Omics A Journal of Integrative Biology最新文献

The coronavirus disease 2019 (COVID-19) pandemic has wreaked havoc globally. Beyond the pandemic, the long-term effects of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus in multiple organ systems are yet to be deciphered. This calls for continued systems science research. Moreover, the host response to SARS-CoV-2 varies person-to-person and gives rise to different degrees of morbidity and mortality. Mass spectrometry (MS) has been a proven asset in studies of the SARS-CoV-2 from an omics systems science lens. To strengthen the proteomics research dedicated to COVID-19, we introduce here a web-based portal, CoVProt. The portal is work in progress and aims for a comprehensive curation of MS-based proteomics data of COVID-19 clinical samples for deep proteomic investigations, data visualization, and easy data accessibility for life sciences innovations and planetary health research community. Currently, CoVProt contains information on 2725 different proteins and 37,125 different peptides from six data sets covering a total of 202 clinical samples. Moreover, all pertinent data sets extracted from the literature have been reanalyzed using a common analysis pipeline developed by combining multiple tools. Going forward, we anticipate that the CoVProt portal will also provide access to the clinical parameters of the patients. The CoVProt (v1.0) portal addresses an existing significant gap to study COVID-19 host proteomics, which, to the best of our knowledge, is the first effort in this direction. We believe that CoVProt is poised to make contributions as a community resource for proteomic applications and aims to broadly support clinical studies to facilitate the discovery of COVID-19 biomarkers and therapeutics with translational potential.

Pharmacomicrobiomics is a rapidly developing field that promises to make significant contributions to predictive, personalized, preventive, and participatory (P4) medicine. This is becoming evident particularly in the field of precision (P4) oncology by taking seriously the crucial role microbiome plays in health and disease. Several studies have already shown that clinicians can harness insights from the microbiome to better predict treatment response, reduce side effects, and improve overall outcomes for cancer patients. Furthermore, pharmacomicrobiomics will undoubtedly play a crucial role in shaping the future of cancer treatment in the era of P4 oncology as we continue to unravel the intricate relationships between the microbiome and cancer. This perspective and innovation analysis discusses the emerging intersection of P4 medicine and P4 oncology, as seen through a lens of pharmacomicrobiomics. A key promise of pharmacomicrobiomics is the development of personalized microbiome-based therapeutics. In all, we suggest that optimizing cancer treatment and prevention by harnessing pharmacomicrobiomics has vast potentials for precision oncology, and personalized medicine using the right drug, at the right dose, for the right patient, and at the right time.

One Health (OH) offers conceptual and applied prospects to advance planetary health and integrative biology in the 21st century. For example, The World Health Organization (WHO) has declared antimicrobial resistance (AMR) one of humanity's top 10 health threats worldwide (AMR). The AMR research, as seen through the OH lens, recognizes the interdependence and the coproduction of the health of humans, nonhuman animals, and the environment (the OH triad). Moreover, research and development (R&D) is required to generate potential solutions to prevent, diagnose, and treat infections and control the spread and emergence of AMR. However, it is still unclear how well the OH approach is integrated into current AMR R&D. In this study, we present a systematic review on the OH funding landscape for cross-sectoral AMR R&D, and its alignment/gaps with the current global strategic agenda on AMR. A systematic literature review was conducted using public databases covering the period between January 2015 and May 2022. We included the studies and reviews on AMR encompassing more than one sector of the OH triad. Out of the 777 included studies, 475 (61%) encompassed the three OH sectors. A key finding of the present systematic review is that the environment was the most neglected sector in the OH triad. AMR surveillance, transmission, and interventions are the most commonly studied priority topics. In addition, both cross-sectoral AMR literature and investments have been increasing since 2017. The operational aspect of AMR is the most researched and funded area. However, certain priority topics in the strategic research and innovation agenda of the Joint Programming Initiative on AMR are underrepresented in OH AMR research, such as diagnosis and therapeutics. To the best of authors' knowledge, this is the first study that systematically reviews the cross-sectoral literature on AMR, classifies it, and aligns and contextualizes it in regard to the funding landscape of AMR. This systematic review identifies neglected areas in AMR R&D and could serve as critical information for policymaking so as to realize the objectives of the Global Action Plan on AMR. Going forward, more cross-sectoral AMR research and funding are needed. As integrative biology and omics systems science are poised to benefit from a rapprochement with the OH lens, the present article highlights the AMR research and funding landscapes.

Hepatitis B virus (HBV) is an enveloped, hepatotropic, noncytopathic virus with a partially double-stranded DNA genome. It infects hepatocytes and is associated with progression to liver fibrosis and cirrhosis, culminating in hepatocellular carcinoma (HCC), accounting for 55% of total HCC cases. MicroRNAs (miRNAs) regulated by HBV play an important role in these pathologies. Mapping the miRNAs responsive to HBV and HBV-specific proteins, including HBV X protein (HBx) that harbor the majority of HBV-human protein interactions, could aid accelerate the diagnostics and therapeutics innovation against the infection and associated diseases. With this in mind, we used a unique annotation strategy whereby we first amassed 362 mature HBV responsive-human Differentially Expressed miRNAs (HBV-hDEmiRs). The core experimentally-validated messenger RNA targets of the HBV-hDEmiRs were mostly associated with viral infections and hepatic inflammation processes. Moreover, our annotation strategy enabled the characterization of HBx-dependent/independent HBV-hDEmiRs as a tool for evaluation of the impact of HBx as a therapeutic target. Bioinformatics analysis of the HBV-human protein-protein interactome revealed new insights into the transcriptional regulatory network of the HBV-hDEmiRs. We performed a comparative analysis of data on miRNAs gathered from HBV infected cell line studies and from tissue studies of fibrosis, cirrhosis, and HCC. Accordingly, we propose hsa-miR-15a-5p that is downregulated by multiple HBV proteins, including HBx, as a potential biomarker of HBV infection, and its progression to HCC. In all, this study underscores (1) the complexity of miRNA regulation in response to HBV infection and its progression into other liver pathologies and (2) provides a regulatory map of HBV-hDEmiRs and the underlying mechanisms modulating their expression through a cross talk between HBV viral proteins and human transcription factors.

With climate emergency, COVID-19, and the rise of planetary health scholarship, the binary of human and ecosystem health has been deeply challenged. The interdependence of human and nonhuman animal health is increasingly acknowledged and paving the way for new frontiers in integrative biology. The convergence of genomics in health, bioinformatics, agriculture, and artificial intelligence (AI) has ushered in a new era of possibilities and applications. However, the sheer volume of genomic/multiomics big data generated also presents formidable sociotechnical challenges in extracting meaningful biological, planetary health and ecological insights. Over the past few years, AI-guided bioinformatics has emerged as a powerful tool for managing, analyzing, and interpreting complex biological datasets. The advances in AI, particularly in machine learning and deep learning, have been transforming the fields of genomics, planetary health, and agriculture. This article aims to unpack and explore the formidable range of possibilities and challenges that result from such transdisciplinary integration, and emphasizes its radically transformative potential for human and ecosystem health. The integration of these disciplines is also driving significant advancements in precision medicine and personalized health care. This presents an unprecedented opportunity to deepen our understanding of complex biological systems and advance the well-being of all life in planetary ecosystems. Notwithstanding in mind its sociotechnical, ethical, and critical policy challenges, the integration of genomics, multiomics, planetary health, and agriculture with AI-guided bioinformatics opens up vast opportunities for transnational collaborative efforts, data sharing, analysis, valorization, and interdisciplinary innovations in life sciences and integrative biology.

The past few years have seen significant advances in the study of complex microbial communities associated with the evolution of sequencing technologies and increasing adoption of whole genome shotgun sequencing methods over the once more traditional Amplicon-based methods. Although these advances have broadened the horizon of meta-omic analyses in planetary health, human health, and ecology from simple sample composition studies to comprehensive taxonomic and metabolic profiles, there are still significant challenges in processing these data. First, there is a widespread lack of standardization in data processing, including software choices and the ease of installing and running attendant software. This can lead to several inconsistencies, making comparing results across studies and reproducing original results difficult. We argue that these drawbacks are especially evident in metatranscriptomic analysis, with most analyses relying on ad hoc scripts instead of pipelines implemented in workflow managers. Additional challenges rely on integrating meta-omic data, since methods have to consider the biases in the library preparation and sequencing methods and the technical noise that can arise from it. Here, we critically discuss the current limitations in metagenomics and metatranscriptomics methods with a view to catalyze future innovations in the field of Planetary Health, ecology, and allied fields of life sciences. We highlight possible solutions for these constraints to bring about more standardization, with ease of installation, high performance, and reproducibility as guiding principles.

High-grade gliomas (HGGs) are among the most aggressive brain tumors and are characterized by dismally low median survival time. Of the many factors influencing the survival of patients with HGGs, proximity to the subventricular zone (SVZ) is one of the key influencers. In this context, 5-amino levulinic acid fluorescence-guided multiple sampling (FGMS) offers the prospect of understanding patient-to-patient molecular heterogeneity driving the aggressiveness of these tumors. Using high-resolution liquid chromatography-mass spectrometry (MS)/MS proteomics for HGGs from seven patients (four SVZ associated and three SVZ nonassociated), this study aimed to uncover the mechanisms driving the aggressiveness in SVZ-associated (SVZ+) HGGs. Differential proteomics analysis revealed significant dysregulation of 11 proteins, of which 9 proteins were upregulated and 2 were downregulated in SVZ+ HGGs compared to SVZ-non-associated (SVZ-) HGGs. The gene set enrichment analysis (GSEA) of the proteomics dataset revealed enrichment of MYC targets V1 and V2, G2M checkpoints, and E2F targets in SVZ+ HGGs. With GSEA, we also compared the pathways enriched in glioma stem cell subpopulations and observed a similar expression trend for most pathways in our data. In conclusion, this study reveals new and emerging insights on pathways that may potentially contribute to greater aggressiveness in SVZ+ HGGs. Future studies using FGMS in larger cohorts are recommended to help uncover the proteomics and molecular basis of aggressiveness and stemness in HGGs.

This article explores the progressive integration of -omics methods, including genomics, metabolomics, and proteomics, into sports research, highlighting the development of the concept of "sportomics." We discuss how sportomics can be used to comprehend the multilevel metabolism during exercise in real-life conditions faced by athletes, enabling potential personalized interventions to improve performance and recovery and reduce injuries, all with a minimally invasive approach and reduced time. Sportomics may also support highly personalized investigations, including the implementation of n-of-1 clinical trials and the curation of extensive datasets through long-term follow-up of athletes, enabling tailored interventions for athletes based on their unique physiological responses to different conditions. Beyond its immediate sport-related applications, we delve into the potential of utilizing the sportomics approach to translate Big Data regarding top-level athletes into studying different human diseases, especially with nontargeted analysis. Furthermore, we present how the amalgamation of bioinformatics, artificial intelligence, and integrative computational analysis aids in investigating biochemical pathways, and facilitates the search for various biomarkers. We also highlight how sportomics can offer relevant information about doping control analysis. Overall, sportomics offers a comprehensive approach providing novel insights into human metabolism during metabolic stress, leveraging cutting-edge systems science techniques and technologies.

Circular RNAs (circRNAs) are of relevance to regenerative medicine and play crucial roles in post-transcriptional and translational regulation of biological processes. circRNAs are a class of RNA molecules that are formed through a unique splicing process, resulting in a covalently closed-loop structure. Recent advancements in RNA sequencing technologies and specialized computational tools have facilitated the identification and functional characterization of circRNAs. These molecules are known to exhibit stability, developmental regulation, and specific expression patterns in different tissues and cell types across various organisms. However, our understanding of circRNA expression and putative function in model organisms for regeneration is limited. In this context, this study reports, for the first time, on the repertoire of circRNAs in axolotl, a widely used model organism for regeneration. We generated RNA-seq data from intact limb, wound, and blastema tissues of axolotl during limb regeneration. The analysis revealed the presence of 35,956 putative axolotl circRNAs, among which 5331 unique circRNAs exhibited orthology with human circRNAs. In silico data analysis underlined the potential roles of axolotl circRNAs in cell cycle, cell death, and cell senescence-related pathways during limb regeneration, suggesting the participation of circRNAs in regulation of diverse functions pertinent to regenerative medicine. These new observations help advance our understanding of the dynamic landscape of axolotl circRNAs, and by extension, inform future regenerative medicine research and innovation that harness this model organism.

Cancer research calls for new approaches that account for the regulatory complexities of biology. We present, in this study, the differential transcriptional regulome (DIFFREG) approach for the identification and prioritization of key transcriptional regulators and apply it to the case of renal cell carcinoma (RCC) biology. Of note, RCC has a poor prognosis and the biomarker and drug discovery studies to date have tended to focus on gene expression independent from mutations and/or post-translational modifications. DIFFREG focuses on the differential regulation between transcription factors (TFs) and their target genes rather than differential gene expression and integrates transcriptome profiling with the human transcriptional regulatory network to analyze differential gene regulation between healthy and RCC cases. In this study, RNA-seq tissue samples (n = 1020) from the Cancer Genome Atlas (TCGA), including healthy and tumor subjects, were integrated with a comprehensive human TF-gene interactome dataset (1122603 interactions between 1289 TFs and 25177 genes). Comparative analysis of DIFFREG profiles, consisting of perturbed TF-gene interactions, from three common subtypes (clear cell RCC, papillary RCC and chromophobe RCC) revealed subtype-specific alterations, supporting the hypothesis that these signatures in the transcriptional regulome profiles may be considered potential biomarkers that may play an important role in elucidating the molecular mechanisms of RCC development and translating knowledge about the genetic basis of RCC into the clinic. In addition, these indicators may help oncologists make the best decisions for diagnosis and prognosis management.