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Motivation Single-cell sequencing assay for transposase-accessible chromatin (scATAC-seq) provides new opportunities to dissect epigenomic heterogeneity and elucidate transcriptional regulatory mechanisms. However, computational modeling of scATAC-seq data is challenging due to its high dimension, extreme sparsity, complex dependencies and high sensitivity to confounding factors from various sources. Results Here, we propose a new deep generative model framework, named SAILER, for analyzing scATAC-seq data. SAILER aims to learn a low-dimensional nonlinear latent representation of each cell that defines its intrinsic chromatin state, invariant to extrinsic confounding factors like read depth and batch effects. SAILER adopts the conventional encoder-decoder framework to learn the latent representation but imposes additional constraints to ensure the independence of the learned representations from the confounding factors. Experimental results on both simulated and real scATAC-seq datasets demonstrate that SAILER learns better and biologically more meaningful representations of cells than other methods. Its noise-free cell embeddings bring in significant benefits in downstream analyses: clustering and imputation based on SAILER result in 6.9% and 18.5% improvements over existing methods, respectively. Moreover, because no matrix factorization is involved, SAILER can easily scale to process millions of cells. We implemented SAILER into a software package, freely available to all for large-scale scATAC-seq data analysis. Availability and implementation The software is publicly available at https://github.com/uci-cbcl/SAILER. Supplementary information Supplementary data are available at Bioinformatics online.

Paraneoplastic neurological syndromes (PNS) are rare immune-mediated disorders, and the detection of onconeural antibodies is helpful for PNS diagnosis. The aim of this study was to investigate the clinical characteristics of patients with PNS with positive onconeural antibodies in a single center in Hubei, China. We retrospectively analyzed the clinical characteristics of 54 patients with positive onconeural antibodies from January 2016 to September 2020. Among 780 patients with suspected PNS, 54 (6.9%) had positive onconeural antibodies. Of those 54 patients, 28 (51.8%) were diagnosed with definite PNS and 13 (24.1%) with possible PNS. Eighteen (33.3%) patients were confirmed with cancer. Ten PNS syndromes were detected among the 28 patients with definite PNS, and they had either classical (12/28, 42.8%) or non-classical syndromes (17/28, 60.7%). Peripheral neuropathy (9/28, 32.1%), subacute cerebellar degeneration (4/28, 14.3%), and limbic encephalitis (4/28, 14.3%) were the most common PNS syndromes. The anti-CV2/CRMP5-antibody was observed most frequently. Lung cancer was the most common tumor type. For patients with possible PNS, peripheral neuropathy was the most common PNS syndrome, and the anti-Tr-antibody was the most frequent onconeural antibody. Immunotherapy was effective in treating PNS. The anti-CV2/CRMP5-antibody was the most subsequently observed antibody. The manifestations of PNS are diverse and include peripheral neuropathy, subacute cerebellar degeneration, and limbic encephalitis. In patients with PNS, lung cancer was the most common tumor.

Yue Zhou,1 Ai-Qing Nie,1 Shang Chen,2 Meng-Meng Wang,1 Rui Yin,1 Bo-Hao Tang,1 Yue-E Wu,1 Fan Yang,1 Bin Du,1 Hai-Yan Shi,3 Xin-Mei Yang,3 Guo-Xiang Hao,1 Xiu-Li Guo,4 Qiu-Ju Han,5 Yi Zheng,1,* Wei Zhao1,3,* 1Department of Clinical Pharmacy, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, People’s Republic of China; 2Institute of Biochemical and Biotechnological Drug, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, People’s Republic of China; 3Department of Pharmacy, Shandong Provincial Qianfoshan Hospital, The First Affiliated Hospital of Shandong First Medical University, Jinan, People’s Republic of China; 4Department of Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, People’s Republic of China; 5Institute of Immunopharmaceutical Sciences, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, People’s Republic of China*These authors contributed equally to this workCorrespondence: Yi Zheng; Wei ZhaoDepartment of Clinical Pharmacy, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, No. 44, Wenhua West Road, Jinan, Shandong Province, People’s Republic of ChinaTel/Fax +86 531 8838 3308Email zhengyi@sdu.edu.cn; zhao4wei2@hotmail.comPurpose: Considering prior investigations on reductions of renal multidrug resistance-associated protein (MRP) 2 and 4 transporters in mice with acute lymphoblastic leukemia (ALL), we sought to characterize the underlying mechanisms responsible for IL-6/STAT3/PXR-mediated changes in the expression of MRP2 and MRP4 in ALL.Subjects and Methods: ALL xenograft models were established and intravenously injected with methotrexate (MTX) of MRPs substrate in NOD/SCID mice. Protein expression of MRPs and associated mechanisms were detected by Western blotting and immunocytochemistry. Plasma concentrations of MTX were determined using high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS).Results: Plasma IL-6 levels in patients with newly diagnosed ALL were increased compared to children with pneumonia. Similarly, plasma IL-6 levels in ALL, ALL-tocilizumab (TCZ, an IL-6 receptor inhibitor) and ALL-S3I-201 (a selective inhibitor of STAT3) mice were increased compared to the control group. The MRP2, MRP4, and PXR expression in HK-2 cells treated with IL-6 were decreased, whereas the p-STAT3 expression was significantly increased compared to the control group results. These results are consistent with clearance of MRPs-mediated MTX in the ALL group. These effects were attenuated by blocking IL-6/STAT3/PXR signaling pathway.Conclusion: Inflammation-mediated changes in pharmacokinetics are thought to be executed through pathways IL-6-activated pathways, which can facilitate a better understanding of the potential for the use of IL-6 to predict the severity of adverse outcomes and the major implications on potential ALL treatments.Keywords: disease-induced pharmacokinetic change, renal transporters, inflammatory cytokines, clearance

What is known and objectives Thiopurines are cornerstone drugs in the treatment of acute lymphoblastic leukaemia (ALL), but their use can be complicated by the incidence of life-threatening leucopenia. Case description We describe a case of a 6-year-old Chinese boy with B-ALL receiving extremely low dose of 6-mercaptopurine (only 4% of recommended dose) during the ALL maintenance therapy phase. What is new and conclusion Complex pharmacogenetic tests and TDM should be recommended in children with complicated ALL to highlight the large individual variability in the responses to 6-MP exposure and the associated adverse effects.

Rapid growth of single-cell transcriptomic data provides unprecedented opportunities for close scrutinizing of dynamical cellular processes. Through investigating epithelial-to-mesenchymal transition (EMT), we develop an integrative tool that combines unsupervised learning of single-cell transcriptomic data and multiscale mathematical modeling to analyze transitions during cell fate decision. Our approach allows identification of individual cells making transition between all cell states, and inference of genes that drive transitions. Multiscale extractions of single-cell scale outputs naturally reveal intermediate cell states (ICS) and ICS-regulated transition trajectories, producing emergent population-scale models to be explored for design principles. Testing on the newly designed single-cell gene regulatory network model and applying to twelve published single-cell EMT datasets in cancer and embryogenesis, we uncover the roles of ICS on adaptation, noise attenuation, and transition efficiency in EMT, and reveal their trade-off relations. Overall, our unsupervised learning method is applicable to general single-cell transcriptomic datasets, and our integrative approach at single-cell resolution may be adopted for other cell fate transition systems beyond EMT.

Mammary and extramammary Paget's Diseases (PD) are a malignant skin cancer characterized by the appearance of Paget cells. Although easily diagnosed, its pathogenesis remains unknown. Here, single-cell RNA-sequencing identified distinct cellular states, novel biomarkers, and signaling pathways - including mTOR, associated with extramammary PD. Interestingly, we identified MSI1 ectopic overexpression in basal epithelial cells of human PD skin, and show that Msi1 overexpression in the epidermal basal layer of mice phenocopies human PD at histopathological, single-cell and molecular levels. Using this mouse model, we identified novel biomarkers of Paget-like cells that translated to human Paget cells. Furthermore, single-cell trajectory, RNA velocity and lineage-tracing analyses revealed a putative keratinocyte-to-Paget-like cell conversion, supporting the in situ transformation theory of disease pathogenesis. Mechanistically, the Msi1-mTOR pathway drives keratinocyte-Paget-like cell conversion, and suppression of mTOR signaling with Rapamycin significantly rescued the Paget-like phenotype in Msi1-overexpressing transgenic mice. Topical Rapamycin treatment improved extramammary PD-associated symptoms in humans, suggesting mTOR inhibition as a novel therapeutic treatment in PD.

BackgroundDiabetic nephropathy (DN) is one of the most common and serious complications of diabetes, and is the most important cause of death for diabetic patients. Baicalin (BAI) has anti-oxidative, anti-inflammatory and anti-apoptotic activities, which play a role in attenuating insulin resistance and protecting the kidney. Moreover, cell-specific targeting of renal tubular cells is an approach to enhance drug accumulation in the kidney.MethodsForty-five Sprague-Dawley rats were divided into four groups. A diabetes model was created using streptozotocin (STZ) intraperitoneally injection. The four groups included: Control group (n = 10), DN (n = 15), BAI treatment (BAI;n = 10) and BAI-LZM treatment (BAI-LZM;n = 10) groups. In the current study, the renoprotection and anti-fibrotic effects of BAI-lysozyme (LZM) conjugate were further investigated in rats with DN induced by STZ compared with BAI treatment alone.ResultsThe results suggest that BAI-LZM better ameliorates renal impairment, metabolic disorder and renal fibrosis than BAI alone in rats with DN, and the potential regulatory mechanism likely involves inhibiting inflammation via the nuclear factor-κB signaling pathway, inhibiting extracellular matrix accumulation via the transforming growth factor-β/Smad3 pathway and regulating cell proliferation via the insulin-like growth factor (IGF)-1/IGF-1 receptor/p38 Mitogen-activated protein kinase (MAPK) pathway. BAI and the kidney-targeted BAI-LZM can utilize the body’s cytoprotective pathways to reactivate autophagy (as indicated by the autophagy markers mechanistic target of rapamycin and sirtuin 1 to ameliorate DN outcomes.ConclusionsOur data support the traditional use ofS. baicalensisas an important anti-DN traditional chinese medicine (TCM), and BAI, above all BAI-LZM, is a promising source for the identification of molecules with anti-DN effects.

Single-cell RNA sequencing (scRNA-seq) provides details for individual cells; however, crucial spatial information is often lost. We present SpaOTsc, a method relying on structured optimal transport to recover spatial properties of scRNA-seq data by utilizing spatial measurements of a relatively small number of genes. A spatial metric for individual cells in scRNA-seq data is first established based on a map connecting it with the spatial measurements. The cell–cell communications are then obtained by “optimally transporting” signal senders to target signal receivers in space. Using partial information decomposition, we next compute the intercellular gene–gene information flow to estimate the spatial regulations between genes across cells. Four datasets are employed for cross-validation of spatial gene expression prediction and comparison to known cell–cell communications. SpaOTsc has broader applications, both in integrating non-spatial single-cell measurements with spatial data, and directly in spatial single-cell transcriptomics data to reconstruct spatial cellular dynamics in tissues., Dissociation of tissues allows high-throughput expression profiling of single cells, but spatial information is lost. Here the authors apply an unbalanced and structured optimal transport method to infer spatial and signalling relationships between cells from scRNA-seq data by integrating it with spatial imaging data.

To investigate the evolutionary history of the recent outbreak of severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) in China, a total of 70 genomes of virus strains from China and elsewhere with sampling dates between 24 December 2019 and 3 February 2020 were analyzed. To explore the potential intermediate animal host of the SARS‐CoV‐2 virus, we reanalyzed virome data sets from pangolins and representative SARS‐related coronaviruses isolates from bats, with particular attention paid to the spike glycoprotein gene. We performed phylogenetic, split network, transmission network, likelihood‐mapping, and comparative analyses of the genomes. Based on Bayesian time‐scaled phylogenetic analysis using the tip‐dating method, we estimated the time to the most recent common ancestor and evolutionary rate of SARS‐CoV‐2, which ranged from 22 to 24 November 2019 and 1.19 to 1.31 × 10−3 substitutions per site per year, respectively. Our results also revealed that the BetaCoV/bat/Yunnan/RaTG13/2013 virus was more similar to the SARS‐CoV‐2 virus than the coronavirus obtained from the two pangolin samples (SRR10168377 and SRR10168378). We also identified a unique peptide (PRRA) insertion in the human SARS‐CoV‐2 virus, which may be involved in the proteolytic cleavage of the spike protein by cellular proteases, and thus could impact host range and transmissibility. Interestingly, the coronavirus carried by pangolins did not have the RRAR motif. Therefore, we concluded that the human SARS‐CoV‐2 virus, which is responsible for the recent outbreak of COVID‐19, did not come directly from pangolins., Highlights We identified a unique peptide (PRRA) insertion in the human SARS‐CoV‐2 virus, which may be involved in the proteolytic cleavage of the spike protein by cellular proteases, and thus could impact host range and transmissibility.

Significance Here, we show that expression of key circadian clock genes in the skin is altered by acute inflammation in mice treated topically with the immune activator imiquimod and by chronic inflammation in human psoriatic lesions. We show time-of-day–dependent activation of the interferon pathway, a key pathway involved in the host defense response. Mice lacking circadian rhythms have greater epidermal hyperplasia and more robust activation of the interferon pathway. Furthermore, we show that daytime-restricted feeding shifts the phase of interferon-sensitive gene expression in mouse skin. These findings demonstrate a role for the circadian clock in a major defense pathway in the skin’s response to microbes and cancer and suggest that timing of feeding can affect this response in the skin., The circadian clock coordinates a variety of immune responses with signals from the external environment to promote survival. We investigated the potential reciprocal relationship between the circadian clock and skin inflammation. We treated mice topically with the Toll-like receptor 7 (TLR7) agonist imiquimod (IMQ) to activate IFN-sensitive gene (ISG) pathways and induce psoriasiform inflammation. IMQ transiently altered core clock gene expression, an effect mirrored in human patient psoriatic lesions. In mouse skin 1 d after IMQ treatment, ISGs, including the key ISG transcription factor IFN regulatory factor 7 (Irf7), were more highly induced after treatment during the day than the night. Nuclear localization of phosphorylated-IRF7 was most prominently time-of-day dependent in epidermal leukocytes, suggesting that these cell types play an important role in the diurnal ISG response to IMQ. Mice lacking Bmal1 systemically had exacerbated and arrhythmic ISG/Irf7 expression after IMQ. Furthermore, daytime-restricted feeding, which affects the phase of the skin circadian clock, reverses the diurnal rhythm of IMQ-induced ISG expression in the skin. These results suggest a role for the circadian clock, driven by BMAL1, as a negative regulator of the ISG response, and highlight the finding that feeding time can modulate the skin immune response. Since the IFN response is essential for the antiviral and antitumor effects of TLR activation, these findings are consistent with the time-of-day–dependent variability in the ability to fight microbial pathogens and tumor initiation and offer support for the use of chronotherapy for their treatment.

SUMMARY Our knowledge of transcriptional heterogeneities in epithelial stem and progenitor cell compartments is limited. Epidermal basal cells sustain cutaneous tissue maintenance and drive wound healing. Previous studies have probed basal cell heterogeneity in stem and progenitor potential, but a comprehensive dissection of basal cell dynamics during differentiation is lacking. Using single-cell RNA sequencing coupled with RNAScope and fluorescence lifetime imaging, we identify three non-proliferative and one proliferative basal cell state in homeostatic skin that differ in metabolic preference and become spatially partitioned during wound re-epithelialization. Pseudotemporal trajectory and RNA velocity analyses predict a quasi-linear differentiation hierarchy where basal cells progress from Col17a1Hi/Trp63Hi state to early-response state, proliferate at the juncture of these two states, or become growth arrested before differentiating into spinous cells. Wound healing induces plasticity manifested by dynamic basal-spinous interconversions at multiple basal transcriptional states. Our study provides a systematic view of epidermal cellular dynamics, supporting a revised “hierarchical-lineage” model of homeostasis., Graphical Abstract, In Brief Haensel et al. performed a comprehensive dissection of the cellular makeup of skin during homeostasis and wound healing and the molecular heterogeneity and cellular dynamics within its stem-cell-containing epidermal basal layer. Their work provides insights and stimulates further investigation into the mechanism of skin maintenance and repair.

The yellow catfish (Pelteobagrus fulvidraco) is an economically important species in eastern Asia, but its aquaculture faces bottlenecks due to the influence of environmental factors. The present study evaluated the effects of dietary carotenoid supplementation on high-temperature stress and disease resistance in yellow catfish. Fish were randomly assigned to one of six treatments: five groups were fed basal diets supplemented with 50, 100, 150, 200 or 250 mg·kg−1 carotenoids, and a control group was fed a basal diet. The feeding experiment lasted 60 d, after which the fish were exposed to acute heat shock for 1 d and then infected with Proteus mirabilis. The results showed that the specific growth rate (SGR) and weight gain (WG) were significantly increased and the feed conversion ratio (FCR) was significantly decreased in the groups supplemented with 100, 150, 200 and 250 mg·kg−1 carotenoids (P

Cell-cell communication is a fundamental process that shapes biological tissue. Historically, studies of cell-cell communication have been feasible for one or two cell types and a few genes. With the emergence of single-cell transcriptomics, we are now able to examine the genetic profiles of individual cells at unprecedented scale and depth. The availability of such data presents an exciting opportunity to construct a more comprehensive description of cell-cell communication. This review discusses the recent explosion of methods that have been developed to infer cell-cell communication from non-spatial and spatial single-cell transcriptomics, two promising technologies which have complementary strengths and limitations. We propose several avenues to propel this rapidly expanding field forward in meaningful ways.

We gratefully acknowledge China National GeneBank at Shenzhen, China for the sequencing strategy and capacity support. We also gratefully acknowledge the laboratories that have contributed publicly available genomes via GISAID: The Public Health Agency of Sweden, Sweden; Public Health Ontario Laboratories, Canada; Pathogen Discovery, Respiratory Viruses Branch, Division of Viral Diseases, Centers for Disease Control and Prevention, United States; Programme in Emerging Infectious Diseases, Duke-NUS Medical School, United State; Alaska State Virology Laboratory, United States; Respiratory Virus Unit, Microbiology Services Colindale, Public Health England, United Kingdom; Laboratory for Functional Genome Analysis, Dept. Genomics, Gene Center of the LMU Munich, Germany; Michigan Department of Health and Human Services, Bureau of Laboratories, United State; National Public Health Laboratory, National Centre for Infectious Diseases, Singapore; BGI-shenzhen & The First Affiliated Hospital of Guangzhou Medical University, China; NSW Health Pathology - Institute of Clinical Pathology and Medical Research; Westmead Hospital; University of Sydney, Australia; BCCDC Public Health Laboratory, Canada; COVID-19 Genomics UK (COG-UK) Consortium, United Kingdom; Guangdong Provincial Institution of Public Health, China; Department of Microbiology, Guangdong Provincial Center for Diseases Control and Prevention, China; Department of Health Technology and Informatics, Faculty of Health and Social Science, The Hong Kong Polytechnic University, Hong Kong; School of Public Health, The University of Hong Kong, Hong Kong; Pathogen Genomics Center, National Institute of Infectious Diseases, Japan; National Research Center for Translational Medicine (Shanghai), Ruijin Hospital affiliated to Shanghai Jiao Tong University School of Medicine & Shanghai Public Health Clinical Center, China; Seattle Flu Study, United States; UW Virology Lab, United States; National Institute for Viral Disease Control & Prevention, CCDC, China; Beijing Institute of Microbiology and Epidemiology, China; National Institute for Viral Disease Control and Prevention, China CDC, China; Inspection Center of Hangzhou Center for Disease Control and Prevention, China; Pathogen Genomics Lab King Abdullah University of Science and Technology (KAUST), Saudi Arabia; Chinese PLA Institute for Disease Control and Prevention, China; National Institute of Health. Department of medical Sciences, Ministry of Public Health, Thailand; Laboratory of Biology, Department of Medicine, Democritus University of Thrace, Greece; Wellcome Sanger Institute for the COVID-19 Genomics UK Consortium, United Kingdom; State Key Laboratory of Biotherapy of Sichuan University, China; SeqCOVID-SPAIN consortium/IBV(CSIC), Spain; Oxford University Clinical Research Unit, Hanoi, Vietnam; Institute of Environmental Science and Research (ESR), New Zealand; Erasmus Medical Center, Netherlands.

Unraveling molecular regulatory networks underlying disease progression is critically important for understanding disease mechanisms and identifying drug targets. The existing methods for inferring gene regulatory networks (GRNs) rely mainly on time-course gene expression data. However, most available omics data from cross-sectional studies of cancer patients often lack sufficient temporal information, leading to a key challenge for GRN inference. Through quantifying the latent progression using random walks-based manifold distance, we propose a latent-temporal progression-based Bayesian method, PROB, for inferring GRNs from the cross-sectional transcriptomic data of tumor samples. The robustness of PROB to the measurement variabilities in the data is mathematically proved and numerically verified. Performance evaluation on real data indicates that PROB outperforms other methods in both pseudotime inference and GRN inference. Applications to bladder cancer and breast cancer demonstrate that our method is effective to identify key regulators of cancer progression or drug targets. The identified ACSS1 is experimentally validated to promote epithelial-to-mesenchymal transition of bladder cancer cells, and the predicted FOXM1-targets interactions are verified and are predictive of relapse in breast cancer. Our study suggests new effective ways to clinical transcriptomic data modeling for characterizing cancer progression and facilitates the translation of regulatory network-based approaches into precision medicine., Author summary Reconstructing gene regulatory network (GRN) is an essential question in systems biology. The lack of temporal information in sample-based transcriptomic data leads to a major challenge for inferring GRN and its translation to precision medicine. To address the above challenge, we propose to decode the latent temporal information underlying cancer progression via ordering patient samples based on transcriptomic similarity, and design a latent-temporal progression-based Bayesian method to infer GRNs from sample-based transcriptomic data of cancer patients. The advantages of our method include its capability to infer causal GRNs (with directed and signed edges) and its robustness to the measurement variability in the data. Performance evaluation using both simulated data and real data demonstrate that our method outperforms other existing methods in both pseudotime inference and GRN inference. Our method is then applied to reconstruct EMT regulatory networks in bladder cancer and to identify key regulators underlying progression of breast cancer. Importantly, the predicted key regulators/interactions are experimentally validated. Our study suggests that inferring dynamic progression trajectory from static expression data of tumor samples helps to uncover regulatory mechanisms underlying cancer progression and to discovery key regulators which may be used as candidate drug targets.

Understanding global communications among cells requires accurate representation of cell-cell signaling links and effective systems-level analyses of those links. We construct a database of interactions among ligands, receptors and their cofactors that accurately represent known heteromeric molecular complexes. We then develop CellChat, a tool that is able to quantitatively infer and analyze intercellular communication networks from single-cell RNA-sequencing (scRNA-seq) data. CellChat predicts major signaling inputs and outputs for cells and how those cells and signals coordinate for functions using network analysis and pattern recognition approaches. Through manifold learning and quantitative contrasts, CellChat classifies signaling pathways and delineates conserved and context-specific pathways across different datasets. Applying CellChat to mouse and human skin datasets shows its ability to extract complex signaling patterns. Our versatile and easy-to-use toolkit CellChat and a web-based Explorer (http://www.cellchat.org/) will help discover novel intercellular communications and build cell-cell communication atlases in diverse tissues., Single-cell methods record molecule expressions of cells in a given tissue, but understanding interactions between cells remains challenging. Here the authors show by applying systems biology and machine learning approaches that they can infer and analyze cell-cell communication networks in an easily interpretable way.

Distinguishing biological from technical variation is crucial when integrating and comparing single-cell genomics datasets across different experiments. Existing methods lack the capability in explicitly distinguishing these two variations, often leading to the removal of both variations. Here, we present an integration method scMC to remove the technical variation while preserving the intrinsic biological variation. scMC learns biological variation via variance analysis to subtract technical variation inferred in an unsupervised manner. Application of scMC to both simulated and real datasets from single-cell RNA-seq and ATAC-seq experiments demonstrates its capability of detecting context-shared and context-specific biological signals via accurate alignment.

The human cerebral cortex undergoes profound structural and functional dynamic variations across the lifespan, whereas the underlying molecular mechanisms remain unclear. Here, with a novel method transcriptome-connectome correlation analysis (TCA), which integrates the brain functional magnetic resonance images and region-specific transcriptomes, we identify age-specific cortex (ASC) gene signatures for adolescence, early adulthood and late adulthood. The ASC gene signatures are significantly correlated with the cortical thickness (P-value Availability: All scripts are written in R. Scripts for the TCA method and related statistics result can be freely accessed at https://github.com/Soulnature/TCA. Additional data related to this paper may be requested from the authors.

Characterizing genome-wide binding profiles of transcription factor (TF) is essential for understanding many biological processes. Although techniques have been developed to assess binding profiles within a population of cells, determining binding profiles at a single cell level remains elusive. Here we report scFAN (Single Cell Factor Analysis Network), a deep learning model that predicts genome-wide TF binding profiles in individual cells. scFAN is pre-trained on genome-wide bulk ATAC-seq, DNA sequence and ChIP-seq data, and utilizes single-cell ATAC-seq to predict TF binding in individual cells. We demonstrate the efficacy of scFAN by studying sequence motifs enriched within predicted binding peaks and investigating the effectiveness of predicted TF peaks for discovering cell types. We develop a new metric “TF activity score” to characterize each cell, and show that the activity scores can reliably capture cell identities. The method allows us to discover and study cellular identities and heterogeneity based on chromatin accessibility profiles.

The vertebrate hindbrain is segmented into rhombomeres (r) initially defined by distinct domains of gene expression. Previous studies have shown that noise-induced gene regulation and cell sorting are critical for the sharpening of rhombomere boundaries, which start out rough in the forming neural plate (NP) and sharpen over time. However, the mechanisms controlling simultaneous formation of multiple rhombomeres and accuracy in their sizes are unclear. We have developed a stochastic multiscale cell-based model that explicitly incorporates dynamic morphogenetic changes (i.e. convergent-extension of the NP), multiple morphogens, and gene regulatory networks to investigate the formation of rhombomeres and their corresponding boundaries in the zebrafish hindbrain. During pattern initiation, the short-range signal, fibroblast growth factor (FGF), works together with the longer-range morphogen, retinoic acid (RA), to specify all of these boundaries and maintain accurately sized segments with sharp boundaries. At later stages of patterning, we show a nonlinear change in the shape of rhombomeres with rapid left-right narrowing of the NP followed by slower dynamics. Rapid initial convergence improves boundary sharpness and segment size by regulating cell sorting and cell fate both independently and coordinately. Overall, multiple morphogens and tissue dynamics synergize to regulate the sizes and boundaries of multiple segments during development., Author summary In segmental pattern formation, chemical gradients control gene expression in a concentration-dependent manner to specify distinct gene expression domains. Despite the stochasticity inherent to such biological processes, precise and accurate borders form between segmental gene expression domains. Previous work has revealed synergy between gene regulation and cell sorting in sharpening borders that are initially rough. However, it is still poorly understood how size and boundary sharpness of multiple segments are regulated in a tissue that changes dramatically in its morphology as the embryo develops. Here we develop a stochastic multiscale cell-base model to investigate these questions. Two novel strategies synergize to promote accurate segment formation, a combination of long- and short-range morphogens plus rapid tissue convergence, with one responsible for pattern initiation and the other enabling pattern refinement.

Unveiling the molecular mechanisms underlying tissue regeneration provides new opportunities to develop treatments for diabetic ulcers and other chronic skin lesions. Here, we show that Ccl2 secretion by epidermal keratinocytes is directly orchestrated by Nrf2, a prominent transcriptional regulator of tissue regeneration that is activated early after cutaneous injury. Through a unique feedback mechanism, we find that Ccl2 from epidermal keratinocytes not only drives chemotaxis of macrophages into the wound but also triggers macrophage expression of EGF, which in turn activates basal epidermal keratinocyte proliferation. Notably, we find dysfunctional activation of Nrf2 in epidermal keratinocytes of diabetic mice after wounding, which partly explains regenerative impairments associated with diabetes. These findings provide mechanistic insight into the critical relationship between keratinocyte and macrophage signaling during tissue repair, providing the basis for continued investigation of the therapeutic value of Nrf2.

Drug elimination alteration has been well reported in acute lymphoblastic leukemia (ALL). Considering that transporters and glomerular filtration influence, to different extents, the drug disposition, and possible side effects, we evaluated the effects of ALL on major renal transporters and glomerular filtration mediated pharmacokinetic changes, as well as expression of renal drug transporters. ALL xenograft models were established and intravenously injected with substrates of renal transporters and glomerular filtration separately in NOD/SCID mice. The plasma concentrations of substrates, after single doses, were determined using high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS). With the development of ALL, protein expression of MDR1, OAT3 and OCT2 were increased by 2.62-fold, 1.70-fold, and 1.45-fold, respectively, whereas expression of MRP2 and MRP4 were significantly decreased by 30.98% and 45.28% in the kidney of ALL groups compared with control groups. Clearance of MDR1-mediated digoxin, OAT3-mediated furosemide, and OCT2-mediated metformin increased by 3.04-fold, 1.47-fold, and 1.26-fold, respectively. However, clearance of MRPs-mediated methotrexate was reduced by 39.5%. These results are consistent with mRNA expression. Clearance of vancomycin and amikacin, as markers of glomerular filtration rate, had a 2.14 and 1.64-fold increase in ALL mice, respectively. The specific alteration of renal transporters and glomerular filtration in kidneys provide a rational explanation for changes in pharmacokinetics for ALL.

How stem cells give rise to epidermis is unclear despite the crucial role the epidermis plays in barrier and appendage formation. Here we use single cell-RNA sequencing to interrogate basal stem cell heterogeneity of human interfollicular epidermis and find four spatially distinct stem cell populations at the top and bottom of rete ridges and transitional positions between the basal and suprabasal epidermal layers. Cell-cell communication modeling suggests that basal cell populations serve as crucial signaling hubs to maintain epidermal communication. Combining pseudotime, RNA velocity, and cellular entropy analyses point to a hierarchical differentiation lineage supporting multi-stem cell interfollicular epidermal homeostasis models and suggest that transitional basal stem cells are stable states essential for proper stratification. Finally, alterations in differentially expressed transitional basal stem cell genes result in severe thinning of human skin equivalents, validating their essential role in epidermal homeostasis and reinforcing the critical nature of basal stem cell heterogeneity., The mechanisms regulating stem cells to give rise to human interfollicular epidermis are unclear. Here, the authors use single cell RNA sequencing to identify heterogeneity within the human neonatal interfollicular epidermis and distinct spatial positioning of at least four basal stem cell populations.

SUMMARY A certain number of epithelial cells in intestinal crypts are DNA damage resistant and contribute to regeneration. However, the cellular mechanism underlying intestinal regeneration remains unclear. Using lineage tracing, we show that cells marked by an Msi1 reporter (Msi1+) are right above Lgr5high cells in intestinal crypts and exhibit DNA damage resistance. Single-cell RNA sequencing reveals that the Msi1+ cells are heterogeneous with the majority being intestinal stem cells (ISCs). The DNA damage-resistant subpopulation of Msi1+ cells is characterized by low-to-negative Lgr5 expression and is more rapidly cycling than Lgr5high radiosensitive crypt base columnar stem cells (CBCs). This enables an efficient repopulation of the intestinal epithelium at early stage when Lgr5high cells are not emerging. Furthermore, relative to CBCs, Msi1+ cells preferentially produce Paneth cells during homeostasis and upon radiation repair. Together, we demonstrate that the DNA damage-resistant Msi1+ cells are cycling ISCs that maintain and regenerate the intestinal epithelium., Graphical Abstract, In Brief Quiescent reserve stem cells in the intestine are thought to activate following irradiation to restore the depleted Lgr5high CBCs. Now, Sheng et al. demonstrate that cycling Msi1+ cells represent DNA damage-resistant ISCs that support efficient repopulation of the intestinal epithelium at the early stage of post-radiation repair, ahead of Lgr5high CBCs.

Gradients of decapentaplegic (Dpp) pattern Drosophila wing imaginal discs, establishing gene expression boundaries at specific locations. As discs grow, Dpp gradients expand, keeping relative boundary positions approximately stationary. Such scaling fails in mutants for Pentagone (pent), a gene repressed by Dpp that encodes a diffusible protein that expands Dpp gradients. Although these properties fit a recent mathematical model of automatic gradient scaling, that model requires an expander that spreads with minimal loss throughout a morphogen field. Here, we show that Pent's actions are confined to within just a few cell diameters of its site of synthesis and can be phenocopied by manipulating non-diffusible Pent targets strictly within the Pent expression domain. Using genetics and mathematical modeling, we develop an alternative model of scaling driven by feedback downregulation of Dpp receptors and co-receptors. Among the model's predictions is a size beyond which scaling fails-something we observe directly in wing discs.

Snail2 has an important role in the epithelial-mesenchymal transition (EMT) and tumor metastasis. Here, we report that Snail2 is highly expressed during TGF-β induced EMT in HL-7702 cells. Additionally, overexpression of Snail2 successfully promotes the migration and invasion of these cells, both in vitro and in a mouse model. Furthermore, our results show that HDAC1 and HDAC3 could suppress the Snail2 gene promoter. Moreover, we find that the acetylation of H3K4 and H3K56 are significantly reduced during the EMT process of liver HL-7702 cells. Thus, our results indicate that HDAC1 and HDAC3 epigenetically suppress the expression of Snail2 during the EMT of liver cells, revealing an opposing function of HDACs during the migration of malignant tumors.

Glioblastoma (GBM) is a fast-growing type of malignant primary brain tumor. To explore the mechanisms in GBM, complex biological networks are used to reveal crucial changes among different biological states, which reflect on the development of living organisms. It is critical to discover the kernel differential subgraph (KDS) that leads to drastic changes. However, identifying the KDS is similar to the Steiner Tree problem that is an NP-hard problem. In this paper, we developed a criterion to explore the KDS (CKDS), which considered the connectivity and scale of KDS, the topological difference of nodes and function relevance between genes in the KDS. The CKDS algorithm was applied to simulated datasets and three single-cell RNA sequencing (scRNA-seq) datasets including GBM, fetal human cortical neurons (FHCN) and neural differentiation. Then we performed the network topology and functional enrichment analyses on the extracted KDSs. Compared with the state-of-art methods, the CKDS algorithm outperformed on simulated datasets to discover the KDSs. In the GBM and FHCN, seventeen genes (one biomarker, nine regulatory genes, one driver genes, six therapeutic targets) and KEGG pathways in KDSs were strongly supported by literature mining that they were highly interrelated with GBM. Moreover, focused on GBM, there were fifteen genes (including ten regulatory genes, three driver genes, one biomarkers, one therapeutic target) and KEGG pathways found in the KDS of neural differentiation process from activated neural stem cells (aNSC) to neural progenitor cells (NPC), while few genes and no pathway were found in the period from NPC to astrocytes (Ast). These experiments indicated that the process from aNSC to NPC is a key differentiation period affecting the development of GBM. Therefore, the CKDS algorithm provides a unique perspective in identifying cell-type-specific genes and KDSs.

Stochasticity (or noise) at cellular and molecular levels has been observed extensively as a universal feature for living systems. However, how living systems deal with noise while performing desirable biological functions remains a major mystery. Regulatory network configurations, such as their topology and timescale, are shown to be critical in attenuating noise, and noise is also found to facilitate cell fate decision. Here we review major recent findings on noise attenuation through regulatory control, the benefit of noise via noise-induced cellular plasticity during developmental patterning, and summarize key principles underlying noise control.

There is increasing evidence reporting that as a common phenomenon in MetS relative diseases, insulin resistance (IR) is regarded as an independent etiological factor and a warning indicator of MetS occurrence. Therefore, for the special group (overweight or obesity), clinical regular monitoring of IR is an important basis for the prevention and early intervention of MetS relative diseases. This surveys reveals that human umbilical cord mesenchymal stem cells (HUC-MSCs)possess a kind of potential: it may become a possible theraphy for IR in type 2 diabetes mellitus (T2DM) and related diseases. Specific emphasis is focused on evaluating the improvement IR function of HUC-MSCs under the background of development in vitro and in vivo. Next, the action mechanisms of HUC-MSCs is discussed, and some of their advantages and disadvantages in the course of clinic application are presented. The final section highlights the application of HUC-MSCs in T2DM and relative diseases at this stage. Up to now, although many questions remain unresolved, we still consider that HUC-MSCs is one of the best therapy ameliorating IR in the future.

Summary The production of germ cells in vitro would open important new avenues for stem biology and human medicine, but the mechanisms of germ cell differentiation are not well understood. The chicken, as a great model for embryology and development, was used in this study to help us explore its regulatory mechanisms. In this study, we reported a comprehensive genome-wide DNA methylation landscape in chicken germ cells, and transcriptomic dynamics was also presented. By uncovering DNA methylation patterns on individual genes, some genes accurately modulated by DNA methylation were found to be associated with cancers and virus infection, e.g., AKT1 and CTNNB1. Chicken-unique markers were also discovered for identifying male germ cells. Importantly, integrated epigenetic mechanisms were explored during male germ cell differentiation, which provides deep insight into the epigenetic processes associated with male germ cell differentiation and possibly improves treatment options to male infertility in animals and humans., Graphical Abstract, Highlights • The mechanisms of stem cell differentiation were explored using the chick embryo model • The orchestrated stem cell differentiation involves multiple epigenetic events • The unique markers in chick embryo were discovered for identifying male germ cells, In this article, Song, Li, and their colleagues reported a comprehensive genome-wide DNA methylation landscape in chicken germ cells. The integrated epigenetic mechanisms were explored, specifically, germ cell differentiation experiences through a highly orchestrated process that involves multiple epigenetic events, which would give a clue for curing male infertility in animals and humans.

Experimental evidence shows that parental psychological stress affects the long-term health of offspring in an inheritable fashion. Although epigenetic mechanisms, including DNA methylation, miRNA, and histone modifications, are involved in transgenerational programming, the underlining mechanisms of transgenerational inheritance remain unsolved. Here, we present a single-cell-based computational model for transgenerational inheritance for investigating the long-term dynamics of phenotype changes in response to parental stress. The model is based on a recent study that has identified the imprinted sperm gene Sfmbt2 as a key target, and incorporates crosstalks among drastically different time scales in mammalian development, including DNA methylation, transcription, cell division, and population dynamics. Computational analysis of the model suggests a positive feedback to DNA methylation in the promoter region of sperm Sfmbt2 gene that provides a possible mechanism to mediate the parental psychological stress reprogramming in offspring. This approach provides a modeling framework for the understanding of the roles that epigenetics play in transgenerational inheritance.

Motivation Single-cell RNA-sequencing (scRNA-seq) offers unprecedented resolution for studying cellular decision-making processes. Robust inference of cell state transition paths and probabilities is an important yet challenging step in the analysis of these data. Results Here we present scEpath, an algorithm that calculates energy landscapes and probabilistic directed graphs in order to reconstruct developmental trajectories. We quantify the energy landscape using ‘single-cell energy’ and distance-based measures, and find that the combination of these enables robust inference of the transition probabilities and lineage relationships between cell states. We also identify marker genes and gene expression patterns associated with cell state transitions. Our approach produces pseudotemporal orderings that are—in combination—more robust and accurate than current methods, and offers higher resolution dynamics of the cell state transitions, leading to new insight into key transition events during differentiation and development. Moreover, scEpath is robust to variation in the size of the input gene set, and is broadly unsupervised, requiring few parameters to be set by the user. Applications of scEpath led to the identification of a cell-cell communication network implicated in early human embryo development, and novel transcription factors important for myoblast differentiation. scEpath allows us to identify common and specific temporal dynamics and transcriptional factor programs along branched lineages, as well as the transition probabilities that control cell fates. Availability and implementation A MATLAB package of scEpath is available at https://github.com/sqjin/scEpath. Supplementary information Supplementary data are available at Bioinformatics online.

Chronic inflammation is a serious risk factor for cancer; however, the routes from inflammation to cancer are poorly understood. On the basis of the processes implicated by frequently mutated genes associated with inflammation and cancer in three organs (stomach, colon, and liver) extracted from the Gene Expression Omnibus, The Cancer Genome Atlas, and Gene Ontology databases, we present a multiscale model of the long-term evolutionary dynamics leading from inflammation to tumorigenesis. The model incorporates cross-talk among interactions on several scales, including responses to DNA damage, gene mutation, cell-cycle behavior, population dynamics, inflammation, and metabolism-immune balance. Model simulations revealed two stages of inflammation-induced tumorigenesis: a precancerous state and tumorigenesis. The precancerous state was mainly caused by mutations in the cell proliferation pathway; the transition from the precancerous to tumorigenic states was induced by mutations in pathways associated with apoptosis, differentiation, and metabolism-immune balance. We identified opposing effects of inflammation on tumorigenesis. Mild inflammation removed cells with DNA damage through DNA damage-induced cell death, whereas severe inflammation accelerated accumulation of mutations and hence promoted tumorigenesis. These results provide insight into the evolutionary dynamics of inflammation-induced tumorigenesis and highlight the combinatorial effects of inflammation and metabolism-immune balance. This approach establishes methods for quantifying cancer risk, for the discovery of driver pathways in inflammation-induced tumorigenesis, and has direct relevance for early detection and prevention and development of new treatment regimes. Cancer Res; 77(22); 6429–41. ©2017 AACR.

The clamworm Perinereis aibuhitensis is a commercially important polychaete in China, but knowledge about the nutritional demands of this species is limited. In this study, the effects of five lipid sources in the diet, namely fish oil (FO), soyabean oil (SO), rapeseed oil (RO), cottonseed oil (CO) and mixed vegetable oil (MO), on growth, whole-body composition and antioxidant parameters of juvenile P. aibuhitensis were evaluated. The results showed that clamworms fed the CO diet had higher specific growth rate (SGR) and protein efficiency ratio (PER) than the other treatments. The accumulation of longer-chain polyunsaturated fatty acids (PUFAs) was observed in P. aibuhitensis, suggesting that P. aibuhitensis had the ability to elongate and desaturate PUFAs with 18C to form longer-chain PUFAs. The values of n-3/n-6 in clamworms fed vegetable oil diets (ranged from 0.20 to 0.31) were much closer to the recommended values for human food compared with FO diet (2.47). Analysis of the antioxidant parameters revealed that clamworms fed the CO diet suffered lower peroxidation burden than those fed FO diet. These results suggested that cottonseed oil is a suitable lipid source for P. aibuhitensis feeds.

The initial rostrocaudal patterning of the neural tube leads to differential expression of Hox genes that contribute to the specification of motor neuron (MN) subtype identity. Although several 3′ Hox mRNAs are expressed in progenitors in a noisy manner, these Hox proteins are not expressed in the progenitors and only become detectable in postmitotic MNs. MicroRNA biogenesis impairment leads to precocious expression and propagates the noise of Hoxa5 at the protein level, resulting in an imprecise Hoxa5-Hoxc8 boundary. Here we uncover, using in silico simulation, two feed-forward Hox-miRNA loops accounting for the precocious and noisy Hoxa5 expression, as well as an ill-defined boundary phenotype in Dicer mutants. Finally, we identify mir-27 as a major regulator coordinating the temporal delay and spatial boundary of Hox protein expression. Our results provide a novel trans Hox-miRNA circuit filtering transcription noise and controlling the timing of protein expression to confer robust individual MN identity., In the spinal cord, some Hox genes are transcribed in progenitors while their proteins are only detected in differentiating postmitotic motor neurons. Here, the authors show that miRNAs (specifically mir-27) regulate post-transcriptional Hoxa5 expression in motor neurons.

In this study, the effects of different salinity levels (9, 12, 15, 18, 21, 24, 27, 30, and 33) on the growth performance, body composition, antioxidant indexes of Perinereis aibuhitensis (initial average mass, 20.4 ± 0.3 mg) and total nitrogen in the substrate were investigated. The survival rate, specific growth rate, feed coefficient, and protein efficiency ratio under different salinity levels were measured. The results showed that the survival rate of P. aibuhitensis at the salinity level of 9 was significantly lower than that of P. aibuhitensis at other salinity levels (P 0.05). On the basis of quadratic polynomial fitting of the relationship between salinity levels and the specific growth rate, feed coefficient, and protein efficiency ratio, it was concluded that 25.36 - 25.9 is the most suitable salinity range for the growth performance of P. aibuhitensis. The main body composition (moisture, crude fat, crude protein, and ash content) was measured at different salinity levels. The results indicated that, with the increase in salinity, the moisture content of P. aibuhitensis decreased gradually; in contrast, the ash content increased gradually, as the salinity level increased. However, in the salinity range of 18 to 33, the difference in ash content was not significant (P > 0.05). Salinity had a significant influence on the crude protein content (P < 0.05), while it had no significant influence on crude body fat. Crude protein showed an increasing trend with the salinity increasing from 9 to 24, and decreased with the salinity increasing from 24 to 33. Antioxidant indexes such as superoxide dismutase, catalase, glutathione, and malondialdehyde (MDA) were analyzed, showing that in vivo MDA content was as low as the antioxidant activity at the salinity level of 24; this means low in vivo contents of active oxygen free radicals and excellent growth performance. This conclusion is consistent with that for other growth indexes. The total nitrogen content of the substrate in which the P. aibuhitensis specimens were cultured for 60 days was higher than the total nitrogen in the soil. With an increase in salinity, the total nitrogen content first decreased and then increased, and the lowest value was observed at the salinity level of about 24.

Background: Amyloid-β deposition and accumulation of autophagic vacuoles are pathologic features of Alzheimer’s disease (AD). Dysregulation of the endosomal–autophagic–lysosomal (EAL) pathway, which impairs amyloid precursor protein processing, is one of the earliest changes in AD. However, the precise role of EAL pathway in neurodegeneration remains unclear. This study aimed to investigate the role of EAL pathway in AD and further study the mechanism of EAL dysfunction. Methods: We used 3-, 7-, and 12-month-old APPswe/PSEN1dE9 (APP/PS1) mice to model different stages of AD with age- and gender-matched wild-type littermates as controls (4–7 mice per group) and detected the changes of EAL markers, endosomal organizers Rab5 and Rab7, autophagosome marker LC3B, and lysosomal proteins Lamp1/2 in cortex and hippocampus by immunohistochemistry and Western blotting analysis. To further explore the mechanism of EAL dysregulation in AD, components of the class III phosphatidylinositol 3-kinase (PI3KC3) complex, activators of Rab7 (Beclin1 and UVRAG), and the negative regulator of Rab7 (Rubicon) were also measured in this two brain regions. Results: In 7-month-old APP/PS1 brain that amyloid beta initiated to accumulate intracellularly, EAL pathway, and related PI3KC3 members, UVRAG and Beclin1 were upregulated both in cortex and hippocampus (all P< 0.05). By the age of 12 months old, when abundant amyloid plaques formed, EAL markers, UVRAG, and Beclin1 were also upregulated in the cortex (all P< 0.05). However, Rab7 was decreased significantly (P = 0.0447), accompanied by a reduction of its activating PI3KC complex component Beclin1 (P = 0.0215) and enhancement of its inhibiting component Rubicon (P = 0.0055) in the hippocampus. Conclusions: Our study implies that EAL pathway, represented as Rab7 and its PI3KC3 regulators’ expressions, showed temporal and spatial variation in brains at different stages of AD. It provides new insights into the role of EAL pathway in pathogenesis and indicates potential therapeutic targets in neurodegenerative diseases. Key words: Autophagy; Class III Phosphatidylinositol 3-Kinase Complex; Endocytosis; Neurodegeneration; Rab7

Cell fate decisions play a pivotal role in development, but technologies for dissecting them are limited. We developed a multifunction new method, Topographer, to construct a "quantitative" Waddington's landscape of single-cell transcriptomic data. This method is able to identify complex cell-state transition trajectories and to estimate complex cell-type dynamics characterized by fate and transition probabilities. It also infers both marker gene networks and their dynamic changes as well as dynamic characteristics of transcriptional bursting along the cell-state transition trajectories. Applying this method to single-cell RNA-seq data on the differentiation of primary human myoblasts, we not only identified three known cell types, but also estimated both their fate probabilities and transition probabilities among them. We found that the percent of genes expressed in a bursty manner is significantly higher at (or near) the branch point (~97%) than before or after branch (below 80%), and that both gene-gene and cell-cell correlation degrees are apparently lower near the branch point than away from the branching. Topographer allows revealing of cell fate mechanisms in a coherent way at three scales: cell lineage (macroscopic), gene network (mesoscopic), and gene expression (microscopic).

Elaborate regulatory feedback processes are thought to make biological development robust, that is, resistant to changes induced by genetic or environmental perturbations. How this might be done is still not completely understood. Previous numerical simulations on reaction-diffusion models of Dpp gradients in Drosophila wing imaginal disc have showed that feedback (of the Hill function type) on (signaling) receptors and/or non-(signaling) receptors are of limited effectiveness in promoting robustness. Spatial nonuniformity of the feedback processes has also been shown theoretically to lead to serious shape distortion and a principal cause for ineffectiveness. Through mathematical modeling and analysis, the present article shows that spatially uniform nonlocal feedback mechanisms typically modify gradient shape through a shape parameter (that does not change with location). This in turn enables us to uncover new multi-feedback instrument for effective promotion of robust signaling gradients.

Elaborate feedback regulatory processes are thought to make biological developments robust, i.e., resistant to changes induced by genetic or environmental perturbations. How this might be done is still not completely understood. Previous numerical simulations on reaction-diffusion models of Dpp gradients in Drosophila wing imaginal disc showed that feedback (of the Hill’s function type) on (signaling) receptors and/or non-(signaling) receptors are of limited effectiveness in promoting robustness. Spatial nonuniformity of the feedback processes is thought to lead to serious shape distortion and a principal cause for ineffectiveness. Through mathematical modeling of a spatially uniform nonlocal feedback mechanism, the present paper provides a theoretical support of these observations. More significantly, the new approach also enables us to uncover in this paper a new, theory-based multi-feedback instrument for broadly effective promotion of robust signaling gradients.

Simultaneous measurements of transcriptomic and epigenomic profiles in the same individual cells provide an unprecedented opportunity to understand cell fates. However, effective approaches for the integrative analysis of such data are lacking. Here, we present a single-cell aggregation and integration (scAI) method to deconvolute cellular heterogeneity from parallel transcriptomic and epigenomic profiles. Through iterative learning, scAI aggregates sparse epigenomic signals in similar cells learned in an unsupervised manner, allowing coherent fusion with transcriptomic measurements. Simulation studies and applications to three real datasets demonstrate its capability of dissecting cellular heterogeneity within both transcriptomic and epigenomic layers and understanding transcriptional regulatory mechanisms.

Many signaling systems execute adaptation under circumstances that require noise attenuation. Here, we identify an intrinsic trade-off existing between sensitivity and noise attenuation in the three-node networks. We demonstrate that although fine-tuning timescales in three-node adaptive networks can partially mediate this trade-off in this context, it prolongs adaptation time and imposes unrealistic parameter constraints. By contrast, four-node networks can effectively decouple adaptation and noise attenuation to achieve dual function without a trade-off, provided that these functions are executed sequentially. We illustrate ideas in seven biological examples, including Dictyostelium discoideum chemotaxis and the p53 signaling network and find that adaptive networks are often associated with a noise attenuation module. Our approach may be applicable to finding network design principles for other dual and multiple functions.

Tissue renewal requires proliferative progenitors with long-lasting potential. Designated stem cells within specialized niches are considered to be the primary mechanism for this requirement. Recent studies show that dispersed equipotent progenitors are sufficient to account for fast-paced cellular dynamics in skin oil glands and foetal gut epithelium.

The use of single-cell transcriptomics has become a major approach to delineate cell subpopulations and the transitions between them. While various computational tools using different mathematical methods have been developed to infer clusters, marker genes, and cell lineage, none yet integrate these within a mathematical framework to perform multiple tasks coherently. Such coherence is critical for the inference of cell-cell communication, a major remaining challenge. Here we present similarity matrix-based optimization for single-cell data analysis (SoptSC), in which unsupervised clustering, pseudotemporal ordering, lineage inference, and marker gene identification are inferred via a structured cell-to-cell similarity matrix. SoptSC then predicts cell-cell communication networks, enabling reconstruction of complex cell lineages that include feedback or feedforward interactions. Application of SoptSC to early embryonic development, epidermal regeneration, and hematopoiesis demonstrates robust identification of subpopulations, lineage relationships, and pseudotime, and prediction of pathway-specific cell communication patterns regulating processes of development and differentiation.

During progression from carcinoma in situ to an invasive tumor, the immune system is engaged in complex sets of interactions with various tumor cells. Tumor cell plasticity also alters disease trajectories via epithelial-to-mesenchymal transition (EMT). Several of the same pathways that regulate EMT are involved in tumor-immune interactions, yet little is known about the mechanisms and consequences of crosstalk between these regulatory processes. Here we introduce a multiscale evolutionary model to describe tumor-immune-EMT interactions and their impact on epithelial cancer progression from in situ to invasive disease. Through in silico analyses of large patient cohorts, we find controllable regions that maximize invasion-free survival. We identify that delaying tumor progression depends crucially on properties of the mesenchymal tumor cell phenotype: its growth rate and its immune-evasiveness. Through analysis of EMT-inflammation-associated data from The Cancer Genome Atlas, we find that association with EMT significantly worsens invasion-free survival probabilities in support of our model, and we predict new genes influencing outcomes in bladder and uterine cancer, including FGF pathway members. These results offer novel means to delay disease progression by regulating properties of EMT through specific gene interactions, and demonstrate the importance of studying cancer-immune interactions in light of EMT.

Directional migration is inherently important for epithelial tissue regeneration and repair, but how it is precisely controlled and coordinated with cell proliferation is unclear. Here, we report that Ovol2, a transcriptional repressor that inhibits epithelial-to-mesenchymal transition (EMT), plays a crucial role in adult skin epithelial regeneration and repair. Ovol2-deficient mice show compromised wound healing characterized by aberrant epidermal cell migration and proliferation, as well as delayed anagen progression characterized by defects in hair follicle matrix cell proliferation and subsequent differentiation. Epidermal keratinocytes and bulge hair follicle stem cells (Bu-HFSCs) lacking Ovol2 fail to expand in culture and display molecular alterations consistent with enhanced EMT and reduced proliferation. Live imaging of wound explants and Bu-HFSCs reveals increased migration speed but reduced directionality, and post-mitotic cell cycle arrest. Remarkably, simultaneous deletion of Zeb1 encoding an EMT-promoting factor restores directional migration to Ovol2-deficient Bu-HFSCs. Taken together, our findings highlight the important function of an Ovol2-Zeb1 EMT-regulatory circuit in controlling the directional migration of epithelial stem and progenitor cells to facilitate adult skin epithelial regeneration and repair.

During early mammalian embryo development, a small number of cells make robust fate decisions at particular spatial locations in a tight time window to form inner cell mass (ICM), and later epiblast (Epi) and primitive endoderm (PE). While recent single-cell transcriptomics data allows scrutinization of heterogeneity of individual cells, consistent spatial and temporal mechanisms the early embryo utilize to robustly form the Epi/PE layers from ICM remain elusive. Here we build a multiscale three-dimensional model for mammalian embryo to recapitulate the observed patterning process from zygote to late blastocyst. By integrating the spatiotemporal information reconstructed from multiple single-cell transcriptomic datasets, the data-informed modeling analysis suggests two major processes critical to the formation of Epi/PE layers: a selective cell-cell adhesion mechanism (via EphA4/EphrinB2) for fate-location coordination and a temporal attenuation mechanism of cell signaling (via Fgf). Spatial imaging data and distinct subsets of single-cell gene expression data are then used to validate the predictions. Together, our study provides a multiscale framework that incorporates single-cell gene expression datasets to analyze gene regulations, cell-cell communications, and physical interactions among cells in complex geometries at single-cell resolution, with direct application to late-stage development of embryogenesis., Author summary Starting as fertilized eggs, mammal embryos develop into fetuses with complex functions through robust spatiotemporal trajectotries. Correct timing of various regulatory mechanisms is an essential prerequisite that keeps developing biological systems on the right track. At the earliest stages of embryo development, cells make robust fate decisions to form inner cell mass which later develops into two cell types forming a particular spatial pattern. Through the lens of a multiscale three-dimensional model with the resolution of single cells in a realistic geometry, we study how timing of regulatory mechanisms ensures the robust developmental process in a tight time window. Assisted by single-cell transcriptomics data, the model revealed how the timing of a gene regulatory mechanism and a spatial mechanistic mechanism impact the pattern formation in early embryo development. We showed that both individual timings of these two mechanisms and the time overlap between them are essential to ensure correct pattern formation. We further validated our findings using distinct subsets of single-cell gene expression data and spatial imaging data. This data-informed multiscale modeling framework has a potential in studying other biological systems and developmental processes utilizing the emerging high-throughput and high-resolution data resources.

The aim of this study was to investigate the phenotypic and genotypic antimicrobial resistance, integrons, and transferability of resistance markers in 243 aerobic bacteria recovered from pork at slaughter in the People's Republic of China. The organisms belonged to 22 genera of gram-negative bacteria (92.2%) and gram-positive bacteria (7.8%). High levels of resistance were detected to tetracycline, trimethoprim-sulfamethoxazole, and ampicillin (36.2 to 54.3%), and lower levels were detected to nitrofurantoin, cefotaxime, gentamicin, ciprofloxacin, and chloramphenicol (7.8 to 29.2%). Across species, genes conferring antimicrobial resistance were observed with the following frequencies: blaTEM, 40.7%; blaCMY-2, 15.2%; blaCTX-M, 11.5%; sul2, 27.2%; sul1, 14.4%; tet(A), 5.4%; tet(L), 5.4%; tet(M), 5.0%; tet(E), 3.7%; tet(C), 3.3%; tet(S), 2.5%; and tet(K), 0.8%. Various antimicrobial resistance genes were found in new carriers: blaTEM in Lactococcus garvieae, Myroides odoratimimus, Aeromonas hydrophila, Staphylococcus sciuri, Raoultella terrigena, Macrococcus caseolyticus, Acinetobacter ursingii, Sphingobacterium sp., and Oceanobacillus sp.; blaCMY-2 in Lactococcus lactis, Klebsiella oxytoca, Serratia marcescens, Acinetobacter baumannii, and Myroides phaeus; tet(L) in M. caseolyticus; sul1 in Vibrio cincinnatiensis; sul2 in Acinetobacter bereziniae, Acinetobacter johnsonii, and V. cincinnatiensis; and the class 1 integron and gene cassette aadA2 in V. cincinnatiensis. Approximately 6.6% of isolates contained class 1 integrons, and one isolate harbored class 2 integrons. Plasmid associated intI1 and androgen receptor- encoding genes were transferred into Escherichia coli J53 and E. coli DH5α by conjugation and transformation experiments, respectively. Our study highlights the importance of aerobic bacteria from pork as reservoirs for antimicrobial resistance genes and mobile genetic elements that can readily be transferred intra- and interspecies.

Key points Using functional mapping assays, we conducted a quantitative assessment of both excitatory and inhibitory synaptic laminar connections to excitatory neurons in layers 2/3–6 of the mouse visual cortex (V1). Laminar-specific synaptic wiring diagrams of excitatory neurons were constructed on the basis of circuit mapping. The present study reveals that that excitatory and inhibitory synaptic connectivity is spatially balanced across excitatory neuronal networks in V1. Abstract In the mammalian neocortex, excitatory neurons provide excitation in both columnar and laminar dimensions, which is modulated further by inhibitory neurons. However, our understanding of intracortical excitatory and inhibitory synaptic inputs in relation to principal excitatory neurons remains incomplete, and it is unclear how local excitatory and inhibitory synaptic connections to excitatory neurons are spatially organized on a layer-by-layer basis. In the present study, we combined whole cell recordings with laser scanning photostimulation via glutamate uncaging to map excitatory and inhibitory synaptic inputs to single excitatory neurons throughout cortical layers 2/3–6 in the mouse primary visual cortex (V1). We find that synaptic input sources of excitatory neurons span the radial columns of laminar microcircuits, and excitatory neurons in different V1 laminae exhibit distinct patterns of layer-specific organization of excitatory inputs. Remarkably, the spatial extent of inhibitory inputs of excitatory neurons for a given layer closely mirrors that of their excitatory input sources, indicating that excitatory and inhibitory synaptic connectivity is spatially balanced across excitatory neuronal networks. Strong interlaminar inhibitory inputs are found, particularly for excitatory neurons in layers 2/3 and 5. This differs from earlier studies reporting that inhibitory cortical connections to excitatory neurons are generally localized within the same cortical layer. On the basis of the functional mapping assays, we conducted a quantitative assessment of both excitatory and inhibitory synaptic laminar connections to excitatory cells at single cell resolution, establishing precise layer-by-layer synaptic wiring diagrams of excitatory neurons in the visual cortex.