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10<h1 class="title site-page-heading">Publications</h1><div class="main publications-page"><section class=pubs-toolbar role=region aria-label="Filter publications"><div class="pubs-toolbar-row pubs-toolbar-row--tabs"><nav class=pubs-tabs role=tablist aria-label="Filter by type"><button type=button class="pubs-tab is-active" data-pub-filter=type data-value role=tab aria-selected=true>All</button><button type=button class=pubs-tab data-pub-filter=type data-value=preprint data-type=preprint role=tab aria-selected=false>Preprints</button><button type=button class=pubs-tab data-pub-filter=type data-value=journal data-type=journal role=tab aria-selected=false>Research articles</button><button type=button class=pubs-tab data-pub-filter=type data-value=review data-type=review role=tab aria-selected=false>Reviews</button><button type=button class=pubs-tab data-pub-filter=type data-value=editorial data-type=editorial role=tab aria-selected=false>Editorials</button><button type=button class=pubs-tab data-pub-filter=type data-value=chapter data-type=chapter role=tab aria-selected=false>Book chapters</button></nav></div><div class="pubs-toolbar-row pubs-toolbar-row--selects"><div class=pubs-yearrange data-pub-yearrange data-min=2016 data-max=2026><span class=pubs-select-label>Year</span>
11<span class=pubs-yearrange-display aria-live=polite><span class=pubs-yearrange-display-min data-pub-yearrange-display=min>2016</span>
12<span class=pubs-yearrange-display-dash aria-hidden=true>–</span>
13<span class=pubs-yearrange-display-max data-pub-yearrange-display=max>2026</span></span><div class=pubs-yearrange-track aria-hidden=true><div class=pubs-yearrange-rail></div><div class=pubs-yearrange-fill data-pub-yearrange-fill></div><input type=range class="pubs-yearrange-input pubs-yearrange-input--min" min=2016 max=2026 step=1 value=2016 aria-label="Start year" data-pub-yearrange-input=min>
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14<label class=pubs-select><span class=pubs-select-label>Journal</span>
15<select data-pub-filter=journal><option value>All</option><option value=Aging>Aging</option><option value="Aging Cell">Aging Cell</option><option value=bioRxiv>bioRxiv</option><option value="Current Biology">Current Biology</option><option value=eLife>eLife</option><option value="Encyclopedia of Bioinformatics and Computational Biology">Encyclopedia of Bioinformatics and Computational Biology</option><option value=F1000Research>F1000Research</option><option value="FEBS Letters">FEBS Letters</option><option value="Genome Biology and Evolution">Genome Biology and Evolution</option><option value="Immunity & Ageing">Immunity & Ageing</option><option value="Mechanisms of Ageing and Development">Mechanisms of Ageing and Development</option><option value="Microbial Ecology">Microbial Ecology</option><option value="Nature Aging">Nature Aging</option><option value="Nature Communications">Nature Communications</option><option value="Nature Reviews Genetics">Nature Reviews Genetics</option><option value="PLOS Computational Biology">PLOS Computational Biology</option><option value="PLOS ONE">PLOS ONE</option><option value="Proceedings of the Royal Society B: Biological Sciences">Proceedings of the Royal Society B: Biological Sciences</option><option value="Science Advances">Science Advances</option><option value="Scientific Reports">Scientific Reports</option>
15<option value="The EMBO Journal">The EMBO Journal</option><option value="Trends in Endocrinology & Metabolism">Trends in Endocrinology & Metabolism</option></select>
16</label><label class=pubs-select><span class=pubs-select-label>Author</span>
17<select data-pub-filter=author><option value>All</option><option value="Aishwarya Alex">Aishwarya Alex</option><option value="Alejandro Hita">Alejandro Hita</option><option value="Aleksandr Stepanov">Aleksandr Stepanov</option><option value="Alex Zhavoronkov">Alex Zhavoronkov</option><option value="Alexander Miguel Monzon">Alexander Miguel Monzon</option><option value="Alexey Tishkin">Alexey Tishkin</option><option value="Ali Metin Büyükkarakaya">Ali Metin Büyükkarakaya</option><option value="Altuğ Kamacıoğlu">Altuğ Kamacıoğlu</option><option value="Ambuj Srivastava">Ambuj Srivastava</option><option value="Amel Bekkar">Amel Bekkar</option><option value="Amparo Latorre">Amparo Latorre</option><option value="Anders Götherström">Anders Götherström</option><option value="Andreas Koeberle">Andreas Koeberle</option><option value="Andrew Fairbairn">Andrew Fairbairn</option><option value="Andrew J. Scott">Andrew J. Scott</option><option value="Anna Kriebs">Anna Kriebs</option><option value="Anne Brunet">Anne Brunet</option><option value="Arev Pelin Sümer">Arev Pelin Sümer</option><option value="Artur Kharinskii">Artur Kharinskii</option><option value="Arvind Singh Mer">Arvind Singh Mer</option><option value="Ashley E. Webb">Ashley E. Webb</option><option value="Ayça Omrak">Ayça Omrak</option><option value="Aysan Poursadegh Zonouzi">Aysan Poursadegh Zonouzi</option><option value="Ayshin Ghalichi">Ayshin Ghalichi</option><option value="Babür Erdem">Babür Erdem</option><option value="Bart Cuypers">Bart Cuypers</option><option value="Becca R. Levy">Becca R. Levy</option><option value="Bérénice A. Benayoun">Bérénice A. Benayoun</option><option value="Berfin Dag">Berfin Dag</option><option value="Bruno Vellas">Bruno Vellas</option><option value="Burak Kizil">Burak Kizil</option><option value="Candice Rafael">Candice Rafael</option><option value="Carla Giner-Delgado">Carla Giner-Delgado</option><option value="Catalina Valdivia">Catalina Valdivia</option><option value="Cemal Can Bilgin">Cemal Can Bilgin</option><option value="Cemil Can Saylan">Cemil Can Saylan</option><option value="Charlotte E. Teunissen">Charlotte E. Teunissen</option><option value="Chase Donnelly">Chase Donnelly</option><option value="Christina Janster">Christina Janster</option><option value="Çiğdem Atakuman">Çiğdem Atakuman</option><option value="Dan DeBlasio">Dan DeBlasio</option><option value="Daniel K Fabian">Daniel K Fabian</option><option value="Daniel K. Fabian">Daniel K. Fabian</option><option value="Daniel W. Belsky">Daniel W. Belsky</option><option value="Daniele Parisi">Daniele Parisi</option><option value="Dario Riccardo Valenzano">Dario Riccardo Valenzano</option><option value="David A. Sinclair">David A. Sinclair</option><option value="David Furman">David Furman</option><option value="Delf-Magnus Kummerfeld">Delf-Magnus Kummerfeld</option><option value="Dena B. Dubal">Dena B. Dubal</option><option value="Denis Volkov">Denis Volkov</option><option value="Dilek Koptekin">Dilek Koptekin</option><option value="Dingding Han">Dingding Han</option><option value="Dmitrii Kichigin">Dmitrii Kichigin</option><option value="Dmitrij Shergin">Dmitrij Shergin</option><option value="Douglas Baird">Douglas Baird</option><option value="Dudley W. Lamming">Dudley W. Lamming</option><option value="E. Ravza Gür">E. Ravza Gür</option><option value="Ece Kocabiyik">Ece Kocabiyik</option><option value="Efejiro Ashano">Efejiro Ashano</option><option value="Eiji Hara">Eiji Hara</option><option value="Ekin Saglican">Ekin Saglican</option><option value="Emilio Cirri">Emilio Cirri</option><option value="Emrah Kırdök">Emrah Kırdök</option><option value="Engin Tatlıdil">Engin Tatlıdil</option><option value="Eren Yüncü">Eren Yüncü</option><option value="Erhan Bıçakçı">Erhan Bıçakçı</option><option value="Eric Verdin">Eric Verdin</option><option value="Etka Yapar">Etka Yapar</option><option value="Eugen Bauer">Eugen Bauer</option><option value="Evandro F. Fang">Evandro F. Fang</option><option value="Evangelia A. Daskalaki">Evangelia A. Daskalaki</option><option value="Evgenij Ineshin">Evgenij Ineshin</option><option value="Evgeniy Kovychev">Evgeniy Kovychev</option><option value="Evrim Fer">Evrim Fer</option><option value="Ezgi Altınışık">Ezgi Altınışık</option><option value="Ezgi Özkurt">Ezgi Özkurt</option><option value="Fabrisia Ambrosio">Fabrisia Ambrosio</option><option value="Farzana Rahman">Farzana Rahman</option><option value="Fatma Betül Dinçaslan">Fatma Betül Dinçaslan</option><option value="Felipe Sierra">Felipe Sierra</option>
17<option value="Fengting Su">Fengting Su</option><option value="Füsun Özer">Füsun Özer</option><option value="Gabriel J. Olguin-Orellana">Gabriel J. Olguin-Orellana</option><option value="Gabriele Morabito">Gabriele Morabito</option><option value="Gaia Zaffaroni">Gaia Zaffaroni</option><option value="Garima Kalakoti">Garima Kalakoti</option><option value="Geetha Saarunya">Geetha Saarunya</option><option value="George A. Kuchel">George A. Kuchel</option><option value="Gökhan Mustafaoğlu">Gökhan Mustafaoğlu</option><option value="Grigorij Ivanov">Grigorij Ivanov</option><option value="Guang-Hui Liu">Guang-Hui Liu</option><option value="Gülşah Merve Kılınç">Gülşah Merve Kılınç</option><option value="Haiyang Hu">Haiyang Hu</option><option value="Hamit Izgi">Hamit Izgi</option><option value="Hamit İzgi">Hamit İzgi</option><option value="Handan Melike Dönertaş">Handan Melike Dönertaş</option><option value="Hannah Walters">Hannah Walters</option><option value="Imane Allali">Imane Allali</option><option value="İnci Togan">İnci Togan</option><option value="Isabela Santos Valentim">Isabela Santos Valentim</option><option value="İsmail Güderer">İsmail Güderer</option><option value="Jan Storå">Jan Storå</option><option value="Janet M Thornton">Janet M Thornton</option><option value="Janet M. Thornton">Janet M. Thornton</option><option value="Jasleen K. Grewal">Jasleen K. Grewal</option><option value="Jenny Tung">Jenny Tung</option><option value="Jens Seidel">Jens Seidel</option><option value="Jerome N. Feige">Jerome N. Feige</option><option value="Jessica Pearson">Jessica Pearson</option><option value="Jin-Tai Yu">Jin-Tai Yu</option><option value="Jing Lu">Jing Lu</option><option value="Jing-Dong J. Han">Jing-Dong J. Han</option><option value="Jinkook Lee">Jinkook Lee</option><option value="Johannes Zimmermann">Johannes Zimmermann</option><option value="John W. Rowe">John W. Rowe</option><option value="Johnathan Labbadia">Johnathan Labbadia</option><option value="Jordan Villalobos-Solís">Jordan Villalobos-Solís</option><option value="José Ignacio Lucas-Lledo">José Ignacio Lucas-Lledo</option><option value="Juan Carlos Izpisua Belmonte">Juan Carlos Izpisua Belmonte</option><option value="Juliana Assis">Juliana Assis</option><option value="Junjie Guan">Junjie Guan</option>
17<option value="Katarzyna Winek">Katarzyna Winek</option><option value="Keenan A. Walker">Keenan A. Walker</option><option value="Kjunnej Pestereva">Kjunnej Pestereva</option><option value="Leonor Rib">Leonor Rib</option><option value="Li Fu">Li Fu</option><option value="Lilia Espada">Lilia Espada</option><option value="Linda P. Fried">Linda P. Fried</option><option value="Linda Partridge">Linda Partridge</option><option value="Lisanna Paladin">Lisanna Paladin</option><option value="Love Dalén">Love Dalén</option><option value="Luca Sperti">Luca Sperti</option><option value="Maja Krzewińska">Maja Krzewińska</option><option value="Manisha Goyal">Manisha Goyal</option><option value="Manisha Kalsan">Manisha Kalsan</option><option value="Marco Necci">Marco Necci</option><option value="Maria A. Ermolaeva">Maria A. Ermolaeva</option><option value="Mark Olenik">Mark Olenik</option><option value="Marouen Ben Guebila">Marouen Ben Guebila</option><option value="Martin Rydén">Martin Rydén</option><option value="Matias Fuentealba">Matias Fuentealba</option><option value="Matías Fuentealba">Matías Fuentealba</option><option value="Matías Fuentealba Valenzuela">Matías Fuentealba Valenzuela</option><option value="Matt Kaeberlein">Matt Kaeberlein</option><option value="Mattias Jakobsson">Mattias Jakobsson</option><option value="Maxim N. Artyomov">Maxim N. Artyomov</option><option value="Mehmet Somel">Mehmet Somel</option><option value="Melike Bayar">Melike Bayar</option><option value="Michael Poeschla">Michael Poeschla</option><option value="Michal Schwartz">Michal Schwartz</option><option value="Ming Xu">Ming Xu</option><option value="Myriam Gorospe">Myriam Gorospe</option><option value="Nadine Chiara Frigger">Nadine Chiara Frigger</option><option value="Nadine Pömpner">Nadine Pömpner</option><option value="Nancy Y. Ip">Nancy Y. Ip</option><option value="Natalija Kashuba">Natalija Kashuba</option><option value="Nazeefa Fatima">Nazeefa Fatima</option><option value="Nicolás N. Moreyra">Nicolás N. Moreyra</option><option value="Nihan Dilşad Dağtaş">Nihan Dilşad Dağtaş</option><option value="Nikolaj Pagh Kristensen">Nikolaj Pagh Kristensen</option><option value="Nikolaos Papadopoulos">Nikolaos Papadopoulos</option><option value="Nikolina Šoštarić">Nikolina Šoštarić</option><option value="Nilson Da Rocha Coimbra">Nilson Da Rocha Coimbra</option><option value="Nir Barzilai">Nir Barzilai</option><option value="Nora Bergfeldt">Nora Bergfeldt</option><option value="Nursen Duha Alioglu">Nursen Duha Alioglu</option><option value="Oleksii Doroshenko">Oleksii Doroshenko</option><option value="Omer Gokcumen">Omer Gokcumen</option><option value="Oskar Hansson">Oskar Hansson</option><option value="Parminder Raina">Parminder Raina</option><option value="Pau Carazo">Pau Carazo</option><option value="Pavel Flegontov">Pavel Flegontov</option><option value="Pavel Mandryka">Pavel Mandryka</option><option value="Philipp Khaitovich">Philipp Khaitovich</option><option value="Pol Alonso-Pernas">Pol Alonso-Pernas</option><option value="Poorya Parvizi">Poorya Parvizi</option><option value="Prasoon Pandey">Prasoon Pandey</option><option value="Prem Aguilar">Prem Aguilar</option><option value="Prerana Shrikant Chaudhari">Prerana Shrikant Chaudhari</option><option value="Qingzhong Ren">Qingzhong Ren</option><option value="R. Gonzalo Parra">R. Gonzalo Parra</option><option value="Recep Ozgur Taskent">Recep Ozgur Taskent</option><option value="Reyhan Yaka">Reyhan Yaka</option><option value="Rhianna Williams">Rhianna Williams</option><option value="Ricardo Rodríguez-Varela">Ricardo Rodríguez-Varela</option><option value="Rori V. Rohlfs">Rori V. Rohlfs</option><option value="Sandeep Kumar Dhanda">Sandeep Kumar Dhanda</option><option value="Sayane Shome">Sayane Shome</option><option value="Sebastien Thuault">Sebastien Thuault</option><option value="Sergio Martínez Cuesta">Sergio Martínez Cuesta</option><option value="Sevilay Güleşen">Sevilay Güleşen</option><option value="Shruti Gupta">Shruti Gupta</option><option value="Shuyun Huang">Shuyun Huang</option><option value="Sinan Can Açan">Sinan Can Açan</option><option value="Sofia Papadimitriou">Sofia Papadimitriou</option><option value="Song Guo">Song Guo</option><option value="Spencer Krieger">
17Spencer Krieger</option><option value="Steve Horvath">Steve Horvath</option><option value="Steven N. Austad">Steven N. Austad</option><option value="Syed Asad Rahman">Syed Asad Rahman</option><option value="Tayyaba Alvi">Tayyaba Alvi</option><option value="Terrie E. Moffitt">Terrie E. Moffitt</option><option value="Tetiana Poliezhaieva">Tetiana Poliezhaieva</option><option value="Thomas A. Rando">Thomas A. Rando</option><option value="Tohru Minamino">Tohru Minamino</option><option value="Tony Wyss-Coray">Tony Wyss-Coray</option><option value="Torsten Günther">Torsten Günther</option><option value="Tülay Karakulak">Tülay Karakulak</option><option value="Ulaş Işıldak">Ulaş Işıldak</option><option value="Vadim N. Gladyshev">Vadim N. Gladyshev</option><option value="Vanesa Pelcastre-Neri">Vanesa Pelcastre-Neri</option><option value="Vera Gorbunova">Vera Gorbunova</option><option value="Veronika R. Kedlian">Veronika R. Kedlian</option><option value="Wim L. Cuypers">Wim L. Cuypers</option><option value="Xu Gao">Xu Gao</option><option value="Yahyah Aman">Yahyah Aman</option><option value="Yasin Gökhan Çakan">Yasin Gökhan Çakan</option><option value="Yasin Kaya">Yasin Kaya</option><option value="Yesid Cuesta-Astroz">Yesid Cuesta-Astroz</option><option value="Yi Wang">Yi Wang</option><option value="Yılmaz Selim Erdal">Yılmaz Selim Erdal</option><option value="Yumna Moosa">Yumna Moosa</option><option value="Yuting Li">Yuting Li</option><option value="Yvonne Gladbach">Yvonne Gladbach</option><option value="Zahida Sultanova">Zahida Sultanova</option><option value="Zeliha Gözde Turan">Zeliha Gözde Turan</option><option value="Zheng Yan">Zheng Yan</option><option value="Zhisong He">Zhisong He</option></select>
18</label><button type=button class=pubs-reset data-pub-filter-reset>Reset</button></div><div class="pubs-toolbar-row pubs-toolbar-row--grouplead">
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20<span class=pubs-checkfilter-label>Group-led only</span></label></div><p class=pubs-legend><strong class=publication-author-lab>Bold</strong>&nbsp;= lab member,
21<sup>*</sup>&nbsp;Equal contribution,
22<sup>#</sup>&nbsp;Corresponding author</p></section><ol class=pubs-list role=feed aria-label=Publications><li class=pubs-card data-pub-type=preprint data-pub-year=2026 data-pub-journal=bioRxiv data-pub-authors="Junjie Guan|Burak Kizil|Garima Kalakoti|Delf-Magnus Kummerfeld|Oleksii Doroshenko|Vanesa Pelcastre-Neri|Nadine Chiara Frigger|Emilio Cirri|Nadine Pömpner|Manisha Goyal|Christina Janster|Johannes Zimmermann|Handan Melike Dönertaş|Katarzyna Winek" data-pub-grouplead=0 style=--pub-stagger:0.00s><article class=pubs-card-inner aria-labelledby=publication-card-title-0><span class=pubs-card-rule aria-hidden=true></span><div class=pubs-card-rail><span class=pubs-card-year>2026</span><span class="pubs-card-type pubs-card-type--preprint">Preprint</span><span class=pubs-card-venue>bioRxiv</span></div><div class=pubs-card-body><h2 class=pubs-card-title id=publication-card-title-0><button type=button class=pubs-card-toggle aria-expanded=false aria-controls=publication-abstract-0>
23<span class=pubs-card-toggle-text>Temporal multi-omic profiling of immune, gut, and microbiome responses to ischemic stroke reveals convergence of host and microbial perturbations one week after brain injury</span>
24<span class=pubs-card-toggle-icon aria-hidden=true><svg width="14" height="14" viewBox="0 0 14 14" fill="none" stroke="currentColor" stroke-width="1.5" stroke-linecap="round"><path d="M3 7h8M7 3v8" class="pubs-toggle-cross"/></svg></span></button></h2><p class=publication-authors-line>Junjie Guan, Burak Kizil, Garima Kalakoti, Delf-Magnus Kummerfeld, Oleksii Doroshenko, Vanesa Pelcastre-Neri, Nadine Chiara Frigger, Emilio Cirri, Nadine Pömpner, Manisha Goyal, Christina Janster, Johannes Zimmermann, <strong class=publication-author-lab>Handan Melike Dönertaş</strong>
24, Katarzyna Winek<sup class=publication-author-mark aria-label="corresponding author">#</sup></p><p class=pubs-card-abstract id=publication-abstract-0 hidden>Ischemic stroke poses a significant medical challenge with limited therapy options, therefore detailed understanding of stroke-associated pathophysiological processes across systems and organs is crucial for further research and identification of novel therapeutic targets. In this manuscript, we provide parallel multi-omic host and gut microbiome characterization on several timepoints (day 1, 7 and 14) in the mouse experimental stroke model (middle cerebral artery occlusion, MCAo). Expanding existing host-derived datasets, we profiled transcriptomes from microglia, brain-infiltrating leukocytes and peripheral leukocytes using single cell RNA sequencing. Our data deliver time-resolved characterization of microglial subtypes and highlight heterogeneous dendritic cell populations as main interaction partners of microglia on all timepoints. In peripheral blood, we did not observe large transcriptomic differences when comparing the immune subsets from MCAo and sham-operated control animals. Here, the neutrophils exhibited most transcriptomic changes on day 1 among all blood leukocytes. Parallel proteomic analysis of 5 intestinal segments (duodenum, jejunum, ileum, caecum and colon) and mesenteric lymph nodes highlighted day 7 as the most important timepoint for changes in the gut-related metabolic pathways especially in the jejunum and colon. Specific hypothesis testing revealed compartmentalized regulation of gut-related immune pathways and proteins related to gut permeability. Finally, gut microbiome analyses (longitudinal metatranscriptomics including day -1, 3, 7, 14, and metagenomics from day 14) highlighted temporally matched changes in microbial gene expression (with day 7 emerging again as the most relevant timepoint), larger overall community perturbations when compared to baseline from day -1 in stroke animals and expansion of facultative anaerobes on day 7.</p><ul class=pubs-card-chips role=list><li class=pubs-card-chip-item><a class="pubs-card-chip pubs-card-chip--doi" href=https://doi.org/10.64898/2026.05.25.727504 rel="noopener noreferrer" target=_blank><span class=pubs-card-chip-label>bioRxiv</span></a></li></ul></div></article></li><li class="pubs-card is-group-led" data-pub-type=review data-pub-year=2026 data-pub-journal="FEBS Letters" data-pub-authors="Mark Olenik|Yi Wang|İsmail Güderer|Tayyaba Alvi|Prasoon Pandey|Handan Melike Dönertaş" data-pub-grouplead=1 style=--pub-stagger:0.05s><article class=pubs-card-inner aria-labelledby=publication-card-title-1><span class=pubs-card-rule aria-hidden=true></span><div class=pubs-card-rail><span class=pubs-card-year>2026</span><span class="pubs-card-type pubs-card-type--review">Review</span><span class=pubs-card-venue>FEBS Letters</span></div><div class=pubs-card-body><h2 class=pubs-card-title id=publication-card-title-1><button type=button class=pubs-card-toggle aria-expanded=false aria-controls=publication-abstract-1>
25<span class=pubs-card-toggle-text>Design and analysis strategies for robust microbiome ageing research</span>
26<span class=pubs-card-toggle-icon aria-hidden=true><svg width="14" height="14" viewBox="0 0 14 14" fill="none" stroke="currentColor" stroke-width="1.5" stroke-linecap="round"><path d="M3 7h8M7 3v8" class="pubs-toggle-cross"/></svg></span></button></h2><p class=publication-authors-line><strong class=publication-author-lab>Mark Olenik</strong>, <strong class=publication-author-lab>Yi Wang</strong>, <strong class=publication-author-lab>İsmail Güderer</strong>, <strong class=publication-author-lab>Tayyaba Alvi</strong>, <strong class=publication-author-lab>Prasoon Pandey</strong>, <strong class=publication-author-lab>Handan Melike Dönertaş</strong><sup class=publication-author-mark aria-label="corresponding author">#</sup></p><p class=pubs-card-abstract id=publication-abstract-1 hidden>The gut microbiome changes systematically with age and associates with age-related morbidity and mortality, establishing it as a candidate biomarker and intervention target for ageing. Realising this potential requires methodological rigour, as distinguishing genuine biological signals from methodological artefacts remains challenging given variable findings across cohorts. This review provides an integrated framework for human microbiome-ageing research, organised around five methodological challenges that will collectively strengthen causal inference. We examine how age-associated factors can correlate with chronological age and may confound the microbiome-age associations, while selection biases shape old-age cohorts towards healthier profiles. We address within-host temporal dynamics and between-individual heterogeneity that require appropriate sampling to distinguish age-related signatures from transient states, and validation strategies that separate ageing from batch effects in predictive models. Mendelian randomisation provides causal leverage when triangulated with longitudinal and interventional evidence. Throughout, we examine how design choices determine the limits of analytical inference. The review concludes with a practical checklist, equipping researchers to strengthen reproducibility, improve generalisability and advance microbiome-based metrics towards validated indicators of biological ageing.</p>
26<ul class=pubs-card-chips role=list><li class=pubs-card-chip-item><a class="pubs-card-chip pubs-card-chip--doi" href=https://doi.org/10.1002/1873-3468.70397 rel="noopener noreferrer" target=_blank><span class=pubs-card-chip-label>Journal</span></a></li></ul></div></article></li><li class="pubs-card is-group-led" data-pub-type=review data-pub-year=2026 data-pub-journal="Nature Reviews Genetics" data-pub-authors="Handan Melike Dönertaş|Linda Partridge" data-pub-grouplead=1 style=--pub-stagger:0.10s><article class=pubs-card-inner aria-labelledby=publication-card-title-2><span class=pubs-card-rule aria-hidden=true></span><div class=pubs-card-rail><span class=pubs-card-year>2026</span><span class="pubs-card-type pubs-card-type--review">Review</span><span class=pubs-card-venue>Nature Reviews Genetics</span></div><div class=pubs-card-body><h2 class=pubs-card-title id=publication-card-title-2><button type=button class=pubs-card-toggle aria-expanded=false aria-controls=publication-abstract-2>
27<span class=pubs-card-toggle-text>Evolutionary genetics of ageing</span>
28<span class=pubs-card-toggle-icon aria-hidden=true><svg width="14" height="14" viewBox="0 0 14 14" fill="none" stroke="currentColor" stroke-width="1.5" stroke-linecap="round"><path d="M3 7h8M7 3v8" class="pubs-toggle-cross"/></svg></span></button></h2><p class=publication-authors-line><strong class=publication-author-lab>Handan Melike Dönertaş</strong><sup class=publication-author-mark aria-label="corresponding author">#</sup>, Linda Partridge<sup class=publication-author-mark aria-label="corresponding author">#</sup></p><p class=pubs-card-abstract id=publication-abstract-2 hidden>Modern humans now routinely survive to advanced ages, in far greater proportions than ancestral populations, and thus experience the consequences of molecular pathways optimized for youth yet still active in old age. Natural selection weakens over the course of adulthood, creating a selection ‘shadow’ in which deleterious late-acting mutations accumulate and alleles with early-life benefits persist despite late-life costs. An evolutionary lens helps us to understand puzzling patterns — from conserved longevity pathways spanning the tree of life to a 100-fold variation in maximum lifespan across vertebrates — and explains why age-related diseases share genetic architectures. Advances in comparative genomics, large-scale human genetic studies and multi-omics ageing biomarkers now enable rigorous testing of evolutionary predictions. This Review integrates evolutionary genetics with molecular mechanisms to clarify why ageing evolves, how it varies across species and individuals, and how these insights can guide healthspan extension.</p><ul class=pubs-card-chips role=list><li class=pubs-card-chip-item><a class="pubs-card-chip pubs-card-chip--doi" href=https://doi.org/10.1038/s41576-026-00959-x rel="noopener noreferrer" target=_blank><span class=pubs-card-chip-label>Journal</span></a></li><li class=pubs-card-chip-item><a class="pubs-card-chip pubs-card-chip--link" href=https://rdcu.be/fh3VU rel="noopener noreferrer" target=_blank><span class=pubs-card-chip-label>Readcube</span></a></li></ul></div></article></li><li class=pubs-card data-pub-type=journal data-pub-year=2026 data-pub-journal="Nature Communications" data-pub-authors="Tetiana Poliezhaieva|Yuting Li|Prerana Shrikant Chaudhari|Ulaş Işıldak|Pol Alonso-Pernas|Isabela Santos Valentim|Fengting Su|Lilia Espada|Melike Bayar|Li Fu|Andreas Koeberle|Handan Melike Dönertaş|Maria A. Ermolaeva" data-pub-grouplead=0 style=--pub-stagger:0.15s><article class=pubs-card-inner aria-labelledby=publication-card-title-3><span class=pubs-card-rule aria-hidden=true></span><div class=pubs-card-rail><span class=pubs-card-year>2026</span><span class="pubs-card-type pubs-card-type--journal">Research Article</span><span class=pubs-card-venue>Nature Communications</span></div><div class=pubs-card-body><h2 class=pubs-card-title id=publication-card-title-3><button type=button class=pubs-card-toggle aria-expanded=false aria-controls=publication-abstract-3>
29<span class=pubs-card-toggle-text>Aging-associated decline of phosphatidylcholine synthesis is a malleable trigger of natural mitochondrial aging</span>
30<span class=pubs-card-toggle-icon aria-hidden=true><svg width="14" height="14" viewBox="0 0 14 14" fill="none" stroke="currentColor" stroke-width="1.5" stroke-linecap="round"><path d="M3 7h8M7 3v8" class="pubs-toggle-cross"/></svg></span></button></h2><p class=publication-authors-line>Tetiana Poliezhaieva, Yuting Li, Prerana Shrikant Chaudhari, <strong class=
30publication-author-lab>Ulaş Işıldak</strong>, Pol Alonso-Pernas, Isabela Santos Valentim, Fengting Su, Lilia Espada, Melike Bayar, Li Fu, Andreas Koeberle, <strong class=publication-author-lab>Handan Melike Dönertaş</strong>, Maria A. Ermolaeva<sup class=publication-author-mark aria-label="corresponding author">#</sup></p><p class=pubs-card-abstract id=publication-abstract-3 hidden>Mitochondrial dysfunction is a prominent hallmark of aging contributing to the decline of metabolic plasticity in late life. While genetic distortions of mitochondrial integrity elicit premature aging, the mechanisms leading to "natural" aging of mitochondria are less clear. Here we use proteomics, lipidomics, genetics and functional tests in wild type Caenorhabditis elegans and long-lived clk-1(qm30) and isp-1(qm150) mitochondrial mutants to identify molecular pathways that support longevity amid persistent mitochondrial inefficiency. These tests and subsequent transcriptomics and metabolomics analyses in humans reveal aging-associated decline of phosphatidylcholine synthesis as a trigger of mitochondrial network disruption, which contributes to mitochondrial dysfunction during normal aging. Moreover, ectopic boosting of phosphatidylcholine levels via diet restores late life mitochondrial integrity in vivo in nematodes and reinstates metabolic resilience in human cell culture tests. We thus describe a previously unrecognized natural driver of mitochondrial decline in aging that is malleable by dietary interventions.</p><ul class=pubs-card-chips role=list><li class=pubs-card-chip-item><a class="pubs-card-chip pubs-card-chip--doi" href=https://doi.org/10.1038/s41467-026-71508-7 rel="noopener noreferrer" target=_blank><span class=pubs-card-chip-label>Journal</span></a></li><li class=pubs-card-chip-item><a class="pubs-card-chip pubs-card-chip--code" href=https://github.com/donertas-group/mt-aging rel="noopener noreferrer" target=_blank><span class=pubs-card-chip-label>Code</span></a></li></ul></div></article></li><li class=pubs-card data-pub-type=journal data-pub-year=2026 data-pub-journal="Nature Aging" data-pub-authors="Gabriele Morabito|Handan Melike Dönertaş|Luca Sperti|Jens Seidel|Aysan Poursadegh Zonouzi|Michael Poeschla|Dario Riccardo Valenzano" data-pub-grouplead=0 style=--pub-stagger:0.20s><article class=pubs-card-inner aria-labelledby=publication-card-title-4><span class=pubs-card-rule aria-hidden=true></span><div class=pubs-card-rail><span class=pubs-card-year>2026</span><span class="pubs-card-type pubs-card-type--journal">Research Article</span><span class=pubs-card-venue>Nature Aging</span></div><div class=pubs-card-body><h2 class=pubs-card-title id=publication-card-title-4><button type=button class=pubs-card-toggle aria-expanded=false aria-controls=publication-abstract-4>
31<span class=pubs-card-toggle-text>Spontaneous aging-associated inflammation and genome instability in the immune system of turquoise killifish</span>
32<span class=pubs-card-toggle-icon aria-hidden=true><svg width="14" height="14" viewBox="0 0 14 14" fill="none" stroke="currentColor" stroke-width="1.5" stroke-linecap="round"><path d="M3 7h8M7 3v8" class="pubs-toggle-cross"/></svg></span></button></h2><p class=publication-authors-line>Gabriele Morabito, <strong class=publication-author-lab>Handan Melike Dönertaş</strong>, Luca Sperti, Jens Seidel, Aysan Poursadegh Zonouzi, Michael Poeschla, Dario Riccardo Valenzano<sup class=publication-author-mark aria-label="corresponding author">#</sup></p><p class=pubs-card-abstract id=publication-abstract-4 hidden>Turquoise killifish (Nothobranchius furzeri) are naturally short-lived vertebrates that recapitulate key aspects of human aging. However, the molecular and cellular causes of systemic aging in killifish are poorly understood. Here we ask whether killifish undergo age-dependent changes in the main hematopoietic organ (kidney marrow), which may contribute to systemic aging. To characterize immune aging in killifish, we used single-cell RNA sequencing, cytometry and functional in vitro assays on kidney marrow cells from young-adult and old killifish, together with proteomic profiling of both kidney marrow-derived cells and plasma. We show that old killifish display increased markers of inflammation;
32 while immune progenitor-like cell clusters from adult killifish display markers of active proliferation and replication-independent DNA repair, immune cell progenitors from old killifish display increased markers of DNA damage. Within less than 10 weeks, killifish exhibit age-related transformations within the immune system, underscoring the value of killifish for developing immune-system-targeted antiaging interventions.</p><ul class=pubs-card-chips role=list><li class=pubs-card-chip-item><a class="pubs-card-chip pubs-card-chip--doi" href=https://doi.org/10.1038/s43587-026-01086-2 rel="noopener noreferrer" target=_blank><span class=pubs-card-chip-label>Journal</span></a></li><li class=pubs-card-chip-item><a class="pubs-card-chip pubs-card-chip--code" href=https://github.com/mdonertas/TK_ImmuneAging_MultiOmics rel="noopener noreferrer" target=_blank><span class=pubs-card-chip-label>Code</span></a></li><li class=pubs-card-chip-item><a class="pubs-card-chip pubs-card-chip--data" href=https://www.ebi.ac.uk/biostudies/studies/S-BSST2265 rel="noopener noreferrer" target=_blank><span class=pubs-card-chip-label>Data</span></a></li><li class=pubs-card-chip-item><a class="pubs-card-chip pubs-card-chip--app" href=https://genome.leibniz-fli.de/shiny/kiamo/ rel="noopener noreferrer" target=_blank><span class=pubs-card-chip-label>App</span></a></li></ul></div></article></li><li class=pubs-card data-pub-type=editorial data-pub-year=2026 data-pub-journal="Nature Aging" data-pub-authors="Fabrisia Ambrosio|Maxim N. Artyomov|Steven N. Austad|Nir Barzilai|Juan Carlos Izpisua Belmonte|Daniel W. Belsky|Bérénice A. Benayoun|Anne Brunet|Handan Melike Dönertaş|Dena B. Dubal|Evandro F. Fang|Jerome N. Feige|Linda P. Fried|David Furman|Xu Gao|Vadim N. Gladyshev|Vera Gorbunova|Myriam Gorospe|Jing-Dong J. Han|Oskar Hansson|Eiji Hara|Steve Horvath|Nancy Y. Ip|George A. Kuchel|Matt Kaeberlein|Dudley W. Lamming|Becca R. Levy|Guang-Hui Liu|Jinkook Lee|Terrie E. Moffitt|Tohru Minamino|Linda Partridge|Parminder Raina|Thomas A. Rando|John W. Rowe|Michal Schwartz|Andrew J. Scott|Felipe Sierra|David A. Sinclair|Charlotte E. Teunissen|Bruno Vellas|Eric Verdin|Keenan A. Walker|Ashley E. Webb|Tony Wyss-Coray|Ming Xu|Jin-Tai Yu|Alex Zhavoronkov|Yahyah Aman|Anna Kriebs|Qingzhong Ren|Hannah Walters|Sebastien Thuault" data-pub-grouplead=0 style=--pub-stagger:0.25s><article class=pubs-card-inner aria-labelledby=publication-card-title-5><span class=pubs-card-rule aria-hidden=true></span><div class=pubs-card-rail><span class=pubs-card-year>2026</span><span class="pubs-card-type pubs-card-type--editorial">Editorial</span><span class=pubs-card-venue>Nature Aging</span></div><div class=pubs-card-body><h2 class=pubs-card-title id=publication-card-title-5><button type=button class=pubs-card-toggle aria-expanded=false aria-controls=publication-abstract-5>
33<span class=pubs-card-toggle-text>Past, present and future perspectives on the science of aging</span>
34<span class=pubs-card-toggle-icon aria-hidden=true><svg width="14" height="14" viewBox="0 0 14 14" fill="none" stroke="currentColor" stroke-width="1.5" stroke-linecap="round"><path d="M3 7h8M7 3v8" class="pubs-toggle-cross"/></svg></span></button></h2><p class=publication-authors-line>Fabrisia Ambrosio, Maxim N. Artyomov, Steven N. Austad, Nir Barzilai, Juan Carlos Izpisua Belmonte, Daniel W. Belsky, Bérénice A. Benayoun, Anne Brunet, <strong class=publication-author-lab>Handan Melike Dönertaş</strong>, Dena B. Dubal, Evandro F. Fang, Jerome N. Feige, Linda P. Fried, David Furman, Xu Gao, Vadim N. Gladyshev, Vera Gorbunova, Myriam Gorospe, Jing-Dong J. Han, Oskar Hansson, Eiji Hara, Steve Horvath, Nancy Y. Ip, George A. Kuchel, Matt Kaeberlein, Dudley W. Lamming, Becca R. Levy, Guang-Hui Liu, Jinkook Lee, Terrie E. Moffitt, Tohru Minamino, Linda Partridge, Parminder Raina, Thomas A. Rando, John W. Rowe, Michal Schwartz, Andrew J. Scott, Felipe Sierra, David A. Sinclair, Charlotte E. Teunissen, Bruno Vellas, Eric Verdin, Keenan A. Walker, Ashley E. Webb, Tony Wyss-Coray, Ming Xu, Jin-Tai Yu, Alex Zhavoronkov, Yahyah Aman, Anna Kriebs, Qingzhong Ren, Hannah Walters, Sebastien Thuault<sup class=publication-author-mark aria-label="corresponding author">#</sup></p><p class=pubs-card-abstract id=publication-abstract-5 hidden>As Nature Aging celebrates its fifth anniversary, the journal asks some of the researchers who contributed to the journal early on to reflect on the past and the future of aging and age-related disease research, the impact of the field on human health now and in the future, and what challenges need to be addressed to ensure sustained progress.</p>
34<ul class=pubs-card-chips role=list><li class=pubs-card-chip-item><a class="pubs-card-chip pubs-card-chip--doi" href=https://doi.org/10.1038/s43587-025-01046-2 rel="noopener noreferrer" target=_blank><span class=pubs-card-chip-label>Journal</span></a></li></ul></div></article></li><li class="pubs-card is-group-led" data-pub-type=preprint data-pub-year=2026 data-pub-journal=bioRxiv data-pub-authors="Jing Lu|İsmail Güderer|Tayyaba Alvi|Mark Olenik|Handan Melike Dönertaş" data-pub-grouplead=1 style=--pub-stagger:0.30s><article class=pubs-card-inner aria-labelledby=publication-card-title-6><span class=pubs-card-rule aria-hidden=true></span><div class=pubs-card-rail><span class=pubs-card-year>2026</span><span class="pubs-card-type pubs-card-type--preprint">Preprint</span><span class=pubs-card-venue>bioRxiv</span></div><div class=pubs-card-body><h2 class=pubs-card-title id=publication-card-title-6><button type=button class=pubs-card-toggle aria-expanded=false aria-controls=publication-abstract-6>
35<span class=pubs-card-toggle-text>Integrative transcriptomic identification of cellular senescence beyond marker limitations</span>
36<span class=pubs-card-toggle-icon aria-hidden=true><svg width="14" height="14" viewBox="0 0 14 14" fill="none" stroke="currentColor" stroke-width="1.5" stroke-linecap="round"><path d="M3 7h8M7 3v8" class="pubs-toggle-cross"/></svg></span></button></h2><p class=publication-authors-line><strong class=publication-author-lab>Jing Lu</strong>, <strong class=publication-author-lab>İsmail Güderer</strong>, <strong class=publication-author-lab>Tayyaba Alvi</strong>, <strong class=publication-author-lab>Mark Olenik</strong>, <strong class=publication-author-lab>Handan Melike Dönertaş</strong><sup class=publication-author-mark aria-label="corresponding author">#</sup></p><p class=pubs-card-abstract id=publication-abstract-6 hidden>Cellular senescence lacks a universal marker and varies across cell types, tissues, and stressors, complicating identification. Using SPiDER SA-β-gal labeled single-cell RNA-seq from regenerating mouse muscle, we found that curated gene sets show opposing enrichment patterns in experimentally defined senescent cells, suggesting apparent concordance in prior studies may reflect circular validation. Machine learning classifiers outperformed marker-centric approaches by capturing coordinated transcriptional features largely absent from differentially expressed genes. These features traced senescence progression, positioning senescent cells at late pseudotime with reduced transcriptional entropy. Ligand-receptor analysis identified IGF signaling as a directional axis of secondary senescence from senescent to non-senescent cells. When applied to bulk RNA-seq and an independent aging dataset, the classifier detected age-associated senescence patterns while the entropy-senescence relationship held across most cell types. These findings demonstrate that transcriptome-based classification provides a robust alternative to marker-centric readouts while enabling mechanistic hypothesis generation.</p><ul class=pubs-card-chips role=list><li class=pubs-card-chip-item><a class="pubs-card-chip pubs-card-chip--doi" href=https://doi.org/10.64898/2026.01.02.697374 rel="noopener noreferrer" target=_blank><span class=pubs-card-chip-label>bioRxiv</span></a></li><li class=pubs-card-chip-item><a class="pubs-card-chip pubs-card-chip--code" href=https://github.com/donertas-group/senClassification_SPiDER rel="noopener noreferrer" target=_blank><span class=pubs-card-chip-label>Code</span></a></li><li class=pubs-card-chip-item><a class="pubs-card-chip pubs-card-chip--data" href=https://www.ebi.ac.uk/biostudies/studies/S-BSST2347 rel="noopener noreferrer" target=_blank><span class=pubs-card-chip-label>Data</span></a></li></ul></div></article></li><li class="pubs-card is-group-led" data-pub-type=preprint data-pub-year=2025 data-pub-journal=bioRxiv data-pub-authors="Handan Melike Dönertaş|Linda Partridge" data-pub-grouplead=1 style=--pub-stagger:0.35s><article class=pubs-card-inner aria-labelledby=publication-card-title-7><span class=pubs-card-rule aria-hidden=true></span><div class=pubs-card-rail><span class=pubs-card-year>2025</span><span class="pubs-card-type pubs-card-type--preprint">Preprint</span><span class=pubs-card-venue>bioRxiv</span></div><div class=pubs-card-body>
36<h2 class=pubs-card-title id=publication-card-title-7><button type=button class=pubs-card-toggle aria-expanded=false aria-controls=publication-abstract-7>
37<span class=pubs-card-toggle-text>The Evolution of Human Ageing Under the Shadow of Demographic Transition</span>
38<span class=pubs-card-toggle-icon aria-hidden=true><svg width="14" height="14" viewBox="0 0 14 14" fill="none" stroke="currentColor" stroke-width="1.5" stroke-linecap="round"><path d="M3 7h8M7 3v8" class="pubs-toggle-cross"/></svg></span></button></h2><p class=publication-authors-line><strong class=publication-author-lab>Handan Melike Dönertaş</strong><sup class=publication-author-mark aria-label="corresponding author">#</sup>, Linda Partridge<sup class=publication-author-mark aria-label="corresponding author">#</sup></p><p class=pubs-card-abstract id=publication-abstract-7 hidden>The demographic transition has the potential to reshape the selective environment acting on the human genome. Here, we apply Hamilton's force-of-selection framework to demographic schedules from 175 countries spanning 74 years (1950 to 2023). In post-transition populations, we observe an extension-dilution trade-off. The age at which selection intensity halves increased by 1.7 years since 1950, yet peak intensity declined by 29.4%. This decline was disproportionately severe at later ages. The ratio of selection intensity at age 20 to intensity at age 40 rose from 17.3 to 25.1, steepening the gradient favouring alleles with early benefits over late-life costs. Post-transition demography allows humans to function for decades beyond ancestral baselines, yet selection pressure to maintain late-life somatic integrity has never been weaker.</p><ul class=pubs-card-chips role=list><li class=pubs-card-chip-item><a class="pubs-card-chip pubs-card-chip--doi" href=https://doi.org/10.64898/2025.12.03.691940 rel="noopener noreferrer" target=_blank><span class=pubs-card-chip-label>bioRxiv</span></a></li><li class=pubs-card-chip-item><a class="pubs-card-chip pubs-card-chip--code" href=https://github.com/donertas-group/human_selection_shadow rel="noopener noreferrer" target=_blank><span class=pubs-card-chip-label>Code</span></a></li><li class=pubs-card-chip-item><a class="pubs-card-chip pubs-card-chip--app" href=https://donertas-group.github.io/human_selection_shadow rel="noopener noreferrer" target=_blank><span class=pubs-card-chip-label>App</span></a></li></ul></div></article></li><li class="pubs-card is-group-led" data-pub-type=journal data-pub-year=2025 data-pub-journal="Microbial Ecology" data-pub-authors="Zahida Sultanova|Handan Melike Dönertaş|Alejandro Hita|Prem Aguilar|Berfin Dag|José Ignacio Lucas-Lledo|Amparo Latorre|Pau Carazo" data-pub-grouplead=1 style=--pub-stagger:0.40s><article class=pubs-card-inner aria-labelledby=publication-card-title-8><span class=pubs-card-rule aria-hidden=true></span><div class=pubs-card-rail><span class=pubs-card-year>2025</span><span class="pubs-card-type pubs-card-type--journal">Research Article</span><span class=pubs-card-venue>Microbial Ecology</span></div><div class=pubs-card-body><h2 class=pubs-card-title id=publication-card-title-8><button type=button class=pubs-card-toggle aria-expanded=false aria-controls=publication-abstract-8>
39<span class=pubs-card-toggle-text>Age-Dependent Gut Microbiota Dynamics and Their Association with Male Life-History Traits in Drosophila melanogaster</span>
40<span class=pubs-card-toggle-icon aria-hidden=true><svg width="14" height="14" viewBox="0 0 14 14" fill="none" stroke="currentColor" stroke-width="1.5" stroke-linecap="round"><path d="M3 7h8M7 3v8" class="pubs-toggle-cross"/></svg></span></button></h2><p class=publication-authors-line>Zahida Sultanova<sup class=publication-author-mark aria-label="equal contribution">*</sup><sup class=publication-author-mark aria-label="corresponding author">#</sup>, <strong class=publication-author-lab>Handan Melike Dönertaş</strong><sup class=publication-author-mark aria-label="equal contribution">*</sup><sup class=publication-author-mark aria-label="corresponding author">#</sup>, Alejandro Hita, Prem Aguilar, Berfin Dag, José Ignacio Lucas-Lledo, Amparo Latorre, Pau Carazo</p><p class=pubs-card-abstract id=publication-abstract-8 hidden>Growing evidence suggests that the gut microbiota is closely intertwined with life-history evolution in a wide range of species, including well-studied model organisms like Drosophila melanogaster. Although recent studies have explored the relationship between gut microbiota and female life-history, the link between gut microbiota and male life-history remains relatively unexplored. In this study, we investigated how gut microbiota changes with male age as well as the associations between gut microbiota composition and male life-history traits in D. melanogaster. Using 22 isolines from the Drosophila melanogaster Genetic Reference Panel (DGRP), we measured lifespan, early/late-life reproduction, and early/late-life physiological performance. We characterized the gut microbiota composition in young (5 days old) and old (26 days old) flies using 16S rDNA sequencing. We observed substantial variation in both male life-history traits and gut microbiota composition across isolines and age groups. Using machine learning, we show that gut microbiota composition could predict the chronological age of the organisms with high accuracy. The most important species contributing to machine learning prediction belonged to the Acetobacter and Ralstonia genera. Associations between gut microbiota and life-history traits were also notable, particularly involving different species from the Acetobacter genus. Our findings suggest that taxa such as Acetobacter may be relevant to the evolutionary ecology of host–microbe interactions in male fruit flies.</p>
40<ul class=pubs-card-chips role=list><li class=pubs-card-chip-item><a class="pubs-card-chip pubs-card-chip--doi" href=https://doi.org/10.1007/s00248-025-02640-y rel="noopener noreferrer" target=_blank><span class=pubs-card-chip-label>Journal</span></a></li><li class=pubs-card-chip-item><a class="pubs-card-chip pubs-card-chip--code" href=https://github.com/mdonertas/DGRP_16S_MaleLH rel="noopener noreferrer" target=_blank><span class=pubs-card-chip-label>Code</span></a></li><li class=pubs-card-chip-item><a class="pubs-card-chip pubs-card-chip--data" href=https://www.ebi.ac.uk/biostudies/studies/S-BSST2029 rel="noopener noreferrer" target=_blank><span class=pubs-card-chip-label>Data</span></a></li><li class=pubs-card-chip-item><a class="pubs-card-chip pubs-card-chip--link" href=https://www.ebi.ac.uk/ena/browser/view/PRJEB88786 rel="noopener noreferrer" target=_blank><span class=pubs-card-chip-label>ENA</span></a></li></ul></div></article></li><li class="pubs-card is-group-led" data-pub-type=chapter data-pub-year=2025 data-pub-journal="Encyclopedia of Bioinformatics and Computational Biology" data-pub-authors="Mark Olenik|Handan Melike Dönertaş" data-pub-grouplead=1 style=--pub-stagger:0.45s><article class=pubs-card-inner aria-labelledby=publication-card-title-9><span class=pubs-card-rule aria-hidden=true></span><div class=pubs-card-rail><span class=pubs-card-year>2025</span><span class="pubs-card-type pubs-card-type--chapter">Book chapter</span><span class=pubs-card-venue>Encyclopedia of Bioinformatics and Computational Biology</span></div><div class=pubs-card-body><h2 class=pubs-card-title id=publication-card-title-9><button type=button class=pubs-card-toggle aria-expanded=false aria-controls=publication-abstract-9>
41<span class=pubs-card-toggle-text>Machine Learning and Omic Data for Prediction of Health and Chronic Diseases</span>
42<span class=pubs-card-toggle-icon aria-hidden=true><svg width="14" height="14" viewBox="0 0 14 14" fill="none" stroke="currentColor" stroke-width="1.5" stroke-linecap="round"><path d="M3 7h8M7 3v8" class="pubs-toggle-cross"/></svg></span></button></h2><p class=publication-authors-line><strong class=publication-author-lab>Mark Olenik</strong><sup class=publication-author-mark aria-label="corresponding author">#</sup>, <strong class=publication-author-lab>Handan Melike Dönertaş</strong><sup class=publication-author-mark aria-label="corresponding author">#</sup></p><p class=pubs-card-abstract id=publication-abstract-9 hidden>Machine learning (ML) combined with diverse omic datasets offers transformative potential for predicting health outcomes and chronic diseases. By leveraging diverse omic data, ML models can identify biomarkers, enhance diagnostic accuracy, and enable personalized treatments. This chapter introduces the fundamental concepts of ML, key omic data sources, and the challenges associated with ML and omic-based disease prediction. Advances in technology, large-scale datasets, interpretable ML algorithms, and the digitization of healthcare are poised to revolutionize medical science, paving the way for precision medicine and early disease detection.</p><ul class=pubs-card-chips role=list><li class=pubs-card-chip-item><a class="pubs-card-chip pubs-card-chip--doi" href=https://doi.org/10.1016/B978-0-323-95502-7.00284-0 rel="noopener noreferrer" target=_blank><span class=pubs-card-chip-label>Chapter</span></a></li></ul></div></article></li><li class="pubs-card is-group-led" data-pub-type=editorial data-pub-year=2023 data-pub-journal="The EMBO Journ
42al" data-pub-authors="Ulaş Işıldak|Handan Melike Dönertaş" data-pub-grouplead=1 style=--pub-stagger:0.50s><article class=pubs-card-inner aria-labelledby=publication-card-title-10><span class=pubs-card-rule aria-hidden=true></span><div class=pubs-card-rail><span class=pubs-card-year>2023</span><span class="pubs-card-type pubs-card-type--editorial">Editorial</span><span class=pubs-card-venue>The EMBO Journal</span></div><div class=pubs-card-body><h2 class=pubs-card-title id=publication-card-title-10><button type=button class=pubs-card-toggle aria-expanded=false aria-controls=publication-abstract-10>
43<span class=pubs-card-toggle-text>Evolutionary paths to mammalian longevity through the lens of gene expression</span>
44<span class=pubs-card-toggle-icon aria-hidden=true><svg width="14" height="14" viewBox="0 0 14 14" fill="none" stroke="currentColor" stroke-width="1.5" stroke-linecap="round"><path d="M3 7h8M7 3v8" class="pubs-toggle-cross"/></svg></span></button></h2><p class=publication-authors-line><strong class=publication-author-lab>Ulaş Işıldak</strong>, <strong class=publication-author-lab>Handan Melike Dönertaş</strong><sup class=publication-author-mark aria-label="corresponding author">#</sup></p><p class=pubs-card-abstract id=publication-abstract-10 hidden>The natural variation in mammalian longevity and its underlying mechanisms remain an active area of aging research. In the latest issue of The EMBO Journ
44al, Liu et al (2023) analyze gene expression levels in 103 mammalian species across three tissues, revealing tissue-specific associations between gene expression patterns and longevity. Remarkably, the study suggests that methionine restriction, a strategy shown to increase lifespan, may extend beyond artificial interventions and is similarly employed by natural selection.</p><ul class=pubs-card-chips role=list><li class=pubs-card-chip-item><a class="pubs-card-chip pubs-card-chip--doi" href=https://doi.org/10.15252/embj.2023114879 rel="noopener noreferrer" target=_blank><span class=pubs-card-chip-label>Journal</span></a></li></ul></div></article></li><li class="pubs-card is-group-led" data-pub-type=journal data-pub-year=2022 data-pub-journal=eLife data-pub-authors="Hamit Izgi|Dingding Han|Shuyun Huang|Ece Kocabiyik|Philipp Khaitovich|Mehmet Somel|Handan Melike Dönertaş" data-pub-grouplead=1 style=--pub-stagger:0.55s><article class=pubs-card-inner aria-labelledby=publication-card-title-11><span class=pubs-card-rule aria-hidden=true></span><div class=pubs-card-rail><span class=pubs-card-year>2022</span><span class="pubs-card-type pubs-card-type--journal">Research Article</span><span class=pubs-card-venue>eLife</span></div><div class=pubs-card-body><h2 class=pubs-card-title id=publication-card-title-11><button type=button class=pubs-card-toggle aria-expanded=false aria-controls=publication-abstract-11>
45<span class=pubs-card-toggle-text>Inter-tissue convergence of gene expression during ageing suggests age-related loss of tissue and cellular identity</span>
46<span class=pubs-card-toggle-icon aria-hidden=true><svg width="14" height="14" viewBox="0 0 14 14" fill="none" stroke="currentColor" stroke-width="1.5" stroke-linecap="round"><path d="M3 7h8M7 3v8" class="pubs-toggle-cross"/></svg></span></button></h2><p class=publication-authors-line>Hamit Izgi, Dingding Han, Shuyun Huang, Ece Kocabiyik, Philipp Khaitovich<sup class=publication-author-mark aria-label="corresponding author">#</sup>, Mehmet Somel<sup class=publication-author-mark aria-label="corresponding author">#</sup>, <strong class=publication-author-lab>Handan Melike Dönertaş</strong><sup class=publication-author-mark aria-label="corresponding author">#</sup></p><p class=pubs-card-abstract id=publication-abstract-11 hidden>Developmental trajectories of gene expression may reverse in their direction during ageing, a phenomenon previously linked to cellular identity loss. Our analysis of cerebral cortex, lung, liver and muscle transcriptomes of 16 mice, covering development and ageing intervals, revealed widespread but tissue-specific ageing-associated expression reversals. Cumulatively, these reversals create a unique phenomenon: mammalian tissue transcriptomes diverge from each other during postnatal development, but during ageing, they tend to converge towards similar expression levels, a process we term Divergence followed by Convergence, or DiCo. We found that DiCo was most prevalent among tissue-specific genes and associated with loss of tissue identity, which is confirmed using data from independent mouse and human datasets. Further, using publicly available single-cell transcriptome data, we showed that DiCo could be driven both by alterations in tissue cell type composition and also by cell-autonomous expression changes within particular cell types.</p><ul class=pubs-card-chips role=list><li class=pubs-card-chip-item><a class="pubs-card-chip pubs-card-chip--doi" href=https://doi.org/10.7554/elife.68048 rel="noopener noreferrer" target=_blank><span class=pubs-card-chip-label>Journal</span></a></li><li class=pubs-card-chip-item><a class="pubs-card-chip pubs-card-chip--code" href=https://github.com/hmtzg/geneexp_mouse rel="noopener noreferrer" target=_blank><span class=pubs-card-chip-label>Code</span></a></li><li class=pubs-card-chip-item><a class="pubs-card-chip pubs-card-chip--data" href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE167665" rel="noopener noreferrer" target=_blank><span class=pubs-card-chip-label>Data</span></a></li></ul></div></article></li><li class="pubs-card is-group-led" data-pub-type=editorial data-pub-year=2022 data-pub-journal=F1000Research data-pub-authors="Yasin Kaya|Tülay Karakulak|Cemil Can Saylan|E. Ravza Gür|Engin Tatlıdil|Sevilay Güleşen|Fatma Betül Dinçaslan|Handan Melike Dönertaş" data-pub-grouplead=1 style=--pub-stagger:0.60s>
46<article class=pubs-card-inner aria-labelledby=publication-card-title-12><span class=pubs-card-rule aria-hidden=true></span><div class=pubs-card-rail><span class=pubs-card-year>2022</span><span class="pubs-card-type pubs-card-type--editorial">Editorial</span><span class=pubs-card-venue>F1000Research</span></div><div class=pubs-card-body><h2 class=pubs-card-title id=publication-card-title-12><button type=button class=pubs-card-toggle aria-expanded=false aria-controls=publication-abstract-12>
47<span class=pubs-card-toggle-text>Lessons from a ten-year-long journey: building a student-driven computational biology society across Turkey</span>
48<span class=pubs-card-toggle-icon aria-hidden=true><svg width="14" height="14" viewBox="0 0 14 14" fill="none" stroke="currentColor" stroke-width="1.5" stroke-linecap="round"><path d="M3 7h8M7 3v8" class="pubs-toggle-cross"/></svg></span></button></h2><p class=publication-authors-line>Yasin Kaya<sup class=publication-author-mark aria-label="corresponding author">#</sup>, Tülay Karakulak, Cemil Can Saylan, E. Ravza Gür, Engin Tatlıdil, Sevilay Güleşen, Fatma Betül Dinçaslan, <strong class=publication-author-lab>Handan Melike Dönertaş</strong><sup class=publication-author-mark aria-label="corresponding author">#</sup></p><p class=pubs-card-abstract id=publication-abstract-12 hidden>The Regional Student Group Turkey (RSG-Turkey) is officially associated with the International Society for Computational Biology (ISCB) Student Council (SC). At the RSG-Turkey, we aim to contribute to the early-career researchers in computational biology and bioinformatics fields by providing opportunities for improving their academic and technical skills in the field. Over the last ten years, we have built a well-known student-driven academic society in Turkey that organizes numerous events every year and continues to grow with over 650 current members. Celebrating the 10th anniversary of RSG-Turkey, in this communication, we share our experiences, five main lessons we learned, and the steps to establish a long-standing academic community: having a clear mission, building a robust structure, effective communication, turning challenges into opportunities, and building collaborations. We believe that our experiences can help students and academics establish long-standing communities in fast-developing areas like bioinformatics.</p><ul class=pubs-card-chips role=list><li class=pubs-card-chip-item><a class="pubs-card-chip pubs-card-chip--doi" href=https://doi.org/10.12688/f1000research.107886.1 rel="noopener noreferrer" target=_blank><span class=pubs-card-chip-label>Journal</span></a></li></ul></div></article></li><li class="pubs-card is-group-led" data-pub-type=editorial data-pub-year=2021 data-pub-journal=F1000Research data-pub-authors="Wim L. Cuypers|Handan Melike Dönertaş|Jasleen K. Grewal|Nazeefa Fatima|Chase Donnelly|Arvind Singh Mer|Spencer Krieger|Bart Cuypers|Farzana Rahman" data-pub-grouplead=1 style=--pub-stagger:0.65s><article class=pubs-card-inner aria-labelledby=publication-card-title-13><span class=pubs-card-rule aria-hidden=true></span><div class=pubs-card-rail><span class=pubs-card-year>2021</span><span class="pubs-card-type pubs-card-type--editorial">Editorial</span><span class=pubs-card-venue>F1000Research</span></div><div class=pubs-card-body><h2 class=pubs-card-title id=publication-card-title-13><button type=button class=pubs-card-toggle aria-expanded=false aria-controls=publication-abstract-13>
49<span class=pubs-card-toggle-text>Highlights from the 16th International Society for Computational Biology Student Council Symposium 2020</span>
50<span class=pubs-card-toggle-icon aria-hidden=true><svg width="14" height="14" viewBox="0 0 14 14" fill="none" stroke="currentColor" stroke-width="1.5" stroke-linecap="round"><path d="M3 7h8M7 3v8" class="pubs-toggle-cross"/></svg></span></button></h2><p class=publication-authors-line>Wim L. Cuypers<sup class=publication-author-mark aria-label="equal contribution">*</sup>, <strong class=publication-author-lab>Handan Melike Dönertaş</strong><sup class=publication-author-mark aria-label="equal contribution">*</sup>, Jasleen K. Grewal<sup class=publication-author-mark aria-label="equal contribution">*</sup>, Nazeefa Fatima, Chase Donnelly, Arvind Singh Mer, Spencer Krieger, Bart Cuypers, Farzana Rahman<sup class=publication-author-mark aria-label="corresponding author">#</sup></p><p class=pubs-card-abstract id=publication-abstract-13 hidden>In this meeting overview, we summarise the scientific program and organisation of the 16th International Society for Computational Biology Student Council Symposium in 2020 (ISCB SCS2020). This symposium was the first virtual edition in an uninterrupted series of symposia that has been going on for 15 years, aiming to unite computational biology students and early career researchers across the globe.</p>
50<ul class=pubs-card-chips role=list><li class=pubs-card-chip-item><a class="pubs-card-chip pubs-card-chip--doi" href=https://doi.org/10.12688/f1000research.53408.1 rel="noopener noreferrer" target=_blank><span class=pubs-card-chip-label>Journal</span></a></li></ul></div></article></li><li class=pubs-card data-pub-type=review data-pub-year=2021 data-pub-journal="Immunity & Ageing" data-pub-authors="Daniel K. Fabian|Matías Fuentealba|Handan Melike Dönertaş|Linda Partridge|Janet M. Thornton" data-pub-grouplead=0 style=--pub-stagger:0.70s><article class=pubs-card-inner aria-labelledby=publication-card-title-14><span class=pubs-card-rule aria-hidden=true></span><div class=pubs-card-rail><span class=pubs-card-year>2021</span><span class="pubs-card-type pubs-card-type--review">Review</span><span class=pubs-card-venue>Immunity & Ageing</span></div><div class=pubs-card-body><h2 class=pubs-card-title id=publication-card-title-14><button type=button class=pubs-card-toggle aria-expanded=false aria-controls=publication-abstract-14>
51<span class=pubs-card-toggle-text>Functional conservation in genes and pathways linking ageing and immunity</span>
52<span class=pubs-card-toggle-icon aria-hidden=true><svg width="14" height="14" viewBox="0 0 14 14" fill="none" stroke="currentColor" stroke-width="1.5" stroke-linecap="round"><path d="M3 7h8M7 3v8" class="pubs-toggle-cross"/></svg></span></button></h2><p class=publication-authors-line>Daniel K. Fabian<sup class=publication-author-mark aria-label="corresponding author">#</sup>, Matías Fuentealba, <strong class=publication-author-lab>Handan Melike Dönertaş</strong>, Linda Partridge, Janet M. Thornton</p><p class=pubs-card-abstract id=publication-abstract-14 hidden>At first glance, longevity and immunity appear to be different traits that have not much in common except the fact that the immune system promotes survival upon pathogenic infection. Substantial evidence however points to a molecularly intertwined relationship between the immune system and ageing. Although this link is well-known throughout the animal kingdom, its genetic basis is complex and still poorly understood. To address this question, we here provide a compilation of all genes concomitantly known to be involved in immunity and ageing in humans and three well-studied model organisms, the nematode worm Caenorhabditis elegans, the fruit fly Drosophila melanogaster, and the house mou
52se Mus musculus. By analysing human orthologs among these species, we identified 7 evolutionarily conserved signalling cascades, the insulin/TOR network, three MAPK (ERK, p38, JNK), JAK/STAT, TGF-β, and Nf-κB pathways that act pleiotropically on ageing and immunity. We review current evidence for these pathways linking immunity and lifespan, and their role in the detrimental dysregulation of the immune system with age, known as immunosenescence. We argue that the phenotypic effects of these pathways are often context-dependent and vary, for example, between tissues, sexes, and types of pathogenic infection. Future research therefore needs to explore a higher temporal, spatial and environmental resolution to fully comprehend the connection between ageing and immunity.</p><ul class=pubs-card-chips role=list><li class=pubs-card-chip-item><a class="pubs-card-chip pubs-card-chip--doi" href=https://doi.org/10.1186/s12979-021-00232-1 rel="noopener noreferrer" target=_blank><span class=pubs-card-chip-label>Journal</span></a></li></ul></div></article></li><li class="pubs-card is-group-led" data-pub-type=preprint data-pub-year=2021 data-pub-journal=bioRxiv data-pub-authors="Etka Yapar|Ekin Saglican|Handan Melike Dönertaş|Ezgi Özkurt|Zheng Yan|Haiyang Hu|Song Guo|Babür Erdem|Rori V. Rohlfs|Philipp Khaitovich|Mehmet Somel" data-pub-grouplead=1 style=--pub-stagger:0.75s><article class=pubs-card-inner aria-labelledby=publication-card-title-15><span class=pubs-card-rule aria-hidden=true></span><div class=pubs-card-rail><span class=pubs-card-year>2021</span><span class="pubs-card-type pubs-card-type--preprint">Preprint</span><span class=pubs-card-venue>bioRxiv</span></div><div class=pubs-card-body><h2 class=pubs-card-title id=publication-card-title-15><button type=button class=pubs-card-toggle aria-expanded=false aria-controls=publication-abstract-15>
53<span class=pubs-card-toggle-text>Convergent evolution of primate testis transcriptomes reflects mating strategy</span>
54<span class=pubs-card-toggle-icon aria-hidden=true><svg width="14" height="14" viewBox="0 0 14 14" fill="none" stroke="currentColor" stroke-width="1.5" stroke-linecap="round"><path d="M3 7h8M7 3v8" class="pubs-toggle-cross"/></svg></span></button></h2><p class=publication-authors-line>Etka Yapar<sup class=publication-author-mark aria-label="equal contribution">*</sup>, Ekin Saglican<sup class=publication-author-mark aria-label="equal contribution">*</sup>, <strong class=publication-author-lab>Handan Melike Dönertaş</strong><sup class=publication-author-mark aria-label="equal contribution">*</sup>, <strong class=publication-author-lab>Ezgi Özkurt</strong>, Zheng Yan, Haiyang Hu, Song Guo, Babür Erdem, Rori V. Rohlfs<sup class=publication-author-mark aria-label="corresponding author">#</sup>, Philipp Khaitovich<sup class=publication-author-mark aria-label="corresponding author">#</sup>, Mehmet Somel<sup class=publication-author-mark aria-label="corresponding author">#</sup></p><p class=pubs-card-abstract id=publication-abstract-15 hidden>In independent mammalian lineages where females mate with multiple males (multi-male mating strategies), males have evolved larger testicles relative to those lineages where females mate with fewer males (single-male mating strategies). Here we study published bulk testis transcriptomes from humans, chimpanzees, gorillas and rhesus macaques, as well as mice and rats. Employing a formal model of adaptive evolution, we find that testis transcriptomes have also evolved convergently, reflecting each species' mating strategy. Using deconvolution, we infer that testis transcriptome divergence patterns largely reflect convergent shifts in tissue cell type composition. However, we also identify modest amounts of convergent evolution at the cell-autonomous level by analyzing cell-type specific transcriptome data from spermatids and spermatocytes. We further show that in the single-male mating primates, human and gorilla, testis transcriptome profiles are paedomorphic relative to those of multi-male primates, chimpanzee and macaque, suggesting that shifts in timing or rate of testis development could underlie convergent changes in testis mass, histology, and transcriptomes. \#\#\# Competing Interest Statement The authors have declared no competing interest.</p>
54<ul class=pubs-card-chips role=list><li class=pubs-card-chip-item><a class="pubs-card-chip pubs-card-chip--doi" href=https://doi.org/10.1101/010553 rel="noopener noreferrer" target=_blank><span class=pubs-card-chip-label>bioRxiv</span></a></li></ul></div></article></li><li class="pubs-card is-group-led" data-pub-type=journal data-pub-year=2021 data-pub-journal="Nature Aging" data-pub-authors="Handan Melike Dönertaş|Daniel K. Fabian|Matías Fuentealba|Linda Partridge|Janet M. Thornton" data-pub-grouplead=1 style=--pub-stagger:0.80s><article class=pubs-card-inner aria-labelledby=publication-card-title-16><span class=pubs-card-rule aria-hidden=true></span><div class=pubs-card-rail><span class=pubs-card-year>2021</span><span class="pubs-card-type pubs-card-type--journal">Research Article</span><span class=pubs-card-venue>Nature Aging</span></div><div class=pubs-card-body><h2 class=pubs-card-title id=publication-card-title-16><button type=button class=pubs-card-toggle aria-expanded=false aria-controls=publication-abstract-16>
55<span class=pubs-card-toggle-text>Common genetic associations between age-related diseases</span>
56<span class=pubs-card-toggle-icon aria-hidden=true><svg width="14" height="14" viewBox="0 0 14 14" fill="none" stroke="currentColor" stroke-width="1.5" stroke-linecap="round"><path d="M3 7h8M7 3v8" class="pubs-toggle-cross"/></svg></span></button></h2><p class=publication-authors-line><strong class=publication-author-lab>Handan Melike Dönertaş</strong><sup class=publication-author-mark aria-label="corresponding author">#</sup>, Daniel K. Fabian, Matías Fuentealba, Linda Partridge, Janet M. Thornton<sup class=publication-author-mark aria-label="corresponding author">#</sup></p><p class=pubs-card-abstract id=publication-abstract-16 hidden>Age is a common risk factor in many diseases, but the molecular basis for this relationship is elusive. In this study we identified four disease clusters from 116 diseases in UK Biobank data, defined by their age-of-onset profiles, and found that diseases with the same onset profile are genetically more similar, suggesting a common etiology. This similarity was not explained by disease categories, co-occurrences or disease cause--effect relationships. Two of the four disease clusters had an increased risk of occurrence from ages 20 and 40 years, respectively. They both showed an association with known aging-related genes, yet differed in functional enrichment and evolutionary profiles. Moreover, they both had age-related expression and methylation changes. We also tested mutation accumulation and antagonistic pleiotropy theories of aging and found support for both. Using genetic and demographic data from the UK Biobank, the authors clustered 116 common diseases based on their age-of-onset profiles and found increased genetic similarity within clusters, suggesting common etiologies. Two of the four disease clusters were associated with aging-related genes but differed in functional enrichment and evolutionary profiles.</p><ul class=pubs-card-chips role=list><li class=pubs-card-chip-item><a class="pubs-card-chip pubs-card-chip--doi" href=https://doi.org/10.1038/s43587-021-00051-5 rel="noopener noreferrer" target=_blank><span class=pubs-card-chip-label>Journal</span></a></li><li class=pubs-card-chip-item><a class="pubs-card-chip pubs-card-chip--code" href=https://github.com/mdonertas/ukbb_ageonset rel="noopener noreferrer" target=_blank><span class=pubs-card-chip-label>Code</span></a></li><li class=pubs-card-chip-item><a class="pubs-card-chip pubs-card-chip--data" href=https://www.ebi.ac.uk/biostudies/studies/S-BSST407 rel="noopener noreferrer" target=_blank><span class=pubs-card-chip-label>Data</span></a></li></ul></div></article></li><li class=pubs-card data-pub-type=journal data-pub-year=2021 data-pub-journal="Mechanisms of Ageing and Development" data-pub-authors="Matias Fuentealba|Daniel K. Fabian|Handan Melike Dönertaş|Janet M. Thornton|Linda Partridge" data-pub-grouplead=0 style=--pub-stagger:0.85s><article class=pubs-card-inner aria-labelledby=publication-card-title-17><span class=pubs-card-rule aria-hidden=true></span><div class=pubs-card-rail><span class=pubs-card-year>2021</span><span class="pubs-card-type pubs-card-type--journal">Research Article</span><span class=pubs-card-venue>Mechanisms of Ageing and Development</span></div><div class=pubs-card-body>
56<h2 class=pubs-card-title id=publication-card-title-17><button type=button class=pubs-card-toggle aria-expanded=false aria-controls=publication-abstract-17>
57<span class=pubs-card-toggle-text>Transcriptomic profiling of long- and short-lived mutant mice implicates mitochondrial metabolism in ageing and shows signatures of normal ageing in progeroid mice</span>
58<span class=pubs-card-toggle-icon aria-hidden=true><svg width="14" height="14" viewBox="0 0 14 14" fill="none" stroke="currentColor" stroke-width="1.5" stroke-linecap="round"><path d="M3 7h8M7 3v8" class="pubs-toggle-cross"/></svg></span></button></h2><p class=publication-authors-line>Matias Fuentealba, Daniel K. Fabian, <strong class=publication-author-lab>Handan Melike Dönertaş</strong>, Janet M. Thornton, Linda Partridge<sup class=publication-author-mark aria-label="corresponding author">#</sup></p><p class=pubs-card-abstract id=publication-abstract-17 hidden>Genetically modified mouse models of ageing are the living proof that lifespan and healthspan can be lengthened or shortened, and provide a powerful context in which to unravel the molecular mechanisms at work. In this study, we analysed and compared gene expression data from 10 long-lived and 8 short-lived mouse models of ageing. Transcriptome-wide correlation analysis revealed that mutations with equivalent effects on lifespan induce more similar transcriptomic changes, especially if they target the same pathway. Using functional enrichment analysis, we identified 58 gene sets with consistent changes in long- and short-lived mice, 55 of which were up-regulated in long-lived mice and down-regulated in short-lived mice. Half of these sets represented genes involved in energy and lipid metabolism, among which Ppargc1a, Mif, Aldh5a1 and Idh1 were frequently observed. Based on the gene sets with consistent changes, and also the whole transcriptome, the gene expression changes during normal ageing resembled the transcriptome of short-lived models, suggesting that accelerated ageing models reproduce partially the molecular changes of ageing. Finally, we identified new genetic interventions that may ameliorate ageing, by comparing the transcriptomes of 51 mouse mutants not previously associated with ageing to expression signatures of long- and short-lived mice and ageing-related changes.</p><ul class=pubs-card-chips role=list><li class=pubs-card-chip-item><a class="pubs-card-chip pubs-card-chip--doi" href=https://doi.org/10.1016/j.mad.2021.111437 rel="noopener noreferrer" target=_blank><span class=pubs-card-chip-label>Journal</span></a></li></ul></div></article></li><li class=pubs-card data-pub-type=journal data-pub-year=2021 data-pub-journal="Genome Biology and Evolution" data-pub-authors="Daniel K Fabian|Handan Melike Dönertaş|Matías Fuentealba|Linda Partridge|Janet M Thornton" data-pub-grouplead=0 style=--pub-stagger:0.90s><article class=pubs-card-inner aria-labelledby=publication-card-title-18><span class=pubs-card-rule aria-hidden=true></span><div class=pubs-card-rail><span class=pubs-card-year>2021</span><span class="pubs-card-type pubs-card-type--journal">Research Article</span><span class=pubs-card-venue>Genome Biology and Evolution</span></div><div class=pubs-card-body><h2 class=pubs-card-title id=publication-card-title-18><button type=button class=pubs-card-toggle aria-expanded=false aria-controls=publication-abstract-18>
59<span class=pubs-card-toggle-text>Transposable Element Landscape in Drosophila Populations Selected for Longevity</span>
60<span class=pubs-card-toggle-icon aria-hidden=true><svg width="14" height="14" viewBox="0 0 14 14" fill="none" stroke="currentColor" stroke-width="1.5" stroke-linecap="round"><path d="M3 7h8M7 3v8" class="pubs-toggle-cross"/></svg></span></button></h2><p class=publication-authors-line>Daniel K Fabian<sup class=publication-author-mark aria-label="corresponding author">#</sup>, <strong class=publication-author-lab>Handan Melike Dönertaş</strong>, Matías Fuentealba, Linda Partridge, Janet M Thornton</p><p class=pubs-card-abstract id=publication-abstract-18 hidden>Transposable elements (TEs) inflict numerous negative effects on health and fitness as they replicate by integrating into new regions of the host genome. Even though organisms employ powerful mechanisms to demobilize TEs, transposons gradually lose repression during aging. The rising TE activity causes genomic instability and was implicated in age-dependent neurodegenerative diseases, inflammation and the determination of lifespan. It is therefore conceivable that long-lived individuals have improved TE silencing mechanisms resulting in reduced TE expression relative to their shorter-lived counterparts and fewer genomic insertions. Here, we test this hypothesis by performing the first genome-wide analysis of TE insertions and expression in populations of Drosophila melanogaster selected for longevity through late-life reproduction for 50-170 generations from four independent studies. Contrary to our expectation, TE families were generally more abundant in long-lived populations compared to non-selected controls. Although simulations showed that this was not expected under neutrality, we found little evidence for selection driving TE abundance differences. Additional RNA-seq analysis revealed a tendency for reducing TE expression in selected populations, which might be more important for lifespan than regulating genomic insertions. We further find limited evidence of parallel selection on genes related to TE regulation and transposition. However, telomeric TEs were genomically and transcriptionally more abundant in long-lived flies, suggesting improved telomere maintenance as a promising TE-mediated mechanism for prolonging lifespan. Our results provide a novel viewpoint indicating that reproduction at old age increases the opportunity of TEs to be passed on to the next generation with little impact on longevity.</p>
60<ul class=pubs-card-chips role=list><li class=pubs-card-chip-item><a class="pubs-card-chip pubs-card-chip--doi" href=https://doi.org/10.1093/gbe/evab031 rel="noopener noreferrer" target=_blank><span class=pubs-card-chip-label>Journal</span></a></li></ul></div></article></li><li class=pubs-card data-pub-type=journal data-pub-year=2021 data-pub-journal="Science Advances" data-pub-authors="Gülşah Merve Kılınç|Natalija Kashuba|Dilek Koptekin|Nora Bergfeldt|Handan Melike Dönertaş|Ricardo Rodríguez-Varela|Dmitrij Shergin|Grigorij Ivanov|Dmitrii Kichigin|Kjunnej Pestereva|Denis Volkov|Pavel Mandryka|Artur Kharinskii|Alexey Tishkin|Evgenij Ineshin|Evgeniy Kovychev|Aleksandr Stepanov|Love Dalén|Torsten Günther|Emrah Kırdök|Mattias Jakobsson|Mehmet Somel|Maja Krzewińska|Jan Storå|Anders Götherström" data-pub-grouplead=0 style=--pub-stagger:0.95s><article class=pubs-card-inner aria-labelledby=publication-card-title-19><span class=pubs-card-rule aria-hidden=true></span><div class=pubs-card-rail><span class=pubs-card-year>2021</span><span class="pubs-card-type pubs-card-type--journal">Research Article</span><span class=pubs-card-venue>Science Advances</span></div><div class=pubs-card-body><h2 class=pubs-card-title id=publication-card-title-19><button type=button class=pubs-card-toggle aria-expanded=false aria-controls=publication-abstract-19>
61<span class=pubs-card-toggle-text>Human population dynamics and Yersinia pestis in ancient northeast Asia</span>
62<span class=pubs-card-toggle-icon aria-hidden=true><svg width="14" height="14" viewBox="0 0 14 14" fill="none" stroke="currentColor" stroke-width="1.5" stroke-linecap="round"><path d="M3 7h8M7 3v8" class="pubs-toggle-cross"/></svg></span></button></h2><p class=publication-authors-line>Gülşah Merve Kılınç<sup class=publication-author-mark aria-label="equal contribution">*</sup><sup class=publication-author-mark aria-label="corresponding author">#</sup>, Natalija Kashuba<sup class=publication-author-mark aria-label="equal contribution">*</sup>, Dilek Koptekin, Nora Bergfeldt, <strong class=publication-author-lab>Handan Melike Dönertaş</strong>, Ricardo Rodríguez-Varela, Dmitrij Shergin, Grigorij Ivanov, Dmitrii Kichigin, Kjunnej Pestereva, Denis Volkov, Pavel Mandryka, Artur Kharinskii, Alexey Tishkin, Evgenij Ineshin, Evgeniy Kovychev, Aleksandr Stepanov, Love Dalén, Torsten Günther, Emrah Kırdök, Mattias Jakobsson, Mehmet Somel, Maja Krzewińska, Jan Storå<sup class=publication-author-mark aria-label="corresponding author">#</sup>, Anders Götherström<sup class=publication-author-mark aria-label="corresponding author">#</sup></p><p class=pubs-card-abstract id=publication-abstract-19 hidden>We present genome-wide data from 40 individuals dating to c.16,900 to 550 years ago in northeast Asia. We describe hitherto unknown gene flow and admixture events in the region, revealing a complex population history. While populations east of Lake Baikal remained relatively stable from the Mesolithic to the Bronze Age, those from Yakutia and west of Lake Baikal witnessed major population transformations, from the Late Upper Paleolithic to the Neolithic, and during the Bronze Age, respectively. We further locate the Asian ancestors of Paleo-Inuits, using direct genetic evidence. Last, we report the most northeastern ancient occurrence of the plague-related bacterium, Yersinia pestis. Our findings indicate the highly connected and dynamic nature of northeast Asia populations throughout the Holocene.</p><ul class=pubs-card-chips role=list><li class=pubs-card-chip-item><a class="pubs-card-chip pubs-card-chip--doi" href=https://doi.org/10.1126/sciadv.abc4587 rel="noopener noreferrer" target=_blank><span class=pubs-card-chip-label>Journal</span></a></li></ul></div></article></li><li class="pubs-card is-group-led" data-pub-type=journal data-pub-year=2020 data-pub-journal="Scientific Reports" data-pub-authors="Ulaş Işıldak|Mehmet Somel|Janet M. Thornton|Handan Melike Dönertaş" data-pub-grouplead=1 style=--pub-stagger:1.00s><article class=pubs-card-inner aria-labelledby=publication-card-title-20><span class=pubs-card-rule aria-hidden=true></span><div class=pubs-card-rail><span class=pubs-card-year>2020</span><span class="pubs-card-type pubs-card-type--journal">Research Article</span><span class=pubs-card-venue>Scientific Reports</span></div><div class=pubs-card-body>
62<h2 class=pubs-card-title id=publication-card-title-20><button type=button class=pubs-card-toggle aria-expanded=false aria-controls=publication-abstract-20>
63<span class=pubs-card-toggle-text>Temporal changes in the gene expression heterogeneity during brain development and aging</span>
64<span class=pubs-card-toggle-icon aria-hidden=true><svg width="14" height="14" viewBox="0 0 14 14" fill="none" stroke="currentColor" stroke-width="1.5" stroke-linecap="round"><path d="M3 7h8M7 3v8" class="pubs-toggle-cross"/></svg></span></button></h2><p class=publication-authors-line><strong class=publication-author-lab>Ulaş Işıldak</strong>, Mehmet Somel, Janet M. Thornton, <strong class=publication-author-lab>Handan Melike Dönertaş</strong><sup class=publication-author-mark aria-label="corresponding author">#</sup></p><p class=pubs-card-abstract id=publication-abstract-20 hidden>Cells in largely non-mitotic tissues such as the brain are prone to stochastic (epi-)genetic alterations that may cause increased variability between cells and individuals over time. Although increased inter-individual heterogeneity in gene expression was previously reported, whether this process starts during development or if it is restricted to the aging period has not yet been studied. The regulatory dynamics and functional significance of putative aging-related heterogeneity are also unknown. Here we address these by a meta-analysis of 19 transcriptome datasets from three independent studies, covering diverse human brain regions. We observed a significant increase in inter-individual heterogeneity during aging (20 + years) compared to postnatal development (0 to 20 years). Increased heterogeneity during aging was consistent among different brain regions at the gene level and associated with lifespan regulation and neuronal functions. Overall, our results show that increased expression heterogeneity is a characteristic of aging human brain, and may influence aging-related changes in brain functions.</p><ul class=pubs-card-chips role=list><li class=pubs-card-chip-item><a class="pubs-card-chip pubs-card-chip--doi" href=https://doi.org/10.1038/s41598-020-60998-0 rel="noopener noreferrer" target=_blank><span class=pubs-card-chip-label>Journal</span></a></li><li class=pubs-card-chip-item><a class="pubs-card-chip pubs-card-chip--code" href=https://mdonertas.github.io/hetAge/ rel="noopener noreferrer" target=_blank><span class=pubs-card-chip-label>Code</span></a></li><li class=pubs-card-chip-item><a class="pubs-card-chip pubs-card-chip--data" href=https://www.ebi.ac.uk/biostudies/studies/S-BSST273 rel="noopener noreferrer" target=_blank><span class=pubs-card-chip-label>Data</span></a></li></ul></div></article></li><li class=pubs-card data-pub-type=editorial data-pub-year=2019 data-pub-journal=F1000Research data-pub-authors="Sayane Shome|R. Gonzalo Parra|Nazeefa Fatima|Alexander Miguel Monzon|Bart Cuypers|Yumna Moosa|Nilson Da Rocha Coimbra|Juliana Assis|Carla Giner-Delgado|Handan Melike Dönertaş|Yesid Cuesta-Astroz|Geetha Saarunya|Imane Allali|Shruti Gupta|Ambuj Srivastava|Manisha Kalsan|Catalina Valdivia|Gabriel J. Olguin-Orellana|Sofia Papadimitriou|Daniele Parisi|Nikolaj Pagh Kristensen|Leonor Rib|Marouen Ben Guebila|Eugen Bauer|Gaia Zaffaroni|Amel Bekkar|Efejiro Ashano|Lisanna Paladin|Marco Necci|Nicolás N. Moreyra|Martin Rydén|Jordan Villalobos-Solís|Nikolaos Papadopoulos|Candice Rafael|Tülay Karakulak|Yasin Kaya|Yvonne Gladbach|Sandeep Kumar Dhanda|Nikolina Šoštarić|Aishwarya Alex|Dan DeBlasio|Farzana Rahman" data-pub-grouplead=0 style=--pub-stagger:1.05s><article class=pubs-card-inner aria-labelledby=publication-card-title-21><span class=pubs-card-rule aria-hidden=true></span><div class=pubs-card-rail><span class=pubs-card-year>2019</span><span class="pubs-card-type pubs-card-type--editorial">Editorial</span><span class=pubs-card-venue>F1000Research</span></div><div class=pubs-card-body><h2 class=pubs-card-title id=publication-card-title-21>Global network of computational biology communities: ISCB's Regional Student Groups breaking barriers</h2><p class=publication-authors-line>Sayane Shome<sup class=publication-author-mark aria-label="corresponding author">#</sup>, R. Gonzalo Parra, Nazeefa Fatima, Alexander Miguel Monzon, Bart Cuypers, Yumna Moosa, Nilson Da Rocha Coimbra, Juliana Assis, Carla Giner-Delgado, <strong class=
64publication-author-lab>Handan Melike Dönertaş</strong>, Yesid Cuesta-Astroz, Geetha Saarunya, Imane Allali, Shruti Gupta, Ambuj Srivastava, Manisha Kalsan, Catalina Valdivia, Gabriel J. Olguin-Orellana, Sofia Papadimitriou, Daniele Parisi, Nikolaj Pagh Kristensen, Leonor Rib, Marouen Ben Guebila, Eugen Bauer, Gaia Zaffaroni, Amel Bekkar, Efejiro Ashano, Lisanna Paladin, Marco Necci, Nicolás N. Moreyra, Martin Rydén, Jordan Villalobos-Solís, Nikolaos Papadopoulos, Candice Rafael, Tülay Karakulak, Yasin Kaya, Yvonne Gladbach, Sandeep Kumar Dhanda, Nikolina Šoštarić, Aishwarya Alex, Dan DeBlasio, Farzana Rahman<sup class=publication-author-mark aria-label="corresponding author">#</sup></p><ul class=pubs-card-chips role=list><li class=pubs-card-chip-item><a class="pubs-card-chip pubs-card-chip--doi" href=https://doi.org/10.12688/f1000research.20408.1 rel="noopener noreferrer" target=_blank><span class=pubs-card-chip-label>Journal</span></a></li></ul></div></article></li><li class=pubs-card data-pub-type=journal data-pub-year=2019 data-pub-journal="Aging Cell" data-pub-authors="Zeliha Gözde Turan|Poorya Parvizi|Handan Melike Dönertaş|Jenny Tung|Philipp Khaitovich|Mehmet Somel" data-pub-grouplead=0 style=--pub-stagger:1.10s><article class=pubs-card-inner aria-labelledby=publication-card-title-22><span class=pubs-card-rule aria-hidden=true></span><div class=pubs-card-rail><span class=pubs-card-year>2019</span><span class="pubs-card-type pubs-card-type--journal">Research Article</span><span class=pubs-card-venue>Aging Cell</span></div><div class=pubs-card-body><h2 class=pubs-card-title id=publication-card-title-22><button type=button class=pubs-card-toggle aria-expanded=false aria-controls=publication-abstract-22>
65<span class=pubs-card-toggle-text>Molecular footprint of Medawar’s mutation accumulation process in mammalian aging</span>
66<span class=pubs-card-toggle-icon aria-hidden=true><svg width="14" height="14" viewBox="0 0 14 14" fill="none" stroke="currentColor" stroke-width="1.5" stroke-linecap="round"><path d="M3 7h8M7 3v8" class="pubs-toggle-cross"/></svg></span></button></h2><p class=publication-authors-line>Zeliha Gözde Turan<sup class=publication-author-mark aria-label="corresponding author">#</sup>, Poorya Parvizi, <strong class=publication-author-lab>Handan Melike Dönertaş</strong>, Jenny Tung, Philipp Khaitovich, Mehmet Somel<sup class=publication-author-mark aria-label="corresponding author">#</sup></p><p class=pubs-card-abstract id=publication-abstract-22 hidden>Abstract Medawar's mutation accumulation hypothesis explains aging by the declining force of natural selection with age: Slightly deleterious germline mutations expressed in old age can drift to fixation and thereby lead to aging-related phenotypes. Although widely cited, empirical evidence for this hypothesis has remained limited. Here, we test one of its predictions that genes relatively highly expressed in old adults should be under weaker purifying selection than genes relatively highly expressed in young adults. Combining 66 transcriptome datasets (including 16 tissues from five mammalian species) with sequence conservation estimates across mammals, here we report that the overall conservation level of expressed genes is lower at old age compared to young adulthood. This age-related decrease in transcriptome conservation (ADICT) is systematically observed in diverse mammalian tissues, including the brain, liver, lung, and artery, but not in others, most notably in the muscle and heart. Where observed, ADICT is driven partly by poorly conserved genes being up-regulated during aging. In general, the more often a gene is found up-regulated with age among tissues and species, the lower its evolutionary conservation. Poorly conserved and up-regulated genes have overlapping functional properties that include responses to age-associated tissue damage, such as apoptosis and inflammation. Meanwhile, these genes do not appear to be under positive selection. Hence, genes contributing to old age phenotypes are found to harbor an excess of slightly deleterious alleles, at least in certain tissues. This supports the notion that genetic drift shapes aging in multicellular organisms, consistent with Medawar's mutation accumulation hypothesis.</p>
66<ul class=pubs-card-chips role=list><li class=pubs-card-chip-item><a class="pubs-card-chip pubs-card-chip--doi" href=https://doi.org/10.1111/acel.12965 rel="noopener noreferrer" target=_blank><span class=pubs-card-chip-label>Journal</span></a></li></ul></div></article></li><li class="pubs-card is-group-led" data-pub-type=journal data-pub-year=2019 data-pub-journal=Aging data-pub-authors="Veronika R. Kedlian|Handan Melike Dönertaş|Janet M. Thornton" data-pub-grouplead=1 style=--pub-stagger:1.15s><article class=pubs-card-inner aria-labelledby=publication-card-title-23><span class=pubs-card-rule aria-hidden=true></span><div class=pubs-card-rail><span class=pubs-card-year>2019</span><span class="pubs-card-type pubs-card-type--journal">Research Article</span><span class=pubs-card-venue>Aging</span></div><div class=pubs-card-body><h2 class=pubs-card-title id=publication-card-title-23><button type=button class=pubs-card-toggle aria-expanded=false aria-controls=publication-abstract-23>
67<span class=pubs-card-toggle-text>The widespread increase in inter-individual variability of gene expression in the human brain with age</span>
68<span class=pubs-card-toggle-icon aria-hidden=true><svg width="14" height="14" viewBox="0 0 14 14" fill="none" stroke="currentColor" stroke-width="1.5" stroke-linecap="round"><path d="M3 7h8M7 3v8" class="pubs-toggle-cross"/></svg></span></button></h2><p class=publication-authors-line>Veronika R. Kedlian<sup class=publication-author-mark aria-label="equal contribution">*</sup>, <strong class=publication-author-lab>Handan Melike Dönertaş</strong><sup class=publication-author-mark aria-label="equal contribution">*</sup>, Janet M. Thornton<sup class=publication-author-mark aria-label="corresponding author">#</sup></p><p class=pubs-card-abstract id=publication-abstract-23 hidden>Aging is broadly defined as a time-dependent progressive decline in the functional and physiological integrity of organisms. Previous studies and evolutionary theories of aging suggest that aging is not a programmed process but reflects dynamic stochastic events. In this study, we test whether transcriptional noise shows an increase with age, which would be expected from stochastic theories. Using human brain transcriptome dataset, we analyzed the heterogeneity in the transcriptome for individual genes and functional pathways, employing different analysis methods and pre-processing steps. We show that unlike expression level changes, changes in heterogeneity are highly dependent on the methodology and the underlying assumptions. Although the particular set of genes that can be characterized as differentially variable is highly dependent on the methods, we observe a consistent increase in heterogeneity at every level, independent of the method. In particular, we demonstrate a weak but reproducible transcriptome-wide shift towards an increase in heterogeneity, with twice as many genes significantly increasing as opposed to decreasing their heterogeneity. Furthermore, this pattern of increasing heterogeneity is not specific but is associated with a wide range of pathways.</p><ul class=pubs-card-chips role=list><li class=pubs-card-chip-item><a class="pubs-card-chip pubs-card-chip--doi" href=https://doi.org/10.18632/aging.101912 rel="noopener noreferrer" target=_blank><span class=pubs-card-chip-label>Journal</span></a></li></ul></div></article></li><li class="pubs-card is-group-led" data-pub-type=review data-pub-year=2019 data-pub-journal="Trends in Endocrinology & Metabolism" data-pub-authors="Handan Melike Dönertaş|Matías Fuentealba|Linda Partridge|Janet M. Thornton" data-pub-grouplead=1 style=--pub-stagger:1.20s><article class=pubs-card-inner aria-labelledby=publication-card-title-24><span class=pubs-card-rule aria-hidden=true></span><div class=pubs-card-rail><span class=pubs-card-year>2019</span><span class="pubs-card-type pubs-card-type--review">Review</span><span class=pubs-card-venue>Trends in Endocrinology & Metabolism</span></div><div class=pubs-card-body><h2 class=pubs-card-title id=publication-card-title-24><button type=button class=pubs-card-toggle aria-expanded=false aria-controls=publication-abstract-24>
69<span class=pubs-card-toggle-text>Identifying Potential Ageing-Modulating Drugs In Silico</span>
70<span class=pubs-card-toggle-icon aria-hidden=true><svg width="14" height="14" viewBox="0 0 14 14" fill="none" stroke="currentColor" stroke-width="1.5" stroke-linecap="round"><path d="M3 7h8M7 3v8" class="pubs-toggle-cross"/></svg></span></button></h2><p class=publication-authors-line>
70<strong class=publication-author-lab>Handan Melike Dönertaş</strong><sup class=publication-author-mark aria-label="equal contribution">*</sup>, Matías Fuentealba<sup class=publication-author-mark aria-label="equal contribution">*</sup>, Linda Partridge, Janet M. Thornton<sup class=publication-author-mark aria-label="corresponding author">#</sup></p><p class=pubs-card-abstract id=publication-abstract-24 hidden>Increasing human life expectancy has posed increasing challenges for healthcare systems. As people age, they become more susceptible to chronic diseases, with an increasing burden of multimorbidity, and the associated polypharmacy. Accumulating evidence from work with laboratory animals has shown that ageing is a malleable process that can be ameliorated by genetic and environmental interventions. Drugs that modulate the ageing process may delay or even prevent the incidence of multiple diseases of ageing. To identify novel, anti-ageing drugs, several studies have developed computational drug-repurposing strategies. We review published studies showing the potential of current drugs to modulate ageing. Future studies should integrate current knowledge with multi-omics, health records, and drug safety data to predict drugs that can improve health in late life.</p><ul class=pubs-card-chips role=list><li class=pubs-card-chip-item><a class="pubs-card-chip pubs-card-chip--doi" href=https://doi.org/10.1016/j.tem.2018.11.005 rel="noopener noreferrer" target=_blank><span class=pubs-card-chip-label>Journal</span></a></li><li class=pubs-card-chip-item><a class="pubs-card-chip pubs-card-chip--code" href=https://github.com/mdonertas/ageing_drug_review rel="noopener noreferrer" target=_blank><span class=pubs-card-chip-label>Code</span></a></li></ul></div></article></li><li class=pubs-card data-pub-type=journal data-pub-year=2019 data-pub-journal="PLOS Computational Biology" data-pub-authors="Matías Fuentealba|Handan Melike Dönertaş|Rhianna Williams|Johnathan Labbadia|Janet M. Thornton|Linda Partridge" data-pub-grouplead=0 style=--pub-stagger:1.25s><article class=pubs-card-inner aria-labelledby=publication-card-title-25><span class=pubs-card-rule aria-hidden=true></span><div class=pubs-card-rail><span class=pubs-card-year>2019</span><span class="pubs-card-type pubs-card-type--journal">Research Article</span><span class=pubs-card-venue>PLOS Computational Biology</span></div><div class=pubs-card-body><h2 class=pubs-card-title id=publication-card-title-25><button type=button class=pubs-card-toggle aria-expanded=false aria-controls=publication-abstract-25>
71<span class=pubs-card-toggle-text>Using the drug-protein interactome to identify anti-ageing compounds for humans</span>
72<span class=pubs-card-toggle-icon aria-hidden=true><svg width="14" height="14" viewBox="0 0 14 14" fill="none" stroke="currentColor" stroke-width="1.5" stroke-linecap="round"><path d="M3 7h8M7 3v8" class="pubs-toggle-cross"/></svg></span></button></h2><p class=publication-authors-line>Matías Fuentealba, <strong class=publication-author-lab>Handan Melike Dönertaş</strong>, Rhianna Williams, Johnathan Labbadia, Janet M. Thornton, Linda Partridge<sup class=publication-author-mark aria-label="corresponding author">#</sup></p><p class=pubs-card-abstract id=publication-abstract-25 hidden>Advancing age is the dominant risk factor for most of the major killer diseases in developed countries. Hence, ameliorating the effects of ageing may prevent multiple diseases simultaneously. Drugs licensed for human use against specific diseases have proved to be effective in extending lifespan and healthspan in animal models, suggesting that there is scope for drug repurposing in humans. New bioinformatic methods to identify and prioritise potential anti-ageing compounds for humans are therefore of interest. In this study, we first used drug-protein interaction information, to rank 1,147 drugs by their likelihood of targeting ageing-related gene products in humans. Among 19 statistically significant drugs, 6 have already been shown to have pro-longevity properties in animal models (p < 0.001). Using the targets of each drug, we established their association with ageing at multiple levels of biological action including pathways, functions and protein interactions. Finally, combining all the data, we calculated a ranked list of drugs that identified tanespimycin, an inhibitor of HSP-90, as the top-ranked novel anti-ageing candidate. We experimentally validated the pro-longevity effect of tanespimycin through its HSP-90 target in Caenorhabditis elegans.</p>
72<ul class=pubs-card-chips role=list><li class=pubs-card-chip-item><a class="pubs-card-chip pubs-card-chip--doi" href=https://doi.org/10.1371/journal.pcbi.1006639 rel="noopener noreferrer" target=_blank><span class=pubs-card-chip-label>Journal</span></a></li></ul></div></article></li><li class="pubs-card is-group-led" data-pub-type=journal data-pub-year=2018 data-pub-journal="Aging Cell" data-pub-authors="Handan Melike Dönertaş|Matías Fuentealba Valenzuela|Linda Partridge|Janet M. Thornton" data-pub-grouplead=1 style=--pub-stagger:1.30s><article class=pubs-card-inner aria-labelledby=publication-card-title-26><span class=pubs-card-rule aria-hidden=true></span><div class=pubs-card-rail><span class=pubs-card-year>2018</span><span class="pubs-card-type pubs-card-type--journal">Research Article</span><span class=pubs-card-venue>Aging Cell</span></div><div class=pubs-card-body><h2 class=pubs-card-title id=publication-card-title-26><button type=button class=pubs-card-toggle aria-expanded=false aria-controls=publication-abstract-26>
73<span class=pubs-card-toggle-text>Gene expression‐based drug repurposing to target aging</span>
74<span class=pubs-card-toggle-icon aria-hidden=true><svg width="14" height="14" viewBox="0 0 14 14" fill="none" stroke="currentColor" stroke-width="1.5" stroke-linecap="round"><path d="M3 7h8M7 3v8" class="pubs-toggle-cross"/></svg></span></button></h2><p class=publication-authors-line><strong class=publication-author-lab>Handan Melike Dönertaş</strong>, Matías Fuentealba Valenzuela, Linda Partridge, Janet M. Thornton<sup class=publication-author-mark aria-label="corresponding author">#</sup></p><p class=pubs-card-abstract id=publication-abstract-26 hidden>Aging is the largest risk factor for a variety of noncommunicable diseases. Model organism studies have shown that genetic and chemical perturbations can extend both lifespan and healthspan. Aging is a complex process, with parallel and interacting mechanisms contributing to its aetiology, posing a challenge for the discovery of new pharmacological candidates to ameliorate its effects. In this study, instead of a target-centric approach, we adopt a systems level drug repurposing methodology to discover drugs that could combat aging in human brain. Using multiple gene expression data sets from brain tissue, taken from patients of different ages, we first identified the expression changes that characterize aging. Then, we compared these changes in gene expression with drug-perturbed expression profiles in the Connectivity Map. We thus identified 24 drugs with significantly associated changes. Some of these drugs may function as antiaging drugs by reversing the detrimental changes that occur during aging, others by mimicking the cellular defence mechanisms. The drugs that we identified included significant number of already identified prolongevity drugs, indicating that the method can discover de novo drugs that meliorate aging. The approach has the advantages that using data from human brain aging data, it focuses on processes relevant in human aging and that it is unbiased, making it possible to discover new targets for aging studies.</p><ul class=pubs-card-chips role=list><li class=pubs-card-chip-item><a class="pubs-card-chip pubs-card-chip--doi" href=https://doi.org/10.1111/acel.12819 rel="noopener noreferrer" target=_blank><span class=pubs-card-chip-label>Journal</span></a></li></ul></div></article></li><li class=pubs-card data-pub-type=journal data-pub-year=2017 data-pub-journal="Proceedings of the Royal Society B: Biological Sciences" data-pub-authors="Gülşah Merve Kılınç|Dilek Koptekin|Çiğdem Atakuman|Arev Pelin Sümer|Handan Melike Dönertaş|Reyhan Yaka|Cemal Can Bilgin|Ali Metin Büyükkarakaya|Douglas Baird|Ezgi Altınışık|Pavel Flegontov|Anders Götherström|İnci Togan|Mehmet Somel" data-pub-grouplead=0 style=--pub-stagger:1.35s><article class=pubs-card-inner aria-labelledby=publication-card-title-27><span class=pubs-card-rule aria-hidden=true></span><div class=pubs-card-rail><span class=pubs-card-year>2017</span><span class="pubs-card-type pubs-card-type--journal">Research Article</span><span class=pubs-card-venue>Proceedings of the Royal Society B: Biological Sciences</span></div><div class=pubs-card-body>
74<h2 class=pubs-card-title id=publication-card-title-27><button type=button class=pubs-card-toggle aria-expanded=false aria-controls=publication-abstract-27>
75<span class=pubs-card-toggle-text>Archaeogenomic analysis of the first steps of Neolithization in Anatolia and the Aegean</span>
76<span class=pubs-card-toggle-icon aria-hidden=true><svg width="14" height="14" viewBox="0 0 14 14" fill="none" stroke="currentColor" stroke-width="1.5" stroke-linecap="round"><path d="M3 7h8M7 3v8" class="pubs-toggle-cross"/></svg></span></button></h2><p class=publication-authors-line>Gülşah Merve Kılınç<sup class=publication-author-mark aria-label="corresponding author">#</sup>, Dilek Koptekin, Çiğdem Atakuman, Arev Pelin Sümer, <strong class=publication-author-lab>Handan Melike Dönertaş</strong>, Reyhan Yaka, Cemal Can Bilgin, Ali Metin Büyükkarakaya, Douglas Baird, Ezgi Altınışık, Pavel Flegontov, Anders Götherström<sup class=publication-author-mark aria-label="corresponding author">#</sup>, İnci Togan<sup class=publication-author-mark aria-label="corresponding author">#</sup>, Mehmet Somel<sup class=publication-author-mark aria-label="corresponding author">#</sup></p><p class=pubs-card-abstract id=publication-abstract-27 hidden>The Neolithic transition in west Eurasia occurred in two main steps: the gradual development of sedentism and plant cultivation in the Near East and the subsequent spread of Neolithic cultures into the Aegean and across Europe after 7000 cal BCE. Here, we use published ancient genomes to investigate gene flow events in west Eurasia during the Neolithic transition. We confirm that the Early Neolithic central Anatolians in the ninth millennium BCE were probably descendants of local hunter-gatherers, rather than immigrants from the Levant or Iran. We further study the emergence of post-7000 cal BCE north Aegean Neolithic communities. Although Aegean farmers have frequently been assumed to be colonists originating from either central Anatolia or from the Levant, our findings raise alternative possibilities: north Aegean Neolithic populations may have been the product of multiple westward migrations, including south Anatolian emigrants, or they may have been descendants of local Aegean Mesolithic groups who adopted farming. These scenarios are consistent with the diversity of material cultures among Aegean Neolithic communities and the inheritance of local forager know-how. The demographic and cultural dynamics behind the earliest spread of Neolithic culture in the Aegean could therefore be distinct from the subsequent Neolithization of mainland Europe.</p><ul class=pubs-card-chips role=list><li class=pubs-card-chip-item><a class="pubs-card-chip pubs-card-chip--doi" href=https://doi.org/10.1098/rspb.2017.2064 rel="noopener noreferrer" target=_blank><span class=pubs-card-chip-label>Journal</span></a></li></ul></div></article></li><li class=pubs-card data-pub-type=journal data-pub-year=2017 data-pub-journal="Genome Biology and Evolution" data-pub-authors="Recep Ozgur Taskent|Nursen Duha Alioglu|Evrim Fer|Handan Melike Dönertaş|Mehmet Somel|Omer Gokcumen" data-pub-grouplead=0 style=--pub-stagger:1.40s><article class=pubs-card-inner aria-labelledby=publication-card-title-28><span class=pubs-card-rule aria-hidden=true></span><div class=pubs-card-rail><span class=pubs-card-year>2017</span><span class="pubs-card-type pubs-card-type--journal">Research Article</span><span class=pubs-card-venue>Genome Biology and Evolution</span></div><div class=pubs-card-body><h2 class=pubs-card-title id=publication-card-title-28><button type=button class=pubs-card-toggle aria-expanded=false aria-controls=publication-abstract-28>
77<span class=pubs-card-toggle-text>Variation and Functional Impact of Neanderthal Ancestry in Western Asia</span>
78<span class=pubs-card-toggle-icon aria-hidden=true><svg width="14" height="14" viewBox="0 0 14 14" fill="none" stroke="currentColor" stroke-width="1.5" stroke-linecap="round"><path d="M3 7h8M7 3v8" class="pubs-toggle-cross"/></svg></span></button></h2><p class=publication-authors-line>Recep Ozgur Taskent, Nursen Duha Alioglu, Evrim Fer, <strong class=
78publication-author-lab>Handan Melike Dönertaş</strong>, Mehmet Somel<sup class=publication-author-mark aria-label="corresponding author">#</sup>, Omer Gokcumen<sup class=publication-author-mark aria-label="corresponding author">#</sup></p><p class=pubs-card-abstract id=publication-abstract-28 hidden>Neanderthals contributed genetic material to modern humans via multiple admixture events. Initial admixture events presumably occurred in Western Asia shortly after humans migrated out of Africa. Despite being a focal point of admixture, earlier studies indicate lower Neanderthal introgression rates in some Western Asian populations as compared with other Eurasian populations. To better understand the genome-wide and phenotypic impact of Neanderthal introgression in the region, we sequenced whole genomes of nine present-day Europeans, Africans, and the Western Asian Druze at high depth, and analyzed available whole genome data from various other populations, including 16 genomes from present-day Turkey. Our results confirmed previous observations that contemporary Western Asian populations, on an average, have lower levels of Neanderthal-introgressed DNA relative to other Eurasian populations. Modern Western Asians also show comparatively high variability in Neanderthal ancestry, which may be attributed to the complex demographic history of the region. We further replicated the previously described depletion of putatively functional sequences among Neanderthal-introgressed haplotypes. Still, we find dozens of common Neanderthal-introgressed haplotypes in the Turkish sample associated with human phenotypes, including anthropometric and metabolic traits, as well as the immune response. One of these haplotypes is unusually long and harbors variants that affect the expression of members of the CCR gene family and are associated with celiac disease. Overall, our results paint a complex first picture of the genomic impact of Neanderthal introgression in the Western Asian populations.</p><ul class=pubs-card-chips role=list><li class=pubs-card-chip-item><a class="pubs-card-chip pubs-card-chip--doi" href=https://doi.org/10.1093/gbe/evx216 rel="noopener noreferrer" target=_blank><span class=pubs-card-chip-label>Journal</span></a></li></ul></div></article></li><li class="pubs-card is-group-led" data-pub-type=journal data-pub-year=2017 data-pub-journal="Scientific Reports" data-pub-authors="Handan Melike Dönertaş|Hamit İzgi|Altuğ Kamacıoğlu|Zhisong He|Philipp Khaitovich|Mehmet Somel" data-pub-grouplead=1 style=--pub-stagger:1.45s><article class=pubs-card-inner aria-labelledby=publication-card-title-29><span class=pubs-card-rule aria-hidden=true></span><div class=pubs-card-rail><span class=pubs-card-year>2017</span><span class="pubs-card-type pubs-card-type--journal">Research Article</span><span class=pubs-card-venue>Scientific Reports</span></div><div class=pubs-card-body><h2 class=pubs-card-title id=publication-card-title-29><button type=button class=pubs-card-toggle aria-expanded=false aria-controls=publication-abstract-29>
79<span class=pubs-card-toggle-text>Gene expression reversal toward pre-adult levels in the aging human brain and age-related loss of cellular identity</span>
80<span class=pubs-card-toggle-icon aria-hidden=true><svg width="14" height="14" viewBox="0 0 14 14" fill="none" stroke="currentColor" stroke-width="1.5" stroke-linecap="round"><path d="M3 7h8M7 3v8" class="pubs-toggle-cross"/></svg></span></button></h2><p class=publication-authors-line><strong class=publication-author-lab>Handan Melike Dönertaş</strong><sup class=publication-author-mark aria-label="corresponding author">#</sup>
80, Hamit İzgi, Altuğ Kamacıoğlu, Zhisong He, Philipp Khaitovich, Mehmet Somel<sup class=publication-author-mark aria-label="corresponding author">#</sup></p><p class=pubs-card-abstract id=publication-abstract-29 hidden>It was previously reported that mRNA expression levels in the prefrontal cortex at old age start to resemble pre-adult levels. Such expression reversals could imply loss of cellular identity in the aging brain, and provide a link between aging-related molecular changes and functional decline. Here we analyzed 19 brain transcriptome age-series datasets, comprising 17 diverse brain regions, to investigate the ubiquity and functional properties of expression reversal in the human brain. Across all 19 datasets, 25 genes were consistently up-regulated during postnatal development and down-regulated in aging, displaying an ``up-down'' pattern that was significant as determined by random permutations. In addition, 113 biological processes, including neuronal and synaptic functions, were consistently associated with genes showing an up-down tendency among all datasets. Genes up-regulated during in vitro neuronal differentiation also displayed a tendency for up-down reversal, although at levels comparable to other genes. We argue that reversals may not represent aging-related neuronal loss. Instead, expression reversals may be associated with aging-related accumulation of stochastic effects that lead to loss of functional and structural identity in neurons.</p><ul class=pubs-card-chips role=list><li class=pubs-card-chip-item><a class="pubs-card-chip pubs-card-chip--doi" href=https://doi.org/10.1038/s41598-017-05927-4 rel="noopener noreferrer" target=_blank><span class=pubs-card-chip-label>Journal</span></a></li></ul></div></article></li><li class=pubs-card data-pub-type=journal data-pub-year=2016 data-pub-journal="Current Biology" data-pub-authors="Gülşah Merve Kılınç|Ayça Omrak|Füsun Özer|Torsten Günther|Ali Metin Büyükkarakaya|Erhan Bıçakçı|Douglas Baird|Handan Melike Dönertaş|Ayshin Ghalichi|Reyhan Yaka|Dilek Koptekin|Sinan Can Açan|Poorya Parvizi|Maja Krzewińska|Evangelia A. Daskalaki|Eren Yüncü|Nihan Dilşad Dağtaş|Andrew Fairbairn|Jessica Pearson|Gökhan Mustafaoğlu|Yılmaz Selim Erdal|Yasin Gökhan Çakan|İnci Togan|Mehmet Somel|Jan Storå|Mattias Jakobsson|Anders Götherström" data-pub-grouplead=0 style=--pub-stagger:1.50s><article class=pubs-card-inner aria-labelledby=publication-card-title-30><span class=pubs-card-rule aria-hidden=true></span><div class=pubs-card-rail><span class=pubs-card-year>2016</span><span class="pubs-card-type pubs-card-type--journal">Research Article</span><span class=pubs-card-venue>Current Biology</span></div><div class=pubs-card-body><h2 class=pubs-card-title id=publication-card-title-30><button type=button class=pubs-card-toggle aria-expanded=false aria-controls=publication-abstract-30>
81<span class=pubs-card-toggle-text>The Demographic Development of the First Farmers in Anatolia</span>
82<span class=pubs-card-toggle-icon aria-hidden=true><svg width="14" height="14" viewBox="0 0 14 14" fill="none" stroke="currentColor" stroke-width="1.5" stroke-linecap="round"><path d="M3 7h8M7 3v8" class="pubs-toggle-cross"/></svg></span></button></h2><p class=publication-authors-line>Gülşah Merve Kılınç<sup class=publication-author-mark aria-label="equal contribution">*</sup>, Ayça Omrak<sup class=publication-author-mark aria-label="equal contribution">*</sup>, Füsun Özer<sup class=publication-author-mark aria-label="equal contribution">*</sup>, Torsten Günther, Ali Metin Büyükkarakaya, Erhan Bıçakçı, Douglas Baird, <strong class=publication-author-lab>Handan Melike Dönertaş</strong>, Ayshin Ghalichi, Reyhan Yaka, Dilek Koptekin, Sinan Can Açan, Poorya Parvizi, Maja Krzewińska, Evangelia A. Daskalaki, Eren Yüncü, Nihan Dilşad Dağtaş, Andrew Fairbairn, Jessica Pearson, Gökhan Mustafaoğlu, Yılmaz Selim Erdal, Yasin Gökhan Çakan, İnci Togan<sup class=publication-author-mark aria-label="corresponding author">#</sup>, Mehmet Somel<sup class=publication-author-mark aria-label="corresponding author">#</sup>, Jan Storå<sup class=publication-author-mark aria-label="corresponding author">#</sup>, Mattias Jakobsson<sup class=publication-author-mark aria-label="corresponding author">#</sup>, Anders Götherström<sup class=publication-author-mark aria-label="corresponding author">#</sup></p><p class=pubs-card-abstract id=publication-abstract-30 hidden>The archaeological documentation of the development of sedentary farming societies in Anatolia is not yet mirrored by a genetic understanding of the human populations involved, in contrast to the spread of farming in Europe [1-3]. Sedentary farming communities emerged in parts of the Fertile Crescent during the tenth millennium and early ninth millennium calibrated (cal) BC and had appeared in central Anatolia by 8300 cal BC [4]. Farming spread into west Anatolia by the early seventh millennium cal BC and quasi-synchronously into Europe, although the timing and process of this movement remain unclear. Using genome sequence data that we generated from nine central Anatolian Neolithic individuals, we studied the transition period from early Aceramic (Pre-Pottery) to the later Pottery Neolithic, when farming expanded west of the Fertile Crescent. We find that genetic diversity in the earliest farmers was conspicuously low, on a par with European foraging groups. With the advent of the Pottery Neolithic, genetic variation within societies reached levels later found in early European farmers. Our results confirm that the earliest Neolithic central Anatolians belonged to the same gene pool as the first Neolithic migrants spreading into Europe. Further, genetic affinities between later Anatolian farmers and fourth to third millennium BC Chalcolithic south Europeans suggest an additional wave of Anatolian migrants, after the initial Neolithic spread but before the Yamnaya-related migrations. We propose that the earliest farming societies demographically resembled foragers and that only after regional gene flow and rising heterogeneity did the farming population expansions into Europe occur.</p>
82<ul class=pubs-card-chips role=list><li class=pubs-card-chip-item><a class="pubs-card-chip pubs-card-chip--doi" href=https://doi.org/10.1016/j.cub.2016.07.057 rel="noopener noreferrer" target=_blank><span class=pubs-card-chip-label>Journal</span></a></li></ul></div></article></li><li class="pubs-card is-group-led" data-pub-type=journal data-pub-year=2016 data-pub-journal="PLOS ONE" data-pub-authors="Handan Melike Dönertaş|Sergio Martínez Cuesta|Syed Asad Rahman|Janet M. Thornton" data-pub-grouplead=1 style=--pub-stagger:1.55s><article class=pubs-card-inner aria-labelledby=publication-card-title-31><span class=pubs-card-rule aria-hidden=true></span><div class=pubs-card-rail><span class=pubs-card-year>2016</span><span class="pubs-card-type pubs-card-type--journal">Research Article</span><span class=pubs-card-venue>PLOS ONE</span></div><div class=pubs-card-body><h2 class=pubs-card-title id=publication-card-title-31><button type=button class=pubs-card-toggle aria-expanded=false aria-controls=publication-abstract-31>
83<span class=pubs-card-toggle-text>Characterising Complex Enzyme Reaction Data</span>
84<span class=pubs-card-toggle-icon aria-hidden=true><svg width="14" height="14" viewBox="0 0 14 14" fill="none" stroke="currentColor" stroke-width="1.5" stroke-linecap="round"><path d="M3 7h8M7 3v8" class="pubs-toggle-cross"/></svg></span></button></h2><p class=publication-authors-line><strong class=publication-author-lab>Handan Melike Dönertaş</strong><sup class=publication-author-mark aria-label="equal contribution">*</sup>, Sergio Martínez Cuesta<sup class=publication-author-mark aria-label="equal contribution">*</sup>, Syed Asad Rahman, Janet M. Thornton<sup class=publication-author-mark aria-label="corresponding author">#</sup></p><p class=pubs-card-abstract id=publication-abstract-31 hidden>The relationship between enzyme-catalysed reactions and the Enzyme Commission (EC) number, the widely accepted classification scheme used to characterise enzyme activity, is complex and with the rapid increase in our knowledge of the reactions catalysed by enzymes needs revisiting. We present a manual and computational analysis to investigate this complexity and found that almost one-third of all known EC numbers are linked to more than one reaction in the secondary reaction databases (e.g., KEGG). Although this complexity is often resolved by defining generic, alternative and partial reactions, we have also found individual EC numbers with more than one reaction catalysing different types of bond changes. This analysis adds a new dimension to our understanding of enzyme function and might be useful for the accurate annotation of the function of enzymes and to study the changes in enzyme function during evolution.</p><ul class=pubs-card-chips role=list><li class=pubs-card-chip-item><a class="pubs-card-chip pubs-card-chip--doi" href=https://doi.org/10.1371/journal.pone.0147952 rel="noopener noreferrer" target=_blank><span class=pubs-card-chip-label>Journal</span></a></li></ul></div></article></li></ol><p class=pubs-empty data-pub-empty hidden>Nothing matches these filters.</p></div>
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Line numbers count LF bytes from the start of the resource, as the search results do. Vendor segments are library code the classifier recognised; they are stored but not indexed. Bytes are shown as Latin1 characters, one per byte.