Omics Profiling Research Articles

The Protein Network in Subcutaneous Fat Biopsies from Patients with AL Amyloidosis: More Than Diagnosis?

AL amyloidosis is caused by the misfolding of immunoglobulin light chains leading to an impaired function of tissues and organs in which they accumulate. Due to the paucity of -omics profiles from undissected samples, few studies have addressed amyloid-related damage system wide. To fill this gap, we evaluated proteome changes in the abdominal subcutaneous adipose tissue of patients affected by the AL isotypes κ and λ. Through our retrospective analysis based on graph theory, we have herein deduced new insights representing a step forward from the pioneering proteomic investigations previously published by our group. ECM/cytoskeleton, oxidative stress and proteostasis were confirmed as leading processes. In this scenario, some proteins, including glutathione peroxidase 1 (GPX1), tubulins and the TRiC complex, were classified as biologically and topologically relevant. These and other results overlap with those already reported for other amyloidoses, supporting the hypothesis that amyloidogenic proteins could induce similar mechanisms independently of the main fibril precursor and of the target tissues/organs. Of course, further studies based on larger patient cohorts and different tissues/organs will be essential, which would be a key point that would allow for a more robust selection of the main molecular players and a more accurate correlation with clinical aspects.

Transcriptome of GH-producing pituitary neuroendocrine tumours and models are significantly affected by somatostatin analogues

Pituitary neuroendocrine tumours (PitNETs) are neoplasms of the pituitary that overproduce hormones or cause unspecific symptoms due to mass effect. Growth hormone overproducing GH-producing PitNETs cause acromegaly leading to connective tissue, metabolic or oncologic disorders. The medical treatment of acromegaly is somatostatin analogues (SSA) in specific cases combined with dopamine agonists (DA), but almost half of patients display partial or full SSA resistance and potential causes of this are unknown. In this study we investigated transcriptomic landscape of GH-producing PitNETs on several levels and functional models—tumour tissue of patients with and without SSA preoperative treatment, tumour derived pituispheres and GH3 cell line incubated with SSA to study effect of medication on gene expression. MGI sequencing platform was used to sequence total RNA from PitNET tissue, pituispheres, mesenchymal stromal stem-like cells (MSC), and GH3 cell cultures, and data were analysed with Salmon—DeSeq2 pipeline. We observed that the GH-producing PitNETs have distinct changes in growth hormone related pathways related to its functional status alongside inner cell signalling, ion transport, cell adhesion and extracellular matrix characteristic patterns. In pituispheres model, treatment regimens (octreotide and cabergoline) affect specific cell proliferation (MKI67) and core functionality pathways (RYR2, COL8A2, HLA-G, ARFGAP1, TGFBR2). In GH3 cells we observed that medication did not have transcriptomic effects similar to preoperative treatment in PitNET tissue or pituisphere model. This study highlights the importance of correct model system selection for cell transcriptomic profiling and data interpretation that could be achieved in future by incorporating NGS methods and detailed cell omics profiling in PitNET model research.

Robust correlation estimation and UMAP assisted topological analysis of omics data for disease subtyping

Deciphering information hidden in the gene expression assays for identifying disease subtypes has significant importance in precision medicine. However, computational limitations thwart this process due to the intricacy of the biological networks and the curse of dimensionality of gene expression data. Therefore, clustering in such scenarios often becomes the first choice of exploratory data analysis to identify natural structures and intrinsic patterns in the data. However, sparse and high dimensional nature of omics data prevents conventional clustering algorithms to discover subtypes that are clinically relevant and statistically significant. Hence, non-linear dimensionality reduction techniques coupled with clustering in such scenarios often becomes imperative to improve the clustering results. In this study, we present a robust pipeline to discover disease subtypes with clinical relevance. Specifically, we focus on discovering patient sub-groups that have a residual life patterns remarkably different from other sub-groups. This is significant because by refining prognosis, subtyping can reduce uncertainty in approximating patients expected outcome. The methodology present is based on robust correlation estimation, UMAP– a non-linear dimensionality reduction method and mapper– a tool from topology. Notably, we suggest a method for improving the robustness of the correlation matrix of gene expression data for improving the clustering results. The performance of the model is evaluated by applying to five cancer datasets obtained through TCGA and comparisons are performed with some state of the art methods of NEMO, RSC-OTRI and SNF with regard to log−rank test and Restricted Life Expectancy Difference. For example in GBM dataset, the minimum separation for any two discovered subtypes is 221 days which is significantly higher than the other methodologies. We also compared the results without using the robust correlation based estimate and observed that robust correlation improves separability between survival curves significantly. From the results we infer that our methodology performs better compared to other methodologies with regard to separating survival curves of patient sub-groups despite using single omics profiles of patients compared to multiple omics profiles of SNF and NEMO. Pathway over-representation analysis is performed on the final clustering results to investigate the biological underpinnings characterizing each subtype.

Using biological constraints to improve prediction in precision oncology

Many gene signatures have been developed by applying machine learning (ML) on omics profiles, however, their clinical utility is often hindered by limited interpretability and unstable performance. Here, we show the importance of embedding prior biological knowledge in the decision rules yielded by ML approaches to build robust classifiers. We tested this by applying different ML algorithms on gene expression data to predict three difficult cancer phenotypes: bladder cancer progression to muscle-invasive disease, response to neoadjuvant chemotherapy in triple-negative breast cancer, and prostate cancer metastatic progression. Wedeveloped two sets of classifiers: mechanistic, by restricting the training to features capturing specific biological mechanisms; and agnostic, in which the training did not use any a priori biological information. Mechanistic models had a similar or better testing performance than their agnostic counterparts, with enhanced interpretability. Our findings support the use of biological constraints to develop robust gene signatures with high translational potential.

Accelerating cancer omics and precision oncology in health care and research: a Lancet Oncology Commission.

We have made substantial progress in developing cancer treatments that target specific molecular vulnerabilities in an increasing number of cancer types.1 Interrogation of complex genomic, transcriptomic, and metabolomic (referred to as omics) profiles, integrated with comprehensive clinical data, represents the essential next step to inform the development and application of selective approaches to cancer prevention, diagnosis, and treatment.2 However, the majority of patients with cancer around the world, and particularly those living in resource-limited settings, cannot access molecular testing and targeted therapies due to regulatory, financial, logistical, educational, and clinical barriers.

Urine-based multi-omic comparative analysis of COVID-19 and bacterial sepsis-induced ARDS

BackgroundAcute respiratory distress syndrome (ARDS), a life-threatening condition during critical illness, is a common complication of COVID-19. It can originate from various disease etiologies, including severe infections, major injury, or inhalation of irritants. ARDS poses substantial clinical challenges due to a lack of etiology-specific therapies, multisystem involvement, and heterogeneous, poor patient outcomes. A molecular comparison of ARDS groups holds the potential to reveal common and distinct mechanisms underlying ARDS pathogenesis.MethodsWe performed a comparative analysis of urine-based metabolomics and proteomics profiles from COVID-19 ARDS patients (n = 42) and bacterial sepsis-induced ARDS patients (n = 17). To this end, we used two different approaches, first we compared the molecular omics profiles between ARDS groups, and second, we correlated clinical manifestations within each group with the omics profiles.ResultsThe comparison of the two ARDS etiologies identified 150 metabolites and 70 proteins that were differentially abundant between the two groups. Based on these findings, we interrogated the interplay of cell adhesion/extracellular matrix molecules, inflammation, and mitochondrial dysfunction in ARDS pathogenesis through a multi-omic network approach. Moreover, we identified a proteomic signature associated with mortality in COVID-19 ARDS patients, which contained several proteins that had previously been implicated in clinical manifestations frequently linked with ARDS pathogenesis.ConclusionIn summary, our results provide evidence for significant molecular differences in ARDS patients from different etiologies and a potential synergy of extracellular matrix molecules, inflammation, and mitochondrial dysfunction in ARDS pathogenesis. The proteomic mortality signature should be further investigated in future studies to develop prediction models for COVID-19 patient outcomes.

Brain Data Standards - A method for building data-driven cell-type ontologies

Large-scale single-cell ‘omics profiling is being used to define a complete catalogue of brain cell types, something that traditional methods struggle with due to the diversity and complexity of the brain. But this poses a problem: How do we organise such a catalogue - providing a standard way to refer to the cell types discovered, linking their classification and properties to supporting data? Cell ontologies provide a partial solution to these problems, but no existing ontology schemas support the definition of cell types by direct reference to supporting data, classification of cell types using classifications derived directly from data, or links from cell types to marker sets along with confidence scores. Here we describe a generally applicable schema that solves these problems and its application in a semi-automated pipeline to build a data-linked extension to the Cell Ontology representing cell types in the Primary Motor Cortex of humans, mice and marmosets. The methods and resulting ontology are designed to be scalable and applicable to similar whole-brain atlases currently in preparation.

Quantitative omics analyses of NCOA4 deficiency reveal an integral role of ferritinophagy in iron homeostasis of hippocampal neuronal HT22 cells.

Neurons require iron to support their metabolism, growth, and differentiation, but are also susceptible to iron-induced oxidative stress and cytotoxicity. Ferritin, a cytosolic iron storage unit, mediates cellular adaptation to fluctuations in iron delivery. NCOA4 has been characterized as a selective autophagic cargo receptor facilitating the mobilization of intracellular iron from ferritin. This process named ferritinophagy results in the degradation of ferritin and the consequent release of iron into the cytosol. Here we demonstrate that NCOA4 is important for the adaptation of the HT22 mouse hippocampal neuronal cell line to cellular iron restriction. Additionally, we determined the pathophysiological implications of impaired ferritinophagy via functional analysis of the omics profile of HT22 cells deficient in NCOA4. NCOA4 silencing impaired ferritin turnover and was cytotoxic when cells were restricted of iron. Quantitative proteomics identified IRP2 accumulation among the most prominent protein responses produced by NCOA4 depletion in HT22 cells, which is indicative of functional iron deficiency. Additionally, proteins of apoptotic signaling pathway were enriched by those responsive to NCOA4 deficiency. Transcriptome profiles of NCOA4 depletion revealed neuronal cell death, differentiation of neurons, and development of neurons as potential diseases and bio functions affected by impaired ferritinophagy, particularly, when iron was restricted. These findings identify an integral role of NCOA4-mediated ferritinophagy in the maintenance of iron homeostasis by HT22 cells, and its potential implications in controlling genetic pathways of neurodevelopment and neurodegenerative diseases.

Detectable Lipidomes and Metabolomes by Different Plasma Exosome Isolation Methods in Healthy Controls and Patients with Advanced Prostate and Lung Cancer.

Circulating exosomes in the blood are promising tools for biomarker discovery in cancer. Due to their heterogeneity, different isolation methods may enrich distinct exosome cargos generating different omic profiles. In this study, we evaluated the effects of plasma exosome isolation methods on detectable multi-omic profiles in patients with non-small cell lung cancer (NSCLC), castration-resistant prostate cancer (CRPC), and healthy controls, and developed an algorithm to quantify exosome enrichment. Plasma exosomes were isolated from CRPC (n = 10), NSCLC (n = 14), and healthy controls (n = 10) using three different methods: size exclusion chromatography (SEC), lectin binding, and T-cell immunoglobulin domain and mucin domain-containing protein 4 (TIM4) binding. Molecular profiles were determined by mass spectrometry of extracted exosome fractions. Enrichment analysis of uniquely detected molecules was performed for each method with MetaboAnalyst. The exosome enrichment index (EEI) scores methods based on top differential molecules between patient groups. The lipidomic analysis detected 949 lipids using exosomes from SEC, followed by 246 from lectin binding and 226 from TIM4 binding. The detectable metabolites showed SEC identifying 191 while lectin binding and TIM4 binding identified 100 and 107, respectively. When comparing uniquely detected molecules, different methods showed preferential enrichment of different sets of molecules with SEC enriching the greatest diversity. Compared to controls, SEC identified 28 lipids showing significant difference in NSCLC, while only 1 metabolite in NSCLC and 5 metabolites in CRPC were considered statistically significant (FDR < 0.1). Neither lectin-binding- nor TIM4-binding-derived exosome lipids or metabolites demonstrated significant differences between patient groups. We observed the highest EEI from SEC in lipids (NSCLC: 871.33) which was also noted in metabolites. These results support that the size exclusion method of exosome extraction implemented by SBI captures more heterogeneous exosome populations. In contrast, lectin-binding and TIM4-binding methods bind surface glycans or phosphatidylserine moieties of the exosomes. Overall, these findings suggest that specific isolation methods select subpopulations which may significantly impact cancer biomarker discovery.

Efficient Bioinspired Feature Selection and Machine Learning Based Framework Using Omics Data and Biological Knowledge Data Bases in Cancer Clinical Endpoint Prediction

Cancer Research has advanced during the past few years. Using high throughput technology and advances in artificial intelligence, it is now possible to improve cancer diagnosis and targeted therapy, by integrating the investigation and analysis of clinical and omics profiles. The high dimensionality and class imbalance of the majority of available data sets represent a serious challenge to the development of computational methods and tools for cancer diagnosis and biomarker discovery. Taking into account multi-omics data further complicates the undertaking. In this paper, we describe a five-step integrative architecture for dealing with the three aforementioned problems by incorporating proteomics data, protein-protein interaction networks, and signaling pathways in order to identify protein biomarkers with a direct association to cancerous patients’ overall survival (OS) and progression free interval (PFI). The core parts of this architecture are a cluster based grey wolf optimization algorithm (CB-GWO) for feature selection and a deep stacked canonical correlation autoencoder (DSCC-AE) for clinical endpoint prediction. A thorough experimental study was carried out to evaluate the performance of the proposed optimization algorithm for feature selection, as well as the performance of the deep learning model in terms of Mathew coefficient correlation (MCC) and Area under the curve (AUC) on breast, lung, colon, and rectum cancers. The results were compared to other methods in the literature. The results are very promising and show the effectiveness of the proposed framework and its ability to outperform the other algorithms and models in terms of AUC (0.91) and MCC (0.64). In addition, hub marker genes with the potential occurence of alterations in colorectal cancer, breast cancer, and lung cancer have been identified.

Nasal Transcriptome and Epigenome Analysis Identifies the Pathogenic Features of Aspirin-Exacerbated Respiratory Disease.

Dysregulation of the arachidonic acid metabolic pathway is the most widely known pathomechanism of aspirin-exacerbated respiratory disease (AERD). This study aimed to perform integrative analysis of transcriptomic and epigenomic profiling with network analysis to determine the novel pathogenic features of AERD. Ten patients with asthma including 5 patients with AERD and another 5 patients with aspirin tolerant asthma (ATA) were enrolled. Nasal scraping was performed and nasal mucosa was used in omics profiling. Peripheral eosinophil counts, sputum eosinophil counts, fractional exhaled nitric oxide levels, and pulmonary function test results were evaluated. Differentially expressed genes (DEGs), differentially methylated probes (DMPs) and differentially correlated genes (DCGs) between patients with AERD and those with ATA were analyzed. Network analysis using ingenuity pathway analysis (IPA) was performed to determine the gene connection network and signaling pathways. In total, 1,736 DEGs, 1,401 DMPs, and 19 pairs for DCGs were identified. Among DCGs, genes related to vesicle transport (e.g., RAB3B and STX2) and sphingolipid dysregulation (e.g., SMPD3) were found to be hypo-methylated and up-regulated in AERD. Using the canonical pathway analysis of IPA with 78 asthma-related DEGs, signaling pathways of T helper cell differentiation/activation and Fcε receptor I were generated. Up-regulation of RORγt and FcER1A were noted in AERD. Gene expression levels of RAB3B, SYNE1, STX2, SMPD3 and RORγt were significantly associated with sputum eosinophil counts. Quantitative real-time polymerase chain reaction was performed and mRNA expression levels of STX2, SMPD3, RORγt, and FcER1A were significantly higher in AERD compared to ATA. Distinct pathogenic features were identified by using integrative multi-omics data analysis in patients with AERD.

Метаболомические исследования при хроническом риносинусите: обзор клинических перспектив

Chronic rhinosinusitis (CRS) is one of the most common otorhinolaryngological diseases, which significantly reduces the quality of life of patients. The etiopathogenesis of CRS has not been fully studied to date, it is problematic to predict the effectiveness of the therapy and the possible period of remission. To solve such issues in modern medicine, metabolomic studies are carried out that study various low-molecular metabolites, cell components in biological material taken from a person: in a cell, tissue, fluid, exhaled air, saliva, bronchial lavage, etc. Metabolomic studies allow identifying possible disease markers and also quantifying them. As a result, a set of individual molecular parameters is compiled that form the patient’s metabolic profile. Metabolomic studies define a new level of clinical prediction in a wide range of complex human diseases, including cardiovascular, oncological diseases, asthma, diabetes, etc. Today, it is typical to use a multiomics approach that combines various molecular omics technologies (genomics, proteomics, transcriptomics, lipidomics, etc.). Based on the identified quantitatively measured biomolecules, it becomes possible to compare genetic variability with complex phenotypic features. Publications on metabolomic studies in CRS are not extensive; however, they report the identified differences in various forms of rhinosinusitis. Metabolomic studies in CRS allow determining the diagnostically significant molecular markers of various forms of chronic sinusitis as well as creating a personal omics profile of the patient and providing an opportunity to control the effectiveness of the therapy. Together with complete data on the patient’s lifestyle and health status, the patient’s personal ohmic profile may be the basis of personalized medicine.

Omics Profiles of Non-GM Tubers from Transgrafted Potato with a GM Scion.

"Transgrafting" is a grafting procedure whereby a transgenic plant body is grafted to a non-transgenic plant body. It is a novel plant breeding technology that allows non-transgenic plants to obtain benefits usually conferred to transgenic plants. Many plants regulate flowering by perceiving the day-length cycle via expression of FLOWERING LOCUS T (FT) in the leaves. The resulting FT protein is translocated to the shoot apical meristem via the phloem. In potato plants, FT is involved in the promotion of tuber formation. Here we investigated the effects of a genetically modified (GM) scion on the edible parts of the non-GM rootstock by using potato plants transformed with StSP6A, a novel potato homolog of the FT gene. Scions prepared from GM or control (wild-type) potato plants were grafted to non-GM potato rootstocks; these were designated as TN and NN plants, respectively. After tuber harvest, we observed no significant differences in potato yield between TN and NN plants. Transcriptomic analysis revealed that only one gene-with unknown function-was differentially expressed between TN and NN plants. Subsequent proteomic analysis indicated that several members of protease inhibitor families, known as anti-nutritional factors in potato, were slightly more abundant in TN plants. Metabolomic analysis revealed a slight increase in metabolite abundance in NN plants, but we observed no difference in the accumulation of steroid glycoalkaloids, toxic metabolites found in potato. Finally, we found that TN and NN plants did not differ in nutrient composition. Taken together, these results indicate that FT expression in scions had a limited effect on the metabolism of non-transgenic potato tubers.

Integrated multi-omics reveals anaplerotic rewiring in methylmalonyl-CoA mutase deficiency

Methylmalonic aciduria (MMA) is an inborn error of metabolism with multiple monogenic causes and a poorly understood pathogenesis, leading to the absence of effective causal treatments. Here we employ multi-layered omics profiling combined with biochemical and clinical features of individuals with MMA to reveal a molecular diagnosis for 177 out of 210 (84%) cases, the majority (148) of whom display pathogenic variants in methylmalonyl-CoA mutase (MMUT). Stratification of these data layers by disease severity shows dysregulation of the tricarboxylic acid cycle and its replenishment (anaplerosis) by glutamine. The relevance of these disturbances is evidenced by multi-organ metabolomics of a hemizygous Mmut mouse model as well as through identification of physical interactions between MMUT and glutamine anaplerotic enzymes. Using stable-isotope tracing, we find that treatment with dimethyl-oxoglutarate restores deficient tricarboxylic acid cycling. Our work highlights glutamine anaplerosis as a potential therapeutic intervention point in MMA.

Integrated omics profiling and network pharmacology uncovers the prognostic genes and multi-targeted therapeutic bioactives to combat lung cancer

Non-small cell lung cancer (NSCLC) is the frequent subtype of lung cancer and the currently used treatment methods, diagnosis, and chemoresistance are relatively ineffective. Determining the pharmacological targets from active biomolecules of medicinal plants has become a frontiers era for biomedical research to develop novel therapies. In view of these scenarios, this pilot study, network pharmacology, cheminformatics, integrative omics, molecular docking and in vitro anti-cancer analysis were performed to unveil the multi-targeted treatment mechanisms of novel plant bioactives to treat lung cancer. Bioactive molecules from medicinal plants were compiled from PubChem. Network pharmacology approach revealed that 29 compounds efficiently target the 390 human and lung cancer associated genes. In addition, comparative analysis was performed and identified the 7 bioactive molecules significantly targeting 8 lung cancer genes. The integrative omics analysis discovered unique genes between the lung cancer and normal lung tissues. These genes were further validated through protein-protein interaction, gene ontology, gene functional and pathway enrichment, boxplot and overall survival analyses to understand the function of unique genes and their involvement in cancer signaling pathways. Survival heatmap analyses identified the significant prognostic genes. Docking results revealed that, lupeol and p-coumaric acid displayed high binding affinities with MIF, CCNB1, FABP4. Hence, we selected these two bioactives for in vitro analysis. Furthermore, these selected bioactives were showed concentration dependent cytotoxicity against the lung adenocarcinoma cells (A549). This holistic study has opened up novel avenues and unravels the cancer prognostic genes which could serve as druggable target and bioactives with anti-cancerous efficacy. Further functional validations are prerequisites to deciphering these bioactives as commercial drug candidates.

Tet2- and Tet3- Mediated Cytosine Hydroxymethylation in Six2 Progenitor Cells in Mice Is Critical for Nephron Progenitor Differentiation and Nephron Endowment.

Epigenetic changes have been proposed to mediate nephron endowment during development, a critical determinant of future renal disease development. Hydroxymethyl cytosine, an epigenetic modification important for gene regulation, is abundant in the human kidney, but its physiologic role and the role of DNA demethylase enzymes encoded by the Tet1 , Tet2 , or Tet3 , which mediate cytosine hydroxymethylation, are unclear. By genetically deleting Tet1 , Tet2 , or Tet3 in nephron progenitors in mice, the authors showed that combined Tet2 and Tet3 loss in nephron progenitors cause defective kidney development, leading to kidney failure and perinatal death. Tet2 and Tet3 deletion also caused an alteration in demethylation and expression of genes critical for nephron formation. These findings establish that Tet2- and Tet3 -mediated cytosine hydroxymethylation in nephron progenitors plays a critical role in nephron endowment. Nephron endowment is a key determinant of hypertension and renal disease in later life. Epigenetic changes have been proposed to mediate fetal programming and nephron number. DNA cytosine methylation, which plays a critical role in gene regulation, is affected by proteins encoded by the ten-eleven translocation (TET) DNA demethylase gene family ( Tet1 , Tet2 , and Tet3 ), but the roles of TET proteins in kidney development and nephron endowment have not been characterized . To study whether epigenetic changes-specifically, active DNA hydroxymethylation mediated by Tet1 , Tet2 , and Tet3- are necessary for nephron progenitor differentiation and nephron endowment, we generated mice with deletion of Tet1 , Tet2 , or Tet3 in Six2-positive nephron progenitors cells (NPCs). We then performed unbiased omics profiling, including whole-genome bisulfite sequencing on isolated Six2-positive NPCs and single-cell RNA sequencing on kidneys from newborn mice. We did not observe changes in kidney development or function in mice with NPC-specific deletion of Tet1 , Tet2 , Tet3 or Tet1 / Tet2 , or Tet1 / Tet3 . On the other hand, mice with combined Tet2 and Tet3 loss in Six2-positive NPCs failed to form nephrons, leading to kidney failure and perinatal death. Tet2 and Tet3 loss in Six2 -positive NPCs resulted in defective mesenchymal to epithelial transition and renal vesicle differentiation. Whole-genome bisulfite sequencing, single-cell RNA sequencing, and gene and protein expression analysis identified a defect in expression in multiple genes, including the WNT- β -catenin signaling pathway, due to a failure in demethylation of these loci in the absence of Tet2 and Tet3 . These findings suggest that Tet2- and Tet3 -mediated active cytosine hydroxymethylation in NPCs play a key role in kidney development and nephron endowment.

Incomplete time-series gene expression in integrative study for islet autoimmunity prediction.

Type 1 diabetes (T1D) outcome prediction plays a vital role in identifying novel risk factors, ensuring early patient care and designing cohort studies. TEDDY is a longitudinal cohort study that collects a vast amount of multi-omics and clinical data from its participants to explore the progression and markers of T1D. However, missing data in the omics profiles make the outcome prediction a difficult task. TEDDY collected time series gene expression for less than 6% of enrolled participants. Additionally, for the participants whose gene expressions are collected, 79% time steps are missing. This study introduces an advanced bioinformatics framework for gene expression imputation and islet autoimmunity (IA) prediction. The imputation model generates synthetic data for participants with partially or entirely missing gene expression. The prediction model integrates the synthetic gene expression with other risk factors to achieve better predictive performance. Comprehensive experiments on TEDDY datasets show that: (1) Our pipeline can effectively integrate synthetic gene expression with family history, HLA genotype and SNPs to better predict IA status at 2 years (sensitivity 0.622, AUC 0.715) compared with the individual datasets and state-of-the-art results in the literature (AUC 0.682). (2) The synthetic gene expression contains predictive signals as strong as the true gene expression, reducing reliance on expensive and long-term longitudinal data collection. (3) Time series gene expression is crucial to the proposed improvement and shows significantly better predictive ability than cross-sectional gene expression. (4) Our pipeline is robust to limited data availability. Availability: Code is available at https://github.com/compbiolabucf/TEDDY.

Fotomics: fourier transform-based omics imagification for deep learning-based cell-identity mapping using single-cell omics profiles

Fotomics: fourier transform-based omics imagification for deep learning-based cell-identity mapping using single-cell omics profiles

Alterations in the omics profiles in mevalonate pathway-inhibited cancer cells

AimsStatins, cholesterol-lowering drugs, are potential therapeutic agents for inhibiting cancer proliferation. However, the mechanisms that mediate the effects of statins, the homeostatic responses of tumor cells to statin therapy, and the modes underlying the antitumor effects of statins remain unclear. Main methodsTo uncover the effects of statins on cancer cells in vitro, we performed transcriptome and metabolome analyses on atorvastatin-treated statin-resistant and statin-sensitive lung cancer cells. Key findingsThe results of Gene Ontology terms and pathway enrichment analyses showed that after 24 h of atorvastatin treatment, the expression of cell cycle- and DNA replication-related genes was significantly decreased in the statin-sensitive cancer cells. The results of metabolome analysis showed that the components of polyamine metabolism and purine metabolism, glycolysis, and pentose phosphate pathway were decreased in the statin-sensitive cancer cells. SignificanceDifferences in cellular properties between statin-sensitive and statin-resistant cancer cells revealed additional candidates for therapeutic targets in statin-treated cancer cells and suggested that inhibiting these metabolic pathways could improve efficacy. In conclusion, combining statins with inhibitors of polyamine metabolism (cell proliferation and protein translation), purine metabolism (DNA synthesis), glycolytic system (energy production), and pentose phosphate pathway (antioxidant stress) might enhance the anticancer effects of statins.

Comprehensive analysis of HHV-6 and HHV-7-related gene signature in prognosis and response to temozolomide of glioma.

Human herpesvirus (HHV)-6 and HHV-7 have been detected in central nervous system and glioma tissue, while their exact role in glioma remains uncertain. Omics profiles and clinical information were downloaded from public databases, including The Cancer Genome Atlas cohort for training set and the Chinese Glioma Genome Atlas cohorts for validation sets. Differentially expressed genes between HHV-6 and HHV-7 infected or noninfected glioma patients were screened for establishing the HHV-6 and HHV-7 infection (HI) model through Lasso regression analysis. Bioinformatics methods were used to analyze the correlation between HI scores and prognosis, metastasis in glioma patients. Predictable efficacy of HI in temozolomide-resistance and HI-related genetic signatures were also explored. The HI model was constructed as: Risk score = (0.014709*DIRAS3) + (0.029787*TEX26) + (0.223492*FBXO39) + (0.074951*MYBL1) + (0.060202*HILS1). The five gene signature showed good performance in predicting survival time for glioma patients, while higher HI score is correlated with malignant features. Moreover, DNA mismatch repair genes were augmented in glioma patients with higher HI score as well as nonresponse to temozolomide treatment, which was in parallel with the transcriptomic result of temozolomide-resistant glioma cell. Targeting the five gene signature is beneficial for prognosis of glioma patients, especially in glioma patients underwent temozolomide treatment.