Biology subjects

Feng, J.

Publications and source records attributed to Feng, J..

At least 19 recordsLinked to original sources

bioRxiv2026

BioIMA: a one-click desktop tool for standardized extraction of phenotypic traits from biological images

Standardized extraction of quantitative phenotypes from images is increasingly important across plant biology, from ecological and evolutionary studies to genetics, breeding, and functional genomics. However, as large image datasets are increasingly used for trait analysis, many biologically relevant traits, including size, shape, color, and spatial patterning, are still measured manually or using fragmented semi-automated workflows. These limitations reduce throughput, reproducibility, and accessibility, especially for researchers without computational expertise. Here, we present BioIMA, an open-source desktop tool for rapid and standardized phenotyping from biological images. BioIMA integrates foundation model-based segmentation with automated trait computation, allowing users to extract quantitative measurements from images through an intuitive graphical interface and without model training. To validate its performance, we quantified a set of knot morphological traits in two Populus species, as these measurements are typically time-consuming to perform manually. Automatic measurements showed strong agreement with manual ImageJ-based measurements (R2 > 0.95), while reducing per-image processing time by approximately 75% (from ~15 s to ~4 s). BioIMA was further applied to diverse plant datasets, including Helianthus and Rhododendron images with varying morphologies and background conditions. Although developed for plant phenotyping, BioIMA may also be extended to other biological samples where region-based size, shape, or color traits are of interest. By combining accessibility and standardization in a lightweight local application, BioIMA provides a practical community resource for image-based phenotyping in ecological and evolutionary studies.

bioinformatics↗

bioRxiv2018

A genetically encoded fluorescent sensor for rapid and specific in vivo detection ofnorepinephrine

Norepinephrine (NE) and epinephrine (Epi), two key biogenic monoamine neurotransmitters, are involved in a wide range of physiological processes. However, their precise dynamics and regulation remain poorly characterized, in part due to limitations of available techniques for measuring these molecules in vivo. Here, we developed a family of GPCR Activation-Based NE/Epi (GRABNE) sensors with a 230% peak {Delta}F/F0 response to NE, good photostability, nanomolar-to-micromolar sensitivities, sub-second rapid kinetics, high specificity to NE vs. dopamine. Viral- or transgenic- mediated expression of GRABNE sensors were able to detect electrical-stimulation evoked NE release in the locus coeruleus (LC) of mouse brain slices, looming-evoked NE release in the midbrain of live zebrafish, as well as optogenetically and behaviorally triggered NE release in the LC and hypothalamus of freely moving mice. Thus, GRABNE sensors are a robust tool for rapid and specific monitoring of in vivo NE/Epi transmission in both physiological and pathological processes.

neuroscience↗

bioRxiv2018

Biofilm Structures of Borrelia burgdorferi Not Only Display More Tolerance to Lyme Antibiotics But Also Cause More Severe Pathology In a Mouse Arthritis Model: Implications for Understanding Persistence, PTLDS and Treatment

Lyme disease, caused by Borrelia burgdorferi, is the most common tick-borne illness in US and Europe. While most patients can be cured with a 2-4 week antibiotic therapy, about 10%-20% patients continue to suffer persistent symptoms of fatigue, pain or joint and muscle aches, and neurocognitive despite the treatment, a condition called post-treatment Lyme disease syndrome (PTLDS). The cause for PTLDS is unclear but one possibility is persistent infection with B. burgdorferi. B. burgdorferi is known to develop morphological variant forms such as round bodies and aggregated biofilm-like microcolonies as a log phase culture consisting of spirochete form grows into stationary phase. Here we isolated biofilm-like microcolony and planktonic form (spirochetal forms and round body) from stationary phase culture and found that the stationary phase planktonic form (SP) and microcolony form (MC) were much more tolerant to the current antibiotics for Lyme disease, doxycycline, ceftriaxone and cefuroxime than log phase spirochete form (LOG). In addition, we also compared the ability of the variant forms to cause disease in a mouse arthritis model. Surprisingly, the MC in particular and the SP caused a more severe arthritis with an earlier onset of inflammation and joint swelling than LOG. MC-infected mice showed significant joint swelling as early as 9 days post-infection, while the LOG and SP did not cause significant swelling. At 21 days, the joint swelling of the MC group dramatically increased and peaked, while the SP showed significant swelling at this time but less severe than the MC group. The LOG infected mice were just beginning to develop joint swelling at 21-day post-infection, with only slight swelling. At 30-day post infection, the SP group mice also developed similar severity of joint swelling as the MC group, but the LOG group still did not show significant swelling. However, at 35-day post infection, all three infected groups showed similar degree of significant joint swelling. Thereafter, the joint swelling of the three infected groups waxed and waned during the 90-day observation. Thus, we established a new biofilm-inocula mediated visual arthritis model that could facilitate more efficient evaluation of treatment regimens for persistent B. burgdorferi infections. Our findings provide new insight about disease pathogenesis and may have implications for understanding PTLDS and PTLDS treatment failure, due to possible biofilm inoculation during tick-bite. This biofilm/persister seeding model may be valid for different microbial infections and facilitate developing more effective treatments of persistent infections in general.

microbiology↗

bioRxiv2018

Auditory and Language Contributions to Neural Encoding of Speech Features in Noisy Environments

Recognizing speech in noisy environments is a challenging task that involves both auditory and language mechanisms. Previous studies have demonstrated noise-robust neural tracking of the speech envelope, i.e., fluctuations in sound intensity, in human auditory cortex, which provides a plausible neural basis for noise-robust speech recognition. The current study aims at teasing apart auditory and language contributions to noise-robust envelope tracking by comparing 2 groups of listeners, i.e., native listeners of the testing language and foreign listeners who do not understand the testing language. In the experiment, speech is mixed with spectrally matched stationary noise at 4 intensity levels and the neural responses are recorded using electroencephalography (EEG). When the noise intensity increases, an increase in neural response gain is observed for both groups of listeners, demonstrating auditory gain control mechanisms. Language comprehension creates no overall boost in the response gain or the envelope-tracking precision but instead modulates the spatial and temporal profiles of envelope-tracking activity. Based on the spatio-temporal dynamics of envelope-tracking activity, the 2 groups of listeners and the 4 levels of noise intensity can be jointly decoded by a linear classifier. All together, the results show that without feedback from language processing, auditory mechanisms such as gain control can lead to a noise-robust speech representation. High-level language processing, however, further modulates the spatial-temporal profiles of the neural representation of the speech envelope.

neuroscience↗

bioRxiv2018

Bats adjust temporal features of echolocation calls but not those of communication calls in response to traffic noise

Summary statementThis study reveals the impact of anthropogenic noise on spectrally distinct vocalizations and the limitations of the acoustic masking hypothesis to explain the vocal response of bats to chronic noise.\n\nAbstractThe acoustic masking hypothesis states that auditory masking may occur if the target sound and interfering sounds overlap spectrally, and it suggests that animals exposed to noise will modify their acoustic signals to increase signal detectability. However, it is unclear if animals will put more effort into changing their signals that spectrally overlap more with the interfering sounds than when the signals overlap less. We examined the dynamic changes in the temporal features of echolocation and communication vocalizations of the Asian particolored bat (Vespertilio sinensis) when exposed to traffic noise. We hypothesized that traffic noise has a greater impact on communication vocalizations than on echolocation vocalizations and predicted that communication vocalization change would be greater than echolocation. The bats started to adjust echolocation vocalizations on the fourth day of noise exposure, including an increased number of call sequences, decreased number of calls, and vocal rate within a call sequence. However, there was little change in the duration of the call sequence. In contrast, these communication vocalization features were not significantly adjusted under noise conditions. These findings suggest that the degree of spectral overlap between noise and animal acoustic signals does not predict the level of temporal vocal response to the noise.

animal behavior and cognition↗

bioRxiv2018

A genetically-encoded fluorescent sensor enables rapid and specific detection of dopamine in flies, fish, and mice

Dopamine (DA) is a central monoamine neurotransmitter involved in many physiological and pathological processes. A longstanding yet largely unmet goal is to measure DA changes reliably and specifically with high spatiotemporal precision, particularly in animals executing complex behaviors. Here we report the development of novel genetically-encoded GPCR-Activation-Based-DA (GRABDA) sensors that enable these measurements. In response to extracellular DA rises, GRABDA sensors exhibit large fluorescence increases ({Delta}F/F0[~]90%) with sub-second kinetics, nanomolar to sub-micromolar affinities, and excellent molecular specificity. Importantly, GRABDA sensors can resolve a single-electrical-stimulus evoked DA release in mouse brain slices, and detect endogenous DA release in the intact brains of flies, fish, and mice. In freely-behaving mice, GRABDA sensors readily report optogenetically-elicited nigrostriatal DA release and depict dynamic mesoaccumbens DA changes during Pavlovian conditioning or during sexual behaviors. Thus, GRABDA sensors enable spatiotemporal precise measurements of DA dynamics in a variety of model organisms while exhibiting complex behaviors.

neuroscience↗

bioRxiv2018

Fusion surface structure, function, and dynamics of gamete fusogen HAP2

HAP2 is a class II gamete fusogen in many eukaryotic kingdoms. A crystal structure of Chlamydomonas HAP2 shows a trimeric fusion state. Domains D1, D2.1 and D2.2 line the 3-fold axis; D3 and a stem pack against the outer surface. Surprisingly, hydrogen-deuterium exchange shows that surfaces of D1, D2.2 and D3 closest to the 3-fold axis are more dynamic than exposed surfaces. Three fusion helices in the fusion loop of each monomer expose hydrophobic residues at the trimer apex that are splayed from the 3-fold axis, leaving a solvent-filled cavity between the fusion loops in each monomer. At the base of the long fusion loop, Arg185 docks in a carbonyl cage. Comparisons to other structures, dynamics, and the greater effect on Chlamydomonas gamete fusion of mutation of axis-proximal than axis-distal fusion helices suggest that the apical portion of each monomer could tilt toward the 3-fold axis with merger of the fusion helices into a common fusion surface.

biochemistry↗

bioRxiv2018

Stiffness Sensing and Cell Motility: Durotaxis and Contact Guidance

Mechanical properties of the substrate plays a vital role in cell motility. Cells are shown to migrate up stiffness gradient (durotaxis) and along aligned fibers in the substrate (contact guidance). Here we present a simple mechanical model for cell migration, by placing a cell on lattice models for biopolymer gels and hydrogels. In our model cells attach to the substrate via focal adhesions (FAs). As the cells contract, forces are generated at the FAs, determining their maturation and detachment. At the same time, the cell also allowed to move and rotate to maintain force and torque balance. Our model, in which the cells only take the information of forces at the FAs, without a prior knowledge of the substrate stiffness or geometry, is able to reproduce both durotaxis and contact guidance.

biophysics↗

bioRxiv2018

A genetically-encoded fluorescent acetylcholine indicator

Acetylcholine (ACh) regulates a diverse array of physiological processes throughout the body, yet cholinergic transmission in the majority of tissues/organs remains poorly understood due primarily to the limitations of available ACh-monitoring techniques. We developed a family of G-protein-coupled receptor activation-based ACh sensors (GACh) with sensitivity, specificity, signal-to-noise ratio, kinetics and photostability suitable for monitoring ACh signals in vitro and in vivo. GACh sensors were validated with transfection, viral and/or transgenic expression in a dozen types of neuronal and non-neuronal cells prepared from several animal species. In all preparations, GACh sensors selectively responded to exogenous and/or endogenous ACh with robust fluorescence signals that were captured by epifluorescent, confocal and/or two-photon microscopy. Moreover, analysis of endogenous ACh release revealed firing pattern-dependent release and restricted volume transmission, resolving two long-standing questions about central cholinergic transmission. Thus, GACh sensors provide a user-friendly, broadly applicable toolbox for monitoring cholinergic transmission underlying diverse biological processes.

neuroscience↗

bioRxiv2018

Topographic diversity of structural connectivity in schizophrenia

The neurobiological heterogeneity of schizophrenia is widely accepted, but it is unclear how mechanistic differences converge to produce the observed phenotype. Establishing a pathophysiological model that accounts for both heterogeneity and phenotypic similarity is essential to inform stratified treatment approaches. In this cross-sectional diffusion tensor imaging (DTI) study, we recruited 77 healthy controls (HC), and 71 patients with DSM-IV diagnosis of schizophrenia (SCZ), and reconstructed the structural connectivity of 90 brain regions covering entire cerebral cortex. We first confirmed the heterogeneity in structural connectivity by showing a reduced inter-individual similarity in SCZ compared with HC. Moreover, we found it was not possible to cluster patients into subgroups with shared patterns of dysconnectivity, indicating a high degree of mechanistic divergence in schizophrenia. Instead of the strength of connectivity between any particular brain regions, we investigated the diversity (or statistically, the variance) of the topographic distribution of the strength was reduced. HC had higher topographic diversity in whole brain structural connectivity compared to the patient group\n\n(P = 2 x 10-6, T = 4.96, Cohen'S d = 0.87). In 62 of the 90 brain regions, the topographic diversity was significantly reduced in patients compared to controls after FDR correction (<0.05). When topographic diversity was used as a discriminant feature for classification between patients and controls, we significantly (P = 4.29 x 10-24) improved the classification accuracy to 79.6% (sensitivity 78.3%, specificity 81.3%). This finding suggests highly individualized pattern of structural dysconnectivity underlying the heterogeneity of schizophrenia converges to a convergent common pathway as reduced topographic diversity for the clinical construct of the disease.

neuroscience↗

bioRxiv2018

Additional Essential Oils with High Activity against Stationary Phase Borrelia burgdorferi

Lyme disease is the most common vector borne-disease in the US. While the majority of the Lyme disease patients can be cured with 2-4 week antibiotic treatment, about 10-20% of patients continue to suffer from persisting symptoms. While the cause of this condition is unclear, persistent infection was proposed as one possibility. It has recently been shown that B. burgdorferi develops dormant persisters in stationary phase cultures that are not killed by the current Lyme antibiotics, and there is interest to identify novel drug candidates that more effectively kill such forms. We previously evaluated 34 essential oils and identified some highly active candidates with excellent activity against biofilm and stationary phase B. burgdorferi. Here we screened another 35 essential oils and found 10 essential oils (garlic, allspice, cumin, palmarosa, myrrh, hedycheim, amyris, thyme white, litsea cubeba, lemon eucalyptus) and the active component of cinnamon bark cinnamaldehyde (CA) at a low concentration of 0.1% to have high activity against stationary phase B. burgdorferi. At a very low 0.05% concentration, garlic, allspice, palmarosa and CA still exhibited strong activity against the stationary phase B. burgdorferi. CA also showed strong activity against replicating B. burgdorferi, with a MIC of 0.02% (or 0.2 g/mL). In subculture studies, the top 5 hits garlic, allspice, myrrh, hedycheim, and litsea cubeba completely eradicated all B. burgdorferi stationary phase cells at 0.1%, while palmarosa, lemon eucalyptus, amyris, cumin, and thyme white failed to do so as shown by visible spirochetal growth after 21-day subculture. At 0.05% concentration, only garlic essential oil and CA sterilized the B. burgdorferi stationary phase culture as shown by no regrowth during subculture, while allspice, myrrh, hedycheim and litsea cubeba all had visible growth during subculture. Future studies are needed to determine if these highly active essential oils could eradicate persistent B. burgdorferi infection in vivo.

microbiology↗

bioRxiv2018

Identifying associations in dense connectomes using structured kernel principal component regression

A powerful and computationally efficient multivariate approach is proposed here, called structured kernel principal component regression (sKPCR), for the identification of associations in the voxel-level dense connectome. The method can identify voxel-phenotype associations based on the voxels whole-brain connectivity pattern, which is applicable to detect linear and non-linear signals for both volume-based and surface-based functional magnetic resonance imaging (fMRI) data. For each voxel, our approach first extracts signals from the spatially smoothed connectivities by structured kernel principal component analysis, and then tests the voxel-phenotype associations via a general linear model. The method derives its power by appropriately modelling the spatial structure of the data. Simulations based on dense connectome data have shown that our method can accurately control the false-positive rate, and it is more powerful than many state-of-the-art approaches, such as the connectivity-wise general linear model (GLM) approach, multivariate distance matrix regression (MDMR), adaptive sum of powered score (aSPU) test, and least-square kernel machine (LSKM). To demonstrate the utility of our approach in real data analysis, we apply these methods to identify voxel-wise difference between schizophrenic patients and healthy controls in two independent resting-state fMRI datasets. The findings of our approach have a better between-sites reproducibility, and a larger proportion of overlap with existing schizophrenia findings. Code for our approach can be downloaded from https://github.com/weikanggong/vBWAS.

neuroscience↗

bioRxiv2017

Uterine progesterone signaling is a target for metformin therapy in polycystic ovary syndrome

Impaired progesterone (P4) signaling is linked to endometrial dysfunction and infertility in women with polycystic ovary syndrome (PCOS). Here we report for the first time that elevated expression of progesterone receptor (PGR) isoforms A and B parallels increased estrogen receptor (ER) expression in PCOS-like rat uteri. The aberrant PGR-targeted gene expression in PCOS-like rats before and after implantation overlaps with dysregulated expression of Fkbp52 and Ncoa2, two genes that contribute to the development of uterine P4 resistance. In vivo and in vitro studies of the effects of metformin on the regulation of the uterine P4 signaling pathway under PCOS conditions showed that metformin directly inhibits the expression of PGR and ER along with the regulation of several genes that are targeted dependently or independently of PGR-mediated uterine implantation. Functionally, metformin treatment corrected the abnormal expression of cell-specific PGR and ER and some PGR-target genes in PCOS-like rats with implantation. Additionally, we documented how metformin contributes to the regulation of the PGR-associated MAPK/ERK/p38 signaling pathway in the PCOS-like rat uterus. Our data provide novel insights into how metformin therapy regulates uterine P4 signaling molecules under PCOS conditions.

physiology↗

bioRxiv2017

RACIPE: A computational tool for Modeling Gene Regulatory Circuits using Randomization

MotivationOne of the major challenges in traditional mathematical modeling of gene regulatory circuits is the insufficient knowledge of kinetic parameters. These parameters are often inferred from existing experimental data and/or educated guesses, which can be time-consuming and error-prone, especially for large networks.\n\nResultsWe present a computational tool based on our newly developed method named random circuit perturbation (RACIPE), to explore the robust dynamical features of gene regulatory circuits without the requirement of detailed kinetic parameters. RACIPE generates an ensemble of circuit models with distinct random parameters and uniquely identifies robust dynamical properties by statistical analysis. Here, we discuss software implementation and illustrate the usage of RACIPE on coupled toggle-switch circuits and a published circuit of B-lymphopoiesis. We expect RACIPE to contribute to a more comprehensive and unbiased understanding of gene regulatory mechanisms.\n\nAvailabilityRACIPE is a free open source software distributed under (Apache 2.0) license and can be downloaded from GitHub (https://github.com/simonhb1990/RACIPE-1.0).\n\nContactMingyang.Lu@jax.org, Herbert.Levine@rice.edu or jonuchic@rice.edu

systems biology↗

bioRxiv2017

Clofazimine targets essential nucleoid associated protein, mycobacterial integration host factor (mIHF), in Mycobacterium tuberculosis

Clofazimine (CFZ) is a phenazine derivative used for treatment of leprosy, MDR-TB and XDR-TB. There is recent interest in understanding how CFZ works following the demonstration of its unique ability to shorten the treatment of MDR-TB. However, the target of CFZ in mycobacteria has remained elusive. Here, we show that CFZ binds to mycobacterial integration host factor (mIHF), which is an essential nucleoid associated protein in mycobacteria involved in DNA protection, chromosome organization and global gene regulation. We demonstrate that CFZ inhibits mIHF binding to DNA and interferes with mycobacterial gene expression. This mode of action is unique among all antibiotics including antimycibacterial agents and may help to explain its unusual action against Mycobacterium tuberculosis. Our study provides new insight about the mechanism of action of this intriguing drug and has implications for developing more effective treatment of TB.

microbiology↗

bioRxiv2017

Cholecystokinin release triggered by presynaptic NMDA receptors produces LTP and sound-sound associative memory formation

Memory is stored in neural networks via changes in synaptic strength mediated in part by NMDA-dependent long-term potentiation (LTP). There is evidence that entorhinal cortex enables neocortical neuroplasticity through cholecystokinin (CCK)-containing neocortical projections. Here we show that a CCKB antagonist blocks high-frequency stimulation (HFS)-induced LTP in the auditory cortex, whereas local infusion of CCK induces LTP. CCK-/- mice lacked neocortical LTP and showed deficits in a cue-cue associative learning paradigm; administration of CCK rescued associative learning. HFS of CCK-containing entorhino-neocortical projection neurons in anesthetized mice enabled cue-cue associative learning. Furthermore, when one cue was pre-conditioned to footshock, the mouse showed a freezing response to the other cue, indicating that the mice had formed an association. HFS-induced neocortical LTP was completely blocked by either NMDA antagonist or CCK-BR antagonist, while application of either NMDA or CCK induced LTP after low-frequency stimulation (LFS). Moreover, in the presence of CCK LTP was still induced, even after blockade of NMDA receptors. Local application of NMDA induced CCK release in the neocortex. To identify how NMDA receptor switches LTP, a stimulation protocol of 25 pulse-pairs was adopted to replace HFS; NMDA-dependent LTP was induced with the inter-pulse intervals between 10 and 100 ms, but not with those of 5 and 200 ms. LTP-mediated plasticity was linked to localization of the NMDA receptor subunit NR2a on cortical CCK terminals originating in the entorhinal cortex. These novel findings suggest that presynaptic NMDA receptors on CCK terminals control the release of CCK, which enables neocortical LTP and formation of cue-cue associative memory.\n\nOne Sentence SummaryPresynaptic NMDA receptors switches the release of CCK from entorhinal neurons, which enables neocortical LTP and formation of sound-sound associative memory.

neuroscience↗

bioRxiv2017

A Rho-GTPase based model explains spontaneous collective migration of neural crest cell clusters

We propose a model to explain the spontaneous collective migration of neural crest cells in the absence of an external gradient of chemoattractants. The model is based on the dynamical interaction between Rac1 and RhoA that is known to regulate the polarization, contact inhibition and co-attraction of neural crest cells. Coupling the reaction-diffusion equations for active and inactive Rac1 and RhoA on the cell membrane with a mechanical model for the overdamped motion of membrane vertices, we show that co-attraction and contact inhibition cooperate to produce persistence of polarity in a cluster of neural crest cells by suppressing the random onset of Rac1 hotspots that may mature into new protrusion fronts. This produces persistent directional migration of cell clusters in corridors. Our model confirms a prior hypothesis that co-attraction and contact inhibition are key to spontaneous collective migration, and provides an explanation of their cooperative working mechanism in terms of Rho GTPase signaling. The model shows that the spontaneous migration is more robust for larger clusters, and is most efficient in a corridor of optimal confinement.

developmental biology↗

bioRxiv2017

The functional and genetic associations of neuroimaging data: a toolbox

Advances in neuroimaging and sequencing techniques provide an unprecedented opportunity to map the function of brain regions and to identify the roots of psychiatric diseases. However, the results generated by most neuroimaging studies, i.e., activated clusters/regions or functional connectivities between brain regions, frequently cannot be conveniently and systematically interpreted, rendering the biological meaning unclear. We describe a Brain Annotation Toolbox (BAT), a toolbox that helps to generate functional and genetic annotations for neuroimaging results. The toolbox can take data from brain regions identified with an atlas, or from brain regions identified as activated in tasks, or from functional connectivity links or networks of links. Then, the voxel-level functional description from the Neurosynth database and the gene expression profile from the Allen Brain Atlas are used to generate functional and genetic knowledge for such region-level data. Parametric (Fishers exact test) or non-parametric (permutation test) statistical tests are adopted to identify significantly related functional descriptors and genes for the neuroimaging results. The validity of the approach is demonstrated by showing that the functional and genetic annotations for specific brain regions are consistent with each other; and further the region by region functional similarity network and gene co-expression networks are highly correlated for many major brain atlases. One application of BAT is to help provide functional and genetic annotations for the newly discovered regions with unknown functions, e.g., the 97 new regions identified in the Human Connectome Project. Importantly too, this toolbox can help understand differences between patients with psychiatric disorders and controls, and this is demonstrated using data for schizophrenia and autism, for which the functional and genetic annotations for the neuroimaging data differences between patients and controls are consistent with each other and help with the interpretation of the differences.

neuroscience↗