Biology subjects

Cho, K.

Publications and source records attributed to Cho, K..

At least 55 records · Page 3Linked to original sources

bioRxiv2023

Molecular signatures associated with successful implantation of the human blastocyst

Embryo implantation in humans is remarkably inefficient for reasons that remain largely unexplained, and high rates of implantation failure remain one of the greatest obstacles in treating infertility. The volume of gene expression data available from human embryos has rapidly accumulated in recent years. However, prioritization of these data to identify the subset of genes that determine successful implantation remains a challenge, in part, because comprehensive analyses cannot be performed on the same embryos that are transferred. Here, we leverage clinical morphologic grading--known for decades to correlate with implantation potential--and transcriptome analyses of matched embryonic and abembryonic samples to identify genes and cell-cell interactions enriched and depleted in human blastocysts of good and poor morphology, genome-wide. Unexpectedly, we discovered that the greatest molecular difference was in the state of the extraembryonic primitive endoderm (PrE), with relative deficiencies in PrE development in embryos of poor morphology at the time of embryo transfer. Together, our results support a model in which implantation success is most strongly reflected by factors and signals from the embryonic compartment and suggest that deficiencies in PrE development, in particular, are common among embryos with reduced implantation potential. Our study provides a valuable resource for those investigating the markers and mechanisms of human embryo implantation.

developmental biology↗

bioRxiv2023

An extended wave of global mRNA deadenylation sets up a switch in translation regulation across the mammalian oocyte-to-embryo transition

The oocyte-to-embryo transition (OET) occurs in the absence of new transcription and relies on post-transcriptional gene regulation, including translational control by mRNA poly(A) tail regulation, where cytoplasmic polyadenylation activates translation and deadenylation leads to translational repression and decay. However, how the transcriptome-wide landscape of mRNA poly(A) tails shapes translation across the OET in mammals remains unknown. Here, we performed long-read RNA sequencing to uncover poly(A) tail lengths and mRNA abundance transcriptome-wide in mice across five stages of development from oocyte to embryo. Integrating these data with recently published ribosome profiling data, we demonstrate that poly(A) tail length is coupled to translational efficiency across the entire OET. We uncover an extended wave of global deadenylation during fertilization, which sets up a switch in translation control between the oocyte and embryo. In the oocyte, short-tailed maternal mRNAs that resist deadenylation in the oocyte are translationally activated, whereas large groups of mRNAs deadenylated without decay in the oocyte are later readenylated to drive translation activation in the early embryo. Our findings provide an important resource and insight into the mechanisms by which cytoplasmic polyadenylation and deadenylation dynamically shape poly(A) tail length in a stage-specific manner to orchestrate development from oocyte to embryo in mammals.

developmental biology↗

bioRxiv2023

Effects of hepatic mitochondrial pyruvate carrier deficiency on de novo lipogenesis and glycerol-mediated gluconeogenesis in mice

The liver coordinates the systemic response to nutrient deprivation and availability by producing glucose from gluconeogenesis during fasting and synthesizing lipids via de novo lipogenesis (DNL) when carbohydrates are abundant. Mitochondrial pyruvate metabolism is thought to play important roles in both gluconeogenesis and DNL. We examined the effects of hepatocyte-specific mitochondrial pyruvate carrier (MPC) deletion on the fasting-refeeding response. Rates of DNL during refeeding were impaired by liver MPC deletion, but this did not reduce intrahepatic lipid content. During fasting, glycerol is converted to glucose by two pathways; a direct cytosolic pathway essentially reversing glycolysis and an indirect mitochondrial pathway requiring the MPC. MPC deletion reduced the incorporation of 13C-glycerol into TCA cycle metabolites but not into newly synthesized glucose. However, suppression of glycerol metabolism did not affect glucose concentrations in fasted hepatocyte-specific MPC-deficient mice. Thus, glucose production by kidney and intestine may compensate for MPC deficiency in hepatocytes.

physiology↗

bioRxiv2023

Inducing vulnerability to InhA inhibition restores isoniazid susceptibility in drug resistant Mycobacterium tuberculosis

Of the approximately 10 million cases of Mycobacterium tuberculosis (Mtb) infections each year, over 10% are resistant to the frontline antibiotic isoniazid (INH). INH resistance is predominantly caused by mutations that decrease the activity of the bacterial enzyme KatG, which mediates conversion of the pro-drug INH to its active form INH-NAD. We previously discovered an inhibitor of Mtb respiration, C10, that enhances the bactericidal activity of INH, prevents the emergence of INH-resistant mutants, and re-sensitizes a collection of INH-resistant mutants to INH through an unknown mechanism. To investigate the mechanism of action of C10, we exploited the toxicity of high concentrations of C10 to select for resistant mutants. We discovered two mutations that confer resistance to the disruption of energy metabolism and allow for growth of Mtb in high C10 concentrations, indicating that growth inhibition by C10 is associated with inhibition of respiration. Using these mutants as well as direct inhibitors of the Mtb electron transport chain, we provide evidence that inhibition of energy metabolism by C10 is neither sufficient nor necessary to potentiate killing by INH. Instead, we find that C10 acts downstream of INH-NAD synthesis, causing Mtb to become particularly sensitive to inhibition of the INH-NAD target, InhA, without changing the concentration of INH-NAD or the activity of InhA, the two predominant mechanisms of potentiating INH. Our studies revealed that there exists a vulnerability in Mtb that can be exploited to render Mtb sensitive to otherwise subinhibitory concentrations of InhA inhibitor. SignificanceIsoniazid (INH) is a critical frontline antibiotic to treat Mycobacterium tuberculosis (Mtb) infections. INH efficacy is limited by its suboptimal penetration of the Mtb-containing lesion and by the prevalence of clinical INH-resistance. We previously discovered a compound, C10, that enhances the bactericidal activity of INH, prevents the emergence of INH-resistant mutants, and re-sensitizes a set of INH-resistant mutants to INH. Resistance is typically mediated by katG mutations that decrease the activation of INH, which is required for INH to inhibit the essential enzyme InhA. Our current work demonstrates that C10 re-sensitizes INH-resistant katG-hypomorphs without enhancing the activation of INH. We furthermore show that C10 causes Mtb to become particularly vulnerable to InhA inhibition without compromising InhA activity on its own. Therefore, C10 represents a novel strategy to curtail the development of INH resistance and to sensitize Mtb to sub-lethal doses of INH, such as those achieved at the infection site.

microbiology↗

bioRxiv2022

Automated Protein Function Description for NovelClass Discovery

Knowledge of protein function is necessary for understanding biological systems, but the discovery of new sequences from high-throughput sequencing technologies far outpaces their functional characterization. Beyond the problem of assigning newly sequenced proteins to known functions, a more challenging issue is discovering novel protein functions. The space of possible functions becomes unlimited when considering designed proteins. Protein function prediction, as it is framed in the case of Gene Ontology term prediction, is a multilabel classification problem with a hierarchical label space. However, this framing does not provide guiding principles for discovering completely novel functions. Here we propose a neural machine translation model in order to generate descriptions of protein functions in natural language. In this way, instead of making predictions in a limited label space, our model generates descriptions in the language space, and thus is capable of composing novel functions. Given the novelty of our approach, we design metrics to evaluate the performance of our model: correctness, specificity and robustness. We provide results of our model in the zero-shot classification setting, scoring functional descriptions that the model has not seen before for proteins that have limited homology to those in the training set. Finally, we show generated function descriptions compared to ground truth descriptions for qualitative evaluation.

genomics↗

bioRxiv2022

EquiFold: Protein Structure Prediction with a Novel Coarse-Grained Structure Representation

Designing proteins to achieve specific functions often requires in silico modeling of their properties at high throughput scale and can significantly benefit from fast and accurate protein structure prediction. We introduce EquiFold, a new end-to-end differentiable, SE(3)-equivariant, all-atom protein structure prediction model. EquiFold uses a novel coarse-grained representation of protein structures that does not require multiple sequence alignments or protein language model embeddings, inputs that are commonly used in other state-of-the-art structure prediction models. Our method relies on geometrical structure representation and is substantially smaller than prior state-of-the-art models. In preliminary studies, EquiFold achieved comparable accuracy to AlphaFold but was orders of magnitude faster. The combination of high speed and accuracy make EquiFold suitable for a number of downstream tasks, including protein property prediction and design.

bioinformatics↗

bioRxiv2022

Probabilisitic Matrix Factorization for Gene Regulatory Network Inference

Inferring gene regulatory networks (GRNs) from single cell data is challenging due to heuristic limitations and a lack of uncertainty estimates in existing methods. To address this, we present Probabilistic Matrix Factorization for Gene Regulatory Network Inference (PMF-GRN). Using single cell expression data, PMF-GRN infers latent factors capturing transcription factor activity and regulatory relationships, incorporating experimental evidence via prior distributions. By utilizing variational inference, we facilitate hyperparameter search for principled model selection and direct comparison to other generative models. We extensively test and benchmark our method using single cell datasets from Saccharomyces cerevisiae, human Peripheral Blood Mononuclear Cells (PBMCs), and BEELINE synthetic data. We discover that PMF-GRN infers GRNs more accurately than current state-of-the-art single-cell GRN inference methods, offering well-calibrated uncertainty estimates for additional interpretability.

genomics↗

bioRxiv2022

TM-Vec: template modeling vectors for fast homology detection and alignment

Exploiting sequence-structure-function relationships in molecular biology and computational modeling relies on detecting proteins with high sequence similarities. However, the most commonly used sequence alignment-based methods, such as BLAST, frequently fail on proteins with low sequence similarity to previously annotated proteins. We developed a deep learning method, TM-Vec, that uses sequence alignments to learn structural features that can then be used to search for structure-structure similarities in large sequence databases. We train TM-Vec to accurately predict TM-scores as a metric of structural similarity for pairs of structures directly from sequence pairs without the need for intermediate computation or solution of structures. For remote homologs (sequence similarity [≤] 10%) that are highly structurally similar (TM-score ? 0.6), we predict TM-scores within 0.026 of their value computed by TM-align. TM-Vec outperforms traditional sequence alignment methods and performs similar to structure-based alignment methods. TM-Vec was trained on the CATH and SwissModel structural databases and it has been tested on carefully curated structure-structure alignment databases that were designed specifically to test very remote homology detection methods. It scales sub-linearly for search against large protein databases and is well suited for discovering remotely homologous proteins.

bioinformatics↗

bioRxiv2022

Mitochondrial Pyruvate Carrier Inhibition Initiates Metabolic Crosstalk to Stimulate Branched Chain Amino Acid Catabolism

ObjectiveThe mitochondrial pyruvate carrier (MPC) has emerged as a therapeutic target for treating insulin resistance, type 2 diabetes, and nonalcoholic steatohepatitis (NASH). We evaluated whether MPC inhibitors (MPCi) might correct impairments in branched chain amino acid (BCAA) catabolism, which are predictive of developing diabetes and NASH. MethodsCirculating BCAA concentrations were measured in people with NASH and type 2 diabetes, who participated in a recent randomized, placebo-controlled Phase IIB clinical trial to test the efficacy and safety of the MPCi MSDC-0602K (EMMINENCE; NCT02784444). In this 52-week trial, patients were randomly assigned to placebo (n = 94) or 250 mg MSDC-0602K (n = 101). Human hepatoma cell lines were used to test the direct effects of various MPCi on BCAA catabolism in vitro. Lastly, we investigated how hepatocyte-specific deletion of MPC2 affects BCAA catabolism in the liver of obese mice. ResultsIn patients with NASH, MSDC-0602K treatment, which led to marked improvements in insulin sensitivity and diabetes, decreased plasma concentrations of BCAAs compared to baseline while placebo had no effect. The rate-limiting enzyme in BCAA catabolism is the mitochondrial branched chain ketoacid dehydrogenase (BCKDH), which is deactivated by phosphorylation. In multiple human hepatoma cell lines, MPCi markedly reduced BCKDH phosphorylation and stimulated branched chain keto acid catabolism; an effect that required the BCKDH phosphatase PPM1K. Mechanistically, the effects of MPCi were linked to activation of the energy sensing AMP-dependent protein kinase (AMPK) and mechanistic target of rapamycin (mTOR) kinase signaling cascades. Finally, BCKDH phosphorylation was reduced in liver of obese, hepatocyte-specific MPC2 knockout (LS-Mpc2-/-) mice compared to wild-type controls concomitant with activation of mTOR signaling in vivo. ConclusionsThese data demonstrate novel cross talk between mitochondrial pyruvate and BCAA metabolism and suggest that MPC inhibition leads to lower plasma BCAA concentrations and BCKDH phosphorylation by activating the AMPK/mTOR axis.

physiology↗

bioRxiv2022

Long-range inhibition synchronizes and updates prefrontal task activity

Changes in patterns of activity within the medial prefrontal cortex enable rodents, non-human primates, and humans to update their behavior to adapt to changes in the environment, e.g., during cognitive tasks1-5. Within medial prefrontal cortex, inhibitory neurons expressing parvalbumin are important for updating strategies in a rule shift task6-8. Nevertheless, causal mechanisms through which parvalbumin neurons recruit specific circuits to produce prefrontal network dynamics that switch from maintaining to updating task-related patterns of activity remain unknown. Here we identify a new long-range inhibitory projection from prefrontal parvalbumin neurons to the contralateral cortex that mediates this process. Whereas nonspecifically inhibiting all callosal projections does not prevent mice from learning rule shifts, selectively inhibiting these callosal parvalbumin projections disrupts rule shift learning, desynchronizes gamma-frequency activity that is necessary for learning8, and suppresses changes in prefrontal activity patterns that normally accompany rule shifts. Thus, callosal parvalbumin projections switch prefrontal circuits from maintaining to updating behavioral strategies by synchronizing callosal communication and preventing it from inappropriately maintaining outdated neural representations. These findings may explain how deficits in prefrontal parvalbumin neurons and gamma synchrony cause impaired behavioral flexibility in schizophrenia9-10, and identify long-range projections from prefrontal parvalbumin neurons as a key circuit locus for understanding and treating these deficits.

neuroscience↗

bioRxiv2021

Function-guided protein design by deep manifold sampling

Protein design is challenging because it requires searching through a vast combinatorial space that is only sparsely functional. Self-supervised learning approaches offer the potential to navigate through this space more effectively and thereby accelerate protein engineering. We introduce a sequence denoising autoencoder (DAE) that learns the manifold of protein sequences from a large amount of potentially unlabelled proteins. This DAE is combined with a function predictor that guides sampling towards sequences with higher levels of desired functions. We train the sequence DAE on more than 20M unlabeled protein sequences spanning many evolutionarily diverse protein families and train the function predictor on approximately 0.5M sequences with known function labels. At test time, we sample from the model by iteratively denoising a sequence while exploiting the gradients from the function predictor. We present a few preliminary case studies of protein design that demonstrate the effectiveness of this proposed approach, which we refer to as "deep manifold sampling", including metal binding site addition, function-preserving diversification, and global fold change.

bioinformatics↗

bioRxiv2021

Sex Differences in Brain Tumor Glutamine Metabolism Reveal Sex-Specific Vulnerabilities to Treatment

Sex differences in normal metabolism are well described, but whether they persist in cancerous tissue is unknown. We assessed metabolite abundance in glioblastoma surgical specimens and found that male glioblastomas are enriched for amino acids, including glutamine. Using PET imaging, we found that gliomas in male patients exhibit significantly higher glutamine uptake. These sex differences were well-modeled in murine transformed astrocytes, in which male cells imported and metabolized more glutamine and were more sensitive to glutaminase 1 (GLS1) inhibition. The sensitivity to GLS1 inhibition in males was driven by their dependence on glutamine-derived glutamate for -ketoglutarate synthesis and TCA cycle replenishment. Females were resistant to GLS1 inhibition through greater pyruvate carboxylase-mediated TCA cycle replenishment. Thus, clinically important sex differences exist in targetable elements of metabolism. Recognition of sex-biased metabolism is an opportunity to improve treatments for all patients through further laboratory and clinical research.

cancer biology↗

bioRxiv2021

Silencing alanine transaminase 2 in diabetic liver attenuates hyperglycemia by reducing gluconeogenesis from amino acids

Hepatic gluconeogenesis from amino acids contributes significantly to diabetic hyperglycemia, but the molecular mechanisms involved are incompletely understood. Alanine transaminases (ALT1 and ALT2) catalyze the interconversion of alanine and pyruvate, which is required for gluconeogenesis from alanine. We found that ALT2 was overexpressed in liver of diet-induced obese and db/db mice and that the expression of the gene encoding ALT2 (GPT2) was downregulated following bariatric surgery in people with obesity. The increased hepatic expression of Gpt2 in db/db liver was mediated by activating transcription factor 4; an endoplasmic reticulum stress-activated transcription factor. Hepatocyte-specific knockout of Gpt2 attenuated incorporation of 13C-alanine into newly synthesized glucose by hepatocytes. In vivo Gpt2 knockdown or knockout in liver had no effect on glucose concentrations in lean mice, but Gpt2 suppression alleviated hyperglycemia in db/db mice. These data suggest that ALT2 plays a significant role in hepatic gluconeogenesis from amino acids in diabetes.

physiology↗

bioRxiv2021

Polycomb group protein CBX7 represses cardiomyocyte proliferation via modulation of the TARDBP/Rbm38 axis

Cardiomyocyte (CM) proliferation notably decreases during the perinatal period. At present, regulatory mechanisms for this loss of proliferative capacity is poorly understood. CBX7, a polycomb group (PcG) protein, regulates the cell cycle but its role in CM proliferation is unknown. Here, we report that CBX7 inhibits proliferation of perinatal CMs by controlling TARDBP/Rbm38 pathway. Gene expression profiling demonstrated that CBX7 expression in the heart was low during the prenatal period, abruptly increased during the perinatal period, and sustained constantly throughout the adulthood. CBX7, when overexpressed via adenoviral transduction in neonatal CMs, reduced proliferation and promoted multinucleation of the CMs. Mutant mice carrying targeted inhibition of CBX7 in CMs exhibited cardiomegaly with increased proliferation of CMs at postnatal stages. Mechanistically, CBX7 interacted with TAR DNA-binding protein 43 (TARDBP) and positively regulated its downstream target, RNA Binding Motif Protein 38 (RBM38). Rbm38 was upregulated in the postnatal hearts and overexpression of RBM38 reduced proliferation of neonatal CMs. Together, this study provides a novel insight into the role of CBX7 in regulation of CM proliferation during the perinatal period.

developmental biology↗

bioRxiv2021

NetTIME: improving multitask transcription factor binding site prediction with base-pair resolution

MotivationMachine learning models for predicting cell-type-specific transcription factor (TF) binding sites have become increasingly more accurate thanks to the increased availability of next-generation sequencing data and more standardized model evaluation criteria. However, knowledge transfer from data-rich to data-limited TFs and cell types remains crucial for improving TF binding prediction models because available binding labels are highly skewed towards a small collection of TFs and cell types. Transfer prediction of TF binding sites can potentially benefit from a multitask learning approach; however, existing methods typically use shallow single-task models to generate low-resolution predictions. Here we propose NetTIME, a multitask learning framework for predicting cell-type-specific transcription factor binding sites with base-pair resolution. ResultsWe show that the multitask learning strategy for TF binding prediction is more efficient than the single-task approach due to the increased data availability. NetTIME trains high-dimensional embedding vectors to distinguish TF and cell-type identities. We show that this approach is critical for the success of the multitask learning strategy and allows our model to make accurate transfer predictions within and beyond the training panels of TFs and cell types. We additionally train a linear-chain conditional random field (CRF) to classify binding predictions and show that this CRF eliminates the need for setting a probability threshold and reduces classification noise. We compare our methods predictive performance with two state-of-the-art methods, Catchitt and Leopard, and show that our method outperforms previous methods under both supervised and transfer learning settings. AvailabilityNetTIME is freely available at https://github.com/ryi06/NetTIME and the code is also archived at https://doi.org/10.5281/zenodo.6994897 Contactrb133@nyu.edu

systems biology↗

bioRxiv2020

NetQuilt: Deep Multispecies Network-based Protein Function Prediction using Homology-informed Network Similarity

Transferring knowledge between species is challenging: different species contain distinct proteomes and cellular architectures, which cause their proteins to carry out different functions via different interaction networks. Many approaches to proteome and biological network functional annotation use sequence similarity to transfer knowledge between species. These similarity-based approaches cannot produce accurate predictions for proteins without homologues of known function, as many functions require cellular or organismal context for meaningful function prediction. In order to supply this context, network-based methods use protein-protein interaction (PPI) networks as a source of information for inferring protein function and have demonstrated promising results in function prediction. However, the majority of these methods are tied to a network for a single species, and many species lack biological networks. In this work, we integrate sequence and network information across multiple species by applying an IsoRank-derived network alignment algorithm to create a meta-network profile of the proteins of multiple species. We then use this integrated multispecies meta-network as input features to train a maxout neural network with Gene Ontology terms as target labels. Our multispecies approach takes advantage of more training examples, and more diverse examples from multiple organisms, and consequently leads to significant improvements in function prediction performance. Further, we evaluate our approach in a setting in which an organisms PPI network is left out, using other organisms network information and sequence homology in order to make predictions for the left-out organism, to simulate cases in which a newly sequenced species has no network information available.

bioinformatics↗

bioRxiv2020

Optimal tuning of weighted kNN- and diffusion-based methods for denoising single cell genomics data

The analysis of single-cell genomics data presents several statistical challenges, and extensive efforts have been made to produce methods for the analysis of this data that impute missing values, address sampling issues and quantify and correct for noise. In spite of such efforts, no consensus on best practices has been established and all current approaches vary substantially based on the available data and empirical tests. The k-Nearest Neighbor Graph (kNN-G) is often used to infer the identities of, and relationships between, cells and is the basis of many widely used dimensionality-reduction and projection methods. The kNN-G has also been the basis for imputation methods using, e.g., neighbor averaging and graph diffusion. However, due to the lack of an agreed-upon optimal objective function for choosing hyperparameters, these methods tend to oversmooth data, thereby resulting in a loss of information with regard to cell identity and the specific gene-to-gene patterns underlying regulatory mechanisms. In this paper, we investigate the tuning of kNN- and diffusion-based denoising methods with a novel non-stochastic method for optimally preserving biologically relevant informative variance in single-cell data. The framework, Denoising Expression data with a Weighted Affinity Kernel and Self-Supervision (DEWAKSS), uses a self-supervised technique to tune its parameters. We demonstrate that denoising with optimal parameters selected by our objective function (i) is robust to preprocessing methods using data from established benchmarks, (ii) disentangles cellular identity and maintains robust clusters over dimension-reduction methods, (iii) maintains variance along several expression dimensions, unlike previous heuristic-based methods that tend to oversmooth data variance, and (iv) rarely involves diffusion but rather uses a fixed weighted kNN graph for denoising. Together, these findings provide a new understanding of kNN- and diffusion-based denoising methods and serve as a foundation for future research. Code and example data for DEWAKSS is available at https://gitlab.com/Xparx/dewakss/-/tree/Tjarnberg2020branch.

bioinformatics↗

bioRxiv2019

Genetic predisposition to myeloproliferative neoplasms implicates hematopoietic stem cell biology

Myeloproliferative neoplasms (MPNs) are blood cancers characterized by excessive production of mature myeloid cells that result from the acquisition of somatic driver mutations in hematopoietic stem cells (HSCs)1. While substantial progress has been made to define the causal somatic mutation profile for MPNs2, epidemiologic studies indicate a significant heritable component for the disease that is among the highest known for all cancers3. However, only a limited set of genetic risk loci have been identified, and the underlying biological mechanisms leading to MPN acquisition remain unexplained. Here, to define the inherited risk profile, we conducted the largest genome-wide association study of MPNs to date (978,913 individuals with 3,224 cases) and identified 14 genome-wide significant loci, as well as a polygenic signature that increases the odds for disease acquisition by nearly 3-fold between the top and median deciles. Interestingly, we find a shared genetic architecture between MPN risk and several hematopoietic traits spanning distinct lineages, as well as an association between increased MPN risk and longer leukocyte telomere length, collectively implicating HSC function and self-renewal. Strikingly, we find a significant enrichment for risk variants mapping to accessible chromatin in HSCs compared with other hematopoietic populations. Finally, gene mapping identifies modulators of HSC biology and targeted variant-to-function analyses suggest likely roles for CHEK2 and GFI1B in altering HSC function to confer disease risk. Overall, we demonstrate the power of human genetic studies to illuminate a previously unappreciated mechanism for MPN risk through modulation of HSC function.

genetics↗