Omics A Journal of Integrative Biology最新文献

Cyclin-dependent kinase 8 (CDK8) is highly expressed in various cancers and common complex human diseases, and an important therapeutic target for drug discovery and development. The CDK8 inhibitors are actively sought after, especially among natural products. We performed a virtual screening using the ZINC library comprising approximately 90,000 natural compounds. We applied Lipinski's rule of five, absorption, distribution, metabolism, excretion, and toxicity properties, and pan-assay interference compounds filter to eliminate promiscuous binders. Subsequently, the filtered compounds underwent molecular docking to predict their binding affinity and interactions with the CDK8 protein. Interaction analysis were carried out to elucidate the interaction mechanism of the screened hits with binding pockets of the CDK8. The ZINC02152165, ZINC04236005, and ZINC02134595 were selected with appreciable specificity and affinity with CDK8. An all-atom molecular dynamic (MD) simulation followed by essential dynamics was performed for 200 ns. Taken together, the results suggest that ZINC02152165, ZINC04236005, and ZINC02134595 can be harnessed as potential leads in therapeutic development. Moreover, the binding of the molecules brings change in protein conformation in a way that blocks the ATP-binding site of the protein, obstructing its kinase activity. These new findings from natural products offer insights into the molecular mechanisms underlying CDK8 inhibition. CDK8 was previously associated with behavioral and neurological diseases such as autism spectrum disorder, and cancers, for example, colorectal, prostate, breast, and acute myeloid leukemia. Hence, we call for further research and experimental validation, and with an eye to inform future clinical drug discovery and development in these therapeutic fields.

Lung adenocarcinoma (LUAD) is a significant planetary health challenge with its high morbidity and mortality rate, not to mention the marked interindividual variability in treatment outcomes and side effects. There is an urgent need for robust systems biomarkers that can help with early cancer diagnosis, prediction of treatment outcomes, and design of precision/personalized medicines for LUAD. The present study aimed at systems biomarkers of LUAD and deployed integrative bioinformatics and machine learning tools to harness gene expression data. Predictive models were developed to stratify patients based on prognostic outcomes. Importantly, we report here several potential key genes, for example, PMEL and BRIP1, and pathways implicated in the progression and prognosis of LUAD that could potentially be targeted for precision/personalized medicine in the future. Our drug repurposing analysis and molecular docking simulations suggested eight drug candidates for LUAD such as heat shock protein 90 inhibitors, cardiac glycosides, an antipsychotic agent (trifluoperazine), and a calcium ionophore (ionomycin). In summary, this study identifies several promising leads on systems biomarkers and drug candidates for LUAD. The findings also attest to the importance of integrative bioinformatics, structural biology and machine learning techniques in biomarker discovery, and precision oncology research and development.

Bottlenecks in moving genomics to real-life applications also include phenomics. This is true not only for genomics medicine and public health genomics but also in ecology and livestock phenomics. This expert narrative review explores the intricate relationship between genetic makeup and observable phenotypic traits across various biological levels in the context of livestock research. We unpack and emphasize the significance of precise phenotypic data in selective breeding outcomes and examine the multifaceted applications of phenomics, ranging from improvement to assessing welfare, reproductive traits, and environmental adaptation in livestock. As phenotypic traits exhibit strong correlations, their measurement alongside specific biological outcomes provides insights into performance, overall health, and clinical endpoints like morbidity and disease. In addition, automated assessment of livestock holds potential for monitoring the dynamic phenotypic traits across various species, facilitating a deeper comprehension of how they adapt to their environment and attendant stressors. A key challenge in genetic improvement in livestock is predicting individuals with optimal fitness without direct measurement. Temporal predictions from unmanned aerial systems can surpass genomic predictions, offering in-depth data on livestock. In the near future, digital phenotyping and digital biomarkers may further unravel the genetic intricacies of stress tolerance, adaptation and welfare aspects of animals enabling the selection of climate-resilient and productive livestock. This expert review thus delves into challenges associated with phenotyping and discusses technological advancements shaping the future of biological research concerning livestock.

In the field of bioinformatics, amplicon sequencing of 16S rRNA genes has long been used to investigate community membership and taxonomic abundance in microbiome studies. As we can observe, shotgun metagenomics has become the dominant method in this field. This is largely owing to advancements in sequencing technology, which now allow for random sequencing of the entire genetic content of a microbiome. Furthermore, this method allows profiling both genes and the microbiome's membership. Although these methods have provided extensive insights into various microbiomes, they solely assess the existence of organisms or genes, without determining their active role within the microbiome. Microbiome scholarship now includes metatranscriptomics to decipher how a community of microorganisms responds to changing environmental conditions over a period of time. Metagenomic studies identify the microbes that make up a community but metatranscriptomics explores the diversity of active genes within that community, understanding their expression profile and observing how these genes respond to changes in environmental conditions. This expert review article offers a critical examination of the computational metatranscriptomics tools for studying the transcriptomes of microbial communities. First, we unpack the reasons behind the need for community transcriptomics. Second, we explore the prospects and challenges of metatranscriptomic workflows, starting with isolation and sequencing of the RNA community, then moving on to bioinformatics approaches for quantifying RNA features, and statistical techniques for detecting differential expression in a community. Finally, we discuss strengths and shortcomings in relation to other microbiome analysis approaches, pipelines, use cases and limitations, and contextualize metatranscriptomics as a tool for clinical metagenomics.

There is a pressing need for novel pharmacological interventions and drug delivery innovations to attenuate the cigarette smoke-associated oxidative stress and lung disease. We report here on the attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR) and metabolomics of Wistar rats exposed to cigarette smoke for 28 days. The animals were treated for 15 days with plain cysteamine given orally or cysteamine as nanoemulsion given orally or via inhalation. The study design also included two control groups as follows: rats exposed to cigarette smoke but did not receive a treatment (diseased control group) and rats neither exposed to cigarette smoke nor a treatment (normal control group). The targeted metabolomics using Parallel Reaction Monitoring showed that in the diseased control group, ornithine, nicotinamide, xanthine, hypoxanthine, and caprolactam were increased compared with the normal control group. In addition, (±)8(9)-DiHET, which was initially downregulated in the diseased control group, exhibited a reversal of this trend with cysteamine nanoemulsion given via inhalation. The cysteamine nanoemulsion delivered by inhalation highlighted the importance of the route of drug administration for targeting the lungs. To the best of our knowledge, this is the first work to use ATR-FTIR and metabolomics in Wistar rat lung tissues, suggesting how cysteamine nanoemulsion can potentially reduce cigarette smoke-induced oxidative damage. The metabolites reported herein have potential implications for discovery of novel theranostics and, thus, to cultivate diagnostic and therapeutic innovation for early prevention and treatment of cigarette smoke-associated lung diseases.

Large investments over many decades in genomics in diverse fields such as precision medicine, plant biology, and recently, in space life science research and astronaut omics were not accompanied by a commensurate focus on high-throughput and granular characterization of phenotypes, thus resulting in a "phenomics lag" in systems science. There are also limits to what can be achieved through increases in sample sizes in genotype-phenotype association studies without commensurate advances in phenomics. These challenges beg a question. What might next-generation phenomics look like, given that the Internet of Things and artificial intelligence offer prospects and challenges for high-throughput digital phenotyping as a key component of next-generation phenomics? While attempting to answer this question, I also reflect on governance of digital technology and next-generation phenomics. I argue that it is timely to broaden the technical discourses through a lens of political theory. In this context, this analysis briefly engages with the recent book "The Earthly Community: Reflections on the Last Utopia," written by the historian and political theorist Achille Mbembe. The question posed by the book, "Will we be able to invent different modes of measuring that might open up the possibility of a different aesthetics, a different politics of inhabiting the Earth, of repairing and sharing the planet?" is directly relevant to healing of human diseases in ways that are cognizant of the interdependency of human and nonhuman animal health, and critical and historically informed governance of digital technologies that promise to benefit next-generation phenomics.

Systems biology and multiomics research expand the prospects of planetary health innovations. In this context, this mini-review unpacks the twin scholarships of glycomedicine and precision medicine in the current era of single-cell multiomics. A significant growth in glycan research has been observed over the past decade, unveiling and establishing co- and post-translational modifications as dynamic indicators of both pathological and physiological conditions. Systems biology technologies have enabled large-scale and high-throughput glycoprofiling and access to data-intensive biological repositories for global research. These advancements have established glycans as a pivotal third code of life, alongside nucleic acids and amino acids. However, challenges persist, particularly in the simultaneous analysis of the glycome and transcriptome in single cells owing to technical limitations. In addition, holistic views of the complex molecular interactions between glycomics and other omics types remain elusive. We underscore and call for a paradigm shift toward the exploration of integrative glycan platforms and analysis methods for single-cell multiomics research and precision medicine biomarker discovery. The integration of multiple datasets from various single-cell omics levels represents a crucial application of systems biology in understanding complex cellular processes and is essential for advancing the twin scholarships of glycomedicine and precision medicine.

Imatinib (IM), a breakthrough in chronic myeloid leukemia (CML) treatment, is accompanied by discontinuation challenges owing to drug intolerance. Although BCR-ABL1 mutation is a key cause of CML resistance, understanding mechanisms independent of BCR-ABL1 is also important. This study investigated the sphingosine-1-phosphate (S1P) signaling-associated genes (SphK1 and S1PRs) and their role in BCR-ABL1-independent resistant CML, an area currently lacking investigation. Through comprehensive transcriptomic analysis of IM-sensitive and IM-resistant CML groups, we identified the differentially expressed genes and found a notable upregulation of SphK1, S1PR2, and S1PR5 in IM-resistant CML. Functional annotation revealed their roles in critical cellular processes such as proliferation and GPCR activity. Their network analysis uncovered significant clusters, emphasizing the interconnectedness of the S1P signaling genes. Further, we identified interactors such as BIRC3, TRAF6, and SRC genes, with potential implications for IM resistance. Additionally, receiver operator characteristic curve analysis suggested these genes' potential as biomarkers for predicting IM resistance. Network pharmacology analysis identified six herbal compounds-ampelopsin, ellagic acid, colchicine, epigallocatechin-3-gallate, cucurbitacin B, and evodin-as potential drug candidates targeting the S1P signaling genes. In summary, this study contributes to efforts to better understand the molecular mechanisms underlying BCR-ABL1-independent CML resistance. Moreover, the S1P signaling genes are promising therapeutic targets and plausible new innovation avenues to combat IM resistance in cancer clinical care in the future.

Congenital heart defects (CHDs) are most prevalent cardiac defects that occur at birth, leading to significant neonatal mortality and morbidity, especially in the developing nations. Among the CHDs, conotruncal heart defects (CTDs) are particularly noteworthy, comprising a significant portion of congenital cardiac anomalies. While advances in imaging and surgical techniques have improved the diagnosis, prognosis, and management of CTDs, their molecular genetics and genomic substrates remain incompletely understood. This expert review covers the recent advances from January 2016 onward and examines the complexities surrounding the genetic etiologies, prevalence, embryology, diagnosis, and clinical management of CTDs. We also emphasize the known copy number variants and single nucleotide variants associated with CTDs, along with the current planetary health research efforts aimed at CTDs in large cohort studies. In all, this comprehensive narrative review of molecular genetics and genomics research and innovation on CTDs draws from and highlights selected works from around the world and offers new ideas for advances in CTD diagnosis, precision medicine interventions, and accurate assessment of prognosis and recurrence risks.