Significant biomagnification of methylmercury in songbird nestlings through a rice-based food web: Insights from stable mercury isotopes.

To elucidate the sources and transfer of mercury (Hg) in terrestrial food chains, particularly in heavily Hg-contaminated rice paddy ecosystems, we collected rice leaves, invertebrates, and Russet Sparrow nestlings from a clear food chain and analyzed the dietary compositions and potential Hg sources using stable Hg isotopes coupled with a Bayesian isotope mixing model (BIMM). Our findings indicated that MeHg exposure is dominant through the dietary route, with caterpillars, grasshoppers, and katydids being the main prey items, while the less provisioned spiders, dragonflies, and mantises contributed the most of the Hg to nestlings. We found minimal MIF but certain MDF in this terrestrial food chain and identified two distinct MeHg sources of dietary exposure and maternal transfer. We firstly found that the dietary route contributed substantially (almost tenfold) more MeHg to the nestlings than maternal transfer. These findings offer new insights into the integration of Hg from the dietary route and maternal transfers, enhancing our understanding of fluctuating Hg exposure risk during the nestling stage. Our study suggested that Hg isotopes combined with BIMM is an effective approach for tracing Hg sources in birds and for gaining in-depth insight into the trophic transfers and biomagnification of MeHg in food chains.

Using live videography observation and Bayesian isotope mixing model to identify food composition and dietary contribution to inorganic mercury and methylmercury intake by songbird nestlings.

Mercury (Hg) exposure is increasing in terrestrial birds; however, studies on its sources are scarce. In the present study, we elucidated the food composition of green-backed tit nestlings from three urban forest parks (CPL, AHL, and LCG) using live videography observation (LVO). Furthermore, the daily dietary intakes of inorganic Hg (IHg) (MDIIHg) and methylmercury (MeHg) (MDIMeHg) were determined using the Bayesian isotope mixing model (BIMM) to uncover the nestlings' specific dietary Hg contribution. Both LVO and BIMM indicated that Lepidoptera (primarily caterpillar) constituted the primary food source for the nestlings in the three forests, accounting for approximately 60% of their diet in all three forest parks. The estimated MDI of Hg revealed that lepidopterans and spiders primarily contributed to IHg exposure, with a co-contribution ratio of 71.8%-97.7%. Unexpectedly, dietary MeHg was mostly derived from spiders; the highest contribution ratio of 93.6% was recorded at CPL, followed by another peak ratio of 92.9% at LCG. However, the dietary exposure was primarily IHg, accounting for 69.8% (AHL), 62.0% (LCG), and 61.3% (CPL) of the nestlings. Our study findings highlight the importance of dietary IHg transfer in evaluating the effects of Hg in nestlings. LVO, coupled with BIMM, is an effective tool for determining the food compositions of songbird nestlings and estimating the contribution of specific diets.

TCR ligand potency differentially impacts PD-1 inhibitory effects on diverse signaling pathways.

Checkpoint blockade revolutionized cancer therapy, but we still lack a quantitative, mechanistic understanding of how inhibitory receptors affect diverse signaling pathways. To address this issue, we developed and applied a fluorescent intracellular live multiplex signal transduction activity reporter (FILMSTAR) system to analyze PD-1-induced suppressive effects. These studies identified pathways triggered solely by TCR or requiring both TCR and CD28 inputs. Using presenting cells differing in PD-L1 and CD80 expression while displaying TCR ligands of distinct potency, we found that PD-1-mediated inhibition primarily targets TCR-linked signals in a manner highly sensitive to peptide ligand quality. These findings help resolve discrepancies in existing data about the site(s) of PD-1 inhibition in T cells while emphasizing the importance of neoantigen potency in controlling the effects of checkpoint therapy.

Theoretical and computational tools to model multistable gene regulatory networks.

The last decade has witnessed a surge of theoretical and computational models to describe the dynamics of complex gene regulatory networks, and how these interactions can give rise to multistable and heterogeneous cell populations. As the use of theoretical modeling to describe genetic and biochemical circuits becomes more widespread, theoreticians with mathematical and physical backgrounds routinely apply concepts from statistical physics, non-linear dynamics, and network theory to biological systems. This review aims at providing a clear overview of the most important methodologies applied in the field while highlighting current and future challenges. It also includes hands-on tutorials to solve and simulate some of the archetypical biological system models used in the field. Furthermore, we provide concrete examples from the existing literature for theoreticians that wish to explore this fast-developing field. Whenever possible, we highlight the similarities and differences between biochemical and regulatory networks and 'classical' systems typically studied in non-equilibrium statistical and quantum mechanics.

Theoretical and computational tools to model multistable gene regulatory networks.

The last decade has witnessed a surge of theoretical and computational models to describe the dynamics of complex gene regulatory networks, and how these interactions can give rise to multistable and heterogeneous cell populations. As the use of theoretical modeling to describe genetic and biochemical circuits becomes more widespread, theoreticians with mathematical and physical backgrounds routinely apply concepts from statistical physics, non-linear dynamics, and network theory to biological systems. This review aims at providing a clear overview of the most important methodologies applied in the field while highlighting current and future challenges. It also includes hands-on tutorials to solve and simulate some of the archetypical biological system models used in the field. Furthermore, we provide concrete examples from the existing literature for theoreticians that wish to explore this fast-developing field. Whenever possible, we highlight the similarities and differences between biochemical and regulatory networks and 'classical' systems typically studied in non-equilibrium statistical and quantum mechanics.

Decoding the coupled decision-making of the epithelial-mesenchymal transition and metabolic reprogramming in cancer.

Cancer metastasis relies on an orchestration of traits driven by different interacting functional modules, including metabolism and epithelial-mesenchymal transition (EMT). During metastasis, cancer cells can acquire a hybrid metabolic phenotype (W/O) by increasing oxidative phosphorylation without compromising glycolysis and they can acquire a hybrid epithelial/mesenchymal (E/M) phenotype by engaging EMT. Both the W/O and E/M states are associated with high metastatic potentials, and many regulatory links coupling metabolism and EMT have been identified. Here, we investigate the coupled decision-making networks of metabolism and EMT. Their crosstalk can exhibit synergistic or antagonistic effects on the acquisition and stability of different coupled metabolism-EMT states. Strikingly, the aggressive E/M-W/O state can be enabled and stabilized by the crosstalk irrespective of these hybrid states' availability in individual metabolism or EMT modules. Our work emphasizes the mutual activation between metabolism and EMT, providing an important step toward understanding the multifaceted nature of cancer metastasis.

A community effort to assess and improve computerized interpretation of 12-lead resting electrocardiogram.

Computerized interpretation of electrocardiogram plays an important role in daily cardiovascular healthcare. However, inaccurate interpretations lead to misdiagnoses and delay proper treatments. In this work, we built a high-quality Chinese 12-lead resting electrocardiogram dataset with 15,357 records, and called for a community effort to improve the performances of CIE through the China ECG AI Contest 2019. This dataset covers most types of ECG interpretations, including the normal type, 8 common abnormal types, and the other type which includes both uncommon abnormal and noise signals. Based on the Contest, we systematically assessed and analyzed a set of top-performing methods, most of which are deep neural networks, with both their commonalities and characteristics. This study establishes the benchmarks for computerized interpretation of 12-lead resting electrocardiogram and provides insights for the development of new methods. Graphical Abstract A community effort to assess and improve computerized interpretation of 12-lead resting electrocardiogram.

Breast cancer dormancy: need for clinically relevant models to address current gaps in knowledge.

Breast cancer is the most commonly diagnosed cancer in the USA. Although advances in treatment over the past several decades have significantly improved the outlook for this disease, most women who are diagnosed with estrogen receptor positive disease remain at risk of metastatic relapse for the remainder of their life. The cellular source of late relapse in these patients is thought to be disseminated tumor cells that reactivate after a long period of dormancy. The biology of these dormant cells and their natural history over a patient's lifetime is largely unclear. We posit that research on tumor dormancy has been significantly limited by the lack of clinically relevant models. This review will discuss existing dormancy models, gaps in biological understanding, and propose criteria for future models to enhance their clinical relevance.

Towards decoding the coupled decision-making of metabolism and epithelial-to-mesenchymal transition in cancer.

Cancer cells have the plasticity to adjust their metabolic phenotypes for survival and metastasis. A developmental programme known as epithelial-to-mesenchymal transition (EMT) plays a critical role during metastasis, promoting the loss of polarity and cell-cell adhesion and the acquisition of motile, stem-cell characteristics. Cells undergoing EMT or the reverse mesenchymal-to-epithelial transition (MET) are often associated with metabolic changes, as the change in phenotype often correlates with a different balance of proliferation versus energy-intensive migration. Extensive crosstalk occurs between metabolism and EMT, but how this crosstalk leads to coordinated physiological changes is still uncertain. The elusive connection between metabolism and EMT compromises the efficacy of metabolic therapies targeting metastasis. In this review, we aim to clarify the causation between metabolism and EMT on the basis of experimental studies, and propose integrated theoretical-experimental efforts to better understand the coupled decision-making of metabolism and EMT.

Deep Multi-instance Networks for Bundle Branch Block Detection from Multi-lead ECG.

Bundle branch block (BBB) is one of the most common cardiac disorder, and can be detected by electro-cardiogram (ECG) signal in clinical practice. Conventional methods adopted some kinds of hand-craft features, whose discriminative power is relatively low. On the other hand, these methods were based on the supervised learning, which required the high cost heartbeat annotation in the training. In this paper, a novel end-to-end deep network was proposed to classify three types of heartbeat: right BBB (RBBB), left BBB (LBBB) and others with a multiple instance learning based training strategy. We trained the proposed method on the China Physiological Signal Challenge 2018 database (CPSC) and tested on the MIT-BIH Arrhythmia database (AR). The proposed method achieved an accuracy of 78.58%, and sensitivity of 84.78% (LBBB), 51.23% (others) and 99.72% (RBBB), better than the baseline methods. Experimental results show that our method would be a good choice for the BBB classification on the ECG dataset with record-level labels instead of heartbeat annotations.

A Weighted Graph Attention Network Based Method for Multi-label Classification of Electrocardiogram Abnormalities.

The multi-label electrocardiogram (ECG) classification is to automatically predict a set of concurrent cardiac abnormalities in an ECG record, which is significant for clinical diagnosis. Modeling the cardiac abnormality dependencies is the key to improving classification performance. To capture the dependencies, we proposed a multi-label classification method based on the weighted graph attention networks. In the study, a graph taking each class as a node was mapped and the class dependencies were represented by the weights of graph edges. A novel weights generation method was proposed by combining the self-attentional weights and the prior learned co-occurrence knowledge of classes. The algorithm was evaluated on the dataset of the Hefei Hi-tech Cup ECG Intelligent Competition for 34 kinds of ECG abnormalities classification. And the micro-f 1 and the macro-f 1 of cross validation respectively were 91.45% and 44.48%. The experiment results show that the proposed method can model class dependencies and improve classification performance.

Drug-Tolerant Idling Melanoma Cells Exhibit Theory-Predicted Metabolic Low-Low Phenotype.

Cancer cells adjust their metabolic profiles to evade treatment. Metabolic adaptation is complex and hence better understood by an integrated theoretical-experimental approach. Using a minimal kinetic model, we predicted a previously undescribed Low/Low (L/L) phenotype, characterized by low oxidative phosphorylation (OXPHOS) and low glycolysis. Here, we report that L/L metabolism is observed in BRAF-mutated melanoma cells that enter a drug-tolerant "idling state" upon long-term MAPK inhibition (MAPKi). Consistently, using publicly available RNA-sequencing data of both cell lines and patient samples, we show that melanoma cells decrease their glycolysis and/or OXPHOS activity upon MAPKi and converge toward the L/L phenotype. L/L metabolism is unfavorable for tumor growth, yet supports successful cell division at ~50% rate. Thus, L/L drug-tolerant idling cells are a reservoir for accumulating mutations responsible for relapse, and it should be considered as a target subpopulation for improving MAPKi outcomes in melanoma treatment.

Decoding the mechanisms underlying cell-fate decision-making during stem cell differentiation by random circuit perturbation.

Stem cells can precisely and robustly undergo cellular differentiation and lineage commitment, referred to as stemness. However, how the gene network underlying stemness regulation reliably specifies cell fates is not well understood. To address this question, we applied a recently developed computational method, random circuit perturbation (RACIPE), to a nine-component gene regulatory network (GRN) governing stemness, from which we identified robust gene states. Among them, four out of the five most probable gene states exhibit gene expression patterns observed in single mouse embryonic cells at 32-cell and 64-cell stages. These gene states can be robustly predicted by the stemness GRN but not by randomized versions of the stemness GRN. Strikingly, we found a hierarchical structure of the GRN with the Oct4/Cdx2 motif functioning as the first decision-making module followed by Gata6/Nanog. We propose that stem cell populations, instead of being viewed as all having a specific cellular state, can be regarded as a heterogeneous mixture including cells in various states. Upon perturbations by external signals, stem cells lose the capacity to access certain cellular states, thereby becoming differentiated. The new gene states and key parameters regulating transitions among gene states proposed by RACIPE can be used to guide experimental strategies to better understand differentiation and design reprogramming. The findings demonstrate that the functions of the stemness GRN is mainly determined by its well-evolved network topology rather than by detailed kinetic parameters.

Quantifying Cancer Epithelial-Mesenchymal Plasticity and its Association with Stemness and Immune Response.

Cancer cells can acquire a spectrum of stable hybrid epithelial/mesenchymal (E/M) states during epithelial-mesenchymal transition (EMT). Cells in these hybrid E/M phenotypes often combine epithelial and mesenchymal features and tend to migrate collectively commonly as small clusters. Such collectively migrating cancer cells play a pivotal role in seeding metastases and their presence in cancer patients indicates an adverse prognostic factor. Moreover, cancer cells in hybrid E/M phenotypes tend to be more associated with stemness which endows them with tumor-initiation ability and therapy resistance. Most recently, cells undergoing EMT have been shown to promote immune suppression for better survival. A systematic understanding of the emergence of hybrid E/M phenotypes and the connection of EMT with stemness and immune suppression would contribute to more effective therapeutic strategies. In this review, we first discuss recent efforts combining theoretical and experimental approaches to elucidate mechanisms underlying EMT multi-stability (i.e., the existence of multiple stable phenotypes during EMT) and the properties of hybrid E/M phenotypes. Following we discuss non-cell-autonomous regulation of EMT by cell cooperation and extracellular matrix. Afterwards, we discuss various metrics that can be used to quantify EMT spectrum. We further describe possible mechanisms underlying the formation of clusters of circulating tumor cells. Last but not least, we summarize recent systems biology analysis of the role of EMT in the acquisition of stemness and immune suppression.

Elucidating cancer metabolic plasticity by coupling gene regulation with metabolic pathways.

Metabolic plasticity enables cancer cells to switch their metabolism phenotypes between glycolysis and oxidative phosphorylation (OXPHOS) during tumorigenesis and metastasis. However, it is still largely unknown how cancer cells orchestrate gene regulation to balance their glycolysis and OXPHOS activities. Previously, by modeling the gene regulation of cancer metabolism we have reported that cancer cells can acquire a stable hybrid metabolic state in which both glycolysis and OXPHOS can be used. Here, to comprehensively characterize cancer metabolic activity, we establish a theoretical framework by coupling gene regulation with metabolic pathways. Our modeling results demonstrate a direct association between the activities of AMPK and HIF-1, master regulators of OXPHOS and glycolysis, respectively, with the activities of three major metabolic pathways: glucose oxidation, glycolysis, and fatty acid oxidation. Our model further characterizes the hybrid metabolic state and a metabolically inactive state where cells have low activity of both glycolysis and OXPHOS. We verify the model prediction using metabolomics and transcriptomics data from paired tumor and adjacent benign tissue samples from a cohort of breast cancer patients and RNA-sequencing data from The Cancer Genome Atlas. We further validate the model prediction by in vitro studies of aggressive triple-negative breast cancer (TNBC) cells. The experimental results confirm that TNBC cells can maintain a hybrid metabolic phenotype and targeting both glycolysis and OXPHOS is necessary to eliminate their metabolic plasticity. In summary, our work serves as a platform to symmetrically study how tuning gene activity modulates metabolic pathway activity, and vice versa.

A Novel Detection Method of Bundle Branch Block from Multi-lead ECG.

Bundle branch block (BBB) is a common conduction block disease and can be diagnosed using electrocardiogram (ECG) signal in clinical practice. In this paper, a novel method was proposed to detect two types of BBB: right BBB (RBBB) and left BBB (LBBB) based on the combination of deep features and several kinds of expert features. We evaluated the proposed method on the MIT-BIH Arrhythmia database (AR) and China Physiological Signal Challenge 2018 database (CPSC). The proposed method achieved an accuracy of 99.96% (AR) in the class-oriented evaluation and an accuracy of 98.76% (AR) and 97.88% (CPSC) in the subject-oriented evaluation, better than the baseline methods. Experimental results show that our method would be a good choice for the detection of the BBB.

Deep Learning Based Patient-Specific Classification of Arrhythmia on ECG signal.

The classification of the heartbeat type is an essential function in the automatical electrocardiogram (ECG) analysis algorithm. The guideline of the ANSI/AAMI EC57 defined five types of heartbeat: non-ectopic or paced beat (N), supraventricular ectopic beat (S), ventricular ectopic beat (V), fusion of a ventricular and normal beat (F), pace beat or fusion of a paced and a normal or beat that cannot be classified (Q). In the work, a deep neural network based method was proposed to classify these five types of heartbeat. After removing the noise from ECG signals by a low-pass filter, the two-lead heartbeat segments with 2-s length were generated on the filtered signals, and classified by an adaptive ResNet model. The proposed method was evaluated on the MIT-BIH Arrhythmia Database with the patient-specific pattern. The overall accuracy was 98.6% and sensitivity of N, S, V, F were 99.4%, 85.4%, 96.6%, 90.6% respectively. Experimental results show that the proposed method achieved a good performance, and would be useful in the clinic practice.

An Electrocardiogram Delineator via Deep Segmentation Network.

Electrocardiogram (ECG) delineation is a process to detect multiple characteristic points, which contain critical diagnostic information about cardiac diseases. We treat the ECG delineation task as an one-dimensional segmentation problem, and propose a novel end-to-end deep learning method to segment sections of ECG signal. Our neural network consists of two parts: a segmentation network composed of multiple 1D Convolutional Neural Networks (CNN) and a postprocessing network composed of a sequential Conditional Random Field (CRF). Our method is trained and validated on QT database. The experimental results show that our method yields competitive overall performance compared with other state-of-the-art works and outperform them on onset of the P wave and offset of the T wave.

Novel Deep Convolutional Neural Network for Cuff-less Blood Pressure Measurement Using ECG and PPG Signals.

Cuff-less blood pressure (BP) is a potential method for BP monitoring because it is undisturbed and continuous monitoring. Existing cuff-less estimation methods are easily influenced by signal noise and non-ideal signal morphology. In this study we propose a novel well-designed Convolutional Neural Network (CNN) model named Deep-BP for BP estimation task. The structure of Deep-BP can help to capture more underlying data features associated with BP than handcrafted features, thus improving the robustness and estimation accuracy. We carry out experiments with and without calibration procedure in training stage to evaluate the performance of new method in different application scenarios. The experiment results show that the Deep-BP model achieves high accuracy and outperforms existing methods, in the experiments both with and without calibration.