Neutrophils are the most abundant leukocyte in humans and provide a critical early line of defense as part of our innate immune system. We perform a comprehensive, genome-wide assessment of the molecular factors critical to proliferation, differentiation, and cell migration in a neutrophil-like cell line. Through the development of multiple migration screen strategies, we specifically probe directed (chemotaxis), undirected (chemokinesis), and 3D amoeboid cell migration in these fast-moving cells. We identify a role for mTORC1 signaling in cell differentiation, which influences neutrophil abundance, survival, and migratory behavior. Across our individual migration screens, we identify genes involved in adhesion-dependent and adhesion-independent cell migration, protein trafficking, and regulation of the actomyosin cytoskeleton. This genome-wide screening strategy, therefore, provides an invaluable approach to the study of neutrophils and provides a resource that will inform future studies of cell migration in these and other rapidly migrating cells. <<<

Wen-Wei Liao, Mobin Asri, Jana Ebler, Daniel Doerr, Marina Haukness, Glenn Hickey, Shuangjia Lu, Julian K Lucas, Jean Monlong, Haley J Abel, Silvia Buonaiuto, Xian H Chang, Haoyu Cheng, Justin Chu, Vincenza Colonna, Jordan M Eizenga, Xiaowen Feng, Christian Fischer, Robert S Fulton, Shilpa Garg, Cristian Groza, Andrea Guarracino, William T Harvey, Simon Heumos, Kerstin Howe, Miten Jain, Tsung-Yu Lu, Charles Markello, Fergal J Martin, Matthew W Mitchell, Katherine M Munson, Moses Njagi Mwaniki, Adam M Novak, Hugh E Olsen, Trevor Pesout, David Porubsky, Pjotr Prins, Jonas A Sibbesen, Jouni Sirén, Chad Tomlinson, Flavia Villani, Mitchell R Vollger, Lucinda L Antonacci-Fulton, Gunjan Baid, Carl A Baker, Anastasiya Belyaeva, Konstantinos Billis, Andrew Carroll, Pi-Chuan Chang, Sarah Cody, Daniel E Cook, Robert M Cook-Deegan, Omar E Cornejo, Mark Diekhans, Peter Ebert, Susan Fairley, Olivier Fedrigo, Adam L Felsenfeld, Giulio Formenti, Adam Frankish, Yan Gao, Nanibaa' A Garrison, Carlos Garcia Giron, Richard E Green, Leanne Haggerty, Kendra Hoekzema, Thibaut Hourlier, Hanlee P Ji, Eimear E Kenny, Barbara A Koenig, Alexey Kolesnikov, Jan O Korbel, Jennifer Kordosky, Sergey Koren, HoJoon Lee, Alexandra P Lewis, Hugo Magalhães, Santiago Marco-Sola, Pierre Marijon, Ann McCartney, Jennifer McDaniel, Jacquelyn Mountcastle, Maria Nattestad, Sergey Nurk, Nathan D Olson, Alice B Popejoy, Daniela Puiu, Mikko Rautiainen, Allison A Regier, Arang Rhie, Samuel Sacco, Ashley D Sanders, Valerie A Schneider, Baergen I Schultz, Kishwar Shafin, Michael W Smith, Heidi J Sofia, Ahmad N Abou Tayoun, Françoise Thibaud-Nissen, Francesca Floriana Tricomi, Justin Wagner, Brian Walenz, Jonathan M D Wood, Aleksey V Zimin, Guillaume Bourque, Mark J P Chaisson, Paul Flicek, Adam M Phillippy, Justin M Zook, Evan E Eichler, David Haussler, Ting Wang, Erich D Jarvis, Karen H Miga, Erik Garrison, Tobias Marschall, Ira M Hall, Heng Li, Benedict Paten <<<

Here the Human Pangenome Reference Consortium presents a first draft of the human pangenome reference. The pangenome contains 47 phased, diploid assemblies from a cohort of genetically diverse individuals. These assemblies cover more than 99% of the expected sequence in each genome and are more than 99% accurate at the structural and base pair levels. Based on alignments of the assemblies, we generate a draft pangenome that captures known variants and haplotypes and reveals new alleles at structurally complex loci. We also add 119 million base pairs of euchromatic polymorphic sequences and 1,115 gene duplications relative to the existing reference GRCh38. Roughly 90 million of the additional base pairs are derived from structural variation. Using our draft pangenome to analyse short-read data reduced small variant discovery errors by 34% and increased the number of structural variants detected per haplotype by 104% compared with GRCh38-based workflows, which enabled the typing of the vast majority of structural variant alleles per sample. <<<

Protection against lethal ()/ () intra-abdominal infection (IAI)-mediated sepsis can be achieved by a novel form of trained innate immunity (TII) involving Gr-1+ myeloid-derived suppressor cells (MDSCs) that are induced by inoculation (immunization) with low virulence species [i.e., ()] that infiltrate the bone marrow (BM). In contrast, more virulent species (i.e., ), even at sub-lethal inocula, fail to induce similar levels of protection. The purpose of the present study was to test the hypothesis that the level of TII-mediated protection induced by strains inversely correlates with damage in the BM as a reflection of virulence. Mice were immunized by intraperitoneal inoculation with several parental and mutant strains of deficient in virulence factors (hyphal formation and candidalysin production), followed by an intraperitoneal challenge 14 d later and monitored for sepsis and mortality. Whole femur bones were collected 24 h and 13 d after immunization and assessed for BM tissue/cellular damage via ferroptosis and histology. While immunization with standard but not sub-lethal inocula of most wild-type strains resulted in considerable mortality, protection against lethal / IAI challenge varied by strain was usually less than that for , with no differences observed between parental and corresponding mutants. Finally, levels of protection afforded by the strains were inversely correlated with BM tissue damage ( = -0.773). TII-mediated protection against lethal sepsis induced by strain immunization inversely correlates with BM tissue/cellular damage as a reflection of localized virulence. <<<

Rujia Chen, Ning Xiao, Yue Lu, Tianyun Tao, Qianfeng Huang, Shuting Wang, Zhichao Wang, Mingli Chuan, Qing Bu, Zhou Lu, Hanyao Wang, Yanze Su, Yi Ji, Jianheng Ding, Ahmed Gharib, Huixin Liu, Yong Zhou, Shuzhu Tang, Guohua Liang, Honggen Zhang, Chuandeng Yi, Xiaoming Zheng, Zhukuan Cheng, Yang Xu, Pengcheng Li, Chenwu Xu, Jinling Huang, Aihong Li, Zefeng Yang <<<

The role of de novo evolved genes from non-coding sequences in regulating morphological differentiation between species/subspecies remains largely unknown. Here, we show that a rice de novo gene GSE9 contributes to grain shape difference between indica/xian and japonica/geng varieties. GSE9 evolves from a previous non-coding region of wild rice Oryza rufipogon through the acquisition of start codon. This gene is inherited by most japonica varieties, while the original sequence (absence of start codon, gse9) is present in majority of indica varieties. Knockout of GSE9 in japonica varieties leads to slender grains, whereas introgression to indica background results in round grains. Population evolutionary analyses reveal that gse9 and GSE9 are derived from wild rice Or-I and Or-III groups, respectively. Our findings uncover that the de novo GSE9 gene contributes to the genetic and morphological divergence between indica and japonica subspecies, and provide a target for precise manipulation of rice grain shape. <<<

BACKGROUND: Emerged coronavirus disease 2019 (COVID-19) is a pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-COV-2). Disease severity is associated with elevated levels of proinflammatory cytokines, such as interleukin-6 (IL-6). Genetic polymorphisms in the regulatory regions of cytokine genes may be associated with differential cytokine production in COVID-19 patients. This study aimed to investigate the association between three potentially functional single-nucleotide polymorphisms (SNPs) in the promoter region of IL-6 and the severity of susceptibility to COVID-19 in an Iranian population.METHODS: In total, 346 individuals (175 patients with severe COVID-19 and 171 patients with mild COVID-19) were recruited for this cohort study. Genomic DNA was extracted from peripheral blood leukocytes of patients to determine the genotypes of three selected SNPs (rs1800795 (-174 G > C), rs1800796 (-572 G > C), and rs1800797 (-597 G > A)) in the promoter region of the IL-6 gene using polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) method.RESULTS: There were no significant differences in the genotype or allele distribution of selected SNPs (rs1800795 (-174 G > C), rs1800796 (-572 G > C), and rs1800797 (-597 G > A)) in the promoter region of the IL-6 gene in patients with severe COVID-19 and patients with mild COVID-19.DISCUSSION: Our study indicated that these SNPs are not associated with COVID-19 severity in the Kurdish population from Kermanshah, Iran. <<<

Pingting Ying, Can Chen, Zequn Lu, Shuoni Chen, Ming Zhang, Yimin Cai, Fuwei Zhang, Jinyu Huang, Linyun Fan, Caibo Ning, Yanmin Li, Wenzhuo Wang, Hui Geng, Yizhuo Liu, Wen Tian, Zhiyong Yang, Jiuyang Liu, Chaoqun Huang, Xiaojun Yang, Bin Xu, Heng Li, Xu Zhu, Ni Li, Bin Li, Yongchang Wei, Ying Zhu, Jianbo Tian, Xiaoping Miao <<<

Genome-wide association studies have identified numerous variants associated with human complex traits, most of which reside in the non-coding regions, but biological mechanisms remain unclear. However, assigning function to the non-coding elements is still challenging. Here we apply Activity-by-Contact (ABC) model to evaluate enhancer-gene regulation effect by integrating multi-omics data and identified 544,849 connections across 20 cancer types. ABC model outperforms previous approaches in linking regulatory variants to target genes. Furthermore, we identify over 30,000 enhancer-gene connections in colorectal cancer (CRC) tissues. By integrating large-scale population cohorts (23,813 cases and 29,973 controls) and multipronged functional assays, we demonstrate an ABC regulatory variant rs4810856 associated with CRC risk (Odds Ratio = 1.11, 95%CI = 1.05-1.16, P = 4.02 × 10) by acting as an allele-specific enhancer to distally facilitate PREX1, CSE1L and STAU1 expression, which synergistically activate p-AKT signaling. Our study provides comprehensive regulation maps and illuminates a single variant regulating multiple genes, providing insights into cancer etiology. <<<

Daniel Levine, Syed Asad Rizvi, Sacha Lévy, Nazreen Pallikkavaliyaveetil, Ruiming Wu, Zihe Zheng, Antonio Oliveira Fonseca, Xingyu Chen, Sina Ghadermarzi, Rahul M. Dhodapkar, David van Dijk <<<

AbstractLarge language models like GPT have shown impressive performance on natural language tasks. Here, we present a novel method to directly adapt these pretrained models to a biological context, specifically single-cell transcriptomics, by representing gene expression data as text. Our Cell2Sentence approach converts each cell’s gene expression profile into a sequence of gene names ordered by expression level. We show that these gene sequences, which we term “cell sentences”, can be used to fine-tune causal language models like GPT-2. Critically, we find that natural language pretraining boosts model performance on cell sentence tasks. When fine-tuned on cell sentences, GPT-2 generates biologically valid cells when prompted with a cell type. Conversely, it can also accurately predict cell type labels when prompted with cell sentences. This demonstrates that language models fine-tuned using Cell2Sentence can gain a biological understanding of single-cell data, while retaining their ability to generate text. Our approach provides a simple, adaptable framework to combine natural language and transcriptomics using existing models and libraries. Our code is available at:https://github.com/vandijklab/cell2sentence-ft. <<<

BACKGROUND: Comparative gene expression studies in apes are fundamentally limited by the challenges associated with sampling across different tissues. Here, we used single-cell RNA sequencing of embryoid bodies to collect transcriptomic data from over 70 cell types in three humans and three chimpanzees.RESULTS: We find hundreds of genes whose regulation is conserved across cell types, as well as genes whose regulation likely evolves under directional selection in one or a handful of cell types. Using embryoid bodies from a human-chimpanzee fused cell line, we also infer the proportion of inter-species regulatory differences due to changes in cis and trans elements between the species. Using the cis/trans inference and an analysis of transcription factor binding sites, we identify dozens of transcription factors whose inter-species differences in expression are affecting expression differences between humans and chimpanzees in hundreds of target genes.CONCLUSIONS: Here, we present the most comprehensive dataset of comparative gene expression from humans and chimpanzees to date, including a catalog of regulatory mechanisms associated with inter-species differences. <<<

Reinforcement learning (RL) models have been influential in characterizing human learning and decision making, but few studies apply them to characterizing human spatial navigation and even fewer systematically compare RL models under different navigation requirements. Because RL can characterize one's learning strategies quantitatively and in a continuous manner, and one's consistency of using such strategies, it can provide a novel and important perspective for understanding the marked individual differences in human navigation and disentangle navigation strategies from navigation performance. One-hundred and fourteen participants completed wayfinding tasks in a virtual environment where different phases manipulated navigation requirements. We compared performance of five RL models (3 model-free, 1 model-based and 1 "hybrid") at fitting navigation behaviors in different phases. Supporting implications from prior literature, the hybrid model provided the best fit regardless of navigation requirements, suggesting the majority of participants rely on a blend of model-free (route-following) and model-based (cognitive mapping) learning in such navigation scenarios. Furthermore, consistent with a key prediction, there was a correlation in the hybrid model between the weight on model-based learning (i.e., navigation strategy) and the navigator's exploration vs. exploitation tendency (i.e., consistency of using such navigation strategy), which was modulated by navigation task requirements. Together, we not only show how computational findings from RL align with the spatial navigation literature, but also reveal how the relationship between navigation strategy and a person's consistency using such strategies changes as navigation requirements change. <<<

Zilong Zeng, Jiaying Zhang, Xinyuan Liang, Lianglong Sun, Yihe Zhang, Weiwei Men, Yanpei Wang, Rui Chen, Haibo Zhang, Shuping Tan, Jia-Hong Gao, Shaozheng Qin, Qiqi Tong, Hongjian He, Sha Tao, Qi Dong, Yong He, Tengda Zhao <<<

AbstractSusceptibility artifacts (SAs), which are inevitable for modern diffusion brain MR images with single-shot echo planar imaging (EPI) protocols in wide large-scale neuroimaging datasets, severely hamper the accurate detection of the human brain white matter structure. While several conventional and deep-learning based distortion correction methods have been proposed, the correction quality and model generality of these approaches are still limited. Here, we proposed the SACNet, a flexible SAs correction (SAC) framework for brain diffusion MR images of various phase-encoding EPI protocols based on an unsupervised learning-based registration convolutional neural network. This method could generate smooth diffeomorphic warps with optional neuroanatomy guidance to correct both geometric and intensity distortions of SAs. By employing near 2000 brain scans covering neonatal, child, adult and traveling participants, our SACNet consistently demonstrates state-of-the-art correction performance and effectively eliminates SAs-related multicenter effects compared with existing SAC methods. To facilitate the development of standard SAC tools for future neuroimaging studies, we also created easy-to-use command lines incorporating containerization techniques for quick user deployment. <<<

Anatoly Buchin, Rebecca de Frates, Anirban Nandi, Rusty Mann, Peter Chong, Lindsay Ng, Jeremy Miller, Rebecca Hodge, Brian Kalmbach, Soumita Bose, Ueli Rutishauser, Stephen McConoughey, Ed Lein, Jim Berg, Staci Sorensen, Ryder Gwinn, Christof Koch, Jonathan Ting, Costas A Anastassiou <<<

Temporal lobe epilepsy is the fourth most common neurological disorder, with about 40% of patients not responding to pharmacological treatment. Increased cellular loss is linked to disease severity and pathological phenotypes such as heightened seizure propensity. While the hippocampus is the target of therapeutic interventions, the impact of the disease at the cellular level remains unclear. Here, we show that hippocampal granule cells change with disease progression as measured in living, resected hippocampal tissue excised from patients with epilepsy. We show that granule cells increase excitability and shorten response latency while also enlarging in cellular volume and spine density. Single-nucleus RNA sequencing combined with simulations ascribes the changes to three conductances: BK, Cav2.2, and Kir2.1. In a network model, we show that these changes related to disease progression bring the circuit into a more excitable state, while reversing them produces a less excitable, "early-disease-like" state. <<<

Heiko Schuster, Wenguang Shao, Tobias Weiss, Patrick G A Pedrioli, Patrick Roth, Michael Weller, David S Campbell, Eric W Deutsch, Robert L Moritz, Oliver Planz, Hans-Georg Rammensee, Ruedi Aebersold, Etienne Caron <<<

The large array of peptides presented to CD8+ T cells by major histocompatibility complex (MHC) class I molecules is referred to as the MHC class I immunopeptidome. Although the MHC class I immunopeptidome is ubiquitous in mammals and represents a critical component of the immune system, very little is known, in any species, about its composition across most tissues and organs in vivo. We applied mass spectrometry (MS) technologies to draft the first tissue-based atlas of the murine MHC class I immunopeptidome in health. Peptides were extracted from 19 normal tissues from C57BL/6 mice and prepared for MS injections, resulting in a total number of 28,448 high-confidence H2D/K-associated peptides identified and annotated in the atlas. This atlas provides initial qualitative data to explore the tissue-specificity of the immunopeptidome and serves as a guide to identify potential tumor-associated antigens from various cancer models. Our data were shared via PRIDE (PXD008733), SysteMHC Atlas (SYSMHC00018) and SWATH Atlas. We anticipate that this unique dataset will be expanded in the future and will find wide applications in basic and translational immunology. <<<

Traditional diagnostic formulations of psychotic disorders have low correspondence with underlying disease neurobiology. This has led to a growing interest in using brain-based biomarkers to capture biologically-informed psychosis constructs. Building upon our prior work on the B-SNIP Psychosis Biotypes, we aimed to examine whether structural MRI (an independent biomarker not used in the Biotype development) can effectively classify the Biotypes. Whole brain voxel-wise grey matter density (GMD) maps from T1-weighted images were used to train and test (using repeated randomized train/test splits) binary L2-penalized logistic regression models to discriminate psychosis cases (n = 557) from healthy controls (CON, n = 251). A total of six models were evaluated across two psychosis categorization schemes: (i) three Biotypes (B1, B2, B3) and (ii) three DSM diagnoses (schizophrenia (SZ), schizoaffective (SAD) and bipolar (BD) disorders). Above-chance classification accuracies were observed in all Biotype (B1 = 0.70, B2 = 0.65, and B3 = 0.56) and diagnosis (SZ = 0.64, SAD = 0.64, and BD = 0.59) models. However, the only model that showed evidence of specificity was B1, i.e., the model was able to discriminate B1 vs. CON and did not misclassify other psychosis cases (B2 or B3) as B1 at rates above nominal chance. The GMD-based classifier evidence for B1 showed a negative association with an estimate of premorbid general intellectual ability, regardless of group membership, i.e. psychosis or CON. Our findings indicate that, complimentary to clinical diagnoses, the B-SNIP Psychosis Biotypes may offer a promising approach to capture specific aspects of psychosis neurobiology. <<<

Jun Li, Melissa J Hubisz, Ethan M Earlie, Mercedes A Duran, Christy Hong, Austin A Varela, Emanuele Lettera, Matthew Deyell, Bernardo Tavora, Jonathan J Havel, Su M Phyu, Amit Dipak Amin, Karolina Budre, Erina Kamiya, Julie-Ann Cavallo, Christopher Garris, Simon Powell, Jorge S Reis-Filho, Hannah Wen, Sarah Bettigole, Atif J Khan, Benjamin Izar, Eileen E Parkes, Ashley M Laughney, Samuel F Bakhoum <<<

Chromosomal instability (CIN) is a driver of cancer metastasis, yet the extent to which this effect depends on the immune system remains unknown. Using ContactTracing-a newly developed, validated and benchmarked tool to infer the nature and conditional dependence of cell-cell interactions from single-cell transcriptomic data-we show that CIN-induced chronic activation of the cGAS-STING pathway promotes downstream signal re-wiring in cancer cells, leading to a pro-metastatic tumour microenvironment. This re-wiring is manifested by type I interferon tachyphylaxis selectively downstream of STING and a corresponding increase in cancer cell-derived endoplasmic reticulum (ER) stress response. Reversal of CIN, depletion of cancer cell STING or inhibition of ER stress response signalling abrogates CIN-dependent effects on the tumour microenvironment and suppresses metastasis in immune competent, but not severely immune compromised, settings. Treatment with STING inhibitors reduces CIN-driven metastasis in melanoma, breast and colorectal cancers in a manner dependent on tumour cell-intrinsic STING. Finally, we show that CIN and pervasive cGAS activation in micronuclei are associated with ER stress signalling, immune suppression and metastasis in human triple-negative breast cancer, highlighting a viable strategy to identify and therapeutically intervene in tumours spurred by CIN-induced inflammation. <<<

Ana Luísa Correia, Joao C Guimaraes, Priska Auf der Maur, Duvini De Silva, Marcel P Trefny, Ryoko Okamoto, Sandro Bruno, Alexander Schmidt, Kirsten Mertz, Katrin Volkmann, Luigi Terracciano, Alfred Zippelius, Marcus Vetter, Christian Kurzeder, Walter Paul Weber, Mohamed Bentires-Alj <<<

The persistence of undetectable disseminated tumour cells (DTCs) after primary tumour resection poses a major challenge to effective cancer treatment. These enduring dormant DTCs are seeds of future metastases, and the mechanisms that switch them from dormancy to outgrowth require definition. Because cancer dormancy provides a unique therapeutic window for preventing metastatic disease, a comprehensive understanding of the distribution, composition and dynamics of reservoirs of dormant DTCs is imperative. Here we show that different tissue-specific microenvironments restrain or allow the progression of breast cancer in the liver-a frequent site of metastasis that is often associated with a poor prognosis. Using mouse models, we show that there is a selective increase in natural killer (NK) cells in the dormant milieu. Adjuvant interleukin-15-based immunotherapy ensures an abundant pool of NK cells that sustains dormancy through interferon-γ signalling, thereby preventing hepatic metastases and prolonging survival. Exit from dormancy follows a marked contraction of the NK cell compartment and the concurrent accumulation of activated hepatic stellate cells (aHSCs). Our proteomics studies on liver co-cultures implicate the aHSC-secreted chemokine CXCL12 in the induction of NK cell quiescence through its cognate receptor CXCR4. CXCL12 expression and aHSC abundance are closely correlated in patients with liver metastases. Our data identify the interplay between NK cells and aHSCs as a master switch of cancer dormancy, and suggest that therapies aimed at normalizing the NK cell pool might succeed in preventing metastatic outgrowth. <<<

Dynamic feature selection, where we sequentially query features to make accurate predictions with a minimal budget, is a promising paradigm to reduce feature acquisition costs and provide transparency into the prediction process. The problem is challenging, however, as it requires both making predictions with arbitrary feature sets and learning a policy to identify the most valuable selections. Here, we take an information-theoretic perspective and prioritize features based on their mutual information with the response variable. The main challenge is learning this selection policy, and we design a straightforward new modeling approach that estimates the mutual information in a discriminative rather than generative fashion. Building on our learning approach, we introduce several further improvements: allowing variable feature budgets across samples, enabling non-uniform costs between features, incorporating prior information, and exploring modern architectures to handle partial input information. We find that our method provides consistent gains over recent state-of-the-art methods across a variety of datasets. <<<

Knowledge of the human microbiome, which is likely a critical factor in the initiation, progression, and prognosis of multiple forms of cancer, is rapidly expanding. In this review, we focus on recent investigations to discern putative, causative microbial species and the microbiome composition and structure currently associated with procarcinogenesis and tumorigenesis at select body sites. We specifically highlight forms of cancer, gastrointestinal and nongastrointestinal, that have significant bacterial associations and well-defined experimental evidence with the aim of generating directions for future experimental and translational investigations to develop a clearer understanding of the multifaceted mechanisms by which microbiota affect cancer formation. SIGNIFICANCE: Emerging and, for some cancers, strong experimental and translational data support the contribution of the microbiome to cancer biology and disease progression. Disrupting microbiome features and pathways contributing to cancer may provide new approaches to improving cancer outcomes in patients. <<<

Hong Yang , Xiao Chen , Zuo-Bing Chen , Le Li , Xue-Ying Li , Francisco Xavier Castellanos , Tong-Jian Bai , Qi-Jing Bo , Jun Cao , Zhi-Kai Chang , Guan-Mao Chen , Ning-Xuan Chen , Wei Chen , Chang Cheng , Yu-Qi Cheng , Xi-Long Cui , Jia Duan , Yiru Fang , Qi-Yong Gong , Wen-Bin Guo , Zheng-Hua Hou , Lan Hu , Li Kuang , Feng Li , Hui-Xian Li , Kai-Ming Li , Tao Li , Yan-Song Liu , Zhe-Ning Liu , Yi-Cheng Long , Bin Lu , Qing-Hua Luo , Hua-Qing Meng , Daihui Peng , Hai-Tang Qiu , Jiang Qiu , Yue-Di Shen , Yu-Shu Shi , Tian-Mei Si , Yan-Qing Tang , Chuan-Yue Wang , Fei Wang , Kai Wang , Li Wang , Xiang Wang , Ying Wang , Yu-Wei Wang , Xiao-Ping Wu , Xin-Ran Wu , Chun-Ming Xie , Guang-Rong Xie , Hai-Yan Xie , Peng Xie , Xiu-Feng Xu , Jian Yang , Jia-Shu Yao , Shu-Qiao Yao , Ying-Ying Yin , Yong-Gui Yuan , Yu-Feng Zang , Ai-Xia Zhang , Hong Zhang , Ke-Rang Zhang , Lei Zhang , Zhi-Jun Zhang , Jing-Ping Zhao , Rubai Zhou , Yi-Ting Zhou , Jun-Juan Zhu , Zhi-Chen Zhu , Chao-Jie Zou , Xi-Nian Zuo , Chao-Gan Yan <<<

AbstractAberrant topological organization of whole-brain networks has been inconsistently reported in studies of patients with major depressive disorder (MDD), reflecting limited sample sizes. To address this issue, we utilized a big data sample of MDD patients from the REST-meta-MDD Project, including 821 MDD patients and 765 normal controls (NCs) from 16 sites. Using the Dosenbach 160 node atlas, we examined whole-brain functional networks and extracted topological features (e.g., global and local efficiency, nodal efficiency, and degree) using graph theory-based methods. Linear mixed-effect models were used for group comparisons to control for site variability; robustness of results was confirmed (e.g., multiple topological parameters, different node definitions, and several head motion control strategies were applied). We found decreased global and local efficiency in patients with MDD compared to NCs. At the nodal level, patients with MDD were characterized by decreased nodal degrees in the somatomotor network (SMN), dorsal attention network (DAN) and visual network (VN) and decreased nodal efficiency in the default mode network (DMN), SMN, DAN, and VN. These topological differences were mostly driven by recurrent MDD patients, rather than first-episode drug naive (FEDN) patients with MDD. In this highly powered multisite study, we observed disrupted topological architecture of functional brain networks in MDD, suggesting both locally and globally decreased efficiency in brain networks. <<<