Suchergebnisse

The origins of neural systems remain unresolved. In contrast to other basal metazoans, ctenophores (comb jellies) have both complex nervous and mesoderm-derived muscular systems. These holoplanktonic predators also have sophisticated ciliated locomotion, behaviour and distinct development. Here we present the draft genome of Pleurobrachia bachei, Pacific sea gooseberry, together with ten other ctenophore transcriptomes, and show that they are remarkably distinct from other animal genomes in their content of neurogenic, immune and developmental genes. Our integrative analyses place Ctenophora as the earliest lineage within Metazoa. This hypothesis is supported by comparative analysis of multiple gene families, including the apparent absence of HOX genes, canonical microRNA machinery, and reduced immune complement in ctenophores. Although two distinct nervous systems are well recognized in ctenophores, many bilaterian neuron-specific genes and genes of 'classical' neurotransmitter pathways either are absent or, if present, are not expressed in neurons. Our metabolomic and physiological data are consistent with the hypothesis that ctenophore neural systems, and possibly muscle specification, evolved independently from those in other animals. ; This work was supported by NSF (NSF-0744649 and NSF CNS-0821622 to L.L.M.; NSF CHE-1111705 to J.V.S.), NIH (1R01GM097502, R01MH097062, R21RR025699 and 5R21DA030118 to L.L.M.; P30 DA018310 to J.V.S.; R01 AG029360 and 1S10RR027052 to E.I.R.), NASA/nNNX13AJ31G (to K.M.H., L.L.M. and K.M.K.), NSERC 458115 and 211598 (J.P.R.), University of Florida Opportunity Funds/McKnight Brain Research and Florida Biodiversity Institute (L.L.M.), Rostock Inc./A.V. Chikunov (E.I.R.), grant from Russian Federation Government 14.B25.31.0033 (Resolution No.220) (E.I.R.). F.A.K., I.S.P. and R.D.were supported by HHMI(55007424),EMBO and MINECO(BFU2012-31329 and Sev-2012-0208). Contributions of AU Marine Biology Program 117 and Molette laboratory 22.

Motivation: Epistasis, the context-dependence of the contribution of an amino acid substitution to fitness, is common in evolution. To detect epistasis, fitness must be measured for at least four genotypes: the reference genotype, two different single mutants and a double mutant with both of the single mutations. For higher-order epistasis of the order n, fitness has to be measured for all 2n genotypes of an n-dimensional hypercube in genotype space forming a "combinatorially complete dataset". So far, only a handful of such datasets have been produced by manual curation. Concurrently, random mutagenesis experiments have produced measurements of fitness and other phenotypes in a high-throughput manner, potentially containing a number of combinatorially complete datasets. Results: We present an effective recursive algorithm for finding all hypercube structures in random mutagenesis experimental data. To test the algorithm, we applied it to the data from a recent HIS3 protein dataset and found all 199,847,053 unique combinatorially complete genotype combinations of dimensionality ranging from two to twelve. The algorithm may be useful for researchers looking for higher-order epistasis in their high-throughput experimental data. Availability: https://github.com/ivankovlab/HypercubeME.git. Supplementary information: Supplementary data are available at Bioinformatics online. ; This work was supported by the European Research Council under the European Union's Seventh Framework Programme (FP7/2007-2013, ERC grant agreement 335980_EinME) and Startup package to the Ivankov laboratory at Skolkovo Institute of Science and Technology. The work was started at the School of Molecular and Theoretical Biology 2017 supported by the Zimin Foundation. N.S.B. was supported by the Woman Scientists Support Grant in Centre for Genomic Regulation (CRG).

Understanding the principles that led to the current complexity of the genetic code is a central question in evolution. Expansion of the genetic code required the selection of new transfer RNAs (tRNAs) with specific recognition signals that allowed them to be matured, modified, aminoacylated, and processed by the ribosome without compromising the fidelity or efficiency of protein synthesis. We show that saturation of recognition signals blocks the emergence of new tRNA identities and that the rate of nucleotide substitutions in tRNAs is higher in species with fewer tRNA genes. We propose that the growth of the genetic code stalled because a limit was reached in the number of identity elements that can be effectively used in the tRNA structure. ; This work was supported in part by the Spanish Ministry of Economy and Competitiveness (grants BIO2012-32200, Sev-2012-0208, and BIO2012-32868 to L.R.d.P., F.A.K., and M.O., respectively) and by the Catalan Government (grants 2014-SGR-0771, 2014-SGR-0974, and 2014-SGR-0134 to L.R.d.P., F.A.K., and M.O., respectively). This work was also supported by the Howard Hughes Medical Institute International Early Career Scientist Program (55007424), by a European Research Council (ERC) Starting Grant (335980_EinME to F.K.), and by a grant from the ERC (ERC_SimDNA to M.O). A.G.T. and C.B. are funded by the Spanish Ministry of Economy and Competitiveness (FPDI-2013-17742 and BES-2013-064004, respectively).

Motivation: Computational prediction of the effect of mutations on protein stability is used by researchers in many fields. The utility of the prediction methods is affected by their accuracy and bias. Bias, a systematic shift of the predicted change of stability, has been noted as an issue for several methods, but has not been investigated systematically. Presence of the bias may lead to misleading results especially when exploring the effects of combination of different mutations. Results: Here we use a protocol to measure the bias as a function of the number of introduced mutations. It is based on a self-consistency test of the reciprocity the effect of a mutation. An advantage of the used approach is that it relies solely on crystal structures without experimentally measured stability values. We applied the protocol to four popular algorithms predicting change of protein stability upon mutation, FoldX, Eris, Rosetta and I-Mutant, and found an inherent bias. For one program, FoldX, we manage to substantially reduce the bias using additional relaxation by Modeller. Authors using algorithms for predicting effects of mutations should be aware of the bias described here. Availability and implementation: All calculations were implemented by in-house PERL scripts. Supplementary information: Supplementary data are available at Bioinformatics online. ; This work was supported by the HHMI International Early Career Scientist Program [55007424], the MINECO [BFU2015-68723-P], Spanish Ministry of Economy and Competitiveness Centro de Excelencia Severo Ochoa 2013-2017 [grant SEV-2012-0208], Secretaria d'Universitats i Recerca del Departament d'Economia i Coneixement de la Generalitat's AGAUR [program 2014 SGR 0974], the European Research Council under the European Union's Seventh Framework Programme [FP7/2007-2013, ERC grant agreement 335980_EinME] and Russian Scientific Foundation (RSF #14-24-00157, the part about I-Mutant calculations). The work was started at the School of Molecular and Theoretical Biology supported by the Dynasty Foundation.

During the COVID-19 pandemic, genomics and bioinformatics have emerged as essential public health tools. The genomic data acquired using these methods have supported the global health response, facilitated the development of testing methods and allowed the timely tracking of novel SARS-CoV-2 variants. Yet the virtually unlimited potential for rapid generation and analysis of genomic data is also coupled with unique technical, scientific and organizational challenges. Here, we discuss the application of genomic and computational methods for efficient data-driven COVID-19 response, the advantages of the democratization of viral sequencing around the world and the challenges associated with viral genome data collection and processing.