Peripheral Lymphoid Volume Expansion and Maintenance Are Controlled by Gut Microbiota via RALDH+ Dendritic Cells.

Lymphocyte homing to draining lymph nodes is critical for the initiation of immune responses. Secondary lymphoid organs of germ-free mice are underdeveloped. How gut commensal microbes remotely regulate cellularity and volume of secondary lymphoid organs remains unknown. We report here that, driven by commensal fungi, a wave of CD45(+)CD103(+)RALDH(+) cells migrates to the peripheral lymph nodes after birth. The arrival of these cells introduces high amounts of retinoic acid, mediates the neonatal to adult addressin switch on endothelial cells, and directs the homing of lymphocytes to both gut-associated lymphoid tissues and peripheral lymph nodes. In adult mice, a small number of these RALDH(+) cells might serve to maintain the volume of secondary lymphoid organs. Homing deficiency of these cells was associated with lymph node attrition in vitamin-A-deficient mice, suggesting a perpetual dependence on retinoic acid signaling for structural and functional maintenance of peripheral immune organs.

The proactive and reactive mechanisms of learned spatial suppression.

Selection history refers to the notion that previous allocations of attention or suppression have the potential to elicit lingering and enduring selection biases that are isolated from goal-driven or stimulus-driven attention. However, in the singleton detection mode task, manipulating the selection history of distractors cannot give rise to pure proactive inhibition. Therefore, we employed a combination of a working memory task and a feature search mode task, simultaneously recording cortical activity using EEG, to investigate the mechanisms of suppression guided by selection history. The results from event-related potential and reaction times showed an enhanced inhibitory performance when the distractor was presented at the high-probability location, along with instances where the target appeared at the high-probability location of distractors. These findings demonstrate that a generalized proactive inhibition bias is learned and processed independent of cognitive resources, which is supported by selection history. In contrast, reactive rejection toward the low-probability location was evident through the Pd component under varying cognitive resource conditions. Taken together, our findings indicated that participants learned proactive inhibition when the distractor was at the high-probability location, whereas reactive rejection was involved at low-probability location.

Altered resting-state brain function in endurance athletes.

Previous research has confirmed significant differences in regional brain activity and functional connectivity between endurance athletes and non-athletes. However, no studies have investigated the differences in topological efficiency of the brain functional network between endurance athletes and non-athletes. Here, we compared differences in regional activities, functional connectivity, and topological properties to explore the functional basis associated with endurance training. The results showed significant correlations between Regional Homogeneity in the motor cortex, visual cortex, cerebellum, and the training intensity parameters. Alterations in functional connectivity among the motor cortex, visual cortex, cerebellum, and the inferior frontal gyrus and cingulate gyrus were significantly correlated with training intensity parameters. In addition, the graph theoretical analysis results revealed a significant reduction in global efficiency among athletes. This decline is mainly caused by decreased nodal efficiency and nodal local efficiency of the cerebellar regions. Notably, the sensorimotor regions, such as the precentral gyrus and supplementary motor areas, still exhibit increased nodal efficiency and nodal local efficiency. This study not only confirms the improvement of regional activity in brain regions related to endurance training, but also offers novel insights into the mechanisms through which endurance athletes undergo changes in the topological efficiency of the brain functional network.

Individualized prediction of anxiety and depressive symptoms using gray matter volume in a non-clinical population.

Machine learning is an emerging tool in clinical psychology and neuroscience for the individualized prediction of psychiatric symptoms. However, its application in non-clinical populations is still in its infancy. Given the widespread morphological changes observed in psychiatric disorders, our study applies five supervised machine learning regression algorithms-ridge regression, support vector regression, partial least squares regression, least absolute shrinkage and selection operator regression, and Elastic-Net regression-to predict anxiety and depressive symptom scores. We base these predictions on the whole-brain gray matter volume in a large non-clinical sample (n = 425). Our results demonstrate that machine learning algorithms can effectively predict individual variability in anxiety and depressive symptoms, as measured by the Mood and Anxiety Symptoms Questionnaire. The most discriminative features contributing to the prediction models were primarily located in the prefrontal-parietal, temporal, visual, and sub-cortical regions (e.g. amygdala, hippocampus, and putamen). These regions showed distinct patterns for anxious arousal and high positive affect in three of the five models (partial least squares regression, support vector regression, and ridge regression). Importantly, these predictions were consistent across genders and robust to demographic variability (e.g. age, parental education, etc.). Our findings offer critical insights into the distinct brain morphological patterns underlying specific components of anxiety and depressive symptoms, supporting the existing tripartite theory from a neuroimaging perspective.

YLC-assembly: large DNA assembly via yeast life cycle.

As an enabling technique of synthetic biology, the scale of DNA assembly largely determines the scale of genetic manipulation. However, large DNA assembly technologies are generally cumbersome and inefficient. Here, we developed a YLC (yeast life cycle)-assembly method that enables in vivo iterative assembly of large DNA by nesting cell-cell transfer of assembled DNA in the cycle of yeast mating and sporulation. Using this method, we successfully assembled a hundred-kilobase (kb)-sized endogenous yeast DNA and a megabase (Mb)-sized exogenous DNA. For each round, over 104 positive colonies per 107 cells could be obtained, with an accuracy ranging from 67% to 100%. Compared with other Mb-sized DNA assembly methods, this method exhibits a higher success rate with an easy-to-operate workflow that avoid in vitro operations of large DNA. YLC-assembly lowers the technical difficulty of Mb-sized DNA assembly and could be a valuable tool for large-scale genome engineering and synthetic genomics.

Design and deep learning of synthetic B-cell-specific promoters.

Synthetic biology and deep learning synergistically revolutionize our ability for decoding and recoding DNA regulatory grammar. The B-cell-specific transcriptional regulation is intricate, and unlock the potential of B-cell-specific promoters as synthetic elements is important for B-cell engineering. Here, we designed and pooled synthesized 23 640 B-cell-specific promoters that exhibit larger sequence space, B-cell-specific expression, and enable diverse transcriptional patterns in B-cells. By MPRA (Massively parallel reporter assays), we deciphered the sequence features that regulate promoter transcriptional, including motifs and motif syntax (their combination and distance). Finally, we built and trained a deep learning model capable of predicting the transcriptional strength of the immunoglobulin V gene promoter directly from sequence. Prediction of thousands of promoter variants identified in the global human population shows that polymorphisms in promoters influence the transcription of immunoglobulin V genes, which may contribute to individual differences in adaptive humoral immune responses. Our work helps to decipher the transcription mechanism in immunoglobulin genes and offers thousands of non-similar promoters for B-cell engineering.

Microstructural and functional substrates underlying dispositional greed and its link with trait but not state impulsivity.

The interplay between personality traits and impulsivity has long been a central theme in psychology and psychiatry. However, the potential association between Greed Personality Traits (GPT) and impulsivity, encompassing both trait and state impulsivity and future time perspective, remains largely unexplored. To address these issues, we employed questionnaires and an inter-temporal choice task to estimate corresponding trait/state impulsivity and collected multi-modal neuroimaging data (resting-state functional imaging: n = 430; diffusion-weighted imaging: n = 426; task-related functional imaging: n = 53) to investigate the underlying microstructural and functional substrates. Behavioral analyses revealed that GPT mediated the association between time perspective (e.g., present fatalism) and trait impulsivity (e.g., motor impulsivity). Functional imaging analyses further identified that brain activation strengths and patterns related to delay length, particularly in the dorsomedial prefrontal cortex, superior parietal lobule, and cerebellum, were associated with GPT. Moreover, individuals with similar levels of greed exhibited analogous spontaneous brain activity patterns, predominantly in the Default Mode Network (DMN), Fronto-Parietal Network (FPN), and Visual Network (VIS). Diffusion imaging analysis observed specific microstructural characteristics in the spinocerebellar/pontocerebellar fasciculus, internal/external capsule, and corona radiata that support the formation of GPT. Furthermore, the corresponding neural activation pattern, spontaneous neural activity pattern, and analogous functional couplings among the aforementioned brain regions mediated the relationships between time perspective and GPT and between GPT and motor impulsivity. These findings provide novel insights into the possible pathway such as time perspective → dispositional greed → impulsivity and uncover their underlying microstructural and functional substrates.

Happy people are always similar: The evidence from brain morphological and functional inter-subject correlations.

A fundamental question in the study of happiness is whether there is neural evidence to support a well-known hypothesis that happy people are always similar while unfortunate people have their own misfortunes. To investigate this, we employed several happiness-related questionnaires to identify potential components of happiness, and further investigated and confirmed their associations with personality, mood, aggressive behaviors, and amygdala reactivity to fearful faces within a substantial sample size of college students (n = 570). Additionally, we examined the functional and morphological similarities and differences among happy individuals using the inter-subject representational similarity analysis (IS-RSA). IS-RSA emphasizes the geometric properties in a high-dimensional space constructed by brain or behavioral patterns and focuses on individual subjects. Our behavioral findings unveiled two factors of happiness: individual and social, both of which mediated the effect of personality traits on individual aggression. Subsequently, mood mediated the impact of happiness on aggressive behaviors across two subgroup splits. Functional imaging data revealed that individuals with higher levels of happiness exhibited reduced amygdala reactivity to fearful faces, as evidenced by a conventional face-matching task (n = 104). Moreover, IS-RSA demonstrated that these participants manifested similar neural activation patterns when processing fearful faces within the visual pathway, but not within the emotional network (e.g., amygdala). Morphological observations (n = 425) indicated that individuals with similar high happiness levels exhibited comparable gray matter volume patterns within several networks, including the default mode network, fronto-parietal network, visual network, and attention network. Collectively, these findings offer early neural evidence supporting the proposition that happy individuals may share common neural characteristics.

The computational and neural substrates of individual differences in impulsivity under loss framework.

Numerous neuroimaging studies have identified significant individual variability in intertemporal choice, often attributed to three neural mechanisms: (1) increased reward circuit activity, (2) decreased cognitive control, and (3) prospection ability. These mechanisms that explain impulsivity, however, have been primarily studied in the gain domain. This study extends this investigation to the loss domain. We employed a hierarchical Bayesian drift-diffusion model (DDM) and the inter-subject representational similarity approach (IS-RSA) to investigate the potential computational neural substrates underlying impulsivity in loss domain across two experiments (n = 155). These experiments utilized a revised intertemporal task that independently manipulated the amounts of immediate and delayed-loss options. Behavioral results demonstrated positive correlations between the drift rate, measured by the DDM, and the impulsivity index K in Exp. 1 (n = 97) and were replicated in Exp. 2 (n = 58). Imaging analyses further revealed that the drift rate significantly mediated the relations between brain properties (e.g., prefrontal cortex activations and gray matter volume in the orbitofrontal cortex and precuneus) and K in Exp. 1. IS-RSA analyses indicated that variability in the drift rate also mediated the associations between inter-subject variations in activation patterns and individual differences in K. These findings suggest that individuals with similar impulsivity levels are likely to exhibit similar value processing patterns, providing a potential explanation for individual differences in impulsivity within a loss framework.

Enhancing clinical utility: deep learning-based embryo scoring model for non-invasive aneuploidy prediction.

BACKGROUND: The best method for selecting embryos ploidy is preimplantation genetic testing for aneuploidies (PGT-A). However, it takes more labour, money, and experience. As such, more approachable, non- invasive techniques were still needed. Analyses driven by artificial intelligence have been presented recently to automate and objectify picture assessments. METHODS: In present retrospective study, a total of 3448 biopsied blastocysts from 979 Time-lapse (TL)-PGT cycles were retrospectively analyzed. The "intelligent data analysis (iDA) Score" as a deep learning algorithm was used in TL incubators and assigned each blastocyst with a score between 1.0 and 9.9. RESULTS: Significant differences were observed in iDAScore among blastocysts with different ploidy. Additionally, multivariate logistic regression analysis showed that higher scores were significantly correlated with euploidy (p < 0.001). The Area Under the Curve (AUC) of iDAScore alone for predicting euploidy embryo is 0.612, but rose to 0.688 by adding clinical and embryonic characteristics. CONCLUSIONS: This study provided additional information to strengthen the clinical applicability of iDAScore. This may provide a non-invasive and inexpensive alternative for patients who have no available blastocyst for biopsy or who are economically disadvantaged. However, the accuracy of embryo ploidy is still dependent on the results of next-generation sequencing technology (NGS) analysis.

Comparative pathogenicity of influenza virus-induced pneumonia mouse model following intranasal and aerosolized intratracheal inoculation.

BACKGROUND: Infection of mice with mouse-adapted strains of influenza virus has been widely used to establish mouse pneumonia models. Intranasal inoculation is the traditional route for constructing an influenza virus-induced pneumonia mouse model, while intratracheal inoculation has been gradually applied in recent years. In this article, the pathogenicity of influenza virus-induced pneumonia mouse models following intranasal and aerosolized intratracheal inoculation were compared. METHODS: By comparing the two ways of influenza inoculation, intranasal and intratracheal, a variety of indices such as survival rate, body weight change, viral titer and load, pathological change, lung wet/dry ratio, and inflammatory factors were investigated. Meanwhile, the transcriptome was applied for the initial exploration of the mechanism underlying the variations in the results between the two inoculation methods. RESULTS: The findings suggest that aerosolized intratracheal infection leads to more severe lung injury and higher viral loads in the lungs compared to intranasal infection, which may be influenced by the initial site of infection, sialic acid receptor distribution, and host innate immunity. CONCLUSION: Intratracheal inoculation is a better method for modelling severe pneumonia in mice than intranasal infection.

Imatinib facilitates gemcitabine sensitivity by targeting epigenetically activated PDGFC signaling in pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) is a malignant tumor with poor prognosis. Gemcitabine-based chemotherapy has become one of the main modalities of its management. However, gemcitabine resistance frequently occurs, leading to failure of PDAC therapy. Platelet-derived growth factors (PDGFs) and their receptors play important roles in cancer progression and chemoresistance. We aimed to investigate the biological function and therapeutic significance of platelet-derived growth factor C (PDGFC) in drug-resistant PDAC. Our study showed that PDGFC was abnormally highly expressed in gemcitabine-resistant PDAC. Silencing PDGFC expression can enhance the therapeutic effect of gemcitabine on PDAC. Mechanistically, the transcription of PDGFC is mediated by H3K27 acetylation, and PDGFC promotes gemcitabine resistance by activating the PDGFR-PI3K-AKT signaling pathway. The PDGFR inhibitor imatinib inhibits the PDGFR pathway. Imatinib and gemcitabine have a synergistic effect on the treatment of PDAC, and imatinib can significantly enhance the anti-tumor effect of gemcitabine in a drug-resistant PDAC patient-derived xenograft model. In conclusion, PDGFC is a potential predictor of gemcitabine-resistant PDAC. Imatinib inhibits PDGFR activation to promote gemcitabine sensitivity in PDAC. Combined modality regimen of imatinib and gemcitabine is likely to translate into clinical trial for the treatment of PDGFC-associated gemcitabine-resistant patients.

Genome-Wide Identification of B-Box Gene Family and Candidate Light-Related Member Analysis of Tung Tree (Vernicia fordii).

Light is one of the most important environmental factors for plant growth. In the production process of tung oil tree cultivation, due to the inappropriate growth of shading conditions, the lower branches are often dry and dead, which seriously affects the yield of tung oil trees. However, little is known about the key factors of light-induced tree photomorphogenesis. In this study, a total of 22 VfBBX family members were identified to provide a reference for candidate genes in tung tree seedlings. All members of the VfBBX family have different numbers of highly conserved B-box domains or CCT domains. Phylogenetic evolution clustered the VfBBX genes into four categories, and the highest density of members was on chromosome 6. Interspecific collinearity analysis suggested that there were six pairs of duplicate genes in VfBBX members, but the expression levels of all family members in different growth and development stages of the tung tree were significantly divergent. After different degrees of shading treatment and physiological data determination of tung tree seedlings, the differential expression level and chlorophyll synthesis genes correlation analysis revealed that VfBBX9 was a typical candidate nuclear localization transcription factor that was significantly differentially expressed in light response. This study systematically identified the VfBBX gene family and provided a reference for studying its molecular function, enhanced the theoretical basis for tung tree breeding, and identified excellent varieties.

Chromosome-level genome of Camellia lanceoleosa provides a valuable resource for understanding genome evolution and self-incompatibility.

The section Oleifera (Theaceae) has attracted attention for the high levels of unsaturated fatty acids found in its seeds. Here, we report the chromosome-scale genome of the sect. Oleifera using diploid wild Camellia lanceoleosa with a final size of 3.00 Gb and an N50 scaffold size of 186.43 Mb. Repetitive sequences accounted for 80.63% and were distributed unevenly across the genome. Camellia lanceoleosa underwent a whole-genome duplication event approximately 65 million years ago (65 Mya), prior to the divergence of C. lanceoleosa and Camellia sinensis (approx. 6-7 Mya). Syntenic comparisons of these two species elucidated the genomic rearrangement, appearing to be driven in part by the activity of transposable elements. The expanded and positively selected genes in C. lanceoleosa were significantly enriched in oil biosynthesis, and the expansion of homomeric acetyl-coenzyme A carboxylase (ACCase) genes and the seed-biased expression of genes encoding heteromeric ACCase, diacylglycerol acyltransferase, glyceraldehyde-3-phosphate dehydrogenase and stearoyl-ACP desaturase could be of primary importance for the high oil and oleic acid content found in C. lanceoleosa. Theanine and catechins were present in the leaves of C. lanceoleosa. However, caffeine can not be dectected in the leaves but was abundant in the seeds and roots. The functional and transcriptional divergence of genes encoding SAM-dependent N-methyltransferases may be associated with caffeine accumulation and distribution. Gene expression profiles, structural composition and chromosomal location suggest that the late-acting self-incompatibility of C. lanceoleosa is likely to have favoured a novel mechanism co-occurring with gametophytic self-incompatibility. This study provides valuable resources for quantitative and qualitative improvements and genome assembly of polyploid plants in sect. Oleifera.

NLRP3 Plays a Key Role in Antihelminth Immunity in the Enteral and Parenteral Stages of Trichinella spiralis-Infected Mice.

Trichinellosis is an important foodborne zoonosis, and no effective treatments are yet available. Nod-like receptor (NLR) plays a critical role in the host response against nematodes. Therefore, we aimed to explore the role of the NLRP3 inflammasome (NLRP3) during the adult, migrating, and encysted stages of Trichinella spiralis infection. The mice were treated with the specific NLRP3 inhibitor MCC950 after inoculation with T. spiralis. Then, the role that NLRP3 plays during T. spiralis infection of mice was evaluated using enzyme-linked immunosorbent assay (ELISA), Western blotting, flow cytometry, histopathological evaluation, bone marrow-derived macrophage (BMDM) stimulation, and immunofluorescence. The in vivo results showed that NLRP3 enhanced the Th1 immune response in the adult and migrating stages and weakened the Th2 immune response in the encysted stage. NLRP3 promoted the release of proinflammatory factors (interferon gamma [IFN-γ]) and suppressed the release of anti-inflammatory factors (interleukin 4 [IL-4]). Pathological changes were also improved in the absence of NLRP3 in mice during T. spiralis infection. Importantly, a significant reduction in adult worm burden and muscle larvae burden at 7 and 35 days postinfection was observed in mice treated with the specific NLRP3 inhibitor MCC950. In vitro, we first demonstrated that NLRP3 in macrophages can be activated by T. spiralis proteins and promotes IL-1ß and IL-18 release. This study revealed that NLRP3 is involved in the host response to T. spiralis infection and that targeted inhibition of NLRP3 enhanced the Th2 response and accelerated T. spiralis expulsion. These findings may help in the development of protocols for controlling trichinellosis.

Developmental validation of a high-resolution panel genotyping 639 Y-chromosome SNP and InDel markers and its evolutionary features in Chinese populations.

Uniparental-inherited haploid genetic marker of Y-chromosome single nucleotide polymorphisms (Y-SNP) have the power to provide a deep understanding of the human evolutionary past, forensic pedigree, and bio-geographical ancestry information. Several international cross-continental or regional Y-panels instead of Y-whole sequencing have recently been developed to promote Y-tools in forensic practice. However, panels based on next-generation sequencing (NGS) explicitly developed for Chinese populations are insufficient to represent the Chinese Y-chromosome genetic diversity and complex population structures, especially for Chinese-predominant haplogroup O. We developed and validated a 639-plex panel including 633 Y-SNPs and 6 Y-Insertion/deletions, which covered 573 Y haplogroups on the Y-DNA haplogroup tree. In this panel, subgroups from haplogroup O accounted for 64.4% of total inferable haplogroups. We reported the sequencing metrics of 354 libraries sequenced with this panel, with the average sequencing depth among 226 individuals being 3,741×. We illuminated the high level of concordance, accuracy, reproducibility, and specificity of the 639-plex panel and found that 610 loci were genotyped with as little as 0.03 ng of genomic DNA in the sensitivity test. 94.05% of the 639 loci were detectable in male-female mixed DNA samples with a mix ratio of 1:500. Nearly all of the loci were genotyped correctly when no more than 25 ng/µL tannic acid, 20 ng/µL humic acid, or 37.5 µM hematin was added to the amplification mixture. More than 80% of genotypes were obtained from degraded DNA samples with a degradation index of 11.76. Individuals from the same pedigree shared identical genotypes in 11 male pedigrees. Finally, we presented the complex evolutionary history of 183 northern Chinese Hans and six other Chinese populations, and found multiple founding lineages that contributed to the northern Han Chinese gene pool. The 639-plex panel proved an efficient tool for Chinese paternal studies and forensic applications.

Development of the prognostic value in lung adenocarcinoma based on anoikis-related genes and initial experimental validation.

BACKGROUND: Lung adenocarcinoma (LUAD) is a highly aggressive cancer in advanced stages and has the highest cancer-related death across the world. Anoikis has emerged as a specific form of apoptotic cell death that may play a vital role in the formation and development of tumors. METHODS: Based on The Cancer Genome Atlas dataset, we developed a novel anoikis-related genes (ARGs) signature in LUAD and evaluated the differences between low and high-risk groups in clinical characteristics, expression patterns, immune cell infiltration, and drug sensitivity, etc. According to multivariate Cox regression analysis, the risk score was identified as a significant independent prognostic factor. The possible biological pathways of ARGs' were assessed by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses. The immune infiltration landscape and risk score of ARGs were analyzed by ESTIMATE and CIBERSORT analysis. A nomogram grounded on six key ARGs and clinicopathological features was provided. Moreover, experiment validation of the expression patterns of six hub ARGs in lung cancer cell lines was conducted. RESULTS: We identified 53 survival-related LUAD anoikis-related differentially expressed genes and finally six hub anoikis genes (LDHA, SLC2A1, SERPINB5, ITGB4, BRCA2, and PIK3R1) were selected to construct an ARG model. The risk model could efficiently cluster the patients into low- and high-risk groups which could accurately predict clinical outcomes for LUAD patients. There is evidence that the prognostic risk score is a remarkable prognostic factor in determining overall survival. Different immune statuses and drug sensitivity between low- and high-risk groups were explored according to functional analysis. On the basis of risk scores and LUAD clinicopathological features, a novel nomogram was developed. Ultimately, all six key genes except for PIK3R1 were proved to be upregulated in LUAD tissues and cell lines by bioinformatics analysis and experimental validation. CONCLUSIONS: The result of the present study suggest that ARGs could be carcinogenic to LUAD and could be used as an effective stratification factor to customize therapies and forecast the survival rate in LUAD patients.

Distributed attribute representation in the superior parietal lobe during probabilistic decision-making.

Several studies have examined the neural substrates of probabilistic decision-making, but few have systematically investigated the neural representations of the two objective attributes of probabilistic rewards, that is, the reward amount and the probability. Specifically, whether there are common or distinct neural activity patterns to represent the objective attributes and their association with the neural representation of the subjective valuation remains largely underexplored. We conducted two studies (nStudy1 = 34, nStudy2 = 41) to uncover distributed neural representations of the objective attributes and subjective value as well as their association with individual probability discounting rates. The amount and probability were independently manipulated to better capture brain signals sensitive to these two attributes and were presented simultaneously in Study 1 and successively in Study 2. Both univariate and multivariate pattern analyses showed that the brain activities in the superior parietal lobule (SPL), including the postcentral gyrus, were modulated by the amount of rewards and probability in both studies. Further, representational similarity analysis revealed a similar neural representation between these two objective attributes and between the attribute and valuation. Moreover, the SPL tracked the subjective value integrated by the hyperbolic function. Probability-related brain activations in the inferior parietal lobule were associated with the variability in individual discounting rates. These findings provide novel insights into a similar neural representation of the two attributes during probabilistic decision-making and perhaps support the common neural coding of stimulus objective properties and subjective value in the field of probabilistic discounting.

Dysregulation of glutamine/glutamate metabolism in COVID-19 patients: A metabolism study in African population and mini meta-analysis.

Coronavirus disease 2019 (COVID-19) remains a serious global threat. The metabolic analysis had been successfully applied in the efforts to uncover the pathological mechanisms and biomarkers of disease severity. Here we performed a quasi-targeted metabolomic analysis on 56 COVID-19 patients from Sierra Leone in western Africa, revealing the metabolomic profiles and the association with disease severity, which was confirmed by the targeted metabolomic analysis of 19 pairs of COVID-19 patients. A meta-analysis was performed on published metabolic data of COVID-19 to verify our findings. Of the 596 identified metabolites, 58 showed significant differences between severe and nonsevere groups. The pathway enrichment of these differential metabolites revealed glutamine and glutamate metabolism as the most significant metabolic pathway (Impact = 0.5; -log10P = 1.959). Further targeted metabolic analysis revealed six metabolites with significant intergroup differences, with glutamine/glutamate ratio significantly associated with severe disease, negatively correlated with 10 clinical parameters and positively correlated with SPO2 (rs = 0.442, p = 0.005). Mini meta-analysis indicated elevated glutamate was related to increased risk of COVID-19 infection (pooled odd ratio [OR] = 2.02; 95% confidence interval [CI]: 1.17-3.50) and severe COVID-19 (pooled OR = 2.28; 95% CI: 1.14-4.56). In contrast, elevated glutamine related to decreased risk of infection and severe COVID-19, the pooled OR were 0.30 (95% CI: 0.20-0.44), and 0.44 (95% CI: 0.19-0.98), respectively. Glutamine and glutamate metabolism are associated with COVID-19 severity in multiple populations, which might confer potential therapeutic target of COVID-19, especially for severe patients.

Climate warming alters the relative importance of plant root and microbial community in regulating the accumulation of soil microbial necromass carbon in a Tibetan alpine meadow.

Climate warming is predicted to considerably affect variations in soil organic carbon (SOC), especially in alpine ecosystems. Microbial necromass carbon (MNC) is an important contributor to stable soil organic carbon pools. However, accumulation and persistence of soil MNC across a gradient of warming are still poorly understood. An 8-year field experiment with four levels of warming was conducted in a Tibetan meadow. We found that low-level (+0-1.5°C) warming mostly enhanced bacterial necromass carbon (BNC), fungal necromass carbon (FNC), and total MNC compared with control treatment across soil layers, while no significant effect was caused between high-level (+1.5-2.5°C) treatments and control treatments. The contributions of both MNC and BNC to soil organic carbon were not significantly affected by warming treatments across depths. Structural equation modeling analysis demonstrated that the effect of plant root traits on MNC persistence strengthened with warming intensity, while the influence of microbial community characteristics waned along strengthened warming. Overall, our study provides novel evidence that the major determinants of MNC production and stabilization may vary with warming magnitude in alpine meadows. This finding is critical for updating our knowledge on soil carbon storage in response to climate warming.