DESI-MSI and METASPACE indicates lipid abnormalities and altered mitochondrial membrane components in diabetic renal proximal tubules

Lipid metabolism disorder in diabetic kidney disease

Diabetic kidney disease (DKD), a significant complication associated with diabetes mellitus, presents limited treatment options. The progression of DKD is marked by substantial lipid disturbances, including alterations in triglycerides, cholesterol, sphingolipids, phospholipids, lipid droplets, and bile acids (BAs). Altered lipid metabolism serves as a crucial pathogenic mechanism in DKD, potentially intertwined with cellular ferroptosis, lipophagy, lipid metabolism reprogramming, and immune modulation of gut microbiota (thus impacting the liver-kidney axis). The elucidation of these mechanisms opens new potential therapeutic pathways for DKD management. This research explores the link between lipid metabolism disruptions and DKD onset.

Sample preparation for fatty acid analysis in biological samples with mass spectrometry-based strategies

Fatty acids (FAs) have attracted many interests for their pivotal roles in many biological processes. Imbalance of FAs is related to a variety of diseases, which makes the measurement of them important in biological samples. Over the past two decades, mass spectrometry (MS) has become an indispensable technique for the analysis of FAs owing to its high sensitivity and precision. Due to complex matrix effect of biological samples and inherent poor ionization efficiency of FAs in MS, sample preparation including extraction and chemical derivatization prior to analysis are often employed. Here, we describe an updated overview of FA extraction techniques, as well as representative derivatization methods utilized in different MS platforms including gas chromatography-MS, liquid chromatography-MS, and mass spectrometry imaging based on different chain lengths of FAs. Derivatization strategies for the identification of double bond location in unsaturated FAs are also summarized and highlighted. The advantages, disadvantages, and prospects of these methods are compared and discussed. This review provides the development and valuable information for sample pretreatment approaches and qualitative and quantitative analysis of interested FAs using different MS-based platforms in complex biological matrices. Finally, the challenges of FA analysis are summarized and the future perspectives are prospected.

Spatial metabolomics method to reveal differential metabolomes in microregions of Panax quinquefolius roots by using ultra-performance liquid chromatography quadrupole/time of flight-mass spectrometry and desorption electrospray ionization mass spectrometry imaging

Panax quinquefolius is a natural homology medicine and food that is rich in bioactive ingredients, such as ginsenosides and polysaccharides. The combination of ultra-performance liquid chromatography quadrupole/time of flight-mass spectrometry (UPLC-Q-TOF/MS) and desorption electrospray ionization mass spectrometry imaging (DESI-MSI) was used for the first time in a spatial metabolomics analysis to comprehensively evaluate the differential components in different microregions of P. quinquefolius. UPLC-Q-TOF/MS and DESI-MSI combined with principal component analysis and orthogonal partial least squares-discriminant analysis were used to screen differential metabolites. UPLC-Q-TOF/MS and DESI-MSI screened 27 and 23 differential metabolites, respectively, among which 15 differential metabolites were identified by both methods. It was found that some components, such as ginsenoside Rg1 and malonyl-ginsenoside Rc, were mainly distributed in P of the transverse slice of P. quinquefolius roots, while ginsenoside Ro and malonyl-ginsenoside Rd were mainly distributed in C. The methods and results of this study could be used to understand the precise localization, biosynthesis, and biological functions of special metabolites in P. quinquefolius.

Tubular dysfunction impairs renal excretion of pseudouridine in diabetic kidney disease

Plasma nucleosides-pseudouridine (PU) and N2N2-dimethyl guanosine (DMG) predict the progression of type 2 diabetic kidney disease (DKD) to end-stage renal disease, but the mechanisms underlying this relationship are not well understood. We used a well-characterized model of type 2 diabetes (db/db mice) and control nondiabetic mice (db/m mice) to characterize the production and excretion of PU and DMG levels using liquid chromatography-mass spectrometry. The fractional excretion of PU and DMG was decreased in db/db mice compared with control mice at 24 wk before any changes to renal function. We then examined the dynamic changes in nucleoside metabolism using in vivo metabolic flux analysis with the injection of labeled nucleoside precursors. Metabolic flux analysis revealed significant decreases in the ratio of urine-to-plasma labeling of PU and DMG in db/db mice compared with db/m mice, indicating significant tubular dysfunction in diabetic kidney disease. We observed that the gene and protein expression of the renal tubular transporters involved with nucleoside transport in diabetic kidneys in mice and humans was reduced. In conclusion, this study strongly suggests that tubular handling of nucleosides is altered in early DKD, in part explaining the association of PU and DMG with human DKD progression observed in previous studies.NEW & NOTEWORTHY Tubular dysfunction explains the association between the nucleosides pseudouridine and N2N2-dimethyl guanosine and diabetic kidney disease.

Next-generation pathology practices with mass spectrometry imaging

Mass spectrometry imaging (MSI) is a powerful technique that reveals the spatial distribution of various molecules in biological samples, and it is widely used in pathology-related research. In this review, we summarize common MSI techniques, including matrix-assisted laser desorption/ionization and desorption electrospray ionization MSI, and their applications in pathological research, including disease diagnosis, microbiology, and drug discovery. We also describe the improvements of MSI, focusing on the accumulation of imaging data sets, expansion of chemical coverage, and identification of biological significant molecules, that have prompted the evolution of MSI to meet the requirements of pathology practices. Overall, this review details the applications and improvements of MSI techniques, demonstrating the potential of integrating MSI techniques into next-generation pathology practices.

Spatial metabolomics principles and application to cancer research

Mass spectrometry imaging (MSI) is an emerging technology in cancer metabolomics. Desorption electrospray ionization (DESI) and matrix-assisted laser desorption ionization (MALDI) MSI are complementary techniques to identify hundreds of metabolites in space with close to single-cell resolution. This technology leap enables research focusing on tumor heterogeneity, cancer cell plasticity, and the communication signals between cancer and stromal cells in the tumor microenvironment (TME). Currently, unprecedented knowledge is generated using spatial metabolomics in fundamental cancer research. Yet, also translational applications are emerging, including the assessment of spatial drug distribution in organs and tumors. Moreover, clinical research investigates the use of spatial metabolomics as a rapid pathology tool during cancer surgeries. Here, we summarize MSI applications, the knowledge gained by this technology in space, future directions, and developments needed.

Determination of short-chain fatty acids as putative biomarkers of cancer diseases by modern analytical strategies and tools: a review

Short-chain fatty acids (SCFAs) are the main metabolites produced by bacterial fermentation of non-digestible carbohydrates in the gastrointestinal tract. They can be seen as the major flow of carbon from the diet, through the microbiome to the host. SCFAs have been reported as important molecules responsible for the regulation of intestinal homeostasis. Moreover, these molecules have a significant impact on the immune system and are able to affect inflammation, cardiovascular diseases, diabetes type II, or oncological diseases. For this purpose, SCFAs could be used as putative biomarkers of various diseases, including cancer. A potential diagnostic value may be offered by analyzing SCFAs with the use of advanced analytical approaches such as gas chromatography (GC), liquid chromatography (LC), or capillary electrophoresis (CE) coupled with mass spectrometry (MS). The presented review summarizes the importance of analyzing SCFAs from clinical and analytical perspective. Current advances in the analysis of SCFAs focused on sample pretreatment, separation strategy, and detection methods are highlighted. Additionally, it also shows potential areas for the development of future diagnostic tools in oncology and other varieties of diseases based on targeted metabolite profiling.

Advances and Emerging Medical Applications of Direct Mass Spectrometry Technologies for Tissue Analysis

Offering superb speed, chemical specificity, and analytical sensitivity, direct mass spectrometry (MS) technologies are highly amenable for the molecular analysis of complex tissues to aid in disease characterization and help identify new diagnostic, prognostic, and predictive markers. By enabling detection of clinically actionable molecular profiles from tissues and cells, direct MS technologies have the potential to guide treatment decisions and transform sample analysis within clinical workflows. In this review, we highlight recent health-related developments and applications of direct MS technologies that exhibit tangible potential to accelerate clinical research and disease diagnosis, including oncological and neurodegenerative diseases and microbial infections. We focus primarily on applications that employ direct MS technologies for tissue analysis, including MS imaging technologies to map spatial distributions of molecules in situ as well as handheld devices for rapid in vivo and ex vivo tissue analysis.

Phosphatidylinositol metabolism of the renal proximal tubule S3 segment is disturbed in response to diabetes

Diabetes is a main risk factor for kidney disease, causing diabetic nephropathy in close to half of all patients with diabetes. Metabolism has recently been identified to be decisive in cell fate decisions and repair. Here we used mass spectrometry imaging (MSI) to identify tissue specific metabolic dysregulation, in order to better understand early diabetes-induced metabolic changes of renal cell types. In our experimental diabetes mouse model, early glomerular glycocalyx barrier loss and systemic metabolic changes were observed. In addition, MSI targeted at small molecule metabolites and glycero(phospho)lipids exposed distinct changes upon diabetes in downstream nephron segments. Interestingly, the outer stripe of the outer medullar proximal tubular segment (PT_S3) demonstrated the most distinct response compared to other segments. Furthermore, phosphatidylinositol lipid metabolism was altered specifically in PT_S3, with one of the phosphatidylinositol fatty acid tails being exchanged from longer unsaturated fatty acids to shorter, more saturated fatty acids. In acute kidney injury, the PT_S3 segment and its metabolism are already recognized as important factors in kidney repair processes. The current study exposes early diabetes-induced changes in membrane lipid composition in this PT_S3 segment as a hitherto unrecognized culprit in the early renal response to diabetes.

SGLT2 inhibitors mitigate kidney tubular metabolic and mTORC1 perturbations in youth-onset type 2 diabetes

The molecular mechanisms of sodium-glucose cotransporter-2 (SGLT2) inhibitors (SGLT2i) remain incompletely understood. Single-cell RNA sequencing and morphometric data were collected from research kidney biopsies donated by young persons with type 2 diabetes (T2D), aged 12 to 21 years, and healthy controls (HCs). Participants with T2D were obese and had higher estimated glomerular filtration rates and mesangial and glomerular volumes than HCs. Ten T2D participants had been prescribed SGLT2i (T2Di[+]) and 6 not (T2Di[-]). Transcriptional profiles showed SGLT2 expression exclusively in the proximal tubular (PT) cluster with highest expression in T2Di(-) patients. However, transcriptional alterations with SGLT2i treatment were seen across nephron segments, particularly in the distal nephron. SGLT2i treatment was associated with suppression of transcripts in the glycolysis, gluconeogenesis, and tricarboxylic acid cycle pathways in PT, but had the opposite effect in thick ascending limb. Transcripts in the energy-sensitive mTORC1-signaling pathway returned toward HC levels in all tubular segments in T2Di(+), consistent with a diabetes mouse model treated with SGLT2i. Decreased levels of phosphorylated S6 protein in proximal and distal tubules in T2Di(+) patients confirmed changes in mTORC1 pathway activity. We propose that SGLT2i treatment benefits the kidneys by mitigating diabetes-induced metabolic perturbations via suppression of mTORC1 signaling in kidney tubules.

Molecular pathways that drive diabetic kidney disease

Kidney disease is a major driver of mortality among patients with diabetes and diabetic kidney disease (DKD) is responsible for close to half of all chronic kidney disease cases. DKD usually develops in a genetically susceptible individual as a result of poor metabolic (glycemic) control. Molecular and genetic studies indicate the key role of podocytes and endothelial cells in driving albuminuria and early kidney disease in diabetes. Proximal tubule changes show a strong association with the glomerular filtration rate. Hyperglycemia represents a key cellular stress in the kidney by altering cellular metabolism in endothelial cells and podocytes and by imposing an excess workload requiring energy and oxygen for proximal tubule cells. Changes in metabolism induce early adaptive cellular hypertrophy and reorganization of the actin cytoskeleton. Later, mitochondrial defects contribute to increased oxidative stress and activation of inflammatory pathways, causing progressive kidney function decline and fibrosis. Blockade of the renin-angiotensin system or the sodium-glucose cotransporter is associated with cellular protection and slowing kidney function decline. Newly identified molecular pathways could provide the basis for the development of much-needed novel therapeutics.

The Mitochondrion: A Promising Target for Kidney Disease

Mitochondrial dysfunction is important in the pathogenesis of various kidney diseases and the mitochondria potentially serve as therapeutic targets necessitating further investigation. Alterations in mitochondrial biogenesis, imbalance between fusion and fission processes leading to mitochondrial fragmentation, oxidative stress, release of cytochrome c and mitochondrial DNA resulting in apoptosis, mitophagy, and defects in energy metabolism are the key pathophysiological mechanisms underlying the role of mitochondrial dysfunction in kidney diseases. Currently, various strategies target the mitochondria to improve kidney function and kidney treatment. The agents used in these strategies can be classified as biogenesis activators, fission inhibitors, antioxidants, mPTP inhibitors, and agents which enhance mitophagy and cardiolipin-protective drugs. Several glucose-lowering drugs, such as glucagon-like peptide-1 receptor agonists (GLP-1-RA) and sodium glucose co-transporter-2 (SGLT-2) inhibitors are also known to have influences on these mechanisms. In this review, we delineate the role of mitochondrial dysfunction in kidney disease, the current mitochondria-targeting treatment options affecting the kidneys and the future role of mitochondria in kidney pathology.

Redox phospholipidomics analysis reveals specific oxidized phospholipids and regions in the diabetic mouse kidney

While it is generally accepted that oxidative stress impacts the diabetic kidney and contributes to pathogenesis, there is a substantial lack of knowledge about the molecular entity and anatomic location of a variety of reactive species. Here we provide a novel "oxidative stress map" of the diabetic kidney - the first of its kind, and identify specific, oxidized and other reactive lipids and their location. We used the db/db mouse model and Desorption Electrospray Ionization (DESI) mass spectrometry combined with heatmap image analysis. We analyzed a comprehensive array of phospholipid peroxide species in normal (db/m) and diabetic (db/db) kidneys using DESI imaging. Oxilipidomics heatmaps of the kidneys were generated focusing on phospholipids and their potential peroxidized products. We identified those lipids that undergo peroxidation in diabetic nephropathy. Several phospholipid peroxides and their spatial distribution were identified that were specific to the diabetic kidney, with significant enrichment in oxygenated phosphatidylethanolamines (PE) and lysophosphatidylethanolamine. Beyond qualitative and semi-quantitative information about the targets, the approach also reveals the anatomic location and the extent of lipid peroxide signal propagation across the kidney. Our approach provides novel, in-depth information of the location and molecular entity of reactive lipids in an organ with a very heterogeneous landscape. Many of these reactive lipids have been previously linked to programmed cell death mechanisms. Thus, the findings may be relevant to understand what impact phospholipid peroxidation has on cell and mitochondria membrane integrity and redox lipid signaling in diabetic nephropathy.

Modular networks and genomic variation during progression from stable angina pectoris through ischemic cardiomyopathy to chronic heart failure

Background

Analyzing disease–disease relationships plays an important role for understanding etiology, disease classification, and drug repositioning. However, as cardiovascular diseases with causative links, the molecular relationship among stable angina pectoris (SAP), ischemic cardiomyopathy (ICM) and chronic heart failure (CHF) is not clear.

Methods

In this study, by integrating the multi-database data, we constructed paired disease progression modules (PDPMs) to identified relationship among SAP, ICM and CHF based on module reconstruction pairs (MRPs) of K-value calculation (a Euclidean distance optimization by integrating module topology parameters and their weights) methods. Finally, enrichment analysis, literature validation and structural variation (SV) were performed to verify the relationship between the three diseases in PDPMs.

Results

Total 16 PDPMs were found with K > 0.3777 among SAP, ICM and CHF, in which 6 pairs in SAP–ICM, 5 pairs for both ICM–CHF and SAP–CHF. SAP–ICM was the most closely related by having the smallest average K-value (K = 0.3899) while the maximum is SAP–CHF (K = 0.4006). According to the function of the validation gene, inflammatory response were through each stage of SAP–ICM–CHF, while SAP–ICM was uniquely involved in fibrosis, and genes were related in affecting the upstream of PI3K–Akt signaling pathway. 4 of the 11 genes (FLT1, KDR, ANGPT2 and PGF) in SAP–ICM–CHF related to angiogenesis in HIF-1 signaling pathway. Furthermore, we identified 62.96% SVs were protein deletion in SAP–ICM–CHF, and 53.85% SVs were defined as protein replication in SAP–ICM, while ICM–CHF genes were mainly affected by protein deletion.

Conclusion

The PDPMs analysis approach combined with genomic structural variation provides a new avenue for determining target associations contributing to disease progression and reveals that inflammation and angiogenesis may be important links among SAP, ICM and CHF progression.

Algorithms and Databases: Unlocking Non-Targeted Screening of Small Molecules with Ambient Ionization Mass Spectrometry

Almost all sectors of analytical chemistry are finding applications for ambient ionization mass spectrometry (AI–MS) because of its ease of use, speed of analysis, and sensitivity. Although emphasis has been placed on developing new hardware that can help analyze unique samples across various applications, there has not been much innovation in the functionality of software tools and mass spectral libraries to support applications like non-targeted searching. In this article, we discuss new algorithms and libraries that have enabled non-targeted analysis of small molecules using AI–MS, as well as some of the key considerations and outstanding questions in the field.

What are we imaging? Software tools and experimental strategies for annotation and identification of small molecules in mass spectrometry imaging

Mass spectrometry imaging (MSI) has become a widespread analytical technique to perform nonlabeled spatial molecular identification. The Achilles' heel of MSI is the annotation and identification of molecular species due to intrinsic limitations of the technique (lack of chromatographic separation and the difficulty to apply tandem MS). Successful strategies to perform annotation and identification combine extra analytical steps, like using orthogonal analytical techniques to identify compounds; with algorithms that integrate the spectral and spatial information. In this review, we discuss different experimental strategies and bioinformatics tools to annotate and identify compounds in MSI experiments. We target strategies and tools for small molecule applications, such as lipidomics and metabolomics. First, we explain how sample preparation and the acquisition process influences annotation and identification, from sample preservation to the use of orthogonal techniques. Then, we review twelve software tools for annotation and identification in MSI. Finally, we offer perspectives on two current needs of the MSI community: the adaptation of guidelines for communicating confidence levels in identifications; and the creation of a standard format to store and exchange annotations and identifications in MSI.

Rapid and Automatic Annotation of Multiple On-Tissue Chemical Modifications in Mass Spectrometry Imaging with Metaspace

On-tissue chemical derivatization is a valuable tool for expanding compound coverage in untargeted metabolomic studies with matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI). Applying multiple derivatization agents in parallel increases metabolite coverage even further but results in large and more complex datasets that can be challenging to analyze. In this work, we present a pipeline to provide rigorous annotations for on-tissue derivatized MSI data using Metaspace. To test and validate the pipeline, maize roots were used as a model system to obtain MSI datasets after chemical derivatization with four different reagents, Girard's T and P for carbonyl groups, coniferyl aldehyde for primary amines, and 2-picolylamine for carboxylic acids. Using this pipeline helped us annotate 631 unique metabolites from the CornCyc/BraChem database compared to 256 in the underivatized dataset, yet, at the same time, shortening the processing time compared to manual processing and providing robust and systematic scoring and annotation. We have also developed a method to remove false derivatized annotations, which can clean 5-25% of false derivatized annotations from the derivatized data, depending on the reagent. Taken together, our pipeline facilitates the use of broadly targeted spatial metabolomics using multiple derivatization reagents.

An Organ-Specific Metabolite Annotation Approach for Ambient Mass Spectrometry Imaging Reveals Spatial Metabolic Alterations of a Whole Mouse Body

Rapid and accurate metabolite annotation in mass spectrometry imaging (MSI) can improve the efficiency of spatially resolved metabolomics studies and accelerate the discovery of reliable in situ disease biomarkers. To date, metabolite annotation tools in MSI generally utilize isotopic patterns, but high-throughput fragmentation-based identification and biological and technical factors that influence structure elucidation are active challenges. Here, we proposed an organ-specific, metabolite-database-driven approach to facilitate efficient and accurate MSI metabolite annotation. Using data-dependent acquisition (DDA) in liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) to generate high-coverage product ions, we identified 1620 unique metabolites from eight mouse organs (brain, liver, kidney, heart, spleen, lung, muscle, and pancreas) and serum. Following the evaluation of the adduct form difference of metabolite ions between LC-MS and airflow-assisted desorption electrospray ionization (AFADESI)-MSI and deciphering organ-specific metabolites, we constructed a metabolite database for MSI consisting of 27,407 adduct ions. An automated annotation tool, MSIannotator, was then created to conduct metabolite annotation in the MSI dataset with high efficiency and confidence. We applied this approach to profile the spatially resolved landscape of the whole mouse body and discovered that metabolites were distributed across the body in an organ-specific manner, which even spanned different mouse strains. Furthermore, the spatial metabolic alteration in diabetic mice was delineated across different organs, exhibiting that differentially expressed metabolites were mainly located in the liver, brain, and kidney, and the alanine, aspartate, and glutamate metabolism pathway was simultaneously altered in these three organs. This approach not only enables robust metabolite annotation and visualization on a body-wide level but also provides a valuable database resource for underlying organ-specific metabolic mechanisms.

Azapodophyllotoxin Causes Lymphoma and Kidney Cancer Regression by Disrupting Tubulin and Monoglycerols

A natural compound screen identified several anticancer compounds, among which azapodophyllotoxin (AZP) was found to be the most potent. AZP caused decreased viability of both mouse and human lymphoma and renal cell cancer (RCC) tumor-derived cell lines. Novel AZP derivatives were synthesized and screened identifying compound NSC750212 to inhibit the growth of both lymphoma and RCC both in vitro and in vivo. A nanoimmunoassay was used to assess the NSC750212 mode of action in vivo. On the basis of the structure of AZP and its mode of action, AZP disrupts tubulin polymerization. Through desorption electrospray ionization mass spectrometry imaging, NSC750212 was found to inhibit lipid metabolism. NSC750212 suppresses monoglycerol metabolism depleting lipids and thereby inhibits tumor growth. The dual mode of tubulin polymerization disruption and monoglycerol metabolism inhibition makes NSC750212 a potent small molecule against lymphoma and RCC.

Uncovering Molecular Heterogeneity in the Kidney With Spatially Targeted Mass Spectrometry

The kidney functions through the coordination of approximately one million multifunctional nephrons in 3-dimensional space. Molecular understanding of the kidney has relied on transcriptomic, proteomic, and metabolomic analyses of kidney homogenate, but these approaches do not resolve cellular identity and spatial context. Mass spectrometry analysis of isolated cells retains cellular identity but not information regarding its cellular neighborhood and extracellular matrix. Spatially targeted mass spectrometry is uniquely suited to molecularly characterize kidney tissue while retaining in situ cellular context. This review summarizes advances in methodology and technology for spatially targeted mass spectrometry analysis of kidney tissue. Profiling technologies such as laser capture microdissection (LCM) coupled to liquid chromatography tandem mass spectrometry provide deep molecular coverage of specific tissue regions, while imaging technologies such as matrix assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) molecularly profile regularly spaced tissue regions with greater spatial resolution. These technologies individually have furthered our understanding of heterogeneity in nephron regions such as glomeruli and proximal tubules, and their combination is expected to profoundly expand our knowledge of the kidney in health and disease.

Differential lipidomics of HK-2 cells and exosomes under high glucose stimulation

Abnormal cellular lipid metabolism has a very important role in the occurrence and progression of diabetic kidney disease (DKD). However, the lipid composition and differential expression by high glucose stimulation of renal tubular cells and their exosomes, which is a vital part of the development of DKD, are largely unknown. In this study, based on targeted lipid analysis by isotope labeling and tandem mass spectrometry, a total of 421 and 218 lipid species were quantified in HK-2 cells and exosomes, respectively. More importantly, results showed that GM3 d18:1/22:0, GM3 d18:1/16:0, GM3 d18:0/16:0, GM3 d18:1/22:1 were significantly increased, while LPE18:1, LPE, CL66:4 (16:1), BMP36:3, CL70:7 (16:1), CL74:8 (16:1) were significantly decreased in high glucose-stimulated HK-2 cells. Also, PG36:1, FFA22:5, PC38:3, SM d18:1/16:1, CE-16:1, CE-18:3, CE-20:5, and CE-22:6 were significantly increased, while GM3 d18:1/24:1, GM3 were significantly decreased in exosomes secreted by high glucose-stimulated HK-2 cells. Furthermore, TAG, PC, CL were decreased significantly in the exosomes comparing with the HK-2 cells, and LPA18:2, LPI22:5, PG32:2, FFA16:1, GM3 d18:1/18:1, GM3 d18:1/20:1, GM3 d18:0/20:0, PC40:6p, TAG52:1(18:1), TAG52:0(18:0), CE-20:5, CE-20:4, CE-22:6 were only found in exosomes. In addition, the expression of PI4P in HK-2 cells decreased under a high glucose state. These data may be useful to provide new targets for exploring the mechanisms of DKD.

Lipidomic approaches to dissect dysregulated lipid metabolism in kidney disease

Dyslipidaemia is a hallmark of chronic kidney disease (CKD). The severity of dyslipidaemia not only correlates with CKD stage but is also associated with CKD-associated cardiovascular disease and mortality. Understanding how lipids are dysregulated in CKD is, however, challenging owing to the incredible diversity of lipid structures. CKD-associated dyslipidaemia occurs as a consequence of complex interactions between genetic, environmental and kidney-specific factors, which to understand, requires an appreciation of perturbations in the underlying network of genes, proteins and lipids. Modern lipidomic technologies attempt to systematically identify and quantify lipid species from biological systems. The rapid development of a variety of analytical platforms based on mass spectrometry has enabled the identification of complex lipids at great precision and depth. Insights from lipidomics studies to date suggest that the overall architecture of free fatty acid partitioning between fatty acid oxidation and complex lipid fatty acid composition is an important driver of CKD progression. Available evidence suggests that CKD progression is associated with metabolic inflexibility, reflecting a diminished capacity to utilize free fatty acids through β-oxidation, and resulting in the diversion of accumulating fatty acids to complex lipids such as triglycerides. This effect is reversed with interventions that improve kidney health, suggesting that targeting of lipid abnormalities could be beneficial in preventing CKD progression.

In situ Metabolomics of Metabolic Reprogramming Involved in a Mouse Model of Type 2 Diabetic Kidney Disease

The in situ metabolic profiling of the kidney is crucial to investigate the complex metabolic reprogramming underlying diabetic kidney disease (DKD) and to allow exploration of potential metabolic targets to improve kidney function. However, as the kidney is a highly heterogeneous organ, traditional metabolomic methods based on bulk analysis that produce an averaged measurement are inadequate. Herein, we employed an in situ metabolomics approach to discover alternations of DKD-associated metabolites and metabolic pathways. A series of histology-specific metabolic disturbances were discovered in situ using airflow-assisted desorption electrospray ionization mass spectrometry imaging (AFADESI-MSI). In combination with integrated metabolomics analysis, five dysfunctional metabolic pathways were identified and located in the kidneys of type-2 DKD mice simultaneously for the first time, including taurine metabolism, arginine and proline metabolism, histidine metabolism, biosynthesis of unsaturated fatty acids, and fatty acid degradation pathways. As crucial nodes of metabolic pathways, five dysregulated rate-limiting enzymes related to altered metabolic pathways were further identified. These findings reveal alternations from metabolites to enzymes at the molecular level in the progression of DKD and provide insights into DKD-associated metabolic reprogramming.

Multimodal Spectroscopic Imaging of Pea Root Nodules to Assess the Nitrogen Fixation in the Presence of Biofertilizer Based on Nod-Factors

Multimodal spectroscopic imaging methods such as Matrix Assisted Laser Desorption/Ionization Mass Spectrometry Imaging (MALDI MSI), Fourier Transform Infrared spectroscopy (FT-IR) and Raman spectroscopy were used to monitor the changes in distribution and to determine semi quantitatively selected metabolites involved in nitrogen fixation in pea root nodules. These approaches were used to evaluate the effectiveness of nitrogen fixation by pea plants treated with biofertilizer preparations containing Nod factors. To assess the effectiveness of biofertilizer, the fresh and dry masses of plants were determined. The biofertilizer was shown to be effective in enhancing the growth of the pea plants. In case of metabolic changes, the biofertilizer caused a change in the apparent distribution of the leghaemoglobin from the edges of the nodule to its centre (the active zone of nodule). Moreover, the enhanced nitrogen fixation and presumably the accelerated maturation form of the nodules were observed with the use of a biofertilizer.

Recent Advances of Ambient Mass Spectrometry Imaging and Its Applications in Lipid and Metabolite Analysis

Ambient mass spectrometry imaging (AMSI) has attracted much attention in recent years. As a kind of unlabeled molecular imaging technique, AMSI can enable in situ visualization of a large number of compounds in biological tissue sections in ambient conditions. In this review, the developments of various AMSI techniques are discussed according to one-step and two-step ionization strategies. In addition, recent applications of AMSI for lipid and metabolite analysis (from 2016 to 2021) in disease diagnosis, animal model research, plant science, drug metabolism and toxicology research, etc., are summarized. Finally, further perspectives of AMSI in spatial resolution, sensitivity, quantitative ability, convenience and software development are proposed.

Changes in plasma and urine metabolites associated with empagliflozin in patients with type 1 diabetes

AIM

To examine the impact of the sodium-glucose co-transporter-2 inhibitor, empagliflozin, on plasma and urine metabolites in participants with type 1 diabetes.

MATERIAL AND METHODS

Participants (n = 40, 50% male, mean age 24.3 years) with type 1 diabetes and without overt evidence of diabetic kidney disease had baseline assessments performed under clamped euglycaemia and hyperglycaemia, on two consecutive days. Participants then proceeded to an 8-week, open-label treatment period with empagliflozin 25 mg/day, followed by repeat assessments under clamped euglycaemia and hyperglycaemia. Plasma and urine metabolites were first grouped into metabolic pathways using MetaboAnalyst software. Principal component analysis was performed to create a representative value for each sufficiently represented metabolic group (false discovery rate ≤ 0.1) for further analysis.

RESULTS

Of the plasma metabolite groups, tricarboxylic acid (TCA) cycle (P < .0001), biosynthesis of unsaturated fatty acids (P = .0045), butanoate (P < .0001), propanoate (P = .0053), and alanine, aspartate and glutamate (P < .0050) metabolites were increased after empagliflozin treatment under clamped euglycaemia. Of the urine metabolite groups, only butanoate metabolites (P = .0005) were significantly increased. Empagliflozin treatment also attenuated the increase in a number of urine metabolites observed with acute hyperglycaemia.

CONCLUSIONS

Empagliflozin was associated with increased lipid and TCA cycle metabolites in participants with type 1 diabetes, suggesting a shift in metabolic substrate use and improved mitochondrial function. These effects result in more efficient energy production and may contribute to end-organ protection by alleviating local hypoxia and oxidative stress.

Urinary 2-Hydroxyglutarate Enantiomers Are Markedly Elevated in a Murine Model of Type 2 Diabetic Kidney Disease

Metabolic reprogramming is a hallmark of diabetic kidney disease (DKD); nutrient overload leads to increased production of metabolic byproducts that may become toxic at high levels. One metabolic byproduct may be 2-hydroxyglutarate (2-HG), a metabolite with many regulatory functions that exists in both enantiomeric forms physiologically. We quantitatively determined the levels of L and D-2HG enantiomers in the urine, plasma, and kidney cortex of db/db mice, a pathophysiologically relevant murine model of type 2 diabetes and DKD. We found increased fractional excretion of both L and D-2HG enantiomers, suggesting increased tubular secretion and/or production of the two metabolites in DKD. Quantitation of TCA cycle metabolites in db/db cortex suggests that TCA cycle overload and an increase in 2-HG precursor substrate, α-ketoglutarate, drive the increased L and D-2HG production in DKD. In conclusion, we demonstrated increased 2-HG enantiomer production and urinary excretion in murine type 2 DKD, which may contribute to metabolic reprogramming and progression of diabetic kidney disease.

Elevated expression of LPCAT1 predicts a poor prognosis and is correlated with the tumour microenvironment in endometrial cancer

Background

Endometrial cancer (EC) is one of the three malignant reproductive tumours that threaten womens lives and health. Glycerophospholipids (GPLs) are important bioactive lipids involved in various physiological and pathological processes, including cancer. Immune infiltration of the tumour microenvironment (TME) is positively associated with the overall survival in EC. Exploring GPL-related factors associated with the TME in endometrial cancer can aid in the prognosis of patients and provide new therapeutic targets.

Methods

Differentially expressed GPL-related genes were identified from TCGA-UCEC datasets and the Molecular Signatures Database (MSigDB). Univariate Cox regression analysis was used to select GPL-related genes with prognostic value. The Random forest algorithm, LASSO algorithm and PPI network were used to identify critical genes. ESTIMATEScore was calculated to identify genes associated with the TME. Then, differentiation analysis and survival analysis of LPCAT1 were performed based on TCGA datasets. GSE17025 and immunohistochemistry (IHC) verified the results of the differentiation analysis. An MTT assay was then conducted to determine the proliferation of EC cells. GO and KEGG enrichment analyses were performed to explore the underlying mechanism of LPCAT1. In addition, we used the ssGSEA algorithm to explore the correlation between LPCAT1 and cancer immune infiltrates.

Results

Twenty-three differentially expressed GPL-related genes were identified, and eleven prognostic genes were selected by univariate Cox regression analysis. Four significant genes were identified by two different algorithms and the PPI network. Only LPCAT1 was significantly correlated with the tumour microenvironment. Then, we found that LPCAT1 was highly expressed in tumour samples compared with that in normal tissues, and lower survival rates were observed in the groups with high LPCAT1 expression. Silencing of LPCAT1 inhibited the proliferation of EC cells. Moreover, the expression of LPCAT1 was positively correlated with the histologic grades and types. The ROC curve indicated that LPCAT1 had good prognostic accuracy. Receptor ligand activity, pattern specification process, regionalization, anterior/posterior pattern specification and salivary secretion pathways were enriched as potential targets of LPCAT1. By using the ssGSEA algorithm, fifteen kinds of tumor-infiltrating cells (TICs) were found to be correlated with LPCAT1 expression.

Conclusion

These findings suggested that LPCAT1 may act as a valuable prognostic biomarker and be correlated with immune infiltrates in endometrial cancer, which may provide novel therapy options for and improved treatment of EC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-01965-1.

Synergizing the potential of bacterial genomics and metabolomics to find novel antibiotics

Antibiotic development based on natural products has faced a long lasting decline since the 1970s, while both the speed and the extent of antimicrobial resistance (AMR) development have been severely underestimated. The discovery of antimicrobial natural products of bacterial and fungal origin featuring new chemistry and previously unknown mode of actions is increasingly challenged by rediscovery issues. Natural products that are abundantly produced by the corresponding wild type organisms often featuring strong UV signals have been extensively characterized, especially the ones produced by extensively screened microbial genera such as streptomycetes. Purely synthetic chemistry approaches aiming to replace the declining supply from natural products as starting materials to develop novel antibiotics largely failed to provide significant numbers of antibiotic drug leads. To cope with this fundamental issue, microbial natural products science is being transformed from a 'grind-and-find' study to an integrated approach based on bacterial genomics and metabolomics. Novel technologies in instrumental analytics are increasingly employed to lower detection limits and expand the space of detectable substance classes, while broadening the scope of accessible and potentially bioactive natural products. Furthermore, the almost exponential increase in publicly available bacterial genome data has shown that the biosynthetic potential of the investigated strains by far exceeds the amount of detected metabolites. This can be judged by the discrepancy between the number of biosynthetic gene clusters (BGC) encoded in the genome of each microbial strain and the number of secondary metabolites actually detected, even when considering the increased sensitivity provided by novel analytical instrumentation. In silico annotation tools for biosynthetic gene cluster classification and analysis allow fast prioritization in BGC-to-compound workflows, which is highly important to be able to process the enormous underlying data volumes. BGC prioritization is currently accompanied by novel molecular biology-based approaches to access the so-called orphan BGCs not yet correlated with a secondary metabolite. Integration of metabolomics, in silico genomics and molecular biology approaches into the mainstream of natural product research will critically influence future success and impact the natural product field in pharmaceutical, nutritional and agrochemical applications and especially in anti-infective research.

Multimodal Imaging Mass Spectrometry: Next Generation Molecular Mapping in Biology and Medicine

Imaging mass spectrometry has become a mature molecular mapping technology that is used for molecular discovery in many medical and biological systems. While powerful by itself, imaging mass spectrometry can be complemented by the addition of other orthogonal, chemically informative imaging technologies to maximize the information gained from a single experiment and enable deeper understanding of biological processes. Within this review, we describe MALDI, SIMS, and DESI imaging mass spectrometric technologies and how these have been integrated with other analytical modalities such as microscopy, transcriptomics, spectroscopy, and electrochemistry in a field termed multimodal imaging. We explore the future of this field and discuss forthcoming developments that will bring new insights to help unravel the molecular complexities of biological systems, from single cells to functional tissue structures and organs.

Storage Conditions of Human Kidney Tissue Sections Affect Spatial Lipidomics Analysis Reproducibility

Lipids often are labile, unstable, and tend to degrade overtime, so it is of the upmost importance to study these molecules in their most native state. We sought to understand the optimal storage conditions for spatial lipidomic analysis of human kidney tissue sections. Specifically, we evaluated human kidney tissue sections on several different days throughout the span of a week using our established protocol for elucidating lipids using high mass resolution matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI). We studied kidney tissue sections stored under five different conditions: open stored at -80 °C, vacuumed sealed and stored at -80 °C, with matrix preapplied before storage at -80 °C, under a nitrogen atmosphere and stored at -80 °C, and at room temperature in a desiccator. Results were compared to data obtained from kidney tissue sections that were prepared and analyzed immediately after cryosectioning. Data was processed using METASPACE. After a week of storage, the sections stored at room temperature showed the largest amount of lipid degradation, while sections stored under nitrogen and at -80 °C retained the greatest number of overlapping annotations in relation to freshly cut tissue. Overall, we found that molecular degradation of the tissue sections was unavoidable over time, regardless of storage conditions, but storing tissue sections in an inert gas at low temperatures can curtail molecular degradation within tissue sections.