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Pulmonary arterial hypertension (PAH) is a life-threatening disease with a poor prognosis, and metabolic abnormalities play a critical role in its development. This study used metabolomics, machine learning algorithms and bioinformatics to screen for potential metabolic biomarkers associated with the diagnosis of PAH. In this study, plasma samples were collected from 17 patients diagnosed with idiopathic pulmonary arterial hypertension (IPAH) and 20 healthy controls. Plasma metabolomic profiling was performed by high-performance liquid chromatography-mass spectrometry. Gene profiles of PAH patients were obtained from the GEO database. Key differentially expressed metabolites (DEMs) and metabolism-related genes were subsequently identified using machine learning algorithms. Twenty differential plasma metabolites associated with IPAH were identified (VIP score > 1 and p < 0 0.05), and enrichment analysis revealed the arginine biosynthesis pathway as the most altered pathway. Using machine learning models, including least absolute shrinkage and selection operator (LASSO), random forest (RF) and support vector machine (SVM), we extracted key metabolites that correlated with clinical phenotypes. Our results suggested that five metabolites, kynurenine, homoserine, tryptophan, AMP, and spermine, are potential biomarkers for IPAH. Bioinformatics analysis also identified 3 metabolism-related genes, MAPK6, SLC7A11 and CDC42BPA, that are strongly correlated with pulmonary hypertension, demonstrating strong predictive power and clinical relevance. Our findings revealed some key genes associated with metabolism in PH, and provided crucial information about complex metabolic reprogramming signals and may lead to the identification of useful metabolic biomarkers for the diagnosis of PAH. [ABSTRACT FROM AUTHOR]

Flavor is a major sensory attribute affecting consumers' preference for cheese products. Differences in cheesemaking change the cheese microenvironment, thereby affecting cheese flavor profiles. A framework for tuning cheese flavor is proposed in this study, which depicts the full picture of flavor development and modulation, from manufacturing and ripening factors through the main biochemical pathways to flavor compounds and flavor notes. Taking semi‐hard and hard cheeses as examples, this review describes how cheese flavor profiles are affected by milk type and applied treatment, fat and salt content, microbiota composition and microbial interactions, ripening time, temperature, and environmental humidity, together with packaging method and material. Moreover, these factors are linked to flavor profiles through their effects on proteolysis, the further catabolism of amino acids, and lipolysis. Acids, alcohols, ketones, esters, aldehydes, lactones, and sulfur compounds are key volatiles, which elicit fruity, sweet, rancid, green, creamy, pungent, alcoholic, nutty, fatty, and sweaty flavor notes, contributing to the overall flavor profiles. Additionally, this review demonstrates how data‐driven modeling techniques can link these influencing factors to resulting flavor profiles. This is done by providing a comprehensive review on the (i) identification of key factors and flavor compounds, (ii) discrimination of cheeses, and (iii) prediction of flavor notes. Overall, this review provides knowledge tools for cheese flavor modulation and sheds light on using data‐driven modeling techniques to aid cheese flavor analysis and flavor prediction. [ABSTRACT FROM AUTHOR]

Abstract Pulmonary arterial hypertension (PAH) is a life-threatening disease with a poor prognosis, and metabolic abnormalities play a critical role in its development. This study used metabolomics, machine learning algorithms and bioinformatics to screen for potential metabolic biomarkers associated with the diagnosis of PAH. In this study, plasma samples were collected from 17 patients diagnosed with idiopathic pulmonary arterial hypertension (IPAH) and 20 healthy controls. Plasma metabolomic profiling was performed by high-performance liquid chromatography-mass spectrometry. Gene profiles of PAH patients were obtained from the GEO database. Key differentially expressed metabolites (DEMs) and metabolism-related genes were subsequently identified using machine learning algorithms. Twenty differential plasma metabolites associated with IPAH were identified (VIP score > 1 and p

Salinity accumulation poses a threat to the production and productivity of economically important crops such as tomatoes (Solanum lycopersicum L.). Currently, salt tolerance breeding programs have been limited by insufficient genetic and physiological knowledge of tolerance-related traits and a lack of an efficient selection domain. For that purpose, we aimed to determine the ability of tomato cultivars to tolerate salt based on seed traits by multiple biochemical pathways. First, we tested three tomato cultivars according to their response to different sodium chloride (NaCl) concentrations (0, 6.3, 9.8, 13.0, and 15.8 dS m−1) and then we analysed their amino acids, organic acids, and phytohormones. Considering the results of germination traits, it is possible to conclude that cultivar H-2274 was more tolerant to salt stress than others. As a result, multivariate discriminant analysis including principal component analysis and two-way hierarchical clustering analyses were constructed and demonstrated that tomato cultivars were separated from each other by the amino acid, organic acid, and phytohormone contents. Considering germination traits of tomato seeds, cv. ‘H-2274’ was more tolerant to salinity than others depending on high proline (29 pmol µl−1) and citric acid (568 ng µl−1) assays. Biochemical variability offers a valuable tool for investigating salt tolerance mechanisms in tomatoes, and it will be appreciated to find high-tolerant tomato cultivar(s) to saline conditions. Also, the findings of this study have significant potential for practical applications in agriculture, particularly in developing salt-tolerant tomato cultivars to enhance productivity in saline environments and address socio-economic challenges.

The application of Silicon modulates the plant defense mechanism by signaling secondary defense metabolites activating signal transduction pathways further triggering the HIPVs which in turn attracts natural enemies thereby suppressing the pest population. Continuous mono culturing of high yielding varieties with intensive cultural practices lead to depletion of available Si in soils and hence exogenous application of Si sources is necessary. Exploiting plant resistance and strengthening the induced defenses through Si and PGR could represent an effective viable alternative to synthetic insecticides that could form an efficient tactic in integrated pest management including biological control. In this context, we underscore recent advancements in understanding Si-mediated enhancement of plant resistance against pests, emphasizing the significance of these findings for potential incorporation into future strategies for crop protection. [ABSTRACT FROM AUTHOR]

Summary: Metabolic flux analysis (MFA) is a valuable tool for quantifying cellular phenotypes and to guide plant metabolic engineering. By introducing stable isotopic tracers and employing mathematical models, MFA can quantify the rates of metabolic reactions through biochemical pathways. Recent applications of isotopically nonstationary MFA (INST‐MFA) to plants have elucidated nonintuitive metabolism in leaves under optimal and stress conditions, described coupled fluxes for fast‐growing algae, and produced a synergistic multi‐organ flux map that is a first in MFA for any biological system. These insights could not be elucidated through other approaches and show the potential of INST‐MFA to correct an oversimplified understanding of plant metabolism. [ABSTRACT FROM AUTHOR]

Introduction: Prader-Willi syndrome (PWS) is a rare disease, which shows a peculiar clinical phenotype, including obesity, which is different from essential obesity (EOB). Metabolomics might represent a valuable tool to reveal the biochemical mechanisms/pathways underlying clinical differences between PWS and EOB. The aim of the present (case-control, retrospective) study was to determine the metabolomic profile that characterizes PWS compared to EOB. Methods: A validated liquid chromatography-tandem mass spectrometry (LC-MS/MS) targeted metabolomic approach was used to measure a total of 188 endogenous metabolites in plasma samples of 32 patients with PWS (F/M = 23/9; age: 31.6 ± 9.2 years; body mass index [BMI]: 42.1 ± 7.0 kg/m²), compared to a sex-, age- and BMI-matched group of patients with EOB (F/M = 23/9; age: 31.4 ± 6.9 years; BMI: 43.5 ± 3.5 kg/m²). Results: Body composition in PWS was different when compared to EOB, with increased fat mass and decreased fat-free mass. Glycemia and HDL cholesterol were higher in patients with PWS than in those with EOB, while insulinemia was lower, as well as heart rate. Resting energy expenditure was lower in the group with PWS than in the one with EOB, a difference that was missed after fat-free mass correction. Carrying out a series of Tobit multivariable linear regressions, adjusted for sex, diastolic blood pressure, and C reactive protein, a total of 28 metabolites was found to be associated with PWS (vs. non-PWS, i.e., EOB), including 9 phosphatidylcholines (PCs) ae, 5 PCs aa, all PCs aa, 7 lysoPCs a, all lysoPCs, 4 acetylcarnitines, and 1 sphingomyelin, all of which were higher in PWS than EOB. Conclusions: PWS exhibits a specific metabolomic profile when compared to EOB, suggesting a different regulation of some biochemical pathways, fundamentally related to lipid metabolism. [ABSTRACT FROM AUTHOR]

The entire scientific world is currently working on bioethanol production from lignocellulosic biomass as the most abundant renewable resource of biofuel to mitigate the acute shortage of fossil fuels in the near future. Bioethanol is the most alternative to petroleum based fossil fuels and being a renewable and environmental friendly cheap energy source. Bioethanol can be produced using a variety of feedstocks, but second generation biofuel production from agricultural waste feedstocks is a promising step. However, economical bioethanol production is constrained by a number of factors, including the transportation and handling of biomass, the effectiveness of pretreatment for delignification of lignocellulosic substrates, enzymatic hydrolysis, and the availability of suitable fermentative organisms. Nowadays, petroleum and natural gas supply 98% of the gasoline required for road transportation, with biofuels providing the remaining 2%. Biofuels are fuels derived from biomass. In 2018, global biofuel output hit a record 154 billion L, with bioethanol accounting for 110 billion L and biodiesel and hydro treated vegetable oil accounting for the remainder. By 2024, global bioethanol output is expected to rise by 20%, reaching 130 billion L. The present review article critically discusses the various agricultural wastes, different pretreatment techniques used for the delignification of waste biomass, their enzymatic hydrolysis and different fermentation techniques used for the conversion of simple sugar to bioethanol. Further, the application of microbes and integration of various modern techniques have also been highlighted for enhanced bioethanol production from agro waste biomasses. [ABSTRACT FROM AUTHOR]

Abscisic acid (ABA) significantly regulates plant growth and development, promoting tuberous root formation in various plants. However, the molecular mechanisms of ABA in the tuberous root development of Pseudostellaria heterophylla are not yet fully understood. This study utilized Illumina sequencing and de novo assembly strategies to obtain a reference transcriptome associated with ABA treatment. Subsequently, integrated transcriptomic and proteomic analyses were used to determine gene expression profiles in P. heterophylla tuberous roots. ABA treatment significantly increases the diameter and shortens the length of tuberous roots. Clustering analysis identified 2,256 differentially expressed genes and 679 differentially abundant proteins regulated by ABA. Gene co-expression and protein interaction networks revealed ABA positively induced 30 vital regulators. Furthermore, we identified and assigned putative functions to transcription factors (PhMYB10, PhbZIP2, PhbZIP, PhSBP) that mediate ABA signaling involved in the regulation of tuberous root development, including those related to cell wall metabolism, cell division, starch synthesis, hormone metabolism. Our findings provide valuable insights into the complex signaling networks of tuberous root development modulated by ABA. It provided potential targets for genetic manipulation to improve the yield and quality of P. heterophylla, which could significantly impact its cultivation and medicinal value.

Introduction: Care towards nutrition is essential for the quality of a sustainable aquaculture product. Since the balance in food affects the growth and production of gametes. The circular economy is made possible through the use of discarded materials. Objective: The aim of this research was to study the fatty acid composition and metabolic pathways in the gametes of Arbacia dufresnii, with a focus on the implications of incorporating shrimp byproducts into aquaculture feeds. Methods: Four different treatments were designed to maintain optimal nutritional quality, particularly in lipids and proteins, based on previous studies. The fatty acid profiles of the feeds and gametes were analyzed by using gas-chromatography, and statistical analyses were conducted to determine significant differences. Results: Significant differences were observed in the abundance (%) of omega-3 (w-3) and omega-6 (w-6) fatty acids. The (w-3) metabolic pathway was more pronounced in the gametes of wild animals and those fed with the experimental feeds. In contrast, the (w-6) metabolic pathway was less relevant in these groups. The (w-3)/(w-6) ratio was highest in the gametes of wild animals. Feeds enriched in fatty acids enhanced their bioaccumulation in the gametes reaching higher concentrations than wild animals. The availability of fatty acids in foods allowed their bioaccumulation in gametes, with concentrations equal to or higher than those observed in animals in their natural environment for certain fatty acids. Conclusions: Incorporating shrimp byproducts in aquaculture feeds demonstrated a promising strategy for resource utilization and organic input generation. The fatty acid composition in the gametes of A. dufresnii was influenced by the diet, highlighting the potential of balanced feeds to enhance the bioaccumulation of essential fatty acids. These findings provide valuable insights for the development of sustainable aquaculture practices and the production of nutritionally enriched seafood products. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy (DR) is a progressive blinding disease, which affects the vision and quality of life of patients, and it severely impacts the society. This complication, caused by abnormal glucose metabolism, leads to structural, functional, molecular, and biochemical abnormalities in the retina. Oxidative stress and inflammation also play pivotal roles in the pathogenic process of DR, leading to mitochondrial damage and a decrease in mitochondrial function. DR causes retinal degeneration in glial and neural cells, while the disappearance of pericytes in retinal blood vessels leads to alterations in vascular regulation and stability. Clinical changes include dilatation and blood flow changes in response to the decrease in retinal perfusion in retinal blood vessels, leading to vascular leakage, neovascularization, and neurodegeneration. The loss of vascular cells in the retina results in capillary occlusion and ischemia. Thus, DR is a highly complex disease with various biological factors, which contribute to its pathogenesis. The interplay between biochemical pathways and non-coding RNAs (ncRNAs) is essential for understanding the development and progression of DR. Abnormal expression of ncRNAs has been confirmed to promote the development of DR, suggesting that ncRNAs such as miRNAs, lncRNAs, and circRNAs have potential as diagnostic biomarkers and theranostic targets in DR. This review provides an overview of the interactions between abnormal biochemical pathways and dysregulated expression of ncRNAs under the influence of hyperglycemic environment in DR. [ABSTRACT FROM AUTHOR]

Breast cancer is a common and deadly disease, so there is a constant need for research to find efficient targets and therapeutic approaches. Breast cancer can be classified on a molecular and histological base. Breast cancer can be divided into ER (estrogen receptor)‐positive and ER‐negative, HER2 (human epidermal growth factor receptor2)‐positive and HER2‐negative subtypes based on the presence of specific biomarkers. Targeting hormone receptors, such as the HER2, progesterone receptor (PR), and ER, is very significant and plays a vital role in the onset and progression of breast cancer. Endocrine treatments and HER2‐targeted drugs are examples of targeted therapies now being used against these receptors. Emerging immune‐based medicines with promising outcomes in the treatment of breast cancer include immune checkpoint inhibitors, cancer vaccines, and adoptive T‐cell therapy. It is also explored how immune cells and the tumor microenvironment affect breast cancer development and treatment response. The major biochemical pathways, signaling cascades, and DNA repair mechanisms that are involved in the development and progression of breast cancer, include the PI3K/AKT/mTOR system, the MAPK pathway, and others. These pathways are intended to be inhibited by a variety of targeted drugs, which are then delivered with the goal of restoring normal cellular function. This review aims to shed light on types of breast cancer with the summarization of different therapeutic approaches which can target different pathways for tailored medicines and better patient outcomes. [ABSTRACT FROM AUTHOR]

IntroductionPrader-Willi syndrome (PWS) is a rare disease, which shows a peculiar clinical phenotype, including obesity, which is different from essential obesity (EOB). Metabolomics might represent a valuable tool to reveal the biochemical mechanisms/pathways underlying clinical differences between PWS and EOB. The aim of the present (case-control, retrospective) study was to determine the metabolomic profile that characterizes PWS compared to EOB.MethodsA validated liquid chromatography-tandem mass spectrometry (LC-MS/MS) targeted metabolomic approach was used to measure a total of 188 endogenous metabolites in plasma samples of 32 patients with PWS (F/M = 23/9; age: 31.6 ± 9.2 years; body mass index [BMI]: 42.1 ± 7.0 kg/m2), compared to a sex-, age- and BMI-matched group of patients with EOB (F/M = 23/9; age: 31.4 ± 6.9 years; BMI: 43.5 ± 3.5 kg/m2).ResultsBody composition in PWS was different when compared to EOB, with increased fat mass and decreased fat-free mass. Glycemia and HDL cholesterol were higher in patients with PWS than in those with EOB, while insulinemia was lower, as well as heart rate. Resting energy expenditure was lower in the group with PWS than in the one with EOB, a difference that was missed after fat-free mass correction. Carrying out a series of Tobit multivariable linear regressions, adjusted for sex, diastolic blood pressure, and C reactive protein, a total of 28 metabolites was found to be associated with PWS (vs. non-PWS, i.e., EOB), including 9 phosphatidylcholines (PCs) ae, 5 PCs aa, all PCs aa, 7 lysoPCs a, all lysoPCs, 4 acetylcarnitines, and 1 sphingomyelin, all of which were higher in PWS than EOB.ConclusionsPWS exhibits a specific metabolomic profile when compared to EOB, suggesting a different regulation of some biochemical pathways, fundamentally related to lipid metabolism.

Herein we trace links between biochemical pathways, pathogenesis, and metabolic diseases to set the stage for new therapeutic advances. Cellular and acellular microorganisms including bacteria and viruses are primary pathogenic drivers that cause disease. Missing from this statement are subcellular compartments, importantly mitochondria, which can be pathogenic by themselves, also serving as key metabolic disease intermediaries. The breakdown of food molecules provides chemical energy to power cellular processes, with mitochondria as powerhouses and ATP as the principal energy carrying molecule. Most animal cell ATP is produced by mitochondrial synthase; its central role in metabolism has been known for >80 years. Metabolic disorders involving many organ systems are prevalent in all age groups. Progressive pathogenic mitochondrial dysfunction is a hallmark of genetic mitochondrial diseases, the most common phenotypic expression of inherited metabolic disorders. Confluent genetic, metabolic, and mitochondrial axes surface in diabetes, heart failure, neurodegenerative disease, and even in the ongoing coronavirus pandemic.

Microscopic, photosynthetic prokaryotes and eukaryotes, collectively referred to as microalgae, are widely studied to improve our understanding of key metabolic pathways (e.g., photosynthesis) and for the development of biotechnological applications. Omics technologies, which are now common tools in biological research, have been shown to be critical in microalgal research. In the past decade, significant technological advancements have allowed omics technologies to become more affordable and efficient, with huge datasets being generated. In particular, where studies focused on a single or few proteins decades ago, it is now possible to study the whole proteome of a microalgae. The development of mass spectrometry-based methods has provided this leap forward with the high-throughput identification and quantification of proteins. This review specifically provides an overview of the use of proteomics in fundamental (e.g., photosynthesis) and applied (e.g., lipid production for biofuel) microalgal research, and presents future research directions in this field.

Arsenic is a ubiquitous toxic metalloid, the concentration of which is beyond WHO safe drinking water standards in many areas of the world, owing to many natural and anthropogenic activities. Long-term exposure to arsenic proves lethal for plants, humans, animals, and even microbial communities in the environment. Various sustainable strategies have been developed to mitigate the harmful effects of arsenic which include several chemical and physical methods, however, bioremediation has proved to be an eco-friendly and inexpensive technique with promising results. Many microbes and plant species are known for arsenic biotransformation and detoxification. Arsenic bioremediation involves different pathways such as uptake, accumulation, reduction, oxidation, methylation, and demethylation. Each of these pathways has a certain set of genes and proteins to carry out the mechanism of arsenic biotransformation. Based on these mechanisms, various studies have been conducted for arsenic detoxification and removal. Genes specific for these pathways have also been cloned in several microorganisms to enhance arsenic bioremediation. This review discusses different biochemical pathways and the associated genes which play important roles in arsenic redox reactions, resistance, methylation/demethylation, and accumulation. Based on these mechanisms, new methods can be developed for effective arsenic bioremediation. [ABSTRACT FROM AUTHOR]

Proteomics tools in the field of equine meat research have been very recently applied to explore the changes in the post-mortem muscle proteome and to discover biomarkers to monitor the variations in its different meat quality traits. The current advances achieved by proteomics in equine meat research are reviewed. Different proteomics techniques (sodium dodecyl sulphate-polyacrylamide gel electrophoresis; two-dimensional polyacrylamide gel electrophoresis; fluorescent two-dimensional difference gel electrophoresis; targeted proteomics; tandem-mass tag labeled proteomics; data-independent analysis proteomics) have been applied in the study of the equine muscle/meat. The studies revealed the biochemical pathways involved in the development of several donkey and horse (foal) meat quality variation. The current knowledge would be useful to develop high-quality products. [ABSTRACT FROM AUTHOR]

Simple Summary: In this research, we analysed the genome of the giant African snail to identify enzymes and reconstruct biochemical pathways related to carbohydrate and glycan metabolism. Fourteen complete pathways of carbohydrate metabolism and seven complete pathways of glycan metabolism were found to be associated with the nutrient acquisition and production of the mucus proteoglycans. The enhanced copy number of amylases, cellulases, and chitinases could give the snail an advantage in terms of food consumption and a fast growth rate. The identified ascorbate biosynthesis pathway could be linked to shell biomineralisation. This understanding will benefit the discovery of valuable enzymes for industrial and medical applications. The giant African snail (Order Stylommatophora: Family Achatinidae), Achatina fulica (Bowdich, 1822), is the most significant and invasive land snail pest. The ecological adaptability of this snail involves high growth rate, reproductive capacity, and shell and mucus production, driven by several biochemical processes and metabolism. The available genomic information for A. fulica provides excellent opportunities to hinder the underlying processes of adaptation, mainly carbohydrate and glycan metabolic pathways toward the shell and mucus formation. The authors analysed the 1.78 Gb draft genomic contigs of A. fulica to identify enzyme-coding genes and reconstruct biochemical pathways related to the carbohydrate and glycan metabolism using a designed bioinformatic workflow. Three hundred and seventy-seven enzymes involved in the carbohydrate and glycan metabolic pathways were identified based on the KEGG pathway reference in combination with protein sequence comparison, structural analysis, and manual curation. Fourteen complete pathways of carbohydrate metabolism and seven complete pathways of glycan metabolism supported the nutrient acquisition and production of the mucus proteoglycans. Increased copy numbers of amylases, cellulases, and chitinases highlighted the snail advantage in food consumption and fast growth rate. The ascorbate biosynthesis pathway identified from the carbohydrate metabolic pathways of A. fulica was involved in the shell biomineralisation process in association with the collagen protein network, carbonic anhydrases, tyrosinases, and several ion transporters. Thus, our bioinformatic workflow was able to reconstruct carbohydrate metabolism, mucus biosynthesis, and shell biomineralisation pathways from the A. fulica genome and transcriptome data. These findings could reveal several evolutionary advantages of the A. fulica snail, and will benefit the discovery of valuable enzymes for industrial and medical applications. [ABSTRACT FROM AUTHOR]

In the era of Omics, metabolomics and metaproteomics have emerged as powerful tools for the deep analysis of the fermentation of various foods. These two are relatively new in the field of science. However, they are already proven to be crucial for a more proper understanding of microbes, their interactions with hosts and surrounding environments, metabolic activity, and intracellular biochemical reactions. Metabolomics is a quantitative and qualitative determination study of metabolites of participated microbes in the process of fermentation that helps to analyze several biochemical pathways concerning chemical changes that occur during different stages of fermentation. Hence, it provides a deep insight into the microscopic world as well as helps to significantly enhance the available nutraceuticals and probiotic characterizations in fermented foods. Evaluating the possible pathways of microbes in generating particular compounds of interest is proven to be beneficial for increasing the bioavailability of nutraceuticals and nutrients. It can be utilized to reshape the general process of fermentation in a more improved and optimized way for better yielding of nutritive components while decreasing the antinutrient components in the specific product. Metabolomics studies can be used to modify strains based on specific needs, for example, different stress-tolerant microbial strains for the optimal production of desired metabolites in stressed conditions, modulation in metabolic pathways to naturally increase the bioavailability of beneficial compounds, etc. These “Omics” methods are paving the way for a more thorough understanding of microbial life, metabolism systems, and genetic characteristics regarding protein synthesis and are undoubtedly becoming a potent weapon or a universal key in modulating fermented food products in a more advanced and sustainable approach possible. This present review aims to constitute a relevant approach for understanding the metabolic processes that can occur or are currently occurring in a given system and for implementing novel strategies focused on solving production problems or improving sustainability.

Pyruvic acid has numerous applications in the food, chemical, and pharmaceutical industries. The high costs of chemical synthesis have prevented the extensive use of pyruvate for many applications. Metabolic engineering and traditional strategies for mutation and selection have been applied to microorganisms to enhance their ability to produce pyruvate. In the past decades, different microbial strains were generated to enhance their pyruvate production capability. In addition to the development of genetic engineering and metabolic engineering in recent years, the metabolic transformation of wild-type yeast, E. coli, and so on to produce high-yielding pyruvate strains has become a hot spot. The strategy and the understanding of the central metabolism directly related to pyruvate production could provide valuable information for improvements in fermentation products. One of the goals of this review was to collect information regarding metabolically engineered strains and the microbial fermentation processes used to produce pyruvate in high yield and productivity. [ABSTRACT FROM AUTHOR]

Multitask multiple kernel learning (MKL) algorithms combine the capabilities of incorporating different data sources into the prediction model and using the data from one task to improve the accuracy on others. However, these methods do not necessarily produce interpretable results. Restricting the solutions to the set of interpretable solutions increases the computational burden of the learning problem significantly, leading to computationally prohibitive run times for some important biomedical applications. That is why we propose a multitask MKL formulation with a clustering of tasks and develop a highly time-efficient solution approach for it. Our solution method is based on the Benders decomposition and treating the clustering problem as finding a given number of tree structures in a graph; hence, it is called the forest formulation. We use our method to discriminate early-stage and late-stage cancers using genomic data and gene sets and compare our algorithm against two other algorithms. The two other algorithms are based on different approaches for linearization of the problem while all algorithms make use of the cutting-plane method. Our results indicate that as the number of tasks and/or the number of desired clusters increase, the forest formulation becomes increasingly favorable in terms of computational performance. [ABSTRACT FROM AUTHOR]

Antibiotic resistance (AR) is an acute problem that results in prolonged and debilitating illnesses. AR mortality worldwide is growing and causes a pressing need to research novel mechanisms of action and untested target molecules. This article presents in silico analyses of eight bacterial riboswitches for their suitability for antibacterial drug targets. Most bacterial riboswitches are located in the 5′-untranslated region of messenger RNAs, act as allosteric cis-acting gene control elements, and have not been found in humans before. Sensing metabolites, the riboswitches regulate the synthesis of vital cellular metabolites in various pathogenic bacteria. The analyses performed in this article represent a complete and informative genome-wide bioinformatics analysis of the adequacy of eight riboswitches as antibacterial drug targets in different pathogenic bacteria based on four criteria. Due to the ability of the riboswitch to control biosynthetic pathways and transport proteins of essential metabolites and the presence/absence of alternative biosynthetic pathways, we classified them into four groups based on their suitability for use as antibacterial drug targets guided by our in silico analyses. We concluded that some of them are promising targets for antibacterial drug discovery, such as the PreQ1, MoCo RNA, cyclic-di-GMP I, and cyclic-di-GMP II riboswitches. [ABSTRACT FROM AUTHOR]

The disruption of gut microbiota caused by antibiotics favors the intestinal colonization of Clostridioides difficile - a Gram-positive, spore-forming anaerobic bacterium that causes potentially fatal gastrointestinal infections. In an endeavor to elucidate the complexities of the gut-brain axis in the context of Clostridium difficile infection (CDI), a murine model has been used to investigate the potential effects of antibiotic administration and subsequent colonization by C. difficile , as well as the impact of three different 10-day treatments (metronidazole, probiotics, and fecal microbiota transplantation), on the cecal metabolome for the first time. This follows our previous research which highlighted the metabolic effect of CDI and these treatments in the brain and employs the same four different metabolomics-based methods (targeted GC-MS/MS, targeted HILIC-MS/MS, untargeted RP-LC-HRMS/MS and untargeted GC-MS). A total of 286 unique metabolites have been identified in the mouse cecal profiles and statistical analysis revealed that CDI, as well as the subsequent treatments, significantly alters cecal metabolites and lipids implicated in various biochemical pathways centered around amino acid metabolism, glycerophospholipid metabolism, and central carbon metabolism. To our knowledge, this study represents the first exploration of the effects of C. difficile -induced colitis and potential treatments on the cecal tissue metabolome.

Varying from other identified cell death pathways, cuproptosis is a new type of regulated cell death characterized by excess Cu ions, abnormal aggregation of lipoylated proteins in TCA cycle, loss of Fe-S cluster proteins, upregulation of HSP70, leading to proteotoxic and oxidative stress. Cuproptosis is highly concerned by scientific community and as the field of cuproptosis further develops, remarkable progress has been made in the verification and mechanism of cuproptosis, and methods used to detect cuproptosis have been continuously improved. According to the characteristic changes of cuproptosis, techniques based on cell death verification, Cu content, morphology, molecular biology of protein levels of cuproptosis-related molecules and biochemical pathways of cuproptosis-related enzyme activity and metabolites of oxidative stress, lipoic acid, TCA cycle, Fe-S cluster proteins, oxidative phosphorylation, cell respiration intensity have been subject to cuproptosis verification and research. In order to further deepen the understanding of detecting cuproptosis, the principle and application of common cuproptosis detection methods are reviewed and categorized in cellular phenomena and molecular mechanism in terms of cell death, Cu content, morphology, molecular biology, biochemical pathways with a flow chart. All the indicating results have been displayed in response to the markers of cuproptosis, their advantages and limitations are summaried, and comparison of cuproptosis and ferroptosis detection is performed in this study. Our collection of methods for cuproptosis detection will provide a great basis for cuproptosis verification and research in the future., Competing Interests: Author RD was employed by Beijing Mercer United International Education Consulting Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Zhang, Deng, Guo, Jiang, Guan, Chen, Zhao, Huang, Liu, Du and Tang.)

The giant African snail (Order Stylommatophora: Family Achatinidae), Achatina fulica (Bowdich, 1822), is the most significant and invasive land snail pest. The ecological adaptability of this snail involves high growth rate, reproductive capacity, and shell and mucus production, driven by several biochemical processes and metabolism. The available genomic information for A. fulica provides excellent opportunities to hinder the underlying processes of adaptation, mainly carbohydrate and glycan metabolic pathways toward the shell and mucus formation. The authors analysed the 1.78 Gb draft genomic contigs of A. fulica to identify enzyme-coding genes and reconstruct biochemical pathways related to the carbohydrate and glycan metabolism using a designed bioinformatic workflow. Three hundred and seventy-seven enzymes involved in the carbohydrate and glycan metabolic pathways were identified based on the KEGG pathway reference in combination with protein sequence comparison, structural analysis, and manual curation. Fourteen complete pathways of carbohydrate metabolism and seven complete pathways of glycan metabolism supported the nutrient acquisition and production of the mucus proteoglycans. Increased copy numbers of amylases, cellulases, and chitinases highlighted the snail advantage in food consumption and fast growth rate. The ascorbate biosynthesis pathway identified from the carbohydrate metabolic pathways of A. fulica was involved in the shell biomineralisation process in association with the collagen protein network, carbonic anhydrases, tyrosinases, and several ion transporters. Thus, our bioinformatic workflow was able to reconstruct carbohydrate metabolism, mucus biosynthesis, and shell biomineralisation pathways from the A. fulica genome and transcriptome data. These findings could reveal several evolutionary advantages of the A. fulica snail, and will benefit the discovery of valuable enzymes for industrial and medical applications.

A bacterial strain ASLT-13 was successfully isolated from activated sludge and identified as Pseudomonas amygdali. Gas chromatograph-mass spectrometer (GC-MS) analysis indicated that strain ASLT-13 could completely mineralize di 2-ethyl hexyl phthalate (DEHP). DEHP was first metabolized from the longer side chain of the benzene ring into shorter branches (Phatlalic mono-esters) like Dibutyl phthalate (DBP) under the action of degrading genes. DBP was then converted into di-methyl phthalate (DMP), and then hydrolysed to phthalic acid (PA). PA was eventually converted to CO2 and H2O through the TCA cycle. The optimal conditions for immobilization were the sodium alginate (SA) concentration of 6%, CaCl2 concentration of 5%, ratio of bacteria and SA of 1:1, crosslinking time of 6 h. Bacterial quantity and community structure in sequencing batch reactors (SBRs) was investigated by q-PCR and high-throughput sequencing. The results indicated that DEHP removal efficiency was significantly enhanced by immobilization. Arthrobacter, Acinetobacter, Bacillus and Rhodococcus were the predominant genera for DEHP degradation. This study suggested that the cell immobilization technology had a potential application in DEHP wastewater treatment. [ABSTRACT FROM AUTHOR]

Untargeted metabolomics (UM) allows for the simultaneous measurement of hundreds of metabolites in a single analytical run. The sheer amount of data generated in UM hampers its use in patient diagnostics because manual interpretation of all features is not feasible. Here, we describe the application of a pathway‐based metabolite set enrichment analysis method to prioritise relevant biological pathways in UM data. We validate our method on a set of 55 patients with a diagnosed inherited metabolic disorder (IMD) and show that it complements feature‐based prioritisation of biomarkers by placing the features in a biological context. In addition, we find that by taking enriched pathways shared across different IMDs, we can identify common drugs and compounds that could otherwise obscure genuine disease biomarkers in an enrichment method. Finally, we demonstrate the potential of this method to identify novel candidate biomarkers for known IMDs. Our results show the added value of pathway‐based interpretation of UM data in IMD diagnostics context. [ABSTRACT FROM AUTHOR]

Tracer metabolomics is a powerful technology for the biomedical community to study and understand disease-inflicted metabolic mechanisms. However, the interpretation of tracer metabolomics results is highly technical, as the metabolites' abundances, tracer incorporation and positions on the metabolic map all must be jointly interpreted. The field is currently lacking a structured approach to help less experienced researchers start the interpretation of tracer metabolomics datasets. We propose an approach using an intuitive visualization concept aided by a novel open-source tool, and provide guidelines on how researchers can apply the approach and the visualization tool to their own datasets. Using a showcase experiment, we demonstrate that the visualization approach leads to an intuitive interpretation that can ease researchers into understanding their tracer metabolomics data. [ABSTRACT FROM AUTHOR]

In this study, the low-salt sufu paste was fermented enhanced by a mixed starter (Pichia fermentans , Kodamaea ohmeri and Lactococcus lactis subsp.) and α-ketoglutarate. Multispecies microbial community is crucial for the flavor formation of sufu paste during post-ripening. Consequently, the primary biochemical pathways of the flavor formation were analyzed using a metagenomic approach in conjunction with gas chromatography mass spectrometry (GC-MS). After post-ripening for 15 d, a total of 86 volatile flavor compounds (VFCs) were tentatively identified, in which 6 compounds were recognized as the main characteristic VFCs. The metagenomics revealed that Actinomucor elegans , Lactobacillus fermentum , Lactococcus lactis subsp., Pichia fermentans , Lactobacillus delbrueckii and Propionibacterium acidipropionici were the dominant species. According to the functional annotation, amino acid metabolisms were identified as the predominant pathways for flavor formation. Among them, aromatic amino acids metabolisms accounted for the highest proportion, up to 41.3%. The VFCs formation pathways mainly involved transamination, decarboxylation, dehydrogenation and oxidation reaction. The amino acid transamination was proposed as a crucial step in VFCs formation, and the corresponding aminotransferases were the key rate-limiting enzymes. These findings have implications for enhancing the flavor quality of sufu paste and elucidating the mechanism of flavor formation in traditional soybean fermented food. [ABSTRACT FROM AUTHOR]

Simple Summary: It is well documented that colorectal cancer (CRC) is the third most common cancer type, responsible for high mortality in developed countries, resulting in a high socio-economic impact. Several biochemical and gene expression pathways explaining the manifestation of this cancer in humans have already been identified. However, explanations for some of the related biophysical mechanisms and their influence on CRC remain elusive. In CRC, biophysics and medical research have already revealed the importance of studying the effects of the stiffness and viscoelasticity of the substrate on cells, as well as the effect of the shear stress of blood and lymphatic vessels on the behavior of cells and tissues. A deeper understanding of the relationship between the biophysical cues and biochemical signals could be advantageous to develop new diagnostic techniques and therapeutic strategies. Being a disease with a high mortality rate, it becomes crucial to dedicate efforts to finding effective, alternative therapeutic strategies. In this review, the mechanobiology of colorectal cancer (CRC) are discussed. Mechanotransduction of CRC is addressed considering the relationship of several biophysical cues and biochemical pathways. Mechanobiology is focused on considering how it may influence epithelial cells in terms of motility, morphometric changes, intravasation, circulation, extravasation, and metastization in CRC development. The roles of the tumor microenvironment, ECM, and stroma are also discussed, taking into account the influence of alterations and surface modifications on mechanical properties and their impact on epithelial cells and CRC progression. The role of cancer-associated fibroblasts and the impact of flow shear stress is addressed in terms of how it affects CRC metastization. Finally, some insights concerning how the knowledge of biophysical mechanisms may contribute to the development of new therapeutic strategies and targeting molecules and how mechanical changes of the microenvironment play a role in CRC disease are presented. [ABSTRACT FROM AUTHOR]

Gene expression changes are linked to air pollutant exposures in in vitro and animal experiments. However, limited data are available on how these outcomes relate to ambient air pollutant exposures in humans. We performed an exploratory analysis testing whether gene expression levels were associated with air pollution exposures in a Los Angeles area cohort of elderly subjects with coronary artery disease. Candidate genes (35) were selected from published studies of gene expression-pollutant associations. Expression levels were measured weekly in 43 subjects (≤ 12 weeks) using quantitative PCR. Exposures included gaseous pollutants O3, nitrogen oxides (NOx), and CO; particulate matter (PM) pollutants elemental and black carbon (EC, BC); and size-fractionated PM mass. We measured organic compounds from PM filter extracts, including polycyclic aromatic hydrocarbons (PAHs), and determined the in vitro oxidative potential of particle extracts. Associations between exposures and gene expression levels were analyzed using mixed-effects regression models. We found positive associations of traffic-related pollutants (EC, BC, primary organic carbon, PM 0.25-2.5 PAH and/or PM 0.25 PAH, and NOx) with NFE2L2, Nrf2-mediated genes (HMOX1, NQO1, and SOD2), CYP1B1, IL1B, and SELP. Findings suggest that NFE2L2 gene expression links associations of traffic-related air pollution with phase I and II enzyme genes at the promoter transcription level.

An ordinary differential equation (ODE) model of the working of the thyroid system for euthyroidism has been presented. As clinical data for thyroid hormones is relatively scarce, such modelling offers potential benefits over wet lab procedures. Genetic algorithms developed for determining of parameters of the ODE system using the available data have been presented and evaluated. This approach enables subject specific parameter estimation towards characterisation of individual thyroid operation. Initially, a simple steady state model was used. Later a cosinor model for the circadian variation of thyroid hormones was used to obtain more reliable results, as indicated through sensitivity analysis in conjunction with other statistical methods. Our parameter determination method has been tested on groups of patients with similar observed values of thyroid stimulating hormone (TSH), free T $_3$ 3 and free T $_4$ 4 (identified through clustering) to determine their parameter values jointly. This approach appears to produce parameter sets with lower variation than parameters determined independently, thus leading to better parameter determination. [ABSTRACT FROM AUTHOR]

Phosphorus (P) acts as one of the essential macronutrients and plays critical roles in regulating growth, development, and yield formation capacity of the crop plants. Elucidating the physiological and molecular processes underlying plant P deprivation responses benefit the crop cultivation with high P use efficiency (PUE). In this study, we investigated the P-associated and agronomic traits as well as the transcriptome profile under contrasting inorganic phosphorus (Pi) levels combined with deficit irrigation, using two wheat cultivars contrasted by PUE (i.e., high PUE Shixin 828 and P deprivation sensitive Jimai 518). The deficient-P (DP) treatment decreased P accumulative amounts, photosynthetic function, and biomass in plants at various growth stages and reduced yields with respect to sufficient-P (SP) condition. Although the two cultivars were comparable on growth and P-associated traits as well as yields under SP, Shixin 828 displayed better growth traits, more P accumulation, and higher yields than Jimai 518 under DP, suggesting that the high PUE cultivars with enhanced P uptake positively affects photosynthetic function, biomass production, and productivity of plants under P deprivation conditions. A large quantity of genes with differential expression patterns, including 2948 upregulated and 1844 downregulated, were identified based on RNA-seq analysis in the Shixin 828 plants treated with P deprivation. These DE genes were shown to be associated with biological process (i.e., metabolic process and cellular process etc.), cellular components (cell body and organelle etc.), and molecular function (binding and catalytic activity), and phytohormone signaling pathways. Transgene analysis on TaZFP1, a gene in ZFP transcription factor family that exhibited upregulated expression in P-deprived plants, validated its role in enhancing P accumulation and P starvation adaptation of plants. Our results suggested that plant P deprivation response is underlying modulation of various physiological processes via modifying gene transcription at global level. Key message: RNA-seq sequencing combined with physiological and transgene analyses dissected the molecular processes related to P-associated and agronomic traits in wheat cultivated under Pi deprivation condition. [ABSTRACT FROM AUTHOR]

Abscisic acid (ABA) significantly regulates plant growth and development, promoting tuberous root formation in various plants. However, the molecular mechanisms of ABA in the tuberous root development of Pseudostellaria heterophylla are not yet fully understood. This study utilized Illumina sequencing and de novo assembly strategies to obtain a reference transcriptome associated with ABA treatment. Subsequently, integrated transcriptomic and proteomic analyses were used to determine gene expression profiles in P. heterophylla tuberous roots. ABA treatment significantly increases the diameter and shortens the length of tuberous roots. Clustering analysis identified 2,256 differentially expressed genes and 679 differentially abundant proteins regulated by ABA. Gene co-expression and protein interaction networks revealed ABA positively induced 30 vital regulators. Furthermore, we identified and assigned putative functions to transcription factors (PhMYB10, PhbZIP2, PhbZIP, PhSBP) that mediate ABA signaling involved in the regulation of tuberous root development, including those related to cell wall metabolism, cell division, starch synthesis, hormone metabolism. Our findings provide valuable insights into the complex signaling networks of tuberous root development modulated by ABA. It provided potential targets for genetic manipulation to improve the yield and quality of P. heterophylla , which could significantly impact its cultivation and medicinal value., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Wang, Yang, Pan, Zhu and Li.)

Pyruvic acid has numerous applications in the food, chemical, and pharmaceutical industries. The high costs of chemical synthesis have prevented the extensive use of pyruvate for many applications. Metabolic engineering and traditional strategies for mutation and selection have been applied to microorganisms to enhance their ability to produce pyruvate. In the past decades, different microbial strains were generated to enhance their pyruvate production capability. In addition to the development of genetic engineering and metabolic engineering in recent years, the metabolic transformation of wild-type yeast, E. coli, and so on to produce high-yielding pyruvate strains has become a hot spot. The strategy and the understanding of the central metabolism directly related to pyruvate production could provide valuable information for improvements in fermentation products. One of the goals of this review was to collect information regarding metabolically engineered strains and the microbial fermentation processes used to produce pyruvate in high yield and productivity.

Cheese has a long history and this naturally fermented dairy product contains a range of distinctive flavors. Microorganisms in variety cheeses are an essential component and play important roles during both cheese production and ripening. However, cheeses from different countries are still handmade, the processing technology is diverse, the microbial community structure is complex and the cheese flavor fluctuates greatly. Therefore, studying the general processing technology and relationship between microbial structure and flavor formation in cheese is the key to solving the unstable quality and standardized production of cheese flavor on basis of maintaining the flavor of cheese. This paper reviews the research progress on the general processing technology and key control points of natural cheese, the biochemical pathways for production of flavor compounds in cheeses, the diversity and the role of yeasts in cheese. Combined with the development of modern detection technology, the evolution of microbial structure, population evolution and flavor correlation in cheese from different countries was analyzed, which is of great significance for the search for core functional yeast microorganisms and the industrialization prospect of traditional fermented cheese.

Cheese has a long history and this naturally fermented dairy product contains a range of distinctive flavors. Microorganisms in variety cheeses are an essential component and play important roles during both cheese production and ripening. However, cheeses from different countries are still handmade, the processing technology is diverse, the microbial community structure is complex and the cheese flavor fluctuates greatly. Therefore, studying the general processing technology and relationship between microbial structure and flavor formation in cheese is the key to solving the unstable quality and standardized production of cheese flavor on basis of maintaining the flavor of cheese. This paper reviews the research progress on the general processing technology and key control points of natural cheese, the biochemical pathways for production of flavor compounds in cheeses, the diversity and the role of yeasts in cheese. Combined with the development of modern detection technology, the evolution of microbial structure, population evolution and flavor correlation in cheese from different countries was analyzed, which is of great significance for the search for core functional yeast microorganisms and the industrialization prospect of traditional fermented cheese. [ABSTRACT FROM AUTHOR]

Antibiotic resistance (AR) is an acute problem that results in prolonged and debilitating illnesses. AR mortality worldwide is growing and causes a pressing need to research novel mechanisms of action and untested target molecules. This article presents in silico analyses of eight bacterial riboswitches for their suitability for antibacterial drug targets. Most bacterial riboswitches are located in the 5′-untranslated region of messenger RNAs, act as allosteric cis-acting gene control elements, and have not been found in humans before. Sensing metabolites, the riboswitches regulate the synthesis of vital cellular metabolites in various pathogenic bacteria. The analyses performed in this article represent a complete and informative genome-wide bioinformatics analysis of the adequacy of eight riboswitches as antibacterial drug targets in different pathogenic bacteria based on four criteria. Due to the ability of the riboswitch to control biosynthetic pathways and transport proteins of essential metabolites and the presence/absence of alternative biosynthetic pathways, we classified them into four groups based on their suitability for use as antibacterial drug targets guided by our in silico analyses. We concluded that some of them are promising targets for antibacterial drug discovery, such as the PreQ1, MoCo RNA, cyclic-di-GMP I, and cyclic-di-GMP II riboswitches.