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Zhou, G.

Publications and source records attributed to Zhou, G..

At least 109 records · Page 6Linked to original sources

Odor induces characteristic time courses of theta, beta and gamma oscillations in human olfactory cortex

Studies of neuronal oscillations have contributed substantial insight into the mechanisms of visual, auditory and somatosensory perception. However, progress in such research in the human olfactory system has lagged behind. As a result, the electrophysiological properties of the human olfactory system are poorly understood, and in particular, whether stimulus-driven high frequency oscillations play a role in odor processing is unknown. Here, we used direct intracranial recordings from human piriform cortex during an odor identification task to show that three key oscillatory rhythms are an integral part of the human olfactory cortical response to smell: odor induces theta, beta and gamma rhythms in human piriform cortex. We further show that these rhythms have distinct relationships with perceptual behavior. Odor-elicited gamma oscillations occur only during trials in which the odor is accurately perceived, and features of gamma oscillations predict odor identification accuracy, suggesting they are critical for odor identity perception in humans. We also found that the amplitude of high-frequency oscillations is organized by the phase of low frequency signals shortly following sniff onset, only when odor is present. Our findings reinforce previous work on theta oscillations, suggest that gamma oscillations in human piriform cortex are important for perception of odor identity, and constitute a robust identification of the characteristic electrophysiological response to smell in the human brain. Future work will determine whether the distinct oscillations we identified reflect distinct perceptual features of odor stimuli.

neuroscience↗

Exploring the dynamic mechanisms of steady-state visual evoked potentials at the whole-brain level: evidence from a computational study

Periodic visual stimulation can induce stable steady-state visual evoked potentials (SSVEPs) distributed in multiple brain regions and has potential applications in both neural engineering and cognitive neuroscience. However, the underlying dynamic mechanisms of SSVEPs at the whole-brain level are still not completely understood. Here, we addressed this issue by simulating the rich dynamics of SSVEPs with a large-scale brain model designed with constraints of neuroimaging data acquired from the human brain. By eliciting activity of the occipital areas using an external periodic stimulus, our model was capable of replicating both the spatial distributions and response features of SSVEPs that were observed in experiments. In particular, we confirmed that alpha-band (8-12 Hz) stimulation could evoke stronger SSVEP responses; this frequency sensitivity was due to nonlinear entrainment and resonance, and could be modulated by endogenous factors in the brain. Interestingly, the stimulus-evoked brain networks also exhibited significant superiority in topological properties near this frequency-sensitivity range, and stronger SSVEP responses were demonstrated to be supported by more efficient functional connectivity at the neural activity level. These findings not only provide insights into the mechanistic understanding of SSVEPs at the whole-brain level but also indicate a bright future for large-scale brain modeling in characterizing the complicated dynamics and functions of the brain.

neuroscience↗

Deficiency of intellectual disability-related gene Brpf1 reduced inhibitory neurotransmission and Map2k7 expression in GABAergic interneurons

Intellectual disability is closely related to impaired GABA neurotransmission. Brpf1 was specifically expressed in medial ganglionic eminence (MGE), a developmental niche of GABAergic interneurons, and patients with BRPF1 mutations were mentally retarded. To test its role in development and function of MGE-derived GABAergic interneurons, we performed immunofluorescence staining, whole-cell patch-clamp, MGE transplantation and mRNA-Seq to understand its effect on neuronal differentiation, dendritic morphology, electrophysiology, migration and gene regulation, using mouse MGE-derived GABAergic interneurons infected with AAV-shBrpf1. We found a decreasing trend on parvalbumin+ interneuron differentiation. Moreover, increased firing threshold, decreased number of evoked APs, and a reduced amplitude of mIPSCs were observed before any significant change of MAP2+ dendritic morphology and in vivo migration appeared. Finally, mRNA-Seq analysis revealed that genes related to neurodevelopment and synaptic transmission such as Map2k7 were dysregulated. Our results demonstrated a key role of Brpf1 in inhibitory neurotransmission and related gene expression of GABAergic interneurons.

neuroscience↗

A fast and robust Bayesian nonparametric method for prediction of complex traits using summary statistics

Genetic prediction of complex traits has great promise for disease prevention, monitoring, and treatment. The development of accurate risk prediction models is hindered by the wide diversity of genetic architecture across different traits, limited access to individual level data for training and parameter tuning, and the demand for computational resources. To overcome the limitations of the most existing methods that make explicit assumptions on the underlying genetic architecture and need a separate validation data set for parameter tuning, we develop a summary statistics-based nonparametric method that does not rely on validation datasets to tune parameters. In our implementation, we refine the commonly used likelihood assumption to deal with the discrepancy between summary statistics and external reference panel. We also leverage the block structure of the reference linkage disequilibrium matrix for implementation of a parallel algorithm. Through simulations and applications to twelve traits, we show that our method is adaptive to different genetic architectures, statistically robust, and computationally efficient. Our method is available at https://github.com/eldronzhou/SDPR.

bioinformatics↗

Characterization of Structural Variation in Tibetans Reveals New Evidence of High-altitude Adaptation and Introgression

Structural variation (SV) acts as an essential mutational force shaping the evolution and function of the human genome. To investigate the role of SVs in high-altitude adaptation (HAA), we here generated a comprehensive catalog of SVs in a Chinese Tibetan (n = 15) and Han (n = 10) population using the nanopore sequencing technology. Among a total of 38,216 unique SVs in the catalog, 27% were sequence-resolved for the first time. We systemically assessed the distribution of these SVs across repeat sequences and functional genomic regions. Through genotyping in additional 189 genomes, we identified 90 Tibetan-Han stratified SVs and 124 candidate adaptive genes. Besides, we discovered 15 adaptive introgressed SV candidates and provided evidence for a deletion of 335 base pairs at 1p36.32. Overall, our results highlight the important role of SVs in the evolutionary processes of Tibetans adaptation to the Qinghai-Tibet Plateau and provide a valuable resource for future HAA studies.

genetics↗

First high-quality reference genome of Amphicarpaea edgeworthii

Amphicarpaea edgeworthii, an annual twining herb, is a widely distributed species and an ideal model for studying complex flowering types and evolutionary mechanisms of species. Herein, we generated a high-quality assembly of A. edgeworthii by using a combination of PacBio, 10x Genomics libraries, and Hi-C mapping technologies. The final 11 chromosome-level scaffolds covered 90.61% of the estimated genome (343.78 Mb), which is the first chromosome-scale assembled genome of an amphicarpic plant. These data will be beneficial for the discovery of genes that control major agronomic traits, spur genetic improvement of and functional genetic studies in legumes, and supply comparative genetic resources for other amphicarpic plants.

genomics↗

A detailed mathematical theory of thalamic and cortical microcircuits based on inference in a generative vision model

Understanding the information processing roles of cortical circuits is an outstanding problem in neuroscience and artificial intelligence. Theory-driven efforts will be required to tease apart the functional logic of cortical circuits from the vast amounts of experimental data on cortical connectivity and physiology. Although the theoretical setting of Bayesian inference has been suggested as a framework for understanding cortical computation, making precise and falsifiable biological mappings need models that tackle the challenge of real world tasks. Based on a recent generative model, Recursive Cortical Networks, that demonstrated excellent performance on visual task benchmarks, we derive a family of anatomically instantiated and functional cortical circuit models. Efficient inference and generalization guided the representational choices in the original computational model. The cortical circuit model is derived by systematically comparing the computational requirements of this model with known anatomical constraints. The derived model suggests precise functional roles for the feed-forward, feedback, and lateral connections observed in different laminae and columns, assigns a computational role for the path through the thalamus, predicts the interactions between blobs and inter-blobs, and offers an algorithmic explanation for the innate inter-laminar connectivity between clonal neurons within a cortical column. The model also explains several visual phenomena, including the subjective contour effect, and neon-color spreading effect, with circuit-level precision. Our work paves a new path forward in understanding the logic of cortical and thalamic circuits.

neuroscience↗

Learning a force field from small-molecule crystal lattice predictions enables consistent sub-Angstrom protein-ligand docking

Accurate and rapid calculation of protein-small molecule interaction energies is critical for computational drug discovery. Because of the large chemical space spanned by drug-like molecules, classical force fields contain thousands of parameters describing atom-pair distance and torsional preferences; each parameter is typically optimized independently on simple representative molecules. Here we describe a new approach in which small-molecule force field parameters are jointly optimized guided by the rich source of information contained within thousands of available small molecule crystal structures. We optimize parameters by requiring that the experimentally determined molecular lattice arrangements have lower energy than all alternative lattice arrangements. Thousands of independent crystal lattice-prediction simulations were run on each of 1,386 small molecule crystal structures, and energy function parameters of an implicit solvent energy model were optimized so native crystal lattice arrangements had lowest energy. The resulting energy model was implemented in Rosetta, together with a rapid genetic algorithm docking method employing grid based scoring and receptor flexibility. The success rate of bound structure recapitulation in cross-docking on 1,112 complexes was improved by more than 10% over previously published methods, with solutions within <1 [A] in over half of the cases. Our results demonstrate that small molecule crystal structures are a rich source of information for systematically improving computational drug discovery.

biophysics↗

Photosynthetic resistance and resilience under drought and rewatering in maize plants

Abnormally altered precipitation patterns induced by climate change have profound global effects on crop production. However, the plant functional responses to various precipitation regimes remain unclear. Here, greenhouse and field experiments were conducted to determine how maize plant functional traits respond to drought, flooding, and rewatering. Drought and flooding hampered photosynthetic capacity, particularly when severe and/or prolonged. Most photosynthetic traits recovered after rewatering, with few compensatory responses. Rewatering often elicited high photosynthetic resilience in plants exposed to severe drought at the end of plant development, with the response strongly depending on the drought severity/duration and plant growth stage. The associations of chlorophyll concentrations with photosynthetically functional activities were stronger during post-tasselling than pre-tasselling, implying an involvement of leaf age/senescence in responses to episodic drought and subsequent rewatering. Coordinated changes in chlorophyll content, gas exchange, fluorescence parameters (PSII quantum efficiency and photochemical/non-photochemical radiative energy dissipation) possibly contributed to the enhanced drought resistance and resilience and suggested a possible regulative trade-off. These findings provide fundamental insights into how plants regulate their functional traits to deal with sporadic alterations in precipitation. Breeding and management of plants with high resistance and resilience traits could help crop production under future climate change.

plant biology↗

Transcriptional analysis of Camellia oleifera inoculated with Colletotrichum fructicola from Different Geographic regions

Anthracnose, caused by Colletotrichum. Spp., is an important disease affecting oil tea (Camellia oleifera). Extensive molecular studies have demonstrated that Colletotrichum fructicola is the dominant pathogen of oil tea anthracnose in China. The study aims to investigate differences in molecular processes and regulatory genes at different stages of infection of C. fructicola, to aide in understanding differences in pathogenic mechanisms of C. fructicola of different geographic populations. Materials and Methods: We compared the pathogenicity of C. fructicola from different populations (Wuzhishan, Hainan province and Shaoyang, Hunan province), and gene expression of representative strains of the two populations before and after inoculation in oil tea using RNA sequencing. In results we found that C. fructicola from Wuzhishan has a stronger ability to infect and impact oil tea leaf tissue. Up-regulated genes in oil tea following infection with the two geographic populations were associated with a number of ribosome-related GO terms and ribosome metabolic related KEGG pathways, and were significantly enriched in galactosidase activity, glutamine family amino acid metabolism, arginine and proline metabolism. In addition, up-regulated gene lists associated with infection in the Wuzhishan strains were significantly enriched purine metabolism pathways as well, while Shaoyang strains were not significantly enriched in these processes. Differences in gene expression were indicative of greater active transcription and translation in the Wuzhishan population while more active glycoprotein metabolism was observed following inoculation with strains from Shaoyang. These results indicate that C. fructicola obtained sugars and amino acids from oil tea tissue to resist host immune pressure. Moreover, more active in transcriptional and translational activity and the greater regulation of purine metabolism pathway in the Wuzhishan strain might contribute to its stronger pathogenicity.

microbiology↗

TMT-Based Quantitative Proteomic Analysis of Intestinal Organoids Infected by Listeria monocytogenes with Different Virulence

Listeria monocytogenes (Lm) is an opportunistic food-borne pathogen that cause listeriosis. L. monocytogenes belonged to different serovars presents with different virulence in the host and caused different host reactions. To investigate the remodeling of host proteome by differently toxic strains, the cellular protein responses of intestinal organoids were analyzed using TMT labeling and high performance liquid chromatography-mass spectrometry. Quantitative proteomic analysis revealed 6564 differentially expressed proteins, of which 5591 proteins were quantified. The fold-change cutoff was set at 1.3 (Lm vs control), the virulent strain caused 102 up-regulated proteins and 52 down-regulated proteins, while the low virulent strain caused 188 up-regulated proteins and 25 down-regulated proteins. These identified proteins were involved in the regulation of essential processes such as biological metabolism, energy metabolism, and immune system process. Some selected proteins were screened by Real-time PCR and Western blotting. These results revealed that differently toxic L. monocytogenes induced similar biological functions and immune responses while had different regulation on differential proteins in the pathway.

microbiology↗

Machine Learning Identifies Novel Candidates for DrugRepurposing in Alzheimer's Disease

Clinical trials of novel therapeutics for Alzheimers Disease (AD) have consumed a large amount of time and resources with largely negative results. Repurposing drugs already approved by the Food and Drug Administration (FDA) for another indication is a more rapid and less expensive option. Repurposing can yield a useful therapeutic and also accelerate proof of concept studies that ultimately lead to a new molecular entity. We present a novel machine learning framework, DRIAD (Drug Repurposing In AD), that quantifies potential associations between the pathology of AD severity (the Braak stage) and molecular mechanisms as encoded in lists of gene names. DRIAD was validated on gene lists known to be associated with AD from other studies and subsequently applied to evaluate lists of genes arising from perturbations in differentiated human neural cell cultures by 80 FDA-approved and clinically tested drugs, producing a ranked list of possible repurposing candidates. Top-scoring drugs were inspected for common trends among their nominal molecular targets and their "off-targets", revealing a high prevalence of kinases from the Janus (JAK), Unc-51-like (ULK) and NIMA-related (NEK) families. These kinase families are known to modulate pathways related to innate immune signaling, autophagy, and microtubule formation and function, suggesting possible disease-modifying mechanisms of action. We propose that the DRIAD method can be used to nominate drugs that, after additional validation and identification of relevant pharmacodynamic biomarker(s), could be evaluated in a clinical trial.

neuroscience↗

Ciprofloxacin Resistance in Hospital and Community-acquired Urinary Tract Infections by Escherichia coli: A Systematic Review and Meta-analysis

In recent years, antimicrobial resistance has been increasingly reported. One main concern is the resistance of gram-negative bacteria like E. coli to ciprofloxacin (fluoroquinolones). Gram-negative bacteria are the main cause of community and hospital-acquired urinary tract infections (UTI). We aimed to review and analyze the data on ciprofloxacin resistance in hospital and community-acquired UTI. A literature search of three electronic databases (PubMed, Medline, and Cochrane) was performed. We considered the papers that were published from January 2004 to May 2019. The search yielded a total of 16097 studies besides 31 studies from a manual search. Filtering yielded 1297 relevant full-text papers. Eighty-three papers, equivalent of 99 cohorts, were finally included in this systematic review and in the analysis. The analysis results suggest that pooled ciprofloxacin resistance for community and hospital-acquired E. coli UTI is 0.27 (95% CI 0.246-0.303) and 0.30 (95% CI 0.22-0.38), respectively. Pooled resistance rates according to regions are 0.43 (95% CI 0.31-0.54) for Asia ensued by Africa 0.31 (95% CI 0.22-0.35), the Middle East 0.21(95% CI 0.13-0.30), Europe 0.18 (95% CI 0.13-0.22), and Australia 0.06 (95% CI 0.04-0.08). The pooled estimates revealed that ciprofloxacin resistance was higher in developing countries compared to that in developed countries, 0.35 (95% CI 0.30-0.40) and 0.13 (95% CI 0.10-0.16), respectively. Finally, plotting resistance over time deemed statistically significant (n= 79, r= 0.29, p= 0.038). Our findings suggest that ciprofloxacin resistance among UTI patients is a highly prevalent and serious issue. The suggested risks are low-income, acquiring hospital infection, and falling in highly-vulnerable regions like Asia and Africa. We also shed light on some approaches to correct the perception of patients and general practitioners (GPs) for antibiotic usage. We also suggest ideas to impede the progress of the post-antibiotic era in countries known for high antibiotic resistance.

microbiology↗

True S-cones are concentrated in the ventral mouse retina for color detection in the upper visual field

Color, an important visual cue for survival, is encoded by comparing signals from photoreceptors with different spectral sensitivities. The mouse retina expresses a short wavelength-sensitive and a middle/long wavelength-sensitive opsin (S- and M-opsin), forming opposing, overlapping gradients along the dorsal-ventral axis. Here, we analyzed the distribution of all cone types across the entire retina for two commonly used mouse strains. We found, unexpectedly, that true S-cones (S-opsin only) are highly concentrated (up to 30% of cones) in ventral retina. Moreover, S-cone bipolar cells (SCBCs) are also skewed towards ventral retina, with wiring patterns matching the distribution of true S-cones. In addition, true S-cones in the ventral retina form clusters, which may augment synaptic input to SCBCs. Such a unique true S-cone pattern forms a basis for mouse color vision, likely reflecting evolutionary adaption to enhance color coding for the upper visual field suitable for mices habitat and behavior.

evolutionary biology↗

Behavioral response of insecticide-resistant mosquitoes against spatial repellent: a modified self-propelled particle model simulation

Rapidly increasing pyrethroid insecticide resistance and changes in vector biting and resting behavior pose serious challenges in malaria control. Mosquito repellents, especially spatial repellents, have received much attention from industry. Many of these repellents contain the same or similar chemicals as those used in insecticides. Does resistance to insecticides affect the efficacy of spatial repellents? We attempted to simulate interactions between mosquitoes and repellents using a machine learning method, the self-propelled particle (SPP) model, which we modified to include attractiveness/repellency effects. We simulated a random walk scenario and scenarios with insecticide susceptible/resistant mosquitoes against repellent alone and against repellent plus attractant (to mimic a human host). We assumed attractant odors and repellent chemicals diffused randomly and omnidirectionally, and that mosquitoes were confined in a cubic cage. We modified the velocity and direction components of SPP using attraction/repulsion rates and concentrations. Simulation results indicated that without attractant/repellent, mosquitoes would fly anywhere in the cage at random. With attractant, mosquitoes might or might not fly toward the source (i.e., the human), depending on the simulation time (and thus the attractant concentration distribution). Eventually, however, all mosquitoes were attracted to the source of the odor. With repellent, results depended on the amount of chemical used and the level of mosquito insecticide resistance. All insecticide-susceptible mosquitoes eventually moved to the corner of the cage farthest from the repellent release point. Surprisingly, a high proportion of highly resistant mosquitoes might reach the attractant release point (the human) earlier in the simulation when repellent was present compare to no repellent was present. At fixed concentration, a high proportion of mosquitoes could be able to reach the host when the relative repellency efficacy (compare to attractant efficacy) was <1, whereas, no mosquitoes reached the host when the relative repellency efficacy was > 1. This result implies that repellent may not be sufficient against highly physiologically insecticide resistant mosquitoes, since very high concentrations of repellent are neither practically feasible nor cost-effective.

ecology↗

Envelope Simulation: A New Adaptive Method for Dose Optimization/* Layout-provided Styles */div.standard { margin-bottom: 2ex;}

We present a flexible and general adaptive experimental design for dose finding clinical trials. This method can be applied in sterotypical settings such as determining a maximum tolerated dose, when the dose response relationship is complex, or when the response is an arbitrary quantitative measure. Our design generalizes dose finding methods such as the continual reassessment method (CRM), modified CRM, and estimation with overdose control (EWOC). Similar to those, our design requires a working mathematical model, which assures efficiency, low bias, and a higher fraction of dose selected near the optimum compared to purely operational designs. Unlike typical model based designs that always employ the same model, our method allows individual dose response models tailored to the circumstance. Simulations are also integral to our design allowing it to account for both known and unknown effects of dose on outcome. This method is applicable to general dose finding problems such as those encountered with modern targeted anti-cancer agents, immunotherapies, and titrations against biomarker outcome measures. The method can also support improvements in the design of the experiment being conducted by providing a platform for concurrent simulations to assess the influence of projected design points. Although we present the design in the context of clinical trials, it is equally applicable to experiments with non-human subjects.

cancer biology↗

A global barley panel revealing genomic signatures of breeding in modern cultivars

The future of plant cultivar improvement lies in the evaluation of genetic resources from currently available germplasm. Recent efforts in plant breeding have been aimed at developing new and improved varieties from poorly adapted crops to suit local environments. However, the impact of these breeding efforts is poorly understood. Here, we assess the contributions of both historical and recent breeding efforts to local adaptation and crop improvement in a global barley panel by analysing the distribution of genetic variants with respect to geographic region or historical breeding category. By tracing the impact breeding had on the genetic diversity of barley released in Australia, where the history of barley production is relatively young, we identify 69 candidate regions within 922 genes that were under selection pressure. We also show that modern Australian barley varieties exhibit 12% higher genetic diversity than historical cultivars. Finally, field-trialling and phenotyping for agriculturally relevant traits across a diverse range of Australian environments suggests that genomic regions under strong breeding selection and their candidate genes are closely associated with key agronomic traits. In conclusion, our combined dataset and germplasm collection provide a rich source of genetic diversity that can be applied to understanding and improving environmental adaptation and enhanced yields. Author summaryTodays gene pool of crop genetic diversity has been shaped during domestication and more recently by breeding. Genetic diversity is vital for crop species to be able to adapt to changing environments. There is concern that recent breeding efforts have eroded the genetic diversity of many domesticated crops including barley. The present study assembled a global panel of barley genotypes with a focus on historical and modern Australian varieties. Genome-wide data was used to detect genes that are thought to have been under selection during crop breeding in Australian barley. The results demonstrate that despite being more extensively bred, modern Australian barley varieties exhibit higher genetic diversity than historical cultivars, countering the common perception that intensive breeding leads to genetic erosion of adaptive diversity in modern cultivars. In addition, some loci (particularly those related to phenology) were subject to selection during the introduction of other barley varieties to Australia - these genes might continue to be important targets in breeding efforts in the face of changing climatic conditions.

genomics↗

Arabidopsis ERF4 and MYB52 Transcription Factors Play Antagonistic Roles in Regulating Homogalacturonan De-methylesterification in Seed Coat Mucilage

The Arabidopsis (Arabidopsis thaliana) seed coat mucilage is a specialized cell wall with pectin as its major component. Pectin is synthesized in the Golgi apparatus with homogalacturonan fully methylesterified, but it must undergo de-methylesterification by pectin methylesterase (PME) after being secreted into the cell wall. This reaction is critical for pectin maturation, but the mechanisms of its transcriptional regulation remain largely unknown. Here, we show that the Arabidopsis ERF4 transcription factor positively regulates pectin de-methylesterification during seed development and directly suppresses the expression of PME INHIBITOR13 (PMEI13), 14, 15 and SUBTILISIN-LIKE SERINE PROTEASE 1.7 (SBT1.7). The erf4 mutant seeds showed repartitioning of mucilage between soluble and adherent layers as a result of decreased PME activity and increased degree of pectin methylesterification. ERF4 physically associates with and antagonizes MYB52 in activating PMEI6, 14 and SBT1.7 and MYB52 also antagonizes ERF4 activity in the regulation of downstream targets. Gene expression studies revealed that ERF4 and MYB52 have opposite effects on pectin de-methylesterification. Genetic analysis indicated that the erf4-2 myb52 double mutant seeds show mucilage phenotype similar to wild-type. Taken together, this study demonstrates that ERF4 and MYB52 antagonize each others activity to maintain the appropriate degree of pectin methylesterification, expanding our understanding of how pectin de-methylesterification is fine-tuned by the ERF4-MYB52 transcriptional complex in the seed mucilage. One-sentence summaryArabidopsis ERF4 and MYB52 transcription factors interact and play antagonistic roles in regulating homogalacturonan de-methylesterification related genes in the seed coat mucilage.

plant biology↗