High-throughput Genomic Technologies Research Articles

Revealing genomic heterogeneity and commonality: A penalized integrative analysis approach accounting for the adjacency structure of measurements.

Advancements in high-throughput genomic technologies have revolutionized the field of disease biomarker identification by providing large-scale genomic data. There is an increasing focus on understanding the relationships among diverse patient groups with distinct disease subtypes and characteristics. Complex diseases exhibit both heterogeneity and shared genomic factors, making it essential to investigate these patterns to accurately detect markers and comprehensively understand the diseases. Integrative analysis has emerged as a promising approach to address this challenge. However, existing studies have been limited by ignoring the adjacency structure of genomic measurements, such as single nucleotide polymorphisms (SNPs) and DNA methylations. In this study, we propose a structured integrative analysis method that incorporates a spline type penalty to accommodate this adjacency structure. We utilize a fused lasso type penalty to identify both heterogeneity and commonality across the groups. Extensive simulations demonstrate its superiority compared to several direct competing methods. The analysis of The Cancer Genome Atlas melanoma data with DNA methylation measurements and GENEVA diabetes data with SNP measurements exhibit that the proposed analysis lead to meaningful findings with better prediction performance and higher selection stability.

Mutation-based therapies for intrahepatic cholangiocarcinoma: new options on the horizon

Intrahepatic cholangiocarcinoma (ICC), a rare but rising global malignancy originating from the bile ducts, poses significant challenges in terms of effective treatment and patient outcomes. While surgical excision remains the curative option, its limited efficacy necessitates more therapeutic strategies, including systemic therapies. The management of ICC involves a multidisciplinary approach, with treatment decisions guided by patient-specific and tumor-specific factors. Gemcitabine-cisplatin (GEMCIS) chemotherapy has been a standard first-line therapy, but recent advancements in immunotherapy, particularly the introduction of durvalumab, have provided new hope. Additionally, gene mutation-based therapies, targeting fibroblast growth factor receptors (FGFRs), isocitrate dehydrogenase-1 (IDH1), human epidermal growth factor receptor-2 (HER2), and B-RAF proto-oncogene (BRAF), offer promising prospects for personalized treatment. High-throughput genomic profiling technologies have facilitated the identification of actionable targets and the development of innovative therapeutic approaches. This review summarizes the mutation-based therapies in ICC, including FDA-approved targeted drugs and ongoing clinical trials, highlighting the evolving landscape of ICC treatment.

Joint inference of clonal structure using single-cell genome and transcriptome sequencing data.

Latest advancements in the high-throughput single-cell genome (scDNA) and transcriptome (scRNA) sequencing technologies enabled cell-resolved investigation of tissue clones. However, it remains challenging to cluster and couple single cells for heterogeneous scRNA and scDNA data generated from the same specimen. In this study, we present a computational framework called CCNMF, which employs a novel Coupled-Clone Non-negative Matrix Factorization technique to jointly infer clonal structure for matched scDNA and scRNA data. CCNMF couples multi-omics single cells by linking copy number and gene expression profiles through their general concordance. It successfully resolved the underlying coexisting clones with high correlations between the clonal genome and transcriptome from the same specimen. We validated that CCNMF can achieve high accuracy and robustness using both simulated benchmarks and real-world applications, including an ovarian cancer cell lines mixture, a gastric cancer cell line, and a primary gastric cancer. In summary, CCNMF provides a powerful tool for integrating multi-omics single-cell data, enabling simultaneous resolution of genomic and transcriptomic clonal architecture. This computational framework facilitates the understanding of how cellular gene expression changes in conjunction with clonal genome alternations, shedding light on the cellular genomic difference of subclones that contributes to tumor evolution.

Prospects for New Antibiotics Discovered through Genome Analysis

Abstract: Antibiotic-resistant illnesses are on the rise worldwide, and the pipeline for developing new antibiotics is drying up. As a result, researchers need to create novel compounds with antimicrobial action. Recent decades have seen a dearth of novel antibiotics because of the reliance on conventional empirical screening procedures using both natural and synthetic chemicals to find them. There is hope that the massive amount of bacterial genome sequence data that has become accessible since the sequencing of the first bacterial genome more than 20 years ago might help lead to the development of new antibiotic drugs. Genes with significant levels of conservation both within and between bacterial species can be found using comparative genomic techniques; these genes may be involved in essential bacterial functions. Bioactive chemicals found in natural products have been successfully used in treating everything from infectious diseases to cancer, but over the past 20-30 years, the effectiveness of screening methods based on fermentation has decreased. Researchers urgently need answers to the unmet demand for bacterial infection resistance. Now more than ever, with the advent of cheap, high-throughput genomic sequencing technology, natural product discovery can be revitalized. Using bioinformatics, investigators may foretell whether or not a certain microbial strain would generate compounds with novel chemical structures, which may have novel modes of action in inhibiting bacterial growth. This manuscript describes how this potential might be utilised, with a particular emphasis on manipulating the expression of dormant biosynthetic gene clusters that are hypothesised to encode new antibiotics. Additionally, it consolidates the work of the past and the present to utilise bacterial genomic data in the identification and development of new antibiotics.

Genomic approaches to enhance adaptive plasticity to cope with soil constraints amidst climate change in wheat.

Climate change is varying the availability of resources, soil physicochemical properties, and rainfall events, which collectively determines soil physical and chemical properties. Soil constraints-acidity (pH<6), salinity (pH≤8.5), sodicity, and dispersion (pH>8.5)-are major causes of wheat yield loss in arid and semiarid cropping systems. To cope with changing environments, plants employ adaptive strategies such as phenotypic plasticity, a key multifaceted trait, to promote shifts in phenotypes. Adaptive strategies for constrained soils are complex, determined by key functional traits and genotype×environment×management interactions. The understanding of the molecular basis of stress tolerance is particularly challenging for plasticity traits. Advances in sequencing and high-throughput genomics technologies have identified functional alleles in gene-rich regions, haplotypes, candidate genes, mechanisms, and in silico gene expression profiles at various growth developmental stages. Our review focuses on favorable alleles for enhanced gene expression, quantitative trait loci, and epigenetic regulation of plant responses to soil constraints, including heavy metal stress and nutrient limitations. A strategy is then described for quantitative traits in wheat by investigating significant alleles and functional characterization of variants, followed by gene validation using advanced genomic tools, and marker development for molecular breeding and genome editing. Moreover, the review highlights the progress of gene editing in wheat, multiplex gene editing, and novel alleles for smart control of gene expression. Application of these advanced genomic technologies to enhance plasticity traits along with soil management practices will be an effective tool to build yield, stability, and sustainability on constrained soils in the face of climate change.

Idiopathic pulmonary fibrosis and the role of genetics in the era of precision medicine.

Idiopathic pulmonary fibrosis (IPF) is a chronic, rare progressive lung disease, characterized by lung scarring and the irreversible loss of lung function. Two anti-fibrotic drugs, nintedanib and pirfenidone, have been demonstrated to slow down disease progression, although IPF mortality remains a challenge and the patients die after a few years from diagnosis. Rare pathogenic variants in genes that are involved in the surfactant metabolism and telomere maintenance, among others, have a high penetrance and tend to co-segregate with the disease in families. Common recurrent variants in the population with modest effect sizes have been also associated with the disease risk and progression. Genome-wide association studies (GWAS) support at least 23 genetic risk loci, linking the disease pathogenesis with unexpected molecular pathways including cellular adhesion and signaling, wound healing, barrier function, airway clearance, and innate immunity and host defense, besides the surfactant metabolism and telomere biology. As the cost of high-throughput genomic technologies continuously decreases and new technologies and approaches arise, their widespread use by clinicians and researchers is efficiently contributing to a better understanding of the pathogenesis of progressive pulmonary fibrosis. Here we provide an overview of the genetic factors known to be involved in IPF pathogenesis and discuss how they will continue to further advance in this field. We also discuss how genomic technologies could help to further improve IPF diagnosis and prognosis as well as for assessing genetic risk in unaffected relatives. The development and validation of evidence-based guidelines for genetic-based screening of IPF will allow redefining and classifying this disease relying on molecular characteristics and contribute to the implementation of precision medicine approaches.

Circular RNAs in eukaryotic cells: origin, characteristics, mechanisms of molecular functioning in human malignant diseases

Circular RNAs (circRNAs) are an evolutionarily conserved novel class of non-coding endogenous RNAs (ncRNAs) found in the eukaryotic transcriptome, originally believed to be aberrant RNA splicing by-products with limited functionality.&#x0D; However, recent advances in highthroughput genomic technology have allowed circRNAs to be characterized in detail and revealed their important functions in controlling various biological and molecular processes, the most essential being gene regulation. Due to structural stability, high expression, availability of microRNA (miRNA) binding sites and tissue-specific expression, circRNAs have become hot topic of research in RNA 2 biology. Unlike linear RNAs, circRNAs are produced differentially by backsplicing exons or lariat introns from a pre-messenger RNA (mRNA) forming covalently closed loop-like molecules missing 3' poly-(A) tail or 5' cap structures, thus rendering them resistant to exonuclease-mediated degradation.&#x0D; Previous studies have revealed multiple roles of circRNAs as sponges for miRNA and RNA-binding proteins (RBP), as well as regulators of transcription, translation, and splicing events. Recent advances in the field suggest that the circRNAs are involved in many human disorders, including cancer and neurodegenerative disorders such as Alzheimers and Parkinsons disease, due to their aberrant expression in different pathological conditions. The circRNAs are stable in cells, owing to their circular structure. Participation of circRNAs in programmed cellular destruction by autophagy is discussed in details. The autophagy is a catabolic process which promotes decomposition and recycling of harmful or redundant biological macromolecules and initiates destruction of ageing cells. Processes how circRNAs influence a course of a disease, including an autophagy are in detail discussed, specifying that it joins at the beginning and upon development of various illnesses, and it can influence drug resistance (for example, antitumor efficiency of Cisplatin).&#x0D; The functional versatility exhibited by circRNAs enables them to serve as potential diagnostic or predictive biomarkers for various diseases. This review discusses the properties, characterization, profiling, and the diverse molecular actions of circRNAs and their usage as potential therapeutic targets in different human malignancies.

Abstract IA007: Does cancer cell state matter? Moving from DNA genotype to RNA phenotype-directed therapies in cancer

Abstract Recent advances in high throughput genomic sequencing technologies have led to a detailed understanding of the genetic alterations that underlie human tumors. However, evidence increasingly indicates that using mutations alone to assign therapies has its limitations, even for cancers with actionable mutational heterogeneity. The advent of single-cell genomic technologies has confirmed extensive mutational heterogeneity in human tumors but also revealed that the complexity of cancer extends to variation in cell transcriptional state. Deciphering whether transcriptional variation informs treatment response heterogeneity represents a new but poorly understood frontier in cancer therapeutics. In pancreatic ductal adenocarcinoma (PDAC), clinically relevant RNA expression states exist but our understanding of their drivers, stability, and relationship to therapeutic response is limited. To examine these attributes systematically, we profiled metastatic biopsies and matched organoid models at single-cell resolution. We identify a new intermediate PDAC transcriptional cell state and uncover distinct site- and state-specific tumor microenvironments. Moreover, we reveal strong organoid culture-specific biases in cancer cell transcriptional state representation and nominate critical factors missing from the ex vivo microenvironment. By adding back specific factors, we restore in vivo expression state heterogeneity and show plasticity in culture models, demonstrating that microenvironmental signals are critical regulators of cell state. Importantly, we prove that non-genetic modulation of cell state can significantly influence drug responses and uncover state-specific vulnerabilities. Our work provides a broadly applicable framework for mapping cell states across in vivo and ex vivo settings, identifying drivers of transcriptional plasticity, and manipulating cell state to target its associated vulnerabilities. Citation Format: Alex K. Shalek. Does cancer cell state matter? Moving from DNA genotype to RNA phenotype-directed therapies in cancer [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer; 2022 Sep 13-16; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2022;82(22 Suppl):Abstract nr IA007.

Predicting prediction: A systematic workflow to analyze factors affecting the classification performance in genomic biomarker discovery.

High throughput technologies in genomics enable the analysis of small alterations in gene expression levels. Patterns of such deviations are an important starting point for the discovery and verification of new biomarker candidates. Identifying such patterns is a challenging task that requires sophisticated machine learning approaches. Currently, there are a variety of classification models, and a common approach is to compare the performance and select the best one for a given classification problem. Since the association between the features of a data set and the performance of a particular classification method is still not fully understood, the main contribution of this work is to provide a new methodology for predicting the prediction results of different classifiers in the field of biomarker discovery. We propose here a three-steps computational workflow that includes an analysis of the data set characteristics, the calculation of the classification accuracy and, finally, the prediction of the resulting classification error. The experiments were carried out on synthetic and microarray datasets. Using this method, we showed that the predictability strongly depends on the discriminatory ability of the features, e.g., sets of genes, in two or multi-class datasets. If a dataset has a certain discriminatory ability, this method enables prediction of the classification performance before applying a learning model. Thus, our results contribute to a better understanding of the relationship between dataset characteristics and the corresponding performance of a machine learning method, and suggest the optimal classification method for a given dataset based on its discriminatory ability.

Insights and advances in integrating multi-omic approaches for potato crop improvement

In the forthcoming era of climate change and degradation of culturable land, there is an urgent need to secure global food supply in a sustainable manner. unraveling the genetic mysteries underlying interactions between functional genetic and metabolic networks through fostering the use of high-throughput -omic technologies can serve as a valuable tool towards characterizing plant phenotypic diversity and plasticity, to mitigate current threats of the climate change scenario on agriculture. Recently, a colossal number of -omic studies, including genomics, transcriptomics, proteomics, metabolomics, epigenomics and metagenomics, have enabled the identification of genes, proteins, and metabolites, that are related to desirable phenotypes, explaining the “holo-genetic” basis of agriculturally important traits, especially under resource-limiting environments. Undoubtedly, the integration of such big datasets with machine learning is highly demanding, mainly due to the lack of universal protocols to predict gene models or networks that govern various key traits. Among other important plant species contributing to food production, potato (Solanum tuberosum L.) represents one of top crop species worldwide, in terms of nutrition contribution, yielding capacity, and as a component of diverse cropping systems, especially for the developing counties. The potato genome is highly heterozygous as a result of self-incompatibility of the diploid potato species, suffering acute inbreeding depression. In this review, we discuss recent developments of high-throughput genomic technologies, as a useful tool for the selection of potato germplasm with improved nutritional value and quality traits.

Salivary gland tumors exhibit distinct miRNA signatures involved in Wnt/β-Catenin signaling in formalin fixed paraffin embedded tissue samples.

Advances in high-throughput genomic technologies have enabled the identification of numerous selective tumor markers. However, adapting these newly identified markers to clinical practice is not always possible because most RNA molecules, including mRNAs of protein-coding genes and long non-coding RNAs, are not stable under laboratory conditions, making their testing a major challenge. In contrast to long RNA molecules, miRNAs offer a great advantage in that they are relatively stable due to their small size. Accordingly, herein we aimed to determine the expression levels of miRNAs that are involved in Wnt/β-catenin signaling pathway in formalin fixed paraffin embedded (FFPE) tissue samples of patients with salivary gland tumors. A total of 42 patients with salivary gland tumors were included in the study. The miRNA expression signatures were evaluated using the RT-qPCR. As a result, β-catenin positivity was observed in all salivary gland tumors without distinguishing between benign and malignant phenotypes. Remarkably, we found that miR-200a and miR-373 were significantly upregulated whereas miR-30c were downregulated in tissues of patients with salivary gland tumors, compared to adjacent healthy tissue samples. In addition, distinct expression signatures of these miRNAs were significantly associated with the clinicopathological findings of patients such as perineural invasion and lymph node metastasis. Additionally, miR-145 and miR-30a were found to be specifically downregulated in a mucoepidermoid carcinoma. Also, miR-26b was selectively increased in pleomorphic adenomas of the salivary gland. Collectively, our findings suggest that these miRNAs may play chief roles in the differential diagnosis of salivary gland tumors.

Causal Discovery in High-dimensional, Multicollinear Datasets.

As the cost of high-throughput genomic sequencing technology declines, its application in clinical research becomes increasingly popular. The collected datasets often contain tens or hundreds of thousands of biological features that need to be mined to extract meaningful information. One area of particular interest is discovering underlying causal mechanisms of disease outcomes. Over the past few decades, causal discovery algorithms have been developed and expanded to infer such relationships. However, these algorithms suffer from the curse of dimensionality and multicollinearity. A recently introduced, non-orthogonal, general empirical Bayes approach to matrix factorization has been demonstrated to successfully infer latent factors with interpretable structures from observed variables. We hypothesize that applying this strategy to causal discovery algorithms can solve both the high dimensionality and collinearity problems, inherent to most biomedical datasets. We evaluate this strategy on simulated data and apply it to two real-world datasets. In a breast cancer dataset, we identified important survival-associated latent factors and biologically meaningful enriched pathways within factors related to important clinical features. In a SARS-CoV-2 dataset, we were able to predict whether a patient (1) had Covid-19 and (2) would enter the ICU. Furthermore, we were able to associate factors with known Covid-19 related biological pathways.

GFI1 KO can develop allergic reaction independently from IL5 and ILC2 cells

Abstract The number of people living with a chronic inflammatory lung disease, such as asthma, is constantly increasing. The development of resistance to existing therapies and the fact that exposure to air pollution and particular matter are continuously increasing have made asthma a major health problem worldwide. We have observed that in mice lacking the transcriptional factor GFI1 (short for “Growth factor independent 1”) where ILC2 are not functional and IL5 is almost absent, a severe asthmatic inflammatory reaction can still be provoked with increased numbers of eosinophils in the airways. This finding strongly suggests that pathways other than those initiated by IL5/IL5R and ILC2 cells exist that play a major role in severe allergic asthma. We hypothesize that the situation in GFI1 KO mice reflects what it is observed in asthmatic patients that are resistant to the recent therapies. We further posit that GFI1 KO mice is a tool to discover new pathways involved in asthmatic reactions different from the known and established mechanisms involving IL5. We are using this unique mouse model (Gfi1 deficient mice) and high throughput genomic sequencing technologies with a specific bioinformatics approach to identify new molecules pathways mediating the inflammatory reaction in asthma. We expect to reveal new molecular signaling pathways that cause lung inflammation during an allergic asthma reaction, specifically those regulating eosinophil recruitment and activities. Our first results show that GFI1 KO seem to be able to support an allergic reaction though IL17 pathway and Th17 cells. We anticipate to identify of molecular targets, which could be used to develop new, more effective therapies for patients with severe allergic asthma.

Application of modular response analysis to medium- to large-size biological systems.

The development of high-throughput genomic technologies associated with recent genetic perturbation techniques such as short hairpin RNA (shRNA), gene trapping, or gene editing (CRISPR/Cas9) has made it possible to obtain large perturbation data sets. These data sets are invaluable sources of information regarding the function of genes, and they offer unique opportunities to reverse engineer gene regulatory networks in specific cell types. Modular response analysis (MRA) is a well-accepted mathematical modeling method that is precisely aimed at such network inference tasks, but its use has been limited to rather small biological systems so far. In this study, we show that MRA can be employed on large systems with almost 1,000 network components. In particular, we show that MRA performance surpasses general-purpose mutual information-based algorithms. Part of these competitive results was obtained by the application of a novel heuristic that pruned MRA-inferred interactions a posteriori. We also exploited a block structure in MRA linear algebra to parallelize large system resolutions.

Abstract PD6-08: Exploring the interplay among ESR1/PIK3CA codon variants, oncogenic pathway alterations and clinical phenotype of metastatic breast cancer (MBC) through circulating tumor DNA (ctDNA) next-generation sequencing (NGS)

Abstract Background: High throughput genomic technologies such as NGS are enhancing the ability to dynamically characterize MBC but their role in describing biological evolution of multiple mutations together remains unclear. ESR1 and PIK3CA are central mutations related to the biology and druggability of hormone-receptor positive, HER2 negative (luminal-like) MBC. The aim of this study was to explore the interplay between oncogenic pathway alterations and ESR1 and PIK3CA codon variants on the impact and clinical phenotype of luminal-like MBC. Methods: The study retrospectively analyzed a multi-institutional cohort comprising 1047 MBC patients (pts) characterized for ctDNA through NGS before treatment start at Northwestern University (Chicago, IL), Massachusetts General Hospital (Boston, MA) and Washington University in St. Louis between 2015-2020. The analysis was then focused on luminal-like MBC. Pathway classification was defined based on previous work (Sanchez-Vega F et al, Cell. 2018) (i.e., RTK, RAS, RAF, MEK, NRF2, ER, WNT, MYC, P53, cell cycle, notch, PI3K). Single nucleotide variations (SNVs) were annotated for their oncogenicity through OncoKB and ClinVar. Only pathogenic variants were included in the models. Associations among, pathway classification, and ESR1/PIK3CA codon variants were explored through stepwise logistic regression. Overall survival (OS) was tested through Cox regression. Results: The luminal-like cohort comprised 702 pts. ESR1 mutations were detected in 166 pts (24%) and PIK3CA in 214 pts (31%). The most common ESR1 gene mutations were found in codons 537 (31%), 538 (21%), 536 (8%) and 380 (7%), while alterations in codons 1047 (38%), 545 (25%), and 542 (20%) were the most common for PIK3CA. Other pathogenic SNVs were observed in 33% and 17% of pts for ESR1 and PIK3CA, respectively with the former being polyclonal. SNVs alterations were mainly observed in the PI3K (35%), P53 (32%), ER (28%), RAS (8%), RTK (8%) and cell cycle (5%) pathways, while copy number variations (CNVs) were detected in the RTK (15%), cell cycle (11%), MYC (7%) PI3K (6%) and RAF (5%) pathways. ESR1 537 variants were associated with alterations in the ER and WNT pathways, 538 with cell cycle, 380 with P53 and ER, 536 with RTK. PIK3CA 1047 variants were associated with alterations in the RTK and P53 pathways, 542 with RTK, RAS and RAF, E545 with PI3K, RAS, cell cycle and P53. 1047 and 542 were also associated with CNVs in the PI3K pathway. Independent prognostic factors in terms of OS were ESR1 537/380 codon variants (HR 1.94 P = 0.001 and HR 2.29 P = 0.047), SNVs in the RAS, cell cycle, and P53 pathways (HR 1.74 P = 0.003 HR 1.84 P = 0.009 and HR 1.56 P &amp;lt; 0.001) and CNVs in the cell cycle pathway (HR 1.96 P &amp;lt; 0.001). Conclusions: This study demonstrated how ESR1 and PIK3CA codon variants, together with alterations in specific oncogenic pathways, can differentially impact the biology and clinical phenotype of luminal-like MBC. As novel endocrine therapy agents such as selective estrogen receptor degraders (SERDs) and PI3K inhibitors are being developed, these results highlight the pivotal role of ctDNA NGS to describe tumor evolution and optimize clinical decision making. Odds Ratio95% Confidence IntervalPESR1 Y537ER SNVs3.341.487.530.004WNT SNVs6.251.4127.740.016ESR1 D538cell cycle SNVs5.221.7915.230.003ESR1 E380P53 SNVs4.801.4116.310.012ER SNVs5.331.3321.400.018ESR1 L536RTK CNVs4.511.1517.690.031PIK3CA H1047RTK SNVs3.751.708.290.001P53 SNVs2.611.584.34&amp;lt; 0.001PI3K CNVs6.082.4515.08&amp;lt; 0.001PIK3CA E542RTK SNVs5.001.9412.880.001RAS SNVs3.651.369.770.01RAF SNVs6.011.0733.870.042PI3K CNVs6.302.2917.36&amp;lt; 0.001PIK3CA E545PI3K SNVs2.881.276.530.011RAS SNVs2.871.186.980.02cell cycle SNVs3.071.088.740.035NRF2 SNVs21.431.29356.520.033P53 SNVs3.752.046.89&amp;lt; 0.001 Citation Format: Lorenzo Gerratana, Andrew A Davis, Marko Velimirovic, Katherine Clifton, Whitney L Hensing, Ami N Shah, Charles S Dai, Carolina Reduzzi, Paolo D’Amico, Qiang Zhang, Firas Wehbe, Seth Wander, William J Gradishar, Amir Behdad, Fabio Puglisi, Cynthia X Ma, Aditya Bardia, Massimo Cristofanilli. Exploring the interplay among ESR1/PIK3CA codon variants, oncogenic pathway alterations and clinical phenotype of metastatic breast cancer (MBC) through circulating tumor DNA (ctDNA) next-generation sequencing (NGS) [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr PD6-08.

Nuclear Receptors in Ovarian Function.

The ovary undergoes cycles of hormone production that regulate physiological changes necessary for folliculogenesis, ovulation and luteinisation, ultimately contributing to female reproductive success. Crucial to these biological processes is stage-specific nuclear receptor signalling. While the transcriptional regulatory roles of steroid receptors in female fertility and especially ovarian functions have long been documented, non-steroid receptors also play an important part in regulating gene expression at various stages of ovarian development. The recent application of high-throughput genomic and transcriptomic technologies has begun to shed light on the molecular mechanisms underlying ovarian nuclear receptor actions and pointed to a complex interplay between highly specific transcription co-regulators as well as between nuclear receptors in mediating mutual as well as unique target genes. Interrelationships between nuclear receptors as well as the involvement of context-specific protein and non-protein co-regulators are likely keys to the precise and specific nuclear receptor action in the ovary. Leveraging such knowledge on the nuclear receptor network is especially valuable in the development of novel fertility treatments as well as female contraceptives.

Identification of key genes associated with survival of glioblastoma multiforme using integrated analysis of TCGA datasets

Background and ObjectiveGlioblastoma (GBM) is the most aggressive type of brain tumor. In spite of having various treatment options, GBM patients usually have a poor prognosis. Genetic markers play a vital role in the progression of the disease. Identification of these novel molecular biomarkers is essential to explain the mechanisms or improve the prognosis of GBM. Advances in high throughput genomic technologies enable the analysis of the varied types of omics data to find biomarkers in GBM. Although data repositories like The Cancer Genome Atlas (TCGA) are rich sources of such multi-omics data, integrating these different genomic datasets of varying quality and patient heterogeneity is challenging. MethodsMulti-omics gene expression datasets from TCGA consisting of DNA methylation, RNA sequencing, and copy number variation (CNV) of GBM patient is obtained to carry out the analysis. The Cox proportional hazards regression model is developed in R to identify significant genes from diverse datasets associated with the patient's survival. (Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) is used as an estimator for the model. Validation is performed to determine the accuracy and corresponding prediction error. ResultsFive key genes are identified from DNA Methylation and RNA sequencing datasets those are ANK1, HOXA9, TOX2, CXCR6, PIGZ, and L3MBTL, KDM5B, CCDC138, NUS1P1, and ARHGAP42, respectively. Higher expression values of these genes determine better survival of the GBM patients. Kaplan-Meier estimate curves show the exact correlation. Lower values of AIC and BIC determine the suitability of the model. The prediction model is validated on the test set and signifies a low error rate. Copy number variation data is also analysed to find the significant chromosomal location of GBM patients associated with chromosome 2,5,6,7,12,13, respectively. Among all nine CNV locations are found to be influencing the progression of GBM. ConclusionIntegrated analysis of multiple omics dataset is carried out to identify significant genes from DNA Methylation and RNA sequencing profiles of 76 common individuals. Copy number variation dataset for the same patients is analyzed to recognize notable locations associated with 22 chromosomes. The survival analysis determines the correlation of these biomarkers with the progression of the disease.

A Distributed Framework for Large-scale Protein-protein Interaction Data Analysis and Prediction Using MapReduce

Protein-protein interactions are of great significance for human to understand the functional mechanisms of proteins. With the rapid development of high-throughput genomic technologies, massive protein-protein interaction (PPI) data have been generated, making it very difficult to analyze them efficiently. To address this problem, this paper presents a distributed framework by reimplementing one of state-of-the-art algorithms, i.e., CoFex, using MapReduce. To do so, an in-depth analysis of its limitations is conducted from the perspectives of efficiency and memory consumption when applying it for large-scale PPI data analysis and prediction. Respective solutions are then devised to overcome these limitations. In particular, we adopt a novel tree-based data structure to reduce the heavy memory consumption caused by the huge sequence information of proteins. After that, its procedure is modified by following the MapReduce framework to take the prediction task distributively. A series of extensive experiments have been conducted to evaluate the performance of our framework in terms of both efficiency and accuracy. Experimental results well demonstrate that the proposed framework can considerably improve its computational efficiency by more than two orders of magnitude while retaining the same high accuracy.

A critical review on the antimicrobial resistance, antibiotic residue and metagenomics‐assisted antimicrobial resistance gene detection in freshwater aquaculture environment

The emergence of antimicrobial resistance is one of the major challenges addressed by the aquaculture industry in recent years. Bacterial resistance to antibiotics is mainly due to the rampant prophylactic use of antibiotics in fish farms. The persistent nature of antibiotics in the system ultimately leads to the development of resistance in environmental bacteria; consequently, resistance can be transmitted to pathogenic and human commensal bacteria via horizontal gene transfer. Increased attention has been paid to antibiotic resistance in aquatic environments due to its significant threat to human health. High-throughput genomic technologies can be used for metagenomic surveillance of antibiotic resistance determinants in environmental bacteria. Continuous monitoring programmes and timely detection of resistant pathogens are unavoidable to curb antimicrobial resistance dissemination in the aquaculture system. The present review focuses on different sources of antimicrobial resistance in aquaculture systems, the current status of antimicrobial use, antimicrobial resistance in ornamental and food fish of freshwater systems, molecular mechanisms involved in resistance development, antibiotic residue in the aquaculture environment and metagenomics-based detection of antimicrobial resistance in the aquaculture environment.

Genome sequencing guide: An introductory toolbox to whole-genome analysis methods.

To fully appreciate genetics, one must understand the link between genotype (DNA sequence) and phenotype (observable characteristics). Advances in high‐throughput genomic sequencing technologies and applications, so‐called “‐omics,” have made genetic sequencing readily available across fields in biology from applications in non‐traditional study organisms to precision medicine. Thus, understanding these tools is critical for any biologist, especially those early in their career. This comprehensive review discusses the chronological development of different sequencing methods, the bioinformatics steps to analyzing this data, and social and ethical issues raised by these techniques that must be discussed and evaluated, including anticipatory guides and discussion questions for active engagement in the classroom. Additionally, the Supporting Information includes a case study to apply technical and ethical concepts from the text.