Discovery Of Signatures Research Articles

EP.06G.14 Discovery of Immune-Related Long Noncoding RNA Signatures in Lung Adenocarcinoma

EP.06G.14 Discovery of Immune-Related Long Noncoding RNA Signatures in Lung Adenocarcinoma

Dynamical structure-function correlations provide robust and generalizable signatures of consciousness in humans

Resting-state functional magnetic resonance imaging evolves through a repertoire of functional connectivity patterns which might reflect ongoing cognition, as well as the contents of conscious awareness. We investigated whether the dynamic exploration of these states can provide robust and generalizable markers for the state of consciousness in human participants, across loss of consciousness induced by general anaesthesia or slow wave sleep. By clustering transient states of functional connectivity, we demonstrated that brain activity during unconsciousness is dominated by a recurrent pattern primarily mediated by structural connectivity and with a reduced capacity to transition to other patterns. Our results provide evidence supporting the pronounced differences between conscious and unconscious brain states in terms of whole-brain dynamics; in particular, the maintenance of rich brain dynamics measured by entropy is a critical aspect of conscious awareness. Collectively, our results may have significant implications for our understanding of consciousness and the neural basis of human awareness, as well as for the discovery of robust signatures of consciousness that are generalizable among different brain conditions.

Genome-wide discovery of selection signatures in four Anatolian sheep breeds revealed by ddRADseq

High-density genomic data analyzed by accurate statistical methods are of potential to enlighten past breeding practices such as selection by unraveling fixed regions. In this study, four native Turkish sheep breeds (80 samples) were genotyped via 296.097 single nucleotide polymorphisms (SNPs) detected by double-digest restriction site-associated DNA (ddRADseq) library preparation combined with the Illumina HiSeq X Ten instrument in order to identify genes under selection pressure. A total of 32, 136, 133, and 119 protein-coding genes were detected under selection pressure by runs of homozygosity (ROH), integrated haplotype score (iHS), the ratio of extended haplotype homozygosity (Rsb), and fixation index (FST) approaches, respectively. Of these, a total of 129 genes were identified by at least two statistical models which overlapped with a total of 52 quantitative trait loci (QTL)-associated SNPs, known to be related to fiber diameter, milk content, body weight, carcass traits, some blood parameters, and entropion. A total of six genes under selection pressure were validated by three statistical approaches five of which are of potential to be integrated into animal breeding since they were associated with wool fiber diameter (ZNF208B), behaviors related to neurocognitive development (CBX1 and NFE2L1), adaptation to high-altitude (SDK1), and anxiety causing internal stress (GSG1L). The sixth gene (COPZ1) turned out to play an important role in coping with different types of cancer in mammals. In particular, ROH analysis uncovered significant findings that the Güney Karaman (GKR) had experienced different selection practices than the Akkaraman (AKR) breed. Moreover, some genes specifically under selection in the GKR breed turned out to be associated with olfaction (OR6K6, OR6N1, OR6N2, and OR4C16), survival during the gestation period (PRR15L), and heat stress (CDK5RAP9). The results of this study imply that GKR may become genetically different from the AKR breed at the genome level due to most probably experiencing different adaptation processes occurring in raised climatic conditions. These differences should be conserved to face future challenges, while other native Turkish sheep breeds could be monitored via genome-wide high-density SNP data to obtain deeper knowledge about the effects of natural selection.

Discovery and Validation of Methylation Signatures in Circulating Cell-Free DNA for the Detection of Colorectal Cancer.

This study was conducted with the primary objective of assessing the performance of cfDNA methylation in the detection of colorectal cancer (CRC). Five tumor tissue, 20 peripheral blood leucocyte, and 169 cfDNA samples were collected for whole-genome bisulfite sequencing (WGBS) analysis. Bioinformatic analysis was conducted to identify differentially methylated regions (DMRs) and their functional characteristics. Quantitative methylation-specific PCR (qMSP) was used to validate the methylation levels of DMRs in the tissues and leucocytes. cfDNA samples from CRC patients and healthy controls were used to evaluate the performance of the DMR analysis. WGBS analysis revealed a decrease in DNA methylation levels in the CpG context in CRC tumor tissues compared with adjacent normal tissues. A total of 132 DMRs in cfDNA were identified as potential markers for diagnosing CRC. In a cohort of 95 CRC patients and 74 healthy controls, a combination of the three DMRs (DAB1, PPP2R5C, and FAM19A5) yielded an AUC of 0.763, achieving 64.21% sensitivity and 78.38% specificity in discriminating CRC patients from healthy controls. This study provides insights into DNA methylation patterns in CRC and identifies a set of DMRs in cfDNA with potential diagnostic value for CRC. These DMRs hold promise as biomarkers for CRC detection, offering promise for non-invasive CRC diagnosis. Further research is warranted to validate these findings in larger cohorts.

The classification of melanocytic gene signatures.

Gene expression profiling technologies have revolutionized cell biology, enabling researchers to identify gene signatures linked to various biological attributes of melanomas, such as pigmentation status, differentiation state, proliferative versus invasive capacity, and disease progression. Although the discovery of gene signatures has significantly enhanced our understanding of melanocytic phenotypes, reconciling the numerous signatures reported across independent studies and different profiling platforms remains a challenge. Current methods for classifying melanocytic gene signatures depend on exact gene overlap and comparison with unstandardized baseline transcriptomes. In this study, we aimed to categorize published gene signatures into clusters based on their similar patterns of expression across clinical cutaneous melanoma specimens. We analyzed nearly 800 melanoma samples from six gene expression repositories and developed a classification framework for gene signatures that is resilient against biases in gene identification across profiling platforms and inconsistencies in baseline standards. Using 39 frequently cited published gene signatures, our analysis revealed seven principal classes of gene signatures that correlate with previously identified phenotypes: Differentiated, Mitotic/MYC, AXL, Amelanotic, Neuro, Hypometabolic, and Invasive. Each class is consistent with the phenotypes that the constituent gene signatures represent, and our classification method does not rely on overlapping genes between signatures. To facilitate broader application, we created WIMMS (what is my melanocytic signature, available at https://wimms.tanlab.org/), a user-friendly web application. WIMMS allows users to categorize any gene signature, determining its relationship to predominantly cited signatures and its representation within the seven principal classes.

Integrated epigenomic exposure signature discovery.

Aim: The epigenome influences gene regulation and phenotypes in response to exposures. Epigenome assessment can determine exposure history aiding in diagnosis.Materials & methods: Here we developed and implemented a machine learning algorithm, the exposure signature discovery algorithm (ESDA), to identify the most important features present in multiple epigenomic and transcriptomic datasets to produce an integrated exposure signature (ES).Results: Signatures were developed for seven exposures including Staphylococcus aureus, human immunodeficiency virus, SARS-CoV-2, influenza A (H3N2) virus and Bacillus anthracis vaccinations. ESs differed in the assays and features selected and predictive value.Conclusion: Integrated ESs can potentially be utilized for diagnosis or forensic attribution. The ESDA identifies the most distinguishing features enabling diagnostic panel development for future precision health deployment.

Integrative multimodality spatial biomarker analysis for tailoring checkpoint inhibitor therapies in non-small cell lung cancer.

e14664 Background: Checkpoint inhibitor therapy has become a cornerstone in the treatment of non-small cell lung cancer (NSCLC). However, the response to checkpoint inhibitors varies widely among patients, with some experiencing significant benefits and others facing adverse reactions without therapeutic gain. The ability to accurately predict which patients will benefit from checkpoint inhibitor therapies is essential for enhancing treatment efficacy and minimizing side effects. In this context, the identification and analysis of predictive biomarkers, including the expression of immune checkpoint proteins such as PD-L1 and TIGIT, and the comprehensive profiling of cytokines in the tumor microenvironment, are of paramount importance. Methods: A multimodal strategy was employed to identify predictive biomarkers for checkpoint inhibitor therapy in non-small cell lung cancer (NSCLC). This approach integrated metabolic, transcriptomic, and proteomic data to achieve a thorough insight into the biomarkers likely to influence patient responses. The diverse data sets were fused through advanced machine learning techniques, creating a holistic dataset. Analysis of this integrated dataset facilitated the discovery of multimodal biomarker signatures, offering the potential to predict which NSCLC patients would positively respond to checkpoint inhibitor treatments. Results: Initial observations revealed considerable variability in the expression of PD-L1 and TIGIT alongside other immune checkpoint molecules, across different regions of the tumor microenvironment in non-small cell lung cancer (NSCLC). Multimodal biomarker signatures, encompassing distinct expressions of immune checkpoint inhibitors, cytokine levels, and the presence of immune cells, showed a strong correlation with patient outcomes following treatment with immune checkpoint inhibitors. Conclusions: Our research highlights the significant promise of using a multimodal stratification method to unravel the complexities of non-small cell lung cancer (NSCLC) and to predict patient responses to immune checkpoint inhibitors. Through the integration of metabolomic, transcriptomic, and proteomic data, we discovered distinctive biomarker signatures that were highly predictive of responses to these immunotherapies.

Bacterial dysbiosis in newly diagnosed treatment naïve pediatric ulcerative colitis in Saudi Arabia.

The role of microbiota in the pathogenesis of ulcerative colitis (UC) has been increasingly recognized. However, most of the reports are from Western populations. In Middle Eastern countries, including Saudi Arabia, little is known about the role of microbiota. Therefore, our aim was to describe the bacterial microbiota profile and signature in pediatric UC in Saudi Arabia. Twenty children with UC and 20 healthy controls enrolled in the study gave stool samples. Twenty rectal mucosal samples were taken from UC and 20 from non-UC controls. Inclusion criteria included newly diagnosed and untreated children and lack of antibiotic exposure for at least 6 months before stool collection was required for children with UC and controls. Bacterial deoxyribonucleic acid was extracted and sequenced using shotgun metagenomic analysis. Statistical analysis included Shannon alpha diversity metrics, Bray-Curtis dissimilarity, DESeq2, and biomarker discovery. The demographic characteristics were similar in children with UC and controls. There was a significant reduction in alpha diversity (P = 0.037) and beta diversity in samples from children with UC (P = 0.001). Many taxa were identified with log2 abundance analysis, revealing 110 and 102 species significantly depleted and enriched in UC, respectively. Eleven bacterial species' signatures were identified. In Saudi Arabian children with UC, we demonstrate a dysbiosis similar to reports from Western populations, possibly related to changes of lifestyle. Microbial signature discovery in this report is an important contribution to research, leading to the development of adjunctive non-invasive diagnostic options in unusual cases of UC.

Large-language models facilitate discovery of the molecular signatures regulating sleep and activity.

Sleep, locomotor and social activities are essential animal behaviors, but their reciprocal relationships and underlying mechanisms remain poorly understood. Here, we elicit information from a cutting-edge large-language model (LLM), generative pre-trained transformer (GPT) 3.5, which interprets 10.2-13.8% of Drosophila genes known to regulate the 3 behaviors. We develop an instrument for simultaneous video tracking of multiple moving objects, and conduct a genome-wide screen. We have identified 758 fly genes that regulate sleep and activities, including mre11 which regulates sleep only in the presence of conspecifics, and NELF-B which regulates sleep regardless of whether conspecifics are present. Based on LLM-reasoning, an educated signal web is modeled for understanding of potential relationships between its components, presenting comprehensive molecular signatures that control sleep, locomotor and social activities. This LLM-aided strategy may also be helpful for addressing other complex scientific questions.

A supervised Bayesian factor model for the identification of multi-omics signatures.

Predictive biological signatures provide utility as biomarkers for disease diagnosis and prognosis, as well as prediction of responses to vaccination or therapy. These signatures are identified from high-throughput profiling assays through a combination of dimensionality reduction and machine learning techniques. The genes, proteins, metabolites, and other biological analytes that compose signatures also generate hypotheses on the underlying mechanisms driving biological responses, thus improving biological understanding. Dimensionality reduction is a critical step in signature discovery to address the large number of analytes in omics datasets, especially for multi-omics profiling studies with tens of thousands of measurements. Latent factor models, which can account for the structural heterogeneity across diverse assays, effectively integrate multi-omics data and reduce dimensionality to a small number of factors that capture correlations and associations among measurements. These factors provide biologically interpretable features for predictive modeling. However, multi-omics integration and predictive modeling are generally performed independently in sequential steps, leading to suboptimal factor construction. Combining these steps can yield better multi-omics signatures that are more predictive while still being biologically meaningful. We developed a supervised variational Bayesian factor model that extracts multi-omics signatures from high-throughput profiling datasets that can span multiple data types. Signature-based multiPle-omics intEgration via lAtent factoRs (SPEAR) adaptively determines factor rank, emphasis on factor structure, data relevance and feature sparsity. The method improves the reconstruction of underlying factors in synthetic examples and prediction accuracy of coronavirus disease 2019 severity and breast cancer tumor subtypes. SPEAR is a publicly available R-package hosted at https://bitbucket.org/kleinstein/SPEAR.

Unveiling the Biomarkers: Identifying Key Signatures for Cancer Hallmarks

INTRODUCTION: Finding biomarkers that are closely associated with cancer-related traits is critical to the advancement of cancer research, especially when it comes to personalised treatment. The objective of this research is to explore multiple biomarker categories, including genetics, proteins, and chemicals, in order to better understand the complex terrain of cancer.
 OBJECTIVES: Few of the objectives include examining a variety of biomarker types, such as chemical, protein, and genetic markers and determining which important biomarker signatures correspond to each cancer hallmark.
 Also the study aims to perform a comparative analysis to show how the SVM model's features incorporating identified biomarkers improves classification performance.
 METHODS: The study includes NLP and ML techniques for the identification and classification of biomarkers for the hallmark of cancer dataset. 
 RESULTS: The discovery of important biomarker signatures connected to every cancer hallmark is one of the study's primary findings. In addition, our new SVM-based classification model performed well in the multilabel text classification of PubMed abstracts, showing a significant improvement in performance when the biomarkers were used as features.
 CONCLUSION: To sum up, this study makes a substantial contribution to the area of cancer research by identifying important biomarker signatures connected to many cancer hallmarks.

Inferring Fundamental Properties of the Flare Current Sheet Using Flare Ribbons: Oscillations in the Reconnection Flux Rates

Magnetic reconnection is understood to be the main physical process that facilitates the transformation of magnetic energy into heat, motion, and particle acceleration during solar eruptions. Yet, observational constraints on reconnection region properties and dynamics are limited due to a lack of high-cadence and high-spatial-resolution observations. By studying the evolution and morphology of postreconnected field-lines footpoints, or flare ribbons and vector photospheric magnetic field, we estimate the magnetic reconnection flux and its rate of change with time to study the flare reconnection process and dynamics of the current sheet above. We compare high-resolution imaging data to study the evolution of the fine structure in flare ribbons as ribbons spread away from the polarity inversion line. Using data from two illustrative events (one M- and X-class flare), we explore the relationship between the ribbon-front fine structure and the temporal development of bursts in the reconnection region. Additionally, we use the RibbonDB database to perform statistical analysis of 73 (C- to X-class) flares and identify quasiperiodic pulsation (QPP) properties using the Wavelet Transform. Our main finding is the discovery of QPP signatures in the derived magnetic reconnection rates in both example events and the large flare sample. We find that the oscillation periods range from 1 to 4 minutes. Furthermore, we find nearly cotemporal bursts in Hard X-ray (HXR) emission profiles. We discuss how dynamical processes in the current sheet involving plasmoids can explain the nearly cotemporal signatures of quasiperiodicity in the reconnection rates and HXR emission.

208 Preliminary Results of Novel Method for Identifying Ischemic Stroke Vesicular Protein Signature in Cardiac Surgery Patients

INTRODUCTION: Acute ischemic stroke (AIS) treatment depends on timely diagnosis. Identifying an efficient, blood-based protein signature for AIS has been limited by the lack of a pre-ischemia control group. Here we report the preliminary results of a model for AIS protein signature discovery using cardiac surgery patients and novel mass spectrometry techniques. METHODS: Twenty patients undergoing aortic arch repair with deep hypothermic circulatory arrest (n = 17) or coronary artery bypass graft (n = 3) were enrolled. Blood samples were obtained immediately pre- and postoperatively. Magnetic resonance imaging (MRI) of the brain was obtained within 72 hours of surgery. Presence of infarct and total ischemic volume was determined. A novel protein enrichment and mass spectrometry analysis technique (MAG-Net) was used to characterize the pre- and post-surgical proteomes present in extracellular vesicles. Analysis of variance (ANOVA) (false discovery rate < 0.05) was used to analyze protein signatures associated with infarction. RESULTS: Post-surgical infarct occurred in 15 patients (75%), with a median volume of 108mm3. A total of 5,376 proteins were identified, 1,125 of which showed a significant difference between paired pre- and post-operative concentrations. Of these 1,125 proteins, 524 proteins were unique to patients without infarction, 340 proteins had a significant change in both populations, and 261 proteins were unique to patients with infarcts. There were no significant proteins for the interaction term comparing pre- and post-operative differences between the infarct/no infarct groups. CONCLUSIONS: This clinical model for AIS protein signature development relies on the high rate of ischemia during cardiac surgery to allow for pre-stroke sample collection. The MAG-Net technique enables efficient processing of biologically relevant extracellular vesicle proteins and analysis of an unbiased protein signature. The preliminary results demonstrate the feasibility of combining these innovations for AIS protein signature discovery.

Exploring salivary gene expression clusters: A bioinformatics approach for advanced diagnosis and prognosis in head and neck squamous cell carcinoma

ObjectiveThis study postulates that distinct patterns in salivary gene expression clusters among HNSCC patients provide valuable insights into diagnostic and prognostic markers for the disease. The aim is to identify critical genes and their protein-protein interactions through bio-computational analyses, contributing to a deeper understanding of HNSCC progression. Ultimately, the hypothesis anticipates the discovery of novel molecular signatures in saliva, potentially enhancing early detection and prognostic precision in head and neck squamous cell carcinoma. Materials and methodsA comprehensive keyword search was conducted to extract 74 salivary gland secretion genes from the Human Protein Atlas. GeneMANIA and STRING databases were employed to analyse protein-protein interactions and predict functional partners. UALCAN facilitated the visualization of salivary gland secretion gene expression patterns in HNSCC, providing insights into survival analysis. ResultsIn HNSCC compared to normal tissue, CHRM3, GYLTL1B, MMP3, and WDR91 were identified as statistically upregulated genes, while the downregulated genes included AGFG2, CCDC64B, CD24, CRISP3, CXCL17, ELF5, KRT7, MANSC1, MYO5C, PRR4, SCGB3A1, SH3YL1, TCN1, TRPV6, and ZG16B. Kaplan–Meier survival curve analysis revealed that high expression of salivary genes such as AGFG2, AMY1A, CCDC64B, CCL28, C4ORF7, GYLTL1B, TMEM211, LMX1B, ODAM, SCGB3A1, and SLC26A9 was significantly associated with poorer overall survival outcomes in HNSCC patients. ConclusionThis study identifies distinct salivary gene expression patterns in HNSCC, highlighting potential diagnostic and prognostic markers. Specific upregulated genes, such as CHRM3 and GYLTL1B, and downregulated genes, including AGFG2 and CCDC64B, offer insights into HNSCC progression. Elevated expression of particular genes correlates with poorer overall survival, emphasising their prognostic significance.

Abstract 4718: Reclaiming a dirty drug: What is the context of vulnerability to arsenic trioxide in glioblastoma

Abstract Glioblastoma (GBM) is the most prevalent type of malignant tumor within the central nervous system. There is a growing demand to develop and adapt new therapeutic options in order to combat the five-year survival rate of 6.8% and improve patient quality of life. Arsenic trioxide (ATO) is the standard of care for myelodysplasia and relapsed or refractory acute promyelocytic leukemia. While currently being classified to have an idiopathic mechanism of action, two specific cytotoxic consequences of ATO have emerged: MNK1 activity and oxidative stress. Across six GBM PDX models a 20-fold difference in sensitivity manifests, indicative of underlying innate sensitivity or resistance to ATO. To correlate this to the proposed mechanisms of action, we observed ATO’s effect on MNK1 activity through eIF4E phosphorylation and glutathione (GSH) levels following treatment. A subset of GBM models exhibited a marked upregulation of eIF4E phosphorylation following treatment with 2 uM ATO. Of the six models, three were chosen to undergo further testing to observe whether this upregulation showed a consistent dose-dependent relationship across the models and correlated with vulnerability to ATO. Combination therapy with the MNK1 inhibitor, ETC-206, displayed mild to moderate synergy on overall cell viability and strong synergy when observing its effects on the glioma stem cell population. This preferential effect on the GSC populations suggests that MNK1i based combination strategies may show greater effectiveness in vivo than indicated by our previous in vitro studies. Nutraceuticals targeting GSH synthesis, Chrysin and Silibinin, displayed a 0.95 to 1.4-fold decrease in ATO IC50 values across all 6 models. To observe whether each nutraceutical impacted GSH levels consistently across all of the tested models, we measured GSH levels following treatment for each individual compound and their combination treatments. These findings further implicate MNK1 activity and cellular response to oxidative stress as markers of ATO sensitivity, however they are not the sole determinates of response. Understanding of the mechanism of action for idiopathic compounds may allow for the discovery of molecular signatures of sensitivity, improving patient selection for clinical trials and the development of new combination approaches to combat resistance in other tumor types. Citation Format: Charles Shaffer, Nanyun Tang, Yue Hao, Karen Fink, George Snipes, Bruce Mickey, Michael Berens. Reclaiming a dirty drug: What is the context of vulnerability to arsenic trioxide in glioblastoma [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 4718.

Predicting risk of endometrial failure: a biomarker signature that identifies a novel disruption independent of endometrial timing in patients undergoing hormonal replacement cycles

To propose a new gene expression signature that identifies endometrial disruptions independent of endometrial luteal phase timing and predicts if patients are at risk of endometrial failure. Multicentric, prospective study. Reproductive medicine research department in a public hospital affiliated with private fertility clinics and a reproductive genetics laboratory. Caucasian women (n = 281; 39.4 ± 4.8 years old with a body mass index of 22.9 ± 3.5 kg/m2) undergoing hormone replacement therapy between July 2018 and July 2021. Endometrial samples from 217 patients met RNA quality criteria for signature discovery and analysis. Endometrial biopsies collected in the mid-secretory phase. Endometrial luteal phase timing-corrected expression of 404 genes and reproductive outcomes of the first single embryo transfer (SET) after biopsy collection to identify prognostic biomarkers of endometrial failure. Removal of endometrial timing variation from gene expression data allowed patients to be stratified into poor (n = 137) or good (n = 49) endometrial prognosis groups on the basis of their clinical and transcriptomic profiles. Significant differences were found between endometrial prognosis groups in terms of reproductive rates: pregnancy (44.6% vs. 79.6%), live birth (25.6% vs. 77.6%), clinical miscarriage (22.2% vs. 2.6%), and biochemical miscarriage (20.4% vs. 0%). The relative risk of endometrial failure for patients predicted as a poor endometrial prognosis was 3.3 times higher than those with a good prognosis. The differences in gene expression between both profiles were proposed as a biomarker, coined the endometrial failure risk (EFR) signature. Poor prognosis profiles were characterized by 59 upregulated and 63 downregulated genes mainly involved in regulation (17.0%), metabolism (8.4%), immune response, and inflammation (7.8%). This EFR signature had a median accuracy of 0.92 (min = 0.88, max = 0.94), median sensitivity of 0.96 (min = 0.91, max = 0.98), and median specificity of 0.84 (min = 0.77, max = 0.88), positioning itself as a promising biomarker for endometrial evaluation. The EFR signature revealed a novel endometrial disruption, independent of endometrial luteal phase timing, present in 73.7% of patients. This EFR signature stratified patients into 2 significantly distinct and clinically relevant prognosis profiles providing opportunities for personalized therapy. Nevertheless, further validations are needed before implementing this gene signature as an artificial intelligence (AI)-based tool to reduce the risk of patients experiencing endometrial failure.

Discovery of common molecular signatures and drug repurposing for COVID-19/Asthma comorbidity: ACE2 and multi-partite networks

ABSTRACT Angiotensin-converting enzyme 2 (ACE2) is identified as the functional receptor for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of the ongoing global coronavirus disease-2019 (COVID-19) pandemic. This study aimed to elucidate potential therapeutic avenues by scrutinizing approved drugs through the identification of the genetic signature associated with SARS-CoV-2 infection in individuals with asthma. This exploration was conducted through an integrated analysis, encompassing interaction networks between the ACE2 receptor and common host (co-host) factors implicated in COVID-19/asthma comorbidity. The comprehensive analysis involved the identification of common differentially expressed genes (cDEGs) and hub-cDEGs, functional annotations, interaction networks, gene set variation analysis (GSVA), gene set enrichment analysis (GSEA), and module construction. Interaction networks were used to identify overlapping disease modules and potential drug targets. Computational biology and molecular docking analyzes were utilized to discern functional drug modules. Subsequently, the impact of the identified drugs on the expression of hub-cDEGs was experimentally validated using a mouse model. A total of 153 cDEGs or co-host factors associated with ACE2 were identified in the COVID-19 and asthma comorbidity. Among these, seven significant cDEGs and proteins – namely, HRAS, IFNG, JUN, CDH1, TLR4, ICAM1, and SCD—were recognized as pivotal host factors linked to ACE2. Regulatory network analysis of hub-cDEGs revealed eight top-ranked transcription factors (TFs) proteins and nine microRNAs as key regulatory factors operating at the transcriptional and post-transcriptional levels, respectively. Molecular docking simulations led to the proposal of 10 top-ranked repurposable drug molecules (Rapamycin, Ivermectin, Everolimus, Quercetin, Estradiol, Entrectinib, Nilotinib, Conivaptan, Radotinib, and Venetoclax) as potential treatment options for COVID-19 in individuals with comorbid asthma. Validation analysis demonstrated that Rapamycin effectively inhibited ICAM1 expression in the HDM-stimulated mice group (p < 0.01). This study unveils the common pathogenesis and genetic signature underlying asthma and SARS-CoV-2 infection, delineated by the interaction networks of ACE2-related host factors. These findings provide valuable insights for the design and discovery of drugs aimed at more effective therapeutics within the context of lung disease comorbidities.

Accurate and sensitive mutational signature analysis with MuSiCal

Mutational signature analysis is a recent computational approach for interpreting somatic mutations in the genome. Its application to cancer data has enhanced our understanding of mutational forces driving tumorigenesis and demonstrated its potential to inform prognosis and treatment decisions. However, methodological challenges remain for discovering new signatures and assigning proper weights to existing signatures, thereby hindering broader clinical applications. Here we present Mutational Signature Calculator (MuSiCal), a rigorous analytical framework with algorithms that solve major problems in the standard workflow. Our simulation studies demonstrate that MuSiCal outperforms state-of-the-art algorithms for both signature discovery and assignment. By reanalyzing more than 2,700 cancer genomes, we provide an improved catalog of signatures and their assignments, discover nine indel signatures absent in the current catalog, resolve long-standing issues with the ambiguous ‘flat’ signatures and give insights into signatures with unknown etiologies. We expect MuSiCal and the improved catalog to be a step towards establishing best practices for mutational signature analysis.

Metabolic phenotyping and global functional analysis facilitate metabolic signature discovery for tuberculosis treatment monitoring

Tracking alterations in polar metabolite and lipid levels during anti-tuberculosis (TB) interventions is an emerging biomarker discovery and validation approach due to its sensitivity in capturing changes and reflecting on the host status. Here, we employed deep plasma metabolic phenotyping to explore the TB patient metabolome during three phases of treatment: at baseline, during intensive phase treatment, and upon treatment completion. Differential metabolites (DMs) in each period were determined, and the pathway-level biological alterations were explored by untargeted metabolomics-guided functional interpretations that bypassed identification. We identified 41 DMs and 39 pathways that changed during intensive phase completion. Notably, levels of certain amino acids including histidine, bile acids, and metabolites of purine metabolism were dramatically increased. The altered pathways included those involved in the metabolism of amino acids, glycerophospholipids, and purine. At the end of treatment, 44 DMs were discovered. The levels of glutamine, bile acids, and lysophosphatidylinositol significantly increased compared to baseline; the levels of carboxylates and hypotaurine declined. In addition, 37 pathways principally associated with the metabolism of amino acids, carbohydrates, and glycan altered at treatment completion. The potential of each DM for diagnosing TB was examined using a cohort consisting of TB patients, those with latent infections, and controls. Logistic regression revealed four biomarkers (taurine, methionine, glutamine, and acetyl-carnitine) that exhibited excellent performance in differential diagnosis. In conclusion, we identified metabolites that could serve as useful metabolic signatures for TB management and elucidated underlying biological processes affected by the crosstalk between host and TB pathogen during treatment.

In-silico discovery of common molecular signatures for which SARS-CoV-2 infections and lung diseases stimulate each other, and drug repurposing.

COVID-19 caused by SARS-CoV-2 is a global health issue. It is yet a severe risk factor to the patients, who are also suffering from one or more chronic diseases including different lung diseases. In this study, we explored common molecular signatures for which SARS-CoV-2 infections and different lung diseases stimulate each other, and associated candidate drug molecules. We identified both SARS-CoV-2 infections and different lung diseases (Asthma, Tuberculosis, Cystic Fibrosis, Pneumonia, Emphysema, Bronchitis, IPF, ILD, and COPD) causing top-ranked 11 shared genes (STAT1, TLR4, CXCL10, CCL2, JUN, DDX58, IRF7, ICAM1, MX2, IRF9 and ISG15) as the hub of the shared differentially expressed genes (hub-sDEGs). The gene ontology (GO) and pathway enrichment analyses of hub-sDEGs revealed some crucial common pathogenetic processes of SARS-CoV-2 infections and different lung diseases. The regulatory network analysis of hub-sDEGs detected top-ranked 6 TFs proteins and 6 micro RNAs as the key transcriptional and post-transcriptional regulatory factors of hub-sDEGs, respectively. Then we proposed hub-sDEGs guided top-ranked three repurposable drug molecules (Entrectinib, Imatinib, and Nilotinib), for the treatment against COVID-19 with different lung diseases. This recommendation is based on the results obtained from molecular docking analysis using the AutoDock Vina and GLIDE module of Schrödinger. The selected drug molecules were optimized through density functional theory (DFT) and observing their good chemical stability. Finally, we explored the binding stability of the highest-ranked receptor protein RELA with top-ordered three drugs (Entrectinib, Imatinib, and Nilotinib) through 100 ns molecular dynamic (MD) simulations with YASARA and Desmond module of Schrödinger and observed their consistent performance. Therefore, the findings of this study might be useful resources for the diagnosis and therapies of COVID-19 patients who are also suffering from one or more lung diseases.