Yuchen Liu

Bio: Yuchen Liu is an academic researcher from Louisiana State University. The author has contributed to research in topics: Medicine & Methanococcus maripaludis. The author has an hindex of 16, co-authored 24 publications receiving 1614 citations. Previous affiliations of Yuchen Liu include University of Georgia & Yale University.

Metabolic, phylogenetic, and ecological diversity of the methanogenic archaea.

Abstract: Although of limited metabolic diversity, methanogenic archaea or methanogens possess great phylogenetic and ecological diversity. Only three types of methanogenic pathways are known: CO(2)-reduction, methyl-group reduction, and the aceticlastic reaction. Cultured methanogens are grouped into five orders based upon their phylogeny and phenotypic properties. In addition, uncultured methanogens that may represent new orders are present in many environments. The ecology of methanogens highlights their complex interactions with other anaerobes and the physical and chemical factors controlling their function.

Sulfur metabolism in archaea reveals novel processes.

Abstract: Studies on sulfur metabolism in archaea have revealed many novel enzymes and pathways and have advanced our understanding on metabolic processes, not only of the archaea, but of biology in general. A variety of dissimilatory sulfur metabolisms, i.e. reactions used for energy conservation, are found in archaea from both the Crenarchaeota and Euryarchaeota phyla. Although not yet fully characterized, major processes include aerobic elemental sulfur (S(0)) oxidation, anaerobic S(0) reduction, anaerobic sulfate/sulfite reduction and anaerobic respiration of organic sulfur. Assimilatory sulfur metabolism, i.e. reactions used for biosynthesis of sulfur-containing compounds, also possesses some novel features. Cysteine biosynthesis in some archaea uses a unique tRNA-dependent pathway. Fe-S cluster biogenesis in many archaea differs from that in bacteria and eukaryotes and requires unidentified components. The eukaryotic ubiquitin system is conserved in archaea and involved in both protein degradation and biosynthesis of sulfur-containing cofactors. Lastly, specific pathways are utilized for the biosynthesis of coenzyme M and coenzyme B, the sulfur-containing cofactors required for methanogenesis.

Global responses of Methanococcus maripaludis to specific nutrient limitations and growth rate.

Abstract: isoleucine and valine showed significant increases, indicating a coordinate regulation of branched-chain amino acids at a post-mRNA level. Leucine limitation also resulted in increased mRNA abundance for ribosomal protein genes, increased rRNA abundance, and decreased mRNA abundance for genes of methanogenesis. In contrast, phosphate limitation induced a specific response, a marked increase in mRNA levels for a phosphate transporter. Some mRNA levels responded to more than one factor; for example, transcripts for flagellum synthesis genes decreased under conditions of leucine limitation and increased under H2 limitation. Increased growth rate resulted in increased mRNA levels for ribosomal protein genes, increased rRNA abundance, and increased mRNA for a gene encoding an S-layer protein.

Fast Tree: Computing Large Minimum-Evolution Trees with Profiles instead of a Distance Matrix

Abstract: Gene families are growing rapidly, but standard methods for inferring phylogenies do not scale to alignments with over 10,000 sequences. We present FastTree, a method for constructing large phylogenies and for estimating their reliability. Instead of storing a distance matrix, FastTree stores sequence profiles of internal nodes in the tree. FastTree uses these profiles to implement neighbor-joining and uses heuristics to quickly identify candidate joins. FastTree then uses nearest-neighbor interchanges to reduce the length of the tree. For an alignment with N sequences, L sites, and a different characters, a distance matrix requires O(N^2) space and O(N^2 L) time, but FastTree requires just O( NLa + N sqrt(N) ) memory and O( N sqrt(N) log(N) L a ) time. To estimate the tree's reliability, FastTree uses local bootstrapping, which gives another 100-fold speedup over a distance matrix. For example, FastTree computed a tree and support values for 158,022 distinct 16S ribosomal RNAs in 17 hours and 2.4 gigabytes of memory. Just computing pairwise Jukes-Cantor distances and storing them, without inferring a tree or bootstrapping, would require 17 hours and 50 gigabytes of memory. In simulations, FastTree was slightly more accurate than neighbor joining, BIONJ, or FastME; on genuine alignments, FastTree's topologies had higher likelihoods. FastTree is available at http://microbesonline.org/fasttree.

MODOMICS: a database of RNA modification pathways. 2017 update.

Abstract: MODOMICS is a database of RNA modifications that provides comprehensive information concerning the chemical structures of modified ribonucleosides, their biosynthetic pathways, the location of modified residues in RNA sequences, and RNA-modifying enzymes. In the current database version, we included the following new features and data: extended mass spectrometry and liquid chromatography data for modified nucleosides; links between human tRNA sequences and MINTbase - a framework for the interactive exploration of mitochondrial and nuclear tRNA fragments; new, machine-friendly system of unified abbreviations for modified nucleoside names; sets of modified tRNA sequences for two bacterial species, updated collection of mammalian tRNA modifications, 19 newly identified modified ribonucleosides and 66 functionally characterized proteins involved in RNA modification. Data from MODOMICS have been linked to the RNAcentral database of RNA sequences. MODOMICS is available at http://modomics.genesilico.pl.

Iterative model-building, structure refinement, and density modification with the PHENIX AutoBuild Wizard

Abstract: Iterative model-building, structure refinement, and density modification with the PHENIX AutoBuild Wizard Thomas C. Terwilliger a* , Ralf W. Grosse-Kunstleve b , Pavel V. Afonine b , Nigel W. Moriarty b , Peter Zwart b , Li-Wei Hung a , Randy J. Read c , Paul D. Adams b* a b Los Alamos National Laboratory, Mailstop M888, Los Alamos, NM 87545, USA Lawrence Berkeley National Laboratory, One Cyclotron Road, Bldg 64R0121, Berkeley, CA 94720, USA. c Department of Haematology, University of Cambridge, Cambridge CB2 0XY, UK. * Email: terwill@lanl.gov or PDAdams@lbl.gov Running title: The PHENIX AutoBuild Wizard Abstract The PHENIX AutoBuild Wizard is a highly automated tool for iterative model- building, structure refinement and density modification using RESOLVE or TEXTAL model- building, RESOLVE statistical density modification, and phenix.refine structure refinement. Recent advances in the AutoBuild Wizard and phenix.refine include automated detection and application of NCS from models as they are built, extensive model completion algorithms, and automated solvent molecule picking. Model completion algorithms in the AutoBuild Wizard include loop-building, crossovers between chains in different models of a structure, and side-chain optimization. The AutoBuild Wizard has been applied to a set of 48 structures at resolutions ranging from 1.1 A to 3.2 A, resulting in a mean R-factor of 0.24 and a mean free R factor of 0.29. The R-factor of the final model is dependent on the quality of the starting electron density, and relatively independent of resolution. Keywords: Model building; model completion; macromolecular models; Protein Data Bank; structure refinement; PHENIX Introduction Iterative model-building and refinement is a powerful approach to obtaining a complete and accurate macromolecular model. The approach consists of cycles of building an atomic model based on an electron density map for a macromolecular structure, refining the structure, using the refined structure as a basis for improving the map, and building a new model. This type of approach has been carried out in a semi-automated fashion for many years, with manual model-building iterating with automated refinement (Jensen, 1997). More recently, with the development first of ARP/wARP (Perrakis et al., 1999), and later other procedures including RESOLVE iterative model-building and refinement (Terwilliger,

Methane emissions from wetlands: biogeochemical, microbial, and modeling perspectives from local to global scales

Abstract: Understanding the dynamics of methane (CH4) emissions is of paramount importance because CH4 has 25 times the global warming potential of carbon dioxide (CO2) and is currently the second most important anthropogenic greenhouse gas. Wetlands are the single largest natural CH4 source with median emissions from published studies of 164 Tg yr 1 , which is about a third of total global emissions. We provide a perspective on important new frontiers in obtaining a better understanding of CH4 dynamics in natural systems, with a focus on wetlands. One of the most exciting recent developments in this field is the attempt to integrate the different methodologies and spatial scales of biogeochemistry, molecular microbiology, and modeling, and thus this is a major focus of this review. Our specific objectives are to provide an up-to-date synthesis of estimates of global CH4 emissions from wetlands and other freshwater aquatic ecosystems, briefly summarize major biogeophysical controls over CH4 emissions from wetlands, suggest new frontiers in CH4 biogeochemistry, examine relationships between methanogen community structure and CH4 dynamics in situ, and to review the current generation of CH4 models. We highlight throughout some of the most pressing issues concerning global change and feedbacks on CH4 emissions from natural ecosystems. Major uncertainties in estimating current and future CH4 emissions from natural ecosystems include the following: (i) A number of important controls over CH4 production, consumption, and transport have not been, or are inadequately, incorporated into existing CH4 biogeochemistry models. (ii) Significant errors in regional and global emission estimates are derived from large spatial-scale extrapolations from highly heterogeneous and often poorly mapped wetland complexes. (iii) The limited number of observations of CH4 fluxes and their associated environmental variables loosely constrains the parameterization of process-based biogeochemistry models.