The methylome and cell-free DNA: current applications in medicine and pediatric disease.

DNA methylation is an epigenetic mechanism that contributes to cell regulation and development, and different methylation patterns allow for the identification of cell and tissue type. Cell-free DNA (cfDNA) is composed of small circulating fragments of DNA found in plasma and urine. Total cfDNA levels correlate with the presence of inflammation and tissue injury in a variety of disease states. Unfortunately, the utility of cfDNA is limited by its lack of tissue or cell-type specificity. However, methylome analysis of cfDNA allows the identification of the tissue or cell type from which cfDNA originated. Thus, methylation patterns in cfDNA from tissues isolated from direct study may provide windows into health and disease states, thereby serving as a "liquid biopsy". This review will discuss methylation and its role in establishing cellular identity, cfDNA as a biomarker and its pathophysiologic role in the inflammatory process, and the ways cfDNA and methylomics can be jointly applied in medicine. IMPACT: Cell-free DNA (cfDNA) is increasingly being used as a noninvasive diagnostic and disease-monitoring tool in pediatric medicine. However, the lack of specificity of cfDNA limits its utility. Identification of cell type-specific methylation signatures can help overcome the limited specificity of cfDNA. As knowledge of the cfDNA methylome improves, cfDNA will be more broadly applied in medicine, such that clinicians will need to understand the methods and applications of its use.

Pharmacovigilance in patients with diabetes: A data-driven analysis identifying specific RAS antagonists with adverse pulmonary safety profiles that have implications for COVID-19 morbidity and mortality.

OBJECTIVES: The current demographic information from China reports that 10%-19% of patients hospitalized with coronavirus disease (COVID-19) were diabetic. Angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin II receptor blockers (ARBs) are considered first-line agents in patients with diabetes because of their nephroprotective effects, but administration of these drugs leads to upregulation of angiotensin-converting enzyme 2 (ACE2), which is responsible for the viral entry of severe acute respiratory distress syndrome coronavirus 2 (SARS-CoV-2). Data are lacking to determine what pulmonary effects ACEIs or ARBs may have in patients with diabetes, which could be relevant in the management of patients infected with SARS-CoV-2. This study aims to assess the prevalence of pulmonary adverse drug effects (ADEs) in patients with diabetes who were taking ACEI or ARBs to provide guidance as to how these medications could affect outcomes in acute respiratory illnesses such as SARS-CoV-2 infection. METHODS: 1DATA, a unique data platform resulting from collaboration across veterinary and human health care, used an intelligent medicine recommender system (1DrugAssist) developed using several national and international databases to evaluate all ADEs reported to the Food and Drug Administration for patients with diabetes taking ACEIs or ARBs. RESULTS: Mining of this data elucidated the proportion of a cluster of pulmonary ADEs associated with specific medications in these classes, which may aid health care professionals in understanding how these medications could worsen or predispose patients with diabetes to infections affecting the respiratory system, specifically COVID-19. Based on this data mining process, captopril was found to have a statistically significantly higher incidence of pulmonary ADEs compared with other ACEIs (P = 0.005) as well as ARBs (P = 0.012), though other specific drugs also had important pulmonary ADEs associated with their use. CONCLUSION: These analyses suggest that pharmacists and clinicians will need to consider the specific medication's adverse event profile, particularly captopril, on how it may affect infections and other acute disease states that alter pulmonary function, such as COVID-19.

Identification of C3b-Binding Small-Molecule Complement Inhibitors Using Cheminformatics.

The complement system is an elegantly regulated biochemical cascade formed by the collective molecular recognition properties and proteolytic activities of more than two dozen membrane-bound or serum proteins. Complement plays diverse roles in human physiology, such as acting as a sentry against invading microorganisms, priming of the adaptive immune response, and removal of immune complexes. However, dysregulation of complement can serve as a trigger for a wide range of human diseases, which include autoimmune, inflammatory, and degenerative conditions. Despite several potential advantages of modulating complement with small-molecule inhibitors, small-molecule drugs are highly underrepresented in the current complement-directed therapeutics pipeline. In this study, we have employed a cheminformatics drug discovery approach based on the extensive structural and functional knowledge available for the central proteolytic fragment of the cascade, C3b. Using parallel in silico screening methodologies, we identified 45 small molecules that putatively bind C3b near ligand-guided functional hot spots. Surface plasmon resonance experiments resulted in the validation of seven dose-dependent C3b-binding compounds. Competition-based biochemical assays demonstrated the ability of several C3b-binding compounds to interfere with binding of the original C3b ligand that guided their discovery. In vitro assays of complement function identified a single complement inhibitory compound, termed cmp-5, and mechanistic studies of the cmp-5 inhibitory mode revealed it acts at the level of C5 activation. This study has led to the identification of a promising new class of C3b-binding small-molecule complement inhibitors and, to our knowledge, provides the first demonstration of cheminformatics-based, complement-directed drug discovery.

Genome-Wide Convergence during Evolution of Mangroves from Woody Plants.

When living organisms independently invade a new environment, the evolution of similar phenotypic traits is often observed. An interesting but contentious issue is whether the underlying molecular biology also converges in the new habitat. Independent invasions of tropical intertidal zones by woody plants, collectively referred to as mangrove trees, represent some dramatic examples. The high salinity, hypoxia, and other stressors in the new habitat might have affected both genomic features and protein structures. Here, we developed a new method for detecting convergence at conservative Sites (CCS) and applied it to the genomic sequences of mangroves. In simulations, the CCS method drastically reduces random convergence at rapidly evolving sites as well as falsely inferred convergence caused by the misinferences of the ancestral character. In mangrove genomes, we estimated ∼400 genes that have experienced convergence over the background level of convergence in the nonmangrove relatives. The convergent genes are enriched in pathways related to stress response and embryo development, which could be important for mangroves' adaptation to the new habitat.

Inhibition of bacterial mevalonate diphosphate decarboxylase by eriochrome compounds.

Mevalonate diphosphate decarboxylase (MDD; EC 4.1.1.33) catalyzes the irreversible decarboxylation of mevalonate diphosphate in the mevalonate pathway to form isopentenyl diphosphate, which is a precursor in the biosynthesis of many essential polyisoprenoid natural products, including sterols. In low G/C Gram-positive bacteria, which utilize the mevalonate pathway, MDD is required for cell viability and thus is a potential target for development of antibiotic drugs. To identify potential inhibitors of the enzyme, the National Cancer Institute's Mechanistic Diversity Set library of compounds was screened for inhibitors of Staphylococcus epidermidis MDD. From this screen, the compound Eriochrome Black A (EBA), an azo dye, was found to inhibit the enzyme with an IC50 value<5µM. Molecular docking of EBA into a crystal structure of S. epidermidis MDD suggested binding at the active site. EBA, along with the related Eriochrome B and T compounds, was evaluated for its ability to not only inhibit enzymatic activity but to inhibit bacterial growth as well. These compounds exhibited competitive inhibition towards the substrate mevalonate diphosphate, with Ki values ranging from 0.6 to 2.7µM. Non-competitive inhibition was observed versus ATP indicating binding of the inhibitor in the mevalonate diphosphate binding site, consistent with molecular docking predictions. Fluorescence quenching analyses also supported active site binding of EBA. These eriochrome compounds are effective at inhibiting S. epidermidis cell growth on both solid media and in liquid culture (MIC50 from 31 to 350µM) raising the possibility that they could be developed into antibiotic leads targeting pathogenic low-G/C Gram-positive cocci.

Identification in Haloferax volcanii of phosphomevalonate decarboxylase and isopentenyl phosphate kinase as catalysts of the terminal enzyme reactions in an archaeal alternate mevalonate pathway.

Mevalonate (MVA) metabolism provides the isoprenoids used in archaeal lipid biosynthesis. In synthesis of isopentenyl diphosphate, the classical MVA pathway involves decarboxylation of mevalonate diphosphate, while an alternate pathway has been proposed to involve decarboxylation of mevalonate monophosphate. To identify the enzymes responsible for metabolism of mevalonate 5-phosphate to isopentenyl diphosphate in Haloferax volcanii, two open reading frames (HVO_2762 and HVO_1412) were selected for expression and characterization. Characterization of these proteins indicated that one enzyme is an isopentenyl phosphate kinase that forms isopentenyl diphosphate (in a reaction analogous to that of Methanococcus jannaschii MJ0044). The second enzyme exhibits a decarboxylase activity that has never been directly attributed to this protein or any homologous protein. It catalyzes the synthesis of isopentenyl phosphate from mevalonate monophosphate, a reaction that has been proposed but never demonstrated by direct experimental proof, which is provided in this account. This enzyme, phosphomevalonate decarboxylase (PMD), exhibits strong inhibition by 6-fluoromevalonate monophosphate but negligible inhibition by 6-fluoromevalonate diphosphate (a potent inhibitor of the classical mevalonate pathway), reinforcing its selectivity for monophosphorylated ligands. Inhibition by the fluorinated analog also suggests that the PMD utilizes a reaction mechanism similar to that demonstrated for the classical MVA pathway decarboxylase. These observations represent the first experimental demonstration in H. volcanii of both the phosphomevalonate decarboxylase and isopentenyl phosphate kinase reactions that are required for an alternate mevalonate pathway in an archaeon. These results also represent, to our knowledge, the first identification and characterization of any phosphomevalonate decarboxylase.

In-silico approaches to assessing multiple high-level drug-drug and drug-disease adverse drug effects.

INTRODUCTION: Pharmacovigilance plays a pivotal role in monitoring adverse events (AEs) related to chemical substances in human/animal populations. With increasing spontaneous-reporting systems, researchers turned to in-silico approaches to efficiently analyze drug safety profiles. Here, we review in-silico methods employed for assessing multiple drug-drug/drug-disease AEs covered by comparative analyses and visualization strategies. AREAS COVERED: Disproportionality, involving multi-stage statistical methodologies and data processing, identifies safety signals among drug-AE pairs. By stratifying data based on disease indications/demographics, researchers address confounders and assess drug safety. Comparative analyses, including clustering techniques and visualization techniques, assess drug similarities, patterns, and trends, calculate correlations, and identify distinct toxicities. Furthermore, we conducted a thorough Scopus search on 'pharmacovigilance,' yielding 5,836 publications spanning 2003 to 2023. EXPERT OPINION: Pharmacovigilance relies on diverse data sources, presenting challenges in the integration of in-silico approaches and requiring compliance with regulations and AI adoption. Systematic use of statistical analyses enables identifications of potential risks with drugs. Frequentist and Bayesian methods are used in disproportionalities, each with its strengths and weaknesses. Integration of pharmacogenomics with pharmacovigilance enables personalized medicine, with AI further enhancing patient engagement. This multidisciplinary approach holds promise, improving drug efficacy and safety, and should be a core mission of One-Health studies.

The role and potential of computer-aided drug discovery strategies in the discovery of novel antimicrobials.

Antimicrobial resistance (AMR) has become more of a concern in recent decades, particularly in infections associated with global public health threats. The development of new antibiotics is crucial to ensuring infection control and eradicating AMR. Although drug discovery and development are essential processes in the transformation of a drug candidate from the laboratory to the bedside, they are often very complicated, expensive, and time-consuming. The pharmaceutical sector is continuously innovating strategies to reduce research costs and accelerate the development of new drug candidates. Computer-aided drug discovery (CADD) has emerged as a powerful and promising technology that renews the hope of researchers for the faster identification, design, and development of cheaper, less resource-intensive, and more efficient drug candidates. In this review, we discuss an overview of AMR, the potential, and limitations of CADD in AMR drug discovery, and case studies of the successful application of this technique in the rapid identification of various drug candidates. This review will aid in achieving a better understanding of available CADD techniques in the discovery of novel drug candidates against resistant pathogens and other infectious agents.

Expression in Haloferax volcanii of 3-hydroxy-3-methylglutaryl coenzyme A synthase facilitates isolation and characterization of the active form of a key enzyme required for polyisoprenoid cell membrane biosynthesis in halophilic archaea.

Enzymes of the isoprenoid biosynthetic pathway in halophilic archaea remain poorly characterized, and parts of the pathway remain cryptic. This situation may be explained, in part, by the difficulty of expressing active, functional recombinant forms of these enzymes. The use of newly available expression plasmids and hosts has allowed the expression and isolation of catalytically active Haloferax volcanii 3-hydroxy-3-methylglutaryl coenzyme A (CoA) synthase (EC 2.3.310). This accomplishment has permitted studies that represent, to the best of our knowledge, the first characterization of an archaeal hydroxymethylglutaryl CoA synthase. Kinetic characterization indicates that, under optimal assay conditions, which include 4 M KCl, the enzyme exhibits catalytic efficiency and substrate saturation at metabolite levels comparable to those reported for the enzyme from nonhalophilic organisms. This enzyme is unique in that it is the first hydroxymethylglutaryl CoA synthase that is insensitive to feedback substrate inhibition by acetoacetyl-CoA. The enzyme supports reaction catalysis in the presence of various organic solvents. Haloferax 3-hydroxy-3-methylglutaryl CoA synthase is sensitive to inactivation by hymeglusin, a specific inhibitor known to affect prokaryotic and eukaryotic forms of the enzyme, with experimentally determined Ki and kinact values of 570 ± 120 nM and 17 ± 3 min(-1), respectively. In in vivo experiments, hymeglusin blocks the propagation of H. volcanii cells, indicating the critical role that the mevalonate pathway plays in isoprenoid biosynthesis by these archaea.

Rate of molecular evolution of the seminal protein gene SEMG2 correlates with levels of female promiscuity.

Postcopulatory sperm competition is a key aspect of sexual selection and is believed to drive the rapid evolution of both reproductive physiology and reproduction-related genes. It is well-established that mating behavior determines the intensity of sperm competition, with polyandry (i.e., female promiscuity) leading to fiercer sperm competition than monandry. Studies in mammals, particularly primates, showed that, owing to greater sperm competition, polyandrous taxa generally have physiological traits that make them better adapted for fertilization than monandrous species, including bigger testes, larger seminal vesicles, higher sperm counts, richer mitochondrial loading in sperm and more prominent semen coagulation. Here, we show that the degree of polyandry can also impact the dynamics of molecular evolution. Specifically, we show that the evolution of SEMG2, the gene encoding semenogelin II, a main structural component of semen coagulum, is accelerated in polyandrous primates relative to monandrous primates. Our study showcases the intimate relationship between sexual selection and the molecular evolution of reproductive genes.

A retrospective study of adverse drug events in anticoagulant administration with relevance to COVID-19.

Initial studies in COVID-19 patients reported lower mortality rates associated with the use of the drug heparin, a widely used anticoagulant. The objective of this analysis was to determine whether there are adverse events associated with the administration of anticoagulants, and specifically how this might apply in patients known to have COVID-19. Data for this study were obtained from the Food and Drug Administration's Adverse Event Reporting System (FAERS) public database and from the NIH's clinical trials website. Proportional Reporting Ratios (PRR) with lower 95% confidence intervals (lower CI) and empirical Bayes geometric mean (EBGM) scores with lower 95% confidence limits were calculated for data from the FAERS database where the adverse events studied mimicked COVID-19 symptoms.

Computational Target-Based Screening of Anti-MRSA Natural Products Reveals Potential Multitarget Mechanisms of Action through Peptidoglycan Synthesis Proteins.

Methicillin-resistant Staphylococcus aureus (MRSA) is one of the leading causes of bacterial infections in both healthcare and community settings. MRSA can acquire resistance to any current antibiotic, which has major implications for its current and future treatment options. As such, it is globally a major focus for infection control efforts. The mechanical rigidity provided by peptidoglycans in the bacteria cell walls makes it a promising target for broad-spectrum antibacterial drug discovery. The development of drugs that can target different stages of the synthesis of peptidoglycan in MRSA may compromise the integrity of its cell wall and consequently result in the rapid decline of diseases associated with this drug-resistant bacteria. The present study is aimed at screening natural products with known in vitro activities against MRSA to identify their potential to inhibit the proteins involved in the biosynthesis of the peptidoglycan cell wall. A total of 262 compounds were obtained when a literature survey was conducted on anti-MRSA natural products (AMNPs). Virtual screening of the AMNPs was performed against various proteins (targets) that are involved in the biosynthesis of the peptidoglycan (PPC) cell wall using Schrödinger software (release 2020-3) to determine their binding affinities. Nine AMNPs were identified as potential multitarget inhibitors against peptidoglycan biosynthesis proteins. Among these compounds, DB211 showed the strongest binding affinity and interactions with six protein targets, representing three stages of peptidoglycan biosynthesis, and thus was selected as the most promising compound. The MD simulation results for DB211 and its proteins indicated that the protein-ligand complexes were relatively stable over the simulation period of 100 ns. In conclusion, DB211 showed the potential to inhibit six proteins involved in the biosynthesis of the peptidoglycan cell wall in MRSA, thus reducing the chance of MRSA developing resistance to this compound. Therefore, DB211 provided a starting point for the design of new compounds that can inhibit multiple targets in the biosynthesis of the peptidoglycan layer in MRSA.

Detection of selection utilizing molecular phylogenetics: a possible approach.

The neutral theory of molecular evolution (Kimura 1985) is the basis for most current statistical tests for detecting selection, mainly using polymorphism data within species, divergence data between species, and/or genomic structures like linkage disequilibrium (Wang et al. 2006). In most cases informative tests can only be constructed with ample variations within these parameters and many common tests are difficult to formulate when identity-by-descent is not clear, for example in gene families or repetitive elements. With the current progress being made toward whole-genome sequencing and re-sequencing efforts, as well as protein sequencing via tandem mass spectrometry where genomic sequencing is lacking, we felt it was necessary to re-visit possible methods for rapid screening and detection of evolutionary outliers. These outliers might be of interest for other research, such as candidate gene association studies or genome annotations, drug- and disease-target searches, and functional studies. We focused on methods that would work on both protein and nucleotide data, could be used on large gene or protein domain families, and could be generated quickly in order for "first pass" annotation of large scale data. For these reasons, we chose properties of trees generated routinely in molecular phylogenetic studies; genetic distance, tree shape and balance, and internal node statistics (Heard 1992). Our current research looking at protein domain family data and phylogenetic trees from PFAM (Finn et al. 2008) suggests this approach towards detecting evolutionary outliers is feasible, but additional work will be necessary to determine the parameters that suggest either positive or negative selection is occurring in specific gene families. This is particularly true when other factors such as rapid duplication and deletion of genes containing these domains is taking place, and we suggest phylogenetic statistics may be useful in combination with existing methodologies for detailed studies of gene family data.

Pulmonary adverse drug event data in hypertension with implications on COVID-19 morbidity.

Hypertension is a recognized comorbidity for COVID-19. The association of antihypertensive medications with outcomes in patients with hypertension is not fully described. However, angiotensin-converting enzyme 2 (ACE2), responsible for host entry of the novel coronavirus (SARS-CoV-2) leading to COVID-19, is postulated to be upregulated in patients taking angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin II receptor blockers (ARBs). Here, we evaluated the occurrence of pulmonary adverse drug events (ADEs) in patients with hypertension receiving ACEIs/ARBs to determine if disparities exist between individual drugs within the respective classes using data from the FDA Spontaneous Reporting Systems. For this purpose, we proposed the proportional reporting ratio to provide a statistical summary for the commonality of an ADE for a specific drug as compared to the entire database for drugs in the same or other classes. In addition, a statistical procedure, multiple logistic regression analysis, was employed to correct hidden confounders when causative covariates are underreported or untrusted to correct analyses of drug-ADE combinations. To date, analyses have been focused on drug classes rather than individual drugs which may have different ADE profiles depending on the underlying diseases present. A retrospective analysis of thirteen pulmonary ADEs showed significant differences associated with quinapril and trandolapril, compared to other ACEIs and ARBs. Specifically, quinapril and trandolapril were found to have a statistically significantly higher incidence of pulmonary ADEs compared with other ACEIs as well as ARBs (P < 0.0001) for group comparison (i.e., ACEIs vs. ARBs vs. quinapril vs. trandolapril) and (P ≤ 0.0007) for pairwise comparison (i.e., ACEIs vs. quinapril, ACEIs vs. trandolapril, ARBs vs. quinapril, or ARBs vs. trandolapril). This study suggests that specific members of the ACEI antihypertensive class (quinapril and trandolapril) have a significantly higher cluster of pulmonary ADEs.

Data mining methodology for response to hypertension symptomology-application to COVID-19-related pharmacovigilance.

Background: Potential therapy and confounding factors including typical co-administered medications, patient's disease states, disease prevalence, patient demographics, medical histories, and reasons for prescribing a drug often are incomplete, conflicting, missing, or uncharacterized in spontaneous adverse drug event (ADE) reporting systems. These missing or incomplete features can affect and limit the application of quantitative methods in pharmacovigilance for meta-analyses of data during randomized clinical trials. Methods: Data from patients with hypertension were retrieved and integrated from the FDA Adverse Event Reporting System; 134 antihypertensive drugs out of 1131 drugs were filtered and then evaluated using the empirical Bayes geometric mean (EBGM) of the posterior distribution to build ADE-drug profiles with an emphasis on the pulmonary ADEs. Afterward, the graphical least absolute shrinkage and selection operator (GLASSO) captured drug associations based on pulmonary ADEs by correcting hidden factors and confounder misclassification. Selected drugs were then compared using the Friedman test in drug classes and clusters obtained from GLASSO. Results: Following multiple filtering stages to exclude insignificant and noise-driven reports, we found that drugs from antihypertensives agents, urologicals, and antithrombotic agents (macitentan, bosentan, epoprostenol, selexipag, sildenafil, tadalafil, and beraprost) form a similar class with a significantly higher incidence of pulmonary ADEs. Macitentan and bosentan were associated with 64% and 56% of pulmonary ADEs, respectively. Because these two medications are prescribed in diseases affecting pulmonary function and may be likely to emerge among the highest reported pulmonary ADEs, in fact, they serve to validate the methods utilized here. Conversely, doxazosin and rilmenidine were found to have the least pulmonary ADEs in selected drugs from hypertension patients. Nifedipine and candesartan were also found by signal detection methods to form a drug cluster, shown by several studies an effective combination of these drugs on lowering blood pressure and appeared an improved side effect profile in comparison with single-agent monotherapy. Conclusions: We consider pulmonary ADE profiles in multiple long-standing groups of therapeutics including antihypertensive agents, antithrombotic agents, beta-blocking agents, calcium channel blockers, or agents acting on the renin-angiotensin system, in patients with hypertension associated with high risk for coronavirus disease 2019 (COVID-19). We found that several individual drugs have significant differences between their drug classes and compared to other drug classes. For instance, macitentan and bosentan from endothelin receptor antagonists show major concern while doxazosin and rilmenidine exhibited the least pulmonary ADEs compared to the outcomes of other drugs. Using techniques in this study, we assessed and confirmed the hypothesis that drugs from the same drug class could have very different pulmonary ADE profiles affecting outcomes in acute respiratory illness. Funding: GJW and MJD accepted funding from BioNexus KC for funding on this project, but BioNexus KC had no direct role in this article.

CrossSearch, a user-friendly search engine for detecting chemically cross-linked peptides in conjugated proteins.

Chemical cross-linking and high resolution MS have been integrated successfully to capture protein interactions and provide low resolution structural data for proteins that are refractive to analyses by NMR or crystallography. Despite the versatility of these combined techniques, the array of products that is generated from the cross-linking and proteolytic digestion of proteins is immense and generally requires the use of labeling strategies and/or data base search algorithms to distinguish actual cross-linked peptides from the many side products of cross-linking. Most strategies reported to date have focused on the analysis of small cross-linked protein complexes (<60 kDa) because the number of potential forms of covalently modified peptides increases dramatically with the number of peptides generated from the digestion of such complexes. We report herein the development of a user-friendly search engine, CrossSearch, that provides the foundation for an overarching strategy to detect cross-linked peptides from the digests of large (>or=170-kDa) cross-linked proteins, i.e. conjugates. Our strategy combines the use of a low excess of cross-linker, data base searching, and Fourier transform ion cyclotron resonance MS to experimentally minimize and theoretically cull the side products of cross-linking. Using this strategy, the (alpha beta gamma delta)(4) phosphorylase kinase model complex was cross-linked to form with high specificity a 170-kDa betagamma conjugate in which we identified residues involved in the intramolecular cross-linking of the 125-kDa beta subunit between its regulatory N terminus and its C terminus. This finding provides an explanation for previously published homodimeric two-hybrid interactions of the beta subunit and suggests a dynamic structural role for the regulatory N terminus of that subunit. The results offer proof of concept for the CrossSearch strategy for analyzing conjugates and are the first to reveal a tertiary structural element of either homologous alpha or beta regulatory subunit of phosphorylase kinase.

Extinction of chromosomes due to specialization is a universal occurrence.

The human X and Y chromosomes evolved from a pair of autosomes approximately 180 million years ago. Despite their shared evolutionary origin, extensive genetic decay has resulted in the human Y chromosome losing 97% of its ancestral genes while gene content and order remain highly conserved on the X chromosome. Five 'stratification' events, most likely inversions, reduced the Y chromosome's ability to recombine with the X chromosome across the majority of its length and subjected its genes to the erosive forces associated with reduced recombination. The remaining functional genes are ubiquitously expressed, functionally coherent, dosage-sensitive genes, or have evolved male-specific functionality. It is unknown, however, whether functional specialization is a degenerative phenomenon unique to sex chromosomes, or if it conveys a potential selective advantage aside from sexual antagonism. We examined the evolution of mammalian orthologs to determine if the selective forces that led to the degeneration of the Y chromosome are unique in the genome. The results of our study suggest these forces are not exclusive to the Y chromosome, and chromosomal degeneration may have occurred throughout our evolutionary history. The reduction of recombination could additionally result in rapid fixation through isolation of specialized functions resulting in a cost-benefit relationship during times of intense selective pressure.

Novel Data Sharing Agreement to Accelerate Big Data Translational Research Projects in the One Health Sphere.

When conducting translational research, the ability to share data generated by researchers and clinicians working with for-profit companies is essential, particularly in cases that involve "one health" data (i.e., data that could come from human, animal, or environmental sources). The 1DATA Project, a collaboration between Kansas State University and the University of Missouri, has examined and overcome some of the barriers to sharing this information for "big data" projects. This article discusses some of the obstacles we encountered, and the ways those obstacles can be surmounted via a novel form of Master Sharing Agreement. Developed in collaboration with industry partners, it is presented here as a template for expediting future one health work.

Making Sense of Pharmacovigilance and Drug Adverse Event Reporting: Comparative Similarity Association Analysis Using AI Machine Learning Algorithms in Dogs and Cats.

Drug-associated adverse events cause approximately 30 billion dollars a year of added health care expense, along with negative health outcomes including patient death. This constitutes a major public health concern. The US Food and Drug Administration (FDA) requires drug labeling to include potential adverse effects for each newly developed drug product. With the advancement in incidence of adverse drug events (ADEs) and potential adverse drug events, published studies have mainly concluded potential ADEs from labeling documents obtained from the FDA's preapproval clinical trials, and very few analyzed their research work based on reported ADEs after widespread use of a drug to animal subjects. The aforesaid procedure of deriving practice based on information from preapproval labeling may misrepresent or deprecate the incidence and prevalence of specific ADEs. In this study, we make the most of the recently disseminated ADE data by the FDA for animal drugs and devices used in animals to address this public and welfare concern. For this purpose, we implemented 5 different methods (Pearson distance, Spearman distance, cosine distance, Yule distance, and Euclidean distance) to determine the most efficient and robust approach to properly discover highly associated ADEs from the reported data and accurately exclude noise-induced reported events, while maintaining a high level of correlation precision. Our comparative analysis of ADEs based on an artificial intelligence (AI) approach for the 5 robust similarity methods revealed high ADE associations for 2 drugs used in dogs and cats. In addition, the described distance methods systematically analyzed and compared ADEs from the drug labeling sections with a specific emphasis on analyzing serious ADEs. Our finding showed that the cosine method significantly outperformed all the other methods by correctly detecting and validating ADEs based on the comparative similarity association analysis compared with ADEs reported by preapproval clinical trials, premarket testing, or postapproval complication experience of FDA-approved animal drugs.

Identification of Novel Regulatory Genes in APAP Induced Hepatocyte Toxicity by a Genome-Wide CRISPR-Cas9 Screen.

Acetaminophen (APAP) is a commonly used analgesic responsible for more than half of acute liver failure cases. Identification of previously unknown genetic risk factors would provide mechanistic insights and novel therapeutic targets for APAP-induced liver injury. This study used a genome-wide CRISPR-Cas9 screen to evaluate genes that are protective against, or cause susceptibility to, APAP-induced liver injury. HuH7 human hepatocellular carcinoma cells containing CRISPR-Cas9 gene knockouts were treated with 15 mM APAP for 30 minutes to 4 days. A gene expression profile was developed based on the 1) top screening hits, 2) overlap of expression data from APAP overdose studies, and 3) predicted affected biological pathways. We further demonstrated the implementation of intermediate time points for the identification of early and late response genes. This study illustrated the power of a genome-wide CRISPR-Cas9 screen to systematically identify novel genes involved in APAP-induced hepatotoxicity and to provide potential targets to develop novel therapeutic modalities.