Palatogenesis is a complex and intricate process involving the formation of the palate through various morphogenetic events highly dependent on the surrounding context. These events comprise outgrowth of palatal shelves from embryonic maxillary prominences, their elevation from a vertical to a horizontal position above the tongue, and their subsequent adhesion and fusion at the midline to separate oral and nasal cavities. Disruptions in any of these processes can result in cleft palate, a common congenital abnormality that significantly affects patient\'s quality of life, despite surgical intervention. Although many genes involved in palatogenesis have been identified through studies on genetically modified mice and human genetics, the precise roles of these genes and their products in signaling networks that regulate palatogenesis remain elusive. Recent investigations have revealed that palatal shelf growth, patterning, adhesion, and fusion are intricately regulated by numerous transcription factors and signaling pathways, including Sonic hedgehog (Shh), bone morphogenetic protein (Bmp), fibroblast growth factor (Fgf), transforming growth factor beta (Tgf-β), Wnt signaling, and others. These studies have also identified a significant number of genes that are essential for palate development. Integrated information from these studies offers novel insights into gene regulatory networks and dynamic cellular processes underlying palatal shelf elevation, contact, and fusion, deepening our understanding of palatogenesis, and facilitating the development of more efficacious treatments for cleft palate.

Precision medicine, the utilization of targeted treatments to address an individual\'s disease, relies on knowledge about the genetic cause of that individual\'s drug response. Here, we present a functional graph (FunGraph) theory to chart comprehensive pharmacogenetic architecture for each and every patient. FunGraph is the combination of functional mapping - a dynamic model for genetic mapping and evolutionary game theory guiding interactive strategies. It coalesces all pharmacogenetic factors into multilayer and multiplex networks that fully capture bidirectional, signed and weighted epistasis. It can visualize and interrogate how epistasis moves in the cell and how this movement leads to patient- and context-specific genetic architecture in response to organismic physiology. We discuss the future implementation of FunGraph to achieve precision medicine.

Psychiatric disorders frequently co-occur and share common symptoms and genetic backgrounds. Previous research has used genome-wide association studies to identify the interrelationships among psychiatric disorders and identify clusters of disorders; however, these methods have limitations in terms of their ability to examine the relationships among disorders as a network structure and their generalizability to the general population. In this study, we explored the network structure of the polygenic risk score (PRS) for 13 psychiatric disorders in a general population (276,249 participants of European ancestry from the UK Biobank) and identified communities and the centrality of the network. In this network, the nodes represented a PRS for each psychiatric disorder and the edges represented the connections between nodes. The psychiatric disorders comprised four robust communities. The first community included attention-deficit hyperactivity disorder, autism spectrum disorder, major depressive disorder, and anxiety disorder. The second community consisted of bipolar I and II disorders, schizophrenia, and anorexia nervosa. The third group included Tourette\'s syndrome and obsessive-compulsive disorder. Cannabis use disorder, alcohol use disorder, and post-traumatic stress disorder make up the fourth community. The PRS of schizophrenia had the highest values for the three metrics (strength, betweenness, and closeness) in the network. Our findings provide a comprehensive genetic network of psychiatric disorders and biological evidence for the classification of psychiatric disorders.

Global genetic networks provide additional information for the analysis of human diseases, beyond the traditional analysis that focuses on single genes or local networks. The Gaussian graphical model (GGM) is widely applied to learn genetic networks because it defines an undirected graph decoding the conditional dependence between genes. Many algorithms based on the GGM have been proposed for learning genetic network structures. Because the number of gene variables is typically far more than the number of samples collected, and a real genetic network is typically sparse, the graphical lasso implementation of GGM becomes a popular tool for inferring the conditional interdependence among genes. However, graphical lasso, although showing good performance in low dimensional data sets, is computationally expensive and inefficient or even unable to work directly on genome-wide gene expression data sets. In this study, the method of Monte Carlo Gaussian graphical model (MCGGM) was proposed to learn global genetic networks of genes. This method uses a Monte Carlo approach to sample subnetworks from genome-wide gene expression data and graphical lasso to learn the structures of the subnetworks. The learned subnetworks are then integrated to approximate a global genetic network. The proposed method was evaluated with a relatively small real data set of RNA-seq expression levels. The results indicate the proposed method shows a strong ability of decoding the interactions with high conditional dependences among genes. The method was then applied to genome-wide data sets of RNA-seq expression levels. The gene interactions with high interdependence from the estimated global networks show that most of the predicted gene-gene interactions have been reported in the literatures playing important roles in different human cancers. Also, the results validate the ability and reliability of the proposed method to identify high conditional dependences among genes in large-scale data sets.

Between October 2021 and April 2022, 317 outbreaks caused by highly pathogenic avian influenza (HPAI) H5N1 viruses were notified in poultry farms in the northeastern Italian regions. The complete genomes of 214 strains were used to estimate the genetic network based on the similarity of the viruses. An exponential random graph model (ERGM) was used to assess the effect of \'at-risk contacts\', \'same owners\', \'in-bound/out-bound risk windows overlap\', \'genetic differences\', \'geographic distances\', \'same species\', and \'poultry company\' on the probability of observing a link within the genetic network, which can be interpreted as the potential propagation of the epidemic via lateral spread or a common source of infection. The variables \'same poultry company\' (Est. = 0.548, C.I. = [0.179; 0.918]) and \'risk windows overlap\' (Est. = 0.339, C.I. = [0.309; 0.368]) were associated with a higher probability of link formation, while the \'genetic differences\' (Est. = -0.563, C.I. = [-0.640; -0.486]) and \'geographic distances\' (Est. = -0.058, C.I. = [-0.078; -0.038]) indicated a reduced probability. The integration of epidemiological data with genomic analyses allows us to monitor the epidemic evolution and helps to explain the dynamics of lateral spreads casting light on the potential diffusion routes. The 2021-2022 epidemic stresses the need to further strengthen the biosecurity measures, and to encourage the reorganization of the poultry production sector to minimize the impact of future epidemics.

BACKGROUND: Indirect interactions between individual solitary mammals, such as the giant panda, are often overlooked because of their nature, yet are important for maintaining the necessary sociality in solitary species.
RESULTS: Here, we determined the genetic identity of all giant panda individuals in a local population and matched these identities with their associations to determine social network of this solitary animal. Total thirty-five giant panda individuals were found in our field survey, and we constructed genetic and social networks for thirty-three individuals who successfully obtained genetic, age and sex information. The results showed that sex had great impact on both social network and genetic network, and age may have the potential to influence the social network of the giant pandas. Adult males, mostly in the central of the social network, which appeared significantly larger network connections than adult females. Due to the female-biased dispersal pattern of wild giant pandas, male-male pairs showed higher relatedness than female-female ones and multi-generational patrilinear assemblages are expected in the study area.
CONCLUSIONS: The relatedness of individuals has an influence on the formation of community social structure of giant pandas, and indirect interactions among solitary giant pandas potentially function to reduce competition for resources and inbreeding.

UNASSIGNED: This study is used to analyze the genetic network of HIV sexual transmission in rural areas of Southwest China after expanding antiretroviral therapy (ART) and to investigate the factors associated with HIV sexual transmission through the genetic network.
UNASSIGNED: This was a longitudinal genetic network study in Guangxi, China. The baseline survey and follow-up study were conducted among patients with HIV in 2015, and among those newly diagnosed from 2016 to 2018, respectively. A generalized estimating equation model was employed to explore the factors associated with HIV transmission through the genetic linkage between newly diagnosed patients with HIV (2016-2018) and those at baseline (2015-2017), respectively.
UNASSIGNED: Of 3,259 identified HIV patient sequences, 2,714 patients were at baseline, and 545 were newly diagnosed patients with HIV at follow-up. A total of 8,691 baseline objectives were observed by repeated measurement analysis. The prevention efficacy in HIV transmission for treated HIV patients was 33% [adjusted odds ratio (AOR): 0.67, 95% confidence interval (CI): 0.48-0.93]. Stratified analyses indicated the prevention efficacy in HIV transmission for treated HIV patients with a viral load (VL) of <50 copies/ml and those treated for 4 years with a VL of <50 copies/ml to be 41 [AOR: 0.59, 95% CI: 0.43-0.82] and 65% [AOR: 0.35, 95% CI: 0.24-0.50], respectively. No significant reduction in HIV transmission occurred among treated HIV patients with VL missing or treated HIV patients on dropout. Some factors were associated with HIV transmission, including over 50 years old, men, Zhuang and other nationalities, with less than secondary schooling, working as a farmer, and heterosexual transmission.
UNASSIGNED: This study reveals the role of ART in reducing HIV transmission, and those older male farmers with less than secondary schooling are at high risk of HIV infection at a population level. Improvements to ART efficacy for patients with HIV and precision intervention on high-risk individuals during the expansion of ART are urgently required.

Exploring the dynamic variations of viral genomes utilizing with a phylogenetic analysis is vital to control the pandemic and stop its waves. Genetic network can be applied to depict the complicated evolution relationships of viral genomes. However, current phylogenetic methods cannot handle the cases with deletions effectively. Therefore, the k-mer natural vector is employed to characterize the compositions and distribution features of k-mers occurring in a viral genome, and construct a one-to-one relationship between a viral genome and its k-mer natural vector. Utilizing the k-mer natural vector, we proposed a novel genetic network to investigate the variations of viral genomes in transmission among humans. With the assistance of genetic network, we identified the super-spreaders that were responsible for the pandemic outbreaks all over the world and chose the parental strains to evaluate the effectiveness of diagnostics, therapeutics, and vaccines. The obtaining results fully demonstrated that our genetic network can truly describe the relationships of viral genomes, effectively simulate virus spread tendency, and trace the transmission routes precisely. In addition, this work indicated that the k-mer natural vector has the ability to capture established hotspots of diversities existing in the viral genomes and understand how genomic contents change over time.

Cancer cells are different from normal cells in regard to phenotypic and functional expression. Cancer is the outcome of aberrant gene expression affecting various cellular signaling pathways. MicroRNAs (MiRs) are small, non-coding RNAs regulating the expression of various protein-coding genes post-transcriptionally and are known to play critical roles in the complicated cellular pathways leading to cell growth, proliferation, development, and apoptosis. MiRs are involved in various cancer-related pathways and function both as tumor suppressor and cancer-causing genes. There is a need for significant biomarkers, and better prognostication of response to a particular treatment and liquid biopsy could be useful to appraise such potential biomarkers. This review has focused on the involvement of anomalous expression of miRs in human pancreatic cancer and the investigation of miR-based biomarkers for disease diagnosis and better therapeutic selection.

Microorganisms often live in complex habitats, where changes in the environment are predictable, providing an opportunity for microorganisms to learn, anticipate the upcoming environmental changes and prepare in advance for better survival and growth. One such environment is the mammalian intestine, where the abundance of different carbon sources is spatially distributed. In this study, we identified seven spatially distributed carbon sources in the mammalian intestine and tested whether Escherichia coli exhibits phenotypes that are consistent with an anticipatory response given their spatial order and abundance within the mammalian intestine. Through RNA-Seq and RT-PCR validation measurements, we found that there was a 67% match in the expression patterns between the measured phenotypes and what would otherwise be expected in the case of anticipatory behavior, while 83% and 0% were in agreement with the homeostatic and random response, respectively. To understand the genetic and phenotypic basis of the discrepancies between the expected and measured anticipatory responses, we thoroughly investigated the discrepancy in D-galactose treatment and the expression of maltose operon in E. coli. Here, the expected anticipatory response, based on the spatial distribution of D-galactose and D-maltose, was that D-galactose should upregulate the maltose operon, but it was the opposite in experimental validation. We performed whole genome random mutagenesis and screening and identified E. coli strains with positive expression of maltose operon in D-galactose. Targeted Sanger sequencing and mutation repair identified that the mutations in the promoter region of malT and in the coding region of the crp gene were the factors responsible for the reversion in the association. Further, to identify why positive association in the D-galactose treatment and the expression of the maltose operon did not evolve naturally, fitness measurements were performed. Fitness experiments demonstrated that the fitness of E. coli strains with a positive association in the D-galactose treatment and the expression of the maltose operon was 12% to 20% lower than that of the wild type strain.