Biology subjects

Cheng, R. R.

Publications and source records attributed to Cheng, R. R..

10 recordsLinked to original sources

bioRxiv2026

The compartmental polarization of interphase human chromosomes within the nucleus: A mechanism for transcriptional regulation of large chromosomes

The structure and dynamics of individual chromosomes are significantly modulated by their local environment within the nucleus. It has long been established that heterochromatin (B compartment loci) associates with the lamina meshwork that lines the inner nuclear envelope. However, the extent to which these interactions perturb the chromosomal organization within a territory remains unclear. Using computer simulations and published imaging data, we further characterize the interaction between chromatin and the lamina and find striking consequences of lamina-association on the chromosomal structural ensemble. We find that lamina-associating chromosomes have their compartmentalization polarized in a direction perpendicular to the nuclear surface. Further, lamina-associated chromosomes exhibit shorter average distances between euchromatin loci (A compartment) than chromosomes without lamina contact. Energy landscape analysis of our simulations reveal that the sequestration of heterochromatin to the lamina allows for euchromatin to form more spatial contacts with other segments of euchromatin. The compaction of euchromatin due to lamina-association also diminishes the mobility of these regions. These findings suggest a mechanism for bringing together euchromatic segments that are separated by large genomic distances within a chromosome, potentially to share transcriptional machinery and enhance transcription for large chromosomes, which are often found at the nuclear periphery.

biophysics↗

bioRxiv2026

3D genome architecture regulates the traffic of transcription factors throughout human chromosomes

Transcription factors (TFs) control the rate of transcription of genetic information by binding to specific DNA sequences. The time needed for a TF to find its specific target sites is a bottleneck to the genetic response mechanism. While TF target site search is a well-studied problem, the effect of genome 3D architecture on the TF target search times is poorly understood. Here, we use accurate and cell-specific 3D structural ensembles of human chromosomes to investigate how the spatial organization of binding sites on chromosomes influences the dynamics of TFs. We use Chromatin Immuno-Precipitation data to map the position of binding sites for several TFs on chromosomal structures and simulate the dynamics of individual TF within chromosomal territories. We find that the distribution of binding sites along chromosomes cooperates with the 3D folding of the chromatin fiber to induce dynamics in which TFs tend to visit sites distributed sequentially along the genome. In this way, genome 3D architecture appears to reduce the time each TF spends in the unbound state while commuting from one target site to the other. At the same time, genome 3D architecture further reduces the flux of TFs between binding sites already well separated along the genome, effectively isolating distant clusters of binding sites. We compare the TF traffic patterns generated by the 3D structures of human chromosomes with those generated by several alternative structural models characterized by increasing randomness. Finally, we study the effect of lengthwise compaction and phase separation, known architectural features of the human genome, in TFs target search. In short, our analysis demonstrates that genome architecture regulates the traffic of TFs within chromosomal territories and reduces the time each TF spends commuting between binding sites.

biophysics↗

bioRxiv2022

Structural Reorganization and Relaxation Dynamics of Axially Stressed Chromosomes

Micromechanical studies of mitotic chromosomes have revealed them to be remarkably extensible objects and informed early models of mitotic chromosome organization. We use a data-driven, coarsegrained polymer modeling approach, capable of generating ensembles of chromosome structures that are quantitatively consistent with experiments, to explore the relationship between the spatial organization of individual chromosomes and their emergent mechanical properties. In particular, we investigate the mechanical properties of our model chromosomes by axially stretching them. Simulated stretching led to a linear force-extension curve for small strain, with mitotic chromosomes behaving about ten-fold stiffer than interphase chromosomes. Studying the relaxation dynamics we found that chromosomes are viscoelastic solids, with a highly liquid-like, viscous behavior in interphase that becomes solid-like in mitosis. This emergent mechanical stiffness in our model originates from lengthwise compaction, an effective potential capturing the activity of loop-extruding SMC complexes. Chromosomes denature under large strains via unraveling, which is characterized by opening of large-scale folding patterns. By quantifying the effect of mechanical perturbations on the chromosomes structural features, our model provides a nuanced understanding of in vivo mechanics of chromosomes.

biophysics↗

bioRxiv2021

Evolutionary differences in the ACE2 reveals the molecular origins of COVID-19 susceptibility

We explore the energetic frustration patterns associated with the binding between the SARS-CoV-2 spike protein and the ACE2 receptor protein in a broad selection of animals. Using energy landscape theory and the concept of energy frustration--theoretical tools originally developed to study protein folding--we are able to identify interactions among residues of the spike protein and ACE2 that result in COVID-19 resistance. This allows us to identify whether or not a particular animal is susceptible to COVID-19 from the protein sequence of ACE2 alone. Our analysis predicts a number of experimental observations regarding COVID-19 susceptibility, demonstrating that this feature can be explained, at least partially, on the basis of theoretical means.

biophysics↗

bioRxiv2020

Exploring Chromosomal Structural Heterogeneity AcrossMultiple Cell Lines

We study the structural ensembles of human chromosomes across different cell types. Using computer simulations, we generate cell-specific 3D chromosomal structures and compare them to recently published chromatin structures obtained through microscopy. We demonstrate using a combination of machine learning and polymer physics simulations that epigenetic information can be used to predict the structural ensembles of multiple human cell lines. The chromosomal structures obtained in silico are quantitatively consistent with those obtained through microscopy as well as DNA-DNA proximity ligation assays. Theory predicts that chromosome structures are fluid and can only be described by an ensemble, which is consistent with the observation that chromosomes exhibit no unique fold. Nevertheless, our analysis of both structures from simulation and microscopy reveals that short segments of chromatin make transitions between a closed conformation and an open dumbbell conformation. This conformational transition appears to be consistent with a two-state process with an effective free energy cost of about four times the effective information theoretic temperature. Finally, we study the conformational changes associated with the switching of genomic compartments observed in human cell lines. Genetically identical but epigenetically distinct cell types appear to rearrange their respective structural ensembles to expose segments of transcriptionally active chromatin, belonging to the A genomic compartment, towards the surface of the chromosome, while inactive segments, belonging to the B compartment, move to the interior. The formation of genomic compartments resembles hydrophobic collapse in protein folding, with the aggregation of denser and predominantly inactive chromatin driving the positioning of active chromatin toward the surface of individual chromosomal territories.

biophysics↗

bioRxiv2019

The Nucleome Data Bank: Web-based Resources to Simulate and Analyze the Three-Dimensional Genome

We introduce the Nucleome Data Bank, a web-based platform to simulate and analyze the three-dimensional organization of genomes. The Nucleome Data Bank enables physics-based simulation of chromosomal structural dynamics through the MEGABASE + MiChroM computational pipeline. The input of the pipeline consists of epigenetic information sourced from the Encode database; the output consists of the trajectories of chromosomal motions that accurately predict Hi-C and FISH data, as well as multiple observations of chromosomal dynamics in vivo. As an intermediate step, users can also generate chromosomal sub-compartment annotations directly from the same epigenetic input, without the use of any DNA-DNA proximity ligation data. Additionally, the Nucleome Data Bank freely hosts both experimental and computational structural genomics data. Besides being able to perform their own genome simulations and download the hosted data, users can also analyze and visualize the same data through custom-designed web-based tools. In particular, the one-dimensional genetic and epigenetic data can be overlaid onto accurate three-dimensional structures of chromosomes, to study the spatial distribution of genetic and epigenetic features. The Nucleome Data Bank aims to be a shared resource to biologists, biophysicists, and all genome scientists. The Nucleome Data Bank (NDB) is available at https://ndb.rice.edu.

bioinformatics↗

bioRxiv2019

Accelerated trans-sulfuration metabolically defines a discrete subclass of ALS patients

Amyotrophic lateral sclerosis (ALS) is a disease characterized by progressive paralysis and death. Most ALS cases are sporadic (sALS) and patient heterogeneity poses a formidable challenge for the development of viable biomarkers and effective therapies. Applying untargeted metabolite profiling on 77 sALS patient-derived primary dermal fibroblast lines and 45 sex/age matched controls, we found that [~]25% of cell lines (termed sALS-1) are characterized by upregulated trans-sulfuration, where methionine-derived homocysteine is channeled into cysteine and glutathione synthesis. sALS-1 fibroblasts exhibit a growth defect when grown under oxidative conditions, that can be fully-rescued by N-acetylcysteine. [U-13C]-glucose tracing shows that activation of the trans-sulfuration pathway is associated with accelerated glucose flux into the TCA cycle. Based on four metabolites, we developed a support vector machine model capable of distinguishing sALS-1 with 97.5% accuracy. Importantly, plasma metabolite profiling identifies a systemic perturbation of cysteine metabolism as a hallmark of sALS-1. These results indicate that sALS patients can be stratified into distinct metabotypes, differently sensitive to metabolic stress, and provides new insights into metabolic biomarkers for personalized sALS therapy.

neuroscience↗

bioRxiv2018

Identification of a Distinct Metabolomic Subtype of Sporadic ALS Patients

Sporadic amyotrophic lateral sclerosis (sALS) is a progressive motor neuron disease resulting in paralysis and death. Genes responsible for familial ALS have been identified, however the molecular basis for sALS is unknown. To discover metabotypic biomarkers that inform on disease etiology, untargeted metabolite profiling was performed on 77 patient-derived dermal fibroblast lines and 45 age/sex-matched controls. Surprisingly, 25% of sALS lines showed upregulated methionine-derived homocysteine, channeled to cysteine and glutathione (GSH). Stable isotope tracing of [U-13C]-glucose showed activation of the trans-sulfuration pathway, associated with accelerated glucose flux into the TCA cycle, glutamate, GSH, alanine, aspartate, acylcarnitines and nucleotide phosphates. A four-molecule support vector machine model distinguished the sALS subtype from controls with 97.5% accuracy. Plasma metabolite profiling identified increased taurine as a hallmark metabolite for this sALS subset, suggesting systemic perturbation of cysteine metabolism. Furthermore, integrated multiomics (mRNAs/microRNAs/metabolites) identified the super-trans-sulfuration pathway as a top hit for the sALS subtype. We conclude that sALS can be stratified into distinct metabotypes, providing for future development of personalized therapies that offer new hope to sufferers.

neuroscience↗

bioRxiv2017

De Novo Prediction of Human Chromosome Structures: Epigenetic Marking Patterns Encode Genome Architecture

Inside the cell nucleus, genomes fold into organized structures that are characteristic of cell type. Here, we show that this chromatin architecture can be predicted de novo using epigenetic data derived from ChIP-Seq. We exploit the idea that chromosomes encode a one-dimensional sequence of chromatin structural types. Interactions between these chromatin types determine the three-dimensional (3D) structural ensemble of chromosomes through a process similar to phase separation. First, a recurrent neural network is used to infer the relation between the epigenetic marks present at a locus, as assayed by ChIP-Seq, and the genomic compartment in which those loci reside, as measured by DNA-DNA proximity ligation (Hi-C). Next, types inferred from this neural network are used as an input to an energy landscape model for chromatin organization (MiChroM) in order to generate an ensemble of 3D chromosome conformations. After training the model, dubbed MEGABASE (Maximum Entropy Genomic Annotation from Biomarkers Associated to Structural Ensembles), on odd numbered chromosomes, we predict the chromatin type sequences and the subsequent 3D conformational ensembles for the even chromosomes. We validate these structural ensembles by using ChIP-Seq tracks alone to predict Hi-C maps as well as distances measured using 3D FISH experiments. Both sets of experiments support the hypothesis of phase separation being the driving process behind compartmentalization. These findings strongly suggest that epigenetic marking patterns encode sufficient information to determine the global architecture of chromosomes and that de novo structure prediction for whole genomes may be increasingly possible.

biophysics↗

bioRxiv2017

Guiding the design of bacterial signaling interactions using a coevolutionary landscape

The selection of amino acid identities that encode new interactions between two-component signaling (TCS) proteins remains a significant challenge. Recent work constructed a co-evolutionary landscape that can be used to select mutations to maintain signal transfer interactions between partner TCS proteins without introducing signal transfer between non-partners (crosstalk). A bigger challenge is to introduce mutations between non-natural partner TCS proteins using the landscape to enhance, suppress, or have a neutral effect on their basal signal transfer rates. This study focuses on the selection of mutations to a response regulator (RR) from Bacilus subtilis and its effect on phosphotransfer with a histidine kinase (HK) from Escherichia Coli. Twelve single-point mutations of the RR protein are selected from the landscape and experimentally expressed to directly test the theoretical predictions on the effect of signal transfer. Differential Scanning Calorimetry is used to monitor any protein stability effects caused by the mutations, which could be detrimental to proper protein function. Of these proteins, seven mutants successfully perturb phosphoryl transfer activity in the computationally predicted manner between the TCS proteins. Furthermore, brute-force exhaustive mutagenesis approaches indicate that only 1% of mutations result in enhanced activity. In comparison, of the six mutations predicted to enhance phosphotransfer, two mutations exhibit a significant enhancement while two mutations are comparable to the wild-type. Thus co-evolutionary landscape theory offers significant improvement over traditional large-scale mutational studies in the efficiency of selecting mutations for protein engineering and design.

bioinformatics↗