Next-generation genomics: an integrative approach

A triple hormone receptor ER, AR, and VDR signature is a robust prognosis predictor in breast cancer

BACKGROUND

Despite evidence indicating the dominance of cell-of-origin signatures in molecular tumor patterns, translating these genome-wide patterns into actionable insights has been challenging. This study introduces breast cancer cell-of-origin signatures that offer significant prognostic value across all breast cancer subtypes and various clinical cohorts, compared to previously developed genomic signatures.

METHODS

We previously reported that triple hormone receptor (THR) co-expression patterns of androgen (AR), estrogen (ER), and vitamin D (VDR) receptors are maintained at the protein level in human breast cancers. Here, we developed corresponding mRNA signatures (THR-50 and THR-70) based on these patterns to categorize breast tumors by their THR expression levels. The THR mRNA signatures were evaluated across 56 breast cancer datasets (5040 patients) using Kaplan-Meier survival analysis, Cox proportional hazard regression, and unsupervised clustering.

RESULTS

The THR signatures effectively predict both overall and progression-free survival across all evaluated datasets, independent of subtype, grade, or treatment status, suggesting improvement over existing prognostic signatures. Furthermore, they delineate three distinct ER-positive breast cancer subtypes with significant survival in differences-expanding on the conventional two subtypes. Additionally, coupling THR-70 with an immune signature identifies a predominantly ER-negative breast cancer subgroup with a highly favorable prognosis, comparable to ER-positive cases, as well as an ER-negative subgroup with notably poor outcome, characterized by a 15-fold shorter survival.

CONCLUSIONS

The THR cell-of-origin signature introduces a novel dimension to breast cancer biology, potentially serving as a robust foundation for integrating additional prognostic biomarkers. These signatures offer utility as a prognostic index for stratifying existing breast cancer subtypes and for de novo classification of breast cancer cases. Moreover, THR signatures may also hold promise in predicting hormone treatment responses targeting AR and/or VDR.

Genome-wide binding analysis unveils critical implication of B-Myb-mediated transactivation in cancers

B-Myb, also known as MYB proto-oncogene like 2 (MYBL2), is an important transcription factor implicated in transcription regulation, cell cycle and tumorigenesis. However, the molecular mechanism underlying B-Myb-controlled transactivation in different cell contexts as well as its functional implication in cancers remains elusive. In this study, we have conducted a comprehensive genome-wide analysis of B-Myb binding sites in multiple immortalized or cancer cell lines and identified its critical target genes. The results revealed that B-Myb regulates a common set of core cell cycle genes and cell type-specific genes through collaboration with other important transcription factors (e.g. NFY and MuvB complex) and binding to cell type-invariant promoters and cell type-specific enhancers and super-enhancers. KIF2C, UBE2C and MYC were further validated as B-Myb target genes. Loss-of-function analysis demonstrated that KIF2C knockdown inhibited tumor cell growth both in vitro and in vivo, suppressed cell motility and cell cycle progression, accompanied with defects in microtubule organization and mitosis, strongly suggesting that KIF2C is a critical regulator of cancer cell growth and mitosis, and maintains high cancer cell motility ability and microtubule dynamics. Pan-cancer transcriptomic analysis revealed that the overexpression of both B-Myb and KIF2C presents as independent prognostic markers in various types of cancer. Notably, B-Myb associates with NFYB, binds to target gene promoters, enhancers and super-enhancers, and provokes a cascade of oncogenic gene expression profiles in cancers. Overall, our results highly suggest the critical implication of B-Myb-mediated gene regulation in cancers, and the promising therapeutic and prognostic potentials of B-Myb and KIF2C for cancer diagnosis and treatment.

Metabolic engineering for compartmentalized biosynthesis of the valuable compounds in Saccharomyces cerevisiae

Saccharomyces cerevisiae is commonly used as a microbial cell factory to produce high-value compounds or bulk chemicals due to its genetic operability and suitable intracellular physiological environment. The current biosynthesis pathway for targeted products is primarily rewired in the cytosolic compartment. However, the related precursors, enzymes, and cofactors are frequently distributed in various subcellular compartments, which may limit targeted compounds biosynthesis. To overcome above mentioned limitations, the biosynthesis pathways are localized in different subcellular organelles for product biosynthesis. Subcellular compartmentalization in the production of targeted compounds offers several advantages, mainly relieving competition for precursors from side pathways, improving biosynthesis efficiency in confined spaces, and alleviating the cytotoxicity of certain hydrophobic products. In recent years, subcellular compartmentalization in targeted compound biosynthesis has received extensive attention and has met satisfactory expectations. In this review, we summarize the recent advances in the compartmentalized biosynthesis of the valuable compounds in S. cerevisiae, including terpenoids, sterols, alkaloids, organic acids, and fatty alcohols, etc. Additionally, we describe the characteristics and suitability of different organelles for specific compounds, based on the optimization of pathway reconstruction, cofactor supplementation, and the synthesis of key precursors (metabolites). Finally, we discuss the current challenges and strategies in the field of compartmentalized biosynthesis through subcellular engineering, which will facilitate the production of the complex valuable compounds and offer potential solutions to improve product specificity and productivity in industrial processes.

Current development of molecular classifications of gastric cancer based on omics (Review)

Gastric cancer (GC) is a complex and heterogeneous disease with significant phenotypic and genetic variation. Traditional classification systems rely mainly on the evaluation of clinical pathological features and conventional biomarkers and might not capture the diverse clinical processes of individual GCs. The latest discoveries in omics technologies such as next‑generation sequencing, proteomics and metabolomics have provided crucial insights into potential genetic alterations and biological events in GC. Clustering strategies for identifying subtypes of GC might offer new tools for improving GC treatment and clinical trial outcomes by enabling the development of therapies tailored to specific subtypes. However, the feasibility and therapeutic significance of implementing molecular classifications of GC in clinical practice need to addressed. The present review examines the current molecular classifications, delineates the prevailing landscape of clinically relevant molecular features, analyzes their correlations with traditional GC classifications, and discusses potential clinical applications.

A novel method for multiple phenotype association studies based on genotype and phenotype network

Joint analysis of multiple correlated phenotypes for genome-wide association studies (GWAS) can identify and interpret pleiotropic loci which are essential to understand pleiotropy in diseases and complex traits. Meanwhile, constructing a network based on associations between phenotypes and genotypes provides a new insight to analyze multiple phenotypes, which can explore whether phenotypes and genotypes might be related to each other at a higher level of cellular and organismal organization. In this paper, we first develop a bipartite signed network by linking phenotypes and genotypes into a Genotype and Phenotype Network (GPN). The GPN can be constructed by a mixture of quantitative and qualitative phenotypes and is applicable to binary phenotypes with extremely unbalanced case-control ratios in large-scale biobank datasets. We then apply a powerful community detection method to partition phenotypes into disjoint network modules based on GPN. Finally, we jointly test the association between multiple phenotypes in a network module and a single nucleotide polymorphism (SNP). Simulations and analyses of 72 complex traits in the UK Biobank show that multiple phenotype association tests based on network modules detected by GPN are much more powerful than those without considering network modules. The newly proposed GPN provides a new insight to investigate the genetic architecture among different types of phenotypes. Multiple phenotypes association studies based on GPN are improved by incorporating the genetic information into the phenotype clustering. Notably, it might broaden the understanding of genetic architecture that exists between diagnoses, genes, and pleiotropy.

Classifying breast cancer using multi-view graph neural network based on multi-omics data

Introduction: As the evaluation indices, cancer grading and subtyping have diverse clinical, pathological, and molecular characteristics with prognostic and therapeutic implications. Although researchers have begun to study cancer differentiation and subtype prediction, most of relevant methods are based on traditional machine learning and rely on single omics data. It is necessary to explore a deep learning algorithm that integrates multi-omics data to achieve classification prediction of cancer differentiation and subtypes. Methods: This paper proposes a multi-omics data fusion algorithm based on a multi-view graph neural network (MVGNN) for predicting cancer differentiation and subtype classification. The model framework consists of a graph convolutional network (GCN) module for learning features from different omics data and an attention module for integrating multi-omics data. Three different types of omics data are used. For each type of omics data, feature selection is performed using methods such as the chi-square test and minimum redundancy maximum relevance (mRMR). Weighted patient similarity networks are constructed based on the selected omics features, and GCN is trained using omics features and corresponding similarity networks. Finally, an attention module integrates different types of omics features and performs the final cancer classification prediction. Results: To validate the cancer classification predictive performance of the MVGNN model, we conducted experimental comparisons with traditional machine learning models and currently popular methods based on integrating multi-omics data using 5-fold cross-validation. Additionally, we performed comparative experiments on cancer differentiation and its subtypes based on single omics data, two omics data, and three omics data. Discussion: This paper proposed the MVGNN model and it performed well in cancer classification prediction based on multiple omics data.

Representation and quantification of module activity from omics data with rROMA

The efficiency of analyzing high-throughput data in systems biology has been demonstrated in numerous studies, where molecular data, such as transcriptomics and proteomics, offers great opportunities for understanding the complexity of biological processes. One important aspect of data analysis in systems biology is the shift from a reductionist approach that focuses on individual components to a more integrative perspective that considers the system as a whole, where the emphasis shifted from differential expression of individual genes to determining the activity of gene sets. Here, we present the rROMA software package for fast and accurate computation of the activity of gene sets with coordinated expression. The rROMA package incorporates significant improvements in the calculation algorithm, along with the implementation of several functions for statistical analysis and visualizing results. These additions greatly expand the package's capabilities and offer valuable tools for data analysis and interpretation. It is an open-source package available on github at: www.github.com/sysbio-curie/rROMA . Based on publicly available transcriptomic datasets, we applied rROMA to cystic fibrosis, highlighting biological mechanisms potentially involved in the establishment and progression of the disease and the associated genes. Results indicate that rROMA can detect disease-related active signaling pathways using transcriptomic and proteomic data. The results notably identified a significant mechanism relevant to cystic fibrosis, raised awareness of a possible bias related to cell culture, and uncovered an intriguing gene that warrants further investigation.

Betulinic acid arrests cell cycle at G2/M phase by up-regulating metallothionein 1G inhibiting proliferation of colon cancer cells

Betulinic acid (BA) is a pentacyclic triterpene found in many plant species and has a broad-spectrum anti-tumor effect in various cancers, including colon cancer (CRC). However, its anticancer mechanism in CRC is no clear. RNA sequencing and bioinformatics analysis showed BA up-regulated 378 genes and down-regulated 137 genes in HT29 cells, while 2303 up-regulated and 1041 down-regulated genes were found in SW480 cells. KEGG enrichment analysis showed BA significantly stimulated the expression of metallothionein 1 (MT1) family genes in both HT29 and SW480 cells. Metallothionein 1G (MT1G) was the gene with the highest upregulation of MT1 family genes induced by BA dose-dependently. High MT1G expression enhanced the sensitivity of CRC cells to BA, whereas, MT1G knockdown had the opposite effect in vitro and in vivo. GSEA and GSCA showed genes affected by BA treatment were involved in cell cycle and G2/M checkpoint in CRC. Flow cytometry further exhibited BA reduced the percentage of G0/G1 cells and increased the percentage of G2/M cells in a dose-dependent manner, which could be rescued by MT1G knockdown. Moreover, MT1G also counteracted the BA-induced changes in cell cycle-related proteins (CDK2 and CDK4) and p-Rb. In summary, we have revealed a new anti-tumor mechanism that BA altered the cell cycle progression of CRC cells by upregulating MT1G gene, thereby inhibiting the proliferation of CRC cells.

Conserved and divergent gene regulatory programs of the mammalian neocortex

Divergence of cis-regulatory elements drives species-specific traits1, but how this manifests in the evolution of the neocortex at the molecular and cellular level remains unclear. Here we investigated the gene regulatory programs in the primary motor cortex of human, macaque, marmoset and mouse using single-cell multiomics assays, generating gene expression, chromatin accessibility, DNA methylome and chromosomal conformation profiles from a total of over 200,000 cells. From these data, we show evidence that divergence of transcription factor expression corresponds to species-specific epigenome landscapes. We find that conserved and divergent gene regulatory features are reflected in the evolution of the three-dimensional genome. Transposable elements contribute to nearly 80% of the human-specific candidate cis-regulatory elements in cortical cells. Through machine learning, we develop sequence-based predictors of candidate cis-regulatory elements in different species and demonstrate that the genomic regulatory syntax is highly preserved from rodents to primates. Finally, we show that epigenetic conservation combined with sequence similarity helps to uncover functional cis-regulatory elements and enhances our ability to interpret genetic variants contributing to neurological disease and traits.

RgnTX: Colocalization analysis of transcriptome elements in the presence of isoform heterogeneity and ambiguity

Colocalization analysis of genomic region sets has been widely adopted to unveil potential functional interactions between corresponding biological attributes, which often serves as the basis for further investigation. A number of methods have been developed for colocalization analysis of genomic elements. However, none of them explicitly considered the transcriptome heterogeneity and isoform ambiguity, making them less appropriate for analyzing transcriptome elements. Here, we developed RgnTX, an R/Bioconductor tool for the colocalization analysis of transcriptome elements with permutation tests. Different from existing approaches, RgnTX directly takes advantage of transcriptome annotation, and offers high flexibility in the null model to simulate realistic transcriptome-wide background, such as the complex alternative splicing patterns. Importantly, it supports the testing of transcriptome elements without clear isoform association, which is often the real scenario due to technical limitations. Proposed package offers a wide selection of pre-defined functions, easy to be utilized by users for visualizing permutation results, calculating shifted z-scores and conducting multiple hypothesis testing under Benjamini-Hochberg correction. Moreover, with synthetic and real datasets, we show that RgnTX novel testing modes return distinct and more significant results compared to existing genome-based methods. We believe RgnTX should make a useful tool to characterize the randomness of the transcriptome, and for conducting statistical association analysis for genomic region sets within the heterogeneous transcriptome. The package now has been accepted by Bioconductor and is freely available at: https://bioconductor.org/packages/RgnTX.

Comparative single cell epigenomic analysis of gene regulatory programs in the rodent and primate neocortex

Sequence divergence of cis- regulatory elements drives species-specific traits, but how this manifests in the evolution of the neocortex at the molecular and cellular level remains to be elucidated. We investigated the gene regulatory programs in the primary motor cortex of human, macaque, marmoset, and mouse with single-cell multiomics assays, generating gene expression, chromatin accessibility, DNA methylome, and chromosomal conformation profiles from a total of over 180,000 cells. For each modality, we determined species-specific, divergent, and conserved gene expression and epigenetic features at multiple levels. We find that cell type-specific gene expression evolves more rapidly than broadly expressed genes and that epigenetic status at distal candidate cis -regulatory elements (cCREs) evolves faster than promoters. Strikingly, transposable elements (TEs) contribute to nearly 80% of the human-specific cCREs in cortical cells. Through machine learning, we develop sequence-based predictors of cCREs in different species and demonstrate that the genomic regulatory syntax is highly preserved from rodents to primates. Lastly, we show that epigenetic conservation combined with sequence similarity helps uncover functional cis -regulatory elements and enhances our ability to interpret genetic variants contributing to neurological disease and traits.

EDomics: a comprehensive and comparative multi-omics database for animal evo-devo

Evolutionary developmental biology (evo-devo) has been among the most fascinating interdisciplinary fields for decades, which aims to elucidate the origin and evolution of diverse developmental processes. The rapid accumulation of omics data provides unprecedented opportunities to answer many interesting but unresolved evo-devo questions. However, the access and utilization of these resources are hindered by challenges particularly in non-model animals. Here, we establish a comparative multi-omics database for animal evo-devo (EDomics, http://edomics.qnlm.ac) containing comprehensive genomes, bulk transcriptomes, and single-cell data across 40 representative species, many of which are generally used as model organisms for animal evo-devo study. EDomics provides a systematic view of genomic/transcriptomic information from various aspects, including genome assembly statistics, gene features and families, transcription factors, transposable elements, and gene expressional profiles/networks. It also exhibits spatiotemporal gene expression profiles at a single-cell level, such as cell atlas, cell markers, and spatial-map information. Moreover, EDomics provides highly valuable, customized datasets/resources for evo-devo research, including gene family expansion/contraction, inferred core gene repertoires, macrosynteny analysis for karyotype evolution, and cell type evolution analysis. EDomics presents a comprehensive and comparative multi-omics platform for animal evo-devo community to decipher the whole history of developmental evolution across the tree of life.

SADLN: Self-attention based deep learning network of integrating multi-omics data for cancer subtype recognition

Integrating multi-omics data for cancer subtype recognition is an important task in bioinformatics. Recently, deep learning has been applied to recognize the subtype of cancers. However, existing studies almost integrate the multi-omics data simply by concatenation as the single data and then learn a latent low-dimensional representation through a deep learning model, which did not consider the distribution differently of omics data. Moreover, these methods ignore the relationship of samples. To tackle these problems, we proposed SADLN: A self-attention based deep learning network of integrating multi-omics data for cancer subtype recognition. SADLN combined encoder, self-attention, decoder, and discriminator into a unified framework, which can not only integrate multi-omics data but also adaptively model the sample's relationship for learning an accurately latent low-dimensional representation. With the integrated representation learned from the network, SADLN used Gaussian Mixture Model to identify cancer subtypes. Experiments on ten cancer datasets of TCGA demonstrated the advantages of SADLN compared to ten methods. The Self-Attention Based Deep Learning Network (SADLN) is an effective method of integrating multi-omics data for cancer subtype recognition.

Interdisciplinary Overview of Lipopeptide and Protein-Containing Biosurfactants

Biosurfactants are amphipathic molecules capable of lowering interfacial and superficial tensions. Produced by living organisms, these compounds act the same as chemical surfactants but with a series of improvements, the most notable being biodegradability. Biosurfactants have a wide diversity of categories. Within these, lipopeptides are some of the more abundant and widely known. Protein-containing biosurfactants are much less studied and could be an interesting and valuable alternative. The harsh temperature, pH, and salinity conditions that target organisms can sustain need to be understood for better implementation. Here, we will explore biotechnological applications via lipopeptide and protein-containing biosurfactants. Also, we discuss their natural role and the organisms that produce them, taking a glimpse into the possibilities of research via meta-omics and machine learning.

Bayesian Genetic Colocalization Test of Two Traits Using coloc

Genetic colocalization is an approach for determining whether a genetic variant at a particular locus is shared across multiple phenotypes. Genome-wide association studies (GWAS) have successfully mapped genetic variants associated with thousands of complex traits and diseases. However, a large proportion of GWAS signals fall in non-coding regions of the genome, making functional interpretation a challenge. Colocalization relies on a Bayesian framework that can integrate summary statistics, for example those derived from GWAS and expression quantitative trait loci (eQTL) mapping, to assess whether two or more independent association signals at a region of interest are consistent with a shared causal variant. The results from a colocalization analysis may be used to evaluate putative causal relationships between omics-based molecular measurements and a complex disease, and can generate hypotheses that may be followed up by tailored experiments. In this article, we present an easy and straightforward protocol for conducting a Bayesian test for colocalization of two traits using the 'coloc' package in R with summary-level results derived from GWAS and eQTL studies. We also provide general guidelines that can assist in the interpretation of findings generated from colocalization analyses. © 2022 Wiley Periodicals LLC. Basic Protocol: Performing a genetic colocalization analysis using the 'coloc' package in R and summary-level data Support Protocol: Installing the 'coloc' R package.

Genome-wide transcriptome profiling of human trabecular meshwork cells treated with TGF-β2

Glaucoma is a complex neurodegenerative disease resulting in progressive optic neuropathy and is a leading cause of irreversible blindness worldwide. Primary open angle glaucoma (POAG) is the predominant form affecting 65.5 million people globally. Despite the prevalence of POAG and the identification of over 120 glaucoma related genetic loci, the underlaying molecular mechanisms are still poorly understood. The transforming growth factor beta (TGF-β) signalling pathway is implicated in the molecular pathology of POAG. To gain a better understanding of the role TGF-β2 plays in the glaucomatous changes to the molecular pathology in the trabecular meshwork, we employed RNA-Seq to delineate the TGF-β2 induced changes in the transcriptome of normal primary human trabecular meshwork cells (HTM). We identified a significant number of differentially expressed genes and associated pathways that contribute to the pathogenesis of POAG. The differentially expressed genes were predominantly enriched in ECM regulation, TGF-β signalling, proliferation/apoptosis, inflammation/wound healing, MAPK signalling, oxidative stress and RHO signalling. Canonical pathway analysis confirmed the enrichment of RhoA signalling, inflammatory-related processes, ECM and cytoskeletal organisation in HTM cells in response to TGF-β2. We also identified novel genes and pathways that were affected after TGF-β2 treatment in the HTM, suggesting additional pathways are activated, including Nrf2, PI3K-Akt, MAPK and HIPPO signalling pathways. The identification and characterisation of TGF-β2 dependent differentially expressed genes and pathways in HTM cells is essential to understand the patho-physiology of glaucoma and to develop new therapeutic agents.

Connectivity in eQTL networks dictates reproducibility and genomic properties

Expression quantitative trait locus (eQTL) analysis associates SNPs with gene expression; these relationships can be represented as a bipartite network with association strength as "edge weights" between SNPs and genes. However, most eQTL networks use binary edge weights based on thresholded FDR estimates: definitions that influence reproducibility and downstream analyses. We constructed twenty-nine tissue-specific eQTL networks using GTEx data and evaluated a comprehensive set of network specifications based on false discovery rates, test statistics, and p values, focusing on the degree centrality-a metric of an SNP or gene node's potential network influence. We found a thresholded Benjamini-Hochberg q value weighted by the Z-statistic balances metric reproducibility and computational efficiency. Our estimated gene degrees positively correlate with gene degrees in gene regulatory networks, demonstrating that these networks are complementary in understanding regulation. Gene degrees also correlate with genetic diversity, and heritability analyses show that highly connected nodes are enriched for tissue-relevant traits.

Omics Technology for the Promotion of Nutraceuticals and Functional Foods

The influence of nutrition and environment on human health has been known for ages. Phytonutrients (7,000 flavonoids and phenolic compounds; 600 carotenoids) and pro-health nutrients—nutraceuticals positively add to human health and may prevent disorders such as cancer, diabetes, obesity, cardiovascular diseases, and dementia. Plant-derived bioactive metabolites have acquired an imperative function in human diet and nutrition. Natural phytochemicals affect genome expression (nutrigenomics and transcriptomics) and signaling pathways and act as epigenetic modulators of the epigenome (nutri epigenomics). Transcriptomics, proteomics, epigenomics, miRNomics, and metabolomics are some of the main platforms of complete omics analyses, finding use in functional food and nutraceuticals. Now the recent advancement in the integrated omics approach, which is an amalgamation of multiple omics platforms, is practiced comprehensively to comprehend food functionality in food science.

S, Patel AB, Chen ZS. An Ayurgenomics Approach: Prakriti-Based Drug Discovery and Development for Personalized Care

Originating in ancient India, Ayurveda is an alternative medicinal approach that provides substantial evidence for a theoretical-level analysis of all aspects of life. Unlike modern medicine, Ayurveda is based upon tridoshas (Vata, pitta, and Kapha) and Prakriti. On the other hand, the research of all the genes involved at the proteomics, metabolomics, and transcriptome levels are referred to as genomics. Geoclimatic regions (deshanupatini), familial characteristics (kulanupatini), and ethnicity (jatiprasakta) have all been shown to affect phenotypic variability. The combination of genomics with Ayurveda known as ayurgenomics provided new insights into tridosha that may pave the way for precision medicine (personalized medicine). Through successful coordination of “omics,” Prakriti-based treatments can help change the existing situation in health care. Prakriti refers to an individual’s behavioral trait, which is established at the moment of birth and cannot be fully altered during one’s existence. Ayurvedic methodologies are based on three Prakriti aspects: aushadhi (medication), vihara (lifestyle), and ahara (diet). A foundation of Prakriti-based medicine, preventative medicine, and improvement of life quality with longevity can be accomplished through these ayurvedic characteristics. In this perspective, we try to understand prakriti’s use in personalized medicine, and how to integrate it with programs for drug development and discovery.

Bibliometric review of ATAC-Seq and its application in gene expression

With recent advances in high-throughput next-generation sequencing, it is possible to describe the regulation and expression of genes at multiple levels. An assay for transposase-accessible chromatin using sequencing (ATAC-seq), which uses Tn5 transposase to sequence protein-free binding regions of the genome, can be combined with chromatin immunoprecipitation coupled with deep sequencing (ChIP-seq) and ribonucleic acid sequencing (RNA-seq) to provide a detailed description of gene expression. Here, we reviewed the literature on ATAC-seq and described the characteristics of ATAC-seq publications. We then briefly introduced the principles of RNA-seq, ChIP-seq and ATAC-seq, focusing on the main features of the techniques. We built a phylogenetic tree from species that had been previously studied by using ATAC-seq. Studies of Mus musculus and Homo sapiens account for approximately 90% of the total ATAC-seq data, while other species are still in the process of accumulating data. We summarized the findings from human diseases and other species, illustrating the cutting-edge discoveries and the role of multi-omics data analysis in current research. Moreover, we collected and compared ATAC-seq analysis pipelines, which allowed biological researchers who lack programming skills to better analyze and explore ATAC-seq data. Through this review, it is clear that multi-omics analysis and single-cell sequencing technology will become the mainstream approach in future research.

Identification of hub genes and pathways in bladder cancer using bioinformatics analysis

Bladder cancer (BC) is the most common malignant tumor of urinary tract system. The aim of this study was to investigate the genetic signatures of bladder cancer (BC) and identify its potential molecular mechanisms. The gene expression profiles of GSE3167 (50 samples, including 41BC and 9 non-cancerous urothelial cells) was downloaded from the GEO database. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) were performed to identify enriched pathways, and a protein-protein interaction (PPI) network was used to identify hub genes and for module analysis. Moreover, we conducted expression and survival analyses to screen and validate hub genes. In total, 1528 DEGs were identified in bladder cancer (BC), including 1212 up-regulated genes and 316 down-regulated genes. Up-regulated differentially expressed genes (DEGs) were significantly enriched in negative regulation of macromolecule metabolic process, macromolecule catabolic process, proteolysis and regulation of cell death, while the down-regulated differentially expressed genes (DEGs) were mainly involved in cell surface receptor linked signal transduction, ion transport, cell-cell signaling and defense response. The top 10 hub genes with the highest degrees were selected from the PPI network. These genes included HSP90AA1, MYH11, MYL9, CNN1, ACTC1, RAN, ENO1, HNRNPC, ACTG2 and YWHAZ. From sub-networks, we found these genes were involved in the proteasome, pathways in cancer and cell cycle. Hence, the identified DEGs and hub genes may be beneficial to elucidate the mechanisms underlying BC.

Multiomics approach reveals a role of translational machinery in shaping maize kernel amino acid composition

Maize (Zea mays) seeds are a good source of protein, despite being deficient in several essential amino acids. However, eliminating the highly abundant but poorly balanced seed storage proteins has revealed that the regulation of seed amino acids is complex and does not rely on only a handful of proteins. In this study, we used two complementary omics-based approaches to shed light on the genes and biological processes that underlie the regulation of seed amino acid composition. We first conducted a genome-wide association study to identify candidate genes involved in the natural variation of seed protein-bound amino acids. We then used weighted gene correlation network analysis to associate protein expression with seed amino acid composition dynamics during kernel development and maturation. We found that almost half of the proteome was significantly reduced during kernel development and maturation, including several translational machinery components such as ribosomal proteins, which strongly suggests translational reprogramming. The reduction was significantly associated with a decrease in several amino acids, including lysine and methionine, pointing to their role in shaping the seed amino acid composition. When we compared the candidate gene lists generated from both approaches, we found a nonrandom overlap of 80 genes. A functional analysis of these genes showed a tight interconnected cluster dominated by translational machinery genes, especially ribosomal proteins, further supporting the role of translation dynamics in shaping seed amino acid composition. These findings strongly suggest that seed biofortification strategies that target the translation machinery dynamics should be considered and explored further.

DNA Methylation and Atopic Diseases

The prevalence of allergic diseases such as asthma is globally increasing, posing threat to the life quality of the affected population. Genome-wide association studies (GWAS) suggest that genetic variations only account for a small proportion of immunoglobulin E (IgE)-mediated type I hypersensitivity. Recently, epigenetics has gained attention as an approach to further understand the missing heritability and underpinning mechanisms of allergic diseases. Furthermore, epigenetic regulation allows the evaluation of the interaction between an individual's genetic predisposition and their environmental exposures. This chapter summarizes several large-scale epigenome-wide association studies (EWAS) on asthma and other allergic diseases and draws a blueprint for future analysis and research direction.

Multi-omics data integration approaches for precision oncology

Next-generation sequencing (NGS) has been pivotal to enhance the molecular characterization of human malignancies, allowing multiple omics data types to be available for cancer researchers and practitioners. In this context,...

Gastric cancer and genomics: review of literature

Gastric cancer (GC) is a major health concern in many countries. GC is a heterogeneous disease stratified by histopathological differences. However, these variations are not used to determine GC management. Next-generation sequencing (NGS) technologies have become widely used, and cancer genomic analysis has recently revealed the relationships between various malignant tumors and genomic information. In 2014, studies using whole-exome sequencing (WES) and whole-genome sequencing (WGS) for GC revealed the entire structure of GC genomics. Genomics with NGS has been used to identify new therapeutic targets for GC. Moreover, personalized medicine to provide specific therapy for targets based on multiplex gene panel testing of tumor tissues has become of clinical use. Recently, immune checkpoint inhibitors (ICIs) have been used for GC treatment; however, their response rates are limited. To predict the anti-tumor effects of ICIs for GC and to select patients suitable for ICI treatment, genomics also provides informative data not only of tumors but also of tumor microenvironments, such as tumor-infiltrating lymphocytes. In therapeutic strategies for unresectable or recurrent malignant tumors, the target is not only the primary lesion but also metastatic lesions, and metastatic lesions are often resistant to chemotherapy. Unlike colorectal carcinoma, there is a heterogeneous status of genetic variants between the primary and metastatic lesions in GC. Liquid biopsy analysis is also helpful for predicting the genomic status of both primary and metastatic lesions. Genomics has become an indispensable tool for GC treatment and is expected to be further developed in the future.

Tumour suppressor TET2 safeguards enhancers from aberrant DNA methylation and epigenetic reprogramming in ERα-positive breast cancer cells

Aberrant DNA methylation is an epigenetic hallmark of malignant tumours. The DNA methylation level is regulated by not only DNA methyltransferases (DNMTs) but also Ten-Eleven Translocation (TET) family proteins. However, the exact role of TET genes in breast cancer remains controversial. Here, we uncover that the ERα-positive breast cancer patients with high TET2 mRNA expression had better overall survival rates. Consistently, knockout of TET2 promotes the tumorigenesis of ERα-positive MCF7 breast cancer cells. Mechanistically, TET2 loss leads to aberrant DNA methylation (gain of 5mC) at a large proportion of enhancers, accompanied by significant reduction in H3K4me1 and H3K27ac enrichment. By analysing the epigenetically reprogrammed enhancers, we identify oestrogen responsive element (ERE) as one of the enriched motifs of transcriptional factors. Importantly, TET2 loss impairs 17beta-oestradiol (E2)-induced transcription of the epigenetically reprogrammed EREs-associated genes through attenuating the binding of ERα. Taken together, these findings shed light on our understanding of the epigenetic mechanisms underlying the enhancer reprogramming during breast cancer pathogenesis.

Expression Profiles of Circulating microRNAs in South African Type 2 Diabetic Individuals on Treatment

Aim: The influence of disease duration and anti-diabetic treatment on epigenetic processes has been described, with limited focus on interactions with microRNAs (miRNAs). miRNAs have been found to play key roles in the regulation of pathways associated with type 2 diabetes mellitus (T2DM), and expression patterns in response to treatment may further promote their use as therapeutic targets in T2DM and its associated complications. We therefore aimed to investigate the expressions of circulating miRNAs (miR-30a-5p, miR-1299, miR-182-5p, miR-30e-3p and miR-126-3p) in newly diagnosed and known diabetics on treatment, in South Africa.

Methods: A total of 1254 participants with an average age of 53.8years were included in the study and classified according to glycaemic status (974 normotolerant, 92 screen-detected diabetes and 188 known diabetes). Whole blood levels of miR-30a-5p, miR-1299, miR-182-5p, miR-30e-3p and miR-126-3p were quantitated using RT-qPCR. Expression analysis was performed and compared across groups.

Results: All miRNAs were significantly overexpressed in subjects with known diabetes when compared to normotolerant individuals, as well as known diabetics vs. screen-detected (p<0.001). Upon performing regression analysis, of all miRNAs, only miR-182-5p remained associated with the duration of the disease after adjustment for type of treatment (OR: 0.127, CI: 0.018–0.236, p=0.023).

Conclusion: Our findings revealed important associations and altered expression patterns of miR-30a-5p, miR-1299, miR-182-5p, miR-30e-3p and miR-126-3p in known diabetics on anti-diabetic treatment compared to newly diagnosed individuals. Additionally, miR-182-5p expression decreased with increasing duration of T2DM. Further studies are, however, recommended to shed light on the involvement of the miRNA in insulin signalling and glucose homeostasis, to endorse its use as a therapeutic target in DM and its associated complications.

A Critical Review of the Current Global Ex Situ Conservation System for Plant Agrobiodiversity. II. Strengths and Weaknesses of the Current System and Recommendations for Its Improvement

In this paper, we review gene bank operations that have an influence on the global conservation system, with the intention to identify critical aspects that should be improved for optimum performance. We describe the role of active and base collections and the importance of linking germplasm conservation and use, also in view of new developments in genomics and phenomics that facilitate more effective and efficient conservation and use of plant agrobiodiversity. Strengths, limitations, and opportunities of the existing global ex situ conservation system are discussed, and measures are proposed to achieve a rational, more effective, and efficient global system for germplasm conservation and sustainable use. The proposed measures include filling genetic and geographic gaps in current ex situ collections; determining unique accessions at the global level for long-term conservation in virtual base collections; intensifying existing international collaborations among gene banks and forging collaborations with the botanic gardens community; increasing investment in conservation research and user-oriented supportive research; improved accession-level description of the genetic diversity of crop collections; improvements of the legal and policy framework; and oversight of the proposed network of global base collections.

Unraveling the versatility of CCD4: Metabolic engineering, transcriptomic and computational approaches

Carotenoids are economically valuable isoprenoids synthesized by plants and microorganisms, which play a paramount role in their overall growth and development. Carotenoid cleavage dioxygenases are a vast group of enzymes that specifically cleave thecarotenoids to produce apocarotenoids. Recently, CCDs are a subject of talk because of their contributions to different aspects of plant growth and due to their significance in the production of economically valuable apocarotenoids. Among them, CCD4 stands unique because of its versatility in performing metabolic roles. This review focuses on the multiple functionalities of CCD4 like pigmentation, volatile apocarotenoid production, stress responses, etc. Interestingly, through our literature survey we arrived at a conclusion that CCD4 could perform functions of other carotenoid cleaving enzymes.The metabolic engineering, transcriptomic, and computational approaches adopted to reveal the contributions of CCD4 were also considered here for the study.Phylogenetic analysis was performed to delve into the evolutionary relationships of CCD4 in different plant groups. A tree of 81CCD genes from 64 plant species was constructed, signifying the presence of well-conserved families. Gene structures were illustrated and the difference in the number and position of exons could be considered as a factor behind functional versatility and substrate tolerance of CCD4 in different plants.

What can we teach Lymnaea and what can Lymnaea teach us?

This review describes the advantages of adopting a molluscan complementary model, the freshwater snail Lymnaea stagnalis, to study the neural basis of learning and memory in appetitive and avoidance classical conditioning; as well as operant conditioning of its aerial respiratory and escape behaviour. We firstly explored 'what we can teach Lymnaea' by discussing a variety of sensitive, solid, easily reproducible and simple behavioural tests that have been used to uncover the memory abilities of this model system. Answering this question will allow us to open new frontiers in neuroscience and behavioural research to enhance our understanding of how the nervous system mediates learning and memory. In fact, from a translational perspective, Lymnaea and its nervous system can help to understand the neural transformation pathways from behavioural output to sensory coding in more complex systems like the mammalian brain. Moving on to the second question: 'what can Lymnaea teach us?', it is now known that Lymnaea shares important associative learning characteristics with vertebrates, including stimulus generalization, generalization of extinction and discriminative learning, opening the possibility to use snails as animal models for neuroscience translational research.

Revisiting social MPE: an integration of molecular pathological epidemiology and social science in the new era of precision medicine

INTRODUCTION

Molecular pathological epidemiology (MPE) is an integrative transdisciplinary area examining the relationships between various exposures and pathogenic signatures of diseases. In line with the accelerating advancements in MPE, social science and its health-related interdisciplinary areas have also developed rapidly. Accumulating evidence indicates the pathological role of social-demographic factors. We therefore initially proposed social MPE in 2015, which aims to elucidate etiological roles of social-demographic factors and address health inequalities globally. With the ubiquity of molecular diagnosis, there are ample opportunities for researchers to utilize and develop the social MPE framework.

AREAS COVERED

Molecular subtypes of breast cancer have been investigated rigorously for understanding its etiologies rooted from social factors. Emerging evidence indicates pathogenic heterogeneity of neurological disorders such as Alzheimer's disease. Presenting specific patterns of social-demographic factors across different molecular subtypes should be promising for advancing the screening, prevention, and treatment strategies of those heterogeneous diseases. This article rigorously reviewed literatures investigating differences of race/ethnicity and socioeconomic status across molecular subtypes of breast cancer and Alzheimer's disease to date.

EXPERT OPINION

With advancements of the multi-omics technologies, we foresee a blooming of social MPE studies, which can address health disparities, advance personalized molecular medicine, and enhance public health.

A novel transcriptional risk score for risk prediction of complex human diseases

Recently polygenetic risk score (PRS) has been successfully used in the risk prediction of complex human diseases. Many studies incorporated internal information, such as effect size distribution, or external information, such as linkage disequilibrium, functional annotation, and pleiotropy among multiple diseases, to optimize the performance of PRS. To leverage on multiomics datasets, we developed a novel flexible transcriptional risk score (TRS), in which messenger RNA expression levels were imputed and weighted for risk prediction. In simulation studies, we demonstrated that single-tissue TRS has greater prediction power than LDpred, especially when there is a large effect of gene expression on the phenotype. Multitissue TRS improves prediction accuracy when there are multiple tissues with independent contributions to disease risk. We applied our method to complex traits, including Crohn's disease, type 2 diabetes, and so on. The single-tissue TRS method outperformed LDpred and AnnoPred across the tested traits. The performance of multitissue TRS is trait-dependent. Moreover, our method can easily incorporate information from epigenomic and proteomic data upon the availability of reference datasets.

Understanding the Gastrointestinal Protective Effects of Polyphenols using Foodomics-Based Approaches

Plant polyphenols are rich sources of natural anti-oxidants and prebiotics. After ingestion, most polyphenols are absorbed in the intestine and interact with the gut microbiota and modulated metabolites produced by bacterial fermentation, such as short-chain fatty acids (SCFAs). Dietary polyphenols immunomodulatory role by regulating intestinal microorganisms, inhibiting the etiology and pathogenesis of various diseases including colon cancer, colorectal cancer, inflammatory bowel disease (IBD) and colitis. Foodomics is a novel high-throughput analysis approach widely applied in food and nutrition studies, incorporating genomics, transcriptomics, proteomics, metabolomics, and integrating multi-omics technologies. In this review, we present an overview of foodomics technologies for identifying active polyphenol components from natural foods, as well as a summary of the gastrointestinal protective effects of polyphenols based on foodomics approaches. Furthermore, we critically assess the limitations in applying foodomics technologies to investigate the protective effect of polyphenols on the gastrointestinal (GI) system. Finally, we outline future directions of foodomics techniques to investigate GI protective effects of polyphenols. Foodomics based on the combination of several analytical platforms and data processing for genomics, transcriptomics, proteomics and metabolomics studies, provides abundant data and a more comprehensive understanding of the interactions between polyphenols and the GI tract at the molecular level. This contribution provides a basis for further exploring the protective mechanisms of polyphenols on the GI system.

SPEAQeasy: a scalable pipeline for expression analysis and quantification for R/bioconductor-powered RNA-seq analyses

BACKGROUND

RNA sequencing (RNA-seq) is a common and widespread biological assay, and an increasing amount of data is generated with it. In practice, there are a large number of individual steps a researcher must perform before raw RNA-seq reads yield directly valuable information, such as differential gene expression data. Existing software tools are typically specialized, only performing one step-such as alignment of reads to a reference genome-of a larger workflow. The demand for a more comprehensive and reproducible workflow has led to the production of a number of publicly available RNA-seq pipelines. However, we have found that most require computational expertise to set up or share among several users, are not actively maintained, or lack features we have found to be important in our own analyses.

RESULTS

In response to these concerns, we have developed a Scalable Pipeline for Expression Analysis and Quantification (SPEAQeasy), which is easy to install and share, and provides a bridge towards R/Bioconductor downstream analysis solutions. SPEAQeasy is portable across computational frameworks (SGE, SLURM, local, docker integration) and different configuration files are provided ( http://research.libd.org/SPEAQeasy/ ).

CONCLUSIONS

SPEAQeasy is user-friendly and lowers the computational-domain entry barrier for biologists and clinicians to RNA-seq data processing as the main input file is a table with sample names and their corresponding FASTQ files. The goal is to provide a flexible pipeline that is immediately usable by researchers, regardless of their technical background or computing environment.

A study of the differential expression profiles of Keshan disease lncRNA/mRNA genes based on RNA-seq

Background

This study aims to analyze the differential expression profiles of lncRNA in Keshan disease (KSD) and to explore the molecular mechanism of the disease occurrence and development.

Methods

RNA-seq technology was used to construct the lncRNA/mRNA expression library of a KSD group (n=10) and a control group (n=10), and then Cuffdiff software was used to obtain the gene lncRNA/mRNA FPKM value as the expression profile of lncRNA/mRNA. The fold changes between the two sets of samples were calculated to obtain differential lncRNA/mRNA expression profiles, and a bioinformatics analysis of differentially expressed genes was performed.

Results

A total of 89,905 lncRNAs and 20,315 mRNAs were detected. Statistical analysis revealed that 921 lncRNAs had obvious differential expression, among which 36 were up-regulated and 885 were down-regulated; 2,771 mRNAs presented with obvious differential expression, among which 253 were up-regulated and 2,518 were down-regulated, and cluster analysis indicated that the gene expression trends among the sample groups were consistent. The differentially expressed lncRNAs were tested for target genes, and 117 genes were found to be regulated by differential lncRNAs, which were concentrated in six signaling pathways, among which the apoptosis FoxO signaling pathway ranked first, so the target genes IGF1R and TGFB2 were screened out.

Conclusions

In this study, RNA-seq technology was used to obtain the differential gene expression profiles of KSD, and bioinformatics analysis was performed to screen out target genes, pointing out the direction for further research into the etiology, pathogenesis and drug treatment targets of KSD.

Establishment of bioinformatics pipeline for deciphering the biological complexities of fragmented sperm transcriptome

Despite the development of several tools for the analysis of the transcriptome data, non-availability of a standard pipeline for analyzing the low quality and fragmented mRNA samples pose a major challenge to the computational molecular biologist for effective interpretation of the data. Hence the present study aimed to establish a bioinformatics pipeline for analyzing the biologically fragmented sperm RNA. Sperm transcriptome data (2 x 75 PE sequencing) generated from bulls (n = 8) of high-fertile (n = 4) and low-fertile (n = 4) classified based on the fertility rate (41.52 ± 1.07 vs 36.04 ± 1.04%) were analyzed with different bioinformatics tools for alignment, quantitation, and differential gene expression studies. TopHat2 was effectual compared to HISAT2 and STAR for sperm mRNA due to the higher exonic (6% vs 2%) mapping percentage and quantitating the low expressed genes. TopHat2 also had significantly strong correlation with STAR (0.871, p = 0.05) and HISAT2 (0.933, p = 0.01). TopHat2 and Cufflinks combo quantitated the number of genes higher than the other combinations. Among the tools (Cuffdiff, DESeq, DESeq2, edgeR, and limma) used for the differential gene expression analysis, edgeR and limma identified the largest number of significantly differentially expressed genes (p < 0.05) with biological relevance. The concordance analysis concurred that edgeR had an edge over the other tools. It also identified a higher number (9.5%) of fertility-related genes to be differentially expressed between the two groups. The present study established that TopHat2, Cufflinks, and edgeR as a suitable pipeline for the analysis of fragmented mRNA from bovine spermatozoa.

ColorCells: a database of expression, classification and functions of lncRNAs in single cells

Although long noncoding RNAs (lncRNAs) have significant tissue specificity, their expression and variability in single cells remain unclear. Here, we developed ColorCells (http://rna.sysu.edu.cn/colorcells/), a resource for comparative analysis of lncRNAs expression, classification and functions in single-cell RNA-Seq data. ColorCells was applied to 167 913 publicly available scRNA-Seq datasets from six species, and identified a batch of cell-specific lncRNAs. These lncRNAs show surprising levels of expression variability between different cell clusters, and has the comparable cell classification ability as known marker genes. Cell-specific lncRNAs have been identified and further validated by in vitro experiments. We found that lncRNAs are typically co-expressed with the mRNAs in the same cell cluster, which can be used to uncover lncRNAs' functions. Our study emphasizes the need to uncover lncRNAs in all cell types and shows the power of lncRNAs as novel marker genes at single cell resolution.

Cardioinformatics: the nexus of bioinformatics and precision cardiology

Cardiovascular disease (CVD) is the leading cause of death worldwide, causing over 17 million deaths per year, which outpaces global cancer mortality rates. Despite these sobering statistics, most bioinformatics and computational biology research and funding to date has been concentrated predominantly on cancer research, with a relatively modest footprint in CVD. In this paper, we review the existing literary landscape and critically assess the unmet need to further develop an emerging field at the multidisciplinary interface of bioinformatics and precision cardiovascular medicine, which we refer to as 'cardioinformatics'.

Feasibility and clinical impact of routine molecular testing of gastrointestinal cancers at a tertiary centre with a multi-gene, tumor-agnostic, next generation sequencing panel

BACKGROUND

High-throughput sequencing technologies are increasingly used in research but limited data are available on the feasibility and value of these when routinely adopted in clinical practice.

MATERIAL AND METHODS

We analyzed all consecutive cancer patients for whom genomic testing by a 48-gene next-generation sequencing (NGS) panel (Truseq Amplicon Cancer Panel, Illumina) was requested as part of standard care in one of the largest Belgian cancer networks between 2014 and 2019. Feasibility of NGS was assessed in all study patients, while the impact of NGS on the decision making was analyzed in the group of gastrointestinal cancer patients.

RESULTS

Tumor samples from 1064 patients with varying tumor types were tested, the number of NGS requests increasing over time (p < .0001). Success rate and median turnaround time were 91.4% and 12.5 days, respectively, both significantly decreasing over time (p ≤ .0002). Non-surgical sampling procedure (OR 7.97, p < .0001), tissue from metastatic site (OR 2.35, p = .0006) and more recent year of testing (OR 1.79, p = .0258) were independently associated with NGS failure. Excluding well-known actionable or clinically relevant mutations which are recommended by international guidelines and commonly tested by targeted sequencing, 57/279 (20.4%) assessable gastrointestinal cancer patients were found to have tumors harboring at least one actionable altered gene according to the OncoKB database. NGS results, however, had a direct impact on management decisions by the treating physician in only 3 cases (1.1%).

CONCLUSIONS

Our findings confirm that NGS is feasible in the clinical setting with acceptably low failure rates and rapid turnaround time. In gastrointestinal cancers, however, NGS-based multiple-gene testing adds very little to standard targeted sequencing, and in routine practice the clinical impact of NGS panels including genes which are not routinely recommended by international guidelines remains limited.

Clover: a clustering-oriented de novo assembler for Illumina sequences

Background

Next-generation sequencing technologies revolutionized genomics by producing high-throughput reads at low cost, and this progress has prompted the recent development of de novo assemblers. Multiple assembly methods based on de Bruijn graph have been shown to be efficient for Illumina reads. However, the sequencing errors generated by the sequencer complicate analysis of de novo assembly and influence the quality of downstream genomic researches.

Results

In this paper, we develop a de Bruijn assembler, called Clover (clustering-oriented de novo assembler), that utilizes a novel k-mer clustering approach from the overlap-layout-consensus concept to deal with the sequencing errors generated by the Illumina platform. We further evaluate Clover’s performance against several de Bruijn graph assemblers (ABySS, SOAPdenovo, SPAdes and Velvet), overlap-layout-consensus assemblers (Bambus2, CABOG and MSR-CA) and string graph assembler (SGA) on three datasets (Staphylococcus aureus, Rhodobacter sphaeroides and human chromosome 14). The results show that Clover achieves a superior assembly quality in terms of corrected N50 and E-size while remaining a significantly competitive in run time except SOAPdenovo. In addition, Clover was involved in the sequencing projects of bacterial genomes Acinetobacter baumannii TYTH-1 and Morganella morganii KT.

Conclusions

The marvel clustering-based approach of Clover that integrates the flexibility of the overlap-layout-consensus approach and the efficiency of the de Bruijn graph method has high potential on de novo assembly. Now, Clover is freely available as open source software from https://oz.nthu.edu.tw/~d9562563/src.html.

Identification and expression analysis of novel splice variants of the human carcinoembryonic antigen-related cell adhesion molecule 19 (CEACAM19) gene using a high-throughput sequencing approach

Alternative splicing is commonly involved in carcinogenesis, being highly implicated in differential expression of cancer-related genes. Recent studies have shown that the human CEACAM19 gene is overexpressed in malignant breast and ovarian tumors, possessing significant biomarker attributes. In the present study, 3' rapid amplification of cDNA ends (3' RACE) and next-generation sequencing (NGS) were used for the detection and identification of novel CEACAM19 transcripts. Bioinformatical analysis of our NGS data revealed novel splice junctions between previously annotated exons and ultimately new exons. Next, fifteen novel CEACAM19 transcripts were identified with Sanger sequencing. Additionally, their expression profile was investigated in a wide panel of human cell lines, using nested PCR with variant-specific primers. The broad expression pattern of the CEACAM19 gene, along with the fact that its overexpression has previously been associated with ovarian and breast cancer progression, indicate the potential of novel CEACAM19 transcripts as putative diagnostic and/or prognostic biomarkers.

PathME: pathway based multi-modal sparse autoencoders for clustering of patient-level multi-omics data

Background

Recent years have witnessed an increasing interest in multi-omics data, because these data allow for better understanding complex diseases such as cancer on a molecular system level. In addition, multi-omics data increase the chance to robustly identify molecular patient sub-groups and hence open the door towards a better personalized treatment of diseases. Several methods have been proposed for unsupervised clustering of multi-omics data. However, a number of challenges remain, such as the magnitude of features and the large difference in dimensionality across different omics data sources.

Results

We propose a multi-modal sparse denoising autoencoder framework coupled with sparse non-negative matrix factorization to robustly cluster patients based on multi-omics data. The proposed model specifically leverages pathway information to effectively reduce the dimensionality of omics data into a pathway and patient specific score profile. In consequence, our method allows us to understand, which pathway is a feature of which particular patient cluster. Moreover, recently proposed machine learning techniques allow us to disentangle the specific impact of each individual omics feature on a pathway score. We applied our method to cluster patients in several cancer datasets using gene expression, miRNA expression, DNA methylation and CNVs, demonstrating the possibility to obtain biologically plausible disease subtypes characterized by specific molecular features. Comparison against several competing methods showed a competitive clustering performance. In addition, post-hoc analysis of somatic mutations and clinical data provided supporting evidence and interpretation of the identified clusters.

Conclusions

Our suggested multi-modal sparse denoising autoencoder approach allows for an effective and interpretable integration of multi-omics data on pathway level while addressing the high dimensional character of omics data. Patient specific pathway score profiles derived from our model allow for a robust identification of disease subgroups.

Promoter-Level Transcriptome Identifies Stemness Associated With Relatively High Proliferation in Pancreatic Cancer Cells

Both pancreatic intraepithelial neoplasia (PanIN), a frequent precursor of pancreatic cancer, and intraductal papillary mucinous neoplasm (IPMN), a less common precursor, undergo several phases of molecular conversions and finally develop into highly malignant solid tumors with negative effects on the quality of life. We approached this long-standing issue by examining the following PanIN/IPMN cell lines derived from mouse models of pancreatic cancer: Ptf1a-Cre; KrasG12D; p53f/+ and Ptf1a-Cre; KrasG12D; and Brg1f/f pancreatic ductal adenocarcinomas (PDAs). The mRNA from these cells was subjected to a cap analysis of gene expression (CAGE) to map the transcription starting sites and quantify the expression of promoters across the genome. Two RNA samples extracted from three individual subcutaneous tumors generated by the transplantation of PanIN or IPMN cancer cell lines were used to generate libraries and Illumina Seq, with four RNA samples in total, to depict discrete transcriptional network between IPMN and PanIN. Moreover, in IPMN cells, the transcriptome tended to be enriched for suppressive and inhibitory biological processes. In contrast, the transcriptome of PanIN cells exhibited properties of stemness. Notably, the proliferation capacity of the latter cells in culture was only minimally constrained by well-known chemotherapy drugs such as GSK690693 and gemcitabine. The various transcriptional factor network systems detected in PanIN and IPMN cells reflect the distinct molecular profiles of these cell types. Further, we hope that these findings will enhance our mechanistic understanding of the characteristic molecular alterations underlying pancreatic cancer precursors. These data may provide a promising direction for therapeutic research.

Integrative factorization of bidimensionally linked matrices

Advances in molecular "omics" technologies have motivated new methodologies for the integration of multiple sources of high-content biomedical data. However, most statistical methods for integrating multiple data matrices only consider data shared vertically (one cohort on multiple platforms) or horizontally (different cohorts on a single platform). This is limiting for data that take the form of bidimensionally linked matrices (eg, multiple cohorts measured on multiple platforms), which are increasingly common in large-scale biomedical studies. In this paper, we propose bidimensional integrative factorization (BIDIFAC) for integrative dimension reduction and signal approximation of bidimensionally linked data matrices. Our method factorizes data into (a) globally shared, (b) row-shared, (c) column-shared, and (d) single-matrix structural components, facilitating the investigation of shared and unique patterns of variability. For estimation, we use a penalized objective function that extends the nuclear norm penalization for a single matrix. As an alternative to the complicated rank selection problem, we use results from the random matrix theory to choose tuning parameters. We apply our method to integrate two genomics platforms (messenger RNA and microRNA expression) across two sample cohorts (tumor samples and normal tissue samples) using the breast cancer data from the Cancer Genome Atlas. We provide R code for fitting BIDIFAC, imputing missing values, and generating simulated data.

High-Throughput Architecture for Discovering Combination Cancer Therapeutics

PURPOSE

The amount of available next-generation sequencing data of tumors, in combination with relevant molecular and clinical data, has significantly increased in the last decade and transformed translational cancer research. Even with the progress made through data-sharing initiatives, there is a clear unmet need for easily accessible analyses tools. These include capabilities to efficiently process large sequencing database projects to present them in a straightforward and accurate way. Another urgent challenge in cancer research is to identify more effective combination therapies.

METHODS

We have created a software architecture that allows the user to integrate and analyze large-scale sequencing, clinical, and other datasets for efficient prediction of potential combination drug targets. This architecture permits predictions for all genes pairs; however, Food and Drug Administration-approved agents are currently lacking for most of the identified gene targets.

RESULTS

By applying this approach, we performed a comprehensive study and analyzed all possible combination partners and identified potentially synergistic target pairs for 38 approved targets currently in clinical use. We further showed which genes could be synergistic prediction markers and potential targets with MAPK/ERK inhibitors for the treatment of melanoma. Moreover, we integrated a graph analytics technique in this architecture to identify pathways that could be targeted synergistically to enhance the efficacy of certain therapeutics in cancer.

CONCLUSION

The architecture and the results presented provide a foundation for discovering effective combination therapeutics.

Microdialysis Workflow for Metabotyping Superficial Pathologies: Application to Burn Injury

Burn injury can be a devastating traumatic injury, with long-term personal and social implications for the patient. The many complex local and disseminating pathological processes underlying burn injury's clinical challenges are orchestrated from the site of injury and develop over time, yet few studies of the molecular basis of these mechanisms specifically explore the local signaling environment. Those that do are typically destructive in nature and preclude the collection of longitudinal temporal data. Burn injury therefore exemplifies a superficial temporally dynamic pathology for which experimental sampling typically prioritizes either specificity to the local burn site or continuous collection from circulation. Here, we present an exploratory approach to the targeted elucidation of complex, local, acutely temporally dynamic interstitia through its application to burn injury. Subcutaneous microdialysis is coupled with ultraperformance liquid chromatography-mass spectrometry (UPLC-MS) analysis, permitting the application of high-throughput metabolomic profiling to samples collected both continuously and specifically from the burn site. We demonstrate this workflow's high yield of burn-altered metabolites including the complete structural elucidation of niacinamide and uric acid, two compounds potentially involved in the pathology of burn injury. Further understanding the metabolic changes induced by burn injury will help to guide therapeutic intervention in the future. This approach is equally applicable to the analysis of other tissues and pathological conditions, so it may further improve our understanding of the metabolic changes underlying a wide variety of pathological processes.