{"id":327,"date":"2019-07-17T19:01:18","date_gmt":"2019-07-17T19:01:18","guid":{"rendered":"https:\/\/labs.bio.cmu.edu\/mcmanus\/?page_id=327"},"modified":"2025-05-26T18:07:59","modified_gmt":"2025-05-26T18:07:59","slug":"translation-regulation-evolution","status":"publish","type":"page","link":"https:\/\/labs.bio.cmu.edu\/mcmanus\/research\/translation-regulation-evolution\/","title":{"rendered":"Upstream Open Reading Frames (uORFs)"},"content":{"rendered":"<p><div class=\"et_pb_with_border et_d4_element et_pb_section et_pb_section_0 et_pb_with_background  et_pb_css_mix_blend_mode et_section_regular et_block_section\" >\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t<div class=\"et_d4_element et_pb_row et_pb_row_0  et_pb_css_mix_blend_mode et_block_row\">\n\t\t\t\t<div class=\"et_d4_element et_pb_column_4_4 et_pb_column et_pb_column_0  et_pb_css_mix_blend_mode et-last-child et_block_column\">\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t<div class=\"et_pb_module et_d4_element et_pb_image et_pb_image_0\">\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t<span class=\"et_pb_image_wrap \"><img loading=\"lazy\" decoding=\"async\" width=\"1779\" height=\"408\" src=\"https:\/\/labs.bio.cmu.edu\/mcmanus\/wp-content\/uploads\/sites\/2\/2025\/03\/cropped-logo5.gif\" alt=\"\" title=\"cropped-logo5.gif\" class=\"wp-image-462\" \/><\/span>\n\t\t\t<\/div>\n\t\t\t<\/div>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t<\/div>\n\t\t\t\t\n\t\t\t\t\n\t\t\t<\/div><div class=\"et_d4_element et_pb_section et_pb_section_1  et_pb_css_mix_blend_mode et_section_regular et_block_section\" >\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t<div class=\"et_d4_element et_pb_row et_pb_row_1  et_pb_css_mix_blend_mode et_block_row\">\n\t\t\t\t<div class=\"et_d4_element et_pb_column_2_5 et_pb_column et_pb_column_1  et_pb_css_mix_blend_mode et_block_column\">\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t<div class=\"et_pb_module et_d4_element et_pb_image et_pb_image_1\">\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t<span class=\"et_pb_image_wrap \"><img loading=\"lazy\" decoding=\"async\" width=\"1187\" height=\"1356\" src=\"https:\/\/labs.bio.cmu.edu\/mcmanus\/wp-content\/uploads\/sites\/2\/2025\/03\/utrs.gif\" alt=\"\" title=\"utrs\" class=\"wp-image-493\" \/><\/span>\n\t\t\t<\/div>\n\t\t\t<\/div><div class=\"et_d4_element et_pb_column_3_5 et_pb_column et_pb_column_2  et_pb_css_mix_blend_mode et-last-child et_block_column\">\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t<div class=\"et_pb_module et_d4_element et_pb_text et_pb_text_0  et_pb_text_align_left et_pb_bg_layout_light\">\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t<div class=\"et_pb_text_inner\"><h1>Upstream Open Reading Frames (uORFs)<\/h1><\/div>\n\t\t\t<\/div><div class=\"et_pb_module et_d4_element et_pb_text et_pb_text_1  et_pb_text_align_left et_pb_bg_layout_light\">\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t<div class=\"et_pb_text_inner\"><p>Changes in gene expression have significant impacts on biological phenotypes, including human disease. These changes can occur in all gene expression processes, including mRNA transcription, alternative splicing, translation into protein, and mRNA and protein turnover. The regulation of these processes involves complex networks comprised of\u00a0<em>cis<\/em>-acting DNA and RNA sequence elements, and\u00a0<em>trans<\/em>-acting factors. Over the past decade it has become increasingly clear that changes in both types of network components have contributed to widespread differences in mRNA abundance, both among and between species. However, species differences in mRNA abundance may not accurately reflect differences in protein expression and comparatively little is known regarding the evolution of post-transcriptional gene regulatory processes. \u00a0We are currently using both laboratory and computational based methods to investigate upstream open reading frames (uORFs), and how they affect translation regulation in yeast and human cells. We are also particularly interested in the structure and function of conserved 5'UTRs.<\/p><\/div>\n\t\t\t<\/div>\n\t\t\t<\/div>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t<\/div>\n\t\t\t\t\n\t\t\t\t\n\t\t\t<\/div><div class=\"et_d4_element et_pb_section et_pb_section_2  et_pb_css_mix_blend_mode et_section_regular et_block_section\" >\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t<div class=\"et_d4_element et_pb_row et_pb_row_2  et_pb_css_mix_blend_mode et_block_row\">\n\t\t\t\t<div class=\"et_d4_element et_pb_column_4_4 et_pb_column et_pb_column_3  et_pb_css_mix_blend_mode et-last-child et_block_column\">\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t<div class=\"et_pb_module et_d4_element et_pb_text et_pb_text_2  et_pb_text_align_left et_pb_bg_layout_light\">\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t<div class=\"et_pb_text_inner\"><h3>Selected Publications<\/h3><\/div>\n\t\t\t<\/div><div class=\"et_pb_module et_d4_element et_pb_text et_pb_text_3  et_pb_text_align_left et_pb_bg_layout_light\">\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t<div class=\"et_pb_text_inner\"><p>Akritava, C, May GE, McManus CJ. (2025) Deciphering the landscape of cis-acting sequences in natural yeast Transcript Leaders. <em><\/em><a href=\"https:\/\/academic.oup.com\/nar\/article\/53\/5\/gkaf165\/8071461\" target=\"_blank\" rel=\"noopener\"><em>Nucleic Acids Research<\/em><\/a><\/p>\n<p>May GE, Akirtava C, Agar-Johnson M, Micic J, Woolford J, <strong>McManus J.<\/strong> (2023) Unraveling the influences of sequence and position on yeast uORF activity using massively parallel reporter systems and machine learning. <a href=\"https:\/\/elifesciences.org\/articles\/69611\" target=\"_blank\" rel=\"noopener\"><em>eLife<\/em><\/a><\/p>\n<p>Akirtava C, May GE, <strong>McManus J.<\/strong> (2022) False-positive IRESes from Hoxa9 and other genes resulting from errors in mammalian 5\u2032 UTR annotations. <em><a href=\"https:\/\/www.pnas.org\/doi\/abs\/10.1073\/pnas.2122170119?url_ver=Z39.88-2003&amp;rfr_id=ori:rid:crossref.org&amp;rfr_dat=cr_pub%20%200pubmed\" target=\"_blank\" rel=\"noopener\">PNAS<\/a>. <\/em><\/p>\n<p>May G, <strong>McManus J. <\/strong>(2022) Multiplexed Analysis of Human uORF Regulatory Functions During the ISR Using PoLib-Seq. <a href=\"https:\/\/link.springer.com\/protocol\/10.1007\/978-1-0716-1975-9_3\" target=\"_blank\" rel=\"noopener\"><em>Methods Mol Biol.<\/em><\/a><\/p>\n<p>May G, <strong>McManus J<\/strong>. (2022)\u00a0 High-Throughput Quantitation of Yeast uORF Regulatory Impacts Using FACS-uORF. <a href=\"https:\/\/link.springer.com\/protocol\/10.1007\/978-1-0716-1851-6_18\" target=\"_blank\" rel=\"noopener\"><em>Methods Mol Biol.<\/em><\/a><\/p>\n<p>Spealman P, Naik A, <strong>McManus J.<\/strong> (2021) uORF-seqr: A Machine Learning-Based Approach to the Identification of Upstream Open Reading Frames in Yeast. <em><a href=\"https:\/\/link.springer.com\/protocol\/10.1007%2F978-1-0716-1150-0_15\" target=\"_blank\" rel=\"noopener\">Methods in Mol. Biol.<\/a><\/em><\/p>\n<p>Lin Y, May G, Kready H, Nazzaro L, Mao M, Spealman P, Creeger Y, <strong>McManus CJ<\/strong>. (2019) Impacts of uORF codon identity and position on translation regulation. <em><a href=\"https:\/\/academic.oup.com\/nar\/advance-article\/doi\/10.1093\/nar\/gkz681\/5545010\" target=\"_blank\" rel=\"noopener\">Nucleic Acids Research<\/a><\/em><\/p>\n<p>Spealman P, Naik A, May G, Kuersten S, Freebert L, Murphy R, <strong>McManus J.<\/strong> (2017) Conserved non-AUG uORFs revealed by a novel regression analysis of ribosome profiling data. <a href=\"http:\/\/genome.cshlp.org\/content\/early\/2018\/01\/12\/gr.221507.117.full.pdf+html?sid=31b4bf76-19da-4c81-8fc3-f5fda20c7e5c\" target=\"_blank\" rel=\"noopener\"><em>Genome Research<\/em><\/a><\/p>\n<p><strong>McManus CJ<\/strong>, May G, Spealman P, Shteyman A. (2013) Ribosome profiling reveals post-transcriptional buffering of divergent gene expression in yeast.<em>\u00a0<a href=\"http:\/\/genome.cshlp.org\/cgi\/pmidlookup?view=long&amp;pmid=24318730\" target=\"_blank\" rel=\"noopener\">Genome Research<\/a><\/em><\/p><\/div>\n\t\t\t<\/div>\n\t\t\t<\/div>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t<\/div>\n\t\t\t\t\n\t\t\t\t\n\t\t\t<\/div><\/p>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":21,"featured_media":0,"parent":8,"menu_order":1,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"<header class=\"entry-header\"><\/header>\r\n<div class=\"entry-content\">\r\n\r\n<img class=\"size-medium wp-image-493 alignleft\" src=\"https:\/\/labs.bio.cmu.edu\/mcmanus\/wp-content\/uploads\/sites\/2\/2025\/03\/utrs-263x300.gif\" alt=\"\" width=\"263\" height=\"300\" \/>\r\n\r\nChanges in gene expression have significant impacts on biological phenotypes, including human disease. These changes can occur in all gene expression processes, including mRNA transcription, alternative splicing, translation into protein, and mRNA and protein turnover. The regulation of these processes involves complex networks comprised of\u00a0<em>cis<\/em>-acting DNA and RNA sequence elements, and\u00a0<em>trans<\/em>-acting factors. Over the past decade it has become increasingly clear that changes in both types of network components have contributed to widespread differences in mRNA abundance, both among and between species. However, species differences in mRNA abundance may not accurately reflect differences in protein expression and comparatively little is known regarding the evolution of post-transcriptional gene regulatory processes. \u00a0We are currently using both laboratory and computational based methods to investigate upstream open reading frames (uORFs), and how they affect translation regulation in yeast and human cells. We are also particularly interested in the structure and function of conserved 5'UTRs.\r\n\r\n<\/div>\r\n<h4><\/h4>\r\n<h4>Selected Publications<\/h4>\r\nAkritava, C, May GE, McManus CJ. (2025) Deciphering the landscape of cis-acting sequences in natural yeast Transcript Leaders. <em>Accepted to <\/em><a href=\"https:\/\/pubmed.ncbi.nlm.nih.gov\/39005336\/\"><em>Nucleic Acids Research<\/em><\/a>\r\n\r\nMay GE, Akirtava C, Agar-Johnson M, Micic J, Woolford J, <strong>McManus J.<\/strong> (2023) Unraveling the influences of sequence and position on yeast uORF activity using massively parallel reporter systems and machine learning. <a href=\"https:\/\/elifesciences.org\/articles\/69611\"><em>eLife<\/em><\/a>\r\n\r\nAkirtava C, May GE, <strong>McManus J.<\/strong> (2022) False-positive IRESes from Hoxa9 and other genes resulting from errors in mammalian 5\u2032 UTR annotations. <em><a href=\"https:\/\/www.pnas.org\/doi\/abs\/10.1073\/pnas.2122170119?url_ver=Z39.88-2003&amp;rfr_id=ori:rid:crossref.org&amp;rfr_dat=cr_pub%20%200pubmed\">PNAS<\/a>. <\/em>\r\n\r\nMay G, <strong>McManus J. <\/strong>(2022) Multiplexed Analysis of Human uORF Regulatory Functions During the ISR Using PoLib-Seq. <a href=\"https:\/\/link.springer.com\/protocol\/10.1007\/978-1-0716-1975-9_3\"><em>Methods Mol Biol.<\/em><\/a>\r\n\r\nMay G, <strong>McManus J<\/strong>. (2022)\u00a0 High-Throughput Quantitation of Yeast uORF Regulatory Impacts Using FACS-uORF. <a href=\"https:\/\/link.springer.com\/protocol\/10.1007\/978-1-0716-1851-6_18\"><em>Methods Mol Biol.<\/em><\/a>\r\n\r\nSpealman P, Naik A, <strong>McManus J.<\/strong> (2021) uORF-seqr: A Machine Learning-Based Approach to the Identification of Upstream Open Reading Frames in Yeast. <em><a href=\"https:\/\/link.springer.com\/protocol\/10.1007%2F978-1-0716-1150-0_15\">Methods in Mol. Biol<\/a><\/em>\r\n\r\nLin Y, May G, Kready H, Nazzaro L, Mao M, Spealman P, Creeger Y, <strong>McManus CJ<\/strong>. (2019) Impacts of uORF codon identity and position on translation regulation. <em><a href=\"https:\/\/academic.oup.com\/nar\/advance-article\/doi\/10.1093\/nar\/gkz681\/5545010\">Nucleic Acids Research<\/a><\/em>\r\n\r\nSpealman P, Naik A, May G, Kuersten S, Freebert L, Murphy R, <strong>McManus J.<\/strong> (2017) Conserved non-AUG uORFs revealed by a novel regression analysis of ribosome profiling data. <a href=\"http:\/\/genome.cshlp.org\/content\/early\/2018\/01\/12\/gr.221507.117.full.pdf+html?sid=31b4bf76-19da-4c81-8fc3-f5fda20c7e5c\"><em>Genome Research<\/em><\/a>.\r\n\r\n<strong>McManus CJ<\/strong>, May G, Spealman P, Shteyman A. (2013) Ribosome profiling reveals post-transcriptional buffering of divergent gene expression in yeast.<em>\u00a0<a href=\"http:\/\/genome.cshlp.org\/cgi\/pmidlookup?view=long&amp;pmid=24318730\">Genome Research<\/a><\/em>\r\n\r\n&nbsp;\r\n\r\n&nbsp;\r\n\r\n&nbsp;\r\n\r\n&nbsp;","_et_gb_content_width":"","footnotes":""},"class_list":["post-327","page","type-page","status-publish","hentry"],"jetpack_sharing_enabled":true,"jetpack_shortlink":"https:\/\/wp.me\/ParKnc-5h","_links":{"self":[{"href":"https:\/\/labs.bio.cmu.edu\/mcmanus\/wp-json\/wp\/v2\/pages\/327","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/labs.bio.cmu.edu\/mcmanus\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/labs.bio.cmu.edu\/mcmanus\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/labs.bio.cmu.edu\/mcmanus\/wp-json\/wp\/v2\/users\/21"}],"replies":[{"embeddable":true,"href":"https:\/\/labs.bio.cmu.edu\/mcmanus\/wp-json\/wp\/v2\/comments?post=327"}],"version-history":[{"count":19,"href":"https:\/\/labs.bio.cmu.edu\/mcmanus\/wp-json\/wp\/v2\/pages\/327\/revisions"}],"predecessor-version":[{"id":710,"href":"https:\/\/labs.bio.cmu.edu\/mcmanus\/wp-json\/wp\/v2\/pages\/327\/revisions\/710"}],"up":[{"embeddable":true,"href":"https:\/\/labs.bio.cmu.edu\/mcmanus\/wp-json\/wp\/v2\/pages\/8"}],"wp:attachment":[{"href":"https:\/\/labs.bio.cmu.edu\/mcmanus\/wp-json\/wp\/v2\/media?parent=327"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}