Peng Xie, Juan Shen, Yi Yang, Xinrui Wang, Wei Liu, Hailong Cao, Yanying Zheng, Chen Wu, Guangyao Mao, Linjin Chen, Jingjing He, Weiheng Zheng, Zepu Yang, Xiao Zhang, Xu Jiang, Xianfa Yang, Ke Fang, Zhao Zhang, Xin Xue, Xueting Chen, Chaoyi Wang, Xing Liu, Ling Liu, Xuebiao Yao, Naihe Jing, Wei Xie, Jin Liu, Hua Cao, Zhuojuan Luo, Xiaodong Fang, Chengqi Lin <<<

Mika Kivimäki, Jaana Pentti, Philipp Frank, Fangyu Liu, Acer Blake, Solja T. Nyberg, Jussi Vahtera, Archana Singh-Manoux, Tony Wyss-Coray, Keenan A. Walker, Linda Partridge, Joni V. Lindbohm <<<

Abstract Social disadvantage, like advanced age, is a risk factor for a broad range of health conditions; however, whether it influences the aging process remains unclear. Here, using a multicohort approach, we investigated the associations of social disadvantage with age-related plasma proteins and age-related diseases. We found proteomic signatures of accelerated immune aging and 14 specific age-related proteins linked to social disadvantage during both early and later life. Individuals experiencing social disadvantage had an increased risk of 66 age-related diseases, with up to 39% of these associations mediated by the 14 age-related proteins (for example, DNAJB9, F2, HSPA1A, BGN). The main enriched pathway involved the upregulation of the pro-inflammatory regulator NF-κB24 and its downstream factor interleukin-8. Our findings support the hypothesis that social disadvantage throughout the life course may accelerate aging, a biological mechanism that could explain why social stratification plays such a fundamental role in determining human health. <<<

Ahyeon Hwang, Mario Skarica, Siwei Xu, Jensine Coudriet, Che Yu Lee, Lin Lin, Rosemarie Terwilliger, Alexa-Nicole Sliby, Jiawei Wang, Tuan Nguyen, Hongyu Li, Min Wu, Yi Dai, Ziheng Duan, Shushrruth Sai Srinivasan, Xiangyu Zhang, Yingxin Lin, Dianne Cruz, P. J. Michael Deans, , Victor E. Alvarez, David Benedek, Alicia Che, Dianne A. Cruz, David A. Davis, Ellen Hoffman, Alfred Kaye, Adam T. Labadorf, Terence M. Keane, Mark W. Logue, Ann McKee, Brian Marx, Mark W. Miller, Crystal Noller, Janitza Montalvo-Ortiz, Meghan Pierce, William K. Scott, Paula Schnurr, Krista DiSano, Thor Stein, Robert Ursano, Erika J. Wolf, Bertrand R. Huber, Daniel Levey, Jill R. Glausier, David A. Lewis, Joel Gelernter, Paul E. Holtzheimer, Matthew J. Friedman, Mark Gerstein, Nenad Sestan, Kristen J. Brennand, Ke Xu, Hongyu Zhao, John H. Krystal, Keith A. Young, Douglas E. Williamson, Alicia Che, Jing Zhang, Matthew J. Girgenti <<<

Post-traumatic stress disorder (PTSD) is a polygenic disorder occurring after extreme trauma exposure. Recent studies have begun to detail the molecular biology of PTSD. However, given the array of PTSD-perturbed molecular pathways identified so far, it is implausible that a single cell type is responsible. Here we profile the molecular responses in over two million nuclei from the dorsolateral prefrontal cortex of 111 human brains, collected post-mortem from individuals with and without PTSD and major depressive disorder. We identify neuronal and non-neuronal cell-type clusters, gene expression changes and transcriptional regulators, and map the epigenomic regulome of PTSD in a cell-type-specific manner. Our analysis revealed PTSD-associated gene alterations in inhibitory neurons, endothelial cells and microglia and uncovered genes and pathways associated with glucocorticoid signalling, GABAergic transmission and neuroinflammation. We further validated these findings using cell-type-specific spatial transcriptomics, confirming disruption of key genes such as SST and FKBP5. By integrating genetic, transcriptomic and epigenetic data, we uncovered the regulatory mechanisms of credible variants that disrupt PTSD genes, including ELFN1, MAD1L1 and KCNIP4, in a cell-type-specific context. Together, these findings provide a comprehensive characterization of the cell-specific molecular regulatory mechanisms that underlie the persisting effects of traumatic stress response on the human prefrontal cortex. <<<

Gustaw Eriksson, Congru Li, Tina Gorsek Sparovec, Anja Dekanski, Sara Torstensson, Sanjiv Risal, Claes Ohlsson, Angelica Lindén Hirschberg, Sophie Petropoulos, Qiaolin Deng, Elisabet Stener-Victorin <<<

Abstract Polycystic ovary syndrome (PCOS) has a negative effect on the receptivity of the endometrium to embryo implantation and increases the risk of miscarriage and endometrial cancer. The cellular and molecular heterogeneity of the endometrium in women with PCOS has not been well studied. Our study presents a comprehensive cellular atlas of the endometrium during the proliferative phase in women with PCOS characterized by overweight and obesity, hyperandrogenism and insulin resistance compared with controls of similar age, weight and body mass index. Analysis of 247,791 isolated endometrial nuclei from 27 biopsies (5 controls and 12 PCOS cases at baseline and 7 after 16 weeks of metformin and 3 after lifestyle intervention) revealed cell-type-specific disease signatures and variations in cellular composition and localization. Samples taken after 16 weeks of metformin treatment and lifestyle management showed extensive recovery of disease-specific endometrial signatures. We linked the specific role of each cell type to clinical features such as hyperandrogenism and insulin resistance, and specific cell types to risk of endometrial and metabolic disease. In addition, potential therapeutic targets such as integrin inhibitors were identified and the role of metformin in restoring endometrial health in patients with PCOS was highlighted. Our findings lay the groundwork to significantly advance the understanding of PCOS-specific endometrial dysfunction for future targeted therapies. <<<

Matthias Diebold, Patrick T. Gauthier, Katharina A. Mayer, Martina Mackova, Christian Hinze, Jessica Chang, Uptal D. Patel, Ekkehard Schütz, Bernd Jilma, Eva Schrezenmeier, Klemens Budde, Georg A. Böhmig, Philip F. Halloran <<<

Abstract A recent randomized controlled trial demonstrated that treatment with anti-CD38 monoclonal antibody felzartamab suppressed antibody-mediated rejection (ABMR) in kidney transplant patients but with recurrence after treatment in some patients. Here we examined the molecular effects of 6 months of felzartamab treatment on biopsies from the trial using genome-wide microarray analysis, comparing pretreatment, end-of-treatment (week 24) and posttreatment (week 52) biopsies from ten patients treated with felzartamab and ten patients in the placebo group. Felzartamab reduced molecular ABMR activity scores in all nine patients with baseline ABMR activity, selectively suppressing interferon gamma-inducible and natural killer cell transcripts, with minimal effect on ABMR stage-related endothelial transcripts. Suppression was often incomplete when ABMR activity was intense, and molecular recurrence was nearly universal by week 52. However, we also found that felzartamab had parenchymal benefits at week 52, slowing the trajectories of molecular injury scores beyond the treatment period, suggesting that suppression of ABMR activity could potentially slow future progression to kidney failure. These data provide preliminary molecular insights into the effects of CD38-directed treatment for ABMR, which have the potential to inform future therapeutic strategies. <<<

Anne-Julie Tessier, Fenglei Wang, Andres Ardisson Korat, A. Heather Eliassen, Jorge Chavarro, Francine Grodstein, Jun Li, Liming Liang, Walter C. Willett, Qi Sun, Meir J. Stampfer, Frank B. Hu, Marta Guasch-Ferré <<<

Areeba Patel, Kirsten Göbel, Sebastian Ille, Felix Hinz, Natalie Schoebe, Henri Bogumil, Jochen Meyer, Michelle Brehm, Helin Kardo, Daniel Schrimpf, Artem Lomakin, Michael Ritter, Pauline Göller, Paul Kerbs, Lisa Pfeifer, Stefan Hamelmann, Christina Blume, Franziska M. Ippen, Natalie Berghaus, Philipp Euskirchen, Leonille Schweizer, Claus Hultschig, Nadine Van Roy, Jo Van Dorpe, Joni Van der Meulen, Siebe Loontiens, Franceska Dedeurwaerdere, Henning Leske, Skarphéðinn Halldórsson, Graeme Fox, Simon Deacon, Inswasti Cahyani, Nadine Holmes, Satrio Wibowo, Rory Munro, Dan Martin, Abid Sharif, Mark Housley, Robert Goldspring, Sebastian Brandner, Somak Roy, Jürgen Hench, Stephan Frank, Andreas Unterberg, Violaine Goidts, Natalie Jäger, Simon Paine, Stuart Smith, Christel Herold-Mende, Wolfgang Wick, Stefan M. Pfister, Einar O. Vik-Mo, Andreas von Deimling, Sandro Krieg, David TW Jones, Matthew Loose, Matthias Schlesner, Martin Sill, Felix Sahm <<<

Hesham ElAbd, Mitchell Pesesky, Gabriel Innocenti, Brian K. Chung, Aya K. H. Mahdy, Valeriia Kriukova, Laila Kulsvehagen, Dennis Strobbe, Claudia Stühler, Gabriele Mayr, Damon H. May, Melanie Prinzensteiner, Tim A. Steiert, Florian Tran, Michel V. Hadjihannas, Rainer Günther, Elisa Rosati, Sören Mucha, Wolfgang Lieb, Malte Ziemann, Astrid Dempfle, Felix Braun, Trine Folseraas, Johannes R. Hov, Espen Melum, Petra Bacher, Martina Sterneck, Tobias J. Weismüller, Henrike Lenzen, Bernd Bokemeyer, Bryan Howie, Harlan S. Robins, Christoph Röcken, Stefan Schreiber, Nina Khanna, Anne-Katrin Pröbstel, Christoph Schramm, Thomas Vogl, Tom H. Karlsen, Andre Franke <<<

Abstract Primary sclerosing cholangitis (PSC) is an idiopathic, progressive and incurable liver disease. Here, we aimed for systematic analyses of adaptive immune responses in PSC. By profiling the T cell repertoires of 504 individuals with PSC and 904 healthy controls, we identified 1,008 clonotypes associated with PSC. A substantial fraction of these clonotypes was restricted to known PSC human leukocyte antigen susceptibility alleles and known to target Epstein–Barr virus (EBV) epitopes. We further utilized phage-immunoprecipitation sequencing to determine antibody epitope repertoires of 120 individuals with PSC and 202 healthy controls, which showed a higher burden of anti-EBV responses in PSC than controls. EBV-specific monoclonal antibodies isolated from B cells in PSC livers corroborated convergent B and T cell responses against EBV. By analyzing electronic health records of >116 million people, we identified an association between infectious mononucleosis and PSC (odds ratio, 12; 95% confidence interval, 6.3–22.9), suggesting a link between EBV and PSC. <<<

Rajappa S. Kenchappa, Laszlo Radnai, Erica J. Young, Natanael Zarco, Li Lin, Athanassios Dovas, Christian T. Meyer, Ashley Haddock, Alice Hall, Katalin Toth, Peter Canoll, Naveen K.H. Nagaiah, Gavin Rumbaugh, Michael D. Cameron, Theodore M. Kamenecka, Patrick R. Griffin, Courtney A. Miller, Steven S. Rosenfeld <<<

The multicellular coordination that underlies tissue homeostasis and disease progression is of fundamental interest. However, how diverse cell types are organized within tissue niches for cohesive functioning remains largely unknown. Here we systematically characterized cross-tissue coordinated cellular modules in healthy tissues, uncovering their spatiotemporal dynamics and phenotypic associations, and examined their rewiring in cancer. We first compiled a comprehensive single-cell transcriptomic atlas from 35 human tissues, revealing substantial inter-tissue variability in cellular composition. By leveraging covariance in cellular abundance, we identified 12 cellular modules with distinct cellular compositions, tissue prevalences and spatial organizations, and demonstrated coordinated intercellular communication within cellular modules using in situ spatial and in vivo perturbation data. Among them, two immune cellular modules in the spleen showed contrasting chronological dynamics with ageing. Analysis of multicellular changes in the breast revealed a menopausal trajectory associated with fibroblast dynamics. Furthermore, interrogation across cancer types uncovered simultaneous rewiring of two types of multicellular ecosystem during tumour progression, including the loss of tissue-specific healthy organization and the emergence of a convergent cancerous ecosystem. These findings reveal fundamental organizing principles of multicellular ecosystems in health and cancer, laying a foundation for further investigations into tissue-level functional coordination across diverse contexts. <<<

Sujing Yuan, Renqiang Sun, Hao Shi, Nicole M. Chapman, Haoran Hu, Cliff Guy, Sherri Rankin, Anil KC, Gustavo Palacios, Xiaoxi Meng, Xiang Sun, Peipei Zhou, Xiaoyang Yang, Stephen Gottschalk, Hongbo Chi <<<

Zedao Liu, Zhenlin Yang, Junqi Wu, Wenjie Zhang, Yuxuan Sun, Chao Zhang, Guangyu Bai, Li Yang, Hongtao Fan, Yawen Chen, Lei Zhang, Benyuan Jiang, Xiaoyan Liu, Xiaoshi Ma, Wei Tang, Chang Liu, Yang Qu, Lixu Yan, Deping Zhao, Yilong Wu, Shun He, Long Xu, Lishan Peng, Xiaowei Chen, Bolun Zhou, Liang Zhao, Zhangyi Zhao, Fengwei Tan, Wanting Zhang, Dingcheng Yi, Xiangjie Li, Qianqian Gao, Guangjian Zhang, Yongjie Wang, Minglei Yang, Honghao Fu, Yongjun Guo, Xueda Hu, Qingyuan Cai, Lu Qi, Yufei Bo, Hui Peng, Zhigang Tian, Yunlang She, Chang Zou, Linnan Zhu, Sijin Cheng, Yi Zhang, Wenzhao Zhong, Chang Chen, Shugeng Gao, Zemin Zhang <<<

Anti-PD-(L)1 treatment is standard for non-small cell lung cancer (NSCLC), but patients show variable responses to the same regimen. The tumor immune microenvironment (TIME) is associated with immunotherapy response, yet the heterogeneous underlying therapeutic outcomes remain underexplored. We applied single-cell RNA and TCR sequencing (scRNA/TCR-seq) to analyze surgical tumor samples from 234 NSCLC patients post-neoadjuvant chemo-immunotherapy. Analyses revealed five distinct TIME subtypes with varying major pathological response (MPR) rates. MPR patients had elevated levels of FGFBP2 NK/NK-like T cells, memory B cells, or effector T cells, while non-MPR patients showed higher CCR8 Tregs. T cell clonal expansion analyses unveiled heterogeneity in non-MPR patients, marked by varying expansions of Tex-relevant cells and CCR8 Tregs. Precursor exhausted T cells (Texp cells) correlated with recurrence-free survival, identifying a patient subgroup with reduced recurrence risk despite lack of MPR. Our study dissects TIME heterogeneity in response to chemoimmunotherapy, offering insights for NSCLC management. <<<

Mirazul Islam, Yilin Yang, Alan J. Simmons, Vishal M. Shah, Krushna Pavan Musale, Yanwen Xu, Naila Tasneem, Zhengyi Chen, Linh T. Trinh, Paola Molina, Marisol A. Ramirez-Solano, Iannish D. Sadien, Jinzhuang Dou, Andrea Rolong, Ken Chen, Mark A. Magnuson, Jeffrey C. Rathmell, Ian G. Macara, Douglas J. Winton, Qi Liu, Hamim Zafar, Reza Kalhor, George M. Church, Martha J. Shrubsole, Robert J. Coffey, Ken S. Lau <<<

AbstractAntigen discovery technologies have largely focused on major histocompatibility complex (MHC) class I-restricted human T cell receptors (TCRs), leaving methods for MHC class II-restricted and mouse TCR reactivities relatively undeveloped. Here we present TCR mapping of antigenic peptides (TCR-MAP), an antigen discovery method that uses a synthetic TCR-stimulated circuit in immortalized T cells to activate sortase-mediated tagging of engineered antigen-presenting cells (APCs) expressing processed peptides on MHCs. Live, tagged APCs can be directly purified for deconvolution by sequencing, enabling TCRs with unknown specificity to be queried against barcoded peptide libraries in a pooled screening context. TCR-MAP accurately captures self-reactivities or viral reactivities with high throughput and sensitivity for both MHC class I-restricted and class II-restricted TCRs. We elucidate problematic cross-reactivities of clinical TCRs targeting the cancer/testis melanoma-associated antigen A3 and discover targets of myocarditis-inciting autoreactive T cells in mice. TCR-MAP has the potential to accelerate T cell antigen discovery efforts in the context of cancer, infectious disease and autoimmunity. <<<

Xiekui Cui, Han Yang, Charles Cai, Cooper Beaman, Xiaoyu Yang, Hongjiang Liu, Xingjie Ren, Zachary Amador, Ian R. Jones, Kathleen C. Keough, Meng Zhang, Tyler Fair, Armen Abnousi, Shreya Mishra, Zhen Ye, Ming Hu, Alex A. Pollen, Katherine S. Pollard, Yin Shen <<<

Mohammed Toufiq, Darawan Rinchai, Eleonore Bettacchioli, Basirudeen Syed Ahamed Kabeer, Taushif Khan, Bishesh Subba, Olivia White, Marina Yurieva, Joshy George, Noemie Jourde-Chiche, Laurent Chiche, Karolina Palucka, Damien Chaussabel <<<

AbstractBackgroundFeature selection is a critical step for translating advances afforded by systems-scale molecular profiling into actionable clinical insights. While data-driven methods are commonly utilized for selecting candidate genes, knowledge-driven methods must contend with the challenge of efficiently sifting through extensive volumes of biomedical information. This work aimed to assess the utility of large language models (LLMs) for knowledge-driven gene prioritization and selection.MethodsIn this proof of concept, we focused on 11 blood transcriptional modules associated with an Erythroid cells signature. We evaluated four leading LLMs across multiple tasks. Next, we established a workflow leveraging LLMs. The steps consisted of: (1) Selecting one of the 11 modules; (2) Identifying functional convergences among constituent genes using the LLMs; (3) Scoring candidate genes across six criteria capturing the gene’s biological and clinical relevance; (4) Prioritizing candidate genes and summarizing justifications; (5) Fact-checking justifications and identifying supporting references; (6) Selecting a top candidate gene based on validated scoring justifications; and (7) Factoring in transcriptome profiling data to finalize the selection of the top candidate gene.ResultsOf the four LLMs evaluated, OpenAI's GPT-4 and Anthropic's Claude demonstrated the best performance and were chosen for the implementation of the candidate gene prioritization and selection workflow. This workflow was run in parallel for each of the 11 erythroid cell modules by participants in a data mining workshop. Module M9.2 served as an illustrative use case. The 30 candidate genes forming this module were assessed, and the top five scoring genes were identified as BCL2L1, ALAS2, SLC4A1, CA1, and FECH. Researchers carefully fact-checked the summarized scoring justifications, after which the LLMs were prompted to select a top candidate based on this information. GPT-4 initially chose BCL2L1, while Claude selected ALAS2. When transcriptional profiling data from three reference datasets were provided for additional context, GPT-4 revised its initial choice to ALAS2, whereas Claude reaffirmed its original selection for this module.ConclusionsTaken together, our findings highlight the ability of LLMs to prioritize candidate genes with minimal human intervention. This suggests the potential of this technology to boost productivity, especially for tasks that require leveraging extensive biomedical knowledge. <<<

Simeng Hu, Can Hu, Jingli Xu, Pengfei Yu, Li Yuan, Ziyu Li, Haohong Liang, Yanqiang Zhang, Jiahui Chen, Qing Wei, Shengjie Zhang, Litao Yang, Dan Su, Yian Du, Zhiyuan Xu, Fan Bai, Xiangdong Cheng <<<

Current computational workflows for comparative analyses of single-cell datasets typically use discrete clusters as input when testing for differential abundance among experimental conditions. However, clusters do not always provide the appropriate resolution and cannot capture continuous trajectories. Here we present Milo, a scalable statistical framework that performs differential abundance testing by assigning cells to partially overlapping neighborhoods on a k-nearest neighbor graph. Using simulations and single-cell RNA sequencing (scRNA-seq) data, we show that Milo can identify perturbations that are obscured by discretizing cells into clusters, that it maintains false discovery rate control across batch effects and that it outperforms alternative differential abundance testing strategies. Milo identifies the decline of a fate-biased epithelial precursor in the aging mouse thymus and identifies perturbations to multiple lineages in human cirrhotic liver. As Milo is based on a cell-cell similarity structure, it might also be applicable to single-cell data other than scRNA-seq. Milo is provided as an open-source R software package at https://github.com/MarioniLab/miloR . <<<

Prashant S Emani, Jason J Liu, Declan Clarke, Matthew Jensen, Jonathan Warrell, Chirag Gupta, Ran Meng, Che Yu Lee, Siwei Xu, Cagatay Dursun, Shaoke Lou, Yuhang Chen, Zhiyuan Chu, Timur Galeev, Ahyeon Hwang, Yunyang Li, Pengyu Ni, Xiao Zhou, PsychENCODE Consortium, Trygve E Bakken, Jaroslav Bendl, Lucy Bicks, Tanima Chatterjee, Lijun Cheng, Yuyan Cheng, Yi Dai, Ziheng Duan, Mary Flaherty, John F Fullard, Michael Gancz, Diego Garrido-Martín, Sophia Gaynor-Gillett, Jennifer Grundman, Natalie Hawken, Ella Henry, Gabriel E Hoffman, Ao Huang, Yunzhe Jiang, Ting Jin, Nikolas L Jorstad, Riki Kawaguchi, Saniya Khullar, Jianyin Liu, Junhao Liu, Shuang Liu, Shaojie Ma, Michael Margolis, Samantha Mazariegos, Jill Moore, Jennifer R Moran, Eric Nguyen, Nishigandha Phalke, Milos Pjanic, Henry Pratt, Diana Quintero, Ananya S Rajagopalan, Tiernon R Riesenmy, Nicole Shedd, Manman Shi, Megan Spector, Rosemarie Terwilliger, Kyle J Travaglini, Brie Wamsley, Gaoyuan Wang, Yan Xia, Shaohua Xiao, Andrew C Yang, Suchen Zheng, Michael J Gandal, Donghoon Lee, Ed S Lein, Panos Roussos, Nenad Sestan, Zhiping Weng, Kevin P White, Hyejung Won, Matthew J Girgenti, Jing Zhang, Daifeng Wang, Daniel Geschwind, Mark Gerstein <<<

Single-cell genomics is a powerful tool for studying heterogeneous tissues such as the brain. Yet, little is understood about how genetic variants influence cell-level gene expression. Addressing this, we uniformly processed single-nuclei, multi-omics datasets into a resource comprising >2.8M nuclei from the prefrontal cortex across 388 individuals. For 28 cell types, we assessed population-level variation in expression and chromatin across gene families and drug targets. We identified >550K cell-type-specific regulatory elements and >1.4M single-cell expression-quantitative-trait loci, which we used to build cell-type regulatory and cell-to-cell communication networks. These networks manifest cellular changes in aging and neuropsychiatric disorders. We further constructed an integrative model accurately imputing single-cell expression and simulating perturbations; the model prioritized ~250 disease-risk genes and drug targets with associated cell types. <<<

Myron G Best, Nik Sol, Irsan Kooi, Jihane Tannous, Bart A Westerman, François Rustenburg, Pepijn Schellen, Heleen Verschueren, Edward Post, Jan Koster, Bauke Ylstra, Najim Ameziane, Josephine Dorsman, Egbert F Smit, Henk M Verheul, David P Noske, Jaap C Reijneveld, R Jonas A Nilsson, Bakhos A Tannous, Pieter Wesseling, Thomas Wurdinger <<<

Tumor-educated blood platelets (TEPs) are implicated as central players in the systemic and local responses to tumor growth, thereby altering their RNA profile. We determined the diagnostic potential of TEPs by mRNA sequencing of 283 platelet samples. We distinguished 228 patients with localized and metastasized tumors from 55 healthy individuals with 96% accuracy. Across six different tumor types, the location of the primary tumor was correctly identified with 71% accuracy. Also, MET or HER2-positive, and mutant KRAS, EGFR, or PIK3CA tumors were accurately distinguished using surrogate TEP mRNA profiles. Our results indicate that blood platelets provide a valuable platform for pan-cancer, multiclass cancer, and companion diagnostics, possibly enabling clinical advances in blood-based "liquid biopsies". <<<