A comparison of rule-based and centroid single-sample multiclass predictors for transcriptomic classification

Cost-effectiveness of de-escalated molecular subtype dependent use of neoadjuvant chemotherapy in patients with muscle-invasive bladder cancer in a Swedish setting

Background

Guidelines recommend neoadjuvant chemotherapy (NAC) and radical cystectomy (RC) for muscle-invasive bladder cancer (MIBC). Current recommendations do not consider genomic profiles, although the Basal/Squamous (Ba/Sq) subtype is less likely to respond to NAC compared to Urothelial-like (Uro) and Genomically Unstable (GU) subtypes. The aim of this study is to perform cost-effectiveness analyses of a de-escalated use of NAC in patients with Ba/Sq tumors and MIBC.

Methods

A cost-effectiveness analysis was performed using a decision analytic Markov model using a healthcare provider perspective. Treatment and prognosis probabilities originated from the Bladder Cancer Data Base, Sweden (BladderBaSe) 2.0. Information on molecular subtype and outcomes was retrieved from published studies, and quality-adjusted life year (QALY) data were obtained from the iROC trial. Costs were collected from the regional healthcare registers in Sweden, utility values were obtained from the literature, and outcomes are presented as incremental cost-effectiveness ratio (ICER). Scenario analyses, along with several one-way and probabilistic sensitivity analyses were performed to capture uncertainties.

Results

At a 5-year time horizon, the model predicts that molecular subtype-based treatment has an ICER of 4,964 Euro/QALY (66,766 Swedish Krona/QALY), which is deemed cost-effective in the Swedish setting. At €7,427 (100,000 SEK) willingness-to-pay threshold, the molecular subtype-based treatment has a 65% probability of being cost-effective. The results were not sensitive to uncertainty analyses.

Conclusion

Molecular subtype-based treatment of MIBC, i.e., refraining from administering NAC to patients with Ba/Sq tumors, is cost-effective compared to the current treatment practices in Sweden.

The importance of data transformation in RNA-Seq preprocessing for bladder cancer subtyping

OBJECTIVE

RNA-Seq provides an accurate quantification of gene expression levels and it is widely used for molecular subtype classification in cancer, with special importance in prognosis. However, the reliability and validity of these analyses can significantly be influenced by how data are processed. In this study we evaluate how RNA-Seq preprocessing methods influence molecular subtype classification in bladder cancer. By benchmarking various aligners, quantifiers and methods of normalization and transformation, we stress the importance of preprocessing choices for accurate and consistent subtype classification.

RESULTS

Our findings highlight that log-transformation plays a crucial role in centroid-based classifiers such as consensusMIBC and TCGAclas, while distribution-free algorithms like LundTax offer robustness to preprocessing variations. Non log-transformed data resulted in low classification rates and poor agreement with reference classifications in consensusMIBC and TCGAclas classifiers. Additionally, LundTax consistently demonstrated better separation among subtypes, compared to consensusMIBC and TCGAclas, regardless of preprocessing methods. Nonetheless, the study is limited by the lack of a true reference for objective assessment of the accuracy of the assigned subtypes. Hence, future work will be necessary to determine the robustness and scalability of the obtained results.

A Versatile and Upgraded Version of the LundTax Classification Algorithm Applied to Independent Cohorts

Stratification of cancer into biologically and molecularly similar subgroups is a cornerstone of precision medicine. The Lund Taxonomy classification system for urothelial carcinoma aims to be applicable across the whole disease spectrum including both non-muscle-invasive and invasive bladder cancer. A successful classification system is one that can be robustly and reproducibly applied to new samples. However, transcriptomic methods used for subtype classification are affected by analytic platform, data preprocessing, cohort composition, and tumor purity. Furthermore, only limited data have been published evaluating the transferability of existing classification algorithms to external data sets. In this study, a single sample classifier was developed based on in-house microarray and RNA-sequencing data, intended to be broadly applicable across studies and platforms. The new classification algorithm was applied to 10 published external bladder cancer cohorts (n = 2560 cases) to evaluate its ability to capture characteristic subtype-associated gene expression signatures and complementary data such as mutations, clinical outcomes, treatment response, or histologic subtypes. The effect of sample purity on the classification results was evaluated by generating low-purity versions of samples in silico. The classifier was robustly applicable across different gene expression profiling platforms and preprocessing methods and was less sensitive to variations in sample purity.

Subclassification of lung adenocarcinoma through comprehensive multi-omics data to benefit survival outcomes

OBJECTIVES

Lung adenocarcinoma (LUAD) is the most common subtype of non-small cell lung cancer. Understanding the molecular mechanisms underlying tumor progression is of great clinical significance. This study aims to identify novel molecular markers associated with LUAD subtypes, with the goal of improving the precision of LUAD subtype classification. Additionally, optimization efforts are directed towards enhancing insights from the perspective of patient survival analysis.

MATERIALS AND METHODS

We propose an innovative feature-selection approach that focuses on LUAD classification, which is comprehensive and robust. The proposed method integrates multi-omics data from The Cancer Genome Atlas (TCGA) and leverages a synergistic combination of max-relevance and min-redundancy, least absolute shrinkage and selection operator, and Boruta algorithms. These selected features were deployed in six machine-learning classifiers: logistic regression, random forest, support vector machine, naive Bayes, k-Nearest Neighbor, and XGBoost.

RESULTS

The proposed approach achieved an area under the receiver operating characteristic curve (AUC) of 0.9958 for LR. Notably, the accuracy and AUC of a composite model incorporating copy number, methylation, as well as RNA- sequencing data for expression of exons, genes, and miRNA mature strands surpassed the accuracy and AUC metrics of models with single-omics data or other multi-omics combinations. Survival analyses, revealed the SVM classifier to elicit optimal classification, outperforming that achieved by TCGA. To enhance model interpretability, SHapley Additive exPlanations (SHAP) values were utilized to elucidate the impact of each feature on the predictions. Gene Ontology (GO) enrichment analysis identified significant biological processes, molecular functions, and cellular components associated with LUAD subtypes.

CONCLUSION

In summary, our feature selection process, based on TCGA multi-omics data and combined with multiple machine learning classifiers, proficiently identifies molecular subtypes of lung adenocarcinoma and their corresponding significant genes. Our method could enhance the early detection and diagnosis of LUAD, expedite the development of targeted therapies and, ultimately, lengthen patient survival.

Molecular Subtypes Are Associated With Clinical Benefit in Cisplatin-Treated Metastatic Urothelial Cancer Patients

PURPOSE

Cisplatin-based combination chemotherapy (CHT) is standard of care in metastatic urothelial cancer (mUC); however, no predictive molecular biomarkers are available for clinical use. The aim of this study was to investigate the impact of molecular subtypes in relation to treatment response and survival in patients with mUC treated with first-line CHT.

PATIENTS AND METHODS

Molecular subtype classification according to the Lund Taxonomy (LundTax) was performed by tumor transcriptomic profiling and immunostaining in a retrospective cohort. Molecular subtypes were investigated in relation to the primary end point overall response rate (ORR) and secondary end points progression-free survival (PFS) and overall survival (OS). Differential gene expression and association to treatment response were explored.

RESULTS

Ninety-five patients with mUC were classified into urothelial-like (Uro, 43%), genomically unstable (GU, 26%), basal squamous-like (Ba/Sq, 20%), mesenchymal-like (Mes-like, 8%), and small cell neuroendocrine-like (Sc/NE, 3%) subtypes. Patients with Mes-like tumors had lower ORR (14%) compared with Uro (70%), GU (77%), Ba/Sq (75%), and Sc/NE (67%; odds ratio, 0.06 [95% CI, 0.01 to 0.54], P = .012). Furthermore, patients with Mes-like tumors had significantly shorter PFS (hazard ratio [HR], 5.18 [95% CI, 2.28 to 11.76], P < .001) and OS (HR, 3.19 [95% CI, 1.45 to 7.03], P = .004). Patients with Uro and GU showed the longest survival. In responders, an enrichment of downregulated stromal- and immune-related genes was seen. Downregulation of interferon-induced transmembrane protein 2 was associated with increased ORR and improved OS.

CONCLUSION

This study identifies different CHT responses by LundTax molecular subtypes in patients with mUC, where the Mes-like subtype was associated with lower response rate and shorter survival.

Exploring genetic diversity and population structure in Cinnamomum cassia (L.) J.Presl germplasm in China through phenotypic, chemical component, and molecular marker analyses

Cinnamomum cassia (L.) J.Presl, a tropical aromatic evergreen tree belonging to the Lauraceae family, is commonly used in traditional Chinese medicine. It is also a traditional spice used worldwide. However, little is currently known about the extent of the genetic variability and population structure of C. cassia. In this study, 71 individuals were collected from seven populations across two geographical provinces in China. Nine morphological features, three chemical components, and single nucleotide polymorphism (SNP) markers were used in an integrated study of C. cassia germplasm variations. Remarkable genetic variation exists in both phenotypic and chemical compositions, and certain traits, such as leaf length, leaf width, volatile oil content, and geographic distribution, are correlated with each other. One-year-old C. cassia seedling leaf length, leaf width, elevation, and volatile oil content were found to be the main contributors to diversity, according to principal component analysis (PCA). Three major groupings were identified by cluster analysis based on the phenotypic and volatile oil data. This was in line with the findings of related research using 1,387,213 SNP markers; crucially, they all demonstrated a substantial link with geographic origin. However, there was little similarity between the results of the two clusters. Analysis of molecular variance (AMOVA) revealed that the genetic diversity of C. Cassia populations was low, primarily among individuals within populations, accounting for 95.87% of the total. Shannon's information index (I) varied from 0.418 to 0.513, with a mean of 0.478 (Na=1.860, Ne =1.584, Ho =0.481, He =0.325, and PPB =86.04%). Genetic differentiation across populations was not significant because natural adaptation or extensive exchange of seeds among farmers between environments, thus maintaining the relationship. Following a population structure analysis using the ADMIXTURE software, 71 accessions were found to be clustered into three groups, with 38% of them being of the pure type, a finding that was further supported by PCA. Future breeding strategies and our understanding of the evolutionary relationships within the C. cassia population would benefit greatly from a thorough investigation of phenotypic, chemical, and molecular markers.

Ensemble methods of rank-based trees for single sample classification with gene expression profiles

Building Single Sample Predictors (SSPs) from gene expression profiles presents challenges, notably due to the lack of calibration across diverse gene expression measurement technologies. However, recent research indicates the viability of classifying phenotypes based on the order of expression of multiple genes. Existing SSP methods often rely on Top Scoring Pairs (TSP), which are platform-independent and easy to interpret through the concept of "relative expression reversals". Nevertheless, TSP methods face limitations in classifying complex patterns involving comparisons of more than two gene expressions. To overcome these constraints, we introduce a novel approach that extends TSP rules by constructing rank-based trees capable of encompassing extensive gene-gene comparisons. This method is bolstered by incorporating two ensemble strategies, boosting and random forest, to mitigate the risk of overfitting. Our implementation of ensemble rank-based trees employs boosting with LogitBoost cost and random forests, addressing both binary and multi-class classification problems. In a comparative analysis across 12 cancer gene expression datasets, our proposed methods demonstrate superior performance over both the k-TSP classifier and nearest template prediction methods. We have further refined our approach to facilitate variable selection and the generation of clear, precise decision rules from rank-based trees, enhancing interpretability. The cumulative evidence from our research underscores the significant potential of ensemble rank-based trees in advancing disease classification via gene expression data, offering a robust, interpretable, and scalable solution. Our software is available at https://CRAN.R-project.org/package=ranktreeEnsemble .

Metastasis and recurrence patterns in the molecular subtypes of urothelial bladder cancer

Urothelial cancer of the urinary bladder frequently metastasizes to lymph-nodes, lungs, liver and bone. A taxonomy for molecular classification exists, but it is unknown if molecular subtypes show tropism for different organs. Here, we study 146 patients with de novo metastatic disease or recurrence after curative treatment. We classify primary tumors using two transcriptomic methods and immunostaining and identify enrichment and depletion of metastatic sites in molecular subtypes using permutation tests. We observed significant depletion of bone metastases in the Basal/squamous molecular subtype, whereas the Urothelial-like subtype entailed an enrichment for metastases to bone. The Genomically unstable subtype was depleted of lung metastases, but enriched for atypical sites, including six out of seven patients with brain metastases. Stroma-rich primary tumor samples were associated with local recurrence, but not with distant sites. Additionally, the proportion with brain or testis metastases differed between systemic chemotherapy regimens (GC vs MVAC) suggesting a sanctuary effect. In conclusion, molecular subtypes of urothelial bladder cancer are significantly associated with specific metastatic sites, suggesting that subtype-specific molecular determinants could exist at various steps in the metastatic cascade.

Tumor-resident Lactobacillus iners confer chemoradiation resistance through lactate-induced metabolic rewiring

Tumor microbiota can produce active metabolites that affect cancer and immune cell signaling, metabolism, and proliferation. Here, we explore tumor and gut microbiome features that affect chemoradiation response in patients with cervical cancer using a combined approach of deep microbiome sequencing, targeted bacterial culture, and in vitro assays. We identify that an obligate L-lactate-producing lactic acid bacterium found in tumors, Lactobacillus iners, is associated with decreased survival in patients, induces chemotherapy and radiation resistance in cervical cancer cells, and leads to metabolic rewiring, or alterations in multiple metabolic pathways, in tumors. Genomically similar L-lactate-producing lactic acid bacteria commensal to other body sites are also significantly associated with survival in colorectal, lung, head and neck, and skin cancers. Our findings demonstrate that lactic acid bacteria in the tumor microenvironment can alter tumor metabolism and lactate signaling pathways, causing therapeutic resistance. Lactic acid bacteria could be promising therapeutic targets across cancer types.

Identification of PPA1 inhibitor candidates for potential repurposing in cancer medicine

Inorganic pyrophosphatase 1 (PPA1) is pivotal to cellular metabolism as it facilitates the hydrolysis of PPi-a by-product of various metabolic processes that influence cell growth and differentiation. Overexpression of PPA1 enzyme has been linked to diminished patient survival and was shown to influence tumor cell dynamics, thereby positioning it as a potential therapy target for a variety of cancers including colorectal cancer, diffuse large B-cell lymphoma, and lung adenocarcinoma. Despite this therapeutic promise, there are no known inhibitors of PPA1 as of today. In this study, we searched for potential PPA1 inhibitors using a molecular docking screen of 30 470 compounds with a history of clinical trials and/or US Food and Drug Administration approval. We specifically targeted the active pocket that coincides with the established catalytic domain. Our screen identified promising hits, which we further subjected to ADMET (absorption, distribution, metabolism, excretion, and toxicity) filtering. Subsequent molecular dynamics (MD) analyses were conducted on devazepide, quinotolast, and tarazepide-the three substances that successfully navigated all filters. MD analyses reinforced the stability of the protein-ligand complexes and confirmed ligand binding, as substantiated by our root mean square deviation, radius of gyration and secondary structures of proteins analyses. Furthermore, Molecular Mechanics Poisson-Boltzmann Surface Area calculations post-MD identified devazepide and quinotolast as showing higher binding affinities; being supported by principal component analysis, free energy landscape, and dynamic cross-correlation matrix results. Overall, our study reveals devazepide and quinotolast as potential candidates for PPA1 inhibition which could be considered for repurposing studies that need further experimental validation. These results not only reveal a potential for clinical repurposing for PPA1 inhibition but they also offer valuable insights into the development of future compounds for targeting the crucial PPA1 enzyme.

Activation of P53 pathway contributes to Xenopus hybrid inviability

Hybrid incompatibility as a kind of reproductive isolation contributes to speciation. The nucleocytoplasmic incompatibility between Xenopus tropicalis eggs and Xenopus laevis sperm (te×ls) leads to specific loss of paternal chromosomes 3L and 4L. The hybrids die before gastrulation, of which the lethal causes remain largely unclear. Here, we show that the activation of the tumor suppressor protein P53 at late blastula stage contributes to this early lethality. We find that in stage 9 embryos, P53-binding motif is the most enriched one in the up-regulated Assay for Transposase-Accessible Chromatin with high-throughput sequencing (ATAC-seq) peaks between te×ls and wild-type X. tropicalis controls, which correlates with an abrupt stabilization of P53 protein in te×ls hybrids at stage 9. Inhibition of P53 activity via either tp53 knockout or overexpression of a dominant-negative P53 mutant or Murine double minute 2 proto-oncogene (Mdm2), a negative regulator of P53, by mRNA injection can rescue the te×ls early lethality. Our results suggest a causal function of P53 on hybrid lethality prior to gastrulation.

The Lund taxonomy for bladder cancer classification - from gene expression clustering to cancer cell molecular phenotypes, and back again

Treatment of bladder cancer patients depends on precise diagnosis. Molecular subtyping by gene expression profiling may contribute substantially to subclassification of bladder cancer. Several classification systems have been proposed. Most of these base their classification on whole biopsy features, and molecular subtypes are therefore often defined by a combination of features from the cancer cells as well as infiltrating noncancer cells. This makes the link to what is seen at the cancer cell level unclear. The aim of the Lund taxonomy (LundTax) has been to align gene expression-level classification with immunohistochemical classification to identify cancer cell phenotypes independent of infiltration and proliferation. A systematic approach was used in which gene expression clusters were validated and adjusted by immunohistochemistry using markers expressed only by the cancer cells. This review provides a rationale for defining molecular subtypes and a step-by-step description of the development of the LundTax with motivations for each modification and extension. As the cancer cell phenotype defined by gene expression profiling corresponds with the immunohistochemistry of cancer cells, the LundTax represents a harmonization of the gene expression and immunohistochemical levels. Furthermore, the classification system is independent of pathological stage and is, thus, applicable to all urothelial carcinomas. A unified classification system relevant for both the molecular biologist and pathologist will facilitate systematization of current treatment practices, as well as the development of new treatments. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

UROSCAN and UROSCANSEQ: a large-scale multicenter effort towards translation of molecular bladder cancer subtypes into clinical practice - from biobank to RNA-sequencing in real time

BACKGROUND

Bladder cancer is molecularly one of the most heterogenous malignancies characterized by equally heterogenous clinical outcomes. Standard morphological assessment with pathology and added immunohistochemical analyses is unable to fully address the heterogeneity, but up to now treatment decisions have been made based on such information only. Bladder cancer molecular subtypes will likely provide means for a more personalized bladder cancer care.

METHODS

To facilitate further development of bladder cancer molecular subtypes and clinical translation, the UROSCAN-biobank was initiated in 2013 to achieve systematic biobanking of preoperative blood and fresh frozen tumor tissue in a population-based setting. In a second phase, we established in 2018 a parallel logistic pipeline for molecular profiling by RNA-sequencing, to develop and validate clinical implementation of molecular subtyping and actionable molecular target identification in real-time.

RESULTS

Until June 2021, 1825 individuals were included in the UROSCAN-biobank, of which 1650 (90%) had primary bladder cancer, 127 (7%) recurrent tumors, and 48 (3%) unknown tumor status. In 159 patients, multiple tumors were sampled, and metachronous tumors were collected in 83 patients. Between 2016 and 2020 the UROSCAN-biobanking included 1122/2999 (37%) of all primary bladder cancer patients in the Southern Healthcare Region. Until June 2021, the corresponding numbers subjected to RNA-sequencing and molecular subtyping was 605 (UROSCANSEQ), of which 52 (9%) samples were not sequenced due to inadequate RNA-quality (n = 47) or technical failure/lost sample (n = 5).

CONCLUSIONS

The UROSCAN-biobanking and UROSCANSEQ-infrastructure for molecular subtyping by real-time RNA-sequencing represents, to our knowledge, the largest effort of evaluating population-wide molecular classification of bladder cancer.

GJB3 promotes pancreatic cancer liver metastasis by enhancing the polarization and survival of neutrophil

Connexins are membrane expressed proteins, which could assemble into hexamers to transfer metabolites and secondary messengers. However, its roles in pancreatic cancer metastasis remains unknown. In this study, by comparing the gene expression patterns in primary pancreatic cancer patients primary and liver metastasis specimens, we found that Gap Junction Protein Beta 3 (GJB3) significantly increased in Pancreatic ductal adenocarcinoma (PDAC) liver metastasis. Animal experiments verified that GJB3 depletion suppressed the hepatic metastasis of PDAC cancer cells. Further, GJB3 over expression increased the neutrophil infiltration. Mechanistic study revealed that GJB3 form channels between PDAC tumor cells and accumulated neutrophil, which transfer cyclic adenosine monophosphate (cAMP) from cancer to neutrophil cells, which supports the survival and polarization. Taken together, our data suggesting that GJB3 could act as a potential therapeutic target of PDAC liver metastasis.

Angiogenesis goes computational - The future way forward to discover new angiogenic targets?

Multi-omics technologies are being increasingly utilized in angiogenesis research. Yet, computational methods have not been widely used for angiogenic target discovery and prioritization in this field, partly because (wet-lab) vascular biologists are insufficiently familiar with computational biology tools and the opportunities they may offer. With this review, written for vascular biologists who lack expertise in computational methods, we aspire to break boundaries between both fields and to illustrate the potential of these tools for future angiogenic target discovery. We provide a comprehensive survey of currently available computational approaches that may be useful in prioritizing candidate genes, predicting associated mechanisms, and identifying their specificity to endothelial cell subtypes. We specifically highlight tools that use flexible, machine learning frameworks for large-scale data integration and gene prioritization. For each purpose-oriented category of tools, we describe underlying conceptual principles, highlight interesting applications and discuss limitations. Finally, we will discuss challenges and recommend some guidelines which can help to optimize the process of accurate target discovery.

Pancancer Analysis of Revealed TDO2 as a Biomarker of Prognosis and Immunotherapy

Background

Tryptophan 2,3-dioxygenase (TDO) encoded by TDO2, a rate-limiting enzyme in the kynurenine pathway, catabolizes tryptophan to kynurenine, evades immune surveillance, and promotes tumor growth. Although accumulating evidence suggests a crucial role of TDO2 during tumor formation and development, systematic evaluation of TDO2 across human cancers has rarely been reported.

Methods

To shed more light on the role of TDO2 in human cancer, we explored the expression profiles of TDO2 and identified its prognostic value in pancancer analysis through TCGA, CCLE, and GTEx databases. We further utilized TCGA data to evaluate the association between TDO2 and tumor immunological features, such as mismatch repair (MMR), tumor immune infiltration, immune checkpoint-related genes, tumor mutational burden (TMB), microsatellite instability (MSI), and DNA methyltransferase (DNMT).

Results

TDO2 exhibited different expression levels in various cancer cell lines. Frequently, TDO2 was detected to be highly expressed in the majority of cancers. In addition, high TDO2 expression was correlated with an unfavorable prognosis for patients in KIRP, LGG, TGCT, and UVM. Moreover, high TDO2 expression level positively correlated with higher immune infiltration, especially dendritic cells. Additionally, there is a close relationship between TDO2 and immune checkpoint-related gene markers, such as LAIR1, CD276, NRP1, CD80, and CD86. Finally, correlation analysis has demonstrated a high-correlation between TDO2 and TMB, MSI, MMR, and DNMT of multiple cancer types.

Conclusion

Therefore, our results suggest that TDO2 can function as a potential prognostic biomarker due to its role in tumor immunity regulation.

Long-term neurological and psychiatric outcomes in patients with aromatic l-amino acid decarboxylase deficiency

INTRODUCTION

l-amino acid decarboxylase deficiency (AADCD) is an ultrarare autosomal recessive defect of biogenic amine synthesis that presents with early-onset encephalopathy progressing to severe neurological impairment and intellectual disability. We aimed to explore neurocognitive and behavioral profiles associated with AADCD and possible factors predicting outcome in more detail.

METHODS

Nine AADCD patients (23.2 ± 10.3 years; range 8-40) underwent systematic clinical and neuropsychological assessment. Diagnostic levels of CSF 5-hydroxyindolacetic acid (5-HIAA) and homovanillic acid (HVA), and DDC genotype (as ascertained by American College of Medical Genetics and Genomics grading) were included in the data analysis.

RESULTS

All AADCD patients were affected by intellectual disability and psychiatric disorders. Movement disorders included parkinsonism-dystonia, dysarthria, and oculogyric crises. CSF 5-HIAA and HVA levels at diagnosis had a significant influence on adaptive behavior and executive function performance. Patients homozygous for DDC pathogenetic variants showed lower CSF 5-HIAA and HVA levels and higher Unified Parkinson's Disease Rating Scale scores. The disease showed a self-limiting clinical course with partial improvement under pharmacological treatment (B6 and dopamine mimetic drugs).

CONCLUSIONS

Patients with AADCD suffer from neuropsychological and psychopathological impairment, which may be improved but not reversed under the present therapeutic approach. However, cognitive functioning should be specifically examined in order to avoid its underestimation on the basis of movement disorder severity. Genotype and biogenic amine level at diagnosis have an important prognostic value.

Peimine inhibits variants of SARS-CoV-2 cell entry via blocking the interaction between viral spike protein and ACE2

Coronavirus disease 2019 (COVID-19) is caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Several vaccines against SARS-CoV-2 have been approved; however, variants of concern (VOCs) can evade vaccine protection. Therefore, developing small compound drugs that directly block the interaction between the viral spike glycoprotein and ACE2 is urgently needed to provide a complementary or alternative treatment for COVID-19 patients. We developed a viral infection assay to screen a library of approximately 126 small molecules and showed that peimine inhibits VOCs viral infections. In addition, a fluorescence resonance energy transfer (FRET) assay showed that peimine suppresses the interaction of spike and ACE2. Molecular docking analysis revealed that peimine exhibits a higher binding affinity for variant spike proteins and is able to form hydrogen bonds with N501Y in the spike protein. These results suggest that peimine, a compound isolated from Fritillaria, may be a potent inhibitor of SARS-CoV-2 variant infection. PRACTICAL APPLICATIONS: In this study, we identified a naturally derived compound of peimine, a major bioactive alkaloid extracted from Fritillaria, that could inhibit SARS-CoV-2 variants of concern (VOCs) viral infection in 293T/ACE2 and Calu-3 lung cells. In addition, peimine blocks viral entry through interruption of spike and ACE2 interaction. Moreover, molecular docking analysis demonstrates that peimine has a higher binding affinity on N501Y in the spike protein. Furthermore, we found that Fritillaria significantly inhibits SARS-CoV-2 viral infection. These results suggested that peimine and Fritillaria could be a potential functional drug and food for COVID-19 patients.

The Lund Molecular Taxonomy Applied to Non-Muscle-Invasive Urothelial Carcinoma

The precise classification of tumors into relevant molecular subtypes will facilitate both future research and optimal treatment. In the present investigation, the Lund Taxonomy system for molecular classification of urothelial carcinoma was applied to two large and independent cohorts of non-muscle-invasive tumors. Of 752 tumors classified, close to 100% were of the luminal subtypes, 95% urothelial-like (Uro; UroA, UroB, or UroC) and 5% genomically unstable. We show that the obtained subtype structure organizes the tumors into groups with specific and coherent gene mutation, genomic, and clinical profiles. The intrasubtype variability in the largest group of tumors, UroA, is caused by infiltration and proliferation, not considered as cancer cell type-defining properties. Within the UroA subtype, a HOXB/late cell-cycle gene expression polarity is described, strongly associated with FGFR3, STAG2, and TP53 mutations, as well as with chromosome 9 losses. Kaplan-Meier analyses identified the genomically unstable subtype as a progression high-risk group, also valid in the subgroup of T1 tumors. Almost all progression events occurred within 12 months in this subtype. In addition, a general progression gene signature was derived that identifies high- and low-risk tumors. All findings were demonstrated in two independent cohorts. We conclude that the Lund Taxonomy system is applicable to both non-muscle- and muscle-invasive tumors and is a useful biological framework for translational studies.

Interrogation of Essentiality in the Reconstructed Haemophilus influenzae Metabolic Network Identifies Lipid Metabolism Antimicrobial Targets: Preclinical Evaluation of a FabH β-Ketoacyl-ACP Synthase Inhibitor

Expediting drug discovery to fight antibacterial resistance requires holistic approaches at system levels. In this study, we focused on the human-adapted pathogen Haemophilus influenzae, and by constructing a high-quality genome-scale metabolic model, we rationally identified new metabolic drug targets in this organism. Contextualization of available gene essentiality data within in silico predictions identified most genes involved in lipid metabolism as promising targets. We focused on the β-ketoacyl-acyl carrier protein synthase III FabH, responsible for catalyzing the first step in the FASII fatty acid synthesis pathway and feedback inhibition. Docking studies provided a plausible three-dimensional model of FabH in complex with the synthetic inhibitor 1-(5-(2-fluoro-5-(hydroxymethyl)phenyl)pyridin-2-yl)piperidine-4-acetic acid (FabHi). Validating our in silico predictions, FabHi reduced H. influenzae viability in a dose- and strain-dependent manner, and this inhibitory effect was independent of fabH gene expression levels. fabH allelic variation was observed among H. influenzae clinical isolates. Many of these polymorphisms, relevant for stabilization of the dimeric active form of FabH and/or activity, may modulate the inhibitory effect as part of a complex multifactorial process with the overall metabolic context emerging as a key factor tuning FabHi activity. Synergies with antibiotics were not observed and bacteria were not prone to develop resistance. Inhibitor administration during H. influenzae infection on a zebrafish septicemia infection model cleared bacteria without signs of host toxicity. Overall, we highlight the potential of H. influenzae metabolism as a source of drug targets, metabolic models as target-screening tools, and FASII targeting suitability to counteract this bacterial infection. IMPORTANCE Antimicrobial resistance drives the need of synergistically combined powerful computational tools and experimental work to accelerate target identification and drug development. Here, we present a high-quality metabolic model of H. influenzae and show its usefulness both as a computational framework for large experimental data set contextualization and as a tool to discover condition-independent drug targets. We focus on β-ketoacyl-acyl carrier protein synthase III FabH chemical inhibition by using a synthetic molecule with good synthetic and antimicrobial profiles that specifically binds to the active site. The mechanistic complexity of FabH inhibition may go beyond allelic variation, and the strain-dependent effect of the inhibitor tested supports the impact of metabolic context as a key factor driving bacterial cell behavior. Therefore, this study highlights the systematic metabolic evaluation of individual strains through computational frameworks to identify secondary metabolic hubs modulating drug response, which will facilitate establishing synergistic and/or more precise and robust antibacterial treatments.