Differential Gene Expression Data Research Articles

Expression and Prognosis of Differential Gene Troponin T1 Between Right and Left Colon Cancers.

Colorectal cancer (CRC) is one of the most common digestive tract tumors in humans. At present, many scholars believe that the primary site of the tumor has a direct and profound impact on its curative effect. There are significant differences in the expression of many genes, tumor microenvironment, and prognosis between the left and right colon. However, there is a lack of detailed studies on whether the differentially expressed genes in the left and right colon significantly impact the prognosis of patients with CRC. Troponin T1 (TNNT1) is an important gene that affects the prognosis difference between left and right colon cancer screening from "The Cancer Genome Atlas" database. By analyzing the differential gene expression data and clinical data of the left and right hemicolons in the database, the online prognostic database was used to screen the key molecules that significantly affect the tumor immune microenvironment and patient prognosis and to predict their functions and pathways. Quantitative reverse transcription-polymerase chain reaction was used to verify the expression difference of TNNT1 in CRC cell lines SW480 and HCT116, and normal human colorectal epithelial cell line FHC. The relationship between TNNT1 expression in 88 pairs of CRC samples and clinical information and pathologic parameters of patients with CRC was analyzed to judge the impact of TNNT1 expression on patient survival. Database analysis showed that TNNT1 was significantly overexpressed in CRC, and TNNT1 was one of the main differential genes between left colon cancer (LCC) and right colon cancer (RCC). The expression of TNNT1 was significantly increased in RCC, which could lead to poor prognosis of patients. Quantitative reverse transcription-polymerase chain reaction indicated that the expression of TNNT1 was significantly up-regulated in CRC cell lines SW480 and HCT116. Eighty-eight immunohistochemistry (IHC) of CRC tissues and adjacent tissues suggested that the expression of TNNT1 in CRC was significantly higher than that in normal adjacent tissues. By analyzing the clinical information and pathologic indicators matched with these clinical samples, we found that high TNNT1 expression in the primary tumor location (right colon) and high N stage (N2, N3) were unfavorable factors affecting the prognosis of patients with CRC. Multivariate Cox regression analysis suggested that high expression of TNNT1 may be an independent risk factor for the prognosis of patients with CRC. As one of the main differential genes between LCC and RCC, TNNT1 is representative to some extent. Its high expression may be one of the reasons why the prognosis of patients with RCC is worse than that of patients with LCC.

Effects of a second iron-dextran injection administered to piglets during lactation on differential gene expression in liver and duodenum at weaning.

Six female littermate piglets were used in an experiment to evaluate the mRNA expression in tissues from piglets given one or two 1mL injections of iron dextran (200mg Fe/mL). All piglets in the litter were administered the first 1mL injection < 24h after birth. On day 7, piglets were paired by weight (mean body weight = 1.72 ± 0.13kg) and one piglet from each pair was randomly selected as control (CON) and the other received a second injection (+Fe). At weaning on day 22, each piglet was anesthetized, and samples of liver and duodenum were taken from the anesthetized piglets and preserved until mRNA extraction. differential gene expression data were analyzed with a fold change cutoff (FC) of |1.2| P < 0.05. Pathway analysis was conducted with Z-score cutoff of P < 0.05. In the duodenum 435 genes were significantly changed with a FC ≥ |1.2| P < 0.05. In the duodenum, Claudin 1 and Claudin 2 were inversely affected by + Fe. Claudin 1 (CLDN1) plays a key role in cell-to-cell adhesion in the epithelial cell sheets and was upregulated (FC = 4.48, P = 0.0423). Claudin 2 (CLDN2) is expressed in cation leaky epithelia, especially during disease or inflammation and was downregulated (FC = -1.41, P = 0.0097). In the liver, 362 genes were expressed with a FC ≥ |1.2| P < 0.05. The gene most affected by a second dose of 200mg Fe was hepcidin antimicrobial peptide (HAMP) with a FC of 40.8. HAMP is a liver-produced hormone that is the main circulating regulator of Fe absorption and distribution across tissues. It also controls the major flows of Fe into plasma by promoting endocytosis and degradation of ferroportin (SLC4A1). This leads to the retention of Fe in Fe-exporting cells and decreased flow of Fe into plasma. Gene expression related to metabolic pathway changes in the duodenum and liver provides evidence for the improved feed conversion and growth rates in piglets given two iron injections preweaning with contemporary pigs in a companion study. In the duodenum, there is a downregulation of gene clusters associated with gluconeogenesis (P < 0.05). Concurrently, there was a decrease in the mRNA expression of genes for enzymes required for urea production in the liver (P < 0.05). These observations suggest that there may be less need for gluconeogenesis, and possibly less urea production from deaminated amino acids. The genomic and pathway analyses provided empirical evidence linking gene expression with phenotypic observations of piglet health and growth improvements.

Functional Connectome Hierarchy in Schizotypy and Its Associations With Expression of Schizophrenia-Related Genes.

Schizotypy has been conceptualized as a continuum of symptoms with marked genetic, neurobiological, and sensory-cognitive overlaps to schizophrenia. Hierarchical organization represents a general organizing principle for both the cortical connectome supporting sensation-to-cognition continuum and gene expression variability across the cortex. However, a mapping of connectome hierarchy to schizotypy remains to be established. Importantly, the underlying changes of the cortical connectome hierarchy that mechanistically link gene expressions to schizotypy are unclear. The present study applied novel connectome gradient on resting-state fMRI data from 1013 healthy young adults to investigate schizotypy-associated sensorimotor-to-transmodal connectome hierarchy and assessed its similarity with the connectome hierarchy of schizophrenia. Furthermore, normative and differential postmortem gene expression data were utilized to examine transcriptional profiles linked to schizotypy-associated connectome hierarchy. We found that schizotypy was associated with a compressed functional connectome hierarchy. Moreover, the pattern of schizotypy-related hierarchy exhibited a positive correlation with the connectome hierarchy observed in schizophrenia. This pattern was closely colocated with the expression of schizophrenia-related genes, with the correlated genes being enriched in transsynaptic, receptor signaling and calcium ion binding. The compressed connectome hierarchy suggests diminished functional system differentiation, providing a novel and holistic system-level basis for various sensory-cognition deficits in schizotypy. Importantly, its linkage with schizophrenia-altered hierarchy and schizophrenia-related gene expression yields new insights into the neurobiological continuum of psychosis. It also provides mechanistic insight into how gene variation may drive alterations in functional hierarchy, mediating biological vulnerability of schizotypy to schizophrenia.

Exploring the pharmacological and molecular mechanisms of Salvia chinensis Benth in colorectal cancer: A network pharmacology and molecular docking study.

While Salvia chinensis Benth (commonly known as "Shijianchuan" in Chinese, and abbreviated as SJC) is commonly used in adjuvant therapy for colorectal cancer (CRC) in traditional Chinese medicine, its mechanism of action remains unclear. In this study, Initially, we examined the impact of SJC on CRC cells in an in vitro setting. Next, we initially retrieved the primary active components and targets of SJC from databases such as TCMSP and existing literature. Subsequently, we integrated differential gene expression data from the GEO database and collected CRC-related targets from resources like DisGeNET. The matching of these datasets enabled the identification of SJC-CRC targets. We constructed a protein-protein interaction network and identified core targets through topological analysis. GO and KEGG enrichment analyses were performed using clusterProfiler. We established networks linking traditional Chinese medicine components to targets and core targets to signaling pathways. Additionally, we performed molecular docking to validate interactions between the main compounds and targets, and employed Western blot analysis to explore how the major components of SJC affect crucial signaling pathways. In this study, SJC inhibited the viability of HCT-116 and HT-29 cells. We identified a total of 11 active components in SJC along with 317 target genes. Among these, there were 8612 target genes associated with CRC, and we successfully matched 276 SJC-CRC target genes. Through topological analysis of the protein-protein interaction network, we pinpointed 20 core targets. It was revealed that SJC effects are linked to genes governing processes like cell apoptosis, proliferation, hypoxia, oxidative stress, and signaling pathways such as PI3K-Akt through GO and KEGG pathway enrichment analyses. Additionally, we applied molecular docking techniques and observed that the majority of active compounds displayed robust binding affinity with the selected targets. In vitro experiments suggested that SJC and its key component, Ursolic acid, may exert its anti-CRC effects by modulating the core PI3K/AKT signaling pathway through inhibiting the phosphorylation of the target Akt1. This discovery is consistent with the predictions derived from network pharmacology methods. This study marks the inaugural utilization of bioinformatics methods in conjunction with in vitro experiments to comprehensively investigate the pharmacological and molecular mechanisms responsible for SJC anti-CRC effects.

198 Differential Gene Expression and Metabolic Pathway Comparisons of Liver and Duodenum from Suckling Piglets Given One or Two Iron Dextran Injections

Abstract Six female littermate pairs were used in an experiment to evaluate the mRNA expression in tissues from piglets given one or two 1 mL injections of iron dextran (200 mg Fe/mL, Uniferon, Pharmacosmos Inc. Watchung, NJ). All pigs in the litter were administered the first 1 mL injection &amp;lt; 24 hours after birth and on d 7, pigs were paired by body weight (BW; 1.72 ± 0.13 kg) and one pig from each pair was randomly selected as control (CON) and the other received a second injection (+Fe). At weaning on d 22, each piglet was anesthetized, and samples of liver and duodenum were taken from the anesthetized piglets and placed in DNA/RNA Shield (Zymo, Irvine, CA) for preservation until mRNA extraction. RNA-Seq libraries were sequenced to a depth of at least 30 million read pairs (150 bp paired-end sequencing) per sample. Differential Gene Expression data were analyzed with GeneSpring (Agilent, Santa Clara, CA) with a fold-change (FC) of |1.2| P &amp;lt; 0.05 (Table 1). Pathway analysis was conducted with Ingenuity Pathway Analysis (IPA QIAGEN, Redwood City, CA) software with Z-score cutoff of P &amp;lt; 0.05. In the duodenum 435 genes were significantly changed with a FC ≥|1.2| P &amp;lt; 0.05. Most notably, Claudin 1 and Claudin 2 were inversely affected by the second dose of iron. Claudin 1 (CLDN1, FC = 4.48, P = 0.0423) is responsible for cell-to-cell adhesion in the epithelial cell sheets and serves as a barrier to prevent water and solutes from passing through the paracellular space. Claudin 2 (CLDN2, FC = -1.41, P = 0.0097) was down regulated because it is expressed in cation leaky epithelia. In the liver 362 genes were expressed with a FC ≥|1.2| P &amp;lt; 0.05. The gene most affected by a second dose of 200 mg Fe was HAMP (Hepcidin Antimicrobial Peptide) with an FC of 40.8. HAMP is a liver-produced hormone that is the main circulating regulator of Fe absorption and distribution across tissues. It also controls the major flows of Fe into plasma by promoting endocytosis and degradation of ferroportin (SLC4A1). This leads to the retention of Fe in Fe-exporting cells and decreased flow of Fe into plasma. Metabolic pathway changes in the duodenum and liver provide evidence for the improved feed conversion and growth rates in pigs given two iron injections pre-weaning. In the duodenum, there is a down regulation of gluconeogenesis (P &amp;lt; 0.05). Concurrently, there is a decrease in the production of urea in the liver (P &amp;lt; 0.05). These observations show that there is less need for gluconeogenesis, thus less urea production from deaminated amino acids. The genomic and pathway analyses provided empirical evidence linking gene expression with phenotypic observations of piglet health and growth improvements.

Differential Gene Expression Data Analysis of ASD Using Random Forest.

Autism spectrum disorder (ASD) is a developmental disability caused by differences in the brain regions. Analysis of differential expression (DE) of transcriptomic data allows for genome-wide analysis of gene expression changes related to ASD. De-novo mutations may play a vital role in ASD, but the list of genes involved is still far from complete. Differentially expressed genes (DEGs) are treated as candidate biomarkers and a small set of DEGs might be identified as biomarkers using either biological knowledge or data-driven approaches like machine learning and statistical analysis. In this study, we employed a machine learning-based approach to identify the differential gene expression between ASD and Typical Development (TD). The gene expression data of 15 ASD and 15 TD were obtained from the NCBI GEO database. Initially, we extracted the data and used a standard pipeline to pre-process the data. Further, Random Forest (RF) was used to discriminate genes between ASD and TD. We identified the top 10 prominent differential genes and compared them with the statistical test results. Our results show that the proposed RF model yields 5-fold cross-validation accuracy, sensitivity and specificity of 96.67%. Further, we obtained precision and F-measure scores of 97.5% and 96.57%, respectively. Moreover, we found 34 unique DEG chromosomal locations having influential contributions in identifying ASD from TD. We have also identified chr3:113322718-113322659 as the most significant contributing chromosomal location in discriminating ASD and TD. Our machine learning-based method of refining DE analysis is promising for finding biomarkers from gene expression profiles and prioritizing DEGs. Moreover, our study reported top 10 gene signatures for ASD may facilitate the development of reliable diagnosis and prognosis biomarkers for screening ASD.

Abstract 4708: Application of spatially resolved transcriptomics to screen multiple tumor biospecimens using tissue microarrays

Abstract The tumor microenvironment is composed of highly heterogeneous structures and cell types that dynamically influence and communicate with each other. Although examination of singular biospecimens is sufficient for diagnostic purposes, it is inadequate and cost prohibitive when scaling for complex and overarching studies. Thus, high density multi-tumor tissue microarrays (TMAs) have been a practical and effective solution for high-throughput molecular analysis of tissues. Introduced more than a decade ago, TMAs have been instrumental in the recent study of tumor biology, the development of diagnostic tests, the establishment of quality control, and the investigation and identification of oncological biomarkers. Here, we demonstrate the pairing of the 10x Genomics Visium Cytassist Spatial Gene Expression Solution and Xenium In-Situ Platform on multi-tumor TMAs to screen for common biomarkers among a cohort of samples. Spatial transcriptomics technology has proven valuable in mapping the whole transcriptome with spatial context (Visium), whereas In Situ (Xenium) enables high-throughput cellular characterization at single-cell resolution. With the addition of our CytAssist platform, we expand on the pre-existing standard Visium solution by facilitating the retrieval of RNA transcriptomic information from tissues placed on standard or archival slides. On the other hand, the novel Xenium platform compliments whole transcriptome Visium data by unlocking the potential to assign transcripts to a particular cell with spatial context and subcellular resolution. The combination of spatial transcriptomics and targeted in situ data with FFPE TMAs promotes a high-throughput method to accelerate the uncovering of molecular signatures suitable to understanding the tumor microenvironment. We showcase the ability to spatially and comprehensively resolve individual oncogene and tumor suppressor genes associated with multiple tumors from a cohort of cancer patients and from multiple different tumor samples. In addition, these markers are mapped back to distinct morphological features within each tissue core, and use differential gene expression data to identify distinct cell types throughout the different patient tissues. By combining the throughput of TMA samples and depth of the Visium and Xenium platforms, the strategy enables greater insights into cell-type specifics while also expanding the spectrum of biospecimen types that can be analyzed. Citation Format: Syrus Mohabbat, Hardeep Singh, Stephen R. Williams, Lauren M M. Gutgesell, David J. Sukovich, Govinda M. Kamath, Hanyoup Kim, Amanda Janesick, Robert Shelansky, Ghezal Beliakoff, Augusto M. Tentori, Albert Kim, Cedric R. Uytingco, Sarah Taylor. Application of spatially resolved transcriptomics to screen multiple tumor biospecimens using tissue microarrays. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4708.

Molecular Landscape of Pelvic Organ Prolapse Provides Insights into Disease Etiology.

Pelvic organ prolapse (POP) represents a major health care burden in women, but its underlying pathophysiological mechanisms have not been elucidated. We first used a case-control design to perform an exome chip study in 526 women with POP and 960 control women to identify single nucleotide variants (SNVs) associated with the disease. We then integrated the functional interactions between the POP candidate proteins derived from the exome chip study and other POP candidate molecules into a molecular landscape. We found significant associations between POP and SNVs in 54 genes. The proteins encoded by 26 of these genes fit into the molecular landscape, together with 43 other POP candidate molecules. The POP landscape is located in and around epithelial cells and fibroblasts of the urogenital tract and harbors four interacting biological processes-epithelial-mesenchymal transition, immune response, modulation of the extracellular matrix, and fibroblast function-that are regulated by sex hormones and TGFB1. Our findings were corroborated by enrichment analyses of differential gene expression data from an independent POP cohort. Lastly, based on the landscape and using vaginal fibroblasts from women with POP, we predicted and showed that metformin alters gene expression in these fibroblasts in a beneficial direction. In conclusion, our integrated molecular landscape of POP provides insights into the biological processes underlying the disease and clues towards novel treatments.

Differential Expression Feature Extraction (DEFE): A Case Study in Wheat FHB RNA-Seq Data Analysis.

In differential gene expression data analysis, one objective is to identify groups of co-expressed genes from a large dataset in order to detect the association between such a group of genes and an experimental condition. This is often done through a clustering approach, such as k-means or bipartition hierarchical clustering, based on particular similarity measures in the grouping process. In such a dataset, the gene differential expression itself is an innate attribute that can be used in the feature extraction process. For example, in a dataset consisting of multiple treatments versus their controls, the expression of a gene in each treatment would have three possible behaviors, upregulated, downregulated, or unchanged. We present in this chapter, a differential expression feature extraction (DEFE) method by using a string consisting of three numerical values at each character to denote such behavior, i.e., 1=up, 2=down, and 0=unchanged, which results in up to 3B differential expression patterns across all B comparisons. This approach has been successfully applied in many research projects, and among these, we demonstrate the strength of DEFE in a case study on RNA-sequencing (RNA-seq) data analysis of wheat challenged with the phytopathogenic fungus, Fusarium graminearum. Combinations of multiple schemes of DEFE patterns revealed groups of genes putatively associated with resistance or susceptibility to FHB.

Pathogenesis of Human Immunodeficiency Virus and Mycobacterium tuberculosis Infection as Revealed by Transcriptome and Interactome Data.

Tuberculosis (TB) among patients with human immunodeficiency virus (HIV) is a major global health burden and contributes to a high mortality rate due to HIV-mediated immunosuppression and subsequent susceptibility to TB. It is imperative to understand the pathogenesis of the association between HIV and TB for therapeutic innovation and preventive medicine. In the present study, we employed transcriptomic and bioinformatic analyses of differential gene expression data obtained from Gene Expression Omnibus (GEO) of the National Center for Biotechnology Information. The expression data of Mycobacterium tuberculosis-infected macrophages and blood samples from TB patients (GSE54992, GSE52819, and GSE19435) and blood samples from HIV patients (GSE30310) were accessed for identification of differentially expressed genes (DEGs). Data from 20 healthy subjects and 19 patients with TB and 16 healthy subjects and 16 patients with HIV were analyzed. We report here the DEGs shared by HIV and TB infection. Moreover, HIV and TB host-pathogen interaction data were collected from BIOGRID, v 4.4.210, for identifying significantly modulated genes' targets and their interactions with the host. Host targets, including PLSCR1 (phospholipid scramblase 1), STAT1 (signal transducer and activator of transcription-1 alpha/beta), FBXO6 (F-box only protein 6), ITGAL (integrin alpha-L), and APP (amyloid beta precursor protein), are commonly modulated in both diseases. The function of these targets was screened from and reconciled with the literature to understand their role in the pathogenesis of HIV and TB. Overall, the study results suggest that these targets may potentially be important contributors to the pathogenesis of this comorbidity. Further experimental work is needed for evaluating these new observations, with a view to future therapeutic innovation for patients with HIV and TB.

DriverMP enables improved identification of cancer driver genes.

Cancer is widely regarded as a complex disease primarily driven by genetic mutations. A critical concern and significant obstacle lies in discerning driver genes amid an extensive array of passenger genes. We present a new method termed DriverMP for effectively prioritizing altered genes on a cancer-type level by considering mutated gene pairs. It is designed to first apply nonsilent somatic mutation data, protein‒protein interaction network data, and differential gene expression data to prioritize mutated gene pairs, and then individual mutated genes are prioritized based on prioritized mutated gene pairs. Application of this method in 10 cancer datasets from The Cancer Genome Atlas demonstrated its great improvements over all the compared state-of-the-art methods in identifying known driver genes. Then, a comprehensive analysis demonstrated the reliability of the novel driver genes that are strongly supported by clinical experiments, disease enrichment, or biological pathway analysis. The new method, DriverMP, which is able to identify driver genes by effectively integrating the advantages of multiple kinds of cancer data, is available at https://github.com/LiuYangyangSDU/DriverMP. In addition, we have developed a novel driver gene database for 10 cancer types and an online service that can be freely accessed without registration for users. The DriverMP method, the database of novel drivers, and the user-friendly online server are expected to contribute to new diagnostic and therapeutic opportunities for cancers.

Admixture mapping identifies novel Alzheimer's disease risk regions in African Americans.

This study used admixture mapping to prioritize the genetic regions associated with Alzheimer's disease (AD) in African American (AA) individuals, followed by ancestry-aware regression analysis to fine-map the prioritized regions. We analyzed 10,271 individuals from 17 different AA datasets. We performed admixture mapping and meta-analyzed the results. We then used regression analysis, adjusting for local ancestry main effects and interactions with genotype, to refine the regions identified from admixture mapping. Finally, we leveraged in silico annotation and differential gene expression data to prioritize AD-related variants and genes. Admixture mapping identified two genome-wide significant loci on chromosomes 17p13.2 (p=2.2×10-5 ) and 18q21.33 (p=1.2×10-5 ). Our fine mapping of the chromosome 17p13.2 and 18q21.33 regions revealed several interesting genes such as the MINK1, KIF1C, and BCL2. Our ancestry-aware regression approach showed that AA individuals have a lower risk of AD if they inherited African ancestry admixture block at the 17p13.2 locus. We identified two genome-wide significant admixture mapping signals: on chromosomes 17p13.2 and 18q21.33, which are novel in African American (AA) populations. Our ancestry-aware regression approach showed that AA individuals have a lower risk of Alzheimer's disease (AD) if they inherited African ancestry admixture block at the 17p13.2 locus. We found that the overall proportion of African ancestry does not differ between the cases and controls that suggest African genetic ancestry alone is not likely to explain the AD prevalence difference between AA and non-Hispanic White populations.

GPSAdb: a comprehensive web resource for interactive exploration of genetic perturbation RNA-seq datasets.

Gene knock-out/down methods are commonly used to explore the functions of genes of interest, but a database that systematically collects perturbed data is not available currently. Manual curation of all the available human cell line perturbed RNA-seq datasets enabled us to develop a comprehensive human perturbation database (GPSAdb, https://www.gpsadb.com/). The current version of GPSAdb collected 3048 RNA-seq datasets associated with 1458 genes, which were knocked out/down by siRNA, shRNA, CRISPR/Cas9, or CRISPRi. The database provides full exploration of these datasets and generated 6096 new perturbed gene sets (up and down separately). GPSAdb integrated the gene sets and developed an online tool, genetic perturbation similarity analysis (GPSA), to identify candidate causal perturbations from differential gene expression data. In summary, GPSAdb is a powerful platform that aims to assist life science researchers to easily access and analyze public perturbed data and explore differential gene expression data in depth.

CTPC, a combined transcriptome data set of human prostate cancer cell lines.

Cell lines are the most used model system in cancer research. The transcriptomic data of established prostate cancer (PCa) cell lines help researchers explore differential gene expressions across the various PCa cell lines. Through large scale datamining, we established a curated Combined Transcriptome dataset of PCa Cell lines (CTPC) which contains the transcriptomic data of 1840 samples of 9 commonly used PCa cell lines including LNCaP, LNCaP-95, LNCaP-abl, C4-2, VCaP, 22Rv1, PC3, DU145, and NCI-H660. The CTPC dataset provides an opportunity for researchers to not only compare gene expression across different PCa cell lines but also retrieve the experiment information and associate the differential gene expression data with meta data, such as gene manipulation and drug treatment information. Additionally, based on the CTPC dataset, we built a platform for users to visualize the data (https://pcatools.shinyapps.io/CTPC_V2/). It is our hope that the combined CTPC dataset and the user-friendly platform are of great service to the PCa research community.

Differential gene expression and identification of growth-related genes in the pituitary gland of South African goats

Growth and carcass quality are economically important traits in goat production. This study investigated differentially expressed genes from the caprine pituitary gland transcriptome of South African indigenous goat breeds of varying growth performances and carcass quality parameters. Tissues were harvested from the pituitary gland of three South African Boer goats and three village ecotype goats all raised under similar conditions simulating intensive commercial production systems. Three additional tissues were harvested from village ecotype goats that were raised extensively on village farms. Between breed differences were investigated by comparing differential gene expression among three South African Boer and three village goats that were both raised under intensive commercial production system at a research farm. Within-breed differences were investigated by comparing differential gene expression among three village goats raised under extensive conditions (on-farm in Pella, S.A. village farming community) and three village goats raised under intensive commercial production system (at ARC research farm in Pretoria, South Africa. Total RNA was isolated from the pituitary gland of 36-week-old animals (n = 9) and sequenced individually in triplicates. An average of 28,298,512 trimmed, and quality-controlled reads/animal were mapped to the goat genome (Capra_hircus.ARS1.94) using HiSat2 software. Transcript assembly and quantification yielded 104 differentially expressed genes for village goats raised under extensive system and 62 for village goats raised under the intensive production system at the false discovery rate (FRD) of ≤0.05 and a fold change of ≥2. Growth-related genes such as POU3F4 and TSHZ1 were highly expressed within breeds raised under both production systems. Conversely, growth-related genes such as FGFR2 and SMPX genes were highly expressed between breeds raised under similar production systems. Ballgown analysis revealed a high expression of GH1 and IGF1 in the intensively raised compared to extensively raised goats. Both genes were also highly expressed in the village goats when compared to the Boer. The differential gene expression data provided insights into genes and molecular mechanisms associated with growth and growth development in goats.

Fatty acid oxidation protects cancer cells from apoptosis by increasing mitochondrial membrane lipids.

Overcoming resistance to chemotherapies remains a major unmet need for cancers, such as triple-negative breast cancer (TNBC). Therefore, mechanistic studies to provide insight for drug development are urgently needed to overcome TNBC therapy resistance. Recently, an important role of fatty acid β-oxidation (FAO) in chemoresistance has been shown. But how FAO might mitigate tumor cell apoptosis by chemotherapy is unclear. Here, we show that elevated FAO activates STAT3 by acetylation via elevated acetyl-coenzyme A (CoA). Acetylated STAT3 upregulates expression of long-chain acyl-CoA synthetase 4 (ACSL4), resulting in increased phospholipid synthesis. Elevating phospholipids in mitochondrial membranes leads to heightened mitochondrial integrity, which in turn overcomes chemotherapy-induced tumor cell apoptosis. Conversely, in both cultured tumor cells and xenograft tumors, enhanced cancer cell apoptosis by inhibiting ASCL4 or specifically targeting acetylated-STAT3 is associated with a reduction in phospholipids within mitochondrial membranes. This study demonstrates a critical mechanism underlying tumor cell chemoresistance.

Model-free Bias Reduction of Storey’s Estimator for the Proportion of True Null Hypotheses

Storey’s bootstrap estimator for the proportion of true null hypotheses ( π0) has a known bias structure but the amount of bias is obviously unknown. A new method has been proposed in this work to reduce bias of the estimator. The proposed method partitions differential gene expression and similar data sets randomly and repeatedly to first approximate the bias and then corrects it in the final estimate. Unlike some recent developments, the method does not require any model assumption. The proposed method depends on some tuning parameters. For practical applicability of the method, ideal choices of the tuning parameters are given based on numerical results. Extensive simulation experiments show favourable results for the proposed method over the popular robust estimators in the literature. Two real-life microarray data sets have been studied to demonstrate the applicability of the final bias and variance reduced estimator. AMS 2010 subject classifications: 62F10, 62F40

Human iPSC-Cardiomyocytes as an Experimental Model to Study Epigenetic Modifiers of Electrophysiology.

The epigenetic landscape and the responses to pharmacological epigenetic regulators in each human are unique. Classes of epigenetic writers and erasers, such as histone acetyltransferases, HATs, and histone deacetylases, HDACs, control DNA acetylation/deacetylation and chromatin accessibility, thus exerting transcriptional control in a tissue- and person-specific manner. Rapid development of novel pharmacological agents in clinical testing—HDAC inhibitors (HDACi)—targets these master regulators as common means of therapeutic intervention in cancer and immune diseases. The action of these epigenetic modulators is much less explored for cardiac tissue, yet all new drugs need to be tested for cardiotoxicity. To advance our understanding of chromatin regulation in the heart, and specifically how modulation of DNA acetylation state may affect functional electrophysiological responses, human-induced pluripotent stem-cell-derived cardiomyocyte (hiPSC-CM) technology can be leveraged as a scalable, high-throughput platform with ability to provide patient-specific insights. This review covers relevant background on the known roles of HATs and HDACs in the heart, the current state of HDACi development, applications, and any adverse cardiac events; it also summarizes relevant differential gene expression data for the adult human heart vs. hiPSC-CMs along with initial transcriptional and functional results from using this new experimental platform to yield insights on epigenetic control of the heart. We focus on the multitude of methodologies and workflows needed to quantify responses to HDACis in hiPSC-CMs. This overview can help highlight the power and the limitations of hiPSC-CMs as a scalable experimental model in capturing epigenetic responses relevant to the human heart.

ΔFBA-Predicting metabolic flux alterations using genome-scale metabolic models and differential transcriptomic data.

Genome-scale metabolic models (GEMs) provide a powerful framework for simulating the entire set of biochemical reactions in a cell using a constraint-based modeling strategy called flux balance analysis (FBA). FBA relies on an assumed metabolic objective for generating metabolic fluxes using GEMs. But, the most appropriate metabolic objective is not always obvious for a given condition and is likely context-specific, which often complicate the estimation of metabolic flux alterations between conditions. Here, we propose a new method, called ΔFBA (deltaFBA), that integrates differential gene expression data to evaluate directly metabolic flux differences between two conditions. Notably, ΔFBA does not require specifying the cellular objective. Rather, ΔFBA seeks to maximize the consistency and minimize inconsistency between the predicted flux differences and differential gene expression. We showcased the performance of ΔFBA through several case studies involving the prediction of metabolic alterations caused by genetic and environmental perturbations in Escherichia coli and caused by Type-2 diabetes in human muscle. Importantly, in comparison to existing methods, ΔFBA gives a more accurate prediction of flux differences.

Novel germline variant in the histone demethylase and transcription regulator KDM4C induces a multi-cancer phenotype

BackgroundGenes involved in epigenetic regulation are central for chromatin structure and gene expression. Specific mutations in these might promote carcinogenesis in several tissue types.MethodsWe used exome, whole-genome and Sanger sequencing...