Gene expression data analysis using Hellinger correlation in weighted gene co-expression networks (WGCNA)

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.

Comprehensive analysis of aging-related gene expression patterns and identification of potential intervention targets

PURPOSE

This study aims to understand the molecular mechanisms underlying the aging process and identify potential interventions to mitigate age-related decline and diseases.

METHODS

This study utilized the GSE168753 dataset to conduct comprehensive differential gene expression analysis and co-expression module analysis. Machine learning and Mendelian randomization analyses were employed to identify core aging-associated genes and potential drug targets. Molecular docking simulations and mediation analysis were also performed to explore potential compounds and mediators involved in the aging process.

RESULTS

The analysis identified 4164 differentially expressed genes, with 1893 upregulated and 2271 downregulated genes. Co-expression analysis revealed 21 modules, including both positively and negatively correlated modules between older age and younger age groups. Further exploration identified 509 aging-related genes with distinct biological functions. Machine learning and Mendelian randomization analyses identified eight core genes associated with aging, including DPP9, GNAZ, and RELL2. Molecular docking simulations suggested resveratrol, folic acid, and ethinyl estradiol as potential compounds capable of attenuating aging through modulation of RELL2 expression. Mediation analysis indicated that eosinophil counts and neutrophil count might act as mediators in the causal relationship between genes and aging-related indicators.

CONCLUSION

This comprehensive study provides valuable insights into the molecular mechanisms of aging and offers important implications for the development of anti-aging therapeutics. Key Messages What is already known on this topic - Prior research outlines aging's complexity, necessitating precise molecular targets for intervention. What this study adds - This study identifies novel aging-related genes, potential drug targets, and therapeutic compounds, advancing our understanding of aging mechanisms. How this study might affect research, practice, or policy - Findings may inform targeted therapies for age-related conditions, influencing future research and clinical practices.

WGCNA reveals a biomarker for cancer-associated fibroblasts to predict prognosis in cervical cancer

BACKGROUND

Cancer-associated fibroblasts (CAFs) are crucial components of the cervical cancer tumor microenvironment, playing a significant role in cervical cancer progression, treatment resistance, and immune evasion, but whether the expression of CAF-related genes can predict clinical outcomes in cervical cancer is still unknown. In this study, we sought to analyze genes associated with CAFs through weighted gene co-expression network analysis (WGCNA) and to create a predictive model for CAFs in cervical cancer.

METHODS

We acquired transcriptome sequencing data and clinical information on cervical cancer patients from the cancer genome atlas (TCGA) and gene expression omnibus (GEO) databases. WGCNA was conducted to identify genes related to CAFs. We developed a prognostic model based on CAF genes in cervical cancer using the least absolute shrinkage and selection operator (LASSO) Cox regression analysis. Single-cell sequencing data analysis and in vivo experiments for validation of hub genes in CAFs.

RESULTS

A prognostic model for cervical cancer was developed based on CAF genes including COL4A1 , LAMC1 , RAMP3 , POSTN , and SERPINF1 . Cervical cancer patients were divided into low- and high-risk groups based on the optimal cutoff value. Patients in the high-risk group had a significantly worse prognosis. Single-cell RNA sequencing data revealed that hub genes in the CAFs risk model were expressed mainly in fibroblasts. The real-time fluorescence quantitative polymerase chain reaction (PCR) results revealed a significant difference in the expression levels of COL4A1 , LAMC1 , POSTN , and SERPINF1 between the cancer group and the normal group ( p < 0.05). Consistently, the results of the immunohistochemical tests exhibited notable variations in COL4A1, LAMC1, RAMP3, POSTN, and SERPINF1 expression between the cancer and normal groups ( p < 0.001).

CONCLUSION

The CAF risk model for cervical cancer constructed in this study can be used to predict prognosis, while the CAF hub genes can be utilized as crucial markers for cervical cancer prognosis.

Identification and validation of core genes associated with intracranial aneurysms through bioinformatics analysis and Mendelian randomization

Intracranial aneurysms (IAs) often go undetected until rupture, leading to significant morbidity and mortality. Identifying biomarkers for early detection of IAs is crucial. The current study attempted to identify core genes linked with IAs and determine their relevance through Mendelian randomization. Limma helped identify differentially expressed genes between IAs and control superficial temporal artery samples. WGCNA was utilized to find IA-related modules and associated genes, which were further evaluated using KEGG and GO analyses to ascertain their potential roles. Five highly associated genes were screened with the CytoHubba plugin of Cytoscape software. ROC curves assessed the diagnostic efficacy of these genes. A two-sample Mendelian randomization evaluated the causal relationship between the core gene PTRPC and IAs, along with its correlation with immune infiltration. WGCNA and differential expression analysis depicted 584 related genes involved in cellular metabolism and chemokine activity. PTPRC was among the top highly associated genes identified through Cytoscape. It showed significant diagnostic value for IAs. Moreover, mendelian randomization depicted that PTPRC in CD4+ T cells is related to IA risk, with an OR of 0.63538 (95 % CI = 0.41636-0.96959, p = 0.03545). No reverse causal relationship was observed between PTPRC and IAs, with an OR of 0.99947 (95 % CI = 0.99719-1.00176, p = 0.65022). Additionally, immune cell infiltration results indicated a positive correlation between PTPRC in IAs with neutrophils and unactivated dendritic cells and a negative association with regulatory T cells (Tregs). PTPRC was identified as a core gene linked with IAs, providing evidence for IA diagnosis and studying molecular mechanisms.

Application of coincidence index in the discovery of co-expressed metabolic pathways

Analyzing transcription data requires intensive statistical analysis to obtain useful biological information and knowledge. A significant portion of this data is affected by random noise or even noise intrinsic to the modeling of the experiment. Without robust treatment, the data might not be explored thoroughly, and incorrect conclusions could be drawn. Examining the correlation between gene expression profiles is one way bioinformaticians extract information from transcriptomic experiments. However, the correlation measurements traditionally used have worrisome shortcomings that need to be addressed. This paper compares five already published and experimented-with correlation measurements to the newly developed coincidence index, a similarity measurement that combines Jaccard and interiority indexes and generalizes them to be applied to vectors containing real values. We used microarray and RNA-Seq data from the archaeonHalobacterium salinarumand the bacteriumEscherichia coli, respectively, to evaluate the capacity of each correlation/similarity measurement. The utilized method explores the co-expressed metabolic pathways by measuring the correlations between the expression levels of enzymes that share metabolites, represented in the form of a weighted graph. It then searches for local maxima in this graph using a simulated annealing algorithm. We demonstrate that the coincidence index extracts larger, more comprehensive, and more statistically significant pathways for microarray experiments. In RNA-Seq experiments, the results are more limited, but the coincidence index managed the largest percentage of significant components in the graph.

Comprehensive analysis of PPP4C's impact on prognosis, immune microenvironment, and immunotherapy response in lung adenocarcinoma using single-cell sequencing and multi-omics

Background

Elevated PPP4C expression has been associated with poor prognostic implications for patients suffering from lung adenocarcinoma (LUAD). The extent to which PPP4C affects immune cell infiltration in LUAD, as well as the importance of associated genes in clinical scenarios, still requires thorough investigation.

Methods

In our investigation, we leveraged both single-cell and comprehensive RNA sequencing data, sourced from LUAD patients, in our analysis. This study also integrated datasets of immune-related genes from InnateDB into the framework. Our expansive evaluation employed various analytical techniques; these included pinpointing differentially expressed genes, constructing WGCNA, implementing Cox proportional hazards models. We utilized these methods to investigate the gene expression profiles of PPP4C within the context of LUAD and to clarify its potential prognostic value for patients. Subsequent steps involved validating the observed enhancement of PPP4C expression in LUAD samples through a series of experimental approaches. The array comprised immunohistochemistry staining, Western blotting, quantitative PCR, and a collection of cell-based assays aimed at evaluating the influence of PPP4C on the proliferative and migratory activities of LUAD cells.

Results

In lung cancer, elevated expression levels of PPP4C were observed, correlating with poorer patient prognoses. Validation of increased PPP4C levels in LUAD specimens was achieved using immunohistochemical techniques. Experimental investigations have substantiated the role of PPP4C in facilitating cellular proliferation and migration in LUAD contexts. Furthermore, an association was identified between the expression of PPP4C and the infiltration of immune cells in these tumors. A prognostic framework, incorporating PPP4C and immune-related genes, was developed and recognized as an autonomous predictor of survival in individuals afflicted with LUAD. This prognostic tool has demonstrated considerable efficacy in forecasting patient survival and their response to immunotherapeutic interventions.

Conclusion

The involvement of PPP4C in LUAD is deeply intertwined with the tumor's immune microenvironment. PPP4C's over-expression is associated with negative clinical outcomes, promoting both tumor proliferation and spread. A prognostic framework based on PPP4C levels may effectively predict patient prognoses in LUAD, as well as the efficacy of immunotherapy strategy. This research sheds light on the mechanisms of immune interaction in LUAD and proposes a new strategy for treatment.

Transcriptome meta-analysis reveals the hair genetic rules in six animal breeds and genes associated with wool fineness

Wool plays an irreplaceable role in the lives of livestock and the textile industry. The variety of hair quality and shape leads to the diversity of its functions and applications, and the finer wool has a higher economic value. In this study, 10 coarse and 10 fine ordos fine wool sheep skin samples were collected for RNA-seq, and coarse and fine skin/hair follicle RNA-seq datasets of other five animal breeds were obtained from NCBI. Weighted gene co-expression network analysis showed that the common genes were clustered into eight modules. Similar gene expression patterns in sheep and rabbits with the same wool types, different gene expression patterns in animal species with different hair types, and brown modules were significantly correlated with species and breeds. GO and KEGG enrichment analyses showed that, most genes in the brown module associated with hair follicle development. Hence, gene expression patterns in skin tissues may determine hair morphology in animal. The analysis of differentially expressed genes revealed that 32 highly expressed candidate genes associated with the wool fineness of Ordos fine wool sheep. Among them, KAZALD1 (grey module), MYOC (brown module), C1QTNF6 (brown module), FOS (tan module), ITGAM, MX2, MX1, and IFI6 genes have been reported to be involved in the regulation of the hair follicle cycle or hair loss. Additionally, 12 genes, including KAZALD1, MYOC, C1QTNF6, and FOS, are differentially expressed across various animal breeds and species. The above results suggest that different sheep breeds share a similar molecular regulatory basis of wool fineness. Finally, the study provides a theoretical reference for molecular breeding of sheep breeds as well as for the investigation of the origin and evolution of animal hair.

Characterizing defective lipid metabolism in the lateral septum of mice treated with olanzapine: implications for its side effects

Schizophrenia significantly impacts cognitive and behavioral functions and is primarily treated with second-generation antipsychotics (SGAs) such as olanzapine. Despite their efficacy, these drugs are linked to serious metabolic side effects which can diminish patient compliance, worsen psychiatric symptoms and increase cardiovascular disease risk. This study explores the hypothesis that SGAs affect the molecular determinants of synaptic plasticity and brain activity, particularly focusing on the lateral septum (LS) and its interactions within hypothalamic circuits that regulate feeding and energy expenditure. Utilizing functional ultrasound imaging, RNA sequencing, and weighted gene co-expression network analysis, we identified significant alterations in the functional connection between the hypothalamus and LS, along with changes in gene expression in the LS of mice following prolonged olanzapine exposure. Our analysis revealed a module closely linked to increases in body weight and adiposity, featuring genes primarily involved in lipid metabolism pathways, notably Apoa1, Apoc3, and Apoh. These findings suggest that olanzapine may influence body weight and adiposity through its impact on lipid metabolism-related genes in the LS. Therefore, the neural circuits connecting the LS and LH, along with the accompanying alterations in lipid metabolism, are likely crucial factors contributing to the weight gain and metabolic side effects associated with olanzapine treatment.

Weighted gene co-expression network analysis reveals genes related to growth performance in Hu sheep

Hu sheep are a unique breed in our country with great reproductive potential, the extent of whose breeding has been steadily rising in recent years. The study subjects in this experiment were 8-month-old Hu sheep (n = 112). First of all, the growth performance, slaughter performance and meat quality of their eye muscle quality were assessed, meanwhile their live weight, carcass weight, body length, body height, chest circumference, chest depth and tube circumference were respectively 33.81 ± 5.47 kg, 17.43 ± 3.21 kg, 60.36 ± 4.41 cm, 63.25 ± 3.88 cm, 72.03 ± 5.02 cm, 30.70 ± 2.32 cm and 7.36 ± 0.56 cm, with a significant difference between rams and ewes (P < 0.01). Following that, transcriptome sequencing was done, and candidate genes related to growth performance were identified using the weighted co-expression network analysis (WGCNA) approach, which was used to identified 15 modules, with the turquoise and blue modules having the strongest association with growth and slaughter performance, respectively. We discovered hub genes such as ARHGAP31, EPS8, AKT3, EPN1, PACS2, KIF1C, C12H1orf115, FSTL1, PTGFRN and IFIH1 in the gene modules connected with growth and slaughter performance. Our research identifies the hub genes associated with the growth and slaughter performance of Hu sheep, which play an important role in their muscle growth, organ and cartilage development, blood vessel development and energy metabolic pathways. Our findings might lead to the development of potentially-useful biomarkers for the selection of growth and slaughterer performance-related attributes of sheep and other livestock.

Physiological and transcriptomic comparisons shed light on the cold stress response mechanisms of Dendrobium spp

BACKGROUND

Dendrobium spp. comprise a group of tropical orchids with ornamental and medicinal value. Dendrobium spp. are sensitive to low temperature, and the underlying cold response regulatory mechanisms in this group are unclear. To understand how these plants respond to cold stress, we compared the transcriptomic responses of the cold-tolerant cultivar 'Hongxing' (HX) and the cold-sensitive cultivar 'Sonia Hiasakul' (SH) to cold stress.

RESULTS

Chemometric results showed that the physiological response of SH in the later stages of cold stress is similar to that of HX throughout the cold treatment. Orthogonal partial least squares discriminant analysis (OPLS-DA) revealed that soluble protein content and peroxidase activity are key physiological parameters for assessing the cold tolerance of these two Dendrobium spp. cultivars. Additionally, weighted gene co-expression network analysis (WGCNA) results showed that many cold response genes and metabolic pathways significantly associated with the physiological indices were enriched in the 12 detected modules. The Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene ontology (GO) enrichment analyses of the 105 hub genes showed that Dendrobium spp. adapt to cold stress by regulating signal transduction, phytohormones, transcription factors, protein translation and modification, functional proteins, biosynthesis and metabolism, cell structure, light, and the circadian clock. Hub genes of the cold stress response network included the remorin gene pp34, the abscisic acid signaling pathway-related genes PROTEIN PHOSPATASE 2 C (PP2C), SNF1-RELATED PROTEIN KINASE 2 (SnRK2), ABRE-BINDING FACTOR 1 (ABF1) and SKI-INTERACTING PROTEIN 17 (SKIP17), the Ca2+ signaling-related GTP diphosphokinase gene CRSH1, the carbohydrate-related gene STARCH SYNTHASE 2 (SS2), the cell wall biosynthesis gene CINNAMYL ALCOHOL DEHYDROGENASE (CAD7), and the endocytosis-related gene VACUOLAR PROTEIN SORTING-ASSOCIATED PROTEIN 52 A (VPS52A).

CONCLUSIONS

The cold-responsive genes and metabolic pathways of Dendrobium spp. revealed in this study provide important insight to enable the genetic enhancement of cold tolerance in Dendrobium spp., and to facilitate cold tolerance breeding in related plants.

Reconstructing the transcriptional regulatory network of probiotic L. reuteri is enabled by transcriptomics and machine learning

Limosilactobacillus reuteri, a probiotic microbe instrumental to human health and sustainable food production, adapts to diverse environmental shifts via dynamic gene expression. We applied the independent component analysis (ICA) to 117 RNA-seq data sets to decode its transcriptional regulatory network (TRN), identifying 35 distinct signals that modulate specific gene sets. Our findings indicate that the ICA provides a qualitative advancement and captures nuanced relationships within gene clusters that other methods may miss. This study uncovers the fundamental properties of L. reuteri's TRN and deepens our understanding of its arginine metabolism and the co-regulation of riboflavin metabolism and fatty acid conversion. It also sheds light on conditions that regulate genes within a specific biosynthetic gene cluster and allows for the speculation of the potential role of isoprenoid biosynthesis in L. reuteri's adaptive response to environmental changes. By integrating transcriptomics and machine learning, we provide a system-level understanding of L. reuteri's response mechanism to environmental fluctuations, thus setting the stage for modeling the probiotic transcriptome for applications in microbial food production.

IMPORTANCE

We have studied Limosilactobacillus reuteri, a beneficial probiotic microbe that plays a significant role in our health and production of sustainable foods, a type of foods that are nutritionally dense and healthier and have low-carbon emissions compared to traditional foods. Similar to how humans adapt their lifestyles to different environments, this microbe adjusts its behavior by modulating the expression of genes. We applied machine learning to analyze large-scale data sets on how these genes behave across diverse conditions. From this, we identified 35 unique patterns demonstrating how L. reuteri adjusts its genes based on 50 unique environmental conditions (such as various sugars, salts, microbial cocultures, human milk, and fruit juice). This research helps us understand better how L. reuteri functions, especially in processes like breaking down certain nutrients and adapting to stressful changes. More importantly, with our findings, we become closer to using this knowledge to improve how we produce more sustainable and healthier foods with the help of microbes.

The RNA sequencing results revealed the expression of different genes and signaling pathways during chemotherapy resistance in peripheral T-cell lymphoma

BACKGROUND

Peripheral T-cell lymphoma (PTCL) is a subtype of non-Hodgkin's lymphoma that occurs primarily at extranodal sites and is commonly treated using chemotherapy and radiotherapy. PTCL is more malignant than other lymphoid tumors, resulting in a poor prognosis.The 5-year recurrence rate remains high, and there is a lack of standard treatment for patients with relapse-resistant disease. However, the molecular mechanisms underlying the resistance of peripheral T-cell lymphoma cells to chemotherapeutic drugs, as well as identifying strategies to overcome drug resistance remains unclear. In this study, we aimed to identify pivotal genes and signaling pathways associated with chemotherapy resistance in PTCL.

METHODS

In this study, a total of 5 healthy controls and 7 clinical patients were enrolled; 4 patients were classified as chemotherapy sensitive, and 3 patients were classified as chemotherapy resistant. Peripheral blood samples were collected from each participant, and total RNA was extracted from the white blood cells. RNA sequencing was conducted on the Illumina HiSeq platform to obtain comprehensive gene expression profiles. Subsequently, the expression patterns of the DEGs associated with the most enriched signaling pathways, with a special focus on cancer-related genes, were validated using quantitative real-time polymerase chain reaction (qRT-PCR) in peripheral TCL patients.

RESULTS

RNA sequencing (RNA-seq) analysis revealed 4063 differentially expressed genes (DEGs) in peripheral T-cell lymphoma specimens from patients with chemotherapy resistance, of which 1128 were upregulated and 2935 were downregulated. Subsequent quantitative gene expression analysis confirmed a differential expression pattern in all the libraries, with 9 downregulated genes and 10 upregulated genes validated through quantitative real-time PCR in 6 clinical specimens from patients with chemotherapy resistance. KEGG pathway analysis revealed significant alterations in several pathways, with 6 downregulated pathways and 9 upregulated pathways enriched in the DEGs. Notably, the TNF signaling pathway, which is extensively regulated, was among the pathways that exhibited significant changes. These findings suggest that DEGs and the TNF signaling pathway may play crucial roles in chemotherapy resistance in peripheral T-cell lymphoma.

CONCLUSION

Our study revealed that the expression of specific genes, including TNFRSF1B, TRADD2, and MAP3K7, may play an important role in chemotherapy resistance in peripheral T-cell lymphoma. Moreover, we identified the downregulation of the TNF signaling pathway, a crucial pathway involved in cell survival, death, and differentiation, as a potential contributor to the development of chemotherapy resistance in peripheral T-cell lymphoma. These findings provide valuable insights into the molecular mechanisms underlying chemotherapy resistance and highlight potential targets for overcoming treatment resistance in this challenging disease.

Potential mechanisms and drug prediction of Rheumatoid Arthritis and primary Sjögren's Syndrome: A public databases-based study

Rheumatoid arthritis (RA) and primary Sjögren's syndrome (pSS) are the most common systemic autoimmune diseases, and they are increasingly being recognized as occurring in the same patient population. These two diseases share several clinical features and laboratory parameters, but the exact mechanism of their co-pathogenesis remains unclear. The intention of this study was to investigate the common molecular mechanisms involved in RA and pSS using integrated bioinformatic analysis. RNA-seq data for RA and pSS were picked up from the Gene Expression Omnibus (GEO) database. Co-expression genes linked with RA and pSS were recognized using weighted gene co-expression network analysis (WGCNA) and differentially expressed gene (DEG) analysis. Then, we screened two public disease-gene interaction databases (GeneCards and Comparative Toxicogenomics Database) for common targets associated with RA and pSS. The DGIdb database was used to predict therapeutic drugs for RA and pSS. The Human microRNA Disease Database (HMDD) was used to screen out the common microRNAs associated with RA and pSS. Finally, a common miRNA-gene network was created using Cytoscape. Four hub genes (CXCL10, GZMA, ITGA4, and PSMB9) were obtained from the intersection of common genes from WGCNA, differential gene analysis and public databases. Twenty-four drugs corresponding to hub gene targets were predicted in the DGIdb database. Among the 24 drugs, five drugs had already been reported for the treatment of RA and pSS. Other drugs, such as bortezomib, carfilzomib, oprozomib, cyclosporine and zidovudine, may be ideal drugs for the future treatment of RA patients with pSS. According to the miRNA-gene network, hsa-mir-21 may play a significant role in the mechanisms shared by RA and pSS. In conclusion, we identified commom targets as potential biomarkers in RA and pSS from publicly available databases and predicted potential drugs based on the targets. A new understanding of the molecular mechanisms associated with RA and pSS is provided according to the miRNA-gene network.

Cord blood lipid correlation network profiles are associated with subsequent attention-deficit/hyperactivity disorder and autism spectrum disorder symptoms at 2 years: a prospective birth cohort study

BACKGROUND

Attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) are neurodevelopmental conditions with early life origins. Alterations in blood lipids have been linked to ADHD and ASD; however, prospective early life data are limited. This study examined (i) associations between the cord blood lipidome and ADHD/ASD symptoms at 2 years of age, (ii) associations between prenatal and perinatal predictors of ADHD/ASD symptoms and cord blood lipidome, and (iii) mediation by the cord blood lipidome.

METHODS

From the Barwon Infant Study cohort (1074 mother-child pairs, 52.3% male children), child circulating lipid levels at birth were analysed using ultra-high-performance liquid chromatography-tandem mass spectrometry. These were clustered into lipid network modules via Weighted Gene Correlation Network Analysis. Associations between lipid modules and ADHD/ASD symptoms at 2 years, assessed with the Child Behavior Checklist, were explored via linear regression analyses. Mediation analysis identified indirect effects of prenatal and perinatal risk factors on ADHD/ASD symptoms through lipid modules.

FINDINGS

The acylcarnitine lipid module is associated with both ADHD and ASD symptoms at 2 years of age. Risk factors of these outcomes such as low income, Apgar score, and maternal inflammation were partly mediated by higher birth acylcarnitine levels. Other cord blood lipid profiles were also associated with ADHD and ASD symptoms.

INTERPRETATION

This study highlights that elevated cord blood birth acylcarnitine levels, either directly or as a possible marker of disrupted cell energy metabolism, are on the causal pathway of prenatal and perinatal risk factors for ADHD and ASD symptoms in early life.

FUNDING

The foundational work and infrastructure for the BIS was sponsored by the Murdoch Children's Research Institute, Deakin University, and Barwon Health. Subsequent funding was secured from the Minderoo Foundation, the European Union's Horizon 2020 research and innovation programme (ENDpoiNTs: No 825759), National Health and Medical Research Council of Australia (NHMRC) and Agency for Science, Technology and Research Singapore [APP1149047], The William and Vera Ellen Houston Memorial Trust Fund (via HOMER Hack), The Shepherd Foundation, The Jack Brockhoff Foundation, the Scobie & Claire McKinnon Trust, the Shane O'Brien Memorial Asthma Foundation, the Our Women Our Children's Fund Raising Committee Barwon Health, the Rotary Club of Geelong, the Ilhan Food Allergy Foundation, Geelong Medical and Hospital Benefits Association, Vanguard Investments Australia Ltd, the Percy Baxter Charitable Trust, and Perpetual Trustees.

Identification of potential crucial genes shared in psoriasis and ulcerative colitis by machine learning and integrated bioinformatics

BACKGROUND

Mounting evidence suggest that there are an association between psoriasis and ulcerative colitis (UC), although the common pathogeneses are not fully understood. Our study aimed to find potential crucial genes in psoriasis and UC through machine learning and integrated bioinformatics.

METHODS

The overlapping differentially expressed genes (DEGs) of the datasets GSE13355 and GSE87466 were identified. Then the functional enrichment analysis was performed. The overlapping genes in LASSO, SVM-RFE and key module in WGCNA were considered as potential crucial genes. The receiver operator characteristic (ROC) curve was used to estimate their diagnostic confidence. The CIBERSORT was conducted to evaluate immune cell infiltration. Finally, the datasets GSE30999 and GSE107499 were retrieved to validate.

RESULTS

112 overlapping DEGs were identified in psoriasis and UC and the functional enrichment analysis revealed they were closely related to the inflammatory and immune response. Eight genes, including S100A9, PI3, KYNU, WNT5A, SERPINB3, CHI3L2, ARNTL2, and SLAMF7, were ultimately identified as potential crucial genes. ROC curves showed they all had high confidence in the test and validation datasets. CIBERSORT analysis indicated there was a correlation between infiltrating immune cells and potential crucial genes.

CONCLUSION

In our study, we focused on the comprehensive understanding of pathogeneses in psoriasis and UC. The identification of eight potential crucial genes may contribute to not only understanding the common mechanism, but also identifying occult UC in psoriasis patients, even serving as therapeutic targets in the future.

Basis with RNA-Seq and WGCNA to explore the effect of Frankincense essential oil on dextran sodium sulfate-induced ulcerative colitis through MAPK/NF-κB signaling

PURPOSE

Frankincense has been shown in studies to have healing benefits for people with ulcerative colitis (UC). However, its underlying mechanisms have not been fully investigated. The objective of this study was to explore the potential molecular mechanisms of Frankincense essential oil (FREO) in improving dextran sodium sulfate (DSS)-induced UC from multiple perspectives.

METHODS

The FREO components were analyzed by GC-MS, and the interactions between the key active components and the mechanism of FREO were determined based on RNA-seq, "quantity-effect" weighting coefficient network pharmacology, WGCNA and pharmacodynamic experiments. The protection of FREO against DSS-induced UC mice was assessed by behavioral and pathological changes through mice. The expression of pro-inflammatory cytokines was measured using enzyme-linked immunosorbent assay. The expression of MAPK and NF-κB-related proteins by the Western Blotting and immunohistochemistry method.

RESULTS

Treatment with FREO significantly improved the symptoms of weight loss, diarrhea, stool blood, and colon shortening in UC mice. Reduced intestinal mucosal damage and the degree of inflammatory cell infiltration in the colon. Decreased TNF-α and IL-6 levels in mice's serum and inhibited phosphorylation of ERK, p65 in MAPK and NF-κB signaling.

CONCLUSION

FREO may decrease the inflammatory response to reduce the symptoms of UC by modulating the MAPK/ NF-κB pathway. This may be due to the synergistic interaction of the effective ingredient Hepten-2-yl tiglate, 6-methyl-5-, Isoneocembrene A and P-Cymene. This study provides a promising drug candidate and a new concept for the treatment of UC.

Co-fractionation-mass spectrometry to characterize native mitochondrial protein assemblies in mammalian neurons and brain

Human mitochondrial (mt) protein assemblies are vital for neuronal and brain function, and their alteration contributes to many human disorders, e.g., neurodegenerative diseases resulting from abnormal protein-protein interactions (PPIs). Knowledge of the composition of mt protein complexes is, however, still limited. Affinity purification mass spectrometry (MS) and proximity-dependent biotinylation MS have defined protein partners of some mt proteins, but are too technically challenging and laborious to be practical for analyzing large numbers of samples at the proteome level, e.g., for the study of neuronal or brain-specific mt assemblies, as well as altered mtPPIs on a proteome-wide scale for a disease of interest in brain regions, disease tissues or neurons derived from patients. To address this challenge, we adapted a co-fractionation-MS platform to survey native mt assemblies in adult mouse brain and in human NTERA-2 embryonal carcinoma stem cells or differentiated neuronal-like cells. The workflow consists of orthogonal separations of mt extracts isolated from chemically cross-linked samples to stabilize PPIs, data-dependent acquisition MS to identify co-eluted mt protein profiles from collected fractions and a computational scoring pipeline to predict mtPPIs, followed by network partitioning to define complexes linked to mt functions as well as those essential for neuronal and brain physiological homeostasis. We developed an R/CRAN software package, Macromolecular Assemblies from Co-elution Profiles for automated scoring of co-fractionation-MS data to define complexes from mtPPI networks. Presently, the co-fractionation-MS procedure takes 1.5-3.5 d of proteomic sample preparation, 31 d of MS data acquisition and 8.5 d of data analyses to produce meaningful biological insights.

Integrated multi-omics analysis of brain aging in female nonhuman primates reveals altered signaling pathways relevant to age-related disorders

The prefrontal cortex (PFC) has been implicated as a key brain region responsible for age-related cognitive decline. Little is known about aging-related molecular changes in PFC that may mediate these effects. To date, no studies have used untargeted discovery methods with integrated analyses to determine PFC molecular changes in healthy female primates. We quantified PFC changes associated with healthy aging in female baboons by integrating multiple omics data types (transcriptomics, proteomics, metabolomics) from samples across the adult age span. Our integrated omics approach using unbiased weighted gene co-expression network analysis to integrate data and treat age as a continuous variable, revealed highly interconnected known and novel pathways associated with PFC aging. We found Gamma-aminobutyric acid (GABA) tissue content associated with these signaling pathways, providing 1 potential biomarker to assess PFC changes with age. These highly coordinated pathway changes during aging may represent early steps for aging-related decline in PFC functions, such as learning and memory, and provide potential biomarkers to assess cognitive status in humans.

Comparative transcriptome analysis reveals the core molecular network in pattern-triggered immunity in Sorghum bicolor

Pathogen-associated molecular pattern (PAMP)-triggered immunity (PTI) is the first line of defense in plant disease resistance. However, the molecular mechanisms of plant PTI vary across species, making it challenging to identify a core set of trait-associated genes. This study aimed to investigate key factors that influence PTI and identify the core molecular network in Sorghum bicolor, a C4 plant. We performed comprehensive weighted gene co-expression network analysis and temporal expression analysis of large-scale transcriptome data from various sorghum cultivars under different PAMP treatments. Our results revealed that the type of PAMP had a stronger influence on the PTI network than did the sorghum cultivar. Following PAMP treatment, 30 genes with stable downregulated expression and 158 genes with stable upregulated expression were identified, including genes encoding potential pattern recognition receptors whose expression was upregulated within 1 h of treatment. PAMP treatment altered the expression of resistance-related, signaling, salt-sensitive, heavy metal-related, and transporter genes. These findings provide novel insights into the core genes involved in plant PTI and are expected to facilitate the identification and application of resistance genes in plant breeding studies.

Genome-Wide Characterization and Functional Analysis of ABCG Subfamily Reveal Its Role in Cutin Formation in Cotton

ATP-binding cassette transporter G (ABCG) has been shown to be engaged in export of broad-spectrum compounds with structural differences, but little is known concerning its role in cutin formation of cotton (Gossypium spp.). In this study, we conduct a genome-wide survey and detected 69, 71, 124 and 131 ABCG genes within G. arboretum, G. raimondii, G. hirsutum and G. barbadense, separately. The above ABCGs could be divided into four groups (Ia, Ib, Ic, II). Some ABCG genes such as GhABCG15, whose homologous gene transports cuticular lipid in Arabidopsis, was preferentially expressed in the development of fiber. A weighted gene co-expression network analysis (WGCNA) demonstrated that GhABCG expression was significantly associated with the amount of 16-Hydroxypalmitate (a main component of cutin precursor) in cotton fibers. Further, silencing of GhABCG15 by virus-induced gene silencing (VIGS) in cotton generated brightened and crinkled leaves as well as reduced thickness of cuticle and increased permeability. Chemical composition analysis showed the cutin content in GhABCG15-silenced leaves had decreased while the wax content had increased. Our results provide an insight for better understanding of the role of the Gossypium ABCG family and revealed the essential role of GhABCGs in cotton cutin formation.

DNA methylation profile of lip tissue from congenital nonsyndromic cleft lip and palate patients by whole-genome bisulfite sequencing

Congenital nonsyndromic cleft lip and palate (NSCLP) is one of the most common malformations worldwide. DNA methylation has been implicated in many diseases. However, its involvement in lip tissue from NSCLP is not well understood. This study aimed to investigate the role of dysregulated DNA methylation in NSCLP. DNA methylation profile was determined in eight injured and five self-normal lip tissue samples from children with NSCLP by whole-genome bisulfite sequencing. A total of 2,711 differentially methylated regions (DMRs), corresponding to 1,231 genes were identified. Given the important role of promoter methylation in regulating gene expression, the promoter DMR-related genes were considered. Bioinformatics analysis demonstrated that some of them showed potential associations with NSCLP. Therefore, the well-known NSCLP susceptibility gene, GLI family zinc finger 2 (GLI2) with an unknown role in its DNA methylation in NSCLP, was selected for further analysis. The promoter hypomethylation and higher mRNA expression level of GLI2 were observed in injured lip tissues by verification in additional samples. Moreover, dual luciferase reporter assay indicated that promoter hypermethylation of GLI2 inhibited its transcription. Overall, this study suggested that abnormal DNA methylation in lip tissue may be correlated with the pathogenesis of congenital NSCLP.