Biology subjects

Noh, H.

Publications and source records attributed to Noh, H..

16 recordsLinked to original sources

bioRxiv2026

NetCrafter: Ontology-derived gene network modeling and interpretation

Understanding the complex nature of multi-functional interactions among genes is crucial for interpreting omics data. We developed NetCrafter, an ontology-driven platform for constructing de novo gene networks that are specific to each input gene list and quantitatively defined by ontology-weighted similarity. By incorporating the probabilistic association of ontology or curated gene sets into a weighted Tanimoto similarity metric, NetCrafter transforms enrichment results into quantitative semantic similarity scores between genes, enabling the creation of context-specific statistical networks. These networks can be further decomposed into optimal sub-networks, facilitating multi-functional interpretation and the identification of gene interaction hotspots. NetCrafter also supports the integration of heterogeneous omics-derived gene lists through consensus ontology scoring. Importantly, this list-specific, quantitative framework reveals functional hotspots and target-biomarker relationships - even in cases where ontology terms alone are not predictive of node-level attributes such as CRISPR efficacy. NetCrafter provides an interactive platform for constructing and interpreting dynamic, context-specific gene networks, leveraging ontology-based functional associations to uncover underlying mechanisms and identify key nodes. It is freely available at https://netcrafter.sookmyung.ac.kr and integrated into Q-omics platform (https://qomics.ai) to enhance the utility of cancer omics data.

bioinformatics↗

bioRxiv2025

Model-based Standardization of Correlation Coefficients Improves Multi-Omic Clustering and Biological Signal Discovery

Multi-omic data pose a particular challenge for Weighted Correlation Network Analysis (WCNA or WGCNA) due to (platform- or) batch-specific characteristics, such as resolution, accuracy, dynamic range, and sources of spurious variation. When unaccounted for, these differences can result in a bias toward single-batch clusters as well as greater sensitivity to "noisier" batches during clustering. Here we propose mitigating these effects using null models fitted separately to the bulk of analyte-analyte correlations within each batch and across each pair of batches. We then map the batch-specific null models to a standard null model, removing batch-dependent distributional differences. This approach is compatible with any correlation-based clustering approach. Since the null model represents information not captured in individual pairwise correlations, we show how to incorporate this additional information into both distance-based clustering and WCNA. For distance-based clustering, we increase distances corresponding to correlations consistent with the null model. For WCNA, we provide a new soft threshold (adjacency) function based on the likelihood of a correlation under the null model. The resulting network can be easily incorporated into the WCNA workflow. These methods are implemented in R package standardcor, and we illustrate the package on simulated data as well as an existing multi-omic dataset.

bioinformatics↗

bioRxiv2025

Robust Serum Proteomic Signatures of APOE2

We previously identified a signature of 16 serum proteins that highlighted a role of the e2 allele of APOE in lipid regulation via apolipoprotein B (APOB) and apolipoprotein E (APOE), and in inflammation. The serum proteins were profiled using the aptamer-based Somalogic technology. Here, we validate and expand the serum protein signature of APOE using a combination of mass-spectrometry, ELISA, Luminex, antibody-based Olink proteomics, and blood transcriptomics. We replicate the association between APOB and the e2 allele of APOE, we correct the pattern of association between APOE genotypes and serum level of APOE, and we detect new associations between APOE genotypes and the complex of apolipoproteins APOC1, APOC4, APOC2, APOC3, APOE, APOF and APOL1. In addition, we discover 13 new proteins that correlate with APOE genotypes. This extended signature includes granule proteins CAMP, CTSG, DEFA3, and MPO secreted from neutrophils and points to olfactomedin 4 (OLFM4) as a new target for the prevention of Alzheimers disease.

genetics↗

bioRxiv2025

Systematic Investigation of Double Emulsion Dewetting Dynamics for the Droplet Microfluidic Production of Giant Unilamellar Vesicles (GUVs) under Biocompatible Conditions

Giant unilamellar vesicles (GUVs) embody biomimetic membranes with compartmentalization that serve not only as simplified models to better understand complex biochemical and biophysical processes, but also as a chassis for the bottom-up assembly of synthetic cells. Recently, double emulsion droplet microfluidics has proven to be a promising platform for their production, offering greater throughput, control, and reproducibility over traditional methods. However, the interplay of parameters--particularly under biocompatible conditions--that influence the complex multiphase fluid dynamics of the dewetting process underlying GUV production has not been thoroughly studied, limiting the democratization of the approach. In this study, we systematically investigate how lipid composition and concentration, aqueous phase conditions, droplet confinement, and fluid dynamics effects promote or impede the dewetting process. We show that the prevalent use of high concentrations of glycerol and P188 are unnecessary, and the altered dewetting dynamics with restricted surfactant usage can be tuned by adjusting chip dimensions and multi-phase compositions. Guided by these findings, we achieved robust, high throughput production of monodisperse GUVs using 0.1% P188 and no glycerol with salts. Our results improve the reliability and accessibility of droplet-microfluidics GUV platforms to catalyze advances in biophysics, synthetic biology, and drug discovery.

synthetic biology↗

bioRxiv2024

Identification and Targeting of Regulators of SARS-CoV-2-Host Interactions in the Airway Epithelium.

Although the impact of SARS-CoV-2 in the lung has been extensively studied, the molecular regulators and targets of the host-cell programs hijacked by the virus in distinct human airway epithelial cell populations remain poorly understood. This is in part ascribed to the use of nonprimary cell systems, overreliance on single-cell gene expression profiling that does not ultimately reflect protein activity, and bias toward the downstream effects rather than their mechanistic determinants. Here we address these issues by network-based analysis of single cell transcriptomic profiles of pathophysiologically relevant human adult basal, ciliated and secretory cells to identify master regulator (MR) protein modules controlling their SARS-CoV-2-mediated reprogramming. This uncovered chromatin remodeling, endosomal sorting, ubiquitin pathways, as well as proviral factors identified by CRISPR analyses as components of the host response collectively or selectively activated in these cells. Large-scale perturbation assays, using a clinically relevant drug library, identified 11 drugs able to invert the entire MR signature activated by SARS-CoV-2 in these cell types. Leveraging MR analysis and perturbational profiles of human primary cells represents a novel mechanism-based approach and resource that can be directly generalized to interrogate signatures of other airway conditions for drug prioritization.

cell biology↗

bioRxiv2024

pyVIPER: A fast and scalable Python package for rank-based enrichment analysis of single-cell RNASeq data

SummarySingle-cell sequencing has revolutionized biomedical research by offering insights into cellular heterogeneity at unprecedented resolution. Yet, the low signal-to-noise ratio, characteristic of single-cell RNA sequencing (scRNASeq), challenges quantitative analyses. We have shown that gene regulatory network (GRN) analysis can help overcome this obstacle and support mechanistic elucidation of cellular state determinants, for example by using the VIPER algorithm to identify Master Regulator (MR) proteins from gene expression data. A key challenge, as the size and complexity of scRNASeq datasets grow, is the need for highly scalable tools supporting the analysis of large-scale datasets with up to hundreds of thousands of cells. To address it, we introduce pyVIPER, a fast, memory-efficient, and highly scalable Python toolkit for assessing protein activity in large-scale scRNASeq datasets. pyVIPER supports multiple enrichment analysis algorithms, data transformation/postprocessing modules, a novel data structure for GRNs manipulation, and seamless integration with AnnData, Scanpy and several widely adopted machine learning libraries. Compared to VIPER, benchmarking reveals orders of magnitude runtime reduction for large datasets--i.e., from hours to minutes-- thus supporting VIPER-based analysis of virtually any large-scale single-cell dataset, as well as integration with other Python-based tools. Availability and ImplementationpyVIPER is available on GitHub (https://github.com/alevax/pyviper) and PyPI (https://pypi.org/project/viper-in-python/). Contactav2729@cumc.columbia.edu Supplementary informationSupplementary data are available at Bioinformatics online. Accompanying data for the tutorials are available on Zenodo (https://zenodo.org/records/10059791).

bioinformatics↗

bioRxiv2023

Identification and Pharmacological Targeting of Treatment-Resistant, Stem-like Breast Cancer Cells for Combination Therapy

Tumors frequently harbor isogenic yet epigenetically distinct subpopulations of multi-potent cells with high tumor-initiating potential--often called Cancer Stem-Like Cells (CSLCs). These can display preferential resistance to standard-of-care chemotherapy. Single-cell analyses can help elucidate Master Regulator (MR) proteins responsible for governing the transcriptional state of these cells, thus revealing complementary dependencies that may be leveraged via combination therapy. Interrogation of single-cell RNA sequencing profiles from seven metastatic breast cancer patients, using perturbational profiles of clinically relevant drugs, identified drugs predicted to invert the activity of MR proteins governing the transcriptional state of chemoresistant CSLCs, which were then validated by CROP-seq assays. The top drug, the anthelmintic albendazole, depleted this subpopulation in vivo without noticeable cytotoxicity. Moreover, sequential cycles of albendazole and paclitaxel--a commonly used chemotherapeutic --displayed significant synergy in a patient-derived xenograft (PDX) from a TNBC patient, suggesting that network-based approaches can help develop mechanism-based combinatorial therapies targeting complementary subpopulations. Statement of significanceNetwork-based approaches, as shown in a study on metastatic breast cancer, can develop effective combinatorial therapies targeting complementary subpopulations. By analyzing scRNA-seq data and using clinically relevant drugs, researchers identified and depleted chemoresistant Cancer Stem-Like Cells, enhancing the efficacy of standard chemotherapies.

systems biology↗

bioRxiv2023

The concordance of length- and sequence-based STRs used in forensic markers with guidance for practice

Next-generation sequencing (NGS) technology has shed light on every aspect of genetic discoveries, including forensic genetics. The Miseq(R) FGx Forensic Genetic System (Verogen) is one of the pioneering forensic NGS that provided a complete system from library preparation to data analysis. The system has been validated by several studies and led to a more practical aspect. Short tandem repeat (STR) is a well-established marker that was designed specifically for human individualization. Since NGS provides different data from fragment analysis, a new STR nomenclature is established to make NGS backward compatible with the previous data. In this study, Thai population were used to evaluate the Miseq(R) FGx Forensic genetic system (Verogen) in practical aspect, including concordance study and forensic population parameters. In summary, we purposed a practical guideline for sequence-based STRs.

genetics↗

bioRxiv2023

STRategy: A support system for collecting and analyzing short tandem repeats for forensic science

Short tandem repeats (STRs) are short repeated sequences commonly found in the human genome. They provide many advantages to forensic sciences, such as identifying individuals, estimating the likelihood of kinship, and analyzing mixtures. Next-generation sequencing (NGS) technologies, e.g., ForenSeq Signature Prep, have been proposed for sequencing STRs, obtaining the sequence of each locus and SNPs, and inferring length-based alleles. However, even though the sequenced STRs from ForenSeq offer more insights into the STRs, which lead to the genetic analysis of population and sub-population structures, no open-source software platform enables the collection and management of STR data from NGS and incorporates related analysis tools in one place. Here, we introduce STRategy, a standalone web-based application supporting essential STR data management and analysis capabilities. The analyzed data will be visualized in various forms, for example, charts, maps, and pattern alignments. The system implemented a role-based access control that allows users to search or access specific data depending on their responsibilities. It enables public users to search for data. In addition, they can view statistical data, for example, detailed alleles and genetic variation. Lab users can add, update, and see the information of individuals and explore pattern alignments for a specific locus within the population. Administrators can customize the system, for example, configure maps according to the samples geographic data, and manage reference STR repeat motifs. We designed and developed the STRategy using software engineering principles for flexible extension and easy deployment utilizing the Docker container. The source code is publicly available at https://github.com/cucpbioinfo/STRategy. Also, we deployed a showcase system on a cloud computing service where its URL is included on the GitHub repository. The current version only supports the ForenSeq sample detail report files.

genetics↗

bioRxiv2021

The in vitro and in vivo potency of CT-P59 against Delta and its associated variants of SARS-CoV-2

The Delta variant originally from India is rapidly spreading across the world and causes to resurge infections of SARS-CoV-2. We previously reported that CT-P59 presented its in vivo potency against Beta and Gamma variants, despite its reduced activity in cell experiments. Yet, it remains uncertain to exert the antiviral effect of CT-P59 on the Delta and its associated variants (L452R). To tackle this question, we carried out cell tests and animal study. CT-P59 showed reduced antiviral activity but enabled neutralization against Delta, Epsilon, and Kappa variants in cells. In line with in vitro results, the mouse challenge experiment with the Delta variant substantiated in vivo potency of CT-P59 showing symptom remission and virus abrogation in the respiratory tract. Collectively, cell and animal studies showed that CT-P59 is effective against the Delta variant infection, hinting that CT-P59 has therapeutic potency for patients infected with Delta and its associated variants. HighlightsO_LICT-P59 exerts the antiviral effect on authentic Delta, Epsilon and Kappa variants in cell-based experiments. C_LIO_LICT-P59 showed neutralizing potency against variants including Delta, Epsilon, Kappa, L452R, T478K and P681H pseudovirus variants. C_LIO_LIThe administration of clinically relevant dose of CT-P59 showed in vivo C_LIO_LIprotection against Delta variants in animal challenge experiment. C_LI

microbiology↗

bioRxiv2021

Therapeutic efficacy of CT-P59 against P.1 variant of SARS-CoV-2

P.1. or gamma variant also known as the Brazil variant, is one of the variants of concern (VOC) which appears to have high transmissibility and mortality. To explore the potency of the CT-P59 monoclonal antibody against P.1 variant, we tried to conduct binding affinity, in vitro neutralization, and in vivo animal tests. In in vitro assays revealed that CT-P59 is able to neutralize P.1 variant in spite of reduction in its binding affinity against a RBD (receptor binding domain) mutant protein including K417T/E484K/N501Y and neutralizing activity against P.1 pseudoviruses and live viruses. In contrast, in vivo hACE2 (human angiotensin-converting enzyme 2)-expressing TG (transgenic) mouse challenge experiment demonstrated that a clinically relevant or lower dosages of CT-P59 is capable of lowering viral loads in the respiratory tract and alleviates symptoms such as body weight losses and survival rates. Therefore, a clinical dosage of CT-P59 could compensate for reduced in vitro antiviral activity in P.1-infected mice, implying that CT-P59 has therapeutic potency for COVID-19 patients infected with P.1 variant. HighlightsO_LICT-P59 could bind to and neutralize P.1 variant, but CT-P59 showed reduced susceptibility in in vitro tests. C_LIO_LIThe clinical dosage of CT-P59 demonstrated in vivo therapeutic potency against P.1 variants in hACE2-expressing mice challenge study. C_LIO_LICT-P59 ameliorates their body weight loss and prevents the lethality in P.1 variant-infected mice. C_LI

microbiology↗

bioRxiv2021

Interrogation of genome-wide, experimentally dissected gene regulatory networks reveals mechanisms underlying dynamic cellular state control

Pooled CRISPRi-mediated silencing of >1,000 transcriptional regulators expressed in single colorectal adenocarcinoma cells, followed by single-cell RNA-seq profiling at two timepoints, 1 day and 4 days, allowed reverse engineering the underlying tumor context-specific, causal regulatory network. Furthermore, the availability of experimentally derived, highly multiplexed gene reporter assays for each regulator, as identified by this analysis, allowed accurate assessment of differential protein activity following silencing of each regulator, thus providing proof-of-concept for generating comprehensive, tissue-specific networks of transcriptional and post-translational interactions. Analysis of this causal network allowed elucidation of complex autoregulatory mechanisms that have eluded previous computational approaches and supported systematic elucidation of cooperative mechanisms, where one regulatory protein can modulate the activity of another regulatory protein, as well as transcriptional mimicry, where one regulatory protein can phenocopy others.

systems biology↗

bioRxiv2020

Network Insights into Improving Drug Target Inference Algorithms

To improve the efficacy of drug research and development (R&D), a better understanding of drug mechanisms of action (MoA) is needed to improve drug discovery. Computational algorithms, such as ProTINA, that integrate protein-protein interactions (PPIs), protein-gene interactions (PGIs) and gene expression data have shown promising performance on drug target inference. In this work, we evaluated how network and gene expression data affect ProTINAs accuracy. Network data predominantly determines the accuracy of ProTINA instead of gene expression, while the size of an interaction network or selecting cell/tissue-specific networks have limited effects on the accuracy. However, we found that protein network betweenness values showed high accuracy in predicting drug targets. Therefore, we suggested a new algorithm, TREAP (https://github.com/ImmuSystems-Lab/TREAP), that combines betweenness values and adjusted p-values for target inference. This algorithm has resulted in higher accuracy than ProTINA using the same datasets.

systems biology↗

bioRxiv2019

DeltaNeTS+: Elucidating the mechanism of drugs and diseases using gene expression and transcriptional regulatory networks

BackgroundKnowledge on the molecular targets of diseases and drugs is crucial for elucidating disease pathogenesis and mechanism of action of drugs, and for driving drug discovery and treatment formulation. In this regard, high-throughput gene transcriptional profiling has become a leading technology, generating whole-genome data on the transcriptional alterations caused by diseases or drug compounds. However, identifying direct gene targets, especially in the background of indirect (downstream) effects, based on differential gene expressions is difficult due to the complexity of gene regulatory network governing the gene transcriptional processes.\n\nResultsIn this work, we developed a network analysis method, called DeltaNeTS+, for inferring direct gene targets of drugs and diseases from gene transcriptional profiles. DeltaNeTS+ relies on a gene regulatory network model to identify direct perturbations to the transcription of genes. Importantly, DeltaNeTS+ is able to combine both steady-state and time-course gene expression profiles, as well as to leverage information on the gene network structure that is increasingly becoming available for a multitude of organisms, including human. We demonstrated the power of DeltaNeTS+ in predicting gene targets using gene expression data in complex organisms, including Caenorhabditis elegans and human cell lines (T-cell and Calu-3). More specifically, in an application to time-course gene expression profiles of influenza A H1N1 (swine flu) and H5N1 (avian flu) infection, DeltaNeTS+ shed light on the key differences of dynamic cellular perturbations caused by the two influenza strains.\n\nConclusionDeltaNeTS+ is an enabling tool to infer gene transcriptional perturbations caused by diseases and drugs from gene transcriptional profiles. By incorporating available information on gene network structure, DeltaNeTS+ produces accurate predictions of direct gene targets from a small sample size (~10s). DeltaNeTS+ can freely downloaded from http://www.github.com/cabsel/deltanetsplus.

bioinformatics↗

bioRxiv2018

Protect TUDCA stimulated CKD-derived hMSCs against the CKD-Ischemic disease via upregulation of PrPC

Although autologous human mesenchymal stem cells (hMSCs) are a promising source for regenerative stem cell therapy, the barriers associated with pathophysiological conditions in this disease limit therapeutic applicability to patients. We proved treatment of CKD-hMSCs with TUDCA enhanced the mitochondrial function of these cells and increased complex I & IV enzymatic activity, increasing PINK1 expression and decreasing mitochondrial O2*- and mitochondrial fusion in a PrPC-dependent pathway. Moreover, TH-1 cells enhanced viability when co-cultured in vitro with TUDCA-treated CKD-hMSC. In vivo, tail vein injection of TUDCA-treated CKD-hMSCs into the mouse model of CKD associated with hindlimb ischemia enhanced kidney recovery, the blood perfusion ratio, vessel formation, and prevented limb loss, and foot necrosis along with restored expression of PrPC in the blood serum of the mice. These data suggest that TUDCA-treated CKD-hMSCs are a promising new autologous stem cell therapeutic intervention that dually treats cardiovascular problems and CKD in patients.

cell biology↗

bioRxiv2017

SALMON: inferring protein targets of compounds by network-based analysis of gene transcriptional profiles

Genome-wide transcriptional profiling provides a global view of cellular state and how this state changes under different treatments (e.g. drugs) or conditions (e.g. healthy and diseased). Here, we present ProTINA (Protein Target Inference by Network Analysis), a network perturbation analysis method for inferring protein targets of compounds from gene transcriptional profiles. ProTINA uses a dynamic model of the cell-type specific protein-gene transcriptional regulation to infer network perturbations from steady state and time-series differential gene expression profiles. A candidate protein target is scored based on the gene networks dysregulation, including enhancement and attenuation of transcriptional regulatory activity of the protein on its downstream genes, caused by drug treatments. For benchmark datasets from three drug treatment studies, ProTINA was able to provide highly accurate protein target predictions and to reveal the mechanism of action of compounds with high sensitivity and specificity. Further, an application of ProTINA to gene expression profiles of influenza A viral infection led to new insights of the early events in the infection.

bioinformatics↗