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As an important seedling source, monospores closely associate with yields in nori farming. However, the molecular mechanism underlying differences in monospore production for different strains remains unknown. Comparative transcriptome analysis was performed to examine gene expression differences between the spore abundant wild-type strain (WT) and spore deficient mutant (Y1) of Pyropia chauhanii. The WT strain that produces monospores in abundance exhibited more differentially expressed genes (DEGs) in both number and higher fold-changes than the Y1 strain incapable of producing monospores, indicating that the specific regulation of genes is involved in monospore production. Three lists of DEGs were obtained between the two strains using intersection and displayed in Venn diagram: one expressed only in WT strain, another expressed only in Y1 strain, and the third shared in both strains. DEGs annotated as homologous genes of Arabidopsis thaliana in these 3 lists were curated for online functional enrichment analysis on Metascape website. Gene regulatory networks of WT were functionally enriched in the processing, proteolysis, and transport of proteins, especially within the small GTPase protein family, which might be account for the monospore production ability, whereas Y1 were functionally enriched in the metabolism of essential substance and utilization of indispensable energy, which might be account for the rapid growth of blades. We found the differentially enriched gene regulatory networks between strains might be the intrinsic mechanisms of the different monospore production traits. These findings provide novel insights into the genes and regulatory networks associated with monospore production abilities, which are essential for developing accurate breeding technologies for optimal release of monospores and increase of total nori production.

The fusion gene MLL-AF4 defines a high-risk subtype of pro-B acute lymphoblastic leukaemia. However, relapse can be associated with a switch from acute lymphoblastic to acute myeloid leukaemia. Here we show that these myeloid relapses share oncogene fusion breakpoints with their matched lymphoid presentations and can originate in either early, multipotent progenitors or committed B-cell precursors. Lineage switching is linked to substantial changes in chromatin accessibility and rewiring of transcriptional programmes indicating that the execution and maintenance of lymphoid lineage differentiation is impaired. We show that this subversion is recurrently associated with the dysregulation of repressive chromatin modifiers, notably the nucleosome remodelling and deacetylation complex, NuRD. In addition to mutations, we show differential expression or alternative splicing of NuRD members and other genes is able to reprogram the B lymphoid into a myeloid gene regulatory network. Lineage switching in MLL-AF4 leukaemia is therefore driven and maintained by defunct epigenetic regulation.Statement of SignificanceWe demonstrate diverse cellular origins of lineage switched relapse within MLL-AF4 pro-B acute leukaemia. Irrespective of the developmental origin of relapse, dysregulation of NuRD and/or other epigenetic machinery underpins fundamental lineage reprogramming with profound implications for the increasing use of epitope directed therapies in this high-risk leukaemia.

Inferring gene co-expression networks from high-throughput gene expression data is an important task in bioinformatics. Many gene networks often exhibit modular structures. Even though several Gaussian graphical model-based methods have been developed to estimate gene co-expression networks by incorporating the modular structural prior, none of them takes into account the modular structures captured by the prior networks (e.g., protein interaction networks). In this study, we propose a novel prior network-dependent gene network inference (pGNI) method to estimate gene co-expression networks by integrating gene expression data and prior protein interaction network data. The underlying modular structure is learned from both sets of data. Through simulation studies, we demonstrate the feasibility and effectiveness of our method. We also apply our method to two real datasets. The modular structures in the networks estimated by our method are biological significant.

Functional genomics and systems biology have opened new doors to previously inaccessible genomic information and holistic approaches to study complex networks of genes and proteins in the central nervous system. The advances are revolutionizing our understanding of the genetic underpinning of cognitive development and decline by facilitating identifications of novel molecular regulators and physiological pathways underlying brain function, and by associating polymorphism and mutations to cognitive dysfunction and neurological diseases. However, our current understanding of these complex gene regulatory mechanisms has yet lacked sufficient mechanistic resolution for further translational breakthroughs. Here we review recent findings from the burgeoning field of epitranscriptomics in association of cognitive functions with a special focus on the epitranscritomic regulation in subcellular locations such as chromosome, synapse, and mitochondria. Although there are important gaps in knowledge, current evidence is suggesting that this layer of RNA regulation may be of particular interest for the spatiotemporally coordinated regulation of gene networks in developing and maintaining brain function that underlie cognitive changes.

Boron (B) is an essential micronutrient for plants, and its deficiency is a widespread nutritional disorder, particularly in high-demanding crops like Brassica napus. Over the past few decades, silicon (Si) has been shown to mitigate plant nutrient deficiencies of different macro- and micro-nutrients. However, the work on B and Si cross-talk has mostly been focused on the alleviation of B toxicity by Si application. In the present study, we investigated the effect of Si application on rapeseed plants grown hydroponically under long-term B deficiency (20 days at 0.1 µM B). In addition, a B-uptake labelling experiment was conducted, and the expression of the genes involved in B uptake were monitored between 2 and 15 days of B shortage. The results showed that Si significantly improved rapeseed plant growth under B deficiency by 34% and 49% in shoots and roots, respectively. It also increased the expression level of BnaNIP5;1 and BOR1;2c in both young leaves and roots. The uptake labelling experiment showed the remobilization of previously fixed 11B from old leaves to new tissues. This study provides additional evidence of the beneficial effects of Si under conditions lacking B by changing the expression of the BnaNIP5;1 gene and by remobilizing 11B to young tissues.

This paper investigates the finite-time delay-free stability criteria for fractional-order gene regulatory networks (FOGRNs) with feedback regulation time delays. The existence of unique equilibrium points of considered systems is established based on the Banach fixed point theorem and Cauchy–Schwarz inequality. Further, when the order γ satisfies 0 γ 1 and 1 γ 2 , some novel delay-free sufficient conditions are derived to ensure the finite-time stability for addressing fractional order systems by using the ideas of generalized Gronwall–Bellman inequality, equivalent norm techniques, and Laplace transform. In the end, the article comes up with two numerical cases to manifest the applicability and superiority of the obtained theoretical results.

We present PALLAS, a practical method for gene regulatory network (GRN) inference from time series data, which employs penalized maximum likelihood and particle swarms for optimization. PALLAS is based on the Partially-Observable Boolean Dynamical System (POBDS) model and thus does not require ad-hoc binarization of the data. The penalty in the likelihood is a LASSO regularization term, which encourages the resulting network to be sparse. PALLAS is able to scale to networks of realistic size under no prior knowledge, by virtue of a novel continuous-discrete Fish School Search particle swarm algorithm for efficient simultaneous maximization of the penalized likelihood over the discrete space of networks and the continuous space of observational parameters. The performance of PALLAS is demonstrated by a comprehensive set of experiments using synthetic data generated from real and artificial networks, as well as real time series microarray and RNA-seq data, where it is compared to several other well-known methods for gene regulatory network inference. The results show that PALLAS can infer GRNs more accurately than other methods, while being capable of working directly on gene expression data, without need of ad-hoc binarization. PALLAS is a fully-fledged program, written in python, and available on GitHub (https://github.com/yukuntan92/PALLAS).

Since the initial sequencing of the human genome in 2001, the field of systems biology has strived to understand gene regulatory mechanisms, in particular the role of transcription factor binding. This is crucial to inform our understanding of diseases such as cancer: regulatory mechanisms are the primary alterations observed in tumors, and they are extensively investigated for treatment and drug design, as well as early detection. Despite the importance of discovering the underlying regulatory mechanisms, the task of inferring them from data while maintaining accuracy, explainability, scalability, and flexibility remains a significant open challenge. This thesis proposes GIRAFFE, a scalable matrix factorization-based algorithm to jointly infer regulatory effects and transcription factor activities from gene expression data. GIRAFFE integrates prior knowledge about regulation to guide the optimization, yielding an interpretable model where regulatory weights are partial effects. Moreover, it can be customized to the requirements of the downstream application by adjusting for variables of interest, such as confounders, and adding sparsity constraints, which help to interpret the regulatory network. We demonstrate the effectiveness of this approach with extensive experiments on synthetic, as well as real world data. Our algorithm outperforms state-of-the-art gene regulatory network inference methods in predicting interactions between transcription factors and target genes. Moreover, it is able to distinguish between activating and inhibitory effects, yielding plausible results in downstream applications such as gene set enrichment analysis.

The architecture of the rice inflorescence is an important determinant of crop yield. The length of the inflorescence and the number of branches are among the key factors determining the number of spikelets, and thus grains, that a plant will develop. In particular, the timing of the identity transition from indeterminate branch meristem to determinate spikelet meristem governs the complexity of the inflorescence. In this context, the ALOG gene TAWAWA1 (TAW1) has been shown to delay the transition to determinate spikelet development in Oryza sativa (rice). Recently, by combining precise laser microdissection of inflorescence meristems with RNA-seq, we observed that two ALOG genes, OsG1-like 1 (OsG1L1) and OsG1L2, have expression profiles similar to that of TAW1. Here, we report that osg1l1 and osg1l2 loss-of-function CRISPR mutants have similar phenotypes to the phenotype of the previously published taw1 mutant, suggesting that these genes might act on related pathways during inflorescence development. Transcriptome analysis of the osg1l2 mutant suggested interactions of OsG1L2 with other known inflorescence architecture regulators and the data sets were used for the construction of a gene regulatory network (GRN), proposing interactions among genes potentially involved in controlling inflorescence development in rice. In this GRN, we selected the homeodomain-leucine zipper transcription factor encoding the gene OsHOX14 for further characterization. The spatiotemporal expression profiling and phenotypical analysis of CRISPR loss-of-function mutants of OsHOX14 suggests that the proposed GRN indeed serves as a valuable resource for the identification of new proteins involved in rice inflorescence development.

Cell state changes in development and disease are controlled by gene regulatory networks, the dynamics of which are difficult to track in real time. In this study, we used an inducible DCM–RNA polymerase subunit b fusion protein which labels active genes and enhancers with a bacterial methylation mark that does not affect gene transcription and is propagated in S-phase. This DCM–RNA polymerase fusion protein enables transcribed genes and active enhancers to be tagged and then examined at later stages of development or differentiation. We apply this DCM-time machine (DCM-TM) technology to study intestinal homeostasis, revealing rapid and coordinated activation of enhancers and nearby genes during enterocyte differentiation. We provide new insights in absorptive–secretory lineage decision-making in intestinal stem cell (ISC) differentiation and show that ISCs retain a unique chromatin landscape required to maintain ISC identity and delineate future expression of differentiation-associated genes. DCM-TM has wide applicability in tracking cell states, providing new insights in the regulatory networks underlying cell state changes.

International audience; In this work, we study the existence of sustained oscillations in the "canonical" repressilator, a basic synthetic circuit of 3 genes leading to sustained oscillations. Previous works mostly used differential equations to study the repressilator. In our work, a pre-existing hybrid modeling framework of gene regulatory networks called HGRN is used to model this system. Compared to differential equations, dynamical properties of HGRNs are easier to prove theoretically due to its lower dynamical complexity. The objective of this work is to find conditions for the existence of sustained oscillations described by separable constraints on parameters. With such separable constraints, each parameter is constrained individually by an interval, which can provide useful information for the design of synthetic circuits. Our two major contributions are the following: firstly, we develop, by using the Poincaré map, a sufficient and necessary condition for the existence of sustained oscillations; then, based on this condition, we give a method using the range enclosure property of Bernstein coefficients to compute compatible separable constraints. By applying this method, we successfully obtain sets of conditions for the existence of sustained oscillations described as separable constraints.

Although at the infancy stage, biomolecular network biology is a comprehensive approach to understand complex biological function in plants. Recent advancements in the accumulation of multi-omics data coupled with computational approach have accelerated our current understanding of the complexities of gene function at the system level. Biomolecular networks such as protein-protein interaction, co-expression and gene regulatory networks have extensively been used to decipher the intricacies of transcriptional reprogramming of hundreds of genes and their regulatory interaction in response to various environmental perturbations mainly in the model plant Arabidopsis. This review describes recent applications of network-based approaches to understand the biological functions in plants and focuses on the challenges and opportunities to harness the full potential of the approach.

Serial homology, i.e., homology between repetitive structures in the same individual organism, is a debated concept in evolutionary developmental biology. The central question is the evolutionary interpretation of “sameness” in the context of the same body. This essay provides a synthetic analysis of the main issues involved in the debate, connecting conceptual problems with current experimental research. It is argued that a concept of serial homology that is not of the all-or-nothing kind can smooth several theoretical inconsistencies, while being more in line with what we know about evolutionary change and the way we investigate it.

Lung cancer (LC) is the leading cause of cancer-related deaths worldwide, with high morbidity and mortality. Non-small cell lung cancer (NSCLC) is a major pathological type of LC and accounts for more than 80% of all cases. Circular RNAs (circRNAs) are a large class of non-coding RNAs (ncRNAs) with covalently closed-loop structures, a high abundance, and tissue-specific expression patterns. They participate in various pathophysiological processes by regulating complex gene networks involved in proliferation, apoptosis, migration, and epithelial-to-mesenchymal transition (EMT), as well as metastasis. A growing number of studies have revealed that the dysregulation of circRNAs contributes to many aspects of cancer progression, such as its occurrence, metastasis, and recurrence, suggesting their great potential as efficient and specific biomarkers in the diagnosis, prognosis, and therapeutic targeting of NSCLC. In this review, we systematically elucidate the characteristics, biogenesis, and functions of circRNAs and focus on their molecular mechanisms in NSCLC progression. Moreover, we highlight their clinical implications in NSCLC treatment., Graphical Abstract, In this review, Liu et al. systematically summarize the biological characteristics, biogenesis, and functions of circRNAs and focus on their mechanisms in non-small cell lung cancer (NSCLC) progression. Moreover, the authors also highlight the potential of circRNAs as diagnostic and prognostic biomarkers and therapeutic targets for NSCLC.

Clostridioides difficile produces toxins TcdA and TcdB during infection. Since the severity of the illness is directly correlated with the level of toxins produced, researchers have long been interested in the regulation mechanisms of toxin production. The advent of new genetics and mutagenesis technologies in C. difficile has allowed a slew of new investigations in the last decade, which considerably improved our understanding of this crucial regulatory network. The current body of work shows that the toxin regulatory network overlaps with the regulatory networks of sporulation, motility, and key metabolic pathways. This implies that toxin production is a complicated process initiated by bacteria in response to numerous host factors during infection. We summarize the existing knowledge about the toxin gene regulatory network here.

Several studies have shown that cancer cells can be "phenotypically reversed", thus achieving a "tumor reversion", by losing malignant hallmarks as migrating and invasive capabilities. These findings suggest that genome activity can switch to assume a different functional configuration, i.e. a different Gene Regulatory Network pattern. Indeed, once "destabilized", cancer cells enter into a critical transition phase that can be adequately "oriented" by yet unidentified morphogenetic factors - acting on both cells and their microenvironment - that trigger an orchestrated array of structural and epigenetic changes. Such process can bypass genetic abnormalities, through rerouting cells toward a benign phenotype. Oocytes and embryonic tissues, obtained by animals and humans, display such "reprogramming" capability, as a number of yet scarcely identified embryo-derived factors can revert the malignant phenotype of several types of tumors. Mechanisms involved in the reversion process include the modification of cell-microenvironment cross talk (mostly through cytoskeleton reshaping), chromatin opening, demethylation, and epigenetic changes, modulation of biochemical pathways, comprising TCTP-p53, PI3K-AKT, FGF, Wnt, and TGF-β-dependent cascades. Results herein discussed promise to open new perspectives not only in the comprehension of cancer biology but also toward different therapeutic options, as suggested by a few preliminary clinical studies.

Human Angiotensin I Converting Enzyme 2 (ACE2) plays an essential role in blood pressure regulation and SARS-CoV-2 entry. ACE2 has a highly conserved, one-to-one ortholog (ace2) in zebrafish, which is an important model for human diseases. However, the zebrafish ace2 expression profile has not yet been studied during early development, between genders, across different genotypes, or in disease. Moreover, a network-based meta-analysis for the extraction of functionally enriched pathways associated with differential ace2 expression is lacking in the literature. Herein, we first identified significant development-, tissue-, genotype-, and gender-specific modulations in ace2 expression via meta-analysis of zebrafish Affymetrix transcriptomics datasets (ndatasets = 107); and the correlation analysis of ace2 meta-differential expression profile revealed distinct positively and negatively correlated local functionally enriched gene networks. Moreover, we demonstrated that ace2 expression was significantly modulated under different physiological and pathological conditions related to development, tissue, gender, diet, infection, and inflammation using additional RNA-seq datasets. Our findings implicate a novel translational role for zebrafish ace2 in organ differentiation and pathologies observed in the intestines and liver.

A key objective of studying biological systems is to design therapeutic intervention strategies for beneficially altering cell dynamics. Derivation of control policies is hindered by the high-dimensional state spaces associated with gene regulatory networks. Hence, it is critical to reduce the network complexity and the paper aims to address this issue by focusing on the distribution of the canalizing power (CP) of the genes in the model. Canalizing genes enforce broad corrective actions on cellular processes and play a crucial role in producing optimal reactions to external stimuli. Therefore, it is critical to reduce the network while preserving the canalizing power of genes. We reduce Boolean networks with perturbation by removing genes with the smallest canalizing power consecutively, and evaluate the stability of canalizing power. A systematic empirical study demonstrates that there are two classes of networks, reducible and irreducible with respect to the preservation of canalizing power of the genes. Based on these observations, we introduce the definition of reducible networks and proceed with the problem of selecting the relevant network features that allow for discriminating networks from the two different classes. We demonstrate the efficacy of the selected features on synthetic and real gene regulatory networks.

Pearl millet is an important staple food crop of poor people which is rich in micronutrients like iron and zinc and amenable for focused breeding for these micronutrients along with high yield. Transcriptome sequencing using ION S5 Next Generation Sequencer generated 43.5 million sequence reads resulting in 83,721 transcripts with N50 of 597 bp and 84.35% of transcripts matched with the pearl millet genome assembly. The genotypes having high Fe and Zn showed differential gene expression during different stages. Of which, 155 were up-regulated and 251were down-regulated while during flowering stage and milking stage 349 and 378 transcripts were differentially expressed, respectively. Gene annotation and GO term showed the presence of transcripts involved in metabolic activities associated with uptake and transport of iron and zinc. In the present study, the 83,721 transcripts were also examined for identification of SSRs. A total of 4,327 SSRs were identified with dominance of tri-nucleotide SSRs in comparison to di-nucleotide SSRs. These EST-SSRs can be used in molecular breeding, genetic diversity analysis and determination of heterozygosity of the allelic loci. Information generated will help in gaining insights into iron and zinc metabolism and develop genotypes with high yield, grain iron and zinc content.

Background Mining gene regulatory network (GRN) is an important avenue for addressing cancer mechanism. Mutations in cancer genome perturb GRN and cause a rewiring in an orchestrated network. Hence, the exploration of gene regulatory network rewiring is significant to discover potential biomarkers and indicators for discriminating cancer phenotypes. Results Here, we propose a new bioinformatics method of identifying biomarkers based on network rewiring in different states. It firstly reconstructs GRN in different phenotypic conditions from gene expression data with a priori background network. We employ the algorithm based on path consistency algorithm and conditional mutual information to delete false-positive regulatory interactions between independent nodes/genes or not closely related gene pairs. And then a differential gene regulatory network (D-GRN) is constructed from the rewiring parts in the two phenotype-specific GRNs. Community detection technique is then applied for D-GRN to detect functional modules. Finally, we apply logistic regression classifier with recursive feature elimination to select biomarker genes in each module individually. The extracted feature genes result in a gene set of biomarkers with impressing ability to distinguish normal samples from controls. We verify the identified biomarkers in external independent validation datasets. For a proof-of-concept study, we apply the framework to identify diagnostic biomarkers of breast cancer. The identified biomarkers obtain a maximum AUC of 0.985 in the internal sample classification experiments. And these biomarkers achieve a maximum AUC of 0.989 in the external validations. Conclusion In conclusion, network rewiring reveals significant differences between different phenotypes, which indicating cancer dysfunctional mechanisms. With the development of sequencing technology, the amount and quality of gene expression data become available. Condition-specific gene regulatory networks that are close to the real regulations in different states will be established. Revealing the network rewiring will greatly benefit the discovery of biomarkers or signatures for phenotypes. D-GRN is a general method to meet this demand of deciphering the high-throughput data for biomarker discovery. It is also easy to be extended for identifying biomarkers of other complex diseases beyond breast cancer.

It is an important task to learn how gene regulatory networks change under different conditions. Several Gaussian graphical model-based methods have been proposed to deal with this task by inferring differential networks from gene expression data. However, most existing methods define the differential networks as the difference of precision matrices, which may include false differential edges caused by the change of conditional variances. In addition, prior information about the condition-specific networks and the differential networks can be obtained from other domains. It is useful to incorporate prior information into differential network analysis. In this study, we propose a new differential network analysis method to address the above challenges. Instead of using the precision matrices, we define the differential networks as the difference of partial correlations, which can exclude the spurious differential edges due to the variants of conditional variances. Furthermore, prior information from multiple hypothesis testing is incorporated using a weighted fused penalty. Simulation studies show that our method outperforms the competing methods. We also apply our method to identify the differential network between luminal A and basal-like subtypes of breast cancers and the differential network between acute myeloid leukemia tumors and normal samples. The hub genes in the differential networks identified by our method carry out important biological functions.

Background The pistil is an essential part of flowers that functions in the differentiation of the sexes and reproduction in plants. The stigma on the pistil can accept pollen to allow fertilization and seed development. Papaya (Carica papaya L.) is a dioecious plant, where female flowers exhibit normal pistil, while the male flowers exhibit aborted pistil at a late stage of pistil development. Results The developmental stages of papaya pistil were analyzed after first dividing it into slices representing the primordium stage 1 (S1), the pre-meiotic stages S2, post-meiotic stage S3, and the mitotic stage S4. The SS scoring algorithm analysis of genes preferentially expressed at different stages revealed differentially expressed genes between male and female flowers. A transcription factor regulatory network for each stage based on the genes that are differentially expressed between male and female flowers was constructed. Some transcription factors related to pistil development were revealed based on the analysis of regulatory networks such as CpAGL11, CpHEC2, and CpSUPL. Based on the specific expression of genes, constructed a gene regulatory subnetwork with CpAGL11-CpSUPL-CpHEC2 functioning as the core. Analysis of the functionally enriched terms in this network reveals several differentially expressed genes related to auxin/ brassinosteroid signal transduction in the plant hormone signal transduction pathway. At the same time, significant differences in the expression of auxin and brassinosteroid synthesis-related genes between male and female flowers at different developmental stages were detected. Conclusions The pistil abortion of papaya might be caused by the lack of expression or decreased expression of some transcription factors and hormone-related genes, affecting hormone signal transduction or hormone biosynthesis. Analysis of aborted and normally developing pistil in papaya provided new insights into the molecular mechanism of pistil development and sex differentiation in dioecious papaya.

Discrete-time probabilistic logic networks (DT-PLNs), of which probabilistic Boolean networks (PBNs) are a special type, are an important qualitative model for gene regulatory networks (GRNs). Although a DT-PLN can predict the long-term behavior of a GRN, using it to describe the transient kinetics at the micro-timescale level remains inconvenient. In this study, we investigate the problems associated with the stability and stabilization of continuous-time probabilistic logic networks (CT-PLNs). First, we demonstrate that the concept of finite-time stability for DT-PLNs cannot be extended to CT-PLNs owing to the nonsingularity of transitional probability matrices (TPMs). Thus, we introduce the concept of asymptotical stability, which is defined as the convergence in distribution of the network state. Second, by developing the theory of invariant subsets for CT-PLNs, a necessary and sufficient condition for asymptotical stability with respect to a subset is proposed, which is expressed in terms of the transition rate matrix (TRM) of probability. Third, for a CT-PLN with input nodes, termed a continuous-time probabilistic logic control network (CT-PLCN), we discuss the subsets that a

The transcriptomic analysis of microarray and RNA-Seq datasets followed our own bioinformatic pipeline to identify a transcriptional regulatory network of lung cancer. Twenty-six transcription factors are dysregulated and co-expressed in most of the lung cancer and pulmonary arterial hypertension datasets, which makes them the most frequently dysregulated transcription factors. Co-expression, gene regulatory, coregulatory, and transcriptional regulatory networks, along with fibration symmetries, were constructed to identify common connection patterns, alignments, main regulators, and target genes in order to analyze transcription factor complex formation, as well as its synchronized co-expression patterns in every type of lung cancer. The regulatory function of the most frequently dysregulated transcription factors over lung cancer deregulated genes was validated with ChEA3 enrichment analysis. A Kaplan–Meier plotter analysis linked the dysregulation of the top transcription factors with lung cancer patients’ survival. Our results indicate that lung cancer has unique and common deregulated genes and transcription factors with pulmonary arterial hypertension, co-expressed and regulated in a coordinated and cooperative manner by the transcriptional regulatory network that might be associated with critical biological processes and signaling pathways related to the acquisition of the hallmarks of cancer, making them potentially relevant tumor biomarkers for lung cancer early diagnosis and targets for the development of personalized therapies against lung cancer.

Embryonic development is driven by molecular instructions encoded by transcriptional regulators that underpin the formation of the body plan. Following gastrulation, the foundation of the embryonic head emerges, being the first major body part to form. Analysis of gastrulating mouse embryos has revealed that the LIM homeobox 1 (LHX1) transcription factor (TF) is indispensable for head and face development. However, the precise function of LHX1 in the initiation of craniofacial morphogenesis has not been fully elucidated. The aim of this project was to identify the direct transcriptional targets and co-factors of LHX1 during the establishment of the embryonic head. We generated an LHX1 anchored gene regulatory network (GRN), utilising multi-omics analytics including RNA-seq, ATAC-seq and DamID-seq in vivo, using mouse embryos. Our data shows that during mid gastrulation, LHX1 is responsible for the modulation of Wnt signalling in the anterior embryo. In late gastrulation, LHX1 acts with OTX2 and FOXA2 to directly downregulate the pluripotency network in the anterior mesendoderm. LHX1 also regulates genes responsible for anterior identity and establishes key signalling gradients including Wnt, Hedgehog, retinoic acid, and FGF signalling. We identified genes expressed in the anterior region of head fold stage mouse embryos such as Kctd1 and Foxd4 as novel downstream targets of LHX1. To understand the function of these TFs during head development, we used chimeric embryos harbouring CRISPR-Cas9-edited mESCs and stem cell-derived embryo models. We showed that Foxd4 is essential for neurulation in the rostral neural tube and for the specification of the cranial neural crest population. A loss of Kctd1 impacted the canonical Wnt signalling pathway, derailing anterior mesendoderm lineage development. Overall, these findings revealed the role of LHX1 in the early formation of head tissue and defined a GRN anchored by LHX1 during embryonic development.

Inom beräkningsbiologin är det snabbt på väg att bli allt vanligare att ta fram genetiska regleringsnätverk (GRN). På grund av storleken på de undersökta nätverken använder många forskare maskininlärning för att härleda GRN från genuttrycksdata, vanligtvis från RNA-seq. Noggrannheten hos sådana moderna metoder kan dock fortfarande förbättras, särskilt när det gäller tidsseriemodellen. I den här forskningen föreslås två tidsseriemodeller för GRN-inferens: GATv2-regressionsmodell och GATv2-länkprediktionsmodell. Den förstnämnda modellen är baserad på idén om en konventionell GRN-inferensmodell som regresserar målgenuttrycksdata med hjälp av data om kandidatkontrollgener. Den senare modellen bygger på den vanliga länkprediktionsmetoden med hjälp av grafiska neurala nätverk som utför binariseringen av om det finns kanter mellan specifika genknutpunkter. GATv2-regressionsmodellen presterar bra i en regressionsuppgift. Noggrannheten för GRN-inferensen är dock låg och var nästan densamma som en slumpmässig utgång. Å andra sidan presterar GATv2-modellen för prediktion av länkar bra om träningsdata är tillräckligt korrekta. Att härleda genreglerande relationer med hög uppskattningsnoggrannhet är dock fortfarande en utmaning inom området GRN-inferens även för partiella genreglerande relationer. Av den anledningen har modellens noggrannhet minskat betydligt på grund av behovet av större noggrannhet i träningsdata. In computational biology, inferring Gene regulatory networks (GRN) is rapidly expanding. Due to the size of the examined networks, many researchers use machine learning to infer GRN from gene expression data, typically from RNA-seq. However, the accuracy of such state-of-the-art methods still has room for improvement, especially for the time-series model. This research proposes two timeseries GRN inference models: GATv2 regression model and GATv2 link prediction model. The former model is based on the idea of conventional GRN inference model that regresses the target gene expression data by using candidate control gene data. The latter model is based on the usual link prediction method by Graph neural networks that performs the binarization task of whether edges exist between specific gene nodes. The GATv2 regression model performs well in a regression task. However, the accuracy of GRN inference is low, and it was almost the same as a random output. On the other hand, the GATv2 link prediction model performs well if the training data is correct enough. However, inferring gene regulatory relationships with high estimation accuracy is still a challenge in the field of GRN inference even for partial gene regulatory relationships. For that reason, the accuracy of the model has significantly decreased because of the need for more accuracy in the training data.

The analysis of possible cell differentiation scenarios of gene regulatory network models can represent an invaluable tool for testing existing hypotheses and developing new biological insights. For this reason, diffeRenTES, an R package that implements the abstractions of a prominent mathematical model of cell differentiation, is presented. diffeRenTES computes tree-like structures describing the progression of the cell differentiation process, starting from Boolean models of gene regulatory networks. Additionally, it offers the possibility to perform robustness analyses of Boolean networks.

Breast cancer (BC) is the second most commonly diagnosed cancer in the world. BC develops due to dysregulation of transcriptional profiles, substantial interpatient variations, genetic mutations, and dysregulation of signaling pathways in breast cells. These events are regulated by many genes such as BRCA1/2, PTEN, TP53, mTOR, TERT, AKT, PI3K and others genes. Treatment options for BC remain a hurdle, which warrants a comprehensive understanding that establishes an interlinking connection between these genes in BC tumorigenesis. Consequently, there is an increasing demand for alternative treatment approaches and the design of more effective treatments. In this regard, it is crucial to build the corresponding transcriptional regulatory networks governing BC by using advanced genetic tools and techniques. In the past, several molecular editing technologies have been used to edit genes with several limitations. Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)/CRISPR Associated Protein 9 (CRISPR/Cas9) recently received wise attention due to its potential in biomedical and therapeutic applications. Here, we review the role of various molecular signalling pathways dysregulated in BC development such as PTEN/PI3K/AKT/mTOR as well as BRCA1/BRCA2/TP53/TERT and their interplay between the related gene networks in BC initiation, progression and development of resistance against available targeted therapeutic agents. Use of CRISPR/Cas9 gene-editing technology to generate BC gene-specific transgenic cell lines and animal models to decipher their role and interactions with other gene products has been employed successfully. Moreover, the significance of using CRISPR/Cas9 technology to develop early BC diagnostic tools and treatments is discussed here.

Background We previously identified differentially expressed genes on the basis of false discovery rate adjusted P value using empirical Bayes moderated tests. However, that approach yielded a subset of differentially expressed genes without accounting for redundancy between the selected genes. Methods This study is a secondary analysis of a case–control study of the effect of antiretroviral therapy on apoptosis pathway genes comprising of 16 cases (HIV infected with mitochondrial toxicity) and 16 controls (uninfected). We applied the maximum relevance minimum redundancy (mRMR) algorithm on the genes that were differentially expressed between the cases and controls. The mRMR algorithm iteratively selects features (genes) that are maximally relevant for class prediction and minimally redundant. We implemented several machine learning classifiers and tested the prediction accuracy of the two mRMR genes. We next used network analysis to estimate and visualize the association among the differentially expressed genes. We employed Markov Random Field or undirected network models to identify gene networks related to mitochondrial toxicity. The Spinglass model was used to identify clusters of gene communities. Results The mRMR algorithm ranked DFFA and TNFRSF1A, two of the upregulated proapoptotic genes, on the top. The overall prediction accuracy was 86%, the two mRMR genes correctly classified 86% of the participants into their respective groups. The estimated network models showed different patterns of gene networks. In the network of the cases, FASLG was the most central gene. However, instead of FASLG, ABL1 and LTBR had the highest centrality in controls. Conclusion The mRMR algorithm and network analysis revealed a new correlation of genes associated with mitochondrial toxicity.

Summary Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative condition characterized by the loss of motor neurons. We utilized single-cell transcriptomics to uncover dysfunctional pathways in degenerating motor neurons differentiated from SOD1 E100G ALS patient-derived induced pluripotent stem cells (iPSCs) and respective isogenic controls. Differential gene expression and network analysis identified activation of developmental pathways and core transcriptional factors driving the ALS motor neuron gene dysregulation. Specifically, we identified activation of SMAD2, a downstream mediator of the transforming growth factor β (TGF-β) signaling pathway as a key driver of SOD1 iPSC-derived motor neuron degeneration. Importantly, our analysis indicates that activation of TGFβ signaling may be a common mechanism shared between SOD1, FUS, C9ORF72, VCP, and sporadic ALS motor neurons. Our results demonstrate the utility of single-cell transcriptomics in mapping disease-relevant gene regulatory networks driving neurodegeneration in ALS motor neurons. We find that ALS-associated mutant SOD1 targets transcriptional networks that perturb motor neuron homeostasis., Highlights • Single-cell transcriptomic analysis of SOD1 E100G ALS iPSC-derived motor neurons • Mapping an ALS-relevant transcriptional network • Upregulation of developmental programs in familial and sporadic ALS motor neurons • Inhibition of TGFβ pathway improves SOD1 ALS iPSC-derived motor neuron survival, In this article, Bhinge and colleagues use single-cell transcriptomics to analyze degenerating motor neurons (MNs) differentiated from SOD1 E100G ALS patient-derived iPSCs. Using network analysis, they uncover upregulation of developmental pathways and TGFβ activation in familial and sporadic ALS MNs. Importantly, they find that inhibition of TGFβ signaling leads to improved survival of SOD1 ALS iPSC-derived MNs.

The multifactorial etiology of stress-related disorders necessitates a constant interrogation of the molecular convergences in preclinical models of stress that use disparate paradigms as stressors spanning from environmental challenges to genetic predisposition to hormonal signaling. Using RNA-sequencing, we investigated the genomic signatures in the ventral hippocampus common to mouse models of stress. Chronic oral corticosterone (CORT) induced increased anxiety- and depression-like behavior in wild-type male mice and male mice heterozygous for the gene coding for brain-derived neurotrophic factor Val66Met, a variant associated with genetic susceptibility to stress. In a separate set of male mice, chronic social defeat stress (CSDS) led to a susceptible or a resilient population, whose proportion was dependent on housing conditions, namely standard housing or enriched environment. Rank-rank-hypergeometric overlap (RRHO), a threshold-free approach that ranks genes by their p value and effect size direction, was used to identify genes from a continuous gradient of significancy that were concordant across groups. In mice treated with CORT and in standard-housed susceptible mice, differentially expressed genes (DEGs) were concordant for gene networks involved in neurotransmission, cytoskeleton function, and vascularization. Weighted gene co-expression analysis generated 54 gene hub modules and revealed two modules in which both CORT and CSDS-induced enrichment in DEGs, whose function was concordant with the RRHO predictions, and correlated with behavioral resilience or susceptibility. These data showed transcriptional concordance across models in which the stress coping depends upon hormonal, environmental, or genetic factors revealing common genomic drivers that embody the multifaceted nature of stress-related disorders.

Mapping gene interactions within tissues/cell types plays a crucial role in understanding the genetic basis of human physiology and disease. Tissue functional gene networks (FGNs) are essential models for mapping complex gene interactions. We present TissueNexus, a database of 49 human tissue/cell line FGNs constructed by integrating heterogeneous genomic data. We adopted an advanced machine learning approach for data integration because Bayesian classifiers, which is the main approach used for constructing existing tissue gene networks, cannot capture the interaction and nonlinearity of genomic features well. A total of 1,341 RNA-seq datasets containing 52,087 samples were integrated for all of these networks. Because the tissue label for RNA-seq data may be annotated with different names or be missing, we performed intensive hand-curation to improve quality. We further developed a user-friendly database for network search, visualization, and functional analysis. We illustrate the application of TissueNexus in prioritizing disease genes. The database is publicly available at https://www.diseaselinks.com/TissueNexus/.

Jointly analyzing transcriptomic data and the existing biological networks can yield more robust and informative feature selection results, as well as better understanding of the biological mechanisms. Selecting and classifying node features over genome-scale networks has become increasingly important in genomic biology and genomic medicine. Existing methods have some critical drawbacks. The first is they do not allow flexible modeling of different subtypes of selected nodes. The second is they ignore nodes with missing values, very likely to increase bias in estimation. To address these limitations, we propose a general modeling framework for Bayesian node classification (BNC) with missing values. A new prior model is developed for the class indicators incorporating the network structure. For posterior computation, we resort to the Swendsen-Wang algorithm for efficiently updating class indicators. BNC can naturally handle missing values in the Bayesian modeling framework, which improves the node classification accuracy and reduces the bias in estimating gene effects. We demonstrate the advantages of our methods via extensive simulation studies and the analysis of the cutaneous melanoma dataset from The Cancer Genome Atlas.

The transcription factors (TFs) OCT4, SOX2 and NANOG are key players of the gene regulatory network of pluripotent stem cells. Evidence accumulated in recent years shows that even small imbalances in the expression levels or relative concentrations of these TFs affect both, the maintenance of pluripotency and cell fate decisions. In addition, many components of the transcriptional machinery including RNA polymerases, cofactors and TFs such as those required for pluripotency, do not distribute homogeneously in the nucleus but concentrate in multiple foci influencing the delivery of these molecules to their DNA-targets. How cells control strict levels of available pluripotency TFs in this heterogeneous space and the biological role of these foci remain elusive. In recent years, a wealth of evidence led to propose that many of the nuclear compartments are formed through a liquid–liquid phase separation process. This new paradigm early penetrated the stem cells field since many key players of the pluripotency circuitry seem to phase-separate. Overall, the formation of liquid compartments may modulate the kinetics of biochemical reactions and consequently regulate many nuclear processes. Here, we review the state-of-the-art knowledge of compartmentalization in the cell nucleus and the relevance of this process for transcriptional regulation, particularly in pluripotent stem cells. We also highlight the recent advances and new ideas in the field showing how compartmentalization may affect pluripotency preservation and cell fate decisions.

The major genetic risk factors for Hirschsprung disease (HSCR) are three common polymorphisms within cis-regulatory elements (CREs) of the receptor tyrosine kinase gene RET, which reduce its expression during enteric nervous system (ENS) development. These risk variants attenuate binding of the transcription factors RARB, GATA2, and SOX10 to their cognate CREs, reduce RET gene expression, and dysregulate other ENS and HSCR genes in the RET–EDNRB gene regulatory network (GRN). Here, we use siRNA, ChIP, and CRISPR-Cas9 deletion analyses in the SK-N-SH cell line to ask how many additional HSCR-associated risk variants reside in RET CREs that affect its gene expression. We identify 22 HSCR-associated variants in candidate RET CREs, of which seven have differential allele-specific in vitro enhancer activity, and four of these seven affect RET gene expression; of these, two enhancers are bound by the transcription factor PAX3. We also show that deleting multiple variant-containing enhancers leads to synergistic effects on RET gene expression. These, coupled with our prior results, show that common sequence variants in at least 10 RET enhancers affect HSCR risk, seven with experimental evidence of affecting RET gene expression, extending the known RET–EDNRB GRN to reveal an extensive regulatory code modulating disease risk at a single gene.

Gene regulatory networks formed by transcription factors play essential roles in the regulation of gene expression during plant reproductive development. These networks integrate endogenous, phytohormonal, and environmental cues. Molecular genetic, biochemical, and chemical analyses performed mainly in Arabidopsis have identified network hub genes and revealed the contributions of individual components to these networks. Here, I outline current understanding of key epigenetic regulatory circuits identified by research on plant reproduction, and highlight significant recent examples of genetic engineering and chemical applications to modulate the epigenetic regulation of gene expression. Furthermore, I discuss future prospects for applying basic plant science to engineer useful floral traits in a predictable manner as well as the potential side effects.

Vertices and edges are made from a network, with the degree of a vertex referring to the number of connected edges. The chance of every vertex possessing a given degree is represented by a network’s degree appropriation, which reveals important global network characteristics. Many fields, including sociology, public health, business, medicine, engineering, computer science, and basic sciences, use network theory. Logistical networks, gene regulatory networks, metabolic networks, social networks, and driven networks are some of the most significant networks. In physical, theoretical, and environmental chemistry, a topological index is a numerical value assigned to a molecular structure/network that is used for correlation analysis. Hexagonal networks of dimension t are used to build hex-derived networks, which have a wide range of applications in computer science, medicine, and engineering. For the third type of hex-derived networks, topological indices of reverse degree based are discussed in this study.

Plant cells communicate information for the regulation of development and responses to external stresses. A key form of this communication is transcriptional regulation, accomplished via complex gene networks operating both locally and systemically. To fully understand how genes are regulated across plant tissues and organs, high resolution, multi-dimensional spatial transcriptional data must be acquired and placed within a cellular and organismal context. Spatial transcriptomics (ST) typically provides a two-dimensional spatial analysis of gene expression of tissue sections that can be stacked to render three-dimensional data. For example, X-ray and light-sheet microscopy provide sub-micron scale volumetric imaging of cellular morphology of tissues, organs, or potentially entire organisms. Linking these technologies could substantially advance transcriptomics in plant biology and other fields. Here, we review advances in ST and 3D microscopy approaches and describe how these technologies could be combined to provide high resolution, spatially organized plant tissue transcript mapping.

Network medicine has proven useful for dissecting genetic organization of complex human diseases. We have previously published HumanNet, an integrated network of human genes for disease studies. Since the release of the last version of HumanNet, many large-scale protein–protein interaction datasets have accumulated in public depositories. Additionally, the numbers of research papers and functional annotations for gene–phenotype associations have increased significantly. Therefore, updating HumanNet is a timely task for further improvement of network-based research into diseases. Here, we present HumanNet v3 (https://www.inetbio.org/humannet/, covering 99.8% of human protein coding genes) constructed by means of the expanded data with improved network inference algorithms. HumanNet v3 supports a three-tier model: HumanNet-PI (a protein–protein physical interaction network), HumanNet-FN (a functional gene network), and HumanNet-XC (a functional network extended by co-citation). Users can select a suitable tier of HumanNet for their study purpose. We showed that on disease gene predictions, HumanNet v3 outperforms both the previous HumanNet version and other integrated human gene networks. Furthermore, we demonstrated that HumanNet provides a feasible approach for selecting host genes likely to be associated with COVID-19.

Boolean networks are a notable model of gene regulatory networks and, particularly, prominent theories discuss how they can capture cellular differentiation processes. One frequent motif in gene regulatory networks, especially in those circuits involved in cell differentiation, is autoregulation. In spite of this, the impact of autoregulation on Boolean network attractor landscape has not yet been extensively discussed in literature. In this paper we propose to model autoregulation as self-loops, and analyse how the number of attractors and their robustness may change once they are introduced in a well-known and widely used Boolean networks model, namely random Boolean networks. Results show that self-loops provide an evolutionary advantage in dynamic mechanisms of cells, by increasing both number and maximal robustness of attractors. These results provide evidence to the hypothesis that autoregulation is a straightforward functional component to consolidate cell dynamics, mainly in differentiation processes.

Ye Zhao,1,* Teng Ma,2,* Duowu Zou1 1Department of Gastroenterology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, People’s Republic of China; 2Department of Thoracic Surgery, Zhongshan Hospital, Fudan University, Shanghai, 200032, People’s Republic of China*These authors contributed equally to this workCorrespondence: Duowu Zou 200025 Tel +86-2164370045-665444Email zdwrjxh66@sjtu.edu.cnPurpose: Transcriptomic studies on gastroesophageal reflux disease are scarce, and gene expression signatures in nonerosive reflux disease (NERD) remain elusive. The aim of the study was to identify gene expression profiles and potential hub genes in NERD.Patients and Methods: We performed RNA sequencing on biopsy samples from nine consecutive patients with NERD and six healthy controls. Differentially expressed genes (DEGs) were analysed with the DESeq2 R package. A DEG-based protein–protein interaction (PPI) network was constructed to filter hub genes using Cytoscape. Weighted gene coexpression network analysis (WGCNA) was conducted to identify the coexpression relationships of all modules and explore the relationship between gene sets and clinical traits.Results: In total, 1195 DEGs were identified, including 649 upregulated and 546 downregulated genes involved in regulating the inflammatory response and epithelial cell differentiation. Overlap of the PPI and WGCNA networks identified five shared genes, namely, THY1, BMP2, LOX, KDR and MMP9, as candidate hub genes in NERD. Quantitative PCR analysis of the expression of these five genes confirmed the sequencing results. Receiver operating characteristic analyses indicated that these hub genes had diagnostic potential for NERD patients. Gene set enrichment analysis confirmed that each hub gene was closely associated with the pathophysiological processes of NERD. In addition, a regulatory network comprising 42 transcription factors (TFs), 28 miRNAs and 5 hub genes was established.Conclusion: The five core genes may be promising biomarkers of NERD. The TF/miRNA/hub gene network can improve the understanding of the molecular mechanisms underlying disease progression.Keywords: nonerosive reflux disease, RNA sequencing, hub gene, bioinformatics analysis, WGCNA, validation

Molecular biomarkers are certain molecules or set of molecules that can be of help for diagnosis or prognosis of diseases or disorders. In the past decades, thanks to the advances in high-throughput technologies, a huge amount of molecular 'omics' data, e.g., transcriptomics and proteomics, have been accumulated. The availability of these omics data makes it possible to screen biomarkers for diseases or disorders. Accordingly, a number of computational approaches have been developed to identify biomarkers by exploring the omics data. In this review, we present a comprehensive survey on the recent progress of identification of molecular biomarkers with machine learning approaches. Specifically, we categorize the machine learning approaches into supervised, un-supervised and recommendation approaches, where the biomarkers including single genes, gene sets and small gene networks. In addition, we further discuss potential problems underlying bio-medical data that may pose challenges for machine learning, and provide possible directions for future biomarker identification.

Gene Regulatory Network (GRN) is formed due to mutual transcriptional regulation within a set of protein coding genes in cellular context of an organism. Computational inference of GRN is important to understand the behavior of each gene in terms of change in its protein production rate (expression level). As Recurrent Neural Network (RNN) is efficient in GRN modeling, a bi-objective RNN formulation has been applied here. Based on Archived Multi Objective Simulated Annealing (AMOSA), four algorithms, namely, AMOSA Revised (AMOSAR), Modified Freezing based AMOSA (AMOFSA), Tabu based AMOSA (AMOTSA) and Modified Freezing and Tabu based AMOSA (AMOFTSA) have been proposed and applied to RNN (treated as GRN) for parameter learning taking four gene expression time series datasets. Comparative studies on the performance of the algorithms (based on each dataset) have been made in terms of the number of GRNs obtained in the final non-dominated front and the performance metrics, namely, recall, precision and f1 score. Two proposed variants, namely, AMOFSA and AMOTSA have been found competitive in performance. Experimental observations and statistical analysis show that, modified algorithms are better than AMOSAR and the state-of-the-art algorithms in respect of the above-mentioned metrics.

Graphical abstract, Botrytis cinerea and Trichoderma atroviride are two relevant fungi in agricultural systems. To gain insights into these organisms’ transcriptional gene regulatory networks (GRNs), we generated a manually curated transcription factor (TF) dataset for each of them, followed by a GRN inference utilizing available sequence motifs describing DNA-binding specificity and global gene expression data. As a proof of concept of the usefulness of this resource to pinpoint key transcriptional regulators, we employed publicly available transcriptomics data and a newly generated dual RNA-seq dataset to build context-specific Botrytis and Trichoderma GRNs under two different biological paradigms: exposure to continuous light and Botrytis-Trichoderma confrontation assays. Network analysis of fungal responses to constant light revealed striking differences in the transcriptional landscape of both fungi. On the other hand, we found that the confrontation of both microorganisms elicited a distinct set of differentially expressed genes with changes in T. atroviride exceeding those in B. cinerea. Using our regulatory network data, we were able to determine, in both fungi, central TFs involved in this interaction response, including TFs controlling a large set of extracellular peptidases in the biocontrol agent T. atroviride. In summary, our work provides a comprehensive catalog of transcription factors and regulatory interactions for both organisms. This catalog can now serve as a basis for generating novel hypotheses on transcriptional regulatory circuits in different experimental contexts.

Glioblastoma is the most aggressive type of brain tumors resistant to a number of antitumor drugs. The problem of therapy and drug treatment course is complicated by extremely high heterogeneity in the benign cell populations, the random arrangement of tumor cells, and polymorphism of their nuclei. The pathogenesis of gliomas needs to be studied using modern cellular technologies, genome- and transcriptome-wide technologies of high-throughput sequencing, analysis of gene expression on microarrays, and methods of modern bioinformatics to find new therapy targets. Functional annotation of genes related to the disease could be retrieved based on genetic databases and cross-validated by integrating complementary experimental data. Gene network reconstruction for a set of genes (proteins) proved to be effective approach to study mechanisms underlying disease progression. We used online bioinformatics tools for annotation of gene list for glioma, reconstruction of gene network and comparative analysis of gene ontology categories. The available tools and the databases for glioblastoma gene analysis are discussed together with the recent progress in this field.

BackgroundGenome-wide association studies have found many genetic risk variants associated with Alzheimer’s disease (AD). However, how these risk variants affect deeper phenotypes such as disease progression and immune response remains elusive. Also, our understanding of cellular and molecular mechanisms from disease risk variants to various phenotypes is still limited. To address these problems, we performed an integrative multi-omics analysis of genotype, transcriptomics, and epigenomics for revealing gene regulatory mechanisms from disease variants to AD phenotypes.MethodFirst, given the population gene expression data of a cohort, we construct and cluster its gene co-expression network to identify gene co-expression modules for various AD phenotypes. Next, we predict transcription factors (TFs) regulating co-expressed genes and AD risk SNPs that interrupt TF binding sites on regulatory elements. Finally, we construct a gene regulatory network (GRN) linking SNPs, interrupted TFs, and regulatory elements to target genes and gene modules for each phenotype in the cohort. This network thus provides systematic insights into gene regulatory mechanisms from risk variants to AD phenotypes.ResultsOur analysis predicted GRNs in three major AD-relevant regions: Hippocampus, Dorsolateral Prefrontal Cortex (DLPFC), Lateral Temporal Lobe (LTL). Comparative analyses revealed cross-region-conserved and region-specific GRNs, in which many immunological genes are present. For instance, SNPs rs13404184 and rs61068452 disrupt SPI1 binding and regulation of AD gene INPP5D in the Hippocampus and LTL. However, SNP rs117863556 interrupts bindings of REST to regulate GAB2 in DLPFC only. Driven by emerging neuroinflammation in AD, we used Covid-19 as a proxy to identify possible regulatory mechanisms for neuroimmunology in AD. To this end, we looked at the GRN subnetworks relating to genes from shared AD-Covid pathways. From those subnetworks, our machine learning analysis prioritized the AD-Covid genes for predicting Covid-19 severity. Decision Curve Analysis also validated our AD-Covid genes outperform known Covid-19 genes for classifying severe Covid-19 patients. This suggests AD-Covid genes along with linked SNPs can be potential novel biomarkers for neuroimmunology in AD. Finally, our results are open-source available as a comprehensive functional genomic map for AD, providing a deeper mechanistic understanding of the interplay among multi-omics, brain regions, gene functions like neuroimmunology, and phenotypes.

The tropical common bean (Phaseolus vulgaris L.) is an obligatory short-day plant that requires relaxation of the photoperiod to induce flowering. Similar to other crops, photoperiod-induced floral initiation depends on the differentiation and maintenance of meristems. In this study, the global changes in transcript expression profiles were analyzed in two meristematic tissues corresponding to the vegetative and inflorescence meristems of two genotypes with different sensitivities to photoperiods. A total of 3396 differentially expressed genes (DEGs) were identified, and 1271 and 1533 were found to be up-regulated and down-regulated, respectively, whereas 592 genes showed discordant expression patterns between both genotypes. Arabidopsis homologues of DEGs were identified, and most of them were not previously involved in Arabidopsis floral transition, suggesting an evolutionary divergence of the transcriptional regulatory networks of the flowering process of both species. However, some genes belonging to the photoperiod and flower development pathways with evolutionarily conserved transcriptional profiles have been found. In addition, the flower meristem identity genes APETALA1 and LEAFY, as well as CONSTANS-LIKE 5, were identified as markers to distinguish between the vegetative and reproductive stages. Our data also indicated that the down-regulation of the photoperiodic genes seems to be directly associated with promoting floral transition under inductive short-day lengths. These findings provide valuable insight into the molecular factors that underlie meristematic development and contribute to understanding the photoperiod adaptation in the common bean., MCIN/AEI PDI2020-114115RB-100, MAPA, ERDF A way of making Europe European Commission European Union NextGenera-tionEU/PRTR