Large-scale Gene Expression Data Research Articles

Potential shared pathogenic mechanisms between endometriosis and inflammatory bowel disease indicate a strong initial effect of immune factors.

Over the past decades, immune dysregulation has been consistently demonstrated being common charactoristics of endometriosis (EM) and Inflammatory Bowel Disease (IBD) in numerous studies. However, the underlying pathological mechanisms remain unknown. In this study, bioinformatics techniques were used to screen large-scale gene expression data for plausible correlations at the molecular level in order to identify common pathogenic pathways between EM and IBD. Based on the EM transcriptomic datasets GSE7305 and GSE23339, as well as the IBD transcriptomic datasets GSE87466 and GSE126124, differential gene analysis was performed using the limma package in the R environment. Co-expressed differentially expressed genes were identified, and a protein-protein interaction (PPI) network for the differentially expressed genes was constructed using the 11.5 version of the STRING database. The MCODE tool in Cytoscape facilitated filtering out protein interaction subnetworks. Key genes in the PPI network were identified through two topological analysis algorithms (MCC and Degree) from the CytoHubba plugin. Upset was used for visualization of these key genes. The diagnostic value of gene expression levels for these key genes was assessed using the Receiver Operating Characteristic (ROC) curve and Area Under the Curve (AUC) The CIBERSORT algorithm determined the infiltration status of 22 immune cell subtypes, exploring differences between EM and IBD patients in both control and disease groups. Finally, different gene expression trends shared by EM and IBD were input into CMap to identify small molecule compounds with potential therapeutic effects. 113 differentially expressed genes (DEGs) that were co-expressed in EM and IBD have been identified, comprising 28 down-regulated genes and 86 up-regulated genes. The co-expression differential gene of EM and IBD in the functional enrichment analyses focused on immune response activation, circulating immunoglobulin-mediated humoral immune response and humoral immune response. Five hub genes (SERPING1、VCAM1、CLU、C3、CD55) were identified through the Protein-protein Interaction network and MCODE.High Area Under the Curve (AUC) values of Receiver Operating Characteristic (ROC) curves for 5hub genes indicate the predictive ability for disease occurrence.These hub genes could be used as potential biomarkers for the development of EM and IBD. Furthermore, the CMap database identified a total of 9 small molecule compounds (TTNPB、CAY-10577、PD-0325901 etc.) targeting therapeutic genes for EM and IBD. Our research revealed common pathogenic mechanisms between EM and IBD, particularly emphasizing immune regulation and cell signalling, indicating the significance of immune factors in the occurence and progression of both diseases. By elucidating shared mechanisms, our study provides novel avenues for the prevention and treatment of EM and IBD.

Large-scale gene expression changes in APP/PSEN1 and GFAP mutation models exhibit high congruence with Alzheimer's disease.

Alzheimer's disease (AD) is a complex neurodegenerative disorder with both genetic and non-genetic causes. Animal research models are available for a multitude of diseases and conditions affecting the central nervous system (CNS), and large-scale CNS gene expression data exist for many of these. Although there are several models specifically for AD, each recapitulates different aspects of the human disease. In this study we evaluate over 500 animal models to identify those with CNS gene expression patterns matching human AD datasets. Approaches included a hypergeometric based scoring system that rewards congruent gene expression patterns but penalizes discordant gene expression patterns. The top two models identified were APP/PS1 transgenic mice expressing mutant APP and PSEN1, and mice carrying a GFAP mutation that is causative of Alexander disease, a primary disorder of astrocytes in the CNS. The APP/PS1 and GFAP models both matched over 500 genes moving in the same direction as in human AD, and both had elevated GFAP expression and were highly congruent with one another. Also scoring highly were the 5XFAD model (with five mutations in APP and PSEN1) and mice carrying CK-p25, APP, and MAPT mutations. Animals with the APOE3 and 4 mutations combined with traumatic brain injury ranked highly. Bulbectomized rats scored high, suggesting anosmia could be causative of AD-like gene expression. Other matching models included the SOD1G93A strain and knockouts for SNORD116 (Prader-Willi mutation), GRID2, INSM1, XBP1, and CSTB. Many top models demonstrated increased expression of GFAP, and results were similar across multiple human AD datasets. Heatmap and Uniform Manifold Approximation Plot results were consistent with hypergeometric ranking. Finally, some gene manipulation models, including for TYROBP and ATG7, were identified with reversed AD patterns, suggesting possible neuroprotective effects. This study provides insight for the pathobiology of AD and the potential utility of available animal models.

Molecular and clinical characterization of ICOS expression in breast cancer through large-scale transcriptome data.

ICOS (Inducible T Cell Costimulator), one of the co-stimulatory B7 superfamily members, was characterized as a co-stimulatory receptor for T-cell enhancement. However, the role of ICOS in breast cancer remains largely unknown. The present study systematically investigated the expression pattern and its relation to clinical characteristics and immunotherapy by integrating multiple clinical cohorts and large-scale gene expression data. This study included 2994 breast tumor samples with transcriptome data and matched clinical data. To make our findings more reliable, we set the TCGA cohort as the discovery set and the METABRIC cohort as the validation set. The expression of ICOS in breast cancer is strongly associated with major clinical and molecular characteristics. There is an association between higher ICOS expression and malignant subtypes and grades of tumors. In addition, gene ontology analysis based on genes significantly correlated with ICOS expression indicated that the expression of ICOS is mainly associated with immune responses and inflammation. We also observed strong correlations between ICOS and other promising immune-checkpoint molecules, including PD1, PDL1, CTLA4, and IDO1. Furthermore, we found that ICOS expression is associated with the response to anti-PDL1 immunotherapy and may serve as a biomarker for immunotherapy prediction. Our results indicated higher ICOS expression is significantly associated with favorable survival in triple-negative breast cancer (TNBC) patients, but not for all subtypes of breast cancer patients. In summary, ICOS correlates with higher malignant breast cancers, and it contributes to the regulation of the immune microenvironment of breast tumors, making it a potential biomarker and immunotherapy target.

An accurate aging clock developed from large-scale gut microbiome and human gene expression data

Accurate measurement of the biological markers of the aging process could provide an "aging clock" measuring predicted longevity and enable the quantification of the effects of specific lifestyle choices on healthy aging. Using machine learning techniques, we demonstrate that chronological age can be predicted accurately from (1) the expression level of human genes in capillary blood and (2) the expression level of microbial genes in stool samples. The latter uses a very large metatranscriptomic dataset, stool samples from 90,303 individuals, which arguably results in a higher quality microbiome-aging model than prior work. Our analysis suggests associations between biological age and lifestyle/health factors, e.g., people on a paleo diet or with IBS tend to have higher model-predicted ages and people on a vegetarian diet tend to have lower model-predicted ages. We delineate the key pathways of systems-level biological decline based on the age-specific features of our model.

Systems immunology-based drug repurposing framework to target inflammation in atherosclerosis

The development of new immunotherapies to treat the inflammatory mechanisms that sustain atherosclerotic cardiovascular disease (ASCVD) is urgently needed. Herein, we present a path to drug repurposing to identify immunotherapies for ASCVD. The integration of time-of-flight mass cytometry and RNA sequencing identified unique inflammatory signatures in peripheral blood mononuclear cells stimulated with ASCVD plasma. By comparing these inflammatory signatures to large-scale gene expression data from the LINCS L1000 dataset, we identified drugs that could reverse this inflammatory response. Ex vivo screens, using human samples, showed that saracatinib—a phase 2a-ready SRC and ABL inhibitor—reversed the inflammatory responses induced by ASCVD plasma. In Apoe−/− mice, saracatinib reduced atherosclerosis progression by reprogramming reparative macrophages. In a rabbit model of advanced atherosclerosis, saracatinib reduced plaque inflammation measured by [18F]fluorodeoxyglucose positron emission tomography–magnetic resonance imaging. Here we show a systems immunology-driven drug repurposing with a preclinical validation strategy to aid the development of cardiovascular immunotherapies.

Integrating Multiple Single-Cell RNA Sequencing Datasets Using Adversarial Autoencoders.

Single-cell RNA sequencing (RNA-seq) has been demonstrated to be a proven method for quantifying gene-expression heterogeneity and providing insight into the transcriptome at the single-cell level. When combining multiple single-cell transcriptome datasets for analysis, it is common to first correct the batch effect. Most of the state-of-the-art processing methods are unsupervised, i.e., they do not utilize single-cell cluster labeling information, which could improve the performance of batch correction methods, especially in the case of multiple cell types. To better utilize known labels for complex dataset scenarios, we propose a novel deep learning model named IMAAE (i.e., integrating multiple single-cell datasets via an adversarial autoencoder) to correct the batch effects. After conducting experiments with various dataset scenarios, the results show that IMAAE outperforms existing methods for both qualitative measures and quantitative evaluation. In addition, IMAAE is able to retain both corrected dimension reduction data and corrected gene expression data. These features make it a potential new option for large-scale single-cell gene expression data analysis.

Gene regulation network inference using k-nearest neighbor-based mutual information estimation: revisiting an old DREAM

BackgroundA cell exhibits a variety of responses to internal and external cues. These responses are possible, in part, due to the presence of an elaborate gene regulatory network (GRN) in every single cell. In the past 20 years, many groups worked on reconstructing the topological structure of GRNs from large-scale gene expression data using a variety of inference algorithms. Insights gained about participating players in GRNs may ultimately lead to therapeutic benefits. Mutual information (MI) is a widely used metric within this inference/reconstruction pipeline as it can detect any correlation (linear and non-linear) between any number of variables (n-dimensions). However, the use of MI with continuous data (for example, normalized fluorescence intensity measurement of gene expression levels) is sensitive to data size, correlation strength and underlying distributions, and often requires laborious and, at times, ad hoc optimization.ResultsIn this work, we first show that estimating MI of a bi- and tri-variate Gaussian distribution using k-nearest neighbor (kNN) MI estimation results in significant error reduction as compared to commonly used methods based on fixed binning. Second, we demonstrate that implementing the MI-based kNN Kraskov–Stoögbauer–Grassberger (KSG) algorithm leads to a significant improvement in GRN reconstruction for popular inference algorithms, such as Context Likelihood of Relatedness (CLR). Finally, through extensive in-silico benchmarking we show that a new inference algorithm CMIA (Conditional Mutual Information Augmentation), inspired by CLR, in combination with the KSG-MI estimator, outperforms commonly used methods.ConclusionsUsing three canonical datasets containing 15 synthetic networks, the newly developed method for GRN reconstruction—which combines CMIA, and the KSG-MI estimator—achieves an improvement of 20–35% in precision-recall measures over the current gold standard in the field. This new method will enable researchers to discover new gene interactions or better choose gene candidates for experimental validations.

Computational approaches for discovering significant microRNAs, microRNA-mRNA regulatory pathways, and therapeutic protein targets in endometrial cancer.

Endometrial cancer (EC) is a urogenital cancer affecting millions of post-menopausal women, globally. This study aims to identify key miRNAs, target genes, and drug targets associated with EC metastasis. The global miRNA and mRNA expression datasets of endometrial tissue biopsies (24 tumors +3 healthy tissues for mRNA and 18 tumor +4 healthy tissues for miRNAs), were extensively analyzed by mapping of DEGs, DEMi, biological pathway enrichment, miRNA-mRNA networking, drug target identification, and survival curve output for differentially expressed genes. Our results reveal the dysregulated expression of 26 miRNAs and their 66 target genes involved in focal adhesions, p53 signaling pathway, ECM-receptor interaction, Hedgehog signaling pathway, fat digestion and absorption, glioma as well as retinol metabolism involved in cell growth, migration, and proliferation of endometrial cancer cells. The subsequent miRNA-mRNA network and expression status analysis have narrowed down to 2 hub miRNAs (hsa-mir-200a, hsa-mir-429) and 6 hub genes (PTCH1, FOSB, PDGFRA, CCND2, ABL1, ALDH1A1). Further investigations with different systems biology methods have prioritized ALDH1A1, ABL1 and CCND2 as potential genes involved in endometrial cancer metastasis owing to their high mutation load and expression status. Interestingly, overexpression of PTCH1, ABL1 and FOSB genes are reported to be associated with a low survival rate among cancer patients. The upregulated hsa-mir-200a-b is associated with the decreased expression of the PTCH1, CCND2, PDGFRA, FOSB and ABL1 genes in endometrial cancer tissue while hsa-mir-429 is correlated with the decreased expression of the ALDH1A1 gene, besides some antibodies, PROTACs and inhibitory molecules. In conclusion, this study identified key miRNAs (hsa-mir-200a, hsa-mir-429) and target genes ALDH1A1, ABL1 and CCND2 as potential biomarkers for metastatic endometrial cancers from large-scale gene expression data using systems biology approaches.

A comparative mRNA- and miRNA transcriptomics reveals novel molecular signatures associated with metastatic prostate cancers.

Background: Prostate cancer (PC) is a fatally aggressive urogenital cancer killing millions of men, globally. Thus, this study aims to identify key miRNAs, target genes, and drug targets associated with prostate cancer metastasis. Methods: The miRNA and mRNA expression datasets of 148 prostate tissue biopsies (39 tumours and 109 normal tissues), were analysed by differential gene expression analysis, protein interactome mapping, biological pathway analysis, miRNA-mRNA networking, drug target analysis, and survival curve analysis. Results: The dysregulated expression of 53 miRNAs and their 250 target genes involved in Hedgehog, ErbB, and cAMP signalling pathways connected to cell growth, migration, and proliferation of prostate cancer cells was detected. The subsequent miRNA-mRNA network and expression status analysis have helped us in narrowing down their number to 3 hub miRNAs (hsa-miR-455-3p, hsa-miR-548c-3p, and hsa-miR-582-5p) and 9 hub genes (NFIB, DICER1, GSK3B, DCAF7, FGFR1OP, ABHD2, NACC2, NR3C1, and FGF2). Further investigations with different systems biology methods have prioritized NR3C1, ABHD2, and GSK3B as potential genes involved in prostate cancer metastasis owing to their high mutation load and expression status. Interestingly, down regulation of NR3C1 seems to improve the prostate cancer patient survival rate beyond 150months. The NR3C1, ABHD2, and GSK3B genes are predicted to be targeted by hsa-miR-582-5p, besides some antibodies, PROTACs and inhibitory molecules. Conclusion: This study identified key miRNAs (miR-548c-3p and miR-582-5p) and target genes (NR3C1, ABHD2, and GSK3B) as potential biomarkers for metastatic prostate cancers from large-scale gene expression data using systems biology approaches.

A novel approach GRNTSTE to reconstruct gene regulatory interactions applied to a case study for rat pineal rhythm gene

Accurate inference and prediction of gene regulatory network are very important for understanding dynamic cellular processes. The large-scale time series genomics data are helpful to reveal the molecular dynamics and dynamic biological processes of complex biological systems. Firstly, we collected the time series data of the rat pineal gland tissue in the natural state according to a fixed sampling rate, and performed whole-genome sequencing. The large-scale time-series sequencing data set of rat pineal gland was constructed, which includes 480 time points, the time interval between adjacent time points is 3 min, and the sampling period is 24 h. Then, we proposed a new method of constructing gene expression regulatory network, named the gene regulatory network based on time series data and entropy transfer (GRNTSTE) method. The method is based on transfer entropy and large-scale time-series gene expression data to infer the causal regulatory relationship between genes in a data-driven mode. The comparative experiments prove that GRNTSTE has better performance than dynamical gene network inference with ensemble of trees (dynGENIE3) and SCRIBE, and has similar performance to TENET. Meanwhile, we proved that the performance of GRNTSTE is slightly lower than that of SINCERITIES method and better than other gene regulatory network construction methods in BEELINE framework, which is based on the BEELINE data set. Finally, the rat pineal rhythm gene expression regulatory network was constructed by us based on the GRNTSTE method, which provides an important reference for the study of the pineal rhythm mechanism, and is of great significance to the study of the pineal rhythm mechanism.

Muscle fiber type-dependence effect of exercise on genomic networks in aged mice models.

Skeletal muscles are made up of various muscle fiber type including slow and fast-twitch fibers. Because each muscle fiber has its own physiological characteristics, the effects of aging and exercise vary depending on the type of muscle fiber. We used bioinformatics screening techniques such as differentially expressed gene analysis, gene ontology analysis and gene set enrichment analysis, to try to understand the genetic differences between muscle fiber types. The experiment and gene expression profiling in this study used the soleus (SOL, slow-twitch muscle) and gastrocnemius (GAS, fast-twitch muscle). According to our findings, fatty acid metabolism is significantly up-regulated in SOL compared to GAS, whereas the glucose metabolism pathway is significantly down-regulated in SOL compared to GAS.Furthermore, apoptosis and myogenesis patterns differ between SOL and GAS. SOL did not show differences in apoptosis due to the aging effect, but apoptosis in GAS was significantly up-regulated with age. Apoptosis in GAS of old groups is significantly reduced after 4 weeks of aerobic exercise, but no such finding was found in SOL. In terms of myogenesis, exercise intervention up-regulated this process in GAS of old groups but not in SOL. Taken together, muscle fiber type significantly interacts with aging and exercise. Despite the importance of the interaction between these factors, large-scale gene expression data has rarely been studied. We hope to contribute to a better understanding of the relationship between muscle fiber type, aging and exercise at the molecular level.

Integrative global co-expression analysis identifies key microRNA-target gene networks as key blood biomarkers for obesity.

Obesity is associated with the quantitative changes in miRNAs and their target genes. However, the molecular basis of their dysregulation and expression status correlations is incompletely understood. Therefore, this study aims to examine the shared differentially expressed miRNAs and their target genes between blood and adipose tissues of obese individuals to identify potential blood-based biomarkers. In this study, 3 gene expression datasets (two mRNA and one miRNA), generated from blood and adipose tissues of 68 obese and 39 lean individuals, were analyzed by a series of robust computational concepts, like protein interactome mapping, functional enrichment of biological pathways and construction of miRNA-mRNA and transcription factor gene networks. The comparison of blood versus tissue datasets has revealed the shared differential expression of 210 genes (59.5% upregulated) involved in lipid metabolism and inflammatory reactions. The blood miRNA (GSE25470) analysis has identified 79 differentially expressed miRNAs (71% downregulated). The miRNA-target gene scan identified regulation of 30 shared genes by 22miRNAs. The gene network analysis has identified the inverse expression correlation between 8 target genes (TP53, DYSF, GAB2, GFRA2, NACC2, FAM53C, JNK and GAB2) and 3 key miRNAs (hsa-mir-940, hsa-mir-765, hsa-mir-612), which are further regulated by 24 key transcription factors. This study identifies potential obesity related blood biomarkers from large-scale gene expression data by computational miRNA-target gene interactome and transcription factor network construction methods.

Reconstruction of dynamic regulatory networks reveals signaling-induced topology changes associated with germ layer specification.

SummaryElucidating regulatory relationships between transcription factors (TFs) and target genes is fundamental to understanding how cells control their identity and behavior. Unfortunately, existing computational gene regulatory network (GRN) reconstruction methods are imprecise, computationally burdensome, and fail to reveal dynamic regulatory topologies. Here, we present Epoch, a reconstruction tool that uses single-cell transcriptomics to accurately infer dynamic networks. We apply Epoch to identify the dynamic networks underpinning directed differentiation of mouse embryonic stem cells (ESCs) guided by multiple signaling pathways, and we demonstrate that modulating these pathways drives topological changes that bias cell fate potential. We also find that Peg3 rewires the pluripotency network to favor mesoderm specification. By integrating signaling pathways with GRNs, we trace how Wnt activation and PI3K suppression govern mesoderm and endoderm specification, respectively. Finally, we identify regulatory circuits of patterning and axis formation that distinguish in vitro and in vivo mesoderm specification.

FoxO3a Inhibits Tamoxifen-Resistant Breast Cancer Progression by Inducing Integrin α5 Expression.

Simple SummaryTamoxifen is a mainstay of adjuvant treatment for estrogen receptor α-positive (ERα+) breast tumors, which account for over 70% of all the diagnosed breast malignancies. Unfortunately, ~30% of patients initially responding to the therapy will eventually relapse with an endocrine-resistant disease. Here, we show that the transcription factor FoxO3a can overcome tamoxifen resistance by inhibiting cell motility and invasiveness, the main features of tumor progression. The underlying mechanism could be ascribed to FoxO3a-dependent transcriptional up-regulation of the integrin α5 subunit of the α5β1 fibronectin receptor, a well-known membrane heterodimer controlling cell adhesion. Indeed, FoxO3a and integrin α5 expression is significantly correlated in ERα+ tumors, and FoxO3a protective effects are lost in cells with reduced levels of integrin α5. Therefore, a pharmacological increase in FoxO3a levels could represent an effective approach to be exploited in combination with tamoxifen treatment in order to restore the sensitivity to the therapy in refractory tumors.Resistance to endocrine therapy is still a major clinical challenge in the management of estrogen receptor α-positive (ERα+) breast cancer (BC). Here, the role of the Forkhead box class O (FoxO)3a transcription factor in tumor progression has been evaluated in tamoxifen-resistant BC cells (TamR), expressing lower levels of FoxO3a compared to sensitive ones. FoxO3a re-expression reduces TamR motility (wound-healing and transmigration assays) and invasiveness (matrigel transwell invasion assays) through the mRNA (qRT-PCR) and protein (Western blot) induction of the integrin α5 subunit of the α5β1 fibronectin receptor, a well-known membrane heterodimer controlling cell adhesion and signaling. The induction occurs through FoxO3a binding to a specific Forkhead responsive core sequence located on the integrin α5 promoter (cloning, luciferase, and ChIP assays). Moreover, FoxO3a failed to inhibit migration and invasion in integrin α5 silenced (siRNA) cells, demonstrating integrin α5 involvement in both processes. Finally, using large-scale gene expression data sets, a strong positive correlation between FoxO3a and integrin α5 in ERα+, but not in ER-negative (ERα−), BC patients emerged. Altogether, our data show how the oncosuppressor FoxO3a, by increasing the expression of its novel transcriptional target integrin α5, reverts the phenotype of endocrine-resistant BC toward a lower aggressiveness.

Discovery of a Carbamoyl Phosphate Synthetase 1-Deficient HCC Subtype With Therapeutic Potential Through Integrative Genomic and Experimental Analysis.

Metabolic reprogramming plays an important role in tumorigenesis. However, the metabolic types of different tumors are diverse and lack in-depth study. Here, through analysis of big databases and clinical samples, we identified a carbamoyl phosphate synthetase 1 (CPS1)-deficient hepatocellular carcinoma (HCC) subtype, explored tumorigenesis mechanism of this HCC subtype, and aimed to investigate metabolic reprogramming as a target for HCC prevention. A pan-cancer study involving differentially expressed metabolic genes of 7,764 tumor samples in 16 cancer types provided by The Cancer Genome Atlas (TCGA) demonstrated that urea cycle (UC) was liver-specific and was down-regulated in HCC. A large-scale gene expression data analysis including 2,596 HCC cases in 7 HCC cohorts from Database of HCC Expression Atlas and 17,444 HCC cases from in-house hepatectomy cohort identified a specific CPS1-deficent HCC subtype with poor clinical prognosis. In vitro and in vivo validation confirmed the crucial role of CPS1 in HCC. Liquid chromatography-mass spectrometry assay and Seahorse analysis revealed that UC disorder (UCD) led to the deceleration of the tricarboxylic acid cycle, whereas excess ammonia caused by CPS1 deficiency activated fatty acid oxidation (FAO) through phosphorylated adenosine monophosphate-activated protein kinase. Mechanistically, FAO provided sufficient ATP for cell proliferation and enhanced chemoresistance of HCC cells by activating forkhead box protein M1. Subcutaneous xenograft tumor models and patient-derived organoids were employed to identify that blocking FAO by etomoxir may provide therapeutic benefit to HCC patients with CPS1 deficiency. In conclusion, our results prove a direct link between UCD and cancer stemness in HCC, define a CPS1-deficient HCC subtype through big-data mining, and provide insights for therapeutics for this type of HCC through targeting FAO.

Assessment of reference genes at six different developmental stages of Schistosoma mansoni for quantitative RT-PCR

Reverse-transcription quantitative real-time polymerase chain reaction (RT-qPCR) is the most used, fast, and reproducible method to confirm large-scale gene expression data. The use of stable reference genes for the normalization of RT-qPCR assays is recognized worldwide. No systematic study for selecting appropriate reference genes for usage in RT-qPCR experiments comparing gene expression levels at different Schistosoma mansoni life-cycle stages has been performed. Most studies rely on genes commonly used in other organisms, such as actin, tubulin, and GAPDH. Therefore, the present study focused on identifying reference genes suitable for RT-qPCR assays across six S. mansoni developmental stages. The expression levels of 25 novel candidates that we selected based on the analysis of public RNA-Seq datasets, along with eight commonly used reference genes, were systematically tested by RT-qPCR across six developmental stages of S. mansoni (eggs, miracidia, cercariae, schistosomula, adult males and adult females). The stability of genes was evaluated with geNorm, NormFinder and RefFinder algorithms. The least stable candidate reference genes tested were actin, tubulin and GAPDH. The two most stable reference genes suitable for RT-qPCR normalization were Smp_101310 (Histone H4 transcription factor) and Smp_196510 (Ubiquitin recognition factor in ER-associated degradation protein 1). Performance of these two genes as normalizers was successfully evaluated with females maintained unpaired or paired to males in culture for 8 days, or with worm pairs exposed for 16 days to double-stranded RNAs to silence a protein-coding gene. This study provides reliable reference genes for RT-qPCR analysis using samples from six different S. mansoni life-cycle stages.

DeepMNN: Deep Learning-Based Single-Cell RNA Sequencing Data Batch Correction Using Mutual Nearest Neighbors.

It is well recognized that batch effect in single-cell RNA sequencing (scRNA-seq) data remains a big challenge when integrating different datasets. Here, we proposed deepMNN, a novel deep learning-based method to correct batch effect in scRNA-seq data. We first searched mutual nearest neighbor (MNN) pairs across different batches in a principal component analysis (PCA) subspace. Subsequently, a batch correction network was constructed by stacking two residual blocks and further applied for the removal of batch effects. The loss function of deepMNN was defined as the sum of a batch loss and a weighted regularization loss. The batch loss was used to compute the distance between cells in MNN pairs in the PCA subspace, while the regularization loss was to make the output of the network similar to the input. The experiment results showed that deepMNN can successfully remove batch effects across datasets with identical cell types, datasets with non-identical cell types, datasets with multiple batches, and large-scale datasets as well. We compared the performance of deepMNN with state-of-the-art batch correction methods, including the widely used methods of Harmony, Scanorama, and Seurat V4 as well as the recently developed deep learning-based methods of MMD-ResNet and scGen. The results demonstrated that deepMNN achieved a better or comparable performance in terms of both qualitative analysis using uniform manifold approximation and projection (UMAP) plots and quantitative metrics such as batch and cell entropies, ARI F1 score, and ASW F1 score under various scenarios. Additionally, deepMNN allowed for integrating scRNA-seq datasets with multiple batches in one step. Furthermore, deepMNN ran much faster than the other methods for large-scale datasets. These characteristics of deepMNN made it have the potential to be a new choice for large-scale single-cell gene expression data analysis.

A single-cell analysis of the Arabidopsis vegetative shoot apex.

The shoot apical meristem allows for reiterative formation of new aerial structures throughout the life cycle of a plant. We use single-cell RNA sequencing to define the cellular taxonomy of the Arabidopsis vegetative shoot apex at the transcriptome level. We find that the shoot apex is composed of highly heterogeneous cells, which can be partitioned into 7 broad populations with 23 transcriptionally distinct cell clusters. We delineate cell-cycle continuums and developmental trajectories of epidermal cells, vascular tissue, and leaf mesophyll cells and infer transcription factors and gene expression signatures associated with cell fate decisions. Integrative analysis of shoot and root apical cell populations further reveals common and distinct features of epidermal and vascular tissues. Our results, thus, offer a valuable resource for investigating the basic principles underlying cell division and differentiation in plants at single-cell resolution.

Joint Lp-Norm and L2,1-Norm Constrained Graph Laplacian PCA for Robust Tumor Sample Clustering and Gene Network Module Discovery.

The dimensionality reduction method accompanied by different norm constraints plays an important role in mining useful information from large-scale gene expression data. In this article, a novel method named Lp-norm and L2,1-norm constrained graph Laplacian principal component analysis (PL21GPCA) based on traditional principal component analysis (PCA) is proposed for robust tumor sample clustering and gene network module discovery. Three aspects are highlighted in the PL21GPCA method. First, to degrade the high sensitivity to outliers and noise, the non-convex proximal Lp-norm (0 < p < 1)constraint is applied on the loss function. Second, to enhance the sparsity of gene expression in cancer samples, the L2,1-norm constraint is used on one of the regularization terms. Third, to retain the geometric structure of the data, we introduce the graph Laplacian regularization item to the PL21GPCA optimization model. Extensive experiments on five gene expression datasets, including one benchmark dataset, two single-cancer datasets from The Cancer Genome Atlas (TCGA), and two integrated datasets of multiple cancers from TCGA, are performed to validate the effectiveness of our method. The experimental results demonstrate that the PL21GPCA method performs better than many other methods in terms of tumor sample clustering. Additionally, this method is used to discover the gene network modules for the purpose of finding key genes that may be associated with some cancers.

Unsupervised Gene Regulatory Network Inference using K-Nearest-Neighbor Based Mutual Information

Biological cells exhibit a variety of responses to internal cues and different external perturbations. This is possible due to elaborate gene regulatory networks (GRNs). The structure of GRN cannot be drawn by reading the genetic code, instead, a series of knockdown experiments on a downstream process or cell state was used to infer the GRN structure. The advancement in large scale gene expression data collection, enables inference of large networks. Basic networks were built by clustering co-expressed genes using a statistical correlation metric, like covariance. Information theory is a widely used correlation metric, as it can detect any correlation (linear and non-linear) between any number of variables (n-dimensional). Using information theory for higher dimensions and continuous data (i.e. normalizes expression data) is sensitive to data size, correlation strength and underlying distributions. To estimate it, few techniques are used: fixed width binning, adaptive partitioning, kernel density estimator and k-nearest neighbor (KNN). Unfortunately, most techniques require user defined parameters and “tweaking” which lead to many different implementations. The era of single cell transcriptome only worsens the problem as data is noisier, and while KNN was shown to be accurate and robust option for any user defined parameter, it was hardly used due to its computational costs. Here, we present a novel GRN inference method that solves two issues in the field: A) Implementation of unsupervised and accurate three-way MI estimator based on KNN, B) Reconstructing the network - we use all two- and three-way MI quantities (total of seven) to generate a digital signature (or “barcode”) to most 3-node network topologies, instead of ranking the interactions according to a single MI quantity with an arbitrary threshold to solve conflicts.