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Novel drug discovery and molecular biological methods, via DNA, RNA and protein changes using structure-function transitions: Transitional structural chemogenomics, transitional structural chemoproteomics and novel multi-stranded nucleic acid microarray.

Nucleic acids and proteins are dynamic molecules that undergo structural changes which control gene expression. The authors have developed two novel techniques, viz., transitional structural chemogenomics and transitional structural chemoproteomics. Transitional structural chemogenomics is used to regulate gene expression, employing ultrasensitive small-molecule drugs targeted toward nucleic acids. Gene expression can be regulated by using chemicals to target transitional changes in the helical conformations of single-stranded (ss-) and double-stranded (ds-) DNA (e.g., B- to Z-DNA), and RNA (e.g., A- to Z-RNA). This method also targets alternative types of ds- and ss-DNA and RNA (e.g., cruciform DNA), and other multi-stranded nucleic acids (e.g., triplex-DNA). Our second technique, transitional structural chemoproteomics, targets a protein before, during or after post-translational modifications which alters its structure and function. Both a proteins' structured and unstructured regions are targeted. These two novel methods represent the next step in the evolution of chemical genomics and chemical proteomics. They allow for two approaches to regulate gene expression, viz., turning genes "on", "off" or variable control (e.g., dimmer switch). This article also discusses the confusion that exists between the term chemical genomics and other related subdisciplines, such as chemical proteomics. Additionally, we have developed a novel multi-stranded DNA, RNA and plasmid microarray which immobilizes intact nondenatured ds-DNA, alternative, and other multiple-stranded nucleic acids onto a substrate surface. This technique represents the next generation of nucleic acid microarrays, which will enhance the characterization of nucleic acids and the drug discovery process. These three novel techniques allow for a multifaceted approach that will greatly enhance the success of molecular biology, the "omics" and drug discovery. They represent the next era of gene expression tools.

Base Sequence↗

Confirmation of the expression of a large set of conserved hypothetical proteins in Shewanella oneidensis MR-1.

High-throughput "omic" technologies have allowed for a relatively rapid, yet comprehensive analysis of the global expression patterns within an organism in response to perturbations. In the current study, 9503 different tryptic peptides were identified with high confidence from capillary liquid chromatography-mass spectrometry analysis of 26 chemostat cultures of Shewanella oneidensis MR-1 under various conditions. Using at least one distinctive and a total of two total peptide identifications per protein, we detected the expression of 758 conserved hypothetical proteins. This included 359 such proteins previously described [Kolker, E., Picone, A.F., Galperin, M.Y., Romine, M.F., Higdon, R., Makarova, K.S., Kolker, N., Anderson, G.A., Qiu, X., Auberry, K.J., Babnigg, G., Beliaev, A.S., Edlefsen, P., Elias, D.A., Gorby, Y.A., Holzman, T., Klappenbach, J.A., Konstantinidis, K.T., Land, M.L., Lipton, M.S., McCue, L.A., Monroe, M., Pasa-Tolic, L., Pinchuk, G., Purvine, S., Serres, M.H., Tsapin, S., Zakrajsek, B.A., Zhu, W., Zhou, J., Larimer, F.W., Lawrence, C.E., Riley, M., Collart, F.R., Yates, J.R., III, Smith, R.D., Giometti, C.S., Nealson, K.H., Fredrickson, J.K., Tiedje, J.M., 2005. Global profiling of Shewanella oneidensis MR-1: expression of hypothetical genes and improved functional annotations. Proc Natl Acad Sci U S A 102, 2099-2104] with an additional 399 reported herein for the first time. The latter 399 proteins ranged from 5.3 to 208.3 kDa, with 44 being of 100 amino acid residues or less. Using a combination of information including peptide detection in cells grown under specific culture conditions and predictive algorithms such as PSORT and PSORT-B, possible/plausible functions are proposed for some conserved hypothetical proteins. Such proteins were found not only to be expressed, but 19 were only expressed under certain culturing conditions, thereby providing insight into potential functions. These findings also impact the genomic annotation for S. oneidensis MR-1 by confirming that these genes code for expressed proteins. Our results indicate that 399 proteins can now be upgraded from "conserved hypothetical protein" to "expressed protein in Shewanella," 19 of which appeared to be expressed under specific culture conditions.

Bacterial Proteins↗

Frontiers in nutrigenomics, proteomics, metabolomics and cancer prevention.

While dietary habits continue to surface as a significant factor that may influence cancer incidence and tumor behavior, there is considerable scientific uncertainty about who will benefit most. Adequate [corrected] knowledge about how the responses depend on an individual's genetic background (nutrigenetic effects), the cumulative effects of food components on genetic expression profiles (nutritional transcriptomics and nutritional epigenomics effects), the occurrence and activity of proteins (proteomic effects) and/or the dose and temporal changes in cellular small molecular weight compounds (metabolomics effects) will [corrected] assist in identifying responders and non-responders. Expanding the information about similarities and differences in the "omic" responses across tissues will not only provide clues about specificity in response to bioactive food components but assist in the identification of surrogate tissues and biomarkers that can be used for predicting a response. Deciphering the importance of each of these potential sites of regulation will be particularly challenging but does hold promise in explaining many of the inconsistencies in the literature.

Anticarcinogenic Agents↗

Prostate cancer and the genomic revolution: Advances using microarray analyses.

The emerging technology of microarray analysis allows the establishment of molecular portraits of prostate cancer and the discovery of novel genes involved in the carcinogenesis process. Many novel genes have already been identified using this technique, and functional analyses of these genes are currently being tested. The combination of microarray analysis with other recently developed high-throughput techniques, such as proteomics, tissue arrays, and gene promoter-methylation, especially using tissue microdissection methods, will provide us with more comprehensive insights into how prostate cancer develops and responds to gene-targeted therapies. Animal models of prostate cancer are being characterized by high throughput techniques to better define the similarities and differences between those models and the human disease, and to determine whether particular models may be useful for specific targeted therapies in pre-clinical studies. Although profiling of mRNA expression provides important information of gene expression, the development of proteomic technologies will allow for an even more precise global insight into cellular signaling and structural alterations during prostate carcinogenesis. Not only will the "omic" revolution change basic science, but it will lead to a new era of molecular medicine.

Animals↗

Construction of precision clinical-proteomics risk model based on machine learning for predicting heart failure in type II diabetes mellitus.

BACKGROUND AND AIMS: Heart failure (HF) is a severe complication in type 2 diabetes mellitus (T2DM), but current risk stratification scores have limited predictive accuracy. We aimed to develop novel prediction tools integrating clinical variables with proteomics to improve risk stratification of hospitalization for HF in T2DM. METHODS AND RESULTS: In this study, we included 2111 UK Biobank participants with T2DM but no prior HF, and profiled 2920 proteins to predict 10-year incident HF hospitalization. Participants were randomly divided into training (70%), tuning (10%), and validation (20%) sets.Three prediction models were developed: a Clinical model based on demographic characteristics, comorbidities, medication use, and laboratory indices; a Protein model based on 40 proteins selected by the Light Gradient Boosting Machine (LGBM); and the Clinical OMics and Protein ASSessment for Heart Failure (COMPASS-HF) model, which integrated both clinical variables and the LGBM-selected proteins. Models were evaluated for area under the curve (AUC), sensitivity, and specificity. During follow-up, 168 participants (7.96%) developed incident HF. The COMPASS-HF model showed better discrimination than the Clinical model, with an AUC of 0.897 (95% CI: 0.850-0.945) versus 0.790 (95% CI: 0.723-0.856). It also demonstrated higher sensitivity (0.882; 95% CI: 0.725-0.967) and consistent performance in subgroups. COMPASS-HF effectively stratified risk of hospitalization for HF, with cumulative incidence rates of 31.9% in the high-risk group and 1.2% in the low-risk group. CONCLUSIONS: By combining clinical and proteomic variables, we developed a high-performance HF prediction model for T2DM, enabling precise risk stratification and informing early intervention strategies.

Humans↗

Potential contribution of the microbiota-gut-brain axis to doxorubicin-associated cognitive impairment: Mechanisms, evidence, and therapeutic opportunities.

Chemotherapy-induced cognitive impairment (CICI), often termed chemobrain, is a clinically important complication of cancer treatment that can affect memory, attention, executive function, and processing speed during and after therapy. Doxorubicin is of particular mechanistic interest because brain parenchymal exposure is limited, yet preclinical studies consistently identify neuroinflammatory, oxidative, vascular, and synaptic abnormalities after treatment. This critical narrative review evaluates whether intestinal injury and disruption of the microbiota-gut-brain axis may contribute to these central effects. Preclinical evidence indicates that doxorubicin can alter microbial community structure, injure the intestinal barrier, modify SCFA-associated taxa or predicted functions, alter selected metabolite profiles, and promote systemic inflammatory and metabolic signaling. These peripheral changes could interact with brain endothelial cells, glia, mitochondria, hippocampal neurogenesis, and synaptic-plasticity pathways. However, the proposed doxorubicin-gut-brain pathway remains a predominantly preclinical and incompletely tested framework. No longitudinal human study has yet established, within the same patients, the temporal sequence linking doxorubicin exposure, microbiome or metabolome changes, systemic inflammation, and objective cognitive outcomes. Existing animal studies also vary in dose, regimen, tumor context, sampling time, microbiome methodology, and control of behavioral or microbiological confounders, while causal rescue experiments remain limited. Key priorities are therefore longitudinal human cohorts with pretreatment baselines and repeated multi-omics and cognitive assessments; animal studies that test temporal precedence and causal rescue or pathway blockade in the same model; mediation analyses that determine whether microbial or metabolic changes lie between treatment and cognitive dysfunction; and mechanism-informed clinical trials that demonstrate target engagement, cognitive benefit, oncology safety, and preservation of antitumor efficacy. Microbiome-directed interventions are promising but remain investigational for doxorubicin-associated CICI.

blood–brain barrier↗

The chemical landscape of plant surface metabolites: Acylsugars as models of ecological function and structural diversity.

Plants produce a multifunctional assortment of specialized metabolites that play important roles in defense, environmental adaptation, and ecological interactions. Among these compounds, acylsugars, nonvolatile metabolites produced primarily in glandular trichomes of Solanaceae species, have emerged as informative model systems for understanding plant surface chemistry. Differences in acyl chain length, branching pattern, saturation, and attachment position generate extensive chemical diversity that influences herbivore deterrence, pathogen resistance, and the physicochemical properties of leaf surfaces. Recent advances in analytical chemistry, particularly liquid chromatography-ion mobility-tandem mass spectrometry (LC-IM-MS/MS), have greatly improved the ability to separate structurally related acylsugar isomers and characterize metabolite complexity at high resolution. When integrated with genomics, transcriptomics, and emerging spatial metabolomics approaches, these analytical tools provide new insights into acylsugar biosynthesis, pathway regulation, evolutionary diversification, and ecological function across plant species. This review positions acylsugars, particularly those of Solanum species, as model systems for understanding how structural diversity, spatial localization, and specialized metabolism shape ecological and physiological function at plant surfaces. We examine acylsugar structural diversity, biosynthetic pathways, ecological and physiological functions, and interactions with environmental and atmospheric processes. Major challenges, including extensive isomeric complexity, incomplete pathway characterization, and difficulties linking chemical structure to biological function, are discussed alongside emerging opportunities in integrative omics, crop improvement, sustainable pest management, and environmental monitoring. Overall, acylsugars provide a powerful model for linking molecular structure, spatial localization, and ecological function, offering broader insight into how specialized metabolism shapes plant adaptation, defense, and environmental interactions.

Acylsugars↗

Shared genetic architecture between major depression and intrinsic brain functional connectome organization.

BACKGROUND: Major depression (MD) is increasingly understood as a disorder characterized by widespread abnormalities in intrinsic brain functional network organization. Although both MD and brain functional connectome architecture are highly heritable, the genetic architecture underlying their relationship remains poorly characterized. METHODS: We integrated genome-wide association studies of MD with 191 ICA-based resting-state functional connectome traits to investigate their shared genetic architecture. These traits captured intrinsic connectome organization across amplitude, functional connectivity, and global connectivity domains. Cross-trait genetic analyses were used to assess pleiotropic overlap between traits. Locus-level and gene-based analyses integrating multi-omics evidence were performed to characterize the biological relevance of shared genetic signals. RESULTS: We identified significant genetic overlap between MD and 148 of 191 brain functional connectome traits. Cross-trait analyses revealed widespread shared genetic signals organized into 627 genomic loci across amplitude, functional connectivity, and global connectivity measures. Among these, 193 loci showed evidence consistent with shared causal variants based on colocalization analyses. Gene-level integration mapped these loci to 1459 protein-coding genes (390 unique genes). Multi-layer prioritization identified 17 high-confidence genes supported by convergent genomic, transcriptomic, and proteomic evidence, with enrichment in neurodevelopmental and lipid-related metabolism pathways. CONCLUSIONS: This study provides a multi-scale characterization of the shared genetic architecture between MD and intrinsic brain functional connectome organization, revealing that shared genetic signals between MD and brain functional systems are distributed across multiple functional levels and converge at the molecular level.

Connectome↗

Targeted lipidomics and drug abuse research.

Contemporaneously with genomics and proteomics technologies, lipidomics may be recognized as the next important emerging technology. The emergence of this important area of "omics" could very well mark the beginning of "the decade of the lipids." A workshop on lipidomics was held in Washington, DC, by the National Institute on Drug Abuse (NIDA) in April 2004. The goal of the workshop was to bring together scientists working at the frontier of lipid research, to discuss their findings in this area and to promote "lipidomics," in general, but also with a special focus on its application to drug abuse research and development of therapies to treat addiction.

Animals↗

Integrative WGBS and ATAC-seq profiling reveals epigenetic and chromatin accessibility signatures associated with clutch length in goose ovaries.

Clutch length is an important reproductive trait in geese, but its epigenetic basis remains poorly characterized. Daily egg production was recorded for 280 individually housed Zi geese, and clutch-related indices were calculated as described in our previous study. Based on these records, six geese with contrasting clutch-length phenotypes were selected and assigned to the long-clutch (LC) and short-clutch (SC) groups. Ovarian tissues from three geese per group were subjected to whole-genome bisulfite sequencing (WGBS) and assay for transposase-accessible chromatin using sequencing (ATAC-seq) to identify candidate epigenetic signatures associated with clutch length. WGBS identified 630,909 differentially methylated regions (DMRs), whereas ATAC-seq identified 902 differentially accessible regions (DARs). Integrated analysis revealed distinct patterns of ovarian DNA methylation and chromatin accessibility between the two groups, suggesting that clutch length variation may be accompanied by epigenomic differences in ovarian tissue. Genes associated with DMRs and/or DARs were enriched in biological processes related to granulosa cell differentiation and endocrine competence, follicular fate regulation, and periovulatory cytoskeletal and signaling remodeling. RERE was prioritized as a candidate locus because it was supported by changes in both DNA methylation and chromatin accessibility, whereas FOXL2, STAR, BAK1, FGF17, PRSS35, ACTR3, and AXIN1 were supported mainly by evidence from a single omics layer. RT-qPCR analysis of selected genes showed expression trends broadly consistent with the corresponding epigenomic differences, providing additional supportive evidence for these candidate associations. Collectively, this study provides an exploratory ovarian epigenomic resource and identifies candidate epigenetic signatures, genes, and biological processes associated with clutch length variation in geese.

DNA methylation↗

Demonstrating the potential of untargeted hair proteomics for personalized biomarkers in stress-associated disorders.

Biomarker research in psychopathology increasingly employs high-dimensional Omics approaches. Yet, proteomics based on human hair remain largely unexplored, despite its potential to efficiently capture stable biological signals accumulated over weeks to months. This study leveraged machine learning to investigate the potential of the hair proteome-all detectable peptides and proteins-as a biomarker source for stress-associated psychopathology. We analyzed protein profiles from hair segments of women with non-suicidal self-injury disorder and healthy controls (N&#x202f;=&#x202f;68). Of 1114 identified proteins, 611 were sufficiently abundant for analyses. Partial Least Squares Discriminant Analysis achieved stable 84.4&#xa0;% cross-validated accuracy for classification of clinical groups (p&#x202f;<&#x202f;.001), outperforming models based on data-derived clusters (60&#xa0;%), stress-related proteins (73&#xa0;%), and simulated hair cortisol from meta-analytic effect sizes (53-59&#xa0;%). Predicted class probabilities strongly correlated with clinical symptoms and well-being (r&#x202f;>&#x202f;.60). Key predictive proteins were linked to pain perception, oxidative stress, and cholesterol homeostasis. Approximately 15&#xa0;% of proteins differed significantly between groups, with the strongest candidates related to ribosomal function-an emerging target in depression. These findings establish hair proteomics as a promising, non-invasive biomarker source for psychiatric research with potential clinical applications in risk assessment and personalized interventions.

Humans↗

Paternal exposure to polystyrene nanoplastics induces inter- and transgenerational bronchopulmonary dysplasia-like damage in male offspring by FtMt hypermethylation-mediated ferroptosis.

Bronchopulmonary dysplasia (BPD) is a major cause of chronic lung disease in both preterm infants and adults, but its etiology remains incompletely understood. In this study, F0 generation mice were exposed to polystyrene nanoplastics (PS-NPs), and F1 to F3 generations were obtained by breeding. Multi-omics sequencing including whole genome methylation sequencing, single cell transcriptome sequencing and transcriptome sequencing was performed on the lungs of offspring. The levels of Fe2+, lipid peroxidation products and key gene expression were determined. Male mice exposed to PS-NPs at environmentally relevant doses produced offspring (F1 and F2) that exhibited a typical BPD-like phenotype. Meanwhile, the F0 males showed diminished sperm motility, demonstrating that paternal PS-NPs exposure constituted an etiological factor for BPD in descendants. Mechanistic studies showed that PS-NPs exposure upregulated the expression of DNA methyltransferase Dnmt3a, leading to global hypermethylation of the sperm genome. Importantly, the hypermethylated promoter signature of the mitochondrial ferritin (FtMt) gene partially resisted epigenetic reprogramming and was transmitted to the lungs of offspring, resulting in persistently low FtMt expression in F1 and F2 lungs. This led to increased intracellular Fe2+ levels, subsequently triggered ferroptosis in alveolar epithelial cells, and ultimately impaired alveolarization. Knockdown of FtMt confirmed that FtMt deficiency was sufficient to induce ferroptosis and BPD-like lung injury both in vitro and in vivo. Furthermore, using in vitro fertilization of F0 sperm combined with Dnmt3a siRNA microinjection, we directly demonstrated that Dnmt3a is a key driver for FtMt to escape reprogramming and maintain its hypermethylation. In summary, this study reveals for the first time that paternal PS-NPs exposure causes BPD through a Dnmt3a-FtMt hypermethylation intergenerational and transgenerational axis, providing an epigenetic basis for understanding paternal derived chronic lung disease and potential targets for early intervention.

Animals↗

Pharmacogenomics of antipsychotic-induced weight gain: A systematic review.

BACKGROUND: Antipsychotic-induced weight gain (AIWG) is a major clinical concern, affecting approximately 30% of patients. Clinical predictors explain only part of AIWG risk. Genetic and molecular variations are hypothesized to contribute to susceptibility. The purpose of this review is to summarize recent results to identify replicated and novel findings. STUDY DESIGN: Applying PRISMA guidelines, we searched MEDLINE, Embase, and PsycINFO (May 2018-May 2026) for studies on genetic and molecular associations with AIWG, extending our prior review. Reviews, editorials, and conference abstracts were excluded. We extracted study characteristics (design, diagnosis, antipsychotic exposure, sample size, ancestry, genetic variants, and AIWG outcomes) (e.g., &#x2265;7% weight gain, BMI change). RESULTS: Fifty-three studies met inclusion criteria. In candidate gene studies, the most consistently replicated genes associated with AIWG were observed for DRD2, HTR2C, and MC4R. Multiple novel associations were identified by genome-wide association studies (GWAS) (e.g., MAP2K1, ZDBF2, PEPD), polygenic risk scores (PRS) (e.g., body mass index PRS), gene expression (e.g., CYP3A4, EP300), and epigenetic analyses (e.g., cg12034943 at CRTC1). CONCLUSIONS: Polymorphisms in candidate genes related to neurotransmission and appetite regulation continue to be investigated for associations with AIWG, while novel findings have emerged from GWAS, gene expression, and epigenetic studies. Evidence remains inconsistent due to limited replication, methodological variability, sparse ancestry data, and geographical underrepresentation. No single genetic variant is ready for clinical use, and multi-omic and multi-ancestry models are needed to improve prediction and clinical utility.

Humans↗

Unveiling tumor heterogeneity by single cell RNA-sequencing: From basic considerations to clinical applications.

Tumor heterogeneity-encompassing diverse cellular phenotypes, genomic alterations, and microenvironmental contexts-is a principal barrier to effective cancer therapy. Single-cell RNA sequencing (scRNA-seq) has transformed our ability to resolve this complexity by capturing transcriptomes at single-cell resolution. Here, we review the technical foundations required for high-quality scRNA-seq studies. We then trace the evolution of scRNA-seq platforms from manual micromanipulation to high-throughput systems, and describe the computational pipelines that enable reliable data interpretation. The application of scRNA-seq is exemplarily shown in the context of lung cancer, where single-cell profiling has revealed (i) the clonal and sub-clonal architecture of tumors, (ii) extensive remodeling of the immune microenvironment, iii) key mechanisms underlying resistance to targeted agents and immune-checkpoint blockade, and (iv) the dynamics of neo-antigen-specific T-cell responses. Integrating machine-learning techniques-such as deep-learning classifiers and graph-based models-with single-cell transcriptomic data has markedly sped up biomarker discovery, produced more accurate risk-stratification scores, and enabled the generation of patient-specific therapeutic predictions. We surveyed the major trial registry ClinicalTrials.gov and identified &#x223c;380&#xa0;ongoing or completed studies that explicitly incorporate scRNA-seq as a correlative or pharmacodynamic endpoint. Overall, the analysis shows that scRNA-seq becomes an increasingly important component of modern trials, providing high-resolution cellular and molecular readouts that complement conventional imaging and bulk-omics endpoints. While key challenges remain, ranging from costs, scalability and need for rigorous validation before routine clinical deployment, ongoing technological advances continue to expand the potential of scRNA-seq as a cornerstone of precision medicine.

Humans↗

Using Large Genomic Biobanks to Generate Insights into Genetic Kidney Disease.

Chronic kidney disease (CKD) affects approximately 9% of the global population, leading to increased risks of end-stage kidney disease (ESKD), cardiovascular disease (CVD), and mortality. Patients with CKD are a huge burden on health care resources globally. CKD is a complex condition influenced by a combination of genetic, environmental, and traditional risk factors. Family studies have suggested heritability rates for CKD ranging from 30% to 75%, and large genomic biobank studies have proven essential in identifying genes with substantial effects on CKD risk and in capturing cumulative genetic risk through polygenic risk scores. These biobanks are crucial for discovering new genes associated with kidney health and disease, and their growing size enhances the power to detect novel genetic associations. Integrating multi-omics technologies such as transcriptomics, metabolomics, and proteomics further enriches our understanding of CKD, while advanced computational tools continue to expand our insights into genetic data. Polygenic risk scores, derived from hundreds of genetic variants with small effect sizes, can help identify individuals at high risk of CKD. Genomic biobanks offer valuable opportunities for early identification and personalized treatment of monogenic kidney disorders, such as autosomal dominant polycystic kidney disease and Alport syndrome. These biobanks help fill knowledge gaps, particularly in individuals with milder or asymptomatic presentations who are often underrepresented in traditional studies. Expanding genomic biobank efforts globally, especially in diverse populations, is vital to enhancing our understanding of the genetic underpinnings of kidney disease. This review highlights the significant contributions of genomic biobanks to advancing our comprehension of the genetics of CKD.

Humans↗

Spatiotemporal single-cell roadmap of human skin wound healing.

Wound healing is vital for human health, yet the details of cellular dynamics and coordination in human wound repair remain largely unexplored. To address this, we conducted single-cell multi-omics analyses on human skin wound tissues through inflammation, proliferation, and remodeling phases of wound repair from the same individuals, monitoring the cellular and molecular dynamics of human skin wound healing at an unprecedented spatiotemporal resolution. This singular roadmap reveals the cellular architecture of the wound margin and identifies FOSL1 as a critical driver of re-epithelialization. It shows that pro-inflammatory macrophages and fibroblasts sequentially support keratinocyte migration like a relay race across different healing stages. Comparison with single-cell data from venous and diabetic foot ulcers uncovers a link between failed keratinocyte migration and impaired inflammatory response in chronic wounds. Additionally, comparing human and mouse acute wound transcriptomes underscores the indispensable value of this roadmap in bridging basic research with clinical innovations.

Humans↗

Nanobioreactor detection of space-associated hematopoietic stem and progenitor cell aging.

Human hematopoietic stem and progenitor cell (HSPC) fitness declines following exposure to stressors that reduce survival, dormancy, telomere maintenance, and self-renewal, thereby accelerating aging. While previous National Aeronautics and Space Administration (NASA) research revealed immune dysfunction in low-earth orbit (LEO), the impact of spaceflight on human HSPC aging had not been studied. To study HSPC aging, our NASA-supported Integrated Space Stem Cell Orbital Research (ISSCOR) team developed bone marrow niche nanobioreactors with lentiviral bicistronic fluorescent, ubiquitination-based cell-cycle indicator (FUCCI2BL) reporter for real-time HSPC tracking in artificial intelligence (AI)-driven CubeLabs. In month-long International Space Station (ISS) missions (SpX-24, SpX-25, SpX-26, and SpX-27) compared with ground controls, FUCCI2BL reporter, whole-genome and transcriptome sequencing, and cytokine arrays demonstrated cell-cycle, inflammatory cytokine, mitochondrial gene, human repetitive element, and apolipoprotein B mRNA editing enzyme, catalytic polypeptide-like 3 (APOBEC3) deregulation together with clonal hematopoietic mutations. Furthermore, HSPC functionally organized multi-omics aging (HSPC-FOMA) analyses revealed reduced telomere maintenance, adenosine deaminase acting on RNA1 (ADAR1) p150 self-renewal gene expression, and replating capacity indicative of space-associated HSPC aging that may limit long-duration spaceflight.

Humans↗

Complementary gene and protein expression studies and integrative approaches in toxicogenomics.

Parallel transcript and protein profiling is a strategy to gain further insight into the mechanisms of toxicity and disease. The technologies used to measure expression at the transcript and protein levels each convey different information and have different technical capabilities that can complement each other when combined. In this review, over twenty studies are considered for the use of -Omics platform, the chemical or disease being profiled, tissues, the number of genes and proteins found by each platform and common expression products. A strategy is suggested for toxicant expression profiling that combines the transcriptomics and proteomics of both the target tissue and blood/serum that could provide a more complete characterization of toxicity as well as synergize expression technologies toward biomarker discovery.

Animals↗