MOSCA 2.0: A bioinformatics framework for metagenomics, metatranscriptomics and metaproteomics data analysis and visualization.

The analysis of meta-omics data requires the utilization of several bioinformatics tools and proficiency in informatics. The integration of multiple meta-omics data is even more challenging, and the outputs of existing bioinformatics solutions are not always easy to interpret. Here, we present a meta-omics bioinformatics pipeline, Meta-Omics Software for Community Analysis (MOSCA), which aims to overcome these limitations. MOSCA was initially developed for analysing metagenomics (MG) and metatranscriptomics (MT) data. Now, it also performs MG and metaproteomics (MP) integrated analysis, and MG/MT analysis was upgraded with an additional iterative binning step, metabolic pathways mapping, and several improvements regarding functional annotation and data visualization. MOSCA handles raw sequencing data and mass spectra and performs pre-processing, assembly, annotation, binning and differential gene/protein expression analysis. MOSCA shows taxonomic and functional analysis in large tables, performs metabolic pathways mapping, generates Krona plots and shows gene/protein expression results in heatmaps, improving omics data visualization. MOSCA is easily run from a single command while also providing a web interface (MOSGUITO). Relevant features include an extensive set of customization options, allowing tailored analyses to suit specific research objectives, and the ability to restart the pipeline from intermediary checkpoints using alternative configurations. Two case studies showcased MOSCA results, giving a complete view of the anaerobic microbial communities from anaerobic digesters and insights on the role of specific microorganisms. MOSCA represents a pivotal advancement in meta-omics research, offering an intuitive, comprehensive, and versatile solution for researchers seeking to unravel the intricate tapestry of microbial communities.

Transcriptomic analyses provide molecular insight into the cold stress response of cold-tolerant alfalfa.

BACKGROUND: Daye No.3 is a novel cultivar of alfalfa (Medicago sativa L.) that is well suited for cultivation in high-altitude regions such as the Qinghai‒Tibet Plateau owing to its high yield and notable cold resistance. However, the limited availability of transcriptomic information has hindered our investigation into the potential mechanisms of cold tolerance in this cultivar. Consequently, we conducted de novo transcriptome assembly to overcome this limitation. Subsequently, we compared the patterns of gene expression in Daye No. 3 during cold acclimatization and exposure to cold stress at various time points. RESULTS: A total of 15 alfalfa samples were included in the transcriptome assembly, resulting in 141.97 Gb of clean bases. A total of 441 DEGs were induced by cold acclimation, while 4525, 5016, and 8056 DEGs were identified at 12 h, 24 h, and 36 h after prolonged cold stress at 4 °C, respectively. The consistency between the RT‒qPCR and transcriptome data confirmed the accuracy and reliability of the transcriptomic data. KEGG enrichment analysis revealed that many genes related to photosynthesis were enriched under cold stress. STEM analysis demonstrated that genes involved in nitrogen metabolism and the TCA cycle were consistently upregulated under cold stress, while genes associated with photosynthesis, particularly antenna protein genes, were downregulated. PPI network analysis revealed that ubiquitination-related ribosomal proteins act as hub genes in response to cold stress. Additionally, the plant hormone signaling pathway was activated under cold stress, suggesting its vital role in the cold stress response of alfalfa. CONCLUSIONS: Ubiquitination-related ribosomal proteins induced by cold acclimation play a crucial role in early cold signal transduction. As hub genes, these ubiquitination-related ribosomal proteins regulate a multitude of downstream genes in response to cold stress. The upregulation of genes related to nitrogen metabolism and the TCA cycle and the activation of the plant hormone signaling pathway contribute to the enhanced cold tolerance of alfalfa.

Flux Calculation for Primary Metabolism Reveals Changes in Allocation of Nitrogen to Different Amino Acid Families When Photorespiratory Activity Changes.

Photorespiration, caused by oxygenation of the enzyme Rubisco, is considered a wasteful process, because it reduces photosynthetic carbon gain, but it also supplies amino acids and is involved in amelioration of stress. Here, we show that a sudden increase in photorespiratory activity not only reduced carbon acquisition and production of sugars and starch, but also affected diurnal dynamics of amino acids not obviously involved in the process. Flux calculations based on diurnal metabolite profiles suggest that export of proline from leaves increases, while aspartate family members accumulate. An immense increase is observed for turnover in the cyclic reaction of glutamine synthetase/glutamine-oxoglutarate aminotransferase (GS/GOGAT), probably because of increased production of ammonium in photorespiration. The hpr1-1 mutant, defective in peroxisomal hydroxypyruvate reductase, shows substantial alterations in flux, leading to a shift from the oxoglutarate to the aspartate family of amino acids. This is coupled to a massive export of asparagine, which may serve in exchange for serine between shoot and root.

An overview of multi-omics technologies in rheumatoid arthritis: applications in biomarker and pathway discovery.

Rheumatoid arthritis (RA) is a chronic inflammatory autoimmune disease with a complex pathological mechanism involving autoimmune response, local inflammation and bone destruction. Metabolic pathways play an important role in immune-related diseases and their immune responses. The pathogenesis of rheumatoid arthritis may be related to its metabolic dysregulation. Moreover, histological techniques, including genomics, transcriptomics, proteomics and metabolomics, provide powerful tools for comprehensive analysis of molecular changes in biological systems. The present study explores the molecular and metabolic mechanisms of RA, emphasizing the central role of metabolic dysregulation in the RA disease process and highlighting the complexity of metabolic pathways, particularly metabolic remodeling in synovial tissues and its association with cytokine-mediated inflammation. This paper reveals the potential of histological techniques in identifying metabolically relevant therapeutic targets in RA; specifically, we summarize the genetic basis of RA and the dysregulated metabolic pathways, and explore their functional significance in the context of immune cell activation and differentiation. This study demonstrates the critical role of histological techniques in decoding the complex metabolic network of RA and discusses the integration of histological data with other types of biological data.

Advancements in biotransformation pathway prediction: enhancements, datasets, and novel functionalities in enviPath.

enviPath is a widely used database and prediction system for microbial biotransformation pathways of primarily xenobiotic compounds. Data and prediction system are freely available both via a web interface and a public REST API. Since its initial release in 2016, we extended the data available in enviPath and improved the performance of the prediction system and usability of the overall system. We now provide three diverse data sets, covering microbial biotransformation in different environments and under different experimental conditions. This also enabled developing a pathway prediction model that is applicable to a more diverse set of chemicals. In the prediction engine, we implemented a new evaluation tailored towards pathway prediction, which returns a more honest and holistic view on the performance. We also implemented a novel applicability domain algorithm, which allows the user to estimate how well the model will perform on their data. Finally, we improved the implementation to speed up the overall system and provide new functionality via a plugin system. SCIENTIFIC CONTRIBUTION: The main scientific contributions are the development of a pathway prediction model applicable to diverse chemicals, a specialized evaluation method for holistic performance assessment, and a novel applicability domain algorithm for user-specific performance estimation. The introduction of two new data sets, and the creation of links to EC classes make enviPath a unique resource in microbial biotransformation research.

Aerobic denitrifying bacterial community with low C/N ratio remove nitrate from micro-polluted water: Metagenomics unravels denitrification pathways.

The efficient nitrogen removal from micro-polluted source water is an international challenge to be solved urgently. However, the inner denitrification mechanism of native aerobic denitrifying bacterial communities in response to carbon scarcity remains relatively unclear. Here, the bacterial community XT6, screened from an oligotrophic reservoir, exhibited aerobic denitrifying capacity under low-carbon environments. Up to 76.79-81.64 % of total organic carbon (TOC) and 51.48-67.60 % of NO3--N were removed by XT6 within 48 h and C/N ratios of 2.0-3.0. Additionally, the nitrogen balance experiments further manifested that 26.27-38.13 % of NO3--N was lost in gaseous form. As the C/N ratio decreased, XT6 tended to generate more extracellular polymeric substances (EPS), with the tightly bound EPS showing the largest increase. Pseudomonas and Variovorax were quite abundant in XT6, constituting 59.69 % and 28.65 % of the total sequences, respectively. Furthermore, metagenomics analysis evidenced that XT6 removed TOC and nitrate through the tricarboxylic acid cycle and aerobic denitrification. Overall, the abovementioned results provide a deeper understanding of the nitrogen metabolic pathways of indigenous aerobic denitrifying bacterial communities with low C/N ratios and offer useful guidance for controlling nitrogen pollution in oligotrophic ecosystems.

Unraveling the core microorganisms and metabolic pathways related to off-flavor compounds formation during Jiang-flavor Baijiu fermentation.

Off-flavor is one of the most frequent and serious causes for the aroma deterioration in Jiang-flavor Baijiu. However, the key compounds and their formation mechanism responsible for off-flavor are still unclear. This study identified 271 volatile compounds from 1 normal and 5 types of off-flavor fermented grains (putrid, rancidity, mud, musty, and burnt) by headspace solid-phase microextraction combined with gas chromatography-mass spectrometry. Using VIP and OAV analysis, 47 key flavor compounds including indole, phenol, isoamyl alcohol, diacetyl, acetic acid, isobutyric acid, and isovaleric acid were found to distinguish normal and off-flavor fermented grains. Furthermore, 40 microbial genera (mainly Monascus, Enterococcus, Dyadobacter, Ottowia, Pseudoxanthomonas, Stenotrophomonas, Pseudomonas, and Xanthomonas) were significantly (p < 0.05, Pearson correlation) related to these 47 compounds. Finally, metabolic pathways for off-flavor compounds formation were constructed. This study provides comprehensive information on the off-flavor compounds and their potential formation mechanism during Jiang-flavor Baijiu fermentation.

Transcriptome analysis reveals the molecular mechanism of differences in growth between photoautotrophy and heterotrophy in Chlamydomonas reinhardtii.

Background: The green alga Chlamydomonas reinhardtii can grow photoautotrophically utilizing light and CO2, and heterotrophically utilizing acetate. The physiological and biochemical responses of autotrophy and heterotrophy are different in C. reinhardtii. However, there is no complete understanding of the molecular physiology between autotrophy and heterotrophy. Therefore, we performed biochemical, molecular and transcriptome analysis of C. reinhardtii between autotrophy and heterotrophy. Results: The cell growth characterization demonstrated that heterotrophic cell had enhanced growth rates, and autotrophic cell accumulated more chlorophyll. The transcriptome data showed that a total of 2,970 differentially expressed genes (DEGs) were identified from photoautotrophy 12h (P12h) to heterotrophy 12h (H12h). The DEGs were involved in photosynthesis, the tricarboxylic acid cycle (TCA), pyruvate and oxidative phosphorylation metabolisms. Moreover, the results of qRT-PCR revealed that the relative expression levels of malate dehydrogenase (MDH), succinate dehydrogenase (SDH), ATP synthase (ATPase), and starch synthase (SSS) were increased significantly from P12h and H12h. The protein activity of NAD-malate dehydrogenase (NAD-MDH) and succinate dehydrogenase (SDH) were significantly higher in the H12h group. Conclusion: The above results indicated that the high growth rate observed in heterotrophic cell may be the effects of environmental or genetic regulation of photosynthesis. Therefore, the identification of novel candidate genes in heterotrophy will contribute to the development of microalga strains with higher growth capacity and better performance for biomass production.

Molecular signatures of longissimus dorsi differ between dairy cattle based on prepartum muscle reserves and branched-chain volatile fatty acid supplementation.

Dairy cattle with high (HM) versus low muscle (LM) reserves exhibit distinct temporal changes in longissimus dorsi muscle depth (LDD) in late gestation. Branched-chain volatile fatty acids (BCVFA) supplementation increased blood glucose levels. We hypothesized that differences in HM and LM reflect distinct muscle metabolism and BCVFA supplementation altered metabolic pathways. At 42 d before expected calving (BEC) Holstein dairy cows were enrolled in a 2 x 2 factorial study of diet and muscle reserves, by assigning to control (CON) or BCVFA supplemented diets and LDD of HM (>4.6 cm) or LM (≤4.6 cm) groups: HM-CON (n=13), HM-BCVFA (n=10), LM-CON (n=9), and LM-BCVFA (n=9). Longisumus dorsi was biopsied at 21 d BEC, total RNA isolated and protein coding gene expression measured with RNA-seq. Between HM and LM 713 genes were differentially expressed and 481 between BCVFA and CON (P<0.05). Transcriptional signatures indicated differential distribution of Type II fibers between groups, with MYH1 greater in LM and MYH2 greater in HM cattle (P<0.05). Signatures of LM cattle relative to HM indicated greater activation of autophagy, ubiquitin-proteasome, and Ca2+-calpain pathways. HM cattle displayed greater expression of genes that encode extracellular matrix proteins and factors that regulate their proteolysis and turnover. BCVFA modified transcriptomes by increasing expression of genes that regulate fatty acid degradation and flux of carbons into the TCA cycle as acetyl CoA. Molecular signatures support distinct metabolic strategies between LM and HM cattle, and that BCVFA supplementation increased substrates for energy generation.

Serum metabolic changes link metal mixture exposures to vascular endothelial inflammation in residents living surrounding rivers near abandoned lead-zinc mines.

Metal exposure is associated with vascular endothelial inflammation, an early pathological phenotype of atherosclerotic cardiovascular events. However, the underlying mechanism linking exposure, metabolic changes, and outcomes remains unclear. We aimed to investigate the metabolic changes underlying the associations of chronic exposure to metal mixtures with vascular endothelial inflammation. We recruited 960 adults aged 20-75 years from residential areas surrounding rivers near abandoned lead-zinc mine and classified them into river area and non-river area exposure groups. Urine levels of 25 metals, Framingham risk score (FRS), and serum concentrations of intercellular adhesion molecule-1 (ICAM-1) and vascular cell adhesion molecule-1 (VCAM-1), as biomarkers of vascular endothelial inflammation, were assessed. A "meet-in-the-middle" approach was applied to identify causal intermediate metabolites and metabolic pathways linking metal exposure to vascular endothelial inflammation in representative metabolic samples from 64 participants. Compared to the non-river area exposure group, the river area exposure group had significantly greater urine concentrations of chromium, copper, cadmium, and lead; lower urine concentrations of selenium; elevated FRS; and increased concentrations of ICAM-1 and VCAM-1. In total, 38 differentially abundant metabolites were identified between the river area and non-river area exposure groups. Among them, 25 metabolites were significantly associated with FRS, 8 metabolites with ICAM-1 expression, and 10 metabolites with VCAM-1 expression. Furthermore, fructose, ornithine, alpha-ketoglutaric acid, urea, and cytidine monophosphate, are potential mediators of the relationship between metal exposure and vascular endothelial inflammation. Additionally, the metabolic changes underlying these effects included changes in arginine and proline metabolism, pyrimidine metabolism, starch and sucrose metabolism, galactose metabolism, arginine biosynthesis, and alanine, aspartate, and glutamate metabolism, suggesting the disturbance of amino acid metabolism, the tricarboxylic acid cycle, nucleotide metabolism, and glycolysis. Overall, our results reveal biomechanisms that may link chronic exposure to multiple metals with vascular endothelial inflammation and elevated cardiovascular risk.

Genetic causality and metabolite pathway identifying the relationship of blood metabolites and psoriasis.

BACKGROUND: Psoriasis is a chronic inflammatory disease that causes significant disability. However, little is known about the underlying metabolic mechanisms of psoriasis. Our study aims to investigate the causality of 975 blood metabolites with the risk of psoriasis. MATERIALS AND METHODS: We mainly applied genetic analysis to explore the possible associations between 975 blood metabolites and psoriasis. The inverse variance weighted (IVW) method was used as the primary analysis to assess the possible association of blood metabolites with psoriasis. Moreover, generalized summary-data-based Mendelian randomization (GSMR) was used as a supplementary analysis. In addition, linkage disequilibrium score regression (LDSC) was used to investigate their genetic correction further. Metabolic pathway analysis of the most suggested metabolites was also performed using MetaboAnalyst 5.0. RESULTS: In our primary analysis, 17 metabolites, including unsaturated fatty acids, phospholipids, and triglycerides traits, were selected as potential factors in psoriasis, with odd ratios (OR) ranging from 0.986 to 1.01. The GSMR method confirmed the above results (ß = 0.001, p < 0.05). LDSC analysis mainly suggested the genetic correlation of psoriasis with genetic correlations (rg) from 0.088 to 0.155. Based on the selected metabolites, metabolic pathway analysis suggested seven metabolic pathways including ketone body that may be prominent pathways for metabolites in psoriasis. CONCLUSION: Our study supports the causal role of unsaturated fatty acid properties and lipid traits with psoriasis. These properties may be regulated by the ketone body metabolic pathway.

Widely targeted metabolomics reveals the phytoconstituent changes in Platostoma palustre leaves and stems at different growth stages.

Platostoma palustre (Blume) A. J. Paton is an important edible and medicinal plant. To gain a comprehensive and clear understanding of the variation patterns of metabolites in P. palustre, we employed the UPLC-MS platform along with widely targeted metabolomics techniques to analyze the metabolites in the stems and leaves of P. palustre at different stages. Our results revealed a total of 1228 detected metabolites, including 241 phenolic acids, 203 flavonoids, 152 lipids, 128 terpenes, 106 amino acids, 79 organic acids, 74 saccharides, 66 alkaloids, 44 lignans, etc. As the growth time increased, the differential metabolites (DAMs) mainly enriched in P. palustre leaves were terpenoids, phenolic acids, and lipids, while the DAMs primarily enriched in stems were terpenoids. Compared to stems, there were more differential flavonoids in leaves, and saccharides and flavonoids were significantly enriched in leaves during the S1 and S2 stages. Additionally, we identified 13, 10, and 23 potential markers in leaf, stem, and leaf vs. stem comparison groups. KEGG enrichment analysis revealed that arginine biosynthesis was the common differential metabolic pathway in different growth stages and tissues. Overall, this study comprehensively analyzed the metabolic profile information of P. palustre, serving as a solid foundation for its further development and utilization.

Gut microbiome-immune interactions and their role in rheumatoid arthritis development.

Objective: The primary objective is to study the impact of gut microbiota and their interactions with diverse immunological markers on the development of rheumatoid arthritis. Methods: This study was performed in Astana, Kazakhstan, and included 77 Kazakh female patients older than 18 years, who met the American College of Rheumatology 2010 classification criteria for rheumatoid arthritis (RA), and 113 healthy controls. The DNA was extracted from fecal samples obtained from all study participants for subsequent sequencing at the 16S rRNA gene V1-V3 locus, facilitating the analysis of the gut microbiome. The Multiplex immunoassay was employed to measure the concentrations of inflammatory cytokines, chemokines, and immunoglobulins in both fecal and plasma samples. Results: Our taxonomic analysis revealed significant differences in the composition of the gut microbiota between the healthy control cohort and the cohort with rheumatoid arthritis RA. Alpha diversity was significantly lower in the RA group. Lachnospiraceae were the most abundant taxon and found to be crucial, showing correlations with immunological markers such as IL5. Additionally, Lachnospiraceae and Oscillospiraceae exhibited the most predictable power and distinguished the composition of both study groups. Conclusion: Our study identifies key differences in the gut microbiome of RA patients, revealing distinct microbial patterns and specific taxa abundance. We highlight potential biomarkers in immunological and bacterial pathways, offering insights into RA development and indicating possibilities for personalized treatment.

UPLC-MS/MS reveals the differences in lipids composition of Camellia oleifera from northern margin distribution area.

The lipids accumulation characteristics in 23Camellia oleifera lines from northern margin distribution area were investigated through quantitative lipidomics. Combined lipids content-function analysis indicated that NQ1, HT1, HT2, ZA2, ZB1, ZB2, and SN2 lines had potential to develop functional foods due to abundant glycerolipids (GLs), glycerophospholipids (GPs), fatty acids (FAs), and prenol lipids (PRs). 673 lipids components were detected, and 293 differential components were identified in NQ1, ZA2, HB1, and HT1. 4 kinds free fatty acids (FFAs) were higher in NQ1, 5 triglycerides (TGs) were higher in HT1, and 2 phosphatidyl serines (PSs) and 1 phosphatidyl glycerol (PG) were higher in ZA2. GLs, GPs, and FFAs had strong relation at intra- and inter-category level. Glycerolipid metabolism, glycerophospholipid metabolism, and fatty acid biosynthesis were the significantly differential lipids pathways. Our study elucidated lipids differences of 23 C. oleifera lines, and offered valuable references for lipids biosynthesis, directional breeding, and lipids utilization.

Biotechnological valorization of levulinic acid as a non-sugar feedstock: New paradigm in biorefineries.

Due to the severe climate crisis, biorefineries have been highlighted as replacements for fossil fuel-derived refineries. In traditional sugar-based biorefineries, levulinic acid (LA) is a byproduct. Nonetheless, in 2002, the US Department of Energy noted that LA is a significant building block obtained from biomass, and the biorefinery paradigm has shifted from being sugar-based to non-sugar-based. Accordingly, LA is of interest in this review since it can be converted into useful precursors and ultimately can broaden the product spectrum toward more valuable products (e.g., fuels, plastics, and pharmaceuticals), thereby enabling the construction of economically viable biorefineries. This study comprehensively reviews LA production techniques utilizing various bioresources. Recent progress in enzymatic and microbial routes for LA valorization and the LA-derived product spectrum and its versatility are discussed. Finally, challenges and future outlooks for LA-based non-sugar biorefineries are suggested.

Current insights into environmental acetochlor toxicity and remediation strategies.

Acetochlor is a selective pre-emergent herbicide that is widely used to control annual grass and broadleaf weeds. However, due to its stable chemical structure, only a small portion of acetochlor exerts herbicidal activity in agricultural applications, while most of the excess remains on the surfaces of plants or enters ecosystems, such as soil and water bodies, causing harm to the environment and human health. In recent years, researchers have become increasingly focused on the repair of acetochlor residues. Compared with traditional physical and chemical remediation methods, microorganisms are the most effective way to remediate chemical pesticide pollution, such as acetochlor, because of their rich species, wide distribution, and diverse metabolic pathways. To date, researchers have isolated and identified many high-efficiency acetochlor-degrading strains, such as Pseudomonas oleovorans, Klebsiella variicola, Bacillus subtilus, Rhodococcus, and Methylobacillus, among others. The microbial degradation pathways of acetochlor include dechlorination, hydroxylation, N-dealkylation, C-dealkylation, and dehydrogenation. In addition, the microbial enzymes, including hydrolase (ChlH), debutoxylase (Dbo), and monooxygenase (MeaXY), responsible for acetochlor biodegradation are also being investigated. In this paper, we review the migration law of acetochlor in the environment, its toxicity to nontarget organisms, and the main metabolic methods. Moreover, we summarize the latest progress in the research on the microbial catabolism of acetochlor, including the efficient degradation of microbial resources, biodegradation metabolic pathways, and key enzymes for acetochlor degradation. At the end of the article, we highlight the existing problems in the current research on acetochlor biodegradation, provide new ideas for the remediation of acetochlor pollution in the environment, and propose future research directions.

Genome-wide association study and pathway analysis to decipher loci associated with Fusarium ear rot resistance in tropical maize germplasm.

Breeding for host resistance is the most efficient and environmentally safe method to curb the spread of fusarium ear rot (FER). However, conventional breeding for resistance to FER is hampered by the complex polygenic nature of this trait, which is highly influenced by environmental conditions. This study aimed to identify genomic regions, single nucleotide polymorphisms (SNPs), and putative candidate genes associated with FER resistance as well as candidate metabolic pathways and pathway genes involved in it. A panel of 151 tropical inbred maize lines were used to assess the genetic architecture of FER resistance over two seasons. During the study period, seven SNPs associated with FER resistance were identified on chromosomes 1, 2, 4, 5, and 9, accounting for 4-11% of the phenotypic variance. These significant markers were annotated into four genes. Seven significant metabolic pathways involved in FER resistance were identified using the Pathway Association Study Tool, the most significant being the superpathway of the glyoxylate cycle. Overall, this study confirmed that resistance to FER is indeed a complex mechanism controlled by several small to medium-effect loci. Our findings may contribute to fast-tracking the efforts to develop disease-resistant maize lines through marker-assisted selection. Supplementary Information: The online version contains supplementary material available at 10.1007/s10722-023-01793-4.

Revolutionizing the probiotic functionality, biochemical activity, antibiotic resistance and specialty genes of Pediococcus acidilactici BCB1H via in-vitro and in-silico approaches.

This study presents a comprehensive genomic exploration, biochemical characterization, and the identification of antibiotic resistance and specialty genes of Pediococcus acidilactici BCB1H strain. The functional characterization, genetic makeup, biological activities, and other considerable parameters have been investigated in this study with a prime focus on antibiotic resistance and specialty gene profiles. The results of this study revealed the unique susceptibility patterns for antibiotic resistance and specialty genes. BCB1H had good in vitro probiotic properties, which survived well in simulated artificial gastrointestinal fluid, and exhibited acid and bile salt resistance. BCB1H didn't produce hemolysis and had certain antibiotic sensitivity, making it a relatively safe LAB strain. Simultaneously, it had good self-coagulation characteristics and antioxidant activity. The EPS produced by BCB1H also had certain antioxidant activity and hypoglycemic function. Moreover, the genome with a 42.4 % GC content and a size of roughly 1.92 million base pairs was analyzed in the genomic investigations. The genome annotation identified 192 subsystems and 1,895 genes, offering light on the metabolic pathways and functional categories found in BCB1H. The identification of specialty genes linked to the metabolism of carbohydrates, stress response, pathogenicity, and amino acids highlighted the strain's versatility and possible uses. This study establishes the groundwork for future investigations by highlighting the significance of using multiple strains to investigate genetic diversity and experimental validation of predicted genes. The results provide a roadmap for utilizing P. acidilactici BCB1H's genetic traits for industrial and medical applications, opening the door to real-world uses in industries including food technology and medicine.

Effects of Various Flavors of Baijiu on the Microbial Communities, Metabolic Pathways, and Flavor Structures of Dongbei Suancai.

This study aimed to assess the effects of Chinese Baijiu with different flavors as supplementary material on microbial communities and flavor formation during inoculated fermentation of Chinese Dongbei Suancai. The results showed that the addition of Fen flavor Baijiu significantly increased the relative abundance of Candida, Luzhou flavor Baijiu increased the relative abundance of Pedobacter and Hannaella, while Maotai flavor Baijiu increased the Chryseobacterium and Kazachstania. A total of 226 volatile metabolites were detected in Suancai fermented when adding different flavors of Baijiu. Furthermore, the significantly upregulated metabolites (p < 0.01) of Suancai after adding Baijiu increased by 328.57%, whereas the significantly downregulated metabolites decreased by 74.60%. Simultaneously, the addition of Baijiu promoted the synthesis and decomposition of amino acids and short-chain fatty acids in the early and middle stages of fermentation. Further, Maotai flavor Baijiu improved the diversification of metabolic pathways in the late stage of Suancai fermentation. The E-nose response showed that sulfur-organic, broad-alcohol, sulfur-chlor was the principal differential flavor in Suancai caused by adding Baijiu with different flavors. Simultaneously, Fen flavor Baijiu and Luzhou flavor Baijiu accelerated the formation of the Suancai flavor. These results indicated that Baijiu with different flavors had significant effects on the flavor formation of inoculated fermented Suancai.

Taste Preferences at Different Ambient Temperatures and Associated Changes in Gut Microbiota and Body Weight in Mice.

Taste, dietary choices, and gut microbiota are often analyzed as major factors of metabolic health. Populations living in cold or hot regions have different dietary habits. This study aims to investigate the potential association among ambient temperature, food taste preferences, and cecal microbiota community profiles in mice. By exposing mice to mixed diets containing sweet, sour, salty, and bitter flavors at low (4 °C) and high (37 °C) ambient temperatures, the taste preferences of mice at both ambient temperatures were in the order of saltiness > sweetness > bitterness > sourness. Exposing mice to sweet, sour, salty, and bitter diets, respectively, revealed that in a low-temperature environment, mice consuming salty (5.00 ± 1.49 g), sweet (4.99 ± 0.35 g), and sour (3.90 ± 0.61 g) diets had significantly higher weight gain compared to those consuming normal feeds (2.34 ± 0.43 g, p < 0.05). Conversely, in a high-temperature environment, no significant changes in body weight were observed among mice consuming different flavored diets (p > 0.05). In a low-temperature environment, mice fed sour and sweet diets showed a significant difference in the gut microbiota composition when compared to those fed a normal diet. A higher abundance of Lachnospiraceae, UBA1819, and Clostridiales was identified as the most significant taxa in the sour group, and a higher abundance of Ruminiclostridium was identified in the sweet group. These differences were associated with microbial pathways involved in carbohydrate metabolism, amino acid metabolism, and energy metabolism. A high-temperature environment exhibited only minor effects on the gut microbiota profile. Overall, our findings provide evidence for temperature-modulated responses to the taste, gut microbiota functions, and body weight changes in mice.