Current genomic deep learning models display decreased performance in cell type-specific accessible regions.

BACKGROUND: A number of deep learning models have been developed to predict epigenetic features such as chromatin accessibility from DNA sequence. Model evaluations commonly report performance genome-wide; however, cis regulatory elements (CREs), which play critical roles in gene regulation, make up only a small fraction of the genome. Furthermore, cell type-specific CREs contain a large proportion of complex disease heritability. RESULTS: We evaluate genomic deep learning models in chromatin accessibility regions with varying degrees of cell type specificity. We assess two modeling directions in the field: general purpose models trained across thousands of outputs (cell types and epigenetic marks) and models tailored to specific tissues and tasks. We find that the accuracy of genomic deep learning models, including two state-of-the-art general purpose models-Enformer and Sei-varies across the genome and is reduced in cell type-specific accessible regions. Using accessibility models trained on cell types from specific tissues, we find that increasing model capacity to learn cell type-specific regulatory syntax-through single-task learning or high capacity multi-task models-can improve performance in cell type-specific accessible regions. We also observe that improving reference sequence predictions does not consistently improve variant effect predictions, indicating that novel strategies are needed to improve performance on variants. CONCLUSIONS: Our results provide a new perspective on the performance of genomic deep learning models, showing that performance varies across the genome and is particularly reduced in cell type-specific accessible regions. We also identify strategies to maximize performance in cell type-specific accessible regions.

Expression genome-wide association study identifies key regulatory variants enriched with metabolic and immune functions in four porcine tissues.

BACKGROUND: Integration of high throughput DNA genotyping and RNA-sequencing data enables the discovery of genomic regions that regulate gene expression, known as expression quantitative trait loci (eQTL). In pigs, efforts to date have been mainly focused on purebred lines for traits with commercial relevance as such growth and meat quality. However, little is known on genetic variants and mechanisms associated with the robustness of an animal, thus its overall health status. Here, the liver, lung, spleen, and muscle transcriptomes of 100 three-way crossbred female finishers were studied, with the aim of identifying novel eQTL regulatory regions and transcription factors (TFs) associated with regulation of porcine metabolism and health-related traits. RESULTS: An expression genome-wide association study with 535,896 genotypes and the expression of 12,680 genes in liver, 13,310 genes in lung, 12,650 genes in spleen, and 12,595 genes in muscle resulted in 4,293, 10,630, 4,533, and 6,871 eQTL regions for each of these tissues, respectively. Although only a small fraction of the eQTLs were annotated as cis-eQTLs, these presented a higher number of polymorphisms per region and significantly stronger associations with their target gene compared to trans-eQTLs. Between 20 and 115 eQTL hotspots were identified across the four tissues. Interestingly, these were all enriched for immune-related biological processes. In spleen, two TFs were identified: ERF and ZNF45, with key roles in regulation of gene expression. CONCLUSIONS: This study provides a comprehensive analysis with more than 26,000 eQTL regions identified that are now publicly available. The genomic regions and their variants were mostly associated with tissue-specific regulatory roles. However, some shared regions provide new insights into the complex regulation of genes and their interactions that are involved with important traits related to metabolism and immunity.

Co-expression in tissue-specific gene networks links genes in cancer-susceptibility loci to known somatic driver genes.

BACKGROUND: The genetic background of cancer remains complex and challenging to integrate. Many somatic mutations within genes are known to cause and drive cancer, while genome-wide association studies (GWAS) of cancer have revealed many germline risk factors associated with cancer. However, the overlap between known somatic driver genes and positional candidate genes from GWAS loci is surprisingly small. We hypothesised that genes from multiple independent cancer GWAS loci should show tissue-specific co-regulation patterns that converge on cancer-specific driver genes. RESULTS: We studied recent well-powered GWAS of breast, prostate, colorectal and skin cancer by estimating co-expression between genes and subsequently prioritising genes that show significant co-expression with genes mapping within susceptibility loci from cancer GWAS. We observed that the prioritised genes were strongly enriched for cancer drivers defined by COSMIC, IntOGen and Dietlein et al. The enrichment of known cancer driver genes was most significant when using co-expression networks derived from non-cancer samples of the relevant tissue of origin. CONCLUSION: We show how genes within risk loci identified by cancer GWAS can be linked to known cancer driver genes through tissue-specific co-expression networks. This provides an important explanation for why seemingly unrelated sets of genes that harbour either germline risk factors or somatic mutations can eventually cause the same type of disease.

The mouse multi-organ proteome from infancy to adulthood.

The early-life organ development and maturation shape the fundamental blueprint for later-life phenotype. However, a multi-organ proteome atlas from infancy to adulthood is currently not available. Herein, we present a comprehensive proteomic analysis of ten mouse organs (brain, heart, lung, liver, kidney, spleen, stomach, intestine, muscle and skin) at three crucial developmental stages (1-, 4- and 8-weeks after birth) acquired using data-independent acquisition mass spectrometry. We detect and quantify 11,533 protein groups across the ten organs and obtain 115 age-related differentially expressed protein groups that are co-expressed in all organs from infancy to adulthood. We find that spliceosome proteins prevalently play crucial regulatory roles in the early-life development of multiple organs, and detect organ-specific expression patterns and sexual dimorphism. This multi-organ proteome atlas provides a fundamental resource for understanding the molecular mechanisms underlying early-life organ development and maturation.

Integrating muti-omics data to identify tissue-specific DNA methylation biomarkers for cancer risk.

The relationship between tissue-specific DNA methylation and cancer risk remains inadequately elucidated. Leveraging resources from the Genotype-Tissue Expression consortium, here we develop genetic models to predict DNA methylation at CpG sites across the genome for seven tissues and apply these models to genome-wide association study data of corresponding cancers, namely breast, colorectal, renal cell, lung, ovarian, prostate, and testicular germ cell cancers. At Bonferroni-corrected P < 0.05, we identify 4248 CpGs that are significantly associated with cancer risk, of which 95.4% (4052) are specific to a particular cancer type. Notably, 92 CpGs within 55 putative novel loci retain significant associations with cancer risk after conditioning on proximal signals identified by genome-wide association studies. Integrative multi-omics analyses reveal 854 CpG-gene-cancer trios, suggesting that DNA methylation at 309 distinct CpGs might influence cancer risk through regulating the expression of 205 unique cis-genes. These findings substantially advance our understanding of the interplay between genetics, epigenetics, and gene expression in cancer etiology.

Self-supervised learning for characterising histomorphological diversity and spatial RNA expression prediction across 23 human tissue types.

As vast histological archives are digitised, there is a pressing need to be able to associate specific tissue substructures and incident pathology to disease outcomes without arduous annotation. Here, we learn self-supervised representations using a Vision Transformer, trained on 1.7 M histology images across 23 healthy tissues in 838 donors from the Genotype Tissue Expression consortium (GTEx). Using these representations, we can automatically segment tissues into their constituent tissue substructures and pathology proportions across thousands of whole slide images, outperforming other self-supervised methods (43% increase in silhouette score). Additionally, we can detect and quantify histological pathologies present, such as arterial calcification (AUROC = 0.93) and identify missing calcification diagnoses. Finally, to link gene expression to tissue morphology, we introduce RNAPath, a set of models trained on 23 tissue types that can predict and spatially localise individual RNA expression levels directly from H&E histology (mean genes significantly regressed = 5156, FDR 1%). We validate RNAPath spatial predictions with matched ground truth immunohistochemistry for several well characterised control genes, recapitulating their known spatial specificity. Together, these results demonstrate how self-supervised machine learning when applied to vast histological archives allows researchers to answer questions about tissue pathology, its spatial organisation and the interplay between morphological tissue variability and gene expression.

Partitioning and aggregating cross-tissue and tissue-specific genetic effects to identify gene-trait associations.

TWAS have shown great promise in extending GWAS loci to a functional understanding of disease mechanisms. In an effort to fully unleash the TWAS and GWAS information, we propose MTWAS, a statistical framework that partitions and aggregates cross-tissue and tissue-specific genetic effects in identifying gene-trait associations. We introduce a non-parametric imputation strategy to augment the inaccessible tissues, accommodating complex interactions and non-linear expression data structures across various tissues. We further classify eQTLs into cross-tissue eQTLs and tissue-specific eQTLs via a stepwise procedure based on the extended Bayesian information criterion, which is consistent under high-dimensional settings. We show that MTWAS significantly improves the prediction accuracy across all 47 tissues of the GTEx dataset, compared with other single-tissue and multi-tissue methods, such as PrediXcan, TIGAR, and UTMOST. Applying MTWAS to the DICE and OneK1K datasets with bulk and single-cell RNA sequencing data on immune cell types showcases consistent improvements in prediction accuracy. MTWAS also identifies more predictable genes, and the improvement can be replicated with independent studies. We apply MTWAS to 84 UK Biobank GWAS studies, which provides insights into disease etiology.

Multi-omic characterization of allele-specific regulatory variation in hybrid pigs.

Hybrid mapping is a powerful approach to efficiently identify and characterize genes regulated through mechanisms in cis. In this study, using reciprocal crosses of the phenotypically divergent Duroc and Lulai pig breeds, we perform a comprehensive multi-omic characterization of regulatory variation across the brain, liver, muscle, and placenta through four developmental stages. We produce one of the largest multi-omic datasets in pigs to date, including 16 whole genome sequenced individuals, as well as 48 whole genome bisulfite sequencing, 168 ATAC-Seq and 168 RNA-Seq samples. We develop a read count-based method to reliably assess allele-specific methylation, chromatin accessibility, and RNA expression. We show that tissue specificity was much stronger than developmental stage specificity in all of DNA methylation, chromatin accessibility, and gene expression. We identify 573 genes showing allele specific expression, including those influenced by parent-of-origin as well as allele genotype effects. We integrate methylation, chromatin accessibility, and gene expression data to show that allele specific expression can be explained in great part by allele specific methylation and/or chromatin accessibility. This study provides a comprehensive characterization of regulatory variation across multiple tissues and developmental stages in pigs.

Tissue of origin detection for cancer tumor using low-depth cfDNA samples through combination of tumor-specific methylation atlas and genome-wide methylation density in graph convolutional neural networks.

BACKGROUND: Cell free DNA (cfDNA)-based assays hold great potential in detecting early cancer signals yet determining the tissue-of-origin (TOO) for cancer signals remains a challenging task. Here, we investigated the contribution of a methylation atlas to TOO detection in low depth cfDNA samples. METHODS: We constructed a tumor-specific methylation atlas (TSMA) using whole-genome bisulfite sequencing (WGBS) data from five types of tumor tissues (breast, colorectal, gastric, liver and lung cancer) and paired white blood cells (WBC). TSMA was used with a non-negative least square matrix factorization (NNLS) deconvolution algorithm to identify the abundance of tumor tissue types in a WGBS sample. We showed that TSMA worked well with tumor tissue but struggled with cfDNA samples due to the overwhelming amount of WBC-derived DNA. To construct a model for TOO, we adopted the multi-modal strategy and used as inputs the combination of deconvolution scores from TSMA with other features of cfDNA. RESULTS: Our final model comprised of a graph convolutional neural network using deconvolution scores and genome-wide methylation density features, which achieved an accuracy of 69% in a held-out validation dataset of 239 low-depth cfDNA samples. CONCLUSIONS: In conclusion, we have demonstrated that our TSMA in combination with other cfDNA features can improve TOO detection in low-depth cfDNA samples.

Genome and tissue-specific transcriptomes of the large-polyp coral, Fimbriaphyllia (Euphyllia) ancora: a recipe for a coral polyp.

Coral polyps are composed of four tissues; however, their characteristics are largely unexplored. Here we report biological characteristics of tentacles (Te), mesenterial filaments (Me), body wall (Bo), and mouth with pharynx (MP), using comparative genomic, morpho-histological, and transcriptomic analyses of the large-polyp coral, Fimbriaphyllia ancora. A draft F. ancora genome assembly of 434 Mbp was created. Morpho-histological and transcriptomic characterization of the four tissues showed that they have distinct differences in structure, primary cellular composition, and transcriptional profiles. Tissue-specific, highly expressed genes (HEGs) of Te are related to biological defense, predation, and coral-algal symbiosis. Me expresses multiple digestive enzymes, whereas Bo expresses innate immunity and biomineralization-related molecules. Many receptors for neuropeptides and neurotransmitters are expressed in MP. This dataset and new insights into tissue functions will facilitate a deeper understanding of symbiotic biology, immunology, biomineralization, digestive biology, and neurobiology in corals.

High-throughput single-molecule long-read RNA sequencing analysis of tissue-specific genes and isoforms in lettuce (Lactuca sativa L.).

Lettuce is one of the most widely cultivated and consumed dicotyledonous vegetables globally. Despite the availability of its reference genome sequence, lettuce gene annotation remains incomplete, impeding comprehensive research and the broad application of genomic resources. Long-read RNA isoform sequencing (Iso-Seq) offers substantial advantages for analyzing RNA alternative splicing and aiding gene annotation, yet it faces throughput limitations. We present the HIT-ISOseq method tailored for bulk sample analysis, significantly enhancing RNA sequencing throughput on the PacBio platform by concatenating cDNA. Here we show, HIT-ISOseq generates 3-4 cDNA molecules per CCS read in lettuce, yielding 15.7 million long reads per PacBio Sequel II SMRT Cell 8 M. We validate its effectiveness in analyzing six lettuce tissue samples, including roots, stems, and leaves, revealing tissue-specific gene expression patterns and RNA isoforms. Leveraging diverse tissue long-read RNA sequencing, we refine the transcript annotation of the lettuce reference genome, expanding its GO and KEGG annotation repertoire. Collectively, this study serves as a foundational reference for genome annotation and the analysis of multi-sample isoform expression, utilizing high-throughput long-read transcriptome sequencing.

Deciphering tissue-specific expression profiles of mitochondrial genome-encoded tRNAs and rRNAs through transcriptomic profiling in buffalo.

BACKGROUND: Mitochondria, essential for cellular energy production through oxidative phosphorylation (OXPHOS), integrate mt-DNA and nuclear-encoded genes. This cooperation extends to the mitochondrial translation machinery, involving crucial mtDNA-encoded RNAs: 22 tRNAs (mt-tRNAs) as adapters and two rRNAs (mt-rRNAs) for ribosomal assembly, enabling mitochondrial-encoded mRNA translation. Disruptions in mitochondrial gene expression can strongly impact energy generation and overall animal health. Our study investigates the tissue-specific expression patterns of mt-tRNAs and mt-rRNAs in buffalo. MATERIAL AND METHODS: To investigate the expression patterns of mt-tRNAs and mt-rRNAs in different tissues and gain a better understanding of tissue-specific variations, RNA-seq was performed on various tissues, such as the kidney, heart, brain, and ovary, from post-pubertal female buffaloes. Subsequently, we identified transcripts that were differentially expressed in various tissue comparisons. RESULTS: The findings reveal distinct expression patterns among specific mt-tRNA and mt-rRNA genes across various tissues, with some exhibiting significant upregulation and others demonstrating marked downregulation in specific tissue contexts. These identified variations reflect tissue-specific physiological roles, underscoring their significance in meeting the unique energy demands of each tissue. Notably, the brain exhibits the highest mtDNA copy numbers and an abundance of mitochondrial mRNAs of our earlier findings, potentially linked to the significant upregulation of mt-tRNAs in brain. This suggests a plausible association between mtDNA replication and the regulation of mtDNA gene expression. CONCLUSION: Overall, our study unveils the tissue-specific expression of mitochondrial-encoded non-coding RNAs in buffalo. As we proceed, our further investigations into tissue-specific mitochondrial proteomics and microRNA studies aim to elucidate the intricate mechanisms within mitochondria, contributing to tissue-specific mitochondrial attributes. This research holds promise to elucidate the critical role of mitochondria in animal health and disease.

Circadian-driven tissue specificity is constrained under caloric restricted feeding conditions.

Tissue specificity is a fundamental property of an organ that affects numerous biological processes, including aging and longevity, and is regulated by the circadian clock. However, the distinction between circadian-affected tissue specificity and other tissue specificities remains poorly understood. Here, using multi-omics data on circadian rhythms in mice, we discovered that approximately 35% of tissue-specific genes are directly affected by circadian regulation. These circadian-affected tissue-specific genes have higher expression levels and are associated with metabolism in hepatocytes. They also exhibit specific features in long-reads sequencing data. Notably, these genes are associated with aging and longevity at both the gene level and at the network module level. The expression of these genes oscillates in response to caloric restricted feeding regimens, which have been demonstrated to promote longevity. In addition, aging and longevity genes are disrupted in various circadian disorders. Our study indicates that the modulation of circadian-affected tissue specificity is essential for understanding the circadian mechanisms that regulate aging and longevity at the genomic level.

The cacao gene atlas: a transcriptome developmental atlas reveals highly tissue-specific and dynamically-regulated gene networks in Theobroma cacao L.

BACKGROUND: Theobroma cacao, the cocoa tree, is a tropical crop grown for its highly valuable cocoa solids and fat which are the basis of a 200-billion-dollar annual chocolate industry. However, the long generation time and difficulties associated with breeding a tropical tree crop have limited the progress of breeders to develop high-yielding disease-resistant varieties. Development of marker-assisted breeding methods for cacao requires discovery of genomic regions and specific alleles of genes encoding important traits of interest. To accelerate gene discovery, we developed a gene atlas composed of a large dataset of replicated transcriptomes with the long-term goal of progressing breeding towards developing high-yielding elite varieties of cacao. RESULTS: We describe the creation of the Cacao Transcriptome Atlas, its global characterization and define sets of genes co-regulated in highly organ- and temporally-specific manners. RNAs were extracted and transcriptomes sequenced from 123 different tissues and stages of development representing major organs and developmental stages of the cacao lifecycle. In addition, several experimental treatments and time courses were performed to measure gene expression in tissues responding to biotic and abiotic stressors. Samples were collected in replicates (3-5) to enable statistical analysis of gene expression levels for a total of 390 transcriptomes. To promote wide use of these data, all raw sequencing data, expression read mapping matrices, scripts, and other information used to create the resource are freely available online. We verified our atlas by analyzing the expression of genes with known functions and expression patterns in Arabidopsis (ACT7, LEA19, AGL16, TIP13, LHY, MYB2) and found their expression profiles to be generally similar between both species. We also successfully identified tissue-specific genes at two thresholds in many tissue types represented and a set of genes highly conserved across all tissues. CONCLUSION: The Cacao Gene Atlas consists of a gene expression browser with graphical user interface and open access to raw sequencing data files as well as the unnormalized and CPM normalized read count data mapped to several cacao genomes. The gene atlas is a publicly available resource to allow rapid mining of cacao gene expression profiles. We hope this resource will be used to help accelerate the discovery of important genes for key cacao traits such as disease resistance and contribute to the breeding of elite varieties to help farmers increase yields.

Mitochondrial lipidomes are tissue specific - low cholesterol contents relate to UCP1 activity.

Lipid composition is conserved within sub-cellular compartments to maintain cell function. Lipidomic analyses of liver, muscle, white and brown adipose tissue (BAT) mitochondria revealed substantial differences in their glycerophospholipid (GPL) and free cholesterol (FC) contents. The GPL to FC ratio was 50-fold higher in brown than white adipose tissue mitochondria. Their purity was verified by comparison of proteomes with ER and mitochondria-associated membranes. A lipid signature containing PC and FC, calculated from the lipidomic profiles, allowed differentiation of mitochondria from BAT of mice housed at different temperatures. Elevating FC in BAT mitochondria prevented uncoupling protein (UCP) 1 function, whereas increasing GPL boosted it. Similarly, STARD3 overexpression facilitating mitochondrial FC import inhibited UCP1 function in primary brown adipocytes, whereas a knockdown promoted it. We conclude that the mitochondrial GPL/FC ratio is key for BAT function and propose that targeting it might be a promising strategy to promote UCP1 activity.

The tissue-resident regulatory T cell pool is shaped by transient multi-tissue migration and a conserved residency program.

The tissues are the site of many important immunological reactions, yet how the immune system is controlled at these sites remains opaque. Recent studies have identified Foxp3+ regulatory T (Treg) cells in non-lymphoid tissues with unique characteristics compared with lymphoid Treg cells. However, tissue Treg cells have not been considered holistically across tissues. Here, we performed a systematic analysis of the Treg cell population residing in non-lymphoid organs throughout the body, revealing shared phenotypes, transient residency, and common molecular dependencies. Tissue Treg cells from different non-lymphoid organs shared T cell receptor (TCR) sequences, with functional capacity to drive multi-tissue Treg cell entry and were tissue-agnostic on tissue homing. Together, these results demonstrate that the tissue-resident Treg cell pool in most non-lymphoid organs, other than the gut, is largely constituted by broadly self-reactive Treg cells, characterized by transient multi-tissue migration. This work suggests common regulatory mechanisms may allow pan-tissue Treg cells to safeguard homeostasis across the body.

HNF4A and HNF1A exhibit tissue specific target gene regulation in pancreatic beta cells and hepatocytes.

HNF4A and HNF1A encode transcription factors that are important for the development and function of the pancreas and liver. Mutations in both genes have been directly linked to Maturity Onset Diabetes of the Young (MODY) and type 2 diabetes (T2D) risk. To better define the pleiotropic gene regulatory roles of HNF4A and HNF1A, we generated a comprehensive genome-wide map of their binding targets in pancreatic and hepatic cells using ChIP-Seq. HNF4A was found to bind and regulate known (ACY3, HAAO, HNF1A, MAP3K11) and previously unidentified (ABCD3, CDKN2AIP, USH1C, VIL1) loci in a tissue-dependent manner. Functional follow-up highlighted a potential role for HAAO and USH1C as regulators of beta cell function. Unlike the loss-of-function HNF4A/MODY1 variant I271fs, the T2D-associated HNF4A variant (rs1800961) was found to activate AKAP1, GAD2 and HOPX gene expression, potentially due to changes in DNA-binding affinity. We also found HNF1A to bind to and regulate GPR39 expression in beta cells. Overall, our studies provide a rich resource for uncovering downstream molecular targets of HNF4A and HNF1A that may contribute to beta cell or hepatic cell (dys)function, and set up a framework for gene discovery and functional validation.

Tissue-specific and endogenous protein labeling with split fluorescent proteins.

The ability to label proteins by fusion with genetically encoded fluorescent proteins is a powerful tool for understanding dynamic biological processes. However, current approaches for expressing fluorescent protein fusions possess drawbacks, especially at the whole organism level. Expression by transgenesis risks potential overexpression artifacts while fluorescent protein insertion at endogenous loci is technically difficult and, more importantly, does not allow for tissue-specific study of broadly expressed proteins. To overcome these limitations, we have adopted the split fluorescent protein system mNeonGreen21-10/11 (split-mNG2) to achieve tissue-specific and endogenous protein labeling in zebrafish. In our approach, mNG21-10 is expressed under a tissue-specific promoter using standard transgenesis while mNG211 is inserted into protein-coding genes of interest using CRISPR/Cas-directed gene editing. Each mNG2 fragment on its own is not fluorescent, but when co-expressed the fragments self-assemble into a fluorescent complex. Here, we report successful use of split-mNG2 to achieve differential labeling of the cytoskeleton genes tubb4b and krt8 in various tissues. We also demonstrate that by anchoring the mNG21-10 component to specific cellular compartments, the split-mNG2 system can be used to manipulate protein localization. Our approach should be broadly useful for a wide range of applications.

[Regulation of Retrotransposons in Drosophila melanogaster Somatic Tissues].

Regulation of retrotransposon activity in somatic tissues is a complex mechanism that has still not been studied in detail. It is strongly believed that siRNA interference is main mechanism of retrotransposon activity regulation outside the gonads, but recently was demonstrated that piRNA interference participates in retrotransposon repression during somatic tissue development. In this work, using RT-PCR, we demonstrated that during ontogenesis piRNA interference determinates retrotransposon expression level on imago stage and retrotransposons demonstrate tissue-specific expression. The major factor of retrotransposon tissue-specific expression is presence of transcription factor binding sites in their regulatory regions.

Muscle-specific lack of Gfpt1 triggers ER stress to alleviate misfolded protein accumulation.

Pathogenic variants in GFPT1, encoding a key enzyme to synthesize UDP-N-acetylglucosamine (UDP-GlcNAc), cause congenital myasthenic syndrome (CMS). We made a knock-in (KI) mouse model carrying a frameshift variant in Gfpt1 exon 9, simulating that found in a patient with CMS. As Gfpt1 exon 9 is exclusively expressed in striated muscles, Gfpt1-KI mice were deficient for Gfpt1 only in skeletal muscles. In Gfpt1-KI mice, (1) UDP-HexNAc, CMP-NeuAc and protein O-GlcNAcylation were reduced in skeletal muscles; (2) aged Gfpt1-KI mice showed poor exercise performance and abnormal neuromuscular junction structures; and (3) markers of the unfolded protein response (UPR) were elevated in skeletal muscles. Denervation-mediated enhancement of endoplasmic reticulum (ER) stress in Gfpt1-KI mice facilitated protein folding, ubiquitin-proteasome degradation and apoptosis, whereas autophagy was not induced and protein aggregates were markedly increased. Lack of autophagy was accounted for by enhanced degradation of FoxO1 by increased Xbp1-s/u proteins. Similarly, in Gfpt1-silenced C2C12 myotubes, ER stress exacerbated protein aggregates and activated apoptosis, but autophagy was attenuated. In both skeletal muscles in Gfpt1-KI mice and Gfpt1-silenced C2C12 myotubes, maladaptive UPR failed to eliminate protein aggregates and provoked apoptosis.