Biology subjects

Shen, H.

Publications and source records attributed to Shen, H..

At least 109 records · Page 6Linked to original sources

bioRxiv2022

CHDbase: A Comprehensive Knowledgebase for Congenital Heart Disease-related Genes and Clinical Manifestations

Congenital heart disease (CHD) is the most common cause of major birth defects, with a prevalence of 1%. Although an increasing number of studies reporting the etiology of CHD, the findings scattered throughout the literature are difficult to retrieve and utilize in research and clinical practice. We therefore developed CHDbase, an evidence-based knowledgebase with CHD-related genes and clinical manifestations manually curated from 1114 publications, linking 1124 susceptibility genes and 3591 variations to more than 300 CHD types and related syndromes. Metadata such as the information of each publication and the selected population and samples, the strategy of studies, and the major findings of study were integrated with each item of research record. We also integrated functional annotations through parsing ~50 databases/tools to facilitate the interpretation of these genes and variations in disease pathogenicity. We further prioritized the significance of these CHD-related genes with a gene interaction network approach, and extracted a core CHD sub-network with 163 genes. The clear genetic landscape of CHD enables the phenotype classification based on the shared genetic origin. Overall, CHDbase provides a comprehensive and freely available resource to study CHD susceptibility, supporting a wide range of users in the scientific and medical communities. CHDbase is accessible at http://chddb.fwgenetics.org/.

genetics↗

bioRxiv2021

The C-terminal tail of polycystin-1 suppresses cystic disease in a mitochondrial enzyme-dependent fashion

Autosomal dominant polycystic kidney disease (ADPKD) is the most prevalent potentially lethal monogenic disorder. Approximately 78% of cases are caused by mutations in the PKD1 gene, which encodes polycystin-1 (PC1). PC1 is a large 462-kDa protein that undergoes cleavage in its N and C-terminal domains. C-terminal cleavage produces fragments that translocate to mitochondria. We show that transgenic expression of a protein corresponding to the final 200 amino acid residues of PC1 in a Pkd1-KO orthologous murine model of ADPKD dramatically suppresses cystic phenotype and preserves renal function. This suppression depends upon an interaction between the C-terminal tail of PC1 and the mitochondrial enzyme Nicotinamide Nucleotide Transhydrogenase. This interaction modulates tubular/cyst cell proliferation, the metabolic profile, mitochondrial function and the redox state. Together, these results suggest that a short fragment of PC1 is sufficient to suppress cystic phenotype and open the door to the exploration of gene therapy strategies for ADPKD.

physiology↗

bioRxiv2021

A modified porous silicon microparticle promotes mucosal delivery of SARS-CoV-2 antigen and induction of potent and durable systemic and mucosal T helper 1 skewed protective immunity

Development of optimal SARS-CoV-2 vaccines to induce potent, long-lasting immunity and provide cross-reactive protection against emerging variants remains a high priority. Here, we report that a modified porous silicon microparticle (mPSM)-adjuvanted SARS-CoV-2 receptor-binding domain (RBD) vaccine activated dendritic cells and generated more potent and durable SARS-CoV-2-specific systemic humoral and type 1 helper T (Th) cell-mediated immune responses than alum-formulated RBD following parenteral vaccination, and protected mice from SARS-CoV-2 and Beta variant infection. mPSM facilitated the uptake of SARS-CoV-2 RBD antigens by nasal and airway epithelial cells. Parenteral and intranasal prime and boost vaccinations with mPSM-RBD elicited potent systemic and lung resident memory T and B cells and SARS-CoV-2 specific IgA responses, and markedly diminished viral loads and inflammation in the lung following SARS-CoV-2 Delta variant infection. Our results suggest that mPSM can serve as potent adjuvant for SARS-CoV-2 subunit vaccine which is effective for systemic and mucosal vaccination.

immunology↗

bioRxiv2021

Epigenomic and transcriptomic prioritization of candidate obesity-risk regulatory GWAS SNPs

Concern about rising rates of obesity has prompted searches for its genetic risk determinants in genome-wide association studies (GWAS). Most genetic variants that contribute to the increased risk of a given trait are probably regulatory single nucleotide polymorphisms (SNPs). However, identifying plausible regulatory SNPs is difficult because of their varied locations relative to their target gene and linkage disequilibrium, which makes most GWAS-derived SNPs only proxies for many fewer functional SNPs. We developed a systematic approach to prioritizing GWAS-derived obesity SNPs using detailed epigenomic and transcriptomic analysis in adipose tissue vs. heterologous tissues. From 50 obesity-related GWAS and 121,064 expanded SNPs, we prioritized 47 potential causal regulatory SNPs (Tier-1 SNPs) for 14 gene loci. A detailed examination of seven of these genes revealed that four (CABLES1, PC, PEMT, and FAM13A) had Tier-1 SNPs that might regulate alternative use of transcription start sites resulting in different polypeptides being generated or different amounts of an intronic microRNA gene being expressed. HOXA11 and long noncoding RNA gene RP11-392O17.1 had Tier-1 SNPs in their 3 or promoter region, respectively, and strong preferences for expression in subcutaneous vs. visceral adipose tissue. ZBED3-AS1 had two intragenic Tier-1 SNPs, each of which might contribute to mediating obesity risk through modulating long-distance chromatin interactions. We conclude that prioritization of regulatory SNP candidates should focus on their surrounding epigenetic features in a trait-relevant tissue. Our approach not only revealed especially credible novel regulatory SNPs, but also helped evaluate previously highlighted obesity GWAS SNPs that were candidates for transcription regulation.

bioinformatics↗

bioRxiv2021

Time-tagged ticker tapes for intracellular recordings

A core taken in a tree today can reveal climate events from centuries past. Here we adapt this idea to record histories of neural activation. We engineered slowly growing intracellular protein fibers which can incorporate diverse fluorescent marks during growth to store linear ticker tape-like histories. An embedded HaloTag reporter incorporated user-supplied HaloTag-ligand dyes, leading to colored stripes whose boundaries mapped fiber growth to wall-clock time. A co-expressed eGFP tag driven by the cFos immediate early gene promoter recorded the history of neural activity. High-resolution multispectral imaging on fixed samples read the cellular histories. We demonstrated recordings of cFos activation in ensembles of cultured neurons with a single-cell absolute accuracy of approximately 39 min over a 12-hour interval. Protein-based ticker tapes have the potential to achieve massively parallel single-cell recordings of multiple physiological modalities.

neuroscience↗

bioRxiv2021

Combinatorial G x G x E CRISPR screening and functional analysis highlights SLC25A39 in mitochondrial GSH transport

The SLC25 carrier family consists of 53 transporters that shuttle nutrients and co-factors across mitochondrial membranes1-3. The family is highly redundant and their transport activities coupled to metabolic state. Here, we introduce a pooled, dual CRISPR screening strategy that knocks out pairs of transporters in four metabolic states -- glucose, galactose, OXPHOS inhibition, and absence of pyruvate -- designed to unmask the inter-dependence of these genes. In total, we screened 63 genes in four metabolic states, corresponding to 2016 single and pair-wise genetic perturbations. We recovered 19 gene-by-environment (GxE) interactions and 9 gene-by-gene (GxG) interactions. One GxE interaction hit illustrated that the fitness defect in the mitochondrial folate carrier (SLC25A32) KO cells was genetically buffered in galactose due to a lack of substrate in de novo purine biosynthesis. Another GxE interaction hit revealed non-equivalence of the paralogous ATP/ADP exchangers (ANTs) with ANT2 specifically required during OXPHOS inhibition. GxG analysis highlighted a buffering interaction between the iron transporter SLC25A37 and the poorly characterized SLC25A39. Mitochondrial metabolite profiling, organelle transport assays, and structure-guided mutagenesis suggests SLC25A39 is critical for mitochondrial glutathione (GSH) transport. Our work underscores the importance of systemetically investigating family-wide genetic interactions between mitochondrial transporters across many metabolic environments.

cell biology↗

bioRxiv2021

A universal approach for integrating super large-scale single-cell transcriptomes by exploring gene rankings

Advancement in single-cell RNA sequencing leads to exponential accumulation of single-cell expression data. However, there is still lack of tools that could integrate these unlimited accumulation of single-cell expression data. Here, we presented a universal approach iSEEEK for integrating super large-scale single-cell expression via exploring expression rankings of top-expressing genes. We developed iSEEEK with 13.7 million single-cells. We demonstrated the efficiency of iSEEEK with canonical single-cell downstream tasks on five heterogenous datasets encompassing human and mouse samples. iSEEEK achieved good clustering performance benchmarked against well-annotated cell labels. In addition, iSEEEK could transfer its knowledge learned from large-scale expression data on new dataset that was not involved in its development. iSEEEK enables identification of gene-gene interaction networks that are characteristic of specific cell types. Our study presents a simple and yet effective method to integrate super large-scale single-cell transcriptomes and would facilitate translational single-cell research from bench to bedside.

bioinformatics↗

bioRxiv2021

Photosensitized Electrospun Nanofibrous Filters for Capturing and Killing Airborne Coronaviruses under Visible Light Irradiation

To address the challenge of the airborne transmission of SARS-CoV-2, photosensitized electrospun nanofibrous membranes were fabricated to effectively capture and inactivate coronavirus aerosols. With an ultrafine fiber diameter ([~] 200 nm) and a small pore size ([~] 1.5 {micro}m), the optimized membranes caught 99.2% of the aerosols of the murine hepatitis virus A59 (MHV-A59), a coronavirus surrogate for SARS-CoV-2. In addition, rose bengal was used as the photosensitizer for the membranes because of its excellent reactivity in generating virucidal singlet oxygen, and the membranes rapidly inactivated 98.9% of MHV-A59 in virus-laden droplets only after 15 min irradiation of simulated reading light. Singlet oxygen damaged the virus genome and impaired virus binding to host cells, which elucidated the mechanism of disinfection at a molecular level. Membrane robustness was also evaluated, and no efficiency reduction for filtering MHV-A59 aerosols was observed after the membranes being exposed to both indoor light and sunlight for days. Nevertheless, sunlight exposure photobleached the membranes, reduced singlet oxygen production, and compromised the performance of disinfecting MHV-A59 in droplets. In contrast, the membranes after simulated indoor light exposure maintained their excellent disinfection performance. In summary, photosensitized electrospun nanofibrous membranes have been developed to capture and kill airborne environmental pathogens under ambient conditions, and they hold promise for broad applications as personal protective equipment and indoor air filters. SynopsisPhotosensitized electrospun nanofibrous filters with excellent capture-and-kill performance against coronaviruses were designed and implemented to prevent the airborne transmission of COVID-19. Table of Contents O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=103 SRC="FIGDIR/small/454404v1_ufig1.gif" ALT="Figure 1"> View larger version (39K): org.highwire.dtl.DTLVardef@171d6corg.highwire.dtl.DTLVardef@18caad5org.highwire.dtl.DTLVardef@23b902org.highwire.dtl.DTLVardef@14746e4_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗

bioRxiv2021

Fully resolved assembly of Cryptosporidium parvum

BackgroundCryptosporidium parvum is an apicomplexan parasite commonly found across many host species with a global infection prevalence in human populations of 7.6%. As such, it is important to understand the diversity and genomic makeup of this prevalent parasite to fight established infections and prohibit further transmission. The basis of every genomic study is a high quality reference genome that has continuity and completeness, thus enabling comprehensive comparative studies. FindingsHere, we provide a highly accurate and complete reference genome of Cryptosporidium parvum. The assembly is based on Oxford Nanopore reads and was improved using Illumina reads for error correction. We also outline how to evaluate and choose from different assembly methods based on two main approaches that can be applied to other Cryptosporidium species. The assembly encompasses 8 chromosomes and includes 13 telomeres that were resolved. Overall, the assembly shows a high completion rate with 98.4% single copy BUSCO genes. ConclusionsThis high quality reference genome of a zoonotic IIaA17G2R1 C. parvum subtype isolate provides the basis for subsequent comparative genomic studies across the Cryptosporidium clade. This will enable improved understanding of diversity, functional and association studies.

genomics↗

bioRxiv2021

Salvage of Ribose from Uridine or RNA Supports Glycolysis when Glucose is Limiting

Summary paragraphGlucose is vital for life, serving both as a source of energy and as a carbon building block for growth. When glucose availability is limiting, alternative nutrients must be harnessed. To identify mechanisms by which cells can tolerate complete loss of glucose, we performed nutrient-sensitized, genome-wide genetic screening and growth assays of 482 pooled PRISM cancer cell lines. We report that catabolism of uridine enables the growth of cells in the complete absence of glucose. While previous studies have shown that the uracil base of uridine can be salvaged to support growth in the setting of mitochondrial electron transport chain deficiency (1), our work shows that the ribose moiety of uridine can be salvaged via a pathway we call "uridinolysis" defined as: [1] the phosphorylytic cleavage of uridine by UPP1/2 into uracil and ribose-1-phosphate (R1P), [2] the conversion of R1P into fructose-6-P and glyceraldehyde-3-P by PGM2 and the non-oxidative branch of the pentose phosphate pathway (non-oxPPP), and [3] their glycolytic utilization to fuel ATP production, biosynthesis and gluconeogenesis. Intriguingly, we report that uridine nucleosides derived from RNA are also a substrate for uridinolysis and that RNA can support growth in glucose-limited conditions. Our results underscore the malleability of central carbon metabolism and raise the provocative hypothesis that RNA can also serve as a potential storage for energy.

biochemistry↗

bioRxiv2021

Compartmap enables inference of higher-order chromatin structure in individual cells from scRNA-seq and scATAC-seq

Single-cell profiling of chromatin structure remains a challenge due to cost, throughput, and resolution. We introduce compartmap to reconstruct higher-order chromatin domains in individual cells from transcriptomic (RNAseq) and epigenomic (ATACseq) assays. In cell lines and primary human samples, compartmap infers higher-order chromatin structure comparable to specialized chromatin capture methods, and identifies clinically relevant structural alterations in single cells. This provides a common lens to integrate transcriptional and epigenomic results, linking higher-order chromatin architecture to gene regulation and to clinically relevant phenotypes in individual cells.

genomics↗

bioRxiv2021

Automated morphological phenotyping using learned shape descriptors and functional maps: A novel approach to geometric morphometrics

The methods of geometric morphometrics are commonly used to quantify morphology in a broad range of biological sciences. The application of these methods to large datasets is constrained by manual landmark placement limiting the number of landmarks and introducing observer bias. To move the field forward, we need to automate morphological phenotyping in ways that capture comprehensive representations of morphological variation with minimal observer bias. Here, we present Morphological Variation Quantifier (morphVQ), a shape analysis pipeline for quantifying, analyzing, and exploring shape variation in the functional domain. morphVQ uses descriptor learning to estimate the functional correspondence between whole triangular meshes in lieu of landmark configurations. With functional maps between pairs of specimens in a dataset we can analyze and explore shape variation. morphVQ uses Consistent ZoomOut refinement to improve these functional maps and produce a new representation of shape variation, area-based and conformal (angular) latent shape space differences (LSSDs). We compare this new representation of shape variation to shape variables obtained via manual digitization and auto3DGM, an existing approach to automated morphological phenotyping. We find that LSSDs compare favorably to modern 3DGM and auto3DGM while being more computationally efficient. By characterizing whole surfaces, our method incorporates more morphological detail in shape analysis. We can classify known biological groupings, such as Genus affiliation with comparable accuracy. The shape spaces produced by our method are similar to those produced by modern 3DGM and to auto3DGM, and distinctiveness functions derived from LSSDs show us how shape variation differs between groups. morphVQ can capture shape in an automated fashion while avoiding the limitations of manually digitized landmarks, and thus represents a novel and computationally efficient addition to the geometric morphometrics toolkit. Author summaryThe quantification of biological shape variation has relied on expert placement of relatively small subsets of landmarks and their analysis using tools of geometric morphometrics (GM). This paper introduces morphVQ, a novel, automated, learning-based approach to shape analysis that approximates the non-rigid correspondence between surface models of bone. With accurate functional correspondence between bones, we can characterize the shape variation within a dataset. Our results demonstrate that morphVQ performs similarly to manual digitization and to an existing automated phenotyping approach, auto3DGM. morphVQ has the advantages of greater computational efficiency and while capturing shape variation directly from surface model representations of bone. We can classify biological shapes to the Genus level with comparable accuracy to previous approaches, and we can demonstrate which aspects of bone shape differ most between groups. The ability to provide comparable accuracy in a Genus level classification with features extracted from morphVQ further guarantees the validity of this approach.

bioinformatics↗

bioRxiv2021

A lipid-mTORC1 nutrient-sensing pathway regulates animal development by peroxisome-derived hormones

Animals have developed many signaling mechanisms that alter cellular and developmental programs in response to changes in nutrients and their derived metabolites, many of which remain to be understood. We recently uncovered that glucosylceramides, a core sphingolipid, act as a critical nutrient signal for overall amino-acid level to promote development by activating the intestinal mTORC1 pathway. However, how the intestinal GlcCer-mTORC1 activity regulates development throughout the whole body is unknown. Through a large-scale genetic screen, we found that the peroxisomes are critical for antagonizing the GlcCer-mTORC1-mediated nutrient signal. Mechanistically, deficiency of glucosylceramide, inactivation of the downstream mTORC1 activity, or prolonged starvation relocated peroxisomes closer to the intestinal apical region to release peroxisomal-beta-oxidation derived hormones that targeting chemosensory neurons to arrest the animal development. Our data illustrated a new gut-brain axis for orchestrating nutrient-sensing dependent development in Caenorhabditis elegans, which may also explain why glucosylceramide and peroxisome become essential in metazoans.

cell biology↗

bioRxiv2021

Population genomics of Salmonella enterica serovar Weltevreden ST365, an emerging predominant causative agent of diarrheal disease

Salmonella enterica serovar Weltevreden is a recently emerged pathogen, and as such we lack a comprehensive knowledge of its microbiology, genomics, epidemiology and biogeography. In this study, we analyzed 174 novel S. Weltevreden isolates including 111 isolates recovered from diarrheal patients in China between 2006 and 2017. Our results demonstrate that the ST365 clone was the predominant causative agent of the diarrhea-outbreak during this period, as vast majority of the isolates recovered from diarrheal patients belonged to this sequence type (97.37%, 74/76). We also determined the ST365 clone as the predominant sequence type of S. Weltevreden from diarrheal patients globally from previously published sequences (97.51%, 196/201). In order to determine the possible antimicrobial genes and virulence factors associated with S. Weltevreden, we performed whole genome sequencing on our novel isolates. We were able to identify a range of key virulence factors associated with S. Weltevreden that are likely to be beneficial to their fitness and pathogenesis. Furthermore, we were able to isolate a novel 100.03-kb IncFII(S) type virulence plasmid that used the same replicon as pSPCV virulence plasmid. Importantly, we demonstrated through plasmid elimination a functional role for this plasmid in bacterial virulence. These findings are critical to further our knowledge of this high consequence pathogen. ImportanceSalmonella Weltevreden is a newly emerged foodborne pathogen and has caused several outbreaks of diarrheal diseases in some regions in the world. However, comprehensive knowledge of microbiology, genomics, epidemiology and biogeography of this newly emerged pathogen is still lack. In this study, we made an unexpected discovery that S. Weltevreden sequence type (ST) 365 is the causative agent in the diarrhea-outbreak in China and many other regions of the world. We also shown that this sequence type was widely recovered from animal, food, and environmental samples collected in different regions in the world. Importantly, we discovered a novel IncFII(S) type virulence plasmid commonly carried by S. Weltevreden strains of both human, animal, and food origins. These data facilitate future studies investigating the emergence of S. Weltevreden involved in diarrheal outbreaks and the global spread of S. Weltevreden strains.

microbiology↗

bioRxiv2021

Cell-to-cell and type-to-type heterogeneity of signaling networks: Insights from the crowd

Recent technological developments allow us to measure the status of dozens of proteins in individual cells. This opens the way to understand the heterogeneity of complex multi-signaling networks across cells and cell-types, with important implications to understand and treat diseases such as cancer. These technologies are however limited to proteins for which antibodies are available and are fairly costly, making predictions of new markers and of existing markers under new conditions a valuable alternative. To assess our capacity to make such predictions and boost further methodological development, we organised the Single Cell Signaling in Breast Cancer DREAM challenge. We used a mass cytometry data set, covering 36 markers in over 4,000 conditions totalling 80 million single cells across 67 breast cancer cell lines. Through four increasingly difficult subchallenges, the participants predicted missing markers, new conditions, and the time course response of single cells to stimuli in the presence and absence of kinase inhibitors. The challenge results show that despite the stochastic nature of signal transduction in single cells, the signaling events are tightly controlled and machine learning methods can accurately predict new experimental data. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=58 SRC="FIGDIR/small/436603v2_ufig1.gif" ALT="Figure 1"> View larger version (24K): org.highwire.dtl.DTLVardef@a95337org.highwire.dtl.DTLVardef@966d5aorg.highwire.dtl.DTLVardef@1e55b18org.highwire.dtl.DTLVardef@bfb5d7_HPS_FORMAT_FIGEXP M_FIG C_FIG Key pointsO_LIOver 80 million single-cell multiplexed measurements across 67 cell lines, 54 conditions and 10 time points to benchmark predictive models of single cell signaling C_LIO_LI73 approaches from 27 teams for predicting response to kinase inhibitors on single cell level, and dynamic response from unperturbed basal omics data C_LIO_LIPredictions of single marker models correlate with measurements with a correlation coefficient of 0.76 C_LIO_LITop models of whole signaling response models perform almost as well as a biological replicate C_LIO_LICell-line specific variation in dynamics can be predicted from basal omics C_LI

bioinformatics↗

bioRxiv2021

Improving Generalizability of Protein Sequence Models with Data Augmentations

AO_SCPLOWBSTRACTC_SCPLOWWhile protein sequence data is an emerging application domain for machine learning methods, small modifications to protein sequences can result in difficult-to-predict changes to the proteins function. Consequently, protein machine learning models typically do not use randomized data augmentation procedures analogous to those used in computer vision or natural language, e.g., cropping or synonym substitution. In this paper, we empirically explore a set of simple string manipulations, which we use to augment protein sequence data when fine-tuning semi-supervised protein models. We provide 276 different comparisons to the Tasks Assessing Protein Embeddings (TAPE) baseline models, with Transformer-based models and training datasets that vary from the baseline methods only in the data augmentations and representation learning procedure. For each TAPE validation task, we demonstrate improvements to the baseline scores when the learned protein representation is fixed between tasks. We also show that contrastive learning fine-tuning methods typically outperform masked-token prediction in these models, with increasing amounts of data augmentation generally improving performance for contrastive learning protein methods. We find the most consistent results across TAPE tasks when using domain-motivated transformations, such as amino acid replacement, as well as restricting the Transformer attention to randomly sampled sub-regions of the protein sequence. In rarer cases, we even find that information-destroying augmentations, such as randomly shuffling entire protein sequences, can improve downstream performance.

bioinformatics↗

bioRxiv2021

An integrated strategy reveals complex glycosylation of erythropoietin using top-down and bottom-up mass spectrometry

The characterization of glycoproteins, like erythropoietin, is challenging due to the structural micro- and macro-heterogeneity of the protein glycosylation. This study presents an in-depth strategy for glycosylation analysis of a first-generation erythropoietin (epoetin beta), including a developed top-down mass spectrometric workflow for N-glycan analysis, bottom-up mass spectrometric methods for site-specific N-glycosylation and a LC-MS approach for O-glycan identification. Permethylated N-glycans, peptides and enriched glycopeptides of erythropoietin were analyzed by nanoLC-MS/MS and de-N-glycosylated erythropoietin was measured by LC-MS, enabling the qualitative and quantitative analysis of glycosylation and different glycan modifications (e.g., phosphorylation and O-acetylation). Extending the coverage of our newly developed Python script to phosphorylated N-glycans enabled the identification of 140 N-glycan compositions (237 N-glycan structures) from erythropoietin. The site-specificity of N-glycans was revealed at glycopeptide level by pGlyco software using different proteases. In total, 215 N-glycan compositions were identified from N-glycan and glycopeptide analysis. Moreover, LC-MS analysis of de-N-glycosylated erythropoietin species identified two different O-glycan compositions, based on the mass shifts between non-O-glycosylated and O-glycosylated species. This integrated strategy allows the in-depth glycosylation analysis of a therapeutic glycoprotein to understand its pharmacological properties and improving the manufacturing processes.

biochemistry↗

bioRxiv2021

Dependency of LKB1-inactivated lung cancer on aberrant CRTC-CREB activation

Lung cancer with loss-of-function of the LKB1 tumor suppressor is a common aggressive subgroup with no effective therapies. LKB1-deficiency induces constitutive activation of cAMP/CREB-mediated transcription by a family of three CREB-regulated transcription coactivators (CRTC1-3). However, the significance and mechanism of CRTC activation in promoting the aggressive phenotype of LKB1-null cancer remain poorly characterized. Here we observed overlapping CRTC expression patterns and mild growth phenotypes of individual CRTC-knockouts in lung cancer, suggesting functional redundancy of CRTC1-3. We consequently designed a dominant-negative mutant (dnCRTC) to block all three CRTCs to bind and co-activate CREB. Expression of dnCRTC efficiently inhibited the aberrantly activated cAMP/CREB-mediated oncogenic transcriptional program induced by LKB1-deficiency, and specifically blocked the growth of LKB1-inactivated lung cancer. Collectively, this study provides direct proof for an essential role of the CRTC-CREB activation in promoting the malignant phenotypes of LKB1-null lung cancer and proposes the CRTC-CREB interaction interface as a novel therapeutic target.

cancer biology↗