Omics A Journal of Integrative Biology最新文献

The study of longevity and its determinants has been revitalized with the rise of microbiome scholarship. The gut microbiota have been established to play essential protective, metabolic, and physiological roles in human health and disease. The gut dysbiosis has been identified as an important factor contributing to the development of multiple diseases. Accordingly, it is reasonable to hypothesize that the gut microbiota of long-living individuals have healthy antiaging-associated gut microbes, which, by extension, might provide specific molecular targets for antiaging treatments and interventions. In the present study, we compared the gut microbiota of Chinese individuals in two different age groups, long-living adults (aged over 90 years) and elderly adults (aged 65-74 years) who were free of major diseases. We found significantly lower relative abundances of bacteria in the genera Sutterella and Megamonas in the long-living individuals. Furthermore, we established that while biological processes such as autophagy (GO:0006914) and telomere maintenance through semiconservative replication (GO:0032201) were enhanced in the long-living group, response to lipopolysaccharide (GO:0032496), nicotinamide adenine dinucleotide oxidation (GO:0006116), and S-adenosyl methionine metabolism (GO:0046500) were weakened. Moreover, the two groups were found to differ with respect to amino acid metabolism. We suggest that these compositional and functional differences in the gut microbiota may potentially be associated with mechanisms that contribute to determining longevity or aging.

Precision oncology promises individually tailored drugs and clinical care for patients with cancer: That is, "the right drug, for the right patient, at the right dose, and at the right time." Although stratification of the risk for treatment resistance and toxicity is key to precision oncology, there are multiple ways in which such stratification can be achieved, for example, genetic, functional pathway based, among others. Moving toward precision oncology is sorely needed in the case of acute lymphoblastic leukemia (ALL) wherein adult patients display survival rates ranging from 30% to 70%. The present study reports on the pathway activity signature of adult B-ALL, with an eye to precision oncology. Transcriptome profiles from three different expression datasets, comprising 346 patients who were adolescents or adults with B-ALL, were harnessed to determine the activity of signaling pathways commonly disrupted in B-ALL. Pathway activity analyses revealed that Ph-like ALL closely resembles Ph-positive ALL. Although this was the case at the average pathway activity level, the pathway activity patterns in B-ALL differ from genetic subtypes. Importantly, clustering analysis revealed that five distinct clusters exist in B-ALL patients based on pathway activity, with each cluster displaying a unique pattern of pathway activation. Identifying pathway-based subtypes thus appears to be crucial, considering the inherent heterogeneity among patients with the same genetic subtype. In conclusion, a pathway-based stratification of the B-ALL could potentially allow for simultaneously targeting highly active pathways within each ALL subtype, and thus might open up new avenues of innovation for personalized/precision medicine in this cancer that continues to have poor prognosis in adult patients compared with the children.

Digital health, an emerging scientific domain, attracts increasing attention as artificial intelligence and relevant software proliferate. Pharmacogenomics (PGx) is a core component of precision/personalized medicine driven by the overarching motto "the right drug, for the right patient, at the right dose, and the right time." PGx takes into consideration patients' genomic variations influencing drug efficacy and side effects. Despite its potentials for individually tailored therapeutics and improved clinical outcomes, adoption of PGx in clinical practice remains slow. We suggest that e-health tools such as clinical decision support systems (CDSSs) can help accelerate the PGx, precision/personalized medicine, and digital health emergence in everyday clinical practice worldwide. Herein, we present a systematic review that examines and maps the PGx-CDSSs used in clinical practice, including their salient features in both technical and clinical dimensions. Using Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines and research of the literature, 29 relevant journal articles were included in total, and 19 PGx-CDSSs were identified. In addition, we observed 10 technical components developed mostly as part of research initiatives, 7 of which could potentially facilitate future PGx-CDSSs implementation worldwide. Most of these initiatives are deployed in the United States, indicating a noticeable lack of, and the veritable need for, similar efforts globally, including Europe.

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.