bioRxiv Science⌕ Search

Biology subjects

Liu, Z.

Publications and source records attributed to Liu, Z..

At least 1,207 records · Page 67Linked to original sources

Bias toward long gene misregulation in synaptic disorders can be an artefact of amplification-based methods

Several recent studies have suggested that genes that are longer than 100 kilobases are more likely to be misregulated in neurological diseases associated with synaptic dysfunction, such as autism and Rett syndrome. These length-dependent transcriptional changes are modest in Mecp2-mutant samples, but, given the low sensitivity of high-throughput transcriptome profiling technology, the statistical significance of these results needs to be re-evaluated. Here, we show that the apparent length-dependent trends previously observed in MeCP2 microarray and RNA-Sequencing datasets, particularly in genes with low fold-changes, disappeared after accounting for baseline variability estimated from randomized control samples. As we found no similar bias with NanoString technology, this long-gene bias seems to be particular to PCR amplification-based platforms. In contrast, authentic long gene effects, such as those caused by topoisomerase inhibition, can be detected even after adjustment for baseline variability. Accurate detection of length-dependent trends requires establishing a baseline from randomized control samples.\n\nHIGHLIGHTSO_LILength-dependent gene misregulation is not intrinsic to Mecp2 disruption.\nC_LIO_LITopoisomerase inhibition produces an authentic long gene bias.\nC_LIO_LIPCR amplification-based high-throughput datasets are biased toward long genes.\nC_LI

neuroscience↗

Boosting ATM Activity Promotes Longevity in Nematodes and Mice

DNA damage accumulates with age1. However, whether and how robust DNA repair machinery promotes longevity is elusive. Here, we demonstrate that activation of ataxia-telangiectasia mutated (ATM) via low dose of chloroquine (CQ) promotes DNA damage clearance, rescues age-related metabolic shift, and extends lifespan in nematodes and mice. Molecularly, ATM phosphorylates SIRT6 deacetylase and thus prevents MDM2-mediated ubiquitination and proteasomal degradation. Extra copies of Sirt6 in Atm-/- mice extend lifespan, accompanied with restored metabolic homeostasis. In a progeria mouse model with low ATM protein level and DNA repair capacity, the treatment with CQ ameliorates premature aging features and extends lifespan. Thus, our data highlights a pro-longevity role of ATM, for the first time establishing direct causal links between robust DNA repair machinery and longevity, and providing therapeutic strategy for progeria and age-related metabolic diseases.

molecular biology↗

DeepMHC: Deep Convolutional Neural Networks for High-performance peptide-MHC Binding Affinity Prediction

Convolutional neural networks (CNN) have been shown to outperform conventional methods in DNA-protien binding specificity prediction. However, whether we can transfer this success to protien-peptide binding affinity prediction depends on appropriate design of the CNN architectue that calls for thorough understanding how to match the architecture to the problem. Here we propose DeepMHC, a deep convolutional neural network (CNN) based protein-peptide binding prediction algorithm for achieving better performance in MHC-I peptide binding affinity prediction than conventional algorithms. Our model takes only raw binding peptide sequences as input without needing any human-designed features and othe physichochemical or evolutionary information of the amino acids. Our CNN models are shown to be able to learn non-linear relationships among the amino acid positions of the peptides to achieve highly competitive performance on most of the IEDB benchmark datasets with a single model architecture and without using any consensus or composite ensemble classifier models. By systematically exploring the best CNN architecture, we identified critical design considerations in CNN architecture development for peptide-MHC binding prediction.

bioinformatics↗

Transcriptome dynamics describe and predict state transition from health to leukemia

Temporal dynamics of gene expression are informative of changes associated with disease development and evolution. Given the complexity of high-dimensional temporal datasets, an analytical framework guided by a robust theory is needed to interpret time-sequential changes and to predict system dynamics. Herein, we use acute myeloid leukemia as a proof-of-principle to model gene expression dynamics in a transcriptome state-space constructed based on time-sequential RNA-sequencing data. We describe the construction of a state-transition model to identify state-transition critical points which accurately predicts leukemia development. We show an analytical approach based on state-transition critical points identified step-wise transcriptomic perturbations driving leukemia progression. Furthermore, the gene(s) trajectory and geometry of the transcriptome state-space provides biologically-relevant gene expression signals that are not synchronized in time, and allows quantification of gene(s) contribution to leukemia development. Therefore, our state-transition model can synthesize information, identify critical points to guide interpretation of transcriptome trajectories and predict disease development.\n\nGraphical Abstract\n\nO_FIG O_LINKSMALLFIG WIDTH=193 HEIGHT=200 SRC=\"FIGDIR/small/238923v2_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (55K):\norg.highwire.dtl.DTLVardef@12eeaccorg.highwire.dtl.DTLVardef@1392af9org.highwire.dtl.DTLVardef@34786dorg.highwire.dtl.DTLVardef@ce78bb_HPS_FORMAT_FIGEXP M_FIG C_FIG In briefThe theory of state-transition is applied to acute myeloid leukemia (AML) to model transcriptome dynamics and trajectories in a state-space, and is used to identify critical points corresponding to critical transcriptomic perturbations that predict leukemia development.\n\nHighlightsO_LILeukemia transcriptome dynamics are modeled as movement in transcriptome state-space\nC_LIO_LIState-transition model and critical points accurately predicts leukemia development\nC_LIO_LICritical point-based approach identifies step-wise transcriptome events in leukemia\nC_LIO_LIState-based geometric analysis provides quantification of leukemogenic contribution\nC_LI

cancer biology↗

Long-range infra-sound acoustic signaling inhuman in vivo

Acupuncture is widely deployed today, but its basic physiology with Qi and meridian is not understood. This letter postulates that Qi is an infrasound wave packet and meridian is the muscle waveguide. Using video cameras and signal processing in an IRB approved clinical experiment we performed a comparison between control and electro-activated statistical tests on the long range (50-80cm) unidirectional transmission of Qi (p = 0.025). In the reverse direction, there is no transmission even in a distance of less than 10 cm (p = 0.545). The rectification is a surprise but in full agreement with Huang Di Nei Jing.

physiology↗

Denaturants alter the flux through multiple pathways in the folding of PDZ domain

Although we understand many aspects of how small proteins (number of residues less than about hundred) fold, it is a major challenge to understand how large proteins self-assemble. To partially overcome this challenge, we performed simulations using the Self-Organized Polymer model with Side Chains (SOP-SC) in guanidinium chloride (GdmCl), using the Molecular Transfer Model (MTM), to describe the folding of the 110-residue PDZ3 domain. The simulations reproduce the folding thermodynamics accurately including the melting temperature (Tm), the stability of the folded state with respect to the unfolded state. We show that the calculated dependence of ln kobs (kobs is the relaxation rate) has the characteristic Chevron shape. The slopes of the Chevron plots are in good agreement with experiments. We show that PDZ3 folds by four major pathways populating two metastable intermediates, in accord with the kinetic partitioning mechanism. The structure of one of the intermediates, populated after polypeptide chain collapse, is structurally similar to an equilibrium intermediate. Surprisingly, the connectivities between the intermediates and hence, the fluxes through the pathways depend on the concentration of GdmCl. The results are used to predict possible outcomes for unfolding of PDZ domain subject to mechanical forces. Our study demonstrates that, irrespective of the size or topology, simulations based on MTM and SOP-SC offer a framework for describing the folding of proteins, mimicking precisely the conditions used in experiments.

biophysics↗

Variational Autoencoder: An Unsupervised Model for Modeling and Decoding fMRI Activity in Visual Cortex

Goal-driven convolutional neural networks (CNN) have been shown to be able to predict and decode cortical responses to natural images or videos. Here, we explored an alternative deep neural network, variational auto-encoder (VAE), as a computational model of the visual cortex. We trained a VAE with a five-layer encoder and a five-layer decoder to learn visual representations from a diverse set of unlabeled images. Inspired by the \"free-energy principle\" in neuroscience, we modeled the brains bottom-up and top-down pathways using the VAEs encoder and decoder, respectively. Following such conceptual relationships, we found that the VAE was able to predict cortical activities observed with functional magnetic resonance imaging (fMRI) from three human subjects watching natural videos. Compared to CNN, VAE resulted in relatively lower prediction accuracies, especially for higher-order ventral visual areas. On the other hand, fMRI responses could be decoded to estimate the VAEs latent variables, which in turn could reconstruct the visual input through the VAEs decoder. This decoding strategy was more advantageous than alternative decoding methods based on partial least square regression. This study supports the notion that the brain, at least in part, bears a generative model of the visual world.

neuroscience↗

A Comprehensive Immunologic Portrait of Triple-Negative Breast Cancer

BackgroundTriple-negative breast cancer (TNBC) is a high-risk malignancy due to its high capacity for invasion and lack of targeted therapy. Immunotherapy continues to demonstrate efficacy in a variety of cancers, and thus may be a promising strategy for TNBC given the limited therapeutic options currently available for TNBC. In this study, we performed a comprehensive portrait of immunologic landscape of TNBC based on 2 large-scale breast cancer genomic data.\n\nMethodsWe compared expression levels of immune-related genes and gene-sets among TNBC, non-TNBC, and normal tissue, and within TNBCs of different genotypic or phenotypic features. Moreover, we explored the association of immune-related genes or gene-sets expression and survival prognosis in TNBC patients.\n\nResultsWe found that almost all analyzed immune-related gene-sets had significantly higher expression levels in TNBC than non-TNBC. These highly expressed gene-sets in TNBC included 15 immune cell type and function, human leukocyte antigen (HLA), cancer testis (CT), tumor-infiltrating lymphocytes (TILs), immune cell infiltrate, regulatory T (Treg) cells, immune checkpoint, cytokine and cytokine receptor, metastasis-promoting, pro-inflammatory and parainflammation (PI) gene-sets. Moreover, TP53-mutated, TNBC had significantly higher expression levels of the immune checkpoint, Treg, PI, and CT gene-sets, and lower expression levels of the immune cell infiltrate gene-set than TP53-wildtype TNBC. Furthermore, we found that elevated expression of most of the immune-related genes in TNBC was associated with the ER-status, while some were associated with both ER-and HER2-status. Elevated expression of the immune-related genes in TNBC was also associated with the high tumor mutation burden (TMB) in TNBC. Finally, elevated expression of the immune-related gene-sets was likely to be associated with better survival prognosis in TNBC.\n\nConclusionsOur findings suggest that TNBC is a breast cancer subtype with particularly strong immunogenicity, and therefore could be propitious to immunotherapeutic options.

cancer biology↗

Dynamic and Selective Low-Complexity Domain Interactions Revealed by Live-Cell Single-Molecule Imaging

Many eukaryotic transcription factors (TFs) contain intrinsically disordered low-complexity domains (LCDs) but how they perform transactivation functions remains unclear. Recent studies report that TF-LCDs can undergo hydrogel formation or liquid-liquid phase separation in vitro. Here, live-cell single-molecule imaging reveals that TF-LCDs form local high concentration interaction hubs at synthetic and endogenous genomic loci. TF-LCD hubs stabilize DNA binding, recruit RNA polymerase II (Pol II) and activate transcription. LCD-LCD interactions within hubs are highly dynamic, display selectivity with binding partners, and are differentially sensitive to disruption by hexanediols. These findings suggest that under physiological conditions, rapid reversible and multivalent LCD-LCD interactions occur between TFs and the Pol II machinery, which underpins a central mechanism for transactivation and plays a key role in gene expression and disease.

biophysics↗

RNA velocity in single cells

RNA abundance is a powerful indicator of the state of individual cells, but does not directly reveal dynamic processes such as cellular differentiation. Here we show that RNA velocity--the time derivative of RNA abundance--can be estimated by distinguishing unspliced and spliced mRNAs in standard single-cell RNA sequencing protocols. We show that RNA velocity is a vector that predicts the future state of individual cells on a timescale of hours. We validate the accuracy of RNA velocity in the neural crest lineage, demonstrate its use on multiple technical platforms, reconstruct the branching lineage tree of the mouse hippocampus, and measure RNA kinetics in human embryonic brain. We expect RNA velocity to greatly aid the analysis of developmental lineages and cellular dynamics, particularly in humans.

genomics↗

Vagal Nerve Stimulation Triggers Wide-Spread Evoked Responses and Changes in Large-Scale Functional Connectivity in Rat Brains

Vagus nerve stimulation (VNS) is a therapy for epilepsy and depression. However, its efficacy varies and its mechanism remains unclear. Prior studies have used functional magnetic resonance imaging (fMRI) to map brain activations with VNS in human brains, but have reported inconsistent findings. The source of inconsistency is likely attributable to the complex temporal characteristics of VNS-evoked fMRI responses that cannot be fully explained by simplified response models in the conventional model-based analysis for activation mapping. To address this issue, we acquired 7-Tesla blood oxygenation level dependent fMRI data from anesthetized Sprague-Dawley rats receiving electrical stimulation at the left cervical vagus nerve. Using spatially independent component analysis, we identified 20 functional brain networks and detected the network-wise activations with VNS in a data-driven manner. Our results showed that VNS activated 15 out of 20 brain networks, and the activated regions covered >76% of the brain volume. The time course of the evoked response was complex and distinct across regions and networks. In addition, VNS altered the strengths and patterns of correlations among brain networks relative to those in the resting state. The most notable changes in network-network interactions were related to the limbic system. Together, such profound and widespread effects of VNS may underlie its unique potential for a wide range of therapeutics to relieve central or peripheral conditions.

neuroscience↗

Acute and chronic toxicity assessment of benzylpenicillin G residue in cooked meat

The current level of penicillin use and its persisting residues in livestock is potentially concerning; the toxicity of penicillin residues in heat-treated animal food products (HAFP) is yet to be elucidated. In this study, the acute and chronic toxicity of benzylpenicillin G (BPG) residues in HAFP was investigated in a mouse model. The calculated LD50 of BPG heated to cooking temperature (BPHCT) was 933.04 mg kg-1 [b.w.] intraperitoneally corresponding to 3.75 times lower than its prototype. Mice fed on the experimental diet containing heat-treated beef with high BPG levels for 6 months displayed a reduction in body weight and altered serum values indicating for liver and renal function. Further, the organ ratios of intestinal and spleen were increased. Histopathological changes were observed in the liver, lung and parenchyma testis tissue. BPHCT residue induced sperm aberration and micronucleated polychromatic erythrocytes formation. Present results indicate that prolonged exposure of BPHCT at higher levels of residue might have an impact on public health. Importantly the toxic concentrations of BPHCT are relatively high compared with levels that would result from the degradation of antibiotic residues in meat from animals that have received a therapeutic dose of BPG.

pharmacology and toxicology↗

A Library of Phosphoproteomic and Chromatin Signatures for Characterizing Cellular Responses to Drug Perturbations

Though the added value of proteomic measurements to gene expression profiling has been demonstrated, profiling of gene expression on its own remains the dominant means of understanding cellular responses to perturbation. Direct protein measurements are typically limited due to issues of cost and scale; however, the recent development of high-throughput, targeted sentinel mass spectrometry assays provides an opportunity for proteomics to contribute at a meaningful scale in high-value areas for drug development. To demonstrate the feasibility of a systematic and comprehensive library of perturbational proteomic signatures, we profiled 90 drugs (in triplicate) in six cell lines using two different proteomic assays -- one measuring global changes of epigenetic marks on histone proteins and another measuring a set of peptides reporting on the phosphoproteome -- for a total of more than 3,400 samples. This effort represents a first-of-its-kind resource for proteomics. The majority of tested drugs generated reproducible responses in both phosphosignaling and chromatin states, but we observed differences in the responses that were cell line-and assay-specific. We formalized the process of comparing response signatures within the data using a concept called connectivity, which enabled us to integrate data across cell types and assays. Furthermore, it facilitated incorporation of transcriptional signatures. Consistent connectivity among cell types revealed cellular responses that transcended cell-specific effects, while consistent connectivity among assays revealed unexpected associations between drugs that were confirmed by experimental follow-up. We further demonstrated how the resource could be leveraged against public domain external datasets to recognize therapeutic hypotheses that are consistent with ongoing clinical trials for the treatment of multiple myeloma and acute lymphocytic leukemia (ALL). These data are available for download via the Gene Expression Omnibus (accession GSE101406), and web apps for interacting with this resource are available at https://clue.io/proteomics.\n\nHighlightsO_LIFirst-of-its-kind public resource of proteomic responses to systematically administered perturbagens\nC_LIO_LIDirect proteomic profiling of phosphosignaling and chromatin states in cells for 90 drugs in six different cell lines\nC_LIO_LIExtends Connectivity Map concept to proteomic data for integration with transcriptional data\nC_LIO_LIEnables recognition of unexpected, cell type-specific activities and potential translational therapeutic opportunities\nC_LI

systems biology↗

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↗

Deep Recurrent Neural Network Reveals a Hierarchy of Process Memory during Dynamic Natural Vision

The human visual cortex extracts both spatial and temporal visual features to support perception and guide behavior. Deep convolutional neural networks (CNNs) provide a computational framework to model cortical representation and organization for spatial visual processing, but unable to explain how the brain processes temporal information. To overcome this limitation, we extended a CNN by adding recurrent connections to different layers of the CNN to allow spatial representations to be remembered and accumulated over time. The extended model, or the recurrent neural network (RNN), embodied a hierarchical and distributed model of process memory as an integral part of visual processing. Unlike the CNN, the RNN learned spatiotemporal features from videos to enable action recognition. The RNN better predicted cortical responses to natural movie stimuli than the CNN, at all visual areas especially those along the dorsal stream. As a fully-observable model of visual processing, the RNN also revealed a cortical hierarchy of temporal receptive window, dynamics of process memory, and spatiotemporal representations. These results support the hypothesis of process memory, and demonstrate the potential of using the RNN for in-depth computational understanding of dynamic natural vision.

neuroscience↗

Immunologic Effect of Polysaccharides Extracted from Sipunculus nudus (SNP) on Hepatoma HepG2-bearing Mice

Since many studies have clarified the biological activity of polysaccharides, we investigated the effect of SNP which was the water-soluble polysaccharides extracted from Sipunculus nudus on Hepatoma HepG2-bearing Mice to verify the potential of SNP as an effective clinical agent for liver cancer therapy. SNP were administered at the doses of 50,100, and 200 mg/kg to HepG2-bearing mice to determine their antitumor effects. SNP had an inhibitory effect on the growth of HepG2 cells and enhanced the immunological effect on HepG2 tumor-bearing mice. SNP increased the expression of IL-2, IFN-{gamma}, and TNF- cytokines in serum, suggesting that SNP can strengthen the antitumor immune response. In addition, SNP increased ATF4, DDIT3, and IkB expression and decreased CYR61, HSP90, and VEGF expression, all of which are proteins involved in antitumor activity and cell death/survival. our results suggested that SNP may be a novel antitumor agent.\n\nSummary statementSNP(polysaccharides extracted from Sipunculus nudus) mediates anti-tumor activity through influencing immunoregulation, and SNP can be explored as a promising candidate for future anticancer drug.

cancer biology↗

Transferring and Generalizing Deep-Learning-based Neural Encoding Models across Subjects

Recent studies have shown the value of using deep learning models for mapping and characterizing how the brain represents and organizes information for natural vision. However, modeling the relationship between deep learning models and the brain (or encoding models), requires measuring cortical responses to large and diverse sets of natural visual stimuli from single subjects. This requirement limits prior studies to few subjects, making it difficult to generalize findings across subjects or for a population. In this study, we developed new methods to transfer and generalize encoding models across subjects. To train encoding models specific to a subject, the models trained for other subjects were used as the prior models and were refined efficiently using Bayesian inference with a limited amount of data from the specific subject. To train encoding models for a population, the models were progressively trained and updated with incremental data from different subjects. For the proof of principle, we applied these methods to functional magnetic resonance imaging (fMRI) data from three subjects watching tens of hours of naturalistic videos, while deep residual neural network driven by image recognition was used to model the visual cortical processing. Results demonstrate that the methods developed herein provide an efficient and effective strategy to establish subject-specific or population-wide predictive models of cortical representations of high-dimensional and hierarchical visual features.

neuroscience↗

DNA 5-Hydroxymethylcytosines from Cell-free Circulating DNA as Diagnostic Biomarkers for Human Cancers

DNA modifications such as 5-methylcytosines (5mC) and 5-hydroxymethylcytosines (5hmC) are epigenetic marks known to affect global gene expression in mammals(1, 2). Given their prevalence in the human genome, close correlation with gene expression, and high chemical stability, these DNA epigenetic marks could serve as ideal biomarkers for cancer diagnosis. Taking advantage of a highly sensitive and selective chemical labeling technology(3), we report here genome-wide 5hmC profiling in circulating cell-free DNA (cfDNA) and in genomic DNA of paired tumor/adjacent tissues collected from a cohort of 90 healthy individuals and 260 patients recently diagnosed with colorectal, gastric, pancreatic, liver, or thyroid cancer. 5hmC was mainly distributed in transcriptionally active regions coincident with open chromatin and permissive histone modifications. Robust cancer-associated 5hmC signatures in cfDNA were identified with specificity for different cancers. 5hmC-based biomarkers of circulating cfDNA demonstrated highly accurate predictive value for patients with colorectal and gastric cancers versus healthy controls, superior to conventional biomarkers, and comparable to 5hmC biomarkers from tissue biopsies. This new strategy could lead to the development of effective blood-based, minimally-invasive cancer diagnosis and prognosis approaches.

cancer biology↗