Biology subjects

Huang, K.

Publications and source records attributed to Huang, K..

At least 199 records · Page 11Linked to original sources

bioRxiv2019

Quantitative, super-resolution localization of small RNAs with sRNA-PAINT

Small RNAs are non-coding RNAs that play important roles in the lives of both animals and plants. They are 21- to 24-nt in length and around 10 nanometers (nm) in size. Their small size and high diversity have made it challenging to develop detection methods that have sufficient resolution and specificity to multiplex and quantify. We created a method, sRNA-PAINT, for the detection of small RNAs with 20 nm resolution by combining the super-resolution method, DNA-based points accumulation in nanoscale topography (DNA-PAINT), and the specificity of locked nucleic acid (LNA) probes for the in situ detection of multiple small RNAs. The method relies on designing probes to target small RNAs that combine DNA oligonucleotides (oligos) for PAINT with LNA-containing oligos for hybridization; therefore, we developed an online tool called "Vetting & Analysis of RNA for in situ Hybridization probes" (VARNISH) for probe design. Our method utilizes advances in DNA-PAINT methodologies, including qPAINT for quantification, and Exchange-PAINT for multiplexing. We demonstrated these capabilities of sRNA-PAINT by detecting and quantifying small RNAs in different cell layers of early developmental stage maize anthers that are important for male sexual reproduction.

cell biology↗

bioRxiv2019

Generation and quality control of maternal plasma lipidomics data associated with preterm birth

Preterm birth is not only one of the most common causes of infant deaths but also a great risk for them to have severe subsequent health problems. The causes of preterm birth may be due to a combination of genetic and environmental factors, however, it remains largely unknown. Here we report an untargeted lipidomics dataset of plasma specimens from 258 pregnant women at the stage of twelve to twenty-five gestational weeks. Among them, 44 had extremely to very preterm births, 54 had moderate preterm births, 71 had late preterm births and 89 had full-term deliveries. The metabolomic profiling was generated with an UPLC-MS in both the positive and negative mode, and putative identification of all the metabolites was provided by searching against online databases. The quality assessment performed on quality control samples showed that the data is reproducible, robust and reliable. Both the raw data files, the raw and processed data matrix were available on MetaboLights, which may be used as a valuable validation dataset for new findings and a test dataset for novel algorithms.

systems biology↗

bioRxiv2019

Allicin regulates energy homeostasis through brown adipose tissue

Background/objectivesDisorder of energy homeostasis can lead to a variety of metabolic diseases, especially obesity. Brown adipose tissue (BAT) is a promising potential therapeutic target for the treatment of obesity and related metabolic diseases. Allicin, a main bioactive ingredient in garlic, has multiple biology and pharmacological function. However, the role of Allicin, in the regulation of metabolic organ, especially the role of activation of BAT, has not been well studied. Here, we analyzed the role of Allicin in whole-body metabolism and the activation of BAT.\n\nResultsAllicin had a significant effect in inhibiting body weight gain, decreasing adiposity, maintaining glucose homeostasis, improving insulin resistance, and ameliorating hepatic steatosis in diet-introduced obesity (DIO) mice. Then we find that Allicin can strongly activate brown adipose tissue (BAT). The activation of brown adipocyte treated with Allicin was also confirmed in mouse primary brown adipocytes.\n\nConclusionAllicin can ameliorate obesity through activating brown adipose tissue. Our findings provide a promising therapeutic approach for the treatment of obesity and metabolic disorders.

molecular biology↗

bioRxiv2019

Impaired peroxisomal import in Drosophila hepatocyte-like cells induces cardiac dysfunction through the pro-inflammatory cytokine Upd3

Age is a major risk factor for cardiovascular diseases. Currently, the non-autonomous regulation of age-related cardiac dysfunction is poorly understood. In the present study, we discover that age-dependent induction of cytokine unpaired 3 (Upd3) in Drosophila oenocytes (hepatocyte-like cells), due to a dampened peroxisomal import function, is the primary non-autonomous mechanism for elevated arrhythmicity in old hearts. We show that Upd3 is significantly up-regulated (52-fold) in aged oenocytes. Oenocyte-specific knockdown of Upd3 is sufficient to block aging-induced cardiac arrhythmia. We further show that the age-dependent induction of Upd3 is triggered by impaired peroxisomal import and elevated JNK signaling in aged oenocytes. Intriguingly, oenocyte-specific over-expression of Pex5, the key peroxisomal import receptor, restores peroxisomal import, blocks age-related Upd3 induction, and alleviates aging- and paraquat-induced cardiac arrhythmicity. Thus, our studies identify an important role of the evolutionarily conserved pro-inflammatory cytokine signaling and hepatocyte-specific peroxisomal import in mediating non-autonomous regulation of cardiac aging.

physiology↗

bioRxiv2019

AimB is a small protein regulator of cell size and MreB assembly

The MreB actin-like cytoskeleton assembles into dynamic polymers that coordinate cell shape in many bacteria. In contrast to most other cytoskeletons, few MreB interacting proteins have been well characterized. Here we identify a small protein from Caulobacter crescentus, AimB, as an Assembly Inhibitor of MreB. AimB overexpression mimics inhibition of MreB polymerization, leading to increased cell width and MreB delocalization. Molecular dynamics simulations suggest that AimB binds MreB at its monomer-monomer protofilament interaction cleft. We validate this model through functional analysis of point mutants in both AimB and MreB, photo-crosslinking studies with site-specific unnatural amino acids, and species-specific activity of AimB. Together, our findings indicate that AimB promotes MreB dynamics by inhibiting monomer-monomer assembly interactions, representing a new mechanism for regulating actin-like polymers and the first identification of a non-toxin MreB assembly inhibitor.

microbiology↗

bioRxiv2019

BURMUDA: A novel deep transfer learning method for single-cell RNA sequencing batch correction reveals hidden high-resolution cellular subtypes

To fully utilize the power of single-cell RNA sequencing (scRNA-seq) technologies for cell lineation and identifying bona fide transcriptional signals, it is necessary to combine data from multiple experiments. We present BERMUDA (Batch-Effect ReMoval Using Deep Autoencoders) -- a novel transfer-learning-based method for batch-effect correction in scRNA-seq data. BERMUDA effectively combines different batches of scRNA-seq data with vastly different cell population compositions and amplifies biological signals by transferring information among batches. We demonstrate that BERMUDA outperforms existing methods for removing batch effects and distinguishing cell types in multiple simulated and real scRNA-seq datasets.

bioinformatics↗

bioRxiv2019

A generalized framework of AMOVA with any number of hierarchies and any level of ploidies

The analysis of molecular variance (AMOVA) is a widely used statistical model in the studies of population genetics and molecular ecology. The classical framework of AMOVA only supports haploid and diploid data, in which the number of hierarchies ranges from two to four. In practice, natural populations can be classified into more hierarchies, and polyploidy is frequently observed in contemporary species. The ploidy level may even vary within the same species, even within the same individual. We generalized the framework of AMOVA such that it can be used for any number of hierarchies and any level of ploidy. Based on this framework, we present four methods to account for the multilocus genotypic and allelic phenotypic data. We use simulated datasets and an empirical dataset to evaluate the performance of our framework. We make freely available our methods in a software, POLYGENE, which is freely available at https://github.com/huangkang1987/.

genetics↗

bioRxiv2019

Physical and data structure of 3D genome

With the textbook view of chromatin folding based on the 30nm fiber being challenged, it has been proposed that interphase DNA has an irregular 10nm nucleosome polymer structure whose folding philosophy is unknown. Nevertheless, experimental advances suggested that such irregular packing is associated with many nontrivial physical properties that are puzzling from a polymer physics point of view. Here, we show that the reconciliation of these exotic properties necessitates modularizing 3D genome into tree data structures on top of, and in striking contrast to the linear topology of DNA double helix. Such functional modules need to be connected and isolated by an open backbone that results in porous and heterogeneous packing in a quasi-self-similar manner as revealed by our electron and optical imaging. Our multi-scale theoretical and experimental results suggest the existence of higher-order universal folding principles for a disordered chromatin fiber to avoid entanglement and fulfill its biological functions.

biophysics↗

bioRxiv2019

Molecular model of the nuclear pore complex reveals a thermoreversible FG-network with distinct territories occupied by different FG motifs

Despite the intensive study of the nuclear pore complex (NPC), its functional core, the central transporter, remains poorly understood. Here, we investigate this unfolded and dynamic part of the NPC using a molecular theory that addresses both entropic and enthalpic effects of the intrinsically disordered phenylalanine-glycine-rich nucleoporins (FG-Nups). Our model shows that the cooperative effect of FG-pairing, specific spacer cohesion, and charge interaction leads to a remarkably elaborate gating structure inside the NPC. In particular, we find sequence-programmed \"phase separation\" between charge-rich and charge-poor regions, and a polarized electrostatic potential throughout the NPC. The model predicts a thermoreversible FG-network with inhomogeneous FG-pairing fraction in space, which features distinct territories of different types of FG motifs. Our theoretical anatomy of the central transporter reveals a clear sequence-structure-function relationship of the FG-Nups, and explains in a self-consistent way how nucleocytoplasmic transport can be efficient yet specific.

cell biology↗

bioRxiv2019

Transmission of human-associated microbiota along family and social networks

The human microbiome, described as an accessory organ because of the crucial functions it provides, is composed of species that are uniquely found in humans1,2. Yet, surprisingly little is known about the impact of routine interpersonal contacts in shaping microbiome composition. In a relatively closed cohort of 287 people from the Fiji Islands, where common barriers to bacterial transmission are absent, we examine putative bacterial transmission in individuals gut and oral microbiomes using strain-level data from both core SNPs and flexible genomic regions. We find a weak signal of transmission, defined by the inferred sharing of genotypes, across many organisms that, in aggregate, reveals strong transmission patterns, most notably within households and between spouses. We find that women harbor strains more closely related to those harbored by their familial and social contacts than men; and that transmission patterns of oral- and gut-associated microbiota need not be the same. Using strain-level data alone, we are able to confidently predict a subset of spouses, highlighting the role of shared susceptibilities, behaviors or social interactions that distinguish specific links in the social network.

microbiology↗

bioRxiv2019

Genotypic frequencies at equilibrium for polysomic inheritance under double-reduction

Polyploids are organisms whose genomes consist of more than two complete sets of chromosomes. Both autopolyploids and allopolyploids may display polysomic inheritance. A peculiarity of polysomic inheritance is multivalent formation during meiosis resulting in double-reduction, which occurs when sister chromatid fragments are segregated into the same gamete. Double-reduction can result in gametes carrying identical-by-descent alleles and slightly increasing homozygosity. This will cause the genotypic frequencies to deviate from expected values and will thus bias the results of standard population genetic analytical methods used in molecular ecology and selective breeding. In this study, we extend existing double-reduction models to account for any even level of ploidy, and derive the symbolic expressions for genotypic frequencies via two methods. Inbreeding coefficients and heterozygosity under double-reduction and inbreeding are also calculated. Numerical solutions obtained by computer simulations are compared with analytical solutions predicted by the model to validate the model.

genetics↗

bioRxiv2019

LAmbDA: Label Ambiguous Domain Adaption Dataset Integration Reduces Batch Effects and Improves Subtype Detection

MotivationRapid advances in single cell RNA sequencing have produced more granular subtypes of cells in multiple tissues from different species. There exists a need to develop rigorous methods that can i) model multiple datasets with ambiguous labels across species and studies and ii) remove systematic biases across datasets and species. ResultsWe developed a species- and dataset-independent transfer learning framework (LAmbDA) to train models on multiple datasets and applied our framework on scRNA-seq experiments. These models mapped corresponding cell types between datasets with inconsistent labels while simultaneously reducing batch effects. We achieved high accuracy in labeling cellular subtypes (weighted accuracy pancreas: 91%, brain: 78%) using LAmbDA Random Forest. LAmbDA Feedforward 1 Layer Neural Network achieved higher weighted accuracy in labeling cellular subtypes than CaSTLe or MetaNeighbor in brain (48%, 32%, 20% respectively). Furthermore, LAmbDA Feedforward 1 Layer Neural Network was the only method to correctly predict ambiguous cellular subtype labels in both pancreas and brain compared to CaSTLe and MetaNeighbor. LAmbDA is model- and dataset-independent and generalizable to diverse data types representing an advance in biocomputing. Availability: github.com/tsteelejohnson91/LAmbDA Contact: kunhuang@iu.edu, jizhan@iu.edu

bioinformatics↗

bioRxiv2019

Integration of Mouse and Human Single-cell RNA Sequencing Infers Spatial Cell-type Composition in Human Brains

Technical advances have enabled the identification of high-resolution cell types within tissues based on single-cell transcriptomics. However, such analyses are restricted in human brain tissue due to the limited number of brain donors. In this study, we integrate mouse and human data to predict cell-type proportions in human brain tissue, and spatially map the resulting cellular composition. By applying feature selection and linear modeling, combinations of human and mouse brain single-cell transcriptomics profiles can be integrated to \"fill in\" missing information. These combined \"in silico chimeric\" datasets are used to model the composition of nine cell types in 3,702 human brain samples in six Allen Human Brain Atlas (AHBA) donors. Cell types were spatially consistent regardless of the scRNA-Seq dataset (91% significantly correlated) or AHBA donor (p-value = 4.43E-20 by t-test) used in the model. Importantly, neuron nuclei location and neuron mRNA location were correlated only after accounting for neural connectivity (p-value = 1.26E-10), which supports the notion that gene expression is a better indicator than nuclei location of cellular localization for cells with large and irregularly shaped cell bodies, such as neurons. These results advocate for the integration of mouse and human data in models of brain tissue heterogeneity.

bioinformatics↗

bioRxiv2019

Antitumor effects of Tv1 venom peptide in liver cancer

A strategy for treating the most common type of liver cancer, hepatocellular carcinoma (HCC) applies a targeted therapy using venom peptides that are selective for ion channels and transporters overexpressed in tumor cells. Here, we report selective anti-HCC properties of Tv1, a venom peptide from the predatory marine terebrid snail, Terebra variegata. Tv1 was applied in vitro to liver cancer cells and administered in vivo to allograft tumor mouse models. Tv1 inhibited the proliferation of murine HCC cells via calcium dependent apoptosis resulting from down-regulation of the cyclooxygenase-2 (COX-2) pathway. Additionally, tumor sizes were significantly reduced in Tv1-treated syngeneic tumor-bearing mice. Tv1s mechanism of action involves binding to specific transient receptor potential (TRP) cation channels that are overexpressed in HCC cell models. Our findings demonstrate the unique potential of venom peptides to function as tumor specific ligands in the quest for targeted cancer therapies.

cancer biology↗

bioRxiv2018

Dicer-like 5 deficiency confers temperature-sensitive male sterility in maize

Small RNAs play important roles during plant development by regulating transcript levels of target mRNAs, maintaining genome integrity, and reinforcing DNA methylation. Dicer-like 5 (Dcl5) is proposed to be responsible for precise slicing in many monocots to generate diverse 24-nt phased, secondary small interfering RNAs (phasiRNAs), which are exceptionally abundant in meiotic anthers of diverse flowering plants. The importance and functions of these phasiRNAs remain unclear. Here, we characterized several mutants of dcl5, including alleles generated by the Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) Cas9 system and a transposon-disrupted allele. We report that dcl5 mutants have few or no 24-nt phasiRNAs, develop short anthers with defective tapetal cells, and exhibit temperature-sensitive male fertility. We propose that DCL5 and 24-nt phasiRNAs are critical for fertility under growth regimes for optimal yield.

plant biology↗

bioRxiv2018

Sex chromosome evolution via two genes

The origin of sex chromosomes has been hypothesized to involve the linkage of factors with antagonistic effects on male and female function. Garden asparagus (Asparagus officinalis L.) is an ideal species to test this hypothesis, as the X and Y chromosomes are cytologically homomorphic and recently evolved from an ancestral autosome pair in association with a shift from hermaphroditism to dioecy. Mutagenesis screens paired with singlemolecule fluorescence in situ hybridization (smFISH) directly implicate Y-specific genes that respectively suppress female organ development and are necessary for male gametophyte development. Comparison of contiguous X and Y chromosome shows that loss of recombination between the genes suppressing female function (SUPPRESSOR OF FEMALE FUNCTION, SOFF) and promoting male function (TAPETAL DEVELOPMENT AND FUNCTION 1, aspTDF1) is due to hemizygosity. We also experimentally demonstrate the function of aspTDF1. These finding provide direct evidence that sex chromosomes can evolve from autosomes via two sex determination genes: a dominant suppressor of femaleness and a promoter of maleness.\n\nOne Sentence SummarySex chromosomes can evolve from autosomes by mutations in two sex determination genes.

genetics↗

bioRxiv2018

Characterization of glutamatergic VTA neural population responses to aversive and rewarding conditioning in freely-moving mice

The Ventral Tegmental Area (VTA) is a midbrain structure known to integrate aversive and rewarding stimuli, a function involving VTA Dopaminergic and GABAergic neurons. VTA also contains a less known population: glutamatergic (VGluT2) neurons. Direct activation of VGluT2 soma evokes rewarding behaviors, while stimulation of their axonal projections to the Nucleus Accumbens (NAc) and the Lateral Habenula (LHb) evokes aversive behaviors. Here, a systematic investigation of the VTAVGluT2+ population response to aversive or rewarding conditioning facilitated our understanding these conflicting properties. We recorded calcium signals from VTA glutamatergic population neurons using fiber photometry in VGluT2-cre mice to investigate how the VTA glutamatergic neuronal population was recruited by aversive and rewarding stimulation, both during unconditioned and conditioned protocols. Our results revealed that, as a population, VTAVGluT2+ neurons responded similarly to unconditioned-aversive and unconditioned-rewarding stimulation. During aversive and rewarding conditioning, the CS-evoked responses gradually increased across trials whilst the US-evoked response remained stable. Retrieval 24 h after conditioning, during which mice received only CS presentation, resulted in VTAVGluT2+ neurons strongly responding to CS presentation and to the expected-US but only for aversive conditioning. The inputs and outputs of VTAVGluT2+ neurons were then investigated using Cholera Toxin B (CTB) and rabies virus, and we propose based on all results that VTAVGluT2+ neurons specialized function may be partially due to their connectivity.

neuroscience↗

bioRxiv2018

ICTD: Inference of cell types and deconvolution -- a next-generation deconvolution method for accurate assess cell population and activities in tumor microenvironment.

We developed a novel deconvolution method, namely Inference of Cell Types and Deconvolution (ICTD) that addresses the fundamental issue of identifiability and robustness in current tissue data deconvolution problem. ICTD provides substantially new capabilities for omics data based characterization of a tissue microenvironment, including (1) maximizing the resolution in identifying resident cell and sub types that truly exists in a tissue, (2) identifying the most reliable marker genes for each cell type, which are tissue and data set specific, (3) handling the stability problem with co-linear cell types, (4) co-deconvoluting with available matched multi-omics data, and (5) inferring functional variations specific to one or several cell types. ICTD is empowered by (i) rigorously derived mathematical conditions of identifiable cell type and cell type specific functions in tissue transcriptomics data and (ii) a semi supervised approach to maximize the knowledge transfer of cell type and functional marker genes identified in single cell or bulk cell data in the analysis of tissue data, and (iii) a novel unsupervised approach to minimize the bias brought by training data. Application of ICTD on real and single cell simulated tissue data validated that the method has consistently good performance for tissue data coming from different species, tissue microenvironments, and experimental platforms. Other than the new capabilities, ICTD outperformed other state-of-the-art devolution methods on prediction accuracy, the resolution of identifiable cell, detection of unknown sub cell types, and assessment of cell type specific functions. The premise of ICTD also lies in characterizing cell-cell interactions and discovering cell types and prognostic markers that are predictive of clinical outcomes.

bioinformatics↗