Antibody production in micro-organisms

Global demand for monoclonal antibody-based therapeutics (Mab’s) far exceeds current production capacity, and is expected to continue to grow based on current development pipelines. Despite their proven efficacy in a large number of indications, equitable use of these drugs is limited by the high cost of CHO-cell based production and purification. Micro-organisms such as yeasts and filamentous fungi present an attractive alternative for antibody production, but will require extensive genetic modification to achieve both high titers and mammalian-like glycosylation patterns in a secreted product that is easily purified. Towards this end, we developed state-of-the-art genetic engineering tools for eight micro-organisms to enable the highly efficient, targeted multiplexed integrations necessary for antibody production in these hosts. We demonstrated successful antibody production in several of these micro-organisms, paving the way to low-cost microbial fermentation to replace CHO fermentation

Empirical Power and Sample Size Calculations for Cluster-Randomized and Cluster-Randomized Crossover Studies

In recent years, the number of studies using a cluster-randomized design has grown dramatically. In addition, the cluster-randomized crossover design has been touted as a methodological advance that can increase efficiency of cluster-randomized studies in certain situations. While the cluster-randomized crossover trial has become a popular tool, standards of design, analysis, reporting and implementation have not been established for this emergent design. We address one particular aspect of cluster-randomized and cluster-randomized crossover trial design: estimating statistical power. We present a general framework for estimating power via simulation in cluster-randomized studies with or without one or more crossover periods. We have implemented this framework in the clusterPower software package for R, freely available online from the Comprehensive R Archive Network. Our simulation framework is easy to implement and users may customize the methods used for data analysis. We give four examples of using the software in practice. The clusterPower package could play an important role in the design of future cluster-randomized and cluster-randomized crossover studies. This work is the first to establish a universal method for calculating power for both cluster-randomized and cluster-randomized clinical trials. More research is needed to develop standardized and recommended methodology for cluster-randomized crossover studies

Fire as a fundamental ecological process: Research advances and frontiers

© 2020 The Authors.Fire is a powerful ecological and evolutionary force that regulates organismal traits, population sizes, species interactions, community composition, carbon and nutrient cycling and ecosystem function. It also presents a rapidly growing societal challenge, due to both increasingly destructive wildfires and fire exclusion in fire‐dependent ecosystems. As an ecological process, fire integrates complex feedbacks among biological, social and geophysical processes, requiring coordination across several fields and scales of study. Here, we describe the diversity of ways in which fire operates as a fundamental ecological and evolutionary process on Earth. We explore research priorities in six categories of fire ecology: (a) characteristics of fire regimes, (b) changing fire regimes, (c) fire effects on above‐ground ecology, (d) fire effects on below‐ground ecology, (e) fire behaviour and (f) fire ecology modelling. We identify three emergent themes: the need to study fire across temporal scales, to assess the mechanisms underlying a variety of ecological feedbacks involving fire and to improve representation of fire in a range of modelling contexts. Synthesis: As fire regimes and our relationships with fire continue to change, prioritizing these research areas will facilitate understanding of the ecological causes and consequences of future fires and rethinking fire management alternatives.Support was provided by NSF‐DEB‐1743681 to K.K.M. and A.J.T. We thank Shalin Hai‐Jew for helpful discussion of the survey and qualitative methods.Peer reviewe

Evaluating the Effects of SARS-CoV-2 Spike Mutation D614G on Transmissibility and Pathogenicity.

Global dispersal and increasing frequency of the SARS-CoV-2 spike protein variant D614G are suggestive of a selective advantage but may also be due to a random founder effect. We investigate the hypothesis for positive selection of spike D614G in the United Kingdom using more than 25,000 whole genome SARS-CoV-2 sequences. Despite the availability of a large dataset, well represented by both spike 614 variants, not all approaches showed a conclusive signal of positive selection. Population genetic analysis indicates that 614G increases in frequency relative to 614D in a manner consistent with a selective advantage. We do not find any indication that patients infected with the spike 614G variant have higher COVID-19 mortality or clinical severity, but 614G is associated with higher viral load and younger age of patients. Significant differences in growth and size of 614G phylogenetic clusters indicate a need for continued study of this variant

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

The evolution of self-control

This work was supported by the National Evolutionary Synthesis Center (NESCent) through support of a working group led by C.L.N. and B.H. NESCent is supported by the National Science Foundation (NSF) EF-0905606. For training in phylogenetic comparative methods, we thank the AnthroTree Workshop (supported by NSF BCS-0923791). Y.S. thanks the National Natural Science Foundation of China (Project 31170995) and National Basic Research Program (973 Program: 2010CB833904). E.E.B. thanks the Duke Vertical Integration Program and the Duke Undergraduate Research Support Office. J.M.P. was supported by a Newton International Fellowship from the Royal Society and the British Academy. L.R.S. thanks the James S. McDonnell Foundation for Award 220020242. L.J.N.B. and M.L.P. acknowledge the National Institutes of Mental Health (R01-MH096875 and R01-MH089484), a Duke Institute for Brain Sciences Incubator Award (to M.L.P.), and a Duke Center for Interdisciplinary Decision Sciences Fellowship (to L.J.N.B.). E.V. and E.A. thank the Programma Nazionale per la Ricerca–Consiglio Nazionale delle Ricerche (CNR) Aging Program 2012–2014 for financial support, Roma Capitale–Museo Civico di Zoologia and Fondazione Bioparco for hosting the Istituto di Scienze e Tecnologie della Cognizione–CNR Unit of Cognitive Primatology and Primate Centre, and Massimiliano Bianchi and Simone Catarinacci for assistance with capuchin monkeys. K.F. thanks the Japan Society for the Promotion of Science (JSPS) for Grant-in-Aid for Scientific Research 20220004. F. Aureli thanks the Stages in the Evolution and Development of Sign Use project (Contract 012-984 NESTPathfinder) and the Integrating Cooperation Research Across Europe project (Contract 043318), both funded by the European Community’s Sixth Framework Programme (FP6/2002–2006). F. Amici was supported by Humboldt Research Fellowship for Postdoctoral Researchers (Humboldt ID 1138999). L.F.J. and M.M.D. acknowledge NSF Electrical, Communications, and Cyber Systems Grant 1028319 (to L.F.J.) and an NSF Graduate Fellowship (to M.M.D.). C.H. thanks Grant-in-Aid for JSPS Fellows (10J04395). A.T. thanks Research Fellowships of the JSPS for Young Scientists (21264). F.R. and Z.V. acknowledge Austrian Science Fund (FWF) Project P21244-B17, the European Research Council (ERC) under the European Union’s Seventh Framework Programme (FP/2007–2013)/ERC Grant Agreement 311870 (to F.R.), Vienna Science and Technology Fund Project CS11-026 (to Z.V.), and many private sponsors, including Royal Canin for financial support and the Game Park Ernstbrunn for hosting the Wolf Science Center. S.M.R. thanks the Natural Sciences and Engineering Research Council (Canada). J.K.Y. thanks the US Department of Agriculture–Wildlife Services–National Wildlife Research Center. J.F.C. thanks the James S. McDonnell Foundation and Alfred P. Sloan Foundation. E.L.M. and B.H. thank the Duke Lemur Center and acknowledge National Institutes of Health Grant 5 R03 HD070649-02 and NSF Grants DGE-1106401, NSF-BCS-27552, and NSF-BCS-25172. This is Publication 1265 of the Duke Lemur Center.Cognition presents evolutionary research with one of its greatest challenges. Cognitive evolution has been explained at the proximate level by shifts in absolute and relative brain volume and at the ultimate level by differences in social and dietary complexity. However, no study has integrated the experimental and phylogenetic approach at the scale required to rigorously test these explanations. Instead, previous research has largely relied on various measures of brain size as proxies for cognitive abilities. We experimentally evaluated these major evolutionary explanations by quantitatively comparing the cognitive performance of 567 individuals representing 36 species on two problem-solving tasks measuring self-control. Phylogenetic analysis revealed that absolute brain volume best predicted performance across species and accounted for considerably more variance than brain volume controlling for body mass. This result corroborates recent advances in evolutionary neurobiology and illustrates the cognitive consequences of cortical reorganization through increases in brain volume. Within primates, dietary breadth but not social group size was a strong predictor of species differences in self-control. Our results implicate robust evolutionary relationships between dietary breadth, absolute brain volume, and self-control. These findings provide a significant first step toward quantifying the primate cognitive phenome and explaining the process of cognitive evolution.PostprintPeer reviewe