Jasper J. Koehorst

Bio: Jasper J. Koehorst is an academic researcher from Wageningen University and Research Centre. The author has contributed to research in topics: RDF & Medicine. The author has an hindex of 16, co-authored 42 publications receiving 1418 citations. Previous affiliations of Jasper J. Koehorst include University of Queensland.

MEMOTE for standardized genome-scale metabolic model testing

Abstract: We acknowledge D. Dannaher and A. Lopez for their supporting work on the Angular parts of MEMOTE; resources and support from the DTU Computing Center; J. Cardoso, S. Gudmundsson, K. Jensen and D. Lappa for their feedback on conceptual details; and P. D. Karp and I. Thiele for critically reviewing the manuscript. We thank J. Daniel, T. Kristjansdottir, J. Saez-Saez, S. Sulheim, and P. Tubergen for being early adopters of MEMOTE and for providing written testimonials. J.O.V. received the Research Council of Norway grants 244164 (GenoSysFat), 248792 (DigiSal) and 248810 (Digital Life Norway); M.Z. received the Research Council of Norway grant 244164 (GenoSysFat); C.L. received funding from the Innovation Fund Denmark (project “Environmentally Friendly Protein Production (EFPro2)”); C.L., A.K., N. S., M.B., M.A., D.M., P.M, B.J.S., P.V., K.R.P. and M.H. received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement 686070 (DD-DeCaF); B.G.O., F.T.B. and A.D. acknowledge funding from the US National Institutes of Health (NIH, grant number 2R01GM070923-13); A.D. was supported by infrastructural funding from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), Cluster of Excellence EXC 2124 Controlling Microbes to Fight Infections; N.E.L. received funding from NIGMS R35 GM119850, Novo Nordisk Foundation NNF10CC1016517 and the Keck Foundation; A.R. received a Lilly Innovation Fellowship Award; B.G.-J. and J. Nogales received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no 686585 for the project LIAR, and the Spanish Ministry of Economy and Competitivity through the RobDcode grant (BIO2014-59528-JIN); L.M.B. has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement 633962 for project P4SB; R.F. received funding from the US Department of Energy, Offices of Advanced Scientific Computing Research and the Biological and Environmental Research as part of the Scientific Discovery Through Advanced Computing program, grant DE-SC0010429; A.M., C.Z., S.L. and J. Nielsen received funding from The Knut and Alice Wallenberg Foundation, Advanced Computing program, grant #DE-SC0010429; S.K.’s work was in part supported by the German Federal Ministry of Education and Research (de.NBI partner project “ModSim” (FKZ: 031L104B)); E.K. and J.A.H.W. were supported by the German Federal Ministry of Education and Research (project “SysToxChip”, FKZ 031A303A); M.K. is supported by the Federal Ministry of Education and Research (BMBF, Germany) within the research network Systems Medicine of the Liver (LiSyM, grant number 031L0054); J.A.P. and G.L.M. acknowledge funding from US National Institutes of Health (T32-LM012416, R01-AT010253, R01-GM108501) and the Wagner Foundation; G.L.M. acknowledges funding from a Grand Challenges Exploration Phase I grant (OPP1211869) from the Bill & Melinda Gates Foundation; H.H. and R.S.M.S. received funding from the Biotechnology and Biological Sciences Research Council MultiMod (BB/N019482/1); H.U.K. and S.Y.L. received funding from the Technology Development Program to Solve Climate Changes on Systems Metabolic Engineering for Biorefineries (grants NRF-2012M1A2A2026556 and NRF-2012M1A2A2026557) from the Ministry of Science and ICT through the National Research Foundation (NRF) of Korea; H.U.K. received funding from the Bio & Medical Technology Development Program of the NRF, the Ministry of Science and ICT (NRF-2018M3A9H3020459); P.B., B.J.S., Z.K., B.O.P., C.L., M.B., N.S., M.H. and A.F. received funding through Novo Nordisk Foundation through the Center for Biosustainability at the Technical University of Denmark (NNF10CC1016517); D.-Y.L. received funding from the Next-Generation BioGreen 21 Program (SSAC, PJ01334605), Rural Development Administration, Republic of Korea; G.F. was supported by the RobustYeast within ERA net project via SystemsX.ch; V.H. received funding from the ETH Domain and Swiss National Science Foundation; M.P. acknowledges Oxford Brookes University; J.C.X. received support via European Research Council (666053) to W.F. Martin; B.E.E. acknowledges funding through the CSIRO-UQ Synthetic Biology Alliance; C.D. is supported by a Washington Research Foundation Distinguished Investigator Award. I.N. received funding from National Institutes of Health (NIH)/National Institute of General Medical Sciences (NIGMS) (grant P20GM125503).

Reverse Methanogenesis and Respiration in Methanotrophic Archaea.

Abstract: Anaerobic oxidation of methane (AOM) is catalyzed by anaerobic methane-oxidizing archaea (ANME) via a reverse and modified methanogenesis pathway. Methanogens can also reverse the methanogenesis pathway to oxidize methane, but only during net methane production (i.e., “trace methane oxidation”). In turn, ANME can produce methane, but only during net methane oxidation (i.e., enzymatic back flux). Net AOM is exergonic when coupled to an external electron acceptor such as sulfate (ANME-1, ANME-2abc, and ANME-3), nitrate (ANME-2d), or metal (oxides). In this review, the reversibility of the methanogenesis pathway and essential differences between ANME and methanogens are described by combining published information with domain based (meta)genome comparison of archaeal methanotrophs and selected archaea. These differences include abundances and special structure of methyl coenzyme M reductase and of multiheme cytochromes and the presence of menaquinones or methanophenazines. ANME-2a and ANME-2d can use electron acceptors other than sulfate or nitrate for AOM, respectively. Environmental studies suggest that ANME-2d are also involved in sulfate-dependent AOM. ANME-1 seem to use a different mechanism for disposal of electrons and possibly are less versatile in electron acceptors use than ANME-2. Future research will shed light on the molecular basis of reversal of the methanogenic pathway and electron transfer in different ANME types.

SPAdes, a new genome assembly algorithm and its applications to single-cell sequencing ( 7th Annual SFAF Meeting, 2012)

Abstract: The lion's share of bacteria in various environments cannot be cloned in the laboratory and thus cannot be sequenced using existing technologies. A major goal of single-cell genomics is to complement gene-centric metagenomic data with whole-genome assemblies of uncultivated organisms. Assembly of single-cell data is challenging because of highly non-uniform read coverage as well as elevated levels of sequencing errors and chimeric reads. We describe SPAdes, a new assembler for both single-cell and standard (multicell) assembly, and demonstrate that it improves on the recently released E+V-SC assembler (specialized for single-cell data) and on popular assemblers Velvet and SoapDeNovo (for multicell data). SPAdes generates single-cell assemblies, providing information about genomes of uncultivatable bacteria that vastly exceeds what may be obtained via traditional metagenomics studies. SPAdes is available online ( http://bioinf.spbau.ru/spades ). It is distributed as open source software.

An updated evolutionary classification of CRISPR–Cas systems

Abstract: The evolution of CRISPR-cas loci, which encode adaptive immune systems in archaea and bacteria, involves rapid changes, in particular numerous rearrangements of the locus architecture and horizontal transfer of complete loci or individual modules. These dynamics complicate straightforward phylogenetic classification, but here we present an approach combining the analysis of signature protein families and features of the architecture of cas loci that unambiguously partitions most CRISPR-cas loci into distinct classes, types and subtypes. The new classification retains the overall structure of the previous version but is expanded to now encompass two classes, five types and 16 subtypes. The relative stability of the classification suggests that the most prevalent variants of CRISPR-Cas systems are already known. However, the existence of rare, currently unclassifiable variants implies that additional types and subtypes remain to be characterized.

Automated Eukaryotic Gene Structure Annotation Using EVidenceModeler and the Program to Assemble Spliced Alignments

Abstract: EVidenceModeler (EVM) is presented as an automated eukaryotic gene structure annotation tool that reports eukaryotic gene structures as a weighted consensus of all available evidence. EVM, when combined with the Program to Assemble Spliced Alignments (PASA), yields a comprehensive, configurable annotation system that predicts protein-coding genes and alternatively spliced isoforms. Our experiments on both rice and human genome sequences demonstrate that EVM produces automated gene structure annotation approaching the quality of manual curation.

C2c2 is a single-component programmable RNA-guided RNA-targeting CRISPR effector

Abstract: The clustered regularly interspaced short palindromic repeat (CRISPR)-CRISPR-associated genes (Cas) adaptive immune system defends microbes against foreign genetic elements via DNA or RNA-DNA interference. We characterize the class 2 type VI CRISPR-Cas effector C2c2 and demonstrate its RNA-guided ribonuclease function. C2c2 from the bacterium Leptotrichia shahii provides interference against RNA phage. In vitro biochemical analysis shows that C2c2 is guided by a single CRISPR RNA and can be programmed to cleave single-stranded RNA targets carrying complementary protospacers. In bacteria, C2c2 can be programmed to knock down specific mRNAs. Cleavage is mediated by catalytic residues in the two conserved Higher Eukaryotes and Prokaryotes Nucleotide-binding (HEPN) domains, mutations of which generate catalytically inactive RNA-binding proteins. These results broaden our understanding of CRISPR-Cas systems and suggest that C2c2 can be used to develop new RNA-targeting tools.

Multiplexed and portable nucleic acid detection platform with Cas13, Cas12a, and Csm6

Abstract: Rapid detection of nucleic acids is integral for clinical diagnostics and biotechnological applications. We recently developed a platform termed SHERLOCK (specific high-sensitivity enzymatic reporter unlocking) that combines isothermal preamplification with Cas13 to detect single molecules of RNA or DNA. Through characterization of CRISPR enzymology and application development, we report here four advances integrated into SHERLOCK version 2 (SHERLOCKv2) (i) four-channel single-reaction multiplexing with orthogonal CRISPR enzymes; (ii) quantitative measurement of input as low as 2 attomolar; (iii) 3.5-fold increase in signal sensitivity by combining Cas13 with Csm6, an auxiliary CRISPR-associated enzyme; and (iv) lateral-flow readout. SHERLOCKv2 can detect Dengue or Zika virus single-stranded RNA as well as mutations in patient liquid biopsy samples via lateral flow, highlighting its potential as a multiplexable, portable, rapid, and quantitative detection platform of nucleic acids.