Functional Gene Modules Research Articles

LMD: Cluster-Independent Multiscale Marker Identification in Single-cell RNA-seq Data.

Identifying accurate cell markers in single-cell RNA-seq data is crucial for understanding cellular diversity and function. Localized Marker Detector (LMD) is a novel tool to identify "localized genes" - genes exclusively expressed in groups of highly similar cells - thereby characterizing cellular diversity in a multi-resolution and fine-grained manner. LMD constructs a cell-cell affinity graph, diffuses the gene expression value across the cell graph, and assigns a score to each gene based on its diffusion dynamics. LMD's candidate markers can be grouped into functional gene modules, which accurately reflect cell types, subtypes, and other sources of variation such as cell cycle status. We apply LMD to mouse bone marrow and hair follicle dermal condensate datasets, where LMD facilitates cross-sample comparisons, identifying shared and sample-specific gene signatures and novel cell populations without requiring batch effect correction or integration methods. Furthermore, we assessed the performance of LMD across nine single-cell RNA sequencing datasets, compared it with six other methods aimed at achieving similar objectives, and found that LMD outperforms the other methods evaluated.

Integrated analysis of miRNAome and transcriptome reveals that microgravity induces the alterations of critical functional gene modules via the regulation of miRNAs in short-term space-flown C. elegans

Microgravity, as a unique hazardous factor encountered in space, can induce a series of harmful effects on living organisms. The impact of microgravity on the pivotal functional gene modules stemming from gene enrichment analysis via the regulation of miRNAs is not fully illustrated. To explore the microgravity-induced alterations in critical functional gene modules via the regulation of miRNAs, in the present study, we proposed a novel bioinformatics algorithm for the integrated analysis of miRNAome and transcriptome from short-term space-flown C. elegans. The samples of C. elegans were exposed to two space conditions, namely spaceflight (SF) and spaceflight control (SC) onboard the International Space Station for 4 days. Additionally, the samples of ground control (GC) were included for comparative analysis. Using the present algorithm, we constructed regulatory networks of functional gene modules annotated from differentially expressed genes (DEGs) and their associated regulatory differentially expressed miRNAs (DEmiRNAs). The results showed that functional gene modules of molting cycle, defense response, fatty acid metabolism, lysosome, and longevity regulating pathway were facilitated by 25 down-regulated DEmiRNAs (e.g., cel-miR-792, cel-miR-65, cel-miR-70, cel-lsy-6, cel-miR-796, etc.) in the SC vs. GC groups, whereas these modules were inhibited by 13 up-regulated DEmiRNAs (e.g., cel-miR-74, cel-miR-229, cel-miR-70, cel-miR-249, cel-miR-85, etc.) in the SF vs. GC groups. These findings indicated that microgravity could significantly alter gene expression patterns and their associated functional gene modules in short-term space-flown C. elegans. Additionally, we identified 34 miRNAs as post-transcriptional regulators that modulated these functional gene modules under microgravity conditions. Through the experimental verification, our results demonstrated that microgravity could induce the down-regulation of five critical functional gene modules (i.e., molting cycle, defense response, fatty acid metabolism, lysosome, and longevity regulating pathways) via the regulation of miRNAs in short-term space-flown C. elegans.

Reverse network diffusion to remove indirect noise for better inference of gene regulatory networks.

Gene regulatory networks (GRNs) are vital tools for delineating regulatory relationships between transcription factors and their target genes. The boom in computational biology and various biotechnologies has made inferring GRNs from multi-omics data a hot topic. However, when networks are constructed from gene expression data, they often suffer from false-positive problem due to the transitive effects of correlation. The presence of spurious noise edges obscures the real gene interactions, which makes downstream analyses, such as detecting gene function modules and predicting disease-related genes, difficult and inefficient. Therefore, there is an urgent and compelling need to develop network denoising methods to improve the accuracy of GRN inference. In this study, we proposed a novel network denoising method named REverse Network Diffusion On Random walks (RENDOR). RENDOR is designed to enhance the accuracy of GRNs afflicted by indirect effects. RENDOR takes noisy networks as input, models higher-order indirect interactions between genes by transitive closure, eliminates false-positive effects using the inverse network diffusion method, and produces refined networks as output. We conducted a comparative assessment of GRN inference accuracy before and after denoising on simulated networks and real GRNs. Our results emphasized that the network derived from RENDOR more accurately and effectively captures gene interactions. This study demonstrates the significance of removing network indirect noise and highlights the effectiveness of the proposed method in enhancing the signal-to-noise ratio of noisy networks. The R package RENDOR is provided at https://github.com/Wu-Lab/RENDOR and other source code and data are available at https://github.com/Wu-Lab/RENDOR-reproduce.

Automated single-cell omics end-to-end framework with data-driven batch inference.

To facilitate single-cell multi-omics analysis and improve reproducibility, we present SPEEDI (Single-cell Pipeline for End to End Data Integration), a fully automated end-to-end framework for batch inference, data integration, and cell type labeling. SPEEDI introduces data-driven batch inference and transforms the often heterogeneous data matrices obtained from different samples into a uniformly annotated and integrated dataset. Without requiring user input, it automatically selects parameters and executes pre-processing, sample integration, and cell type mapping. It can also perform downstream analyses of differential signals between treatment conditions and gene functional modules. SPEEDI's data-driven batch inference method works with widely used integration and cell-typing tools. By developing data-driven batch inference, providing full end-to-end automation, and eliminating parameter selection, SPEEDI improves reproducibility and lowers the barrier to obtaining biological insight from these valuable single-cell datasets. The SPEEDI interactive web application can be accessed at https://speedi.princeton.edu/.

ISuRe-HadCre is an essential tool for effective conditional genetics.

Methods for modifying gene function at high spatiotemporal resolution in mice have revolutionized biomedical research, with Cre-loxP being the most widely used technology. However, the Cre-loxP technology has several drawbacks, including weak activity, leakiness, toxicity, and low reliability of existing Cre-reporters. This is mainly because different genes flanked by loxP sites (floxed) vary widely in their sensitivity to Cre-mediated recombination. Here, we report the generation, validation, and utility of iSuRe-HadCre, a new dual Cre-reporter and deleter mouse line that avoids these drawbacks. iSuRe-HadCre achieves this through a novel inducible dual-recombinase genetic cascade that ensures that cells expressing a fluorescent reporter had only transient Cre activity, that is nonetheless sufficient to effectively delete floxed genes. iSuRe-HadCre worked reliably in all cell types and for the 13 floxed genes tested. This new tool will enable the precise, efficient, and trustworthy analysis of gene function in entire mouse tissues or in single cells.

Single-Cell Multi-Omics Map of Cell Type-Specific Mechanistic Drivers of Multiple Sclerosis Lesions.

In progressive multiple sclerosis (MS), compartmentalized inflammation plays a pivotal role in the complex pathology of tissue damage. The interplay between epigenetic regulation, transcriptional modifications, and location-specific alterations within white matter (WM) lesions at the single-cell level remains underexplored. We examined intracellular and intercellular pathways in the MS brain WM using a novel dataset obtained by integrated single-cell multi-omics techniques from 3 active lesions, 3 chronic active lesions, 3 remyelinating lesions, and 3 control WM of 6 patients with progressive MS and 3 non-neurologic controls. Single-nucleus RNA-seq and ATAC-seq were combined and additionally enriched with newly conducted spatial transcriptomics from 1 chronic active lesion. Functional gene modules were then validated in our previously published bulk tissue transcriptome data obtained from 73 WM lesions of patients with progressive MS and 25 WM of non-neurologic disease controls. Our analysis uncovered an MS-specific oligodendrocyte genetic signature influenced by the KLF/SP gene family. This modulation has potential associations with the autocrine iron uptake signaling observed in transcripts of transferrin and its receptor LRP2. In addition, an inflammatory profile emerged within these oligodendrocytes. We observed unique cellular endophenotypes both at the periphery and within the chronic active lesion. These include a distinct metabolic astrocyte phenotype, the importance of FGF signaling among astrocytes and neurons, and a notable enrichment of mitochondrial genes at the lesion edge populated predominantly by astrocytes. Our study also identified B-cell coexpression networks indicating different functional B-cell subsets with differential location and specific tendencies toward certain lesion types. The use of single-cell multi-omics has offered a detailed perspective into the cellular dynamics and interactions in MS. These nuanced findings might pave the way for deeper insights into lesion pathogenesis in progressive MS.

Single-cell biclustering for cell-specific transcriptomic perturbation detection in AD progression

The pathogenesis of Alzheimer disease (AD) involves complex gene regulatory changes across different cell types. To help decipher this complexity, we introduce single-cell Bayesian biclustering (scBC), a framework for identifying cell-specific gene network biomarkers in scRNA and snRNA-seq data. Through biclustering, scBC enables the analysis of perturbations in functional gene modules at the single-cell level. Applying the scBC framework to AD snRNA-seq data reveals the perturbations within gene modules across distinct cell groups and sheds light on gene-cell correlations during AD progression. Notably, our method helps to overcome common challenges in single-cell data analysis, including batch effects and dropout events. Incorporating prior knowledge further enables the framework to yield more biologically interpretable results. Comparative analyses on simulated and real-world datasets demonstrate the precision and robustness of our approach compared to other state-of-the-art biclustering methods. scBC holds potential for unraveling the mechanisms underlying polygenic diseases characterized by intricate gene coexpression patterns.

Prior knowledge-guided multilevel graph neural network for tumor risk prediction and interpretation via multi-omics data integration.

The interrelation and complementary nature of multi-omics data can provide valuable insights into the intricate molecular mechanisms underlying diseases. However, challenges such as limited sample size, high data dimensionality and differences in omics modalities pose significant obstacles to fully harnessing the potential of these data. The prior knowledge such as gene regulatory network and pathway information harbors useful gene-gene interaction and gene functional module information. To effectively integrate multi-omics data and make full use of the prior knowledge, here, we propose a Multilevel-graph neural network (GNN): a hierarchically designed deep learning algorithm that sequentially leverages multi-omics data, gene regulatory networks and pathway information to extract features and enhance accuracy in predicting survival risk. Our method achieved better accuracy compared with existing methods. Furthermore, key factors nonlinearly associated with the tumor pathogenesis are prioritized by employing two interpretation algorithms (i.e. GNN-Explainer and IGscore) for neural networks, at gene and pathway level, respectively. The top genes and pathways exhibit strong associations with disease in survival analyses, many of which such as SEC61G and CYP27B1 are previously reported in the literature.

Cognitive Impairment in Nonagenarians: Potential Metabolic Mechanisms Revealed by the Synergy of In Silico Gene Expression Modeling and Pathway Enrichment Analysis.

Previous studies examining the molecular and genetic basis of cognitive impairment, particularly in cohorts of long-living adults, have mainly focused on associations at the genome or transcriptome level. Dozens of significant dementia-associated genes have been identified, including APOE, APOC1, and TOMM40. However, most of these studies did not consider the intergenic interactions and functional gene modules involved in cognitive function, nor did they assess the metabolic changes in individual brain regions. By combining functional analysis with a transcriptome-wide association study, we aimed to address this gap and examine metabolic pathways in different areas of the brain of older adults. The findings from our previous genome-wide association study in 1155 older adults, 179 of whom had cognitive impairment, were used as input for the PrediXcan gene prediction algorithm. Based on the predicted changes in gene expression levels, we conducted a transcriptome-wide association study and functional analysis using the KEGG and HALLMARK databases. For a subsample of long-living adults, we used logistic regression to examine the associations between blood biochemical markers and cognitive impairment. The functional analysis revealed a significant association between cognitive impairment and the expression of NADH oxidoreductase in the cerebral cortex. Significant associations were also detected between cognitive impairment and signaling pathways involved in peroxisome function, apoptosis, and the degradation of lysine and glycan in other brain regions. Our approach combined the strengths of a transcriptome-wide association study with the advantages of functional analysis. It demonstrated that apoptosis and oxidative stress play important roles in cognitive impairment.

Temporal changes of gene expression in health, schizophrenia, bipolar disorder, and major depressive disorder

The molecular events underlying the development, manifestation, and course of schizophrenia, bipolar disorder, and major depressive disorder span from embryonic life to advanced age. However, little is known about the early dynamics of gene expression in these disorders due to their relatively late manifestation. To address this, we conducted a secondary analysis of post-mortem prefrontal cortex datasets using bioinformatics and machine learning techniques to identify differentially expressed gene modules associated with aging and the diseases, determine their time-perturbation points, and assess enrichment with expression quantitative trait loci (eQTL) genes. Our findings revealed early, mid, and late deregulation of expression of functional gene modules involved in neurodevelopment, plasticity, homeostasis, and immune response. This supports the hypothesis that multiple hits throughout life contribute to disease manifestation rather than a single early-life event. Moreover, the time-perturbed functional gene modules were associated with genetic loci affecting gene expression, highlighting the role of genetic factors in gene expression dynamics and the development of disease phenotypes. Our findings emphasize the importance of investigating time-dependent perturbations in gene expression before the age of onset in elucidating the molecular mechanisms of psychiatric disorders.

Inferring co-expression networks of Arabidopsis thaliana genes during their interaction with Trichoderma spp.

Fungi of the Trichoderma genus are called "biostimulants" because they promote plant growth and development and induce disease resistance. We used conventional transcriptome and gene co-expression analyses to understand the molecular response of the plant Arabidopsis thaliana to inoculation with Trichoderma atroviride or Trichoderma virens. The transcriptional landscape of the plant during the interaction with these fungi showed a reduction in functions such as reactive oxygen species production, defense mechanisms against pathogens, and hormone signaling. T. virens, as opposed to T. atroviride, was more effective at downregulating genes related to terpenoid metabolism, root development, and chemical homeostasis. Through gene co-expression analysis, we found functional gene modules that closely link plant defense with hypoxia. Notably, we found a transcription factor (locus AT2G47520) with two functional domains of interest: a DNA-binding domain and an N-terminal cysteine needed for protein stability under hypoxia. We hypothesize that the transcription factor can bind to the promoter sequence of the GCC-box that is connected to pathogenesis by positioned weight matrix analysis.

Molecular bases for strong phenotypic effects of single synonymous codon substitutions in the E. coli ccdB toxin gene

BackgroundSingle synonymous codon mutations typically have only minor or no effects on gene function. Here, we estimate the effects on cell growth of ~ 200 single synonymous codon mutations in an operonic context by mutating almost all positions of ccdB, the 101-residue long cytotoxin of the ccdAB Toxin-Antitoxin (TA) operon to most degenerate codons. Phenotypes were assayed by transforming the mutant library into CcdB sensitive and resistant E. coli strains, isolating plasmid pools, and subjecting them to deep sequencing. Since autoregulation is a hallmark of TA operons, phenotypes obtained for ccdB synonymous mutants after transformation in a RelE toxin reporter strain followed by deep sequencing provided information on the amount of CcdAB complex formed.ResultsSynonymous mutations in the N-terminal region involved in translation initiation showed the strongest non-neutral phenotypic effects. We observe an interplay of numerous factors, namely, location of the codon, codon usage, t-RNA abundance, formation of anti-Shine Dalgarno sequences, predicted transcript secondary structure, and evolutionary conservation in determining phenotypic effects of ccdB synonymous mutations. Incorporation of an N-terminal, hyperactive synonymous mutation, in the background of the single synonymous codon mutant library sufficiently increased translation initiation, such that mutational effects on either folding or termination of translation became more apparent. Introduction of putative pause sites not only affects the translational rate, but might also alter the folding kinetics of the protein in vivo.ConclusionIn summary, the study provides novel insights into diverse mechanisms by which synonymous mutations modulate gene function. This information is useful in optimizing heterologous gene expression in E. coli and understanding the molecular bases for alteration in gene expression that arise due to synonymous mutations.

Network Biomarker Detection From Gene Co-Expression Network Using Gaussian Mixture Model Clustering.

Finding network biomarkers from gene co-expression networks (GCNs) has attracted a lot of research interest. A network biomarker is a topological module, i.e., a group of densely connected nodes in a GCN, in which the gene expression values correlate with sample labels. Compared with biomarkers based on single genes, network biomarkers are not only more robust in separating samples from different categories, but are also able to better interpret the molecular mechanism of the disease. The previous network biomarker detection methods either employ distance based clustering methods or search for cliques in a GCN to detect topological modules. The first strategy assumes that the topological modules should be spherical in shape, and the second strategy requires all nodes to be fully connected. However, the relations between genes are complex, as a result, genes in the same biological process may not be directly, strongly connected. Therefore, the shapes of those modules could be oval or long strips. Hence, the shapes of gene functional modules and gene disease modules may not meet the aforementioned constraints in the previous methods. Thus, previous methods may break up the genes belonging to the same biological process into different topological modules due to those constraints. To address this issue, we propose a novel network biomarker detection method by using Gaussian mixture model clustering which allows more flexibility in the shapes of the topological modules. We have evaluated the performance of our method on a set of eight TCGA cancer datasets. The results show that our method can detect network modules that possess better discriminate power, and provide biological insights.

Asymmetric Evolution of Protein Domains in the Leucine-Rich Repeat Receptor-Like Kinase Family of Plant Signaling Proteins.

The coding sequences of developmental genes are expected to be deeply conserved, with cis-regulatory change driving the modulation of gene function. In contrast, proteins with roles in defense are expected to evolve rapidly, in molecular arms races with pathogens. However, some gene families include both developmental and defense genes. In these families, does the tempo and mode of evolution differ between genes with divergent functions, despite shared ancestry and structure? The leucine-rich repeat receptor-like kinase (LRR-RLKs) protein family includes members with roles in plant development and defense, thus providing an ideal system for answering this question. LRR-RLKs are receptors that traverse plasma membranes. LRR domains bind extracellular ligands; RLK domains initiate intracellular signaling cascades in response to ligand binding. In LRR-RLKs with roles in defense, LRR domains evolve faster than RLK domains. To determine whether this asymmetry extends to LRR-RLKs that function primarily in development, we assessed evolutionary rates and tested for selection acting on 11 subfamilies of LRR-RLKs, using deeply sampled protein trees. To assess functional evolution, we performed heterologous complementation assays in Arabidopsis thaliana (Arabidopsis). We found that the LRR domains of all tested LRR-RLK proteins evolved faster than their cognate RLK domains. All tested subfamilies of LRR-RLKs had strikingly similar patterns of molecular evolution, despite divergent functions. Heterologous transformation experiments revealed that multiple mechanisms likely contribute to the evolution of LRR-RLK function, including escape from adaptive conflict. Our results indicate specific and distinct evolutionary pressures acting on LRR versus RLK domains, despite diverse organismal roles for LRR-RLK proteins.

Variations in Physiology and Genomic Function of Prochlorococcus Across the Eastern Indian Ocean

AbstractThe widespread distribution of Prochlorococcus can be attributed to the extensive genetic diversity that allows them to adapt to various oligotrophic environments. We investigated the adaptation of Prochlorococcus to nutrient environments in the surface eastern Indian Ocean (EIO, 16.5°N to 20°S, 88°E) in November 2018. The growth rate of the Prochlorococcus population and its response to macronutrient enrichments () and the abundance of functional gene modules related to nutrient utilization were examined by on‐deck incubation experiments and metagenomic analysis, respectively. Although the dissolved inorganic nitrogen was depleted (∼58 nM) and the Prochlorococcus populations were dominated by the high‐light‐adapted II ecotype, Prochlorococcus populations showed distinct physiological patterns, especially the response to macronutrient enrichments, indicating their adaptation to local nutrient environments. At the northernmost station in the Bay of Bengal, the significant increase in growth rate with macronutrient enrichments and the highest abundance of the phosphate starvation response two‐component regulatory system module indicated adaptation to phosphorus‐limited environments. In the southern EIO, the insignificant increase in growth rate with macronutrient enrichment and higher abundance of the iron complex transport system module suggested adaptation to iron‐limited environments. However, genomic characteristics are not always associated with physiological characteristics. The abundance of the nitrate/nitrite transport system module was higher in the southern EIO, where the growth of Prochlorococcus relied on regenerated nitrogen sources as revealed by incubation experiments. These results reflected the complexity of Prochlorococcus adaptation especially in chronically oligotrophic environments, which was better revealed by combining physiological and genomic analyses.

Performance evaluation, enzymatic activity change and metagenomic analysis of sequencing batch reactor under divalent zinc stress

Divalent zinc (Zn2+) are widely detected in domestic and industrial wastewater, and it is essential to evaluate the effect of Zn2+ on wastewater biological treatment process due to its bio-toxicity. In this study, the nitrogen removal rates and their corresponding enzymatic activities of sequencing batch reactor decreased with the increase of Zn2+ concentration. The Zn2+ accumulation in activated sludge caused significant antioxidant response, and the reactive oxygen species (ROS) production and antioxidant enzymatic activities were positively correlated with Zn2+ concentration. The presence of Zn2+ inhibited the metabolic pathways related to energy production and electron transport. The abundance decreases of nitrification and denitrification functional genes led to the deterioration of nitrogen removal performance under Zn2+ stress. The correlation analysis between functional gene modules and microbial genera revealed that Zoogloea had obvious Zn2+ resistance. This study can provide the insights into the influencing mechanism of Zn2+ on the biological nitrogen removal process.

Structured Sparse Regularization based Random Vector Functional Link Networks for DNA N4-methylcytosine sites prediction

As an epigenetic modification that plays an important role in modifying gene function and controlling gene expression during cell development, DNA N4-methylcytosine (4mC) is still lack of researching. It is therefore necessary to accurately predict the 4mC sites to make fully aware of its mechanism and function. In this paper, we propose a novel model which is called Structural Sparse Regularized Random Vector Functional Link Network (SSR-RVFL) for predicting 4mC sites. Compared with other state-of-the-art methods, SSR-RVFL performs better and achieves higher prediction accuracy. There are total six benchmark datasets used in the experiments, namely C.elegans, D.elanogaster, E.coli, A.thaliana G.subterraneus and G.pickeringii. Our model improves the accuracy by 0.42%, 0.45%, 0.48%, 0.91%, 0.66% and 0.7% on these six benchmark datasets respectively, so it can be regarded as a more effective prediction tool.

Pooled Genome-Scale CRISPR Screens in Single Cells.

Assigning functions to genes and learning how to control their expression are part of the foundation of cell biology and therapeutic development. An efficient and unbiased method to accomplish this is genetic screening, which historically required laborious clone generation and phenotyping and is still limited by scale today. The rapid technological progress on modulating gene function with CRISPR-Cas and measuring it in individual cells has now relaxed the major experimental constraints and enabled pooled screening with complex readouts from single cells. Here, we review the principles and practical considerations for pooled single-cell CRISPR screening. We discuss perturbation strategies, experimental model systems, matching the perturbation to the individual cells, reading out cell phenotypes, and data analysis. Our focus is on single-cell RNA sequencing and cell sorting-based readouts, including image-enabled cell sorting. We expect this transformative approach to fuel biomedical research for the next several decades.

Spatially resolved single-cell translatomics at molecular resolution.

The precise control of messenger RNA (mRNA) translation is a crucial step in posttranscriptional gene regulation of cellular physiology. However, it remains a challenge to systematically study mRNA translation at the transcriptomic scale with spatial and single-cell resolution. Here, we report the development of ribosome-bound mRNA mapping (RIBOmap), a highly multiplexed three-dimensional in situ profiling method to detect cellular translatome. RIBOmap profiling of 981 genes in HeLa cells revealed cell cycle-dependent translational control and colocalized translation of functional gene modules. We mapped 5413 genes in mouse brain tissues, yielding spatially resolved single-cell translatomic profiles for 119,173 cells and revealing cell type-specific and brain region-specific translational regulation, including translation remodeling during oligodendrocyte maturation. Our method detected widespread patterns of localized translation in neuronal and glial cells in intact brain tissue networks.

Guilt by Association: Inflammation and Shared Genetic Risk Between Stress-Related and Immune Disorders

Guilt by Association: Inflammation and Shared Genetic Risk Between Stress-Related and Immune Disorders