Differential Network Analysis Research Articles

Fungal communities shift with soybean cyst nematode abundance in soils

Plant parasitic nematodes pose a significant threat to food security. Management strategies for these pathogens are limited, and additional tools for their control are needed. Some fungi have shown promise for biocontrol, however, their success in the field has varied. In contrast, some fungal plant pathogens form synergistic associations with nematodes resulting in increased plant disease severity. However, how both groups of fungi change with different plant parasitic nematode abundances in the soil is underexplored. In this study, we used the soybean cyst nematode as a model to understand these changes. We sampled soil from 171 Ohio soybean fields in 2019 and 2021 and determined soybean cyst nematode abundance. We identified the fungi in the samples through amplicon sequencing of the 18S-ITS rDNA regions. Edaphoclimatic factors were used to classify samples into geographic regions to account for environmental differences between sampling locations. We hypothesized that fungal communities would be influenced by both region and soybean cyst nematode abundance. K-near neighbor analysis revealed that fungal communities follow regional patterns. We also found that soybean cyst nematode abundance was associated with changes in these communities regardless of the region. Two potential nematophagous fungi were found to be prevalent in Ohio through core community analysis, although they were enriched when soybean cyst nematode abundance was high. Lastly, differential network analysis showed that interactions among fungal community members change when soybean cyst nematode is present in the soil. Together these results suggest that this nematode significantly shifts the fungal community composition in field soils.

Association of circulating long-chain free fatty acids and incident diabetes risk among normoglycemic Chinese adults: a prospective nested case–control study

BackgroundLong-chain free fatty acids (FFAs) are associated with risk of incident diabetes. However, a comprehensive assessment of the associations in normoglycemic populations is lacking. ObjectivesOur study aimed to comprehensively investigate the prospective associations and patterns of FFA profiles with diabetes risk among normoglycemic Chinese adults. MethodsThis is a prospective nested case–control study from the China Cardiometabolic Disease and Cancer Cohort (4C) study. We quantitatively measured 53 serum FFAs using a targeted metabolomics approach in 1707 incident diabetes subjects and 1707 propensity score–matched normoglycemic controls. Conditional logistic regression models were employed to estimate odds ratios (ORs) for associations. Least Absolute Shrinkage and Selection Operator (LASSO) penalty regression and quantile g-computation (qg-comp) analyses were implemented to estimate the association between multi-FFA exposures and incident diabetes. ResultsThe majority of odd-chain FFAs exhibited an inverse association with incident diabetes, wherein the ORs per SD increment of all 7 saturated fatty acids (SFAs), monounsaturated fatty acid (MUFA) 15:1, and polyunsaturated fatty acid (PUFA) 25:2 were ranging from 0.79 to 0.88 (95% CIs ranging between 0.71 and 0.97). Even-chain FFAs comprised 99.3% of total FFAs and displayed heterogeneity with incident diabetes. SFAs with 18–26 carbon atoms are inversely linked to incident diabetes, with ORs ranging from 0.81 to 0.86 (95% CIs ranging between 0.73 and 0.94). MUFAs 26:1 (OR: 0.85; 95% CI: 0.76, 0.94), PUFAs 20:4 (OR: 0.84; 95% CI: 0.75, 0.94), and 24:2 (OR: 0.87; 95% CI: 0.78, 0.97) demonstrated significant associations. In multi-FFA exposure model, 24 FFAs were significantly associated with incident diabetes, most of which were consistent with univariate results. The mixture OR was 0.78 (95% CI: 0.61, 0.99; P = 0.04159). Differential correlation network analysis revealed pre-existing perturbations in intraclass and interclass FFA coregulation before diabetes onset. ConclusionsThese findings underscore the variations in diabetes risk associated with FFAs across chain length and unsaturation degree, highlighting the importance of recognizing FFA subtypes in the pathogenesis of diabetes.

Exploring the Early Molecular Pathogenesis of Osteoarthritis Using Differential Network Analysis of Human Synovial Fluid

The molecular mechanisms that drive the onset and development of osteoarthritis (OA) remain largely unknown. In this exploratory study, we used a proteomic platform (SOMAscan assay) to measure the relative abundance of more than 6000 proteins in synovial fluid (SF) from knees of human donors with healthy or mildly degenerated tissues and knees with late-stage OA from patients undergoing knee replacement surgery. Using a linear mixed effects model, we estimated the differential abundance of 6251 proteins between the three groups. We found 583 proteins upregulated in the late-stage OA, including MMP1, MMP13 and IL6. Further, we selected 760 proteins (800 aptamers) based on absolute fold changes between the healthy and mild degeneration groups. To those, we applied Gaussian Graphical Models (GGMs) to analyze the conditional dependence of proteins and to identify key proteins and subnetworks involved in early OA pathogenesis. After regularization and stability selection, we identified 102 proteins involved in GGM networks. Notably, network complexity was lost in the protein graph for mild degeneration when compared to controls, suggesting a disruption in the regular protein interplay. Furthermore, among our main findings were several downregulated (in mild degeneration versus healthy) proteins with unique interactions in the healthy group, one of which, SLCO5A1, has not previously been associated with OA. Our results suggest that this protein is important for healthy joint function. Further, our data suggests that SF proteomics, combined with GGMs, can reveal novel insights into the molecular pathogenesis and identification of biomarker candidates for early-stage OA.

Cyclosporine A-induced systemic metabolic perturbations in rats: A comprehensive metabolome analysis

A better understanding of cyclosporine A (CsA)-induced nephro- and hepatotoxicity at the molecular level is necessary for safe and effective use. Utilizing a sophisticated study design, this study explored metabolic alterations after long-term CsA treatment in vivo. Rats were exposed to CsA with 4, 10, and 25 mg/kg for 4 weeks and then sacrificed to obtain liver, kidney, urine, and serum for untargeted metabolomics analysis. Differential network analysis was conducted to explore the biological relevance of metabolites significantly altered by toxicity-induced disturbance. Dose-dependent toxicity was observed in all biospecimens. The toxic effects were characterized by alterations of metabolites related to energy metabolism and cellular membrane composition, which could lead to the cholestasis-induced accumulation of bile acids in the tissues. The unfavorable impacts were also demonstrated in the serum and urine. Intriguingly, phenylacetylglycine was increased in the kidney, urine, and serum treated with high doses versus controls. Differential correlation network analysis revealed the strong correlations of deoxycytidine and guanosine with other metabolites in the network, which highlighted the influence of repeated CsA exposure on DNA synthesis. Overall, prolonged CsA administration had system-level dose-dependent effects on the metabolome in treated rats, suggesting the need for careful usage and dose adjustment.

Differential network connectivity analysis for microbiome data adjusted for clinical covariates using jackknife pseudo-values

BackgroundA recent breakthrough in differential network (DN) analysis of microbiome data has been realized with the advent of next-generation sequencing technologies. The DN analysis disentangles the microbial co-abundance among taxa by comparing the network properties between two or more graphs under different biological conditions. However, the existing methods to the DN analysis for microbiome data do not adjust for other clinical differences between subjects.ResultsWe propose a Statistical Approach via Pseudo-value Information and Estimation for Differential Network Analysis (SOHPIE-DNA) that incorporates additional covariates such as continuous age and categorical BMI. SOHPIE-DNA is a regression technique adopting jackknife pseudo-values that can be implemented readily for the analysis. We demonstrate through simulations that SOHPIE-DNA consistently reaches higher recall and F1-score, while maintaining similar precision and accuracy to existing methods (NetCoMi and MDiNE). Lastly, we apply SOHPIE-DNA on two real datasets from the American Gut Project and the Diet Exchange Study to showcase the utility. The analysis of the Diet Exchange Study is to showcase that SOHPIE-DNA can also be used to incorporate the temporal change of connectivity of taxa with the inclusion of additional covariates. As a result, our method has found taxa that are related to the prevention of intestinal inflammation and severity of fatigue in advanced metastatic cancer patients.ConclusionSOHPIE-DNA is the first attempt of introducing the regression framework for the DN analysis in microbiome data. This enables the prediction of characteristics of a connectivity of a network with the presence of additional covariate information in the regression. The R package with a vignette of our methodology is available through the CRAN repository (https://CRAN.R-project.org/package=SOHPIE), named SOHPIE (pronounced as Sofie). The source code and user manual can be found at https://github.com/sjahnn/SOHPIE-DNA.

Differential Gene Regulatory Network Analysis between Azacitidine-Sensitive and -Resistant Cell Lines.

Azacitidine, a DNA methylation inhibitor, is employed for the treatment of acute myeloid leukemia (AML). However, drug resistance remains a major challenge for effective azacitidine chemotherapy, though several studies have attempted to uncover the mechanisms of azacitidine resistance. With the aim to identify the mechanisms underlying acquired azacitidine resistance in cancer cell lines, we developed a computational strategy that can identify differentially regulated gene networks between drug-sensitive and -resistant cell lines by extending the existing method, differentially coexpressed gene sets (DiffCoEx). The technique specifically focuses on cell line-specific gene network analysis. We applied our method to gene networks specific to azacitidine sensitivity and identified differentially regulated gene networks between azacitidine-sensitive and -resistant cell lines. The molecular interplay between the metallothionein gene family, C19orf33, ELF3, GRB7, IL18, NRN1, and RBM47 were identified as differentially regulated gene network in drug resistant cell lines. The biological mechanisms associated with azacitidine and AML for the markers in the identified networks were verified through the literature. Our results suggest that controlling the identified genes (e.g., the metallothionein gene family) and "cellular response"-related pathways ("cellular response to zinc ion", "cellular response to copper ion", and "cellular response to cadmium ion", where the enriched functional-related genes are MT2A, MT1F, MT1G, and MT1E) may provide crucial clues to address azacitidine resistance in patients with AML. We expect that our strategy will be a useful tool to uncover patient-specific molecular interplay that provides crucial clues for precision medicine in not only gastric cancer but also complex diseases.

Proteomic Analysis of a Rat Streptozotocin Model Shows Dysregulated Biological Pathways Implicated in Alzheimer's Disease.

Alzheimer's Disease (AD) is an age-related neurodegenerative disorder characterized by progressive memory loss and cognitive impairment, affecting 35 million individuals worldwide. Intracerebroventricular (ICV) injection of low to moderate doses of streptozotocin (STZ) in adult male Wistar rats can reproduce classical physiopathological hallmarks of AD. This biological model is known as ICV-STZ. Most studies are focused on the description of behavioral and morphological aspects of the ICV-STZ model. However, knowledge regarding the molecular aspects of the ICV-STZ model is still incipient. Therefore, this work is a first attempt to provide a wide proteome description of the ICV-STZ model based on mass spectrometry (MS). To achieve that, samples from the pre-frontal cortex (PFC) and hippocampus (HPC) of the ICV-STZ model and control (wild-type) were used. Differential protein abundance, pathway, and network analysis were performed based on the protein identification and quantification of the samples. Our analysis revealed dysregulated biological pathways implicated in the early stages of late-onset Alzheimer's disease (LOAD), based on differentially abundant proteins (DAPs). Some of these DAPs had their mRNA expression further investigated through qRT-PCR. Our results shed light on the AD onset and demonstrate the ICV-STZ as a valid model for LOAD proteome description.

Global gene expression profile of proliferative verrucous leukoplakia and its underlying biological disease mechanisms

BackgroundProliferative verrucous leukoplakia (PVL) is a rare and enigmatic oral potentially malignant disorder which almost invariably results in oral squamous cell carcinoma (OSCC). The aims of this project were to use transcriptome profiling to characterise PVL gene expression patterns for biomarker identification and gain insight into the molecular aetiopathogenesis of PVL. MethodsForty-three oral cavity mucosal biopsies from 32 patients with oral lesions clinically compatible with either PVL or non-PVL conventional oral leukoplakia (OLK) underwent transcriptome profiling by RNA sequencing. Data was analysed by hierarchical clustering, differential gene expression, functional enrichment and network analysis, sparse partial least squares discriminant analysis sPLS-DA, and immune cell phenotypic estimation. ResultsWe found 464 genes significantly differentially expressed at least 2-fold between PVL and non-PVL OLK (193 up and 271 down). HOX genes, including HOXA1 and HOXB7, keratin-associated proteins (KRTAPs) and olfactory receptor G proteins (OR) were significantly upregulated in PVL. Other upregulated genes in PVL included FOS, WNT16 and IFNA1. Pathway analysis showed that there was a significant downregulation of connective tissue signalling in PVL. Classifying multivariate models based upon 22 genes discriminated PVL from non-PVL OLK. Bioinformatic profiling showed that immune cell profiles in PVL and OLK were similar except that fibroblast markers were reduced in PVL. ConclusionThese results demonstrate that PVL and conventional OLK are molecularly distinct with upregulation of many cancer-associated genes. They provide insight into the pathogenesis of PVL and show that biomarker based molecular diagnostics is feasible to discriminate and inform diagnosis and management.

Differential eigengene network analysis reveals benzo[a]pyrene and 2,3,7,8-tetrachlorodibenzo-p-dioxin consensus regulatory network in human liver cell line HepG2

Aryl hydrocarbon receptor (AHR) is one of the main mediators of the toxic effects of benzo[a]pyrene (BaP) and 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD). However, a vast number of BaP- and TCDD-affected genes may suggest a more complex transcriptional regulatory network driving common adverse effects of these two chemicals. Unlike TCDD, BaP is rapidly metabolized in the liver, yielding products with a questionable ability to bind and activate AHR. In this study, we used transcriptomics data from the BaP- and TCCD-exposed human liver cell line HepG2, and performed differential eigengene network analysis to understand the correlation among genes and to untangle the common regulatory mechanism in the action of BaP and TCDD. The genes were grouped into 11 meta-modules with an overall preservation of 0.72 and were also segregated into three consensus time clusters: 12, 24, and 48 h. The analysis showed that the consensus genes in each time cluster were either directly regulated by the AHR or the AHR-TF interactions. Some TFs form a direct physical interaction with AHR such as ESR1, FOXA1, and E2F1, whereas others, including CTCF, RXRA, FOXO1, CEBPA, CEBPB, and TP53 show an indirect interaction with AHR. The analysis of biological processes (BPs) identified unique and common BPs in BaP and TCDD samples, with DNA damage response detected in all three time points. In summary, we identified a consensus transcriptional regulatory network common for BaP and TCDD consisting of direct AHR targets and AHR-TF targets. This analysis sheds new light on the common mechanism of action of a genotoxic (BaP) and non-genotoxic (TCDD) chemical in liver cells.

Pan-cancer analysis revealing the multidimensional expression and prognostic and immunologic roles of TGFB1 in cancer.

This study aimed to perform an integrated pan-cancer analysis to characterize the expression patterns, prognostic value, genetic alterations, and immunologic roles of transforming growth factor beta 1 (TGFB1) across diverse human cancer types. Bioinformatics analyses were conducted using multiple public databases including The Cancer Genome Atlas, Genotype-Tissue Expression, Clinical Proteomic Tumor Analysis Consortium, TIMER2, GEPIA2, cBioPortal, StringDB, and others. Differential expression, survival, immune correlation, and protein interaction network analyses were performed. TGFB1 was overexpressed in several tumor types compared with that in normal tissues. High TGFB1 expression was associated with an advanced stage and poorer prognosis in certain cancers. TGFB1 mutations occurred in 1.3% of 10,967 cases surveyed. TGFB1 expression correlated with tumor-infiltrating immune cells and immunotherapy-related genes. This comprehensive multi-omics analysis revealed the complex expression and prognostic landscape of TGFB1 across cancers. TGFB1 is emerging as a potential immunotherapeutic target in certain contexts. Further research should elucidate its multifaceted tumor-promoting and tumor-suppressive mechanisms. Our pan-cancer analysis provides new insights into TGFB1 as a prognostic biomarker and immunotherapeutic target in human cancers, and our findings may guide future preclinical and clinical investigations of TGFB1-directed therapies.

SOHPIE: statistical approach via pseudo-value information and estimation for differential network analysis of microbiome data.

The SOHPIER package implements a novel functionality for "multivariable" differential co-abundance network (DN, hereafter) analyses of microbiome data. It incorporates a regression approach that adjusts for additional covariates for DN analyses. This distinguishes from previous prominent approaches in DN analyses such as MDiNE and NetCoMi which do not feature a covariate adjustment of finding taxa that are differentially connected (DC, hereafter) between individuals with different clinical and phenotypic characteristics. SOHPIE with a vignette is available on CRAN repository https://CRAN.R-project.org/package=SOHPIE and published under General Public License (GPL) version 3 license.

Differential co-expression network analysis elucidated genes associated with sensitivity to farnesyltransferase inhibitor and prognosis of acute myeloid leukemia.

Acute myeloid leukemia (AML) is a heterogeneous disease and the most common form of acute leukemia with a poor prognosis. Due to its complexity, the disease requires the identification of biomarkers for reliable prognosis. To identify potential disease genes that regulate patient prognosis, we used differential co-expression network analysis and transcriptomics data from relapsed, refractory, and previously untreated AML patients based on their response to treatment in the present study. In addition, we combined functional genomics and transcriptomics data to identify novel and therapeutically potential systems biomarkers for patients who do or do not respond to treatment. As a result, we constructed co-expression networks for response and non-response cases and identified a highly interconnected group of genes consisting of SECISBP2L, MAN1A2, PRPF31, VASP, and SNAPC1 in the response network and a group consisting of PHTF2, SLC11A2, PDLIM5, OTUB1, and KLRD1 in the non-response network, both of which showed high prognostic performance with hazard ratios of 4.12 and 3.66, respectively. Remarkably, ETS1, GATA2, AR, YBX1, and FOXP3 were found to be important transcription factors in both networks. The prognostic indicators reported here could be considered as a resource for identifying tumorigenesis and chemoresistance to farnesyltransferase inhibitor. They could help identify important research directions for the development of new prognostic and therapeutic techniques for AML.

Exploring explainable machine learning and Shapley additive exPlanations (SHAP) technique to uncover key factors of HNSC cancer: An analysis of the best practices

ObjectiveHead and Neck Squamous Cell Carcinoma (HNSC) is characterized by poor prognosis and easy recurrence. Aberrant induction of autophagy and long non-coding RNAs (LncRNAs) have emerged as crucial factors in diagnosing and predicting HNSC. The goal of this study is to show how machine learning may help categorize HNSC subjects into risk categories. Moreover, we used SHAP architectures to offer explanations and interpretations. MethodsIn this study, we screened autophagy-related LncRNAs (DEARLs) using differential analysis and co-expression network analysis, identified DEARLs with prognostic ability using LASSO regression and RF classification, built a model based on multivariate Cox regression, subsequently validated its predictive capability. Eight machine learning methods were used to construct prediction models. SHAP analysis was established based on the best model, and the output of the ARLGS model was interpreted to filter out significant DEARLs. Finally, the ARLGS model was used for pan-cancer analysis. ResultsWe determined Nine DEARLs with predictive ability, and then these DEARLs were utilized to construct an ARLGS model. Kaplan-Meier (KM) analysis was performed to verify that cohorts with high ARLGS exhibited lower overall survival (OS) compared to those with low ARLGS, both in the train and test set. Furthermore, ROC curve analysis demonstrated the model's strong performance for HNSC patients at 1-, 3-, and 5-year (AUC > 70 %). Comparing the prediction models established by eight machine learning methods, we found that the xgboost model has the highest accuracy. Based on this, we performed SHAP analysis and found that the five DEARLs with the highest SHAP values were MIAT, LINC01343, LINC01305, and SLCO4A1.AS1 and GATA2.AS1 reflects the importance of these five DEARLs. Finally, we obtained the differential expression, immune signature, mutation, and drug sensitivity signatures of DEARLs in 33 cancers using pan-cancer analysis. ConclusionWe developed an ARLGS model with good reliability using LASSO and RF. We also found that the Xgboost model was the best, and can be used for SHAP analysis to find important DEARLs, solving the problem of the uninterpretable nature of machine learning methods. Thus, such an approach provided important guidance for assessing the risk of HNSC patients and developing customized treatments.

Short-term consumption of highly processed diets varying in macronutrient content impair the sense of smell and brain metabolism in mice

ObjectiveFood processing greatly contributed to increased food safety, diversity, and accessibility. However, the prevalence of highly palatable and highly processed food in our modern diet has exacerbated obesity rates and contributed to a global health crisis. While accumulating evidence suggests that chronic consumption of such foods is detrimental to sensory and neural physiology, it is unclear whether its short-term intake has adverse effects. Here, we assessed how short-term consumption (<2 months) of three diets varying in composition and macronutrient content influence olfaction and brain metabolism in mice. MethodsThe diets tested included a grain-based standard chow diet (CHOW; 54% carbohydrate, 32% protein, 14% fat; #8604 Teklad Rodent diet , Envigo Inc.), a highly processed control diet (hpCTR; 70% carbohydrate, 20% protein, 10% fat; #D12450B, Research Diets Inc.), and a highly processed high-fat diet (hpHFD; 20% carbohydrate, 20% protein, 60% fat; #D12492, Research Diets Inc.). We performed behavioral and metabolic phenotyping, electro-olfactogram (EOG) recordings, brain glucose metabolism imaging, and mitochondrial respirometry in different brain regions. We also performed RNA-sequencing (RNA-seq) in the nose and across several brain regions, and conducted differential expression analysis, gene ontology, and network analysis. ResultsWe show that short-term consumption of the two highly processed diets, but not the grain-based diet, regardless of macronutrient content, adversely affects odor-guided behaviors, physiological responses to odorants, transcriptional profiles in the olfactory mucosa and brain regions, and brain glucose metabolism and mitochondrial respiration. ConclusionsEven short periods of highly processed food consumption are sufficient to cause early olfactory and brain abnormalities, which has the potential to alter food choices and influence the risk of developing metabolic disease.

PRANA: an R package for differential co-expression network analysis with the presence of additional covariates

BackgroundAdvances in sequencing technology and cost reduction have enabled an emergence of various statistical methods used in RNA-sequencing data, including the differential co-expression network analysis (or differential network analysis). A key benefit of this method is that it takes into consideration the interactions between or among genes and do not require an established knowledge in biological pathways. As of now, none of existing softwares can incorporate covariates that should be adjusted if they are confounding factors while performing the differential network analysis.ResultsWe develop an R package PRANA which a user can easily include multiple covariates. The main R function in this package leverages a novel pseudo-value regression approach for a differential network analysis in RNA-sequencing data. This software is also enclosed with complementary R functions for extracting adjusted p-values and coefficient estimates of all or specific variable for each gene, as well as for identifying the names of genes that are differentially connected (DC, hereafter) between subjects under biologically different conditions from the output.ConclusionHerewith, we demonstrate the application of this package in a real data on chronic obstructive pulmonary disease. PRANA is available through the CRAN repositories under the GPL-3 license: https://cran.r-project.org/web/packages/PRANA/index.html.

PSI-13 Effects of a Postbiotic Program Consisting of Liquid and Solid Saccharomyces Cerevisiae Fermentation Products on Ruminal Microbiome Composition and Microbial Functions of Finishing Beef Steers

Abstract We assessed the effects of a postbiotic program consisting of liquid and solid Saccharomyces cerevisiae fermentation products (SCFP) on the ruminal microbiome and fermentation characteristics of beef steers. Eight Holstein steers (BW 467 ± 13.9 kg) with rumen cannulas were used in a crossover design study with 21-d per period and 7-d washout in between periods. Steers were blocked based on initial BW and assigned to one of two treatments: 1) Control, basal diet, or 2) SCFP, basal diet plus one day feeding of LiquiCare RTU (Diamond V, Cedar Rapids, IA; dosed via rumen cannula at 11 mL/100 kg BW), followed by daily feeding of 12 g/d NaturSafe (Diamond V, top-dressed). Rumen fluid samples were collected on d 0, 1, 2, 3, 5 and 21 at 0, 4, 8, or 12 h post morning feeding via rumen cannula for pH, VFA and NH3-N analyses. Samples collected at 4 h post morning feeding on d 0, 2 and 21 were subjected to DNA extraction and shotgun Nanopore sequencing to determine microbiome composition and carbohydrate active enzymes (CAZy) functional potential. Statistical model included the fixed effects of treatment, period, day and their interactions and random effect of animal. Feeding SCFP tended to reduce ruminal lactate (0.09 vs. 0.02 mM, P = 0.10), and reduced ruminal NH3-N (4.86 vs. 6.83 mg/dL, P = 0.04) on d 21. Ruminal total VFA concentration was increased (P = 0.01) by feeding SCFP 4-h post-feeding on d 5 and numerically increased 4-h post-feeding on day 21 (133.3 vs. 151.2 mM, P = 0.12). Treatments did not affect species or CAZy alpha- and beta-diversity. Steers received liquid postbiotic on d 2 exhibited a positive correlation (P &amp;lt; 0.05) between propionate molar proportion and glycosyl transferases (GT), glycoside hydrolases (GH), carbohydrate esterases, and carbohydrate binding modules (CBM); and a negative correlation (P &amp;lt; 0.05) between acetate molar proportion and polysaccharide lyases, GT, GH, and CBM, which was not observed in Control. Additionally, SCFP steers had higher (P = 0.05) CBM functional potential on d 21 than on d 0, while CAZy class abundances did not change over time in Control steers. Differential network analysis indicated a greater normalized degree for GT in the SCFP steers compared with Control on d 21. There were 2, 6 and 12 differently abundant species (P &amp;lt; 0.01) between SCFP and Control on d 0, 2 and 21, respectively, suggesting supplementing SCFP altered the abundances of more species over time. Our results showed that the postbiotic program altered the metabolic pathways of ruminal microbes toward greater propionate production, and improved abundance of carbohydrate binding modules. Our results also showed that glycosyl transferases had a greater promotive role in the ruminal enzymatic network of SCFP steers.

Construction of a molecular regulatory network related to fat deposition by multi-tissue transcriptome sequencing of Jiaxian red cattle

Intramuscular fat (IMF) refers to the fat that accumulates between muscle bundles or within muscle cells, whose content significantly impacts the taste, tenderness, and flavor of meat products, making it a crucial economic characteristic in livestock production. However, the intricate mechanisms governing IMF deposition, involving non-coding RNAs (ncRNAs), genes, and complex regulatory networks, remain largely enigmatic. Identifying adipose tissue-specific genes and ncRNAs is paramount to unravel these molecular mysteries. This study, conducted on Jiaxian red cattle, harnessed whole transcriptome sequencing to unearth the nuances of circRNAs and miRNAs across seven distinct tissues. The interplay of these ncRNAs was assessed through differential expression analysis and network analysis. These findings are not only pivotal in unveiling the intricacies of fat deposition mechanisms but also lay a robust foundation for future research, setting the stage for enhancing IMF content in Jiaxian red cattle breeding.

ScGRN: a comprehensive single-cell gene regulatory network platform of human and mouse.

Gene regulatory networks (GRNs) are interpretable graph models encompassing the regulatory interactions between transcription factors (TFs) and their downstream target genes. Making sense of the topology and dynamics of GRNs is fundamental to interpreting the mechanisms of disease etiology and translating corresponding findings into novel therapies. Recent advances in single-cell multi-omics techniques have prompted the computational inference of GRNs from single-cell transcriptomic and epigenomic data at an unprecedented resolution. Here, we present scGRN (https://bio.liclab.net/scGRN/), a comprehensive single-cell multi-omics gene regulatory network platform of human and mouse. The current version of scGRN catalogs 237051 cell type-specific GRNs (62 999692 TF-target gene pairs), covering 160 tissues/cell lines and 1324 single-cell samples. scGRN is the first resource documenting large-scale cell type-specific GRN information of diverse human and mouse conditions inferred from single-cell multi-omics data. We have implemented multiple online tools for effective GRN analysis, including differential TF-target network analysis, TF enrichment analysis, and pathway downstream analysis. We also provided details about TF binding to promoters, super-enhancers and typical enhancers of target genes in GRNs. Taken together, scGRN is an integrative and useful platform for searching, browsing, analyzing, visualizingand downloading GRNs of interest, enabling insight into the differences in regulatory mechanisms across diverse conditions.

Transcriptional dissection of symptomatic profiles across the brain of men and women with depression

Major depressive disorder (MDD) is one of the most important causes of disability worldwide. While recent work provides insights into the molecular alterations in the brain of patients with MDD, whether these molecular signatures can be associated with the expression of specific symptom domains remains unclear. Here, we identified sex-specific gene modules associated with the expression of MDD, combining differential gene expression and co-expression network analyses in six cortical and subcortical brain regions. Our results show varying levels of network homology between males and females across brain regions, although the associations between these structures and the expression of MDD remain highly sex specific. We refined these associations to several symptom domains and identified transcriptional signatures associated with distinct functional pathways, including GABAergic and glutamatergic neurotransmission, metabolic processes and intracellular signal transduction, across brain regions associated with distinct symptomatic profiles in a sex-specific fashion. In most cases, these associations were specific to males or to females with MDD, although a subset of gene modules associated with common symptomatic features in both sexes were also identified. Together, our findings suggest that the expression of distinct MDD symptom domains associates with sex-specific transcriptional structures across brain regions.

A distinctive subgingival microbiome in patients with periodontitis and Alzheimer's disease compared with cognitively unimpaired periodontitis patients.

Periodontitis is caused by dysbiosis of oral microbes and is associated with increased cognitive decline in Alzheimer's disease (AD), and recently, a potential functional link was proposed between oral microbes and AD. We compared the oral microbiomes of patients with or without AD to evaluate the association between oral microbes and AD in periodontitis. Periodontitis patients with AD (n = 15) and cognitively unimpaired periodontitis patients (CU) (n = 14) were recruited for this study. Each patient underwent an oral examination and neuropsychological evaluation. Buccal, supragingival and subgingival plaque samples were collected, and microbiomes were analysed by next-generation sequencing. Alpha diversity, beta diversity, linear discriminant analysis effect size, analysis of variance-like differential expression analysis and network analysis were used to compare group oral microbiomes. All 29 participants had moderate to severe periodontitis. Group buccal and supragingival samples were indistinguishable, but subgingival samples demonstrated significant alpha and beta diversity differences. Differential analysis showed subgingival samples of the AD group had higher prevalence of Atopobium rimae, Dialister pneumosintes, Olsenella sp. HMT 807, Saccharibacteria (TM7) sp. HMT 348 and several species of Prevotella than the CU group. Furthermore, subgingival microbiome network analysis revealed a distinct, closely connected network in the AD group comprised of various Prevotella spp. and several anaerobic bacteria. A unique microbial composition was discovered in the subgingival region in the AD group. Specifically, potential periodontal pathogens were found to be more prevalent in the subgingival plaque samples of the AD group. These bacteria may possess a potential to worsen periodontitis and other systemic diseases. We recommend that AD patients receive regular, careful dental check-ups to ensure proper oral hygiene management.