Rizi Ai

Neuronal subtypes and diversity revealed by single-nucleus RNA sequencing of the human brain

Single-nucleus gene expression Identifying the genes expressed at the level of a single cell nucleus can better help us understand the human brain. Blue et al. developed a single-nuclei sequencing technique, which they applied to cells in classically defined Brodmann areas from a postmortem brain. Clustering of gene expression showed concordance with the area of origin and defining 16 neuronal subtypes. Both excitatory and inhibitory neuronal subtypes show regional variations that define distinct cortical areas and exhibit how gene expression clusters may distinguish between distinct cortical areas. This method opens the door to widespread sampling of the genes expressed in a diseased brain and other tissues of interest. Science, this issue p. 1586 Individual neurons have specific transcriptomic signatures and transcriptomic heterogeneity. The human brain has enormously complex cellular diversity and connectivities fundamental to our neural functions, yet difficulties in interrogating individual neurons has impeded understanding of the underlying transcriptional landscape. We developed a scalable approach to sequence and quantify RNA molecules in isolated neuronal nuclei from a postmortem brain, generating 3227 sets of single-neuron data from six distinct regions of the cerebral cortex. Using an iterative clustering and classification approach, we identified 16 neuronal subtypes that were further annotated on the basis of known markers and cortical cytoarchitecture. These data demonstrate a robust and scalable method for identifying and categorizing single nuclear transcriptomes, revealing shared genes sufficient to distinguish previously unknown and orthologous neuronal subtypes as well as regional identity and transcriptomic heterogeneity within the human brain.

Normalization and noise reduction for single cell RNA-seq experiments

UNLABELLED A major roadblock towards accurate interpretation of single cell RNA-seq data is large technical noise resulted from small amount of input materials. The existing methods mainly aim to find differentially expressed genes rather than directly de-noise the single cell data. We present here a powerful but simple method to remove technical noise and explicitly compute the true gene expression levels based on spike-in ERCC molecules. AVAILABILITY AND IMPLEMENTATION The software is implemented by R and the download version is available at http://wanglab.ucsd.edu/star/GRM. CONTACT wei-wang@ucsd.edu SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Joint-specific DNA methylation and transcriptome signatures in rheumatoid arthritis identify distinct pathogenic processes

Stratifying patients on the basis of molecular signatures could facilitate development of therapeutics that target pathways specific to a particular disease or tissue location. Previous studies suggest that pathogenesis of rheumatoid arthritis (RA) is similar in all affected joints. Here we show that distinct DNA methylation and transcriptome signatures not only discriminate RA fibroblast-like synoviocytes (FLS) from osteoarthritis FLS, but also distinguish RA FLS isolated from knees and hips. Using genome-wide methods, we show differences between RA knee and hip FLS in the methylation of genes encoding biological pathways, such as IL-6 signalling via JAK-STAT pathway. Furthermore, differentially expressed genes are identified between knee and hip FLS using RNA-sequencing. Double-evidenced genes that are both differentially methylated and expressed include multiple HOX genes. Joint-specific DNA signatures suggest that RA disease mechanisms might vary from joint to joint, thus potentially explaining some of the diversity of drug responses in RA patients.

T-Analyst: a program for efficient analysis of protein conformational changes by torsion angles

T-Analyst is a user-friendly computer program for analyzing trajectories from molecular modeling. Instead of using Cartesian coordinates for protein conformational analysis, T-Analyst is based on internal bond-angle-torsion coordinates in which internal torsion angle movements, such as side-chain rotations, can be easily detected. The program computes entropy and automatically detects and corrects angle periodicity to produce accurate rotameric states of dihedrals. It also clusters multiple conformations and detects dihedral rotations that contribute hinge-like motions. Correlated motions between selected dihedrals can also be observed from the correlation map. T-Analyst focuses on showing changes in protein flexibility between different states and selecting representative protein conformations for molecular docking studies. The program is provided with instructions and full source code in Perl.

DNA Methylome Signature in Synoviocytes From Patients With Early Rheumatoid Arthritis Compared to Synoviocytes From Patients With Longstanding Rheumatoid Arthritis

Epigenetics can contribute to pathogenic mechanisms in autoimmunity. We recently identified an imprinted DNA methylation pattern in rheumatoid arthritis (RA) fibroblast-like synoviocytes (FLS) involving multiple genes in pathways implicated in cell migration, matrix regulation and immune responses.(1,2) To understand when alterations in DNA methylation occur in RA and the specificity of the methylation changes in RA, we compared differentially methylated loci (DMLs) of early RA (ERA), juvenile idiopathic arthritis (JIA) to longstanding RA (LRA) and osteoarthritis (OA). This article is protected by copyright. All rights reserved

Synergistic regulation and ligand-induced conformational changes of tryptophan synthase.

Conformational changes of enzyme complexes are often related to regulating and creating an optimal environment for efficient chemistry. We investigated the synergistic regulation of the tryptophan synthase (TRPS) complex, studied for decades as a model of allosteric regulation and substrate channeling within protein complexes. TRPS is a bifunctional tetrameric alphabetabetaalpha enzyme complex that exhibits cooperative motions of the alpha- and beta-subunits by tightly controlled allosteric interactions. We have delineated the atomically detailed dynamics and conformational changes of TRPS in the absence and presence of substrates using molecular dynamics simulations. The computed energy and entropy associated with the protein motions also offer mechanistic insights into the conformational fluctuations and the ligand binding mechanism. The flexible alpha-L6 loop samples both open and partially closed conformations in the ligand-free state but shifts to fully closed conformations when its substrates are present. The fully closed conformations are induced by favorable protein-ligand interactions but are partly compensated by configurational entropy loss. Considerable local rearrangements exist during ligand binding processes when the system is searching for energy minima. The motion of the region that closes the beta-subunit during catalysis, the COMM domain, couples with the motion of the alpha-subunit, although the fluctuations are smaller than in the flexible loop regions. Because of multiple conformations of ligand-free TRPS in the open and partially closed states, the alpha-L6 loop fluctuations have preferential directionality, which may facilitate the fully closed conformations induced by alpha- and beta-substrates binding to both subunits. Such cooperative and directional motion may be a general feature that contributes to catalysis in many enzyme complexes.

Ligand-specific homology modeling of human cannabinoid (CB1) receptor

Cannabinoid (CB1) receptor is a therapeutic drug target, and its structure and conformational changes after ligand binding are of great interest. To study the protein conformations in ligand bound state and assist in drug discovery, CB1 receptor homology models are needed for computer-based ligand screening. The known CB1 ligands are highly diverse structurally, so CB1 receptor may undergo considerable conformational changes to accept different ligands, which is challenging for molecular docking methods. To account for the flexibility of CB1 receptor, we constructed four CB1 receptor models based on four structurally distinct ligands, HU-210, ACEA, WIN55212-2 and SR141716A, using the newest X-ray crystal structures of human β₂ adrenergic receptor and adenosine A(2A) receptor as templates. The conformations of these four CB1-ligand complexes were optimized by molecular dynamics (MD) simulations. The models revealed interactions between CB1 receptor and known binders suggested by experiments and could successfully discriminate known ligands and non-binders in our docking assays. MD simulations were used to study the most flexible ligand, ACEA, in its free and bound states to investigate structural mobility achieved by the rearrangement of the fatty acid chain. Our models may capture important conformational changes of CB1 receptor to help improve accuracy in future CB1 drug screening.

Assessing characteristics of RNA amplification methods for single cell RNA sequencing

BackgroundRecently, measurement of RNA at single cell resolution has yielded surprising insights. Methods for single-cell RNA sequencing (scRNA-seq) have received considerable attention, but the broad reliability of single cell methods and the factors governing their performance are still poorly known.ResultsHere, we conducted a large-scale control experiment to assess the transfer function of three scRNA-seq methods and factors modulating the function. All three methods detected greater than 70% of the expected number of genes and had a 50% probability of detecting genes with abundance greater than 2 to 4 molecules. Despite the small number of molecules, sequencing depth significantly affected gene detection. While biases in detection and quantification were qualitatively similar across methods, the degree of bias differed, consistent with differences in molecular protocol. Measurement reliability increased with expression level for all methods and we conservatively estimate measurements to be quantitative at an expression level greater than ~5–10 molecules.ConclusionsBased on these extensive control studies, we propose that RNA-seq of single cells has come of age, yielding quantitative biological information.

DNA Methylome Signature in Early Rheumatoid Arthritis Synoviocytes Compared with Longstanding Rheumatoid Arthritis Synoviocytes

Epigenetics can contribute to pathogenic mechanisms in autoimmunity. We recently identified an imprinted DNA methylation pattern in rheumatoid arthritis (RA) fibroblast-like synoviocytes (FLS) involving multiple genes in pathways implicated in cell migration, matrix regulation and immune responses.(1,2) To understand when alterations in DNA methylation occur in RA and the specificity of the methylation changes in RA, we compared differentially methylated loci (DMLs) of early RA (ERA), juvenile idiopathic arthritis (JIA) to longstanding RA (LRA) and osteoarthritis (OA). This article is protected by copyright. All rights reserved

Homology modeling of cannabinoid receptors: discovery of cannabinoid analogues for therapeutic use.

Cannabinoids represent a promising class of compounds for developing novel therapeutic agents. Since the isolation and identification of the major psychoactive component Δ(9)-THC in Cannabis sativa in the 1960s, numerous analogues of the classical plant cannabinoids have been synthesized and tested for their biological activity. These compounds primarily target the cannabinoid receptors 1 (CB1) and Cannabinoid receptors 2 (CB2). This chapter focuses on CB1. Despite the lack of crystal structures for CB1, protein-based homology modeling approaches and molecular docking methods can be used in the design and discovery of cannabinoid analogues. Efficient synthetic approaches for therapeutically interesting cannabinoid analogues have been developed to further facilitate the drug discovery process.