Therapeutic applications of synthetic gene/genetic circuits: a patent review.

A significant limitation of numerous current genetic engineering therapy approaches is their limited control over the strength, timing, or cellular context of their therapeutic effect. Synthetic gene/genetic circuits are synthetic biology approaches that can control the generation, transformation, or depletion of a specific DNA, RNA, or protein and provide precise control over gene expression and cellular behavior. They can be designed to perform logical operations by carefully selecting promoters, repressors, and other genetic components. Patent search was performed in Espacenet, resulting in 38 selected patents with 15 most frequent international classifications. Patent embodiments were categorized into applications for the delivery of therapeutic molecules, treatment of infectious diseases, treatment of cancer, treatment of bleeding, and treatment of metabolic disorders. The logic gates of selected genetic circuits are described to comprehensively demonstrate their therapeutic applications. Synthetic gene circuits can be customized for precise control of therapeutic interventions, leading to personalized therapies that respond specifically to individual patient needs, enhancing treatment efficacy and minimizing side effects. They can be highly sensitive biosensors that provide real-time therapy by accurate monitoring various biomarkers or pathogens and appropriately synthesizing a therapeutic molecule. Synthetic gene circuits may also lead to the development of advanced regenerative therapies and to implantable biodevices that produce on-demand bioactive molecules. However, this technology faces challenges for commercial profitability. The genetic circuit designs need adjustments for specific applications, and may have disadvantages like toxicity from multiple regulators, homologous recombination, context dependency, resource overuse, and environmental variability.

Water deficit response in nodulated soybean roots: a comprehensive transcriptome and translatome network analysis.

BACKGROUND: Soybean establishes a mutualistic interaction with nitrogen-fixing rhizobacteria, acquiring most of its nitrogen requirements through symbiotic nitrogen fixation. This crop is susceptible to water deficit; evidence suggests that its nodulation status-whether it is nodulated or not-can influence how it responds to water deficit. The translational control step of gene expression has proven relevant in plants subjected to water deficit. RESULTS: Here, we analyzed soybean roots' differential responses to water deficit at transcriptional, translational, and mixed (transcriptional + translational) levels. Thus, the transcriptome and translatome of four combined-treated soybean roots were analyzed. We found hormone metabolism-related genes among the differentially expressed genes (DEGs) at the translatome level in nodulated and water-restricted plants. Also, weighted gene co-expression network analysis followed by differential expression analysis identified gene modules associated with nodulation and water deficit conditions. Protein-protein interaction network analysis was performed for subsets of mixed DEGs of the modules associated with the plant responses to nodulation, water deficit, or their combination. CONCLUSIONS: Our research reveals that the stand-out processes and pathways in the before-mentioned plant responses partially differ; terms related to glutathione metabolism and hormone signal transduction (2 C protein phosphatases) were associated with the response to water deficit, terms related to transmembrane transport, response to abscisic acid, pigment metabolic process were associated with the response to nodulation plus water deficit. Still, two processes were common: galactose metabolism and branched-chain amino acid catabolism. A comprehensive analysis of these processes could lead to identifying new sources of tolerance to drought in soybean.

Resequencing of Brazilian locally adapted cattle breeds revealed variants in candidate genes and transcription factors for meat fatty acid profile.

The beef cattle industry has experienced a shift driven by a market demand for healthier meat, cost efficiency and environmental sustainability in recent years. Consequently, there has been a growing focus on the fatty acids content and functions of meat in cattle breeding programmes. Besides, a deeper understanding of the biological mechanisms influencing the expression of different phenotypes related to fatty acid profiles is crucial. In this study, we aimed to identify Single-Nucleotide Variants (SNV) and Insertion/Deletion (InDels) DNA variants in candidate genes related to fatty acid profiles described in genomic, transcriptomic and proteomic studies conducted in beef cattle breeds. Utilizing whole-genome re-sequencing data from Brazilian locally adapted bovine breeds, namely Caracu and Pantaneiro, we identified SNVs and InDels associated with 23,947 genes. From these, we identified 318 candidate genes related to fatty acid profiles that contain variants. Subsequently, we select only genes with SNVs and InDels in their promoter, 5' UTR and coding region. Through the gene-biological process network, approximately 19 genes were highlighted. Furthermore, considering the studied trait and a literature review, we selected the main transcription factors (TF). Functional analysis via gene-TF network allowed us to identify the 30 most likely candidate genes for meat fatty acid profile in cattle. LIPE, MFSD2A and SREBF1 genes were highlighted in networks due to their biological importance. Further dissection of these genes revealed 15 new variants found in promoter regions of Caracu and Pantaneiro sequences. The gene networks facilitated a better functional understanding of genes and TF, enabling the identification of variants potentially related to the expression of candidate genes for meat fatty acid profiles in cattle.

Gene Regulatory Networks: Improving Inferences with Transfer Learning.

Deep transfer learning improves the inference of gene regulatory networks in human cells, reveals disease-associated genes, and identifies network-based druggable targets in human heart disease.

Spatial biology of Ising-like synthetic genetic networks.

BACKGROUND: Understanding how spatial patterns of gene expression emerge from the interaction of individual gene networks is a fundamental challenge in biology. Developing a synthetic experimental system with a common theoretical framework that captures the emergence of short- and long-range spatial correlations (and anti-correlations) from interacting gene networks could serve to uncover generic scaling properties of these ubiquitous phenomena. RESULTS: Here, we combine synthetic biology, statistical mechanics models, and computational simulations to study the spatial behavior of synthetic gene networks (SGNs) in Escherichia coli quasi-2D colonies growing on hard agar surfaces. Guided by the combined mechanisms of the contact process lattice simulation and two-dimensional Ising model (CPIM), we describe the spatial behavior of bi-stable and chemically coupled SGNs that self-organize into patterns of long-range correlations with power-law scaling or short-range anti-correlations. These patterns, resembling ferromagnetic and anti-ferromagnetic configurations of the Ising model near critical points, maintain their scaling properties upon changes in growth rate and cell shape. CONCLUSIONS: Our findings shed light on the spatial biology of coupled and bistable gene networks in growing cell populations. This emergent spatial behavior could provide insights into the study and engineering of self-organizing gene patterns in eukaryotic tissues and bacterial consortia.

miR-6087 Might Regulate Cell Cycle-Related mRNAs During Cardiomyogenesis of hESCs.

MicroRNAs (miRNAs) are small noncoding RNAs that act as negative regulators of gene expression at the post-transcriptional level, promoting mRNA degradation or translation repression. Despite the well-described presence of miRNAs in various human tissues, there is still a lack of information about the relationship between miRNAs and the translation regulation in human embryonic stem cells (hESCs) during cardiomyogenesis. Here, we investigate RNA-seq data from hESCs, focusing on distinct stages of cardiomyogenesis and searching for polysome-bound miRNAs that could be involved in translational regulation. We identify miR-6087 as a differentially expressed miRNA at latest steps of cardiomyocyte differentiation. We analyzed the coexpression pattern between the differentially expressed mRNAs and miR-6087, evaluating whether they are predicted targets of the miRNA. We arranged the genes into an interaction network and identified BLM, RFC4, RFC3, and CCNA2 as key genes of the network. A post hoc analysis of the key genes suggests that miR-6087 could act as a regulator of the cell cycle in hESC during cardiomyogenesis.

Senescence-Associated Glycine max (Gm)NAC Genes: Integration of Natural and Stress-Induced Leaf Senescence.

Leaf senescence is a genetically regulated developmental process that can be triggered by a variety of internal and external signals, including hormones and environmental stimuli. Among the senescence-associated genes controlling leaf senescence, the transcriptional factors (TFs) comprise a functional class that is highly active at the onset and during the progression of leaf senescence. The plant-specific NAC (NAM, ATAF, and CUC) TFs are essential for controlling leaf senescence. Several members of Arabidopsis AtNAC-SAGs are well characterized as players in elucidated regulatory networks. However, only a few soybean members of this class display well-known functions; knowledge about their regulatory circuits is still rudimentary. Here, we describe the expression profile of soybean GmNAC-SAGs upregulated by natural senescence and their functional correlation with putative AtNAC-SAGs orthologs. The mechanisms and the regulatory gene networks underlying GmNAC081- and GmNAC030-positive regulation in leaf senescence are discussed. Furthermore, new insights into the role of GmNAC065 as a negative senescence regulator are presented, demonstrating extraordinary functional conservation with the Arabidopsis counterpart. Finally, we describe a regulatory circuit which integrates a stress-induced cell death program with developmental leaf senescence via the NRP-NAC-VPE signaling module.

Different mating contexts lead to extensive rewiring of female brain coexpression networks in the guppy.

Understanding the basis of behavior requires dissecting the complex waves of gene expression that underlie how the brain processes stimuli and produces an appropriate response. In order to determine the dynamic nature of the neurogenomic network underlying mate choice, we use transcriptome sequencing to capture the female neurogenomic response in two brain regions involved in sensory processing and decision-making under different mating and social contexts. We use differential coexpression (DC) analysis to evaluate how gene networks in the brain are rewired when a female evaluates attractive and nonattractive males, greatly extending current single-gene approaches to assess changes in the broader gene regulatory network. We find the brain experiences a remarkable amount of network rewiring in the different mating and social contexts we tested. Further analysis indicates the network differences across contexts are associated with behaviorally relevant functions and pathways, particularly learning, memory and other cognitive functions. Finally, we identify the loci that display social context-dependent connections, revealing the basis of how relevant neurological and metabolic pathways are differentially recruited in distinct social contexts. More broadly, our findings contribute to our understanding of the genetics of mating and social behavior by identifying gene drivers behind behavioral neural processes, illustrating the utility of DC analysis in neurosciences and behavior.

Binary Expression Enhances Reliability of Messaging in Gene Networks.

The promoter state of a gene and its expression levels are modulated by the amounts of transcription factors interacting with its regulatory regions. Hence, one may interpret a gene network as a communicating system in which the state of the promoter of a gene (the source) is communicated by the amounts of transcription factors that it expresses (the message) to modulate the state of the promoter and expression levels of another gene (the receptor). The reliability of the gene network dynamics can be quantified by Shannon's entropy of the message and the mutual information between the message and the promoter state. Here we consider a stochastic model for a binary gene and use its exact steady state solutions to calculate the entropy and mutual information. We show that a slow switching promoter with long and equally standing ON and OFF states maximizes the mutual information and reduces entropy. That is a binary gene expression regime generating a high variance message governed by a bimodal probability distribution with peaks of the same height. Our results indicate that Shannon's theory can be a powerful framework for understanding how bursty gene expression conciliates with the striking spatio-temporal precision exhibited in pattern formation of developing organisms.

Transcriptomic analysis at organ and time scale reveals gene regulatory networks controlling the sulfate starvation response of Solanum lycopersicum.

BACKGROUND: Sulfur is a major component of biological molecules and thus an essential element for plants. Deficiency of sulfate, the main source of sulfur in soils, negatively influences plant growth and crop yield. The effect of sulfate deficiency on plants has been well characterized at the physiological, transcriptomic and metabolomic levels in Arabidopsis thaliana and a limited number of crop plants. However, we still lack a thorough understanding of the molecular mechanisms and regulatory networks underlying sulfate deficiency in most plants. In this work we analyzed the impact of sulfate starvation on the transcriptome of tomato plants to identify regulatory networks and key transcriptional regulators at a temporal and organ scale. RESULTS: Sulfate starvation reduces the growth of roots and leaves which is accompanied by major changes in the organ transcriptome, with the response being temporally earlier in roots than leaves. Comparative analysis showed that a major part of the Arabidopsis and tomato transcriptomic response to sulfate starvation is conserved between these plants and allowed for the identification of processes specifically regulated in tomato at the transcript level, including the control of internal phosphate levels. Integrative gene network analysis uncovered key transcription factors controlling the temporal expression of genes involved in sulfate assimilation, as well as cell cycle, cell division and photosynthesis during sulfate starvation in tomato roots and leaves. Interestingly, one of these transcription factors presents a high identity with SULFUR LIMITATION1, a central component of the sulfate starvation response in Arabidopsis. CONCLUSIONS: Together, our results provide the first comprehensive catalog of sulfate-responsive genes in tomato, as well as novel regulatory targets for future functional analyses in tomato and other crops.

Integración de la Bioinformática en la caracterización Clínica y Genómica de Retinitis Pigmentosa, Síndrome de Noonan y Síndrome de Retraso Mental Martin-Probs, en un mismo individuo. Reporte de un Caso / Integration of Bioinformatics in the Clinical and Genomic Characterization of Retinitis Pigmentosa, Noonan Syndrome and Martin-Probs Mental Retardation Syndrome, in the Same Individual. Case report

Introducción: El avance en las técnicas bioinformáticas ha permitido realizar acercamientos y mejoras en los diagnósticos clínicos, correlacionando genotipo ­ fenotipo y permitiendo el acercamiento a una terapia personalizada. Objetivo: Realizar mediante técnicas bioinformáticas, la caracterización molecular y de expresión génica de una paciente con manifestaciones clínicas (dismorfias, retraso en el desarrollo) de una enfermedad compleja (poligénica). Materiales y métodos: Se realizó la secuenciación de exoma completo a partir de una muestra de sangre periférica. Se analizaron los datos obtenidos mediante análisis in-sílico, utilizando programas como SIFT, Mutation Tester, UMD y Provean, para determinar la significancia clínica de variantes encontradas; además se usó programa GeneMania para determinar las interacciones génicas. Resultados:Se encontraron 3 variantes en los genes SEMA4A, PTPN11 y RAB40A, asociados a Retinitis pigmentosa 35, Síndrome de Noonan y Sindrome de retraso mental Martin-Probs, respectivamente; encontrando según los softwares predictores, en el primer caso un significado clínico aparentemente benigno, y en los dos últimos genes un significado clínico patogénico. El análisis de redes génicas reveló alteraciones en funciones biológicas como la señalización mediada por fosfatidilinositol, respuesta al factor del crecimiento fibroblástico, vía de señalización de neutrofina y la morfogénesis de vasos sanguíneo que permitieron explicar gran parte de la sintomatología observada. Conclusión: El análisis personalizado de las patologías complejas mediante el uso de la clínica, herramientas genómicas y bioinformaticas han permitido un avance significativo en las técnicas para el procesamiento y análisis de datos, beneficiando los estudios científicos que permiten el acercamiento a un correcto diagnóstico y adecuada consejería genética.

Introduction: Advances in bioinformatics techniques have allowed approaches and improvements in clinical diagnoses, correlating genotype - phenotype and allowing the approach to personalized therapy. Objective: In order to perform the molecular characterization and gene expression in a patient with complex clinical manifestations through bioinformatics techniques, complete exome sequencing was performed by a peripheral blood sample to a woman with facial dysmorphisms and developmental disorders. Material and methods: We analyzed the data obtained by in-silico analysis, using programs such as SIFT, Mutation Tester, UMD and Provean, to determine the clinical significance of the found variants and GeneMania program was used to determine gene interactions. Results: 3 variants were found in the genes SEMA4A, PTPN11 and RAB40A, associated with Retinitis pigmentosa 35, Noonan Syndrome and Mental Retardation Syndrome Martin-Probs, respectively; according to the predictive softwares, in the first case an apparently benign clinical meaning, and in the last two genes a clinical pathogenic meaning. The analysis of gene networks revealed alterations in biological functions such as signaling mediated by phosphatidylinositol, response to the fibroblastic growth factor, neutrophin signaling pathway and blood vessel morphogenesis that allowed us to explain a large part of the observed symptomatology. Conclusion: The personalized analysis of complex pathologies through the use of clinical, genomic and bioinformatic tools has allowed a significant advance in techniques for processing and analyzing data, benefiting scientific studies that allow the approach to a correct diagnosis and adequate genetic counseling.

Genes related to maintenance of autophagy and successful aging / Genes relacionados à manutenção da autofagia e envelhecimento bem-sucedido

ABSTRACT Considering aging as a phenomenon in which there is a decline in essential processes for cell survival, we investigated the autophagic and proteasome pathways in three different groups: young, older and oldest old male adults. The expression profile of autophagic pathway-related genes was carried out in peripheral blood, and the proteasome quantification was performed in plasma. No significant changes were found in plasma proteasome concentrations or in correlations between proteasome concentrations and ages. However, some autophagy- and/or apoptosis-related genes were differentially expressed. In addition, the network and enrichment analysis showed an interaction between four of the five differentially expressed genes and an association of these genes with the transcriptional process. Considering that the oldest old individuals maintained both the expression of genes linked to the autophagic machinery, and the proteasome levels, when compared with the older group, we concluded that these factors could be considered crucial for successful aging.

RESUMO Considerando o envelhecimento como um fenômeno em que há um declínio nos processos essenciais a sobrevivência celular, investigamos as vias autofágica e proteassômica em três grupos: jovens, idosos e longevos. O perfil de expressão dos genes relacionados à via autofágica foi analisado em sangue periférico, e a quantificação do proteassoma realizada em plasma. Não foram encontradas alterações significativas nas concentrações plasmáticas de proteassoma ou na correlação entre as concentrações de proteassoma e as idades. No entanto, alguns genes relacionados a autofagia e / ou apoptose foram expressos diferencialmente. Além disso, as análises de rede e de enriquecimento mostraram uma interação entre quatro dos cinco genes diferencialmente expressos e a associação desses ao processo transcricional. Considerando que os indivíduos longevos mantiveram tanto a expressão de genes ligados à maquinaria autofágica, quanto os níveis de proteassoma quando comparados aos idosos, concluímos que esses fatores poderiam ser considerados cruciais para o envelhecimento bem-sucedido.

CEMiTool: a Bioconductor package for performing comprehensive modular co-expression analyses.

BACKGROUND: The analysis of modular gene co-expression networks is a well-established method commonly used for discovering the systems-level functionality of genes. In addition, these studies provide a basis for the discovery of clinically relevant molecular pathways underlying different diseases and conditions. RESULTS: In this paper, we present a fast and easy-to-use Bioconductor package named CEMiTool that unifies the discovery and the analysis of co-expression modules. Using the same real datasets, we demonstrate that CEMiTool outperforms existing tools, and provides unique results in a user-friendly html report with high quality graphs. Among its features, our tool evaluates whether modules contain genes that are over-represented by specific pathways or that are altered in a specific sample group, as well as it integrates transcriptomic data with interactome information, identifying the potential hubs on each network. We successfully applied CEMiTool to over 1000 transcriptome datasets, and to a new RNA-seq dataset of patients infected with Leishmania, revealing novel insights of the disease's physiopathology. CONCLUSION: The CEMiTool R package provides users with an easy-to-use method to automatically implement gene co-expression network analyses, obtain key information about the discovered gene modules using additional downstream analyses and retrieve publication-ready results via a high-quality interactive report.

Gestation under chronic constant light leads to extensive gene expression changes in the fetal rat liver.

Recent reports account for altered metabolism in adult offspring from pregnancy subjected to abnormal photoperiod, suggesting fetal programming of liver physiology. To generate a pipeline of subsequent mechanistic experiments addressing strong candidate genes, here we investigated the effects of constant gestational light on the fetal liver transcriptome. At 10 days of gestation, dams were randomized in two groups (n = 7 each): constant light (LL) and normal photoperiod (12 h light/12 h dark; LD). At 18 days of gestation, RNA was isolated from the fetal liver and subjected to DNA microarray (Affymetrix platform for 28,000 genes). Selected differential mRNAs were validated by quantitative PCR (qPCR), while integrated transcriptional changes were analyzed with Ingenuity Pathway Analysis and other bioinformatics tools. Comparison of LL relative to LD fetal liver led to the following findings. Significant differential expression was found for 3,431 transcripts (1,960 upregulated and 1,471 downregulated), with 393 of them displaying ≥ 1.5-fold change. We validated 27 selected transcripts by qPCR, which displayed fold-change values highly correlated with microarray (r(2) = 0.91). Different markers of nonalcoholic fatty liver disease were either upregulated (e.g., Ndn and Pnpla3) or downregulated (e.g., Gnmt, Bhmt1/2, Sult1a1, Mpo, and Mat1a). Diverse pathways were altered, including hematopoiesis, coagulation cascade, complement system, and carbohydrate and lipid metabolism. The microRNAs 7a-1, 431, 146a, and 153 were upregulated, while the abundant hepatic miRNA 122 was downregulated. Constant gestational light induced extensive modification of the fetal liver transcriptome. A number of differentially expressed transcripts belong to fundamental functional pathways, potentially contributing to long-term liver disease.

Gene networks and transcription factor motifs defining the differentiation of stem cells into hepatocyte-like cells.

BACKGROUND & AIMS: The differentiation of stem cells to hepatocyte-like cells (HLC) offers the perspective of unlimited supply of human hepatocytes. However, the degree of differentiation of HLC remains controversial. To obtain an unbiased characterization, we performed a transcriptomic study with HLC derived from human embryonic and induced stem cells (ESC, hiPSC) from three different laboratories. METHODS: Genome-wide gene expression profiles of ESC and HLC were compared to freshly isolated and up to 14days cultivated primary human hepatocytes. Gene networks representing successful and failed hepatocyte differentiation, and the transcription factors involved in their regulation were identified. RESULTS: Gene regulatory network analysis demonstrated that HLC represent a mixed cell type with features of liver, intestine, fibroblast and stem cells. The "unwanted" intestinal features were associated with KLF5 and CDX2 transcriptional networks. Cluster analysis identified highly correlated groups of genes associated with mature liver functions (n=1057) and downregulated proliferation associated genes (n=1562) that approach levels of primary hepatocytes. However, three further clusters containing 447, 101, and 505 genes failed to reach levels of hepatocytes. Key TF of two of these clusters include SOX11, FOXQ1, and YBX3. The third unsuccessful cluster, controlled by HNF1, CAR, FXR, and PXR, strongly overlaps with genes repressed in cultivated hepatocytes compared to freshly isolated hepatocytes, suggesting that current in vitro conditions lack stimuli required to maintain gene expression in hepatocytes, which consequently also explains a corresponding deficiency of HLC. CONCLUSIONS: The present gene regulatory network approach identifies key transcription factors which require modulation to improve HLC differentiation.

Gene networks as a tool to understand transcriptional regulation

Gene regulatory networks, or simply gene networks (GNs), have shown to be a promising approach that the bioinformatics community has been developing for studying regulatory mechanisms in biological systems. GNs are built from the genome-wide high-throughput gene expression data that are often available from DNA microarray experiments. Conceptually, GNs are (un)directed graphs, where the nodes correspond to the genes and a link between a pair of genes denotes a regulatory interaction that occurs at transcriptional level. In the present study, we had two objectives: 1) to develop a framework for GN reconstruction based on a Bayesian network model that captures direct interactions between genes through nonparametric regression with B-splines, and 2) to demonstrate the potential of GNs in the analysis of expression data of a real biological system, the yeast pheromone response pathway. Our framework also included a number of search schemes to learn the network. We present an intuitive notion of GN theory as well as the detailed mathematical foundations of the model. A comprehensive analysis of the consistency of the model when tested with biological data was done through the analysis of the GNs inferred for the yeast pheromone pathway. Our results agree fairly well with what was expected based on the literature, and we developed some hypotheses about this system. Using this analysis, we intended to provide a guide on how GNs can be effectively used to study transcriptional regulation. We also discussed the limitations of GNs and the future direction of network analysis for genomic data. The software is available upon request.