Exposure to elevated glucocorticoid during development primes altered transcriptional responses to acute stress in adulthood.

Early life stress (ELS) is a major risk factor for developing psychiatric disorders, with glucocorticoids (GCs) implicated in mediating its effects in shaping adult phenotypes. In this process, exposure to high levels of developmental GC (hdGC) is thought to induce molecular changes that prime differential adult responses. However, identities of molecules targeted by hdGC exposure are not completely known. Here, we describe lifelong molecular consequences of hdGC exposure using a newly developed zebrafish double-hit stress model, which shows altered behaviors and stress hypersensitivity in adulthood. We identify a set of primed genes displaying altered expression only upon acute stress in hdGC-exposed adult fish brains. Interestingly, this gene set is enriched in risk factors for psychiatric disorders in humans. Lastly, we identify altered epigenetic regulatory elements following hdGC exposure. Thus, our study provides comprehensive datasets delineating potential molecular targets mediating the impact of hdGC exposure on adult responses.

The Alpine ibex (Capra ibex) gut microbiome, seasonal dynamics, and potential application in lignocellulose bioconversion.

Aiming to shed light on the biology of wild ruminants, we investigated the gut microbiome seasonal dynamics of the Alpine ibex (Capra ibex) from the Central Italian Alps. Feces were collected in spring, summer, and autumn during non-invasive sampling campaigns. Samples were analyzed by 16S rRNA amplicon sequencing, shotgun metagenomics, as well as targeted and untargeted metabolomics. Our findings revealed season-specific compositional and functional profiles of the ibex gut microbiome that may allow the host to adapt to seasonal changes in available forage, by fine-tuning the holobiont catabolic layout to fully exploit the available food. Besides confirming the importance of the host-associated microbiome in providing the phenotypic plasticity needed to buffer dietary changes, we obtained species-level genome bins and identified minimal gut microbiome community modules of 11-14 interacting strains as a possible microbiome-based solution for the bioconversion of lignocellulose to high-value compounds, such as volatile fatty acids.

Comparative analysis of integrative classification methods for multi-omics data.

Recent advances in sequencing, mass spectrometry, and cytometry technologies have enabled researchers to collect multiple 'omics data types from a single sample. These large datasets have led to a growing consensus that a holistic approach is needed to identify new candidate biomarkers and unveil mechanisms underlying disease etiology, a key to precision medicine. While many reviews and benchmarks have been conducted on unsupervised approaches, their supervised counterparts have received less attention in the literature and no gold standard has emerged yet. In this work, we present a thorough comparison of a selection of six methods, representative of the main families of intermediate integrative approaches (matrix factorization, multiple kernel methods, ensemble learning, and graph-based methods). As non-integrative control, random forest was performed on concatenated and separated data types. Methods were evaluated for classification performance on both simulated and real-world datasets, the latter being carefully selected to cover different medical applications (infectious diseases, oncology, and vaccines) and data modalities. A total of 15 simulation scenarios were designed from the real-world datasets to explore a large and realistic parameter space (e.g. sample size, dimensionality, class imbalance, effect size). On real data, the method comparison showed that integrative approaches performed better or equally well than their non-integrative counterpart. By contrast, DIABLO and the four random forest alternatives outperform the others across the majority of simulation scenarios. The strengths and limitations of these methods are discussed in detail as well as guidelines for future applications.

Epigenomic features associated with body temperature stabilize tissues during cold exposure in cold-resistant pigs.

Cold stress in low-temperature environments can trigger changes in gene expression, but epigenomics regulation of temperature stability in vital tissues, including the fat and diencephalon, is still unclear. Here, we explore the cold-induced changes in epigenomic features in the diencephalon and fat tissues of two cold-resistant Chinese pig breeds, Min and Enshi black (ES) pigs, utilizing H3K27ac CUT&Tag, RNA-seq, and selective signature analysis. Our results show significant alterations in H3K27ac modifications in the diencephalon of Min pigs and the fat of ES pigs after cold exposure. Dramatic changes in H3K27ac modifications in Min pigs are primarily associated with genes involved in energy metabolism and hormone regulation, whereas those in ES pigs are primarily associated with immunity-related genes. Moreover, transcription factors PRDM1 and HSF1, which show evidence of selection, are enriched in genomic regions presenting cold-responsive alterations in H3K27ac modification in the Min pig diencephalon and ES pig fat, respectively. Our results indicate the diversity of epigenomic response mechanisms to cold exposure between Min and ES pigs, providing unique epigenetic resources for studies of low-temperature adaptation in large mammals.

RBMS1 reflects a distinct microenvironment and promotes tumor progression in ocular melanoma.

Ocular melanoma, including uveal melanoma (UM) and conjunctival melanoma (CM), is the most common ocular cancer among adults with a high rate of recurrence and poor prognosis. Loss of epigenetic homeostasis disturbed gene expression patterns, resulting in oncogenesis. Herein, we comprehensively analyzed the DNA methylation, transcriptome profiles, and corresponding clinical information of UM patients through multiple machine-learning algorithms, finding that a methylation-driven gene RBMS1 was correlated with poor clinical outcomes of UM patients. RNA-seq and single-cell RNA-seq analyses revealed that RBMS1 reflected diverse tumor microenvironments, where high RBMS1 expression marked an immune active TME. Furthermore, we found that tumor cells were identified to have the higher communication probability in RBMS1+ state. The functional enrichment analysis revealed that RBMS1 was associated with pigment granule and melanosome, participating in cell proliferation as well as apoptotic signaling pathway. Biological experiments were performed and demonstrated that the silencing of RBMS1 inhibited ocular melanoma proliferation and promoted apoptosis. Our study highlighted that RBMS1 reflects a distinct microenvironment and promotes tumor progression in ocular melanoma, contributing to the therapeutic customization and clinical decision-making.

Multiplexed imaging to reveal tissue dendritic cell spatial localisation and function.

Dendritic cells (DCs) play a pivotal role in immune surveillance, acting as sentinels that coordinate immune responses within tissues. Although differences in the identity and functional states of DC subpopulations have been identified through multiparametric flow cytometry and single-cell RNA sequencing, these methods do not provide information about the spatial context in which the cells are located. This knowledge is crucial for understanding tissue organisation and cellular cross-talk. Recent developments in multiplex imaging techniques can now offer insights into this complex spatial and functional landscape. This review provides a concise overview of these imaging methodologies, emphasising their application in identifying DCs to delineate their tissue-specific functions and aiding newcomers in navigating this field.

Integrative multi-omics analysis unravels the host response landscape and reveals a serum protein panel for early prognosis prediction for ARDS.

BACKGROUND: The multidimensional biological mechanisms underpinning acute respiratory distress syndrome (ARDS) continue to be elucidated, and early biomarkers for predicting ARDS prognosis are yet to be identified. METHODS: We conducted a multicenter observational study, profiling the 4D-DIA proteomics and global metabolomics of serum samples collected from patients at the initial stage of ARDS, alongside samples from both disease control and healthy control groups. We identified 28-day prognosis biomarkers of ARDS in the discovery cohort using the LASSO method, fold change analysis, and the Boruta algorithm. The candidate biomarkers were validated through parallel reaction monitoring (PRM) targeted mass spectrometry in an external validation cohort. Machine learning models were applied to explore the biomarkers of ARDS prognosis. RESULTS: In the discovery cohort, comprising 130 adult ARDS patients (mean age 72.5, 74.6% male), 33 disease controls, and 33 healthy controls, distinct proteomic and metabolic signatures were identified to differentiate ARDS from both control groups. Pathway analysis highlighted the upregulated sphingolipid signaling pathway as a key contributor to the pathological mechanisms underlying ARDS. MAP2K1 emerged as the hub protein, facilitating interactions with various biological functions within this pathway. Additionally, the metabolite sphingosine 1-phosphate (S1P) was closely associated with ARDS and its prognosis. Our research further highlights essential pathways contributing to the deceased ARDS, such as the downregulation of hematopoietic cell lineage and calcium signaling pathways, contrasted with the upregulation of the unfolded protein response and glycolysis. In particular, GAPDH and ENO1, critical enzymes in glycolysis, showed the highest interaction degree in the protein-protein interaction network of ARDS. In the discovery cohort, a panel of 36 proteins was identified as candidate biomarkers, with 8 proteins (VCAM1, LDHB, MSN, FLG2, TAGLN2, LMNA, MBL2, and LBP) demonstrating significant consistency in an independent validation cohort of 183 patients (mean age 72.6 years, 73.2% male), confirmed by PRM assay. The protein-based model exhibited superior predictive accuracy compared to the clinical model in both the discovery cohort (AUC: 0.893 vs. 0.784; Delong test, P < 0.001) and the validation cohort (AUC: 0.802 vs. 0.738; Delong test, P = 0.008). INTERPRETATION: Our multi-omics study demonstrated the potential biological mechanism and therapy targets in ARDS. This study unveiled several novel predictive biomarkers and established a validated prediction model for the poor prognosis of ARDS, offering valuable insights into the prognosis of individuals with ARDS.

Unveiling the molecular landscape of cognitive aging: insights from polygenic risk scores, DNA methylation, and gene expression.

BACKGROUND: Aging represents a significant risk factor for the occurrence of cerebral small vessel disease, associated with white matter (WM) lesions, and to age-related cognitive alterations, though the precise mechanisms remain largely unknown. This study aimed to investigate the impact of polygenic risk scores (PRS) for WM integrity, together with age-related DNA methylation, and gene expression alterations, on cognitive aging in a cross-sectional healthy aging cohort. The PRSs were calculated using genome-wide association study (GWAS) summary statistics for magnetic resonance imaging (MRI) markers of WM integrity, including WM hyperintensities, fractional anisotropy (FA), and mean diffusivity (MD). These scores were utilized to predict age-related cognitive changes and evaluate their correlation with structural brain changes, which distinguish individuals with higher and lower cognitive scores. To reduce the dimensionality of the data and identify age-related DNA methylation and transcriptomic alterations, Sparse Partial Least Squares-Discriminant Analysis (sPLS-DA) was used. Subsequently, a canonical correlation algorithm was used to integrate the three types of omics data (PRS, DNA methylation, and gene expression data) and identify an individual "omics" signature that distinguishes subjects with varying cognitive profiles. RESULTS: We found a positive association between MD-PRS and long-term memory, as well as a correlation between MD-PRS and structural brain changes, effectively discriminating between individuals with lower and higher memory scores. Furthermore, we observed an enrichment of polygenic signals in genes related to both vascular and non-vascular factors. Age-related alterations in DNA methylation and gene expression indicated dysregulation of critical molecular features and signaling pathways involved in aging and lifespan regulation. The integration of multi-omics data underscored the involvement of synaptic dysfunction, axonal degeneration, microtubule organization, and glycosylation in the process of cognitive aging. CONCLUSIONS: These findings provide valuable insights into the biological mechanisms underlying the association between WM coherence and cognitive aging. Additionally, they highlight how age-associated DNA methylation and gene expression changes contribute to cognitive aging.

Advancing lung adenocarcinoma prognosis and immunotherapy prediction with a multi-omics consensus machine learning approach.

Lung adenocarcinoma (LUAD) is a tumour characterized by high tumour heterogeneity. Although there are numerous prognostic and immunotherapeutic options available for LUAD, there is a dearth of precise, individualized treatment plans. We integrated mRNA, lncRNA, microRNA, methylation and mutation data from the TCGA database for LUAD. Utilizing ten clustering algorithms, we identified stable multi-omics consensus clusters (MOCs). These data were then amalgamated with ten machine learning approaches to develop a robust model capable of reliably identifying patient prognosis and predicting immunotherapy outcomes. Through ten clustering algorithms, two prognostically relevant MOCs were identified, with MOC2 showing more favourable outcomes. We subsequently constructed a MOCs-associated machine learning model (MOCM) based on eight MOCs-specific hub genes. Patients characterized by a lower MOCM score exhibited better overall survival and responses to immunotherapy. These findings were consistent across multiple datasets, and compared to many previously published LUAD biomarkers, our MOCM score demonstrated superior predictive performance. Notably, the low MOCM group was more inclined towards 'hot' tumours, characterized by higher levels of immune cell infiltration. Intriguingly, a significant positive correlation between GJB3 and the MOCM score (R = 0.77, p < 0.01) was discovered. Further experiments confirmed that GJB3 significantly enhances LUAD proliferation, invasion and migration, indicating its potential as a key target for LUAD treatment. Our developed MOCM score accurately predicts the prognosis of LUAD patients and identifies potential beneficiaries of immunotherapy, offering broad clinical applicability.

Microbiome modeling: a beginner's guide.

Microbiomes, comprised of diverse microbial species and viruses, play pivotal roles in human health, environmental processes, and biotechnological applications and interact with each other, their environment, and hosts via ecological interactions. Our understanding of microbiomes is still limited and hampered by their complexity. A concept improving this understanding is systems biology, which focuses on the holistic description of biological systems utilizing experimental and computational methods. An important set of such experimental methods are metaomics methods which analyze microbiomes and output lists of molecular features. These lists of data are integrated, interpreted, and compiled into computational microbiome models, to predict, optimize, and control microbiome behavior. There exists a gap in understanding between microbiologists and modelers/bioinformaticians, stemming from a lack of interdisciplinary knowledge. This knowledge gap hinders the establishment of computational models in microbiome analysis. This review aims to bridge this gap and is tailored for microbiologists, researchers new to microbiome modeling, and bioinformaticians. To achieve this goal, it provides an interdisciplinary overview of microbiome modeling, starting with fundamental knowledge of microbiomes, metaomics methods, common modeling formalisms, and how models facilitate microbiome control. It concludes with guidelines and repositories for modeling. Each section provides entry-level information, example applications, and important references, serving as a valuable resource for comprehending and navigating the complex landscape of microbiome research and modeling.

A comprehensive multi-omics analysis reveals unique signatures to predict Alzheimer's disease.

Background: Complex disorders, such as Alzheimer's disease (AD), result from the combined influence of multiple biological and environmental factors. The integration of high-throughput data from multiple omics platforms can provide system overviews, improving our understanding of complex biological processes underlying human disease. In this study, integrated data from four omics platforms were used to characterise biological signatures of AD. Method: The study cohort consists of 455 participants (Control:148, Cases:307) from the Religious Orders Study and Memory and Aging Project (ROSMAP). Genotype (SNP), methylation (CpG), RNA and proteomics data were collected, quality-controlled and pre-processed (SNP = 130; CpG = 83; RNA = 91; Proteomics = 119). Using a diagnosis of Mild Cognitive Impairment (MCI)/AD combined as the target phenotype, we first used Partial Least Squares Regression as an unsupervised classification framework to assess the prediction capabilities for each omics dataset individually. We then used a variation of the sparse generalized canonical correlation analysis (sGCCA) to assess predictions of the combined datasets and identify multi-omics signatures characterising each group of participants. Results: Analysing datasets individually we found methylation data provided the best predictions with an accuracy of 0.63 (95%CI = [0.54-0.71]), followed by RNA, 0.61 (95%CI = [0.52-0.69]), SNP, 0.59 (95%CI = [0.51-0.68]) and proteomics, 0.58 (95%CI = [0.51-0.67]). After integration of the four datasets, predictions were dramatically improved with a resulting accuracy of 0.95 (95% CI = [0.89-0.98]). Conclusion: The integration of data from multiple platforms is a powerful approach to explore biological systems and better characterise the biological signatures of AD. The results suggest that integrative methods can identify biomarker panels with improved predictive performance compared to individual platforms alone. Further validation in independent cohorts is required to validate and refine the results presented in this study.

Elucidating the role of angiogenesis-related genes in colorectal cancer: a multi-omics analysis.

Background: Angiogenesis plays a pivotal role in colorectal cancer (CRC), yet its underlying mechanisms demand further exploration. This study aimed to elucidate the significance of angiogenesis-related genes (ARGs) in CRC through comprehensive multi-omics analysis. Methods: CRC patients were categorized according to ARGs expression to form angiogenesis-related clusters (ARCs). We investigated the correlation between ARCs and patient survival, clinical features, consensus molecular subtypes (CMS), cancer stem cell (CSC) index, tumor microenvironment (TME), gene mutations, and response to immunotherapy. Utilizing three machine learning algorithms (LASSO, Xgboost, and Decision Tree), we screen key ARGs associated with ARCs, further validated in independent cohorts. A prognostic signature based on key ARGs was developed and analyzed at the scRNA-seq level. Validation of gene expression in external cohorts, clinical tissues, and blood samples was conducted via RT-PCR assay. Results: Two distinct ARC subtypes were identified and were significantly associated with patient survival, clinical features, CMS, CSC index, and TME, but not with gene mutations. Four genes (S100A4, COL3A1, TIMP1, and APP) were identified as key ARCs, capable of distinguishing ARC subtypes. The prognostic signature based on these genes effectively stratified patients into high- or low-risk categories. scRNA-seq analysis showed that these genes were predominantly expressed in immune cells rather than in cancer cells. Validation in two external cohorts and through clinical samples confirmed significant expression differences between CRC and controls. Conclusion: This study identified two ARG subtypes in CRC and highlighted four key genes associated with these subtypes, offering new insights into personalized CRC treatment strategies.

Biodegradation of humic acids by Streptomyces rochei to promote the growth and yield of corn.

Humic acids (HAs) are organic macromolecules that play an important role in improving soil properties, plant growth and agronomic parameters. However, the feature of relatively complex aromatic structure makes it difficult to be degraded, which restricts the promotion to the crop growth. Thus, exploring microorganisms capable of degrading HAs may be a potential solution. Here, a HAs-degrading strain, Streptomyces rochei L1, and its potential for biodegradation was studied by genomics, transcriptomics, and targeted metabolomics analytical approaches. The results showed that the high molecular weight HAs were cleaved to low molecular aliphatic and aromatic compounds and their derivatives. This cleavage may be associated with the laccase (KatE). In addition, the polysaccharide deacetylase (PdgA) catalyzes the removal of acetyl groups from specific sites on the HAs molecule, resulting in structural changes. The field experiment showed that the degraded HAs significantly promote the growth of corn seedlings and increase the corn yield by 3.6 %. The HAs-degrading products, including aromatic and low molecular weight aliphatic substances as well as secondary metabolites from S. rochei L1, might be the key components responsible for the corn promotion. Our findings will advance the application of HAs as soil nutrients for the green and sustainable agriculture.

Multi-omics revealed anti-fatigue property of polyphenol from areca nut.

BACKGROUND: Areca nut polyphenols (AP) that extracted from areca nut, have been demonstrated for their potential of anti-fatigue effects. However, the underlying mechanisms for the anti-fatigue properties of AP has not been fully elucidated to date. Previous studies have predominantly concentrated on single aspects, such as antioxidation and anti-inflammation, yet have lacked comprehensive multi-dimensional analyses. PURPOSE: To explore the underlying mechanism of AP in exerting anti-fatigue effects. METHODS: In this study, we developed a chronic sleep deprivation-induced fatigue model and used physiological, hematological, and biochemical indicators to evaluate the anti- fatigue efficacy of AP. Additionally, a multi-omics approach was employed to reveal the anti-fatigue mechanisms of AP from the perspective of microbiome, metabolome, and proteome. RESULTS: The detection of physiology, hematology and biochemistry index indicated that AP markedly alleviate mice fatigue state induced by sleep deprivation. The 16S rRNA sequencing showed the AP promoted the abundance of probiotics (Odoribacter, Dubosiella, Marvinbryantia, and Eubacterium) and suppressed harmful bacteria (Ruminococcus). On the other hand, AP was found to regulate the expression of colonic proteins, such as increases of adenosine triphosphate (ATP) synthesis and mitochondrial function related proteins, including ATP5A1, ATP5O, ATP5L, ATP5H, NDUFA, NDUFB, NDUFS, and NDUFV. Serum metabolomic analysis revealed AP upregulated the levels of anti-fatigue amino acids, such as taurine, leucine, arginine, glutamine, lysine, and l-proline. Hepatic proteins express levels, especially tricarboxylic acid (TCA) cycle (CS, SDHB, MDH2, and DLST) and redox-related proteins (SOD1, SOD2, GPX4, and PRDX3), were significantly recovered by AP administration. Spearman correlation analysis uncovered the strong correlation between microbiome, metabolome and proteome, suggesting the anti-fatigue effects of AP is attribute to the energy homeostasis and redox balance through gut-liver axis. CONCLUSION: AP increased colonic ATP production and improve mitochondrial function by regulating gut microbiota, and further upregulated anti-fatigue amino acid levels in the blood. Based on the gut-liver axis, AP upregulated the hepatic tricarboxylic acid cycle and oxidoreductase-related protein expression, regulating energy homeostasis and redox balance, and ultimately exerting anti-fatigue effects. This study provides insights into the anti-fatigue mechanisms of AP, highlighting its potential as a therapeutic agent.

Multi-omics analyses and botanical perfumer hypothesis provide insights into the formation and maintenance of aromatic characteristics of Dendrobium loddigesii flowers.

Dendrobium loddigesii, a member of the Orchidaceae family, is a valuable horticultural crop known for its aromatic qualities. However, the mechanisms responsible for the development of its aromatic characteristics remain poorly understood. To elucidate these underlying mechanisms, we assembled the first chromosome-level reference genome of D. loddigesii using PacBio HiFi-reads, Illumina short-reads, and Hi-C data. The assembly comprises 19 pseudochromosomes with N50 contig and N50 scaffold sizes of 55.15 and 89.94 Mb, respectively, estimating the genome size to be 1.68 Gb, larger than that of other sequenced Dendrobium species. During the flowering stages, we conducted a comprehensive analysis combining volatilomics and transcriptomics to understand the characteristics and biosynthetic mechanisms pathways of the floral scent. Our findings emphasize the significant contribution of aromatic terpenoids, especially monoterpenoids, in defining the floral aroma. Furthermore, we identified two crucial terpene synthase (TPS) genes that play a key role in maintaining the aroma during flowering. Through the integration volatilomics data with catalytic assays of DlTPSbs proteins, we identified specific compounds responsible for the aromatic characteristics of D. loddigesii. This integrated analysis of the genome, transcriptome, and volatilome, offers valuable insights into the development and preservation of D. loddigesii's aromatic characteristics, setting the stage for further exploration of the botanical perfumer hypothesis.

PANAMA-enabled high-sensitivity dual nanoflow LC-MS metabolomics and proteomics analysis.

High-sensitivity nanoflow liquid chromatography (nLC) is seldom employed in untargeted metabolomics because current sample preparation techniques are inefficient at preventing nanocapillary column performance degradation. Here, we describe an nLC-based tandem mass spectrometry workflow that enables seamless joint analysis and integration of metabolomics (including lipidomics) and proteomics from the same samples without instrument duplication. This workflow is based on a robust solid-phase micro-extraction step for routine sample cleanup and bioactive molecule enrichment. Our method, termed proteomic and nanoflow metabolomic analysis (PANAMA), improves compound resolution and detection sensitivity without compromising the depth of coverage as compared with existing widely used analytical procedures. Notably, PANAMA can be applied to a broad array of specimens, including biofluids, cell lines, and tissue samples. It generates high-quality, information-rich metabolite-protein datasets while bypassing the need for specialized instrumentation.

PPARγ/NF-κB axis contributes to cold-induced resolution of experimental colitis and preservation of intestinal barrier.

BACKGROUND: Environmental stress is a significant contributor to the development of inflammatory bowel disease (IBD). The involvement of temperature stimulation in the development of IBD remains uncertain. Our preliminary statistical data suggest that the prevalence of IBD is slightly lower in colder regions compared to non-cold regions. The observation indicates that temperature changes may play a key role in the occurrence and progression of IBD. Here, we hypothesized that cold stress has a protective effect on IBD. METHODS: The cold exposure model for mice was placed in a constant temperature and humidity chamber, maintained at a temperature of 4 °C. Colitis models were induced in the mice using TNBS or DSS. To promote the detection methods more clinically, fluorescence confocal endoscopy was used to observe the mucosal microcirculation status of the colon in the live model. Changes in the colonic wall of the mice were detected using 9.4 T Magnetic Resonance Imaging (MRI) imaging and in vivo fluorescence imaging. Hematoxylin and eosin (H&E) and Immunofluorescence (IF) staining confirmed the pathological alterations in the colons of sacrificed mice. Molecular changes at the protein level were assessed through Western blotting and Enzyme-Linked Immunosorbent Assay (ELISA) assays. RNA sequencing (RNA-seq) and metabolomics (n = 18) were jointly analyzed to investigate the biological changes in the colon of mice treated by cold exposure. RESULTS: Cold exposure decreased the pathologic and disease activity index scores in a mouse model. Endomicroscopy revealed that cold exposure preserved colonic mucosal microcirculation, and 9.4 T MRI imaging revealed alleviation of intestinal wall thickness. In addition, the expression of the TLR4 and PP65 proteins was downregulated and epithelial cell junctions were strengthened after cold exposure. Intriguingly, we found that cold exposure reversed the decrease in ZO-1 and occludin protein levels in dextran sulfate sodium (DSS)- and trinitrobenzenesulfonic acid-induced colitis mouse models. Multi-omics analysis revealed the biological landscape of DSS-induced colitis under cold exposure and identified that the peroxisome proliferator-activated receptor (PPAR) signaling pathway mediates the effects of cold on colitis. Subsequent administration of rosiglitazone (PPAR agonist) enhanced the protective effect of cold exposure on colitis, whereas GW9662 (PPAR antagonist) administration mitigated these protective effects. Overall, cold exposure ameliorated the progression of mouse colitis through the PPARγ/NF-κB signaling axis and preserved the intestinal mucosal barrier. CONCLUSION: Our study provides a mechanistic link between intestinal inflammation and cold exposure, providing a theoretical framework for understanding the differences in the prevalence of IBD between the colder regions and non-cold regions, and offering new insights into IBD therapy.

Evaluation of sperm quality and male fertility: The use of molecular markers in boar sperm and seminal plasma.

In pig production, the optimization of artificial insemination (AI) efficiency significantly relies on the accurate assessment of semen quality and fertility of boars. Traditional methods such as conventional seminogram techniques, although long-standing, exhibit limited sensitivity in predicting boar fertility, warranting the exploration of novel molecular markers. This review synthesizes the current knowledge on the utilization of molecular markers for semen quality evaluation and male fertility prediction in boars, providing an in-depth examination of molecular markers in this context. Specifically, the present work delves into the potential of OMICs technologies, encompassing genetic and genomic approaches, transcriptomics, proteomics, and metabolomics. A diverse array of molecular markers, including genomic regions associated with sperm quality and male fertility, chromatin integrity, mitochondrial DNA content, mRNA and non-coding RNA signatures, as well as proteins and metabolites in sperm and seminal plasma, are identified as promising molecular markers for fertility prediction in boars. Furthermore, the need of validating biomarkers and their practical implementation in AI centres is here emphasized. Addressing these considerations and integrating molecular markers within the swine breeding field holds the potential to enhance reproductive management practices and optimize productivity in boar breeding programs. This integration can significantly improve overall efficiency within the pig breeding industry.

Multi-omics analysis reveals drivers of loss of ß-cell function after newly diagnosed autoimmune type 1 diabetes: An INNODIA multicenter study.

AIMS: Heterogeneity in the rate of ß-cell loss in newly diagnosed type 1 diabetes patients is poorly understood and creates a barrier to designing and interpreting disease-modifying clinical trials. Integrative analyses of baseline multi-omics data obtained after the diagnosis of type 1 diabetes may provide mechanistic insight into the diverse rates of disease progression after type 1 diabetes diagnosis. METHODS: We collected samples in a pan-European consortium that enabled the concerted analysis of five different omics modalities in data from 97 newly diagnosed patients. In this study, we used Multi-Omics Factor Analysis to identify molecular signatures correlating with post-diagnosis decline in ß-cell mass measured as fasting C-peptide. RESULTS: Two molecular signatures were significantly correlated with fasting C-peptide levels. One signature showed a correlation to neutrophil degranulation, cytokine signalling, lymphoid and non-lymphoid cell interactions and G-protein coupled receptor signalling events that were inversely associated with a rapid decline in ß-cell function. The second signature was related to translation and viral infection was inversely associated with change in ß-cell function. In addition, the immunomics data revealed a Natural Killer cell signature associated with rapid ß-cell decline. CONCLUSIONS: Features that differ between individuals with slow and rapid decline in ß-cell mass could be valuable in staging and prediction of the rate of disease progression and thus enable smarter (shorter and smaller) trial designs for disease modifying therapies as well as offering biomarkers of therapeutic effect.

Tackling neurodegeneration in vitro with omics: a path towards new targets and drugs.

Drug discovery is a generally inefficient and capital-intensive process. For neurodegenerative diseases (NDDs), the development of novel therapeutics is particularly urgent considering the long list of late-stage drug candidate failures. Although our knowledge on the pathogenic mechanisms driving neurodegeneration is growing, additional efforts are required to achieve a better and ultimately complete understanding of the pathophysiological underpinnings of NDDs. Beyond the etiology of NDDs being heterogeneous and multifactorial, this process is further complicated by the fact that current experimental models only partially recapitulate the major phenotypes observed in humans. In such a scenario, multi-omic approaches have the potential to accelerate the identification of new or repurposed drugs against a multitude of the underlying mechanisms driving NDDs. One major advantage for the implementation of multi-omic approaches in the drug discovery process is that these overarching tools are able to disentangle disease states and model perturbations through the comprehensive characterization of distinct molecular layers (i.e., genome, transcriptome, proteome) up to a single-cell resolution. Because of recent advances increasing their affordability and scalability, the use of omics technologies to drive drug discovery is nascent, but rapidly expanding in the neuroscience field. Combined with increasingly advanced in vitro models, which particularly benefited from the introduction of human iPSCs, multi-omics are shaping a new paradigm in drug discovery for NDDs, from disease characterization to therapeutics prediction and experimental screening. In this review, we discuss examples, main advantages and open challenges in the use of multi-omic approaches for the in vitro discovery of targets and therapies against NDDs.