Multi-omics and clustering analyses reveal the mechanisms underlying unmet needs for patients with lung adenocarcinoma and identify potential therapeutic targets.

BACKGROUND: The cancer genome contains several driver mutations. However, in some cases, no known drivers have been identified; these remaining areas of unmet needs, leading to limited progress in cancer therapy. Whole-genome sequencing (WGS) can identify non-coding alterations associated with the disease. Consequently, exploration of non-coding regions using WGS and other omics data such as ChIP-sequencing (ChIP-seq) to discern novel alterations and mechanisms related to tumorigenesis have been attractive these days. METHODS: Integrated multi-omics analyses, including WGS, ChIP-seq, DNA methylation, and RNA-sequencing (RNA-seq), were conducted on samples from patients with non-clinically actionable genetic alterations (non-CAGAs) in lung adenocarcinoma (LUAD). Second-level cluster analysis was performed to reinforce the correlations associated with patient survival, as identified by RNA-seq. Subsequent differential gene expression analysis was performed to identify potential druggable targets. RESULTS: Differences in H3K27ac marks in non-CAGAs LUAD were found and confirmed by analyzing RNA-seq data, in which mastermind-like transcriptional coactivator 2 (MAML2) was suppressed. The down-regulated genes whose expression was correlated to MAML2 expression were associated with patient prognosis. WGS analysis revealed somatic mutations associated with the H3K27ac marks in the MAML2 region and high levels of DNA methylation in MAML2 were observed in tumor samples. The second-level cluster analysis enabled patient stratification and subsequent analyses identified potential therapeutic target genes and treatment options. CONCLUSIONS: We overcome the persistent challenges of identifying alterations or driver mutations in coding regions related to tumorigenesis through a novel approach combining multi-omics data with clinical information to reveal the molecular mechanisms underlying non-CAGAs LUAD, stratify patients to improve patient prognosis, and identify potential therapeutic targets. This approach may be applicable to studies of other cancers with unmet needs.

Integrative proteome and metabolome analyses reveal molecular basis of the tail resorption during the metamorphic climax of Nanorana pleskei.

During the metamorphosis of anuran amphibians, the tail resorption process is a necessary and crucial change. One subject that has received relatively little or no attention is the expression patterns of proteins and metabolites in the different tail portions during metamorphosis, especially in highland amphibians. The mechanisms of tail resorption in three portions (the tip, middle and root) of the tail were investigated in N. pleskei G43 tadpole based on two omics (proteomic and metabolomic). Integrin αVß3 was found to be high expressed in the distal portion of the tail, which could improve the sensitiveness to thyroid hormones in the distal portion of the tail. Muscle regression displayed a spatial pattern with stronger regression in distal and weaker one in proximal portion. Probably, this stronger regression was mainly performed by the proteases of proteasome from the active translation by ribosomes. The suicide model and murder model coexisted in the tail resorption. Meanwhile, fatty acids, amino acids, pyrimidine, and purine which derived from the breakdown of tissues can be used as building blocks or energy source for successful metamorphosis. Our data improved a better comprehension of the tail resorption mechanisms underlying the metamorphism of N. pleskei tadpole through identifying important participating proteins and metabolites.

Multi-omics analysis of aggregative multicellularity.

All organisms have to carefully regulate their gene expression, not least during development. mRNA levels are often used as proxy for protein output; however, this approach ignores post-transcriptional effects. In particular, mRNA-protein correlation remains elusive for organisms that exhibit aggregative rather than clonal multicellularity. We addressed this issue by generating a paired transcriptomics and proteomics time series during the transition from uni-to multicellular stage in the social ameba Dictyostelium discoideum. Our data reveals that mRNA and protein levels correlate highly during unicellular growth, but decrease when multicellular development is initiated. This accentuates that transcripts alone cannot accurately predict protein levels. The dataset provides a useful resource to study gene expression during aggregative multicellular development. Additionally, our study provides an example of how to analyze and visualize mRNA and protein levels, which should be broadly applicable to other organisms and conditions.

HDAC1 and HDAC2 orchestrate Wnt signaling to regulate neural progenitor transition during brain development.

Tightly controlled neurogenesis is crucial for generating the precise number of neurons and establishing the intricate architecture of the cortex, with deficiencies often leading to neurodevelopmental disorders. Neuroepithelial progenitors (NPs) transit into radial glial progenitors (RGPs) to initiate neural differentiation, yet the governing mechanisms remain elusive. Here, we found that histone deacetylases 1 and 2 (HDAC1/2) mediated suppression of Wnt signaling is essential for the NP-to-RGP transition. Conditional depletion of HDAC1/2 from NPs upregulated Wnt signaling genes, impairing the transition to RGPs and resulting in rosette structures within the neocortex. Multi-omics analysis revealed that HDAC1/2 are critical for downregulating Wnt signaling, identifying Wnt9a as a key target. Overexpression of Wnt9a led to an increased population of NPs and the disruption of cortical organization. Notably, Wnt inhibitor administration partially rescued the disrupted cortical architecture. Our findings reveal the significance of tightly controlled Wnt signaling through epigenetic mechanisms in neocortical development.

CelltypeR: A flow cytometry pipeline to characterize single cells from brain organoids.

Motivated by the cellular heterogeneity in complex tissues, particularly in brain and induced pluripotent stem cell (iPSC)-derived brain models, we developed a complete workflow to reproducibly characterize cell types in complex tissues. Our approach combines a flow cytometry (FC) antibody panel with our computational pipeline CelltypeR, enabling dataset aligning, unsupervised clustering optimization, cell type annotating, and statistical comparisons. Applied to human iPSC derived midbrain organoids, it successfully identified the major brain cell types. We performed fluorescence-activated cell sorting of CelltypeR-defined astrocytes, radial glia, and neurons, exploring transcriptional states by single-cell RNA sequencing. Among the sorted neurons, we identified subgroups of dopamine neurons: one reminiscent of substantia nigra cells most vulnerable in Parkinson's disease. Finally, we used our workflow to track cell types across a time course of organoid differentiation. Overall, our adaptable analysis framework provides a generalizable method for reproducibly identifying cell types across FC datasets in complex tissues.

Construction of a predictive model for gastric cancer neuroaggression and clinical validation analysis: A single-center retrospective study.

BACKGROUND: This study investigated the construction and clinical validation of a predictive model for neuroaggression in patients with gastric cancer. Gastric cancer is one of the most common malignant tumors in the world, and neuroinvasion is the key factor affecting the prognosis of patients. However, there is a lack of systematic analysis on the construction and clinical application of its prediction model. This study adopted a single-center retrospective study method, collected a large amount of clinical data, and applied statistics and machine learning technology to build and verify an effective prediction model for neuroaggression, with a view to providing scientific basis for clinical treatment decisions and improving the treatment effect and survival rate of patients with gastric cancer. AIM: To investigate the value of a model based on clinical data, spectral computed tomography (CT) parameters and image omics characteristics for the preoperative prediction of nerve invasion in patients with gastric cancer. METHODS: A retrospective analysis was performed on 80 gastric cancer patients who underwent preoperative energy spectrum CT at our hospital between January 2022 and August 2023, these patients were divided into a positive group and a negative group according to their pathological results. Clinicopathological data were collected, the energy spectrum parameters of primary gastric cancer lesions were measured, and single factor analysis was performed. A total of 214 image omics features were extracted from two-phase mixed energy images, and the features were screened by single factor analysis and a support vector machine. The variables with statistically significant differences were included in logistic regression analysis to construct a prediction model, and the performance of the model was evaluated using the subject working characteristic curve. RESULTS: There were statistically significant differences in sex, carbohydrate antigen 199 expression, tumor thickness, Lauren classification and Borrmann classification between the two groups (all P < 0.05). Among the energy spectrum parameters, there were statistically significant differences in the single energy values (CT60-CT110 keV) at the arterial stage between the two groups (all P < 0.05) and statistically significant differences in CT values, iodide group values, standardized iodide group values and single energy values except CT80 keV at the portal vein stage between the two groups (all P < 0.05). The support vector machine model with the largest area under the curve was selected by image omics analysis, and its area under the curve, sensitivity, specificity, accuracy, P value and parameters were 0.843, 0.923, 0.714, 0.925, < 0.001, and c:g 2.64:10.56, respectively. Finally, based on the logistic regression algorithm, a clinical model, an energy spectrum CT model, an imaging model, a clinical + energy spectrum model, a clinical + imaging model, an energy spectrum + imaging model, and a clinical + energy spectrum + imaging model were established, among which the clinical + energy spectrum + imaging model had the best efficacy in diagnosing gastric cancer nerve invasion. The area under the curve, optimal threshold, Youden index, sensitivity and specificity were 0.927 (95%CI: 0.850-1.000), 0.879, 0.778, 0.778, and 1.000, respectively. CONCLUSION: The combined model based on clinical features, spectral CT parameters and imaging data has good value for the preoperative prediction of gastric cancer neuroinvasion.

Inhibition of xanthine oxidase alleviated pancreatic necrosis via HIF-1α-regulated LDHA and NLRP3 signaling pathway in acute pancreatitis.

Acute pancreatitis (AP) is a potentially fatal condition with no targeted treatment options. Although inhibiting xanthine oxidase (XO) in the treatment of AP has been studied in several experimental models and clinical trials, whether XO is a target of AP and what its the main mechanism of action is remains unclear. Here, we aimed to re-evaluate whether XO is a target aggravating AP other than merely generating reactive oxygen species that trigger AP. We first revealed that XO expression and enzyme activity were significantly elevated in the serum and pancreas of necrotizing AP models. We also found that allopurinol and febuxostat, as purine-like and non-purine XO inhibitors, respectively, exhibited protective effects against pancreatic acinar cell death in vitro and pancreatic damage in vivo at different doses and treatment time points. Moreover, we observed that conditional Xdh overexpression aggravated pancreatic necrosis and severity. Further mechanism analysis showed that XO inhibition restored the hypoxia-inducible factor 1-alpha (HIF-1α)-regulated lactate dehydrogenase A (LDHA) and NOD-like receptor family pyrin domain containing 3 (NLRP3) signaling pathways and reduced the enrichment of 13C6-glucose to 13C3-lactate. Lastly, we observed that clinical circulatory XO activity was significantly elevated in severe cases and correlated with C-reactive protein levels, while pancreatic XO and urate were also increased in severe AP patients. These results together indicated that proper inhibition of XO might be a promising therapeutic strategy for alleviating pancreatic necrosis and preventing progression of severe AP by downregulating HIF-1α-mediated LDHA and NLRP3 signaling pathways.

Multi-omics analysis to reveal the synergistic mechanism underlying the multiple ingredients of Stephania tetrandra extract on rheumatoid arthritis through the PI3K/Akt signaling pathway.

Background: Stephania tetrandra has been used for treating rheumatic diseases for thousands of years in rural areas of China. Several studies have found that tetrandrine and fangchinoline can inactivate the PI3K/Akt signaling pathway by reducing the expression and phosphorylation of AKT. However, the mechanism underlying the therapeutic actions of S. tetrandra on RA is not well known. Methods: In this study, we determined the molecular mechanism of the therapeutic effects of the multiple ingredients of S. tetrandra extract (STE) on collagen-induced arthritic (CIA) rats by integrating pharmacometabolomics, proteomics, and PTMomics. Results: In the multi-omics joint analysis, first, the expression signatures of proteins, PTMs, metabolites, and STE ingredients were profiled in CIA rats PBMCs that underwent STE treatment. Bioinformatics analysis were subsequently probed that STE mainly regulated tryptophan metabolism, inflammatory response, and cell adhesion pathways in CIA rats. The interrelated pathways were further constructed, and the findings revealed that STE attenuated the inflammatory response and proliferation of PBMCs in CIA rats by mediating the key targets of the PI3K/Akt pathway, including Hint1, ACP1, FGR, HSP90@157W + dioxidation, and Prkca@220N + 845.4540 Da. The rheumatic functions of Hint1 and ACP1 were further confirmed by applying a transcriptomic data of RA patients who clinically received abatacept therapy. Furthermore, a cross-ome correlation analysis was performed and major in vivo ingredients of STE, including coclaurine-N-glucuronide, Me,coclaurine-O-glc, N-gluA-schefferine, corydamine, corypamine, tetrandrine, and fangchiniline, were found to act on these targerts to inactivate the PI3K/Akt pathway. Conclusion: These results elucidated the molecular mechanism by which the ingredients of STE mediate the expression of the key targets in the PI3K/Akt pathway, leading to anti-rheumatic functions. The findings of this study provided new insights into the synergistic effect of STE against arthritis in rats.

Integrated multi-omics analysis identifies features that predict human pluripotent stem cell-derived progenitor differentiation to cardiomyocytes.

Human pluripotent stem cell-derived cardiomyocytes (hPSC-CMs) are advancing cardiovascular development and disease modeling, drug testing, and regenerative therapies. However, hPSC-CM production is hindered by significant variability in the differentiation process. Establishment of early quality markers to monitor lineage progression and predict terminal differentiation outcomes would address this robustness and reproducibility roadblock in hPSC-CM production. An integrated transcriptomic and epigenomic analysis assesses how attributes of the cardiac progenitor cell (CPC) affect CM differentiation outcome. Resulting analysis identifies predictive markers of CPCs that give rise to high purity CM batches, including TTN, TRIM55, DGKI, MEF2C, MAB21L2, MYL7, LDB3, SLC7A11, MAB21L2, and CALD1. Predictive models developed from these genes provide high accuracy in determining terminal CM purities at the CPC stage. Further, insights into mechanisms of batch failure and dominant non-CM cell types generated in failed batches are elucidated. Namely EMT, MAPK, and WNT signaling emerge as significant drivers of batch divergence, giving rise to off-target populations of fibroblasts/mural cells, skeletal myocytes, epicardial cells, and a non-CPC SLC7A11+ subpopulation. This study demonstrates how integrated multi-omic analysis of progenitor cells can identify quality attributes of that progenitor and predict differentiation outcomes, thereby improving differentiation protocols and increasing process robustness.

A Contrastive-Learning-Based Deep Neural Network for Cancer Subtyping by Integrating Multi-Omics Data.

BACKGROUND: Accurate identification of cancer subtypes is crucial for disease prognosis evaluation and personalized patient management. Recent advances in computational methods have demonstrated that multi-omics data provides valuable insights into tumor molecular subtyping. However, the high dimensionality and small sample size of the data may result in ambiguous and overlapping cancer subtypes during clustering. In this study, we propose a novel contrastive-learning-based approach to address this issue. The proposed end-to-end deep learning method can extract crucial information from the multi-omics features by self-supervised learning for patient clustering. RESULTS: By applying our method to nine public cancer datasets, we have demonstrated superior performance compared to existing methods in separating patients with different survival outcomes (p < 0.05). To further evaluate the impact of various omics data on cancer survival, we developed an XGBoost classification model and found that mRNA had the highest importance score, followed by DNA methylation and miRNA. In the presented case study, our method successfully clustered subtypes and identified 14 cancer-related genes, of which 12 (85.7%) were validated through literature review. CONCLUSIONS: Our findings demonstrate that our method is capable of identifying cancer subtypes that are both statistically and biologically significant. The code about COLCS is given at: https://github.com/Mercuriiio/COLCS .

Integrated epigenomic exposure signature discovery.

Aim: The epigenome influences gene regulation and phenotypes in response to exposures. Epigenome assessment can determine exposure history aiding in diagnosis.Materials & methods: Here we developed and implemented a machine learning algorithm, the exposure signature discovery algorithm (ESDA), to identify the most important features present in multiple epigenomic and transcriptomic datasets to produce an integrated exposure signature (ES).Results: Signatures were developed for seven exposures including Staphylococcus aureus, human immunodeficiency virus, SARS-CoV-2, influenza A (H3N2) virus and Bacillus anthracis vaccinations. ESs differed in the assays and features selected and predictive value.Conclusion: Integrated ESs can potentially be utilized for diagnosis or forensic attribution. The ESDA identifies the most distinguishing features enabling diagnostic panel development for future precision health deployment.

This article introduces ESDA, a new analytic tool for integrating multiple data types to identify the most distinguishing features following an exposure. Using the ESDA, we were able to identify signatures of infectious diseases. The results of the study indicate that integration of multiple types of large datasets can be used to identify distinguishing features for infectious diseases. Understanding the changes from different exposures will enable development of diagnostic tests for infectious diseases that target responses from the patient. Using the ESDA, we will be able to build a database of human response signatures to different infections and simplify diagnostic testing in the future.

Integrated multi-omics approaches reveal the neurotoxicity of triclocarban in mouse brain.

Triclocarban (TCC) is an antimicrobial ingredient that commonly incorporated in many household and personal care products, raising public concerns about its potential health risks. Previous research has showed that TCC could cross the blood-brain barrier, but to date our understanding of its potential neurotoxicity at human-relevant concentrations remains lacking. In this study, we observed anxiety-like behaviors in mice with continuous percutaneous exposure to TCC. Subsequently, we combined lipidomic, proteomic, and metabolic landscapes to investigate the underlying mechanisms of TCC-related neurotoxicity. The results showed that TCC exposure dysregulated the proteins involved in endocytosis and neurodegenerative disorders in mouse cerebrum. Brain energy homeostasis was also altered, as evidenced by the perturbation of pyruvate metabolism, TCA cycle, and oxidative phosphorylation, which in turn caused mitochondrial dysfunction. Meanwhile, the changing trends of sphingolipid signaling pathway and overproduction of mitochondrial reactive oxygen species (mROS) could enhance the neural apoptosis. The in vitro approach further demonstrated that TCC exposure promoted apoptosis, accompanied by the overproduction of mROS and alteration in the mitochondrial membrane potential in N2A cells. Together, dysregulated endocytosis, mROS-related mitochondrial dysfunction and neural cell apoptosis are considered to be crucial factors for TCC-induced neurotoxicity, which may contribute to the occurrence and development of neurodegenerative disorders. Our findings provide novel perspectives for the mechanisms of TCC-triggered neurotoxicity.

Milletomics: a metabolomics centered integrated omics approach toward genetic progression.

Producing alternative staple foods like millet will be essential to feeding ten billion people by 2050. The increased demand for millet is driving researchers to improve its genetic variation. Millets include protein, dietary fiber, phenolic substances, and flavonoid components. Its climate resilience makes millet an appealing crop for agronomic sustainability. Integrative omics technologies could potentially identify and develop millets with desirable phenotypes that may have high agronomic value. Millets' salinity and drought tolerance have been enhanced using transcriptomics. In foxtail, finger, and pearl millet, proteomics has discovered salt-tolerant protein, phytohormone-focused protein, and drought tolerance. Metabolomics studies have revealed that certain metabolic pathways including those involving lignin, flavonoids, phenylpropanoid, and lysophospholipids are critical for many processes, including seed germination, photosynthesis, energy metabolism, and the synthesis of bioactive chemicals necessary for drought tolerance. Metabolomics integration with other omics revealed metabolome engineering and trait-specific metabolite creation. Integrated metabolomics and ionomics are still in the development stage, but they could potentially assist in comprehending the pathway of ionomers to control nutrient levels and biofortify millet. Epigenomic analysis has shown alterations in DNA methylation patterns and chromatin structure in foxtail and pearl millets in response to abiotic stress. Whole-genome sequencing utilizing next-generation sequencing is the most proficient method for finding stress-induced phytoconstituent genes. New genome sequencing enables novel biotechnological interventions including genome-wide association, mutation-based research, and other omics approaches. Millets can breed more effectively by employing next-generation sequencing and genotyping by sequencing, which may mitigate climate change. Millet marker-assisted breeding has advanced with high-throughput markers and combined genotyping technologies.

Biotechnological studies towards improvement of finger millet using multi-omics approaches.

A plethora of studies have uncovered numerous important genes with agricultural significance in staple crops. However, when it comes to orphan crops like minor millet, genomic research lags significantly behind that of major crops. This situation has promoted a focus on exploring research opportunities in minor millets, particularly in finger millet, using cutting-edge methods. Finger millet, a coarse cereal known for its exceptional nutritional content and ability to withstand environmental stresses represents a promising climate-smart and nutritional crop in the battle against escalating environmental challenges. The existing traditional improvement programs for finger millet are insufficient to address global hunger effectively. The lack of utilization of high-throughput platforms, genome editing, haplotype breeding, and advanced breeding approaches hinders the systematic multi-omics studies on finger millet, which are essential for pinpointing crucial genes related to agronomically important and various stress responses. The growing environmental uncertainties have widened the gap between the anticipated and real progress in crop improvement. To overcome these challenges a combination of cutting-edge multi-omics techniques such as high-throughput sequencing, speed breeding, mutational breeding, haplotype-based breeding, genomic selection, high-throughput phenotyping, pangenomics, genome editing, and more along with integration of deep learning and artificial intelligence technologies are essential to accelerate research efforts in finger millet. The scarcity of multi-omics approaches in finger millet leaves breeders with limited modern tools for crop enhancement. Therefore, leveraging datasets from previous studies could prove effective in implementing the necessary multi-omics interventions to enrich the genetic resource in finger millet.

Subchronic Chloroform Exposure Causes Intestinal Damage and Induces Gut Microbiota Disruption and Metabolic Dysregulation in Mice.

Chloroform is a prevalent toxic environmental pollutant in urban settings, posing risks to human health through exposure via various mediums such as air and tap water. The gut microbiota plays a pivotal role in maintaining host health. However, there is a paucity of research elucidating the impact of chloroform exposure on the gut microbiota. In this investigation, 18 SPF Kunming female mice were stratified into three groups (n = 6) and subjected to oral gavage with chloroform doses equivalent to 0, 50, and 150 mg/kg of body weight over 30 days. Our findings demonstrate that subchronic chloroform exposure significantly perturbs hematological parameters in mice and induces histopathological alterations in cecal tissues, consequently engendering marked disparities in the functional composition of cecal microbiota and metabolic equilibrium of cecal contents. Ultimately, our investigation revealed a statistically robust correlation, exhibiting a high degree of significance, between the intestinal microbiome composition and the metabolites that were differentially expressed consequent to chloroform exposure.

Genome-Wide Identification of Pentatricopeptide Repeat (PPR) Gene Family and Multi-Omics Analysis Provide New Insights Into the Albinism Mechanism of Kandelia obovata Propagule Leaves.

Pentatricopeptide repeat (PPR) gene family constitutes one of the largest gene families in plants, which mainly participate in RNA editing and RNA splicing of organellar RNAs, thereby affecting the organellar development. Recently, some evidence elucidated the important roles of PPR proteins in the albino process of plant leaves. However, the functions of PPR genes in the woody mangrove species have not been investigated. In this study, using a typical true mangrove Kandelia obovata, we systematically identified 298 PPR genes and characterized their general features and physicochemical properties, including evolutionary relationships, the subcellular localization, PPR motif type, the number of introns and PPR motifs, and isoelectric point, and so forth. Furthermore, we combined genome-wide association studies (GWAS) and transcriptome analysis to identify the genetic architecture and potential PPR genes associated with propagule leaves colour variations of K. obovata. As a result, we prioritized 16 PPR genes related to the albino phenotype using different strategies, including differentially expressed genes analysis and genetic diversity analysis. Further analysis discovered two genes of interest, namely Maker00002998 (PLS-type) and Maker00003187 (P-type), which were differentially expressed genes and causal genes detected by GWAS analysis. Moreover, we successfully predicted downstream target chloroplast genes (rps14, rpoC1 and rpoC2) bound by Maker00002998 PPR proteins. The experimental verification of RNA editing sites of rps14, rpoC1, and rpoC2 in our previous study and the verification of interaction between Maker00002998 and rps14 transcript using in vitro RNA pull-down assays revealed that Maker00002998 PPR protein might be involved in the post-transcriptional process of chloroplast genes. Our result provides new insights into the roles of PPR genes in the albinism mechanism of K. obovata propagule leaves.

Soil volatilomics uncovers tight linkage between soybean presence and soil omics profiles in agricultural fields.

Securing a stable food supply and achieving sustainable agricultural production are essential for mitigating future food insecurity. Soil metabolomics is a promising tool for capturing soil status, which is a critical issue for future sustainable food security. This study aims to provide deeper insights into the status of soybean-grown fields under varying soil conditions over three years by employing comprehensive soil volatile organic compound (VOC) profiling, also known as soil volatilomics. Profiling identified approximately 200 peaks in agricultural fields. The soil of soybean-presented plots exhibited markedly higher VOC levels than those of non-soybean plots during the flowering season. Pentanoic acid, 2,2,4-trimethyl-3-carboxyisopropyl, isobutyl ester, a discriminative soil VOC, was identified through multivariate data analysis as a distinctively present VOC in fields with or without soybean plants during the flowering period. Soil VOC profiles exhibited strong correlations with soil-related omics datasets (soil ionome, microbiome, metabolome, and physics) and no significant correlations with root microbiome and rhizosphere chemicals. These findings indicate that soil VOC profiles could serve as a valuable indicator for assessing soil status, thereby supporting efforts to ensure future global food security.

Emerging methods and techniques for cancer biomarker discovery.

Modern cancer research depends heavily on the identification and validation of biomarkers because they provide important information about the diagnosis, prognosis, and response to treatment of the cancer. This review will provide a comprehensive overview of cancer biomarkers, including their development phases and recent breakthroughs in transcriptomics and computational techniques for detecting these biomarkers. Blood-based biomarkers have great potential for non-invasive tumor dynamics and treatment response monitoring. These include circulating tumor DNA, exosomes, and microRNAs. Comprehensive molecular profiles are provided by multi-omic technologies, which combine proteomics, metabolomics, and genomes to support the identification of biomarkers and the targeting of therapeutic interventions. Genetic changes are detected by next-generation sequencing, and patterns of protein expression are found by protein arrays and mass spectrometry. Tumor heterogeneity and clonal evolution can be understood using metabolic profiling and single-cell studies. It is projected that the use of several biomarkers-genetic, protein, mRNA, microRNA, and DNA profiles, among others-will rise, enabling multi-biomarker analysis and improving individualised treatment plans. Biomarker identification and patient outcome prediction are further improved by developments in AI algorithms and imaging techniques. Robust biomarker validation and reproducibility require cooperation between industry, academia, and doctors. Biomarkers can provide individualized care, meet unmet clinical needs, and enhance patient outcomes despite some obstacles. Precision medicine will continue to take shape as scientific research advances and the integration of biomarkers with cutting-edge technologies continues to offer a more promising future for personalized cancer care.

MCMV-infected maize attracts its insect vector Frankliniella occidentalis by inducing ß-myrcene.

Maize lethal necrosis is attributed to the accumulation of maize chlorotic mottle virus (MCMV), an invasive virus transmitted by insect vectors. The western flower thrips (WFT) can shift host to maize, thus promoting the spread of MCMV. However, our understanding of the characteristics and interactions involved in the transmission of MCMV is still limited. This study finds that non-viruliferous WFTs showed a 57.56% higher preference for MCMV-infected maize plants compared to healthy maize plants, while viruliferous WFTs showed a 53.70% higher preference for healthy maize plants compared to MCMV-infected maize plants. We also show for the first time that both adults and larvae of WFT could successfully acquire MCMV after 1 min of acquisition access period (AAP), and after 48 h of AAP, WFT could transmit MCMV in an inoculation access period of 1 h without a latent period. Both adults and larvae of WFT can transmit MCMV for up to 2 days. Furthermore, the decreasing number of viruliferous WFTs and transmission rates as time progressed, together with the transcriptomic evidence, collectively suggest that WFTs transmit MCMV in a semi-persistent method, a mode of transmission requiring minutes to several hours for acquisition access and having a retention time of several hours to a few days. Additionally, ß-myrcene can attract WFTs significantly and is detected in Nicotiana benthamiana plants transiently expressing MCMV CP (coat protein), which is consistent with results in MCMV-infected maize plants through the metabolomic profiling and the preference analyses of WFT. Therefore, this study demonstrates the indirect interaction between MCMV and WFT by inducing maize to synthesize ß-myrcene to attract insect vectors. The exploration of specific interactions between MCMV and WFT could help to expand the mechanism studies of virus-vector-host plant interaction and put forward a new insight for the combined control of MCMV and WFT through the manipulation of plant volatiles and key insect genes.