IMOVNN: incomplete multi-omics data integration variational neural networks for gut microbiome disease prediction and biomarker identification.

The gut microbiome has been regarded as one of the fundamental determinants regulating human health, and multi-omics data profiling has been increasingly utilized to bolster the deep understanding of this complex system. However, stemming from cost or other constraints, the integration of multi-omics often suffers from incomplete views, which poses a great challenge for the comprehensive analysis. In this work, a novel deep model named Incomplete Multi-Omics Variational Neural Networks (IMOVNN) is proposed for incomplete data integration, disease prediction application and biomarker identification. Benefiting from the information bottleneck and the marginal-to-joint distribution integration mechanism, the IMOVNN can learn the marginal latent representation of each individual omics and the joint latent representation for better disease prediction. Moreover, owing to the feature-selective layer predicated upon the concrete distribution, the model is interpretable and can identify the most relevant features. Experiments on inflammatory bowel disease multi-omics datasets demonstrate that our method outperforms several state-of-the-art methods for disease prediction. In addition, IMOVNN has identified significant biomarkers from multi-omics data sources.

Vacancy Suppression and Resonant Level Rendering Extraordinary Power Factor in Sn0.99In0.01Te/Tourmaline Composite.

SnTe, as a potential medium-temperature thermoelectric material, reaches a maximum power factor (PF) usually above 750 K, which is not conducive to continuous high-power output in practical applications. In this study, PF is maintained at high values between 18.5 and 25 µW cm-1 K-2 for Sn0.99In0.01Te-x wt% tourmaline samples within the temperature range of 323 to 873 K, driving the highest PFeng of 1.2 W m-1 K-1 and PFave of 22.5 µW cm-1 K-2, over 2.5 times that of pristine SnTe. Such an extraordinary PF is attributed to the synergy of resonant levels and Sn vacancy suppression. Specifically, the Seebeck coefficient increases dramatically, reaching 88 µV K-1 at room temperature. Meanwhile, by Sn vacancy suppression, carrier concentration, and mobility are optimized to ≈1019 cm-3 and 740 cm2 V-1 s-1, respectively. With the tourmaline compositing, Sn vacancies are further suppressed and the thermal conductivity simultaneously decreases, with the minimum lattice thermal conductivity of 0.9 W m-1 K-1. Finally, the zT value ≈0.8 is obtained in the Sn0.99In0.01Te sample. The peak of the power output density reaches 0.89 W cm-2 at a temperature difference of 600 K. Such SnTe alloys with high and "temperature-independent" PF will offer an option for realizing high output power in thermoelectric devices.

Recent Advances in Habenula Imaging Technology: A Comprehensive Review.

The habenula (Hb) is involved in many natural human behaviors, and the relevance of its alterations in size and neural activity to several psychiatric disorders and addictive behaviors has been presumed and investigated in recent years using magnetic resonance imaging (MRI). Although the Hb is small, an increasing number of studies have overcome the difficulties in MRI. Conventional structural-based imaging also has great defects in observing the Hb contrast with adjacent structures. In addition, more and more attention should be paid to the Hb's functional, structural, and quantitative imaging studies. Several advanced MRI methods have recently been employed in clinical studies to explore the Hb and its involvement in psychiatric diseases. This review summarizes the anatomy and function of the human Hb; moreover, it focuses on exploring the human Hb with noninvasive MRI approaches, highlighting strategies to overcome the poor contrast with adjacent structures and the need for multiparametric MRI to develop imaging markers for diagnosis and treatment follow-up. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 2.

The therapeutic potential of dietary intervention: based on the mechanism of a tryptophan derivative-indole propionic acid on metabolic disorders.

Tryptophan (TRP) contributes to individual immune homeostasis and good condition via three complex metabolism pathways (5-hydroxytryptamine (5-HT), kynurenine (KP), and gut microbiota pathway). Indole propionic acid (IPA), one of the TRP derivatives of the microbiota pathway, has raised more attention because of its impact on metabolic disorders. Here, we retrospect increasing evidence that TRP metabolites/IPA derived from its proteolysis impact host health and disease. IPA can activate the immune system through aryl hydrocarbon receptor (AHR) and/or Pregnane X receptor (PXR) as a vital mediator among diet-caused host and microbe cross-talk. Different levels of IPA in systemic circulation can predict the risk of NAFLD, T2DM, and CVD. IPA is suggested to alleviate cognitive impairment from oxidative damage, reduce gut inflammation, inhibit lipid accumulation and attenuate the symptoms of NAFLD, putatively enhance the intestinal epithelial barrier, and maintain intestinal homeostasis. Now, we provide a general description of the relationships between IPA and various physiological and pathological processes, which support an opportunity for diet intervention for metabolic diseases.

Collaborative Multiple Players to Address Label Sparsity in Quality Prediction of Batch Processes.

For decades, soft sensors have been extensively renowned for their efficiency in real-time tracking of expensive variables for advanced process control. However, despite the diverse efforts lavished on enhancing their models, the issue of label sparsity when modeling the soft sensors has always posed challenges across various processes. In this paper, a fledgling technique, called co-training, is studied for leveraging only a small ratio of labeled data, to hone and formulate a more advantageous framework in soft sensor modeling. Dissimilar to the conventional routine where only two players are employed, we investigate the efficient number of players in batch processes, making a multiple-player learning scheme to assuage the sparsity issue. Meanwhile, a sliding window spanning across both time and batch direction is used to aggregate the samples for prediction, and account for the unique 2D correlations among the general batch process data. Altogether, the forged framework can outperform the other prevalent methods, especially when the ratio of unlabeled data is climbing up, and two case studies are showcased to demonstrate its effectiveness.

MOF-Derived Mn2 O3 Nanocage with Oxygen Vacancies as Efficient Cathode Catalysts for Li-O2 Batteries.

Designing efficient and cost-effective electrocatalysts is the primary imperative for addressing the pivotal concerns confronting lithium-oxygen batteries (LOBs). The microstructure of the catalyst is one of the key factors that influence the catalytic performance. This study proceeds to the advantage of metal-organic frameworks (MOFs) derivatives by annealing manganese 1,2,3-triazolate (MET-2) at different temperatures to optimize Mn2 O3 crystals for special microstructures. It is found that at 350 °C annealing temperature, the derived Mn2 O3 nanocage maintains the structure of MOF, the inherited high porosity and large specific surface area provide more channels for Li+ and O2 diffusion, beside the oxygen vacancies on the surface of Mn2 O3 nanocages enhance the electrocatalytic activity. With the synergy of unique structure and rich oxygen vacancies, the Mn2 O3 nanocage exhibits ultrahigh discharge capacity (21 070.6 mAh g-1 at 500 mA g-1 ) and excellent cycling stability (180 cycles at the limited capacity of 600 mAh g-1 with a current of 500 mA g-1 ). This study demonstrates that the Mn2 O3 nanocage structure containing oxygen vacancies can significantly enhance catalytic performance for LOBs, which provide a simple method for structurally designed transition metal oxide electrocatalysts.

Bifidobacterium longum subsp. infantis as widespread bacteriocin gene clusters carrier stands out among the Bifidobacterium.

Bifidobacterium is the dominant genus, particularly in the intestinal tract niche of healthy breast-fed infants, and many of these strains have been proven to elicit positive effects on infant development. In addition to its effective antimicrobial activity against detrimental microorganisms, it helps to improve the intestinal microbiota balance. The isolation and identification of bacteriocins from Bifidobacterium have been limited since the mid-1980s, leading to an underestimation of its ability for bacteriocin production. Here, we employed a silicon-based search strategy to mine 354 putative bacteriocin gene clusters (BGCs), most of which have never been reported, from the genomes of 759 Bifidobacterium strains distributed across 9 species. Consistent with previous reports, most Bifidobacterium strains did not carry or carry only a single BGC; however, Bifidobacterium longum subsp. infantis, in contrast to other Bifidobacterium species, carried numerous BGCs, including lanthipeptides, lasso peptides, thiopeptides, and class IId bacteriocins. The antimicrobial activity of the crude bacteriocins and transcription analysis confirmed its potential for bacteriocin biosynthesis. Additionally, we investigated the association of bacteriocins with the phylogenetic positions of their homologs from other genera and niches. In conclusion, this study re-examines a few Bifidobacterium species traditionally regarded as a poor source of bacteriocins. These bacteriocin genes impart a competitive advantage to Bifidobacterium in colonizing the infant intestinal tract. IMPORTANCE Development of the human gut microbiota commences from birth, with bifidobacteria being among the first colonizers of the newborn intestinal tract and dominating it for a considerable period. To date, the genetic basis for the successful adaptation of bifidobacteria to this particular niche remains unclear since studies have mainly focused on glycoside hydrolase and adhesion-related genes. Bacteriocins are competitive factors that help producers maintain colonization advantages without destroying the niche balance; however, they have rarely been reported in Bifidobacterium. The advancement in sequencing methods and bacteriocin databases enables the use of a silicon-based search strategy for the comprehensive and rapid re-evaluation of the bacteriocin distribution of Bifidobacterium. Our study revealed that B. infantis carries abundant bacteriocin biosynthetic gene clusters for the first time, presenting new evidence regarding the competitive interactions of Bifidobacterium in the infant intestinal tract.

Diversity within the species Clostridium butyricum: pan-genome, phylogeny, prophage, carbohydrate utilization, and antibiotic resistance.

AIMS: Clostridium butyricum has been recognized as a strong candidate for the "next generation of probiotics" due to its beneficial roles on humans. Owing to our current understanding of this species is limited, it is imperative to unveil the genetic variety and biological properties of C. butyricum on sufficient strains. METHODS AND RESULTS: We isolated 53 C. butyricum strains and collected 25 publicly available genomes to comprehensively assess the genomic and phenotypic diversity of this species. Average nucleotide identity and phylogeny suggested that multiple C. butyricum strains might share the same niche. Clostridium butyricum genomes were replete with prophage elements, but the CRISPR-positive strain efficiently inhibited prophage integration. Clostridium butyricum utilizes cellulose, alginate, and soluble starch universally, and shows general resistance to aminoglycoside antibiotics. CONCLUSIONS: Clostridium butyricum exhibited a broad genetic diversity from the extraordinarily open pan-genome, extremely convergent core genome, and ubiquitous prophages. In carbohydrate utilization and antibiotic resistance, partial genotypes have a certain guiding significance for phenotypes.

Status quo of advanced cancer patients participating in shared decision-making in China: a mixed study.

PURPOSE: Patients with advanced cancer are usually willing to participate in shared decision-making (SDM), but in clinical practice, patient participation is easily ignored. This study aimed to analyze the current SDM status of advanced cancer patients and related factors. METHODS: In quantitative research, we administered a cross-sectional survey to 513 advanced cancer patients in 16 tertiary hospitals in China. A sociodemographic information questionnaire, the Control Preference Scale (CPS), and Perceived-Involvement in Care Scale (PICS) were used to analyze current SDM status and influencing factors. Our qualitative research was based on the Ottawa Decision Support Framework (ODSF), and 17 advanced cancer patients were interviewed to explore their perceptions of SDM. RESULTS: Our quantitative results show that patients' actual and expected decision-making participation differed; statistically significant influencing factors were age, insurance, and whether patients were worried about the therapeutic effects. We also found that dynamic decision-making mode changes, disease information acquisition, decision-making participation obstacles, and family members' roles affected patients' SDM through qualitative interviews. CONCLUSION: Advanced cancer patients' SDM in China is dominated by sharing and continuously fluctuates. Influenced by Chinese traditional culture, family members play an important role in SDM. In clinical work, we should pay attention to the dynamic changes in patients' participation in decision-making and the role of family members.

PCSK9 Inhibitors in Multi-Branch Lesions in Coronary Artery Disease with Substandard Lipid-Lowering Effects.

Objective: This study was carried out to evaluate the clinical efficacy of proprotein convertase chymotrypsin 9 (PCSK9) inhibitors in multi-branch lesions in coronary artery disease with substandard lipid-lowering effects. Methods: This retrospective study collected the clinical data of 100 patients with multiple coronary artery diseases admitted to our hospital between May 2020 and August 2022 for analysis. The eligible patients were assigned to either a PCSK9 inhibitor group or a control group at a ratio of 1:1 by their dosing regimens, with 50 cases in each group. Outcome measures for the clinical efficacy of PCSK9 inhibitors included lipid levels, low-density lipoprotein cholesterol (LDL-C) changes, serum concentrations of coronary artery disease-related inflammatory factors, improvement of angina questionnaire scores, adverse reactions, and major cardiovascular adverse events. Results: PCSK9 inhibitors resulted in significantly lower serum concentrations of total cholesterol (TC), LDL-C, and ApoB and higher high-density lipoprotein cholesterol (HDL-C) levels versus conventional lipid-lowering medication (P < .05). The two arms exhibited similar serum concentrations of triglyceride (TG) and ApoA1 after treatment (P > .05). With LDL-C<1.4 mmol/L as the cut-off for desirable blood lipid levels, 47 (94%) patients reached the standard after in the PCSK9 inhibitors group, while no eligible cases were reported in the control group (P < .05). PCSK9 inhibitors provided a marked reduction in the serum concentrations of high-sensitivity C-reactive protein in the patients. Patients had higher angina stability (AS), angina flare (AF), physical limitation (PL), and treatment satisfaction (TS) scores after PCSK9 inhibitor administration versus after conventional medication (P < .05). PCSK9 inhibitors were associated with a significantly lower incidence of adverse cardiovascular events (10%) versus conventional medication (42%) (P < .05). Conclusion: PCSK9 inhibitors significantly improve the LDL-C concentrations of patients with multiple lesions of coronary artery disease who have failed to meet lipid-lowering targets, this enables physicians to more effectively manage patients' cholesterol levels, consequently reducing their cardiovascular risk. Moreover, these inhibitors have the potential to enhance patients' quality of life by alleviating relieve angina symptoms. These findings offer valuable insights into managing multi-branch coronary artery disease.

Design and Experiment of Capacitive Rice Online Moisture Detection Device.

To solve the problems of poor stability and low monitoring precision in the online detection of rice moisture in the drying tower, we designed an online detection device for rice moisture at the outlet of the drying tower. The structure of a tri-plate capacitor was adopted, and the electrostatic field of the tri-plate capacitor was simulated using COMSOL software. A central composite design of three factors and five levels was carried out with the thickness, spacing, and area of the plates as the influencing factors and the capacitance-specific sensitivity as the test index. This device was composed of a dynamic acquisition device and a detection system. The dynamic sampling device was found to achieve dynamic continuous sampling and static intermittent measurements of rice using a ten-shaped leaf plate structure. The hardware circuit of the inspection system with STM32F407ZGT6 as the main control chip was designed to realize stable communication between the master and slave computers. Additionally, an optimized BP neural network prediction model based on the genetic algorithm was established using the MATLAB software. Indoor static and dynamic verification tests were also carried out. The results showed that the optimal plate structure parameter combination includes a plate thickness of 1 mm, plate spacing of 100 mm, and relative area of 18,000.069 mm2 while satisfying the mechanical design and practical application needs of the device. The structure of the BP neural network was 2-90-1, the length of individual code in the genetic algorithm was 361, and the prediction model was trained 765 times to obtain a minimum MSE value of 1.9683 × 10-5, which was lower than that of the unoptimized BP neural network with an MSE of 7.1215 × 10-4. The mean relative error of the device was 1.44% under the static test and 2.103% under the dynamic test, which met the accuracy requirements for the design of the device.

Machine learning framework for gut microbiome biomarkers discovery and modulation analysis in large-scale obese population.

BACKGROUND: The gut microbiome has proven to be an important factor affecting obesity; however, it remains a challenge to identify consistent biomarkers across geographic locations and perform precisely targeted modulation for obese individuals. RESULTS: This study proposed a systematic machine learning framework and applied it to 870 human stool metagenomes across five countries to obtain comprehensive regional shared biomarkers and conduct a personalized modulation analysis. In our pipeline, a heterogeneous ensemble feature selection diagram is first developed to determine an optimal subset of biomarkers through the aggregation of multiple techniques. Subsequently, a deep reinforcement learning method was established to alter the targeted composition to the desired healthy target. In this manner, we can realize personalized modulation by counterfactual inference. Consequently, a total of 42 species were identified as regional shared biomarkers, and they showed good performance in distinguishing obese people from the healthy group (area under curve (AUC) =0.85) when demonstrated on validation datasets. In addition, by pooling all counterfactual explanations, we found that Akkermansia muciniphila, Faecalibacterium prausnitzii, Prevotella copri, Bacteroides dorei, Bacteroides eggerthii, Alistipes finegoldii, Alistipes shahii, Eubacterium sp. _CAG_180, and Roseburia hominis may be potential broad-spectrum targets with consistent modulation in the multi-regional obese population. CONCLUSIONS: This article shows that based on our proposed machine-learning framework, we can obtain more comprehensive and accurate biomarkers and provide modulation analysis for the obese population. Moreover, our machine-learning framework will also be very useful for other researchers to further obtain biomarkers and perform counterfactual modulation analysis in different diseases.

Neuregulin-4 attenuates diabetic cardiomyopathy by regulating autophagy via the AMPK/mTOR signalling pathway.

BACKGROUND: Diabetic cardiomyopathy is characterized by left ventricle dysfunction, cardiomyocyte apoptosis, and interstitial fibrosis and is a serious complication of diabetes mellitus (DM). Autophagy is a mechanism that is essential for maintaining normal heart morphology and function, and its dysregulation can produce pathological effects on diabetic hearts. Neuregulin-4 (Nrg4) is an adipokine that exerts protective effects against metabolic disorders and insulin resistance. The aim of this study was to explore whether Nrg4 could ameliorate DM-induced myocardial injury by regulating autophagy. METHODS: Four weeks after the establishment of a model of type 1 diabetes in mice, the mice received Nrg4 treatment (with or without an autophagy inhibitor) for another 4 weeks. The cardiac functions, histological structures and cardiomyocyte apoptosis were investigated. Autophagy-related protein levels along with related signalling pathways that regulate autophagy were evaluated. In addition, the effects of Nrg4 on autophagy were also determined in cultured primary cardiomyocytes. RESULTS: Nrg4 alleviated myocardial injury both in vivo and in vitro. The autophagy level was decreased in type 1 diabetic mice, and Nrg4 intervention reactivated autophagy. Furthermore, Nrg4 intervention was found to activate autophagy via the AMPK/mTOR signalling pathway. Moreover, when autophagy was suppressed or the AMPK/mTOR pathway was inhibited, the beneficial effects of Nrg4 were diminished. CONCLUSION: Nrg4 intervention attenuated diabetic cardiomyopathy by promoting autophagy in type 1 diabetic mice. Additionally, Nrg4 induced autophagy via the AMPK/mTOR signalling pathway.

A multiphase dietetic protocol incorporating an improved ketogenic diet enhances weight loss and alters the gut microbiome of obese people.

The prevalence of obesity and its associated diseases is increasing. In the current study, 15 obese subjects took part in a 12-week multiphase dietetic protocol incorporating an improved ketogenic diet (MDP-i-KD) (KYLLKS 201806). We investigated the effects of the MDP-i-KD on the anthropometric parameters and the gut microbiota of obese subjects. Our results showed that the MDP-i-KD led to significant reductions in body mass index in obese subjects. The MDP-i-KD significantly decreased the relative abundance of the Lachnospiraceae_ND3007_group, the Eubacterium_hallii_group, and Pseudomonas and Blautia. In addition, gut microbiota co-occurrence networks in obese subjects were restructured to a more healthy condition after weight loss. These results show that the MDP-i-KD enhanced weight loss, which may be associated with dietary-induced changes in the gut microbiome. Our results emphasise the importance of determining the interaction between the host and microbial cells to comprehensively understand the mechanism by which diet affects host physiology and the microbiota.

Limosilactobacillus reuteri Attenuates Atopic Dermatitis via Changes in Gut Bacteria and Indole Derivatives from Tryptophan Metabolism.

Gut bacteria are closely associated with the development of atopic dermatitis (AD) due to their immunoregulatory function. Indole derivatives, produced by gut bacteria metabolizing tryptophan, are ligands to activate the aryl hydrocarbon receptor (AHR), which plays a critical role in attenuating AD symptoms. Limosilactobacillus reuteri, a producer of indole derivatives, regulates mucosal immunity via activating the AHR signaling pathway. However, the effective substance and mechanism of L. reuteri in the amelioration of AD remain to be elucidated. In this research, we found that L. reuteri DYNDL22M62 significantly improved AD-like symptoms in mice by suppressing IgE levels and the expressions of thymic stromal lymphopoietin (TSLP), IL-4, and IL-5. L. reuteri DYNDL22M62 induced an increase in the production of indole lactic acid (ILA) and indole propionic acid (IPA) via targeted tryptophan metabolic analysis and the expression of AHR in mice. Furthermore, L. reuteri DYNDL22M62 increased the proportions of Romboutsia and Ruminococcaceae NK4A214 group, which were positively related to ILA, but decreased Dubosiella, which was negatively related to IPA. Collectively, L. reuteri DYNDL22M62 with the role of modulating gut bacteria and the production of indole derivatives may attenuate AD via activating AHR in mice.

Efficacy of Probiotic Compounds in Relieving Constipation and Their Colonization in Gut Microbiota.

A number of studies have confirmed the relationship between constipation and gut microbiota. Additionally, many human and animal experiments have identified probiotics as effectors for the relief of constipation symptoms. In this study, probiotic compounds, including Lactobacillus acidophilus LA11-Onlly, Lacticaseibacillus rhamnosus LR22, Limosilactobacillus reuteri LE16, Lactiplantibacillus plantarum LP-Onlly, and Bifidobacterium animalis subsp. lactis BI516, were administered to mice with loperamide-induced constipation, and the impacts of these strains on constipation-related indicators and gut microbiota were evaluated. The effects of probiotic compounds on constipation relief were associated with various aspects, including gastrointestinal transit rate, number and weight of stools, serum and intestinal gastrointestinal regulatory hormones, and serum cytokines. Some of the probiotic compounds, including Limosilactobacillus reuteri, Lactiplantibacillus plantarum, and Lacticaseibacillus rhamnosus, were found to colonize the intestinal tract. Furthermore, higher dosages promoted the colonization of specific strains. This study yields a new perspective for the clinical use of probiotics to improve constipation symptoms by combining strains with different mechanisms for the alleviation of constipation.

The role of phenylalanine hydroxylase in lipogenesis in the oleaginous fungus Mortierella alpina.

Phenylalanine hydroxylase (PAH) catalyses the irreversible hydroxylation of phenylalanine to tyrosine, which is the rate-limiting reaction in phenylalanine metabolism in animals. A variety of polyunsaturated fatty acids can be synthesized by the lipid-producing fungus Mortierella alpina, which has a wide range of industrial applications in the production of arachidonic acid. In this study, RNA interference (RNAi) with the gene PAH was used to explore the role of phenylalanine hydroxylation in lipid biosynthesis in M. alpina. Our results indicated that PAH knockdown decreased the PAH transcript level by approximately 55% and attenuated cellular fatty acid biosynthesis. Furthermore, the level of NADPH, which is a critical reducing agent and the limiting factor in lipogenesis, was decreased in response to PAH RNAi, in addition to the downregulated transcription of other genes involved in NADPH production. Our study indicates that PAH is part of an overall enzymatic and regulatory mechanism supplying NADPH required for lipogenesis in M. alpina.

Enhanced Power Factor and Figure of Merit of Cu2ZnSnSe4-Based Thermoelectric Composites by Ag Alloying.

The quaternary chalcogenide composites Cu2ZnSn1-xAgxSe4 (0 ≤ x ≤ 0.075) have been successfully synthesized by high-temperature melting and annealing followed by hot-pressing. The phase structure of the bulk sample has been analyzed by powder X-ray diffraction and Rietveld refinement combined with Raman spectroscopy to confirm Cu2ZnSnSe4 as the main phase with ZnSe and Cu5Zn8 secondary phases. The thermoelectric properties of all specimens have been investigated in the temperature range of 300-700 K. The replacement of Sn by Ag significantly enhances the electrical transport properties by providing extra charge carriers. The tremendous reduction in electrical resistivity enhances the power factor, and a maximum power factor of 804 µW K-2 m-1 is achieved at 673 K for the specimen with 5% Ag content. Furthermore, increased point defects increase phonon scattering, resulting in reduced thermal conductivity. The combined effect of improved power factor and suppressed thermal conductivity provides a good boost to the dimensionless figure of merit. The maximum figure of merit of zT = 0.25 has been achieved at 673 K for Cu2ZnSn0.95Ag0.05Se4, which is 2.5 times the value of the parent sample.

The role of MTHFDL in mediating intracellular lipogenesis in oleaginous Mortierella alpina.

The oleaginous fungus Mortierella alpina can synthesize a variety of polyunsaturated fatty acids, which are used extensively in industry for the production of arachidonic acid (AA). NADPH is the limiting factor and critical reducing agent in lipid biosynthesis. In the folate cycle, methylenetetrahydrofolate dehydrogenase (MTHFDL) catalyzes the conversion of methylene tetrahydrofolate into 10-formyl-tetrahydrofolate with the reduction of NADP+ to NADPH. MTHFDL RNAi was used to investigate the role of the folate cycle in lipogenesis. Gene knockdown decreased the transcript levels of MTHFDL by about 50 % and attenuated cell fatty acid synthesis. The observation of decreased NADPH levels and downregulated NADPH-producing genes in response to MTHFDL RNAi indicates a novel aspect of the NADPH regulatory mechanism. Thus, our study demonstrates that MTHFDL plays key role in the mediation of NADPH in lipogenesis in M. alpina.

Potential of gut microbiome for detection of autism spectrum disorder.

Autism spectrum disorder (ASD) is a neuro developmental disorder characterized by a series of abnormal social behaviors. The increasing prevalence of ASD has led to the discovery of a correlation with the intestinal microbiome in many studies. In our research, we evaluated 297 subjects, including 169 individuals with ASD and 128 neurotypical subjects, from the Sequence Read Archive database. We conducted a series of analyses, including alpha-diversity, phylogenetic profiles, and functional profiles, to explore the correlation between the gut microbiome and ASD. The principal component analysis (PCA) indicated that ASD and neurotypical subjects could be divided based on the unweighted UniFrac distance. The genera Prevotella, Roseburia, Ruminococcus, Megasphaera, and Catenibacterium might be biomarkers of ASD after linear discriminant analysis effect size (LEfSe) evaluation and Random Forest analysis, respectively. The functional analysis found six significant pathways between ASD and neurotypical subjects, including oxidative phosphorylation, nucleotide excision repair, peptidoglycan biosynthesis, photosynthesis, photosynthesis proteins, and two-component system. Based on these alterations of the intestinal microbiome in ASD subjects, we developed four machine learning models: random forest (RF), Multilayer Perceptron (MLP), kernelized support vector machines with the RBF kernel (SVMs), and Decision trees (DT). Notably, the RF model after RF selection was superior, with an F1 score of 0.74 and area under the curve of 0.827(0.004), suggesting the reliability and generalizability of predictive model. Besides, the validation performance of RF model after RF selection could be 0.75(0.01) on external cohort collected by our laboratory. Our study advances the understanding of human gut microbiome in ASD that designing and evaluating microbially based interventions of ASD.