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The elucidation of genetic architecture and molecular regulatory networks underlying complex traits remains a significant challenge in life science, largely due to the substantial background effects that arise from epistasis and gene-environment interactions. The chromosome segment substitution line (CSSL) is an ideal material for genetic and molecular dissection of complex traits due to its near-isogenic properties; yet a comprehensive analysis, from the basic identification of substitution segments to advanced regulatory network, is still insufficient. Here, we developed two cotton CSSL populations on the Gossypium hirsutum background, representing wide adaptation and high lint yield, with introgression from G. barbadense, representing superior fibre quality. We sequenced 99 CSSLs that demonstrated significant differences from G. hirsutum in fibre, and characterized 836 dynamic fibre transcriptomes in three crucial developmental stages. We developed a workflow for precise resolution of chromosomal substitution segments; the genome sequencing revealed substitutions collectively representing 87.25% of the G. barbadense genome. Together, the genomic and transcriptomic survey identified 18 novel fibre-quality-related quantitative trait loci with high genetic contributions and the comprehensive landscape of fibre development regulation. Furthermore, analysis determined unique cis-expression patterns in CSSLs to be the driving force for fibre quality alteration; building upon this, the co-expression regulatory network revealed biological relationships among the noted pathways and accurately described the molecular interactions of GhHOX3, GhRDL1 and GhEXPA1 during fibre elongation, along with reliable predictions for their interactions with GhTBA8A5. Our study will enhance more strategic employment of CSSL in crop molecular biology and breeding programmes., (© 2024 The Author(s). Plant Biotechnology Journal published by Society for Experimental Biology and The Association of Applied Biologists and John Wiley & Sons Ltd.)

Chronic obstructive pulmonary disease (COPD) is an etiologically complex disease characterized by acute exacerbations and stable phases. We aimed to identify biological functions modulated in specific COPD conditions, using whole blood samples collected in the AERIS clinical study (NCT01360398). Considered conditions were exacerbation onset, severity of airway obstruction, and presence of respiratory pathogens in sputum samples. With an integrative multi-network gene community detection (MNGCD) approach, we analyzed expression profiles to identify communities of correlated genes. The approach combined different layers of gene interactions for each explored condition/subset of samples: gene expression similarity, protein-protein interactions, transcription factors, and microRNAs validated regulons. Heme metabolism, interferon-alpha, and interferon-gamma pathways were modulated in patients at both exacerbation and stable-state visits, but with the involvement of distinct sets of genes. An important gene community was enriched with G2M checkpoint, E2F targets, and mitotic spindle pathways during exacerbation. Targets of TAL1 regulator and hsa-let-7b - 5p microRNA were modulated with increasing severity of airway obstruction. Bacterial infections with Moraxella catarrhalis and, particularly, Haemophilus influenzae triggered a specific cellular and inflammatory response in acute exacerbations, indicating an active reaction of the host to infections. In conclusion, COPD is a complex multifactorial disease that requires in-depth investigations of its causes and features during its evolution and whole blood transcriptome profiling can contribute to capturing some relevant regulatory mechanisms associated with this disease. In this work, we explored multi-network modeling that integrated diverse layers of regulatory gene networks and enhanced our comprehension of the biological functions implicated in the COPD pathogenesis., (© 2024. GSK.)

Elucidating the emergent dynamics of cellular differentiation networks is crucial to understanding cell-fate decisions. Toggle switch - a network of mutually repressive lineage-specific transcription factors A and B - enables two phenotypes from a common progenitor: (high A, low B) and (low A, high B). However, the dynamics of networks enabling differentiation of more than two phenotypes from a progenitor cell has not been well-studied. Here, we investigate the dynamics of a four-node network A, B, C, and D inhibiting each other, forming a toggle tetrahedron. Our simulations show that this network is multistable and predominantly allows for the co-existence of six hybrid phenotypes where two of the nodes are expressed relatively high as compared to the remaining two, for instance (high A, high B, low C, low D). Finally, we apply our results to understand naïve CD4 + T cell differentiation into Th1, Th2, Th17 and Treg subsets, suggesting Th1/Th2/Th17/Treg decision-making to be a two-step process., (© 2024. The Author(s).)

Background: As noncoding RNAs, circular RNAs (circRNAs) can act as microRNA (miRNA) sponges due to their abundant miRNA binding sites, allowing them to regulate gene expression and influence disease development. Accurately identifying circRNA-miRNA associations (CMAs) is helpful to understand complex disease mechanisms. Given that biological experiments are time consuming and labor intensive, alternative computational methods to predict CMAs are urgently needed., Results: This study proposes a novel computational model named CMAGN, which incorporates several advanced computational methods, for predicting CMAs. First, similarity networks for circRNAs and miRNAs are constructed according to their sequences. Graph attention autoencoder is then applied to these networks to generate the first representations of circRNAs and miRNAs. The second representations of circRNAs and miRNAs are obtained from the CMA network via node2vec. The similarity networks of circRNAs and miRNAs are reconstructed on the basis of these new representations. Finally, network consistency projection is applied to the reconstructed similarity networks and the CMA network to generate a recommendation matrix., Conclusion: Five-fold cross-validation of CMAGN reveals that the area under ROC and PR curves exceed 0.96 on two widely used CMA datasets, outperforming several existing models. Additional tests elaborate the reasonability of the architecture of CMAGN and uncover its strengths and weaknesses., (© 2024. The Author(s).)

Background: Osteoarthritis (OA) is marked by the progressive degradation of joint cartilage and subchondral bone. The precise molecular mechanisms driving meniscus deterioration in OA, especially at the single-cell level, remain poorly understood., Method: We analyzed two datasets from the GEO database, GSE220243 and GSE98918, focusing on meniscus tissue sequencing data from OA and non-OA patients. The standard Seurat procedure was employed to process single-cell data and identify differentially expressed genes (DEGs). Immune cell infiltration was assessed using the Microenvironment Cell Populations (MCP) counter and CIBERSORT algorithms. For the microarray data, DEGs were identified with the limma package, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed using ClusterProfiler. The overlapping DEGs from both datasets were imported into Cytoscape to generate protein-protein interaction (PPI) networks and identify hub genes. Transcription factor (TF) and miRNA interaction networks were analyzed using NetworkAnalyst, and gene-related predictive drugs were enriched through the DSigDB platform., Result: After quality control, 34,763 cells from the OA patients and 34,145 cells from the healthy controls were analyzed. UMAP identified and SingleR annotated 14 cell clusters. The 10 largest cell clusters were selected for further analysis. The OA group exhibited a notable increase in macrophages and a reduction in cytotoxic lymphocytes and endothelial cells in the meniscus. In GSE98918, 220 DEGs were identified, and the MCODE plug-in in Cytoscape pinpointed a key module containing 12 candidate genes. The MCC methodfiltered the top 20 DEGs in each GSE220243 cluster. Overlapping DEGs from GSE220243 and GSE98918 identified COL1A1, COL3A1, COL5A2, COL6A3, LOX, and VEGFA as significantly decreased in OA, with COL3A1, COL5A2, LOX, and VEGFA upregulated in meniscal chondrocytes. The interaction network highlighted 3 key miRNAs and 13 shared TFs. Ten gene-related predictive drug molecules were identified., Conclusion: This research highlights crucial genes in the OA meniscus and uncovers their differing regulatory patterns between chondrocytes and non-chondrocytes. These findings enhance our understanding of the molecular mechanisms driving OA pathogenesis and aid in identifying potential drug targets., (© 2024. The Author(s).)

Neuregulin1 (Nrg1) signaling is critical for neuronal development and function from fate specification to synaptic plasticity. Type III Nrg1 is a synaptic protein which engages in bidirectional signaling with its receptor ErbB4. Forward signaling engages ErbB4 phosphorylation, whereas back signaling engages two known mechanisms: (1) local axonal PI3K-AKT signaling and (2) cleavage by γ-secretase resulting in cytosolic release of the intracellular domain (ICD), which can traffic to the nucleus (Bao et al., 2003; Hancock et al., 2008). To dissect the contribution of these alternate signaling strategies to neuronal development, we generated a transgenic mouse with a missense mutation (V 321 L) in the Nrg1 transmembrane domain that disrupts nuclear back signaling with minimal effects on forward signaling or local back signaling and was previously found to be associated with psychosis (Walss-Bass et al., 2006). We combined RNA sequencing, retroviral fate mapping of neural stem cells, behavioral analyses, and various network analyses of transcriptomic data to investigate the effect of disrupting Nrg1 nuclear back signaling in the dentate gyrus (DG) of male and female mice. The V 321 L mutation impairs nuclear translocation of the Nrg1 ICD and alters gene expression in the DG. V 321 L mice show reduced stem cell proliferation, altered cell cycle dynamics, fate specification defects, and dendritic dysmorphogenesis. Orthologs of known schizophrenia (SCZ)-susceptibility genes were dysregulated in the V 321 L DG. These genes coordinated a larger network with other dysregulated genes. Weighted gene correlation network analysis and protein interaction network analyses revealed striking similarity between DG transcriptomes of V 321 L mouse and humans with SCZ., Competing Interests: The authors declare no competing financial interests., (Copyright © 2024 Rajebhosale et al.)

Energy and its dissipation are fundamental to all living systems, including cells. Insufficient abundance of energy carriers─as caused by the additional burden of artificial genetic circuits─shifts a cell's priority to survival, also impairing the functionality of the genetic circuit. Moreover, recent works have shown the importance of energy expenditure in information transmission. Despite living organisms being non-equilibrium systems, non-equilibrium models capable of accounting for energy dissipation and non-equilibrium response curves are not yet employed in genetic design automation (GDA) software. To this end, we introduce Energy Aware Technology Mapping, the automated design of genetic logic circuits with respect to energy efficiency and functionality. The basis for this is an energy aware non-equilibrium steady state model of gene expression, capturing characteristics like energy dissipation─which we link to the entropy production rate─and transcriptional bursting, relevant to eukaryotes as well as prokaryotes. Our evaluation shows that a genetic logic circuit's functional performance and energy efficiency are disjoint optimization goals. For our benchmark, energy efficiency improves by 37.2% on average when comparing to functionally optimized variants. We discover a linear increase in energy expenditure and overall protein expression with the circuit size, where Energy Aware Technology Mapping allows for designing genetic logic circuits with the energetic costs of circuits that are one to two gates smaller. Structural variants improve this further, while results show the Pareto dominance among structures of a single Boolean function. By incorporating energy demand into the design, Energy Aware Technology Mapping enables energy efficiency by design. This extends current GDA tools and complements approaches coping with burden in vivo .

Synthetic sRNAs show promise as tools for targeted and programmable gene expression manipulation. However, the design of high-efficiency synthetic sRNAs is a challenging task that necessitates careful consideration of multiple factors. Therefore, this study aims to investigate rational design strategies that significantly and robustly enhance the efficiency of synthetic sRNAs. This is achieved by optimizing the following parameters: the sRNA scaffold, mRNA binding affinity, Hfq protein expression level, and mRNA secondary structure. By utilizing optimized synthetic sRNAs within a positive feedback circuit, we effectively addressed the issue of gene expression leakage─an enduring challenge in synthetic biology that undermines the reliability of genetic circuits in bacteria. Our designed synthetic sRNAs successfully prevented gene expression leakage, thus averting unintended circuit activation caused by initial expression noise, even in the absence of signal molecules. This result shows that high-efficiency synthetic sRNAs not only enable precise gene knockdown for metabolic engineering but also ensure the robust performance of synthetic circuits. The strategies developed here hold significant promise for broad applications across diverse biotechnological fields, establishing synthetic sRNAs as pivotal tools in advancing synthetic biology and gene regulation.

Chronic obstructive pulmonary disease (COPD) is a complex disease influenced by well-established environmental exposures (most notably, cigarette smoking) and incompletely defined genetic factors. The chromosome 4q region harbors multiple genetic risk loci for COPD, including signals near HHIP, FAM13A, GSTCD, TET2, and BTC. Leveraging RNA-Seq data from lung tissue in COPD cases and controls, we estimated the co-expression network for genes in the 4q region bounded by HHIP and BTC (~70MB), through partial correlations informed by protein-protein interactions. We identified several co-expressed gene pairs based on partial correlations, including NPNT-HHIP, BTC-NPNT and FAM13A-TET2, which were replicated in independent lung tissue cohorts. Upon clustering the co-expression network, we observed that four genes previously associated to COPD: BTC, HHIP, NPNT and PPM1K appeared in the same network community. Finally, we discovered a sub-network of genes differentially co-expressed between COPD vs controls (including FAM13A, PPA2, PPM1K and TET2). Many of these genes were previously implicated in cell-based knock-out experiments, including the knocking out of SPP1 which belongs to the same genomic region and could be a potential local key regulatory gene. These analyses identify chromosome 4q as a region enriched for COPD genetic susceptibility and differential co-expression., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: EKS has received grant support from Bayer and Northpond Laboratories. PJC has received grant support from Sanofi and Bayer and consulting fees from Verona Pharma. CPH has received grant support from Bayer, Boehringer-Ingelheim, and Vertex, and consulting fees from Chiesi, Ono, Sanofi, and Takeda. MHC has received grant support from Bayer., (Copyright: © 2024 Gentili et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: The potential of microglia as a target for Alzheimer's disease (AD) treatment is promising, yet the clinical and pathological diversity within microglia, driven by genetic factors, poses a significant challenge. Subtyping AD is imperative to enable precise and effective treatment strategies. However, existing subtyping methods fail to comprehensively address the intricate complexities of AD pathogenesis, particularly concerning genetic risk factors. To address this gap, we have employed systems biology approaches for AD subtyping and identified potential therapeutic targets., Methods: We constructed patient-specific microglial molecular regulatory network models by utilizing existing literature and single-cell RNA sequencing data. The combination of large-scale computer simulations and dynamic network analysis enabled us to subtype AD patients according to their distinct molecular regulatory mechanisms. For each identified subtype, we suggested optimal targets for effective AD treatment., Results: To investigate heterogeneity in AD and identify potential therapeutic targets, we constructed a microglia molecular regulatory network model. The network model incorporated 20 known risk factors and crucial signaling pathways associated with microglial functionality, such as inflammation, anti-inflammation, phagocytosis, and autophagy. Probabilistic simulations with patient-specific genomic data and subsequent dynamics analysis revealed nine distinct AD subtypes characterized by core feedback mechanisms involving SPI1, CASS4, and MEF2C. Moreover, we identified PICALM, MEF2C, and LAT2 as common therapeutic targets among several subtypes. Furthermore, we clarified the reasons for the previous contradictory experimental results that suggested both the activation and inhibition of AKT or INPP5D could activate AD through dynamic analysis. This highlights the multifaceted nature of microglial network regulation., Conclusions: These results offer a means to classify AD patients by their genetic risk factors, clarify inconsistent experimental findings, and advance the development of treatments tailored to individual genotypes for AD., (© 2024. The Author(s).)

Growing evidence supports the transcription of enhancer RNAs (eRNAs) and their important roles in gene regulation. However, their interactions with other biomolecules and their corresponding functionality remain poorly understood. In an attempt to facilitate mechanistic research, this study presents eRNA-IDO, the first integrative computational platform for the identification, interactome discovery, and functional annotation of human eRNAs. eRNA-IDO comprises two modules: eRNA-ID and eRNA-Anno. Functionally, eRNA-ID can identify eRNAs from de novo assembled transcriptomes. eRNA-ID includes eight kinds of enhancer makers, enabling users to customize enhancer regions flexibly and conveniently. In addition, eRNA-Anno provides cell-/tissue-specific functional annotation for both new and known eRNAs by analyzing the eRNA interactome from prebuilt or user-defined networks between eRNAs and protein-coding genes. The prebuilt networks include the Genotype-Tissue Expression (GTEx)-based co-expression networks in normal tissues, The Cancer Genome Atlas (TCGA)-based co-expression networks in cancer tissues, and omics-based eRNA-centric regulatory networks. eRNA-IDO can facilitate research on the biogenesis and functions of eRNAs. The eRNA-IDO server is freely available at http://bioinfo.szbl.ac.cn/eRNA_IDO/., (© The Author(s) 2024. Published by Oxford University Press and Science Press on behalf of the Beijing Institute of Genomics, Chinese Academy of Sciences / China National Center for Bioinformation and Genetics Society of China.)

Elucidating the molecular dependencies behind the cancer-type specificity of driver mutations may reveal new therapeutic opportunities. We hypothesized that developmental programs would impact the transduction of oncogenic signaling activated by a driver mutation and shape its cancer-type specificity. Therefore, we designed a computational analysis framework by combining single-cell gene expression profiles during fetal organ development, latent factor discovery, and information theory-based differential network analysis to systematically identify transcription factors that selectively respond to driver mutations under the influence of organ-specific developmental programs. After applying this approach to VHL mutations, which are highly specific to clear cell renal cell carcinoma (ccRCC), we revealed important regulators downstream of VHL mutations in ccRCC and used their activities to cluster patients with ccRCC into three subtypes. This classification revealed a more significant difference in prognosis than the previous mRNA profile-based method and was validated in an independent cohort. Moreover, we found that EP300, a key epigenetic factor maintaining the regulatory network of the subtype with the worst prognosis, can be targeted by a small inhibitor, suggesting a potential treatment option for a subset of patients with ccRCC. This work demonstrated an intimate relationship between organ development and oncogenesis from the perspective of systems biology, and the method can be generalized to study the influence of other biological processes on cancer driver mutations., (© 2024. The Author(s).)

Deciphering cellular responses to genetic perturbations is fundamental for a wide array of biomedical applications. However, there are three main challenges: predicting single-genetic-perturbation outcomes, predicting multiple-genetic-perturbation outcomes and predicting genetic outcomes across cell lines. Here we introduce Subtask Decomposition Modeling for Genetic Perturbation Prediction (STAMP), a flexible artificial intelligence strategy for genetic perturbation outcome prediction and downstream applications. STAMP formulates genetic perturbation prediction as a subtask decomposition problem by resolving three progressive subtasks in a problem decomposition manner, that is, identifying postperturbation differentially expressed genes, determining the expression change directions of differentially expressed genes and finally estimating the magnitudes of gene expression changes. STAMP exhibits a substantial improvement over the existing approaches on three subtasks and beyond, including the ability to identify key regulatory genes and pathways on small samples and to reveal precise genetic interactions of diverse types., (© 2024. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Computational synthetic lethality (SL) method has become a promising strategy to identify SL gene pairs for targeted cancer therapy and cancer medicine development. Feature representation for integrating various biological networks is crutial to improve the identification performance. However, previous feature representation, such as matrix factorization and graph neural network, projects gene features onto latent variables by keeping a specific geometric metric. There is a lack of models of gene representational latent space with considerating multiple dimentionalities correlation and preserving latent geometric structures in both sample and feature spaces. Therefore, we propose a novel method to model gene Latent Space using matrix Tri-Factorization (LSTF) to obtain gene representation with embedding variables resulting from the potential interpretation of synthetic lethality. Meanwhile, manifold subspace regularization is applied to the tri-factorization to capture the geometrical manifold structure in the latent space with gene PPI functional and GO semantic embeddings. Then, SL gene pairs are identified by the reconstruction of the associations with gene representations in the latent space. The experimental results illustrate that LSTF is superior to other state-of-the-art methods. Case study demonstrate the effectiveness of the predicted SL associations.

Dysfunctional lactate metabolism in the brain has been implicated in neuroinflammation, Aβ deposition, and cell disturbance, all of which play a significant role in the pathogenesis of Alzheimer's disease (AD). In this study, we aimed to investigate the lactate metabolism-related genes (LMRGs) in AD via an integrated bulk RNA and single-nuclei RNA sequencing (snRNA-seq) analysis, with a specific focus on microglia. We obtained 26 HC and 24 AD snRNA-seq samples originated from human prefrontal cortex in Gene Expression Omnibus (GEO) database and collected 873 LMRGs from three databases, namely MSigDB, The Human Protein Atlas and GeneCards. Bulk RNA was analyzed with LMRG characteristics in AD by using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), the protein-protein interaction (PPI), CytoHubba-MCC, Support Vector Machine (SVM) algorithms analyses. Then we conducted the Receiver Operating Characteristic (ROC) curve, correlation, and connection network analyses for biomarkers. Their differential expression validation was performed using AlzData database. The single-nuclei RNA analysis of microglia was applied to identify hub genes and pathways using cell-cell communication analysis and high dimensional Weighted Gene Co-Expression Network Analysis (hdWGCNA). Support Vector Machine (SVM) algorithm showed an AUC of 0.967, a sensitivity of 93.30% and a specificity of 100.00%. Our analysis identified biomarkers with LMRG characteristics, namely INSR, CDKL1, and PNISR. ROC analysis revealed that each of these biomarkers exhibited excellent diagnostic potential, as evidenced by their respective area under the curve (AUC) values: INSR (AUC: 0.679), CDKL1 (AUC: 0.788), and PNISR (AUC: 0.724). Correlation analysis showed that biomarkers exhibited a positive correlation with each other. Connection network illustrated their shared biological processes: aging, phosphorylation, metabolic process, and apoptosis. Cell-cell communication analysis revealed that GALECTIN signaling pathway was exclusively expressed in AD microglia, and only LGALS9 exhibited significant overexpression. HdWGCNA identified FTH1 as a hub gene enriched in ferroptosis and mineral absorption pathways within microglia. The roles of INSR, CDKL1, PNISR, LGALS9, and FTH1 should be taken into account to enhance our understanding of lactate metabolism in the context of AD., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Pancreatic cancer is one of the most malignant cancers with rapid progression and poor prognosis. The use of transcriptional data can be effective in finding new biomarkers for pancreatic cancer. Many network-based methods used to identify cancer biomarkers are proposed, among which the combination of network controllability appears. However, most of the existing methods do not study RNA, rely on priori and mutations information, or can only achieve classification tasks. In this study, we propose a method combined Relational Graph Convolutional Network and Deep Q-Network called RDDriver to identify pancreatic cancer biomarkers based on multi-layer heterogeneous transcriptional regulation network. Firstly, we construct a regulation network containing long non-coding RNA, microRNA, and messenger RNA. Secondly, Relational Graph Convolutional Network is used to learn the node representation. Finally, we use the idea of Deep Q-Network to build a model, which score and prioritize each RNA with the Popov-Belevitch-Hautus criterion. We train RDDriver on three small simulated networks, and calculate the average score after applying the model parameters to the regulation networks separately. To demonstrate the effectiveness of the method, we perform experiments for comparison between RDDriver and other eight methods based on the approximate benchmark of three types cancer drivers RNAs.

Cell-cell interactions orchestrate complex functions in multicellular organisms, forming a regulatory network for diverse biological processes. Their disruption leads to disease states. Recent advancements - including single-cell sequencing and spatial transcriptomics, coupled with powerful bioengineering and molecular tools - have revolutionized our understanding of how cells respond to each other. Notably, spatial transcriptomics allows us to analyze gene expression changes based on cell proximity, offering a unique window into the impact of cell-cell contact. Additionally, computational approaches are being developed to decipher how cell contact governs the symphony of cellular responses. This review explores these cutting-edge approaches, providing valuable insights into deciphering the intricate cellular changes influenced by cell-cell communication., Competing Interests: Declaration of interests The authors declare that they have no conflicts of interest., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Recent advances in measurement technologies, particularly single-cell RNA sequencing (scRNA-seq), have revolutionized our ability to acquire large amounts of omics-level data on cellular states. As measurement techniques evolve, there has been an increasing need for data analysis methodologies, especially those focused on cell-type identification and inference of gene regulatory networks (GRNs). We have developed a new method named BootCellNet, which employs smoothing and resampling to infer GRNs. Using the inferred GRNs, BootCellNet further infers the minimum dominating set (MDS), a set of genes that determines the dynamics of the entire network. We have demonstrated that BootCellNet robustly infers GRNs and their MDSs from scRNA-seq data and facilitates unsupervised identification of cell clusters using scRNA-seq datasets of peripheral blood mononuclear cells and hematopoiesis. It has also identified COVID-19 patient-specific cells and their potential regulatory transcription factors. BootCellNet not only identifies cell types in an unsupervised and explainable way but also provides insights into the characteristics of identified cell types through the inference of GRNs and MDS., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Yutaro Kumagai. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: Autism spectrum disorder (ASD) is a class of complex neurodevelopment disorders with high genetic heterogeneity. Long non-coding RNAs (lncRNAs) are vital regulators that perform specific functions within diverse cell types and play pivotal roles in neurological diseases including ASD. Therefore, exploring lncRNA regulation would contribute to deciphering ASD molecular mechanisms. Existing computational methods utilize bulk transcriptomics data to identify lncRNA regulation in all of samples, which could reveal the commonalities of lncRNA regulation in ASD, but ignore the specificity of lncRNA regulation across various cell types., Results: Here, we present Cycle (Cell type-specific lncRNA regulatory network) to construct the landscape of cell type-specific lncRNA regulation in ASD. We have found that each ASD cell type is unique in lncRNA regulation, and more than one-third and all cell type-specific lncRNA regulatory networks are characterized as scale-free and small-world, respectively. Across 17 ASD cell types, we have discovered 19 rewired and 11 stable modules, along with eight rewired and three stable hubs within the constructed cell type-specific lncRNA regulatory networks. Enrichment analysis reveals that the discovered rewired and stable modules and hubs are closely related to ASD. Furthermore, more similar ASD cell types tend to be connected with higher strength in the constructed cell similarity network. Finally, the comparison results demonstrate that Cycle is a potential method for uncovering cell type-specific lncRNA regulation., Conclusion: Overall, these results illustrate that Cycle is a promising method to model the landscape of cell type-specific lncRNA regulation, and provides insights into understanding the heterogeneity of lncRNA regulation between various ASD cell types., (© 2024. The Author(s).)

The Cold Spring Harbor Laboratory (CSHL) Summer Course on Synthetic Biology, established in 2013, has emerged as a premier platform for immersive education and research in this dynamic field. Rooted in CSHL's rich legacy of biological discovery, the course offers a comprehensive exploration of synthetic biology's fundamentals and applications. Led by a consortium of faculty from diverse institutions, the course structure seamlessly integrates practical laboratory sessions, exploratory research rotations, and enriching seminars by leaders in the field. Over the years, the curriculum has evolved to cover essential topics such as cell-free transcription-translation, DNA construction, computational modeling of gene circuits, engineered gene regulation, and CRISPR technologies. In this review, we describe the history, development, and structure of the course, and discuss how elements of the course might inform the development of other short courses in synthetic biology. We also demonstrate the course's impact beyond the lab with a summary of alumni contributions to research, education, and entrepreneurship. Through these efforts, the CSHL Summer Course on Synthetic Biology remains at the forefront of shaping the next generation of synthetic biologists.

The progress and utility of synthetic biology is currently hindered by the lengthy process of studying literature and replicating poorly documented work. Reconstruction of crucial design information through post hoc curation is highly noisy and error-prone. To combat this, author participation during the curation process is crucial. To encourage author participation without overburdening them, an ML-assisted curation tool called SeqImprove has been developed. Using named entity recognition, called entity normalization, and sequence matching, SeqImprove creates machine-accessible sequence data and metadata annotations, which authors can then review and edit before submitting a final sequence file. SeqImprove makes it easier for authors to submit sequence data that is FAIR (findable, accessible, interoperable, and reusable).

Background: Many approaches have been developed to overcome technical noise in single cell RNA-sequencing (scRNAseq). As researchers dig deeper into data-looking for rare cell types, subtleties of cell states, and details of gene regulatory networks-there is a growing need for algorithms with controllable accuracy and fewer ad hoc parameters and thresholds. Impeding this goal is the fact that an appropriate null distribution for scRNAseq cannot simply be extracted from data in which ground truth about biological variation is unknown (i.e., usually)., Results: We approach this problem analytically, assuming that scRNAseq data reflect only cell heterogeneity (what we seek to characterize), transcriptional noise (temporal fluctuations randomly distributed across cells), and sampling error (i.e., Poisson noise). We analyze scRNAseq data without normalization-a step that skews distributions, particularly for sparse data-and calculate p values associated with key statistics. We develop an improved method for selecting features for cell clustering and identifying gene-gene correlations, both positive and negative. Using simulated data, we show that this method, which we call BigSur (Basic Informatics and Gene Statistics from Unnormalized Reads), captures even weak yet significant correlation structures in scRNAseq data. Applying BigSur to data from a clonal human melanoma cell line, we identify thousands of correlations that, when clustered without supervision into gene communities, align with known cellular components and biological processes, and highlight potentially novel cell biological relationships., Conclusions: New insights into functionally relevant gene regulatory networks can be obtained using a statistically grounded approach to the identification of gene-gene correlations., (© 2024. The Author(s).)

The regulation of genes can be mathematically described by input-output functions that are typically assumed to be time invariant. This fundamental assumption underpins the design of synthetic gene circuits and the quantitative understanding of natural gene regulatory networks. Here, we found that this assumption is challenged in mammalian cells. We observed that a synthetic reporter gene can exhibit unexpected transcriptional memory, leading to a shift in the dose-response curve upon a second induction. Mechanistically, we investigated the cis-dependency of transcriptional memory, revealing the necessity of promoter DNA methylation in establishing memory. Furthermore, we showed that the synthetic transcription factor's effective DNA binding affinity underlies trans-dependency, which is associated with its capacity to undergo biomolecular condensation. These principles enabled modulating memory by perturbing either cis- or trans-regulation of genes. Together, our findings suggest the potential pervasiveness of transcriptional memory and implicate the need to model mammalian gene regulation with time-varying input-output functions. A record of this paper's transparent peer review process is included in the supplemental information., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Identifying meaningful patterns in data is crucial for understanding complex biological processes, particularly in transcriptomics, where genes with correlated expression often share functions or contribute to disease mechanisms. Traditional correlation coefficients, which primarily capture linear relationships, may overlook important nonlinear patterns. We introduce the clustermatch correlation coefficient (CCC), a not-only-linear coefficient that utilizes clustering to efficiently detect both linear and nonlinear associations. CCC outperforms standard methods by revealing biologically meaningful patterns that linear-only coefficients miss and is faster than state-of-the-art coefficients such as the maximal information coefficient. When applied to human gene expression data from genotype-tissue expression (GTEx), CCC identified robust linear relationships and nonlinear patterns, such as sex-specific differences, that are undetectable by standard methods. Highly ranked gene pairs were enriched for interactions in integrated networks built from protein-protein interactions, transcription factor regulation, and chemical and genetic perturbations, suggesting that CCC can detect functional relationships missed by linear-only approaches. CCC is a highly efficient, next-generation, not-only-linear correlation coefficient for genome-scale data. A record of this paper's transparent peer review process is included in the supplemental information., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Most cancer types lack targeted therapeutic options, and when first-line targeted therapies are available, treatment resistance is a huge challenge. Recent technological advances enable the use of assay for transposase-accessible chromatin with sequencing (ATAC-seq) and RNA sequencing (RNA-seq) on patient tissue in a high-throughput manner. Here, we present a computational approach that leverages these datasets to identify drug targets based on tumor lineage. We constructed gene regulatory networks for 371 patients of 22 cancer types using machine learning approaches trained with three-dimensional genomic data for enhancer-to-promoter contacts. Next, we identified the key transcription factors (TFs) in these networks, which are used to find therapeutic vulnerabilities, by direct targeting of either TFs or the proteins that they interact with. We validated four candidates identified for neuroendocrine, liver, and renal cancers, which have a dismal prognosis with current therapeutic options., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 Elsevier Inc. All rights reserved.)

R package pathlinkR is designed to aid transcriptomic analyses by streamlining and simplifying the process of analyzing and interpreting differentially expressed genes derived from human RNA-Seq data. It provides an integrated approach to performing pathway enrichment and network-based analyses, while also producing publication-quality figures to summarize these results, allowing users to more efficiently interpret their findings and extract biological meaning from large amounts of data. pathlinkR is available to install from the software repository Bioconductor at https://bioconductor.org/packages/pathlinkR/, with support available through the Bioconductor forums. The code, example, and supporting data is available on the GitHub repository at https://github.com/hancockinformatics/pathlinkR, under the GPL-3.0 license, where users may report problems or make suggestions using GitHub's issue system., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Blimkie et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: There is increasing evidence that inflammation plays a key role in the pathophysiology of periodontitis (PT) and Alzheimer's disease (AD), but the roles of inflammation in linking PT and AD are not clear. Our aim is to analyze the potential molecular mechanisms between these two diseases using bioinformatics and systems biology approaches., Methods: To elucidate the link between PT and AD, we selected shared genes (SGs) with gene-disease-association scores of ≥ 0.1 from the Disease Gene Network (DisGeNET) database, followed by extracting the hub genes. Based on these genes, we constructed gene ontology (GO) enrichment analysis, pathway enrichment analysis, protein-protein interaction (PPI) networks, transcription factors (TFs)-gene networks, microRNAs (miRNAs)-gene regulatory networks, and gene-disease association analyses. Finally, the Drug Signatures database (DSigDB) was utilized to predict candidate molecular drugs related to hub genes., Results: A total of 21 common SGs between PT and AD were obtained. Cell cytokine activity, inflammatory response, and extracellular membrane were the most important enriched items in GO analysis. Interleukin-10 Signaling, LTF Danger Signal Response Pathway, and RAGE Pathway were identified as important shared pathways. IL6, IL10, IL1B, TNF, IFNG, CXCL8, CCL2, MMP9, TLR4 were identified as hub genes. Both shared pathways and hub genes are closely related to endoplasmic reticulum (ER) stress and mitochondrial dysfunction. Importantly, glutathione, simvastatin, and dexamethasone were identified as important candidate drugs for the treatment of PT and AD., Conclusions: There is a close link between PT and AD pathogenesis, which may involve in the inflammation, ER and mitochondrial function., (© 2024. The Author(s).)

The gene regulatory network (GRN) of biological cells governs a number of key functionalities that enable them to adapt and survive through different environmental conditions. Close observation of the GRN shows that the structure and operational principles resemble an artificial neural network (ANN), which can pave the way for the development of wet-neuromorphic computing systems. Genes are integrated into gene-perceptrons with transcription factors (TFs) as input, where the TF concentration relative to half-maximal RNA concentration and gene product copy number influences transcription and translation via weighted multiplication before undergoing a nonlinear activation function. This process yields protein concentration as the output, effectively turning the entire GRN into a gene regulatory neural network (GRNN). In this paper, we establish nonlinear classifiers for molecular machine learning using the inherent sigmoidal nonlinear behavior of gene expression. The eigenvalue-based stability analysis, tailored to system parameters, confirms maximum-stable concentration levels, minimizing concentration fluctuations and computational errors. Given the significance of the stabilization phase in GRNN computing and the dynamic nature of the GRN, alongside potential changes in system parameters, we utilize the Lyapunov stability theorem for temporal stability analysis. Based on this GRN-to-GRNN mapping and stability analysis, three classifiers are developed utilizing two generic multilayer sub-GRNNs and a sub-GRNN extracted from the Escherichia coli GRN. Our findings also reveal the adaptability of different sub-GRNNs to suit different application requirements., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Chilean peach growers have achieved worldwide recognition for their high-quality fruit products. Among the main factors influencing peach fruit quality, sweetness is pivotal for maintaining the market's competitiveness. Numerous studies have been conducted in different peach-segregating populations to unravel SSC regulation. However, different cultivars may also have distinct genetic conformation, and other factors, such as environmental conditions, can significantly impact SSC. Using a transcriptomic approach with a gene co-expression network analysis, we aimed to identify the regulatory mechanism that controls the sugar accumulation process in an 'O × N' peach population. This population was previously studied through genomic analysis, associating LG5 with the genetic control of the SSC trait. The results obtained in this study allowed us to identify 91 differentially expressed genes located on chromosome 5 of the peach genome as putative new regulators of sugar accumulation in peach, together with a regulatory network that involves genes directly associated with sugar transport (PpSWEET15), cellulose biosynthesis (PpCSLG2), flavonoid biosynthesis (PpPAL1), pectin modifications (PpPG, PpPL and PpPMEi), expansins (PpEXPA1 and PpEXPA8) and several transcription factors (PpC3H67, PpHB7, PpRVE1 and PpCBF4) involved with the SSC phenotype. These results contribute to a better understanding of the genetic control of the SSC trait for future breeding programs in peaches., (© 2024. The Author(s).)

Link prediction (LP) is a task for the identification of potential, missing and spurious links in complex networks. Protein-protein interaction (PPI) networks are important for understanding the underlying biological mechanisms of diseases. Many complex networks have been constructed using LP methods; however, there are a limited number of studies that focus on disease-related gene predictions and evaluate these genes using various evaluation criteria. The main objective of the study is to investigate the effect of a simple ensemble method in disease related gene predictions. Local similarity indices (LSIs) based disease related gene predictions were integrated by a simple ensemble decision method, simple majority voting (SMV), on the PPI network to detect accurate disease related genes. Human PPI network was utilized to discover potential disease related genes using four LSIs for the gene prediction. LSIs discovered potential links between disease related genes, which were obtained from OMIM database for gastric, colorectal, breast, prostate and lung cancers. LSIs based disease related genes were ranked due to their LSI scores in descending order for retrieving the top 10, 50 and 100 disease related genes. SMV integrated four LSIs based predictions to obtain SMV based the top 10, 50 and 100 disease related genes. The performance of LSIs based and SMV based genes were evaluated separately by employing overlap analyses, which were performed with GeneCard disease-gene relation dataset and Gene Ontology (GO) terms. The GO-terms were used for biological assessment for the inferred gene lists by LSIs and SMV on all cancer types. Adamic-Adar (AA), Resource Allocation Index (RAI), and SMV based gene lists are generally achieved good performance results on all cancers in both overlap analyses. SMV also outperformed on breast cancer data. The increment in the selection of the number of the top ranked disease related genes also enhanced the performance results of SMV., Competing Interests: The author declares that they have no competing interests., (© 2024 Cingiz.)

Inferring the driving regulatory programs from comparative analysis of gene expression data is a cornerstone of systems biology. Many computational frameworks were developed to address this problem, including our iPAGE (information-theoretic Pathway Analysis of Gene Expression) toolset that uses information theory to detect non-random patterns of expression associated with given pathways or regulons. Our recent observations, however, indicate that existing approaches are susceptible to the technical biases that are inherent to most real world annotations. To address this, we have extended our information-theoretic framework to account for specific biases and artifacts in biological networks using the concept of conditional information. To showcase pyPAGE, we performed a comprehensive analysis of regulatory perturbations that underlie the molecular etiology of Alzheimer's disease (AD). pyPAGE successfully recapitulated several known AD-associated gene expression programs. We also discovered several additional regulons whose differential activity is significantly associated with AD. We further explored how these regulators relate to pathological processes in AD through cell-type specific analysis of single cell and spatial gene expression datasets. Our findings showcase the utility of pyPAGE as a precise and reliable biomarker discovery in complex diseases such as Alzheimer's disease., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Bakulin et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

In several carcinomas, including hepatocellular carcinoma, it has been demonstrated that cancer stem cells (CSCs) have enhanced invasiveness and therapy resistance compared to differentiated cancer cells. Mathematical-computational tools could be valuable for integrating experimental results and understanding the phenotypic plasticity mechanisms for CSCs emergence. Based on the literature review, we constructed a Boolean model that recovers eight stable states (attractors) corresponding to the gene expression profile of hepatocytes and mesenchymal cells in senescent, quiescent, proliferative, and stem-like states. The epigenetic landscape associated with the regulatory network was analyzed. We observed that the loss of p53, p16, RB, or the constitutive activation of β-catenin and YAP1 increases the robustness of the proliferative stem-like phenotypes. Additionally, we found that p53 inactivation facilitates the transition of proliferative hepatocytes into stem-like mesenchymal phenotype. Thus, phenotypic plasticity may be altered, and stem-like phenotypes related to CSCs may be easier to attain following the mutation acquisition., (© 2024. The Author(s).)

Long non-coding RNAs (lncRNAs) have emerged as important regulators of many biological processes, although their regulatory roles remain poorly characterized in woody plants, especially in gymnosperms. A major challenge of working with lncRNAs is to assign functional annotations, since they have a low coding potential and low cross-species conservation. We utilised an existing RNA-Sequencing resource and performed short RNA sequencing of somatic embryogenesis developmental stages in Norway spruce (Picea abies L. Karst). We implemented a pipeline to identify lncRNAs located within the intergenic space (lincRNAs) and generated a co-expression network including protein coding, lincRNA and miRNA genes. To assign putative functional annotation, we employed a guilt-by-association approach using the co-expression network and integrated these results with annotation assigned using semantic similarity and co-expression. Moreover, we evaluated the relationship between lincRNAs and miRNAs, and identified which lincRNAs are conserved in other species. We identified lincRNAs with clear evidence of differential expression during somatic embryogenesis and used network connectivity to identify those with the greatest regulatory potential. This work provides the most comprehensive view of lincRNAs in Norway spruce and is the first study to perform global identification of lincRNAs during somatic embryogenesis in conifers. The data have been integrated into the expression visualisation tools at the PlantGenIE.org web resource to enable easy access to the community. This will facilitate the use of the data to address novel questions about the role of lincRNAs in the regulation of embryogenesis and facilitate future comparative genomics studies., (© 2024 The Author(s). Physiologia Plantarum published by John Wiley & Sons Ltd on behalf of Scandinavian Plant Physiology Society.)

Starch and seed storage protein (SSP) composition profoundly impact wheat grain yield and quality. To unveil regulatory mechanisms governing their biosynthesis, transcriptome, and epigenome profiling is conducted across key endosperm developmental stages, revealing that chromatin accessibility, H3K27ac, and H3K27me3 collectively regulate SSP and starch genes with varying impact. Population transcriptome and phenotype analyses highlight accessible promoter regions' crucial role as a genetic variation resource, influencing grain yield and quality in a core collection of wheat accessions. Integration of time-serial RNA-seq and ATAC-seq enables the construction of a hierarchical transcriptional regulatory network governing starch and SSP biosynthesis, identifying 42 high-confidence novel candidates. These candidates exhibit overlap with genetic regions associated with grain size and quality traits, and their functional significance is validated through expression-phenotype association analysis among wheat accessions and loss-of-function mutants. Functional analysis of wheat abscisic acid insensitive 3-A1 (TaABI3-A1) with genome editing knock-out lines demonstrates its role in promoting SSP accumulation while repressing starch biosynthesis through transcriptional regulation. Excellent TaABI3-A1 Hap1 with enhanced grain weight is selected during the breeding process in China, linked to altered expression levels. This study unveils key regulators, advancing understanding of SSP and starch biosynthesis regulation and contributing to breeding enhancement., (© 2024 The Author(s). Advanced Science published by Wiley‐VCH GmbH.)

Dynamic disease pathways are a combination of complex dynamical processes among bio-molecules in a cell that leads to diseases. Network modeling of disease pathways considers disease-related bio-molecules (e.g. DNA, RNA, transcription factors, enzymes, proteins, and metabolites) and their interaction (e.g. DNA methylation, histone modification, alternative splicing, and protein modification) to study disease progression and predict therapeutic responses. These bio-molecules and their interactions are the basic elements in the study of the misregulation in the disease-related gene expression that lead to abnormal cellular responses. Gene regulatory networks, cell signaling networks, and metabolic networks are the three major types of intracellular networks for the study of the cellular responses elicited from extracellular signals. The disease-related cellular responses can be prevented or regulated by designing control strategies to manipulate these extracellular or other intracellular signals. The paper reviews the regulatory mechanisms, the dynamic models, and the control strategies for each intracellular network. The applications, limitations and the prospective for modeling and control are also discussed.

A major objective in genomics is to design interventions that can shift undesirable behaviors of such systems (i.e., those associated with cancers) into desirable ones. Several intervention policies have been developed in recent years, including dynamic and structural interventions. These techniques aim at making targeted changes to cell dynamics upon intervention, without considering the cell's defensive mechanisms to interventions. This simplified assumption often leads to early and short-term success of interventions, followed by partial or full recurrence of diseases. This is due to the fact that cells often have dynamic and intelligent responses to interventions through internal stimuli. This paper models gene regulatory networks (GRNs) using the Boolean network with perturbation. The dynamic and adaptive battle between intervention and the cell is modeled as a two-player zero-sum game, where intervention and the cell fight against each other with fully opposite objectives. An optimal intervention policy is obtained as a Nash equilibrium solution, through which the intervention is stochastic, ensuring the optimal solution to all potential cell responses. We analytically analyze the superiority of the proposed intervention policy against existing intervention techniques. Comprehensive numerical experiments using the p53-MDM2 negative feedback loop regulatory network and melanoma network demonstrate the high performance of the proposed method.

Intracranial aneurysms (IAs) often go undetected until rupture, leading to significant morbidity and mortality. Identifying biomarkers for early detection of IAs is crucial. The current study attempted to identify core genes linked with IAs and determine their relevance through Mendelian randomization. Limma helped identify differentially expressed genes between IAs and control superficial temporal artery samples. WGCNA was utilized to find IA-related modules and associated genes, which were further evaluated using KEGG and GO analyses to ascertain their potential roles. Five highly associated genes were screened with the CytoHubba plugin of Cytoscape software. ROC curves assessed the diagnostic efficacy of these genes. A two-sample Mendelian randomization evaluated the causal relationship between the core gene PTRPC and IAs, along with its correlation with immune infiltration. WGCNA and differential expression analysis depicted 584 related genes involved in cellular metabolism and chemokine activity. PTPRC was among the top highly associated genes identified through Cytoscape. It showed significant diagnostic value for IAs. Moreover, mendelian randomization depicted that PTPRC in CD4+ T cells is related to IA risk, with an OR of 0.63538 (95 % CI = 0.41636-0.96959, p = 0.03545). No reverse causal relationship was observed between PTPRC and IAs, with an OR of 0.99947 (95 % CI = 0.99719-1.00176, p = 0.65022). Additionally, immune cell infiltration results indicated a positive correlation between PTPRC in IAs with neutrophils and unactivated dendritic cells and a negative association with regulatory T cells (Tregs). PTPRC was identified as a core gene linked with IAs, providing evidence for IA diagnosis and studying molecular mechanisms., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

The complexity, scale, and uncertainty in regulatory networks (e.g., gene regulatory networks and microbial networks) regularly pose a huge uncertainty in their models. These uncertainties often cannot be entirely reduced using limited and costly data acquired from the normal condition of systems. Meanwhile, regulatory networks often suffer from the non-identifiability issue, which refers to scenarios where the true underlying network model cannot be clearly distinguished from other possible models. Perturbation or excitation is a well-known process in systems biology for acquiring targeted data to reveal the complex underlying mechanisms of regulatory networks and overcome the non-identifiability issue. We consider a general class of Boolean network models for capturing the activation and inactivation of components and their complex interactions. Assuming partial available knowledge about the interactions between components of the networks, this paper formulates the inference process through the maximum aposteriori (MAP) criterion. We develop a Bayesian lookahead policy that systematically perturbs regulatory networks to maximize the performance of MAP inference under the perturbed data. This is achieved by optimally formulating the perturbation process in a reinforcement learning context and deriving a scalable deep reinforcement learning perturbation policy to compute near-optimal Bayesian policy. The proposed method learns the perturbation policy through planning without the need for any real data. The high performance of the proposed approach is demonstrated by comprehensive numerical experiments using the well-known mammalian cell cycle and gut microbial community networks.

The pan-cancer initiative aims to study the origin patterns of cancer cell, the processes of carcinogenesis, and the signaling pathways from a perspective that spans across different types of cancer. The construction of the pan-cancer related gene regulatory network is helpful to excavate the commonalities in regulatory relationships among different types of cancers. It also aids in understanding the mechanisms behind cancer occurrence and development, which is of great scientific significance for cancer prevention and treatment. In light of the high dimension and large sample size of pan-cancer omics data, a causal pan-cancer gene regulation network inference algorithm based on stochastic complexity is proposed. With the network construction strategy of local first and then global, the stochastic complexity is used in the conditional independence test and causal direction inference for the candidate adjacent node set of the target nodes. This approach aims to decrease the time complexity and error rate of causal network learning. By applying this algorithm to the sample data of seven types of cancers in the TCGA database, including breast cancer, lung adenocarcinoma, and so on, the pan-cancer related causal regulatory networks are constructed, and their biological significance is verified. The experimental results show that this algorithm can eliminate the redundant regulatory relationships effectively and infer the pan-cancer regulatory network more accurately (https://github.com/LindeEugen/CNI-SC)., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

The identification of cancer driver genes (CDGs) poses challenges due to the intricate interdependencies among genes and the influence of measurement errors and noise. We propose a novel energy-constrained diffusion (ECD)-based model for identifying CDGs, termed ECD-CDGI. This model is the first to design an ECD-Attention encoder by combining the ECD technique with an attention mechanism. ECD-Attention encoder excels at generating robust gene representations that reveal the complex interdependencies among genes while reducing the impact of data noise. We concatenate topological embedding extracted from gene-gene networks through graph transformers to these gene representations. We conduct extensive experiments across three testing scenarios. Extensive experiments show that the ECD-CDGI model possesses the ability to not only be proficient in identifying known CDGs but also efficiently uncover unknown potential CDGs. Furthermore, compared to the GNN-based approach, the ECD-CDGI model exhibits fewer constraints by existing gene-gene networks, thereby enhancing its capability to identify CDGs. Additionally, ECD-CDGI is open-source and freely available. We have also launched the model as a complimentary online tool specifically crafted to expedite research efforts focused on CDGs identification., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Wang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The evolution of a population by means of genetic drift and natural selection operating on a gene regulatory network (GRN) of an individual has not been scrutinized in depth. Thus, the relative importance of various evolutionary forces and processes on shaping genetic variability in GRNs is understudied. In this study, we implemented a simulation framework, called EvoNET, that simulates forward-in-time the evolution of GRNs in a population. The fitness effect of mutations is not constant, rather fitness of each individual is evaluated on the phenotypic level, by measuring its distance from an optimal phenotype. Each individual goes through a maturation period, where its GRN may reach an equilibrium, thus deciding its phenotype. Afterwards, individuals compete to produce the next generation. We examine properties of the GRN evolution, such as robustness against the deleterious effect of mutations and the role of genetic drift. We are able to confirm previous hypotheses regarding the effect of mutations and we provide new insights on the interplay between random genetic drift and natural selection., Competing Interests: The authors declare that they have no competing interests., (© 2024 Papadadonakis et al.)

In this review paper we summarize a recent progress on the problem of describing range of dynamics supported by a network. We show that there is natural connection between network models consisting of collections of multivalued monotone boolean functions and ordinary differential equations models. We show how to construct such collections and use them to answer questions about prevalence of cellular phenotypes that correspond to equilibria of network models., (© 2024. The Author(s).)

The identification of cancer driver genes is crucial for early detection, effective therapy, and precision medicine of cancer. Cancer is caused by the dysregulation of several genes at various levels of regulation. However, current techniques only capture a limited amount of regulatory information, which may hinder their efficacy. In this study, we present IMI-driver, a model that integrates multi-omics data into eight biological networks and applies Multi-view Collaborative Network Embedding to embed the gene regulation information from the biological networks into a low-dimensional vector space to identify cancer drivers. We apply IMI-driver to 29 cancer types from The Cancer Genome Atlas (TCGA) and compare its performance with nine other methods on nine benchmark datasets. IMI-driver outperforms the other methods, demonstrating that multi-level network integration enhances prediction accuracy. We also perform a pan-cancer analysis using the genes identified by IMI-driver, which confirms almost all our selected candidate genes as known or potential drivers. Case studies of the new positive genes suggest their roles in cancer development and progression., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Shi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Inference of causal transcriptional regulatory networks (TRNs) from transcriptomic data suffers notoriously from false positives. Approaches to control the false discovery rate (FDR), for example, via permutation, bootstrapping, or multivariate Gaussian distributions, suffer from several complications: difficulty in distinguishing direct from indirect regulation, nonlinear effects, and causal structure inference requiring "causal sufficiency," meaning experiments that are free of any unmeasured, confounding variables. Here, we use a recently developed statistical framework, model-X knockoffs, to control the FDR while accounting for indirect effects, nonlinear dose-response, and user-provided covariates. We adjust the procedure to estimate the FDR correctly even when measured against incomplete gold standards. However, benchmarking against chromatin immunoprecipitation (ChIP) and other gold standards reveals higher observed than reported FDR. This indicates that unmeasured confounding is a major driver of FDR in TRN inference. A record of this paper's transparent peer review process is included in the supplemental information., Competing Interests: Declaration of interests A.B. is a stockholder for Alphabet, Inc.; has consulted for Third Rock Ventures; and is a founder of CellCipher, Inc., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

One snapshot of the peer review process for "Transcriptome data are insufficient to control false discoveries in regulatory network inference" (Kernfeld et al., 2024). 1 ., Competing Interests: Declaration of interests The author declares no competing interests., (Copyright © 2024. Published by Elsevier Inc.)

Heritability and genetic covariance/correlation quantify the marginal and shared genetic effects across traits. They offer insights on the genetic architecture of complex traits and diseases. To explore how genetic variations contribute to brain function variations, we estimated heritability and genetic correlation across cortical thickness, surface area, and volume of 33 anatomically predefined regions in left and right hemispheres, using summary statistics of genome-wide association analyses of 31,968 participants in the UK Biobank. To characterize the relationships between these regions of interest, we constructed a genetic network for these regions using recursive two-way cut-offs in similarity matrices defined by genetic correlations. The inferred genetic network matches the brain lobe mapping more closely than the network inferred from phenotypic similarities. We further studied the associations between the genetic network for brain regions and 30 complex traits through a novel composite-linkage disequilibrium score regression method. We identified seven significant pairs, which offer insights on the genetic basis for regions of interest mediated by cortical measures., Competing Interests: Declaration of competing interest The authors declare that they have no conflict of interest., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Ligand-receptor systems, covalent modification cycles, and transcriptional networks are the fundamental components of cell signaling and gene expression systems. While their behavior in reaching a steady-state regime under step-like stimulation is well understood, their response under repetitive stimulation, particularly at early time stages is poorly characterized. Yet, early-stage responses to external inputs are arguably as informative as late-stage ones. In simple systems, a periodic stimulation elicits an initial transient response, followed by periodic behavior. Transient responses are relevant when the stimulation has a limited time span, or when the stimulated component's timescale is slow as compared to the timescales of the downstream processes, in which case the latter processes may be capturing only those transients. In this study, we analyze the frequency response of simple motifs at different time stages. We use dose-conserved pulsatile input signals and consider different metrics versus frequency curves. We show that in ligand-receptor systems, there is a frequency preference response in some specific metrics during the transient stages, which is not present in the periodic regime. We suggest this is a general system-level mechanism that cells may use to filter input signals that have consequences for higher order circuits. In addition, we evaluate how the described behavior in isolated motifs is reflected in similar types of responses in cascades and pathways of which they are a part. Our studies suggest that transient frequency preferences are important dynamic features of cell signaling and gene expression systems, which have been overlooked., (© 2024. The Author(s).)

This study aimed to perform exhaustive bioinformatic analysis by using GSE29221 micro-array maps obtained from healthy controls and Type 2 Diabetes (T2DM) patients. Raw data are downloaded from the Gene Expression Omnibus database and processed by the limma package in R software to identify Differentially Expressed Genes (DEGs). Gene ontology functional analysis and Kyoto Gene Encyclopedia and Genome Pathway analysis are performed to determine the biological functions and pathways of DEGs. A protein interaction network is constructed using the STRING database and Cytoscape software to identify key genes. Finally, immune infiltration analysis is performed using the Cibersort method. This study has implications for understanding the underlying molecular mechanism of T2DM and provides potential targets for further research., Competing Interests: Declaration of competing interest The authors declare no conflicts of interest., (Copyright © 2024 HCFMUSP. Published by Elsevier España, S.L.U. All rights reserved.)

Background: Nucleus pulposus cells survive in a hypoxic, acidic, nutrient-poor, and hypotonic microenvironment. Consequently, they maintain low proliferation and undergo autophagy to protect themselves from cellular stress. Therefore, we aimed to identify autophagy-related biomarkers involved in intervertebral disc degeneration pathogenesis., Methods: Autophagy-related differentially expressed genes were derived from the intersection between the public GSE147383 microarray data set to identify differentially expressed genes and online databases to identify autophagy-related genes. Furthermore, we assessed their biological functions with gene annotation and enrichment analysis in the Metscape portal. Then, the STRING database and Cytoscape software allowed inferring a protein-protein interaction (PPI) network and identifying hub genes. In addition, to predict transcription factors that may regulate the hub genes, we used the GeneMANIA website. Finally, the competing endogenous RNA prediction tools and Cytoscape were also used to construct an mRNA-miRNA-lncRNA network., Results: A total of 123 autophagy-related differentially expressed genes were identified, they were mainly involved in phosphoinositide 3-kinase-Akt signaling, autophagy animal, and apoptosis pathways. Nine were identified as hub genes (PTEN, MYC, CTNNB1, JUN, BECN1, ERBB2, FOXO3, ATM, and FN1) and 36 transcription factors were associated with them. Finally, an autophagy-associated competing endogenous RNA network was constructed based on the 9 hub genes., Conclusions: Nine hub genes were identified and a network of competing endogenous RNA associated with autophagy was established. They can be used as autophagy-related biomarkers of intervertebral disc degeneration and for further exploration., (Copyright © 2023. Published by Elsevier Inc.)