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      Loss of a gluconeogenic muscle enzyme contributed to adaptive metabolic traits in hummingbirds

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          Abstract

          Hummingbirds possess distinct metabolic adaptations to fuel their energy-demanding hovering flight, but the underlying genomic changes are largely unknown. Here, we generated a chromosome-level genome assembly of the long-tailed hermit and screened for genes that have been specifically inactivated in the ancestral hummingbird lineage. We discovered that FBP2 (fructose-bisphosphatase 2), which encodes a gluconeogenic muscle enzyme, was lost during a time period when hovering flight evolved. We show that FBP2 knockdown in an avian muscle cell line up-regulates glycolysis and enhances mitochondrial respiration, coincident with an increased mitochondria number. Furthermore, genes involved in mitochondrial respiration and organization have up-regulated expression in hummingbird flight muscle. Together, these results suggest that FBP2 loss was likely a key step in the evolution of metabolic muscle adaptations required for true hovering flight.

          Loss leads to gain

          Hummingbirds display true hovering flight, an incredibly energy-intensive activity. Although much is known about the physiology of this movement, little has been known about the genetics underlying its evolution. Osipova et al . screened newly generated and previously sequenced bird genomes to search for key changes facilitating this high-energy locomotion. They found that a gluconeogenic muscle enzyme, FBP2, was lost as hovering flight evolved. Knockouts of this gene in avian cell lines led to an increase in glycolysis, mitochondria production, and mitochondrial respiration, all leading to higher energy efficiency. These results also illustrate how the loss of a gene can be adaptive. —SNV

          Abstract

          FBP2 loss in hummingbirds coincided with the evolution of true hovering flight and likely contributed to muscle adaptations.

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          Most cited references66

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            Is Open Access

            Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

            In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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              Fast and accurate short read alignment with Burrows–Wheeler transform

              Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ∼10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package. Availability: http://maq.sourceforge.net Contact: rd@sanger.ac.uk
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                Author and article information

                Contributors
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                Journal
                Science
                Science
                American Association for the Advancement of Science (AAAS)
                0036-8075
                1095-9203
                January 13 2023
                January 13 2023
                : 379
                : 6628
                : 185-190
                Affiliations
                [1 ]Max Planck Institute of Molecular Cell Biology and Genetics, Pfotenhauerstr. 108, 01307 Dresden, Germany.
                [2 ]Max Planck Institute for the Physics of Complex Systems, Nöthnitzer Str. 38, 01187 Dresden, Germany.
                [3 ]Center for Systems Biology Dresden, Pfotenhauerstr. 108, 01307 Dresden, Germany.
                [4 ]LOEWE Centre for Translational Biodiversity Genomics, Senckenberganlage 25, 60325 Frankfurt, Germany.
                [5 ]Senckenberg Research Institute, Senckenberganlage 25, 60325 Frankfurt, Germany.
                [6 ]DRESDEN concept Genome Center, Technische Universität Dresden, 01062 Dresden, Germany.
                [7 ]Evolution of Sensory Systems Research Group, Max Planck Institute for Ornithology, Seewiesen, Germany.
                [8 ]University of British Columbia, Vancouver, Vancouver, BC V6T 1Z4, Canada.
                [9 ]Structure and Motion Laboratory, Royal Veterinary College, University of London, London, UK.
                [10 ]Department of Behavioural Neurobiology, Max Planck Institute for Ornithology, Seewiesen, Germany.
                [11 ]Roche Institute for Translational Bioengineering, Grenzacherstrasse 124, 4070 Basel, Switzerland.
                Article
                10.1126/science.abn7050
                36634192
                469208a4-2a0a-49ba-9a58-566535ed415f
                © 2023
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