Male obesity causes adipose mitochondrial dysfunction in F_1 mouse progeny via a let-7-DICER axis

Domain: chemistry
Published: 2026-02-24
Editorial outcome: major_revision

Abstract
Male obesity negative affects gametic function and offspring metabolism. We here describe that (F0) obesity and weight loss in male mice reversibly alter metabolism and impair adipose mitochondrial function. These metabolic aberrations are transmitted to male offsprings (F1), which display reduced mitochondrial gene expression. Mechanistically, we identify microRNAs let-7d/e as epigenetic mediators induced in obese F0 sperm and in F0/F1 adipose tissue, where they silence the miRNA processor DICER1 and impair mitochondrial activity. Microinjecting let-7d/e into lean zygotes phenocopies the paternal obesity phenotype, inducing glucose intolerance and mitochondrial gene suppression in sired offspring. Single-cell RNA sequencing of blastomeres reveals that let-7d/e impair oxidative metabolism in early embryos. Furthermore, lifestyle-induced weight loss in males with obesity downregulates human HSA-LET-7D/E in semen, indicating a conserved role for let-7 in transmission of metabolic health. These findings demonstrate that microRNA let-7 in sperm reprograms offspring metabolism by modulating mitochondrial function during early development.

Introduction
Genome-wide association studies have demonstrated that genetic (i.e., DNA) variants only explain 20% of obesity heritability1. 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 environmental exposures, might increase obesity susceptibility and contribute to disease incidences. This form of epigenetic inheritance, if convincingly demonstrated2, is particularly intriguing for cases of paternal programming where affected fathers contribute lifestyle-affected sperm to embryos (F1 generation, so-called “intergenerational” inheritance) or even in situations that arise as a consequence of F1 phenotypes but in F2 and beyond (so-called “transgenerational” inheritance). Epidemiological support for the existence of hereditary, paternally-acquired traits comes from historical records demonstrating that patterns of ancestral food supply affect cardiometabolic traits for two generations3,4, showing that individuals can carry increased risks of cardiometabolic dysfunction if their parents or even grandparents were obese or malnourished5. In line with these correlative observations in humans, mechanistic studies in isogenic (i.e., genetically identical) mouse strains like C57BL/6 demonstrated that paternal obesity impairs metabolism in offsprings (i.e., F1 generation and beyond) and that low-protein6 or high-fat diet feeding7–9 and stress-related cues like depression-like behavior10, fear-conditioning11 and psychological traumata12 impart highly specific epigenetic behavioral and metabolic effects in F1 offsprings. Recently, beneficial types of epigenetic programming in adipose tissue were demonstrated where offsprings of mice exposed to cold temperatures display increased metabolically favorable brown adipose tissue13, suggesting an unexpected plasticity of the molecular and cellular mechanisms underlying hereditary epigenetic responses.

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 gametes14. 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 flies15–18, thus species where sncRNAs-evoked gametic gene silencing processes are well understood19. In contrast to DNA-linked marks, sncRNAs are mobile in nature and subject to microvesicular trafficking between cells and organs and secreted by somatic cell types in testes20,21. Thus, due to their high abundance of miRNAs in sperm, their dynamic changes following diet exposures22,23, their exchange between sperm and oocytes23 sperm-born sncRNAs have the unique potential for exchanging information about past lifestyle choices to zygotes and F1 progeny. Consistent with this, metabolic trait/disease-associated miRNAs can affect intergenerational phenotypes and phenocopy F0 lifestyles if microinjected24,25 or removed from zygotes26. Beyond controlling gametic epigenomes, miRNAs are also essential for germ cell development processes such as spermatogenesis27 and embryogenesis28, and they are exchanged between zygotes and other cell types12,25,29–31. 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 inflammation15, nutrition29,31 to stress12,30 alter gametic miRNA repertoires and F1 phenotypes in non-overlapping ways, the intriguing possibility exists that environmental experiences are encoded in individual gametic sncRNA signatures and that these sperm sncRNAs have the potential to transmit complex lifestyle exposures, given the high bandwidth of RNA-mediated effects32.

Adipose tissue is a multifunctional endocrine organ that coordinates tissue-level (autocrine, juxtacrine, paracrine) and systemic (endocrine) responses by release of adipokines, lipids, inflammatory factors, and, 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 resistance36, aberrant gene expression in adipocytes37, vascular remodeling and immune cell infiltration33. Mitochondria in adipose tissue exert important functions for tissue/energy homeostasis, particularly during nutrient deprivation/excess35: Whereas brown adipocytes in contain a high numbers of mitochondria to generate heat by dissipation of the electron transport chain (ETC) proton gradients38, 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 obesity39, and reduced mitochondrial function in fat causes metabolic imbalance40–43. 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 obesity39,43 and aging44. 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 improvement45. Recently, miRNAs were identified as important regulators of mitochondrial formation and function. Known mitochondrial microRNAs or ‘mito-miRs' include miR-143, miR-181a, miR-378 that instigate, whereas miR-532 impairs mitochondrial function, mitochondrial translocation and mt-RNA homeostasis46–48. 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 mice49–51.

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 F1 progeny. Using integrative analyses of adipose, liver and sperm miRNA:mRNA gene networks, we identify several let-7 isoforms, including let-7d/e, as being induced by obesity across cell types and generations. Mechanistically, we implicate let-7d/e driven suppression of DICER1 and the concomitant declines in mitochondrial function in adipocytes in inherited mouse phenotypes. This suggess sperm-born let-7d/e as an intergenerational contributing factor for mitochondrial dysfunction and glucose impairments in the F1 offspring generation.

Results
Paternal obesity elicits intergenerational impairments in adipose mitochondrial function

Whilst intergenerational effects of paternal (F0) obesity on offspring (F1) 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/6J male mice, and fed 6-week-old animals with 1) low-fat diet (LF) for 18 weeks (F0 LF), 2) high-fat diet (HF) for 18 weeks (F0 HF) or 3) HF for 9 weeks, followed by 9 weeks of LF-driven weight loss (F0 HF-LF, Fig. 1a) to allow at least three cycles of spermatogenesis. For each F0 group, we mated eight randomly selected mice with unexposed lean virgin, female C57BL/6J mice, and exposed both male and female F1 progeny of each group, i.e., F1 (F0 LF, n=44 pups), F1 (F0 HF, n=52 pups) and F1 (F0 HF-LF, n=38 pups) mice to LF after weaning to test for metabolic and metabolic sequel of F0 diet exposure and removal. We did not additionally challenge F1 mice with HF given that effects of HF and epigenetic F0 effects were reported as cumulative with regard to F1 phenotypes52 (Fig. 1b). Noteworthy, we detected no alterations in fecundity, male-to-female pup ratio and litter sizes (Fig. S1a, b), suggesting that F0 obesity did not affect development and embryonic implantation rates nor indirectly affected maternal provisioning after birth due to different litter sizes, two potential confounders.

When phenotyping F0 and male F1 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 F1 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 F0 HF mice, but also in their F1 (F0 HF) offsprings. Whilst circulating F1 insulin (Fig. S2b) was unchanged, we found that obesity increased iWAT (Fig. S2c) and eWAT (Fig. 1f) masses and elevated serum levels of leptin, an adipokine reflecting adipocyte size and numbers, in F0 and F1 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 F0 and F1 (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 F0 and male F1. When phenotyping female offsprings of lean, obese and weight-regressed F0 fathers, we observed no changes in F1 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 F0, 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 mass spectrometry to determine differentially abundant proteins in F0 and F1 eWAT. We observed 980/431 proteins increased in F0 HF compared to F0 LF mice and in F1 (F0 HF) compared to F1 (F0 LF) mice. Conversely, 632/305 proteins were less abundant comparing F0 HF to F0 LF mice (Fig. S3a) and F1 (F0 HF) compared to F1 (F0 LF) mice (Fig. S3b). Using the STRING database (https://string-db.org/) followed by Gene Ontology (GO) analysis of overrepresented biological processes (GOBP), we observed that F0 HF mice (Fig. 1k) and F1 (F0 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 F0 HF and F1 (F0 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 like translocase of outer mitochondrial membrane 20 (TOMM20). In contrast, upregulated GOBP terms in F0 HF mice (Fig. S3a) and their F1 (F0 HF) progeny (Fig. S3b) included unrelated GOBP terms like protein translation, Golgi-mediated transport and secretion, as well as protein localization. Importantly, abundance of metabolite- (Fig. S4a–c) and lipid-based (Fig. S4d, e) biomarkers that can reflect systemic mitochondrial dysfunction, for instance tricarboxylic 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 adipocytes53,54 were induced, whilst mito-protective long-chain C18:1/C24:1 ceramides were reduced in F0 HF mice (Fig. S4h). Thus, male obesity downregulates proteins associated with mitochondrial processes in obese F0 and F1 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 the expression of mitochondria-associated miRNA-mRNA networks

We next turned our attention to in detail study of gene-regulatory processes at the messenger and miRNA level that might underlie the observed repression of mitochondria-associated proteins in F0 and F1 adipose tissue, and to this end, performed mRNA-seq of eWAT and liver of F0 and F1 groups and performed miRNA and mRNA differential expression analysis, specifically focussing on mitochondrial processes. Comparing F0 HF to F0 LF eWAT using adjusted p values (p adj) ≤ 0.05, we detected yielded 3265 upregulated and 2890 downregulated genes (Fig. 2a). When comparing F1 (F0 HF) to F1 (F0 LF) mice, we detected no significantly regulated genes when accounting for multiple testing (p adj). Yet when specifically interrogating differential expression to reflect the more variable transcriptional effects in F1 mice, we found 766 upregulated and 893 downregulated genes using unadjusted p Values (p) ≤ 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 cilia function in F1. Importantly, we confirmed our proteomic findings and detected overrepresented GOBP related to lipid catabolism, ATP synthesis, OxPhos and ETC downregulated in adipose tissue of F0 HF mice (Fig. 2c) and their F1 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 a large extent reversible by weight loss, as comparing F0 HF to F0 HF-LF eWAT yielded 2687 upregulated and 2237 downregulated genes using p adj (Fig. S5a), whilst comparing F1 (F0 HF) to F1 (F0 HF-LF) eWAT yielded 547 upregulated and 732 downregulated 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).

Given that miRNAs are important (rheostatic) components in post-transcriptional gene network and have previously been linked to mitochondrial maturation and function55, albeit not in adipocytes, we next wondered whether concerted downregulation of mitochondrial programs in obese eWAT might (partially) be instigated by altered expression of mito-miRs1 like let-72, miR-3783, miR-1814, miR-1435 that functionally support mitochondrial formation and oxidative metabolism. Thus, when conducting small RNA (sRNA)-seq to determine differential miRNA expression, we found that F0 obesity repressed mito-miRs that positively affect mitochondrial biogenesis and function, for instance miR-143-3p56, miR-181a-2-3p57,58, miR-18259, miR-20060 and miR-378a-3p61 in eWAT of F0 HF (Fig. 2e) and F1 (F0 HF, Fig. 2f) mice, whereas mitochondria-inactivating miR-53262 was induced in F0 HF (Fig. S6) and F1 (F0 HF, Fig. S6) eWAT, suggesting that miRNA dysregulation contributed to mitochondrial impediment observed in obese F0 eWAT. Integration of miRNA-mRNA NGS datasets with adipocyte-specific miRNA-mRNA-Argonaute 2 interactomes using AGO-HITS-CLIP6 revealed that miR-378 and miR-143, despite being predicted, did not coincide 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 F0 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 F0 HF (Fig. S5e) and F1 (F0 HF, Fig. S5f) mice. Thus, F0 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 specific the observed heritability of mitochondrial dysfunction was as a cellular and metabolic process. Further, we aim to address whether this impairment in mitochondrial gene expression was confined to adipose tissue. Thus, we next 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 dysfunction33, 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 (Retnlb) were induced in F0 HF, but not F1 eWAT (Fig. S6e, f), emphasizing that a meta-inflammatory tone is not passed on from F0 HF mice.

Given the dysfunction in a specialized 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 F0 LF segregated from F0 HF and F0 HF-LF, arguing for mostly irreversible effects of obesity in F0 liver (Fig. S7a) and modest effects of F0 obesity on F1 liver transcriptomes (Fig. S7b). When comparing F0 HF to F0 LF livers, we found 2,474 up- and 2,612 downregulated genes with p adj ≤ 0.05 (Fig. S7c), whereas comparing F1 (F0 HF) to F1 (F0 LF) without adjusting for multiple testing (p ≤ 0.05) yielded 716 up- and 546 downregulated genes (Fig. S7d). GSEA63 of differentially regulated mRNAs identified lipid metabolism and organelle formation as being induced, whereas repressed terms included lipoprotein trafficking, endoplasmic reticulum and ribosome, yet no transcriptional evidence for altered mitochondrial processes in the liver of obese mice and their F1 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 F0 HF mice, but not lean F0 or F1 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 F0 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 F0 HF, but not F1, mice. Thus, combinatorial analyses of transcriptional and histological changes argue against prominent effects of paternal obesity onto F1 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 humans22; and it is therefore conceivable that F0 HF feeding and weight loss alters spermic sncRNA levels that can then, following oocyte fertilization, be passed on to fertilized oocytes (zygotes) as shown recently23. By changing sRNA composition in zygotes, sncRNAs thus have the ability to post-transcriptionally affect decision points in embryogenesis, thereby contributing to the observed glucose intolerance and mitochondrial protein repression observed in F1 offsprings sired by F0 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 fragments26 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 fertilization, a scenario also conceivable for other types of mitochondria-associated sncRNAs, for example mito-miRs. To map the effects of paternal obesity and weight loss on spermic sncRNAs and to gain insights into sRNA-driven metabolic adaptations in F0 HF sperm, we next isolated motile spermatozoa using swim-up assays and charted differential spermic sncRNA responses as a consequence of body weight gain and losses and found sperm harbored 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 an 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. S9a), but only 5’ fragments of mt-Ts-1 were significantly changed in sperm from F0 HF and F0 HF-LF diet (Fig. b). As miRNAs are abundant in somatic cell types and sperm, harbor 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 F0 obesity and weight loss caused a coordinated and inverse (i.e., downregulation and upregulation, respectively) effect on miRNAome regulation (Fig. 3d, e), a finding previously observed by our labs51 and others49,50,64 for obese adipose tissues; in the latter case, at least partially due to 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 sperm21: 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 downregulated, whilst inactivating mito-miRs of the let-7 family upregulated in obese F0 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 F0 eWAT and sperm and F1 eWAT (Fig. 3g–i). As increases of let-7c were reported in intergenerational rat studies by us8 and others65 and because let-7 impairs embryo implantation and glucose homeostasis when overexpressed in mice66,67, 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.

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, the observation that environmentally and artificially induced (e.g., elicited by miRNA microinjection) alteration in zygote miRNA (pools) trigger abnormalities in progeny25,68, and because embryonic trajectories require maturation of pre- into mature miRNAs69, we next tested the functional relevance of let-7 gain-of-function in one-cell embryos for F1 phenotypes. We focused on let-7d-5p and let-7e-5p given that these exhibit the most concordant increases amongst let-7 family members in F0 and F1 eWAT and sperm (Fig. 3g–i), and because let-7 has been connected to embryonic cell fate decisions70,71. 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 F0 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/6J mice using standard protocols29 (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 analyzed 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 downregulated mitochondrial processes in F MI mice (Fig. 4h). Thus, zygotic delivery of let-7 at levels corresponding to few F0 HF sperm cells, causes glucose intolerance and aberrant mitochondrial gene regulation in mice derived from these embryos.

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 embryos20,72,73, 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 F1 phenotypes if perturbed by miRNA delivery, despite the physiological levels of miRNA delivery and overt absence of embryonic lethality. Nonetheless, it is conceivable that alterations in zygotic miRNAs still entail far-reaching consequences in sired mice due to a combination of altered developmental, epigenetic and metabolic processes73. Based on the specific mitochondrial alterations, but absence of increased let-7 levels in F1 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-seq274 (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 evidence4,75 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 humans2. As it is becoming appreciated that nutritional and metabolic cues shape human sncRNA expression22, we tested effects of weight change on semen let-7 in a small cohort of n=15 men with obesity (BMI, 39.49 ± 1.80) undergoing voluntary weight loss77 and quantified hsa-let-7 using TaqMan qPCR. Intriguingly, we found that hsa-let-7d-5p and hsa-let-7e-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/e-5p affects murine embryogenesis by impairing mitochondrial gene regulation and similar processes might be occurring following weight loss in humans with obesity.

Adipocyte let-7 impairs mitochondrial function via post-transcriptional silencing of DICER1

Beyond F0 HF sperm (Fig. 3g–i), we found let-7d-5p and let-7e-5p upregulated in F0/F1 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 F0/F1 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 the highly sequence-related let-7i can impair commitment of mitochondria-rich adipocytes78. Yet, whether 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 (1768 upregulated, 1387 downregulated genes, Fig. 5b) than let-7e-5p (557 upregulated, 893 downregulated genes, Fig. 5c) compared to cel-mir-67, yet both let-7 isoforms downregulated processes related to lipid metabolism, Insulin-AKT (Protein Kinase B) signaling 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). Dicer1 3’UTRs and exons harbored several independent let-7 seeds of which, after mutation of putative let-7 microRNA 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 in 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 whether 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). We further validated that not only DICER1 but also other mitochondrial proteins like CPT2, IDH2 were downregulated and partially restored in F0 HF eWAT (Fig. 5l, m) supporting our hypothesis that obesity impairs mitochondrial respiration and gene regulation, at least partially, by post-transcriptional silencing of DICER1 via let-7 (Fig. 5m). Thus, obesity-associated let-7 impairs mitochondrial oxidative respiration by silencing of DICER1 in mature adipocytes.

Methods
Mice housing
All animal studies received ethical approval and were approved by the Ministry of Environment and Agriculture Denmark (Miljø- og Fødevarestyrelsen), under license 2023-15-0201-01544. All mice were housed under a 12-h light/12-h dark cycle in a temperature-controlled (22°C) and humidity-controlled facility that 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 the HF-fed mice were randomly assigned to the diet regression group, which was given LFD. LFD is carefully chosen to match the food source (animal vs plant) and the 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 F0 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 an 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 100 IU 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. For area under the curve (AUC) calculations, we have normalized serum [Glu] values to [Glu, T0] of each animal to account for intra-animal variability in glucose responses and relative glucose responses.

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 2000×g for 15 min. The supernatant was collected and stored in −80°C.

Sperm collection
The prospective intervention study complies with relevant ethical regulations and 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). The primary outcomes have been published77. Briefly, mature sperm cells were collected from the cauda epididymis. Sperm cells are allowed to swim out from the cauda epididymis and incubate in phosphate-buffered saline (PBS) at 37°C for 15 min. Supernatant containing sperms was passed through a 40-µm cell strainer and 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 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, 5 IU/female, 47 h later. After the second injection, females were set in breeding with C57BL/6J stud males. The next morning, they were monitored for the formation of a vaginal plug (indicating mating had 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 10 mg/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 a 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 pseudopregnant 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 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 into the embryo at the 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% C02. 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 Ca2+ and Mg2+ 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 μl of RNAse inhibitor mix (containing 0.12 μl Triton-X-100 10%, 0.1 μl RNAse inhibitor (Takara (Cat. No. 2313B, 40 U/μl), 1.78 μl MilliQ water (RNAse-free)). Plates were then 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 16,000×g 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. The 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.

Lipidomics analysis. For lipidomics 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, 95% 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. An 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 ProteoWizard83. Lipidomics data were annotated in MS-Dial (v. 4.9) against the incorporated lipid-blast84 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 were 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 statTarget85.

Data analysis. For metabolomics pathway enrichment analysis (KEGG pathways of Mus musculus), comparing F0 LF and F0 HF serum (parameters 'Scatter plot', 'Hypergeometric test', 'relative-betweenness Centrality', 'Use all compounds in the selected pathway library') of differentially abundant polar metabolites (p ≤ 0.1) were loaded into Metaboanalyst 6.0. For lipidomics and metabolomics, principal component analysis and heatmap representation of an indicated number 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 Centring'.

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 PFA 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 (<5% hepatocytes with large fat vacuoles), S1 (5–33%), S2 (>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 from 6 mice of all F0 and F1 groups were performed on at least 75 adipocyte profiles in each tissue.

Proteomics from epididymal white adipose tissue. Deep frozen fat pads (200 mg of epididymal adipose tissue) were powdered with a mortar-pestle and subjected to homogenization with ceramic beads (Percellys, 5500 rpm, 2×10 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 20,000×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 8 M 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 (2 M 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 (StageTip)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. Productions 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 MaxQuant88 (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 Perseus89 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 were 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 Z-scored and hierarchically clustered using the Euclidean distance model. Resulting clusters were merged onto the whole dataset, and a Fisher's Exact Test was 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 F0 sperm, library preparation and sRNA-seq were performed at Cologne Centre 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 NovaSeq 6000 serie no. A00316. (2) Small RNA sequencing from F0 and F1 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 2×50 bp paired-end sequencing on Illumina Novaseq 6000.

RNA seq data analysis
Small RNA sequencing of spermatozoa. SncRNA-seq analysis of murine sperm was performed 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 one read per sample, and a size of 15–75 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 pipeline90. 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 R91 using respective Bioconductor packages92.

mRNA sequencing. Sequencing reads were aligned to the mouse reference genome from Gencode (vM25)93 using STAR aligner (v2.7.9a)94, and gene features were counted by using featureCounts v (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 voomLmFit97 function 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 = 1,000,000), respectively. Gene permutation 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 before74. 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 µl nuclease-free water were added to each well, and the lysis protocol was run when the samples were incubated at 72°C for 3 min in a 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 MgCl2 (1 M), 0.5 µL of DTT (100 mM), 0.5 µL of SuperScript III (200 U/µL), and 0.29 µl 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 IS PCR 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 Tapestation 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 Tapestation using the High Sensitivity DNA assay. All libraries were individually normalized and diluted to a concentration of the pooled library to 5.4 ng/µL with an average size ~515 bp (16 nM). The libraries were sequenced on Illumina NextSeq 500 in a single-end 75-bp format (FC-404-2005, Illumina). Single-nuclei libraries were sequenced at an average depth of 3 million reads.

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, Denmark77. The semen samples were collected from patients living with obesity (≥ 30 kg/m2) via masturbation following 2 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). 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 primary outcomes have been published77. 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 3.75 U/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 wild-type (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 (mirus bio) 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, DLRAs (Promega) were performed. The relative activities of firefly luciferase, indicating the interaction between miRNA and the 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. The level of mature hsa-miR-16 was used as an 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 OCRs 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% CO2. 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 1× PBS and changed to 180 µl Assay Medium in a non-CO2 incubator at 37°C. Calibration was started using calibration plates, measuring O2 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 (Mito Stress Kit). 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 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, 1:11,000 dilution), VDAC (Cell Signaling Technologies #4661, 1:11,000 dilution), HK2 (Cell Signaling Technologies #2897, 1:1,1000 dilution), SLC25A24 (Abcam Ab221120, 1:1000 dilution), CPT2 (Abcam Ab181114, 1:1,1000 dilution), CRAT (AP Proteintech #15170, 1:1000) and IDH2 (Cell Signaling Technologies #60322, 1:1,1000 dilution). Relative protein quantification was normalized by HSC70 (Santa Cruz sc-7298, 1:1000 dilution) or HSP90 (Cell Signaling Technologies #4877, 1:1000 dilution).

Access to reagents. All requests for data and reagent sharing should be directed to the corresponding author (janwilhelmkornfeld@bmb.sdu.dk).

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 the figure legends, respectively.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Huang et al. describe the intergenerational consequences of paternal obesity in mice by focusing on a diet-induced adipose-to-sperm miRNA axis involved in mitochondrial function.\nThe authors have compiled a significant amount of data which, while making the story compelling and interesting, leaves it difficult to follow. In general all my previous comments have been addressed and for this version, I do have the following general suggestions:\n1. F1 glucose phenotypes - Still not clear to me what the n values mean, is it families? offspring? and - should the latter be the case - how have the offspring been selected?\n2. F1 glucose phenotypes - given the slight but detectable differences in fasting glucose levels, it would be important to also show normalized glucose responses to understand whether the \"glucose tolerance\" phenotype is an exclusive function of the impaired fasting glycaemia\n3. sperm smallRNA analysis - The authors have now included most of the smallRNA biotypes in the analysis, but I still miss the nuclear-encoded tRNAs, which have been implicated in paternal effects and might mirror miRNAs.\n4. Single blastomere RNA-seq - What is the specific advantage and rationale of looking at single blastomeres, rather than running a bulk RNA-seq on single morula?\n5. General writing - please go through the ms for typos (eg. offsprings) and correct figure indications.", "reviewer_2": "The revised manuscript is markedly improved and satisfactorily addresses most of the concerns raised in the previous round. Only two minor issues remain:\n1. Overstatement of causality – The manuscript seems to imply that sperm-borne let-7 drives the entire inter-generational phenotype. Given that loss-of-function evidence is not presented, it would be more accurate to describe let-7 as “a contributing factor” rather than “the driver.” Please soften the wording accordingly.\n2. Image clarity for western blots – Several immunoblots (notably CRAT in Fig. 5f and the blots in Fig. 5i, 5L) are blurry and difficult to interpret. Higher-resolution images would improve readability.", "reviewer_3": "The authors studied how paternal obesity influence offspring metabolic phenotypes. The authors found that sperm miRNAs, especially let-7 family members influence offspring by targeting mitochondrial function and TCA cycle crossing tissues, providing new insight into altered mitochondrial gene regulation and transcriptional rewiring during embryogenesis and in adult adipose tissue. While phenotypic and transcriptional changes are described, how sperm miRNA alterations mechanistically translate into tissue-specific mitochondrial dysfunction in offspring remains insufficiently elucidated. The observation that adipose tissue, but not other tissues like liver, shows transcriptional reversibility and mitochondrial regulation lacks mechanistic insight. The cause of adipose-specific sensitivity and the role of other tissues in glucose intolerance need clarification.\n1.The study focuses on the short-term effect of metabolic changes in the offspring, but it lacks long-term follow-up to determine whether these effects last into later stages of life. A more comprehensive assay over time could provide insights into whether paternal obesity-induced epigenetic changes might contribute to age-related diseases such as type 2 diabetes or cardiovascular disease, which are common in individuals with a history of obesity.\n2.The sperm miRNA profile was analyzed, but a broader and more comprehensive analysis could have been performed to explore the full spectrum of small non-coding RNAs (sncRNAs) that might be involved in transmitting the effects of paternal obesity. Exploring additional types of sncRNAs, such as piRNAs or other miRNAs, would help clarify whether let-7 alone is responsible for the observed effects or whether other regulatory RNAs also contribute to intergenerational inheritance.\n3.The study focuses on a specific genetic background (C57BL/6N mice), which is a commonly used strain in metabolic research. However, it is unclear whether the observed intergenerational effects are common to other genetic backgrounds in mice.\n4.The study mainly uses RNA sequencing and some histological analyses to assess the impact of paternal obesity. It will be great to show the context of mitochondrial morphology, such as electronic microscope, would provide a better understanding of the cellular and tissue-specific consequences of paternal obesity. It would be useful to compare the effects in other organs involved in metabolic regulation, such as muscle or pancreas, to demonstrate whether the effects are specific to adipose tissue or are more widespread.\n5.Given that DICER1 is a global regulator of miRNA maturation, its knockdown could have widespread effects beyond mitochondrial biology. The specificity of this pathway in relation to mitochondrial impairment remains unclear.\n6.The study uses LNA to knock down Dicer1. LNAs can have off-target interactions, especially when targeting highly conserved regions.\n7.The idea that adipocyte-derived miRNAs are horizontally transferred to sperm via epididysomes remains unclear. No experiments, such as labeling, tracking of vesicle cargo, are conducted to demonstrate this cross-tissue miRNA transfer. These miRNA elevations could result from parallel responses to obesity rather than reflecting active cross-tissue transfer.\n8.There are still other possibility from other epigenetic vectors such as DNA methylation, histone modifications that may also mediate intergenerational effects."}, "author_response": "We thank the reviewer for her/his encouraging remark and appreciation of the work performed to consolidate our findings. We do agree that a substantial set of data has been obtained and presented and have therefore restructured the paper to allow following the paper more easily. This involved presenting the data in a more natural order (for instance, but not limited to a side-by-side presentation of F0/F1 data to allow direct comparisons of metabolic and gene-regulatory effects).\nGiven the (intergenerational, multi organ, microRNA-mRNA datasets) ressource character of the paper, we decided to make the full dataset available to the community but, whenever possible, we have moved non-essential data to the Supplementary Information part of the paper for more clarity.\nTo not overcomplicate the paper, we have included the new data from the revision process in this rebuttal letter. We hope the letter can be made public in combination with the reviewers’ questions.\nQuestion 1.\nF1 glucose phenotypes - Still not clear to me what the n values mean, is it families? offspring? and - should the latter be the case - how have the offspring been selected?\nResponse 1.\nWe apologies for being unclear here: The n in the ipGTT represent the total number individual F1 mice. Per the 3 F1 groups we obtained 6-10 litters, and we randomly chose 1-2 mice per individual litter for subsequent metabolic and molecular analyses to end up with n=10 mice per each F1 group.\nQuestion 2.\nF1 glucose phenotypes - given the slight but detectable differences in fasting glucose levels, it would be important to also show normalized glucose responses to understand whether the \"glucose tolerance\" phenotype is an exclusive function of the impaired fasting glycaemia\nResponse 2.\nWe agree that the glucose excursion during an intraperitoneal GTT often reflects the basal (e.g.., 6h fasted) glycemia before the glucose challenge at T0. To account for this phenomenon, we had in our paper normalised glucose responses to individual glucose values at T0 in AUC calculations shown in Fig.S2a,b and as suggested by the reviewers. We have added the sentence ‘For area under the curve (AUC) calculations, we have normalised serum [Glu] values to [Glu, T0] of each animal to account for intra-animal variability in glucose responses and relative glucose responses’ to the Methods parts to avoid ambiguity.\nQuestion 3.\nSperm smallRNA analysis - The authors have now included most of the smallRNA biotypes in the analysis, but I still miss the nuclear-encoded tRNAs, which have been implicated in paternal effects and might mirror miRNAs.\nResponse 3.\nWe agree that it is worthwhile to investigate nuclear-encoded tRNA fragments (tsRNAs) that have been described in the context of spermic alterations in obesity (PMID 26721685, 26721680). To our surprise, we did not detect significant trends in tsRNAs that were previously associated with epigenetic phenotypes, for instance Gly-GCC and Glu-CTC. We interpret this to be a potential consequence of the different diet feeding performed in aforementioned studies such as PMID 26721685 (19 versus 10% protein for control and our low-protein diet, LPD) or PMID 26721680 (similar HFD as our study, but different control diets) or different durations of diet feeding (PMID 26721685: 6-9 weeks LPD; PMID 26721680: 22 weeks HFD) compared to our study and, finally, depending on whether IVF was performed (PMID 26721685: Yes; PMID 26721680: No) which could all obfuscate obtaining similar results in our study.\nQuestion 4.\nSingle blastomere RNA-seq - What is the specific advantage and rationale of looking at single blastomeres, rather than running a bulk RNA-seq on single morula?\nResponse 4.\nWhen designing the scRNA-seq experiment we anticipated to detect inter-embryo / inter-blastomere differences as observed for mouse 2-/4-blastomere stage embryos (PMID: 25096407). To our surprise, the strong transcriptional effects following let-7 injection masked the intercellular differences. Thus, one might have a posteriori have resorted to bulk RNA-seq on single morulae, as suggested, but we think that providing the single-cell resolved data to be appropriate at this point.\nQuestion 5.\nGeneral writing - please go through the ms for typos (eg. offsprings) and correct figure indications.\nResponse 5:\nWe apologize for the remaining typographical errors that were also overlooked using a spelling program. We manually went through the paper and corrected mistakes with a focus on genitive case.\nReply:\nWe are happy to learn that this reviewer appreciates our additional analysis and that we were able to address most of his/her concerns.\nQuestion 6.\nOverstatement of causality – The manuscript seems to imply that sperm-borne let-7 drives the entire inter-generational phenotype. Given that loss-of-function evidence is not presented, it would be more accurate to describe let-7 as “a contributing factor” rather than “the driver.” Please soften the wording accordingly.\nResponse 6.\nWe agree that overstatements compromise the impact of our work and have throughout the manuscript mitigated statements not backed up by data. We also appreciate that, while spermic let-7 has been associated with intergenerational epigenetic effects in obese rodents (PMID: 26977389) and in this study, other sncRNA have been described (miR-19, mt-tRNAs and mt-tsRNAs, reviewed in PMID: 32415866) that might contribute to the intergenerational effects on glucose metabolism and mitochondrial function that we described. Whilst these studies did not perform sncRNA injection experiments, some of them have demonstrated sperm-oocyte transfer (PMID: 38839949). Thus, we throughout the paper we referred to let-7 as a contributing factor and not driver of heritable effects.\nQuestion 7.\nImage clarity for western blots – Several immunoblots (notably CRAT in Fig. 5f and the blots in Fig. 5i, 5L) are blurry and difficult to interpret. Higher-resolution images would improve readability.\nResponse 7.1:\nWe acknowledge that the quality of Western Blot analyses can be improved and the low affinity of antibodies targeting mitochondrial ETC / TCA components constitutes an important limitation, particularly when interrogating lipid-rich tissues like fat. Despite several attempts, the quality for CRAT could not be improved. Thus, despite our luciferase data suggesting CRAT to be targeted by let-7 in HEK cells, we could not adress this functional interaction in adipocytes (Fig.5f). Given this, and the fact that we do not further pursue the role of CRAT in adipocytes, we omitted CRAT Western Blots. More limiting is the poor quality of DICER1 Western Blots. Post-transcriptional silencing of DICER1 by let-7 is central to our paper, and despite this interaction being shown in other cellular contexts such as cancer cells (PMID: 18700235). DICER1 Western Blot remains cumbersome due to the large size of the protein (217kDa) thus inefficacy of transfer as well as its poor (tryptic) digest which leads to DICER1 peptides not being present in proteomic datasets, also ours.\nResponse 7.2:\nAlthough not explicitly asked for by the reviewers, we unsolicitedly aimed to strengthen the functional interaction between let-7 and reduced DICER1 in adipocytes, and to address the point of hitherto relying only on let-7 loss-of-function gain-of-function experiments, we have additionally performed let-7d/e inhibition using locked nucleic acid (LNA) inhibitors in primary adipocytes. As expected, densitometric quantification revealed that loss of let-7 increased levels of DICER1 protein, but not other that of other predicted let-7d/e targets (HK2, CPT2). The fact that both let-7 gain-of-function (old version of the paper), but also loss-of-function (included in this rebuttal letter) antagonise and restore DICER1 protein, we are confident regarding our major claim of functionally linking let-7 abundances to post-transcriptional repression of DICER1.\nReply:\nWe thank the reviewer for his/her points and for thoroughly assessing our work. We agree with the reviewer and the editor that these points warrant a detailed analysis of our sncRNA data, of testing the possibility of horizontal let-7 transfer and additional experiments regarding the function of DICER1 in mitochondrial regulation in adipocytes. We hope that by our extensive additional experiments we have addressed the reviewer’s concerns and hope that our manuscript of sufficiently quality for publication in Nature Communications.\nQuestion 8:\nThe study focuses on the short-term effect of metabolic changes in the offspring, but it lacks long-term follow-up to determine whether these effects last into later stages of life. A more comprehensive assay over time could provide insights into whether paternal obesity-induced epigenetic changes might contribute to age-related diseases such as type 2 diabetes or cardiovascular disease, which are common in individuals with a history of obesity.\nResponse 8:\nWe agree that analysing the effect of time that F1 phenotypes need to unfold is an important consideration. In our initial paper we chose to interrogate F1 metabolism after 21 weeks of LFD feeding to align with the duration of F0 diet interventions. We could not extend the duration of HFD/LFD feeding beyond 24 weeks of age due to welfare considerations and ethical approvals that limit the duration of HFD feeding in F0, but also as we consider conducting a full (F1) ageing study beyond the scope of this paper.\nAs we were nonetheless interested in testing whether F1 phenotypes occurred earlier than at 24 weeks of age (TP2), we analysed F1 mice also at 15 weeks of age (TP1, data hitherto not shown in the paper). In contrast to F1 mice analysed and sacrificed at TP2, no alterations in glucose tolerance and insulin sensitivity evidenced by ipGTT/ipITT can be observed in male and female mice at TP1. Thus, our data suggest that F1 effects require a time window of >12 weeks post-weaning and LFD feeding to unfold, and/or an additional F1 HFD burden (PMID: 26974008).\nQuestion 9.\nThe sperm miRNA profile was analyzed, but a broader and more comprehensive analysis could have been performed to explore the full spectrum of small non-coding RNAs (sncRNAs) that might be involved in transmitting the effects of paternal obesity. Exploring additional types of sncRNAs, such as piRNAs or other miRNAs, would help clarify whether let-7 alone is responsible for the observed effects or whether other regulatory RNAs also contribute to intergenerational inheritance.\nResponse 9:\nWe acknowledged the importance of addressing the point of whether other sncRNAs beyond let-7 contribute to epigenetic phenotypes, which has also been raised by Reviewer 2 (see rebuttal to Q6). To address the contribution of other sncRNAs than microRNAs in somatic tissues (eWAT), we quantified cumulative counts and unique no. of sequences sncRNA biotypes across F0/F1 eWAT and F0 sperm using the same computational workflow for all three datasets. Here, comparison was performed on a sequence level, annotating them to Ensembl’s non-coding and coding RNA references, as well as piRBase and miRBase to investigate a broad spectrum of biotypes.\nAs described in the literature (PMID: 31681757) we observe the highest variation between small RNA biotypes in sperm, whereas eWAT exhibited the lowest variation (limited to expression of microRNAs and piRNAs for F0 eWAT). We think this argues against performing an even more thorough analysis of non-microRNA sncRNA biotypes in F0/F1 adipose tissue. This would only be meaningful in our eyes for piRNAs, but these are functionally connected to transposon silencing and germline maintenance, whereas an implication of piRNAs in (multi/inter)-generational epigenetic memory has only been described in lower organisms (PMID: 22738725). Finally, while we appreciate the potential implication of other sncRNAs in intergenerational crosstalk (notably 5’ tsRNA fragments) tsRNA were not differentially expressed in eWAT. Thus, we opted not to perform microinjection experiments using tsRNAs, we think that functionally delineating their contribution, i.e., by additional embryonic microinjection experiments (and scRNA-seq) as done for mmu-let-7d/e is beyond the scope of the study. Furthermore, as described in Q6, we have toned down the statement that let7 drives all the effects to \"are a factor\" of the effect.\nQuestion 10.\nThe study focuses on a specific genetic background (C57BL/6N mice), which is a commonly used strain in metabolic research. However, it is unclear whether the observed intergenerational effects are common to other genetic backgrounds in mice.\nResponse 10.\nWe think that this is an excellent point and repeating these studies in other (obesity-resistent) genetic background such as Sv/129 or outbred strains would add to the rigor of our findings. Given that let-7c has been described in an intergenerational rodent obesity study that used non-syngenic rats as model (PMID: 26977389), we think repeating our study in another extensive mouse cohort does not warrant the amount of resources needed to do so.\nQuestion 11a.\nThe study mainly uses RNA sequencing and some histological analyses to assess the impact of paternal obesity. It will be great to show the context of mitochondrial morphology, such as electron microscope, which would provide a better understanding of the cellular and tissue-specific consequences of paternal obesity.\nResponse 11b.\nIn addition to the transcriptomic and histological analyses presented in the study, our data further indicate alterations in mitochondrial morphology in epididymal white adipocytes of obese fathers. Specifically, we observed that paternal obesity (F0 HF) leads to smaller and more irregularly shaped and fragmented mitochondria compared to F0 LF/F0 HF-LF, likely reflecting the TEM manifestations of mitochondrial dysfunction that were published recently in Nature Metabolism (PMID 38286821) and before (PMID: 16501941, 15520860). Thus, these morphological changes support the downregulation of mitochondria-related pathways identified in our multi-omics analyses.\nQuestion 11b.\nIt would be useful to compare the effects in other organs involved in metabolic regulation, such as muscle or pancreas, to demonstrate whether the effects are specific to adipose tissue or are more widespread.\nResponse 11b.\nWe appreciate that our current analysis which focuses on transcriptional effects in mitochondrial genes might be (too) limited by investigating mitochondrial gene expression only in liver and adipose tissue. We thus designed qPCR primers for genes deregulated in obese F0 and F1 adipose tissue and measured the expression of these genes in other F0 and F1 metabolic tissues:\nImportantly, when interrogating expression of mitochondrial gene expression(mtCox2), nuclearly-encoded mitochondrial proteins (Tfam, Cox5b, Sdhb, Ndufa1, Ndufs1, Ndufa8, Atp5a1, Cox6a2, Atpaf2, Atp5if1, Atp5g3) and regulators of nuclear expression of mitochondrial proteins (Nrf2) in the heart, a tissue dependent on oxidative respiration, kidney and skeletal muscle, two organs important for glucose regulation, we did not observe alterations in mitochondrial genes, but also not of membrane calcium-transporting ATPase 2 (Atp2b2). We conclude that the alterations in glucose tolerance and systemic alterations in metabolites indicative of impaired mitochondrial function that we reported for F0 HFD exposed animals (and F1 progeny) triggers mitochondrial dysfunction predominantly in epididymal white adipose tissue, but not in the other metabolic tissues that we investigated. We aimed to isolate RNA from pancreati which we isolated but unfortunately the RNA integrity was insufficient to perform qPCR analysis.\nQuestion 12.\nGiven that DICER1 is a global regulator of miRNA maturation, its knockdown could have widespread effects beyond mitochondrial biology. The specificity of this pathway in relation to mitochondrial impairment remains unclear.\nResponse 12.\nAlthough the role of adipose DICER1 in mitochondrial gene regulation has been explored in the context of organismal ageing and calorie restriction (PMID: 27241713), a thorough investigation of the metabolic roles of DICER1 in mature adipocytes has not been performed yet, so this is a valid point that we wanted to address with additional experiments. To address the role of DICER1 selectively in adipocytes, we resorted to a new cellular model (Rocha et al. Unpublished, PMID: 33268375) that ablates DICER1 in immortalised, mature adipocytes of subcutaneous origin in a Tamoxifen-inducible manner (DICER1-KO cells). Using this system, we investigated mitochondrial respiration using Seahorse and found that, as seen using LNA-mediated DICER1 knockdown, that genetic deletion of DICER1 also blunted oxygen consumption, indicating reduced mitochondrial numbers and/or function.\nQuestion 13.\nThe study uses LNA to knock down Dicer1. LNAs can have off-target interactions, especially when targeting highly conserved regions.\nResponse 13.\nWe acknowledge the limitations of LNA-mediated DICER1 knockdown and risk of off-target effects, particularly when using short LNA GapmeR. As shown in point 12, we validated the mitochondrial (respiratory) defects of inducible DICER1 knockouts in mature adipocytes (DICER1-KO cells) and in adipose tissue of conditional DICER1-KO mice, reenforcing the functional connection between DICER1 deficiency and transcriptional mitochondrial impairment across several (KD, KO) orthologous cellular and mouse models.\nQuestion 14.\nThe idea that adipocyte-derived miRNAs are horizontally transferred to sperm via epididysomes remains unclear. No experiments, such as labeling, tracking of vesicle cargo, are conducted to demonstrate this cross-tissue miRNA transfer. These miRNA elevations could result from parallel responses to obesity rather than reflecting active cross-tissue transfer.\nResponse 14.\nWe think this is an excellent point which we address using new data from our lab and additional re-analyses of public data. To address this question comprehensively, we used the Dicer1-AdcKO mouse line to define and measure ’sentinel’ microRNAs that are absent from KO fat and tracked their regulation in sperm (R14.1) and addressed trafficking of let-7 isoforms using public data (R14.2).\nResponse 14.1 Transfer of mature ’sentinel’ microRNAs from adipose tissue to sperm\nTo get insight into the transfer of adipocyte-derived microRNAs into spermatozoa, we used the adipocyte-specific DICER1-AdcKO model to deplete microRNAs in adipocytes as described by us and others (PMID: 22958919, 26900752, 24983316). In this context testing let-7 is not ideal these microRNAs are ubiquitous and highly abundant in circulation. Thus, DICER1-AdcKO evoked loss of let-7 in adipocytes might not meaningfully reduce sperm let-7 level; particularly considering compensatory mechanisms that we observed in other settings.\nInstead, we focussed on microRNAs that are known to be exchanged by microvesicular (MV) transfer (PMID: 39285292), are highly abundant in fat, and have in the past been proven to be adipocyte DICER KO-sensitive miRNAs. miR-16-5p and miR-92a-3p fulfill this criterion and accordingly were repressed in adipose tissue of DICER1-AdcKO mice, whereas adipocyte DICER insensitive miRNAs such as miR-193b-5p and miR-99b-5p were unaffected by DICER1 knockout.\nResponse 14.2 Transfer of let-7 isoforms from testicular cell types to sperm:\nGiven that we only validated horizontal transfer of microRNAs between fat tissue and sperm by determining the exchange miR-16-5p/92a-3p, but not let7 (which was also not directly affected by DICER1 KO), we next specifically addressed the transfer of let-7 isoforms between testicular cell types and sperm: For this, we re-analysed data from a study that used testicular germ cell (GC)-specific (Dgcr8fl/fl; Stra8-iCre+/-) and epididymes (Epi) specific (Dgcr8fl/fl; Defb41-iCre). Knockout of Dgcr8 was used to achieve microRNA loss in GC and Epi, respectively. Following microRNA depletion in GC/Epi, sRNA-seq in sperm was performed to delineate the cellular origin of spermic microRNA and possibility for horizontal transfer of microRNAs (including let-7) between testicular somatic and germ cells. Intriguingly, Dgcr8 KO in epididymis did not cause a reduction in most microRNAs including let-7 in sperm (top), whereas deficiency of microRNAs in testicular germ cells did. This suggested to us that provisioning of let-7 to sperm must happen early during sperm formation, as deleting maturation of let-7 and most other microRNAs secreted from the cauda epididymes are insufficient to change sperm microRNA payloads.\nResponse 15.\nWe acknowledge the existence of other gene-regulatory (chromatin based) modalities that have been reported to affect intergenerational traits. As our focus was on specifically investigating sncRNA which (in contrast to DNA methylation and histone PTM) have the capacity to be physically exchanged between tissues and cells, and because we here demonstrate that certain microRNA even undergo horizontal transfer between adipocytes and sperm (response to question 14), we consider performing additional omics beyond the scope of this paper but will include this shortcoming in the the discussion."}, {"round": 2, "reviewer_comments": {"reviewer_1": "I'd like to thank the authors for the additional effort they've put into satisfying our - hopefully fair and logical - requests and improving their study.\nI'm still left with some minor concerns I'd like to have clarified. The reason for this is that the study is an important study and deals with a topic, which is receiving more and more attention by the scientific and clinical communities but - perhaps most importantly - by the public. Our duty is to deliver clear and solid informations.\nHere my specific points (called remaining doubts here):\nQuestion 1.\nF1 glucose phenotypes - Still not clear to me what the n values mean, is it families? offspring? and -\nshould the latter be the case - how have the offspring been selected?\nResponse 1.\nWe apologies for being unclear here: The n in the ipGTT represent the total number individual F1\nmice. Per the 3 F1 groups we obtained 6-10 litters, and we randomly chose 1-2 mice per individual\nlitter for subsequent metabolic and molecular analyses to end up with n=10 mice per each F1 group.\nRemaining doubt 1.\nThanks a lot for the clarification. Based on this approach I would have chosen - if possible - more mice per litter (at least 4) to be able to do post-hoc clustering based on litter and account for litter-to-litter variation. That said, looking at the IPGTT data and considering your mouse selection approach, are you not surprised by the relatively small variability you observed, compared to the one you'd have expected from combining 1-2 mice/litter across 10 litters?\nQuestion 2.\nF1 glucose phenotypes - given the slight but detectable differences in fasting glucose levels, it would\nbe important to also show normalized glucose responses to understand whether the \"glucose\ntolerance\" phenotype is an exclusive function of the impaired fasting glycaemia\nResponse 2.\nWe agree that the glucose excursion during an intraperitoneal GTT often reflects the basal (e.g.., 6h\nfasted) glycemia before the glucose challenge at T0. To account for this phenomenon, we had in our\npaper normalised glucose responses to individual glucose values at T0 in AUC calculations shown in\nFig.S2a,b and as suggested by the reviewers. We have added the sentence ‘For area under the curve\n(AUC) calculations, we have normalised serum [Glu] values to [Glu, T0] of each animal to account for\nintra-animal variability in glucose responses and relative glucose responses’ to the Methods parts to\navoid ambiguity.\nRemaining doubt 2\nThanks a lot for this. From the normalized AUC data, there seems to be no significant impact of the paternal perturbations on\noffspring metabolic control (body weight, glucose tolerance and insulin sensitivity). Are the differences in fasting glucose\nsignificant? Is the glucose tolerance phenotype of the F1 mice generated after zygotic microinjection confirmed after\nnormalization to basal glucose levels (which seem to be different there as well)? How do these results change the\ninterpretation and impact of your study? Were adipose tissue analyses (H&E, mRNA-seq, proteomics ecc.) done on the\nwhole cohort? If not, how were these samples selected?\nThese points and limitations of the study should be clearly addressed in the discussion together with a potential explanation\nfor the adipose phenotyped being uncoupled from organismal metabolic control. Would this adipose phenotype be a\nsignature of increased obesity and metabolic disease risk in the F1? In this case, the authors should study F1 mice\nsusceptibility to diet-induced obesity and metabolic derangement.\nI have no other point to be addressed.", "reviewer_2": "The authors have addressed all my concerns.", "reviewer_3": "The authors have solved my concerns, no more comments."}, "author_response": "We would like to sincerely thank you for your time and constructive feedback on our revised manuscript. We are very grateful for your positive assessment and for acknowledging the effort we invested in the revisions.\nWe strongly agree with your sentiment that this study addresses an important topic of increasing public and scientific interest. We share your view entirely that it is our collective duty to deliver clear and solid information to the community.\nWe appreciate the opportunity to clarify the remaining points you raised. Below, we have addressed each specific doubt in a point-by-point manner.\nQuestion 1 (Revision 1).\nF1 glucose phenotypes - Still not clear to me what the n values mean, is it families? offspring? and -\nshould the latter be the case - how have the offspring been selected?\nResponse (Revision 1).\nWe apologies for being unclear here: The n in the ipGTT represent the total number of individual F1 mice. Per the 3 x F1 groups we obtained 6-10 litters, and we randomly chose 1-2 mice per individual litter for subsequent metabolic and molecular analyses to end up with n=10 mice per each F1 group.\nRemaining doubt (Revision 2).\nThanks a lot for the clarification. Based on this approach I would have chosen - if possible - more mice per litter (at least n=4) to be able to do post-hoc clustering based on litter and account for litter-to-litter variation. That said, looking at the IPGTT data and considering your mouse selection approach, are you not surprised by the relatively small variability you observed, compared to the one you'd have expected from combining 1-2 mice/litter across 10 litters?\nResponse (Revision 2):\nWe sincerely thank the reviewer for this insightful follow-up comment and for raising this important point regarding experimental design and litter-to-litter variation. We completely agree that sampling more mice per litter (e.g., n=4) to specifically analyze the litter effect using post-hoc clustering is a robust statistical approach. We would like to clarify why this approach was not feasible in our context, and why our chosen strategy gives us confidence in the low variability observed.\ni. Practical Constraints on Sampling:\nAs the reviewer noted (\"if possible\"), obtaining n=4 mice per litter was practically challenging. In our C57BL/6J colony, the litter sizes typically range from 4-8 pups. Furthermore, as our study investigates potential sex differences (as mentioned in the Methods), we analyzed males and females separately. It was extremely difficult to consistently obtain n=4 pups of the same sex from a single litter.\nii. Rationale for Our Sampling Strategy:\nFaced with these constraints, we intentionally adopted the strategy of sampling 1-2 mice (of the same sex) per litter across a larger number of litters (6-10). This strategy was specifically chosen to account for litter-to-litter variation by distributing this potential variance across the entire experimental group (n=10). This approach ensures that our group average is not skewed by one or two \"atypical\" litters but instead represents a more robust biological average derived from a wider genetic and maternal background.\niii. Addressing the \"Surprise\" (Low Variability):\nThe reviewer astutely points out the relatively small variability in our ipGTT data. We were also encouraged by this observation and since our data are presented as means ± SEM, combined with a relatively large sample size, the error bars appear smaller.\nIf the litter-to-litter variation was more pronounced, our method of sampling across 10 different litters would indeed have resulted in high variability. However, the low variability we observed suggests two potential underlying reasons: (a) The metabolic phenotype (F1 glucose phenotype) we are studying is highly penetrant and robust. (b) Our sampling strategy successfully \"averaged out\" the minor variations between litters, allowing the true, consistent biological effect of the paternal diet to emerge clearly.\nWe hope this clarifies that our experimental design was a deliberate choice to maximize statistical power and biological relevance given the practical constraints, and that the resulting low variability strengthens our confidence in the observed phenotype. We appreciate the reviewer's meticulous attention to this detail.\nQuestion 2 (Revision 1).\nF1 glucose phenotypes - given the slight but detectable differences in fasting glucose levels, it would be important to also show normalized glucose responses to understand whether the \"glucose tolerance\" phenotype is an exclusive function of the impaired fasting glycaemia\nResponse (Revision 1).\nWe agree that the glucose excursion during an intraperitoneal GTT often reflects the basal (e.g.., 6h fasted) glycemia before the glucose challenge at T0. To account for this phenomenon, we had in our paper normalized glucose responses to individual glucose values at T0 in AUC calculations shown in Fig.S2a, b and as suggested by the reviewers. We have added the sentence ‘For area under the curve (AUC) calculations, we have normalized serum [Glu] values to [Glu, T0] of each animal to account for intra-animal variability in glucose responses and relative glucose responses’ to the Methods parts to avoid ambiguity.\nRemaining doubt (Revision 2):\nThanks a lot for this. From the normalized AUC data, there seems to be no significant impact of the paternal perturbations on offspring metabolic control (body weight, glucose tolerance and insulin sensitivity).\n• Are the differences in fasting glucose significant?\n• Is the glucose tolerance phenotype of the F1 mice generated after zygotic microinjection confirmed after normalization to basal glucose levels (which seem to be different there as well)?\n• How do these results change the interpretation and impact of your study?\n• Were adipose tissue analyses (H&E, mRNA-seq, proteomics etc.) done on the whole cohort? If not, how were these samples selected?\nThese points and limitations of the study should be clearly addressed in the discussion together with a potential explanation for the adipose phenotype being uncoupled from organismal metabolic control.\n• Would this adipose phenotype be a signature of increased obesity and metabolic disease risk in the F1?\nIn this case, the authors should study F1 mice susceptibility to diet-induced obesity and metabolic derangement. I have no other point to be addressed.\nWe sincerely thank for this critical and insightful analysis of the normalized data, which touches upon the central interpretation of our study. We will address each point directly.\ni. Fasting Glucose and Normalized Data: After normalization, the impact on overall metabolic control and glycemia appears rather subtle. To answer the specific questions:\n• The differences in fasting glucose for the F1 generation were not significant (P = 0.1483), and this was also the case for the Fm mice.\n• Similarly, the milder phenotype observed in the zygotic microinjection (Fm) group did not reach statistical significance after normalization to basal glucose levels.\nii. Interpretation and Impact of these Results: We agree that the metabolic phenotype in the F1 and Fm generations is, as we have faithfully presented, milder than that in the F0 generation. This is expected, as the paternal influence was shown to be more pronounced when dietary programming occurred over several generations of HFD/WD feeding. Otherwise, the intergenerational effect diminishes over generations when offspring are not exposed to a \"second hit\" (e.g., a high-fat diet challenge, see PMID: 33783350). However, we respectfully argue that these findings do not diminish the study's impact; rather, they refine it.\nWhile the T0-normalized AUC did not reach the P < 0.05 threshold, other key metrics suggest a persistent, subtle dysregulation:\n• The normalized AUC values still show a meaningful trend toward impaired tolerance (F1, P = 0.0935; Fm, P = 0.0965).\n• Crucially, the glucose levels at the peak time points of the GTT and ITT did reach statistical significance (as shown in Figure 1d, e), indicating a clear, albeit subtle, impairment in glucose handling.\niii. Adipose Tissue Analyses (Sample Selection): Regarding the adipose tissue analyses, these were not performed on the entire cohort. Reasons for that were 1) of a biological nature, i.e., the amount of eWAT collected from a single lean F1 (F0-LF) mouse was often insufficient for all downstream multi-omics analyses and but also 2) the fact that we had to balance robust biological insights (i.e., high number of samples) with costs and manpower constraints.\nGiven that the no. was based on empirical knowledge in the group on the necessity of biological replicates to extract biological insights with high statistical power.\n• For histology, mRNA-seq, and small RNA-seq, samples were selected from those that met our rigorous quality control standards (i.e., sufficient RNA quantity and integrity quality, and RIN numbers) to ensure high NGS quality but in a manner that that ensured that the selection faithfully represented physiological parameters (adiposity, glycemia) found in the entire group.\n• For proteomics, we had no choice but choose the individual with enough mass of tissue, especially in lean F1 (F0-LF) group.\niv. Interpreting the \"Uncoupled\" Phenotype: We thank the reviewer for bringing up this point and we respectfully propose another interpretation: They might not be uncoupled.\nInstead, our data could be interpreted in a way that suggests that the milder systemic phenotype is a direct in F1 (and less so in Fm) consequence of a specific epigenetic inheritance occurring only in the adipose tissue.\nOur reasoning is as follows:\n• The main physiological observations pointing towards adipose-specific hereditary effects are surprisingly robust. Even when F1 (F0 HF) were not rendered obese themselves, they exhibited significant differences in leptin and eWAT hypertrophy, thus closely mirroring the endocrine effects seen in their (obese) F0 HF fathers. This clearly demonstrates a strong, tissue-specific epigenetic inheritance of adipose dysfunction.\n• Systemic glucose control inherently relies on multi-organ endocrine crosstalk. Mild systemic alterations in glucose homeostasis (like a non-significant normalized AUC) does not rule out effects emanating from gene malregulation in adipose tissue (which we provide strong evidence for). We do not rule out that this dysfunction prompts secondary effects in liver, muscle, pancreas, and adipose tissue. But at the level of mitochondrial gene regulation (the main mechanistic focus of our paper), we fail to observe similar transcriptional effects in the liver (Figure S8), muscle and heart mitochondrial function (our previous response 11 to Reviewer #3).\nTherefore, our central finding is that in the absence of hereditary mitochondrial dysfunction in the liver, heart and muscle, the isolated, inherited adipose hypertrophy is, by itself, sufficient to cause a partial but significant impairment in glucose metabolism (as shown by the peak GTT/ITT glucose excursions in Figure 1d, e). The multi-omics data from the F1 generation further provides the molecular-level evidence linking this inherited adipose phenotype to the observed metabolic control.\nWe agree with the reviewer that this adipose phenotype likely represents a \"signature of risk.\" and others have meticulously experimentally validated this notion in related mouse paradigms (PMID: 26974008). However, based on our data, we cannot unequivocally decipher whether our F1 constitutes a signature of potential future body weight gains (this would need additional F1 HFD feeding), but rather an instigator of metabolic susceptibility and a predisposition to glucose intolerance, offering crucial insights into the etiology of glucose alterations and (pre)-T2D in non-obese patients. Our study focused on the latter, and this is described clearly in lines 154-156."}, {"round": 3, "reviewer_comments": {"reviewer_1": "I'd like to thank the authors for addressing all my tedious observations. I have no further comments.", "reviewer_4": "This manuscript investigates the process by which parental diet can affect offspring metabolism. The authors clearly take great care to minimize confounding factors. The focus on paternal diet is valuable as it eliminates effects of the maternal-foetal interaction. This is greatly appreciated in such studies that can be invalidated by poor experimental design. The strength of the manuscript, however, lies in the uncovering of a specific mechanism (miRNAs) that can recapitulate the environmental effects artificially through micro-injection. As it therefore bridges an important gap between parental environment and offspring phenotype with a molecular mechanism, I would recommend this manuscript for publication.\nI would also like to commend the authors in their care to refer to their findings as “intergenerational” rather than “transgenerational” or less specific terms, although the definition by which they distinguish between the two in their introduction confused me initially. It would clarify the findings to state that such intergenerational effects are only observed or expected to last a single generation. This is especially true given that the mechanism they uncover (let-7) cannot self-propagate (as miRNAs, unlike some other classes of sRNA, have no self-reinforcing mechanism) and so is unlikely to extend to a transgenerational effect, but is rather parentally-deposited in the gametes in direct response to the environment (diet).\nWith regards to the authors’ responses to comments made by Reviewer 1:\nRemaining Doubt 1:\nThe authors clarify their reasoning for the selection a small number of mice per litter. Given the constraints imposed by the litter size and the separation of sexes (which is essential) they explain well the decision to instead sample a large number of litters, rather than a large number of pups per litter. This is not ideal, but is justified. While the low variability is somewhat surprising, if the authors are sure of their reporting then this may be accounted for, as they say, by high penetrance and averaging across litters which are, after all, isogenic for the express purpose of reducing variability.\nRemaining Doubt 2:\nThe authors have responded well to the points raised by the reviewer. Given that the paternal effect may act as a kind of “priming” for the offspring in preparation of a similar environment (high fat diet) the most obvious effects should be observed upon exposure to that environment anew. By removing the “second pulse” of environmental signal, the authors are necessarily measuring smaller effects, that may not manifest in the same outward phenotypes. However, the removal of this second pulse is essential to completely exclude any primary effects of the environment on the offspring epigenome/phenotype. By observing the offspring in the low-fat conditions, the authors ensure that any effects observed are due to paternal inheritance. This conclusion is strengthened by the mechanistic detail supplied by the injection of sncRNA that recapitulates this effect.\nAs the reviewer points out, great care must be taken in this field which is receiving increasing media attention. However, the use of an isogenic strain, the elimination of environmental perturbation and the search for a molecular/mechanistic explanation indicate the authors are being as careful as possible.\nGiven the large amount of work already put in by the authors to prepare their manuscript and address reviewer comments, I have no further changes to request, except perhaps the clarification of “intergenerational” and “transgenerational” mentioned above, or a further indication in the discussion that this mechanism is unlikely to last further generations. These are, however, minor points."}, "author_response": "We thank the arbritraring reviewer for appreciating the experiment rigor and novelty of our study and our focus on deciphering on a novel molecular / cellular mechanism in intergenerational epigenetic heredity with potential implications for human metabolic regulation and germline processes.\nWe strongly align with R4 in carefully distinguishing between ‘intergenerational’ and ‘transgenerational’ effects, not the least as these presumably involve different molecular and developmental processes (e.g., exposure of male PGC to an obesogenic environment or not). As rightly put forward by R4, we have no evidence that the proposed, let7-driven, germline mechanism transcends the F1 generation. As this distinction between IG/TG heredity is of utmost important to us, we have changed the text in passages where this, as rightly mentioned by R4, was unclear.\n´Remaining Doubt 1:\nRemaining Doubt 1:\nThe authors clarify their reasoning for the selection a small number of mice per litter. Given the constraints imposed by the litter size and the separation of sexes (which is essential) they explain well the decision to instead sample a large number of litters, rather than a large number of pups per litter. This is not ideal, but is justified. While the low variability is somewhat surprising, if the authors are sure of their reporting then this may be accounted for, as they say, by high penetrance and averaging across litters which are, after all, isogenic for the express purpose of reducing variability.\nResponse:\nWe thank R4 in highlighting the limitations of conducting intergenerational mouse studies and appreciating the careful deliberations we have done to mitigate (but not remove) potential confounders. As alluded to in earlier rebuttals, the strong consistency of F1 glucose responses was a surprise for us but we also think that additional mouse studies are needed to ascertain whether this reduced variability is statistically different from earlier reports showing more variable effects.\nRemaining Doubt 2:\nThe authors have responded well to the points raised by the reviewer. Given that the paternal effect may act as a kind of “priming” for the offspring in preparation of a similar environment (high fat diet) the most obvious effects should be observed upon exposure to that environment anew. By removing the “second pulse” of environmental signal, the authors are necessarily measuring smaller effects, that may not manifest in the same outward phenotypes. However, the removal of this second pulse is essential to completely exclude any primary effects of the environment on the offspring epigenome/phenotype. By observing the offspring in the low-fat conditions, the authors ensure that any effects observed are due to paternal inheritance. This conclusion is strengthened by the mechanistic detail supplied by the injection of sncRNA that recapitulates this effect.\nAs the reviewer points out, great care must be taken in this field which is receiving increasing media attention. However, the use of an isogenic strain, the elimination of environmental perturbation and the search for a molecular/mechanistic explanation indicate the authors are being as careful as possible.\nResponse:\nWe thank R4 in emphasising that the focus of our mouse study was not to reproduce earlier studies with F1 HFD challenges but to discern novel mechanisms (let7) and elucidate “true” epigenetic sequelae of F0 exposure. We would like to stress though that even our careful design cannot rule out “priming” environmental and maternal behavioral effects on offspring phenotypes: For instance, we opted for natural mating, thus females e.g., were exposed to the lean, obese or weight-regressed male, and we cannot by principle rule out reduced maternal care of progeny from obese fathers. As female mice are polyandric, this would probably be a stronger concern when housed with several (lean and obese) males, which was not the case here."}]

Decision letter