The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases

AFP: Finding pathways accounting for stoichiometry along with atom group tracking in metabolic network

Automatically finding novel pathways plays an important role in the initial designs of metabolic pathways in synthetic biology and metabolic engineering. Although path-finding methods have been successfully applied in identifying valuable synthetic pathways, few efforts have been made in fusing atom group tracking into building stoichiometry model to search metabolic pathways from arbitrary start compound via Mixed Integer Linear Programming (MILP). We propose a novel method called AFP to find metabolic pathways by incorporating atom group tracking into reaction stoichiometry via MILP. AFP tracks the movements of atom groups in the reaction stoichiometry to construct MILP model to search the pathways containing atom groups exchange in the reactions and adapts the MILP model to provide the options of searching pathways from an arbitrary or given compound to the target compound. Combining atom group tracking with reaction stoichiometry to build MILP model for pathfinding may promote the search of well-designed alternative pathways at the stoichiometric modeling level. The experimental comparisons to the known pathways show that our proposed method AFP is more effective to recover the known pathways than other existing methods and is capable of discovering biochemically feasible pathways producing the metabolites of interest.

Integrating functional metagenomics to decipher microbiome-immune interactions

Microbial metabolites can be viewed as the cytokines of the microbiome, transmitting information about the microbial and metabolic environment of the gut to orchestrate and modulate local and systemic immune responses. Still, many immunology studies focus solely on the taxonomy and community structure of the gut microbiota rather than its functions. Early sequencing-based microbiota profiling approaches relied on PCR amplification of small regions of bacterial and fungal genomes to facilitate identification of the microbes present. However, recent microbiome analysis methods, particularly shotgun metagenomic sequencing, now enable culture-independent profiling of microbiome functions and metabolites in addition to taxonomic characterization. In this review, we showcase recent advances in functional metagenomics methods and applications and discuss the current limitations and potential avenues for future development. Importantly, we highlight a few examples of key areas of opportunity in immunology research where integrating functional metagenomic analyses of the microbiome can substantially enhance a mechanistic understanding of microbiome-immune interactions and their contributions to health and disease states.

Low magnesium in conjunction with high homocysteine increases DNA damage in healthy middle aged Australians

PURPOSE

Magnesium is one of the most common elements in the human body and plays an important role as a cofactor of enzymes required for DNA replication and repair and many other biochemical mechanisms including sensing and regulating one-carbon metabolism deficiencies. Low intake of magnesium can increase the risk of many diseases, in particular, chronic degenerative disorders. However, its role in prevention of DNA damage has not been studied fully in humans so far. Therefore, we tested the hypothesis that magnesium deficiency either on its own or in conjunction with high homocysteine (Hcy) induces DNA damage in vivo in humans.

METHODS

The present study was carried out in 172 healthy middle aged subjects from South Australia. Blood levels of magnesium, Hcy, folate and vitamin B12 were measured. Cytokinesis-Block Micronucleus cytome assay was performed to measure three DNA damage biomarkers: micronuclei (MN), nucleoplasmic bridges (NPBs) and nuclear buds (NBuds) in peripheral blood lymphocytes.

RESULTS

Data showed that magnesium and Hcy are significantly inversely correlated with each other (r = - 0.299, p < 0.0001). Furthermore, magnesium is positively correlated both with folate (p = 0.002) and vitamin B12 (p = 0.007). Magnesium is also significantly inversely correlated with MN (p < 0.0001) and NPB (p < 0.0001). Individuals with low magnesium and high Hcy exhibited significantly higher frequency of MN and NPBs compared to those with high magnesium and low Hcy (p < 0.0001). Furthermore, there was an interactive effect between these two factors as well in inducing MN (p = 0.01) and NPB (p = 0.048).

CONCLUSIONS

The results obtained in the present study indicate for the first time that low in vivo levels of magnesium either on its own or in the presence of high Hcy increases DNA damage as evident by higher frequencies of MN and NPBs.

Carbapenem-resistant Acinetobacter baumannii (CRAB): metabolic adaptation and transcriptional response to human urine (HU)

Carbapenem-resistant Acinetobacter baumannii (CRAB) is a major human pathogen and a research priority for developing new antimicrobial agents. CRAB is a causative agent of a variety of infections in different body sites. One of the manifestations is catheter-associated urinary tract infection, which exposes the bacteria to the host's urine, creating a particular environment. Exposure of two CRAB clinical isolates, AB5075 and AMA40, to human urine (HU) resulted in the differential expression levels of 264 and 455 genes, respectively, of which 112 were common to both strains. Genes within this group play roles in metabolic pathways such as phenylacetic acid (PAA) catabolism, the Hut system, the tricarboxylic acid (TCA) cycle, and other processes like quorum sensing and biofilm formation. These results indicate that the presence of HU induces numerous adaptive changes in gene expression of the infecting bacteria. These modifications presumably help bacteria establish and thrive in the hostile conditions in the urinary tract. These analyses advance our understanding of CRAB's metabolic adaptations to human fluids, as well as expanding knowledge on bacterial responses to distinct human fluids containing different concentrations of human serum albumin (HSA).

Magnesium and the Hallmarks of Aging

Magnesium is an essential ion in the human body that regulates numerous physiological and pathological processes. Magnesium deficiency is very common in old age. Age-related chronic diseases and the aging process itself are frequently associated with low-grade chronic inflammation, called 'inflammaging'. Because chronic magnesium insufficiency has been linked to excessive generation of inflammatory markers and free radicals, inducing a chronic inflammatory state, we formerly hypothesized that magnesium inadequacy may be considered among the intermediaries helping us explain the link between inflammaging and aging-associated diseases. We show in this review evidence of the relationship of magnesium with all the hallmarks of aging (genomic instability, telomere attrition, epigenetic alterations, loss of proteostasis, deregulated nutrient sensing, mitochondrial dysfunction, cellular senescence, stem cell exhaustion, altered intercellular communication, disabled autophagy, dysbiosis, and chronic inflammation), which may positively affect the human healthspan. It is feasible to hypothesize that maintaining an optimal balance of magnesium during one's life course may turn out to be a safe and economical strategy contributing to the promotion of healthy aging. Future well-designed studies are necessary to further explore this hypothesis.

16S rRNA metagenomic dataset on endophytic bacterial community of the cashew plant (Anacardium occidentale L.) grown in Dak Lak Province of Vietnam

Vietnam is currently one of the largest producers and exporters of cashew nuts in the world. Cashew (Anacardium occidentale L.) is one of the main industrial crops cultivated in Dak Lak Province of Vietnam. Comprehending the endophytic bacteria of this plant, a new biofertilizer for sustainable cashew nut production can be progressed. In this report, the cashew root sample was collected from cashew fields in 2021 in Dak Lak. The DNeasy Powersoil kit was used to extract the genomic DNA of endophytic bacteria from the root sample. The 16S rRNA genes (V1-V9 regions) were amplified by PCR, and libraries of amplicons were prepared using the Swift amplicon 16S plus ITS panel kit. The Illumina MiSeq platform was applied to sequence amplicon libraries using 16S rRNA metagenomics. Taxonomic analyses showed that Gammaproteobacteria (38.77 %) and Alphaproteobacteria (37.76 %) were the predominant classes among the endophytic bacteria. Functional analyses revealed that biosynthesis (72.78 %) was the primary function of the endophytic bacterial community. Raw sequences (Fastq files) have been deposited in Mendeley Data [1]. The obtained data provide insight into the endophytic bacterial community of cashews cultivated in Dak Lak Province of Vietnam. The data are valuable for further developing a new biofertilizer for cashew nut production using endophytic bacteria. Ours is the first report about endophytic bacterial communities of cashews cultivated in this province as well as the Central Highlands of Vietnam.

Changes in the urinary proteome of rats after short-term intake of magnesium L-threonate(MgT)

Introduction

Magnesium (Mg) is an important mineral in living organisms. Magnesium has multiple functions in the human body, wherein it plays an important therapeutic and preventive role in a variety of diseases.

Methods

Urine samples of rats before and after gavage of magnesium L-threonate (MgT) were collected, and the urinary proteome was identified using the LC-MS/MS technique and analyzed using various databases.

Results and discussion

The results illustrated that the urinary proteome of rats was significantly altered after short-term intake of magnesium supplements and that the differential proteins and the biological functions were related to magnesium. This study innovatively establishes a method to study nutrients from the perspective of urine proteomics. This work demonstrates that the urinary proteome is capable of reflecting the effects of nutrient intake on the organism in a more systematic and comprehensive manner and has the potential to provide clues for clinical nutrition research and practice.

A new metabolic model of Drosophila melanogaster and the integrative analysis of Parkinson's disease

High conservation of the disease-associated genes between flies and humans facilitates the common use of Drosophila melanogaster to study metabolic disorders under controlled laboratory conditions. However, metabolic modeling studies are highly limited for this organism. We here report a comprehensively curated genome-scale metabolic network model of Drosophila using an orthology-based approach. The gene coverage and metabolic information of the draft model derived from a reference human model were expanded via Drosophila-specific KEGG and MetaCyc databases, with several curation steps to avoid metabolic redundancy and stoichiometric inconsistency. Furthermore, we performed literature-based curations to improve gene-reaction associations, subcellular metabolite locations, and various metabolic pathways. The performance of the resulting Drosophila model (8,230 reactions, 6,990 metabolites, and 2,388 genes), iDrosophila1 (https://github.com/SysBioGTU/iDrosophila), was assessed using flux balance analysis in comparison with the other currently available fly models leading to superior or comparable results. We also evaluated the transcriptome-based prediction capacity of iDrosophila1, where differential metabolic pathways during Parkinson's disease could be successfully elucidated. Overall, iDrosophila1 is promising to investigate system-level metabolic alterations in response to genetic and environmental perturbations.

DksA is a conserved master regulator of stress response in Acinetobacter baumannii

Coordination of bacterial stress response mechanisms is critical for long-term survival in harsh environments for successful host infection. The general and specific stress responses of well-studied Gram-negative pathogens like Escherichia coli are controlled by alternative sigma factors, archetypically RpoS. The deadly hospital pathogen Acinetobacter baumannii is notoriously resistant to environmental stresses, yet it lacks RpoS, and the molecular mechanisms driving this incredible stress tolerance remain poorly defined. Here, using functional genomics, we identified the transcriptional regulator DksA as a master regulator for broad stress protection and virulence in A. baumannii. Transcriptomics, phenomics and in vivo animal studies revealed that DksA controls ribosomal protein expression, metabolism, mutation rates, desiccation, antibiotic resistance, and host colonization in a niche-specific manner. Phylogenetically, DksA was highly conserved and well-distributed across Gammaproteobacteria, with 96.6% containing DksA, spanning 88 families. This study lays the groundwork for understanding DksA as a major regulator of general stress response and virulence in this important pathogen.

Intestinal Epithelial Inactivity of Dual Oxidase 2 Results in Microbiome-Mediated Metabolic Syndrome

BACKGROUND & AIMS

Metabolic syndrome (MetS) is characterized by obesity, glucose intolerance, and hepatic steatosis. Alterations in the gut microbiome play important roles in the development of MetS. However, the mechanisms by which this occurs are poorly understood. Dual oxidase 2 (DUOX2) is an antimicrobial reduced nicotinamide adenine dinucleotide phosphate oxidase expressed in the gut epithelium. Here, we posit that epithelial DUOX2 activity provides a mechanistic link between the gut microbiome and the development of MetS.

METHODS

Mice carrying an intestinal epithelial-specific deletion of dual oxidase maturation factor 1/2 (DA IEC-KO), and wild-type littermates were fed a standard diet and killed at 24 weeks. Metabolic alterations were determined by glucose tolerance, lipid tests, and body and organ weight measurements. DUOX2 activity was determined by Amplex Red. Intestinal permeability was determined by fluorescein isothiocyanate-dextran, microbial translocation assessments, and portal vein lipopolysaccharide measurements. Metagenomic analysis of the stool microbiome was performed. The role of the microbiome was assessed in antibiotic-treated mice.

RESULTS

DA IEC-KO males showed increased body and organ weights accompanied by glucose intolerance and increased plasma lipid and liver enzyme levels, and increased adiposity in the liver and adipose tissue. Expression of F4/80, CD68, uncoupling protein 1, carbohydrate response element binding protein, leptin, and adiponectin was altered in the liver and adipose tissue of DA IEC-KO males. DA IEC-KO males produced less epithelial H2O2, had altered relative abundance of Akkermansiaceae and Lachnospiraceae in stool, and showed increased portal vein lipopolysaccharides and intestinal permeability. Females were protected from barrier defects and MetS, despite producing less H2O2. Antibiotic depletion abrogated all MetS phenotypes observed.

CONCLUSIONS

Intestinal epithelial inactivity of DUOX2 promotes MetS in a microbiome-dependent manner.

Artificial Intelligence: A Promising Tool in Exploring the Phytomicrobiome in Managing Disease and Promoting Plant Health

There is increasing interest in harnessing the microbiome to improve cropping systems. With the availability of high-throughput and low-cost sequencing technologies, gathering microbiome data is becoming more routine. However, the analysis of microbiome data is challenged by the size and complexity of the data, and the incomplete nature of many microbiome databases. Further, to bring microbiome data value, it often needs to be analyzed in conjunction with other complex data that impact on crop health and disease management, such as plant genotype and environmental factors. Artificial intelligence (AI), boosted through deep learning (DL), has achieved significant breakthroughs and is a powerful tool for managing large complex datasets such as the interplay between the microbiome, crop plants, and their environment. In this review, we aim to provide readers with a brief introduction to AI techniques, and we introduce how AI has been applied to areas of microbiome sequencing taxonomy, the functional annotation for microbiome sequences, associating the microbiome community with host traits, designing synthetic communities, genomic selection, field phenotyping, and disease forecasting. At the end of this review, we proposed further efforts that are required to fully exploit the power of AI in studying phytomicrobiomes.

The Urinary Excretion of Magnesium as an Effective Magnesium Deficiency State Indicator: A Controlled Intervention Trial

With the western influence in our diets, food consumption has changed, and our magnesium (Mg) intake is no longer optimal. Serum Mg (S-Mg) level is currently used as an indicator of Mg deficiency and is strictly regulated via compensatory mechanisms. It is believed that a 24-h urine collection can be used to evaluate potential Mg deficiency. This study aimed to assess whether Mg deficiency state as found in urine Mg (U-Mg) excretion and improving such deficiency with a diet that meets the Recommended Dietary Allowances (RDAs) of Mg for 15 d. Healthy Japanese women were recruited for Study 1 (n=22) and Study 2 (n=10). Study 1 was 1-d balance test, where fasting blood and 24-h urine samples were collected. Study 2 was 15-d diet load test, where fasting blood (days 1, 7, and 15) and 24-h urine (odd days) were collected. All test meals were made certain to have met the RDA for Mg for women in their 20s. In Studies 1 and 2, S-Mg was within the normal range. In Study 1, U-Mg excretion was 67.7±17.0 mg/d, with a large dispersion. In Study 2, U-Mg excretion on days 7 and 15 was significantly higher than on day 1, but have no significant differences in U-Mg excretion between days 7-15. U-Mg excretion can be a valuable indicator to evaluate Mg state. In young women, improvements in Mg deficient state were observed after 7-15 d of taking meals that met the RDAs of Mg.

Comparison of Clinical and Laboratory Findings at Different Clinical Stages in Cats Naturally Infected with Feline Coronavirus

Feline coronavirus (FCoV) infections occur commonly in cats, with entrocyte and monocyte-macrophage tropism. Most FCoV-infected cats remain asymp tomatic, but up to 10% develop fatal feline infectious peritonitis (FIP). This study aims to investigate the diagnostic utility of clinical and laboratory examinations including serum and effusion AGP levels in cats either with symptomatic effusive FIP or asymptomatic feline enteric coronavirus (FECV). The study included 40 cats with effusive FIP and 10 cats with FECV infection. The FIP group was divided into two subgroups: abdominal (AE; n=30) and thoracic effusion (TE; n=10). Clinical and laboratory examinations, including serum or effusion AGP measurement, were performed. Among all the groups, TE group had higher body temperature, heart and respiratory rates (P<0.000). Compared with the FECV group, the FIP group had lower pH and HCO3 levels and higher base excess and lactate levels (P<0.05). The leukocyte and lymphocyte counts were higher and the hematocrit was lower in the AE group among all the groups (P<0.023). MCV was lower in the FIP group compared to the FECV group (P<0.002). In the AE group, total protein level was the lowest and the AST, GGT, total bilirubin and cholesterol levels were the highest (P<0.032) among all the groups. Magnesium level was lower in the FIP group compared to the FECV group (P<0.044). Although the serum AGP level was highest in the TE group among all groups (P<0.004), the AGP levels of cats with FECV were similar to the AE group (P>0.05). Since FECV-positive cats will likely develop FIP, differences in clinical and laboratory findings in FECV-positive cats were identified. Among them, pH, HCO3, base excess, lactate, MCV and magnesium were found to be important in the course of the disease, and AGP in the evaluation of the presence of an inflammatory state. It was concluded that clinical, laboratory and serum AGP evaluation could be used in the index of suspicion of development of FIP and FECV.

Ex vivo metabolomics-A hypothesis-free approach to identify native substrate(s) and product(s) of orphan enzymes

Over the past decade, the number of fully sequenced genomes has increased at an awe-inspiring pace. Similarly, the quality and scope of tools for the prediction of both protein structure and function has seen vast improvements. However, to pinpoint the exact function of a protein, for instance the exact reaction catalyzed by an enzyme, experimental evidence is crucial. At the same time, this step is the main bottleneck when generating a conclusive model for the function of an enzyme and to interpret its function in a physiological context. Hence, a comprehensive experimental strategy for functional annotation of enzymes that is as efficient as possible is required. Ex vivo metabolomics is a powerful non-targeted approach that overcomes several of the challenges inherent to in vitro characterization of enzymes with unknown functions. By incubating the recombinant enzyme of interest in a quasi-native metabolite extract from its tissue of origin under specific environmental and developmental conditions, the complete native substrate range can be tested in a single assay. This unlocks compounds that are commercially unavailable or otherwise difficult to procure. Coupled with non-targeted metabolomics analysis, ex vivo has the capability to test for and identify even unexpected substrates and assign the respective products of the enzymatic reaction.

Magnesium Homeostasis: Lessons from Human Genetics

Mg2+, the fourth most abundant cation in the body, serves as a co-factor for about 600 cellular enzymes. One third of ingested Mg2+ is absorbed from the gut through a saturable transcellular process and a concentration-dependent paracellular process. Absorbed Mg2+ is excreted by the kidney and maintains serum Mg2+ within a narrow range of 0.7 to 1.25 mmol/L. The reabsorption of Mg2+ by the nephron is characterized by paracellular transport in the proximal tubule and thick ascending limb. The nature of the transport pathways in the gut epithelia and thick ascending limb has emerged from an understanding of the molecular mechanisms responsible for rare monogenetic disorders presenting with clinical hypomagnesemia. These human disorders due to loss-of function mutations, in concert with mouse models have led to a deeper understanding of Mg2+ transport in the gut and renal tubule. This review focuses on the nature of the transporters and channels revealed by human and mouse genetics and how they are integrated into an understanding of human Mg2+ physiology.

Ionized Magnesium: Interpretation and Interest in Atrial Fibrillation

BACKGROUND

Magnesium (Mg) is often used to manage de novo atrial fibrillation (AF) in the emergency department (ED) and intensive care unit (ICU). Point of care measurement of ionized magnesium (iMg) allows a rapid identification of patients with impaired magnesium status, however, unlike ionized calcium, the interpretation of iMg is not entirely understood. Thus, we evaluated iMg reference values, correlation between iMg and plasmatic magnesium (pMg), and the impact of pH and albumin variations on iMg levels. Secondary objectives were to assess the incidence of hypomagnesemia in de novo AF.

METHODS

A total of 236 emergency department and intensive care unit patients with de novo AF, and 198 control patients were included. Reference values were determined in the control population. Correlation and concordance between iMg and pMg were studied using calcium (ionized and plasmatic) as a control in the whole study population. The impact of albumin and pH was assessed in the discordant iMg and pMg values. Lastly, we assessed the incidence of ionized hypomagnesemia (hypoMg) among de novo AF.

RESULTS

The reference range values established in our study for iMg were: 0.48-0.65 mmol/L (the manufacturers were: 0.45-0.60 mmol/L). A strong correlation was observed between pMg and iMg (r = 0.85), but, unlike for calcium values, there was no significant impact of pH and albumin in iMg/pMg interpretation. The incidence of hypoMg among de novo AF patients was 8.5% (12.7% using our ranges). When using our ranges, we found a significant link (p = 0.01) between hyopMg and hypokalemia.

CONCLUSION

We highlight the need for more accurate reference range values of iMg. Furthermore, our results suggest that blood Mg content is not identical to that of calcium. The incidence of ionized hypomagnesemia among de novo AF patients in our study is 8.5%.

Complete genome sequence analysis of a plant growth-promoting phylloplane Bacillus altitudinis FD48 offers mechanistic insights into priming drought stress tolerance in rice

Bacillus altitudinis FD48 is a multifunctional plant growth-promoting bacterium isolated from the phylloplane of rice and survives at --10 bars of osmotic potential (--1.0 MPa). It also serves as an ideal PGPM against drought stress by triggering antioxidant defense mechanisms in rice. To further unravel the genetic determinants of osmotic stress tolerance and plant growth-promoting traits, the whole genome sequence of FD48 was compared with its related strains. The whole genome analysis revealed a single chromosome with a total length of 3,752,533 bp (3.7 Mb) and an average G + C ratio of 41.19%. A total of 4029 genes were predicted, of which 3964 (98.4%) were protein-encoding genes (PEGs) and 65 (1.6%) were non-protein-coding genes. The interaction of FD48 with the host plants is associated with many chemotactic and motility-related genes. The ability of FD48 to colonize plants and maintain plant growth under adverse environmental conditions was evidenced by the presence of genes for plant nutrient acquisition, phytohormone synthesis, trehalose, choline, and glycine betaine biosynthesis, microbial volatile organic compounds (acetoin synthesis), heat and cold shock chaperones, translation elongation factor TU (Ef-Tu), siderophore production, DEAD/DEAH boxes, and non- ribosomal peptide synthase clusters (bacilysin, fengycin, and bacitracin). This study sheds light on the drought stress-resilient mechanism, metabolic pathways and potential activity, and plant growth-promoting traits of B. altitudinis FD48 at the genetic level.

Genome-Scale Modeling and Systems Metabolic Engineering of Vibrio natriegens for the Production of 1,3-Propanediol

The fastest-growing bacterium Vibrio natriegens is a highly promising next-generation workhorse for molecular biology and industrial biotechnology. In this work, we described the workflows for developing genome-scale metabolic models and genome-editing protocols for engineering Vibrio natriegens. A case study for metabolic engineering of Vibrio natriegens for the production of 1,3-propanediol was also presented.

Kinetic characterization of a new phenol degrading Acinetobacter towneri strain isolated from landfill leachate treating bioreactor

The objective of this study was to establish and to mathematically describe the phenol degrading properties of a new Acinetobacter towneri CFII-87 strain, isolated from a bioreactor treating landfill leachate. For this purpose, the biokinetic parameters of phenol biodegradation at various initial phenol concentrations of the A. towneri CFII-87 strain have been experimentally measured, and four different mathematical inhibition models (Haldane, Yano, Aiba and Edwards models) have been used to simulate the substrate-inhibited phenol degradation process. The results of the batch biodegradation experiments show that the new A. towneri CFII-87 strain grows on and metabolizes phenol up to 1000 mg/L concentration, manifests significant substrate inhibition and lag time only at concentrations above 800 mg/L phenol, and has a maximum growth rate at 300 mg/L initial phenol concentration. The comparison of the model predictions with the experimental phenol and biomass data revealed that the Haldane, Aiba and Edwards models can be used with success to describe the phenol biodegradation process by A. towneri CFII-87, while the Yano model, especially at higher initial phenol concentrations, fails to describe the process. The best performing inhibition model was the Edwards model, presenting correlation coefficients of R² > 0.98 and modelling efficiency of ME > 0.94 for the prediction of biomass and phenol concentrations on the validation datasets. The calculated biokinetic model parameters place this new strain among the bacteria with the highest tolerance towards phenol. The results suggest that the A. towneri CFII-87 strain can potentially be used in the treatment of phenolic wastewaters.

Pathway expression analysis

This paper introduces a pathway expression framework as an approach for constructing derived biomarkers. The pathway expression framework incorporates the biological connections of genes leading to a biologically relevant model. Using this framework, we distinguish between shedding subjects post-infection and all subjects pre-infection in human blood transcriptomic samples challenged with various respiratory viruses: H1N1, H3N2, HRV (Human Rhinoviruses), and RSV (Respiratory Syncytial Virus). Additionally, pathway expression data is used for selecting discriminatory pathways from these experiments. The classification results and selected pathways are benchmarked against standard gene expression based classification and pathway ranking methodologies. We find that using the pathway expression data along with selected pathways, which have minimal overlap with high ranking pathways found by traditional methods, improves classification rates across experiments.

New globally distributed bacterial phyla within the FCB superphylum

Microbes in marine sediments play crucial roles in global carbon and nutrient cycling. However, our understanding of microbial diversity and physiology on the ocean floor is limited. Here, we use phylogenomic analyses of thousands of metagenome-assembled genomes (MAGs) from coastal and deep-sea sediments to identify 55 MAGs that are phylogenetically distinct from previously described bacterial phyla. We propose that these MAGs belong to 4 novel bacterial phyla (Blakebacterota, Orphanbacterota, Arandabacterota, and Joyebacterota) and a previously proposed phylum (AABM5-125-24), all of them within the FCB superphylum. Comparison of their rRNA genes with public databases reveals that these phyla are globally distributed in different habitats, including marine, freshwater, and terrestrial environments. Genomic analyses suggest these organisms are capable of mediating key steps in sedimentary biogeochemistry, including anaerobic degradation of polysaccharides and proteins, and respiration of sulfur and nitrogen. Interestingly, these genomes code for an unusually high proportion (~9% on average, up to 20% per genome) of protein families lacking representatives in public databases. Genes encoding hundreds of these protein families colocalize with genes predicted to be involved in sulfur reduction, nitrogen cycling, energy conservation, and degradation of organic compounds. Our findings advance our understanding of bacterial diversity, the ecological roles of these bacteria, and potential links between novel gene families and metabolic processes in the oceans.

Revealing the Phenotypic and Genomic Background for PHA Production from Rapeseed-Biodiesel Crude Glycerol Using Photobacterium ganghwense C2.2

Polyhydroxyalkanoates (PHA) are promising biodegradable and biocompatible bioplastics, and extensive knowledge of the employed bacterial strain's metabolic capabilities is necessary in choosing economically feasible production conditions. This study aimed to create an in-depth view of the utilization of Photobacterium ganghwense C2.2 for PHA production by linking a wide array of characterization methods: metabolic pathway annotation from the strain's complete genome, high-throughput phenotypic tests, and biomass analyses through plate-based assays and flask and bioreactor cultivations. We confirmed, in PHA production conditions, urea catabolization, fatty acid degradation and synthesis, and high pH variation and osmotic stress tolerance. With urea as a nitrogen source, pure and rapeseed-biodiesel crude glycerol were analyzed comparatively as carbon sources for fermentation at 20 °C. Flask cultivations yielded 2.2 g/L and 2 g/L PHA at 120 h, respectively, with molecular weights of 428,629 g/mol and 81,515 g/mol. Bioreactor batch cultivation doubled biomass accumulation (10 g/L and 13.2 g/L) in 48 h, with a PHA productivity of 0.133 g/(L·h) and 0.05 g/(L·h). Thus, phenotypic and genomic analyses determined the successful use of Photobacterium ganghwense C2.2 for PHA production using urea and crude glycerol and 20 g/L NaCl, without pH adjustment, providing the basis for a viable fermentation process.

Wild Panax plants adapt to their thermal environment by harboring abundant beneficial seed endophytic bacteria

The seed microbiome of crop wild relatives is a potential reservoir of beneficial traits that potentially improve their host plant resilience to fluctuating environments and pathogenic threats. Herein, we studied the seed microbiome of three species of the medicinal genus Panax (P. vietnamensis, P. japonicas, and P. stipuleanatus) collected from seven locations in Southwest China. We used qPCR and metabarcoding high-throughput sequencing to target both endophytic bacteria and fungi. Seed bacterial absolute abundance (1.1 × 109∼1.0 × 107 gene copy numbers per gram seed) was substantially higher than that of fungi (7.6 × 105∼3.7 × 102). Host plant genotype was the main driver of seed microbiome composition for both bacteria and fungi. Panax growing hypothermal environments significantly shaped their seed endophytic bacterial but not fungal microbiota. The three Panax species’ seeds harbored unique microbes [averaged ∼150 amplicon sequence variants (ASVs)], sharing only 12 bacterial ASVs (half affiliated to Halomonas) and four fungal ASVs. Network analysis showed that the Panax seed endophytic bacteria tend to form inter-weaved functional modules that are majorly connected by core members from the genus Halomonas, Pseudomonas, and Pantoea. These genera have been associated with nutrient cycling, plant, disease suppression, and tolerance to environmental fluctuation. Together, these novel insights may shade light on the ecological strategies of wild Panax plants adaptation to their thermal environment by possessing abundant beneficial seed endophytic bacteria.

Nuclear Magnetic Resonance-Measured Ionized Magnesium Is Inversely Associated with Type 2 Diabetes in the Insulin Resistance Atherosclerosis Study

The aims were to optimize a nuclear magnetic resonance (NMR)-based assay for quantifying ionized or free magnesium and investigate its association with type 2 diabetes (T2D). A high-throughput, ionized magnesium assay was optimized and evaluated. Plasma magnesium was quantified, and associations with T2D were ascertained in Insulin Resistance Atherosclerosis Study (IRAS) participants. Coefficients of variation for the ionized magnesium assay ranged from 0.7−1.5% for intra-assay and 4.2−4.7% for inter-assay precision. In IRAS (n = 1342), ionized magnesium was significantly lower in subjects with prediabetes and T2D than in normoglycemic subjects, and lower in participants with T2D than those with prediabetes (p < 0.0001). Cross-sectional regression analyses revealed that magnesium was associated with T2D at baseline in models adjusted for multiple clinical risk factors (p = 0.032). This association appeared to be modified by sex, in such a way that the associations were present in women (OR = 0.54 (95% CI 0.37−0.79), p = 0.0015) and not in men (OR = 0.98 (95% CI 0.71−1.35), p = 0.90). Longitudinal regression analyses revealed an inverse association between magnesium and future T2D in the total population (p = 0.035) that was attenuated by LP-IR (p = 0.22). No interactions were detected between magnesium and age, race, BMI, glucose, insulin, triglycerides, or LPIR for the prospective association with future T2D. However, a significant interaction between magnesium and sex was present, now with a trend for an association in men (OR = 0.75 (95% CI 0.55−1.02), p = 0.065 and absence of an association in women (OR = 1.01 (0.76−1.33), p = 0.97). Conclusions: lower ionized magnesium, as measured by an NMR-based assay optimized for accuracy and precision, was associated cross-sectionally with T2D at baseline and longitudinally with incident T2D in IRAS.

Alzheimer's disease as an autoimmune disorder of innate immunity endogenously modulated by tryptophan metabolites

Introduction

Alzheimer's disease (AD) is characterized by neurotoxic immuno-inflammation concomitant with cytotoxic oligomerization of amyloid beta (Aβ) and tau, culminating in concurrent, interdependent immunopathic and proteopathic pathogeneses.

Methods

We performed a comprehensive series of in silico, in vitro, and in vivo studies explicitly evaluating the atomistic-molecular mechanisms of cytokine-mediated and Aβ-mediated neurotoxicities in AD.  Next, 471 new chemical entities were designed and synthesized to probe the pathways identified by these molecular mechanism studies and to provide prototypic starting points in the development of small-molecule therapeutics for AD.

Results

In response to various stimuli (e.g., infection, trauma, ischemia, air pollution, depression), Aβ is released as an early responder immunopeptide triggering an innate immunity cascade in which Aβ exhibits both immunomodulatory and antimicrobial properties (whether bacteria are present, or not), resulting in a misdirected attack upon "self" neurons, arising from analogous electronegative surface topologies between neurons and bacteria, and rendering them similarly susceptible to membrane-penetrating attack by antimicrobial peptides (AMPs) such as Aβ. After this self-attack, the resulting necrotic (but not apoptotic) neuronal breakdown products diffuse to adjacent neurons eliciting further release of Aβ, leading to a chronic self-perpetuating autoimmune cycle.  AD thus emerges as a brain-centric autoimmune disorder of innate immunity. Based upon the hypothesis that autoimmune processes are susceptible to endogenous regulatory processes, a subsequent comprehensive screening program of 1137 small molecules normally present in human brain identified tryptophan metabolism as a regulator of brain innate immunity and a source of potential endogenous anti-AD molecules capable of chemical modification into multi-site therapeutic modulators targeting AD's complex immunopathic-proteopathic pathogenesis.

Discussion

 Conceptualizing AD as an autoimmune disease, identifying endogenous regulators of this autoimmunity, and designing small molecule drug-like analogues of these endogenous regulators represents a novel therapeutic approach for AD.

Genomics-Based Reconstruction and Predictive Profiling of Amino Acid Biosynthesis in the Human Gut Microbiome

The human gut microbiota (HGM) have an impact on host health and disease. Amino acids are building blocks of proteins and peptides, also serving as precursors of many essential metabolites including nucleotides, cofactors, etc. Many HGM community members are unable to synthesize some amino acids (auxotrophs), while other members possess complete biosynthetic pathways for these nutrients (prototrophs). Metabolite exchange between auxotrophs and prototrophs affects microbial community structure. Previous studies of amino acid biosynthetic phenotypes were limited to model species or narrow taxonomic groups of bacteria. We analyzed over 2800 genomes representing 823 cultured HGM species with the aim to reconstruct biosynthetic pathways for proteinogenic amino acids. The genome context analysis of incomplete pathway variants allowed us to identify new potential enzyme variants in amino acid biosynthetic pathways. We further classified the studied organisms with respect to their pathway variants and inferred their prototrophic vs. auxotrophic phenotypes. A cross-species comparison was applied to assess the extent of conservation of the assigned phenotypes at distinct taxonomic levels. The obtained reference collection of binary metabolic phenotypes was used for predictive metabolic profiling of HGM samples from several large metagenomic datasets. The established approach for metabolic phenotype profiling will be useful for prediction of overall metabolic properties, interactions, and responses of HGM microbiomes as a function of dietary variations, dysbiosis and other perturbations.

Magnesium Nutritional Status, Risk Factors, and the Associations with Glucose Parameters of Childbearing Women in the China Adult Chronic Disease and Nutrition Surveillance (2015)

Magnesium is an essential element and participates in many metabolic pathways. Inadequate magnesium levels may lead to various health problems such as type 2 diabetes (T2DM), hypertension, and cancer. But the role of Mg in childbearing women of China is still a relatively narrow researched field. We aimed to assess the Mg nutritional status, explore the risk factors of Mg deficiency, and the associations between Mg and glucose parameters among childbearing women in a nationally representative sample. A total of 1895 18–44 years childbearing women were recruited from the China Adult Chronic Disease and Nutrition Surveillance (2015). Multivariate logistic regression was used to explore the risk factors for Mg deficiency and estimate the odds ratios (ORs) and 95% confidence intervals (95% CIs) for the risk of hyperglycemia. The mean value of Mg was 0.87 mmol/L and the prevalence of deficiency was 4.69%. The risk factors of Mg deficiency (Mg < 0.75 mmol/L) was city-type of rural (p = 0.045), while calcium (p = 0.001), LDL-C (p = 0.024), age group of 26–35 years (p = 0.016), 36–44 years (p = 0.006), and CNNM2 rs3740393 genotypes of GC (p = 0.027) were protective factors. It was also found that magnesium deficiency induces an increase in plasma glucose (p = 0.001). Compared with the reference range, Mg < 0.75 mmol/L would have a 6.53 fold risk for T2DM, a 5.31 fold risk for glucose-hyperglycemia, and a 9.60 fold risk for HbA1c-hyperglycemia. Consistently, there was a negative association between plasma Mg and blood glucose parameters in the dose–response study. More attention should be paid to the nutritional status of magnesium and the impact of magnesium deficiency on human health.

Genome-Scale Metabolic Modelling Approach to Understand the Metabolism of the Opportunistic Human Pathogen Staphylococcus epidermidis RP62A

Staphylococcus epidermidis is a common commensal of collagen-rich regions of the body, such as the skin, but also represents a threat to patients with medical implants (joints and heart), and to preterm babies. Far less studied than Staphylococcus aureus, the mechanisms behind this increasingly recognised pathogenicity are yet to be fully understood. Improving our knowledge of the metabolic processes that allow S. epidermidis to colonise different body sites is key to defining its pathogenic potential. Thus, we have constructed a fully curated, genome-scale metabolic model for S. epidermidis RP62A, and investigated its metabolic properties with a focus on substrate auxotrophies and its utilisation for energy and biomass production. Our results show that, although glucose is available in the medium, only a small portion of it enters the glycolytic pathways, whils most is utilised for the production of biofilm, storage and the structural components of biomass. Amino acids, proline, valine, alanine, glutamate and arginine, are preferred sources of energy and biomass production. In contrast to previous studies, we have shown that this strain has no real substrate auxotrophies, although removal of proline from the media has the highest impact on the model and the experimental growth characteristics. Further study is needed to determine the significance of proline, an abundant amino acid in collagen, in S. epidermidis colonisation.

Analogous Metabolic Decoupling in Pseudomonas putida and Comamonas testosteroni Implies Energetic Bypass to Facilitate Gluconeogenic Growth

Gluconeogenic carbon metabolism is not well understood, especially within the context of flux partitioning between energy generation and biomass production, despite the importance of gluconeogenic carbon substrates in natural and engineered carbon processing. Here, using multiple omics approaches, we elucidate the metabolic mechanisms that facilitate gluconeogenic fast-growth phenotypes in Pseudomonas putida and Comamonas testosteroni, two Proteobacteria species with distinct metabolic networks. In contrast to the genetic constraint of C. testosteroni, which lacks the enzymes required for both sugar uptake and a complete oxidative pentose phosphate (PP) pathway, sugar metabolism in P. putida is known to generate surplus NADPH by relying on the oxidative PP pathway within its characteristic cyclic connection between the Entner-Doudoroff (ED) and Embden-Meyerhoff-Parnas (EMP) pathways. Remarkably, similar to the genome-based metabolic decoupling in C. testosteroni, our 13C-fluxomics reveals an inactive oxidative PP pathway and disconnected EMP and ED pathways in P. putida during gluconeogenic feeding, thus requiring transhydrogenase reactions to supply NADPH for anabolism in both species by leveraging the high tricarboxylic acid cycle flux during gluconeogenic growth. Furthermore, metabolomics and proteomics analyses of both species during gluconeogenic feeding, relative to glycolytic feeding, demonstrate a 5-fold depletion in phosphorylated metabolites and the absence of or up to a 17-fold decrease in proteins of the PP and ED pathways. Such metabolic remodeling, which is reportedly lacking in Escherichia coli exhibiting a gluconeogenic slow-growth phenotype, may serve to minimize futile carbon cycling while favoring the gluconeogenic metabolic regime in relevant proteobacterial species. IMPORTANCE Glycolytic metabolism of sugars is extensively studied in the Proteobacteria, but gluconeogenic carbon sources (e.g., organic acids, amino acids, aromatics) that feed into the tricarboxylic acid (TCA) cycle are widely reported to produce a fast-growth phenotype, particularly in species with biotechnological relevance. Much remains unknown about the importance of glycolysis-associated pathways in the metabolism of gluconeogenic carbon substrates. Here, we demonstrate that two distinct proteobacterial species, through genetic constraints or metabolic regulation at specific metabolic nodes, bypass the oxidative PP pathway during gluconeogenic growth and avoid unnecessary carbon fluxes by depleting protein investment into connected glycolysis pathways. Both species can leverage instead the high TCA cycle flux during gluconeogenic feeding to meet NADPH demand. Importantly, lack of a complete oxidative pentose phosphate pathway is a widespread metabolic trait in Proteobacteria with a gluconeogenic carbon preference, thus highlighting the important relevance of our findings toward elucidating the metabolic architecture in these bacteria.

Paenarthrobacter sp. GOM3 Is a Novel Marine Species With Monoaromatic Degradation Relevance

Paenarthrobacter sp. GOM3, which is a strain that represents a new species-specific context within the genus Paenarthrobacter, is clearly a branched member independent of any group described thus far. This strain was recovered from marine sediments in the Gulf of Mexico, and despite being isolated from a consortium capable of growing with phenanthrene as a sole carbon source, this strain could not grow successfully in the presence of this substrate alone. We hypothesized that the GOM3 strain could participate in the assimilation of intermediate metabolites for the degradation of aromatic compounds. To date, there are no experimental reports of Paenarthrobacter species that degrade polycyclic aromatic hydrocarbons (PAHs) or their intermediate metabolites. In this work, we report genomic and experimental evidence of metabolic benzoate, gentisate, and protocatechuate degradation by Paenarthrobacter sp. GOM3. Gentisate was the preferred substrate with the highest volumetric consumption rate, and genomic analysis revealed that this strain possesses multiple gene copies for the specific transport of gentisate. Furthermore, upon analyzing the GOM3 genome, we found five different dioxygenases involved in the activation of aromatic compounds, suggesting its potential for complete remediation of PAH-contaminated sites in combination with strains capable of assimilating the upper PAH degradation pathway. Additionally, this strain was characterized experimentally for its pathogenic potential and in silico for its antimicrobial resistance. An overview of the potential ecological role of this strain in the context of other members of this taxonomic clade is also reported.

DTYMK Expression Predicts Prognosis and Chemotherapeutic Response and Correlates with Immune Infiltration in Hepatocellular Carcinoma

Introduction

Hepatocellular carcinoma (HCC) is the most common malignant tumor of the liver. Identifying specific molecular markers that can predict HCC prognosis is extremely important. The protein deoxythymidylate kinase (DTYMK) has been reported to contribute to unfavorable prognosis in non-small cell lung cancer patients, but its role in the prediction of HCC patient prognosis has not been clarified.

Methods

Samples from the TCGA and GEO databases were consecutively enrolled for gene expression analysis, clinicopathology analysis, immune microenvironment analysis and chemotherapeutic response prediction. The results were validated using 86 samples from the First Affiliated Hospital of Sun Yat-sen University. Cox regression analysis was used to analyze the effect of DTYMK on progression-free survival (PFS) and overall survival (OS). Functional enrichment analysis was used to describe the marker pathways that were significantly related to DTYMK. TIMER (Tumor Immune Estimation Resource), TISIDB (Tumor and Immune System Interaction DataBase) and CIBERSORT (Cell type Identification By Estimating Relative Subsets Of RNA Transcripts) were used to explore the immune microenvironment.

Results

We found that DTYMK expression upregulation is associated with poor prognosis in HCC patients and tightly related to the pathways regulating the cell cycle and acid metabolism. Our findings revealed that hepatocellular carcinoma cell lines with high DTYMK expression were more sensitive to sorafenib and many other chemotherapeutic drugs. We also found an inhibiting effect of DTYMK on the immune microenvironment in the process of tumorigenesis.

Discussion

We found that DTYMK has potential as a new prognostic and chemotherapeutic response biomarker for HCC patients and correlates with the immune microenvironment in HCC. However, there are some deficiencies in our study. First, this is a retrospective study that may lead to selection bias. Second, the protein expression of DTYMK was investigated via immunohistochemical analysis. Finally, we did not explore the exact underlying molecular mechanisms of DTYMK in tumorigenesis in this study, which is needed to be clarified in future research.

Virus-associated organosulfur metabolism in human and environmental systems

Viruses influence the fate of nutrients and human health by killing microorganisms and altering metabolic processes. Organosulfur metabolism and biologically derived hydrogen sulfide play dynamic roles in manifestation of diseases, infrastructure degradation, and essential biological processes. Although microbial organosulfur metabolism is well studied, the role of viruses in organosulfur metabolism is unknown. Here, we report the discovery of 39 gene families involved in organosulfur metabolism encoded by 3,749 viruses from diverse ecosystems, including human microbiomes. The viruses infect organisms from all three domains of life. Six gene families encode for enzymes that degrade organosulfur compounds into sulfide, whereas others manipulate organosulfur compounds and may influence sulfide production. We show that viral metabolic genes encode key enzymatic domains, are translated into protein, and are maintained after recombination, and sulfide provides a fitness advantage to viruses. Our results reveal viruses as drivers of organosulfur metabolism with important implications for human and environmental health.

METAnnotatorX2: a Comprehensive Tool for Deep and Shallow Metagenomic Data Set Analyses

The use of bioinformatic tools for read-based taxonomic and functional analyses of metagenomic data sets, including their assembly and management, is rather fragmentary due to the absence of an accepted gold standard. Moreover, most currently available software tools need input of millions of reads and rely on approximations in data analysis in order to reduce computing times. These issues result in suboptimal results in terms of accuracy, sensitivity, and specificity when used either for the reconstruction of taxonomic or functional profiles through read analysis or analysis of genomes reconstructed by metagenomic assembly. Moreover, the recent introduction of novel DNA sequencing technologies that generate long reads, such as Nanopore and PacBio, represent a valuable data resource that still suffers from a lack of dedicated tools to perform integrated hybrid analysis alongside short read data. In order to overcome these limitations, here we describe a comprehensive bioinformatic platform, METAnnotatorX2, aimed at providing an optimized user-friendly resource which maximizes output quality, while also allowing user-specific adaptation of the pipeline and straightforward integrated analysis of both short and long read data. To further improve performance quality and accuracy of taxonomic assignment of reads and contigs, custom preprocessed and taxonomically revised genomic databases for viruses, prokaryotes, and various eukaryotes were developed. The performance of METAnnotatorX2 was tested by analysis of artificial data sets encompassing viral, archaeal, bacterial, and eukaryotic (fungal) sequence reads that simulate different biological matrices. Moreover, real biological samples were employed to validate in silico results. IMPORTANCE We developed a novel tool, i.e., METAnnotatorX2, that includes a number of new advanced features for analysis of deep and shallow metagenomic data sets and is accompanied by (regularly updated) customized databases for archaea, bacteria, fungi, protists, and viruses. Both software and databases were developed so as to maximize sensitivity and specificity while including support for shallow metagenomic data sets. Through extensive tests performed on Illumina and Nanopore artificial data sets, we demonstrated the high performance of the software to not only extract taxonomic and functional information from sequence reads but also to assemble and process genomes from metagenomic data. The robustness of these functionalities was validated using "real-life" data sets obtained from Illumina and Nanopore sequencing of biological samples. Furthermore, the performance of METAnnotatorX2 was compared to other available software tools for analysis of shotgun metagenomics data.

Genome analysis to decipher syntrophy in the bacterial consortium 'SCP' for azo dye degradation

Background

A bacterial consortium SCP comprising three bacterial members, viz. Stenotrophomonas acidaminiphila APG1, Pseudomonas stutzeri APG2 and Cellulomonas sp. APG4 was developed for degradation of the mono-azo dye, Reactive Blue 28. The genomic analysis of each member of the SCP consortium was done to elucidate the catabolic potential and role of the individual organism in dye degradation.

Results

The genes for glycerol utilization were detected in the genomes of APG2 and APG4, which corroborated with their ability to grow on a minimal medium containing glycerol as the sole co-substrate. The genes for azoreductase were identified in the genomes of APG2 and APG4, while no such trait could be determined in APG1. In addition to co-substrate oxidation and dye reduction, several other cellular functions like chemotaxis, signal transduction, stress-tolerance, repair mechanisms, aromatic degradation, and copper tolerance associated with dye degradation were also annotated. A model for azo dye degradation is postulated, representing the predominant role of APG4 and APG2 in dye metabolism while suggesting an accessory role of APG1.

Conclusions

This exploratory study is the first-ever attempt to divulge the genetic basis of azo-dye co-metabolism by cross-genome comparisons and can be harnessed as an example for demonstrating microbial syntrophy.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12866-021-02236-9.

Integrated omics analyses reveal differential gene expression and potential for cooperation between denitrifying polyphosphate and glycogen accumulating organisms

Unusually high accumulation of the potent greenhouse gas nitrous oxide (N₂ O) has previously been documented in denitrifying biological phosphorus (P) removal bioprocesses, but the roles of differential denitrification gene expression patterns and ecological interactions between key functional groups in driving these emissions are not well understood. To address these knowledge gaps, we applied genome-resolved metagenomics and metatranscriptomics to a denitrifying bioprocess enriched in as-yet-uncultivated denitrifying polyphosphate accumulating organisms (PAOs) affiliated with Candidatus Accumulibacter. The six transcriptionally most active populations in the community included three co-occurring Accumulibacter strains affiliated with clades IF (a novel clade identified in this study), IA and IC, a competing glycogen accumulating organism (GAO) affiliated with Competibacteraceae (GAO1), a Gammaproteobacteria PR6 and an Anaerolineae CH7. Strongly elevated expression of nitrite reductase genes compared to nitrous oxide reductase genes was observed in the overall community and in Accumulibacter populations, suggesting a strong role for differential gene expression in driving N₂ O accumulation. Surprisingly, while ~90% of the nirS gene transcripts were expressed by the three co-occurring PAO populations, ~93% of the norB gene transcripts were expressed by GAO1 and ~75% of the norZ gene transcripts were mapped to PR6 and several other non-PAO flanking populations. This suggests the potential for cooperation between flanking populations and PAOs in reducing denitrification intermediates. Such cooperation may benefit the community by reducing the accumulation of toxic nitric oxide.

Tobacco Smoking and the Fecal Microbiome in a Large, Multi-ethnic Cohort

BACKGROUND

Increasing evidence suggests that tobacco smoking, a well-known driver of carcinogenesis, influences the gut microbiome; however, these relationships remain understudied in diverse populations. Thus, we performed an analysis of smoking and the gut microbiome in a subset of 803 adults from the multi-ethnic NYU FAMiLI study.

METHODS

We assessed fecal microbiota using 16S rRNA gene sequencing, and clustered samples into Amplicon Sequence Variants using QIIME2. We evaluated inferred microbial pathway abundance using PICRUSt. We compared population β-diversity, and relative taxonomic and functional pathway abundance, between never smokers, former smokers, and current smokers.

RESULTS

We found that the overall composition of the fecal microbiome in former and current smokers differs significantly from that of never smokers. The taxa Prevotella and Veillonellaceae were enriched in current and former smokers, whereas the taxa Lachnospira and Tenericutes were depleted, relative to never smokers. These shifts were consistent across racial and ethnic subgroups. Relative to never smokers, the abundance of taxa enriched in current smokers were positively correlated with the imputed abundance of pathways involving smoking-associated toxin breakdown and response to reactive oxygen species (ROS).

CONCLUSIONS

Our findings suggest common mechanisms of smoking associated microbial change across racial subgroups, regardless of initial microbiome composition. The correlation of these differentials with ROS exposure pathways may suggest a role for these taxa in the known association between smoking, ROS and carcinogenesis.

IMPACT

Smoking shifts in the microbiome may be independent of initial composition, stimulating further studies on the microbiome in carcinogenesis and cancer prevention.

Metabolic networks of the Nicotiana genus in the spotlight: content, progress and outlook

Manually curated metabolic databases residing at the Sol Genomics Network comprise two taxon-specific databases for the Solanaceae family, i.e. SolanaCyc and the genus Nicotiana, i.e. NicotianaCyc as well as six species-specific databases for Nicotiana tabacum TN90, N. tabacum K326, Nicotiana benthamiana, N. sylvestris, N. tomentosiformis and N. attenuata. New pathways were created through the extraction, examination and verification of related data from the literature and the aid of external database guided by an expert-led curation process. Here we describe the curation progress that has been achieved in these databases since the first release version 1.0 in 2016, the curation flow and the curation process using the example metabolic pathway for cholesterol in plants. The current content of our databases comprises 266 pathways and 36 superpathways in SolanaCyc and 143 pathways plus 21 superpathways in NicotianaCyc, manually curated and validated specifically for the Solanaceae family and Nicotiana genus, respectively. The curated data have been propagated to the respective Nicotiana-specific databases, which resulted in the enrichment and more accurate presentation of their metabolic networks. The quality and coverage in those databases have been compared with related external databases and discussed in terms of literature support and metabolic content.

Gut lactate-producing bacteria promote CD4 T cell recovery on Anti-retroviral therapy in HIV-infected patients

Anti-retroviral therapy (ART) effectively suppresses viral replication in HIV-infected patients, however CD4 + cell restoration to normal value is not achieved by 15–20% of patients who are called immune non-responders. Gut microbiota composition has been shown to influence host immunity. Herein, to identify intestinal microbial agents that may influence the CD4 recovery in HIV-infected patients, we utilized a “Quasi-paired cohort” method to analyze intestinal metagenome data from immunological responders (IRs) and immunological non-responders (INRs). This method identified significant enrichment for Streptococcus sp. and related lactate-producing bacteria (LAB) in IRs. In a validation cohort, positive correlations between the abundance of these LAB and the post-ART CD4 + recovery was observed, and a prediction model based on these LAB performed well in predicting immune recovery. Finally, experiments using a germ-free mouse model of antibody-induced CD4 + cell depletion showed that supplementation with a lactate-producing commensal Streptococcus thermophilus strongly promoted CD4 recovery. In conclusion, our study identified a group of LAB that was associated with enhanced immune recovery in post-ART HIV-infected patients and promotes CD4 + cell restoration in a mouse model. These findings favour supplementation of LAB commensal as a therapeutic strategy for CD4 + cell count improvement in HIV-infected patients.

Machine learning approach to gene essentiality prediction: a review

 

Essential genes are critical for the growth and survival of any organism. The machine learning approach complements the experimental methods to minimize the resources required for essentiality assays. Previous studies revealed the need to discover relevant features that significantly classify essential genes, improve on the generalizability of prediction models across organisms, and construct a robust gold standard as the class label for the train data to enhance prediction. Findings also show that a significant limitation of the machine learning approach is predicting conditionally essential genes. The essentiality status of a gene can change due to a specific condition of the organism. This review examines various methods applied to essential gene prediction task, their strengths, limitations and the factors responsible for effective computational prediction of essential genes. We discussed categories of features and how they contribute to the classification performance of essentiality prediction models. Five categories of features, namely, gene sequence, protein sequence, network topology, homology and gene ontology-based features, were generated for Caenorhabditis elegans to perform a comparative analysis of their essentiality prediction capacity. Gene ontology-based feature category outperformed other categories of features majorly due to its high correlation with the genes' biological functions. However, the topology feature category provided the highest discriminatory power making it more suitable for essentiality prediction. The major limiting factor of machine learning to predict essential genes conditionality is the unavailability of labeled data for interest conditions that can train a classifier. Therefore, cooperative machine learning could further exploit models that can perform well in conditional essentiality predictions.

SHORT ABSTRACT

Identification of essential genes is imperative because it provides an understanding of the core structure and function, accelerating drug targets' discovery, among other functions. Recent studies have applied machine learning to complement the experimental identification of essential genes. However, several factors are limiting the performance of machine learning approaches. This review aims to present the standard procedure and resources available for predicting essential genes in organisms, and also highlight the factors responsible for the current limitation in using machine learning for conditional gene essentiality prediction. The choice of features and ML technique was identified as an important factor to predict essential genes effectively.

Magnesium: Biochemistry, Nutrition, Detection, and Social Impact of Diseases Linked to Its Deficiency

Magnesium plays an important role in many physiological functions. Habitually low intakes of magnesium and in general the deficiency of this micronutrient induce changes in biochemical pathways that can increase the risk of illness and, in particular, chronic degenerative diseases. The assessment of magnesium status is consequently of great importance, however, its evaluation is difficult. The measurement of serum magnesium concentration is the most commonly used and readily available method for assessing magnesium status, even if serum levels have no reliable correlation with total body magnesium levels or concentrations in specific tissues. Therefore, this review offers an overview of recent insights into magnesium from multiple perspectives. Starting from a biochemical point of view, it aims at highlighting the risk due to insufficient uptake (frequently due to the low content of magnesium in the modern western diet), at suggesting strategies to reach the recommended dietary reference values, and at focusing on the importance of detecting physiological or pathological levels of magnesium in various body districts, in order to counteract the social impact of diseases linked to magnesium deficiency.

gapseq: informed prediction of bacterial metabolic pathways and reconstruction of accurate metabolic models

Genome-scale metabolic models of microorganisms are powerful frameworks to predict phenotypes from an organism's genotype. While manual reconstructions are laborious, automated reconstructions often fail to recapitulate known metabolic processes. Here we present gapseq ( https://github.com/jotech/gapseq ), a new tool to predict metabolic pathways and automatically reconstruct microbial metabolic models using a curated reaction database and a novel gap-filling algorithm. On the basis of scientific literature and experimental data for 14,931 bacterial phenotypes, we demonstrate that gapseq outperforms state-of-the-art tools in predicting enzyme activity, carbon source utilisation, fermentation products, and metabolic interactions within microbial communities.

Complete genome sequence of Photobacterium ganghwense C2.2: A new polyhydroxyalkanoate production candidate

Polyhydroxyalkanoates (PHAs) are biodegradable bioplastics that can be manufactured sustainably and represent a promising green alternative to petrochemical-based plastics. Here, we describe the complete genome of a new marine PHA-producing bacterium-Photobacterium ganghwense (strain C2.2), which we have isolated from the Black Sea seashore. This new isolate is psychrotolerant and accumulates PHA when glycerol is provided as the main carbon source. Transmission electron microscopy, specific staining with Nile Red visualized via epifluorescence microscopy and gas chromatography analysis confirmed the accumulation of PHA. This is the only PHA-producing Photobacterium for which we now have a complete genome sequence, allowing us to investigate the pathways for PHA production and other secondary metabolite synthesis pathways. The de novo assembly genome, obtained using open-source tools, comprises two chromosomes (3.5, 2 Mbp) and a megaplasmid (202 kbp). We identify the entire PHA synthesis gene cluster that encodes a class I PHA synthase, a phasin, a 3-ketothiolase, and an acetoacetyl-CoA reductase. No conventional PHA depolymerase was identified in strain C2.2, but a putative lipase with extracellular amorphous PHA depolymerase activity was annotated, suggesting that C2.2 is unable to degrade intracellular PHA. A complete pathway for the conversion of glycerol to acetyl-CoA was annotated, in accordance with its ability to convert glycerol to PHA. Several secondary metabolite biosynthetic gene clusters and a low number of genes involved in antibiotic resistance and virulence were also identified, indicating the strain's suitability for biotechnological applications.

Ecological insights into assembly processes and network structures of bacterial biofilms in full-scale biologically active carbon filters under ozone implementation

To address the adverse effects of harmful algal blooms, there are increased demands over the implementation of ozone coupled with biologically active carbon (BAC) filters in the drinking water treatment plants. Although the microbial biofilms are vital elements to support the proper performance of BAC filters, except for taxonomic affiliations, little is known about the assembly mechanisms of microbial communities in the full-scale BAC filters. This study aimed to examine how the assembly processes and their associated factors (e.g., influent characteristics, biological interactions) drive the temporal dynamics of bacterial communities in full-scale BAC filters, which underwent ozone implementation (five consecutive seasons from 2017 to 2018). The results revealed that along with the increase of bacterial taxonomic richness and evenness, stochastic processes became more crucial to determine the bacterial community assembly in the summer and autumn after ozone implementation (relative contribution: 61.23% and 83.75%, respectively). Moreover, their corresponding networks possessed simple network structures with lower modularity than other seasons, which implied lesser biological interactions among bacterial populations. The correlation between taxonomic and predicted functional diversities using functional redundancy index indicated that relatively high levels of bacterial functional redundancy (>0.83) were generally present in BAC filters. However, compared to other seasons, significantly higher degrees of functional redundancy existed in the summer and autumn after ozone implementation (0.85 ± 0.01 and 0.86 ± 0.01, respectively). Overall, this work improves our understanding of the microbial ecology of full-scale BAC filters by providing a conceptual framework that characterizes bacterial biofilm assembly processes relevant to performance optimization of full-scale BAC filters.

Magnesium in Infectious Diseases in Older People

Reduced magnesium (Mg) intake is a frequent cause of deficiency with age together with reduced absorption, renal wasting, and polypharmacotherapy. Chronic Mg deficiency may result in increased oxidative stress and low-grade inflammation, which may be linked to several age-related diseases, including higher predisposition to infectious diseases. Mg might play a role in the immune response being a cofactor for immunoglobulin synthesis and other processes strictly associated with the function of T and B cells. Mg is necessary for the biosynthesis, transport, and activation of vitamin D, another key factor in the pathogenesis of infectious diseases. The regulation of cytosolic free Mg in immune cells involves Mg transport systems, such as the melastatin-like transient receptor potential 7 channel, the solute carrier family, and the magnesium transporter 1 (MAGT1). The functional importance of Mg transport in immunity was unknown until the description of the primary immunodeficiency XMEN (X-linked immunodeficiency with Mg defect, Epstein–Barr virus infection, and neoplasia) due to a genetic deficiency of MAGT1 characterized by chronic Epstein–Barr virus infection. This and other research reporting associations of Mg deficit with viral and bacterial infections indicate a possible role of Mg deficit in the recent coronavirus disease 2019 (COVID-19) and its complications. In this review, we will discuss the importance of Mg for the immune system and for infectious diseases, including the recent pandemic of COVID-19.

Enhancement of polyhydroxyalkanoate production by co-feeding lignin derivatives with glycerol in Pseudomonas putida KT2440

BACKGROUND

Efficient utilization of all available carbons from lignocellulosic biomass is critical for economic efficiency of a bioconversion process to produce renewable bioproducts. However, the metabolic responses that enable Pseudomonas putida to utilize mixed carbon sources to generate reducing power and polyhydroxyalkanoate (PHA) remain unclear. Previous research has mainly focused on different fermentation strategies, including the sequential feeding of xylose as the growth stage substrate and octanoic acid as the PHA-producing substrate, feeding glycerol as the sole carbon substrate, and co-feeding of lignin and glucose. This study developed a new strategy-co-feeding glycerol and lignin derivatives such as benzoate, vanillin, and vanillic acid in Pseudomonas putida KT2440-for the first time, which simultaneously improved both cell biomass and PHA production.

RESULTS

Co-feeding lignin derivatives (i.e. benzoate, vanillin, and vanillic acid) and glycerol to P. putida KT2440 was shown for the first time to simultaneously increase cell dry weight (CDW) by 9.4-16.1% and PHA content by 29.0-63.2%, respectively, compared with feeding glycerol alone. GC-MS results revealed that the addition of lignin derivatives to glycerol decreased the distribution of long-chain monomers (C10 and C12) by 0.4-4.4% and increased the distribution of short-chain monomers (C6 and C8) by 0.8-3.5%. The ¹H-¹³C HMBC, ¹H-¹³C HSQC, and ¹H-¹H COSY NMR analysis confirmed that the PHA monomers (C6-C14) were produced when glycerol was fed to the bacteria alone or together with lignin derivatives. Moreover, investigation of the glycerol/benzoate/nitrogen ratios showed that benzoate acted as an independent factor in PHA synthesis. Furthermore, ¹H, ¹³C and ³¹P NMR metabolite analysis and mass spectrometry-based quantitative proteomics measurements suggested that the addition of benzoate stimulated oxidative-stress responses, enhanced glycerol consumption, and altered the intracellular NAD⁺/NADH and NADPH/NADP⁺ ratios by up-regulating the proteins involved in energy generation and storage processes, including the Entner-Doudoroff (ED) pathway, the reductive TCA route, trehalose degradation, fatty acid β-oxidation, and PHA biosynthesis.

CONCLUSIONS

This work demonstrated an effective co-carbon feeding strategy to improve PHA content/yield and convert lignin derivatives into value-added products in P. putida KT2440. Co-feeding lignin break-down products with other carbon sources, such as glycerol, has been demonstrated as an efficient way to utilize biomass to increase PHA production in P. putida KT2440. Moreover, the involvement of aromatic degradation favours further lignin utilization, and the combination of proteomics and metabolomics with NMR sheds light on the metabolic and regulatory mechanisms for cellular redox balance and potential genetic targets for a higher biomass carbon conversion efficiency.

Magnesium and Hypertension in Old Age

Hypertension is a complex condition in which various actors and mechanisms combine, resulting in cardiovascular and cerebrovascular complications that today represent the most frequent causes of mortality, morbidity, disability, and health expenses worldwide. In the last decades, there has been an exceptional amount of experimental, epidemiological, and clinical studies confirming a close relationship between magnesium deficit and high blood pressure. Multiple mechanisms may help to explain the bulk of evidence supporting a protective effect of magnesium against hypertension and its complications. Hypertension increases sharply with advancing age, hence older persons are those most affected by its negative consequences. They are also more frequently at risk of magnesium deficiency by multiple mechanisms, which may, at least in part, explain the higher frequency of hypertension and its long-term complications. The evidence for a favorable effect of magnesium on hypertension risk emphasizes the importance of broadly encouraging the intake of foods such as vegetables, nuts, whole cereals and legumes, optimal dietary sources of magnesium, avoiding processed food, which are very poor in magnesium and other fundamental nutrients, in order to prevent hypertension. In some cases, when diet is not enough to maintain an adequate magnesium status, magnesium supplementation may be of benefit and has been shown to be well tolerated.

A comparative UHPLC-Q/TOF-MS-based eco-metabolomics approach reveals temperature adaptation of four Nepenthes species

Nepenthes, as the largest family of carnivorous plants, is found with an extensive geographical distribution throughout the Malay Archipelago, specifically in Borneo, Philippines, and Sumatra. Highland species are able to tolerate cold stress and lowland species heat stress. Our current understanding on the adaptation or survival mechanisms acquired by the different Nepenthes species to their climatic conditions at the phytochemical level is, however, limited. In this study, we applied an eco-metabolomics approach to identify temperature stressed individual metabolic fingerprints of four Nepenthes species: the lowlanders N. ampullaria, N. rafflesiana and N. northiana, and the highlander N. minima. We hypothesized that distinct metabolite regulation patterns exist between the Nepenthes species due to their adaptation towards different geographical and altitudinal distribution. Our results revealed not only distinct temperature stress induced metabolite fingerprints for each Nepenthes species, but also shared metabolic response and adaptation strategies. The interspecific responses and adaptation of N. rafflesiana and N. northiana likely reflected their natural habitat niches. Moreover, our study also indicates the potential of lowlanders, especially N. ampullaria and N. rafflesiana, to produce metabolites needed to deal with increased temperatures, offering hope for the plant genus and future adaption in times of changing climate.

A Comparative Evaluation of Tools to Predict Metabolite Profiles From Microbiome Sequencing Data

Metabolomic analyses of human gut microbiome samples can unveil the metabolic potential of host tissues and the numerous microorganisms they support, concurrently. As such, metabolomic information bears immense potential to improve disease diagnosis and therapeutic drug discovery. Unfortunately, as cohort sizes increase, comprehensive metabolomic profiling becomes costly and logistically difficult to perform at a large scale. To address these difficulties, we tested the feasibility of predicting the metabolites of a microbial community based solely on microbiome sequencing data. Paired microbiome sequencing (16S rRNA gene amplicons, shotgun metagenomics, and metatranscriptomics) and metabolome (mass spectrometry and nuclear magnetic resonance spectroscopy) datasets were collected from six independent studies spanning multiple diseases. We used these datasets to evaluate two reference-based gene-to-metabolite prediction pipelines and a machine-learning (ML) based metabolic profile prediction approach. With the pre-trained model on over 900 microbiome-metabolome paired samples, the ML approach yielded the most accurate predictions (i.e., highest F1 scores) of metabolite occurrences in the human gut and outperformed reference-based pipelines in predicting differential metabolites between case and control subjects. Our findings demonstrate the possibility of predicting metabolites from microbiome sequencing data, while highlighting certain limitations in detecting differential metabolites, and provide a framework to evaluate metabolite prediction pipelines, which will ultimately facilitate future investigations on microbial metabolites and human health.

Comparative genomics of Clostridium species associated with vacuum-packed meat spoilage

Bacterial species belonging to the genus Clostridium have been recognized as causative agents of blown pack spoilage (BPS) in vacuum packed meat products. Whole-genome sequencing of six New Zealand psychrotolerant clostridia isolates derived from three meat production animal types and their environments was performed to examine their roles in BPS. Comparative genome analyses have provided insight into the genomic diversity and physiology of these bacteria and divides clostridia into two separate species clusters. BPS-associated clostridia encode a large and diverse spectrum of degradative carbohydrate-active enzymes (CAZymes) that enable them to utilize the intramuscular carbohydrate stores and facilitate sporulation. In total, 516 glycoside hydrolases (GHs), 93 carbohydrate esterases (CEs), 21 polysaccharide lyases (PLs), 434 glycosyl transferases (GTs) and 211 carbohydrate-binding protein modules (CBM) with predicted activities involved in the breakdown and transport of carbohydrates were identified. Clostridia genomes have different patterns of CAZyme families and vary greatly in the number of genes within each CAZy category, suggesting some level of functional redundancy. These results suggest that BPS-associated clostridia occupy similar environmental niches but apply different carbohydrate metabolism strategies to be able to co-exist and cause meat spoilage.