SCTC: inference of developmental potential from single-cell transcriptional complexity.

Inferring the developmental potential of single cells from scRNA-Seq data and reconstructing the pseudo-temporal path of cell development are fundamental but challenging tasks in single-cell analysis. Although single-cell transcriptional diversity (SCTD) measured by the number of expressed genes per cell has been widely used as a hallmark of developmental potential, it may lead to incorrect estimation of differentiation states in some cases where gene expression does not decrease monotonously during the development process. In this study, we propose a novel metric called single-cell transcriptional complexity (SCTC), which draws on insights from the economic complexity theory and takes into account the sophisticated structure information of scRNA-Seq count matrix. We show that SCTC characterizes developmental potential more accurately than SCTD, especially in the early stages of development where cells typically have lower diversity but higher complexity than those in the later stages. Based on the SCTC, we provide an unsupervised method for accurate, robust, and transferable inference of single-cell pseudotime. Our findings suggest that the complexity emerging from the interplay between cells and genes determines the developmental potential, providing new insights into the understanding of biological development from the perspective of complexity theory.

Predicting metabolite-disease associations based on auto-encoder and non-negative matrix factorization.

Metabolism refers to a series of orderly chemical reactions used to maintain life activities in organisms. In healthy individuals, metabolism remains within a normal range. However, specific diseases can lead to abnormalities in the levels of certain metabolites, causing them to either increase or decrease. Detecting these deviations in metabolite levels can aid in diagnosing a disease. Traditional biological experiments often rely on a lot of manpower to do repeated experiments, which is time consuming and labor intensive. To address this issue, we develop a deep learning model based on the auto-encoder and non-negative matrix factorization named as MDA-AENMF to predict the potential associations between metabolites and diseases. We integrate a variety of similarity networks and then acquire the characteristics of both metabolites and diseases through three specific modules. First, we get the disease characteristics from the five-layer auto-encoder module. Later, in the non-negative matrix factorization module, we extract both the metabolite and disease characteristics. Furthermore, the graph attention auto-encoder module helps us obtain metabolite characteristics. After obtaining the features from three modules, these characteristics are merged into a single, comprehensive feature vector for each metabolite-disease pair. Finally, we send the corresponding feature vector and label to the multi-layer perceptron for training. The experiment demonstrates our area under the receiver operating characteristic curve of 0.975 and area under the precision-recall curve of 0.973 in 5-fold cross-validation, which are superior to those of existing state-of-the-art predictive methods. Through case studies, most of the new associations obtained by MDA-AENMF have been verified, further highlighting the reliability of MDA-AENMF in predicting the potential relationships between metabolites and diseases.

Phosphorylation of PPDPF via IL6-JAK2 activates the Wnt/ß-catenin pathway in colorectal cancer.

Inflammation plays an important role in the initiation and progression of colorectal cancer (CRC) and leads to ß-catenin accumulation in colitis-related CRC. However, the mechanism remains largely unknown. Here, pancreatic progenitor cell differentiation and proliferation factor (PPDPF) is found to be upregulated in CRC and significantly correlated with tumor-node-metastasis (TNM) stages and survival time. Knockout of PPDPF in the intestinal epithelium shortens crypts, decreases the number of stem cells, and inhibits the growth of organoids and the occurrence of azoxymethane (AOM)/dextran sodium sulfate (DSS)-induced CRC. Mechanistically, PPDPF is found to interact with Casein kinase 1α (CK1α), thereby disrupting its binding to Axin, disassociating the ß-catenin destruction complex, decreasing the phosphorylation of ß-catenin, and activating the Wnt/ß-catenin pathway. Furthermore, interleukin 6 (IL6)/Janus kinase 2 (JAK2)-mediated inflammatory signals lead to phosphorylation of PPDPF at Tyr16 and Tyr17, stabilizing the protein. In summary, this study demonstrates that PPDPF is a key molecule in CRC carcinogenesis and progression that connects inflammatory signals to the Wnt/ß-catenin signaling pathway, providing a potential novel therapeutic target.

Robots as models of evolving systems.

Experimental robobiological physics can bring insights into biological evolution. We present a development of hybrid analog/digital autonomous robots with mutable diploid dominant/recessive 6-byte genomes. The robots are capable of death, rebirth, and breeding. We map the quasi-steady-state surviving local density of the robots onto a multidimensional abstract "survival landscape." We show that robot death in complex, self-adaptive stress landscapes proceeds by a general lowering of the robotic genetic diversity, and that stochastically changing landscapes are the most difficult to survive.

An entropy-based approach for assessing the directional persistence of cell migration.

Cell migration, which is primarily characterized by directional persistence, is essential for the development of normal tissues and organs, as well as for numerous pathological processes. However, there is a lack of simple and efficient tools to analyze the systematic properties of persistence based on cellular trajectory data. Here, we present a novel approach, the entropy of angular distribution , which combines cellular turning dynamics and Shannon entropy to explore the statistical and time-varying properties of persistence that strongly correlate with cellular migration modes. Our results reveal the changes in the persistence of multiple cell lines that are tightly regulated by both intra- and extracellular cues, including Arpin protein, collagen gel/substrate, and physical constraints. Significantly, some previously unreported distinctive details of persistence have also been captured, helping to elucidate how directional persistence is distributed and evolves in different cell populations. The analysis suggests that the entropy of angular distribution-based approach provides a powerful metric for evaluating directional persistence and enables us to better understand the relationships between cellular behaviors and multiscale cues, which also provides some insights into the migration dynamics of cell populations, such as collective cell invasion.

AttnPep: A Self-Attention-Based Deep Learning Method for Peptide Identification in Shotgun Proteomics.

In shotgun proteomics, the proteome search engine analyzes mass spectra obtained by experiments, and then a peptide-spectra match (PSM) is reported for each spectrum. However, most of the PSMs identified are incorrect, and therefore various postprocessing software have been developed for reranking the peptide identifications. Yet these methods suffer from issues such as dependency on distribution, reliance on shallow models, and limited effectiveness. In this work, we propose AttnPep, a deep learning model for rescoring PSM scores that utilizes the Self-Attention module. This module helps the neural network focus on features relevant to the classification of PSMs and ignore irrelevant features. This allows AttnPep to analyze the output of different search engines and improve PSM discrimination accuracy. We considered a PSM to be correct if it achieves a q-value <0.01 and compared AttnPep with existing mainstream software PeptideProphet, Percolator, and proteoTorch. The results indicated that AttnPep found an average increase in correct PSMs of 9.29% relative to the other methods. Additionally, AttnPep was able to better distinguish between correct and incorrect PSMs and found more synthetic peptides in the complex SWATH data set.

A phase Ib/II study of cadonilimab (PD-1/CTLA-4 bispecific antibody) plus anlotinib as first-line treatment in patients with advanced non-small cell lung cancer.

BACKGROUND: Cadonilimab is a bispecific antibody that simultaneously targets programmed cell death receptor-1 and cytotoxic T lymphocyte-associated antigen-4. This study aimed to assess the safety and efficacy of cadonilimab plus anlotinib for the first-line treatment of advanced non-small cell lung cancer (NSCLC) without sensitizing EGFR/ALK/ROS1 mutations. METHODS: Patients received cadonilimab 15 mg/kg and 10 mg/kg every three weeks (Q3W) plus anlotinib at doses of 10 or 12 mg once daily for two weeks on a one-week-off schedule. The primary endpoints included safety and objective response rate (ORR). RESULTS: Sixty-nine treatment-naïve patients received cadonilimab 15 mg/kg Q3W combination (n = 49) and 10 mg/kg Q3W combination (n = 20). Treatment-related adverse events (TRAEs) were reported in 48 (98.0%) and 19 (95.0%) patients, with grade ≥3 TRAEs occurring in 29 (59.2%) and five (25.0%) patients, respectively. TRAEs leading to cadonilimab discontinuation occurred in eight (16.3%) and one (5.0%) patients in the cadonilimab 15 mg/kg Q3W and 10 mg/kg Q3W dosing groups. The confirmed ORRs were 51.0% (25/49) and 60.0% (12/20) accordingly. CONCLUSIONS: Cadonilimab 10 mg/kg Q3W plus anlotinib showed manageable safety and promising efficacy as a first-line chemo-free treatment for advanced NSCLC. GOV IDENTIFIER: NCT04646330.

Machine learning model for the prediction of gram-positive and gram-negative bacterial bloodstream infection based on routine laboratory parameters.

BACKGROUND: Bacterial bloodstream infection is responsible for the majority of cases of sepsis and septic shock. Early recognition of the causative pathogen is pivotal for administration of adequate empiric antibiotic therapy and for the survival of the patients. In this study, we developed a feasible machine learning (ML) model to predict gram-positive and gram-negative bacteremia based on routine laboratory parameters. METHODS: Data for 2118 patients with bacteremia were obtained from the Medical Information Mart for Intensive Care dataset. Patients were randomly split into the training set and test set by stratified sampling, and 374 routine laboratory blood test variables were retrieved. Variables with missing values in more than 40% of the patients were excluded. Pearson correlation test was employed to eliminate redundant features. Five ML algorithms were used to build the model based on the selected features. Additionally, 132 patients with bacteremia who were treated at Qilu Hospital of Shandong University were included in an independent test cohort to evaluate the model. RESULTS: After feature selection, 32 variables remained. All the five ML algorithms performed well in terms of discriminating between gram-positive and gram-negative bacteremia, but the performance of convolutional neural network (CNN) and random forest (RF) were better than other three algorithms. Consider of the interpretability of models, RF was chosen for further test (ROC-AUC = 0.768; 95%CI = 0.715-0.798, with a sensitivity of 75.20% and a specificity of 63.79%). To expand the application of the model, a decision tree (DT) was built utilizing the major variables, and it achieved an AUC of 0.679 (95%CI = 0.632-0.723), a sensitivity of 66%, and a specificity of 67.82% in the test cohort. When tested in the Qilu Hospital cohort, the ROC-AUC of the RF and DT models were 0.666 (95%CI = 0.579-0.746) and 0.615 (95%CI = 0.526-0.698), respectively. Finally, a software was developed to make the RF- and DT-based prediction models easily accessible. CONCLUSION: The present ML-based models could effectively discriminate between gram-positive and gram-negative bacteremia based on routine laboratory blood test results. This simple model would be beneficial in terms of guiding timely antibiotic selection and administration in critically ill patients with bacteremia before their pathogen test results are available.

A hierarchical opportunistic screening model for osteoporosis using machine learning applied to clinical data and CT images.

BACKGROUND: Osteoporosis is a common metabolic skeletal disease and usually lacks obvious symptoms. Many individuals are not diagnosed until osteoporotic fractures occur. Bone mineral density (BMD) measured by dual-energy X-ray absorptiometry (DXA) is the gold standard for osteoporosis detection. However, only a limited percentage of people with osteoporosis risks undergo the DXA test. As a result, it is vital to develop methods to identify individuals at-risk based on methods other than DXA. RESULTS: We proposed a hierarchical model with three layers to detect osteoporosis using clinical data (including demographic characteristics and routine laboratory tests data) and CT images covering lumbar vertebral bodies rather than DXA data via machine learning. 2210 individuals over age 40 were collected retrospectively, among which 246 individuals' clinical data and CT images are both available. Irrelevant and redundant features were removed via statistical analysis. Consequently, 28 features, including 16 clinical data and 12 texture features demonstrated statistically significant differences (p < 0.05) between osteoporosis and normal groups. Six machine learning algorithms including logistic regression (LR), support vector machine with radial-basis function kernel, artificial neural network, random forests, eXtreme Gradient Boosting and Stacking that combined the above five classifiers were employed as classifiers to assess the performances of the model. Furthermore, to diminish the influence of data partitioning, the dataset was randomly split into training and test set with stratified sampling repeated five times. The results demonstrated that the hierarchical model based on LR showed better performances with an area under the receiver operating characteristic curve of 0.818, 0.838, and 0.962 for three layers, respectively in distinguishing individuals with osteoporosis and normal BMD. CONCLUSIONS: The proposed model showed great potential in opportunistic screening for osteoporosis without additional expense. It is hoped that this model could serve to detect osteoporosis as early as possible and thereby prevent serious complications of osteoporosis, such as osteoporosis fractures.

CITEMOXMBD: A flexible single-cell multimodal omics analysis framework to reveal the heterogeneity of immune cells.

Simultaneous measurement of multiple modalities in single-cell analysis, represented by CITE-seq, is a promising approach to link transcriptional changes to cellular phenotype and function, requiring new computational methods to define cellular subtypes and states based on multiple data types. Here, we design a flexible single-cell multimodal analysis framework, called CITEMO, to integrate the transcriptome and antibody-derived tags (ADT) data to capture cell heterogeneity from the multi omics perspective. CITEMO uses Principal Component Analysis (PCA) to obtain a low-dimensional representation of the transcriptome and ADT, respectively, and then employs PCA again to integrate these low-dimensional multimodal data for downstream analysis. To investigate the effectiveness of the CITEMO framework, we apply CITEMO to analyse the cell subtypes of Cord Blood Mononuclear Cells (CBMC) samples. Results show that the CITEMO framework can comprehensively analyse single-cell multimodal samples and accurately identify cell subtypes. Besides, we find some specific immune cells that co-express multiple ADT markers. To better describe the co-expression phenomenon, we introduce the co-expression entropy to measure the heterogeneous distribution of the ADT combinations. To further validate the robustness of the CITEMO framework, we analyse Human Bone Marrow Cell (HBMC) samples and identify different states of the same cell type. CITEMO has an excellent performance in identifying cell subtypes and states for multimodal omics data. We suggest that the flexible design idea of CITEMO can be an inspiration for other single-cell multimodal tasks. The complete source code and dataset of the CITEMO framework can be obtained from https://github.com/studentiz/CITEMO.

Shannon entropy for time-varying persistence of cell migration.

Cell migration, which can be significantly affected by intracellular signaling pathways and extracellular matrix, plays a crucial role in many physiological and pathological processes. Cell migration is typically modeled as a persistent random walk, which depends on two critical motility parameters, i.e., migration speed and persistence time. It is generally very challenging to efficiently and accurately quantify the migration dynamics from noisy experimental data. Here, we introduce the normalized Shannon entropy (SE) based on the FPS of cellular velocity autocovariance function to quantify migration dynamics. The SE introduced here possesses a similar physical interpretation as the Gibbs entropy for thermal systems in that SE naturally reflects the degree of order or randomness of cellular migration, attaining the maximal value of unity for purely diffusive migration (i.e., SE = 1 for the most "random" dynamics) and the minimal value of 0 for purely ballistic dynamics (i.e., SE = 0 for the most "ordered" dynamics). We also find that SE is strongly correlated with the migration persistence but is less sensitive to the migration speed. Moreover, we introduce the time-varying SE based on the WPS of cellular dynamics and demonstrate its superior utility to characterize the time-dependent persistence of cell migration, which typically results from complex and time-varying intra- or extracellular mechanisms. We employ our approach to analyze experimental data of in vitro cell migration regulated by distinct intracellular and extracellular mechanisms, exhibiting a rich spectrum of dynamic characteristics. Our analysis indicates that the SE and wavelet transform (i.e., SE-based approach) offers a simple and efficient tool to quantify cell migration dynamics in complex microenvironment.

Deriving time-varying cellular motility parameters via wavelet analysis.

Cell migration, which is regulated by intracellular signaling pathways (ICSP) and extracellular matrix (ECM), plays an indispensable role in many physiological and pathological process such as normal tissue development and cancer metastasis. However, there is a lack of rigorous and quantitative tools for analyzing the time-varying characteristics of cell migration in heterogeneous microenvironment, resulted from, e.g. the time-dependent local stiffness due to microstructural remodeling by migrating cells. Here, we develop a wavelet-analysis approach to derive the time-dependent motility parameters from cell migration trajectories, based on the time-varying persistent random walk model. In particular, the wavelet denoising and wavelet transform are employed to analyze migration velocities and obtain the wavelet power spectrum. Subsequently, the time-dependent motility parameters are derived via Lorentzian power spectrum. Our results based on synthetic data indicate the superiority of the method for estimating the intrinsic transient motility parameters, robust against a variety of stochastic noises. We also carry out a systematic parameter study and elaborate the effects of parameter selection on the performance of the method. Moreover, we demonstrate the utility of our approach via analyzing experimental data ofin vitrocell migration in distinct microenvironments, including the migration of MDA-MB-231 cells in confined micro-channel arrays and correlated migration of MCF-10A cells due to ECM-mediated mechanical coupling. Our analysis shows that our approach can be as a powerful tool to accurately derive the time-dependent motility parameters, and further analyze the time-dependent characteristics of cell migration regulated by complex microenvironment.

Emergent Field-Driven Robot Swarm States.

We present an ecology-inspired form of active matter consisting of a robot swarm. Each robot moves over a planar dynamic resource environment represented by a large light-emitting diode array in search of maximum light intensity; the robots deplete (dim) locally by their presence the local light intensity and seek maximum light intensity. Their movement is directed along the steepest local light intensity gradient; we call this emergent symmetry breaking motion "field drive." We show there emerge dynamic and spatial transitions similar to gas, crystalline, liquid, glass, and jammed states as a function of robot density, resource consumption rates, and resource recovery rates. Paradoxically the nongas states emerge from smooth, flat resource landscapes, not rough ones, and each state can directly move to a glassy state if the resource recovery rate is slow enough, at any robot density.

Marker-Assisted Development and Evaluation of Monogenic Lines of Rice cv. Kaohsiung 145 Carrying Blast Resistance Genes.

Rice blast is a serious threat to global rice production. Large-scale and long-term cultivation of rice varieties with a single blast resistance gene usually leads to breakdown of resistance. To effectively control rice blast in Taiwan, marker-assisted backcrossing was conducted to develop monogenic lines carrying different blast resistance genes in the genetic background of an elite japonica rice cultivar, Kaohsiung 145 (KH145). Eleven International Rice Research Institute (IRRI)-bred blast-resistant lines (IRBLs) showing broad-spectrum resistance to local Pyricularia oryzae isolates were used as resistance donors. Sequencing analysis revealed that the recurrent parent, KH145, does not carry known resistance alleles at the target Pi2/9, Pik, Pita, and Ptr loci. For each IRBL × KH145 cross, we screened 21 to 370 (average of 108) plants per generation from the BC1F1 to BC3F1/BC4F1 generation. A total of 1,499 BC3F2/BC4F2 lines carrying homozygous resistance alleles were selected and self-crossed for four to six successive generations. The derived lines were also evaluated for background genotype using genotyping by sequencing, for blast resistance under artificial inoculation and natural infection conditions, and for agronomic performance in multiple field trials. In Chiayi and Taitung blast nurseries in 2018 to 2020, Pi2, Pi9, and Ptr conferred high resistance, Pi20 and Pik-h moderate resistance, and Pi1, Pi7, Pik-p, and Pik susceptibility to leaf blast; only Pi2, Pi9, and Ptr conferred effective resistance against panicle blast. The monogenic lines showed agronomic traits, yield, and grain quality similar to those of KH145, suggesting the potential of growing a mixture of lines to achieve durable resistance in the field.

It doesn't always pay to be fit: success landscapes.

Landscapes play an important role in many areas of biology, in which biological lives are deeply entangled. Here we discuss a form of landscape in evolutionary biology which takes into account (1) initial growth rates, (2) mutation rates, (3) resource consumption by organisms, and (4) cyclic changes in the resources with time. The long-term equilibrium number of surviving organisms as a function of these four parameters forms what we call a success landscape, a landscape we would claim is qualitatively different from fitness landscapes which commonly do not include mutations or resource consumption/changes in mapping genomes to the final number of survivors. Although our analysis is purely theoretical, we believe the results have possibly strong connections to how we might treat diseases such as cancer in the future with a deeper understanding of the interplay between resource degradation, mutation, and uncontrolled cell growth.

Dynamically Re-Organized Collagen Fiber Bundles Transmit Mechanical Signals and Induce Strongly Correlated Cell Migration and Self-Organization.

Correlated cell migration in fibrous extracellular matrix (ECM) is important in many biological processes. During migration, cells can remodel the ECM, leading to the formation of mesoscale structures such as fiber bundles. However, how such mesoscale structures regulate correlated single-cells migration remains to be elucidated. Here, using a quasi-3D in vitro model, we investigate how collagen fiber bundles are dynamically re-organized and guide cell migration. By combining laser ablation technique with 3D tracking and active-particle simulations, we definitively show that only the re-organized fiber bundles that carry significant tensile forces can guide strongly correlated cell migration, providing for the first time a direct experimental evidence supporting that matrix-transmitted long-range forces can regulate cell migration and self-organization. This force regulation mechanism can provide new insights for studies on cellular dynamics, fabrication or selection of biomedical materials in tissue repairing, and many other biomedical applications.

FluConvert and IniFlu: a suite of integrated software to identify novel signatures of emerging influenza viruses with increasing risk.

BACKGROUND: The pandemic threat of influenza has attracted great attention worldwide. To assist public health decision-makers, new suites of tools are needed to rapidly process and combine viral information retrieved from public-domain databases for a better risk assessment. RESULTS: Using our recently developed FluConvert and IniFlu software, we automatically processed and rearranged sequence data by standard viral nomenclature, determined the group-related consensus sequences, and identified group-specific polygenic signatures. The software possesses powerful ability to integrate viral, clinical, and epidemiological data. We demonstrated that both multiple basic amino acids at the cleavage site of the HA gene and also at least 11 more evidence-based viral amino acid substitutions present in global highly pathogenic avian influenza H5N2 viruses during the years 2009-2016 that are associated with viral virulence and human infection. CONCLUSIONS: FluConvert and IniFlu are useful to monitor and assess all subtypes of influenza viruses with pandemic potential. These programs are implemented through command-line and user-friendly graphical interfaces, and identify molecular signatures with virological, epidemiological and clinical significance. FluConvert and IniFlu are available at https://apps.flutures.com or https://github.com/chinrur/FluConvert_IniFlu.

ADAM17 promotes the invasion of hepatocellular carcinoma via upregulation MMP21.

BACKGROUND: The upregulation of ADAM17 has been reported to be associated with invasion and metastasis in various tumors, however the molecular mechanism of ADAM17 in the progression of hepatocellular carcinoma (HCC) remain to be clarified. Human matrix metalloproteinase 21 (MMP21), the newest member of the MMP gene family, has been suggested to play an important role in embryogenesis and tumor progression. So far, nothing is known about the relationship between ADAM17 and MMP21. METHODS: In this study, the expression level of ADAM17 and MMP21 in HCC tissues was measured by immunohistochemistry. The Scratch wounding assay and Transwell were used to identify the invasion and metastasis ability. ELISA was used to evaluate the production of MMP21. Coimmunoprecipitation experiments demonstrated a direct association between ADAM17 and MMP21. HPLC was used to confirmed that ADAM17 participated in the maturation of MMP21. RESULTS: Our present data indicated that ADAM17 and MMP21 was significantly upregulated in human HCC tissues. Knockdown of ADAM17 in HCC inhibited cell invasion and metastasis. Moreover, ADAM17 regulates the secretion and expression of MMP21. Furthermore we discovered a direct association between ADAM17 and MMP21, and we also found MMP21 prodomain could be cleaved by ADAM17. CONCLUSION: Our data suggest that ADAM17 plays an important role in the development of HCC invasion and metastasis and this function may be implement by MMP21.

Psychological distress of suicide attempters predicts one-year suicidal deaths during 2007-2016: A population-based study.

BACKGROUND/PURPOSE: Of the risk factors for suicide, prior attempt is regarded as one of the strongest for subsequent attempts or completed suicide. This large-scale cohort study aims to examine whether the distress level of individual mental symptoms and general psychopathology measured at the index attempt can predict subsequent suicide death within one year. METHODS: The participants were 104,441 suicide attempters first reported to the Taiwan National Suicide Surveillance System during 2007-2016, who completed the five-item Brief Symptom Rating Scale (BSRS-5) at the index attempt. Kaplan-Meier survival curve analysis with log-rank test and Cox regression model were used to examine whether the level of psychological distress could predict the cumulative incidence of re-attempted suicidal death in the following year. RESULTS: In total, 1254 (1.2%) participants subsequently killed themselves within one year. Survival curves analysis and Cox regression modelling indicated that levels of distress of individual items (i.e., suicide ideation, depression, inferiority, anxiety, hostility and insomnia) and total BSRS-5 scores were significantly correlated with the incidence of subsequent suicidal death within one year for both genders. CONCLUSION: The study revealed that self-rated psychological distress was a significant and sustained predictor of re-attempted suicide death within one year after the index attempt. These results imply that suicide is not only an issue of acute crisis, but also a prolonged problem of lasting psychological distress. The BSRS-5 assessment could provide a symptom profile on which to develop a pertinent person-centered approach to prevent subsequent suicide attempts.