Slc25a24 , Fig. 5b ,c ; in blue) repressed upon let-7 delivery. Importantly, we also detected pivotal genes in miRNA maturation such as Ribonuclease III DICER1 (Dicer1) and Argonaute RISC Catalytic Complex 2 (Ago2, Fig. 5b ,c ; in red ) . Next, we experimentally validated these predicted let-7 targets by dual luciferase reporter assays (DLRA)-based miRNA-mRNA 3’UTR interaction studies in HEK293 cells for genes linked to mitochondrial lipid and ATP uptake ( Slc25a24, Cpt2 ), glycolysis ( Hk2 , Pdk2 ), TCA activity ( Sdha , Fig. 5d ) and miRNA processing ( Dicer1, Ago2 ). Download figure Open in new tab Figure 5: Adipocyte let-7 blunts mitochondrial function via a novel let-7-DICER1 axis (a) Visualisation of high-confidence STRING interaction networks of let-7 targets predicted by AGO2-CLIP miRNA:mRNA interactions and subsetted for proteins downregulated comparing F 0 LF (n=6) versus F 0 HF (n=6) in eWAT from male C57BL/6N mice. Submodules containing mitochondrial proteins are marked by a red square and proteins marked by red dots are AGO2-CLIP predicted let-7 targets and downregulated in F0 HF versus F 0 LF eWAT. (b,c ) Volcano plot of significantly up-(grey) and down-regulated (white) genes in 1°eWA transfected with ( b ) let-7-d-5p versus cel-mir-67 , yielding 974 up– and 1,295 down-regulated genes and ( c ) let-7-e-5p versus cel-mir-67 , yielding 754 up– and 633 down-regulated genes. Genes connected to mitochondrial regulation are marked by blue, genes involved in miRNA maturation by red dots. (d) Relative luciferase activity in HEK293 cells after transfection of pmirGLO dual luciferase reporter assay (DLRA) constructs harbouring wild-type 3′-UTR or exon containing let-7 miRNA responsive elements (MRE) of indicated mitochondrial genes and ( e ) wild-type and mutated let-7 MREs in Dicer1 transfected with 100 nM of cel-mir-67 , let-7d-5p and let-7d-5p mimics. Data represent n≥3 independent biological replicates, each performed in n=3 technical replicates. (f-h) Western blot analysis of 1°eWA transfected with cel-mir-67 , let-7d-5p and let-7d-5p mimics performed using the indicated antibodies detecting (f) mitochondrial and (g) DICER1 proteins. HSC70 served as loading control. (h) Densitometric quantification of ( f,g) representing n=3 biological replicates. (i) Western blot ( top ) and quantitative RT-PCR ( bottom ) quantification of DICER1 in 1°eWA transfected using two independent locked nucleic acid (LNA) inhibitors targeting Dicer1 mRNA. Data represent n=3 biological replicates, each performed in n=3 technical replicates. (j,k) Oxygen consumption rates ( j, OCR) and extracellular acidification rates ( k , ECAR) in 1°eWA transfected with 100 nM of negative control LNA , Dicer1 LNA1 and Dicer1 LNA2 and stimulated with oligomycin (O), FCCP (F) and Antimycin A plus Rotenone (A/R). Experiments are representative of n≥3 independent biological replicates. (l) Western blot analysis of eWAT from F 0 LF (n=4), F 0 HF (n=4), F 0 HF-LF (n=4) male C57BL/6N mice performed using the indicated antibodies detecting mitochondrial and DICER1 proteins. HSP90 served as loading control. (m) Densitometric quantification of ( l) representing n=4 biological replicates. (n) Illustration of proposed molecular mechanism connecting obesity and elevated let-7 in adipocytes to post-transcriptional silencing of DICER1 and impaired oxidative phosphorylation and respiration. Two-tailed Student’s t-test (j,k) for each timepoint or one-way ANOVA ( d,e,h ) followed by Tukey’s multiple comparisons test were used for statistical analysis. Data are presented as mean ± standard error with individual values shown for n≤10. P-Values are indicated in the panel or represented as letters ( a , p <0.05, cel-mir-67 versus let-7d-5p ; b , p <0.05, cel-mir-67 versus let-7e-5p ). Dicer1 3’UTRs and exons harboured several independent let-7 seeds of which, after mutation of putative let-7 responsive elements (MREs), two from exonic regions became refractory to let-7 repression, demonstrating direct negative effects of let-7 on DICER1 translation ( Fig. 5e ). We strived to validate let-7 targets identified in HEK293 cells adipocytes, and delivered let-7 and control mimics to 1°eWA and confirmed that let-7 also repressed DICER1 in white adipocytes, yet caused only modest reductions in mitochondria-/TCA-/glycolysis-associated proteins upon overexpression ( Fig. 5f -h ), prompting us to hypothesize that the chronic let-7 induction seen in obese adipocytes might repress mitochondrial function rather via sustained DICER1 loss than by direct let-7 effects on mitochondrial function; and that Dicer1 inactivation might be a better proxy for mimicking the prolonged upregulation in let-7d-5p and let-7e-5p in obesity than acute overexpression of the miRNA. To address if Dicer1 silencing because of sustained let-7 upregulation is sufficient to phenocopy in vitro the obesity-evoked impediments in mitochondrial function, we designed two independent Dicer1- targeting locked nucleic acid (LNA) inhibitors that repressed Dicer1 at the mRNA and protein level, yet with varying efficiencies ( Fig. 5i ) and interrogated mitochondrial respiration using Seahorse. We found that Dicer1 knockdown reduced oxygen consumption rates (OCR) as proxy for mitochondrial respiration ( Fig. 5j ) and extracellular acidification rates (ECAR) as proxy for glycolytic rates in 1°eWA ( Fig. 5k ) supporting our hypothesis that obesity impairs mitochondrial respiration and gene regulation, at least partially, by post-transcriptional silencing of DICER1 via let-7 ( Fig. 5l ). Thus, obesity-associated let-7 impairs mitochondrial oxidative respiration by silencing of DICER1 in mature adipocytes . Discussion High transcriptional plasticity of mitochondrial dysfunction in adipose tissue In our study we found that hepatic expression changes inflicted by HFD feeding where to a large extend irreversible, whereas adipose tissue miRNA/mRNA transcriptomes and translatomes, and particularly mitochondrial OxPhos and TCA gene sets, reverted to a state ante . Similar studies have investigated the effects of reversible (i.e., diet– and exercise-evoked weight gain and loss) on hepatic and adipose transcriptomes: Gonzalez-Francesa et al. observed 42 that sustained diet-induced weight loss correlated with appreciable adipose transcriptional plasticity and mitochondrial gene expression, where short-term, exercise induced did not. Thus, durations of weight loss, and differences in control diet exposure might reflect the importance of nutritional and exposure factors during weight loss interventions on tissue responses in obesity. Despite not performing profiling of chromatic accessibility, transcriptional factor (TF) binding site enrichment analysis revealed that transcriptional activities of mitochondrial biogenesis factors like Nuclear Respiratory Factor 1 ( Nrf1 ) and expression of transcriptional (co)-regulators such as Peroxisome proliferator-activated receptor gamma coactivator 1-alpha ( Ppargc1a ) and Nrf1 79 were repressed in obese fat (not shown). As Brandao et al. reported 80 that ablation of DICER1 in adipocytes repressed Ppargc1a / Nrf1 , we can conceive a scenario where reduced DICER1 (for instance due to increased let-7d/e-5p ) drives and/or synergizes with reduced Ppargc1a / Nrf1 levels to impair mitochondrial biogenesis in hypercaloric adipocytes. Rewiring of embryonal development by gametic miRNAs In C. elegans and D. melanogaster , germline gene silencing by small Noncoding RNAs (sncRNA) is well-understood and similar processes are suggested for mammals 19 . miRNAs are not only critical for sperm cell formation 27 and embryogenesis 28 , but can also be transferred from sperm to zygotes and it is plausible that oocyte-sperm fusion alters zygotic miRNA pools, affect embryonic development, and instructs phenotypic traits and somatic gene expression in offspring as observed in our intergenerational paradigm. In contrast to DNA-intrinsic epigenetic modifications like DNA methylation (DNAme) and histone post-translational modifications, miRNAs diffuse between and across cellular compartments, are highly abundant in most cells, and, crucially, can be exchanged between cells through extravesicular transport 32 . As mature sperm is transcriptionally inert, and no mechanism exists how sperm senses endocrine information and translates this into differential miRNA levels, sperm relies on ‘decoding somatic cell types’ that possess these sentinel features and are capable of instructing sperm by transfer of miRNAs and other sncRNAs like mitochondrial tRNA fragments that are exchanged between sperm and oocyte at fertilisation 23 . Furthermore, epididymal cells, somatic epithelial cells of the extragonadal male reproductive tracts, secrete epididysomes , microvesicles that fuse with nascent sperm 81 , and as we observed several miRNAs concordantly regulated in F 0 adipose tissue and sperm, horizontal miRNA transfer from adipocytes to sperm, potentially via epididysomes is a plausible scenario that still lacks experimental proof in our paradigm. Finally, altering miRNA levels like miR-199a-5p in zygotes by microinjection is sufficient to reprogram embryonal metabolism 82 , highlighting that miRNA alterations can rewire embryonal development and metabolism which are still compatible with fetal development. Methods Mice All animal experiments were approved by the Ministry of Environment and Agriculture Denmark (Miljø-og Fødevarestyrelsen), the license no. 2018-15-0201-01544. All mice were housed under a 12-h light/ 12-h dark cycle in a temperature and humidity-controlled facility and had ad libitum access to diets and drinking water. 4-week-old C57BL/6J male mice (Janvier Labs) were fed with a chow diet (NIH-31, Zeigler Brothers Inc., 8% calories from fat) for 2 weeks of acclimatization and then were fed with either a low-fat diet (LF, D12450J, 10% kcal from fat, Research Diet) or a high-fat diet (HF, D12492, 60% kcal from fat, Research Diet) for 9 weeks. After 9 weeks of feeding, half of HF-fed mice were randomly assigned to the diet regression group which was given LFD. LFD is carefully chosen to match food source (animal vs plant) and micronutrient composition of HFD. Mouse husbandry All male virgin mice were maintained on the assigned diet until 21 weeks of age and mated with 10-week-old C57BL/6J female virgin mice (Janvier Labs). 8-week-old C57BL/6J female mice were fed with a chow diet for 2 weeks of acclimatization and then were fed with LF during breeding and lactation. Mating was set up for 5 days in a female’s cage and vaginal plugs were checked every morning. All mice, including the F 0 HF group, were maintained on LF during breeding to remove confounding effects from maternal diets. Males were sent from breeding cages to the original cage to be fed with the assigned diet for additional 2 weeks and then sacrificed. The offspring from all groups of founders consumed only LF. Representative male offspring from six litters per group were included in all analyses. Intraperitoneal glucose and insulin tolerance tests Mice were fasted for 6 h for glucose-tolerance tests. Fasting glucose levels were measured and then mice were intraperitoneally injected with 2 g/kg body weight of 20 % glucose solution (B. Braun Medical A/S). For intraperitoneal insulin-tolerance tests, random-fed glucose levels were measured, and mice were intraperitoneally injected with 0.75 IU/kg body weight of 100IU stock insulin (Novo Nordisk). Blood glucose levels were measured at 15, 30, 60, 90, and 120 min after injecting glucose or insulin using a CONTOUR®NEXT glucometer (Bayer). For ipGTT/ipITT, we omitted mice with strongly delayed rises in glucose and no discernible drop in blood glucose, respectively, from analyses as this was the consequence of mal injection outside the peritoneum. Tissue collection Mice were sacrificed by carbon dioxide euthanasia. Livers, eWAT, iWAT and BAT were weighed, snap-frozen in liquid nitrogen, and stored in –80 °C. Pieces from liver and eWAT were fixed in 4% paraformaldehyde (PFA) for histology analysis. For serum collection, blood collected by the cardiac puncture was centrifuged at 2,000 g for 15 min. The supernatant was collected and stored in –80 °C. Sperm collection Mature sperm cells were collected from the cauda epididymis. Briefly, sperm cells are allowed to swim out from the cauda epididymis and incubate in PBS at 37 °C for 15 min. Supernatant containing sperms was passed through a 40-µm cell strainer and then incubated in a somatic cell lysis buffer (0.5 % Triton-X and 0.1 % SDS) for 40 min on ice. Sperms were centrifuged at 600 g for 5 min and the pellets were washed with PBS and pelted again at 600 g for 5 min. The supernatant was discarded, and the pellets were snap-frozen in liquid nitrogen and stored at –80 °C. Zygotic miRNA microinjection and embryo implantation Ovulation of prepubescent (4-week-old) C57BL/6J females was induced by intraperitoneal (IP) injection of PMSG, 5 IU/female, followed by IP injection of hCG, 5IU/female, 47h later. After the second injection, females were set in breeding with C57BL/6J stud males. Next morning, they were monitored for formation of a vaginal plug (indicating mating has occurred during the night). Females were euthanized and oviducts were dissected to harvest the cumuli containing zygotes and unfertilized oocytes. Cumuli were disaggregated by incubation in a 10mg/ml solution of Hyaluronidase (Sigma-Aldrich ref. H4272) in M2 medium (Sigma-Aldrich ref. M7167) at RT for 10 minutes. Zygotes were sorted out, assessed by the presence of the second polar body and cultured in KSOM medium (Merck Millipore ref. MR-106-D). Oocytes were discarded. Each miRNA was diluted at 10 ng/ul in microinjection buffer (5 mM Tris, 0.1 mM EDTA, pH 7.4) as stock solution and kept at –80 °C. The day of microinjection, 1,5 ng/ul solutions were prepared from the stock, and injected in the pronuclei of the zygotes following standard microinjection procedures. miRIDIAN mimics were ordered from Dharmacon: Inert miRNA control is based on C.elegans cel-mir-67 which is not expressed in mouse or human cells (5‘-UCACAACCUCCUAGAAAGAGUAGA-3‘, cat. no. CN-001000-01-05), mmu-let-7d-5p (cat. no. C-310486-07-0002) and mmu-let-7e-5p (cat. no. C-310507-07-0005). Injected zygotes were cultured in KSOM until the microinjection session was completed and then cryopreserved by the “Slow Freezing” Method (Manipulating the Mouse Embryo, a Laboratory Manual, fourth edition, Chapter 16, Richard Behringer et al. Cold Spring Harbor Laboratory Press, 2014) and transported to SDU at –150 °C for embryo transfer and recovery. The frozen microinjected embryos were thawed at SDU and cultured in KSOM (Merck, MR-121-D) for 40 minutes (Manipulating the Mouse Embryo, a Laboratory Manual, fourth edition, Chapter 16, Richard Behringer et al. Cold Spring Harbor Laboratory Press, 2014). Viable embryos with intact zona pellucida were selected for further transfer. These embryos were then washed in 10 drops of M2 and surgically transferred to the oviducts of 0.5 dpc pseudo pregnant females. Pseudopregnancy was induced in the females by mating them with a vasectomized male. Prior to implantation, the pseudo pregnant females were anesthetized with ketamine (100 mg/kg) and xylazine (10 mg/kg) through intraperitoneal injection, and the surgical embryo transfer procedure was performed following the protocol described by Behringer et al. (Manipulating the Mouse Embryo, a Laboratory Manual, fourth edition, Chapter 6, Richard Behringer et al. Cold Spring Harbor Laboratory Press, 2014). Postoperative analgesic treatment with carprofen (5 mg/kg) was administered subcutaneously for 48 hours. 8-cell-morula blastomere disaggregation miRNAs were injected in the embryo at zygote stage as described above. After injection, embryos were incubated in KSOM (KSOM-Embryomax advanced embryo medium, Merck Millipore #MD-101-D) for 48-56 hours at 37 °C, in 5% C0 2 . The culture medium was refreshed daily. In the afternoon of the third day of culture, when the embryos had reached the 8-cell morula stage, they were incubated at RT in Tyrod’s solution (Sigma-Aldrich T1788) for up to 30 seconds until the zona pellucida was completely dissolved. Afterwards the naked morulae were washed through three drops of M2 medium (Sigma-Aldrich M7167) and finally incubated in KSOM without Ca 2+ and Mg 2+ at RT for 20 minutes. Then, embryos were moved to a new drop of fresh M2 medium and there, pipetted up and down repeatedly to facilitate the blastomere disaggregation. When single blastomeres were obtained, they were individually transferred to single wells of a 96-well single-cell-sequencing plate from VWR (Cat. No. 82006-636) with 2 ul of RNAse inhibitor mix (containing 0.12 ul Triton X-100 10 %, 0.1 ul RNAse inhibitor (Takara (Cat. No. 2313B, 40 U/ul), 1.78 ul MilliQ water (RNAse-free)). Plates were them frozen at –80 °C until the sequencing was performed. Lipidomics and Metabolomics Serum Extraction 100 µL of serum was mixed with 1000 µL of a 2:1 chloroform/methanol solution and 200 µL of water. Four blank samples, each containing 100 µL of water, were processed in parallel. The mixtures were shaken at 1000 rpm for 30 minutes at 4°C, followed by centrifugation at 16,000g for 10 minutes at 4 °C to separate the phases. The aqueous phase was re-extracted with 350 µL of an 86:14:1 chloroform/methanol/water solution. The organic phase from this re-extraction was combined with the corresponding organic phase from the initial extraction. Aqueous fraction was dried using SpeedVac, while the organic phase was dried under a stream of nitrogen. For subsequent analysis, the dried aqueous fractions were reconstituted in 30 µL of 1 % formic acid for metabolomics. The organic phase was reconstituted in 30 µL of lipidomic solvent A (5:1:4 isopropanol/methanol/water with 5 mM ammonium acetate and 0.1 % acetic acid). A pooled quality control sample was prepared for each analytical setup by combining 5 µL from each sample, excluding blanks. Lipidomic analysis For lipidomic analysis, 2.5 µL of each sample was injected into an Agilent 1290 Infinity UPLC system (Agilent Technologies, Santa Clara, CA, USA) equipped with a Zorbax Eclipse Plus C18 guard column (2.1 × 5 mm, 1.8 µm) and an analytical column (2.1 × 150 mm, 1.8 µm), maintained at 50 °C. Analytes were eluted at a flow rate of 400 µL/min using eluent A (water with 5 mM ammonium acetate and 0.1 % acetic acid) and eluent B (99:1 isopropanol/water with 5 mM ammonium acetate and 0.1 % acetic acid). The gradient was as follows: 0 % B from 0 to 1 min, 0-25 % B from 1 to 1.5 min, 25-95 % B from 1.5 to 12 min, 95 % B from 12 to 14 min, and 95-0 % B from 14 to 15 min, followed by a 3-minute equilibration. For metabolomics analysis, 1.5 µL of each sample was injected into the same UPLC-MS system, but with a ZORBAX Eclipse Plus C18 guard column (2.1 × 50 mm, 1.8 µm) and an analytical column (2.1 × 150 mm, 1.8 µm) maintained at 40°C. Eluent A was 0.1 % formic acid, and eluent B was 0.1 % formic acid in acetonitrile. The gradient was as follows: 3 % B from 0 to 1.5 min, 3-40 % B from 1.5 to 4.5 min, 40-95 % B from 4.5 to 7.5 min, 9 5% B from 7.5 to 10.1 min, and 95-3% B from 10.1 to 10.5 min, followed by a 3.5-minute equilibration. Both analyses were performed on a 6530B quadrupole time-of-flight mass spectrometer (Agilent Technologies, Santa Clara, CA, USA) for mass spectrometric detection in positive and negative ion modes. MS1 mode settings included a scanning range of 50-1700 m/z for lipidomic and 10-1050 m/z for metabolomics, with 2 scans/sec. Internal calibration was performed using Hexakis (1H,1H,3H-tetrafluoropropoxy) phosphazene delivered through a second needle in the ion source via an isocratic pump running at 20 µL/min. Iterative data-dependent MS2 mode was used for fragmentation in both analyses with the following settings: collision energy at 40 V (lipidomics) and 20-40 V (metabolomics), precursor threshold at 5000 counts, and active exclusion after 2 spectra, with re-inclusion after 0.5 min. Data Processing and Normalization All data was converted. mzML format using ProteoWizard 83 . Lipidomics data was annotated in MS-Dial (v. 4.9) against the incorporated lipid-blast 84 with an MS1/MS2 mass tolerance of 0.005/0.01 Da and a minimum identification score of 70 %. The annotated compounds were exported to PCDL manager B.08.00 (Agilent Technologies) to create a database for area extraction in Profinder 10.0 (Agilent Technologies). Metabolomics data was processed in MzMine (v2.59) utilizing modules such as ADAP chromatogram builder and deconvolution. Compounds were annotated at Metabolomics Standards Initiative (MSI) levels 3 using the libraries of National Institute of Standards and Technology 2017 (NIST17) and MassBank of North America (MoNa). Features with signals less than 5 times those in blanks or missing in more than 20 % of QC samples were removed and signals were QC corrected using statTarget 85 . Data analysis For metabolomics pathway enrichment analysis (KEGG pathways of Mus Musculus) comparing F 0 LF and F 0 HF serum (parameters ‘Scatter plot’, ‘Hypergeometric test’, ‘relative-betweenness Centrality’, ‘Use all compounds in the selected pathway library’) of differentially abundant polar metabolites (pV ≤ 0.1) were loaded into Metaboanalyst 6.0. For lipidomics and metabolomics principal component analysis and heatmap representation of indicated no. of differentially abundant lipids and metabolites were generated using ‘Statistical Feature’ (one-factor option), Sample normalization = ‘Normalization by sum‘, Data transformation = ‘Log transformation (base 10)’ and Data scaling = ‘Mean Centering‘. Serum insulin and leptin ELISA Serum insulin and leptin levels were measured by mouse insulin ELISA kit (Mercodia, Uppsala, Sweden) and mouse leptin ELISA kit (Crystal Chem, IL, USA) according to manufactures’ protocol. Histology and staining Liver and adipose tissues fixed with 4 % of paraformaldehyde were used for histology analyses. Formalin-fixed, paraffin-embedded tissues were cut into 3 μm sections. Sections were stained by haematoxylin and eosin (H&E) and Sirius Red by standard procedures at the Department of Pathology at Odense University Hospital, Odense, Denmark. Immunohistochemistry for CD45 was performed on Ventana BenchMark Ultra (Roche) with ready-to-use CD45 (LCA) (2B11 & PD7/26) Mouse Monoclonal Antibody. A liver pathologist (SD) assigned steatosis, lobular inflammation, ballooning, fibrosis stage, and portal inflammation. Steatosis was graded as S0 (33-66%), and S3 (>66% hepatocytes with large fat vacuoles) 86 . Lobular inflammation (0-3) and hepatocellular ballooning (0-2) were assessed according to the NAS-CRN Activity Score by Kleiner et al. 86 . Also, portal inflammation was assessed, using a 4-tiered score by Kleine et al. Liver fibrosis stage was assessed according to NAS-CRN for fibrosis; F-0, no fibrosis; F-1, perisinusoidal or periportal fibrosis; F-2, perisinusoidal and portal/periportal fibrosis; F-3, bridging fibrosis; F-4, cirrhosis. Measurement of adipocyte volumes The volume weighted mean volume of adipocytes was determined from the H&E sections with newCAST stereology software (Visiopharm), autodisector; measurements on systematic, randomly selected fields were performed on by at least 75 adipocyte profiles in each tissue. Bulk proteomics from epididymal white adipose tissue Deep frozen fat pads (200 mg of epididymal adipose tissue) were powdered with mortar-pestle and subjected to homogenization with ceramic beads (Percellys, 5500 rpm, 2 x 10 seconds, 30 seconds break) in the buffer containing 50 mM HEPES pH 7.4, 1 % Triton X-100, 100 mM NaF, 10 mM sodium orthovanadate, 10 mM EDTA, 0.2 % SDS, 100 mM NaCl. Tissue homogenates were further processed to remove the nucleic acid traces (Diagenode SA; 10 min, cycle 30/30 seconds, at 4 °C). Floating fat and tissue debris were removed from protein extracts by centrifugation at 20000 x g. 150 mg of proteins were precipitated with 4x volume of ice-cold acetone and washed with ice-cold acetone twice. Air-dry pellets were resuspended in 8M Urea/50 mM triethylammonium bicarbonate (TAEB) buffer supplemented with a protease inhibitor cocktail (Sigma). 50 mg of the samples were incubated with 5 mM dithiothreitol at 25°C for 60 min, followed by treatment with 40 mM chloroacetamide for 30 min (room temperature, light-protected). Lys-C protease (enzyme: substrate ratio of 1:75) was added for 4 hours at 25 °C. The TAEB-diluted sample (2M Urea final) was supplemented with trypsin (1:75 ratio) followed by overnight digestion at 25 °C. Acidified peptides (1 % formic acid final) were purified on the SDR-RP (C18) multi-stop-and-go-tip (Stage Tip) 87 . LC MS/MS Label-Free Quantitative Proteomic Analysis from epididymal white adipose tissue All samples were analyzed by the Proteomics Facility on a Q Exactive Plus Orbitrap mass spectrometer coupled to an EASY nLC 1000 (Thermo Scientific). Peptides were loaded with solvent A (0.1 % formic acid in water) onto an in-house packed analytical column (50 cm – 75 µm I.D., filled with 2.7 µm Poroshell EC120 C18, Agilent) and separated with 150 min gradients. Peptides were chromatographically separated at a constant flow rate of 250 nL/min using the following gradient: 4-6 % solvent B (0.1 % formic acid in 80 % acetonitrile) within 5.0 min, 6-23 % solvent B within 120.0 min, 23-54 % solvent B within 7.0 min, 54-85 % solvent B within 6.0 min, followed by washing and column equilibration. The mass spectrometer was operated in data-dependent acquisition mode. The MS1 survey scan was acquired from 300-1750 m/z at a resolution of 70,000. The top 10 most abundant peptides were isolated within a 2.1 Th window and subjected to HCD fragmentation at a normalized collision energy of 27 %. The AGC target was set to 5E5 charges, allowing a maximum injection time of 60 ms. Product ions were detected in the Orbitrap at a resolution of 17,500. Precursors were dynamically excluded for 20.0 s. All mass spectrometric raw data were processed with MaxQuant 88 (version 1.5.3.8, using default parameters and searched against the canonical murine Uniprot reference proteome (UP589, downloaded on 28/08/2020). Label-free quantification option was enabled as was the match-between runs option between replicates. Follow-up analysis was done in Perseus 89 1.6.15. Hits from the decoy database, the contaminant list, and those only identified by modified peptides were removed. Afterwards, results were filtered for data completeness in replicate groups and LFQ values imputed using sigma downshift with standard settings. Finally, FDR-controlled T-tests between sample groups were performed with s0 = 0.2. Proteins were annotated on GOBP, GOCC, KEGG and Pfam terms. Afterwards, all one-way ANOVA (s= 0.2) significant candidates were Zscored and hierarchically clustered using the Euclidean distance model. Resulting clusters were merged onto the whole data set and a Fisher Exact Test calculated on cluster level with the whole dataset as background. RNA isolation from mouse tissue and primary adipocytes Total RNAs from tissues, sperm, and primary adipocytes were isolated using the TRI reagent (Sigma) followed by clean-up with RPE buffer (Qiagen, Germany). The quality of RNA was validated by the Agilent RNA 6000 Nano-Kit in Agilent 2100 Bioanalyzer according to the manufacturer’s protocol. (Agilent Technologies, Waldbronn, Germany). RNA sequencing Small RNA sequencing: (1) For sRNA-seq from F 0 sperm, library preparation and sRNA-seq was performed at Cologne Center for Genomics, Germany. 50 ng of total RNA was used for library preparation followed by size selection (Small RNA-Seq Library Prep Kit, Lexogen). The library was sequenced in 1×50-bp single-end reads on NovaSeq6000 serie no. A00316. (2) Small RNA sequencing from F 0 and F 1 eWAT was carried out after quality control, 125 ng of total RNA was used for library preparation (Nextflex library kit) followed by sequencing by 1×50-bp single-end reads on Illumina Novaseq 6000. mRNA sequencing: mRNA sequencing was performed in-house. After quality control, 1 µg of total RNA was used for library preparation (NEBNext RNA prep kit) followed by 2×50bp paired-end sequencing on Illumina Novaseq 6000. RNAseq data analysis Small RNA sequencing of spermatozoa SncRNA-seq analysis of murine sperm was analyzed with Seqpac ver. 1.2.0, where sncRNA biotypes were annotated with 1 mismatch against Ensembl protein and non-coding RNA from GRCm39, miRbase, piRNA from piRBase ver. 3.0, tRNA from tRNAscan-SE, rRNA from RNAcentral and mitochondria from GRCm39:MT. Biotypes were assessed based on the following hierarchy: miRNAs, mitochondrial rRNAs, rRNAs, mitochondrial protein-coding mRNAs, mitochondrial tRNAs, tRNAs, piRNAs, nuclear protein coding mRNAs, lncRNAs, and miscRNAs. Baseline filtering of 1 read per sample and size of 15 to 75 nt long was performed. Normalization was performed with counts per million (cpm). Small RNA sequencing of somatic (adipose) tissue Adapter sequences were trimmed from raw miRNA-seq reads using Cutadapt. The processed reads were mapped to the reference genome and aligned using the Bowtie algorithm within the miRDeep2 pipeline 90 . Normalization was performed using the Trimmed Mean of M-values (TMM) method to account for sequencing depth and composition bias. Subsequent data analysis, including differential expression analysis, was conducted in R 91 using respective Bioconductor packages 92 . mRNA sequencing Sequencing reads were aligned to the mouse reference genome from Gencode (vM25) 93 using STAR aligner (v 2.7.9a) 94 and gene features were counted by using featureCountsv (2.0.3) 95 . The genes with low expression were filtered out using filterByExpr function with default settings from edgeR (v4.0.16) 96 R package. Differential expression analyses were performed using edgeR’s voomLmFit 97 unction with sample quality weights (sample.weights = TRUE) and linear model “0 + group”, whereas group represents the combination of all experimental groups of interest. The experimental conditions and linear models used for each dataset are summarized below. Genes with adjusted p-values below 0.05 were considered as differentially expressed. Gene enrichment analyses were performed by clusterProfiler (v4.10.1) 98 R package using both over-representation analysis (ORA) and gene-set enrichment analysis (GSEA) using differentially expressed genes and log-fold-change (logFC) ranked genes (n permutation = 1,000,000), respectively. Gene sets were retrieved from Gene Ontology (GO.db v3.18.0) and Reactome (reactome.db, v1.86.2) databases. Smart-seq2 mRNA sequencing of murine blastomeres Custom-made Smart-seq2 protocol was implemented as before 74 . Briefly, after thawing the plate, 1 µL of dNTP mix (10 mM), 0.1 µL of oligo-dT (100 µM, /5Biosg/AAGCAGTGGTATCAACGCAGAGTAC(T)30VN-3′) and 0.9 ul nuclease-free water was added to each well and the lysis protocol was run when the samples were incubated at 72°C for 3 min in thermocycler. Samples were then put on ice for at least 1 min. Next, 5.60 µL of reverse-transcription mix containing 0.25 µL of RNase I, 2 µL of 5× first-strand buffer, 2 µL of betaine (5 M), 0.06 µL of MgCl 2 (1 M), 0.5 µL of DTT (100 mM), 0.5 µL of SuperScript III (200 U/µL), and 0,29 ul of nuclease-free water was added to each well and reverse transcription was carried out as follows: 42 °C-90:00, 42 °C-hold, 42 °C—12:20, 10 cycles (50 °C-2:00, 42 °C-2:00), 39 °C-12:00, 70 °C-15:00, 4 °C hold. After the first 90 min of RT, 0.4 µL of 12,5 µM TSO (0.83 µM final concentration) was added to each reaction at room temperature. The plate was resealed, centrifuged for 1 min at 700 g, and placed back on the thermocycler to resume cycling from 42 °C-12:20. After reverse transcription, 15 µL of cDNA enrichment PCR reaction mix containing 12.5 µL of 2× KAPA HiFi HotStart ReadyMix, 0.25 µL of ISPCR primer (10 µM, /5Biosg/AAGCAGTGGTATCAACGCAGAGT-3′), and 2.25 µL of nuclease-free water was added to each well and cDNA enrichment PCR was performed as follows for 21 cycles: 98 °C-3:00, 7 cycles (98 °C-0:20, 60 °C-4:00, and 72 °C-6:00), 7 cycles (98 °C-0:20, 64 °C-0:30, and 72 °C-6:00), and 7 cycles (98 °C-0:20, 67 °C-0:30, and 72 °C-7:00), 72 °C-10:00, 4 °C-hold. The cDNA purification was performed using AMPure XP beads at a ratio of 1:1 (Beckmann). For elution, Qiagen elution buffer (EB) was used. cDNA concentrations were measured using the Qubit 3.0 fluorometer according to the manufacturer’s protocol, and all cDNA samples were analyzed on Agilent Tape station using the High Sensitivity DNA assay. Dual-indexed Illumina Nextera XT sequencing libraries were prepared using 25 % of the recommended reaction volumes of Nextera XT components. All Nextera XT libraries were prepared using 125 pg of input cDNA for tagmentation, and were subsequently enriched for 12 PCR cycles, and purified using AMPure XP beads (Beckmann) at a sample:bead ratio of 0.6:1. Library concentrations were measured using Qubit 3.0 fluorometer, and the fragments were analyzed on the Agilent Tape station using the High Sensitivity DNA assay. All libraries were individually normalized and diluted to a concentration of pooled library to 5.4 ng/µL with average size ∼515bp (16 nM). The libraries were sequenced on Illumina NextSeq500 in a single-end 75-bp format (FC-404-2005, Illumina). Single-nuclei libraries were sequenced at an average depth of 3 million reads. Data availability – NGS Illumina sRNA-seq and mRNA-seq datasets from F 0 and F 1 liver and eWAT (2) Illumina sRNA-seq from F 0 sperm (3) Illumina mRNA-seq from offsprings sired from of miRNA-injected zygotes (F m /F MI ) eWAT (4) Illumina mRNA-seq from 1° eWA overexpressing cel-mir-67 (negative control), let-7d-5p and let-7e-5p mimics and (5) Smart-Seq2 single-cell mRNA-seq data from miRNA-injected 8-cell embryos are available as Gene Expression Omnibus (GEO) SuperSeries GSE280278. The data are available to the reviewers’ discretion using url : https://tinyurl.com/3kdey4sk ( reviewer token : inqlkwqknhanxsn) Data availability – Proteomics The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD042800. The data are available to the reviewer discretion: Username: reviewer_pxd042800{at}ebi.ac.uk , Password: QHj9fv33 Data availability – Metabolomics & lipidomics All metabolomic and lipidomic datasets are available at the Metabolomics Workbench 99 data repository as project ID PR002238 ( https://www.metabolomicsworkbench.org ). Male participants, sperm samples and analysis The intervention study was approved by the Danish National Committee on Health Research Ethics (SJ-769), the Danish Data Protection Agency (REG-048-2019) and performed at the Fertility Clinic, Zealand University Hospital, Denmark 77 . The semen samples were collected from obese patients (≥ 30 kg/m 2 ) via masturbation following two days of abstinence. The sperm analyses were performed in the IVF laboratory in an authorized tissue establishment. The other semen samples were diluted with MHM-C (Fujifilm) and frozen immediately for further analysis. The semen samples were collected via masturbation following two days of abstinence, and the samples were analyzed a maximum of two to three hours after production. After liquefaction, ejaculate volume and sperm concentration (total, motile and progressive) were assessed using a microscope (Leica DM750). Sperm concentration was determined with a CellVision counting chamber (CellVision Technology, Heerhugowaard, the Netherlands). Total sperm count was calculated as the product between ejaculate volume and sperm concentration. The sperm analyses were performed in the IVF laboratory in an authorized tissue establishment. Study design and approvals This prospective intervention study was conducted between May 2020 and April 2022 at the Fertility Clinic, Zealand University Hospital, Denmark. The study was approved by the Danish National Committee on Health Research Ethics (SJ-769), the Danish Data Protection Agency (REG-048-2019) and registered on the Clinical Trial website ( www.ClinicalTrials.gov identifier NCT04721938 ). All participants provided written informed consent. Primary adipocyte culture and transfection Epididymal white adipose tissues (eWAT) from 6-to 8-week-old male C57BL/6J mice were removed, minced, and digested with pre-warmed collagenase media (500 U/mL collagenase type 2 (LS004183, Worthington) and 3.75U/mL DNase I (10104159001, Sigma). Isolated cells were seeded in 6-well plates and grown in DMEM/Ham’s F12 medium (21331, Vendor: Gibco), supplemented with 0.1 % Biotin/D-Pantothenate (33 mM/17 mM), 1 % penicillin-streptomycin, and 20 % FBS. Until reaching 80 % confluency, primary cells were induced by induction medium with 1 µM rosiglitazone, 850 nM insulin, 1 µM dexamethasone, 250 µM 3-isobutyl-1-methyl-xanthin (IBMX) and 10 % FBS. Subsequently, the medium was changed every other day by a differentiation medium containing 1 µM rosiglitazone, 850 nM insulin and 10 % FBS. 100 nM of Negative Control and mmu-let-7-d/e-5p mimics or LNA oligos targeting Dicer1 were reversely transfected using Lipofectamine RNAiMAX (Thermo Fisher Scientific) at day 4 after induction. Completely differentiated cells were harvested at 7 days after induction for further analysis. Plasmid design and dual luciferase assay For the generation of reporter constructs, the specific region of murine Ago2 , Dicer-1 , Hif1an , Hk2 , Cpt2 , Crat , Pcx , Idh2 , Sdha , Sdhaf3 , Pdk2 , Slc25a22 and Slc25a24 containing either wildtype (wt) or mutated (mut) let-7 binding sites was cloned behind the translation stop codon of the firefly-luciferase gene in pmirGLO Dual-Luciferase vector using custom DNA oligos provided by IDT. Renilla luciferase coding in the same plasmid was used as a control reporter for normalization purposes. To report the miRNA activity in mammalian cells, HEK 293 cells were reversely transfected in 96-well plates with 60 ng DNA using the TransIT transfection reagent (mirusbio) for 3 days and forwardly transfected with let-7d/e-5p mimics for 2 days. HEK-293 cells were cultured in DMEM medium (21013, Gibco) supplemented with 5.6 mM L-glutamine, 1 % penicillin-streptomycin, and 10 % FBS. After 72-h vector transfection and 48-h miRNA mimics transfection, dual luciferase reporter assays (Promega) were performed. The relative activities of firefly luciferase indicating the interaction between miRNA and specific region of targeted mRNA were normalized by the activities of Renilla luciferase. TaqMan assay and general qPCR To quantify the levels of mature miRNAs, TaqMan MicroRNA Reverse Transcription Kit (4366596, Thermo Fisher), TaqMan MicroRNA Assays and TaqMan Universal Master Mix II (4440042, Thermo Fisher) were applied according to manufacturer’s protocols. Level of mature hsa-miR-16 was used as internal control in human samples. The relative expression of mature miRNA was calculated using a comparative method (2-∂∂CT) according to the ABI Relative Quantification Method. Determination of oxygen consumption rates and measurement of glycolytic activity Primary adipocytes from epididymal SVF (1°eWA) were seeded into Agilent Seahorse XFe96 Bioanalyzer microplates. Induction and differentiation were performed as described above. After four days of differentiation, 16,000 per well mature adipocytes were re-seeded and transfected with LNA oligos targeting Dicer1 in DMEM/Ham’s F-12 medium plus 10 % Fetal Bovine Serum, 1 % P/S, 0.1 % Biotin and 0.1 % Pantothenic acid (Growth medium) at 37° C and 5 % CO 2 . Seahorse measurement was conducted at day 7 from differentiation. For each seahorse plate, the corresponding calibration plate was prepared 24 hours prior to experiments using 200 µl XF Seahorse Calibrant Agilent per well. Corresponding media were prepared before the experiment and consisted of Assay Medium supplemented with 25 mM Glucose, 1 mM Glutamine, 2 mM Sodium Pyruvate, set to pH = 7.4 and filtered sterile. The calibration plate possessed a cartridge having 4 pockets per well. Shortly before the measurement pocket A was filled with 20 µl 20 µM Oligomycin, pocket B with 22 µl 20 µM FCCP and pocket C with 25 µl 5 µM Antimycin A and Rotenone. One hour before measurements, plates were washed with 1x PBS and changed to 180 µl Assay Medium in a non-CO 2 incubator at 37° C. Calibration was started using calibration plates, measuring O 2 and pH LED Value/emission/Initial reference Delta for each well. All measurements started with measuring basal values, followed by injection of Oligomycin, FCCP, and Rotenone plus Antimycin A (MitoStressKit). Measurement parameters were: Mix 3 min, wait 0 min, measure 3 min with each reagent’s effect assessed within three consecutive measurement cycles with a total duration of 18 min per reagent injection. Protein extraction and western blot analysis After being washed once by phosphate-buffered saline (PBS), the treated cells at harvest time were lysed by RIPA buffer supplemented with cOmplete and PhosSTOP and quantified by Pierce BCA Protein Assay Kit (23227, Thermo Fisher). Western blot analyses were carried out according to standard protocols with primary antibodies against COXIV (Cell Signaling Technologies #4850, 1:1,1000 dilution), Cytochrome C (Cell Signaling Technologies #4280, 1:1,1000 dilution), HSP60 (Cell Signaling Technologies #412165, 1:1,1000 dilution), PHB1 (Cell Signaling Technologies #2426, 1:1,1000 dilution), pyruvate dehydrogenase (Cell Signaling Technologies #3205, 1:1,1000 dilution), SDHA (Cell Signaling Technologies #11998, 1:1,1000 dilution), SOD1 (Cell Signaling Technologies #4266, 1:1,1000 dilution), VDAC (Cell Signaling Technologies #4661, 1:1,1000 dilution), HK2 (Cell Signaling Technologies #2897, 1:1,1000 dilution), CPT2 (Abcam Ab181114, 1:1,1000 dilution) and IDH2 (Cell Signaling Technologies #60322, 1:1,1000 dilution). Relative protein quantification was normalized by HSC70 (Santa Cruz sc-7298, 1:1000 dilution). Statistical analysis Data were shown as mean ± standard error. One-way ANOVA and two-way ANOVA followed by Tukey’s multiple comparisons test were used for statistical analysis in in vivo experiments. Significant differences between groups were noted in figure legends respectively. Supplementary figure legends Download figure Open in new tab Figure S1: Paternal obesity and weight loss do not affect reproductive fitness and litter sizes. (a) Total numbers, number of male and female offsprings, sex ratios and (b) litter sizes of pups conceived by (F 0 LF, n=8), (F 0 HF, n=8) and F 1 (F 0 HF-LF, n=8) bucks. Download figure Open in new tab Figure S2: Paternal obesity causes heritable alterations in glucose metabolism in male but not in female mice. (a) Blood glucose area under the curve (AUC) of glucose levels during intraperitoneal glucose tolerance test in F 0 LF (n=6), F 0 HF (n=6) and F 0 HF-LF (n=7, left ) mice F 1 (F 0 LF, n=10), F 1 (F 0 HF, n=10) and F 1 (F 0 HF-LF, n=10, right ) mice. (b) Serum insulin levels as measured by ELISA in F 0 LF (n=6), F 0 HF (n=6) and F 0 HF-LF (n=7, left ) and F 1 (F 0 LF, n=6), F 1 (F 0 HF, n=8) and F 1 (F 0 HF-LF, n=8) mice. (c) Relative inguinal white adipose tissue (iWAT) weights in F 0 LF (n=6), F 0 HF (n=6) and F 0 HF-LF (n=7, left ) and F 1 (F 0 LF, n=10), F 1 (F 0 HF, n=10) and F 1 (F 0 HF-LF, n=7, right ) mice. (d) Body weights of F 1 (F 0 LF, n=11), F 1 (F 0 HF, n=12) and F 1 (F 0 HF-LF, n=13) female C57BL/6N mice. (e) Blood glucose during intraperitoneal glucose tolerance test in F 1 (F 0 LF, n=8), F 1 (F 0 HF, n=10) and F 1 (F 0 HF-LF, n=10) female C57BL/6N mice. (f) AUC of glucose levels during intraperitoneal glucose tolerance test in F 1 (F 0 LF, n=8), F 1 (F 0 HF, n=10) and F 1 (F 0 HF-LF, n=10) female C57BL/6N mice. (g) Blood glucose during intraperitoneal insulin tolerance test in F 1 (F 0 LF, n=7), F 1 (F 0 HF, n=9) and F 1 (F 0 HF-LF, n=9) female C57BL/6N mice. (h) Random-fed serum glucose levels in F 1 (F 0 LF, n=13), F 1 (F 0 HF, n=12) and F 1 (F 0 HF-LF, n=13) female C57BL/6N mice. ( i) Relative organ weights in indicated tissue from F 1 (F 0 LF, n=9), F 1 (F 0 HF, n=10) and F 1 (F 0 HF-LF, n=8) female C57BL/6N mice. Two-tailed Student’s t-test ( d,e ) for individual timepoints or one-way ANOVA followed by Tukey’s multiple comparisons ( a-c,f ) were used for statistical analysis. Data are presented as mean ± standard error with individual values shown for n≤10. p-Values are indicated in the panel or represented as letters ( a , p <0.05, F 0 LF versus F 0 HF or F 1 (F 0 LF) versus F 1 (F 0 HF); b , p <0.05, F 0 HF versus F 0 HF-LF or F 1 (F 0 HF) versus F 1 (F 0 HF-LF); c , p <0.05, F 0 LF versus F 0 HF-LF or F 1 (F 0 LF) versus F 1 (F 0 HF-LF)). Download figure Open in new tab Figure S3: Effects of paternal obesity on F 0 and F 1 eWAT proteomes. ( a,b ) Visualisation of high-confidence STRING interaction networks of proteins upregulated in ( a ) F 0 LF (n=6) vs F 0 HF (n=6) and ( b ) F 1 (F 0 LF, n=6), F 1 (F 0 HF, n=6) eWAT ( top ) and GOBP enrichment of differentially regulated proteins ( bottom ). Download figure Open in new tab Figure S4: Paternal obesity elicits alterations in circulating mitochondria-associated metabolites and lipids. ( a-c ) Principal component analysis (PCA) representing PC1-2 (a) heatmap of the 74 most differentially regulated serum metabolites (c) and ( b ) GOBP term enrichment obtained by integrating differentially abundant serum metabolites and differentially expressed eWAT genes determined by RNA-seq in eWAT of F 0 LF versus F 0 HF male C57L/6N mice and integrated using by Metaboanalyst 6.0. ( d,e ) PCA representing PC1-2 (d) and heatmap of the 100 most differentially regulated serum lipids (e) . Metabolites and lipids were determined by ultra-performance liquid chromatography in serum of F 0 LF compared to F 0 HF (n=6 per group) male C57BL/6N mice. (f-h) Relative abundances of (f ) mitochondrial tricarbon acid intermediates (g) mitochondrial Carnitine lipid carriers (h) mitochondria-inactivating short-chain and mitochondria-promoting long-chain ceramides in serum of F 0 LF (n=6), F 0 HF (n=6) and F 0 HF-LF (n=7) mice. One-way ANOVA followed by Tukey’s multiple comparisons test were used for statistical analysis and P vales are indicated in each panel. Download figure Open in new tab Figure S5: Paternal weight loss corrects obesity-associated mitochondrial dysfunction in eWAT. ( a,b ) Volcano plot of significantly up-(grey) and down-regulated (white) mRNAs in eWAT from (a) F 0 HF versus F 0 HF-LF with 2,779 up– and 1,741 down-regulated mRNAs and ( b ) F 1 (F 0 HF) versus F 1 (F 0 HF-F) with 1,118 up– and 550 down-regulated mRNAs. Plots show log10 transformed fold-changes (fc) and log10-transformed (a) adjusted or (b) non-adjusted p-Values of mRNA changes. Mitochondrial complex genes are annotated in the plot. (c,d) GSEA and GOBP enrichment of differentially expressed mRNAs from (a,b). Terms linked to mitochondrial respiration, ATP synthesis and Complex I are marked with blue bars. (e,f) Volcano plot of significantly regulated miRNAs in eWAT from (e) F 0 HF versus F 0 HF-F with 147 up– and 148 down-regulated miRNAs and ( f ) F 1 (F 0 HF) versus F 1 (F 0 HF-LF) with 92 up– and 37 downregulated miRNAs. Plots depict log10 transformed fc and p-Values of mRNA changes. Mitochondria-activating miRNAs are annotated in pink. Download figure Open in new tab Figure S6: Paternal obesity does not affect F 1 adipose miRNA expression and meta-inflammation. ( a,b) Multidimensional scaling (MDS) plots and PC1/2 of mRNA changes in eWAT of (a) F 0 LF (n=6), F 0 HF (n=6) and F 0 HF-LF (n=6) and ( b ) F 1 (F 0 HF, n=4), F 1 (F 0 HF, n=6) and F 1 (F 0 HF-LF, n=6) male C57BL/6N mice determined by mRNA-seq. (c,d) Volcano plot of significantly regulated miRNAs in eWAT from (c) F 0 HF versus F 0 LF with 154 up– and 193 down-regulated miRNAs and ( d ) F 1 (F 0 HF) versus F 1 (F 0 LF) with 62 up– and 87 down-regulated miRNAs. Plots depict log10 transformed fc and p-Values of mRNA changes. Mitochondria-inactivating miRNAs are annotated in light blue. (e,f) Expression changes reflected by normalised mRNA-seq counts from (e) F 0 LF (n=6), F 0 HF (n=6) and F 0 HF-LF (n=6) and ( f ) F 1 (F 0 HF, n=4), F 1 (F 0 HF, n=5) and F 1 (F 0 HF-LF, n=6) male C57BL/6N mice. Genes represent markers of classically (M1) or alternatively (M2) activated macrophages/monocytes. One-way ANOVA followed by Tukey’s multiple comparisons ( e,f ) were used for statistical analysis. Data are presented as mean ± standard error with individual values shown for n≤10.p-Values are represented as letters ( a , p <0.05, F 0 LF versus F 0 HF or F 1 (F 0 LF) versus F 1 (F 0 HF); b , p <0.05, F 0 HF versus F 0 HF-LF or F 1 (F 0 HF) versus F 1 (F 0 HF-LF); c , p <0.05, F 0 LF versus F 0 HF-LF or F 1 (F 0 LF) versus F 1 (F 0 HF-LF)). Download figure Open in new tab Figure S7: Paternal obesity does not affect mitochondrial gene expression in F 0 and F 1 liver. ( a,b) MDS plots and PC1/2 of mRNA changes in liver of (a) F 0 LF (n=6), F 0 HF (n=6) and F 0 HF-LF (n=6) and ( b ) F 1 (F 0 HF, n=4), F 1 (F 0 HF, n=5) and F 1 (F 0 HF-LF, n=6) male C57BL/6N mice determined by mRNA-seq. (c,d) Volcano plot of significantly up-(grey) and down-regulated (white) genes in liver from ( c ) F 0 HF versus F 0 LF, 3,312 up– and 1,597 down-regulated genes and ( d ) F 1 (F 0 HF) versus F 1 (F 0 LF), 457 upregulated and 1,041 downregulated genes. Plots depict log10 transformed fold-changes and log10-transformed (c) adjusted or (d) non-adjusted p-Values of expression changes. (e,f) GSEA and GOBP enrichment of biological processes expression changes shown in ( c,d ). Download figure Open in new tab Figure S8: Paternal obesity and weight loss does not affect liver morphology and lipid metabolism in F 1 progeny. ( a-f) Representative (a,b) histological (haematoxylin/eosine staining cellular structures), (c,d) fibrosis (Sirius Red staining collagen) and (e,f) pan-leukocyte (anti-CD45 stains leukocytes) stained formalin-fixed paraffine embedded sections in livers of (a,c,e) F 0 LF, F 0 HF and F 0 HF-LF and (b,d,f) F 1 (F 0 LF), F 1 (F 0 HF) and F 1 (F 0 HF-LF) male C57BL/6N mice. ( g,h ) Pathologist assessment of macrovesicular and microvesicular steatosis, lobular inflammation and NAS fibrosis scores in livers of livers of F 0 LF, F 0 HF and F 0 HF-LF as well as in F 1 (F 0 LF), F 1 (F 0 HF) and F 1 (F 0 HF-LF, n=6 per group). (i,j) Expression changes reflected by normalised mRNA-seq counts from F 0 LF (n=6), F 0 HF (n=6) and F 0 HF-LF (n=6, left ) and ( f ) F 1 (F 0 HF, n=4), F 1 (F 0 HF, n=5) and F 1 (F 0 HF-LF, n=5, right ) male C57BL/6N mice. Data are shown for genes in liver (i) lipid and lipoprotein metabolism and (j) mitochondrial beta-oxidation. Data in (i,j) are presented as mean ± standard error with individual values shown for n≤10. p-Values are shown in the panels or represented as letters ( a , p <0.05, F 0 LF versus F 0 HF or F 1 (F 0 LF) versus F 1 (F 0 HF); b , p <0.05, F 0 HF versus F 0 HF-LF or F 1 (F 0 HF) versus F 1 (F 0 HF-LF); c , p <0.05, F 0 LF versus F 0 HF-LF or F 1 (F 0 LF) versus F 1 (F 0 HF-LF)). Download figure Open in new tab Figure S9: Paternal obesity and weight loss cause only modest changes in spermatozoa mitochondrial tRNA fragments. Fragments with a mean cpm above 10 shown. (a) Sequences mapped to mitochondrial tRNA, presented as mean cpm per diet. (b) Sum cpm per mitochondrial tsRNA fragment type. Each point represents one sample. (*p<0.05, One-way ANOVA with Tukey’s multiple comparisons). n=6 per diet, pink=F 0 LF, beige= F 0 HF, blue= F 0 HF-LF. Download figure Open in new tab Figure S10: Zygotic microinjection of obesity associated let-7 does not affect embryonic viability and implantation. Total numbers of zygotes microinjected with cel-mir-67 , let-7d-5p and let-7e-5p , total numbers of pups born and relative survival rates of miRNA-proficient embryos. Figure S11: Obesity-associated let-7 impairs adipocyte mitochondrial function via silencing of DICER1. GSEA-GOBP analysis of differentially abundant mRNAs in 8-cell blastomeres derived from cel-mir-67 (n=22), let-7d-5p (n=19) and let-7e-5p (n=20) injected zygotes (corresponding Fig. 4j ). Acknowledgements We thank the CECAD Proteomics Core Facility for support with proteomics analyses, the Laboratory Manager, Stine Ravn, at The Zealand Fertility Clinic for handling and analysing the sperm samples. We are thankful for technical support from Ronni Nielsen and specialists from the Functional Genomics and Metabolism NGS team. We wish to thank Bente Møller and Irina Korshuniova from BRIC Single Cell Genomics Core Facility for excellent help for Smart-seq2 experiments. Smart-seq2 work was partly supported by CellX (The Danish Single Cell Examination Platform) funded by the Danish Research Agency through the Danish national research infrastructure program (5229-0009B). JWK, JHP and BDML received funding from the European Research Council Starting Grant TransGenRNA (#675014). JWK, CH, and NS received funding from Sygeforsikring Denmark, University of Southern Denmark and Danish Diabetes Academy that is funded by the Novo Nordisk Foundation (NNF17SA0031406). JWK received support from the NNF Challenge (#33444) and Bioscience and Basic Biomedicine Programs of the NNF (#28416). CH received grants from National Taiwan University (113L7494) and the National Science and Technology, Taiwan (114-2314-B-002-055-). MAM received support from Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) and Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), and Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES). PK and MRF were supported by the Vetenskapsrådet Consolidator Grant 2022-03953 ‘InSync‘. Footnotes We substantially reorganised the paper and included additional analyses. References 1. ↵ Trerotola , M. , Relli , V. , Simeone , P. & Alberti , S . Epigenetic inheritance and the missing heritability . Human genomics 9 , 17 , doi: 10.1186/s40246-015-0041-3 ( 2015 ). OpenUrl CrossRef PubMed 2. ↵ Horsthemke , B . A critical view on transgenerational epigenetic inheritance in humans . Nature communications 9 , 2973 , doi: 10.1038/s41467-018-05445-5 ( 2018 ). OpenUrl CrossRef PubMed 3. ↵ Bygren , L. O. , Kaati , G. & Edvinsson , S . Longevity determined by paternal ancestors’ nutrition during their slow growth period . Acta Biotheor 49 , 53 – 59 , doi: 10.1023/a:1010241825519 ( 2001 ). OpenUrl CrossRef PubMed Web of Science 4. ↵ Pembrey , M. E. et al. Sex-specific, male-line transgenerational responses in humans . European journal of human genetics: EJHG 14 , 159 – 166 , doi: 10.1038/sj.ejhg.5201538 ( 2006 ). OpenUrl CrossRef PubMed Web of Science 5. ↵ Lumey , L. H. , Stein , A. D. , Kahn , H. S. & Romijn , J. A . Lipid profiles in middle-aged men and women after famine exposure during gestation: the Dutch Hunger Winter Families Study . Am J Clin Nutr 89 , 1737 – 1743 , doi: 10.3945/ajcn.2008.27038 ( 2009 ). OpenUrl Abstract / FREE Full Text 6. ↵ Carone , B. R. et al. Paternally induced transgenerational environmental reprogramming of metabolic gene expression in mammals . Cell 143 , 1084 – 1096 , doi: 10.1016/j.cell.2010.12.008 ( 2010 ). OpenUrl CrossRef PubMed Web of Science 7. ↵ Ng , S. F. et al. Paternal high-fat diet consumption induces common changes in the transcriptomes of retroperitoneal adipose and pancreatic islet tissues in female rat offspring . FASEB journal: official publication of the Federation of American Societies for Experimental Biology 28 , 1830 – 1841 , doi: 10.1096/fj.13-244046 ( 2014 ). OpenUrl CrossRef PubMed Web of Science 8. ↵ de Castro Barbosa , T. , et al. High-fat diet reprograms the epigenome of rat spermatozoa and transgenerationally affects metabolism of the offspring . Molecular metabolism 5 , 184 – 197 , doi: 10.1016/j.molmet.2015.12.002 ( 2016 ). OpenUrl CrossRef PubMed 9. ↵ de Castro Barbosa , T. , Alm , P. S. , Krook , A. , Barres , R. & Zierath , J. R. Paternal high-fat diet transgenerationally impacts hepatic immunometabolism . FASEB journal: official publication of the Federation of American Societies for Experimental Biology 33 , 6269 – 6280 , doi: 10.1096/fj.201801879RR ( 2019 ). OpenUrl CrossRef PubMed 10. ↵ Wang , Y. et al. Sperm microRNAs confer depression susceptibility to offspring . Sci Adv 7 , doi: 10.1126/sciadv.abd7605 ( 2021 ). OpenUrl FREE Full Text 11. ↵ Dias , B. G. & Ressler , K. J . Parental olfactory experience influences behavior and neural structure in subsequent generations . Nature neuroscience 17 , 89 – 96 , doi: 10.1038/nn.3594 ( 2014 ). OpenUrl CrossRef PubMed 12. ↵ Gapp , K. et al. Implication of sperm RNAs in transgenerational inheritance of the effects of early trauma in mice . Nature neuroscience 17 , 667 – 669 , doi: 10.1038/nn.3695 ( 2014 ). OpenUrl CrossRef PubMed 13. ↵ Sun , W. et al. Cold-induced epigenetic programming of the sperm enhances brown adipose tissue activity in the offspring . Nature medicine 24 , 1372 – 1383 , doi: 10.1038/s41591-018-0102-y ( 2018 ). OpenUrl CrossRef PubMed 14. ↵ Skvortsova , K. , Iovino , N. & Bogdanovic , O . Functions and mechanisms of epigenetic inheritance in animals . Nature reviews. Molecular cell biology 19 , 774 – 790 , doi: 10.1038/s41580-018-0074-2 ( 2018 ). OpenUrl CrossRef PubMed 15. ↵ Rechavi , O. , Minevich , G. & Hobert , O . Transgenerational inheritance of an acquired small RNA-based antiviral response in C. elegans . Cell 147 , 1248 – 1256 , doi: 10.1016/j.cell.2011.10.042 ( 2011 ). OpenUrl CrossRef PubMed Web of Science 16. Fire , A. et al. Potent and specific genetic interference by double-stranded RNA in Caenorhabditis elegans . Nature 391 , 806 – 811 , doi: 10.1038/35888 ( 1998 ). OpenUrl CrossRef PubMed Web of Science 17. Ashe , A. et al. piRNAs can trigger a multigenerational epigenetic memory in the germline of C. elegans . Cell 150 , 88 – 99 , doi: 10.1016/j.cell.2012.06.018 ( 2012 ). OpenUrl CrossRef PubMed Web of Science 18. ↵ de Vanssay , A. et al. Paramutation in Drosophila linked to emergence of a piRNA-producing locus . Nature 490 , 112 – 115 , doi: 10.1038/nature11416 ( 2012 ). OpenUrl CrossRef PubMed Web of Science 19. ↵ Rassoulzadegan , M. et al. RNA-mediated non-mendelian inheritance of an epigenetic change in the mouse . Nature 441 , 469 – 474 , doi: 10.1038/nature04674 ( 2006 ). OpenUrl CrossRef PubMed Web of Science 20. ↵ Gross , N. , Kropp , J. & Khatib , H . MicroRNA Signaling in Embryo Development . Biology (Basel ) 6 , doi: 10.3390/biology6030034 ( 2017 ). OpenUrl CrossRef PubMed 21. ↵ Sharma , U. et al. Small RNAs Are Trafficked from the Epididymis to Developing Mammalian Sperm . Developmental cell 46 , 481 – 494 e486 , doi: 10.1016/j.devcel.2018.06.023 ( 2018 ). OpenUrl CrossRef PubMed 22. ↵ Natt , D. et al. Human sperm displays rapid responses to diet . PLoS Biol 17 , e3000559 , doi: 10.1371/journal.pbio.3000559 ( 2019 ). OpenUrl CrossRef PubMed 23. ↵ Tomar , A. et al. Epigenetic inheritance of diet-induced and sperm-borne mitochondrial RNAs . Nature 630 , 720 – 727 , doi: 10.1038/s41586-024-07472-3 ( 2024 ). OpenUrl CrossRef PubMed 24. ↵ Grandjean , V. et al. RNA-mediated paternal heredity of diet-induced obesity and metabolic disorders . Scientific reports 5 , 18193 , doi: 10.1038/srep18193 ( 2015 ). OpenUrl CrossRef PubMed 25. ↵ Rodgers , A. B. , Morgan , C. P. , Leu , N. A. & Bale , T. L . Transgenerational epigenetic programming via sperm microRNA recapitulates effects of paternal stress . Proceedings of the National Academy of Sciences of the United States of America 112 , 13699 – 13704 , doi: 10.1073/pnas.1508347112 ( 2015 ). OpenUrl Abstract / FREE Full Text 26. ↵ Sharma , U. et al. Biogenesis and function of tRNA fragments during sperm maturation and fertilization in mammals . Science , doi: 10.1126/science.aad6780 ( 2015 ). OpenUrl Abstract / FREE Full Text 27. ↵ Korhonen , H. M. et al. Dicer is required for haploid male germ cell differentiation in mice . PloS one 6 , e24821 , doi: 10.1371/journal.pone.0024821 ( 2011 ). OpenUrl CrossRef PubMed 28. ↵ Bernstein , E. et al. Dicer is essential for mouse development . Nature genetics 35 , 215 – 217 , doi: 10.1038/ng1253 ( 2003 ). OpenUrl CrossRef PubMed Web of Science 29. ↵ Chen , Q. et al. Sperm tsRNAs contribute to intergenerational inheritance of an acquired metabolic disorder . Science , doi: 10.1126/science.aad7977 ( 2015 ). OpenUrl Abstract / FREE Full Text 30. ↵ Rodgers , A. B. , Morgan , C. P. , Bronson , S. L. , Revello , S. & Bale , T. L . Paternal stress exposure alters sperm microRNA content and reprograms offspring HPA stress axis regulation . The Journal of neuroscience: the official journal of the Society for Neuroscience 33 , 9003 – 9012 , doi: 10.1523/JNEUROSCI.0914-13.2013 ( 2013 ). OpenUrl Abstract / FREE Full Text 31. ↵ Cropley , J. E. , Suter , C. M. , Beckman , K. B. & Martin , D. I . Germ-line epigenetic modification of the murine A vy allele by nutritional supplementation . Proceedings of the National Academy of Sciences of the United States of America 103 , 17308 – 17312 , doi: 10.1073/pnas.0607090103 ( 2006 ). OpenUrl Abstract / FREE Full Text 32. ↵ Zhang , Y. , Shi , J. , Rassoulzadegan , M. , Tuorto , F. & Chen , Q . Sperm RNA code programmes the metabolic health of offspring . Nature reviews. Endocrinology 15 , 489 – 498 , doi: 10.1038/s41574-019-0226-2 ( 2019 ). OpenUrl CrossRef PubMed 33. ↵ Sun , K. , Kusminski , C. M. & Scherer , P. E . Adipose tissue remodeling and obesity . The Journal of clinical investigation 121 , 2094 – 2101 , doi: 10.1172/JCI45887 ( 2011 ). OpenUrl CrossRef PubMed Web of Science 34. Scherer , P. E . Adipose tissue: from lipid storage compartment to endocrine organ . Diabetes 55 , 1537 – 1545 , doi: 10.2337/db06-0263 ( 2006 ). OpenUrl Abstract / FREE Full Text 35. ↵ Lee , J. H. et al. The Role of Adipose Tissue Mitochondria: Regulation of Mitochondrial Function for the Treatment of Metabolic Diseases . Int J Mol Sci 20 , doi: 10.3390/ijms20194924 ( 2019 ). OpenUrl CrossRef 36. ↵ Czech , M. P . Mechanisms of insulin resistance related to white, beige, and brown adipocytes . Molecular metabolism 34 , 27 – 42 , doi: 10.1016/j.molmet.2019.12.014 ( 2020 ). OpenUrl CrossRef PubMed 37. ↵ Kahn , C. R. , Wang , G. & Lee , K. Y . Altered adipose tissue and adipocyte function in the pathogenesis of metabolic syndrome . The Journal of clinical investigation 129 , 3990 – 4000 , doi: 10.1172/JCI129187 ( 2019 ). OpenUrl CrossRef PubMed 38. ↵ Bartelt , A. & Heeren , J . Adipose tissue browning and metabolic health . Nature reviews. Endocrinology 10 , 24 – 36 , doi: 10.1038/nrendo.2013.204 ( 2014 ). OpenUrl CrossRef PubMed 39. ↵ Schottl , T. , Kappler , L. , Braun , K. , Fromme , T. & Klingenspor , M . Limited mitochondrial capacity of visceral versus subcutaneous white adipocytes in male C57BL/6N mice . Endocrinology 156 , 923 – 933 , doi: 10.1210/en.2014-1689 ( 2015 ). OpenUrl CrossRef PubMed 40. ↵ Heinonen , S. , Jokinen , R. , Rissanen , A. & Pietilainen , K. H . White adipose tissue mitochondrial metabolism in health and in obesity . Obes Rev 21 , e12958 , doi: 10.1111/obr.12958 ( 2020 ). OpenUrl CrossRef PubMed 41. Kusminski , C. M. et al. MitoNEET-driven alterations in adipocyte mitochondrial activity reveal a crucial adaptive process that preserves insulin sensitivity in obesity . Nature medicine 18 , 1539 – 1549 , doi: 10.1038/nm.2899 ( 2012 ). OpenUrl CrossRef PubMed 42. ↵ Gonzalez-Franquesa , A. et al. Remission of obesity and insulin resistance is not sufficient to restore mitochondrial homeostasis in visceral adipose tissue . Redox Biol 54 , 102353 , doi: 10.1016/j.redox.2022.102353 ( 2022 ). OpenUrl CrossRef 43. ↵ Schottl , T. , Kappler , L. , Fromme , T. & Klingenspor , M . Limited OXPHOS capacity in white adipocytes is a hallmark of obesity in laboratory mice irrespective of the glucose tolerance status . Molecular metabolism 4 , 631 – 642 , doi: 10.1016/j.molmet.2015.07.001 ( 2015 ). OpenUrl CrossRef PubMed 44. ↵ Soro-Arnaiz , I. et al. Role of Mitochondrial Complex IV in Age-Dependent Obesity . Cell reports 16 , 2991 – 3002 , doi: 10.1016/j.celrep.2016.08.041 ( 2016 ). OpenUrl CrossRef PubMed 45. ↵ Wang , Y. et al. Metformin Improves Mitochondrial Respiratory Activity through Activation of AMPK . Cell reports 29 , 1511 – 1523 e1515 , doi: 10.1016/j.celrep.2019.09.070 ( 2019 ). OpenUrl CrossRef PubMed 46. ↵ Borralho , P. M. , Rodrigues , C. M. & Steer , C. J . microRNAs in Mitochondria: An Unexplored Niche . Adv Exp Med Biol 887 , 31 – 51 , doi: 10.1007/978-3-319-22380-3_3 ( 2015 ). OpenUrl CrossRef PubMed 47. Bandiera , S. et al. Nuclear outsourcing of RNA interference components to human mitochondria . PloS one 6 , e20746 , doi: 10.1371/journal.pone.0020746 ( 2011 ). OpenUrl CrossRef PubMed 48. ↵ Barrey , E. et al. Pre-microRNA and mature microRNA in human mitochondria . PloS one 6 , e20220 , doi: 10.1371/journal.pone.0020220 ( 2011 ). OpenUrl CrossRef PubMed 49. ↵ Mori , M. A. et al. Role of MicroRNA Processing in Adipose Tissue in Stress Defense and Longevity . Cell metabolism 16 , 336 – 347 , doi:S1550-4131(12)00322-1 [pii] 10.1016/j.cmet.2012.07.017 ( 2012 ). OpenUrl CrossRef PubMed Web of Science 50. ↵ Mori , M. A. et al. Altered miRNA processing disrupts brown/white adipocyte determination and associates with lipodystrophy . The Journal of clinical investigation 124 , 3339 – 3351 , doi: 10.1172/JCI73468 ( 2014 ). OpenUrl CrossRef PubMed 51. ↵ Oliverio , M. et al. Dicer1-miR-328-Bace1 signalling controls brown adipose tissue differentiation and function . Nature cell biology 18 , 328 – 336 , doi: 10.1038/ncb3316 ( 2016 ). OpenUrl CrossRef PubMed 52. ↵ Huypens , P. et al. Epigenetic germline inheritance of diet-induced obesity and insulin resistance . Nature genetics 48 , 497 – 499 , doi: 10.1038/ng.3527 ( 2016 ). OpenUrl CrossRef PubMed 53. ↵ Turpin , S. M. et al. Obesity-induced CerS6-dependent C16:0 ceramide production promotes weight gain and glucose intolerance . Cell metabolism 20 , 678 – 686 , doi: 10.1016/j.cmet.2014.08.002 ( 2014 ). OpenUrl CrossRef PubMed 54. ↵ Hammerschmidt , P. et al. CerS6-dependent ceramide synthesis in hypothalamic neurons promotes ER/mitochondrial stress and impairs glucose homeostasis in obese mice . Nature communications 14 , 7824 , doi: 10.1038/s41467-023-42595-7 ( 2023 ). OpenUrl CrossRef PubMed 55. ↵ Cui , Q. , Yu , Z. , Purisima , E. O. & Wang , E . Principles of microRNA regulation of a human cellular signaling network . Mol Syst Biol 2 , 46 , doi: 10.1038/msb4100089 ( 2006 ). OpenUrl Abstract / FREE Full Text 56. ↵ Hong , H. et al. MicroRNA-143 promotes cardiac ischemia-mediated mitochondrial impairment by the inhibition of protein kinase Cepsilon . Basic Res Cardiol 112 , 60 , doi: 10.1007/s00395-017-0649-7 ( 2017 ). OpenUrl CrossRef PubMed 57. ↵ Das , S. et al. miR-181c regulates the mitochondrial genome, bioenergetics, and propensity for heart failure in vivo . PloS one 9 , e96820 , doi: 10.1371/journal.pone.0096820 ( 2014 ). OpenUrl CrossRef PubMed 58. ↵ Roman , B. et al. Nuclear-mitochondrial communication involving miR-181c plays an important role in cardiac dysfunction during obesity . J Mol Cell Cardiol 144 , 87 – 96 , doi: 10.1016/j.yjmcc.2020.05.009 ( 2020 ). OpenUrl CrossRef PubMed 59. ↵ Alhadidi , Q. M. et al. MiR-182 Inhibition Protects Against Experimental Stroke in vivo and Mitigates Astrocyte Injury and Inflammation in vitro via Modulation of Cortactin Activity . Neurochemical research 47 , 3682 – 3696 , doi: 10.1007/s11064-022-03718-6 ( 2022 ). OpenUrl CrossRef PubMed 60. ↵ Lee , H. et al. microRNA-200a-3p enhances mitochondrial elongation by targeting mitochondrial fission factor . BMB reports 50 , 214 – 219 , doi: 10.5483/bmbrep.2017.50.4.006 ( 2017 ). OpenUrl CrossRef PubMed 61. ↵ Carrer , M. et al. Control of mitochondrial metabolism and systemic energy homeostasis by microRNAs 378 and 378 . Proceedings of the National Academy of Sciences of the United States of America 109 , 15330 – 15335 , doi:1207605109 [pii] 10.1073/pnas.1207605109 ( 2012 ). OpenUrl Abstract / FREE Full Text 62. ↵ Wang , J. X. et al. MicroRNA-532-3p regulates mitochondrial fission through targeting apoptosis repressor with caspase recruitment domain in doxorubicin cardiotoxicity . Cell Death Dis 6 , e1677 , doi: 10.1038/cddis.2015.41 ( 2015 ). OpenUrl CrossRef PubMed 63. ↵ Subramanian , A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles . Proceedings of the National Academy of Sciences of the United States of America 102 , 15545 – 15550 , doi:0506580102 [pii] 10.1073/pnas.0506580102 ( 2005 ). OpenUrl Abstract / FREE Full Text 64. ↵ Madsen , S. et al. Rapid downregulation of DICER is a hallmark of adipose tissue upon high-fat diet feeding . Molecular and cellular endocrinology 595 , 112413 , doi: 10.1016/j.mce.2024.112413 ( 2025 ). OpenUrl CrossRef 65. ↵ Brieno-Enriquez , M. A. et al. Exposure to endocrine disruptor induces transgenerational epigenetic deregulation of microRNAs in primordial germ cells . PloS one 10 , e0124296 , doi: 10.1371/journal.pone.0124296 ( 2015 ). OpenUrl CrossRef PubMed 66. ↵ Zhu , H. et al. The Lin28/let-7 axis regulates glucose metabolism . Cell 147 , 81 – 94 , doi: 10.1016/j.cell.2011.08.033 ( 2011 ). OpenUrl CrossRef PubMed Web of Science 67. ↵ Frost , R. J. & Olson , E. N . Control of glucose homeostasis and insulin sensitivity by the Let-7 family of microRNAs . Proceedings of the National Academy of Sciences of the United States of America 108 , 21075 – 21080 , doi: 10.1073/pnas.1118922109 ( 2011 ). OpenUrl Abstract / FREE Full Text 68. ↵ Fullston , T. et al. Paternal obesity initiates metabolic disturbances in two generations of mice with incomplete penetrance to the F2 generation and alters the transcriptional profile of testis and sperm microRNA content . FASEB journal: official publication of the Federation of American Societies for Experimental Biology 27 , 4226 – 4243 , doi: 10.1096/fj.12-224048 ( 2013 ). OpenUrl CrossRef PubMed Web of Science 69. ↵ Cheong , A. W. et al. MicroRNA Let-7a and dicer are important in the activation and implantation of delayed implanting mouse embryos . Human reproduction 29 , 750 – 762 , doi: 10.1093/humrep/det462 ( 2014 ). OpenUrl CrossRef PubMed Web of Science 70. ↵ Liu , W. et al. Expression of microRNA let-7 in cleavage embryos modulates cell fate determination and formation of mouse blastocystsdagger . Biol Reprod 107 , 1452 – 1463 , doi: 10.1093/biolre/ioac181 ( 2022 ). OpenUrl CrossRef PubMed 71. ↵ Kolenda , T. , Przybyla , W. , Teresiak , A. , Mackiewicz , A. & Lamperska , K. M . The mystery of let-7d – a small RNA with great power . Contemp Oncol (Pozn ) 18 , 293 – 301 , doi: 10.5114/wo.2014.44467 ( 2014 ). OpenUrl CrossRef PubMed 72. ↵ Yuan , S. et al. Sperm-borne miRNAs and endo-siRNAs are important for fertilization and preimplantation embryonic development . Development 143 , 635 – 647 , doi: 10.1242/dev.131755 ( 2016 ). OpenUrl Abstract / FREE Full Text 73. ↵ Suh , N. & Blelloch , R . Small RNAs in early mammalian development: from gametes to gastrulation . Development 138 , 1653 – 1661 , doi: 10.1242/dev.056234 ( 2011 ). OpenUrl Abstract / FREE Full Text 74. ↵ Pfisterer , U. et al. Identification of epilepsy-associated neuronal subtypes and gene expression underlying epileptogenesis . Nature communications 11 , 5038 , doi: 10.1038/s41467-020-18752-7 ( 2020 ). OpenUrl CrossRef PubMed 75. ↵ Tobi , E. W. et al. DNA methylation signatures link prenatal famine exposure to growth and metabolism . Nature communications 5 , 5592 , doi: 10.1038/ncomms6592 ( 2014 ). OpenUrl CrossRef PubMed 76. ↵ Schulz , L. C . The Dutch Hunger Winter and the developmental origins of health and disease . Proceedings of the National Academy of Sciences of the United States of America 107 , 16757 – 16758 , doi: 10.1073/pnas.1012911107 ( 2010 ). OpenUrl FREE Full Text 77. ↵ Jepsen , I. E. et al. Turning waiting time into treatment time: Weight reduction by a lifestyle intervention programme for patients with obesity before fertility treatment. Reproductive , Female and Child Health 2 , 133 – 142 , doi: 10.1002/rfc2.46 ( 2023 ). OpenUrl CrossRef 78. ↵ Giroud , M. et al. Let-7i-5p represses brite adipocyte function in mice and humans . Scientific reports 6 , 28613 , doi: 10.1038/srep28613 ( 2016 ). OpenUrl CrossRef PubMed 79. ↵ Popov , L. D . Mitochondrial biogenesis: An update . J Cell Mol Med 24 , 4892 – 4899 , doi: 10.1111/jcmm.15194 ( 2020 ). OpenUrl CrossRef PubMed 80. ↵ Brandao , B. B. et al. Dynamic changes in DICER levels in adipose tissue control metabolic adaptations to exercise . Proceedings of the National Academy of Sciences of the United States of America 117 , 23932 – 23941 , doi: 10.1073/pnas.2011243117 ( 2020 ). OpenUrl Abstract / FREE Full Text 81. ↵ Sullivan , R. & Saez , F . Epididymosomes, prostasomes, and liposomes: their roles in mammalian male reproductive physiology . Reproduction 146 , R21 – 35 , doi: 10.1530/REP-13-0058 ( 2013 ). OpenUrl Abstract / FREE Full Text 82. ↵ Tan , K. et al. Downregulation of miR-199a-5p Disrupts the Developmental Potential of In Vitro-Fertilized Mouse Blastocysts . Biol Reprod 95 , 54 , doi: 10.1095/biolreprod.116.141051 ( 2016 ). OpenUrl CrossRef PubMed 83. ↵ Chambers , M. C. et al. A cross-platform toolkit for mass spectrometry and proteomics . Nature biotechnology 30 , 918 – 920 , doi: 10.1038/nbt.2377 ( 2012 ). OpenUrl CrossRef PubMed 84. ↵ Kind , T. et al. LipidBlast in silico tandem mass spectrometry database for lipid identification . Nature methods 10 , 755 – 758 , doi: 10.1038/nmeth.2551 ( 2013 ). OpenUrl CrossRef PubMed Web of Science 85. ↵ Luan , H. , Ji , F. , Chen , Y. & Cai , Z . statTarget: A streamlined tool for signal drift correction and interpretations of quantitative mass spectrometry-based omics data . Anal Chim Acta 1036 , 66 – 72 , doi: 10.1016/j.aca.2018.08.002 ( 2018 ). OpenUrl CrossRef PubMed 86. ↵ Kleiner , D. E. et al. Design and validation of a histological scoring system for nonalcoholic fatty liver disease . Hepatology 41 , 1313 – 1321 , doi: 10.1002/hep.20701 ( 2005 ). OpenUrl CrossRef PubMed Web of Science 87. ↵ Rappsilber , J. & Mann , M . Analysis of the topology of protein complexes using cross-linking and mass spectrometry . CSH Protoc 2007 , pdb prot4594 , doi: 10.1101/pdb.prot4594 ( 2007 ). OpenUrl CrossRef 88. ↵ Tyanova , S. , Mann , M. & Cox , J. MaxQuant for in-depth analysis of large SILAC datasets . Methods in molecular biology 1188 , 351 - 364 , doi: 10.1007/978-1-4939-1142-4_24 ( 2014 ). OpenUrl CrossRef PubMed 89. ↵ Tyanova , S. et al. The Perseus computational platform for comprehensive analysis of (prote)omics data . Nature methods 13 , 731 – 740 , doi: 10.1038/nmeth.3901 ( 2016 ). OpenUrl CrossRef PubMed 90. ↵ Friedlander , M. R. , Mackowiak , S. D. , Li , N. , Chen , W. & Rajewsky , N . miRDeep2 accurately identifies known and hundreds of novel microRNA genes in seven animal clades . Nucleic acids research 40 , 37 – 52 , doi: 10.1093/nar/gkr688 ( 2012 ). OpenUrl CrossRef PubMed Web of Science 91. ↵ Chan , B. K. C . Data Analysis Using R Programming . Adv Exp Med Biol 1082 , 47 – 122 , doi: 10.1007/978-3-319-93791-5_2 ( 2018 ). OpenUrl CrossRef PubMed 92. ↵ Reimers , M. & Carey , V. J . Bioconductor: an open source framework for bioinformatics and computational biology . Methods Enzymol 411 , 119 – 134 , doi: 10.1016/S0076-6879(06)11008-3 ( 2006 ). OpenUrl CrossRef PubMed Web of Science 93. ↵ Frankish , A. et al. GENCODE reference annotation for the human and mouse genomes . Nucleic acids research 47 , D766 – D773 , doi: 10.1093/nar/gky955 ( 2019 ). OpenUrl CrossRef PubMed 94. ↵ Dobin , A. et al. STAR: ultrafast universal RNA-seq aligner . Bioinformatics 29 , 15 – 21 , doi: 10.1093/bioinformatics/bts635 ( 2013 ). OpenUrl CrossRef PubMed Web of Science 95. ↵ Liao , Y. , Smyth , G. K. & Shi , W . featureCounts: an efficient general purpose program for assigning sequence reads to genomic features . Bioinformatics 30 , 923 – 930 , doi: 10.1093/bioinformatics/btt656 ( 2014 ). OpenUrl CrossRef PubMed Web of Science 96. ↵ Robinson , M. D. , McCarthy , D. J. & Smyth , G. K . edgeR: a Bioconductor package for differential expression analysis of digital gene expression data . Bioinformatics 26 , 139 – 140 , doi: 10.1093/bioinformatics/btp616 ( 2010 ). OpenUrl CrossRef PubMed Web of Science 97. Law , C. W. , Chen , Y. , Shi , W. & Smyth , G . K. voom: Precision weights unlock linear model analysis tools for RNA-seq read counts . Genome biology 15 , R29 , doi: 10.1186/gb-2014-15-2-r29 ( 2014 ). OpenUrl CrossRef PubMed 98. ↵ Wu , T. et al. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data . Innovation (Camb ) 2 , 100141 , doi: 10.1016/j.xinn.2021.100141 ( 2021 ). OpenUrl CrossRef PubMed 99. ↵ Sud , M. et al. Metabolomics Workbench: An international repository for metabolomics data and metadata, metabolite standards, protocols, tutorials and training, and analysis tools . Nucleic acids research 44 , D463 – 470 , doi: 10.1093/nar/gkv1042 ( 2016 ). OpenUrl CrossRef PubMed View the discussion thread. Back to top Previous Next Posted April 03, 2025. Download PDF Email Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Male obesity causes adipose mitochondrial dysfunction in F1 progeny via a let-7-DICER axis Message Subject (Your Name) has forwarded a page to you from bioRxiv Message Body (Your Name) thought you would like to see this page from the bioRxiv website. 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