Early hepatic proteomic signatures reveal metabolic changes in high-fat-induced obesity in rats

The prevalence of diet-related obesity is increasing dramatically worldwide, making it important to understand the associated metabolic alterations in the liver. It is well known that obesity is a multifactorial condition that is the result of complex integration between many gene expressions and dietary factors. Obesity alone or in conjunction with other chronic diseases such as diabetes and insulin resistance causes many health problems and is considered a major risk factor for developing non-alcoholic steatohepatitis (NASH) and cirrhosis. In this study, we aimed to understand the molecular mechanisms underlying early hepatic changes in the pathophysiology of high-fat diet (HFD)-induced abdominal obesity in rats. Hepatic protein profiles of normal diet and HFD-induced obesity for 24 weeks were analysed using two-dimensional differential gel electrophoresis (DIGE) and protein identification by MS. Fifty-two proteins were identified by matrix-assisted laser desorption ionization-time of flight (MALDI-TOF), and computer-assisted DIGE image software analysis showed that eighteen major proteins were significantly differentially expressed between comparable groups, with 2·0–4·0-fold change/more (P < 0·01). These proteins are regulated in response to a HFD, and differentially expressed proteins are involved in key metabolic pathways such as lipid metabolism, energy metabolism, detoxification, urea cycle and hepatic Ca homoeostasis. In addition, Western blot and immunohistochemistry of liver-specific arginase-1 (Arg-1) showed significant increased expression in the liver of high-fat-fed rats (P < 0·01). Further, Arg-1 expression was correlated with NASH patients with obesity-related fibrosis (F0–F4). It is concluded that high-fat content may affect changes in liver pathways and may be a therapeutic target for obesity-related liver disease. Arg-1 expressions may be a potential pathological marker for assessing the progression of the disease.

Multi-omics analysis identifies potential microbial and metabolite diagnostic biomarkers of bacterial vaginosis

BACKGROUND

Bacterial vaginosis (BV) is a common clinical manifestation of a perturbed vaginal ecology associated with adverse sexual and reproductive health outcomes if left untreated. The existing diagnostic modalities are either cumbersome or require skilled expertise, warranting alternate tests. Application of machine-learning tools to heterogeneous and high-dimensional multi-omics datasets finds promising potential in data integration and may aid biomarker discovery.

OBJECTIVES

The present study aimed to evaluate the potential of the microbiome and metabolome-derived biomarkers in BV diagnosis. Interpretable machine-learning algorithms were used to evaluate the utility of an integrated-omics-derived classification model.

METHODS

Vaginal samples obtained from reproductive-age group women with (n = 40) and without BV (n = 40) were subjected to 16S rRNA amplicon sequencing and LC-MS-based metabolomics. The vaginal microbiome and metabolome were characterized, and machine-learning analysis was performed to build a classification model using biomarkers with the highest diagnostic accuracy.

RESULTS

Microbiome-based diagnostic model exhibited a ROC-AUC (10-fold CV) of 0.84 ± 0.21 and accuracy of 0.79 ± 0.18, and important features were Aerococcus spp., Mycoplasma hominis, Sneathia spp., Lactobacillus spp., Prevotella spp., Gardnerella spp. and Fannyhessea vaginae. The metabolome-derived model displayed superior performance with a ROC-AUC of 0.97 ± 0.07 and an accuracy of 0.92 ± 0.08. Beta-leucine, methylimidazole acetaldehyde, dimethylethanolamine, L-arginine and beta cortol were among key predictive metabolites for BV. A predictive model combining both microbial and metabolite features exhibited a high ROC-AUC of 0.97 ± 0.07 and accuracy of 0.94 ± 0.08 with diagnostic performance only slightly superior to the metabolite-based model.

CONCLUSION

Application of machine-learning tools to multi-omics datasets aid biomarker discovery with high predictive performance. Metabolome-derived classification models were observed to have superior diagnostic performance in predicting BV than microbiome-based biomarkers.

Metabolic alterations unravel the maternofetal immune responses with disease severity in pregnant women infected with SARS-CoV-2

Pregnancy being an immune compromised state, coronavirus disease of 2019 (COVID-19) disease poses high risk of premature delivery and threat to fetus. Plasma metabolome regulates immune cellular responses, therefore we aimed to analyze the change in plasma secretome, metabolome, and immune cells with disease severity in COVID-19 positive pregnant females and their cord blood. COVID-19 reverse transcriptase-polymerase chain reaction positive pregnant females (n = 112) with asymptomatic (Asy) (n = 82), mild (n = 21), or moderate (n = 9) disease, healthy pregnant (n = 18), COVID-19 positive nonpregnant females (n = 7) were included. Eighty-two cord blood from COVID-19 positive and seven healthy cord blood were also analyzed. Mother's peripheral blood and cord blood were analyzed for untargeted metabolome profiling and cytokines by using high-resolution mass spectrometry and cytokine bead array. Immune scan was performed only in mothers' blood by flow cytometry. In Asy severe acute respiratory syndrome coronavirus 2 infection, the amino acid metabolic pathways such as glycine, serine, l-lactate, and threonine metabolism were upregulated with downregulation of riboflavin and tyrosine metabolism. However, with mild-to-moderate disease, the pyruvate and nicotinamide adenine dinucleotide (NAD+ ) metabolism were mostly altered. Cord blood mimicked the mother's metabolomic profiles by showing altered valine, leucine, isoleucine, glycine, serine, threonine in Asy and NAD+ , riboflavin metabolism in mild and moderate. Additionally, with disease severity tumor necrosis factor-α, interferon (IFN)-α, IFN-γ, interleukin (IL)-6 cytokine storm, IL-9 was raised in both mothers and neonates. Pyruvate, NAD metabolism and increase in IL-9 and IFN-γ had an impact on nonclassical monocytes, exhausted T and B cells. Our results demonstrated that immune-metabolic interplay in mother and fetus is influenced with increase in IL-9 and IFN-γ regulated pyruvate, lactate tricarboxylic acid, and riboflavin metabolism with context to disease severity.

Differential blood transcriptome modules predict response to corticosteroid therapy in alcoholic hepatitis

Background & Aims

In patients with severe alcoholic hepatitis (SAH), little is known about the profile of peripheral blood mononuclear cells (PBMCs) at baseline and during corticosteroid therapy, among those who can be treated successfully with steroids (steroid-responders [R] and those who cannot (steroid-non-responders [NR]); 2 groups with different outcomes.

Methods

We performed RNA-seq analysis in PBMCs from 32 patients with definite SAH, at baseline and after 7 days of corticosteroids. The data were sorted into R and NR (n = 16, each group) using the Lille model and 346 blood transcription modules (BTMs) were identified. BTMs are predefined modules of highly co-expressed PBMC genes, which can determine specific immune cell types and cellular functions. The activity of each BTM was taken as the mean value of its member genes.

Results

At baseline, 345 BTMs had higher activity (i.e. were upregulated) in NR relative to R. The 100 most upregulated BTMs in NR, included several modules related to lymphoid lineage (T, B, and natural killer [NK] cells), modules for cell division and mitochondrial respiratory electron transport chain (ETC, relating to energy production), but only a few modules of myeloid cells. Correlation studies of BTM activities found features of significantly greater activation/proliferation and differentiation for T and B cells in NR relative to R. After 7 days of corticosteroids, NR had no significant changes in BTM activities relative to baseline, whereas R had downregulation of BTMs related to innate and adaptive immunity.

Conclusions

At baseline and during corticosteroid therapy, increased activity in the PBMCs of gene modules related to activation/proliferation and differentiation of T and B cells, NK cells, and mitochondrial ETC, is a hallmark of SAH patients who are steroid-non-responders.

Lay summary

Patients with severe alcoholic hepatitis receive steroid therapy as the main line of treatment; however, this treatment is ineffective in some patients. This only becomes apparent after 7 days of steroid therapy. We have developed an approach where it can be estimated if a patient is going to respond or not to steroid therapy using the gene expression information of blood cells. This method will allow clinicians to assess the response of patients to steroids earlier, and will help them in adopting alternate strategies if the treatment is found to be ineffective in a particular patient.

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RNA-seq is an unprecedented tool for analysis of the bulk peripheral blood mononuclear cells (PBMCs) transcriptome.

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Co-expressed genes in the bulk PBMC transcriptome can be grouped as blood transcriptional modules (BTMs).

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Patients with severe alcoholic hepatitis, non-responsive to corticosteroids, have a distinct BTM profile at baseline.

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Deconvolution of RNA-seq data using CIBERSORTx showed increases in different populations of B cells, CD4 and CD8 T cells, and NK cells.

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Baseline FACS analysis of PBMCs can be reflective of immune cells identified by RNA-seq data analysis.

Microvesicles in hepatic and peripheral vein can predict nonresponse to corticosteroid therapy in severe alcoholic hepatitis

BACKGROUND

Severe alcoholic hepatitis patients have high mortality and limited response to corticosteroids. Microvesicles reflect cellular stress and disease conditions.

AIMS

To investigate whether microvesicles are associated with severity, response to steroid therapy and inflammation in severe alcoholic hepatitis.

METHODS

Microvesicles originating from different cells were studied pre-therapy in 101 patients; (71 responder to corticosteroid therapy and 30 nonresponders) and 20 healthy controls. Microvesicles and cells were determined in peripheral and hepatic vein samples using flow cytometry and correlated with outcomes. Inflammatory signalling pathways and functional alterations of immune cells after stimulation with microvesicles were also investigated.

RESULTS

Microvesicles mean levels were higher in nonresponders for T cells (CD3⁺ CD4⁺ ; 10.1 MV/μL vs 5.4; P = 0.06), macrophages (CD68⁺ CD11b⁺ ; 136.5 vs 121.9 MV/μL; P = 0.01), haematopoietic stem-cells (CD45⁺ CD34⁺ ; 116.8 vs 13.4 MV/μL; P = 0.0001) and hepatocytes (ASGPR⁺ ; 470 vs 361 MV/μL; P = 0.01); the latter two predicting steroid nonresponse in 94% patients at baseline in peripheral plasma. Microvesicle levels correlated with histological and liver disease severity indices. Whereas, in non-responders hepatic vein CD34+ cells were lower (P = 0.02), the CD34+ microvesicles there from were higher (P = 0.04), thus suggesting impaired regeneration. Also, microvesicles of 0.2-0.4 μm size were higher in nonresponders (P < 0.03) at baseline. Microvesicles from patients trigger more (P = 0.04) ROS generation, TNF-α production (P = 0.04) and up-regulate pro-inflammatory cytokine related genes in neutrophils in vitro.

CONCLUSIONS

Pre-therapy peripheral plasma levels of CD34⁺ and ASGPR⁺ microvesicles are reliable non-invasive markers of steroid nonresponse and mortality in patients with severe alcoholic hepatitis.