Biology subjects

Cheng, R. R.

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

3 recordsLinked to original sources

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↗