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Ren, H.

Publications and source records attributed to Ren, H..

At least 109 records · Page 6Linked to original sources
bioRxiv2020

Viral mimicry of p65/RelA transactivation domain to inhibit NF-κB activation

Sensing of virus infection activates NF-{kappa}B to induce the expression of interferons, cytokines and chemokines to initiate the antiviral response. Viruses antagonise these antiviral defences by interfering with immune sensing and blocking the actions of antiviral and inflammatory molecules. Here, we show that a viral protein mimics the transactivation domain of the p65 subunit of NF-{kappa}B. The C terminus of vaccinia virus (VACV) protein F14 (residues 51-73) activates transcription when fused to a DNA-binding domain-containing protein and F14 associates with NF-{kappa}B co-activator CBP, disrupting p65-CBP interaction. Consequently, F14 diminishes CBP-mediated acetylation of p65 and the downstream recruitment of the transcriptional regulator BRD4 to the promoter of the NF-{kappa}B-responsive genes CXCL10 and CCL2, hence inhibiting their expression. Conversely, the recruitment of BRD4 to the promoters of NFKBIA, which encodes the inhibitor of NF-{kappa}B (I{kappa}B), and CXCL8 remains unaffected in the presence of either F14 or JQ1, a competitive inhibitor of BRD4 bromodomains, indicating its recruitment is acetylation-independent. Therefore, unlike other viral NF-{kappa}B antagonists, F14 is a selective inhibitor of NF-{kappa}B-dependent gene expression. A VACV strain lacking F14 showed that it contributes to virulence in an intradermal model of infection. Our results uncover a mechanism by which viruses disarm the antiviral defences through molecular mimicry of a conserved host protein and provide insight into the regulation of NF-{kappa}B-dependent gene expression by BRD4.

microbiology↗
bioRxiv2020

An expanded gene catalog of mouse gut metagenome

High-quality and comprehensive reference gene catalogs are essential for metagenomic research. The rather low diversity of samples used to construct existing catalogs of mouse gut metagenomes limits the size and numbers of identified genes in existing catalogs. We therefore established an expanded gene catalog of genes in the mouse gut metagenomes (EMGC) containing >5.8 million genes by integrating 88 newly sequenced samples, 86 mouse-gut-related bacterial genomes and 3 existing gene catalogs. EMGC increases the number on non-redundant genes by more than one million genes compared to the so far most extensive catalog. More than 50% of the genes in EMGC were taxonomically assigned and 30% were functionally annotated. 902 Metagenomic species (MGS) assigned to 122 taxa are identified based on the EMGC. The EMGC-based analysis of samples from groups of mice originating from different animal providers, housing laboratories and genetic strains substantiated that diet is a major contributor to differences in composition and functional potential of the gut microbiota irrespective of differences in environment and genetic background. We envisage that EMGC will serve as an efficient and resource-saving reference dataset for future metagenomic studies in mice.

bioinformatics↗
bioRxiv2020

Pancreatic α and β cells are globally phase-locked

The Ca2+ modulated pulsatile secretion of glucagon and insulin by pancreatic and {beta} cells plays a key role in glucose homeostasis. However, how and {beta} cells coordinate via paracrine interaction to produce various Ca2+ oscillation patterns is still elusive. Using a microfluidic device and transgenic mice in which and {beta} cells were labeled with different colors, we were able to record islet Ca2+ signals at single cell level for long times. Upon glucose stimulation, we observed heterogeneous Ca2+ oscillation patterns intrinsic to each islet. After a transient period, the oscillations of and {beta} cells were globally phase-locked, i.e., the two types of cells in an islet each oscillate synchronously but with a phase shift between the two. While the activation of cells displayed a fixed time delay of ~20 s to that of {beta} cells, {beta} cells activated with a tunable delay after the cells. As a result, the tunable phase shift between and {beta} cells set the islet oscillation period and pattern. Furthermore, we demonstrated that the phase shift can be modulated by glucagon. A mathematical model of islet Ca2+ oscillation taking into consideration of the paracrine interaction was constructed, which quantitatively agreed with the experimental data. Our study highlights the importance of cell-cell interaction to generate stable but tunable islet oscillation patterns.

systems biology↗
bioRxiv2020

Species packing and the latitudinal gradient in local beta-diversity

The decline in species richness at higher latitudes is among the most fundamental patterns in ecology. Whether changes in species composition across space (beta-diversity) contribute to this gradient of overall species richness (gamma-diversity) remains hotly debated. Previous studies that failed to resolve the issue suffered from a well-known tendency for small samples in areas with high gamma-diversity to have inflated measures of beta-diversity. Here, we provide here a novel analytical test, using beta-diversity metrics that correct the gamma-diversity and sampling biases, to compare beta-diversity and species packing across a latitudinal gradient in tree species richness of 21 large forest plots along a large environmental gradient in East Asia. We demonstrate that after accounting for topography and correcting the gamma-diversity bias, tropical forests still have higher beta-diversity than temperate analogs. This suggests that beta-diversity contributes to the latitudinal species richness gradient as a component of gamma-diversity. Moreover, both niche specialization and niche marginality (a measure of niche spacing along an environmental gradient) also increase towards the equator, after controlling for the effect of topographic heterogeneity. This supports the joint importance of tighter species packing and larger niche space in tropical forests while also demonstrating the importance of local processes in controlling beta-diversity.

ecology↗
bioRxiv2020

Cellbow: a robust customizable cell segmentation program

Time-lapse live cell imaging of a growing cell population is routine in many biological investigations. A major challenge in imaging analysis is accurate segmentation, a process to define the boundaries of cells based on raw image data. Current segmentation methods relying on single boundary features have problems in robustness when dealing with inhomogeneous foci which invariably happens in cell population imaging. Here, we demonstrated that combined with multi-layer training set strategy, a neural-network-based algorithm Cellbow can achieve accurate and robust segmentation of cells in broad and general settings. It can also facilitate long-term tracking of cell growth and division. Furthermore, Cellbow is customizable and generalizable. It is broadly applicable to segmenting fluorescent images of diverse cell types with no further training needed. For bright-field images, only a small set of sample images of the specific cell type from the user may be needed for training. To facilitate the application of Cellbow, we provide a website on which one can online test the software, as well as an ImageJ plugin for the user to visualize the performance before software installation.

systems biology↗
bioRxiv2020

Foundation species across a latitudinal gradient in China

AbstractFoundation species play important roles in structuring forest communities and ecosystems. Foundation species are difficult to identify without long-term observations or experiments and their foundational roles rarely are identified before they are declining or threatened. We used new statistical criteria based on size-frequency distributions, species diversity, and spatial codispersion among woody plants to identify potential ("candidate") foundation species in 12 large forest dynamics plots spanning 26 degrees of latitude in China. We used these data to identify a suite of candidate foundation species in Chinese forests; test the hypothesis that foundation woody plant species are more frequent in the temperate zone than in the tropics; and compare these results with comparable data from the Americas to suggest candidate foundation genera in Northern Hemisphere forests. We identified more candidate foundation species in temperate plots than in subtropical or tropical plots, and this relationship was independent of the latitudinal gradient in overall species richness. Two species of Acer, the canopy tree Acer ukurunduense and the shrubby treelet Acer barbinerve were the only two species that met both criteria in full to be considered as candidate foundation species. When we relaxed the diversity criteria, Acer, Tilia, and Juglans spp., and Corlyus mandshurica were frequently identified as candidate foundation species. In tropical plots, the tree Mezzettiopsis creaghii and the shrubs or treelets Aporusa yunnanensis and Ficus hispida had some characteristics associated with foundation species. Species diversity of co-occurring woody species was negatively associated with basal area of candidate foundation species more frequently at 5- and 10-m spatial grains (scale) than at a 20-m grain. Conversely, Bray-Curtis dissimilarity was positively associated with basal area of candidate foundation species more frequently at 5-m than at 10- or 20-m grains. Our data support the hypothesis that foundation species should be more common in temperate than in tropical or boreal forests, and suggest that in the Northern Hemisphere that Acer be investigated further as a foundation tree genus.

ecology↗
bioRxiv2020

Accurate Diagnosis of Colorectal Cancer Based on Histopathology Images Using Artificial Intelligence

BackgroundAccurate and robust pathological image analysis for colorectal cancer (CRC) diagnosis is time-consuming and knowledge-intensive, but is essential for CRC patients treatment. The current heavy workload of pathologists in clinics/hospitals may easily lead to unconscious misdiagnosis of CRC based on their daily image analyses. MethodsBased on a state-of-the-art transfer-learned deep convolutional neural network in artificial intelligence (AI), we proposed a novel patch aggregation strategy for clinic CRC prediction/diagnosis using weakly labeled pathological whole slide image (WSI) patches. This approach was trained and validated using an unprecedented and enormously large number of 170,099 patches, >14,680 WSIs, from >9,631 subjects that covered diverse and representative clinical cases from multi-independent-sources across China, U.S., and Germany. ResultsOur innovative AI tool was consistently nearly perfectly agreed with (average Kappa-statistic 0.896) and even often better than most of the experienced expert pathologists when tested in diagnosing CRC WSIs from multi-centers. The average area under the receiver operating characteristics curve (AUC) of AI was greater than that of the pathologists (0.981 vs 0.970) and achieved the best performance among the application of other AI methods to CRC diagnosis. Our AI-generated heatmap highlights the image regions of cancer tissue/cells. ConclusionsThis first-ever generalizable AI system can handle large amounts of WSIs consistently and robustly without potential bias due to fatigue commonly experienced by clinical pathologists. Hence, it will drastically alleviate the heavy clinical burden of daily pathology diagnosis, and improve the treatment for CRC patients. This tool is generalizable to other cancer diagnosis based on image recognition.

pathology↗
bioRxiv2020

Multiple approaches for massively parallel sequencing of HCoV-19 genomes directly from clinical samples

COVID-19 has caused a major epidemic worldwide, however, much is yet to be known about the epidemiology and evolution of the virus. One reason is that the challenges underneath sequencing HCoV-19 directly from clinical samples have not been completely tackled. Here we illustrate the application of amplicon and hybrid capture (capture)-based sequencing, as well as ultra-high-throughput metatranscriptomic (meta) sequencing in retrieving complete genomes, inter-individual and intra-individual variations of HCoV-19 from clinical samples covering a range of sample types and viral load. We also examine and compare the bias, sensitivity, accuracy, and other characteristics of these approaches in a comprehensive manner. This is, to date, the first work systematically implements amplicon and capture approaches in sequencing HCoV-19, as well as the first comparative study across methods. Our work offers practical solutions for genome sequencing and analyses of HCoV-19 and other emerging viruses.

genomics↗
bioRxiv2020

Synthesis of diagnostic quality cancer pathology images

Deep learning-based computer vision methods have recently made remarkable breakthroughs in the analysis and classification of cancer pathology images. However, there has been relatively little investigation of the utility of deep neural networks to synthesize medical images. In this study, we evaluated the efficacy of generative adversarial networks (GANs) to synthesize high resolution pathology images of ten histological types of cancer, including five cancer types from The Cancer Genome Atlas (TCGA) and the five major histological subtypes of ovarian carcinoma. The quality of these images was assessed using a comprehensive survey of board-certified pathologists (n = 9) and pathology trainees (n = 6). Our results show that the real and synthetic images are classified by histotype with comparable accuracies, and the synthetic images are visually indistinguishable from real images. Furthermore, we trained deep convolutional neural networks (CNNs) to diagnose the different cancer types and determined that the synthetic images perform as well as additional real images when used to supplement a small training set. These findings have important applications in proficiency testing of medical practitioners and quality assurance in clinical laboratories. Furthermore, training of computer-aided diagnostic systems can benefit from synthetic images where labeled datasets are limited (e.g., rare cancers). We have created a publicly available website where clinicians and researchers can attempt questions from the image survey at http://gan.aimlab.ca/.

bioinformatics↗
bioRxiv2020

Dissolved oxygen dynamics reveal biogeochemical tipping points driven by river corridor hydrology

Riverbeds can have an important impact on large-scale fluxes of biogeochemically active solutes in river corridor systems. The hyporheic zone is an important area of both hydrology and biogeochemistry, particularly in a hydropeaked river system where rapid variations in river stage height, hydraulic gradients, and residence times occur. We measured several biogeochemical and hydrological parameters at three different subsurface depths in the riverbed of the Columbia River in Washington state, a complex hydropeaked river system with significant subsurface heterogeneity. During the study, episodes of significant dissolved oxygen (DO) change were observed. The DO signal changes were the most apparent, compared to more modest changes in other biogeochemical markers. While DO is often associated with biological activity, we ultimately found that the notable DO excursions were associated with hydrologic gradients. Here we describe hydrologic perturbations and biogeochemical responses in terms of hydrobiogeochemical regimes. Two different forms of DO response to hydraulic gradient perturbation were observed, defining different hydrobiogeochemical regimes. The system tips abruptly from one regime to the other, exhibiting threshold behavior that is uncaptured in current estimations of cumulative influences of subsurface processes from reach to earth system scales. Plain language summaryThe water in a river is constantly flowing in and out of the rivers bed. While it is in the bed, microbes process environmentally and ecologically important nutrients and other compounds. The exchange of water between the river and the riverbed is largely controlled by the stage of the river. Many processes, both natural and human, can cause rapid changes in the river stage. These rapid stage changes can cause abrupt changes in the riverbeds ability to process nutrients and other compounds. In this study, we use dissolved oxygen as a window into riverbed microbial and chemical processing. We see that relatively rapid changes in river stage can cause abrupt changes in the concentrations of chemicals in the riverbed, indicating fundamental changes in how the riverbed processes are occurring. Understanding these abrupt and fundamental changes is important to our overall understanding of the ecological function of river corridor systems. 3 key pointsO_LIA hydrobiogeochemical regime is defined by the functional form of the relationship of a hydrologic perturbation and biogeochemical marker. C_LIO_LIShort-term hydrologic perturbations drive tipping points between distinct hydrobiogeochemical regimes. C_LIO_LILinear and hysteretic dissolved oxygen dynamics define distinct regimes as observed throughout the hyporheic zone. C_LI

biochemistry↗
bioRxiv2020

Catabolism of 3-hydroxypyridine by Ensifer adhaerens HP1: a novel four-component gene encoding 3-hydroxypyridine dehydrogenase HpdA catalyzes the first step of biodegradation

3-Hydroxypyridine (3HP) is an important natural pyridine derivative. Ensifer adhaerens HP1 can utilize 3HP as the sole source of carbon, nitrogen and energy to grow. However, the genes responsible for the degradation of 3HP remain unknown. In this study, we predicted that a gene cluster, designated 3hpd, may be responsible for the degradation of 3HP. The initial hydroxylation of 3HP is catalyzed by a four-component dehydrogenase (HpdA1A2A3A4), leading to the formation of 2,5-dihydroxypyridine (2,5-DHP) in E. adhaerens HP1. In addition, the SRPBCC component in HpdA existed as a separate subunit, which is different from other SRPBCC-containing molybdohydroxylases acting on N-heterocyclic aromatic compounds. Our findings provide a better understanding of the microbial degradation of pyridine derivatives in nature. Additionally, research on the origin of the discovered four-component dehydrogenase with a separate SRPBCC domain may be of great significance. Importance3-Hydroxypyridine is an important building block for synthesizing drugs, herbicides and antibiotics. Although the microbial degradation of 3-hydroxypyridine has been studied for many years, the molecular mechanisms remain unclear. Here, we show that 3hpd is responsible for the catabolism of 3-hydroxypyridine. The 3hpd gene cluster was found to be widespread in Actinobacteria, Rubrobacteria, Thermoleophilia, and Alpha-, Beta-, and Gammaproteobacteria, and the genetic organization of the 3hpd gene clusters in these bacteria showed high diversity. Our findings provide new insight into the catabolism of 3-hydroxypyridine in bacteria.

microbiology↗
bioRxiv2019

Advanced Whole Genome Sequencing Using a Complete PCR-free Massively Parallel Sequencing (MPS) Workflow

BackgroundSystematic errors can be introduced from DNA amplification during massively parallel sequencing (MPS) library preparation and sequencing array formation. Polymerase chain reaction (PCR)-free genomic library preparation methods were previously shown to improve whole genome sequencing (WGS) quality on the Illumina platform, especially in calling insertions and deletions (InDels). We hypothesized that substantial InDel errors continue to be introduced by the remaining PCR step of DNA cluster generation. In addition to library preparation and sequencing, data analysis methods are also important for the accuracy of the output data.In recent years, several machine learning variant calling pipelines have emerged, which can correct the systematic errors from MPS and improve the data performance of variant calling. ResultsHere, PCR-free libraries were sequenced on the PCR-free DNBSEQ arrays from MGI Tech Co., Ltd. (referred to as MGI) to accomplish the first true PCR-free WGS which the whole process is truly not only PCR-free during library preparation but also PCR-free during sequencing. We demonstrated that PCR-based WGS libraries have significantly (about 5 times) more InDel errors than PCR-free libraries.Furthermore, PCR-free WGS libraries sequenced on the PCR-free DNBSEQ platform have up to 55% less InDel errors compared to the NovaSeq platform, confirming that DNA clusters contain PCR-generated errors.In addition, low coverage bias and less than 1% read duplication rate was reproducibly obtained in DNBSEQ PCR-free using either ultrasonic or enzymatic DNA fragmentation MGI kits combined with MGISEQ-2000. Meanwhile, variant calling performance (single-nucleotide polymorphisms (SNPs) F-score>99.94%, InDels F-score>99.6%) exceeded widely accepted standards using machine learning (ML) methods (DeepVariant or DNAscope). ConclusionsEnabled by the new PCR-free library preparation kits, ultra high-thoughput PCR-free sequencers and ML-based variant calling, true PCR-free DNBSEQ WGS provides a powerful solution for improving WGS accuracy while reducing cost and analysis time, thus facilitating future precision medicine, cohort studies, and large population genome projects.

genomics↗
bioRxiv2019

Cytokines and schizophrenia revisited: a two-sample multi-marker Mendelian randomization approach

BackgroundSchizophrenia is a complex mental disorder with recent evidence suggesting a critical immune component underpinning its pathophysiology. Two-sample Mendelian randomization (MR) provided an opportunity to probe the immune changes in schizophrenia by harnessing the increasing availability of summary-level data from large GWAS consortia. ObjectiveTo map the extensive immune response of schizophrenia in terms of cytokines/chemokines and to explore the effect of cytokines induced by schizophrenia (SCZ-induced cytokines) on the brain structure and function Sources and methodsUsing the summary-level data generated from GWAS of schizophrenia, cytokines in the peripheral blood and imaging-derived phenotypes (IDPs), we performed two rounds of two-sample MR analysis; the identified cytokines from first round of analysis (schizophrenia => cytokines) were modeled for its underlying structure and subsequent clustering analysis further grouped SCZ-induced cytokines based on their genetic similarities. The multi-phenotype summary statistics of each cytokine module were then used as instrumental variables (IVs) for the second round of MR analysis to detect their effect on brain structure and function. ResultsThe first round of MR analysis identified nine cytokines, the highlight of which includes IL18 (OR = 1.292, P = 8.37 x 10-42) and TNFa (OR = 0.721, P = 7.33 x 10-6), to be causally associated with schizophrenia. These SCZ-induced cytokines could be clustered into three modules. The second round of MR analysis (cytokine module => IDPs) indicated that module B (SCGFb-IP10-CTACK-IL6) significantly increased the level of IDPs including IDP_T1_SIENAX_peripheral_grey_normalised_volume ({beta} = 0.0453, P = 4.40x1010), IDP_dMRI_TBSS_MD_Posterior_corona_radiata_R ({beta}= 0.0584, P = 8.89x 10-16) and IDP_dMRI_TBSS_MD_Cingulum_hippocampus_R ({beta} = 0.0563, P = 9.88x 10-15), with module C (IL18-GROa-TNFa) increasing the level of IDP_dMRI_TBSS_L2_Posterior_thalamic_radiation_R ({beta}= 0.0341, P = 2.67x 10-6). ConclusionOur study, for the first time, mapped the causal link from schizophrenia to the comprehensive immune responses, and the findings suggest immune networks play a role in pathophysiology of schizophrenia by mediating the deviations of total gray matter volume and white matter fibers possibly in the mesolimbic system.

bioinformatics↗
bioRxiv2019

Analysis of single-cell gene pair coexpression landscapes by stochastic kinetic modeling reveals gene-pair interactions in development

Single-cell transcriptomics is advancing discovery of the molecular determinants of cell identity, while spurring development of novel data analysis methods. Stochastic mathematical models of gene regulatory networks help unravel the dynamic, molecular mechanisms underlying cell-to-cell heterogeneity, and can thus aid interpretation of heterogeneous cell-states revealed by single-cell measurements. However, integrating stochastic gene network models with single cell data is challenging. Here, we present a method for analyzing single-cell gene-pair coexpression patterns, based on biophysical models of stochastic gene expression and interaction dynamics. We first developed a high-computational-throughput approach to stochastic modeling of gene-pair coexpression landscapes, based on numerical solution of gene network Master Equations. We then comprehensively catalogued coexpression patterns arising from tens of thousands of gene-gene interaction models with different biochemical kinetic parameters and regulatory interactions. From the computed landscapes, we obtain a low-dimensional "shape-space" describing distinct types of coexpression patterns. We applied the theoretical results to analysis of published single cell RNA sequencing data and uncovered complex dynamics of coexpression among gene pairs during embryonic development. Our approach provides a generalizable framework for inferring evolution of gene-gene interactions during critical cell-state transitions.

systems biology↗
bioRxiv2019

Spatial expression pattern of ZNF391 gene in the brains of patients with schizophrenia, bipolar disorders or major depressive disorder identifies new cross-disorder biotypes: A trans-diagnostic, top-down approach

BackgroundGiven the struggle in the field of psychiatry to realize the precise diagnosis and treatment, it is in an urgent need to redefine psychiatric disorders based on objective biomarkers. The results generated from large psychiatric genomic consortia show us some new vantage points to understand the pathophysiology of psychiatric disorders. Nevertheless, how to relate these captured signals to the more refined disease dimensions has yet to be established.\n\nMethodsWe chose a top-down, cross-disorder approach by using the summary statistics of GWAS from large psychiatric genomic consortia to build a genomic structural equation model (SEM) for SCZ, BD and MDD to detect their common factor (CF), and to map a potential causal core gene for the CF, followed by the transcriptional prediction of the identified causal gene in our sample and the discovery of new biotypes based on the prediction pattern of the causal gene in the brain. We then characterized the biotypes in the context of their demographic features, cognitive functions and neuroimaging traits.\n\nOutcomesA common factor emerged from a well-fitting genomic SEM of SCZ, BD and MDD (loading 0.42, 0.35 and 0.09 for SCZ, BD and MDD, respectively). One genomic region in chromosome 6 was implicated in the genetic make-up of the common factor, with fine-mapping analysis marking ZNF391 as a potential causal core gene (posterior possibility = 0.96). Gene expression inference analysis identified eight brain regions showing different expression levels of ZNF391 between patients and controls, with three biotypes arising from clustering patients based on their expression pattern of ZNF391 in the brain. The three biotypes performed significantly differently in working memory (PDMS_TC = 0.015, PDMS_TC_A = 0.0318, PDMS_t0D = 0.015) and demonstrated different gray matter volumes in right inferior frontal orbital gyrus (RIFOG) in the same order as working memory (biotype 3 > biotype 2 > biotype 1, PRIFOG = 0.0027). Using ZNF391 as instrumental variable (IV), a partial casual path could be linked from RIFOG to working memory ({beta}RIFOG->DMS_TC0D = 4.95, P = 0.0056; {beta} RIFOG->DMS_TC = 2.53, P = 0.059; {beta}RIFOG->DMS_TC_A = 2.57, P = 0.056).\n\nInterpretationThe general predisposition to several psychiatric disorders may be influenced by variations of ZNF391, through its effects on right inferior frontal orbital gyrus and working memory. This illustrates the potential of a trans-diagnostic, top-down approach in understanding the commonality of psychiatric disorders.\n\nEvidence before this studyThe results from recent cross-disorder genome-wide association studies (GWAS)using large samples indicate that there is notable genetic overlapping between psychiatric disorders. However, the structural relationship of these disorders at the genomic level and the details of refined disease dimensions affected by the associated loci in a cross-disorder pattern remains unknown. We searched the published studies (up to Sep 7, 2019) in PubMed using the combination of the following keywords \"((cross disorder) OR (schizophrenia AND bipolar disorder AND major depressive disorder) AND (genome AND structural equation) AND (cognition OR imaging))\", no published study was found. We then removed the term \"structural equation\", 23 original studies were found. To the best of our knowledge, none of these studies explored the organized structure between three disorders. Further, of 23 articles we found, the majority of them took an approach of either polygenic risk score (PRS) or candidate gene to test the association with either psychological traits such as loneliness or neuroimaging measures in one (schizophrenia) or two (schizophrenia and bipolar) disorders. Hitherto, no study has been conducted to redefine three disorders based on the biological markers generated from the cross-disorder genomic studies.\n\nAdded value of this studyAdopting a novel approach of genomic structural equation modelling, we used the latest results of GWAS of three major psychiatric disorders to detect their common factor, further, to identify the loci associated with such as a common factor, and the locis transcription consequences in the brain. Propelled by the phenomenon \"genes do not read DSM\", we used a cutting-edge clustering algorithm to redefine three disorders based on the cerebral spatial expression pattern of associated core gene. Our study provides another piece of evidence as to the potentials of utilizing the signals arising from large population-scale GWAS to dissect and redefine psychiatric disorders.\n\nImplications of all the available evidenceConsistent with previous case-control cross-disorder GWAS, our study suggests that a common factor exists in three major psychiatric disorders and the biological information of core gene associated with the common factor could be used as an objective marker to explain three disorders and their pathophysiology.

genomics↗
bioRxiv2019

Machine learning aided analyses of thousands of draft genomes reveal plant- and environment-specific features of activated sludge process

Microorganisms in activated sludge (AS) play key roles in the wastewater treatment process. However, the ecological behavior of microorganisms in AS and their differences with microorganisms in other environments have mainly been studied using 16S rRNA gene that may not truly represent their in-situ functions. Here, we present 2045 bacterial and archaeal metagenome-assembled genomes (MAGs) recovered from 1.35 Tb of metagenomic sequencing data generated from 114 AS samples of 23 full-scale wastewater treatment plants (WWTPs). The average completeness and contamination of the MAGs are 82.0% and 2.0%, respectively. We find that the AS MAGs have obviously plant-specific features and few proteins are shared by different WWTPs, especially for WWTPs located in geographically distant areas. Despite the differences, specific functional traits (e.g. functions related to aerobic metabolism, nutrient sensing/acquisition, biofilm formation, etc.) of AS MAGs could be identified by a machine learning approach, and based on these traits, AS MAGs could be differentiated from MAGs of other environments with an accuracy of 96.6%. Our work provides valuable genome resources for future investigation of the AS microbiome and also introduces a novel approach to understand the microbial ecology in different ecosystems.

microbiology↗
bioRxiv2019

Distinct gut metagenomics and metaproteomics signatures in prediabetics and treatment-naïve type 2 diabetics

BackgroundThe gut microbiota plays important roles in modulating host metabolism. Previous studies have demonstrated differences in the gut microbiome of T2D and prediabetic individuals compared to healthy individuals, with distinct disease-related microbial profiles being reported in groups of different age and ethnicity. However, confounding factors such as anti-diabetic medication hamper identification of the gut microbial changes in disease development.\n\nMethodWe used a combination of in-depth metagenomics and metaproteomics analyses of faecal samples from treatment-naive type 2 diabetic (TN-T2D, n=77), pre-diabetic (Pre-DM, n=80), and normal glucose tolerant (NGT, n=97) individuals to investigate compositional and functional changes of the gut microbiota and the faecal content of microbial and host proteins in Pre-DM and treatment-naive T2D individuals to elucidate possible host-microbial interplays characterising different disease stages.\n\nFindingsWe observed distinct differences characterizing the gut microbiota of these three groups and validated several key features in an independent TN-T2D cohort. We also demonstrated that the content of several human antimicrobial peptides and pancreatic enzymes differed in faecal samples between three groups, such as reduced faecal level of antimicrobial peptides and pancreatic enzymes in TN-T2D.\n\nInterpretationOur findings suggest a complex, disease stage-dependent interplay between the gut microbiota and the host and emphasize the value of metaproteomics to gain further insight into interplays between the gut microbiota and the host.\n\nFundingNational Key Research and Development Program of China, No. 2017YFC0909703, Shenzhen Municipal Government of China, No. JCYJ20170817145809215, and National Natural Science Foundation of China, No. 31601073.

microbiology↗
bioRxiv2019

Metagenomic Next-generation Sequencing of Cerebrospinal Fluid for the Diagnosis of Central Nervous System Infections: A Multicentre Prospective Study

BackgroundInfectious encephalitis and meningitis are often treated empirically without identification of the causative pathogen. Metagenomic next-generation sequencing (mNGS) is a high throughput technology that enables the detection of pathogens independent of prior clinical or laboratory information.\n\nMethodsThe present study was a multicentre prospective evaluation of mNGS of cerebrospinal fluid (CSF) for the diagnosis of suspected central nervous system infections.\n\nResultsA total of 276 patients were enrolled in this study between Jan 1, 2017 and Jan 1, 2018. Identification of an etiologic pathogen in CSF by mNGS was achieved in 101 patients (36.6%). mNGS detected 11 bacterial species, 7 viral species, 2 fungal species, and 2 parasitic species. The five leading positive detections were varicella-zoster virus (17), Mycobacterium tuberculosis (14), herpes simplex virus 1 (12), Epstein-Barr virus (12), and Cryptococcus neoformans (7). False positives occurred in 12 (4.3%) patients with bacterial infections known to be widespread in hospital environments. False negatives occurred in 16 (5.8%) patients and included bacterial, viral and fungal aetiologies.\n\nConclusionsmNGS of CSF is a powerful diagnostic method to identify the pathogen for many central nervous system infections.

genomics↗