Biology subjects

Qi, Y.

Publications and source records attributed to Qi, Y..

12 recordsLinked to original sources

bioRxiv2026

A Hymenoptera-restricted gene mediating ant castes co-opts deeply conserved machinery to control organ size

Lineage-specific genes are widespread and have been implicated as phenotypic innovation inducers, but how they acquire complex developmental functions remains poorly understood. Ant queens and workers develop dramatically different organ sizes from identical genomes under juvenile hormone (JH) control, yet the molecular effectors translating JH signalling into caste-specific organ growth remain unknown. Here we identify torch, a Hymenoptera-restricted gene, as the most consistently gyne-biased and JH-responsive gene across 68 ant species. Knockdown of torch in virgin queens of Monomorium pharaonis produces a worker-like, multi-organ growth-restricted phenotype. Mechanistically, torch harbours an E-box-like motif activated by the JH receptor Gce-Tai and acts as a GA-repeat-binding transcription factor that regulates Hippo signalling, the deeply conserved organ-size control pathway in animals. Expressing torch heterologously in mice and a growth-restricted Drosophila background shows that the gene retained its general growth-promoting activity across more than 700 million years of animal evolution in lineages that lack the gene, establishing that its function is mediated through conserved rather than ant-specific machinery. A lineage-specific gene can therefore acquire complex morphogenetic function by co-opting ancient organ-size circuitry, providing a general route by which novel genes can drive phenotypic innovation.

evolutionary biology↗

bioRxiv2018

Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks

Here we present Perturb-ATAC, a method which combines multiplexed CRISPR interference or knockout with genome-wide chromatin accessibility profiling in single cells, based on the simultaneous detection of CRISPR guide RNAs and open chromatin sites by assay of transposase-accessible chromatin with sequencing (ATAC-seq). We applied Perturb-ATAC to transcription factors (TFs), chromatin-modifying factors, and noncoding RNAs (ncRNAs) in [~]4,300 single cells, encompassing more than 63 unique genotype-phenotype relationships. Perturb-ATAC in human B lymphocytes uncovered regulators of chromatin accessibility, TF occupancy, and nucleosome positioning, and identified a hierarchical organization of TFs that govern B cell state, variation, and disease-associated cis-regulatory elements. Perturb-ATAC in primary human epidermal cells revealed three sequential modules of cis-elements that specify keratinocyte fate, orchestrated by the TFs JUNB, KLF4, ZNF750, CEBPA, and EHF. Combinatorial deletion of all pairs of these TFs uncovered their epistatic relationships and highlighted genomic co-localization as a basis for synergistic interactions. Thus, Perturb-ATAC is a powerful and general strategy to dissect gene regulatory networks in development and disease.\n\nHighlightsO_LIA new method for simultaneous measurement of CRISPR perturbations and chromatin state in single cells.\nC_LIO_LIPerturb-ATAC reveals regulatory factors that control cis-element accessibility, trans-factor occupancy, and nucleosome positioning.\nC_LIO_LIPerturb-ATAC reveals regulatory modules of coordinated trans-factor activity in B lymphoblasts.\nC_LIO_LIKeratinocyte differentiation is orchestrated by synergistic activities of co-binding TFs on cis-elements.\nC_LI

genomics↗

bioRxiv2018

Human cytomegalovirus RNA2.7 regulates host cell cycle and facilitates viral DNA replication by inhibiting RNA polymerase II phosphorylation

Human cytomegalovirus (HCMV) is a ubiquitous pathogen belongs to the beta herpesvirus family. RNA2.7 is a viral long non-coding RNA accounting for more than 20% of total viral transcripts at early time of infection. By construction of RNA2.7 deleted mutant and genome transcriptomic analysis, RNA2.7 is demonstrated to repress host cellular RNA polymerase II (Pol II)-dependent transcription through inhibiting the phosphorylation of RNA polymerase II (Pol II). Co-immunoprecipitation, RNA immunoprecipitation and RNA electrophoretic mobility shift assay are followed to investigate its mechnism. A 145nt-in-length fragment in RNA2.7 is identified to bind to Pol II and block the interaction between Pol II and phosphorylated cyckin-dependent kinase 9 (phospho-CDK9). By inhibiting Pol II phosphorylation, RNA2.7 decreases the transcription and expression levels of chromatin licensing and DNA replication factor 1 (Cdt1) and cell division cycle gene 6 (Cdc6). Through above way, RNA2.7 prevents the entry of cells into S phase and facilitates viral DNA replication. Our results discover the functions of HCMV RNA2.7 in regulation of Pol II phosphorylation and cell cycle control during infection.\n\nAuthor summaryHuman cytomegalovirus (HCMV) RNA2.7 is a viral lncRNA that is most abundant during infection. Here we show that a 145nt-in-length fragment in RNA2.7 binds to RNA polymerase II (Pol II) and blocks the interaction between Pol II and phosphorylated cyckin-dependent kinase 9 (phospho-CDK9). By inhibiting Pol II phosphorylation, RNA2.7 decreases the transcription and expression levels of chromatin licensing and DNA replication factor 1 (Cdt1) and cell division cycle gene 6 (Cdc6), and blocks host cells entering into S phase. RNA2.7 is confirmed to facilitate viral DNA replication through decreasing Cdt1 and Cdc6. Therefore, our results discover the functions of HCMV RNA2.7 in regulation of Pol II phosphorylation and cell cycle control during infection.

microbiology↗

bioRxiv2018

Attend and Predict: Understanding Gene Regulation by Selective Attention on Chromatin

The past decade has seen a revolution in genomic technologies that enabled a flood of genome-wide profiling of chromatin marks. Recent literature tried to understand gene regulation by predicting gene expression from large-scale chromatin measurements. Two fundamental challenges exist for such learning tasks: (1) genome-wide chromatin signals are spatially structured, high-dimensional and highly modular; and (2) the core aim is to understand what the relevant factors are and how they work together. Previous studies either failed to model complex dependencies among input signals or relied on separate feature analysis to explain the decisions. This paper presents an attention-based deep learning approach, AttentiveChrome, that uses a unified architecture to model and to interpret dependencies among chromatin factors for controlling gene regulation. AttentiveChrome uses a hierarchy of multiple Long Short-Term Memory (LSTM) modules to encode the input signals and to model how various chromatin marks cooperate automatically. AttentiveChrome trains two levels of attention jointly with the target prediction, enabling it to attend differentially to relevant marks and to locate important positions per mark. We evaluate the model across 56 different cell types (tasks) in humans. Not only is the proposed architecture more accurate, but its attention scores provide a better interpretation than state-of-the-art feature visualization methods such as saliency maps.1

bioinformatics↗

bioRxiv2018

GaKCo: a Fast GApped k-mer string Kernel using COunting

String Kernel (SK) techniques, especially those using gapped k-mers as features (gk), have obtained great success in classifying sequences like DNA, protein, and text. However, the state-of-the-art gk-SK runs extremely slow when we increase the dictionary size ({Sigma}) or allow more mismatches (M). This is because current gk-SK uses a trie-based algorithm to calculate co-occurrence of mismatched substrings resulting in a time cost proportional to O({Sigma}M). We propose a fast algorithm for calculating Gapped k-mer Kernel using Counting (GaKCo). GaKCo uses associative arrays to calculate the co-occurrence of substrings using cumulative counting. This algorithm is fast, scalable to larger{Sigma} and M, and naturally parallelizable. We provide a rigorous asymptotic analysis that compares GaKCo with the state-of-the-art gk-SK. Theoretically, the time cost of GaKCo is independent of the{Sigma} M term that slows down the trie-based approach. Experimentally, we observe that GaKCo achieves the same accuracy as the state-of-the-art and outperforms its speed by factors of 2, 100, and 4, on classifying sequences of DNA (5 datasets), protein (12 datasets), and character-based English text (2 datasets). 1

bioinformatics↗

bioRxiv2018

A large-scale whole-genome sequencing analysis reveals highly specific genome editing by both Cas9 and Cpf1 nucleases in rice

Targeting specificity has been an essential issue for applying genome editing systems in functional genomics, precise medicine and plant breeding. Understanding the scope of off-target mutations in Cas9 or Cpf1-edited crops is critical for research and regulation. In plants, only limited studies had used whole-genome sequencing (WGS) to test off-target effects of Cas9. However, the cause of numerous discovered mutations is still controversial. Furthermore, WGS based off-target analysis of Cpf1 has not been reported in any higher organism to date. Here, we conducted a WGS analysis of 34 plants edited by Cas9 and 15 plants edited by Cpf1 in T0 and T1 generations along with 20 diverse control plants in rice, a major food crop with a genome size of ~380 Mb. The sequencing depth ranged from 45X to 105X with reads mapping rate above 96%. Our results clearly show that most mutations in edited plants were created by tissue culture process, which caused ~102 to 148 single nucleotide variations (SNVs) and ~32 to 83 insertions/deletions (indels) per plant. Among 12 Cas9 single guide RNAs (sgRNAs) and 3 Cpf1 CRISPR RNAs (crRNAs) assessed by WGS, only one Cas9 sgRNA resulted in off-target mutations in T0 lines at sites predicted by computer programs. Moreover, we cannot find evidence for bona fide off-target mutations due to continued expression of Cas9 or Cpf1 with guide RNAs in T1 generation. Taken together, our comprehensive and rigorous analysis of WGS big data across multiple sample types suggests both Cas9 and Cpf1 nucleases are very specific in generating targeted DNA modifications and off-targeting can be avoided by designing guide RNAs with high specificity.

molecular biology↗

bioRxiv2018

Predicting three-dimensional genome organization with chromatin states

We introduce a computational model to simulate chromatin structure and dynamics. Starting from one-dimensional genomics and epigenomics data that are available for hundreds of cell types, this model enables de novo prediction of chromatin structures at five-kilo-base resolution. Simulated chromatin structures recapitulate known features of genome organization, including the formation of chromatin loops, topologically associating domains (TADs) and compartments, and are in quantitative agreement with chromosome conformation capture experiments and super-resolution microscopy measurements. Detailed characterization of the predicted structural ensemble reveals the dynamical flexibility of chromatin loops and the presence of cross-talk among neighboring TADs. Analysis of the models energy function uncovers distinct mechanisms for chromatin folding at various length scales.

biophysics↗

bioRxiv2017

Metabolic Interactions Between Dynamic Bacterial Subpopulations

Within multi-species microbial communities, individual species are known to occupy distinct metabolic niches. By contrast, it has remained largely unclear whether and how metabolic specialization occurs within clonal bacterial populations. The possibility of such metabolic specialization in clonal populations raises several questions: Does specialization occur, and if it does, which metabolic processes are involved? How is specialization coordinated? How rapidly do cells switch between states? And finally, what functions might metabolic specialization provide? One potential function of metabolic specialization could be to manage overflow metabolites such as acetate, which presents a toxic challenge due to low pH, and protective pH-neutral overflow metabolites. Here we show that exponentially dividing Bacillus subtilis cultures divide into distinct interacting metabolic subpopulations including one population that produces acetate, and another population that differentially expresses metabolic genes for the production of acetoin, a pH-neutral storage molecule. These subpopulations grew at distinct rates, and cells switched dynamically between states, with acetate influencing the relative sizes of the different subpopulations. These results show that clonal populations can use metabolic specialization to control the environment through a process of dynamic, environmentally-sensitive state-switching.

microbiology↗

bioRxiv2017

Continuous interdomain orientation distributions reveal components of binding thermodynamics

1The flexibility of biological macromolecules is an important structural determinant of function. Unfortunately, the correlations between different motional modes are poorly captured by discrete ensemble representations. Here, we present new ways to both represent and visualize correlated interdomain motions. Interdomain motions are determined directly from residual dipolar couplings (RDCs), represented as a continuous conformational distribution, and visualized using the disk-on-sphere (DoS) representation. Using the DoS representation, features of interdomain motions, including correlations, are intuitively visualized. The representation works especially well for multidomain systems with broad conformational distributions. This analysis also can be extended to multiple probability density modes, using a Bingham mixture model. We use this new paradigm to study the interdomain motions of staphylococcal protein A, which is a key virulence factor contributing to the pathogenicity of S. aureus. We capture the smooth transitions between important states and demonstrate the utility of continuous distribution functions for computing components of binding thermodynamics. Such insights allow the dissection the dynamic structural components of functionally important intermolecular interactions.

biophysics↗

bioRxiv2017

Native and engineered clifednamide biosynthesis in multiple Streptomyces spp.

Polycyclic tetramate macrolactam (PTM) natural products are produced by actinomycetes and other bacteria. PTMs are often bioactive, and the simplicity of their biosynthetic clusters make them attractive for bioengineering. Clifednamide-type PTMs from Streptomyces sp. JV178 contain a distinctive ketone group, suggesting the existence of a novel PTM oxidizing enzyme. Here, we report the new cytochrome P450 enzyme (CftA) is required for clifednamide production. Genome mining was used to identify several new clifednamide producers, some having improved clifednamide yields. Using a parallel synthetic biology approach, CftA isozymes were used to engineer the ikarugamycin pathway of Streptomyces sp. NRRL F-2890 to yield clifednamides. Further, we observed that strong CftA expression leads to the production of a new PTM, clifednamide C. We demonstrate the utility of both genome mining and synthetic biology to rapidly increase clifednamide production and identify a PTM tailoring enzyme for rational molecule design.

synthetic biology↗

bioRxiv2017

Opportunities And Obstacles For Deep Learning In Biology And Medicine

Deep learning, which describes a class of machine learning algorithms, has recently showed impressive results across a variety of domains. Biology and medicine are data rich, but the data are complex and often ill-understood. Problems of this nature may be particularly well-suited to deep learning techniques. We examine applications of deep learning to a variety of biomedical problems--patient classification, fundamental biological processes, and treatment of patients--and discuss whether deep learning will transform these tasks or if the biomedical sphere poses unique challenges. We find that deep learning has yet to revolutionize or definitively resolve any of these problems, but promising advances have been made on the prior state of the art. Even when improvement over a previous baseline has been modest, we have seen signs that deep learning methods may speed or aid human investigation. More work is needed to address concerns related to interpretability and how to best model each problem. Furthermore, the limited amount of labeled data for training presents problems in some domains, as do legal and privacy constraints on work with sensitive health records. Nonetheless, we foresee deep learning powering changes at both bench and bedside with the potential to transform several areas of biology and medicine.

bioinformatics↗

bioRxiv2017

Neuron Type-Specific Mechanical Regulation Of Voltage-Gated Ca2+ Channels And Excitability In Hippocampal And Trigeminal Ganglion Neurons

Increasing evidence suggests that the mechanical properties of extracellular matrix regulate central and peripheral neuronal functions. We thus investigated the CaV channels in hippocampal and trigeminal ganglion (TG) neurons cultured on substrates with different stiffness. Patch-clamp current recordings showed that stiff substrate augmented the CaV channel currents in hippocampal and TG neurons and additionally induced a leftward shift in the voltage-dependent channel activation curve in small TG neurons. Combination with using selective channel blockers revealed that substrate stiffness preferentially regulated the N-type channel current in hippocampal and medium TG neurons but the T-type channel current in small TG neurons. Current-clamp recordings further demonstrated that stiff substrate enhanced the excitability of small TG neurons, which was ablated by blocking the T-type channel. Treatment of neurons on the stiff substrate with low-dose blebbistatin reduced both the N-type channel current in hippocampal and medium TG neurons and the T-type channel current in small TG neurons to the levels in neurons on the soft substrate, whereas treatment of neurons on the soft substrate with calcium A increased both the N-type channel current in hippocampal and medium TG neurons and the T-type channel current in small TG neurons to the levels in neurons on the stiff substrate, thus consistently supporting critical involvement of actomyosin in mechanical sensing. Taken together, our results reveal neuron type-specific mechanical regulation of the Cav channels and excitability in the nervous system. Such information is useful for neural tissue engineering and regeneration.

biophysics↗