Design Principle of Heparanase Inhibitors: A Combined In Vitro and In Silico Study.

Heparanase (HPSE) is an enzyme that cleaves heparan sulfate (HS) side chains from heparan sulfate proteoglycans (HSPGs). Overexpression of HPSE is associated with various types of cancer, inflammation, and immune disorders, making it a highly promising therapeutic target. Previously developed HPSE inhibitors that have advanced to clinical trials are polysaccharide-derived compounds or their mimetics; however, these molecules tend to suffer from poor bioavailability, side effects via targeting other saccharide binding proteins, and heterogeneity. Few small-molecule inhibitors have progressed to the preclinical or clinical stages, leaving a gap in HPSE drug discovery. In this study, a novel small molecule that can inhibit HPSE activity was discovered through high-throughput screening (HTS) using an ultrasensitive HPSE probe. Computational tools were employed to elucidate the mechanisms of inhibition. The essential structural features of the hit compound were summarized into a structure-activity relationship (SAR) theory, providing insights into the future design of HPSE small-molecule inhibitors.

Quantitative trait locus analysis of gray leaf spot resistance in the maize IBM Syn10 DH population.

KEY MESSAGE: The exploration and dissection of a set of QTLs and candidate genes for gray leaf spot disease resistance using two fully assembled parental genomes may help expedite maize resistance breeding. The fungal disease of maize known as gray leaf spot (GLS), caused by Cercospora zeae-maydis and Cercospora zeina, is a significant concern in China, Southern Africa, and the USA. Resistance to GLS is governed by multiple genes with an additive effect and is influenced by both genotype and environment. The most effective way to reduce the cost of production is to develop resistant hybrids. In this study, we utilized the IBM Syn 10 Doubled Haploid (IBM Syn10 DH) population to identify quantitative trait loci (QTLs) associated with resistance to gray leaf spot (GLS) in multiple locations. Analysis of seven distinct environments revealed a total of 58 QTLs, 49 of which formed 12 discrete clusters distributed across chromosomes 1, 2, 3, 4, 8 and 10. By comparing these findings with published research, we identified colocalized QTLs or GWAS loci within eleven clustering intervals. By integrating transcriptome data with genomic structural variations between parental individuals, we identified a total of 110 genes that exhibit both robust disparities in gene expression and structural alterations. Further analysis revealed 19 potential candidate genes encoding conserved resistance gene domains, including putative leucine-rich repeat receptors, NLP transcription factors, fucosyltransferases, and putative xyloglucan galactosyltransferases. Our results provide a valuable resource and linked loci for GLS marker resistance selection breeding in maize.

Morphological profiling for drug discovery in the era of deep learning.

Morphological profiling is a valuable tool in phenotypic drug discovery. The advent of high-throughput automated imaging has enabled the capturing of a wide range of morphological features of cells or organisms in response to perturbations at the single-cell resolution. Concurrently, significant advances in machine learning and deep learning, especially in computer vision, have led to substantial improvements in analyzing large-scale high-content images at high throughput. These efforts have facilitated understanding of compound mechanism of action, drug repurposing, characterization of cell morphodynamics under perturbation, and ultimately contributing to the development of novel therapeutics. In this review, we provide a comprehensive overview of the recent advances in the field of morphological profiling. We summarize the image profiling analysis workflow, survey a broad spectrum of analysis strategies encompassing feature engineering- and deep learning-based approaches, and introduce publicly available benchmark datasets. We place a particular emphasis on the application of deep learning in this pipeline, covering cell segmentation, image representation learning, and multimodal learning. Additionally, we illuminate the application of morphological profiling in phenotypic drug discovery and highlight potential challenges and opportunities in this field.

Decision model for durable clinical benefit from front- or late-line immunotherapy alone or with chemotherapy in non-small cell lung cancer.

BACKGROUND: Predictive biomarkers and models of immune checkpoint inhibitors (ICIs) have been extensively studied in non-small cell lung cancer (NSCLC). However, evidence for many biomarkers remains inconclusive, and the opaqueness of machine learning models hinders practicality. We aimed to provide compelling evidence for biomarkers and develop a transparent decision tree model. METHODS: We consolidated data from 3,288 ICI-treated patients with NSCLC across real-world multicenter, public cohorts and the Choice-01 trial (ClinicalTrials.gov: NCT03856411). Over 50 features were examined for predicting durable clinical benefits (DCBs) from ICIs. Noteworthy biomarkers were identified to establish a decision tree model. Additionally, we explored the tumor microenvironment and peripheral CD8+ programmed death-1 (PD-1)+ T cell receptor (TCR) profiles. FINDINGS: Multivariate logistic regression analysis identified tumor histology, PD-ligand 1 (PD-L1) expression, tumor mutational burden, line, and regimen of ICI treatment as significant factors. Mutation subtypes of EGFR, KRAS, KEAP1, STK11, and disruptive TP53 mutations were associated with DCB. The decision tree (DT10) model, using the ten clinicopathological and genomic markers, showed superior performance in predicting DCB in the training set (area under the curve [AUC] = 0.82) and consistently outperformed other models in test sets. DT10-predicted-DCB patients manifested longer survival, an enriched inflamed tumor immune phenotype (67%), and higher peripheral TCR diversity, whereas the DT10-predicted-NDB (non-durable benefit) group showed an enriched desert immune phenotype (86%) and higher peripheral TCR clonality. CONCLUSIONS: The model effectively predicted DCB after front-/subsequent-line ICI treatment, with or without chemotherapy, for squamous and non-squamous lung cancer, offering clinicians valuable insights into efficacy prediction using cost-effective variables. FUNDING: This study was supported by the National Key R&D Program of China.

3D reconstruction of a gastrulating human embryo.

Progress in understanding early human development has been impeded by the scarcity of reference datasets from natural embryos, particularly those with spatial information during crucial stages like gastrulation. We conducted high-resolution spatial transcriptomics profiling on 38,562 spots from 62 transverse sections of an intact Carnegie stage (CS) 8 human embryo. From this spatial transcriptomic dataset, we constructed a 3D model of the CS8 embryo, in which a range of cell subtypes are identified, based on gene expression patterns and positional register, along the anterior-posterior, medial-lateral, and dorsal-ventral axis in the embryo. We further characterized the lineage trajectories of embryonic and extra-embryonic tissues and associated regulons and the regionalization of signaling centers and signaling activities that underpin lineage progression and tissue patterning during gastrulation. Collectively, the findings of this study provide insights into gastrulation and post-gastrulation development of the human embryo.

Novel PET Imaging Probe for Quantitative Detection of Senescence In Vivo.

Real-time detection of cellular senescence remains a clinical challenge. Here, we aimed to develop a positron emission tomography (PET) imaging probe targeting senescence-associated ß-galactosidase (SA-ß-Gal), the most widely used biomarker of cellular senescence, and investigate its performance for real-time in vivo quantitative detection of cellular senescence. A stable PET imaging agent [68Ga]Ga-BGal was obtained with a high labeling yield (90.0 ± 4.3%) and a radiochemical purity (>95%). [68Ga]Ga-BGal displayed high sensitivity and specificity for ß-Gal both in vitro and in vivo. The reaction and uptake of the probe correlated with the ß-Gal concentration and reaction time. In PET imaging, high ß-Gal-expressing CT26.CL25 tumors and doxorubicin-treated HeLa tumors showed high signals from [68Ga]Ga-BGal, while a low signal was observed in CT26.WT and untreated HeLa tumors. In summary, we showcased successful PET imaging of senescence in preclinical models using probe [68Ga]Ga-BGal. This finding holds the potential for translating senescence imaging into clinical applications.

A Fluorogenic Green Merocyanine-Based Probe to Detect Heparanase-1 Activity.

Heparanase-1 (HPSE-1), an endo-ß-D-glucuronidase, is an extracellular matrix (ECM) remodeling enzyme that degrades heparan sulfate (HS) chains of heparan sulfate proteoglycans (HSPGs). HPSE-1 functions to remodel the ECM and thereby disseminate cells, liberate HS-bound bioactive molecules, and release biologically active HS fragments. Being the only known enzyme for the cleavage of HS, HPSE-1 regulates a number of fundamental cellular processes including cell migration, cytokine regulation, angiogenesis, and wound healing. Overexpression of HPSE-1 has been discovered in most cancers, inflammatory diseases, viral infections, among others. As an emerging therapeutic target, the biological role of HPSE-1 remains to be explored but is hampered by a lack of research tools. To expand the chemical tool-kit of fluorogenic probes to interrogate HPSE-1 activity, we design and synthesized a fluorogenic green disaccharide-based HPSE-1 probe using our design strategy of tuning the electronic effect of the aryl aglycon. The novel probe exhibits a highly sensitive 278-fold fluorescence turn-on response in the presence of recombinant human HPSE-1, while emitting green light at 560 nm, enabling the fluorescence imaging of HPSE-1 activity in cells.

Chemical toolbox to interrogate Heparanase-1 activity.

The development of a robust chemical toolbox to interrogate the activity of heparanase-1 (HPSE-1), an endo-ß-d-glucuronidase and the only known enzyme that cleaves heparan sulfate (HS), has become critically important. The primary function of HPSE-1, cleaving HS side chains from heparan sulfate proteoglycans (HSPGs), regulates the integrity of the extracellular matrix (ECM) and the bioavailability of active, heparan sulfate-binding partners such as enzymes, growth factors, chemokines, and cytokines. HPSE-1 enzymatic activity is strictly regulated and has been found to play fundamental roles in pathophysiological processes. HPSE-1 is significantly overexpressed under various conditions including cancer, metastasis, angiogenesis, and inflammation, making HPSE-1 a promising therapeutic and diagnostic target. Chemical tools that can detect and image HPSE-1 activity in vitro and/or in vivo can help drive the discovery of novel and efficacious anti-HPSE-1 drugs, investigate the basic biology of HPSE-1, and help serve as a diagnostic tool in clinical applications. Here, we will give an overview of the common chemical tools to detect HPSE-1 activity and highlight the novel heparanase probes recently developed in our lab.

Hypercholesterolemia Is Associated With Dysregulation of Lipid Metabolism and Poor Prognosis in Primary Biliary Cholangitis.

BACKGROUND & AIMS: Hypercholesterolemia is frequently diagnosed in patients with primary biliary cholangitis (PBC). However, its association with the prognosis and lipid metabolism is unknown. In this study, we aimed to investigate the prognostic value of baseline total cholesterol (TC) levels in PBC and characterized the associated lipid metabolism. METHODS: Five hundred and thirty-one patients with PBC without prior cirrhosis-related complications were randomly divided into the derivation and validation cohorts at a ratio of 7:3. Complete clinical data were obtained and analyzed. The endpoints were defined as liver-related death, liver transplantation, and cirrhosis-related complications. Lipidomics was performed in 89 patients and 28 healthy controls. RESULTS: Baseline TC was independently associated with poor liver-related outcomes, and adjusted C-statistics were 0.80 (95% confidence interval [CI]: 0.74-0.85) and 0.88 (95% CI: 0.78-0.91) in the derivation and validation cohorts, respectively. The predictive ability of TC for disease outcomes was stable over time and comparable with the Globe score. The 200 mg/dL cut-off optimally divided patients into low- and high-TC groups. A combination of TC and Globe score provided a more accurate stratification of patients into risk subgroups. Lipidomics indicated an up-regulation of lipid families in high-TC patients. Pathway analysis of 66 up-regulated lipids revealed the dysregulation of glycerophospholipid and sphingolipid metabolism in high-TC patients, which were associated with poor liver-related outcomes. CONCLUSIONS: Our results indicate that patients with PBC having baseline TC levels above 200 mg/dL have unique lipidome characteristics and are at a higher risk of poor liver-related outcomes.

Morphological Profiling for Drug Discovery in the Era of Deep Learning.

Morphological profiling is a valuable tool in phenotypic drug discovery. The advent of high-throughput automated imaging has enabled the capturing of a wide range of morphological features of cells or organisms in response to perturbations at the single-cell resolution. Concurrently, significant advances in machine learning and deep learning, especially in computer vision, have led to substantial improvements in analyzing large-scale high-content images at high-throughput. These efforts have facilitated understanding of compound mechanism-of-action (MOA), drug repurposing, characterization of cell morphodynamics under perturbation, and ultimately contributing to the development of novel therapeutics. In this review, we provide a comprehensive overview of the recent advances in the field of morphological profiling. We summarize the image profiling analysis workflow, survey a broad spectrum of analysis strategies encompassing feature engineering- and deep learning-based approaches, and introduce publicly available benchmark datasets. We place a particular emphasis on the application of deep learning in this pipeline, covering cell segmentation, image representation learning, and multimodal learning. Additionally, we illuminate the application of morphological profiling in phenotypic drug discovery and highlight potential challenges and opportunities in this field.

Generation of DNAzyme in Bacterial Cells by a Bacterial Retron System.

DNAzymes are catalytically active single-stranded DNAs in which DNAzyme 10-23 (Dz 10-23) consists of a catalytic core and a substrate-binding arm that reduces gene expression through sequence-specific mRNA cleavage. However, the in vivo application of Dz 10-23 depends on exogenous delivery, which leads to its inability to be synthesized and stabilized in vivo, thus limiting its application. As a unique reverse transcription system, the bacterial retron system can synthesize single-stranded DNA in vivo using ncRNA msr/msd as a template. The objective of this work is to reduce target gene expression using Dz 10-23 generated in vivo by the retron system. In this regard, we successfully generated Dz 10-23 by cloning the Dz 10-23 coding sequence into the retron msd gene and tested its ability to reduce specific gene expression by examining the mRNA levels of cfp encoding cyan fluorescence protein and other functional genes such as mreB and ftsZ. We found that Dz had different repressive effects when targeting different mRNA regions, and in general, the repressive effect was stronger when targeting downstream of mRNAs. Our results also suggested that the reduction effect was due to cleavage of the substrate mRNA by Dz 10-23 rather than the antisense effect of the substrate-binding arm. Therefore, this study not only provided a retron-based method for the intracellular generation of Dz 10-23 but also demonstrated that Dz 10-23 could reduce gene expression by cleaving target mRNAs in cells. We believe that this new strategy would have great potential in the regulation of gene expression.

Identifying optimal candidates for autologous peripheral blood stem cell therapy in patients with decompensated liver cirrhosis: a prognostic scoring system.

BACKGROUND: Stem cell transplantation shows great potential to improve the long-term survival of cirrhosis patients. However, therapeutic effects may not be homogeneous across the whole study population. This study constructed an easy-to-use nomogram to improve prognostic prediction and aid in treatment decision making for cirrhotic patients. METHODS: From August 2005 to April 2019, 315 patients with decompensated cirrhosis receiving autologous peripheral blood stem cell (PBSC) transplantation were enrolled in this study. They were randomly classified into training (2/3) and validation (1/3) groups. A predictive model was developed using Cox proportional hazard models and subsequently validated. The predictive performance of the model was evaluated and also compared with other prognostic models. RESULTS: Age, creatinine, neutrophil-to-lymphocyte ratio, and Child-Turcotte-Pugh class were included in the nomogram as prognostic variables. The nomogram showed high discrimination power concerning the area under receiver operating characteristic curves (3/5-year AUC: 0.742/0.698) and good consistency suggested by calibration plots. Patients could be accurately stratified into poor- and good-outcome groups regarding liver-transplantation free survival after receiving PBSC therapy (P < 0.001). Compared with poor-outcome group, the liver function of patients listed for liver transplantation in the good-outcome group was significantly improved (P < 0.001). Besides, our nomogram achieved a higher C-index (0.685, 95% CI 0.633-0.738) and better clinical utility compared with other conventional prognostic models. CONCLUSIONS: The proposed nomogram facilitated an accurate prognostic prediction for patients with decompensated cirrhosis receiving PBSC transplantation. Moreover, it also held the promise to stratify patients in clinical trials or practice to implement optimal treatment regimens for individuals.

Global potential distribution of mangroves: Taking into account salt marsh interactions along latitudinal gradients.

Mangrove is one of the most productive and sensitive ecosystems in the world. Due to the complexity and specificity of mangrove habitat, the development of mangrove is regulated by several factors. Species distribution models (SDMs) are effective tools to identify the potential habitats for establishing and regenerating the ecosystem. Such models usually include exclusively environmental factors. Nevertheless, recent studies have challenged this notion and highlight the importance of including biotic interactions. Both factors are necessary for a mechanistic understanding of the mangrove distribution in order to promote the protection and restoration of mangroves. Thus, we present a novel approach of combining environmental factors and interactions with salt marsh for projecting mangrove distributions at the global level and within latitudinal zones. To test the salt marsh interaction, we fit the MaxEnt model with two predicting sets: (1) environments only and (2) environments + salt marsh interaction index (SII). We found that both sets of models had good predictive ability, although the SII improved model performance slightly. Potential distribution areas of mangrove decrease with latitudes, and are controlled by biotic and abiotic factors. Temperature, precipitation and wind speed are generally critical at both global scale and ecotones along latitudes. SII is important on global scale, with a contribution of 5.9%, ranking 6th, and is particularly critical in the 10-30°S and 20-30°N zone. Interactions with salt marsh, including facilitation and competition, are shown to affect the distribution of mangroves at the zone of coastal ecotone, especially in the latitudinal range from 10° - 30°. The contribution of SII to mangrove distribution increases with latitudes due to the difference in the adaptive capacity of salt marsh plants and mangroves to environments. Totally, this study identified and quantified the effects of salt marsh on mangrove distribution by establishing the SII. The results not only facilitate to establish a more accurate mangrove distribution map, but also improve the efficiency of mangrove restoration by considering the salt marsh interaction in the mangrove management projects. In addition, the method of incorporating biotic interaction into SDMs through establish the biotic interaction index has contributed to the development of SDMs.

Could the New pN Staging Classification Better Predict the Prognosis of Penile Cancer? A Population-Based Analysis

Background: The 8th edition of the American Joint Committee on Cancer (AJCC) has made new revisions to the N staging of penile cancer (PeCa). This study aimed to evaluate the prognostic value of the new N staging classification. Methods: This cohort was included from the Surveillance, Epidemiology, and End Results (SEER) database (1988–2016). Overall survival (OS) and cancer-specific survival (CSS) were evaluated using Kaplan–Meier survival curve. The Cox proportional hazards model was employed to calculate hazard ratio (HR) and 95% confidence intervals (CI). Results: Among the included 583 patients, 270 patients had only one positive inguinal lymph node (ILNP), 115 had two ILNPs, and 198 had 3 or more ILNPs. Kaplan–Meier analysis indicated that The OS and CSS of patients with ILNP = 2 were not statistically different from those with ILNP = 1 (p = 0.394; p = 0.760), but had OS and CSS benefit over those with ILNP ≥3 (p = 0.017; p = 0.020). Cox proportional hazards regression analysis indicated that patients with ILNP = 2 and ILNP = 1 have similar OS and CSS (HR = 0.80, p = 0.153; HR = 0.74, p = 0.148), but patients with ILNP ≥3 had worse OS and CSS than patients with ILNP = 2 (HR = 1.56, p = 0.007; HR = 1.86, p = 0.003). Conclusions: PeCa patients with only one or two lymph node metastases had similar survival outcomes. AJCC 8th edition pN staging has a better discriminative ability to predict the prognosis and can accurately stratify mortality risk in PeCa (AU)