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Interferon (IFN) activity exhibits a gender bias in human skin, skewed toward females. We show that HERC6, an IFN-induced E3 ubiquitin ligase, is induced in human keratinocytes through the epidermal type I IFN; IFN-κ. HERC6 knockdown in human keratinocytes results in enhanced induction of interferon-stimulated genes (ISGs) upon treatment with a double-stranded (ds) DNA STING activator cGAMP but not in response to the RNA-sensing TLR3 agonist. Keratinocytes lacking HERC6 exhibit sustained STING-TBK1 signaling following cGAMP stimulation through modulation of LATS2 and TBK1 activity, unmasking more robust ISG responses in female keratinocytes. This enhanced female-biased immune response with loss of HERC6 depends on VGLL3, a regulator of type I IFN signature. These data identify HERC6 as a previously unrecognized negative regulator of ISG expression specific to dsDNA sensing and establish it as a regulator of female-biased immune responses through modulation of STING signaling.

Multi-omics approaches have been successfully applied to investigate pregnancy and health outcomes at a molecular and genetic level in several studies. As omics technologies advance, research areas are open to study further. Here we discuss overall trends and examples of successfully using omics technologies and techniques (e.g., genomics, proteomics, metabolomics, and metagenomics) to investigate the molecular epidemiology of pregnancy. In addition, we outline omics applications and study characteristics of pregnancy for understanding fundamental biology, causal health, and physiological relationships, risk and prediction modeling, diagnostics, and correlations.

Type 2 innate lymphoid cells (ILC2s) have emerged as pivotal regulators in the pathogenesis of diseases, with their roles in inflammation, metabolism, and tissue homeostasis becoming increasingly recognized. This review provides an overview of the current understanding of ILC2s in inflammation and metabolic disorders, including their functional contributions. Moreover, we will discuss how these cells adapt their metabolic processes to support their function and survival and how their metabolic requirements change under different physiological and pathological conditions. Lastly, we will review recent omics studies that have provided insights into the molecular and cellular characteristics of ILC2s. This includes transcriptomic, proteomic, and metabolomic analyses that have elucidated the gene expression profiles, protein interactions, and metabolic networks, respectively, associated with ILC2s. These studies have advanced our understanding of the functional diversity of ILC2s and their involvement in metabolic disease. [ABSTRACT FROM AUTHOR]

Accurate identification of bacterial strains in clinical samples is essential to provide an appropriate antibiotherapy to the patient and reduce the prescription of broad-spectrum antimicrobials, leading to antibiotic resistance. In this study, we utilized the combination of a multidimensional analytical technique, liquid chromatography-ion mobility-tandem mass spectrometry (LC-IM-MS/MS), and machine learning to accurately identify and distinguish 11 Escherichia coli (E. coli) strains in artificially contaminated urine samples. Machine learning was utilized on the LC-IM-MS/MS data of the inoculated urine samples to reveal lipid, metabolite, and peptide isomeric biomarkers for the identification of the bacteria strains. Tandem MS and LC separation proved effective in discriminating diagnostic isomers in the negative ion mode, while IM separation was more effective in resolving conformational biomarkers in the positive ion mode. Using hierarchical clustering, the strains are clustered accurately according to their group highlighting the uniqueness of the discriminating biomarkers to the class of each E. coli strain. These results show the great potential of using LC-IM-MS/MS and machine learning for targeted omics applications to diagnose infectious diseases in various environmental and clinical samples accurately. [ABSTRACT FROM AUTHOR]

Balancing the tradeoff between quantity and quality of phenotypic data is critical in omics studies. Measurements below the limit of quantification (BLQ) are often tagged in quality control fields, but these flags are currently underutilized in human genetics studies. Extreme phenotype sampling is advantageous for mapping rare variant effects. We hypothesize that genetic drivers, along with environmental and technical factors, contribute to the presence of BLQ flags. Here, we introduce "hypometric genetics" (hMG) analysis and uncover a genetic basis for BLQ flags, indicating an additional source of genetic signal for genetic discovery, especially from phenotypic extremes. Applying our hMG approach to n = 227,469 UK Biobank individuals with metabolomic profiles, we reveal more than 5% heritability for BLQ flags and report biologically relevant associations, for example, at APOC3 , APOA5 , and PDE3B loci. For common variants, polygenic scores trained only for BLQ flags predict the corresponding quantitative traits with 91% accuracy, validating the genetic basis. For rare coding variant associations, we find an asymmetric 65.4% higher enrichment of metabolite-lowering associations for BLQ flags, highlighting the impact of putative loss-of-function variants with large effects on phenotypic extremes. Joint analysis of binarized BLQ flags and the corresponding quantitative metabolite measurements improves power in Bayesian rare variant aggregation tests, resulting in an average of 181% more prioritized genes. Our approach is broadly applicable to omics profiling. Overall, our results underscore the benefit of integrating quality control flags and quantitative measurements and highlight the advantage of joint analysis of population-based samples and phenotypic extremes in human genetics studies. [Display omitted] Tanigawa and Kellis introduce "hypometric genetics," studying the genetic basis of binarized traits representing the presence of below-the-limit-of-quantification (BLQ) quality control indicators. Their joint analysis of BLQ and quantitative traits leads to improved power in genetic discovery, highlighting the value of considering population-based samples and phenotypic extremes in genetic studies. [ABSTRACT FROM AUTHOR]

Many soils face dual challenges of cadmium (Cd) contamination and salinization. However, the response of crops, especially wheat, to combined Cd and salinity stress is not understood. Here, wheat was grown in a hydroponic model for 14 days under single and combined Cd and NaCl stresses. Growth parameters, tissue Cd2+ and Na+ contents, and leaf chlorophyll (Chl), O2•−, and MDA levels were determined. Comparative transcriptomic and metabolomic analyses of the leaves were performed. The results showed that combined stress had a greater inhibitory effect on Chl contents and generated more O2•− and MDA, resulting in more severe wheat growth retardation than those under Cd or NaCl stress. Stress-induced decrease in Chl levels may be attributed to the inhibition of Chl biosynthesis, activation of Chl degradation, or a decline in glutamate content. Cd addition weakened the promotional effect of NaCl on SOS1 gene expression, thereby increasing the Na+ content. Contrastingly, NaCl supplementation downregulated the Nramp and ZIP gene expressions related to Cd uptake and transport, thereby impeding Cd2+ accumulation. All stresses enhanced tryptophan content via promoting tryptophan biosynthesis. Meanwhile, Cd and NaCl stresses activated phenylpropanoid biosynthesis and purine metabolism, respectively, thereby increasing the levels of caffeic acid, fumaric acid, and uric acid. Activating the TCA cycle was important in the wheat's response to combined stress. Additionally, NaCl and combined stresses affected starch and sucrose metabolism, resulting in sucrose and trehalose accumulation. Our findings provide a comprehensive understanding of the response of wheat to the combined Cd and salinity stress. [ABSTRACT FROM AUTHOR]

Introduction: Rice is a vital staple food for many countries, and its yield is known to be significantly affected by various abiotic stresses, which are expected to intensify with climate change, posing a threat to global food security. Abscisic acid (ABA), a crucial plant growth regulator, plays a crucial role in plant responses to these abiotic stresses. It influences several processes, such as seed dormancy, leaf gas exchange, reactive oxygen species (ROS) scavenging, ion toxicity reduction, and root elongation, all of which contribute to enhancing plant survival under stress. Methods: This article reviews recent research on ABA-mediated gene responses and expressions involved in rice plant architecture and its response to abiotic stress. Results and discussion: Abscisic acid responses were primarily driven by changes in gene expression. Expression analyses of the gene related to ABA biosynthesis or catabolism indicated several changes in plant architecture, such as changes in leaf angle, delayed flowering, and modifications in growth regulators. Additionally, tolerance-related mechanisms, such as increased ROS scavenging, reduced membrane leakage, and vacuolar compartmentation of toxic radicals, were activated under single or multiple stress conditions. While these adaptations may improve plant survival and yield sustainability under stress, they may not necessarily enhance yield potential in environments affected by drought, salinity, or heat stress. ABA expression was also associated with improved pollen viability, grain-filling potential, and seed setting under abiotic stresses such as heat, which could enhance seed yield in such challenging environments. [ABSTRACT FROM AUTHOR]

Background: Breast cancer (BRCA) represents a substantial global health challenge marked by inadequate early detection rates. The complex interplay between the tumor immune microenvironment and fatty acid metabolism in BRCA requires further investigation to elucidate the specific role of lipid metabolism in this disease. Methods: We systematically integrated nine machine learning algorithms into 184 unique combinations to develop a consensus model for lipid metabolism-related prognostic genes (LMPGS). Additionally, transcriptomics analysis provided a comprehensive understanding of this prognostic signature. Using the ESTIMATE method, we evaluated immune infiltration among different risk subgroups and assessed their responsiveness to immunotherapy. Tailored treatments were screened for specific risk subgroups. Finally, we verified the expression of key genes through in vitro experiments. Results: We identified 259 differentially expressed genes (DEGs) related to lipid metabolism through analysis of the cancer genome atlas program (TCGA) database. Subsequently, via univariate Cox regression analysis and C-index analysis, we developed an optimal machine learning algorithm to construct a 21-gene LMPGS model. We used optimal cutoff values to divide the lipid metabolism prognostic gene scores into two groups according to high and low scores. Our study revealed distinct biological functions and mutation landscapes between high-scoring and low-scoring patients. The low-scoring group presented a greater immune score, whereas the high-scoring group presented enhanced responses to both immunotherapy and chemotherapy drugs. Single-cell analysis highlighted significant upregulation of CPNE3 in epithelial cells. Moreover, by employing molecular docking, we identified niclosamide as a potential targeted therapeutic drug. Finally, our experiments demonstrated high expression of MTMR9 and CPNE3 in BRCA and their significant correlation with prognosis. Conclusion: By employing bioinformatics and diverse machine learning algorithms, we successfully identified genes associated with lipid metabolism in BRCA and uncovered potential therapeutic agents, thereby offering novel insights into the mechanisms and treatment strategies for BRCA. [ABSTRACT FROM AUTHOR]

Seaweed aquaculture is gaining traction globally as a solution to many climate issues. However, seaweeds themselves are also under threat of anthropogenically driven climate change. Here, we summarize climate-related challenges to the seaweed aquaculture industry, with a focus on the developing trade in the North Atlantic. Specifically, we summarize three main challenges: i) abiotic change; ii) extreme events; and iii) disease & herbivory. Abiotic change includes negative effects of ocean warming and acidification, as well as altered seasonality due to ocean warming. This can lower biomass yield and change biochemical composition of the seaweeds. Extreme events can cause considerable damage and loss to seaweed farms, particularly due to marine heatwaves, storms and freshwater inputs. Seaweed diseases have a higher chance of proliferating under environmentally stressful conditions such as ocean warming and decreased salinity. Herbivory causes loss of biomass but is not well researched in relation to seaweed aquaculture in the North Atlantic. Despite challenges, opportunities exist to improve resilience to climate change, summarized in three sections: i) future proof site selection; ii) advances in breeding and microbiome manipulation; and iii) restorative aquaculture. We present a case study where we use predictive modelling to illustrate suitable habitat for seaweed cultivation in the North Atlantic under future ocean warming. Notably, there was a large loss of suitable habitat for cultivating Alaria esculenta and Laminaria digitata. We show how selection and priming and microbe inoculates may be a cost-effective and scalable solution to improve disease- and thermal tolerance. Co-cultivation of seaweeds may increase both yield and biodiversity co-benefits. Finally, we show that aquaculture and restoration can benefit from collaborating on nursery techniques and push for improved legislation. [ABSTRACT FROM AUTHOR]

A number of consequences of climate change, notably salinity, put global food security at risk by impacting the development and production of lentils. Salinity-induced stress alters lentil genetics, resulting in severe developmental issues and eventual phenotypic damage. Lentils have evolved sophisticated signaling networks to combat salinity stress. Lentil genomics and transcriptomics have discovered key genes and pathways that play an important role in mitigating salinity stress. The development of saline-smart cultivars can be further revolutionized by implementing proteomics, metabolomics, miRNAomics, epigenomics, phenomics, ionomics, machine learning, and speed breeding approaches. All these cutting-edge approaches represent a viable path toward creating saline-tolerant lentil cultivars that can withstand climate change and meet the growing demand for high-quality food worldwide. The review emphasizes the gaps that must be filled for future food security in a changing climate while also highlighting the significant discoveries and insights made possible by omics and other state-of-the-art biotechnological techniques. [ABSTRACT FROM AUTHOR]

Although as an analytical method with high specificity and high sensitivity, mass spectrometry (MS) has a wide range of applications in many fields, it still needs other technologies as the assist and supplement to enhance the scope and capability of analysis. Coupling with ion mobility (IM) can make an enhancement effect in the field of pharmaceutical analysis as a supplementary method. The two-dimensional mass technology improves the confidence of compounds annotations while increasing peak capacity, with the gradual deepening of theoretical research on IM-MS, it has shown unique advantages in the complex analysis conditions. IM-MS owns great potential for improving the depth, range, dimension of in-depth drug research. In this review, the principle, instruments and methods, applications, advantages and limitations of IM-MS are described. Here, we also elaborate on the prospects in structural evaluation, separation, and identification of complex compounds for the drug discovery and development phase and the great advantages of macromolecules and omics. [ABSTRACT FROM AUTHOR]

Drug-induced liver injury (DILI) refers to drug-mediated damage to the structure and function of the liver, ranging from mild elevation of liver enzymes to severe hepatic insufficiency, and in some cases, progressing to liver failure. The mechanisms and clinical symptoms of DILI are diverse due to the varying combination of drugs, making clinical treatment and prevention complex. DILI has significant public health implications and is the primary reason for post-marketing drug withdrawals. The search for reliable preclinical models and validated biomarkers to predict and investigate DILI can contribute to a more comprehensive understanding of adverse effects and drug safety. In this review, we examine the progress of research on DILI, enumerate in vitro models with potential benefits, and highlight cellular molecular perturbations that may serve as biomarkers. Additionally, we discuss omics approaches frequently used to gather comprehensive datasets on molecular events in response to drug exposure. Finally, three commonly used gene modulation techniques are described, highlighting their application in identifying causal relationships in DILI. Altogether, this review provides a thorough overview of ongoing work and approaches in the field of DILI. [ABSTRACT FROM AUTHOR]

Traditional risk assessment methodologies in toxicology have relied upon animal testing, despite concerns regarding interspecies consistency, reproducibility, costs, and ethics. New Approach Methodologies (NAMs), including cell culture and multi-level omics analyses, hold promise by providing mechanistic information rather than assessing organ pathology. However, NAMs face limitations, like lacking a whole organism and restricted toxicokinetic interactions. This is an inherent challenge when it comes to the use of omics data from in vitro studies for the prediction of organ toxicity in vivo. One solution in this context are comparative in vitro–in vivo studies as they allow for a more detailed assessment of the transferability of the respective NAM data. Hence, hepatotoxic and nephrotoxic pesticide active substances were tested in human cell lines and the results subsequently related to the biology underlying established effects in vivo. To this end, substances were tested in HepaRG and RPTEC/tERT1 cells at non-cytotoxic concentrations and analyzed for effects on the transcriptome and parts of the proteome using quantitative real-time PCR arrays and multiplexed microsphere-based sandwich immunoassays, respectively. Transcriptomics data were analyzed using three bioinformatics tools. Where possible, in vitro endpoints were connected to in vivo observations. Targeted protein analysis revealed various affected pathways, with generally fewer effects present in RPTEC/tERT1. The strongest transcriptional impact was observed for Chlorotoluron in HepaRG cells (increased CYP1A1 and CYP1A2 expression). A comprehensive comparison of early cellular responses with data from in vivo studies revealed that transcriptomics outperformed targeted protein analysis, correctly predicting up to 50% of in vivo effects. [ABSTRACT FROM AUTHOR]

Aortic aneurysm is a life-threatening condition and mechanisms underlying its formation and progression are still incompletely understood. Omics approach has brought new insights to identify a broad spectrum of biomarkers and better understand cellular and molecular pathways involved. Omics generate a large amount of data and several studies have highlighted that artificial intelligence (AI) and techniques such as machine learning (ML)/deep learning (DL) can be of use in analyzing such complex datasets. However, only a few studies have so far reported the use of ML/DL for omics analysis in aortic aneurysms. The aim of this study is to summarize recent advances on the use of ML/DL for omics analysis to decipher aortic aneurysm pathophysiology and develop patient-tailored risk prediction models. In the light of current knowledge, we discuss current limits and highlight future directions in the field. [ABSTRACT FROM AUTHOR]

The effects of physical activity and sedentary behavior on human health are well known, however, the molecular mechanisms are poorly understood. Growing evidence points to physical activity as an important modulator of the composition and function of microbial communities, while evidence of sedentary behavior is scarce. We aimed to synthesize and meta-analyze the current evidence about the effects of physical activity and sedentary behavior on microbiome across different body sites and in different populations. A systematic search in PubMed, Web of Science, Scopus and Cochrane databases was conducted until September 2022. Random-effects meta-analyses including cross-sectional studies (active vs. inactive/athletes vs. non-athletes) or trials reporting the chronic effect of physical activity interventions on gut microbiome alpha-diversity in healthy individuals were performed. Ninety-one studies were included in this systematic review. Our meta-analyses of 2632 participants indicated no consistent effect of physical activity on microbial alpha-diversity, although there seems to be a trend toward a higher microbial richness in athletes compared to non-athletes. Most of studies reported an increase in short-chain fatty acid-producing bacteria such as Akkermansia , Faecalibacterium , Veillonella or Roseburia in active individuals and after physical activity interventions. Physical activity levels were positively associated with the relative abundance of short-chain fatty acid-producing bacteria. Athletes seem to have a richer microbiome compared to non-athletes. However, high heterogeneity between studies avoids obtaining conclusive information on the role of physical activity in microbial composition. Future multi-omics studies would enhance our understanding of the molecular effects of physical activity and sedentary behavior on the microbiome. [ABSTRACT FROM AUTHOR]

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Graph neural networks (GNNs) process the graph-structured data using neural networks and have proven successful in various graph processing tasks. Currently, graph pooling operators have emerged as crucial components that bridge the gap between node representation learning and diverse graph-level tasks by transforming node representations into graph representations. Given the rapid growth and widespread adoption of graph pooling, this review aims to summarize the existing graph pooling operators for GNNs and their representative applications in omics. Specifically, we first present a comprehensive taxonomy of existing graph pooling algorithms, expanding the categorization for both global and hierarchical pooling operators, and for the first time reviewing the inverse operation of graph pooling, named unpooling. Next, we describe the general evaluation framework for graph pooling operators, encompassing three fundamental aspects: experimental setup, ablation analysis, and model interpretation. We also discuss open issues that significantly influence the design of graph pooling operators, including complexity, connectivity, adaptability, additional loss, and attention mechanisms. Finally, we summarize bioinformatics applications of graph pooling operators in omics, including graphs of gene interaction, medical images, and protein structures for drug discovery and disease diagnosis. Furthermore, we showcase the impact of graph pooling operators on research in specific real-world domains, with a focus on prediction performance and model interpretability. This review provides methodological insights in machine learning based graph modeling and related omics research, as well as an ongoing resource by gathering related papers and code in a dedicated GitHub repository (https://github.com/Hou-WJ/Graph-Pooling-Operators-and-Bioinformatics-Applications). [ABSTRACT FROM AUTHOR]

Increasing global life expectancy motivates investigations of molecular mechanisms of aging and age‐related diseases. This study examines age‐associated changes in red blood cells (RBCs), the most numerous host cell in humans. Four cohorts, including healthy individuals and patients with sickle cell disease, were analyzed to define age‐dependent changes in RBC metabolism. Over 15,700 specimens from 13,757 humans were examined, a major expansion over previous studies of RBCs in aging. Multi‐omics approaches identified chronological age‐related alterations in the arginine pathway with increased arginine utilization in RBCs from older individuals. These changes were consistent across healthy and sickle cell disease cohorts and were influenced by genetic variation, sex, and body mass index. Integrating multi‐omics data and metabolite quantitative trait loci (mQTL) in humans and 525 diversity outbred mice functionally linked metabolism of arginine during RBC storage to increased vesiculation—a hallmark of RBC aging—and lower post‐transfusion hemoglobin increments. Thus, arginine metabolism is a biomarker of RBC and organismal aging, suggesting potential new targets for addressing sequelae of aging. [ABSTRACT FROM AUTHOR]

In nature, germination of orchid seeds and early plant development rely on a symbiotic association with orchid mycorrhizal (ORM) fungi. These fungi provide the host with the necessary nutrients and facilitate the transition from embryos to protocorms. Despite recent advances in omics technologies, our understanding of this symbiosis remains limited, particularly during the initial stages of the interaction. To address this gap, we employed transcriptomics and metabolomics to investigate the early responses occurring in the mycorrhizal fungus Tulasnella sp. isolate SV6 when co-cultivated with orchid seeds of Serapias vomeracea. The integration of data from gene expression and metabolite profiling revealed the activation of some fungal signalling pathways before the establishment of the symbiosis. Prior to seed contact, an indole-related metabolite was produced by the fungus, and significant changes in the fungal lipid profile occurred throughout the symbiotic process. Additionally, the expression of plant cell wall-degrading enzymes (PCWDEs) was observed during the pre-symbiotic stage, as the fungus approached the seeds, along with changes in amino acid metabolism. Thus, the dual-omics approach employed in this study yielded novel insights into the symbiotic relationship between orchids and ORM fungi and suggest that the ORM fungus responds to the presence of the orchid seeds prior to contact. [ABSTRACT FROM AUTHOR]

Background: Neuroblastoma (NB), a prevalent extracranial solid tumor in children, stems from the neural crest. Omics technologies are extensively employed in NB, and We analyzed published articles on NB omics to understand the research trends and hot topics in NB omics. Method: We collected all articles related to NB omics published from 2005 to 2023 from the Web of Science Core Collection database. Subsequently, we conducted analyses using VOSviewer, CiteSpace, Bibliometrix, and the Bibliometric online analysis platform (https://bibliometric.com/). Results: We included a total of 514 articles in our analysis. The increasing number of publications in this field since 2020 indicates growing attention to NB omics, gradually entering a mature development stage. These articles span 50 countries and 1,000 institutions, involving 3,669 authors and 292 journals. The United States has the highest publication output and collaboration with other countries, with Germany being the most frequent collaborator. Capital Medical University and the German Cancer Research Center are the institutions with the highest publication count. The Journal of Proteome Research and the Journal of Biological Chemistry are the most prolific journal and most co-cited journal, respectively. Wang, W, and Maris, JM are the scholars with the highest publication count and co-citations in this field. "Neuroblastoma" and "Expression" are the most frequent keywords, while "classification," "Metabolism," "Cancer," and "Diagnosis" are recent key terms. The article titled "Neuroblastoma" by John M. Maris is the most cited reference in this analysis. Conclusion: The continuous growth in NB omics research underscores its increasing significance in the scientific community. Omics technologies have facilitated the identification of potential biomarkers, advancements in personalized medicine, and the development of novel therapeutic strategies. Despite these advancements, the field faces significant challenges, including tumor heterogeneity, data standardization issues, and the translation of research findings into clinical practice. [ABSTRACT FROM AUTHOR]

Summary: In the marine environment, seaweeds (i.e. marine macroalgae) provide a wide range of ecological services and economic benefits. Like land plants, seaweeds do not provide these services in isolation, rather they rely on their associated microbial communities, which together with the host form the seaweed holobiont. However, there is a poor understanding of the mechanisms shaping these complex seaweed–microbe interactions, and of the evolutionary processes underlying these interactions. Here, we identify the current research challenges and opportunities in the field of seaweed holobiont biology. We argue that identifying the key microbial partners, knowing how they are recruited, and understanding their specific function and their relevance across all seaweed life history stages are among the knowledge gaps that are particularly important to address, especially in the context of the environmental challenges threatening seaweeds. We further discuss future approaches to study seaweed holobionts, and how we can apply the holobiont concept to natural or engineered seaweed ecosystems. [ABSTRACT FROM AUTHOR]

Trichoderma spp. is known for its ability to enhance plant growth and suppress disease, but the mechanisms for its interaction with host plants and pathogens remain unclear. This study investigated the transcriptomics and metabolomics of peanut plants (Arachis hypogaea L.) inoculated with Trichoderma harzianum QT20045, in the absence and presence of the stem rot pathogen Sclerotium rolfsii JN3011. Under the condition without pathogen stress, the peanut seedlings inoculated with QT20045 showed improved root length and plant weight, increased indole acetic acid (IAA) production, and reduced ethylene level, with more active 1-aminocyclopropane-1-carboxylate acid (ACC) synthase (ACS) and ACC oxidase (ACO), compared with the non-inoculated control. Under the pathogen stress, the biocontrol efficacy of QT20045 against S. rolfsii was 78.51%, with a similar effect on plant growth, and IAA and ethylene metabolisms to the condition with no biotic stress. Transcriptomic analysis of peanut root revealed that Trichoderma inoculation upregulated the expression of certain genes in the IAA family but downregulated the genes in the ACO family (AhACO1 and AhACO) and ACS family (AhACS3 and AhACS1) consistently in the absence and presence of pathogens. During pathogen stress, QT20045 inoculation leads to the downregulation of the genes in the pectinesterase family to keep the host plant's cell wall stable, along with upregulation of the AhSUMM2 gene to activate plant defense responses. In vitro antagonistic test confirmed that QT20045 suppressed S. rolfsii growth through mechanisms of mycelial entanglement, papillary protrusions, and decomposition. Our findings highlight that Trichoderma inoculation is a promising tool for sustainable agriculture, offering multiple benefits from pathogen control to enhanced plant growth and soil health. [ABSTRACT FROM AUTHOR]

Many soils face dual challenges of cadmium (Cd) contamination and salinization. However, the response of crops, especially wheat, to combined Cd and salinity stress is not understood. Here, wheat was grown in a hydroponic model for 14 days under single and combined Cd and NaCl stresses. Growth parameters, tissue Cd2+ and Na+ contents, and leaf chlorophyll (Chl), O2•-, and MDA levels were determined. Comparative transcriptomic and metabolomic analyses of the leaves were performed. The results showed that combined stress had a greater inhibitory effect on Chl contents and generated more O2•- and MDA, resulting in more severe wheat growth retardation than those under Cd or NaCl stress. Stress-induced decrease in Chl levels may be attributed to the inhibition of Chl biosynthesis, activation of Chl degradation, or a decline in glutamate content. Cd addition weakened the promotional effect of NaCl on SOS1 gene expression, thereby increasing the Na+ content. Contrastingly, NaCl supplementation downregulated the Nramp and ZIP gene expressions related to Cd uptake and transport, thereby impeding Cd2+ accumulation. All stresses enhanced tryptophan content via promoting tryptophan biosynthesis. Meanwhile, Cd and NaCl stresses activated phenylpropanoid biosynthesis and purine metabolism, respectively, thereby increasing the levels of caffeic acid, fumaric acid, and uric acid. Activating the TCA cycle was important in the wheat's response to combined stress. Additionally, NaCl and combined stresses affected starch and sucrose metabolism, resulting in sucrose and trehalose accumulation. Our findings provide a comprehensive understanding of the response of wheat to the combined Cd and salinity stress. [ABSTRACT FROM AUTHOR]

Featured Application: Featured Application: The potential application of the work relates to novel digital twins of patients or their organs based on artificial intelligence. Digital society strategies in healthcare include the rapid development of digital twins (DTs) for patients and human organs in medical research and the use of artificial intelligence (AI) in clinical practice to develop effective treatments in a cheaper, quicker, and more effective manner. This is facilitated by the availability of large historical datasets from previous clinical trials and other real-world data sources (e.g., patient biometrics collected from wearable devices). DTs can use AI models to create predictions of future health outcomes for an individual patient in the form of an AI-generated digital twin to support the rapid assessment of in silico intervention strategies. DTs are gaining the ability to update in real time in relation to their corresponding physical patients and connect to multiple diagnostic and therapeutic devices. Support for this form of personalized medicine is necessary due to the complex technological challenges, regulatory perspectives, and complex issues of security and trust in this approach. The challenge is also to combine different datasets and omics to quickly interpret large datasets in order to generate health and disease indicators and to improve sampling and longitudinal analysis. It is possible to improve patient care through various means (simulated clinical trials, disease prediction, the remote monitoring of apatient's condition, treatment progress, and adjustments to the treatment plan), especially in the environments of smart cities and smart territories and through the wider use of 6G, blockchain (and soon maybe quantum cryptography), and the Internet of Things (IoT), as well as through medical technologies, such as multiomics. From a practical point of view, this requires not only efficient validation but also seamless integration with the existing healthcare infrastructure. [ABSTRACT FROM AUTHOR]

Dinoflagellates are a remarkable group of protists, not only for their association with harmful algal blooms and coral reefs but also for their numerous characteristics deviating from the rules of eukaryotic biology. Genome research on dinoflagellates has lagged due to their immense genome sizes in most species (~ 1-250 Gbp). Nevertheless, the last decade marked a fruitful era of dinoflagellate genomics, with 27 genomes sequenced and many insights attained. This review aims to synthesize information from these genomes, along with other omic data, to reflect on where we are now in understanding dinoflagellates and where we are heading in the future. The most notable insights from the decade-long genomics work include: (1) dinoflagellate genomes have been expanded in multiple times independently, probably by a combination of rampant retroposition, accumulation of repetitive DNA, and genome duplication; (2) Symbiodiniacean genomes are highly divergent, but share about 3,445 core unigenes concentrated in 219 KEGG pathways; (3) Most dinoflagellate genes are encoded unidirectionally and are not intron-poor; (4) The dinoflagellate nucleus has undergone extreme evolutionary changes, including complete or nearly complete loss of nucleosome and histone H1, and acquisition of dinoflagellate viral nuclear protein (DVNP); (5) Major basic nuclear protein (MBNP), histone-like protein (HLP), and bacterial HU-like protein (HCc) belong to the same protein family, and MBNP can be the unifying name; (6) Dinoflagellate gene expression is regulated by poorly understood mechanisms, but microRNA and other epigenetic mechanisms are likely important; (7) Over 50% of dinoflagellate genes are "dark" and their functions remain to be deciphered using functional genetics; (8) Initial insights into the genomic basis of parasitism and mutualism have emerged. The review then highlights functionally unique and interesting genes. Future research needs to obtain a finished genome, tackle large genomes, characterize the unknown genes, and develop a quantitative molecular ecological model for addressing ecological questions. [ABSTRACT FROM AUTHOR]

Simple Summary: Multi-omics has become a crucial tool in cancer biomarker discovery over the last decade, including for prostate cancer. This approach integrates data from various biological layers, enhancing our understanding of complex, heterogeneous cancers. Multi-omics accelerates precision medicine by identifying new biomarkers and therapeutic targets, potentially improving treatment options for advanced prostate cancer patients. This review discusses traditional prostate cancer biomarkers and highlights recent single-omics and multi-omics advances that have improved clinical diagnostic and prognostic capabilities. Prostate cancer remains a significant health challenge, being the most prevalent non-cutaneous cancer in men worldwide. This review discusses the critical advancements in biomarker discovery using single-omics and multi-omics approaches. Multi-omics, integrating genomic, transcriptomic, proteomic, metabolomic, and epigenomic data, offers a comprehensive understanding of the molecular heterogeneity of prostate cancer, leading to the identification of novel biomarkers and therapeutic targets. This holistic approach not only enhances the specificity and sensitivity of prostate cancer detection but also supports the development of personalized treatment strategies. Key studies highlighted include the identification of novel genes, genetic mutations, peptides, metabolites, and potential biomarkers through multi-omics analyses, which have shown promise in improving prostate cancer management. The integration of multi-omics in clinical practice can potentially revolutionize prostate cancer prognosis and treatment, paving the way for precision medicine. This review underscores the importance of continued research and the application of multi-omics to overcome current challenges in prostate cancer diagnosis and therapy. [ABSTRACT FROM AUTHOR]

Simple Summary: HER2 is a well-known driver of worse outcomes for many types of cancer, including prostate cancer. While HER2 has been previously evaluated in prostate cancer bench studies and clinical trials, there has been a lack of diversity in the experimental designs and protocols. For this reason, it has only recently been reported that Black men with prostate cancer may have a higher prevalence of HER2 overexpression. To thoroughly address health inequities that exist for Black men with prostate cancer, it is critical to utilize diverse biospecimens and enroll diverse study participants into studies that evaluate genetic and molecular contributors to worse prognosis for high-risk populations. In this review, we reconsider the role of HER2 in prostate cancer with an approach that incorporates the effects of race and genetic ancestry. Human epidermal growth factor receptor 2 (HER2) is a major driver of disease progression, treatment resistance, and worse survival for patients with various types of cancers, including prostate cancer. However, key bench studies and clinical trials have failed to evaluate the role of HER2 in prostate cancer using racially diverse experimental designs and protocols. This lack of diversity represents what has been the status quo of cancer research in the United States for decades. In the case of prostate cancer, homogenic study designs are problematic as Black men are much more likely to be diagnosed and die from aggressive and incurable forms of the disease. Therefore, the strategic inclusion of biospecimens collected from Black patients as well as the recruitment and enrollment of Black men into prostate cancer clinical trials is necessary to comprehensively evaluate genetic and molecular factors that contribute to variable outcomes in this high-risk population. Additionally, a higher prevalence of HER2 expression in Black men was recently reported in a small cohort of prostate cancer patients and may contribute to worsened prognosis. In this review, we carefully consider the role of HER2 in prostate cancer while, for the first time, taking into account the influences of race and genetic ancestry. [ABSTRACT FROM AUTHOR]

Patients with chronic kidney disease (CKD) have increased oxidative stress and chronic inflammation, which may escalate the production of advanced glycation end‐products (AGEs). High soluble receptor for AGE (sRAGE) and low estimated glomerular filtration rate (eGFR) levels are associated with CKD and aging. We evaluated whether eGFR calculated from creatinine and cystatin C share pleiotropic genetic factors with sRAGE. We employed whole‐genome sequencing and correlated meta‐analyses on combined genome‐wide association study (GWAS) p‐values in 4182 individuals (age range: 24–110) from the Long Life Family Study (LLFS). We also conducted transcriptome‐wide association studies (TWAS) on whole blood in a subset of 1209 individuals. We identified 59 pleiotropic GWAS loci (p < 5 × 10−8) and 17 TWAS genes (Bonferroni‐p < 2.73 × 10−6) for eGFR traits and sRAGE. TWAS genes, LSP1 and MIR23AHG, were associated with eGFR and sRAGE located within GWAS loci, lncRNA‐KCNQ1OT1 and CACNA1A/CCDC130, respectively. GWAS variants were eQTLs in the kidney glomeruli and tubules, and GWAS genes predicted kidney carcinoma. TWAS genes harbored eQTLs in the kidney, predicted kidney carcinoma, and connected enhancer‐promoter variants with kidney function‐related phenotypes at p < 5 × 10−8. Additionally, higher allele frequencies of protective variants for eGFR traits were detected in LLFS than in ALFA‐Europeans and TOPMed, suggesting better kidney function in healthy‐aging LLFS than in general populations. Integrating genomic annotation and transcriptional gene activity revealed the enrichment of genetic elements in kidney function and aging‐related processes. The identified pleiotropic loci and gene expressions for eGFR and sRAGE suggest their underlying shared genetic effects and highlight their roles in kidney‐ and aging‐related signaling pathways. [ABSTRACT FROM AUTHOR]

Mango has frequently encountered severe climate and environmental challenges such as low temperatures, seriously affecting the sustainable development of the industry. In the study, physiological measurements showed that the activities of superoxide dismutase (SOD) and peroxidase (POD) were found to be higher in Jinhuang (JH) mango plants than those of Tainong (TN) mango plants under cold stress, indicating cold tolerant (JH) and non-cold tolerant (TN) mango varieties were firstly determined. Subsequently, transcriptomics showed 8,337 and 7,996 differentially expressed genes (DEGs) were respectively identified in JH and TN mango varieties treated at 4 °C for 36 h, while more DEGs (10,683 and 10,723) were screened when treated at 4 °C for 72 h. Quantitative real-time PCR (qRT-PCR) of the selected DEGs confirmed their transcriptional levels displayed agreement to the transcriptome data. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses showed two primary cold resistant regulation pathways, photosynthesis-antenna proteins pathway and photosynthesis pathway, were both significant annotated in the two mango varieties, indicating share the common regulation mechanism response to cold stress. However, five specific cold resistant pathways, such as amino acid and carbohydrate metabolisms, were identified in JH mango variety with cold stress for longer duration, indicating the specific regulation pathways in the cold tolerant mango varieties. Furthermore, 43 ethylene-responsive transcription factors (ERFs) were significantly annotated in JH mango after cold-treated for 72 h comparing with the control group, and three of them ERF109-1, ERF017-1 and ERF017-2 were highly expressed, which may play important regulatory roles in plant cold resistance. These results provided insights into the primary and specific molecular mechanisms of different mango varieties resistance to chill. [ABSTRACT FROM AUTHOR]

Chinese cabbage, Brassica rapa L. subsp. pekinensis is a crucial and extensively consumed vegetable in the world, especially Eastern Asia. The market demand for this leafy vegetable increases year by year, resulting in multiple challenges for agricultural researchers worldwide. Multi-omic approaches and the integration of functional genomics helps us understand the relationships between Chinese cabbage genomes and phenotypes under specific physiological and environmental conditions. However, challenges exist in integrating multi-omics for the functional analysis of genes and for developing potential traits for Chinese cabbage improvement. However, the panomics platform allows for the integration of complex omics, enhancing our understanding of molecular regulator networks in Chinese cabbage agricultural traits. In addition, the agronomic features of Chinese cabbage are significantly impacted by the environment. The expression of these agricultural features is tightly regulated by a combination of signals from both the internal regulatory network and the external growth environment. To comprehend the molecular process of these characteristics, it is necessary to have a prior understanding of molecular breeding for the objective of enhancing quality. While the use of various approaches in Chinese cabbage is still in its early stages, recent research has shown that it has the potential to uncover new regulators both rapidly and effectively, leading to updated regulatory networks. In addition, the utilization of the efficient transformation technique in conjunction with gene editing using CRISPR/Cas9 will result in a reduction in time requirements and facilitate a more precise understanding of the role of the regulators. Numerous studies about Chinese cabbage have been conducted in the past two decades, but a comprehensive review about its genome still limited. This review provides a concise summary of the latest discoveries in genomic research related to Brassica and explores the potential future developments for this species. [ABSTRACT FROM AUTHOR]

Osteoporosis, characterized by low BMD, is a highly heritable metabolic bone disorder. Although single nucleotide variations (SNVs) have been extensively studied, they explain only a fraction of BMD heritability. Although genomic structural variations (SVs) are large-scale genomic alterations that contribute to genetic diversity in shaping phenotypic variations, the role of SVs in osteoporosis susceptibility remains poorly understood. This study aims to identify and prioritize genes that harbor BMD-related SVs. We performed whole genome sequencing on 4982 subjects from the Louisiana Osteoporosis Study. To obtain high-confidence SVs, the detection of SVs was performed using an ensemble approach. The SVs were tested for association with BMD variation at the hip (HIP), femoral neck (FNK), and lumbar spine (SPN), respectively. Additionally, we conducted co-occurrence analysis using multi-omics approaches to prioritize the identified genes based on their functional importance. Stratification was employed to explore the sex- and ethnicity-specific effects. We identified significant SV-BMD associations: 125 for FNK-BMD, 99 for SPN-BMD, and 83 for HIP-BMD. We observed SVs that were commonly associated with both FNK and HIP BMDs in our combined and stratified analyses. These SVs explain 13.3% to 19.1% of BMD variation. Novel bone-related genes emerged, including LINC02370, ZNF family genes, and ZDHHC family genes. Additionally, FMN2, carrying BMD-related deletions, showed associations with FNK or HIP BMDs, with sex-specific effects. The co-occurrence analysis prioritized an RNA gene LINC00494 and ZNF family genes positively associated with BMDs at different skeletal sites. Two potential causal genes, IBSP and SPP1, for osteoporosis were also identified. Our study uncovers new insights into genetic factors influencing BMD through SV analysis. We highlight BMD-related SVs, revealing a mix of shared and specific genetic influences across skeletal sites and gender or ethnicity. These findings suggest potential roles in osteoporosis pathophysiology, opening avenues for further research and therapeutic targets. [ABSTRACT FROM AUTHOR]

Ganoderma sp., the fungal agent causing basal stem rot (BSR), poses a severe threat to global oil palm production. Alarming increases in BSR occurrences within oil palm growing zones are attributed to varying effectiveness in its current management strategies. Asymptomatic progression of the disease and the continuous monoculture of oil palm pose challenges for prompt and effective management. Therefore, the development of precise, early, and timely detection techniques is crucial for successful BSR management. Conventional methods such as visual assessments, culture-based assays, and biochemical and physiological approaches prove time-consuming and lack specificity. Serological-based diagnostic methods, unsuitable for fungal diagnostics due to low sensitivity, assay affinity, cross-contamination which further underscores the need for improved techniques. Molecular PCR-based assays, utilizing universal, genus-specific, and species-specific primers, along with functional primers, can overcome the limitations of conventional and serological methods in fungal diagnostics. Recent advancements, including real-time PCR, biosensors, and isothermal amplification methods, facilitate accurate, specific, and sensitive Ganoderma detection. Comparative whole genomic analysis enables high-resolution discrimination of Ganoderma at the strain level. Additionally, omics tools such as transcriptomics, proteomics, and metabolomics can identify potential biomarkers for early detection of Ganoderma infection. Innovative on-field diagnostic techniques, including remote methods like volatile organic compounds profiling, tomography, hyperspectral and multispectral imaging, terrestrial laser scanning, and Red-Green-Blue cameras, contribute to a comprehensive diagnostic approach. Ultimately, the development of point-of-care, early, and cost-effective diagnostic techniques accessible to farmers is vital for the timely management of BSR in oil palm plantations. [ABSTRACT FROM AUTHOR]

Background/Objectives: Multisystem Inflammatory Syndrome in Children (MIS-C) is a post-infectious complication of COVID-19. MIS-C has overlapping features with other pediatric inflammatory disorders including Kawasaki Disease (KD), Macrophage Activation Syndrome (MAS), Toxic Shock Syndrome and sepsis. The exact mechanisms responsible for the clinical overlap between MIS-C and these conditions remain unclear, and biomarkers that could distinguish MIS-C from its clinical mimics are lacking. This study aimed to provide an overview of how proteomic methods, like Mass Spectrometry (MS) and affinity-based proteomics, can offer a detailed understanding of pathophysiology and aid in the diagnosis and prognosis of MIS-C. Methods: A narrative review of relevant studies published up to July 2024 was conducted. Results: We identified 15 studies and summarized their key proteomic findings. These studies investigated the serum or plasma proteome of MIS-C patients using MS, Proximity Extension, or Aptamer-based assays. The studies associated the proteomic profile of MIS-C with laboratory and clinical parameters and/or compared it with that of other diseases including acute COVID-19, KD, MAS, pediatric rheumatic diseases, sepsis and myocarditis or pericarditis following COVID-19 mRNA immunization. Depending on the method and the control group, different proteins were increased or decreased in the MIS-C group. The limitations and challenges in MIS-C proteomic research are also discussed, and future research recommendations are provided. Conclusions: Although proteomics appear to be a promising approach for understanding the pathogenesis and uncovering candidate biomarkers in MIS-C, proteomic studies are still needed to recognize and validate biomarkers that could accurately discriminate MIS-C from its clinical mimics. [ABSTRACT FROM AUTHOR]

Necrotizing enterocolitis (NEC) is the most prevalent and potentially fatal intestinal injury mainly affecting premature infants, with significant long-term consequences for those who survive. This review explores the scale of the problem, highlighting advancements in epidemiology, the understanding of pathophysiology, and improvements in the prediction and diagnosis of this complex, multifactorial, and multifaced disease. Additionally, we focus on the potential role of metabolomics in distinguishing NEC from other conditions, which could allow for an earlier and more accurate classification of intestinal injuries in infants. By integrating metabolomic data with other diagnostic approaches, it is hoped to enhance our ability to predict outcomes and tailor treatments, ultimately improving care for affected infants. [ABSTRACT FROM AUTHOR]

The Religious Order Study and Memory and Aging Project (ROSMAP) is an initiative that integrates two longitudinal cohort studies, which have been collecting clinicopathological and molecular data since the early 1990s. This extensive dataset includes a wide array of omic data, revealing the complex interactions between molecular levels in neurodegenerative diseases (ND) and aging. Neurodegenerative diseases (ND) are frequently associated with morbidity and cognitive decline in older adults. Omics research, in conjunction with clinical variables, is crucial for advancing our understanding of the diagnosis and treatment of neurodegenerative diseases. This summary reviews the extensive omics research--encompassing genomics, transcriptomics, proteomics, metabolomics, epigenomics, and multiomics--conducted through the ROSMAP study. It highlights the significant advancements in understanding the mechanisms underlying neurodegenerative diseases, with a particular focus on Alzheimer's disease. [ABSTRACT FROM AUTHOR]

Achalasia is a rare and complex esophageal disease of unknown etiology characterized by difficulty in swallowing due to the lack of opening of the lower esophageal sphincter and the absence of esophageal peristalsis. Recent advancements in technology for analyzing DNA, RNA and biomolecules in high-throughput techniques are offering new opportunities to better understand the etiology and the pathogenetic mechanisms underlying achalasia. Through this narrative review of the scientific literature, we aim to provide a comprehensive assessment of the state-of-the-art knowledge on omics of achalasia, with particular attention to those considered relevant to the pathogenesis of the disease. The notion and importance of the multi-omics approach, its limitations and future directions are also introduced, and it is highlighted how the integration of single omics data will lead to new insights into the development of achalasia and offer clinical tools which will allow early diagnosis and better patient management. [ABSTRACT FROM AUTHOR]

The therapeutic usage of physical stimuli is framed in a highly heterogeneous research area, with variable levels of maturity and of translatability into clinical application. In particular, electrostimulation is deeply studied for its application on the autonomous nervous system, but less is known about the anti- inflammatory effects of such stimuli beyond the inflammatory reflex. Further, reproducibility and meta-analyses are extremely challenging, owing to the limited rationale on dosage and experimental standardization. It is specifically to address the fundamental question on the anti-inflammatory effects of electricity on biological systems, that we propose a series of controlled experiments on the effects of direct and alternate current delivered on a standardized 3D bioconstruct constituted by fibroblasts and keratinocytes in a collagen matrix, in the presence or absence of TNF- α as conventional inflammation inducer. This selected but systematic exploration, with transcriptomics backed by metabolomics at specific time points allows to obtain the first systemic overview of the biological functions at stake, highlighting the differential anti-inflammatory potential of such approaches, with promising results for 5 V direct current stimuli, correlating with the wound healing process. With our results, we wish to set the base for a rigorous systematic approach to the problem, fundamental towards future elucidations of the detailed mechanisms at stake, highlighting both the healing and damaging potential of such approaches. [ABSTRACT FROM AUTHOR]

Personalized nutrition (PN) has gained much attention as a tool for empowerment of consumers to promote changes in dietary behavior, optimizing health status and preventing diet related diseases. Generalized implementation of PN faces different obstacles, one of the most relevant being metabolic characterization of the individual. Although omics technologies allow for assessment the dynamics of metabolism with unprecedented detail, its translatability as affordable and simple PN protocols is still difficult due to the complexity of metabolic regulation and to different technical and economical constrains. In this work, we propose a conceptual framework that considers the dysregulation of a few overarching processes, namely Carbohydrate metabolism, lipid metabolism, inflammation, oxidative stress and microbiota-derived metabolites, as the basis of the onset of several non-communicable diseases. These processes can be assessed and characterized by specific sets of proteomic, metabolomic and genetic markers that minimize operational constrains and maximize the information obtained at the individual level. Current machine learning and data analysis methodologies allow the development of algorithms to integrate omics and genetic markers. Reduction of dimensionality of variables facilitates the implementation of omics and genetic information in digital tools. This framework is exemplified by presenting the EU-Funded project PREVENTOMICS as a use case. [ABSTRACT FROM AUTHOR]

Probiotics are amply studied and applied dietary supplements of greater consumer acceptance. Nevertheless, the emerging evidence on probiotics-mediated potential risks, especially among immunocompromised individuals, necessitates careful and in-depth safety studies. The traditional probiotic safety evaluation methods investigate targeted phenotypic traits, such as virulence factors and antibiotic resistance. However, the rapid innovation in omics technologies has offered an impactful means to ultimately sequence and unknot safety-related genes or their gene products at preliminary levels. Further validating the genome features using an array of phenotypic tests would provide an absolute realization of gene expression dynamics. For safety studies in animal models, the in vivo toxicity evaluation guidelines of chemicals proposed by the Organization for Economic Co-operation and Development (OECD) have been meticulously adopted in probiotic research. Future research should also focus on coupling genome-scale safety analysis and establishing a link to its transcriptome, proteome, or metabolome for a fine selection of safe probiotic strains. Considering the studies published over the years, it can be inferred that the safety of probiotics is strain-host-dose-specific. Taken together, an amalgamation of in silico, in vitro, and in vivo approaches are necessary for a fine scale selection of risk-free probiotic strain for use in human applications. [ABSTRACT FROM AUTHOR]

This document provides a summary of various research articles related to antibiotic resistance and treatment strategies. The articles cover topics such as the development of a multi-epitope vaccine against P. melaninogenica infection, the identification of critical factors in antibiotic resistance development in Vibrio mimicus SCCF01, the investigation of antibiotic resistance mechanisms in carbapenem-resistant Acinetobacter baumannii, the molecular mechanism of antibiotic resistance in Weissella cibaria, the role of ISKpn element in conferring mgrB gene mutations in hypervirulent colistin-resistant Klebsiella pneumonia, the potential of medicinal plant-derived compounds as adjunct therapy for tuberculosis, optimization of dosage regimens for colistin and sitafloxacin against A. baumannii, mechanisms responsible for antibiotic resistance in A. baumannii, the structure and functions of integrons and their role in antibiotic resistance, the bacterial composition of extended boar semen and its impact on sperm quality, antibiotic prescribing trends for respiratory tract infections before and during the COVID-19 pandemic, and the current state of antibiotic resistance and emerging challenges. The articles aim to provide insights into the mechanisms of antibiotic resistance and develop effective treatment strategies. [Extracted from the article]

Rationale: Chronic obstructive pulmonary disease (COPD) results from gene–environment interactions over the lifetime. These interactions are captured by epigenetic changes, such as DNA methylation. Objectives: To systematically review the evidence form epigenome-wide association studies related to COPD and lung function. Methods: A systematic literature search performed on PubMed, Embase, and Cumulative Index to Nursing and Allied Health Literature (CINAHL) databases identified 1,947 articles that investigated epigenetic changes associated with COPD and/or lung function; 17 of them met our eligibility criteria, from which data were manually extracted. Differentially methylated positions (DMPs) and/or annotated genes were considered replicated if identified by two or more studies with a P < 1 × 10−4. Measurements and Main Results: Ten studies profiled DNA methylation changes in blood and seven in respiratory samples, including surgically resected lung tissue (n = 3), small airway epithelial brushings (n = 2), BAL (n = 1), and sputum (n = 1). Main results showed: 1) high variability in study design, covariates, and effect sizes, which prevented a formal meta-analysis; 2) in blood samples, 51 DMPs were replicated in relation to lung function and 12 related to COPD; 3) in respiratory samples, 42 DMPs were replicated in relation to COPD but none in relation to lung function; and 4) in COPD versus control studies, 123 genes (2.6% of total) were shared between one or more blood and one or more respiratory samples and associated with chronic inflammation, ion transport, and coagulation. Conclusions: There is high heterogeneity across published COPD and/or lung function epigenome-wide association studies. A few genes (n = 123; 2.6%) were replicated in blood and respiratory samples, suggesting that blood can recapitulate some changes in respiratory tissues. These findings have implications for future research. Systematic Review [protocol] registered with Open Science Framework (OSF). [ABSTRACT FROM AUTHOR]

This article explores the significance of developing new techniques and technical advances in experimental botany to enhance our understanding of plant biology. It specifically focuses on the importance of microscopy and gene editing technology in plant research. The article highlights the different branches of microscopy and their advantages and limitations, as well as specific research articles that utilize microscopy techniques to study autophagosomes, peptide-receptor binding, and redox dynamics. It also discusses the use of CRISPR/Cas9 gene editing technology in plants and provides resources for planning and performing gene editing experiments. The article emphasizes the need for a combination of advanced methods in plant biology research and acknowledges the contributions of previous methodologies to scientific progress. [Extracted from the article]

Biomarkers are valuable tools in clinical research where they allow to predict susceptibility to diseases, or response to specific treatments. Likewise, biomarkers can be extremely useful in the biomanufacturing of therapeutic proteins. Indeed, constraints such as short timelines and the need to find hyper-productive cells could benefit from a data-driven approach during cell line and process development. Many companies still rely on large screening capacities to develop productive cell lines, but as they reach a limit of production, there is a need to go from empirical to rationale procedures. Similarly, during bioprocessing runs, substrate consumption and metabolism wastes are commonly monitored. None of them possess the ability to predict the culture behavior in the bioreactor. Big data driven approaches are being adapted to the study of industrial mammalian cell lines, enabled by the publication of Chinese hamster and CHO genome assemblies which allowed the use of next-generation sequencing with these cells, as well as continuous proteome and metabolome annotation. However, if these different -omics technologies contributed to the characterization of CHO cells, there is a significant effort remaining to apply this knowledge to biomanufacturing methods. The correlation of a complex phenotype such as high productivity or rapid growth to the presence or expression level of a specific biomarker could save time and effort in the screening of manufacturing cell lines or culture conditions. In this review we will first discuss the different biological molecules that can be identified and quantified in cells, their detection techniques, and associated challenges. We will then review how these markers are used during the different steps of cell line and bioprocess development, and the inherent limitations of this strategy. • Biomarkers characterize the physiological state of cells and help predict their behavior. • A biomarker can consist of any macromolecule present in the cell that can be detected and quantified. • Discovering and identifying biomarkers requires specific and customized methods. • In an industrial context, biomarkers enable a more rational and efficient approach to bioprocessing. • Biomarkers are often specific to environmental conditions and, as such, have limitations. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Lentil (Lens culinaris Medik.) is a cool season legume crop that plays vital roles in food and nutritional security, mostly in the least developed countries. Lentil is often cultivated in dry and semi-dry regions, where the primary abiotic factor is drought, which negatively impacts lentil growth and development, resulting in a reduction of yield. To withstand drought-induced multiple negative effects, lentil plants evolved a variety of adaptation strategies that can be classified within three broad categories of drought tolerance mechanisms (i.e., escape, avoidance, and tolerance). Lentil adapts to drought by the modulation of various traits in the root system, leaf architecture, canopy structure, branching, anatomical features, and flowering process. Furthermore, the activation of certain defensive biochemical pathways as well as the regulation of gene functions contributes to lentil drought tolerance. Plant breeders typically employ conventional and mutational breeding approaches to develop lentil varieties that can withstand drought effects; however, little progress has been made in developing drought-tolerant lentil varieties using genomics-assisted technologies. This review highlights the current understanding of morpho-physiological, biochemical, and molecular mechanisms of lentil adaptation to drought stress. We also discuss the potential application of omics-assisted breeding approaches to develop lentil varieties with superior drought tolerance traits. [ABSTRACT FROM AUTHOR]

Hypertension is a major contributor to premature death, owing to the associated increased risk of damage to the heart, brain and kidneys. Although hypertension is manageable by medication and lifestyle changes, the risk increases with age. In an increasingly aged society, the incidence of hypertension is escalating, and is expected to increase the prevalence of (cerebro)vascular events and their associated mortality. Adherence to plant‐based diets improves blood pressure and vascular markers in individuals with hypertension. Food flavonoids have an inhibitory effect towards angiotensin‐converting enzyme (ACE1) and although this effect is greatly diminished upon metabolization, their microbial metabolites have been found to improve endothelial nitric oxide synthase (eNOS) activity. Considering the transmembrane location of ACE1 and eNOS, the ability of (poly)phenols to interact with membrane lipids modulate the cell membrane's biophysical properties and impact on nitric oxide (·NO) synthesis and bioavailability, remain poorly studied. Herein, we provide an overview of the current knowledge on the lipid remodeling of endothelial membranes with age, its impact on the cell membrane's biophysical properties and ·NO permeability across the endothelial barrier. We also discuss the potential of (poly)phenols and other plant‐based compounds as key players in hypertension management, and address the caveats and challenges in adopted methodologies. [ABSTRACT FROM AUTHOR]

Deep learning applications have had a profound impact on many scientific fields, including functional genomics. Deep learning models can learn complex interactions between and within omics data; however, interpreting and explaining these models can be challenging. Interpretability is essential not only to help progress our understanding of the biological mechanisms underlying traits and diseases but also for establishing trust in these model's efficacy for healthcare applications. Recognizing this importance, recent years have seen the development of numerous diverse interpretability strategies, making it increasingly difficult to navigate the field. In this review, we present a quantitative analysis of the challenges arising when designing interpretable deep learning solutions in functional genomics. We explore design choices related to the characteristics of genomics data, the neural network architectures applied, and strategies for interpretation. By quantifying the current state of the field with a predefined set of criteria, we find the most frequent solutions, highlight exceptional examples, and identify unexplored opportunities for developing interpretable deep learning models in genomics. [ABSTRACT FROM AUTHOR]

The endemic, green-lipped mussel (Perna canaliculus), trademarked as Greenshell™ mussel, contributes most to the New Zealand aquaculture industry based on tonnage and export value. Research on mussel immunity is motivated greatly by economical and biosecurity necessities. Indeed, mussel aquaculture is threatened by pathogenic micro-organisms and environmental stressors. As such there is a need to understand the mechanisms that drive mussel immune responses and the associated interactions with the environment. Specifically, this review (1) analyses the existing immunological studies conducted on P. canaliculus, (2) evaluates the literature pertaining to mussel immunity at the cellular and humoral levels, (3) identifies and discusses pathogens that are relevant to P. canaliculus, (4) focuses on the virulent factors employed by mussel pathogens likely to induce diseases, (5) provides a comprehensive analysis of the response mechanisms employed by mussels to various stressors, and (6) explores omics applications and future perspectives in mussel immunology. Finally, this review highlights various strategies from immunological research, such as gene rearrangement, probiotics, immunostimulants, and selective breeding, promising to enhance mussel health and resilience in aquaculture. By exploring these immunological findings and their practical applications, this review contributes to sustainable mussel aquaculture, improving productivity and disease management in the industry. [ABSTRACT FROM AUTHOR]

Genetically engineered cyanobacterial strains that have improved growth rate, biomass productivity, and metabolite productivity could be a better option for sustainable bio-metabolite production. The global demand for biobased metabolites with nutraceuticals and health benefits has increased due to their safety and plausible therapeutic and nutritional utility. Cyanobacteria are solar-powered green cellular factories that can be genetically tuned to produce metabolites with nutraceutical and pharmaceutical benefits. The present review discusses biotechnological endeavors for producing bioprospective compounds from genetically engineered cyanobacteria and discusses the challenges and troubleshooting faced during metabolite production. This review explores the cyanobacterial versatility, the use of engineered strains, and the techno-economic challenges associated with scaling up metabolite production from cyanobacteria. Challenges to produce cyanobacterial bioactive compounds with remarkable nutraceutical values have been discussed. Additionally, this review also summarises the challenges and future prospects of metabolite production from genetically engineered cyanobacteria as a sustainable approach. [ABSTRACT FROM AUTHOR]