Biology subjects

Long, Q.

Publications and source records attributed to Long, Q..

At least 37 records · Page 2Linked to original sources

bioRxiv2023

Integrative genomics reveals the polygenic basis of seedlessness in grapevine

Seedlessness is a crucial quality trait in table grape (Vitis vinifera L.) breeding. However, the development of seeds involved intricate regulations, while the polygenic basis of seed abortion remains unclear. Here, we combine comparative genomics, population genetics, quantitative genetics, and integrative genomics to unravel the evolution and polygenic basis of seedlessness in grapes. We generated four haplotype-resolved telomere-to-telomere (T2T) genomes for two seedless grape cultivars, Thompson Seedless (TS, syn. Sultania) and Black Monukka (BM). Comparative genomics identified a [~]4.25 Mb hemizygous inversion on Chr10 specific in seedless cultivars, with seedless-associated genes VvTT16 and VvSUS2 located at breakpoints. Population genomic analyses of 548 grapevine accessions revealed two distinct clusters of seedless cultivars, tracing the origin of the seedlessness trait back to Sultania. Introgression, rather than convergent selection, shaped the evolutionary history of seedlessness in grape improvement. Genome-wide association study (GWAS) analysis identified 110 quantitative trait loci (QTLs) associated with 634 candidate genes, including novel candidate genes, such as three 11S GLOBULIN SEED STORAGE PROTEIN and two CYTOCHROME P450 genes, and well-known genes like VviAGL11. Integrative genomic analyses resulted in 339 core candidate genes categorized into 13 groups related to seed development. Machine learning based genomic selection achieved a remarkable 99% precision in predicting grapevine seedlessness. Our findings highlight the polygenic nature of seedless and provide novel candidate genes for molecular genetics and an effective prediction for seedlessness in grape genomic breeding.

genetics↗

bioRxiv2023

The Bias of Using Cross-Validation in Genomic Predictions and Its Correction

Cross-validation (CV) is a widely used technique in statistical learning for model evaluation and selection. Meanwhile, various of statistical learning methods, such as Generalized Least Square (GLS), Linear Mixed-Effects Models (LMM), and regularization methods are commonly used in genomic predictions, a field that utilizes DNA polymorphisms to predict phenotypic traits. However, due to high dimensionality, relatively small sample sizes, and data sparsity in genomic data, CV in these scenarios may lead to an underestimation of the generalization error. In this work, we analyzed the bias of CV in eight methods: Ordinary Least Square (OLS), GLS, LMM, Lasso, Ridge, elastic-net (ENET), and two hybrid methods: one combining GLS with Ridge regularization (GLS+Ridge), and the other combining LMM with Ridge regularization (LMM+Ridge). Leveraging genomics data from the 1,000 Genomes Project and simulated phenotypes, our investigation revealed the presence of bias in all these methods. To address this bias, we adapted a variance-structure method known as Cross-Validation Correction (CVc). This approach aims to rectify the cross-validation error by providing a more accurate estimate of the generalization error. To quantify the performance of our adapted CVc towards all these methods, we applied the trained model to an independently generated dataset, which served as a gold standard for validating the models and calculating the generalization error. The outcomes show that, by leveraging CVc, we corrected the CV bias for most of the methods mentioned above, with two exceptions that are unrectifiable methods: ENET and Lasso. Our work revealed the substantial bias in the use of CV in genomics, a phenomenon under-appreciated by the field of statistical genomics and medicine. Additionally, we demonstrated that bias-corrected models may be formed by adapting CVc, although more work is needed to cover the full spectrum.

bioinformatics↗

bioRxiv2023

GeneCompass: Deciphering Universal Gene Regulatory Mechanisms with Knowledge-Informed Cross-Species Foundation Model

Deciphering the universal gene regulatory mechanisms in diverse organisms holds great potential to advance our knowledge of fundamental life process and facilitate research on clinical applications. However, the traditional research paradigm primarily focuses on individual model organisms, resulting in limited collection and integration of complex features on various cell types across species. Recent breakthroughs in single-cell sequencing and advancements in deep learning techniques present an unprecedented opportunity to tackle this challenge. In this study, we developed GeneCompass, the first knowledge-informed, cross-species foundation model pre-trained on an extensive dataset of over 120 million single-cell transcriptomes from human and mouse. During pre-training, GeneCompass effectively integrates four types of biological prior knowledge to enhance the understanding of gene regulatory mechanisms in a self-supervised manner. Fine-tuning towards multiple downstream tasks, GeneCompass outperforms competing state-of-the-art models in multiple tasks on single species and unlocks new realms of cross-species biological investigation. Overall, GeneCompass marks a milestone in advancing knowledge of universal gene regulatory mechanisms and accelerating the discovery of key cell fate regulators and candidate targets for drug development.

bioinformatics↗

bioRxiv2023

Tissue-specific transfer-learning enables retasking of a general comprehensive model to a specific domain

Machine learning (ML) has proven successful in biological data analysis. However, may require massive training data. To allow broader use of ML in the full spectrum of biology and medicine, including sample-sparse domains, re-directing established models to specific tasks by add-on training via a moderate sample may be promising. Transfer learning (TL), a technique migrating pre-trained models to new tasks, fits in this requirement. Here, by TL, we retasked Enformer, a comprehensive model trained by massive data, tailored to breast cancers using breast-specific data. Its performance has been validated through statistical accuracy of predictions, annotation of genetic variants, and mapping of variants associated with breast cancer. By allowing the flexibility of adding dedicated training data, our TL protocol unlocks future discovery within specific domains with moderate add-on samples by standing on the shoulders of giant models.

bioinformatics↗

bioRxiv2023

Prokaryotic microbial diversity analysis and metabolic function prediction of salt lakes on the Qinghai-Tibet Plateau

The Dong Taijinar Salt Lake (DT) and Xi Taijinar Salt Lake (XT) have been widely studied as mineral-rich areas. However, little is known about the composition and distribution of the microbial communities in these two lakes. In this study, metagenomics sequencing was used to analyze the diversity and potential functions of the microbial communities in DT and XT. According to our report, the salinity of DT (332.18-358.30 g/L) was 10 times higher than that of XT (20.09-36.83 g/L). Interestingly, the dominant domain in DT was Archaea (96.16%), while that in XT was Bacteria (93.09%). The distribution of Bacteria in the DT revealed 33 phyla and 1717 genera. The dominant genus in DT was Marinobacillus, which was positively correlated with total phosphorus content. There were four main phyla and 153 genera identified in the Archaea of DT. The most abundant Archaea genera in DT were Natronomonas (24.61%) and Halorubrum (23.69%), which were mainly positively correlated with the Na+, Ca2+, and Cl- contents. Similarly, there were 33 phyla and 1906 genera of Bacteria in XT, and Loktanella was the dominant genus. The archaeal taxonomy in XT mainly included four phyla and 149 genera. Proteobacteria and Euryarchaeota were the most abundant bacterial and archaeal phyla in the two salt lakes. Analysis of the halophilic mechanisms of the microorganisms identified in these two salt lakes revealed that the Bacteria in XT preferred to synthesize compatible solutes, whereas the Archaea in DT preferred a "salt-in" adaptation strategy in salt-stressed environments. IMPORTANCEThe Qinghai-Tibet Plateau is the origin of many lakes and mountains in China. Among them, the Dong Taijinar and Xi Taijinar salt lakes are important biological resources with unknown microbial community compositions and functional potentials. The results of this study revealed significant differences in the distribution of Bacteria and Archaea between the two salt lakes. Salinity mainly drives lower biodiversity and restricted bacterial growth and metabolism in the high-salinity and near-saturated Dong Taijinar Salt Lake. This study not only identifies the key microorganisms in two penetrating salt lakes, but also provides insights into the mechanisms of salinity tolerance and the unknown ecological functions of microorganisms in extreme environments.

microbiology↗

bioRxiv2023

Gradient-based implementation of linear model outperforms deep learning models

Deep learning has been widely considered more effective than traditional statistical models in modeling biological complex data such as single-cell omics. Here we show the devil is hidden in details: by adapting a modern gradient solver to a traditional linear mixed model, we showed that conventional models can outperform deep models in terms of both speed and accuracy. This work reveals the potential of re-implementing traditional models with modern solvers.

bioinformatics↗

bioRxiv2023

Autoencoder-transformed transcriptome improves genotype-phenotype association studies

Transcriptome-wide association study (TWAS) is an emerging model leveraging gene expressions to direct genotype-phenotype association mapping. A key component in TWAS is the prediction of gene expressions; and many statistical approaches have been developed along this line. However, a problem is that many genes have low expression heritability, limiting the performance of any predictive model. In this work, hypothesizing that appropriate denoising may improve the quality of expression data (including heritability), we propose AE-TWAS, which adds a transformation step before conducting standard TWAS. The transformation is composed of two steps by first splitting the whole transcriptome into co-expression networks (modules) and then using autoencoder (AE) to reconstruct the transcriptome data within each module. This transformation removes noise (including nonlinear ones) from the transcriptome data, paving the path for downstream TWAS. We showed two inspiring properties of AE-TWAS: (1) After transformation, the transcriptome data enjoy higher expression heritability at the low-heritability spectrum and possess higher connectivity within the modules. (2) The transferred transcriptome indeed enables better performance of TWAS; and moreover, the newly formed highly connected genes (i.e., hub genes) are more functionally relevant to diseases, evidenced by their functional annotations and overlap with TWAS hits.

bioinformatics↗

bioRxiv2023

Mecp2 Fine-tunes Quiescence Exit by Targeting Nuclear Receptors

Quiescence (G0) maintenance and exit are crucial for tissue homeostasis and regeneration in mammals. Here, we show that methyl-CpG binding protein 2 (Mecp2) expression is cell cycle-dependent and negatively regulates quiescence exit in cultured cells and in an injury-induced liver regeneration mouse model. Specifically, acute reduction of Mecp2 is required for efficient quiescence exit, as deletion of Mecp2 accelerates, while overexpression of Mecp2 delays quiescence exit, and forced expression of Mecp2 after Mecp2 conditional knockout rescues cell cycle reentry. The E3 ligase Nedd4 mediates the ubiquitination and degradation of Mecp2, and thus facilitates quiescence exit. Genome-wide study uncovered the dual role of Mecp2 in preventing quiescence exit by transcriptionally activating metabolic genes while repressing proliferation-associated genes. Particularly, disruption of two nuclear receptors (NRs), Rara or Nr1h3, accelerates quiescence exit, mimicking the Mecp2 depletion phenotype. Our studies unravel a previously unrecognized role for Mecp2 as an essential regulator of quiescence exit and tissue regeneration.

cell biology↗

bioRxiv2023

T2T-YAO: a Telomere-to-telomere Assembled Diploid Reference Genome for Han Chinese

Since its initial release in 2001, the human reference genome has been continuously improved in both continuity and accuracy, and the recently-released telomere-to-telomere version--T2T-CHM13--reaches its top quality after 20 years of effort. However, T2T-CHM13 does not represent an authentic diploid human genome, but rather one derived from a simplified, nearly homozygous genome of a hydatidiform mole cell line. To address this limitation and provide an alternative pertinent to the Chinese population, the largest ethnic group in the world, we have assembled a complete diploid human genome of a male Han Chinese, T2T-YAO, which includes telomere-to-telomere assemblies for all the 22+X+M and 22+Y chromosomes in his two haploids inherited separately from his parents. Both haplotypes contain no artificial sequences or model nucleotides and possess a high quality comparable to CHM13, with fewer than one error per [~]14 Mb. Derived from the individual who lives in the aboriginal region of Han Chinese, T2T-YAO shows clear ancestry and potential genetic continuity from the ancient ancestors of the Han population. Each haplotype of T2T-YAO possesses [~]340 Mb exclusive sequences and [~]3100 unique genes as compared to CHM13, and their genome sequences show greater genetic distance to CHM13 than to each other in terms of nucleotide polymorphism and structural variations. The construction of T2T-YAO would serve as a high-quality diploid reference that enables precise delineation of genomic variations in a haplotype-sensitive manner, which could advance our understandings in human evolution, hereditability of diseases and phenotypes, especially within the context of the unique variations of the Chinese population.

genetics↗

bioRxiv2023

An expression-directed linear mixed model (edLMM) discovering low-effect genetic variants

Detecting genetic variants with low effect sizes using a moderate sample size is difficult, hindering downstream efforts to learn pathology and estimating heritability. In this work, by utilizing informative weights learned from training genetically predicted gene expression models, we formed an alternative approach to estimate the polygenic term in a linear mixed model (LMM). Our LMM estimates the genetic background by incorporating their relevance to gene expression. Our protocol, expression-directed linear mixed model (edLMM), enables the discovery of subtle signals of low-effect variants using moderate sample size. By applying edLMM to cohorts of around 5,000 individuals with either binary (WTCCC) or quantitative (NFBC1966) traits, we demonstrated its power gain at the low-effect end of the genetic etiology spectrum. In aggregate, the additional low-effect variants detected by edLMM substantially improved estimation of missing heritability. edLMM moves precision medicine forward by accurately detecting the contribution of low-effect genetic variants to human diseases.

genetics↗

bioRxiv2023

A statistical method for image-mediated association studies discovers genes and pathways associated with four brain disorders

Brain imaging and genomics are critical tools enabling characterization of the genetic basis of brain disorders. However, imaging large cohorts is expensive, and may be unavailable for legacy datasets used for genome-wide association studies (GWAS). Using an integrated feature selection/aggregation model, we developed Image-Mediated Association Study (IMAS), which utilizes borrowed imaging/genomics data to conduct association mapping in legacy GWAS cohorts. By leveraging the UK Biobank image-derived phenotypes (IDPs), IMAS discovered genetic bases underlying four neuropsychiatric disorders and verified them by analyzing annotations, pathways, and expression Quantitative Trait Loci (eQTLs). A cerebellar-mediated mechanism was identified to be common to the four disorders. Simulations show that, if the goal is identifying genetic risk, IMAS is more powerful than a hypothetical protocol in which the imaging results were available in the GWAS dataset. This implies the feasibility of reanalyzing legacy GWAS datasets without conducting additional imaging, yielding cost-savings for integrated analysis of genetics and imaging.

bioinformatics↗

bioRxiv2023

A genetic program for vascular pericyte precursors

Brain pericytes are one of the critical cell types that regulate endothelial barrier function and activity, thus ensuring adequate blood flow to the brain. The genetic pathways guiding undifferentiated cells into mature pericytes are not well understood. We show here that pericyte precursor populations from both neural crest and head mesoderm of zebrafish express the transcription factor nkx3.1 develop into brain pericytes. We identify the gene signature of these precursors, and show that an nkx3.1, foxf2a, and cxcl12b -expressing pericyte precursor population is present around the basilar artery prior to artery formation and pericyte recruitment. The precursors later spread throughout the brain and differentiate to express canonical pericyte markers. Cxcl12b-Cxcr4 signaling is required for pericyte attachment and differentiation. Further, both nkx3.1 and cxcl12b are necessary and sufficient in regulating pericyte number as loss inhibits and gain increases pericyte number. Through genetic experiments we have defined a precursor population for brain pericytes and identified genes critical for their differentiation.

developmental biology↗

bioRxiv2023

Phosphorylated trimeric SOSS1 complex and RNA polymerase II trigger liquid-liquid phase separation at double-strand breaks

The most toxic forms of DNA damage are double-strand breaks (DSBs). We have previously shown that RNA polymerase II (RNAPII), phosphorylated at tyrosine 1 (Y1P) on the C- terminal domain, transcribes RNA at DSBs to promote efficient DNA repair. However, it is still unknown how transcription is regulated at DSBs. Here, we show that the trimeric SOSS1 complex (hSSB1, INTS3, and c9orf80) binds to Y1P RNAPII in response to DNA damage, hSSB1 binds to R-loops, and formation of the SOSS1 complex is required for the coexistence of replication protein A (RPA) and hSSB1 at DSBs. The damage-activated tyrosine kinase c- Abl phosphorylates hSSB1 to enable its binding to Y1P RNAPII and its recruitment to DSBs. Finally, we show both in vitro and in vivo that the SOSS1 complex and RNAPII form dynamic repair compartments at DSBs via liquid-liquid phase separation (LLPS). The loss of the trimeric SOSS1 leads to impaired DNA repair, highlighting its biological importance in the RNA-dependent DNA damage response. TeaserTrimeric SOSS1 complex and transcription contribute to phase separation at double-strand DNA breaks.

molecular biology↗

bioRxiv2023

CATE: A fast and scalable CUDA implementation to conduct highly parallelized evolutionary tests on large scale genomic data.

Statistical tests for molecular evolution provide quantifiable insights into the selection pressures that govern a genomes evolution. Increasing sample sizes used for analysis leads to higher statistical power. However, this requires more computational nodes or longer computational time. CATE (CUDA Accelerated Testing of Evolution) is a computational solution to this problem comprised of two main innovations. The first is a file organization system coupled with a novel search algorithm and the second is a large-scale parallelization of algorithms using both GPU and CPU. CATE is capable of conducting evolutionary tests such as Tajimas D, Fu and Lis, and Fay and Wus test statistics, McDonald-Kreitman Neutrality Index, Fixation Index, and Extended Haplotype Homozygosity. CATE is magnitudes faster than standard tools with benchmarks estimating it being on average over 180 times faster. For instance, CATE processes all 54,849 human genes for all 22 autosomal chromosomes across the five super populations present in the 1000 Genomes Project in less than thirty minutes while counterpart software took 3.62 days. This proven framework has the potential to be adapted for GPU-accelerated large-scale parallel analyses of many evolutionary and genomic analyses.

bioinformatics↗

bioRxiv2022

Differential expression patterns and potential regulatory function of piRNAs in western honey bee larval response to Ascosphaera apis infestation

piRNAs are a class of small non-coding RNAs that play an essential part in genomic defense, as well as in modulation of gene expression and diverse biological processes such as host-pathogen interaction. However, little is known about the expression pattern of the regulatory function of piRNAs in interactions between honey bees and pathogens. In this work, on the basis of our previously obtained high-quality small RNA-seq datasets from western honey bee (Apis mellifera) larval guts, the differential expression profile of piRNAs in A. mellifera larval guts after Ascosphaera apis infestation was analyzed, followed by structural characterization, target prediction, and regulatory network investigation. The potential roles of differentially expressed piRNAs (DEpiRNAs) in regulating host response, especially immune response, were further discussed. In this study, 504, 657, and 587 piRNAs were respectively identified in the 4-, 5-, and 6-day-old larval guts infected by A. apis, with 411 (53.24%) piRNAs shared. The length distribution of these piRNAs ranged from 24 nt to 33 nt and their first base had a "C" bias, similar to piRNAs discovered in other mammals and insects. Additionally, 96, 103, and 143 DEpiRNAs were detected in the 4-, 5-, and 6-day-old comparison groups; among these, piR-ame-149736, piR-ame-1066173, and piR-ame-1125190 were the most up-regulated, while piR-ame-1202932 was the most down-regulated. There were 68 DEpiRNAs shared between these three comparison groups. The targets of the DEpiRNAs in the three comparison groups were engaged in a suite of crucial functions associated with biological processes, molecular function, and cellular components, including molecular transducer activity, biological regulation, and membrane part. These targets were also relevant to diverse vital pathways such as the phosphatidylinositol signaling system, inositol phosphate metabolism, and Wnt signaling pathway. Further investigation demonstrated that targets of DEpiRNAs were involved in three energy metabolism-related pathways, seven development-associated signaling pathways, and seven immune-relevant pathways, including lysosome and endocytosis, as well as the MAPK and Jak-STAT signaling pathways. The expression trends of five randomly selected DEpiRNAs were verified using a combination of RT-PCR and RT-qPCR. Moreover, the expression levels of six genes targeted by piR-ame-945760 were detected by RT-qPCR, with the results showing that their expression trends were the same as the expression trend of piR-ame-945760, indicative of the positive correlation between piR-ame-945760 and these targets. These results suggest that A.apis infestation increased the overall expression level of piRNAs and altered the expression pattern of piRNAs in A. mellifera larval guts. DEpiRNAs potentially participate in the A. apis response of the host by modulating the expression of target genes associated with energy metabolism and development, as well as cellular and humoral immune response. Our findings not only offer novel insights into A. mellifera larva-A. apis interaction, but also lay the groundwork for clarifying the mechanisms underlying DEpiRNA-regulated larval response.

molecular biology↗

bioRxiv2022

First identification and investigation of piRNAs in the larval guts of Asian honey bee, Apis cerana

Piwi-interacting RNAs (piRNAs), a kind of small non-coding RNAs (ncRNAs), play pivotal parts in maintaining the genomic stability and modulating biological processes such as growth and development via regulation of gene expression. However, piRNAs in Asian honey bee (Apis cerana) is still largely unknown at present. In this current work, on basis of previously gained high-quality small RNA-seq datasets, piRNAs in the larval guts of Apis cerana cerana, the nominate species of A. cerana, was for the first time identified, followed by in-depth investigation of the regulatory roles of differentially expressed piRNAs (DEpiRNAs) in the developmental process of the A. c. cerana. Here, a total of 621 piRNAs were identified in the A. c. cerana larval guts, among which 499 piRNAs were shared by 4- (Ac4 group), 5- (Ac5 group), and 6-day-old (Ac6 group) larval guts, while the numbers of unique ones were 79, 37, and 11, respectively. piRNAs each group were ranged from 24 nt to 33 nt in length, and the first base of piRNAs had a cytosine (C) bias. Additionally, five up-regulated and five down-regulated piRNAs were identified in the Ac4 vs. Ac5 comparison group, 9 of which could target 9, 011 mRNAs; these targets were involved in 41 GO terms and 137 pathways. Comparatively, 22 up-regulated piRNAs were detected in the Ac5 vs. Ac6 comparison group, 21 of which could target 28, 969 mRNAs; these targets were engaged in 46 functional terms and 164 pathways. The results suggested the overall alteration of expression pattern of piRNAs during the developmental process of A. c. cerana larvae. Regulatory network analysis showed that piR-bmo-748815 and piR-bmo-512574 in the Ac4 vs. Ac5 comparison group as well as piR-bmo-716466 and piR-bmo-828146 in the Ac5 vs. Ac6 comparison group linked to the highest number of targets. Further investigation indicated that targets of DEpiRNAs in the above-mentioned two comparison groups could be annotated to several growth and development-associated pathways, such as Jak/STAT, TGF-{beta}, and Wnt signaling pathways, indicating the involvement of DEpiRNAs in modulating larval gut development via these crucial pathways. Moreover, the expression trends of six randomly selected DEpiRNAs were verified using a combination of stem-loop RT-PCR and RT-qPCR. These results not only provide a novel insight into the development of the A. c. cerana larval guts, but also lay a foundation for uncovering the epigenetic mechanism underlying the larval gut development.

molecular biology↗

bioRxiv2022

Transcriptome-wide characterization of piRNAs during the developmental process of European honey bee larval guts

piRNAs play pivotal roles in suppressing transposons, maintaining genome stability, regulating gene expression, and modulating development and immunity. However, there are few piRNA-associated studies on honey bee, and the regulatory role of piRNAs in the development of bee guts is largely unknown. In this current work, the differential expression pattern of piRNAs during the developmental process of the European honey bee (Apis mellifera) was analyzed, and target prediction of differentially expressed piRNAs (DEpiRNAs) was then conducted, followed by investigation of regulatory networks and the potential function of DEpiRNAs in regulating larval gut development. Here, a total of 843 piRNAs were identified in the larval guts of A. mellifera; among these, 764 piRNAs were shared by 4- (Am4 group), 5- (Am5 group), and 6-day-old (Am6 group) larval guts, while 11, 67, and 1, respectively, were unique. The first base of piRNAs in each group had a cytosine (C) bias. Additionally, 61 up-regulated and 17 down-regulated piR-NAs were identified in the Am4 vs. Am5 comparison group, further targeting 9, 983 genes, which were involved in 50 GO terms and 142 pathways, while two up-regulated and five down-regulated piRNAs were detected in the Am5 vs. Am6 comparison group, further targeting 1, 936 genes, which were engaged in 41 functional terms and 101 pathways. piR-ame-742536 and piR-ame-856650 in the Am4 vs. Am5 comparison group as well as piR-ame-592661 and piR-ame-31653 in the Am5 vs. Am6 comparison group were found to link to the highest number of targets. Further analysis indicated that targets of DEpiRNAs in these two comparison groups were seven development-associated signaling pathways such as Hippo and Notch signaling pathways, seven immune-associated pathways including endocytosis and the Jak/STAT signaling pathway, and three energy metabolism pathways, namely sulfur metabolism, nitrogen metabolism, and oxidative phosphorylation. Moreover, the expression trends of five randomly selected DEpiRNAs were verified based on stem-loop RT-PCR and RT-qPCR. These results were suggestive of the overall alteration of piRNAs during the larval developmental process and demonstrated that DEpiRNAs potentially modulate development-, immune-, and energy metabolism-associated pathways by regulating expression of corresponding genes via target binding, further affecting the development of A. mellifera larval guts. Our data offer novel insights into the development of bee guts and lay a basis for clarifying the developmental mechanism underlying the larval guts of European honey bee.

molecular biology↗

bioRxiv2022

Senescent cells develop PDK4-dependent hypercatabolism and form an acidic microenvironment to drive cancer resistance

Cellular senescence is a state of stable growth arrest, usually accompanied by development of the senescence-associated secretory phenotype (SASP). Although senescent cells remain metabolically active, little is known about their metabolic landscape and in vivo pathophysiological implications. Here we show that expression of the pyruvate dehydrogenase (PDH) inhibitory enzyme, pyruvate dehydrogenase kinase 4 (PDK4), is significantly upregulated in human senescent stromal cells. Preferentially expressed upon genotoxicity-induced senescence (GIS), PDK4 is negatively correlated with posttreatment survival of cancer patients. Upon cellular senescence, PDK4 shifts glucose metabolic flux from oxidative phosphorylation to aerobic glycolysis, causing enhanced lactate production and forming an acidic microenvironment. However, distinct from the cancer cell-featured Warburg effect, senescent cells maintain an intensive use of pyruvate through the tricarboxylic acid cycle (TCA), displaying increased respiration and redox activity, indicative of a special form of metabolic reprogramming. Conditioned media from PDK4+ stromal cells change global expression and promote malignancy of recipient cancer cells in vitro and accelerate tumor progression in vivo. Pharmacologically targeting PDK4 restrains the adverse effects of PDK4 in cell-based assays, while promoting tumor regression and extending posttreatment survival in preclinical trials. Together, our study substantiates the hypercatabolic nature of senescent cells, and reveals a metabolic link between senescence-associated acidic microenvironment and age-related pathologies including but not limited to cancer.

cell biology↗