Choice of library size normalization and statistical methods for differential gene expression analysis in balanced two-group comparisons for RNA-seq studies

STAT5B leukemic mutations, altering SH2 tyrosine 665, have opposing impacts on immune gene programs

STAT5B is a vital transcription factor for lymphocytes. Here, the function of two STAT5B mutations from human T-cell leukemias: one substituting tyrosine 665 with phenylalanine (STAT5BY665F) and the other with histidine (STAT5BY665H), was interrogated. In silico modeling predicted divergent energetic effects on homodimerization with a range of pathogenicity. In primary T cells in vitro, STAT5BY665F showed gain-of-function, whereas STAT5BY665H demonstrated loss-of-function. Introducing the mutation into the mouse genome illustrated that the gain-of-function Stat5b Y665F mutation resulted in accumulation of CD8+ effector and memory and CD4+ regulatory T cells, altering CD8+/CD4+ ratios. In contrast, STAT5BY665H "knock-in" mice showed diminished CD8+ effector and memory and CD4+ regulatory T cells. In contrast to WT STAT5B, the STAT5BY665F variant displayed greater STAT5 phosphorylation, DNA binding, and transcriptional activity after cytokine activation, whereas the STAT5BY665H variant resembled a null. The work exemplifies how joining in silico and in vivo studies of single nucleotides deepens our understanding of disease-associated variants, revealing structural determinants of altered function, defining mechanistic roles, and, specifically here, identifying a gain-of-function variant that does not directly induce hematopoietic malignancy.

The therapeutic potential of Rosiglitazone in modulating scar formation through PPAR-γ pathway

The prevention and treatment of scars has always posed a challenge in the medical field. Researchers have reached the consensus that safe, effective and affordable treatments are needed. Here, by conducting non-targeted metabolomics and RNA sequencing experiments, we revealed that a significant number of metabolites and genes related to glucose and lipid metabolism underwent changes during scar formation, with peroxisome proliferator-activated receptor-γ (PPAR-γ) exerting a profound influence. Considering that rosiglitazone is a selective orally active PPAR-γ receptor agonist, scar models were induced in rats, and rosiglitazone was administered at different dosages. We characterized rosiglitazone as a crucial mediator in a rat scar model in vivo and in vitro in two models of transforming growth factor β1(TGF-β1) stimulated fibroblasts (NIH 3T3 and 3T3 L1). Functionally, activation of PPAR-γ with rosiglitazone effectively impedes fibrosis and mitigates scar formation. Rosiglitazone also inhibits some inflammatory factors, and downregulates triglyceride, lactic acid, glycogen and lactic dehydrogenase levels in rat scars. Conversely, rosiglitazone increases adenosine triphosphate (ATP) production and increases free fatty acid levels and the activity of acetyl-CoA carboxylase, fatty acid synthetase, succinate dehydrogenase. Collectively, these findings shed light on the underlying mechanisms and suggest that the use of rosiglitazone could be a promising therapeutic approach to alleviate fibrosis and reduce scar formation.

Comparative enzymatic browning transcriptome analysis of three apple cultivars unravels a conserved regulatory network related to stress responses

Enzymatic browning (EB) endangers the adaptation of apple fruit cultivars to new markets, affecting organoleptic properties and producing economic losses. Polyphenol oxidases and polyphenol compounds play a key role in EB development in apple. However, the regulation of apple response to EB remains to be uncovered. In this study, three apple cultivars with different EB phenotypes ranging from low to high browning in apple pulp were used to study transcriptomic changes over time after fresh cutting (0, 30 and 60 min). This study allowed the identification of 1448 differentially expressed genes (DEGs), revealing both shared and genotype-specific responses, particularly in the affected metabolic pathways associated with EB. At 60 min (T60 vsT0), 77 DEGs were shared by all genotypes, suggesting a conserved regulatory network. This network included genes encoding for protein families such as calcium-binding proteins, heat-shock proteins, redox-responsive transcription factors, WRKY family transcription factors, zinc finger family proteins and disease resistance proteins among others. A co-expressed gene cluster, identified through Weighed Gene Co-Expression Network Analysis (WGCNA), was found to correlate with EB and included 323 genes enriched in several biological terms according to Gene Ontology analysis. Moreover, a more detailed analysis of identified WGCNA gene cluster regulatory sequences allowed the detection of cis-regulatory elements belonging to CAMTA, WRKY and WUSCHEL transcription factor families. The identification of these sequences alongside with an abundant and diverse amount of overexpressed transcription factors from various families (WRKY, ERF, GRAS, GATA, etc.) point out to a highly regulated stress-response that is strictly connected to innate plant immunity. These findings provide valuable insights into the molecular mechanism involved in apple fresh-cut browning and offer new potential targets for EB regulation.

Smoking-Associated Changes in Gene Expression in Coronary Artery Disease Patients Using Matched Samples

Smoking is a well known risk factor for coronary artery disease (CAD). However, the effects of smoking on gene expression in the blood of CAD subjects in Hungary have not been extensively studied. This study aimed to identify differentially expressed genes (DEGs) associated with smoking in CAD subjects. Eleven matched samples based on age and gender were selected for analysis in this study. All subjects were non-obese, non-alcoholic, non-diabetic, and non-hypertensive and had moderate to severe stenosis of one or more coronary arteries, confirmed by coronary angiography. Whole blood samples were collected using PAXgene tubes. Next-generation sequencing was employed using the NextSeq 500 system to generate high-throughput sequencing data for transcriptome profiling. The differentially expressed genes were analyzed using the R programming language. Results: The study revealed that smokers exhibited non-significant higher levels of total cholesterol, low-density lipoprotein-cholesterol, and triglycerides compared to non-smokers (p > 0.05), although high-density lipoprotein-cholesterol was also elevated. Despite this, the overall lipid profile of smokers remained less favorable. Non-smokers had a higher BMI (p = 0.02). Differential gene expression analysis identified 58 DEGs, with 38 upregulated in smokers. The key upregulated genes included LILRB5 (log2FC = 2.88, p = 1.05 × 10-5) and RELN (log2FC = 3.31, p = 0.024), while RNF5_2 (log2FC = -5.29, p = 0.028) and IGHV7-4-1_1 (log2FC = -2.86, p = 0.020) were notably downregulated. Heatmap analysis showed a distinct clustering of gene expression profiles between smokers and non-smokers. However, GO analysis did not identify significant biological pathways associated with the DEGs. Conclusions: This research illuminates smoking's biological effects, aiding personalized medicine for predicting and treating smoking-related diseases.

Reduced bioenergetics and mitochondrial fragmentation in human primary cytotrophoblasts induced by an EGFR-targeting chemical mixture

Exposures to complex environmental chemical mixtures during pregnancy reach and target the feto-placental unit. This study investigates the influence of environmental chemical mixtures on placental bioenergetics. Recognizing the essential role of the epidermal growth factor receptor (EGFR) in placental development and its role in stimulating glycolysis and mitochondrial respiration in trophoblast cells, we explored the effects of chemicals known to disrupt EGFR signaling on cellular energy production. Human primary cytotrophoblasts (hCTBs) and a first-trimester extravillous trophoblast cell line (HTR-8/SVneo) were exposed to a mixture of EGFR-interfering chemicals, including atrazine, bisphenol S, niclosamide, PCB-126, PCB-153, and trans-nonachlor. An RNA sequencing approach revealed that the mixture altered the transcriptional signature of genes involved in cellular energetics. Next, the impact of the mixture on cellular bioenergetics was evaluated using a combination of mitochondrial and glycolytic stress tests, ATP production, glucose consumption, lactate synthesis, and super-resolution imaging. The chemical mixture did not alter basal oxygen consumption but diminished the maximum respiratory capacity in a dose-dependent manner, indicating a disruption of mitochondrial function. The respiratory capacity and ATP production were increased by EGF, while the Chem-Mix reduced both EGF- and non-EGF-mediated oxygen consumption rate in hCTBs. A similar pattern was observed in the glycolytic medium acidification, with EGF increasing the acidification, and the Chem-Mix blocking EGF-induced glycolytic acidification. Furthermore, direct stochastic optical reconstruction microscopy (dSTORM) imaging demonstrated that the Chem-Mix led to a reduction of the mitochondrial network architecture, with findings supported by a decrease in the abundance of OPA1, a mitochondrial membrane GTPase involved in mitochondrial fusion. In conclusion, we demonstrated that a mixture of EGFR-disrupting chemicals alters mitochondrial remodeling, resulting in disturbed cellular bioenergetics, reducing the capacity of human cytotrophoblast cells to generate energy. Future studies should investigate the mechanism by which mitochondrial dynamics are disrupted and the pathological significance of these findings.

Autophagic Regulation of Adipogenesis Through TP53INP2: Insights from In Silico and In Vitro Analysis

Obesity is an epidemic disease associated with multimorbidity resulting in higher mortality risk. The imbalance between energy storage and expenditure is the prime factor in the prognosis of the disease. Specifically, excessive lipid storage through adipogenesis leads to obesity. Adipogenesis is the process that converts preadipocytes into mature adipocytes by regulating major transcription factors like PPARγ and C/EBPα, contributes to lipid storage in adipose tissue. On the contrary, autophagy is a self-degradative process that maintains homeostasis in adipose tissue by regulating adipogenesis and lipolysis. TP53INP2 is a key player that regulates the autophagy process, and it negatively regulates adipogenesis and lipid storage. The gene expression profile GSE93637 was retrieved from the GEO database and analyzed using an integrated bioinformatics approach. The differentially expressed genes (DEGs) were analyzed using R-Bioconductor for TP53INP2 knockdown microarray dataset of 3T3L1 cells, and the DEGs were analyzed for the functional enrichment analysis. Further, the genes involved in the potential biological and molecular functions were evaluated for pathway enrichment analysis by KEGG (Kyoto Encyclopedia of Genes and Genomes). A total of 726 DEGs were found including 391 upregulated and 335 downregulated genes. Further, the functional and pathway enrichment analysis was employed to identify the highly interacting genes, and we identified a total of 56 genes that are highly interacting through a protein-protein interaction network. The DEGs mainly regulate the Peroxisome proliferator-activated receptor (PPAR) signaling pathway, lipolysis, and autophagy. Further, we investigated the associated Hub genes for enriched pathway genes and found the involvement of two autophagic genes ATG7 and sequestosome 1 (p62). In addition, in vitro studies of qRT-PCR (Quantitative real-time polymerase chain reaction) and Western blot analysis revealed that increased autophagy resulted in reduced lipid storage through down-regulation of the adipogenic gene. Moreover, increased expression of autophagic gene TP53INP2 and ATG7 facilitates the down-regulation of p62 and PPARγ gene resulting in lipolysis in mature adipocytes through autophagy. There is no specific treatment to reduce obesity other than a caloric diet and exercise. Hence, this study provides sufficient evidence to conclude that TP53INP2 negatively regulates adipogenesis and increases the degradation of lipids in mature adipocytes which is crucial for reducing obesity. Therefore, it is plausible to consider TP53INP2 as a promising therapeutic target for managing adipogenesis and obesity. However, further studies are necessary to validate their functional and molecular pathway analysis in the regulation of adipogenesis and obesity.

Integrated analysis reveals the regulatory mechanism of the neddylation inhibitor MLN4924 on the metabolic dysregulation in rabbit granulosa cells

BACKGROUND

Neddylation, an important post-translational modification (PTM) of proteins, plays a crucial role in follicular development. MLN4924 is a small-molecule inhibitor of the neddylation-activating enzyme (NAE) that regulates various biological processes. However, the regulatory mechanisms of neddylation in rabbit ovarian cells have not been emphasized. Here, the transcriptome and metabolome profiles in granulosa cells (GCs) treated with MLN4924 were utilized to identify differentially expressed genes, followed by pathway analysis to precisely define the altered metabolisms.

RESULTS

The results showed that 563 upregulated and 910 downregulated differentially expressed genes (DEGs) were mainly enriched in pathways related to cancer, cell cycle, PI3K-AKT, progesterone-mediated oocyte maturation, and PPAR signaling pathway. Furthermore, we characterized that MLN4924 inhibits PPAR-mediated lipid metabolism, and disrupts the cell cycle by promoting the apoptosis and proliferation of GCs. Importantly, we found the reduction of several metabolites in the MLN4924 treated GCs, including glycerophosphocholine, arachidic acid, and palmitic acid, which was consistent with the deregulation of PPAR signaling pathways. Furthermore, the increased metabolites included 6-Deoxy-6-sulfo-D-glucono-1,5-lactone and N-Acetyl-D-glucosaminyldiphosphodolichol. Combined with transcriptome data analyses, we identified genes that strongly correlate with metabolic dysregulation, particularly those related to glucose and lipid metabolism. Therefore, neddylation inhibition may disrupt the energy metabolism of GCs.

CONCLUSIONS

These results provide a foundation for in-depth research into the role and molecular mechanism of neddylation in ovary development.

Genome scale CRISPR screens identify actin capping proteins as key modulators of therapeutic responses to radiation and immunotherapy

Radiotherapy (RT), is a fundamental treatment for malignant tumors and is used in over half of cancer patients. As radiation can promote anti-tumor immune effects, a promising therapeutic strategy is to combine radiation with immune checkpoint inhibitors (ICIs). However, the genetic determinants that impact therapeutic response in the context of combination therapy with radiation and ICI have not been systematically investigated. To unbiasedly identify the tumor intrinsic genetic factors governing such responses, we perform a set of genome-scale CRISPR screens in melanoma cells for cancer survival in response to low-dose genotoxic radiation treatment, in the context of CD8 T cell co-culture and with anti-PD1 checkpoint blockade antibody. Two actin capping proteins, Capza3 and Capg, emerge as top hits that upon inactivation promote the survival of melanoma cells in such settings. Capza3 and Capg knockouts (KOs) in mouse and human cancer cells display persistent DNA damage due to impaired homology directed repair (HDR); along with increased radiation, chemotherapy, and DNA repair inhibitor sensitivity. However, when cancer cells with these genes inactivated were exposed to sublethal radiation, inactivation of such actin capping protein promotes activation of the STING pathway, induction of inhibitory CEACAM1 ligand expression and resistance to CD8 T cell killing. Patient cancer genomics analysis reveals an increased mutational burden in patients with inactivating mutations in CAPG and/or CAPZA3, at levels comparable to other HDR associated genes. There is also a positive correlation between CAPG expression and activation of immune related pathways and CD8 T cell tumor infiltration. Our results unveil the critical roles of actin binding proteins for efficient HDR within cancer cells and demonstrate a previously unrecognized regulatory mechanism of therapeutic response to radiation and immunotherapy.

WASF3 overexpression affects the expression of circular RNA hsa-circ-0100153, which promotes breast cancer progression by sponging hsa-miR-31, hsa-miR-767-3p, and hsa-miR-935

Background

The WASF3 gene has been linked to promoting metastasis in breast cancer (BC) cells, and low expression reduces invasion potential. Circular RNAs (circRNAs) function as microRNA (miRNA) modulators and are involved in cancer progression, but the relationship between these factors remains unclear.

Methods

This study used bioinformatics methods and a computational approach to investigate the role of circRNAs and miRNAs in the context of WASF3 overexpression. Differentially expressed mRNAs, circRNAs, and miRNAs were identified using Gene Expression Omnibus (GEO) datasets. A competing endogenous RNA (ceRNA) network was constructed based on circRNA-miRNA pairs and miRNA-mRNA pairs. Functional and pathway enrichment analyses were predicted using a circRNA-miRNA-mRNA network.

Results

RNA expression patterns were significantly different between normal and tumor samples. A total of 190 circRNAs, 76 miRNAs, and 678 mRNAs were differentially expressed. The analysis of the circRNA-miRNA-mRNA regulatory network revealed interactions between hsa-circ-0100153, hsa-miR-31, hsa-miR-767-3p, and hsa-miR-935 with WASF3 in cancer. These interactions primarily function in DNA replication and the cell cycle.

Conclusions

This study reveals a mechanism by which WASF3 overexpression affects the expression of circRNAs hsa-circ-0100153, promoting BC progression by sponging hsa-miR-31/hsa-miR-767-3p /hsa-miR-935. This mechanism may increase the invasive potential of cancers, in addition to other reported molecular mechanisms involving the WASF3 gene.

Validation of potential RNA biomarkers for prostate cancer diagnosis and monitoring in plasma and urinary extracellular vesicles

Introduction: Prostate cancer (PCa), one of the most prevalent malignancies affecting men worldwide, presents significant challenges in terms of early detection, risk stratification, and active surveillance. In recent years, liquid biopsies have emerged as a promising non-invasive approach to complement or even replace traditional tissue biopsies. Extracellular vesicles (EVs), nanosized membranous structures released by various cells into body fluids, have gained substantial attention as a source of cancer biomarkers due to their ability to encapsulate and transport a wide range of biological molecules, including RNA. In this study, we aimed to validate 15 potential RNA biomarkers, identified in a previous EV RNA sequencing study, using droplet digital PCR. Methods: The candidate biomarkers were tested in plasma and urinary EVs collected before and after radical prostatectomy from 30 PCa patients and their diagnostic potential was evaluated in a test cohort consisting of 20 benign prostate hyperplasia (BPH) and 20 PCa patients' plasma and urinary EVs. Next, the results were validated in an independent cohort of plasma EVs from 31 PCa and 31 BPH patients. Results: We found that the levels of NKX3-1 (p = 0.0008) in plasma EVs, and tRF-Phe-GAA-3b (p < 0.0001) tRF-Lys-CTT-5c (p < 0.0327), piR-28004 (p = 0.0081) and miR-375-3p (p < 0.0001) in urinary EVs significantly decreased after radical prostatectomy suggesting that the main tissue source of these RNAs is prostate and/or PCa. Two mRNA biomarkers-GLO1 and NKX3-1 showed promising diagnostic potential in distinguishing between PCa and BPH with AUC of 0.68 and 0.82, respectively, in the test cohort and AUC of 0.73 and 0.65, respectively, in the validation cohort, when tested in plasma EVs. Combining these markers in a biomarker model yielded AUC of 0.85 and 0.71 in the test and validation cohorts, respectively. Although the PSA levels in the blood could not distinguish PCa from BPH in our cohort, adding PSA to the mRNA biomarker model increased AUC from 0.71 to 0.76. Conclusion: This study identified two novel EV-enclosed RNA biomarkers-NKX3-1 and GLO1-for the detection of PCa, and highlights the complementary nature of GLO1, NKX3-1 and PSA as combined biomarkers in liquid biopsies of PCa.

Unveiling metabolic integration in psyllids and their nutritional endosymbionts through comparative transcriptomics analysis

Psyllids, a group of insects that feed on plant sap, have a symbiotic relationship with an endosymbiont called Carsonella. Carsonella synthesizes essential amino acids and vitamins for its psyllid host, but lacks certain genes required for this process, suggesting a compensatory role of psyllid host genes. To investigate this, gene expression was compared between two psyllid species, Bactericera cockerelli and Diaphorina citri, in specialized cells where Carsonella resides (bacteriomes). Collaborative psyllid genes, including horizontally transferred genes, showed patterns of conserved gene expression; however, species-specific patterns were also observed, suggesting differences in the nutritional metabolism between psyllid species. Also, the recycling of nitrogen in bacteriomes may primarily rely on glutamate dehydrogenase (GDH). Additionally, lineage-specific gene clusters were differentially expressed in B. cockerelli and D. citri bacteriomes and are highlighted here. These findings shed light on potential host adaptations for the regulation of this symbiosis due to host, microbiome, and environmental differences.

Translating Ribosome Affinity Purification (TRAP) and Bioinformatic RNA-Seq Analysis in Post-metamorphic Xenopus laevis

Recent technical advances provide the ability to isolate and purify mRNAs from genetically distinct cell types so as to provide a broader view of gene expression as they relate to gene networks. These tools allow the genome of organisms undergoing different developmental or diseased states and environmental or behavioral conditions to be compared. Translating ribosome affinity purification (TRAP), a method using transgenic animals expressing a ribosomal affinity tag (ribotag) that targets ribosome-bound mRNAs, allows for the rapid isolation of genetically distinct populations of cells. In this chapter, we provide stepwise methods for carrying out an updated protocol for using the TRAP method in the South African clawed frog Xenopus laevis. A discussion of the experimental design and necessary controls and their rationale, along with a description of the bioinformatic steps involved in analyzing the Xenopus laevis translatome using TRAP and RNA-Seq, is also provided.

Novel and known transcriptional targets of ALS/FTD protein TDP-43: Meta-analysis and interactive graphical database

TDP-43 proteinopathy is the major pathology in amyotrophic lateral sclerosis (ALS) and tau-negative frontotemporal dementia (FTD). Mounting evidence implicates loss of normal TDP-43 RNA processing function as a key pathomechanism. However, the RNA targets of TDP-43 differ by report, and have never been formally collated or compared between models and disease, hampering understanding of TDP-43 function. Here, we conducted re-analysis and meta-analysis of publicly available RNA-sequencing datasets from six TDP-43-knockdown models, and TDP-43-immunonegative neuronal nuclei from ALS/ FTD brain, to identify differentially expressed genes (DEGs) and exon usage (DEU) events. There was little overlap in DEGs between knockdown models, but PFKP, STMN2, CFP, KIAA1324 and TRHDE were common targets and were also differentially expressed in TDP-43-immunonegative neurons. DEG enrichment analysis revealed diverse biological pathways including immune and synaptic functions. Common DEU events in human datasets included well-known targets POLDIP3 and STMN2, and novel targets EXD3, MMAB, DLG5 and GOSR2. Our interactive database https://phpstack-449938-2576646.cloudwaysapps.com/ allows further exploration of TDP-43 DEG and DEU targets. Together, these data identify TDP-43 targets that can be exploited therapeutically or to validate loss-of-function processes.

SAREV: A review on statistical analytics of single-cell RNA sequencing data

Due to the development of next-generation RNA sequencing (NGS) technologies, there has been tremendous progress in research involving determining the role of genomics, transcriptomics and epigenomics in complex biological systems. However, scientists have realized that information obtained using earlier technology, frequently called 'bulk RNA-seq' data, provides information averaged across all the cells present in a tissue. Relatively newly developed single cell (scRNA-seq) technology allows us to provide transcriptomic information at a single-cell resolution. Nevertheless, these high-resolution data have their own complex natures and demand novel statistical data analysis methods to provide effective and highly accurate results on complex biological systems. In this review, we cover many such recently developed statistical methods for researchers wanting to pursue scRNA-seq statistical and computational research as well as scientific research about these existing methods and free software tools available for their generated data. This review is certainly not exhaustive due to page limitations. We have tried to cover the popular methods starting from quality control to the downstream analysis of finding differentially expressed genes and concluding with a brief description of network analysis.

RNA Sequencing Reveals the Upregulation of FOXO Signaling Pathway in Porphyromonas gingivalis Persister-Treated Human Gingival Epithelial Cells

Porphyromonas gingivalis as the keystone periodontopathogen plays a critical role in the pathogenesis of periodontitis, and crucially accounts for inflammatory comorbidities such as cardiovascular disease and Alzheimer's disease. We recently identified the existence of P. gingivalis persisters and revealed the unforeseen perturbation of innate response in human gingival epithelial cells (HGECs) due to these noxious persisters. Herein, RNA sequencing revealed how P. gingivalis persisters affected the expression profile of cytokine genes and related signaling pathways in HGECs. Results showed that metronidazole-treated P. gingivalis persisters (M-PgPs) impaired the innate host defense of HGECs, in a similar fashion to P. gingivalis. Notably, over one thousand differentially expressed genes were identified in HGECs treated with M-PgPs or P. gingivalis with reference to the controls. Gene Ontology and KEGG pathway analysis demonstrated significantly enriched signaling pathways, such as FOXO. Importantly, the FOXO1 inhibitor rescued the M-PgP-induced disruption of cytokine expression. This study suggests that P. gingivalis persisters may perturb innate host defense, through the upregulation of the FOXO signaling pathway. Thus, the current findings could contribute to developing new approaches to tackling P. gingivalis persisters for the effective control of periodontitis and P. gingivalis-related inflammatory comorbidities.

Exaggerated false positives by popular differential expression methods when analyzing human population samples

When identifying differentially expressed genes between two conditions using human population RNA-seq samples, we found a phenomenon by permutation analysis: two popular bioinformatics methods, DESeq2 and edgeR, have unexpectedly high false discovery rates. Expanding the analysis to limma-voom, NOISeq, dearseq, and Wilcoxon rank-sum test, we found that FDR control is often failed except for the Wilcoxon rank-sum test. Particularly, the actual FDRs of DESeq2 and edgeR sometimes exceed 20% when the target FDR is 5%. Based on these results, for population-level RNA-seq studies with large sample sizes, we recommend the Wilcoxon rank-sum test.

OUP accepted manuscript

Deep sequencing has become one of the most popular tools for transcriptome profiling in biomedical studies. While an abundance of computational methods exists for ‘normalizing’ sequencing data to remove unwanted between-sample variations due to experimental handling, there is no consensus on which normalization is the most suitable for a given data set. To address this problem, we developed ‘DANA’—an approach for assessing the performance of normalization methods for microRNA sequencing data based on biology-motivated and data-driven metrics. Our approach takes advantage of well-known biological features of microRNAs for their expression pattern and chromosomal clustering to simultaneously assess (i) how effectively normalization removes handling artifacts and (ii) how aptly normalization preserves biological signals. With DANA, we confirm that the performance of eight commonly used normalization methods vary widely across different data sets and provide guidance for selecting a suitable method for the data at hand. Hence, it should be adopted as a routine preprocessing step (preceding normalization) for microRNA sequencing data analysis. DANA is implemented in R and publicly available at https://github.com/LXQin/DANA.

Flimma: a federated and privacy-aware tool for differential gene expression analysis

Aggregating transcriptomics data across hospitals can increase sensitivity and robustness of differential expression analyses, yielding deeper clinical insights. As data exchange is often restricted by privacy legislation, meta-analyses are frequently employed to pool local results. However, the accuracy might drop if class labels are inhomogeneously distributed among cohorts. Flimma ( https://exbio.wzw.tum.de/flimma/ ) addresses this issue by implementing the state-of-the-art workflow limma voom in a federated manner, i.e., patient data never leaves its source site. Flimma results are identical to those generated by limma voom on aggregated datasets even in imbalanced scenarios where meta-analysis approaches fail.

The multicellular signalling network of ovarian cancer metastases

BACKGROUND

Transcoelomic spread is the major route of metastasis of ovarian high-grade serous carcinoma (HGSC) with the omentum as the major metastatic site. Its unique tumour microenvironment with its large populations of adipocytes, mesothelial cells and immune cells establishes an intercellular signaling network that is instrumental for metastatic growth yet poorly understood.

METHODS

Based on transcriptomic analysis of tumour cells, tumour-associated immune and stroma cells we defined intercellular signaling pathways for 284 cytokines and growth factors and their cognate receptors after bioinformatic adjustment for contaminating cell types. The significance of individual components of this network was validated by analysing clinical correlations and potentially pro-metastatic functions, including tumour cell migration, pro-inflammatory signal transduction and TAM expansion.

RESULTS

The data show an unexpected prominent role of host cells, and in particular of omental adipocytes, mesothelial cells and fibroblasts (CAF), in sustaining this signaling network. These cells, rather than tumour cells, are the major source of most cytokines and growth factors in the omental microenvironment (n = 176 vs. n = 13). Many of these factors target tumour cells, are linked to metastasis and are associated with a short survival. Likewise, tumour stroma cells play a major role in extracellular-matrix-triggered signaling. We have verified the functional significance of our observations for three exemplary instances. We show that the omental microenvironment (i) stimulates tumour cell migration and adhesion via WNT4 which is highly expressed by CAF; (ii) induces pro-tumourigenic TAM proliferation in conjunction with high CSF1 expression by omental stroma cells and (iii) triggers pro-inflammatory signaling, at least in part via a HSP70-NF-κB pathway.

CONCLUSIONS

The intercellular signaling network of omental metastases is majorly dependent on factors secreted by immune and stroma cells to provide an environment that supports ovarian HGSC progression. Clinically relevant pathways within this network represent novel options for therapeutic intervention.

A zebrafish screen reveals Renin-angiotensin system inhibitors as neuroprotective via mitochondrial restoration in dopamine neurons

Parkinson’s disease (PD) is a common neurodegenerative disorder without effective disease-modifying therapeutics. Here, we establish a chemogenetic dopamine (DA) neuron ablation model in larval zebrafish with mitochondrial dysfunction and robustness suitable for high-content screening. We use this system to conduct an in vivo DA neuron imaging-based chemical screen and identify the Renin-Angiotensin-Aldosterone System (RAAS) inhibitors as significantly neuroprotective. Knockdown of the angiotensin receptor 1 (agtr1) in DA neurons reveals a cell-autonomous mechanism of neuroprotection. DA neuron-specific RNA-seq identifies mitochondrial pathway gene expression that is significantly restored by RAAS inhibitor treatment. The neuroprotective effect of RAAS inhibitors is further observed in a zebrafish Gaucher disease model and Drosophila pink1-deficient PD model. Finally, examination of clinical data reveals a significant effect of RAAS inhibitors in delaying PD progression. Our findings reveal the therapeutic potential and mechanisms of targeting the RAAS pathway for neuroprotection and demonstrate a salient approach that bridges basic science to translational medicine.

Parkinson’s disease is caused by the slow death and deterioration of brain cells, in particular of the neurons that produce a chemical messenger known as dopamine. Certain drugs can mitigate the resulting drop in dopamine levels and help to manage symptoms, but they cause dangerous side-effects. There is no treatment that can slow down or halt the progress of the condition, which affects 0.3% of the population globally.

Many factors, both genetic and environmental, contribute to the emergence of Parkinson’s disease. For example, dysfunction of the mitochondria, the internal structures that power up cells, is a known mechanism associated with the death of dopamine-producing neurons.

Zebrafish are tiny fish which can be used to study Parkinson’s disease, as they are easy to manipulate in the lab and share many characteristics with humans. In particular, they can be helpful to test the effects of various potential drugs on the condition.

Here, Kim et al. established a new zebrafish model in which dopamine-producing brain cells die due to their mitochondria not working properly; they then used this assay to assess the impact of 1,403 different chemicals on the integrity of these cells. A group of molecules called renin-angiotensin-aldosterone (RAAS) inhibitors was shown to protect dopamine-producing neurons and stopped them from dying as often. These are already used to treat high blood pressure as they help to dilate blood vessels. In the brain, however, RAAS worked by restoring certain mitochondrial processes.

Kim et al. then investigated whether these results are relevant in other, broader contexts. They were able to show that RAAS inhibitors have the same effect in other animals, and that Parkinson’s disease often progresses more slowly in patients that already take these drugs for high blood pressure.

Taken together, these findings therefore suggest that RAAS inhibitors may be useful to treat Parkinson’s disease, as well as other brain illnesses that emerge because of mitochondria not working properly. Clinical studies and new ways to improve these drugs are needed to further investigate and capitalize on these potential benefits.

Effect of high variation in transcript expression on identifying differentially expressed genes in RNA-seq analysis

Great efforts have been made on the algorithms that deal with RNA-seq data to enhance the accuracy and efficiency of differential expression (DE) analysis. However, no consensus has been reached on the proper threshold values of fold change and adjusted p-value for filtering differentially expressed genes (DEGs). It is generally believed that the more stringent the filtering threshold, the more reliable the result of a DE analysis. Nevertheless, by analyzing the impact of both adjusted p-value and fold change thresholds on DE analyses, with RNA-seq data obtained for three different cancer types from the Cancer Genome Atlas (TCGA) database, we found that, for a given sample size, the reproducibility of DE results became poorer when more stringent thresholds were applied. No matter which threshold level was applied, the overlap rates of DEGs were generally lower for small sample sizes than for large sample sizes. The raw read count analysis demonstrated that the transcript expression of the same gene in different samples, whether in tumor groups or in normal groups, showed high variations, which resulted in a drastic fluctuation in fold change values and adjustedp-values when different sets of samples were used. Overall, more stringent thresholds did not yield more reliable DEGs due to high variations in transcript expression; the reliability of DEGs obtained with small sample sizes was more susceptible to these variations. Therefore, less stringent thresholds are recommended for screening DEGs. Moreover, large sample sizes should be considered in RNA-seq experimental designs to reduce the interfering effect of variations in transcript expression on DEG identification.

Short-term high fat diet alters genes associated with metabolic and vascular dysfunction during adolescence in rats: a pilot study

BACKGROUND

Diet-induced metabolic dysfunction precedes multiple disease states including diabetes, heart disease, and vascular dysfunction. The critical role of the vasculature in disease progression is established, yet the details of how gene expression changes in early cardiovascular disease remain an enigma. The objective of the current pilot project was to evaluate whether a quantitative assessment of gene expression within the aorta of six-week old healthy male Sprague-Dawley rats compared to those exhibiting symptoms of metabolic dysfunction could reveal potential mediators of vascular dysfunction.

METHODS

RNA was extracted from the aorta of eight rats from a larger experiment; four animals fed a high-fat diet (HFD) known to induce symptoms of metabolic dysfunction (hypertension, increased adiposity, fasting hyperglycemia) and four age-matched healthy animals fed a standard chow diet (CHOW). The bioinformatic workflow included Gene Ontology (GO) biological process enrichment and network analyses.

RESULTS

The resulting network contained genes relevant to physiological processes including fat and protein metabolism, oxygen transport, hormone regulation, vascular regulation, thermoregulation, and circadian rhythm. The majority of differentially regulated genes were downregulated, including several associated with circadian clock function. In contrast, leptin and 3-hydroxy-3-methylglutaryl-CoA synthase 2 (Hmgcs2) were notably upregulated. Leptin is involved in several major energy balance signaling pathways and Hmgcs2 is a mitochondrial enzyme that catalyzes the first reaction of ketogenesis.

CONCLUSION

Together, these data describe changes in gene expression within the aortic wall of HFD rats with early metabolic dysfunction and highlight potential pathways and signaling intermediates that may impact the development of early vascular dysfunction.

Integrated Analysis of miR-430 on Steroidogenesis-Related Gene Expression of Larval Rice Field Eel Monopterus albus

The present study aims to reveal the mechanism by which miR-430s regulate steroidogenesis in larval rice field eel Monopterus albus. To this end, M. albus embryos were respectively microinjected with miRNA-overexpressing mimics (agomir430a, agomir430b, and agomir430c) or miRNA-knockdown inhibitors (antagomir430a, antagomir430b, and antagomir430c). Transcriptome profiling of the larvae indicated that a total of more than 149 differentially expressed genes (DEGs) were identified among the eight treatments. Specifically, DEGs related to steroidogenesis, the GnRH signaling pathway, the erbB signaling pathway, the Wnt signaling pathway, and other pathways were characterized in the transcriptome. We found that steroidogenesis-related genes (hydroxysteroid 17-beta dehydrogenase 3 (17β-hsdb3), hydroxysteroid 17-beta dehydrogenase 7 (17β-hsdb7), hydroxysteroid 17-beta dehydrogenase 12 (17β-hsdb12), and cytochrome P450 family 19 subfamily a (cyp19a1b)) were significantly downregulated in miR-430 knockdown groups. The differential expressions of miR-430 in three gonads indicated different roles of three miR-430 (a, b, and c) isoforms in regulating steroidogenesis and sex differentiation. Mutation of the miR-430 sites reversed the downregulation of cytochrome P450 family 17 (cyp17), cyp19a1b, and forkhead box L2 (foxl2) reporter activities by miR-430, indicating that miR-430 directly interacted with cyp17, cyp19a1b, and foxl2 genes to inhibit their expressions. Combining these findings, we concluded that miR-430 regulated the steroidogenesis and the biosynthesis of steroid hormones by targeting cyp19a1b in larval M. albus. Our results provide a novel insight into steroidogenesis at the early stage of fish at the molecular level.

The overexpression of DNA repair genes in invasive ductal and lobular breast carcinomas: Insights on individual variations and precision medicine

In the era of precision medicine, analyzing the transcriptomic profile of patients is essential to tailor the appropriate therapy. In this study, we explored transcriptional differences between two invasive breast cancer subtypes; infiltrating ductal carcinoma (IDC) and lobular carcinoma (LC) using RNA-Seq data deposited in the TCGA-BRCA project. We revealed 3854 differentially expressed genes between normal ductal tissues and IDC. In addition, IDC to LC comparison resulted in 663 differentially expressed genes. We then focused on DNA repair genes because of their known effects on patients' response to therapy and resistance. We here report that 36 DNA repair genes are overexpressed in a significant number of both IDC and LC patients' samples. Despite the upregulation in a significant number of samples, we observed a noticeable variation in the expression levels of the repair genes across patients of the same cancer subtype. The same trend is valid for the expression of miRNAs, where remarkable variations between patients' samples of the same cancer subtype are also observed. These individual variations could lie behind the differential response of patients to treatment. The future of cancer diagnostics and therapy will inevitably depend on high-throughput genomic and transcriptomic data analysis. However, we propose that performing analysis on individual patients rather than a big set of patients' samples will be necessary to ensure that the best treatment is determined, and therapy resistance is reduced.

Galaxy and MEAN Stack to Create a User-Friendly Workflow for the Rational Optimization of Cancer Chemotherapy

One aspect of personalized medicine is aiming at identifying specific targets for therapy considering the gene expression profile of each patient individually. The real-world implementation of this approach is better achieved by user-friendly bioinformatics systems for healthcare professionals. In this report, we present an online platform that endows users with an interface designed using MEAN stack supported by a Galaxy pipeline. This pipeline targets connection hubs in the subnetworks formed by the interactions between the proteins of genes that are up-regulated in tumors. This strategy has been proved to be suitable for the inhibition of tumor growth and metastasis in vitro. Therefore, Perl and Python scripts were enclosed in Galaxy for translating RNA-seq data into protein targets suitable for the chemotherapy of solid tumors. Consequently, we validated the process of target diagnosis by (i) reference to subnetwork entropy, (ii) the critical value of density probability of differential gene expression, and (iii) the inhibition of the most relevant targets according to TCGA and GDC data. Finally, the most relevant targets identified by the pipeline are stored in MongoDB and can be accessed through the aforementioned internet portal designed to be compatible with mobile or small devices through Angular libraries.

High heterogeneity undermines generalization of differential expression results in RNA-Seq analysis

BACKGROUND

RNA sequencing (RNA-Seq) has been widely applied in oncology for monitoring transcriptome changes. However, the emerging problem that high variation of gene expression levels caused by tumor heterogeneity may affect the reproducibility of differential expression (DE) results has rarely been studied. Here, we investigated the reproducibility of DE results for any given number of biological replicates between 3 and 24 and explored why a great many differentially expressed genes (DEGs) were not reproducible.

RESULTS

Our findings demonstrate that poor reproducibility of DE results exists not only for small sample sizes, but also for relatively large sample sizes. Quite a few of the DEGs detected are specific to the samples in use, rather than genuinely differentially expressed under different conditions. Poor reproducibility of DE results is mainly caused by high variation of gene expression levels for the same gene in different samples. Even though biological variation may account for much of the high variation of gene expression levels, the effect of outlier count data also needs to be treated seriously, as outlier data severely interfere with DE analysis.

CONCLUSIONS

High heterogeneity exists not only in tumor tissue samples of each cancer type studied, but also in normal samples. High heterogeneity leads to poor reproducibility of DEGs, undermining generalization of differential expression results. Therefore, it is necessary to use large sample sizes (at least 10 if possible) in RNA-Seq experimental designs to reduce the impact of biological variability and DE results should be interpreted cautiously unless soundly validated.

Rapid single cell evaluation of human disease and disorder targets using REVEAL: SingleCell™

BACKGROUND

Single-cell (sc) sequencing performs unbiased profiling of individual cells and enables evaluation of less prevalent cellular populations, often missed using bulk sequencing. However, the scale and the complexity of the sc datasets poses a great challenge in its utility and this problem is further exacerbated when working with larger datasets typically generated by consortium efforts. As the scale of single cell datasets continues to increase exponentially, there is an unmet technological need to develop database platforms that can evaluate key biological hypotheses by querying extensive single-cell datasets. Large single-cell datasets like Human Cell Atlas and COVID-19 cell atlas (collection of annotated sc datasets from various human organs) are excellent resources for profiling target genes involved in human diseases and disorders ranging from oncology, auto-immunity, as well as infectious diseases like COVID-19 caused by SARS-CoV-2 virus. SARS-CoV-2 infections have led to a worldwide pandemic with massive loss of lives, infections exceeding 7 million cases. The virus uses ACE2 and TMPRSS2 as key viral entry associated proteins expressed in human cells for infections. Evaluating the expression profile of key genes in large single-cell datasets can facilitate testing for diagnostics, therapeutics, and vaccine targets, as the world struggles to cope with the on-going spread of COVID-19 infections.

MAIN BODY

In this manuscript we describe REVEAL: SingleCell, which enables storage, retrieval, and rapid query of single-cell datasets inclusive of millions of cells. The array native database described here enables selecting and analyzing cells across multiple studies. Cells can be selected using individual metadata tags, more complex hierarchical ontology filtering, and gene expression threshold ranges, including co-expression of multiple genes. The tags on selected cells can be further evaluated for testing biological hypotheses. One such example includes identifying the most prevalent cell type annotation tag on returned cells. We used REVEAL: SingleCell to evaluate the expression of key SARS-CoV-2 entry associated genes, and queried the current database (2.2 Million cells, 32 projects) to obtain the results in < 60 s. We highlighted cells expressing COVID-19 associated genes are expressed on multiple tissue types, thus in part explains the multi-organ involvement in infected patients observed worldwide during the on-going COVID-19 pandemic.

CONCLUSION

In this paper, we introduce the REVEAL: SingleCell database that addresses immediate needs for SARS-CoV-2 research and has the potential to be used more broadly for many precision medicine applications. We used the REVEAL: SingleCell database as a reference to ask questions relevant to drug development and precision medicine regarding cell type and co-expression for genes that encode proteins necessary for SARS-CoV-2 to enter and reproduce in cells.

Transcriptomic profiling in Cutaneous Leishmaniasis patients

INTRODUCTION

Cutaneous leishmaniasis (CL), caused by different Leishmania parasite species, is associated with parasite-induced immune-mediated skin inflammation and ulceration. Whereas many CL studies focus on gene expression signatures in mouse models, the transcriptional response driving human patients in the field is less characterized. Human studies in CL disease provide the opportunity to directly investigate the host-pathogen interaction in the cutaneous lesion site.

AREAS COVERED

Advances in high-throughput sequencing technologies, particularly their application for evaluation of the global gene expression changes, have made transcriptomics as a powerful tool to understand the pathogen-host molecular interactions.

EXPERT COMMENTARY

In this review, we focus on the transcriptomics studies that have been performed so far on human blood or tissue-driven samples to investigate Leishmania parasites interplay with the CL patients. Further, we summarize microarray and RNA-seq studies associated with lesion biopsies of CL patients to discuss how current whole genome analysis along with systems biology approaches have developed novel CL biomarkers for further applications, not only for research, but also for accelerating vaccine development.