Molecular Annotation Research Articles

Streamlining Phenotype Classification and Highlighting Feature Candidates: A Screening Method for Non-Targeted Ion Mobility Spectrometry-Mass Spectrometry (IMS-MS) Data.

Nontargeted analysis (NTA) is increasingly utilized for its ability to identify key molecular features beyond known targets in complex samples. NTA is particularly advantageous in exploratory studies aimed at identifying phenotype-associated features or molecules able to classify various sample types. However, implementing NTA involves extensive data analyses and labor-intensive annotations. To address these limitations, we developed a rapid data screening capability compatible with NTA data collected on a liquid chromatography, ion mobility spectrometry, and mass spectrometry (LC-IMS-MS) platform that allows for sample classification while highlighting potential features of interest. Specifically, this method aggregates the thousands of IMS-MS spectra collected across the LC space for each sample and collapses the LC dimension, resulting in a single summed IMS-MS spectrum for screening. The summed IMS-MS spectra are then analyzed with a bootstrapped Lasso technique to identify key regions or coordinates for phenotype classification via support vector machines. Molecular annotations are then performed by examining the features present in the selected coordinates, highlighting potential molecular candidates. To demonstrate this summed IMS-MS screening approach, we applied it to clinical plasma lipidomic NTA data and exposomic NTA data from water sites with varying contaminant levels. Distinguishing coordinates were observed in both studies, enabling the evaluation of phenotypic molecular annotations and resulting in screening models capable of classifying samples with up to a 25% increase in accuracy compared to models using annotated data.

An Immunofluorescence-Guided Segmentation Model in Hematoxylin and Eosin Images Is Enabled by Tissue Artifact Correction Using a Cycle-Consistent Generative Adversarial Network

Despite recent advances, the adoption of computer vision methods into clinical and commercial applications has been hampered by the limited availability of accurate ground truth tissue annotations required to train robust supervised models. Generating such ground truth can be accelerated by annotating tissue molecularly using immunofluorescence (IF) staining and mapping these annotations to a post-IF hematoxylin and eosin (H&E) (terminal H&E) stain. Mapping the annotations between IF and terminal H&E increases both the scale and accuracy by which ground truth could be generated. However, discrepancies between terminal H&E and conventional H&E caused by IF tissue processing have limited this implementation. We sought to overcome this challenge and achieve compatibility between these parallel modalities using synthetic image generation, in which a cycle-consistent generative adversarial network was applied to transfer the appearance of conventional H&E such that it emulates terminal H&E. These synthetic emulations allowed us to train a deep learning model for the segmentation of epithelium in terminal H&E that could be validated against the IF staining of epithelial-based cytokeratins. The combination of this segmentation model with the cycle-consistent generative adversarial network stain transfer model enabled performative epithelium segmentation in conventional H&E images. The approach demonstrates that the training of accurate segmentation models for the breadth of conventional H&E data can be executed free of human expert annotations by leveraging molecular annotation strategies such as IF, so long as the tissue impacts of the molecular annotation protocol are captured by generative models that can be deployed prior to the segmentation process.

Radiogenomic analysis for predicting lymph node metastasis and molecular annotation of radiomic features in pancreatic cancer

BackgroundTo provide a preoperative prediction model for lymph node metastasis in pancreatic cancer patients and provide molecular information of key radiomic features.MethodsTwo cohorts comprising 151 and 54 pancreatic cancer patients were included in the analysis. Radiomic features from the tumor region of interests were extracted by using PyRadiomics software. We used a framework that incorporated 10 machine learning algorithms and generated 77 combinations to construct radiomics-based models for lymph node metastasis prediction. Weighted gene coexpression network analysis (WGCNA) was subsequently performed to determine the relationships between gene expression levels and radiomic features. Molecular pathways enrichment analysis was performed to uncover the underlying molecular features.ResultsPatients in the in-house cohort (mean age, 61.3 years ± 9.6 [SD]; 91 men [60%]) were separated into training (n = 105, 70%) and validation (n = 46, 30%) cohorts. A total of 1,239 features were extracted and subjected to machine learning algorithms. The 77 radiomic models showed moderate performance for predicting lymph node metastasis, and the combination of the StepGBM and Enet algorithms had the best performance in the training (AUC = 0.84, 95% CI = 0.77–0.91) and validation (AUC = 0.85, 95% CI = 0.73–0.98) cohorts. We determined that 15 features were core variables for lymph node metastasis. Proliferation-related processes may respond to the main molecular alterations underlying these features.ConclusionsMachine learning-based radiomics could predict the status of lymph node metastasis in pancreatic cancer, which is associated with proliferation-related alterations.

Immuno-oncologic profiling of pediatric brain tumors reveals major clinical significance of the tumor immune microenvironment

With the success of immunotherapy in cancer, understanding the tumor immune microenvironment (TIME) has become increasingly important; however in pediatric brain tumors this remains poorly characterized. Accordingly, we developed a clinical immune-oncology gene expression assay and used it to profile a diverse range of 1382 samples with detailed clinical and molecular annotation. In low-grade gliomas we identify distinct patterns of immune activation with prognostic significance in BRAF V600E-mutant tumors. In high-grade gliomas, we observe immune activation and T-cell infiltrates in tumors that have historically been considered immune cold, as well as genomic correlates of inflammation levels. In mismatch repair deficient high-grade gliomas, we find that high tumor inflammation signature is a significant predictor of response to immune checkpoint inhibition, and demonstrate the potential for multimodal biomarkers to improve treatment stratification. Importantly, while overall patterns of immune activation are observed for histologically and genetically defined tumor types, there is significant variability within each entity, indicating that the TIME must be evaluated as an independent feature from diagnosis. In sum, in addition to the histology and molecular profile, this work underscores the importance of reporting on the TIME as an essential axis of cancer diagnosis in the era of personalized medicine.

BIOM-03. IMMUNO-ONCOLOGIC PROFILING OF PEDIATRIC BRAIN TUMORS REVEALS MAJOR CLINICAL SIGNIFICANCE OF THE TUMOR IMMUNE MICROENVIRONMENT

Abstract With the success of immunotherapy in cancer, understanding the tumor immune microenvironment (TIME) has become increasingly important; however in pediatric brain tumors this remains poorly characterized. Accordingly, we developed a clinical immune-oncology gene expression assay, including the 18-gene tumor inflammation signature (TIS) as a marker of overall immune activation, and validated this with immunohistochemistry for key cell-type markers. We used this assay, in conjunction with public data from the Pediatric Brain Tumor Atlas (PBTA), to profile a diverse range of 1382 samples in total with detailed clinical and molecular annotation. Overall among the main types of pediatric brain tumors, low-grade gliomas (LGG) had the highest inflammation levels (regardless of genetic driver alteration) and medulloblastomas the lowest. In LGG we identified three distinct patterns of immune activation with prognostic significance in BRAF V600E-mutant tumors, specifically that higher inflammation predicted worse outcomes. In high-grade gliomas (HGG), we observed immune activation and T-cell infiltrates in tumors that have historically been considered exclusively immune cold. There were genomic correlates of inflammation levels, with BRAF-V600E-mutant HGG having the highest inflammation levels and suggesting these may be candidates for combination MEK inhibition and immunotherapy. In mismatch repair deficient HGG, we found that high TIS was a significant predictor of response to immune checkpoint inhibition, and demonstrated the potential for multimodal biomarkers (TIS plus tumor mutation burden) to improve treatment stratification. Importantly, while overall patterns of immune activation were observed for histologically and genetically defined tumor types, there was significant variability within each entity, indicating that the TIME must be evaluated as an independent feature from diagnosis. In sum, in addition to the histology and molecular profile, this work underscores the importance of reporting on the TIME as an essential axis of cancer diagnosis in the era of personalized medicine.

MosaicMPI: a framework for modular data integration across cohorts and -omics modalities.

Advances in molecular profiling have facilitated generation of large multi-modal datasets that can potentially reveal critical axes of biological variation underlying complex diseases. Distilling biological meaning, however, requires computational strategies that can perform mosaic integration across diverse cohorts and datatypes. Here, we present mosaicMPI, a framework for discovery of low to high-resolution molecular programs representing both cell types and states, and integration within and across datasets into a network representing biological themes. Using existing datasets in glioblastoma, we demonstrate that this approach robustly integrates single cell and bulk programs across multiple platforms. Clinical and molecular annotations from cohorts are statistically propagated onto this network of programs, yielding a richly characterized landscape of biological themes. This enables deep understanding of individual tumor samples, systematic exploration of relationships between modalities, and generation of a reference map onto which new datasets can rapidly be mapped. mosaicMPI is available at https://github.com/MorrissyLab/mosaicMPI.

A combined metagenome and disease enrichment analysis of nitrosamines present in smokeless tobacco, wiz using molecular docking and functional annotation approach

Smokeless tobacco products (SLTs) are a major concern due to the associated health risks of the carcinogens, toxins, dynamic microbial system and other harmful chemicals present in them. Procured tobacco leaves are sweetened, flavored and processed to yield the various noncombustible forms of SLTs. These SLTs contain a discrete microbial ecosystem which may contribute to the formation of tobacco specific nitrosamines (TSNAs), endotoxins and other pro inflammatory components. In the present study Whole Metagenome Sequencing was carried out for the exploration of microbial communities followed by their functional profiling. In addition to this, novel targets for TSNAs were predicted using the Swiss Target Prediction tool. Further, disease enrichment analysis of various TSNAs present in Wiz was done using Molecular Docking and functional annotation approach using David database. Bacterial, fungal and viral species were found in the sample where more than 50 % of total bacterial species were Proteobacteria followed by Actinobacteria, Firmicutes, Apicomplexa, Bacteroidetes, Basidiomycota and Cyanobacteria phyla. Total 22,766 genes with 64.9 % GC content were found, which were associated with nitrogen metabolism, antibiotic resistance, multidrug transportation, multitoxins, and pro-inflammation. The presence of phages in wiz showed the possibility of horizontal gene transfer. The pathway enrichment analysis of various TSNAs of wiz were found to be associated with numerous diseases, such as Parkinson's disease, schizophrenia, mental depression, Hypertension, male infertility, Acute Kidney disease and various kinds of cancers.This study will help to understand the possible role of microbiota, TSNAs and their genetic contribution to the overall product's effect on human health. The raw data was submitted to NCBI Short Read Archive (SRA) under Bio Project accession number PRJNA837388.

Comparative Analysis of Cyanotoxins in Fishponds in Nigeria and South Africa

Over the decades, the aquaculture sector has witnessed substantial growth, contributing significantly to the nation’s economy. However, the menace of CyanoHABs threatens the sustainability of fish farming. Considering the possible hazards linked to cyanotoxins in food and water, a comparative study design between commercial fish in Nigeria and South Africa was employed to investigate cyanotoxins in the water from fishponds. Six commercial fishponds in Calabar Municipality—Nigeria and Duthuni—South Africa with varying climatic zones were selected. Water samples from the ponds were collected at intervals during different seasons (summer, winter, dry, and wet seasons) to capture climate-induced variation. Liquid chromatography–mass spectrometry (LCMS) in combination with the metabolites database was used for the identification of toxic cyanometabolites in water samples. The molecular networking approach, coupled with the Global Natural Products Social Molecular Networking (GNPS) database and CANOPUS annotation, enabled the putative identification of cyanometabolites. The resulting molecular network unveiled discernible clusters representing related molecule families, aiding in the identification of both known cyanotoxins and unfamiliar analogues. Furthermore, the molecular network revealed that water samples from different fishponds shared specific metabolites, including ethanesulfonic acid, pheophorbide A, cholic acid, phenylalanine, amyl amine, phosphocholine (PC), and sulfonic acid, despite variations in location, local climatic factors, and sampling sites. The fishponds in Nigeria showed the presence of multiple cyanotoxin classes in the dry, wet, and summer seasons in the water. Aflatoxin was identified in all sampling sites in Nigeria (N1, N2, and N3). The Duthuni, South Africa, sampling sites (P1, P2, and P3) exhibited the presence of microginins and microcystins. All the fishponds displayed a widespread occurrence of anabaenopeptins, aplysiatoxins, aflatoxin, microcolins, and marabmids during the selected summer. In conclusion, the untargeted metabolome analysis, guided by GNPS, proved highly effective in identifying both toxic and non-toxic metabolites in fishponds.

Abstract 4239: Pancreatic cancer organoids from the Human Cancer Models Initiative biobank reflect disease genotypes, capture patient heterogeneity, and are amenable to therapeutic screening

Abstract Pancreatic cancer is the fourth leading cause of cancer-related death in the United States (~3% of all cancer cases) and has one of the lowest 5-year relative survival rates. There is a lack of clinically representative, easily available, validated, in vitro pancreatic cancer models that reflect the genomic and phenotypic diversity of the disease. The Human Cancer Models Initiative (HCMI) is an international collaborative project devoted to the development and distribution of approximately 1,000 novel human primary tissue-derived tumor models supported with clinical and molecular annotation. These models were manufactured, characterized, and validated at a not-for-profit biobank and made available for academic and commercial use in basic research, compound screening, and drug development. Available pancreatic cancer organoids (PCOs) from the HCMI biobank (n=46 unique models, n=41 unique donors) were acquired and subjected to a targeted next-generation sequencing hybridization capture, pan-cancer panel of 663 genes. Data were compared to publicly available clinical and genotyping results from the patient’s tumor (n=42) that the model was derived from as well as cancer datasets such as The Cancer Genome Atlas (TCGA). A subset of models was cultured following standard procedures and tested for response to chemotherapeutics (cisplatin, nab-paclitaxel), KRAS-targeting inhibitors (MRTX1133, KR-pep-2D), or PARP inhibitors (PJ34, olaparib) at three time points. A luminescent ATP viability assay was used to assess response. PCOs from the HCMI were predominantly derived from primary tumors (75%), had an adenocarcinoma ductal histological type, and were from female patients (56%) with an average age of 65 years at diagnosis. Models had mutations in key disease-relevant driver genes including KRAS, TP53, SMAD4, and/or CDKN2A at frequencies similar to the TGCA cohort. Sequencing revealed mutations in genes less commonly associated with pancreatic cancer, including BRCA, APC, BRAF, TGFBR2, and ARID1A, albeit at lower frequencies. Most models had mutations in KRAS including G12D, G12V, G12C, and G12R variants. Significant but not complete concordance was seen in comparison with the mutation status of the patient’s respective tumor, indicating potential genotypic drift had occurred. Response to acute treatment varied by model, genotype, timepoint, and drug. Chemotherapeutics like Cisplatin were broadly cytotoxic while MRTX1133 exhibited greater toxicity in models with KRAS G12D variants as compared with KRAS wild type. Patient-derived cancer organoids from the HCMI recapitulate genotypes seen in patient populations and respond to therapeutic treatment in vitro. Citation Format: Stephen Friend, Rachel Demerath, Matthew Graziano, James Clinton. Pancreatic cancer organoids from the Human Cancer Models Initiative biobank reflect disease genotypes, capture patient heterogeneity, and are amenable to therapeutic screening [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4239.

Abstract 5479: Spatially resolved molecular features in the tumor-immune microenvironment of germline BRCA1/2 mutated versus BRCA wild type high grade serous ovarian cancers

Abstract High-grade serous ovarian cancer (HGSC) is the most common and aggressive subtype of epithelial ovarian cancer. Importantly, germline mutations in BRCA1/2 give rise to HGSC. Because most HGSC are diagnosed at stage III/IV, outcomes are poor and more effective therapies are needed. Although immune checkpoint inhibitors show promise for a number of cancer types, efficacy has been variable in HGSC. To help provide new clues to the molecular features of HGSC, we set out to elucidate the tumor microenvironment of HGSC using spatial transcriptomics approaches. Specifically, we applied 10x Genomics Visium Spatial Transcriptomics (ST) technology to 48 samples representing primary HGSC tumors from 24 patients, including 15 BRCA1/2 mutated cases and 9 BRCA non-mutated cases. In addition to individual sample analysis, we normalized data from 64,784 total features across all sections and performed an integrated cluster analysis to get an overall picture of the cellular architecture of HGSC, but to also directly investigate differences in the tumor microenvironment based on BRCA mutation status. The resulting 17 integrated clusters were subjected to molecular annotation using GSEA, ESTIMATE, and CIBERSORTx, which revealed feature clusters representing important cell types defined by marker genes. Analysis of the entire dataset identified a primary tumor cluster defined by the expression of known ovarian cancer genes including WFDC2 and “don’t eat me” marker genes CD24 and CD47. However, the predominant tumor cell population in BRCA mutated cases was marked by the expression of MUC1, EPCAM, CLDN3, and CD24, while BRCA non-mutated tumor cell cluster was marked by EFNA1, ID1, LSR, and CD9. Importantly, both groups revealed the presence of a specialized monocyte derived macrophage cluster defined by the expression of LYZ and genes involved in cholesterol metabolism including APOE and APOC1. These lipid-associated macrophages are M2 polarized and shown to be associated with poor outcome. Additional analyses are underway to determine spatial dependencies of various tumor and immunological cell compartments. These data provide novel insights into the HGSC tumor microenvironment that may influence HGSC’s vulnerability to immunological therapies. Citation Format: Jing Qian, Lynda D. Roman, Rania Bassiouni, Seeta Rajpara, Monica Neuman, Varun Khetan, David W. Craig, Joseph W. Carlson, John D. Carpten. Spatially resolved molecular features in the tumor-immune microenvironment of germline BRCA1/2 mutated versus BRCA wild type high grade serous ovarian cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5479.

Multi-locus phylogenies revealed a new record of Entoloma species (Basidiomycota, Agaricales) responsible for gastrointestinal poisoning

The frequency of mushroom poisoning has increased worldwide. In Thailand, reports on mycetism between 2013 and 2021 included 18 cases of Entoloma poisoning. Incorrect identification of toxic Entoloma species occurs during wild mushroom foraging, and their consumption results in gastrointestinal irritation. Morphological distinction of mushroom leftovers is difficult; however, we aimed to characterize 40 clinical samples to the genus level using macroscopic and microscopic analyses. Furthermore, species identification of the poisonous Entoloma was performed based on multi-locus phylogenetic analyses of the nuclear internal transcribed spacer region, nuclear large subunit ribosomal DNA, and mitochondrial small subunit ribosomal DNA. Phylogenetic trees reconstructed using maximum likelihood and Bayesian methods demonstrated that forty samples of mushroom remnants formed eight robust clades with six identified species. The findings confirmed the taxonomic identity of E. griseolazulinum as a new record of species causing gastrointestinal poisoning. The use of molecular annotation provided a foundation for the development of accurate methods for identification of toxic mushrooms.

Diagnostic evaluation in bone marrow failure disorders: what have we learnt to help inform the transplant decision in 2024 and beyond?

Aplastic anemia (AA) is the prototypical bone marrow failure syndrome. In the current era of readily available 'molecular annotation', application of comprehensive next-generation sequencing panels has generated novel insights into underlying pathogenetic mechanisms, potentially leading to improvements in personalized therapeutic approaches. New evidence has emerged as to the role of somatic loss of HLA class I allele expression in 'immune-mediated' AA, associated molecular aberrations, and risk of clonal evolution. A deeper understanding has emerged regarding the role of 'myeloid' gene mutations in this context, translating patho-mechanistic insights derived from wider clinical and translational research within the myeloid disorder arena. Here, we review contemporary 'tools' which aid in confirmation of a diagnosis of AA, with an additional focus on their potential in guiding therapeutic options. A specific emphasis is placed upon interpretation and integration of this detailed diagnostic information and how this may inform optimal transplantation strategies.

Evergene: an interactive webtool for large-scale gene-centric analysis of primary tumours.

The data sharing of large comprehensive cancer research projects, such as The Cancer Genome Atlas (TCGA), has improved the availability of high-quality data to research labs around the world. However, due to the volume and inherent complexity of high-throughput omics data, analysis of this is limited by the capacity for performing data processing through programming languages such as R or Python. Existing webtools lack functionality that supports large-scale analysis; typically, users can only input one gene, or a gene list condensed into a gene set, instead of individual gene-level analysis. Furthermore, analysis results are usually displayed without other sample-level molecular or clinical annotations. To address these gaps in the existing webtools, we have developed Evergene using R and Shiny. Evergene is a user-friendly webtool that utilizes RNA-sequencing data, alongside other sample and clinical annotation, for large-scale gene-centric analysis, including principal component analysis (PCA), survival analysis (SA), and correlation analysis (CA). Moreover, Evergene achieves in-depth analysis of cancer transcriptomic data which can be explored through dimensional reduction methods, relating gene expression with clinical events or other sample information, such as ethnicity, histological classification, and molecular indices. Lastly, users can upload custom data to Evergene for analysis. Evergene webtool is available at https://bshihlab.shinyapps.io/evergene/. The source code and example user input dataset are available at https://github.com/bshihlab/evergene.

Mining Secondary Metabolites of Chassalia curviflora Leaves Using SIRIUS and High-Resolution Mass Spectrometry Data

Chassalia curviflora (Wall ex Kurz.) Thwaites is a flowering plant from Rubiaceae family. Previous phytochemical investigation on Chassalia species led to the isolation of benzoic acid, benzoquinone derivatives, flavonoids, monoterpenoid indole alkaloids and its essential oil composition. However, these are still scarce in terms of its scientific report on studied species. This study was carried out to investigate fast screening of secondary metabolites by using data-dependent acquisition (DDA) LC-MS/MS analysis of methanolic extract of the leaf of the C. curviflora in positive ionization mode. The data were analysed using computational mass spectrometry method from molecular structure database search implemented in Sirius. This led in discovering of 13 known secondary metabolites from a distinct tandem MS data. De novo molecular formula annotations and predictions from SIRIUS suggested five major class compounds classified as coumarin, benzofuran, fatty acids, esters and triterpenes. Stigmasterol 12 and β-sitosterol 13 were successfully isolated, thus used to verify the data obtained from the Sirius. With our results, we advocate the MS-based approach as a useful starting method for the dereplication of compounds from the complex crude extract of plants.

Deep learning links localized digital pathology phenotypes with transcriptional subtype and patient outcome in glioblastoma.

Deep learning has revolutionized medical image analysis in cancer pathology, where it had a substantial clinical impact by supporting the diagnosis and prognostic rating of cancer. Among the first available digital resources in the field of brain cancer is glioblastoma, the most common and fatal brain cancer. At the histologic level, glioblastoma is characterized by abundant phenotypic variability that is poorly linked with patient prognosis. At the transcriptional level, 3 molecular subtypes are distinguished with mesenchymal-subtype tumors being associated with increased immune cell infiltration and worse outcome. We address genotype-phenotype correlations by applying an Xception convolutional neural network to a discovery set of 276 digital hematozylin and eosin (H&E) slides with molecular subtype annotation and an independent The Cancer Genome Atlas-based validation cohort of 178 cases. Using this approach, we achieve high accuracy in H&E-based mapping of molecular subtypes (area under the curve for classical, mesenchymal, and proneural = 0.84, 0.81, and 0.71, respectively; P < 0.001) and regions associated with worse outcome (univariable survival model P < 0.001, multivariable P = 0.01). The latter were characterized by higher tumor cell density (P < 0.001), phenotypic variability of tumor cells (P < 0.001), and decreased T-cell infiltration (P = 0.017). We modify a well-known convolutional neural network architecture for glioblastoma digital slides to accurately map the spatial distribution of transcriptional subtypes and regions predictive of worse outcome, thereby showcasing the relevance of artificial intelligence-enabled image mining in brain cancer.

Identification and bioinformatic analysis of invA gene of Salmonella in free range chicken.

Salmonella is a serious cause of the health issues in human and animal worldwide. Salmonella has been isolated from different biological samples and it considers as the key role in induction of inflammation of gastrointestinal tract which in turn cause diarrhoea in different species. To further understand the involvement of Salmonella in contaminating and infecting fresh eggs and meat of free-range chicken. This study aimed to establish the microbiological and molecular detections of Salmonella in the cloaca of the free-range chicken and to identify predicted biological functions using Kyoto Encyclopedia of Gene and Genomic (KEGG) pathways and protein-protein interaction. Cloacal swabs were collected from free range chicken raised in the local farm in Duhok city. The isolates were cultured and biochemical test performed using XLD and TSI, respectively. Molecular detection and functional annotation of invA gene was carried out using Conventional PCR and bioinformatics approaches. The present study found that Salmonella was detected in 36 out of 86 samples using microbiological methods. To confirm these findings, invA gene was utilised and 9 out of 36 Salmonella isolates have shown a positive signal of invA by agarose gel. In addition, bioinformatic analysis revealed that invA gene was mainly associated with bacterial secretion processes as well as their KEGG terms and Protein-Protein Interaction were involved in bacterial invasion and secretion pathways. These findings suggested that invA gene plays important role in regulating colonization and invasion processes of Salmonella within the gut host in the free range chicken.

Clinical Significance and Molecular Annotation for PD-L1 Negative Advanced Non-Small Cell Lung Cancer with Sensitivity to Responsive to Dual PD-1/CTLA-4 Blockade.

Immunotherapy has become the standard treatment for driving gene-negative advanced non-small cell lung cancer (NSCLC). However, compared to PD-L1-positive patients, the efficacy of Anti-PD-(L)1 monotherapy is suboptimal in PD-L1-negative advanced NSCLC. In this study, we aim to analyze the optimal immunotherapy approach for PD-L1-negative NSCLC patients and develop a new nomogram to enhance the clinical predictability of immunotherapy for NSCLC patients. In this study, we retrieved clinical information and genomic data from cBioPortal for NSCLC patients undergoing immunotherapy. Cox regression analyses were utilized to screen the clinical information and genomic data that related to survival. The prognostic-relate genes function was studied by comprehensive bioinformatics analyses. The Kaplan-Meier plot method was employed for survival analysis. A total of 199 PD-L1-negative NSCLC patients were included in this study. Among them, 165 patients received Anti-PD-(L)1 monotherapy, while 34 patients received Anti-PD-(L)1+Anti-CTLA-4 combination therapy. The Anti-PD-(L)1+Anti-CTLA-4 combination therapy demonstrated significantly higher PFS compared to the Anti-PD-(L)1 monotherapy. The mutation status of KRAS, ANO1, COL14A1, LTBP1. ERBB4 and PCSK5 were found to correlate with PFS. Utilizing the clinicopathological parameters and genomic data of the patients, a novel nomogram was developed to predict the prognosis of Anti-PD-(L)1+Anti-CTLA-4 combination therapy. Our study revealed that KRAS, ANO1, COL14A1, LTBP1. ERBB4 and PCSK5 mutation could serve as predictive biomarkers for patients with Anti-PD-(L)1+Anti-CTLA-4 combination therapy. Our systematic nomogram demonstrates significant potential in predicting the prognosis for NSCLC patients with responsive to dual PD-1/CTLA-4 blockade.

A multi-module structure labelled molecular network orients the chemical profiles of traditional Chinese medicine prescriptions: Xiaoyao San, as an example

Ultra-high-performance liquid chromatography-mass spectrometry (UHPLC-MS) technology has emerged as a crucial tool for identifying components in traditional Chinese medicine (TCM). However, the characterization of the chemical profiles of TCM prescriptions (TCMPs) which often consist of multiple herbal medicines and contain diverse structural types, presents several challenges, such as component overlapping and time-consuming. In this study, a novel strategy known as the multi-module structure labelled molecular network (MSLMN), which integrates molecular networking, database annotation, and cluster analysis techniques, has been successfully proposed, which facilitates the identification of chemical constituents by leveraging a high-structural similarity ion list derived from the MSLMN. It has been effectively applied to analyze the chemical profile of Xiaoyao San (XYS), a classical TCMP. Through the MSLMN method, a total of 302 chemical constituents were identified, covering nine structural types in XYS. Furthermore, a validated and quantitative analytical method using UHPLC-QqQ-MS/MS technology was developed for 31 identified chemicals, encompassing all eight herbal medicines present in XYS, and the developed analytical approach was applied to investigate the content distribution across 40 different batches of commercially available XYS. In total, the proposed strategy has practical significance for improving the insight into the chemical profile of XYS and serves as a valuable approach for handling complex system data based on UHPLC-MS, particularly for TCMPs.

Predicting Clonal Evolution to Secondary Myeloid Neoplasms in Aplastic Anemia through Machine Learning

Background: One of the major long-term complications in aplastic anemia (AA) is the clonal evolution to secondary myeloid neoplasms (sMNs) such as acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS) 1. Hematopoietic stem cell transplantation (HSCT) is largely considered a curative treatment option for AA and is effective at preventing the clonal evolution of sMNs 2. However, patients over 40 years old or those with poor donor options are contraindicated for HSCT due to elevated post-transplant mortality rates 3. When treated with non-transplant strategies, patients with AA have indeed a high risk for acquisition/expansion of somatic myeloid mutations, with some harbingers of future clonal progression to full-blown MDS or AML. Older age, presence of myeloid mutations other than BCOR/L and PIGA at onset, and lack of response to standard immunosuppressive treatment (IST) have been identified as factors predictive for subsequent sMN evolution 4,5,6. If an individual patient's risk of developing a sMN could be assessed through a predictive model that uses information collected at initial AA diagnosis, providers may consider HSCT for traditionally contraindicated patients. However, no such predictive model that could aid in risk assessment currently exists. Methods: In our study, we utilized a large, multi-institutional cohort of patients diagnosed with AA to generate a comprehensive dataset. This dataset contained 76 variables, encompassing relevant clinical and molecular annotations. On this dataset, we trained a machine learning (ML) model to predict patients who would evolve sMNs. Variables with the highest feature impact score (FIS) in predicting clonal evolution to sMN were then chosen to be included in the final ML model, and the algorithm's fitness was calculated using the Leave-One-Out cross-validation method. Variables missing in more than 30% of subjects were imputed using K-Nearest Neighbors (N=20). Of our initial cohort of 455 patients, 213 patients were excluded from the study. 35 patients were excluded because they were treated with HSCT. 39 pediatric cases (defined as &amp;lt;18 years old at the time of diagnosis) were excluded from the study. 91 patients were removed for not receiving specific treatment (IST or HSCT). The remaining 77 patients were excluded due to having a sMN detected at the time of or prior to AA diagnosis, insufficient data, or detection of a germline mutation indicative of an inherited pathophysiology. Our final cohort included a total of 242 acquired AA patients from UT Southwestern Medical Center and Cleveland Clinic. Results: Among our final cohort of 242 patients, 119 (49.2%) were male. The cohort's mean and median ages at AA diagnosis were 53.1 years (SD = 18.9 years) and 58.1 years, respectively. The median follow-up time was 4.04 years. 80 (33.1%) of our patients had a clinically significant PNH clone (defined as &amp;gt;0.001%). 29 patients developed sMNs, with 24 developing MDS and 5 developing AML. Of patients who underwent clonal evolution to sMNs, the median age at the time of AA diagnosis and sMN diagnosis was 63.1 years and 67.2 years, respectively. The median time elapsed from initial AA diagnosis to sMN evolution was 3.78 years. The median time elapsed between initial AA diagnosis and earliest IST administration was 51 days. Chi-squared analysis showed that patients who received ATG treatment were more likely to develop sMNs, with 78.0% of all patients receiving ATG, while 96.6% of patients who developed sMNs had received ATG (p=0.00927). Our ML model achieved a sensitivity of 0.759, a specificity of 0.728, and an AUC of 0.78 using the ten variables with the highest feature impact score (FIS) to predict clonal evolution [Table 1]. All of the variables except for the presence of a PNH clone are positively correlated with sMN evolution. Nine of these variables are supported by existing literature 7,8. The feature with the third highest feature impact score, the time elapsed (days) between diagnosis and IST treatment, has not been established by previous studies and may be an important consideration in establishing optimal AA treatment guidelines. Conclusion: The strategic combination of this ML algorithm with clinical expertise has tremendous potential in improving health outcomes. Identification of patients at high risk for sMN and offering HSCT as a curative option upfront may lead to improved overall survival in AA patients.

MSIpixel: a fully automated pipeline for compound annotation and quantitation in mass spectrometry imaging experiments.

Mass spectrometry imaging (MSI) is commonly used to map the spatial distribution of small molecules within complex biological matrices. One of the major challenges in imaging MS-based spatial metabolomics is molecular identification and metabolite annotation, to address this limitation, annotation is often complemented with parallel bulk LC-MS2-based metabolomics to confirm and validate identifications. Here we applied MSI method, utilizing data-dependent acquisition, to visualize and identify unknown molecules in a single instrument run. To reach this aim we developed MSIpixel, a fully automated pipeline for compound annotation and quantitation in MSI experiments. It overcomes challenges in molecular identification, and improving reliability and comprehensiveness in MSI-based spatial metabolomics.