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Despite collective efforts to understand the complex regulation of reproductive traits, no causative genes and/or mutations have been reported yet. By integrating genomics and transcriptomics data, potential regulatory mechanisms may be unveiled, providing opportunities to dissect the genetic factors governing fertility. Herein, we identified regulatory variants from RNA-Seq data associated with gene expression regulation in the uterine luminal epithelial cells of beef cows. We identified 4676 cis and 7682 trans eQTLs (expression quantitative trait loci) affecting the expression of 1120 and 2503 genes, respectively (FDR < 0.05). These variants affected the expression of transcription factor coding genes (71 cis and 193 trans eQTLs) and genes previously reported as differentially expressed between pregnant and nonpregnant cows. Functional over-representation analysis highlighted pathways related to metabolism, immune response, and hormone signaling (estrogen and GnRH) affected by eQTL-regulated genes (p-value ≤ 0.01). Furthermore, eQTLs were enriched in QTL regions for 13 reproduction-related traits from the CattleQTLdb (FDR ≤ 0.05). Our study provides novel insights into the genetic basis of reproductive processes in cattle. The underlying causal mechanisms modulating the expression of uterine genes warrant further investigation. [ABSTRACT FROM AUTHOR]

Simple Summary: Cow–calf production is the foundation of the U.S. beef industry. However, infertility and sub-fertility limit its sustainability. In this review, we discuss the progress made as a result of the implementation of genomics and transcriptomics technologies to understand the genetic basis underlying fertility-related traits in cattle. We highlight the opportunities and limitations of using such technologies to discover genes and regulatory mechanisms and discuss the importance of translating research findings from bench to on-farm applications. Female fertility is the foundation of the cow–calf industry, impacting both efficiency and profitability. Reproductive failure is the primary reason why beef cows are sold in the U.S. and the cause of an estimated annual gross loss of USD 2.8 billion. In this review, we discuss the status of the genomics, transcriptomics, and systems genomics approaches currently applied to female fertility and the tools available to cow–calf producers to maximize genetic progress. We highlight the opportunities and limitations associated with using genomic and transcriptomic approaches to discover genes and regulatory mechanisms related to beef fertility. Considering the complex nature of fertility, significant advances in precision breeding will rely on holistic, multidisciplinary approaches to further advance our ability to understand, predict, and improve reproductive performance. While these technologies have advanced our knowledge, the next step is to translate research findings from bench to on-farm applications. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is a branch of computer science that allows machines to work efficiently, can analyze complex data. The research focused on AI has increased tremendously, and its role in healthcare service and research is emerging at a greater pace. This review elaborates on the opportunities and challenges of AI in healthcare and pharmaceutical research. The literature was collected from domains such as PubMed, Science Direct and Google scholar using specific keywords and phrases such as 'Artificial intelligence', 'Pharmaceutical research', 'drug discovery', 'clinical trial', 'disease diagnosis', etc. to select the research and review articles published within the last five years. The application of AI in disease diagnosis, digital therapy, personalized treatment, drug discovery and forecasting epidemics or pandemics was extensively reviewed in this article. Deep learning and neural networks are the most used AI technologies; Bayesian nonparametric models are the potential technologies for clinical trial design; natural language processing and wearable devices are used in patient identification and clinical trial monitoring. Deep learning and neural networks were applied in predicting the outbreak of seasonal influenza, Zika, Ebola, Tuberculosis and COVID-19. With the advancement of AI technologies, the scientific community may witness rapid and cost-effective healthcare and pharmaceutical research as well as provide improved service to the general public. [ABSTRACT FROM AUTHOR]

Conjugated bile acids (BA) are significantly elevated in several liver pathologies and in the metastatic lymph node (LN). However, the effects of BAs on pathological lymphangiogenesis remains unknown. The current study explores the effects of BAs on lymphangiogenesis. BA levels were elevated in the LN and serum of Mdr2−/− mice (model of sclerosing cholangitis) compared to control mice. Liver and LN tissue sections showed a clear expansion of the lymphatic network in Mdr2−/− mice, indicating activated lymphangiogenic pathways. Human lymphatic endothelial cells (LECs) expressed BA receptors and a direct treatment with conjugated BAs enhanced invasion, migration, and tube formation. BAs also altered the LEC metabolism and upregulated key metabolic genes. Further, BAs induced the production of reactive oxygen species (ROS), that in turn phosphorylated the redox-sensitive kinase p90RSK, an essential regulator of endothelial cell dysfunction and oxidative stress. Activated p90RSK increased the SUMOylation of the Prox1 transcription factor and enhanced VEGFR3 expression and 3-D LEC invasion. BA-induced ROS in the LECs, which led to increased levels of Yes-associated protein (YAP), a lymphangiogenesis regulator. The suppression of cellular YAP inhibited BA-induced VEGFR3 upregulation and lymphangiogenic mechanism. Overall, our data shows the expansion of the lymphatic network in presclerotic liver disease and establishes a novel mechanism whereby BAs promote lymphangiogenesis. [ABSTRACT FROM AUTHOR]

Cellular senescence—the irreversible cell cycle arrest driven by a variety of mechanisms and, more specifically, the senescence-associated secretory phenotype (SASP)—is an important area of research in the context of different age-related diseases, such as cardiovascular disease and cancer. SASP factors play both beneficial and detrimental roles in age-related disease progression depending on the source of the SASPs, the target cells, and the microenvironment. The impact of senescence and the SASP on different cell types, the immune system, and the vascular system has been widely discussed. However, the impact of replicative or stress-induced senescence on lymphatic biology and pathological lymphangiogenesis remains underexplored. The lymphatic system plays a crucial role in the maintenance of body fluid homeostasis and immune surveillance. The perturbation of lymphatic function can hamper normal physiological function. Natural aging or stress-induced premature aging influences the lymphatic vessel structure and function, which significantly affect the role of lymphatics in tumor dissemination and metastasis. In this review, we focus on the role of senescence on lymphatic pathobiology, its impact on cancer, and potential therapeutic interventions to manipulate the aged or senescent lymphatic system for disease management. [ABSTRACT FROM AUTHOR]

Reproductive failure is a major contributor to inefficiency within the cow-calf industry. Particularly problematic is the inability to diagnose heifer reproductive issues prior to pregnancy diagnosis following their first breeding season. Therefore, we hypothesized that gene expression from the peripheral white blood cells at weaning could predict the future reproductive potential of beef heifers. To investigate this, the gene expression was measured using RNA-Seq in Angus–Simmental crossbred heifers sampled at weaning and retrospectively classified as fertile (FH, n = 8) or subfertile (SFH, n = 7) after pregnancy diagnosis. We identified 92 differentially expressed genes between the groups. Network co-expression analysis identified 14 and 52 hub targets. ENSBTAG00000052659, OLR1, TFF2, and NAIP were exclusive hubs to the FH group, while 42 hubs were exclusive to the SFH group. The differential connectivity between the networks of each group revealed a gain in connectivity due to the rewiring of major regulators in the SFH group. The exclusive hub targets from FH were over-represented for the CXCR chemokine receptor pathway and inflammasome complex, while for the SFH, they were over-represented for immune response and cytokine production pathways. These multiple interactions revealed novel targets and pathways predicting reproductive potential at an early stage of heifer development. [ABSTRACT FROM AUTHOR]

Simple Summary: Female reproductive failure is still a challenge for the beef industry. Several biological processes that underlie fertility-related traits, such as the establishment of pregnancy and embryo survival, are still unclear. Increased availability of transcriptomic data has allowed a deep investigation of the potential mechanisms involved in fertility. This study investigated candidate gene biomarkers predictive of pregnancy status and underlying fertility-related networks. To this end, we integrated gene expression profiles through supervised machine learning and gene network modeling. We identified nine biologically relevant endometrial gene biomarkers that could discriminate against pregnancy status in cows. These biomarkers were co-expressed with genes critical for uterine receptivity, including endometrial tissue remodeling, focal adhesion, and embryo development. This study outlined key pathways involved with pregnancy success and provided predictive candidate biomarkers for pregnancy outcome in cows. Reproductive failure is still a challenge for beef producers and a significant cause of economic loss. The increased availability of transcriptomic data has shed light on the mechanisms modulating pregnancy success. Furthermore, new analytical tools, such as machine learning (ML), provide opportunities for data mining and uncovering new biological events that explain or predict reproductive outcomes. Herein, we identified potential biomarkers underlying pregnancy status and fertility-related networks by integrating gene expression profiles through ML and gene network modeling. We used public transcriptomic data from uterine luminal epithelial cells of cows retrospectively classified as pregnant (P, n = 25) and non-pregnant (NP, n = 18). First, we used a feature selection function from BioDiscML and identified SERPINE3, PDCD1, FNDC1, MRTFA, ARHGEF7, MEF2B, NAA16, ENSBTAG00000019474, and ENSBTAG00000054585 as candidate biomarker predictors of pregnancy status. Then, based on co-expression networks, we identified seven genes significantly rewired (gaining or losing connections) between the P and NP networks. These biomarkers were co-expressed with genes critical for uterine receptivity, including endometrial tissue remodeling, focal adhesion, and embryo development. We provided insights into the regulatory networks of fertility-related processes and demonstrated the potential of combining different analytical tools to prioritize candidate genes. [ABSTRACT FROM AUTHOR]

Reproductive failure remains a significant challenge to the beef industry. The omics technologies have provided opportunities to improve reproductive efficiency. We used a multistaged analysis from blood profiles to integrate metabolome (plasma) and transcriptome (peripheral white blood cells) in beef heifers. We used untargeted metabolomics and RNA-Seq paired data from six AI-pregnant (AI-P) and six nonpregnant (NP) Angus-Simmental crossbred heifers at artificial insemination (AI). Based on network co-expression analysis, we identified 17 and 37 hub genes in the AI-P and NP groups, respectively. Further, we identified TGM2, TMEM51, TAC3, NDRG4, and PDGFB as more connected in the NP heifers' network. The NP gene network showed a connectivity gain due to the rewiring of major regulators. The metabolomic analysis identified 18 and 15 hub metabolites in the AI-P and NP networks. Tryptophan and allantoic acid exhibited a connectivity gain in the NP and AI-P networks, respectively. The gene–metabolite integration identified tocopherol-a as positively correlated with ENSBTAG00000009943 in the AI-P group. Conversely, tocopherol-a was negatively correlated in the NP group with EXOSC2, TRNAUIAP, and SNX12. In the NP group, α-ketoglutarate-SMG8 and putrescine-HSD17B13 were positively correlated, whereas a-ketoglutarate-ALAS2 and tryptophan-MTMR1 were negatively correlated. These multiple interactions identified novel targets and pathways underlying fertility in bovines. [ABSTRACT FROM AUTHOR]

Cholangiocarcinoma (CCA), or cancer of bile duct epithelial cells, is a very aggressive malignancy characterized by early lymphangiogenesis in the tumor microenvironment (TME) and lymph node (LN) metastasis which correlate with adverse patient outcome. However, the specific roles of lymphatic endothelial cells (LECs) that promote LN metastasis remains unexplored. Here we aimed to identify the dynamic molecular crosstalk between LECs and CCA cells that activate tumor-promoting pathways and enhances lymphangiogenic mechanisms. Our studies show that inflamed LECs produced high levels of chemokine CXCL5 that signals through its receptor CXCR2 on CCA cells. The CXCR2-CXCL5 signaling axis in turn activates EMT (epithelial-mesenchymal transition) inducing MMP (matrix metalloproteinase) genes such as GLI, PTCHD, and MMP2 in CCA cells that promote CCA migration and invasion. Further, rate of mitochondrial respiration and glycolysis of CCA cells was significantly upregulated by inflamed LECs and CXCL5 activation, indicating metabolic reprogramming. CXCL5 also induced lactate production, glucose uptake, and mitoROS. CXCL5 also induced LEC tube formation and increased metabolic gene expression in LECs. In vivo studies using CCA orthotopic models confirmed several of these mechanisms. Our data points to a key finding that LECs upregulate critical tumor-promoting pathways in CCA via CXCR2-CXCL5 axis, which further augments CCA metastasis. [ABSTRACT FROM AUTHOR]

Feed efficiency (FE) is an economically important trait. Thus, reliable predictors would help to reduce the production cost and provide sustainability to the pig industry. We carried out metabolome-transcriptome integration analysis on 40 purebred Duroc and Landrace uncastrated male pigs to identify potential gene-metabolite interactions and explore the molecular mechanisms underlying FE. To this end, we applied untargeted metabolomics and RNA-seq approaches to the same animals. After data quality control, we used a linear model approach to integrate the data and find significant differently correlated gene-metabolite pairs separately for the breeds (Duroc and Landrace) and FE groups (low and high FE) followed by a pathway over-representation analysis. We identified 21 and 12 significant gene-metabolite pairs for each group. The valine-leucine-isoleucine biosynthesis/degradation and arginine-proline metabolism pathways were associated with unique metabolites. The unique genes obtained from significant metabolite-gene pairs were associated with sphingolipid catabolism, multicellular organismal process, cGMP, and purine metabolic processes. While some of the genes and metabolites identified were known for their association with FE, others are novel and provide new avenues for further research. Further validation of genes, metabolites, and gene-metabolite interactions in larger cohorts will elucidate the regulatory mechanisms and pathways underlying FE. [ABSTRACT FROM AUTHOR]

Inhibition of cholinesterases remains one of a few available treatment strategies for neurodegenerative dementias such as Alzheimer's disease and related conditions. The current study was inspired by previous data on anticholinesterase properties of diterpenoids from Perovskia atriplicifolia and other Lamiaceae species. The acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) inhibition by the three new natural compounds—(1R,15R)-1-acetoxycryptotanshinone (1), (1R)-1-acetoxytanshinone IIA (2), and (15R)-1-oxoaegyptinone A (3)—as well as, new for this genus, isograndifoliol (4) were assessed. Three of these compounds exhibited profound inhibition of butyrylcholinesterase (BChE) and much weaker inhibition of acetylcholinesterase (AChE). All compounds (1–4) selectively inhibited BChE (IC50 = 2.4, 7.9, 50.8, and 0.9 µM, respectively), whereas only compounds 3 and 4 moderately inhibited AChE (IC50 329.8 µM and 342.9 µM). Molecular docking and in silico toxicology prediction studies were also performed on the active compounds. Natural oxygenated norditerpenoids from the traditional Central Asian medicinal plant P. atriplicifolia are selective BChE inhibitors. Their high potential makes them useful candidate molecules for further investigation as lead compounds in the development of a natural drug against dementia caused by neurodegenerative diseases. [ABSTRACT FROM AUTHOR]