Congenital Zika syndrome (CZS) comprises a set of clinical manifestations that can be presented by neonates born to mothers infected by the Zika virus (ZIKV). CZS-associated phenotypes include neurological, skeletal, and systemic alterations and long-term developmental sequelae. One of the most frequently reported clinical conditions is microcephaly characterized by a reduction in head circumference and cognitive complications. Nevertheless, the associations among the diverse signaling pathways underlying CZS phenotypes remain to be elucidated. To shed light on CZS, we have extensively reviewed the morphological anomalies resulting from ZIKV infection, as well as genes and proteins of interest obtained from the published literature. With this list of genes or proteins, we performed computational analyses to explore the cellular processes, molecular mechanisms, and molecular pathways related to ZIKV infection. Therefore, in this review, we comprehensively describe the morphological abnormalities caused by congenital ZIKV infection and, through the analysis noted above, propose common molecular pathways altered by ZIKV that could explain both central nervous system and craniofacial skeletal alterations.

Our study utilized genome-wide association studies (GWAS) to link nucleotide variants to traits in Populus trichocarpa, a species with rapid linkage disequilibrium decay. The aim was to overcome the challenge of interpreting statistical associations at individual loci without sufficient biological context, which often leads to reliance solely on gene annotations from unrelated model organisms. We employed an integrative approach that included GWAS targeting multiple traits using three individual techniques for lignocellulose phenotyping, expression quantitative trait loci (eQTL) analysis to construct transcriptional regulatory networks around each candidate locus and co-expression analysis to provide biological context for these networks, using lignocellulose biosynthesis in Populus trichocarpa as a case study. The research identified three candidate genes potentially involved in lignocellulose formation, including one previously recognized gene (Potri.005G116800/VND1, a critical regulator of secondary cell wall formation) and two genes (Potri.012G130000/AtSAP9 and Potri.004G202900/BIC1) with newly identified putative roles in lignocellulose biosynthesis. Our integrative approach offers a framework for providing biological context to loci associated with trait variation, facilitating the discovery of new genes and regulatory networks.

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.

The plant cuticle is a complex extracellular lipid barrier that has multiple protective functions. We investigated cuticle deposition by integrating metabolomics and transcriptomics data gathered from six different maize seedling organs of four genotypes, the inbred lines B73 and Mo17, and their reciprocal hybrids. These datasets captured the developmental transition of the seedling from heterotrophic skotomorphogenic growth to autotrophic photomorphogenic growth, which is a transition that is highly vulnerable to environmental stresses. Statistical interrogation of these data reveals that the predominant determinant of cuticle composition is seedling organ type, whereas the seedling genotype has a smaller effect on this phenotype. Gene-to-metabolite associations assessed by integrated statistical analyses identified three gene networks connected with the deposition of different elements of the cuticle: a) cuticular waxes; b) monomers of lipidized cell wall biopolymers, including cutin and suberin; and c) both of these elements. These gene networks reveal three metabolic programs that appear to support cuticle deposition, including processes of chloroplast biogenesis, lipid metabolism, and molecular regulation (e.g., transcription factors, post-translational regulators and phytohormones). This study demonstrates the wider physiological metabolic context that can determine cuticle deposition and lays the groundwork for new targets for modulating properties of this protective barrier.

BACKGROUND: The genetic background of cancer remains complex and challenging to integrate. Many somatic mutations within genes are known to cause and drive cancer, while genome-wide association studies (GWAS) of cancer have revealed many germline risk factors associated with cancer. However, the overlap between known somatic driver genes and positional candidate genes from GWAS loci is surprisingly small. We hypothesised that genes from multiple independent cancer GWAS loci should show tissue-specific co-regulation patterns that converge on cancer-specific driver genes.
RESULTS: We studied recent well-powered GWAS of breast, prostate, colorectal and skin cancer by estimating co-expression between genes and subsequently prioritising genes that show significant co-expression with genes mapping within susceptibility loci from cancer GWAS. We observed that the prioritised genes were strongly enriched for cancer drivers defined by COSMIC, IntOGen and Dietlein et al. The enrichment of known cancer driver genes was most significant when using co-expression networks derived from non-cancer samples of the relevant tissue of origin.
CONCLUSIONS: We show how genes within risk loci identified by cancer GWAS can be linked to known cancer driver genes through tissue-specific co-expression networks. This provides an important explanation for why seemingly unrelated sets of genes that harbour either germline risk factors or somatic mutations can eventually cause the same type of disease.

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.

UNASSIGNED: Studies have shown that cardiovascular health (CVH) is related to depression. We aimed to identify gene networks jointly associated with depressive symptoms and cardiovascular health metrics using the whole blood transcriptome.
UNASSIGNED: We analyzed human blood transcriptomic data to identify gene co-expression networks, termed gene modules, shared by Beck\'s depression inventory (BDI-II) scores and cardiovascular health (CVH) metrics as markers of depression and cardiovascular health, respectively. The BDI-II scores were derived from Beck\'s Depression Inventory, a 21-item self-report inventory that measures the characteristics and symptoms of depression. CVH metrics were defined according to the American Heart Association criteria using seven indices: smoking, diet, physical activity, body mass index (BMI), blood pressure, total cholesterol, and fasting glucose. Joint association of the modules, identified with weighted co-expression analysis, as well as the member genes of the modules with the markers of depression and CVH were tested with multivariate analysis of variance (MANOVA).
UNASSIGNED: We identified a gene module with 256 genes that were significantly correlated with both the BDI-II score and CVH metrics. Based on the MANOVA test results adjusted for age and sex, the module was associated with both depression and CVH markers. The three most significant member genes in the module were YOD1, RBX1, and LEPR. Genes in the module were enriched with biological pathways involved in brain diseases such as Alzheimer\'s, Parkinson\'s, and Huntington\'s.
UNASSIGNED: The identified gene module and its members can provide new joint biomarkers for depression and CVH.

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.

This study explores the genetic risk associations with autism spectrum disorder (ASD) using graph neural networks (GNNs), leveraging the Sfari dataset and protein interaction network (PIN) data. We built a gene network with genes as nodes, chromosome band location as node features, and gene interactions as edges. Graph models were employed to classify the autism risk associated with newly introduced genes (test set). Three classification tasks were undertaken to test the ability of our models: binary risk association, multi-class risk association, and syndromic gene association. We tested graph convolutional networks, Graph Sage, graph transformer, and Multi-Layer Perceptron (Baseline) architectures on this problem. The Graph Sage model consistently outperformed the other models, showcasing its utility in classifying ASD-related genes. Our ablation studies show that the chromosome band location and protein interactions contain useful information for this problem. The models achieved 85.80% accuracy on the binary risk classification, 81.68% accuracy on the multi-class risk classification, and 90.22% on the syndromic classification.

An intrinsic and genetically determined ripening program of tomato fruits often depends upon the appropriate activation of tissue- and stage-specific transcription factors in space and time. The past two decades have yielded considerable progress in detailing these complex transcriptional as well as hormonal regulatory circuits paramount to fleshy fruit ripening. This non-linear ripening process is strongly controlled by the MADS-box and NOR family of proteins, triggering a transcriptional response associated with the progression of fruit ripening. Deepening insights into the connection between MADS-RIN and plant hormones related transcription factors, such as ERFs and ARFs, further conjugates the idea that several signaling units work in parallel to define an output fruit ripening transcriptome. Besides these TFs, the role of other families of transcription factors such as MYB, GLK, WRKY, GRAS and bHLH have also emerged as important ripening regulators. Other regulators such as EIN and EIL proteins also determine the transcriptional landscape of ripening fruits. Despite the abundant knowledge of the complex spectrum of ripening networks in the scientific domain, identifying more ripening effectors would pave the way for a better understanding of fleshy fruit ripening at the molecular level. This review provides an update on the transcriptional regulators of tomato fruit ripening.