Temporal turnover of viral biodiversity and functional potential in intertidal wetlands.

As the central members of the microbiome networks, viruses regulate the composition of microbial communities and drive the nutrient cycles of ecosystems by lysing host cells. Therefore, uncovering the dynamic patterns and the underlying ecological mechanisms mediating the tiniest viral communities across space and through time in natural ecosystems is of crucial importance for better understanding the complex microbial world. Here, the temporal dynamics of intertidal viral communities were investigated via a time-series sampling effort. A total of 1911 viral operational taxonomic units were recovered from 36 bimonthly collected shotgun metagenomes. Functionally important auxiliary metabolic genes involved in carbohydrate, sulfur, and phosphorus metabolism were detected, some of which (e.g., cysH gene) were stably present within viral genomes over time. Over the sampling period, strong and comparable temporal turnovers were observed for intertidal viromes and their host microbes. Winter was determined as the pivotal point for the shifts in viral diversity patterns. Notably, the viral micro-diversity covaried with the macro-diversity, following similar temporal patterns. The relative abundances of viral taxa also covaried with their host prokaryotes. Meanwhile, the virus-host relationships at the whole community level were relatively stable. Further statistical analyses demonstrated that the dynamic patterns of viral communities were highly deterministic, for which temperature was the major driver. This study provided valuable mechanistic insights into the temporal turnover of viral communities in complex ecosystems such as intertidal wetlands.

Comprehensive profiles of sulfur cycling microbial communities along a mangrove sediment depth.

The sulfur (S) cycle is an important biogeochemical cycle with profound implications for both cellular- and ecosystem-level processes by diverse microorganisms. Mangrove sediments are a hotspot of biogeochemical cycling, especially for the S cycle with high concentrations of S compounds. Previous studies have mainly focused on some specific inorganic S cycling processes without paying specific attention to the overall S-cycling communities and processes as well as organic S metabolism. In this study, we comprehensively analyzed the distribution, ecological network and assembly mechanisms of S cycling microbial communities and their changes with sediment depths using metagenome sequencing data. The results showed that the abundance of gene families involved in sulfur oxidation, assimilatory sulfate reduction, and dimethylsulfoniopropionate (DMSP) cleavage and demethylation decreased with sediment depths, while those involved in S reduction and dimethyl sulfide (DMS) transformation showed an opposite trend. Specifically, glpE, responsible for converting S2O32- to SO32-, showed the highest abundance in the surface sediment and decreased with sediment depths; in contrast, high abundances of dmsA, responsible for converting dimethyl sulfoxide (DMSO) to DMS, were identified and increased with sediment depths. We identified Pseudomonas and Streptomyces as the main S-cycling microorganisms, while Thermococcus could play an import role in microbial network connections in the S-cycling microbial community. Our statistical analysis showed that both taxonomical and functional compositions were generally shaped by stochastic processes, while the functional composition of organic S metabolism showed a transition from stochastic to deterministic processes. This study provides a novel perspective of diversity distribution of S-cycling functions and taxa as well as their potential assembly mechanisms, which has important implications for maintaining mangrove ecosystem functions.

Development of a quantitative index system for evaluating the quality of electronic medical records in disease risk intelligent prediction.

OBJECTIVE: This study aimed to develop and validate a quantitative index system for evaluating the data quality of Electronic Medical Records (EMR) in disease risk prediction using Machine Learning (ML). MATERIALS AND METHODS: The index system was developed in four steps: (1) a preliminary index system was outlined based on literature review; (2) we utilized the Delphi method to structure the indicators at all levels; (3) the weights of these indicators were determined using the Analytic Hierarchy Process (AHP) method; and (4) the developed index system was empirically validated using real-world EMR data in a ML-based disease risk prediction task. RESULTS: The synthesis of review findings and the expert consultations led to the formulation of a three-level index system with four first-level, 11 second-level, and 33 third-level indicators. The weights of these indicators were obtained through the AHP method. Results from the empirical analysis illustrated a positive relationship between the scores assigned by the proposed index system and the predictive performances of the datasets. DISCUSSION: The proposed index system for evaluating EMR data quality is grounded in extensive literature analysis and expert consultation. Moreover, the system's high reliability and suitability has been affirmed through empirical validation. CONCLUSION: The novel index system offers a robust framework for assessing the quality and suitability of EMR data in ML-based disease risk predictions. It can serve as a guide in building EMR databases, improving EMR data quality control, and generating reliable real-world evidence.

Finely manipulating room temperature phosphorescence by dynamic lanthanide coordination toward multi-level information security.

Room temperature phosphorescence materials have garnered significant attention due to their unique optical properties and promising applications. However, it remains a great challenge to finely manipulate phosphorescent properties to achieve desirable phosphorescent performance on demand. Here, we show a feasible strategy to finely manipulate organic phosphorescent performance by introducing dynamic lanthanide coordination. The organic phosphors of terpyridine phenylboronic acids possessing excellent coordination ability are covalently embedded into a polyvinyl alcohol matrix, leading to ultralong organic room temperature phosphorescence with a lifetime of up to 0.629 s. Notably, such phosphorescent performance, including intensity and lifetime, can be well controlled by varying the lanthanide dopant. Relying on the excellent modulable performance of these lanthanide-manipulated phosphorescence films, multi-level information encryption including attacker-misleading and spatial-time-resolved applications is successfully demonstrated with greatly improved security level. This work opens an avenue for finely manipulating phosphorescent properties to meet versatile uses in optical applications.

LncRNA IRAIN overcomes imatinib resistance in chronic myeloid leukemia via NF-κB/CD44 pathway inhibition.

The development of tyrosine kinase inhibitors (TKIs) has revolutionarily increased the overall survival of patients with chronic myeloid leukemia (CML). However, drug resistance remains a major obstacle. Here, we demonstrated that a BCR-ABL1-independent long non-coding RNA, IRAIN, is constitutively expressed at low levels in CML, resulting in imatinib resistance. IRAIN knockdown decreased the sensitivity of CD34+ CML blasts and cell lines to imatinib, whereas IRAIN overexpression significantly increased sensitivity. Mechanistically, IRAIN downregulates CD44, a membrane receptor favorably affecting TKI resistance, by binding to the nuclear factor kappa B subunit p65 to reduce the expression of p65 and phosphorylated p65. Therefore, the demethylating drug decitabine, which upregulates IRAIN, combined with imatinib, formed a dual therapy strategy which can be applied to CML with resistance to TKIs.

Metagenomic-based discovery and comparison of the lignin degrading potential of microbiomes in aquatic and terrestrial ecosystems via the LCdb database.

Lignin, as an abundant organic carbon, plays a vital role in the global carbon cycle. However, our understanding of the global lignin-degrading microbiome remains elusive. The greatest barrier has been absence of a comprehensive and accurate functional gene database. Here, we first developed a curated functional gene database (LCdb) for metagenomic profiling of lignin degrading microbial consortia. Via the LCdb, we draw a clear picture describing the global biogeography of communities with lignin-degrading potential. They exhibit clear niche differentiation at the levels of taxonomy and functional traits. The terrestrial microbiomes showed the highest diversity, yet the lowest correlations. In particular, there were few correlations between genes involved in aerobic and anaerobic degradation pathways, showing a clear functional redundancy property. In contrast, enhanced correlations, especially closer inter-connections between anaerobic and aerobic groups, were observed in aquatic consortia in response to the lower diversity. Specifically, dypB and dypA, are widespread on Earth, indicating their essential roles in lignin depolymerization. Estuarine and marine consortia featured the laccase and mnsod genes, respectively. Notably, the roles of archaea in lignin degradation were revealed in marine ecosystems. Environmental factors strongly influenced functional traits, but weakly shaped taxonomic groups. Null mode analysis further verified that composition of functional traits was deterministic, while taxonomic composition was highly stochastic, demonstrating that the environment selects functional genes rather than taxonomic groups. Our study not only develops a useful tool to study lignin degrading microbial communities via metagenome sequencing but also advances our understanding of ecological traits of these global microbiomes.

Transcriptomic analysis of nitrogen metabolism pathways in Klebsiella aerogenes under nitrogen-rich conditions.

The acceleration of the nitrogen cycle and the nitrogen excess observed in some coastal waters has increased interest into understanding the biochemical and molecular basis of nitrogen metabolism in various microorganisms. To investigate nitrogen metabolism of a novel heterotrophic nitrification and aerobic denitrification bacterium Klebsiella aerogenes strain (B23) under nitrogen-rich conditions, we conducted physiological and transcriptomic high-throughput sequencing analyses on strain B23 cultured on potassium nitrate-free or potassium nitrate-rich media. Overall, K. aerogenes B23 assimilated 82.47% of the nitrate present into cellular nitrogen. Further, 1,195 differentially expressed genes were observed between K. aerogenes B23 cultured on potassium nitrate-free media and those cultured on potassium nitrate-rich media. Gene annotation and metabolic pathway analysis of the transcriptome were performed using a series of bioinformatics tools, including Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and Non-Redundant Protein Database annotation. Accordingly, the nitrogen metabolism pathway of K. aerogenes B23 was analyzed; overall, 39 genes were determined to be involved in this pathway. Differential expression analysis of the genes involved in the nitrogen metabolism pathway demonstrated that, compared to the control, FNR, NarK/14945, fdx, gshA, proB, proA, gapA, argH, artQ, artJ, artM, ArgR, GAT1, prmB, pyrG, glnS, and Ca1 were significantly upregulated in the nitrogen-treated K. aerogenes B23; these genes have been established to be involved in the regulation of nitrate, arginine, glutamate, and ammonia assimilation. Further, norV, norR, and narI were also upregulated in nitrogen-treated K. aerogenes B23; these genes are involved in the regulation of NO metabolism. These differential expression results are important for understanding the regulation process of key nitrogen metabolism enzyme genes in K. aerogenes B23. Therefore, this study establishes a solid foundation for further research into the expression regulation patterns of nitrogen metabolism-associated genes in K. aerogenes B23 under nitrogen-rich conditions; moreover, this research provides essential insight into how K. aerogenes B23 utilizes nutritional elements.

Regulation of Zbp1 by miR-99b-5p in microglia controls the development of schizophrenia-like symptoms in mice.

Current approaches to the treatment of schizophrenia have mainly focused on the protein-coding part of the genome; in this context, the roles of microRNAs have received less attention. In the present study, we analyze the microRNAome in the blood and postmortem brains of schizophrenia patients, showing that the expression of miR-99b-5p is downregulated in both the prefrontal cortex and blood of patients. Lowering the amount of miR-99b-5p in mice leads to both schizophrenia-like phenotypes and inflammatory processes that are linked to synaptic pruning in microglia. The microglial miR-99b-5p-supressed inflammatory response requires Z-DNA binding protein 1 (Zbp1), which we identify as a novel miR-99b-5p target. Antisense oligonucleotides against Zbp1 ameliorate the pathological effects of miR-99b-5p inhibition. Our findings indicate that a novel miR-99b-5p-Zbp1 pathway in microglia might contribute to the pathogenesis of schizophrenia.

Radiotherapy-Induced Astrocyte Senescence Promotes an Immunosuppressive Microenvironment in Glioblastoma to Facilitate Tumor Regrowth.

Accumulating evidence suggests that changes in the tumor microenvironment caused by radiotherapy are closely related to the recurrence of glioma. However, the mechanisms by which such radiation-induced changes are involved in tumor regrowth have not yet been fully investigated. In the present study, how cranial irradiation-induced senescence in non-neoplastic brain cells contributes to glioma progression is explored. It is observed that senescent brain cells facilitated tumor regrowth by enhancing the peripheral recruitment of myeloid inflammatory cells in glioblastoma. Further, it is identified that astrocytes are one of the most susceptible senescent populations and that they promoted chemokine secretion in glioma cells via the senescence-associated secretory phenotype. By using senolytic agents after radiotherapy to eliminate these senescent cells substantially prolonged survival time in preclinical models. The findings suggest the tumor-promoting role of senescent astrocytes in the irradiated glioma microenvironment and emphasize the translational relevance of senolytic agents for enhancing the efficacy of radiotherapy in gliomas.

Targeting Compact and Ordered Emitters by Supramolecular Dynamic Interactions for High-performance Organic Ambient Phosphorescence.

Purely organic room-temperature phosphorescence (RTP) materials have received intense attention due to their fascinating optical properties and advanced optoelectronic applications. The promotion of intersystem crossing (ISC) and minimalization of nonradiative dissipation under ambient conditions are key prerequisites for realizing high-performance organic RTP; However, the ISC process is generally inefficient for organic fluorogens and the populated triplet excitons are always too susceptible to be well stabilized by conventional means. Particularly, organizing organic fluorophores into compact and ordered entities by supramolecular dynamic interactions has proven to be a newly-emerged strategy to boost the ISC process greatly and suppress the non-radiative relaxations immensely, facilitating the population and stabilization of triplet excitons to access high-performance organic RTP. Consequently, well-defined organic emitters enable robust RTP emission even in the solution state, thus greatly extending the applications. Here, this review is focused on a timely and brief introduction to recent progress in tailoring ordered high-performance RTP emitters by supramolecular dynamic interactions. Their typical preparation strategies, optoelectronic properties, and applications are thoroughly summarized. In the summary section, key challenges and perspectives of this field are highlighted to suggest potential directions for future study.

Components of the tumor immune microenvironment based on m-IHC correlate with prognosis and subtype of triple-negative breast cancer.

BACKGROUND AND AIM: The spatial distribution and interactions of cells in the tumor immune microenvironment (TIME) might be related to the different responses of triple-negative breast cancer (TNBC) to immunomodulators. The potential of multiplex IHC (m-IHC) in evaluating the TIME has been reported, but the efficacy is insufficient. We aimed to research whether m-IHC results could be used to reflect the TIME, and thus to predict prognosis and complement the TNBC subtyping system. METHODS: The clinical, imaging, and prognosis data for 86 TNBC patients were retrospectively reviewed. CD3, CD4, CD8, Foxp3, PD-L1, and Pan-CK markers were stained by m-IHC. Particular cell spatial distributions and interactions in the TIME were evaluated with the HALO multispectral analysis platform. Then, we calculated the prognostic value of components of the TIME and their correlations with TNBC transcriptomic subtypes and MRI radiomic features reflecting TNBC subtypes. RESULTS: The components of the TIME score were established by m-IHC and demonstrated positive prognostic value for TNBC (p = 0.0047, 0.039, <0.0001 for DMFS, RFS, and OS). The score was calculated from several indicators, including Treg% in the tumor core (TC) or stromal area (SA), PD-L1+ cell% in the SA, CD3 + cell% in the TC, and PD-L1+ /CD8+ cells in the invasive margin and SA. According to the TNBC subtyping system, a few TIME indicators were significantly different in different subtypes and significantly correlated with MRI radiomic features reflecting TNBC subtypes. CONCLUSION: We demonstrated that the m-IHC-based quantitative score and indicators related to the spatial distribution and interactions of cells in the TIME can aid in the accurate diagnosis of TNBC in terms of prognosis and classification.

Artificial intelligence in breast imaging: Current situation and clinical challenges.

Breast cancer ranks among the most prevalent malignant tumours and is the primary contributor to cancer-related deaths in women. Breast imaging is essential for screening, diagnosis, and therapeutic surveillance. With the increasing demand for precision medicine, the heterogeneous nature of breast cancer makes it necessary to deeply mine and rationally utilize the tremendous amount of breast imaging information. With the rapid advancement of computer science, artificial intelligence (AI) has been noted to have great advantages in processing and mining of image information. Therefore, a growing number of scholars have started to focus on and research the utility of AI in breast imaging. Here, an overview of breast imaging databases and recent advances in AI research are provided, the challenges and problems in this field are discussed, and then constructive advice is further provided for ongoing scientific developments from the perspective of the National Natural Science Foundation of China.

Radiogenomic-based multiomic analysis reveals imaging intratumor heterogeneity phenotypes and therapeutic targets.

Intratumor heterogeneity (ITH) profoundly affects therapeutic responses and clinical outcomes. However, the widespread methods for assessing ITH based on genomic sequencing or pathological slides, which rely on limited tissue samples, may lead to inaccuracies due to potential sampling biases. Using a newly established multicenter breast cancer radio-multiomic dataset (n = 1474) encompassing radiomic features extracted from dynamic contrast-enhanced magnetic resonance images, we formulated a noninvasive radiomics methodology to effectively investigate ITH. Imaging ITH (IITH) was associated with genomic and pathological ITH, predicting poor prognosis independently in breast cancer. Through multiomic analysis, we identified activated oncogenic pathways and metabolic dysregulation in high-IITH tumors. Integrated metabolomic and transcriptomic analyses highlighted ferroptosis as a vulnerability and potential therapeutic target of high-IITH tumors. Collectively, this work emphasizes the superiority of radiomics in capturing ITH. Furthermore, we provide insights into the biological basis of IITH and propose therapeutic targets for breast cancers with elevated IITH.

Baseline levels of miR-223-3p correlate with the effectiveness of electroconvulsive therapy in patients with major depression.

There is a strong medical need to develop suitable biomarkers to improve the diagnosis and treatment of depression, particularly in predicting response to certain therapeutic approaches such as electroconvulsive therapy (ECT). MicroRNAs are small non-coding RNAs that have the ability to influence the transcriptome as well as proteostasis at the systems level. Here, we investigate the role of circulating microRNAs in depression and response prediction towards ECT. Of the 64 patients with treatment-resistant major depression (MDD) who received ECT treatment, 62.5% showed a response, defined as a reduction of ≥50% in the MADRS total score from baseline. We performed smallRNA sequencing in blood samples that were taken before the first ECT, after the first and the last ECT. The microRNAome was compared between responders and non-responders. Co-expression network analysis identified three significant microRNA modules with reverse correlation between ECT- responders and non-responders, that were amongst other biological processes linked to inflammation. A candidate microRNA, namely miR-223-3p was down-regulated in ECT responders when compared to non-responders at baseline. In line with data suggesting a role of miR-223-3p in inflammatory processes we observed higher expression levels of proinflammatory factors Il-6, Il-1b, Nlrp3 and Tnf-α in ECT responders at baseline when compared to non-responders. ROC analysis of confirmed the diagnostic power of miR-223-3p demarcating ECT-responders from non-responder subjects (AUC = 0.76, p = 0.0031). Our data suggest that miR-223-3p expression and related cytokine levels could serve as predictors of response to ECT in individuals with treatment-resistant depressive disorders.

Bacteria rather than fungi mediate the chemodiversity of dissolved organic matter in a mudflat intertidal zone.

Dissolved organic matter (DOM) in natural ecosystems is intimately associated with microbial communities. However, it remains unclear whether the diversity patterns followed by microbes can be transmitted to DOM compounds. Considering the structural properties of DOM compounds and the roles of microbes in ecosystems, we hypothesized that bacteria tended to be more closely associated with DOM compounds than fungi. To test the above hypothesis and bridge this knowledge gap, the diversity patterns and ecological processes for the DOM compounds, and the bacterial and fungal communities in a mudflat intertidal zone were comparatively investigated. As a result, spatial scaling patterns followed by microbes, including the diversity-area and distance-decay relationships, were also observed for DOM compounds. Lipid-like and aliphatic-like molecules comprised the major DOM compounds associated with environmental factors. Both the alpha- and beta-chemodiversity of DOM compounds were significantly associated with the diversity of bacterial communities, but not fungal communities. Co-occurrence ecological network analysis suggested that DOM compounds were more frequently associated with bacteria than fungi. Further, consistent community assembly patterns were observed for DOM and bacterial communities, but not fungal communities. Integrating multiple lines of evidence, this study demonstrated that bacteria rather than fungi mediated the chemodiversity of DOM in the mudflat intertidal zone. This study elucidates the spatial patterns of complex dissolved organic matter (DOM) pools in the intertidal ecosystem, shedding light on the intricate relationship between DOM compounds and bacterial communities.

Fully automating LI-RADS on MRI with deep learning-guided lesion segmentation, feature characterization, and score inference.

Introduction: Leveraging deep learning in the radiology community has great potential and practical significance. To explore the potential of fitting deep learning methods into the current Liver Imaging Reporting and Data System (LI-RADS) system, this paper provides a complete and fully automatic deep learning solution for the LI-RADS system and investigates its model performance in liver lesion segmentation and classification. Methods: To achieve this, a deep learning study design process is formulated, including clinical problem formulation, corresponding deep learning task identification, data acquisition, data preprocessing, and algorithm validation. On top of segmentation, a UNet++-based segmentation approach with supervised learning was performed by using 33,078 raw images obtained from 111 patients, which are collected from 2010 to 2017. The key innovation is that the proposed framework introduces one more step called feature characterization before LI-RADS score classification in comparison to prior work. In this step, a feature characterization network with multi-task learning and joint training strategy was proposed, followed by an inference module to generate the final LI-RADS score. Results: Both liver segmentation and feature characterization models were evaluated, and comprehensive statistical analysis was conducted with detailed discussions. Median DICE of liver lesion segmentation was able to achieve 0.879. Based on different thresholds, recall changes within a range of 0.7 to 0.9, and precision always stays high greater than 0.9. Segmentation model performance was also evaluated on the patient level and lesion level, and the evaluation results of (precision, recall) on the patient level were much better at approximately (1, 0.9). Lesion classification was evaluated to have an overall accuracy of 76%, and most mis-classification cases happen in the neighboring categories, which is reasonable since it is naturally difficult to distinguish LI-RADS 4 from LI-RADS 5. Discussion: In addition to investigating the performance of the proposed model itself, extensive comparison experiment was also conducted. This study shows that our proposed framework with feature characterization greatly improves the diagnostic performance which also validates the effectiveness of the added feature characterization step. Since this step could output the feature characterization results instead of simply generating a final score, it is able to unbox the black-box for the proposed algorithm thus improves the explainability.

Environmental heterogeneity and dispersal limitation simultaneously determine the spatial scaling of different microbial functional groups.

Uncovering the mechanisms driving patterns of diversity across space and through time is of critical importance in microbial community ecology. Previous studies suggest that microorganisms also follow the same spatial scaling patterns as macro-organisms. However, it remains unclear whether different microbial functional groups differ in spatial scaling and how different ecological processes may contribute to such differences. In this study, two typical spatial scaling patterns, taxa-area (TAR) and distance-decay relationships (DDR), were investigated for the whole prokaryotic community and seven microbial functional groups using marker genes, including amoA (AOA), amoA (AOB), aprA, dsrB, mcrA, nifH and nirS. Different microbial functional groups harbored different spatial scaling patterns. Microbial functional groups had weaker TAR slope coefficients than the whole prokaryotic community. The archaeal ammonia-oxidizing group, however, displayed a stronger DDR pattern than the bacterial ammonia-oxidizing group. For both TAR and DDR, rare subcommunities were mainly responsible for the observed microbial spatial scaling patterns. Significant associations between environmental heterogeneity and spatial scaling metrics were observed for multiple microbial functional groups. Dispersal limitation, which positively correlated with phylogenetic breadth, was also strongly associated with the strength of microbial spatial scaling. The results demonstrated that environmental heterogeneity and dispersal limitation simultaneously contributed to microbial spatial scaling patterns. This study links microbial spatial scaling patterns with ecological processes, providing mechanistic insights into the typical diversity patterns followed by microbes.

Quantitative study on the three-dimensional defense of Puzhuang Suo-Fort ancient wall and the moat.

The spatial layout of the coastal forts defense system of the Ming Dynasty of China has been studied in a relatively comprehensive way. Nonetheless, ancient defense mechanisms have not been fully revealed. Previous studies have focused more on the macro and meso levels. Studies into its microscopic construction mechanism need to be enhanced. This research attempts to quantify and validate the rationality of the ancient microscopic defense mechanism, using the ancient defense mechanism of Pu Zhuang Suo-Fort in Zhejiang Province as an instance. This study concentrates on the distribution of firepower strength beyond the walls of coastal defense forts, as well as the effect of wall height on firepower defense capabilities. There is a specific firepower attenuation area near the walls due to the firing blind area in the coastal forts defense system. And the construction of the moat plays an additive role in its defensive capability. Meanwhile, the height of the fort wall will also affect the range of the firing blind zone under Yangmacheng. In theory, there is a reasonable height range of the wall and a proper position of the moat. This height range can meet both good economy and defensive capabilities. In turn, the position of the moats and the height of the walls can verify the rationality of the construction mechanism of the coastal forts' defense system.

Distinct Ecological Processes Mediate Domain-Level Differentiation in Microbial Spatial Scaling.

The spatial scaling of biodiversity, such as the taxa-area relationship (TAR) and distance-decay relationship (DDR), is a typical ecological pattern that is followed by both microbes and macrobes in natural ecosystems. Previous studies focusing on microbes mainly aimed to address whether and how different types of microbial taxa differ in spatial scaling patterns, leaving the underlying mechanisms largely untouched. In this study, the spatial scaling of different microbial domains and their associated ecological processes in an intertidal zone were comparatively investigated. The significant spatial scaling of biodiversity could be observed across all microbial domains, including archaea, bacteria, fungi, and protists. Among them, archaea and fungi were found with much stronger DDR slopes than those observed in bacteria and protists. For both TAR and DDR, rare subcommunities were mainly responsible for the observed spatial scaling patterns, except for the DDR of protists and bacteria. This was also evidenced by extending the TAR and DDR diversity metrics to Hill numbers. Further statistical analyses demonstrated that different microbial domains were influenced by different environmental factors and harbored distinct local community assembly processes. Of these, drift was mainly responsible for the compositional variations of bacteria and protists. Archaea were shaped by strong homogeneous selection, whereas fungi were more affected by dispersal limitation. Such differing ecological processes resulted in the domain-level differentiation of microbial spatial scaling. This study links ecological processes with microbial spatial scaling and provides novel mechanistic insights into the diversity patterns of microbes that belong to different trophic levels. IMPORTANCE As the most diverse and numerous life form on Earth, microorganisms play indispensable roles in natural ecological processes. Revealing their diversity patterns across space and through time is of essential importance to better understand the underlying ecological mechanisms controlling the distribution and assembly of microbial communities. However, the diversity patterns and their underlying ecological mechanisms for different microbial domains and/or trophic levels require further exploration. In this study, the spatial scaling of different microbial domains and their associated ecological processes in a mudflat intertidal zone were investigated. The results showed different spatial scaling patterns for different microbial domains. Different ecological processes underlie the domain-level differentiation of microbial spatial scaling. This study links ecological processes with microbial spatial scaling to provide novel mechanistic insights into the diversity patterns of microorganisms that belong to different trophic levels.

To assemble or not to assemble: metagenomic profiling of microbially mediated biogeochemical pathways in complex communities.

High-throughput profiling of microbial functional traits involved in various biogeochemical cycling pathways using shotgun metagenomic sequencing has been routinely applied in microbial ecology and environmental science. Multiple bioinformatics data processing approaches are available, including assembly-based (single-sample assembly and multi-sample assembly) and read-based (merged reads and raw data). However, it remains not clear how these different approaches may differ in data analyses and affect result interpretation. In this study, using two typical shotgun metagenome datasets recovered from geographically distant coastal sediments, the performance of different data processing approaches was comparatively investigated from both technical and biological/ecological perspectives. Microbially mediated biogeochemical cycling pathways, including nitrogen cycling, sulfur cycling and B12 biosynthesis, were analyzed. As a result, multi-sample assembly provided the most amount of usable information for targeted functional traits, at a high cost of computational resources and running time. Single-sample assembly and read-based analysis were comparable in obtaining usable information, but the former was much more time- and resource-consuming. Critically, different approaches introduced much stronger variations in microbial profiles than biological differences. However, community-level differences between the two sampling sites could be consistently observed despite the approaches being used. In choosing an appropriate approach, researchers shall balance the trade-offs between multiple factors, including the scientific question, the amount of usable information, computational resources and time cost. This study is expected to provide valuable technical insights and guidelines for the various approaches used for metagenomic data analysis.