Bin Fu, Jiaoyang Liao, Shuanghong Chen, Wei Li, Qiudao Wang, Jian Hu, Fei Yang, Shenlin Hsiao, Yanhong Jiang, Liren Wang, Fangping Chen, Yuanjin Zhang, Xin Wang, Dali Li, Mingyao Liu, Yuxuan Wu <<<

Gene editing to disrupt the GATA1-binding site at the +58 BCL11A erythroid enhancer could induce γ-globin expression, which is a promising therapeutic strategy to alleviate β-hemoglobinopathy caused by HBB gene mutation. In the present study, we report the preliminary results of an ongoing phase 1/2 trial (NCT04211480) evaluating safety and efficacy of gene editing therapy in children with blood transfusion-dependent β-thalassemia (TDT). We transplanted BCL11A enhancer-edited, autologous, hematopoietic stem and progenitor cells into two children, one carrying the β/β genotype, classified as the most severe type of TDT. Primary endpoints included engraftment, overall survival and incidence of adverse events (AEs). Both patients were clinically well with multilineage engraftment, and all AEs to date were considered unrelated to gene editing and resolved after treatment. Secondary endpoints included achieving transfusion independence, editing rate in bone marrow cells and change in hemoglobin (Hb) concentration. Both patients achieved transfusion independence for >18 months after treatment, and their Hb increased from 8.2 and 10.8 g dl at screening to 15.0 and 14.0 g dl at the last visit, respectively, with 85.46% and 89.48% editing persistence in bone marrow cells. Exploratory analysis of single-cell transcriptome and indel patterns in edited peripheral blood mononuclear cells showed no notable side effects of the therapy. <<<

Yusheng Cai, Muzhao Xiong, Zijuan Xin, Chengyu Liu, Jie Ren, Xiying Yang, Jinghui Lei, Wei Li, Feifei Liu, Qun Chu, Yiyuan Zhang, Jian Yin, Yanxia Ye, Dingyi Liu, Yanling Fan, Shuhui Sun, Yaobin Jing, Qian Zhao, Liyun Zhao, Shanshan Che, Yandong Zheng, Haoteng Yan, Shuai Ma, Si Wang, Juan Carlos Izpisua Belmonte, Jing Qu, Weiqi Zhang, Guang-Hui Liu <<<

Regeneration across tissues and organs exhibits significant variation throughout the body and undergoes a progressive decline with age. To decode the relationships between aging and regenerative capacity, we conducted a comprehensive single-cell transcriptome analysis of regeneration in eight tissues from young and aged mice. We employed diverse analytical models to study tissue regeneration and unveiled the intricate cellular and molecular mechanisms underlying the attenuated regenerative processes observed in aged tissues. Specifically, we identified compromised stem cell mobility and inadequate angiogenesis as prominent contributors to this age-associated decline in regenerative capacity. Moreover, we discovered a unique subset of Arg1 macrophages that were activated in young tissues but suppressed in aged regenerating tissues, suggesting their important role in age-related immune response disparities during regeneration. This study provides a comprehensive single-cell resource for identifying potential targets for interventions aimed at enhancing regenerative outcomes in the aging population. <<<

Within the context of screening tests, it is important to avoid misconceptions about sensitivity, specificity, and predictive values. In this article, therefore, foundations are first established concerning these metrics along with the first of several aspects of pliability that should be recognized in relation to those metrics. Clarification is then provided about the definitions of sensitivity, specificity, and predictive values and why researchers and clinicians can misunderstand and misrepresent them. Arguments are made that sensitivity and specificity should usually be applied only in the context of describing a screening test's attributes relative to a reference standard; that predictive values are more appropriate and informative in actual screening contexts, but that sensitivity and specificity can be used for screening decisions about individual people if they are extremely high; that predictive values need not always be high and might be used to advantage by adjusting the sensitivity and specificity of screening tests; that, in screening contexts, researchers should provide information about all four metrics and how they were derived; and that, where necessary, consumers of health research should have the skills to interpret those metrics effectively for maximum benefit to clients and the healthcare system. <<<

Lian Narunsky-Haziza, Gregory D. Sepich-Poore, Ilana Livyatan, Omer Asraf, Cameron Martino, Deborah Nejman, Nancy Gavert, Jason E. Stajich, Guy Amit, Antonio González, Stephen Wandro, Gili Perry, Ruthie Ariel, Arnon Meltser, Justin P. Shaffer, Qiyun Zhu, Nora Balint-Lahat, Iris Barshack, Maya Dadiani, Einav N. Gal-Yam, Sandip Pravin Patel, Amir Bashan, Austin D. Swafford, Yitzhak Pilpel, Rob Knight, Ravid Straussman <<<

Deep convolutional neural networks (DCNNs) trained for face identification can rival and even exceed human-level performance. The ways in which the internal face representations in DCNNs relate to human cognitive representations and brain activity are not well understood. Nearly all previous studies focused on static face image processing with rapid display times and ignored the processing of naturalistic, dynamic information. To address this gap, we developed the largest naturalistic dynamic face stimulus set in human neuroimaging research (700+ naturalistic video clips of unfamiliar faces). We used this naturalistic dataset to compare representational geometries estimated from DCNNs, behavioral responses, and brain responses. We found that DCNN representational geometries were consistent across architectures, cognitive representational geometries were consistent across raters in a behavioral arrangement task, and neural representational geometries in face areas were consistent across brains. Representational geometries in late, fully connected DCNN layers, which are optimized for individuation, were much more weakly correlated with cognitive and neural geometries than were geometries in late-intermediate layers. The late-intermediate face-DCNN layers successfully matched cognitive representational geometries, as measured with a behavioral arrangement task that primarily reflected categorical attributes, and correlated with neural representational geometries in known face-selective topographies. Our study suggests that current DCNNs successfully capture neural cognitive processes for categorical attributes of faces but less accurately capture individuation and dynamic features. <<<

Yingjie Bian, Wei Li, Daniel M Kremer, Peter Sajjakulnukit, Shasha Li, Joel Crespo, Zeribe C Nwosu, Li Zhang, Arkadiusz Czerwonka, Anna Pawłowska, Houjun Xia, Jing Li, Peng Liao, Jiali Yu, Linda Vatan, Wojciech Szeliga, Shuang Wei, Sara Grove, J Rebecca Liu, Karen McLean, Marcin Cieslik, Arul M Chinnaiyan, Witold Zgodziński, Grzegorz Wallner, Iwona Wertel, Karolina Okła, Ilona Kryczek, Costas A Lyssiotis, Weiping Zou <<<

Abnormal epigenetic patterns correlate with effector T cell malfunction in tumours, but the cause of this link is unknown. Here we show that tumour cells disrupt methionine metabolism in CD8 T cells, thereby lowering intracellular levels of methionine and the methyl donor S-adenosylmethionine (SAM) and resulting in loss of dimethylation at lysine 79 of histone H3 (H3K79me2). Loss of H3K79me2 led to low expression of STAT5 and impaired T cell immunity. Mechanistically, tumour cells avidly consumed methionine and outcompeted T cells for methionine by expressing high levels of the methionine transporter SLC43A2. Genetic and biochemical inhibition of tumour SLC43A2 restored H3K79me2 in T cells, thereby boosting spontaneous and checkpoint-induced tumour immunity. Moreover, methionine supplementation improved the expression of H3K79me2 and STAT5 in T cells, and this was accompanied by increased T cell immunity in tumour-bearing mice and patients with colon cancer. Clinically, tumour SLC43A2 correlated negatively with T cell histone methylation and functional gene signatures. Our results identify a mechanistic connection between methionine metabolism, histone patterns, and T cell immunity in the tumour microenvironment. Thus, cancer methionine consumption is an immune evasion mechanism, and targeting cancer methionine signalling may provide an immunotherapeutic approach. <<<

We propose an effective denoising diffusion model for generatinghigh-resolution images (e.g., 1024$\times$512), trained on small-size imagepatches (e.g., 64$\times$64). We name our algorithm Patch-DM, in which a newfeature collage strategy is designed to avoid the boundary artifact whensynthesizing large-size images. Feature collage systematically crops andcombines partial features of the neighboring patches to predict the features ofa shifted image patch, allowing the seamless generation of the entire image dueto the overlap in the patch feature space. Patch-DM produces high-quality imagesynthesis results on our newly collected dataset of nature images(1024$\times$512), as well as on standard benchmarks of smaller sizes(256$\times$256), including LSUN-Bedroom, LSUN-Church, and FFHQ. We compare ourmethod with previous patch-based generation methods and achievestate-of-the-art FID scores on all four datasets. Further, Patch-DM alsoreduces memory complexity compared to the classic diffusion models. <<<

Qiming Sun, Xing Zhang, Samragni Banerjee, Peng Bao, Marc Barbry, Nick S Blunt, Nikolay A Bogdanov, George H Booth, Jia Chen, Zhi-Hao Cui, Janus J Eriksen, Yang Gao, Sheng Guo, Jan Hermann, Matthew R Hermes, Kevin Koh, Peter Koval, Susi Lehtola, Zhendong Li, Junzi Liu, Narbe Mardirossian, James D McClain, Mario Motta, Bastien Mussard, Hung Q Pham, Artem Pulkin, Wirawan Purwanto, Paul J Robinson, Enrico Ronca, Elvira R Sayfutyarova, Maximilian Scheurer, Henry F Schurkus, James E T Smith, Chong Sun, Shi-Ning Sun, Shiv Upadhyay, Lucas K Wagner, Xiao Wang, Alec White, James Daniel Whitfield, Mark J Williamson, Sebastian Wouters, Jun Yang, Jason M Yu, Tianyu Zhu, Timothy C Berkelbach, Sandeep Sharma, Alexander Yu Sokolov, Garnet Kin-Lic Chan <<<

PySCF is a Python-based general-purpose electronic structure platform that supports first-principles simulations of molecules and solids as well as accelerates the development of new methodology and complex computational workflows. This paper explains the design and philosophy behind PySCF that enables it to meet these twin objectives. With several case studies, we show how users can easily implement their own methods using PySCF as a development environment. We then summarize the capabilities of PySCF for molecular and solid-state simulations. Finally, we describe the growing ecosystem of projects that use PySCF across the domains of quantum chemistry, materials science, machine learning, and quantum information science. <<<

Over time, the human DNA methylation landscape accrues substantial damage, which has been associated with a broad range of age-related diseases, including cardiovascular disease and cancer. Various age-related DNA methylation changes have been described, including at the level of individual CpGs, such as differential and variable methylation, and at the level of the whole methylome, including entropy and correlation networks. Here, we review these changes in the ageing methylome as well as the statistical tools that can be used to quantify them. We detail the evidence linking DNA methylation to ageing phenotypes and the longevity strategies aimed at altering both DNA methylation patterns and machinery to extend healthspan and lifespan. Lastly, we discuss theories on the mechanistic causes of epigenetic ageing. <<<

Wenyi Yang, Pingping Wang, Meng Luo, Yideng Cai, Chang Xu, Guangfu Xue, Xiyun Jin, Rui Cheng, Jinhao Que, Fenglan Pang, Yuexin Yang, Huan Nie, Qinghua Jiang, Zhigang Liu, Zhaochun Xu <<<

MOTIVATION: Cell-cell interactions (CCIs) play critical roles in many biological processes such as cellular differentiation, tissue homeostasis, and immune response. With the rapid development of high throughput single-cell RNA sequencing (scRNA-seq) technologies, it is of high importance to identify CCIs from the ever-increasing scRNA-seq data. However, limited by the algorithmic constraints, current computational methods based on statistical strategies ignore some key latent information contained in scRNA-seq data with high sparsity and heterogeneity.RESULTS: Here, we developed a deep learning framework named DeepCCI to identify meaningful CCIs from scRNA-seq data. Applications of DeepCCI to a wide range of publicly available datasets from diverse technologies and platforms demonstrate its ability to predict significant CCIs accurately and effectively. Powered by the flexible and easy-to-use software, DeepCCI can provide the one-stop solution to discover meaningful intercellular interactions and build CCI networks from scRNA-seq data.AVAILABILITY AND IMPLEMENTATION: The source code of DeepCCI is available online at https://github.com/JiangBioLab/DeepCCI. <<<

Xiaoyi Sun, Jin Liu, Qing Ma, Jia Duan, Xindi Wang, Yuehua Xu, Zhilei Xu, Ke Xu, Fei Wang, Yanqing Tang, Yong He, Mingrui Xia <<<

Schizophrenia (SCZ) is a highly heterogeneous disorder with remarkable intersubject variability in clinical presentations. Previous neuroimaging studies in SCZ have primarily focused on identifying group-averaged differences in the brain connectome between patients and healthy controls (HCs), largely neglecting the intersubject differences among patients. We acquired whole-brain resting-state functional MRI data from 121 SCZ patients and 183 HCs and examined the intersubject variability of the functional connectome (IVFC) in SCZ patients and HCs. Between-group differences were determined using permutation analysis. Then, we evaluated the relationship between IVFC and clinical variables in SCZ. Finally, we used datasets of patients with bipolar disorder (BD) and major depressive disorder (MDD) to assess the specificity of IVFC alteration in SCZ. The whole-brain IVFC pattern in the SCZ group was generally similar to that in HCs. Compared with the HC group, the SCZ group exhibited higher IVFC in the bilateral sensorimotor, visual, auditory, and subcortical regions. Moreover, altered IVFC was negatively correlated with age of onset, illness duration, and Brief Psychiatric Rating Scale scores and positively correlated with clinical heterogeneity. Although the SCZ shared altered IVFC in the visual cortex with BD and MDD, the alterations of IVFC in the sensorimotor, auditory, and subcortical cortices were specific to SCZ. The alterations of whole-brain IVFC in SCZ have potential implications for the understanding of the high clinical heterogeneity of SCZ and the future individualized clinical diagnosis and treatment of this disease. <<<

Understanding the regulatory landscape of the human genome is a long-standing objective of modern biology. Using the reference-free alignment across 241 mammalian genomes produced by the Zoonomia Consortium, we charted evolutionary trajectories for 0.92 million human candidate cis-regulatory elements (cCREs) and 15.6 million human transcription factor binding sites (TFBSs). We identified 439,461 cCREs and 2,024,062 TFBSs under evolutionary constraint. Genes near constrained elements perform fundamental cellular processes, whereas genes near primate-specific elements are involved in environmental interaction, including odor perception and immune response. About 20% of TFBSs are transposable element-derived and exhibit intricate patterns of gains and losses during primate evolution whereas sequence variants associated with complex traits are enriched in constrained TFBSs. Our annotations illuminate the regulatory functions of the human genome. <<<

Jizeng Jia, Guangyao Zhao, Danping Li, Kai Wang, Chuizheng Kong, Pingchuan Deng, Xueqing Yan, Xueyong Zhang, Zefu Lu, Shujuan Xu, Yuannian Jiao, Kang Chong, Xu Liu, Dangqun Cui, Guangwei Li, Yijing Zhang, Chunguang Du, Liang Wu, Tianbao Li, Dong Yan, Kehui Zhan, Feng Chen, Zhiyong Wang, Lichao Zhang, Xiuying Kong, Zhengang Ru, Daowen Wang, Lifeng Gao <<<

Despite recent progress in crop genomics studies, the genomic changes brought about by modern breeding selection are still poorly understood, thus hampering genomics-assisted breeding, especially in polyploid crops with compound genomes such as common wheat (Triticum aestivum). In this work, we constructed genome resources for the modern elite common wheat variety Aikang 58 (AK58). Comparative genomics between AK58 and the landrace cultivar Chinese Spring (CS) shed light on genomic changes that occurred through recent varietal improvement. We also explored subgenome diploidization and divergence in common wheat and developed a homoeologous locus-based genome-wide association study (HGWAS) approach, which was more effective than single homoeolog-based GWAS in unraveling agronomic trait-associated loci. A total of 123 major HGWAS loci were detected using a genetic population derived from AK58 and CS. Elite homoeologous haplotypes (HHs), formed by combinations of subgenomic homoeologs of the associated loci, were found in both parents and progeny, and many could substantially improve wheat yield and related traits. We built a website where users can download genome assembly sequence and annotation data for AK58, perform blast analysis, and run JBrowse. Our work enriches genome resources for wheat, provides new insights into genomic changes during modern wheat improvement, and suggests that efficient mining of elite HHs can make a substantial contribution to genomics-assisted breeding in common wheat and other polyploid crops. <<<

Jie-Yi Shi, Xiaodong Wang, Guang-Yu Ding, Zhou Dong, Jing Han, Zehui Guan, Li-Jie Ma, Yuxuan Zheng, Lei Zhang, Guan-Zhen Yu, Xiao-Ying Wang, Zhen-Bin Ding, Ai-Wu Ke, Haoqing Yang, Liming Wang, Lirong Ai, Ya Cao, Jian Zhou, Jia Fan, Xiyang Liu, Qiang Gao <<<

OBJECTIVE: Tumour pathology contains rich information, including tissue structure and cell morphology, that reflects disease progression and patient survival. However, phenotypic information is subtle and complex, making the discovery of prognostic indicators from pathological images challenging.DESIGN: An interpretable, weakly supervised deep learning framework incorporating prior knowledge was proposed to analyse hepatocellular carcinoma (HCC) and explore new prognostic phenotypes on pathological whole-slide images (WSIs) from the Zhongshan cohort of 1125 HCC patients (2451 WSIs) and TCGA cohort of 320 HCC patients (320 WSIs). A 'tumour risk score (TRS)' was established to evaluate patient outcomes, and then risk activation mapping (RAM) was applied to visualise the pathological phenotypes of TRS. The multi-omics data of The Cancer Genome Atlas(TCGA) HCC were used to assess the potential pathogenesis underlying TRS.RESULTS: Survival analysis revealed that TRS was an independent prognosticator in both the Zhongshan cohort (p<0.0001) and TCGA cohort (p=0.0003). The predictive ability of TRS was superior to and independent of clinical staging systems, and TRS could evenly stratify patients into up to five groups with significantly different prognoses. Notably, sinusoidal capillarisation, prominent nucleoli and karyotheca, the nucleus/cytoplasm ratio and infiltrating inflammatory cells were identified as the main underlying features of TRS. The multi-omics data of TCGA HCC hint at the relevance of TRS to tumour immune infiltration and genetic alterations such as the FAT3 and RYR2 mutations.CONCLUSION: Our deep learning framework is an effective and labour-saving method for decoding pathological images, providing a valuable means for HCC risk stratification and precise patient treatment. <<<

ABSTRACT Streptomycin (Sm) is a commonly used antibiotic for its efficacy against diverse bacteria. The plant pathogen Agrobacterium fabrum is a model for studying pathogenesis and interkingdom gene transfer. Streptomycin-resistant variants of A. fabrum are commonly employed in genetic analyses, yet mechanisms of resistance and susceptibility to streptomycin in this organism have not previously been investigated. We observe that resistance to a high concentration of streptomycin arises at high frequency in A. fabrum , and we attribute this trait to the presence of a chromosomal gene ( strB ) encoding a putative aminoglycoside phosphotransferase. We show how strB , along with rpsL (encoding ribosomal protein S12) and rsmG (encoding a 16S rRNA methyltransferase), modulates streptomycin sensitivity in A. fabrum . IMPORTANCE The plant pathogen Agrobacterium fabrum is a widely used model bacterium for studying biofilms, bacterial motility, pathogenesis, and gene transfer from bacteria to plants. Streptomycin (Sm) is an aminoglycoside antibiotic known for its broad efficacy against gram-negative bacteria. A. fabrum exhibits endogenous resistance to somewhat high levels of streptomycin, but the mechanism underlying this resistance has not been elucidated. Here, we demonstrate that this resistance is caused by a chromosomally encoded streptomycin-inactivating enzyme, StrB, that has not been previously characterized in A. fabrum . Furthermore, we show how the genes rsmG , rpsL , and strB jointly modulate streptomycin susceptibility in A. fabrum . <<<

Currently, the field of low-dose CT (LDCT) denoising is dominated by supervised learning based methods, which need perfectly registered pairs of LDCT and its corresponding clean reference image (normal-dose CT). However, training without clean labels is more practically feasible and significant, since it is clinically impossible to acquire a large amount of these paired samples. In this paper, a self-supervised denoising method is proposed for LDCT imaging.The proposed method does not require any clean images. In addition, the perceptual loss is used to achieve data consistency in feature domain during the denoising process. Attention blocks used in decoding phase can help further improve the image quality.In the experiments, we validate the effectiveness of our proposed self-supervised framework and compare our method with several state-of-the-art supervised and unsupervised methods. The results show that our proposed model achieves competitive performance in both qualitative and quantitative aspects to other methods.Our framework can be directly applied to most denoising scenarios without collecting pairs of training data, which is more flexible for real clinical scenario. <<<

PURPOSE: To investigate the differentially expressed proteins (DEP) between high myopia and moderate myopia on the anterior corneal stroma.METHODS: Tandem mass tag (TMT) quantitative proteomics was utilized to reveal proteins. DEPs were screened by the multiple change of more than 1.2 times or less than 0.83 and the P value < 0.05. The DEPs were functional annotated by Gene Ontology (GO) terms. Proteins and protein interaction (PPI) networks were conducted with String online tool. Parallel reaction monitoring (PRM) data processing was used to verify the TMT proteomics results.RESULTS: There are 36 DEPs between high myopia and moderate myopia on the anterior corneal stroma, of which 11 proteins are upregulated, 25 proteins are downregulated. The GO analysis demonstrated keratinocyte migration and structural constituent of cytoskeleton that are significantly changed with most of the proteins decreased in high myopic corneas. Keratin 16 (KRT16) and erythrocyte membrane protein band 4.1-like protein 4B are the only two proteins involved in both functions. The PPI analysis showed keratin type II cytoskeletal 6A (KRT6A) and KRT16 that have strong connections. Immunoglobulin lambda variable 8-61(IGLV8-61) and nicotinamide phosphoribosyl transferase (NAMPT) have consistent results with the TMT.CONCLUSIONS: The high myopic corneas have 36 DEPs compared to the moderate myopic corneas on the anterior corneal stroma. Keratinocyte migrations and structural constituent of cytoskeleton are weakened in high myopic corneas, which may partly account for the lower corneal biomechanics in high myopic eyes. The lower expressed KRT16 plays important roles in high myopic corneas. <<<