WGCNA: an R package for weighted correlation network analysis

Drought-induced molecular changes in crown of various barley phytohormone mutants

One of the main signal transduction pathways that modulate plant growth and stress responses, including drought, is the action of phytohormones. Recent advances in omics approaches have facilitated the exploration of plant genomes. However, the molecular mechanisms underlying the response in the crown of barley, which plays an essential role in plant performance under stress conditions and regeneration after stress treatment, remain largely unclear. The objective of the present study was the elucidation of drought-induced molecular reactions in the crowns of different barley phytohormone mutants. We verified the hypothesis that defects of gibberellins, brassinosteroids, and strigolactones action affect the transcriptomic, proteomic, and hormonal response of barley crown to the transitory drought influencing plant development under stress. Moreover, we assumed that due to the strong connection between strigolactones and branching the hvdwarf14.d mutant, with dysfunctional receptor of strigolactones, manifests the most abundant alternations in crowns and phenotype under drought. Finally, we expected to identify components underlying the core response to drought which are independent of the genetic background. Large-scale analyses were conducted using gibberellins-biosynthesis, brassinosteroids-signaling, and strigolactones-signaling mutants, as well as reference genotypes. Detailed phenotypic evaluation was also conducted. The obtained results clearly demonstrated that hormonal disorders caused by mutations in the HvGA20ox2, HvBRI1, and HvD14 genes affected the multifaceted reaction of crowns to drought, although the expression of these genes was not induced by stress. The study further detected not only genes and proteins that were involved in the drought response and reacted specifically in mutants compared to the reaction of reference genotypes and vice versa, but also the candidates that may underlie the genotype-universal stress response. Furthermore, candidate genes involved in phytohormonal interactions during the drought response were identified. We also found that the interplay between hormones, especially gibberellins and auxins, as well as strigolactones and cytokinins may be associated with the regulation of branching in crowns exposed to drought. Overall, the present study provides novel insights into the molecular drought-induced responses that occur in barley crowns.

The STAT1-SLC31A1 axis: Potential regulation of cuproptosis in diabetic retinopathy

BACKGROUND

By identifying molecular biological markers linked to cuproptosis in diabetic retinopathy (DR), new pathobiological pathways and more accessible diagnostic markers can be developed.

METHODS

The datasets related to DR were acquired from the Gene Expression Omnibus database, while genes associated with cuproptosis were sourced from previously published compilations. Consensus clustering was conducted to delineate distinct DR subclasses. Feature genes were identified utilizing weighted correlation network analysis (WGCNA). Additionally, two machine-learning algorithms were employed to refine the selection of feature genes. Finally, we conducted preliminary validation experiments to ascertain the involvement of cuproptosis in DR development and the transcriptional regulation of critical genes using both the streptozotocin-induced diabetic mouse model and the high glucose-induced BV2 model.

RESULTS

In the STZ-induced diabetic mouse retinas, a decrease in the expression of cuproptosis signature proteins (FDX1, DLAT, and NDUFS8) suggested the occurrence of cuproptosis in DR. Subsequently, the expression of eight cuproptosis differential genes was validated through the STZ-induced diabetes and oxygen-induced retinopathy (OIR) models, with the key gene SLC31A1 showing upregulation in both models and dataset species. Further analyses, including weighted gene co-expression network analysis, GSVA, and immune infiltration analysis, indicated a close correlation between cuproptosis and microglia function. Additionally, validation in an in vitro model of microglia indicated the occurrence of cuproptosis in microglia under high glucose conditions, alongside abnormal expression of STAT1 with SLC31A1.

CONCLUSION

Our findings suggest that STAT1/SLC31A1 may pave the way for a deeper comprehension of the mechanistic basis of DR and offer potential therapeutic avenues.

PANoptosis signaling enables broad immune response in psoriasis: From pathogenesis to new therapeutic strategies

Background

Accumulating evidence suggests that regulated cell death, such as pyroptosis, apoptosis, and necroptosis, is deeply involved in the pathogenesis of psoriasis. As a newly recognized form of systematic cell death, PANoptosis is involved in a variety of inflammatory disorders through amplifying inflammatory and immune cascades, but its role in psoriasis remains elusive.

Objectives

To reveal the role of PANoptosis in psoriasis for a potential therapeutic strategy.

Methods

Multitranscriptomic analysis and experimental validation were used to identify PANoptosis signaling in psoriasis. RNA-seq and scRNA-seq analyses were performed to establish a PANoptosis-mediated immune response in psoriasis, which revealed hub genes through WGCNA and predicted disulfiram as a potential drug. The effect and mechanism of disulfiram were verified in imiquimod (IMQ)-induced psoriasis.

Results

Here, we found a highlighted PANoptosis signature in psoriasis patients through multitranscriptomic analysis and experimental validation. Based on this, two distinct PANoptosis patterns (non/high) were identified, which were the options for clinical classification. The high-PANoptosis-related group had a higher response rate to immune cell infiltration (such as M1 macrophages and keratinocytes). Subsequently, WGCNA showed the hub genes (e.g., S100A12, CYCS, NOD2, STAT1, HSPA4, AIM2, MAPK7), which were significantly associated with clinical phenotype, PANoptosis signature, and identified immune response in psoriasis. Finally, we explored disulfiram (DSF) as a candidate drug for psoriasis through network pharmacology, which ameliorated IMQ-mediated psoriatic symptoms through antipyroptosis-mediated inflammation and enhanced apoptotic progression. By analyzing the specific ligand-receptor interaction pairs within and between cell lineages, we speculated that DSF might exert its effects by targeting keratinocytes directly or targeting M1 macrophages to downregulate the proliferation of keratinocytes.

Conclusions

PANoptosis with its mediated immune cell infiltration provides a roadmap for research on the pathogenesis and therapeutic strategies of psoriasis.

ExomiRHub: A comprehensive database for hosting and analyzing human disease-related extracellular microRNA transcriptomics data

Extracellular microRNA (miRNA) expression data generated by different laboratories exhibit heterogeneity, which poses challenges for biologists without bioinformatics expertise. To address this, we introduce ExomiRHub (http://www.biomedical-web.com/exomirhub/), a user-friendly database designed for biologists. This database incorporates 191 human extracellular miRNA expression datasets associated with 112 disease phenotypes, 62 treatments, and 24 genotypes, encompassing 29,198 and 23 sample types. ExomiRHub also integrates 16,012 miRNA transcriptomes of 156 cancer subtypes from The Cancer Genome Atlas. All the data in ExomiRHub were further standardized and curated with annotations. The platform offers 25 analytical functions, including differential expression, co-expression, Weighted Gene Co-Expression Network Analysis (WGCNA), feature selection, and functional enrichment, enabling users to select samples, define groups, and customize parameters for analyses. Moreover, ExomiRHub provides a web service that allows biologists to analyze their uploaded miRNA expression data. Four additional tools were developed to evaluate the functions and targets of miRNAs and miRNA variations. Through ExomiRHub, we identified extracellular miRNA biomarkers associated with angiogenesis for monitoring glioma progression, demonstrating its potential to significantly accelerate the discovery of extracellular miRNA biomarkers.

Identification and Validation of Aging Related Genes Signature in Chronic Obstructive Pulmonary Disease

PURPOSE

Chronic Obstructive Pulmonary Disease (COPD) is regarded as an accelerated aging disease. Aging-related genes in COPD are still poorly understood.

METHOD

Data set GSE76925 was obtained from the Gene Expression Omnibus (GEO) database. The "limma" package identified the differentially expressed genes. The weighted gene co-expression network analysis (WGCNA) constructes co-expression modules and detect COPD-related modules. The least absolute shrinkage and selection operator (LASSO) and the support vector machine recursive feature elimination (SVM-RFE) algorithms were chosen to identify the hub genes and the diagnostic ability. Three external datasets were used to identify differences in the expression of hub genes. Real-time reverse transcription polymerase chain reaction (RT-qPCR) was used to verify the expression of hub genes.

RESULT

We identified 15 differentially expressed genes associated with aging (ARDEGs). The SVM-RFE and LASSO algorithms pinpointed four potential diagnostic biomarkers. Analysis of external datasets confirmed significant differences in PIK3R1 expression. RT-qPCR results indicated decreased expression of hub genes. The ROC curve demonstrated that PIK3R1 exhibited strong diagnostic capability for COPD.

CONCLUSION

We identified 15 differentially expressed genes associated with aging. Among them, PIK3R1 showed differences in external data sets and RT-qPCR results. Therefore, PIK3R1 may play an essential role in regulating aging involved in COPD.

Novel immune cross-talk between inflammatory bowel disease and IgA nephropathy

The mechanisms underlying the complex correlation between immunoglobulin A nephropathy (IgAN) and inflammatory bowel disease (IBD) remain unclear. This study aimed to identify the optimal cross-talk genes, potential pathways, and mutual immune-infiltrating microenvironments between IBD and IgAN to elucidate the linkage between patients with IBD and IgAN. The IgAN and IBD datasets were obtained from the Gene Expression Omnibus (GEO). Three algorithms, CIBERSORTx, ssGSEA, and xCell, were used to evaluate the similarities in the infiltrating microenvironment between the two diseases. Weighted gene co-expression network analysis (WGCNA) was implemented in the IBD dataset to identify the major immune infiltration modules, and the Boruta algorithm, RFE algorithm, and LASSO regression were applied to filter the cross-talk genes. Next, multiple machine learning models were applied to confirm the optimal cross-talk genes. Finally, the relevant findings were validated using histology and immunohistochemistry analysis of IBD mice. Immune infiltration analysis showed no significant differences between IBD and IgAN samples in most immune cells. The three algorithms identified 10 diagnostic genes, MAPK3, NFKB1, FDX1, EPHX2, SYNPO, KDF1, METTL7A, RIDA, HSDL2, and RIPK2; FDX1 and NFKB1 were enhanced in the kidney of IBD mice. Kyoto Encyclopedia of Genes and Genomes analysis showed 15 mutual pathways between the two diseases, with lipid metabolism playing a vital role in the cross-talk. Our findings offer insights into the shared immune mechanisms of IgAN and IBD. These common pathways, diagnostic cross-talk genes, and cell-mediated abnormal immunity may inform further experimental studies.

The molecular subtypes of autoimmune diseases

Autoimmune diseases (ADs) are characterized by their complexity and a wide range of clinical differences. Despite patients presenting with similar symptoms and disease patterns, their reactions to treatments may vary. The current approach of personalized medicine, which relies on molecular data, is seen as an effective method to address the variability in these diseases. This review examined the pathologic classification of ADs, such as multiple sclerosis and lupus nephritis, over time. Acknowledging the limitations inherent in pathologic classification, the focus shifted to molecular classification to achieve a deeper insight into disease heterogeneity. The study outlined the established methods and findings from the molecular classification of ADs, categorizing systemic lupus erythematosus (SLE) into four subtypes, inflammatory bowel disease (IBD) into two, rheumatoid arthritis (RA) into three, and multiple sclerosis (MS) into a single subtype. It was observed that the high inflammation subtype of IBD, the RA inflammation subtype, and the MS "inflammation & EGF" subtype share similarities. These subtypes all display a consistent pattern of inflammation that is primarily driven by the activation of the JAK-STAT pathway, with the effective drugs being those that target this signaling pathway. Additionally, by identifying markers that are uniquely associated with the various subtypes within the same disease, the study was able to describe the differences between subtypes in detail. The findings are expected to contribute to the development of personalized treatment plans for patients and establish a strong basis for tailored approaches to treating autoimmune diseases.

Prognostic iron-metabolism signature robustly stratifies single-cell characteristics of hepatocellular carcinoma

Cancer immunotherapy has shown to be a promising method in treating hepatocellular carcinoma (HCC), but suboptimal responses in patients are attributed to cellular and molecular heterogeneity. Iron metabolism-related genes (IRGs) are important in maintaining immune system homeostasis and have the potential to help develop new strategies for HCC treatment. Herein, we constructed and validated the iron-metabolism gene prognostic index (IPX) using univariate Cox proportional hazards regression and LASSO Cox regression analysis, successfully categorizing HCC patients into two groups with distinct survival risks. Then, we performed single-sample gene set enrichment analysis, weighted correlation network analysis, gene ontology enrichment analysis, cellular lineage analysis, and SCENIC analysis to reveal the key determinants underlying the ability of this model based on bulk and single-cell transcriptomic data. We identified several driver transcription factors specifically activated in specific malignant cell sub-populations to contribute to the adverse survival outcomes in the IPX-high subgroup. Within the tumor microenvironment (TME), T cells displayed significant diversity in their cellular characteristics and experienced changes in their developmental paths within distinct clusters identified by IPX. Interestingly, the proportion of Treg cells was increased in the high-risk group compared with the low-risk group. These results suggest that iron-metabolism could be involved in reshaping the TME, thereby disrupting the cell cycle of immune cells. This study utilized IRGs to construct a novel and reliable model, which can be used to assess the prognosis of patients with HCC and further clarify the molecular mechanisms of IRGs in HCC at single-cell resolution.

Identification the role of necroptosis in rheumatoid arthritis by WGCNA network

Rheumatoid arthritis (RA) is the predominant manifestation of inflammatory arthritis, distinguished by an increasing burden of morbidity and mortality. The intricate interplay of genes and signalling pathways involved in synovial inflammation in patients with RA remains inadequately comprehended. This study aimed to ascertain the role of necroptosis in RA, as along with their associations with immune cell infiltration. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were employed to identify central genes for RA. In this study, identified total of 28 differentially expressed genes (DEGs) were identified in RA. Utilising WGCNA, two co-expression modules were generated, with one module demonstrating the strongest correlation with RA. Through the integration of differential gene expression analysis, a total of 5 intersecting genes were discovered. These 5 hub genes, namely fused in sarcoma (FUS), transformer 2 beta homolog (TRA2B), eukaryotic translation elongation factor 2 (EEF2), cleavage and polyadenylation specific factor 6 (CPSF6) and signal transducer and activator of transcription 3 (STAT3) were found to possess significant diagnostic value as determined by receiver operating characteristic (ROC) curve analysis. The close association between the concentrations of various immune cells is anticipated to contribute to the diagnosis and treatment of RA. Furthermore, the infiltration of immune cells mentioned earlier is likely to exert a substantial influence on the initiation of this disease.

Identification of key modules in metabolic syndrome induced by second-generation antipsychotics based on co-expression network analysis

Background

Second-generation antipsychotics (SGAs) frequently cause metabolic syndrome (MetS), which raises the risk of heart disease, type 2 diabetes, morbid obesity, atherosclerosis, and hypertension. MetS also impairs cognitive function in patients with schizophrenia. However, the fundamental reasons of MetS caused by SGAs are not yet fully understood. Thus, we aimed to identify potential therapeutic targets for MetS induced by SGAs.

Methods

The serum biochemical parameters and the RNA-sequencing of peripheral blood mononuclear cells were measured in three groups (healthy controls and patients with schizophrenia with and without MetS taking SGAs). The study of the weighted gene co-expression network was utilized to pinpoint modules that were significantly connected to clinical markers.

Results

Statistical analysis showed significant differences in triglyceride and high-density lipoprotein among the three groups. The TNF signaling pathway, TGF-β signaling pathway, fatty acid metabolism, NF-kappa B signaling pathway, MAPK signaling pathway, and Toll-like receptor signaling pathway were the pathways that were primarily enriched in the two unique co-expression network modules that were found. Finally, five specific genes (TNF, CXCL8, IL1B, TIMP1, and ESR1) associated with metabolism and immunity pathways were identified.

Conclusions

This study indicated that SGAs differentially induced MetS of patients with schizophrenia through metabolic and inflammation-related pathways. Therefore, the potential side effects of drugs on inflammatory processes need to be considered when using SGAs for the treatment of schizophrenia.

Oligodendrocyte-specific expression of PSG8-AS1 suggests a role in myelination with prognostic value in oligodendroglioma

The segmentally duplicated Pregnancy-specific glycoprotein (PSG) locus on chromosome 19q13 may be one of the most rapidly evolving in the human genome. It comprises ten coding genes (PSG1-9, 11) and one predominantly non-coding gene (PSG10) that are expressed in the placenta and gut, in addition to several poorly characterized long non-coding RNAs. We report that long non-coding RNA PSG8-AS1 has an oligodendrocyte-specific expression pattern and is co-expressed with genes encoding key myelin constituents. PSG8-AS1 exhibits two peaks of expression during human brain development coinciding with the most active periods of oligodendrogenesis and myelination. PSG8-AS1 orthologs were found in the genomes of several primates but significant expression was found only in the human, suggesting a recent evolutionary origin of its proposed role in myelination. Additionally, because co-deletion of chromosomes 1p/19q is a genomic marker of oligodendroglioma, expression of PSG8-AS1 was examined in these tumors. PSG8-AS1 may be a promising diagnostic biomarker for glioma, with prognostic value in oligodendroglioma.

Analysis and identification of ferroptosis-related diagnostic markers in rheumatoid arthritis

BACKGROUND

Rheumatoid arthritis (RA) is an autoimmune, inflammatory joint disease. There is growing evidence that ferroptosis is involved in the pathogenesis of RA. This study aimed to search for diagnostic markers of ferroptosis in RA and to analyse the potential mechanisms and clinical value.

MATERIALS AND METHODS

RA-associated datasets were used from the publicly available GEO database. Three methods of machine learning were applied to screen biomarkers. The diagnostic efficacy of the results was also verified by receiver operating characteristic (ROC) curve, external dataset, qRT-PCR and Western blot. Enrichment analysis was performed in this process, while protein-protein interaction (PPI) analysis and immune infiltration correlation analysis were performed using biomarkers, and competing endogenous RNA (ceRNA) networks were constructed to search for prospective therapeutic targets.

RESULTS

MMP13 and GABARAPL1 can be used as ferroptosis diagnostic genes in RA. The ROC curve and PPI result demonstrated that MMP13 and GABARAPL1 had an excellent diagnostic value. The results of signature genes in the external dataset, qRT-PCR and Western blot further confirm our findings. The enrichment analysis showed that p53, MAPK and NOD-like receptor signalling pathways may be involved in the process of ferroptosis in RA. In addition, two ferroptosis diagnostic genes in RA participate in the occurrence of ferroptosis in RA via oxidative stress, metabolism and immune response. Immune infiltration analysis showed that RA extensively infiltrated B cells, T cells, macrophages and other immune cells. Persistent immune activation may be an essential reason for the progression of ferroptosis in RA. We also obtained five potential therapeutic agents for RA and some long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) regulating ferroptosis diagnostic genes.

CONCLUSIONS

Our study suggests that MMP13 and GABARAPL1, which are closely linked with oxidative stress and immunological modulation, can be used as ferroptosis-related potential diagnostic markers in RA and provide new clues regarding the diagnostic and therapeutic targets of ferroptosis in RA.

In-depth metaproteomics analysis reveals the protein profile and metabolism characteristics in pork during refrigerated storage

Alterations in microbiotas and endogenous enzymes have been implicated in meat deterioration. However, the factors that mediate the interactions between meat quality and microbiome profile were inadequately investigated. In this study, we collected pork samples throughout the refrigeration period and employed metaproteomics to characterize both the pork and microbial proteins. Our findings demonstrated that pork proteins associated with the catabolic process are upregulated during storage compared to the initial stage. Pseudomonas, Clostridium, Goodfellowiella, and Gonapodya contribute to the spoilage process. Notably, we observed an elevated abundance of microbial proteins related to glycolytic enzymes in refrigerated pork, identifying numerous proteins linked to biogenic amine production, thus highlighting their essential role in microbial decay. Further, we reveal that many of these microbial proteins from Pseudomonas are ribosomal proteins, promoting enzyme synthesis by enhancing transcription and translation. This study provides intrinsic insights into the underlying mechanisms by which microorganisms contribute to meat spoilage.

Analysis of genes differentially expressed in the cortex of mice with the Tbl1xr1Y446C/Y446C variant

Transducin β-like 1 X-linked receptor 1 (mouse Tbl1xr1) or TBL1X/Y related 1 (human TBL1XR1), part of the NCoR/SMRT corepressor complex, is involved in nuclear receptor signaling. Variants in TBL1XR1 cause a variety of neurodevelopmental disorders including Pierpont syndrome caused by the p.Tyr446Cys variant. We recently reported a mouse model carrying the Tbl1xr1Y446C/Y446C variant as a model for Pierpont syndrome. To obtain insight into mechanisms involved in altered brain development we studied gene expression patterns in the cortex of mutant and wild type (WT) mice, using RNA-sequencing, differentially expressed gene (DEG) analysis, gene set enrichment analysis (GSEA), weighted gene correlation network analysis (WGCNA) and hub gene analysis. We validated results in mutated mouse cortex, as well as in BV2 and SK-N-AS cell lines, in both of which Tbl1xr1 was knocked down by siRNA. Two DEGs (adj.P. Val < 0.05) were found in the cortex, Mpeg1 (downregulated in mutant mice) and 2900052N01Rik (upregulated in mutant mice). GSEA, WGCNA and hub gene analysis demonstrated changes in genes involved in ion channel function and neuroinflammation in the cortex of the Tbl1xr1Y446C/Y446C mice. The lowered expression of ion channel genes Kcnh3 and Kcnj4 mRNA was validated in the mutant mouse cortex, and increased expression of TRIM9, associated with neuroinflammation, was confirmed in the SK-N-AS cell line. Conclusively, our results show altered expression of genes involved in ion channel function and neuroinflammation in the cortex of the Tbl1xr1Y446C/Y446C mice. These may partly explain the impaired neurodevelopment observed in individuals with Pierpont syndrome and related TBL1XR1-related disorders.

Relationships between radiation, wildfire and the soil microbial communities in the Chornobyl Exclusion Zone

There is considerable uncertainty regarding radiation's effects on biodiversity in natural complex ecosystems typically subjected to multiple environmental disturbances and stresses. In this study we characterised the relationships between soil microbial communities and estimated total absorbed dose rates to bacteria, grassy vegetation and trees in the Red Forest region of the Chornobyl Exclusion Zone. Samples were taken from sites of contrasting ecological histories and along burn and no burn areas following a wildfire. Estimated total absorbed dose rates to bacteria reached levels one order of magnitude higher than those known to affect bacteria in laboratory studies. Sites with harsher ecological conditions, notably acidic pH and low soil moisture, tended to have higher radiation contamination levels. No relationship between the effects of fire and radiation were observed. Microbial groups that correlated with high radiation sites were mostly classified to taxa associated with high environmental stress habitats or stress resistance traits. Distance-based linear models and co-occurrence analysis revealed that the effects of radiation on the soil microbiome were minimal. Hence, the association between high radiation sites and specific microbial groups is more likely a result of the harsher ecological conditions in these sites, rather than due to radiation itself. In this study, we provide a starting point for understanding the relationship between soil microbial communities and estimated total absorbed radiation dose rates to different components of an ecosystem highly contaminated with radiation. Our results suggest that soil microbiomes adapted to natural soil conditions are more likely to be resistant to ionising radiation than expected from laboratory studies, which demonstrates the importance of assessing the impact of ionising radiation on soil microbial communities under field conditions.

Functional investigation and two-sample Mendelian randomization study of primary biliary cholangitis hub genes

BACKGROUND

The identification of specific gene expression patterns is crucial for understanding the mechanisms underlying primary biliary cholangitis (PBC) and finding relevant biomarkers for diagnosis and therapeutic evaluation.

AIM

To determine PBC-associated hub genes and assess their clinical utility for disease prediction.

METHODS

PBC expression data were obtained from the Gene Expression Omnibus database. Overlapping genes from differential expression analysis and weighted gene co-expression network analysis (WGCNA) were identified as key genes for PBC. Kyoto Encyclopedia of Genes and Genomes and Gene Ontology analyses were performed to explore the potential roles of key genes. Hub genes were identified in protein-protein interaction (PPI) networks using the Degree algorithm in Cytoscape software. The relationship between hub genes and immune cells was investigated. Finally, a Mendelian randomization study was conducted to determine the causal effects of hub genes on PBC.

RESULTS

We identified 71 overlapping key genes using differential expression analysis and WGCNA. These genes were primarily enriched in pathways related to cytokine-cytokine receptor interaction, and Th1, Th2, and Th17 cell differentiation. We utilized Cytoscape software and identified five hub genes (CD247, IL10, CCL5, CCL3, and STAT3) in PPI networks. These hub genes showed a strong correlation with immune cell infiltration in PBC. However, inverse variance weighting analysis did not indicate the causal effects of hub genes on PBC risk.

CONCLUSION

Hub genes can potentially serve as valuable biomarkers for PBC prediction and treatment, thereby offering significant clinical utility.

De novo mutations promote inflammation in children with STAT3 gain-of-function syndrome by affecting IL-1β expression

STAT3 gain-of-function syndrome, characterized by early-onset autoimmunity and primary immune regulatory disorder, remains poorly understood in terms of its immunological mechanisms. We employed whole-genome sequencing of familial trios to elucidate the pivotal role of de novo mutations in genetic diseases. We identified 37 high-risk pathogenic loci affecting 23 genes, including a novel STAT3 c.508G>A mutation. We also observed significant down-regulation of pathogenic genes in affected individuals, potentially associated with inflammatory responses regulated by PTPN14 via miR378c. These findings enhance our understanding of the pathogenesis of STAT3 gain-of-function syndrome and suggest potential therapeutic strategies. Notably, combined JAK inhibitors and IL-6R antagonists may offer promising treatment avenues for mitigating the severity of STAT3 gain-of-function syndrome.

Deciphering dynamic interactions between spermatozoa and the ovarian microenvironment through integrated multi-omics approaches in viviparous Sebastes schlegelii

The ovarian microenvironment plays a crucial role in ensuring the reproductive success of viviparous teleosts. However, the molecular mechanism underlying the interaction between spermatozoa and the ovarian microenvironment has remained elusive. This study aimed to contribute to a better understanding of this process in black rockfish (Sebastes schlegelii) using integrated multi-omics approaches. The results demonstrated significant upregulation of ovarian complement-related proteins and pattern recognition receptors, along with remodeling of glycans on the surface of spermatozoa at the early spermatozoa-storage stage (1 month after mating). As spermatozoa were stored over time, ovarian complement proteins were progressively repressed by tryptophan and hippurate, indicating a remarkable adaptation of spermatozoa to the ovarian microenvironment. Before fertilization, a notable upregulation of cellular junction proteins was observed. The study revealed that spermatozoa bind to ZPB2a protein through GSTM3 and that ZPB2a promotes spermatozoa survival and movement in a GSTM3-dependent manner. These findings shed light on a key mechanism that influences the dynamics of spermatozoa in the female reproductive tract, providing valuable insights into the molecular networks regulating spermatozoa adaptation and survival in species with internal fertilization.

Optimizing tradeoff strategies of soil microbial community between metabolic efficiency and resource acquisition along a natural regeneration chronosequence

The tradeoff between community-level soil microbial metabolic efficiency and resource acquisition strategies during natural regeneration remains unclear. Herein, we examined variations in soil extracellular enzyme activity, microbial metabolic quotient (qCO2), and microbial carbon use efficiency (CUE) along a chronosequence of natural regeneration by sampling secondary forests at 1, 10, 20, 30, 40, and 100 years after rubber plantation (RP) clearance. The results showed that the natural logarithms of carbon (C)-, nitrogen (N)-, and phosphorus (P)-acquiring enzyme activities were 1:1.68:1.37 and 1:1.54:1.38 in the RP and secondary forests, respectively, thus demonstrating that microbial metabolism was co-limited by N and P. Moreover, the soil microbial C limitation initially increased (1-40 years) and later decreased (100 years). Overall, the qCO2 increased, decreased, and then increased again in the initial (< 10 years), middle (10-40 years), and late (100 years) successional stages, respectively. Except for specific P-acquiring enzyme activities, the changes in other indicators with natural regeneration were consistent in the dry and wet seasons. Both qCO2 and CUE were mainly predicted by microbial community composition and physiological traits. These results indicate that soil microbial communities could employ tradeoff strategies between metabolic efficiency and resource acquisition to cope with variations in resources. Our findings provide new information on tradeoff strategies between metabolic efficiency and resource acquisition during natural regeneration.

Integrated multi-omics analysis reveals molecular changes associated with chronic lipid accumulation following contusive spinal cord injury

Functional and pathological recovery after spinal cord injury (SCI) is often incomplete due to the limited regenerative capacity of the central nervous system (CNS), which is further impaired by several mechanisms that sustain tissue damage. Among these, the chronic activation of immune cells can cause a persistent state of local CNS inflammation and damage. However, the mechanisms that sustain this persistent maladaptive immune response in SCI have not been fully clarified yet. In this study, we integrated histological analyses with proteomic, lipidomic, transcriptomic, and epitranscriptomic approaches to study the pathological and molecular alterations that develop in a mouse model of cervical spinal cord hemicontusion. We found significant pathological alterations of the lesion rim with myelin damage and axonal loss that persisted throughout the late chronic phase of SCI. This was coupled by a progressive lipid accumulation in myeloid cells, including resident microglia and infiltrating monocyte-derived macrophages. At tissue level, we found significant changes of proteins indicative of glycolytic, tricarboxylic acid cycle (TCA), and fatty acid metabolic pathways with an accumulation of triacylglycerides with C16:0 fatty acyl chains in chronic SCI. Following transcriptomic, proteomic, and epitranscriptomic studies identified an increase of cholesterol and m6A methylation in lipid-droplet-accumulating myeloid cells as a core feature of chronic SCI. By characterizing the multiple metabolic pathways altered in SCI, our work highlights a key role of lipid metabolism in the chronic response of the immune and central nervous system to damage.

Hepatic stearoyl-CoA desaturase-1 deficiency induces fibrosis and hepatocellular carcinoma-related gene activation under a high carbohydrate low fat diet

Stearoyl-CoA desaturase-1 (SCD1) is a pivotal enzyme in lipogenesis, which catalyzes the synthesis of monounsaturated fatty acids (MUFA) from saturated fatty acids, whose ablation downregulates lipid synthesis, preventing steatosis and obesity. Yet deletion of SCD1 promotes hepatic inflammation and endoplasmic reticulum stress, raising the question of whether hepatic SCD1 deficiency promotes further liver damage, including fibrosis. To delineate whether SCD1 deficiency predisposes the liver to fibrosis, cirrhosis, and hepatocellular carcinoma (HCC), we employed in vivo SCD1 deficient global and liver-specific mouse models fed a high carbohydrate low-fat diet and in vitro established AML12 mouse cells. The absence of liver SCD1 remarkably increased the saturation of liver lipid species, as indicated by lipidomic analysis, and led to hepatic fibrosis. Consistently, SCD1 deficiency promoted hepatic gene expression related to fibrosis, cirrhosis, and HCC. Deletion of SCD1 increased the circulating levels of Osteopontin, known to be increased in fibrosis, and alpha-fetoprotein, often used as an early marker and a prognostic marker for patients with HCC. De novo lipogenesis or dietary supplementation of oleate, an SCD1-generated MUFA, restored the gene expression related to fibrosis, cirrhosis, and HCC. Although SCD1 deficient mice are protected against obesity and fatty liver, our results show that MUFA deprivation results in liver injury, including fibrosis, thus providing novel insights between MUFA insufficiency and pathways leading to fibrosis, cirrhosis, and HCC under lean non-steatotic conditions.

The nicotine demethylase CYP82E4 is essential for the formation of red dapples on flue-cured leaves of cherry-red tobacco

Common flue-cured tobacco (Nicotiana tabacum L.) primarily accumulates nicotine, and its flue-cured leaves exhibit a lemon appearance. In contrast, a spontaneous cherry-red variant (CR60) primarily accumulates nornicotine, accompanied by distinctive red dapples on the cured leaves. In this study, suppression of conversion of nicotine to nornicotine by genome editing resulted in decreased nornicotine and N-acyl nornicotines (NacNNs), and the subsequent disappearance of red dapples in CR60. Conversely, overexpression of CYP82E4 increased nornicotine and NacNNs accumulation, inducing a red dapple phenotype in common tobacco. Notably, nicotine conversion triggered significant alterations in leaf total sugars, alkaloids, and nitrogens. Metabolome analyses using 1352 identified compounds indicated nicotine conversion dramatically affected the entire metabolic network and induced unique metabolic responses across diverse genetic backgrounds. Further WGCNA analysis revealed that nicotine conversion caused substantial contents variation of alkaloids, flavonoids and amino acids and derivatives in cured leaves. Overall, this research provides valuable insights into the mechanisms underlying red dapple formation in cherry-red tobacco, elucidating profound influence of nicotine conversion on entire metabolic network.

The effects of climate and soil depth on living and dead bacterial communities along a longitudinal gradient in Chile

Soil bacterial communities play a critical role in shaping soil stability and formation, exhibiting a dynamic interaction with local climate and soil depth. We employed an innovative DNA separation method to characterize microbial assemblages in low-biomass environments such as deserts and distinguish between intracellular DNA (iDNA) and extracellular DNA (eDNA) in soils. This approach, combined with analyses of physicochemical properties and co-occurrence networks, investigated soil bacterial communities across four sites representing diverse climatic gradients (i.e., arid, semi-arid, Mediterranean, and humid) along the Chilean Coastal Cordillera. The separation method yielded a distinctive unimodal pattern in the iDNA pool alpha diversity, increasing from arid to semi-arid climates and decreasing in humid environments, highlighting the rapid feedback of the iDNA community to increasing soil moisture. In the arid region, harsh surface conditions restrict bacterial growth, leading to peak iDNA abundance and diversity occurring in slightly deeper layers than the other sites. Our findings confirmed the association between specialist bacteria and ecosystem-functional traits. We observed transitions from Halomonas and Delftia, resistant to extreme arid environments, to Class AD3 and the genus Bradyrhizobium, associated with plants and organic matter in humid environments. The distance-based redundancy analysis (dbRDA) analysis revealed that soil pH and moisture were the key parameters that influenced bacterial community variation. The eDNA community correlated slightly better with the environment than the iDNA community. Soil depth was found to influence the iDNA community significantly but not the eDNA community, which might be related to depth-related metabolic activity. Our investigation into iDNA communities uncovered deterministic community assembly and distinct co-occurrence modules correlated with unique bacterial taxa, thereby showing connections with sites and key environmental factors. The study additionally revealed the effects of climatic gradients and soil depth on living and dead bacterial communities, emphasizing the need to distinguish between iDNA and eDNA pools.

lncRNA-gene network analysis reveals the effects of early maternal nutrition on mineral homeostasis and energy metabolism in the fetal liver transcriptome of beef heifers

Maternal nutrition during pregnancy influences fetal development; however, the regulatory markers of fetal programming across different gestational phases remain underexplored in livestock models. Herein, we investigated the regulatory role of long non-coding RNAs (lncRNAs) on fetal liver gene expression, the impacts of maternal vitamin and mineral supplementation, and the rate of maternal body weight gain during the periconceptual period. To this end, crossbred Angus heifers (n=31) were randomly assigned to a 2×2 factorial design to evaluate the main effects of the rate of weight gain (low gain [LG, avg. daily gain of 0.28 kg/day] vs. moderate gain [MG, avg. daily gain of 0.79 kg/day]) and vitamins and minerals supplementation (VTM vs. NoVTM). On day 83±0.27 of gestation, fetuses were collected for morphometric measurements, and fetal liver was collected for transcriptomic and mineral analyses. The maternal diet significantly affected fetal liver development and mineral reserves. Using an RNA-Seq approach, we identified 320 unique differentially expressed genes (DEGs) across all six comparisons (FDR <0.05). Furthermore, lncRNAs were predicted through the FEELnc pipeline, revealing 99 unique differentially expressed lncRNAs (DELs). The over-represented pathways and biological processes (BPs) were associated with energy metabolism, Wnt signaling, CoA carboxylase activity, and fatty acid metabolism. The DEL-regulated BPs were associated with metal ion transport, pyrimidine metabolism, and classical energy metabolism-related glycolytic, gluconeogenic, and TCA cycle pathways. Our findings suggest that lncRNAs regulate mineral homeostasis- and energy metabolism-related gene networks in the fetal liver in response to early maternal nutrition.

ERP29 regulates the proliferation of endometrial carcinoma via M6A modification

AIMS

Endoplasmic reticulum protein 29 (ERP29) is crucial for endoplasmic reticulum stress (ERS). M6A plays an important role in the progression of endometrial cancer (EC). The study investigated the role of ERS-related gene (ERP29) and m6A in EC.

MATERIALS AND METHODS

We screened ERS-related genes based on the GEO dataset, GSEA dataset and TCGA-UCEC database using WGCNA and two machine learning algorithms. The m6A-related GEO dataset was employed to identify the ERS-related hub genes with m6A. Expression of hub genes in different cell types were visualize through scRNA-seq data analyzing. Using qPCR, Western blot, and Immunohistochemical assays to detect the expression of ERP29, the effect of ERP29 on cancer cell proliferation was investigated through CCK8, EdU and clone formation experiments. M6A modifications were studied using m6A Dot blot and MeRIP-qPCR. Finally, we conducted rescue experiments.

KEY FINDINGS

Ten ERS-related hub genes with m6A were identified. ERP29 is highly expressed in EC. ERP29 knockdown inhibits EC cell proliferation. METTL3 overexpression increases the ERP29 mRNA m6A and decreases the expression of ERP29. Cycloleucine (Cyc), a nucleic acid methylation inhibitor, treatment reduces ERP29 mRNA m6A and increases the expression of ERP29. Cyc rescue the low expression of ERP29 caused by overexpression of METTL3 through m6A. ERP29 knockdown rescued the increased proliferation of EC cells caused by low m6A.

SIGNIFICANCE

ERP29 is highly expressed in EC. m6A regulates ERP29 expression and affects the proliferation of endometrial cancer cells. This represents the premise for applying ERP29 and m6A modifications in diagnosing and treating EC.

Weighted gene co-expression network analysis identifies important modules and hub genes involved in the regulation of breast muscle yield in broilers

OBJECTIVE

Increasing breast meat production is one of the primary goals of the broiler industry. Over the past few decades, tremendous progress has been made in genetic selection and the identification of candidate genes for improving the breast muscle mass. However, the molecular network contributing to muscle production traits in chickens still needs to be further illuminated.

METHODS

A total of 150 1-day-old male 817 broilers were reared in a floor litter system. At the market age of 50 d, eighteen healthy 817 broilers were slaughtered and the left pectoralis major muscle sample from each bird was collected for RNA-seq sequencing. The birds were then plucked and eviscerated and the whole breast muscle was removed and weighed. Breast muscle yield was calculated as the ratio of the breast muscle weight to the eviscerated weight. To identify the co-expression networks and hub genes contributing to breast muscle yield in chickens, we performed weighted gene co-expression network analysis (WGCNA) based on the 18 transcriptome datasets of pectoralis major muscle from eighteen 817 broilers.

RESULTS

The WGCNA analysis classified all co-expressed genes in the pectoral muscle of 817 broilers into 44 modules. Among these modules, the turquoise and skyblue3 modules were found to be most significantly positively (r = 0.78, p = 1e-04) and negatively (r = -0.57, p = 0.01) associated with breast meat yield, respectively. Further analysis identified several hub genes (e.g., DLX3, SH3RF2, TPM1, CAV3, MYF6, and CFL2) that involved in muscle structure and muscle development were identified as potential regulators of breast meat production.

CONCLUSION

The present study has advanced our understanding of the molecular regulatory networks contributing to muscle growth and breast muscle production and will contribute to the molecular breeding of chickens in the future.

Lipocalin-2-mediated intestinal epithelial cells pyroptosis via NF-κB/NLRP3/GSDMD signaling axis adversely affects inflammation in colitis

Ulcerative colitis (UC) is a major inflammatory bowel disease (IBD) characterized by intestinal epithelium damage. Recently, Lipocalin-2 (LCN2) has been identified as a potential fecal biomarker for patients with UC. However, further investigation is required to explore its pro-inflammatory role in UC and the underlying mechanism. The biological analysis revealed that Lcn2 serves as a putative signature gene in the colon mucosa of patients with UC and its association with the capsase/pyroptosis signaling pathway in UC. In wild-type mice with DSS-induced colitis, LCN2 overexpression in colon mucosa via in vivo administration of Lcn2 overexpression plasmid resulted in exacerbation of colitis symptoms and epithelium damage, as well as increased expression levels of pyroptosis markers (cleaved caspase1, GSDMD, IL-1β, HMGB1 and IL-18). Additionally, we observed downregulation in the expression levels of pyroptosis markers following in vivo silencing of LCN2. However, the pro-inflammatory effect of LCN2 overexpression was effectively restrained in GSDMD-KO mice. Moreover, single-cell RNA-sequencing analysis revealed that Lcn2 was predominantly expressed in the intestinal epithelial cells (IECs) within the colon mucosa of patients with UC. We found that LCN2 effectively regulated pyroptosis events by modulating the NF-κB/NLRP3/GSDMD signaling axis in NCM460 cells stimulated by LPS and ATP. These findings demonstrate the pro-inflammatory role of LCN2 in colon epithelium and provide a potential target for inhibiting pyroptosis in UC.

Identification and experimental validation of immune-related gene PPARG is involved in ulcerative colitis

BACKGROUND

The pathophysiology of ulcerative colitis (UC) is believed to be heavily influenced by immunology, which presents challenges for both diagnosis and treatment. The main aims of this study are to deepen our understanding of the immunological characteristics associated with the disease and to identify valuable biomarkers for diagnosis and treatment.

METHODS

The UC datasets were sourced from the GEO database and were analyzed using unsupervised clustering to identify different subtypes of UC. Twelve machine learning algorithms and Deep learning model DNN were developed to identify potential UC biomarkers, with the LIME and SHAP methods used to explain the models' findings. PPI network is used to verify the identified key biomarkers, and then a network connecting super enhancers, transcription factors and genes is constructed. Single-cell sequencing technology was utilized to investigate the role of Peroxisome Proliferator Activated Receptor Gamma (PPARG) in UC and its correlation with macrophage infiltration. Furthermore, alterations in PPARG expression were validated through Western blot (WB) and immunohistochemistry (IHC) in both in vitro and in vivo experiments.

RESULT

By utilizing bioinformatics techniques, we were able to pinpoint PPARG as a key biomarker for UC. The expression of PPARG was significantly reduced in cell models, UC animal models, and colitis models induced by dextran sodium sulfate (DSS). Interestingly, overexpression of PPARG was able to restore intestinal barrier function in H2O2-induced IEC-6 cells. Additionally, immune-related differentially expressed genes (DEGs) allowed for efficient classification of UC samples into neutrophil and mitochondrial metabolic subtypes. A diagnostic model incorporating the three disease-specific genes PPARG, PLA2G2A, and IDO1 demonstrated high accuracy in distinguishing between the UC group and the control group. Furthermore, single-cell analysis revealed that decreased PPARG expression in colon tissue may contribute to the polarization of M1 macrophages through activation of inflammatory pathways.

CONCLUSION

In conclusion, PPARG, a gene related to immunity, has been established as a reliable potential biomarker for the diagnosis and treatment of UC. The immune response it controls plays a key role in the progression and development of UC by enabling interaction between characteristic biomarkers and immune infiltrating cells.

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

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

Endotype-driven Co-module mechanisms of danhong injection in the Co-treatment of cardiovascular and cerebrovascular diseases: A modular-based drug and disease integrated analysis

ETHNOPHARMACOLOGICAL RELEVANCE

Cardiovascular and cerebrovascular diseases are the leading causes of death worldwide and interact closely with each other. Danhong Injection (DHI) is a widely used preparation for the co-treatment of brain and heart diseases (CTBH). However, the underlying molecular endotype mechanisms of DHI in the CTBH remain unclear.

AIM OF THIS STUDY

To elucidate the underlying endotype mechanisms of DHI in the CTBH.

MATERIALS AND METHODS

In this study, we proposed a modular-based disease and drug-integrated analysis (MDDIA) strategy for elucidating the systematic CTBH mechanisms of DHI using high-throughput transcriptome-wide sequencing datasets of DHI in the treatment of patients with stable angina pectoris (SAP) and cerebral infarction (CI). First, we identified drug-targeted modules of DHI and disease modules of SAP and CI based on the gene co-expression networks of DHI therapy and the protein-protein interaction networks of diseases. Moreover, module proximity-based topological analyses were applied to screen CTBH co-module pairs and driver genes of DHI. At the same time, the representative driver genes were validated via in vitro experiments on hypoxia/reoxygenation-related cardiomyocytes and neuronal cell lines of H9C2 and HT22.

RESULTS

Seven drug-targeted modules of DHI and three disease modules of SAP and CI were identified by co-expression networks. Five modes of modular relationships between the drug and disease modules were distinguished by module proximity-based topological analyses. Moreover, 13 targeted module pairs and 17 driver genes associated with DHI in the CTBH were also screened. Finally, the representative driver genes AKT1, EDN1, and RHO were validated by in vitro experiments.

CONCLUSIONS

This study, based on clinical sequencing data and modular topological analyses, integrated diseases and drug targets. The CTBH mechanism of DHI may involve the altered expression of certain driver genes (SRC, STAT3, EDN1, CYP1A1, RHO, RELA) through various enriched pathways, including the Wnt signaling pathway.

Network-based integration of omics, physiological and environmental data in real-world Elbe estuarine Zander

Coastal and estuarine environments are under endogenic and exogenic pressures jeopardizing survival and diversity of inhabiting biota. Information of possible synergistic effects of multiple (a)biotic stressors and holobiont interaction are largely missing in estuaries like the Elbe but are of importance to estimate unforeseen effects on animals' physiology. Here, we seek to leverage host-transcriptional RNA-seq and gill mucus microbial 16S rRNA metabarcoding data coupled with physiological and abiotic measurements in a network analysis approach to decipher the impact of multiple stressors on the health of juvenile Sander lucioperca along one of the largest European estuaries. We find mesohaline areas characterized by gill tissue specific transcriptional responses matching osmosensing and tissue remodeling. Liver transcriptomes instead emphasized that zander from highly turbid areas were undergoing starvation which was supported by compromised body condition. Potential pathogenic bacteria, including Shewanella, Acinetobacter, Aeromonas and Chryseobacterium, dominated the gill microbiome along the freshwater transition and oxygen minimum zone. Their occurrence coincided with a strong adaptive and innate transcriptional immune response in host gill and enhanced energy demand in liver tissue supporting their potential pathogenicity. Taken together, we show physiological responses of a fish species and its microbiome to abiotic factors whose impact is expected to increase with consequences of climate change. We further present a method for the close-meshed detection of the main stressors and bacterial species with disease potential in a highly productive ecosystem.

Machine learning-based autophagy-related prognostic signature for personalized risk stratification and therapeutic approaches in bladder cancer

OBJECTIVE

Bladder cancer (BCa) is a highly lethal urological malignancy characterized by its notable histological heterogeneity. Autophagy has swiftly emerged as a diagnostic and prognostic biomarker in diverse cancer types. Nonetheless, the currently accessible autophagy-related signature specific to BCa remains limited.

METHODS

A refined autophagy-related signature was developed through a 10-fold cross-validation framework, incorporating 101 combinations of machine learning algorithms. The performance of this signature in predicting prognosis and response to immunotherapy was thoroughly evaluated, along with an exploration of potential drug targets and compounds. In vitro and in vivo experiments were conducted to verify the regulatory mechanism of hub gene.

RESULTS

The autophagy-related prognostic signature (ARPS) has exhibited superior performance in predicting the prognosis of BCa compared to the majority of clinical features and other developed markers. Higher ARPS is associated with poorer prognosis and reduced sensitivity to immunotherapy. Four potential targets and five therapeutic agents were screened for patients in the high-ARPS group. In vitro and vivo experiments have confirmed that FKBP9 promotes the proliferation, invasion, and metastasis of BCa.

CONCLUSIONS

Overall, our study developed a valuable tool to optimize risk stratification and decision-making for BCa patients.

Differential network analysis reveals the key role of the ECM-receptor pathway in α-particle-induced malignant transformation

Space particle radiation is a major environmental factor in spaceflight, and it is known to cause body damage and even trigger cancer, but with unknown molecular etiologies. To examine these causes, we developed a systems biology approach by focusing on the co-expression network analysis of transcriptomics profiles obtained from single high-dose (SE) and multiple low-dose (ME) α-particle radiation exposures of BEAS-2B human bronchial epithelial cells. First, the differential network and pathway analysis based on the global network and the core modules showed that genes in the ME group had higher enrichment for the extracellular matrix (ECM)-receptor interaction pathway. Then, collagen gene COL1A1 was screened as an important gene in the ME group assessed by network parameters and an expression study of lung adenocarcinoma samples. COL1A1 was found to promote the emergence of the neoplastic characteristics of BEAS-2B cells by both in vitro experimental analyses and in vivo immunohistochemical staining. These findings suggested that the degree of malignant transformation of cells in the ME group was greater than that of the SE, which may be caused by the dysregulation of the ECM-receptor pathway.

The Proteomic Landscape of Monocytes in Response to Colorectal Cancer Cells

Colorectal cancer (CRC) involves a complex interaction between tumor cells and immune cells, notably monocytes, leading to immunosuppression. This study explored these interactions using in vitro coculture systems of THP-1 cells and CRC cell lines, employing quantitative proteomics to analyze protein changes in monocytes. Multiple analytical methods were utilized to delineate the altered proteomic landscape, identify key proteins, and their associated functional pathways for comprehensive data analysis. Differentially expressed proteins (DEPs) were selected and validated by cross-referencing them with publicly available TCGA and GEO data sets to explore their potential clinical significance. Our analysis identified 161 up-regulated and 130 down-regulated DEPs. The enrichment results revealed impairments in adhesion and innate immune functions in monocytes, potentially facilitating cancer progression. The down-regulation of FN1, THSB1, and JUN may contribute to these impairments. Furthermore, the overexpression of ADAMTSL4, PRAM1, GPNMB, and NPC2 on monocytes was associated with unfavorable prognostic outcomes in CRC patients, suggesting potential biomarkers or therapeutic targets. This study illustrated the proteomic landscape of monocytes in response to CRC cells, providing clues for future investigations of the crosstalk between cancer cells and monocytes within the tumor microenvironment.

AutoFocus: a hierarchical framework to explore multi-omic disease associations spanning multiple scales of biomolecular interaction

Recent advances in high-throughput measurement technologies have enabled the analysis of molecular perturbations associated with disease phenotypes at the multi-omic level. Such perturbations can range in scale from fluctuations of individual molecules to entire biological pathways. Data-driven clustering algorithms have long been used to group interactions into interpretable functional modules; however, these modules are typically constrained to a fixed size or statistical cutoff. Furthermore, modules are often analyzed independently of their broader biological context. Consequently, such clustering approaches limit the ability to explore functional module associations with disease phenotypes across multiple scales. Here, we introduce AutoFocus, a data-driven method that hierarchically organizes biomolecules and tests for phenotype enrichment at every level within the hierarchy. As a result, the method allows disease-associated modules to emerge at any scale. We evaluated this approach using two datasets: First, we explored associations of biomolecules from the multi-omic QMDiab dataset (n = 388) with the well-characterized type 2 diabetes phenotype. Secondly, we utilized the ROS/MAP Alzheimer's disease dataset (n = 500), consisting of high-throughput measurements of brain tissue to explore modules associated with multiple Alzheimer's Disease-related phenotypes. Our method identifies modules that are multi-omic, span multiple pathways, and vary in size. We provide an interactive tool to explore this hierarchy at different levels and probe enriched modules, empowering users to examine the full hierarchy, delve into biomolecular drivers of disease phenotype within a module, and incorporate functional annotations.

Cross-Species Insights into Trophoblast Invasion During Placentation Governed by Immune-Featured Trophoblast Cells

Proper development of the placenta, the transient support organ forms after embryo implantation, is essential for a successful pregnancy. However, the regulation of trophoblast invasion, which is most important during placentation, remains largely unknown. Here, rats, mice, and pigs are used as biomedical models, used scRNA-seq to comparatively elucidate the regulatory mechanism of placental trophoblast invasion, and verified it using a human preeclampsia disease model combined with scStereo-seq. A dual-featured type of immune-featured trophoblast (iTrophoblast) is unexpectedly discovered. Interestingly, iTrophoblast only exists in invasive placentas and regulates trophoblast invasion during placentation. In a normally developing placenta, iTrophoblast gradually transforms from an immature state into a functional mature state as it develops. Whereas in the developmentally abnormal preeclamptic placenta, disordered iTrophoblast transformation leads to the accumulation of immature iTrophoblasts, thereby disrupting trophoblast invasion and ultimately leading to the progression of preeclampsia.

Integrated analyses reveal CST7 and DUSP5 regulate Th2 cells differentiation to promote chronic HBV infection

Chronicity of HBV infection is a complex process influenced by both viral and host factors. Understanding the complex interplay between HBV and cellular immunity is critical. In this study, we used bulk expression datasets for CHB liver tissue from GSE83148 and GSE84044, and scRNA-seq data of CHB liver samples from GSE182159 to find critical genes and immune cells accounted for CHB. We first identified DEGs closely associated with CHB by WGCNA and these genes were intricately linked to differentiation of Th2 cells, which were significantly higher in CHB and positively associated with ALT, AST, HBV-DNA, Scheuer grade and Scheuer stage. Among these DEGs, CST7 and DUSP5 highly expressed in CHB and positively associated with ALT, AST, HBV-DNA, Scheuer grade and Scheuer stage. Moreover, through scRNA-seq, we also found that CST7 and DUSP5 upregulated in Th2 cells and regulated differentiation of naive CD4+ T cells to Th2 cells. Finally, in-vitro studies also showed that HBV infection could significantly up-regulate DUSP5 and CST7 expression. This research strongly revealed that HBV could up-regulate CST7 and DUSP5 to drive differentiation of naive CD4+ T cells into Th2 cells and contribute to CHB, which may pave the way for immunotherapeutic interventions.

Inference of single-cell network using mutual information for scRNA-seq data analysis

BACKGROUND

With the advance in single-cell RNA sequencing (scRNA-seq) technology, deriving inherent biological system information from expression profiles at a single-cell resolution has become possible. It has been known that network modeling by estimating the associations between genes could better reveal dynamic changes in biological systems. However, accurately constructing a single-cell network (SCN) to capture the network architecture of each cell and further explore cell-to-cell heterogeneity remains challenging.

RESULTS

We introduce SINUM, a method for constructing the SIngle-cell Network Using Mutual information, which estimates mutual information between any two genes from scRNA-seq data to determine whether they are dependent or independent in a specific cell. Experiments on various scRNA-seq datasets with different cell numbers based on eight performance indexes (e.g., adjusted rand index and F-measure index) validated the accuracy and robustness of SINUM in cell type identification, superior to the state-of-the-art SCN inference method. Additionally, the SINUM SCNs exhibit high overlap with the human interactome and possess the scale-free property.

CONCLUSIONS

SINUM presents a view of biological systems at the network level to detect cell-type marker genes/gene pairs and investigate time-dependent changes in gene associations during embryo development. Codes for SINUM are freely available at https://github.com/SysMednet/SINUM .

TIMM9 as a prognostic biomarker in multiple cancers and its associated biological processes

TIMM9 has been identified as a mediator of essential functions in mitochondria, but its association with pan-cancer is poorly understood. We herein employed bioinformatics, computational chemistry techniques and experiments to investigate the role of TIMM9 in pan-cancer. Our analysis revealed that overexpression of TIMM9 was significantly associated with tumorigenesis, pathological stage progression, and metastasis. Missense mutations (particularly the S49L variant), copy number variations (CNV) and methylation alterations in TIMM9 were found to be associated with poor cancer prognosis. Moreover, TIMM9 was positively related with cell cycle progression, mitochondrial and ribosomal function, oxidative phosphorylation, TCA cycle activity, innate and adaptive immunity. Additionally, we discovered that TIMM9 could be regulated by cancer-associated signaling pathways, such as the mTOR pathway. Using molecular simulations, we identified ITFG1 as the protein that has the strongest physical association with TIMM9, which show a promising structural complement.

Comparative transcriptomics provides insights into molecular mechanisms of zinc tolerance in the ectomycorrhizal fungus Suillus luteus

Zinc (Zn) is a major soil contaminant and high Zn levels can disrupt growth, survival, and reproduction of fungi. Some fungal species evolved Zn tolerance through cell processes mitigating Zn toxicity, although the genes and detailed mechanisms underlying mycorrhizal fungal Zn tolerance remain unexplored. To fill this gap in knowledge, we investigated the gene expression of Zn tolerance in the ectomycorrhizal fungus Suillus luteus. We found that Zn tolerance in this species is mainly a constitutive trait that can also be environmentally dependent. Zinc tolerance in S. luteus is associated with differences in the expression of genes involved in metal exclusion and immobilization, as well as recognition and mitigation of metal-induced oxidative stress. Differentially expressed genes were predicted to be involved in transmembrane transport, metal chelation, oxidoreductase activity, and signal transduction. Some of these genes were previously reported as candidates for S. luteus Zn tolerance, while others are reported here for the first time. Our results contribute to understanding the mechanisms of fungal metal tolerance and pave the way for further research on the role of fungal metal tolerance in mycorrhizal associations.

Physiological and comprehensive transcriptome analysis reveals distinct regulatory mechanisms for aluminum tolerance of Trifolium repens

It is estimated that up to 50 % of arable lands worldwide are acidic, and most crops are severely inhibited due to the high active aluminum (Al). Trifolium repens is an excellent legume forage with a certain acid tolerance, although it is affected by Al toxicity in acidic soil. In this study, physiological and transcriptomic responses of different white clover varieties were analyzed when exposed to a high-level of Al stress. The results revealed that Trifolium repens had a high level of Al toxicity tolerance, and accumulated nearly 70 % of Al3+ in its roots. Al toxicity significantly inhibited the root length and root activity, decreased the chlorophyll (Chl) content and photosynthetic pigments, while significantly increased the intercellular CO2 concentration (Ci). The content of malondialdehyde (MDA), electrolyte leakage (EL), proline and reactive oxygen species (ROS) were significantly accumulated under Al stress. Furthermore, a total of 27,480 differentially expressed genes (DEGs) were identified after the treatment. Gene ontology (GO) and Kyoto encyclopedia of Genes and Genomes (KEGG) pathway analysis showed that most Al-responsive genes enriched to chloroplast thylakoid membrane, chloroplast stroma and photosynthesis in Haifa leaf while in MAG leaf highly enriched in response to regulation of defense response, which could induce the different tolerance of the two cultivars to Al stress. Besides, pectin methylesterase (PME), glycosyl transferases (GT1) and chalcone synthase genes associated with cell wall biosynthesis may improve the Al accumulation and enhance tolerance of Al toxicity. The results established here would help to understand the morphological structure, physiological and biochemical response, and molecular mechanism of white clover under Al tolerance.

Function-Genes and Disease-Genes Prediction Based on Network Embedding and One-Class Classification

Using genes which have been experimentally-validated for diseases (functions) can develop machine learning methods to predict new disease/function-genes. However, the prediction of both function-genes and disease-genes faces the same problem: there are only certain positive examples, but no negative examples. To solve this problem, we proposed a function/disease-genes prediction algorithm based on network embedding (Variational Graph Auto-Encoders, VGAE) and one-class classification (Fast Minimum Covariance Determinant, Fast-MCD): VGAEMCD. Firstly, we constructed a protein-protein interaction (PPI) network centered on experimentally-validated genes; then VGAE was used to get the embeddings of nodes (genes) in the network; finally, the embeddings were input into the improved deep learning one-class classifier based on Fast-MCD to predict function/disease-genes. VGAEMCD can predict function-gene and disease-gene in a unified way, and only the experimentally-verified genes are needed to provide (no need for expression profile). VGAEMCD outperforms classical one-class classification algorithms in Recall, Precision, F-measure, Specificity, and Accuracy. Further experiments show that seven metrics of VGAEMCD are higher than those of state-of-art function/disease-genes prediction algorithms. The above results indicate that VGAEMCD can well learn the distribution characteristics of positive examples and accurately identify function/disease-genes.

Transcriptomic network underlying physiological alterations in the stem of Myricaria laxiflora in response to waterlogging stress

Myricaria laxiflora is an endangered shrub plant with remarkable tolerance to waterlogging stress, however, little attention has been paid to understanding the underlying mechanisms. Here, physiological and transcriptomic approaches were applied to uncover the physiological and molecular reconfigurations in the stem of M. laxiflora in response to waterlogging stress. The accumulation of the contents of H2O2 and malonaldehyde (MDA) alongside increased activities of enzymes for scavenging the reactive oxygen species (ROS) in the stem of M. laxiflora were observed under waterlogging stress. The principal component analysis (PCA) of transcriptomes from five different timepoints uncovered PC1 counted for 17.3 % of total variations and separated the treated and non-treated samples. A total of 8714 genes in the stem of M. laxiflora were identified as differentially expressed genes (DEGs) under waterlogging stress, which could be assigned into two different subgroups with distinct gene expression patterns and biological functions. The DEGs involved in glycolysis were generally upregulated, whereas opposite results were observed for nitrogen uptake and the assimilation pathway. The contents of abscisic acid (ABA) and jasmonic acid (JA) were sharply decreased alongside the decreased mRNA levels of the genes involved in corresponding synthesis pathways upon waterlogging stress. A network centered by eight key transcription factors has been constructed, which uncovered the inhibited cell division processes in the stem of M. laxiflora upon waterlogging stress. Taken together, the obtained results showed that glycolysis, nitrogen metabolism and meristem activities played an important role in the stem of M. laxiflora in response to waterlogging stress.

Molecular subtypes and nomogram for predicting the prognosis of cervical cancer based on a matrix-immune signature

Cervical cancer is a kind of tumor related to chronic HPV infection. Currently, the treatment of cervical cancer is guided mainly by clinicopathological factors. The role of tumor microenvironment in the prognosis and treatment of cervical cancer has been ignored. We aimed to use bioinformatics to identify the molecular subtypes in cervical cancer and construct a predictive nomogram combining a matrix-immune signature (MIS) and clinicopathological factors to support treatment decisions. Two cervical cancer subtypes with different prognoses were identified based on matrix- and immune-genes in TCGA-CESC. The MIS was developed using Cox regression and Lasso algorithm and verified in the Cancer Genome Characterization Initiative (CGCI) using time-dependent receiver operating characteristic (ROC) curve analysis. Multivariable analysis identified lymph node metastases, lymphovascular space invasion, and the MIS as independent prognostic factors, which were used to construct the predictive nomogram. The areas under the ROC curve of the model were 0.872, 0.879, and 0.803 for the 1-, 3-, and 5-year periods, respectively. The C-index was 0.845. Calibration curves confirmed the excellent prognosis prediction of the nomogram. The nomogram indicted a 3-year survival rate of > 90% in patients with a total score > 110.1. The constructed predictive nomogram has significant implications for prognostic assessment and treatment selection in cervical cancer.

Stress responses in an Arctic microalga (Pelagophyceae) following sudden salinity change revealed by gene expression analysis

Marine microbes that have for eons been adapted to stable salinity regimes are confronted with sudden decreases in salinity in the Arctic Ocean. The episodic freshening is increasing due to climate change with melting multi-year sea-ice and glaciers, greater inflows from rivers, and increased precipitation. To investigate algal responses to lowered salinity, we analyzed the responses and acclimatation over 24 h in a non-model Arctic marine alga (pelagophyte CCMP2097) following transfer to realistic lower salinities. Using RNA-seq transcriptomics, here we show rapid differentially expressed genes related to stress oxidative responses, proteins involved in the photosystem and circadian clock, and those affecting lipids and inorganic ions. After 24 h the pelagophyte adjusted to the lower salinity seen in the overexpression of genes associated with freezing resistance, cold adaptation, and salt tolerance. Overall, a suite of ancient widespread pathways is recruited enabling the species to adjust to the stress of rapid salinity change.

Transcriptome analysis reveals miRNA expression profiles in hypothalamus tissues during the sexual development of Jining grey goats

BACKGROUND

Exploring the physiological and molecular mechanisms underlying goat sexual maturation can enhance breeding practices and optimize reproductive efficiency and is therefore substantially important for practical breeding purposes. As an essential neuroendocrine organ in animals, the hypothalamus is involved in sexual development and other reproductive processes in female animals. Although microRNAs (miRNAs) have been identified as significant regulators of goat reproduction, there is a lack of research on the molecular regulatory mechanisms of hypothalamic miRNAs that are involved in the sexual development of goats. Therefore, we examined the dynamic changes in serum hormone profiles and hypothalamic miRNA expression profiles at four developmental stages (1 day (neonatal, D1, n = 5), 2 months (prepubertal, M2, n = 5), 4 months (sexual maturity, M4, n = 5), and 6 months (breeding period, M6, n = 5)) during sexual development in Jining grey goats.

RESULTS

Transcriptome analysis revealed 95 differentially expressed miRNAs (DEMs) in the hypothalamus of goats across the four developmental stages. The target genes of these miRNAs were significantly enriched in the GnRH signalling pathway, the PI3K-Akt signalling pathway, and the Ras signalling pathway (P < 0.05). Additionally, 16 DEMs are common among the M2 vs. D1, M4 vs. D1, and M6 vs. D1 comparisons, indicating that the transition from D1 to M2 represents a potentially critical period for sexual development in Jining grey goats. The bioinformatics analysis results indicate that miR-193a/miR-193b-3p-Annexin A7 (ANXA7), miR-324-5p-Adhesion G protein-coupled receptor A1 (ADGRA1), miR-324-3p-Erbb2 receptor tyrosine kinase 2 (ERBB2), and miR-324-3p-Rap guanine nucleotide exchange factor 3 (RAPGEF3) are potentially involved in biological processes such as hormone secretion, energy metabolism, and signal transduction. In addition, we further confirmed that miR-324-3p targets the regulatory gene RAPGEF3.

CONCLUSION

These results further enrich the expression profile of hypothalamic miRNAs in goats and provide important insights for studying the regulatory effects of hypothalamic miRNAs on the sexual development of goats after birth.

Identification of gene co-expression modules from zebrafish brain data: Applications in psychiatry illustrated through alcohol-related traits

Cumulative evidence suggests that zebrafish is a useful model in psychiatric research. Weighted Gene Co-expression Network Analysis (WGCNA) enables the reduction of genome-wide expression data to modules of highly co-expressed genes, which are hypothesized to interact within molecular networks. In this study, we first applied WGCNA to zebrafish brain expression data across different experimental conditions. Then, we characterized the different co-expression modules by gene-set enrichment analysis and hub gene-phenotype association. Finally, we analyzed association of polygenic risk scores (PRSs) based on genes of some interesting co-expression modules with alcohol dependence in 524 patients and 729 controls from Galicia, using competitive tests. Our approach revealed 34 co-expression modules in the zebrafish brain, with some showing enrichment in human synaptic genes, brain tissues, or brain developmental stages. Moreover, certain co-expression modules were enriched in psychiatry-related GWAS and comprised hub genes associated with psychiatry-related traits in both human GWAS and zebrafish models. Expression patterns of some co-expression modules were associated with the tested experimental conditions, mainly with substance withdrawal and cold stress. Notably, a PRS based on genes from co-expression modules exclusively associated with substance withdrawal in zebrafish showed a stronger association with human alcohol dependence than PRSs based on randomly selected brain-expressed genes. In conclusion, our analysis led to the identification of co-expressed gene modules that may model human brain gene networks involved in psychiatry-related traits. Specifically, we detected a cluster of co-expressed genes whose expression was exclusively associated with substance withdrawal in zebrafish, which significantly contributed to alcohol dependence susceptibility in humans.

RNA-seq analyses on gametogenic tissues of alfalfa (Medicago sativa) revealed plant reproduction- and ploidy-related genes

BACKGROUND

In alfalfa (Medicago sativa), the coexistence of interfertile subspecies (i.e. sativa, falcata and coerulea) characterized by different ploidy levels (diploidy and tetraploidy) and the occurrence of meiotic mutants capable of producing unreduced (2n) gametes, have been efficiently combined for the establishment of new polyploids. The wealth of agronomic data concerning forage quality and yield provides a thorough insight into the practical benefits of polyploidization. However, many of the underlying molecular mechanisms regarding gene expression and regulation remained completely unexplored. In this study, we aimed to address this gap by examining the transcriptome profiles of leaves and reproductive tissues, corresponding to anthers and pistils, sampled at different time points from diploid and tetraploid Medicago sativa individuals belonging to progenies produced by bilateral sexual polyploidization (dBSP and tBSP, respectively) and tetraploid individuals stemmed from unilateral sexual polyploidization (tUSP).

RESULTS

Considering the crucial role played by anthers and pistils in the reduced and unreduced gametes formation, we firstly analyzed the transcriptional profiles of the reproductive tissues at different stages, regardless of the ploidy level and the origin of the samples. By using and combining three different analytical methodologies, namely weighted-gene co-expression network analysis (WGCNA), tau (τ) analysis, and differentially expressed genes (DEGs) analysis, we identified a robust set of genes and transcription factors potentially involved in both male sporogenesis and gametogenesis processes, particularly in crossing-over, callose synthesis, and exine formation. Subsequently, we assessed at the same floral stage, the differences attributable to the ploidy level (tBSP vs. dBSP) or the origin (tBSP vs. tUSP) of the samples, leading to the identification of ploidy and parent-specific genes. In this way, we identified, for example, genes that are specifically upregulated and downregulated in flower buds in the comparison between tBSP and dBSP, which could explain the reduced fertility of the former compared to the latter materials.

CONCLUSIONS

While this study primarily functions as an extensive investigation at the transcriptomic level, the data provided could represent not only a valuable original asset for the scientific community but also a fully exploitable genomic resource for functional analyses in alfalfa.

Shared patterns of glial transcriptional dysregulation link Huntington's disease and schizophrenia

Huntington's disease and juvenile-onset schizophrenia have long been regarded as distinct disorders. However, both manifest cell-intrinsic abnormalities in glial differentiation, with resultant astrocytic dysfunction and hypomyelination. To assess whether a common mechanism might underlie the similar glial pathology of these otherwise disparate conditions, we used comparative correlation network approaches to analyse RNA-sequencing data from human glial progenitor cells (hGPCs) produced from disease-derived pluripotent stem cells. We identified gene sets preserved between Huntington's disease and schizophrenia hGPCs yet distinct from normal controls that included 174 highly connected genes in the shared disease-associated network, focusing on genes involved in synaptic signalling. These synaptic genes were largely suppressed in both schizophrenia and Huntington's disease hGPCs, and gene regulatory network analysis identified a core set of upstream regulators of this network, of which OLIG2 and TCF7L2 were prominent. Among their downstream targets, ADGRL3, a modulator of glutamatergic synapses, was notably suppressed in both schizophrenia and Huntington's disease hGPCs. Chromatin immunoprecipitation sequencing confirmed that OLIG2 and TCF7L2 each bound to the regulatory region of ADGRL3, whose expression was then rescued by lentiviral overexpression of these transcription factors. These data suggest that the disease-associated suppression of OLIG2 and TCF7L2-dependent transcription of glutamate signalling regulators may impair glial receptivity to neuronal glutamate. The consequent loss of activity-dependent mobilization of hGPCs may yield deficient oligodendrocyte production, and hence the hypomyelination noted in these disorders, as well as the disrupted astrocytic differentiation and attendant synaptic dysfunction associated with each. Together, these data highlight the importance of convergent glial molecular pathology in both the pathogenesis and phenotypic similarities of two otherwise unrelated disorders, Huntington's disease and schizophrenia.

Profiling of Phakopsora pachyrhizi transcriptome revealed co-expressed virulence effectors as prospective RNA interference targets for soybean rust management

Soybean rust (SBR), caused by an obligate biotrophic pathogen Phakopsora pachyrhizi, is a devastating disease of soybean worldwide. However, the mechanisms underlying plant invasion by P. pachyrhizi are poorly understood, which hinders the development of effective control strategies for SBR. Here we performed detailed histological characterization on the infection cycle of P. pachyrhizi in soybean and conducted a high-resolution transcriptional dissection of P. pachyrhizi during infection. This revealed P. pachyrhizi infection leads to significant changes in gene expression with 10 co-expressed gene modules, representing dramatic transcriptional shifts in metabolism and signal transduction during different stages throughout the infection cycle. Numerous genes encoding secreted protein are biphasic expressed, and are capable of inhibiting programmed cell death triggered by microbial effectors. Notably, three co-expressed P. pachyrhizi apoplastic effectors (PpAE1, PpAE2, and PpAE3) were found to suppress plant immune responses and were essential for P. pachyrhizi infection. Double-stranded RNA coupled with nanomaterials significantly inhibited SBR infection by targeting PpAE1, PpAE2, and PpAE3, and provided long-lasting protection to soybean against P. pachyrhizi. Together, this study revealed prominent changes in gene expression associated with SBR and identified P. pachyrhizi virulence effectors as promising targets of RNA interference-based soybean protection strategy against SBR.