Omics Levels Research Articles

Drug Sensitivity Testing for Cancer Therapy, Key Areas.

Cancer is a high-mortality disease (9.6 million deaths in 2018 worldwide). Given various anticancer drugs, drug selection plays a key role in patient survival in clinical trials. Drug Sensitivity Testing (DST), one of the leading drug selective systems, was widely practiced for therapeutic promotion in the clinic. Notably, DSTs assist in drug selection that benefits drug responses against cancer from 20-22% to 30-35% over the past two decades. The relationship between drug resistance in vitro and drug treatment benefits was associated with different tumor origins and subtypes. Medical theory and underlying DST mechanisms remain poorly understood until now. The study of the clinical scenario, sustainability and financial support for mechanism and technical promotions is indispensable. Despite the great technical advance, therapeutic prediction and drug selection by DST needs to be miniature, versatility and cost-effective in the clinic. Multi-parameters and automation of DST should be a future trend. Advanced biomedical knowledge and clinical approaches to translating oncologic profiles into drug selection were the main focuses of DST developments. With a great technical stride, the clinical architecture of the DST platform was entering higher levels (drug response testing at any stage of cancer patients and miniaturization of tumor samples). The cancer biology and pharmacology for drug selection mutually benefit the clinic. New proposals to reveal more therapeutic information and drug response prediction at genetic, molecular and omics levels should be estimated overall. By upholding this goal of non-invasive, versatility and automation, DST could save the life of several thousand annually worldwide. In this article, new insights into DST novelty and development are highlighted.

Oncogenic signaling pathway dysregulation landscape reveals the role of pathways at multiple omics levels in pan-cancer.

Dysregulation of signaling pathways plays an essential role in cancer. However, there is not a comprehensive understanding on how oncogenic signaling pathways affect the occurrence and development with a common molecular mechanism of pan-cancer. Here, we investigated the oncogenic signaling pathway dysregulation by using multi-omics data on patients from TCGA from a pan-cancer perspective to identify commonalities across different cancer types. First, the pathway dysregulation profile was constructed by integrating typical oncogenic signaling pathways and the gene expression of TCGA samples, and four molecular subtypes with significant phenotypic and clinical differences induced by different oncogenic signaling pathways were identified: TGF-β+ subtype; cell cycle, MYC, and NF2− subtype; cell cycle and TP53+ subtype; and TGF-β and TP53− subtype. Patients in the TGF-β+ subtype have the best prognosis; meanwhile, the TGF-β+ subtype is associated with hypomethylation. Moreover, there is a higher level of immune cell infiltration but a slightly worse survival prognosis in the cell cycle, MYC, and NF2− subtype patients due to the effect of T-cell dysfunction. Then, the prognosis and subtype classifiers constructed by differential genes on a multi-omics level show great performance, indicating that these genes can be considered as biomarkers with potential therapeutic and prognostic significance for cancers. In summary, our study identified four oncogenic signaling pathway–driven patterns presented as molecular subtypes and their related potential prognostic biomarkers by integrating multiple omics data. Our discovery provides a perspective for understanding the role of oncogenic signaling pathways in pan-cancer.

Dissecting super-enhancer heterogeneity: time to re-examine cancer subtypes?

The heterogeneity of transcriptional regulations by super-enhancers (SEs) is poorly understood in human cancers. Herein, we summarize a bioinformatics workflow for genome-wide SE profiling and identification of subtype-specific SEs and regulatory networks. Dissecting SE heterogeneity provides new insights into cancer biology and alternative therapeutic strategies for cancer precision medicine.

Priming crops for the future: rewiring stress memory.

The agricultural sector must produce resilient and climate-smart crops to meet the increasing needs of global food production. Recent advancements in elucidating the mechanistic basis of plant stress memory have provided new opportunities for crop improvement. Stress memory-coordinated changes at the organismal, cellular, and various omics levels prepare plants to be more responsive to reoccurring stress within or across generation(s). The exposure to a primary stress, or stress priming, can also elicit a beneficial impact when encountering a secondary abiotic or biotic stress through the convergence of synergistic signalling pathways, referred to as cross-stress tolerance. 'Rewired plants' with stress memory provide a new means to stimulate adaptable stress responses, safeguard crop reproduction, and engineer climate-smart crops for the future.

The Big Picture of Neurodegeneration: A Meta Study to Extract the Essential Evidence on Neurodegenerative Diseases in a Network-Based Approach.

The common features of all neurodegenerative diseases, including Alzheimer's disease, Parkinson's disease, Amyotrophic Lateral Sclerosis (ALS), and Huntington's disease, are the accumulation of aggregated and misfolded proteins and the progressive loss of neurons, leading to cognitive decline and locomotive dysfunction. Still, they differ in their ultimate manifestation, the affected brain region, and the kind of proteinopathy. In the last decades, a vast number of processes have been described as associated with neurodegenerative diseases, making it increasingly harder to keep an overview of the big picture forming from all those data. In this meta-study, we analyzed genomic, transcriptomic, proteomic, and epigenomic data of the aforementioned diseases using the data of 234 studies in a network-based approach to study significant general coherences but also specific processes in individual diseases or omics levels. In the analysis part, we focus on only some of the emerging findings, but trust that the meta-study provided here will be a valuable resource for various other researchers focusing on specific processes or genes contributing to the development of neurodegeneration.

Causal Relations between Exposome and Stroke: A Mendelian Randomization Study.

Background and Purpose To explore the causal relationships of elements of the exposome with ischemic stroke and its subtypes at the omics level and to provide evidence for stroke prevention. Methods We conducted a Mendelian randomization study between exposure and any ischemic stroke (AIS) and its subtypes (large-artery atherosclerotic disease [LAD], cardioembolic stroke [CE], and small vessel disease [SVD]). The exposure dataset was the UK Biobank involving 361,194 subjects, and the outcome dataset was the MEGASTROKE consortium including 52,000 participants. Results We found that higher blood pressure (BP) (systolic BP: odds ratio [OR], 1.02; 95% confidence interval [CI], 1.01 to 1.04; diastolic BP: OR, 1.03; 95% CI, 1.01 to 1.05; pulse pressure: OR, 1.03; 95% CI, 1.00 to 1.06), atrial fibrillation (OR, 1.18; 95% CI, 1.13 to 1.25), and diabetes (OR, 1.13; 95% CI, 1.07 to 1.18) were significantly associated with ischemic stroke. Importantly, higher education (OR, 0.69; 95% CI, 0.60 to 0.79) decreased the risk of ischemic stroke. Higher systolic BP (OR, 1.06; 95% CI, 1.02 to 1.10), pulse pressure (OR, 1.08; 95% CI, 1.02 to 1.14), diabetes (OR, 1.28; 95% CI, 1.13 to 1.45), and coronary artery disease (OR, 1.58; 95% CI, 1.25 to 2.00) could cause LAD. Atrial fibrillation could cause CE (OR, 1.90; 95% CI, 1.71 to 2.11). For SVD, higher systolic BP (OR, 1.04; 95% CI, 1.00 to 1.07), diastolic BP (OR, 1.06; 95% CI, 1.01 to 1.12), and diabetes (OR, 1.22; 95% CI, 1.10 to 1.36) were causal factors. Conclusions The study revealed elements of the exposome causally linked to ischemic stroke and its subtypes, including conventional causal risk factors and novel protective factors such as higher education.

MO701: Origin of Proteins in Peritoneal Dialysis Explained by a Transcriptomics/Proteomics Cross-Over Analysis

Abstract BACKGROUND AND AIMS Peritoneal dialysis effluent (PDE) is a rich but underexplored source of molecular markers for therapy monitoring and investigation of deregulated processes during PD. Modern high performance mass spectrometry (MS) and sequencing methods allow monitoring of hundreds of analytes in parallel. For understanding PD related processes on a systems biology level, a multilevel omics approach is particularly attractive. Here, we investigate the cellular transcriptome and cell-free proteome of PDE samples in combination with the publicly available Human Plasma Proteome Database to investigate the origin of proteins found in peritoneal dialysis effluent. METHOD Samples were obtained from clinically stable patients on chronic peritoneal dialysis during a highly standardized clinical routine check. The effluent material was separated into a cellular and cell-free component. Soluble proteins in the cell-free compartment were processed using our equalizing and TMT-labeling workflow followed by LC-MS. The cellular material was subjected to RNA sequencing. The Human Plasma Proteome database (peptideatlas.org/hupo/hppp) was used for referencing plasma proteins and estimating plasma concentration. A bioinformatic workflow conjoined information from the datasets to reveal novel insights into the ‘PD effluentome,’ especially clarifying the source of proteins found in PDE. RESULTS Combining two targeted metabolomics methods enabled detecting 207 unique metabolites in cell-free PDE. Metabolites not detected in our samples were included in the panel for in vitro studies of cellular systems. A mixed-effect ANOVA of all metabolites demonstrated dwell time-dependent concentration changes in 173 metabolites. Post-hoc testing revealed most metabolites to be changed between 1 and 16 h [ON] of fluid dwell (160), followed by 114 and 46 differently concentrated metabolites between 4 and 16 h and 1 and 4 h of dwell, respectively. We quantified 9797 transcripts in PD-effluent cells and 2729 solved proteins in PD effluent. A total of 342 proteins were filtered from plasma, while 800 proteins were attributable to local production. A quantitative analysis of the interaction proteome and cellular transcripts of roughly 1700 protein-transcript pairs showed clusters of proteins explained by overexpression in peritoneal cells compared to plasma concentrations. CONCLUSION Multi-omic profiling of PD effluent proved to be a valuable approach for revealing small molecule related changes during PD treatment. The exploitation of PD effluent information on multiple omics levels as identified by our bioinformatic approach has been shown to improve our understanding of the molecular processes in the peritoneal cavity and their role in development of complications for ultimately improving PD therapy. The combinatorial investigation of proteome and transcriptome of PD effluent represents a first step in identifying locally produced proteins for further validation as biomarkers of peritoneal health in peritoneal dialysis patients. Proteins of plasma origin could be tested for their value as diagnostic tools in monitoring treatment success and protein transport over the peritoneal barrier. Our work suggests feasibility of multi-omics approaches to investigate cell-derived biomarkers for their involvement in pathomechanisms relevant in PD.

Serum autoantibodyome reveals that healthy individuals share common autoantibodies

SUMMARYAutoantibodies are a hallmark of both autoimmune disease and cancer, but they also occur in healthy individuals. Here, we perform a meta-analysis of nine datasets and focus on the common autoantibodies shared by healthy individuals. We report 77 common autoantibodies based on the protein microarray data obtained from probing 182 healthy individual sera on 7,653 human proteins and an additional 90 healthy individual sera on 1,666 human proteins. There is no gender bias; however, the number of autoantibodies increase with age, plateauing around adolescence. We use a bioinformatics pipeline to determine possible molecular-mimicry peptides that can contribute to the elicitation of these common autoantibodies. There is enrichment of intrinsic properties of proteins like hydrophilicity, basicity, aromaticity, and flexibility for common autoantigens. Subcellular localization and tissue-expression analysis reveal that several common autoantigens are sequestered from the circulating autoantibodies.

Global Transcriptomic Analysis of Bacteriophage-Host Interactions between a Kayvirus Therapeutic Phage and Staphylococcus aureus.

ABSTRACTKayviruses are polyvalent broad host range staphylococcal phages with a potential to combat staphylococcal infections. However, the implementation of rational phage therapy in medicine requires a thorough understanding of the interactions between bacteriophages and pathogens at omics level. To evaluate the effect of a phage used in therapy on its host bacterium, we performed differential transcriptomic analysis by RNA-Seq from bacteriophage K of genus Kayvirus infecting two Staphylococcus aureus strains, prophage-less strain SH1000 and quadruple lysogenic strain Newman. The temporal transcriptional profile of phage K was comparable in both strains except for a few loci encoding hypothetical proteins. Stranded sequencing revealed transcription of phage noncoding RNAs that may play a role in the regulation of phage and host gene expression. The transcriptional response of S. aureus to phage K infection resembles a general stress response with differential expression of genes involved in a DNA damage response. The host transcriptional changes involved upregulation of nucleotide, amino acid and energy synthesis and transporter genes and downregulation of host transcription factors. The interaction of phage K with variable genetic elements of the host showed slight upregulation of gene expression of prophage integrases and antirepressors. The virulence genes involved in adhesion and immune evasion were only marginally affected, making phage K suitable for therapy.IMPORTANCE Bacterium Staphylococcus aureus is a common human and veterinary pathogen that causes mild to life-threatening infections. As strains of S. aureus are becoming increasingly resistant to multiple antibiotics, the need to search for new therapeutics is urgent. A promising alternative to antibiotic treatment of staphylococcal infections is a phage therapy using lytic phages from the genus Kayvirus. Here, we present a comprehensive view on the phage-bacterium interactions on transcriptomic level that improves the knowledge of molecular mechanisms underlying the Kayvirus lytic action. The results will ensure safer usage of the phage therapeutics and may also serve as a basis for the development of new antibacterial strategies.

Proteomics-driven identification of short open reading frame-encoded peptides.

Accumulating evidence has shown that a large number of short open reading frames (sORFs) also have the ability to encode proteins. The discovery of sORFs opens up a new research area, leading to the identification and functional study of sORF encoded peptides (SEPs) at the omics level. Besides bioinformatics prediction and ribosomal profiling, mass spectrometry (MS) has become a significant tool as it directly detects the sequence of SEPs. Though MS-based proteomics methods have proved to be effective for qualitative and quantitative analysis of SEPs, the detection of SEPs is still a great challenge due to their low abundance and short sequence. To illustrate the progress in method development, we described and discussed the main steps of large-scale proteomics identification of SEPs, including SEP extraction and enrichment, MS detection, data processing and quality control, quantification, and function prediction and validation methods.

Target-oriented prioritization: targeted selection strategy by integrating organismal and molecular traits through predictive analytics in breeding

Genomic prediction in crop breeding is hindered by modeling on limited phenotypic traits. We propose an integrative multi-trait breeding strategy via machine learning algorithm, target-oriented prioritization (TOP). Using a large hybrid maize population, we demonstrate that the accuracy for identifying a candidate that is phenotypically closest to an ideotype, or target variety, achieves up to 91%. The strength of TOP is enhanced when omics level traits are included. We show that TOP enables selection of inbreds or hybrids that outperform existing commercial varieties. It improves multiple traits and accurately identifies improved candidates for new varieties, which will greatly influence breeding.

HDL quality features revealed by proteome‒lipidome connectivity are associated with atherosclerotic disease.

Lipoprotein, especially high-density lipoprotein (HDL), particles are composed of multiple heterogeneous subgroups containing various proteins and lipids. The molecular distribution among these subgroups is closely related to cardiovascular disease (CVD). Here, we established high-resolution proteomics and lipidomics (HiPL) methods to depict the molecular profiles across lipoprotein (Lipo-HiPL) and HDL (HDL-HiPL) subgroups by optimizing the resolution of anion-exchange chromatography and comprehensive quantification of proteins and lipids on the omics level. Furthermore, based on the Pearson correlation coefficient analysis of molecular profiles across high-resolution subgroups, we achieved the relationship of proteome‒lipidome connectivity (PLC) for lipoprotein and HDL particles. By application of these methods to high-fat, high-cholesterol diet-fed rabbits and acute coronary syndrome (ACS) patients, we uncovered the delicate dynamics of the molecular profile and reconstruction of lipoprotein and HDL particles. Of note, the PLC features revealed by the HDL-HiPL method discriminated ACS from healthy individuals better than direct proteome and lipidome quantification or PLC features revealed by the Lipo-HiPL method, suggesting their potential in ACS diagnosis. Together, we established HiPL methods to trace the dynamics of the molecular profile and PLC of lipoprotein and even HDL during the development of CVD.

Multi-omics & pathway analysis identify potential roles for tumor N-acetyl aspartate accumulation in murine models of castration-resistant prostate cancer

SummaryCastration-resistant prostate cancer (CRPC) is incurable and remains a significant worldwide challenge (Oakes and Papa, 2015). Matched untargeted multi-level omic datasets may reveal biological changes driving CRPC, identifying novel biomarkers and/or therapeutic targets. Untargeted RNA sequencing, proteomics, and metabolomics were performed on xenografts derived from three independent sets of hormone naive and matched CRPC human cell line models of local, lymph node, and bone metastasis grown as murine orthografts. Collectively, we tested the feasibility of muti-omics analysis on models of CRPC in revealing pathways of interest for future validation investigation. Untargeted metabolomics revealed NAA and NAAG commonly accumulating in CRPC across three independent models and proteomics showed upregulation of related enzymes, namely N-acetylated alpha-linked acidic dipeptidases (FOLH1/NAALADL2). Based on pathway analysis integrating multiple omic levels, we hypothesize that increased NAA in CRPC may be due to upregulation of NAAG hydrolysis via NAALADLases providing a pool of acetyl Co-A for upregulated sphingolipid metabolism and a pool of glutamate and aspartate for nucleotide synthesis during tumor growth.

An exposomic framework to uncover environmental drivers of aging.

The exposome, the environmental complement of the genome, is an omics level characterization of an individual’s exposures. There is growing interest in uncovering the role of the environment in human health using an exposomic framework that provides a systematic and unbiased analysis of the non-genetic drivers of health and disease. Many environmental toxicants are associated with molecular hallmarks of aging. An exposomic framework has potential to advance understanding of these associations and how modifications to the environment can promote healthy aging in the population. However, few studies have used this framework to study biological aging. We provide an overview of approaches and challenges in using an exposomic framework to investigate environmental drivers of aging. While capturing exposures over a life course is a daunting and expensive task, the use of historical data can be a practical way to approach this research.

Multi-Omics Integration in a Twin Cohort and Predictive Modeling of Blood Pressure Values.

Abnormal blood pressure is strongly associated with risk of high-prevalence diseases, making the study of blood pressure a major public health challenge. Although biological mechanisms underlying hypertension at the single omic level have been discovered, multi-omics integrative analyses using continuous variations in blood pressure values remain limited. We used a multi-omics regression-based method, called sparse multi-block partial least square, for integrative, explanatory, and predictive interests in study of systolic and diastolic blood pressure values. Various datasets were obtained from the Finnish Twin Cohort for up to 444 twins. Blocks of omics—including transcriptomic, methylation, metabolomic—data as well as polygenic risk scores and clinical data were integrated into the modeling and supported by cross-validation. The predictive contribution of each omics block when predicting blood pressure values was investigated using external participants from the Young Finns Study. In addition to revealing interesting inter-omics associations, we found that each block of omics heterogeneously improved the predictions of blood pressure values once the multi-omics data were integrated. The modeling revealed a plurality of clinical, transcriptomic, and metabolomic factors consistent with the literature and that play a leading role in explaining unit variations in blood pressure. These findings demonstrate (1) the robustness of our integrative method to harness results obtained by single omics discriminant analyses, and (2) the added value of predictive and exploratory gains of a multi-omics approach in studies of complex phenotypes such as blood pressure.

Is it time to reassess variant annotation?

Variant annotation is one of the most essential steps in selecting candidates for further investigation. With the advancement in functional genomics, new variant annotation tools focus on annotation based on empirically generated data instead of theoretically based predictions. This is a direct result of the large national and international consortia that generated enormous experiment-based or validated data at multiple omics levels. Here, we highlight the recent empirically based annotation methods and discuss their strengths and weaknesses.

Effects of diisononyl phthalate exposure on the oxidative stress and gut microorganisms in earthworms (Eisenia fetida)

Phthalate esters (PAEs) are widely used as plasticizers and can be ubiquitously detected in environment. However, the toxic effects and mechanisms of diisononyl phthalate (DINP) on earthworms are still poorly understood. In this study, earthworms (Eisenia fetida) were exposed to DINP at various doses (0, 300, 600, 1200, and 2400 mg/kg) to investigate their subchronic toxicity. The results demonstrated that the reactive oxygen species (ROS) levels displayed an “increase-decrease” trend with the increasing DINP doses after DINP exposure on days 7, 14, 21, and 28. The malondialdehyde (MDA) content increased with increasing DINP doses on days 7, 14, and then decreased on days 21, 28. The values of superoxide dismutase (SOD), catalase (CAT), and glutathione S-transferase (GST) showed similar variation patterns and reached a maximum level on 21 d. Moreover, on day 28, the SOD and CAT gene expression levels were upregulated, while the GST gene expression levels were downregulated. Meanwhile, 16S rRNA genes of E. fetida gut bacteria and surrounding soil bacteria were measured after 28 days of exposure to DINP. The Chao index of E. fetida gut bacteria decreased when the treatment with the highest concentration (2400 mg/kg) was applied. At the phylum level, the abundance of Chloroflexi was significantly lower in the gut of E. fetida. In addition, the abundance of Proteobacteria at the phylum level and Ottowia at the genus level significantly increased in the surrounding soil. Overall, our results shed light on the toxic mechanism of DINP at biochemical, molecular, and omics levels, and contributed to a better understanding of the ecotoxicity of DINP.

Identification of Novel Lung Cancer Driver Genes Connecting Different Omics Levels With a Heat Diffusion Algorithm.

Cancer driver gene is a type of gene with abnormal alterations that initiate or promote tumorigenesis. Driver genes can be used to reveal the fundamental pathological mechanisms of tumorigenesis. These genes may have pathological changes at different omics levels. Thus, identifying cancer driver genes involving two or more omics levels is essential. In this study, a computational investigation was conducted on lung cancer driver genes. Four omics levels, namely, epigenomics, genomics, transcriptomics, and post-transcriptomics, were involved. From the driver genes at each level, the Laplacian heat diffusion algorithm was executed on a protein–protein interaction network for discovering latent driver genes at this level. A following screen procedure was performed to extract essential driver genes, which contained three tests: permutation, association, and function tests, which can exclude false-positive genes and screen essential ones. Finally, the intersection operation was performed to obtain novel driver genes involving two omic levels. The analyses on obtained genes indicated that they were associated with fundamental pathological mechanisms of lung cancer at two corresponding omics levels.

Deep latent space fusion for adaptive representation of heterogeneous multi-omics data.

The integration of multi-omics data makes it possible to understand complex biological organisms at the system level. Numerous integration approaches have been developed by assuming a common underlying data space. Due to the noise and heterogeneity of biological data, the performance of these approaches is greatly affected. In this work, we propose a novel deep neural network architecture, named Deep Latent Space Fusion (DLSF), which integrates the multi-omics data by learning consistent manifold in the sample latent space for disease subtypes identification. DLSF is built upon a cycle autoencoder with a shared self-expressive layer, which can naturally and adaptively merge nonlinear features at each omics level into one unified sample manifold and produce adaptive representation of heterogeneous samples at the multi-omics level. We have assessed DLSF on various biological and biomedical datasets to validate its effectiveness. DLSF can efficiently and accurately capture the intrinsic manifold of the sample structures or sample clusters compared with other state-of-the-art methods, and DLSF yielded more significant outcomes for biological significance, survival prognosis and clinical relevance in application of cancer study in The Cancer Genome Atlas. Notably, as a deep case study, we determined a new molecular subtype of kidney renal clear cell carcinoma that may benefit immunotherapy in the viewpoint of multi-omics, and we further found potential subtype-specific biomarkers from multiple omics data, which were validated by independent datasets. In addition, we applied DLSF to identify potential therapeutic agents of different molecular subtypes of chronic lymphocytic leukemia, demonstrating the scalability of DLSF in diverse omics data types and application scenarios.

Development of high-resolution multidimensional native protein microfluidic chip electrophoresis fingerprinting and its application in the quick analysis of unknown microorganisms

The unascertained, constant mutation and emergence of new types of microorganisms present significant challenges to their detection. Differing from the focus on the limited local 16S rRNA gene or protein markers, characteristic whole fingerprint technologies at the omic level are particularly suitable for unknown analytes since accurate knowledge about the constituents is not necessarily required. Herein, through a combination of several innovative strategies, including pure water isotachophoresis integrated (2 + 1)D electrophoresis, inversion-funnel peak stacking channel geometry and COMSOL computer-aided fluid simulation, high-resolution whole protein 2D native microfluidic chip electrophoresis was achieved within less than 1 min. The highest ever reported peak capacity for native 2D chip electrophoresis was obtained. Furthermore, taking Escherichia coli, Staphylococcus aureus, and Bacillus subtilis as model analytes without protein biomarker information, the feasibility of the identification and semiqualification of unknown microbes in pure or mixed samples was explored with the utilisation of original algorithms, including SIFT feature abstraction and a global information entropy combined support vector machine. As such, the multidisciplinary cooperation in the present study demonstrates monstrated promising prospects for microfluidic chip electropherogram fingerprint-based quick microorganism assays, biointeraction studies, and drug screenings, even if the analytes are not fully ascertained.