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      Gene expression phylogenies and ancestral transcriptome reconstruction resolves major transitions in the origins of pregnancy

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          Abstract

          Structural and physiological changes in the female reproductive system underlie the origins of pregnancy in multiple vertebrate lineages. In mammals, the glandular portion of the lower reproductive tract has transformed into a structure specialized for supporting fetal development. These specializations range from relatively simple maternal nutrient provisioning in egg-laying monotremes to an elaborate suite of traits that support intimate maternal-fetal interactions in Eutherians. Among these traits are the maternal decidua and fetal component of the placenta, but there is considerable uncertainty about how these structures evolved. Previously, we showed that changes in uterine gene expression contributes to several evolutionary innovations during the origins of pregnancy (Mika et al., 2021b). Here, we reconstruct the evolution of entire transcriptomes (‘ancestral transcriptome reconstruction’) and show that maternal gene expression profiles are correlated with degree of placental invasion. These results indicate that an epitheliochorial-like placenta evolved early in the mammalian stem-lineage and that the ancestor of Eutherians had a hemochorial placenta, and suggest maternal control of placental invasiveness. These data resolve major transitions in the evolution of pregnancy and indicate that ancestral transcriptome reconstruction can be used to study the function of ancestral cell, tissue, and organ systems.

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          IQ-TREE: A Fast and Effective Stochastic Algorithm for Estimating Maximum-Likelihood Phylogenies

          Large phylogenomics data sets require fast tree inference methods, especially for maximum-likelihood (ML) phylogenies. Fast programs exist, but due to inherent heuristics to find optimal trees, it is not clear whether the best tree is found. Thus, there is need for additional approaches that employ different search strategies to find ML trees and that are at the same time as fast as currently available ML programs. We show that a combination of hill-climbing approaches and a stochastic perturbation method can be time-efficiently implemented. If we allow the same CPU time as RAxML and PhyML, then our software IQ-TREE found higher likelihoods between 62.2% and 87.1% of the studied alignments, thus efficiently exploring the tree-space. If we use the IQ-TREE stopping rule, RAxML and PhyML are faster in 75.7% and 47.1% of the DNA alignments and 42.2% and 100% of the protein alignments, respectively. However, the range of obtaining higher likelihoods with IQ-TREE improves to 73.3-97.1%.
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            ModelFinder: Fast Model Selection for Accurate Phylogenetic Estimates

            Model-based molecular phylogenetics plays an important role in comparisons of genomic data, and model selection is a key step in all such analyses. We present ModelFinder, a fast model-selection method that greatly improves the accuracy of phylogenetic estimates. The improvement is achieved by incorporating a model of rate-heterogeneity across sites not previously considered in this context, and by allowing concurrent searches of model-space and tree-space.
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              IQ-TREE 2: New Models and Efficient Methods for Phylogenetic Inference in the Genomic Era

              Abstract IQ-TREE (http://www.iqtree.org, last accessed February 6, 2020) is a user-friendly and widely used software package for phylogenetic inference using maximum likelihood. Since the release of version 1 in 2014, we have continuously expanded IQ-TREE to integrate a plethora of new models of sequence evolution and efficient computational approaches of phylogenetic inference to deal with genomic data. Here, we describe notable features of IQ-TREE version 2 and highlight the key advantages over other software.
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                Author and article information

                Contributors
                Role: Reviewing Editor
                Role: Senior Editor
                Journal
                eLife
                Elife
                eLife
                eLife
                eLife Sciences Publications, Ltd
                2050-084X
                30 June 2022
                2022
                : 11
                : e74297
                Affiliations
                [1 ] Department of Human Genetics, University of Chicago ( https://ror.org/024mw5h28) Chicago United States
                [2 ] Department of Organismal Biology and Anatomy, University of Chicago ( https://ror.org/024mw5h28) Chicago United States
                [3 ] School of Life and Environmental Sciences, University of Sydney ( https://ror.org/0384j8v12) Sydney Australia
                [4 ] Charles Perkins Centre,University of Sydney ( https://ror.org/0384j8v12) Sydney Australia
                [5 ] Department of Biological Sciences, University at Buffalo, State University of New York ( https://ror.org/01y64my43) Buffalo,Newyork United States
                Vanderbilt University ( https://ror.org/02vm5rt34) United States
                Pennsylvania State University ( https://ror.org/04p491231) United States
                Vanderbilt University ( https://ror.org/02vm5rt34) United States
                Vanderbilt University ( https://ror.org/02vm5rt34) United States
                University of Manchester ( https://ror.org/027m9bs27) United Kingdom
                Author information
                https://orcid.org/0000-0002-2170-9364
                https://orcid.org/0000-0001-5765-9699
                https://orcid.org/0000-0001-5311-3824
                Article
                74297
                10.7554/eLife.74297
                9275820
                35770963
                88d1d923-cfc9-4850-a38a-2e695da834b7
                © 2022, Mika et al

                This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

                History
                : 30 September 2021
                : 29 June 2022
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000912, March of Dimes Foundation;
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000861, Burroughs Wellcome Fund;
                Award ID: 1013760
                Award Recipient :
                The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
                Categories
                Research Advance
                Developmental Biology
                Evolutionary Biology
                Custom metadata
                Mammals evolved an invasive placenta early in the origins of pregnancy.

                Life sciences
                pregnancy,placenta,mammals,reptiles,ancestral reconstruction,devoevo,none
                Life sciences
                pregnancy, placenta, mammals, reptiles, ancestral reconstruction, devoevo, none

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