Biology subjects

Deng, Y.

Publications and source records attributed to Deng, Y..

At least 253 records · Page 14Linked to original sources

bioRxiv2020

Recurrent Dynamics of Rupture Transitions of Single Giant Vesicles at Solid Surfaces

Single giant vesicles (GVs) rupture spontaneously from their salt-laden suspension onto solid surfaces. At hydrophilic surfaces, they rupture via a recurrent burst-heal dynamics: during burst, single pores nucleate at the contact boundary of the adhering vesicles facilitating asymmetric spreading and producing a "heart" shaped membrane patch. During the healing phase, the competing pore closure produces a daughter vesicle. At hydrophobic surfaces, by contrast, the GVs rupture via a distinctly different, yet recurrent, bouncing ball rhythm: Rendered tense by the substrate interactions, GVs porate and spread monomolecular layer on the hydrophobic surface in a symmetric manner. Here too, the competition from pore closure produces a daughter vesicle, which re-engages with the substrate. In both cases, the pattern of burst-reseal events repeats multiple times splashing and spreading the vesicular fragments as bilayer patches at the solid surface in a pulsatory manner. These remarkable recurrent dynamics arise not because of the elastic properties of the solid surface but because the competition between membrane spreading and pore healing, prompted by the surface-energy dependent adhesion, determine the course of the topological transition. STATEMENT OF SIGNIFICANCEGiant lipid vesicles adhering to a solid surface experience strong mechanical stresses. The contacting membrane segment loses thermal fluctuations and accumulates mechanical tension, the equilibration of which can give rise to global shape changes, lipid phase separation, and traction forces. Beyond a threshold tension, vesicles porate, unravel, and spread. Here, we find that a competition from pore-healing can make rupture iterative, rather than a single all-or-nothing event. During burst, single pores expand, spreading a lipid bilayer on the hydrophilic surface and a monolayer on the hydrophobic one. During heal, pore-healing can produce daughter vesicles. This burst-reseal event reiterates "splashing" portions of single vesicles at the solid surface and "bouncing" the remainder as a secondary vesicle in multiple steps.

biophysics↗

bioRxiv2020

Single-soma transcriptomics of tangle-bearing neurons in Alzheimer's disease reveals the signatures of tau-associated synaptic dysfunction

Aggregation of hyperphosphorylated tau in neurofibrillary tangles (NFTs) is closely associated with neuronal death and cognitive decline in Alzheimers disease (AD). To define the signatures that distinguish between aggregation-prone and resistant cell states in AD, we developed a FACS-based method for the high-throughput isolation and transcriptome profiling of individual cells with cytoplasmic aggregates and profiled 63,110 somas from human AD brains. By comparing NFT-bearing and NFT-free somas within and across neuronal subtypes, we identified the cell-type-specific and shared states. NFT-bearing neurons shared a marked upregulation of genes associated with synaptic transmission, including a core set of 63 genes enriched for synaptic vesicle cycle and transsynaptic signaling, whereas glucose metabolism and oxidative phosphorylation changes were highly neuronal-subtype-specific. Apoptosis was modestly enriched in NFT-bearing neurons despite the strong link between tau and cell death. Our datasets provide a resource for investigating tau-mediated neurodegeneration and a platform for biomarker and drug target discovery.

neuroscience↗

bioRxiv2020

A Genetic Model Therapy Proposes a Critical Role for Liver Dysfunction in Mitochondrial Biology and Disease

The clinical and largely unpredictable heterogeneity of phenotypes in patients with mitochondrial disorders demonstrates the ongoing challenges in the understanding of this semi-autonomous organelle in biology and disease. Here we present a new animal model that recapitulates key components of Leigh Syndrome, French Canadian Type (LSFC), a mitochondrial disorder that includes diagnostic liver dysfunction. LSFC is caused by allelic variations in the Leucine Rich Pentatricopeptide repeat-containing motif (LRPPRC) gene. LRPPRC has native functions related to mitochondrial mRNA polyadenylation and translation as well as a role in gluconeogenesis. We used the Gene-Breaking Transposon (GBT) cassette to create a revertible, insertional mutant zebrafish line in the LRPPRC gene. lrpprc zebrafish homozygous mutants displayed impaired muscle development, liver function and lowered levels of mtDNA transcripts and are lethal by 12dpf, all outcomes similar to clinical phenotypes observed in patients. Investigations using an in vivo lipidomics approach demonstrated accumulation of non-polar lipids in these animals. Transcript profiling of the mutants revealed dysregulation of clinically important nuclearly encoded and mitochondrial transcripts. Using engineered liver-specific rescue as a genetic model therapy, we demonstrate survival past the initial larval lethality, as well as restored normal gut development, mitochondrial morphology and triglyceride levels functionally demonstrating a critical role for the liver in the pathophysiology of this model of mitochondrial disease. Understanding the molecular mechanism of the liver-mediated genetic rescue underscores the potential to improve the clinical diagnostic and therapeutic developments for patients suffering from these devastating disorders.

genetics↗

bioRxiv2020

Rab18 Binds PLIN2 and ACSL3 to Mediate Lipid Droplet Dynamics

Rab18 has been linked to lipid metabolism and metabolic diseases in different model systems, but the mechanism of Rab18-mediated lipid droplet (LD) dynamics in muscle cells remains elusive. Here, we report that Rab18 plays an essential role in oleic acid (OA)-induced LD growth and formation in mouse myoblast cell line C2C12. Rab18 was translocated from endoplasmic reticulum (ER) to LDs during the LD growth induced by OA in C2C12 cells, which was directly regulated by perilipin 2 (PLIN2), a LD resident protein. LD-associated Rab18 bound with the C terminus of PLIN2, and the LD localization of Rab18 was diminished after PLIN2 deletion. Moreover, loss of function of Rab18 led to less triacylglycerol (TAG) accumulation and fewer but larger LD formation. In contrast, expression of wild type Rab18 and a constitutively active Rab18 (Q67L) mutant resulted in elevated TAG content and LD number. Furthermore, LD-associated Rab18 interacted with acyl-CoA synthetase long-chain family member 3 (ACSL3) and in turn, promoted the LD localization of ACSL3, which may play an important role in the accumulation of TAG induced by OA. These data showed that Rab18 was recruited to LD after OA treatment, and formed a complex with PLIN2 and ACSL3, which contributes to TAG accumulating and LD growth.

cell biology↗

bioRxiv2020

Electron Event Representation (EER) data enables efficient cryoEM file storage with full preservation of spatial and temporal resolution

Direct detector device (DDD) cameras have revolutionized electron cryomicroscopy (cryoEM) with their high detective quantum efficiency (DQE) and output of movie data. A high ratio of camera frame rate (frames/sec) to camera exposure rate (electrons/pixel/sec) allows electron counting, which further improves DQE and enables recording of super-resolution information. Movie output also allows for computational correction of specimen movement and compensation for radiation damage. However, these movies come at the cost of producing large volumes of data. It is common practice to sum groups of successive camera frames to reduce the final frame rate, and therefore file size, to one suitable for storage and image processing. This reduction in the cameras temporal resolution requires decisions to be made during data acquisition that may result in the loss of information that could have been advantageous during image analysis. Here we present experimental analysis of a new Electron Event Representation (EER) data format for electron counting DDD movies, which is enabled by new hardware developed by Thermo Fisher Scientific for their Falcon DDD cameras. This format enables recording of DDD movies at the raw camera frame rate without sacrificing either spatial or temporal resolution. Experimental data demonstrate that the method retains super-resolution information and allows correction of specimen movement at the physical frame rate of the camera while maintaining manageable file sizes. The EER format will enable the development of new methods that can utilize the full spatial and temporal resolution of DDD cameras.

biophysics↗

bioRxiv2020

Classifying protein structures into folds by convolutional neural networks, distance maps, and persistent homology

The fold classification of a protein reveals valuable information about its shape and function. It is important to find a mapping between protein structures and their folds. There are numerous machine learning techniques to predict protein folds from 1-dimensional (1D) protein sequences, but there are few machine learning methods to directly class protein 3D (tertiary) structures into predefined folds (e.g. folds defined in the SCOP database). We develop a 2D-convolutional neural network to classify any protein structure into one of 1232 folds. We extract two classes of input features for each protein: residue-residue distance matrix and persistent homology images derived from 3D protein structures. Due to restrictions in computing resources, we sample every other point in the carbon alpha chain to generate a reduced distance map representation. We find that it does not lead to significant loss in accuracy. Using the distance matrix, we achieve an accuracy of 95.2% on the SCOP dataset. With persistence homology images of 100 x 100 resolution, we achieve an accuracy of 56% on SCOPe 2.07 dataset. Combining the two kinds of features further improves classification accuracy. The source code of our method (PRO3DCNN) is available at https://github.com/jianlin-cheng/PRO3DCNN.

bioinformatics↗

bioRxiv2020

Identification of a Micropeptide on Lipid Droplet That Mediates Lipid Storage and Insulin Sensitivity

Over the past decade, great progress in sequencing technologies and computational biology has revealed that the majority of the mammalian genome considered to be noncoding is rich in functional elements able to produce proteins. Many RNA molecules, mis-annotated as noncoding, actually harbor small open reading frames that are predicted to code for proteins. Some of those proteins have been verified to play critical roles in multiple biological processes. The lipid droplet (LD) is a unique cellular organelle, conserved from bacteria to humans, and is closely associated with cellular lipid metabolism and metabolic disorders. No noncoding RNA-coded proteins have been identified on LDs. Here, for the first time, we searched the organelle for their presence. After the enrichment of small proteins of LDs isolated from myoblasts, we used mass spectrometry coupled with our lab made protein database to identify LD-associated noncoding RNA-encoded proteins (LDANPs). A total of 15 new proteins were identified. One of them was studied further and termed LDANP1. LDANP1 was localized on LDs by imaging, cell fractionation, and immunogold labeling. Like LD resident proteins, LDANP1 was degraded by the proteasome. Using the CRISPR/Cas9-mediated genome editing technique, the endogenous expression of LDANP1 was validated. The stable expression of LDANP1 suppressed the accumulation of triacylglycerol in oleic acid treated myoblasts and inhibited the rescue of palmitate-inhibited insulin sensitivity by oleic acid. In summary, we report for the first time that translatable, nominally noncoding RNA-derived proteins, which are new and cannot be identified using current research methods, were associated with LDs and that among these, LDANP1 modulated lipid metabolism and insulin sensitivity. The discovery of noncoding RNA-encoded proteins on LDs paves a new way for the research of LDs and lipid metabolism.

cell biology↗

bioRxiv2020

Functional characterization of germline variants in the TMEM127 tumor suppressor reveals novel insights into its membrane topology and trafficking

PurposeTo better understand the function of the transmembrane protein TMEM127, a poorly known tumor suppressor gene associated with pheochromocytomas, paragangliomas and renal carcinomas, we evaluated patient-derived germline variants. MethodsSubcellular localization and steady-state levels of 21 tumor-associated, transiently expressed TMEM127 variants were compared to the wild-type protein using immunofluorescence and immunoblot analysis, respectively, in cells genetically modified to lack endogenous TMEM127. Membrane topology and endocytic mechanisms were also assessed. ResultsWe identified three subgroups of mutations and determined that 15 of the 21 variants (71%), including 9 of 15 missense variants (60%), are pathogenic or likely pathogenic, through loss of membrane binding ability, stability and/or internalization capability. Investigation into an N-terminal cluster of missense variants uncovered a previously unrecognized transmembrane domain, indicating that TMEM127 is a four-, not a three-, transmembrane domain-containing protein. Additionally, a C-terminal variant with predominant plasma membrane localization revealed an atypical, extended acidic, dileucine-based motif required for TMEM127 internalization through clathrin-mediated endocytosis. ConclusionWe characterized the functional deficits of several germline TMEM127 variants and identified novel structure-function features of TMEM127, namely, a fourth transmembrane domain and an endocytic motif. These findings will assist in TMEM127 variant interpretation and will help guide future studies investigating the cellular role of TMEM127.

genetics↗

bioRxiv2020

A simple, scalable approach to building a cross-platform transcriptome atlas

Gene expression atlases have transformed our understanding of the development, composition and function of human tissues. New technologies promise improved cellular or molecular resolution, and have led to the identification of new cell types, or better defined cell states. But as new technologies emerge, information derived on old platforms becomes obsolete. We demonstrate that it is possible to combine a large number of different profiling experiments summarised from dozens of laboratories and representing hundreds of donors, to create an integrated molecular map of human tissue. As an example, we combine 850 samples from 38 platforms to build an integrated atlas of human blood cells. We achieve robust and unbiased cell type clustering using a variance partitioning method, selecting genes with low platform bias relative to biological variation. Other than an initial rescaling, no other transformation to the primary data is applied through batch correction or renormalisation. Additional data, including single-cell datasets, can be projected for comparison, classification and annotation. The resulting atlas provides a multi-scaled approach to visualise and analyse the relationships between sets of genes and blood cell lineages, including the maturation and activation of leukocytes in vivo and in vitro. In allowing for data integration across hundreds of studies, we address a key reproduciblity challenge which is faced by any new technology. This allows us to draw on the deep phenotypes and functional annotations that accompany traditional profiling methods, and provide important context to the high cellular resolution of single cell profiling. Here, we have implemented the blood atlas in the open access Stemformatics.org platform, drawing on its extensive collection of curated transcriptome data. The method is simple, scalable and amenable for rapid deployment in other biological systems or computational workflows. Graphical abstractRecursive approach to generating a multi-scaled atlas. Top panel: The method integrates data from all cell types in the Stemformatics database, and shows clear division of samples into global categories of stromal, pluripotent or blood (inset) cell types. Bottom panel: Integration of only the blood cell subsets provides a blood atlas. Projection of external samples (green) onto the blood atlas. Samples are coloured by curated annotations derived from the original studies, and can be viewed at Stemformatics.org O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=182 SRC="FIGDIR/small/984468v1_ufig1.gif" ALT="Figure 1"> View larger version (40K): org.highwire.dtl.DTLVardef@2de08dorg.highwire.dtl.DTLVardef@1f18284org.highwire.dtl.DTLVardef@afd32aorg.highwire.dtl.DTLVardef@1507502_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2020

Structure-based drug design, virtual screening and high-throughput screening rapidly identify antiviral leads targeting COVID-19

A new coronavirus (CoV) identified as COVID-19 virus is the etiological agent responsible for the 2019-2020 viral pneumonia outbreak that commenced in Wuhan1-4. Currently there is no targeted therapeutics and effective treatment options remain very limited. In order to rapidly discover lead compounds for clinical use, we initiated a program of combined structure-assisted drug design, virtual drug screening and high-throughput screening to identify new drug leads that target the COVID-19 virus main protease (Mpro). Mpro is a key CoV enzyme, which plays a pivotal role in mediating viral replication and transcription, making it an attractive drug target for this virus5,6. Here, we identified a mechanism-based inhibitor, N3, by computer-aided drug design and subsequently determined the crystal structure of COVID-19 virus Mpro in complex with this compound. Next, through a combination of structure-based virtual and high-throughput screening, we assayed over 10,000 compounds including approved drugs, drug candidates in clinical trials, and other pharmacologically active compounds as inhibitors of Mpro. Six of these inhibit Mpro with IC50 values ranging from 0.67 to 21.4 M. Ebselen also exhibited promising antiviral activity in cell-based assays. Our results demonstrate the efficacy of this screening strategy, which can lead to the rapid discovery of drug leads with clinical potential in response to new infectious diseases where no specific drugs or vaccines are available.

biochemistry↗

bioRxiv2020

What drives the assembly of plant-associated protist microbiomes?

In a field experiment we investigated the influence of the environmental filters soil type and plant species identity on rhizosphere community assembly of Cercozoa, a dominant group of (mostly bacterivorous) soil protists. The experiment was set up with two plant species, lettuce and potato, grown in an experimental plot system with three contrasting soils. Plant species (14%) and rhizosphere origin (vs. bulk soil) with 13%, together explained four times more variation in cercozoan beta diversity than the three soil types (7% explained variation in beta diversity). Our results clearly confirm the existence of plant species-specific protist communities. Network analyses of bacteria-Cercozoa rhizosphere communities identified scale-free small world topologies, indicating mechanisms of self-organization. While the assembly of rhizosphere bacterial communities is bottom-up controlled through the resource supply from root (secondary) metabolites, our results support the hypothesis that the net effect may depend on the strength of top-down control by protist grazers. Since grazing of protists has a strong impact on the composition and functioning of bacteria communities, protists expand the repertoire of plant genes by functional traits, and should be considered as protist microbiomes in analogy to bacterial microbiomes. HighlightMicrobiomes of rhizosphere protists are plant species-specific and tightly co-evolving with their bacterial prey, thereby extending and modifying the functional repertoire of the bacterial-plant symbiosis.

plant biology↗

bioRxiv2020

Synergism and antagonism of two distinct, but confused, Nrf1 factors in integral regulation of the nuclear-to mitochondrial respiratory and antioxidant transcription networks

There is hitherto no literature available for explaining two distinct, but confused Nrf1 transcription factors, because they shared the same abbreviations from nuclear factor erythroid 2-related factor 1 (also called Nfe2l1) and nuclear respiratory factor (originally designated -Pal). Thus, we have here identified that Nfe2l1Nrf1 and -PalNRF1 exert synergistic and antagonistic roles in integrative regulation of the nuclear-to-mitochondrial respiratory and antioxidant transcription profiles. In mouse embryonic fibroblasts (MEFs), knockout of Nfe2l1-/- leads to substantial decreases in expression levels of -PalNRF1 and Nfe2l2, together with TFAM (mitochondrial transcription factor A) and other target genes. Similar inhibitory results were determined in Nfe2l2-/- MEFs, with an exception that GSTa1 and Aldh1a1 were distinguishably up-regulated in Nfe2l1-/- MEFs. Such synergistic contributions of Nfe2l1 and Nfe2l2 to the positive regulation of -PalNRF1 and TFAM were validated in Keap1-/- MEFs. However, human -PalNRF1 expression was unaltered by hNfe2l1-/-, hNfe2l2-/-{Delta}TA or even hNfe2l1-/-+siNrf2, albeit TFAM was activated by Nfe2l1 but inhibited by Nfe2l2; such an antagonism occured in HepG2 cells. Conversely, almost all of mouse Nfe2l1, Nfe2l2 and co-target genes were down-expressed in -PalNRF1+/- MEFs. On the contrary, up-regulation of human Nfe2l1, Nfe2l2 and relevant reporter genes took place after silencing of -PalNRF1, but their down-regulation occurred upon ectopic expression of -PalNRF1. Furtherly, Pitx2 (pituitary homeobox 2) was also identified as a direct upstream regulator of Nfe2l1 and TFAM, besides -PalNRF1. Overall, these across-talks amongst Nfe2l1, Nfe2l2 and -PalNRF1, along with Pitx2, are integrated from the endoplasmic reticulum to the nuclear-to-mitochondrial communication for targeting TFAM, in order to finely tune the cellular respiratory and antioxidant gene transcription networks, albeit they differ between the mouse and the human.

molecular biology↗

bioRxiv2020

Fine-tuning biosensor dynamic range based on rational design of cross-ribosome-binding sites in bacteria

Currently, predictive translation tuning of regulatory elements to the desired output of transcription factor based biosensors remains a challenge. The gene expression of a biosensor system must exhibit appropriate translation intensity, which is controlled by the ribosome-binding site (RBS), to achieve fine-tuning of its dynamic range (i.e., fold change in gene expression between the presence and absence of inducer) by adjusting the translation initiation rate of the transcription factor and reporter. However, existing genetically encoded biosensors generally suffer from unpredictable translation tuning of regulatory elements to dynamic range. Here, we elucidated the connections and partial mechanisms between RBS, translation initiation rate, protein folding and dynamic range, and presented a rational design platform that predictably tuned the dynamic range of biosensors based on deep learning of large datasets cross-RBSs (cRBSs). A library containing 24,000 semi-rationally designed cRBSs was constructed using DNA microarray, and was divided into five sub-libraries through fluorescence-activated cell sorting. To explore the relationship between cRBSs and dynamic range, we established a classification model with the cRBSs and average dynamic range of five sub-libraries to accurately predict the dynamic range of biosensors based on convolutional neural network in deep learning. Thus, this work provides a powerful platform to enable predictable translation tuning of RBS to the dynamic range of biosensors.

bioinformatics↗

bioRxiv2020

Derived ecological niches of indoor microbes are crucial for asthma symptoms in university dormitories

Increasing evidences from home environment indicate that microbiome community is associated with asthma. However, indoor microbiome composition can be highly diverse and dynamic, and thus current studies fail to produce consistent association. Chinese university dormitories are special high-density dwellings with a standard built environment and personal characteristics for occupants, which can be used to disentangle the complex interactions between microbes, environmental characteristics and asthma. Settled air dust and floor dust was collected from 87 dormitory rooms in Shanxi University. Bacterial community was characterized by 16S rRNA amplicon sequencing. Students (n = 357) were surveyed for asthma symptoms. Asthma symptoms were not associated with the overall bacterial richness, but associated with different phylogenetic classes. Taxa richness and abundance in Clostridia and Bacteroidia were positively associated with asthma (p < 0.05), and these taxa were mainly derived from human gut. Taxa richness (p < 0.1) and abundance (p < 0.05) in Alphaproteobacteria and Actinobacteria were protectively associated with asthma, and these taxa were mainly derived from outdoor environment. Building age, floor and curtain cleaning frequency shaped the overall bacterial community of air dust (p < 0.05). Frequent curtain cleaning increased the relative abundance of 10 protective genera (p < 0.05), and old buildings had mix effects to protective genera (p < 0.05). Our data shows that taxa from different phylogenetic classes and ecological niches have different health effects, indicating the importance of incorporating evolutionary and ecological concepts in revealing general patterns in the microbiome asthma association analysis.

microbiology↗

bioRxiv2019

Explaining the genetic causality for complex diseases via deep association kernel learning

The genetic effect explains the causality from genetic mutation to the development of complex diseases. Existing genome-wide association study (GWAS) approaches are always built under a linear assumption, restricting their generalization in dissecting complicated causality such as the recessive genetic effect. Therefore, a sophisticated and general GWAS model that can work with different types of genetic effects is highly desired. Here, we introduce a Deep Association Kernel learning (DAK) model to enable automatic causal genotype encoding for GWAS at pathway level. DAK can detect both common and rare variants with complicated genetic effects that existing approaches fail. When applied to real-world GWAS data, our approach discovered potential casual pathways that could be explained by alternative biological studies.

bioinformatics↗

bioRxiv2019

Neutrophil: Airway Epithelial Interactions Result in Increased Epithelial Damage and Viral Clearance during RSV Infection

Respiratory syncytial virus (RSV) is a major cause of paediatric respiratory disease. Large numbers of neutrophils are recruited into the airways of children with severe RSV disease. It is not clear whether or how neutrophils enhance recovery from disease or contribute to its pathology. Using an in vitro model of the differentiated airway epithelium, we found that addition of physiological concentrations of neutrophils to RSV infected nasal cultures was associated with greater epithelial damage with lower ciliary activity, cilia loss, less tight junction expression (ZO-1) and more detachment of epithelial cells than seen with RSV infection alone. This was also associated with a decrease in infectious virus and fewer RSV positive cells in cultures after neutrophil exposure compared to pre-exposure. Epithelial damage in response to RSV infection was associated with neutrophil activation (within 1h), and neutrophil degranulation with significantly greater cellular expression of CD11b, MPO and higher neutrophil elastase and myeloperoxidase activity in apical surface medias compared to that from mock-infected AECs. We also recovered more apoptotic neutrophils from RSV infected cultures (>40%), compared to <5% in mock infected cultures after 4h. The results of this study could provide important insights into the role of neutrophils in host response in the airway.

immunology↗

bioRxiv2019

Visualization of two architectures in class-II CAP-dependent transcription activation

Transcription activation by cyclic AMP receptor protein (CAP) is the classic paradigm of transcription regulation in bacteria. CAP was suggested to activate transcription on class-II promoters via a recruitment and isomerization mechanism. However, whether and how it modifies RNA polymerase (RNAP) to initiate transcription remains unclear. Here we report cryo-EM structures of an intact E. coli class-II CAP-dependent transcription activation complex (TAC) with and without de novo RNA transcript. The structures reveal two distinct architectures of TAC and show that CAP-binding induces substantial conformational changes in all the subunits of RNAP and consequently widens the main cleft of RNAP considerably to facilitate DNA promoter entering and formation of initiation open complex. These structural changes vanish during further RNA transcript synthesis. The observations in this study suggest a unique activation mechanism on class-II promoters that CAP activates transcription by first remodeling RNAP conformation and then stabilizing initiation complex.

molecular biology↗

bioRxiv2019

Distinct microbiome composition of floor and air dust distort richness estimation in university dormitories

The number of culture-independent indoor microbiome study has increased remarkably in recent years, but microbial composition among different sampling strategies remains poorly characterized and their impact to downstream microbiome analysis is also not clear. In this study, we reported a case study of microbial composition of floor and air dust in 87 dormitory rooms of Shanxi University, China. Floor and air dust were collected by vacuum cleaner and petri-dish, respectively, and the bacterial composition was characterized by 16S rRNA sequencing. The composition of floor and air dust differed significantly (R2 = 0.65, p < 0.001, Adonis), and Pseudomonas dominated in floor dust (75.1%) but was less common in air dust (1.9%). The top genera in air dust, including Ralstonia (15.6%), Pelomonas (11.3%), Anoxybacillus (9.3%) and Ochrobactrum (6.2%), all accounted for < 1% abundance in floor dust. The dominant Pseudomonas in floor dust swamped low frequency organisms, leading to significant lower number of operational taxonomic units (OTUs) compared with air dust in the same sequencing depth. The different microbial composition of floor and air dust can lead to differences in downstream bioinformatics analyses. We searched the dormitory microbiome against ~200,000 samples deposited in Microbiome Search Engine (MSE), and found that the compositions of floor dust samples were similar to samples from building environment and human nasopharynx, whereas the compositions of the air dust samples were similar to mosquito tissues. ImportanceIncreasing number of indoor microbiome studies has been conducted in recent years, but the impact of sampling strategy is far from clear. In this study, we reported that the floor and air sampling can lead to drastic variation in microbial composition and downstream analyses in university dormitories, including microbial diversity estimation and compositional similarity search. Thus, the bioinformatics analysis results need to be interpreted with caution in microbiome studies, and it may be necessary to collected samples from different sampling strategies to comprehensively characterize the microbial composition and exposure to human occupants in an indoor environment.

microbiology↗