Biology subjects

Huang, L.

Publications and source records attributed to Huang, L..

At least 19 recordsLinked to original sources

bioRxiv2018

Structural Organization of a Type III-A CRISPR Effector Subcomplex Determined by X-ray Crystallography and Cryo-EM

Clustered regularly interspaced short palindromic repeats (CRISPR) and their associated Cas proteins provide an immune-like response in many prokaryotes against extraneous nucleic acids. CRISPR-Cas systems are classified into different classes and types. Class 1 CRISPR-Cas systems form multi-protein effector complexes that includes a guide RNA (crRNA) used to identify the target for destruction. Here we present crystal structures of Staphylococcus epidermidis Type III-A CRISPR subunits Csm2 and Csm3 and a 5.2 [A] resolution single-particle cryo-electron microscopy (cryo-EM) reconstruction of an effector subcomplex including the crRNA. The structures help to clarify the quaternary architecture of Type III-A effector complexes, as well as to provide details on crRNA binding, target RNA binding and cleavage, and intermolecular interactions essential for effector complex assembly. The structures allow a better understanding of the organization of Type III-A CRISPR effector complexes as well as highlighting the overall similarities and differences with other Class 1 effector complexes.

biophysics↗

bioRxiv2018

In vivo assembly and trafficking of olfactory Ionotropic Receptors

lonotropic Receptors (IRs) are a large, divergent subfamily of ionotropic glutamate receptors(iGluRs), with roles in chemosensation, thermosensation and hygrosensation. Analogous to the synaptic targeting mechanisms of their iGluR ancestors, IRs are thought to form complexes of broadly-expressed co-receptors and selectively-expressed tuning receptors to localise to sensory cilia. While tuning receptors extracellular ligand-binding domain (LBD) defines sensory specificity, the role of this domain in co-receptors is unclear. We identify a coreceptor-specific sequence in the LBD, which contains a single N-glycosylation site. Combining molecular genetic and cell biological analyses, we show that this site is dispensable for assembly of IR complexes in olfactory sensory neurons, but essential for endoplasmic reticulum exit of some,but not all, IR complexes. Our data reveal an important role for the IR co-receptor LBD in control of intracellular transport, provide novel insights into the stoichiometry and assembly of IR complexes, and uncover an unexpected heterogeneity in the trafficking regulation of this sensory receptor family.

neuroscience↗

bioRxiv2018

Unsupervised integration of multimodal dataset identifies novel signatures of health and disease

Modern medicine is rapidly moving towards a data-driven paradigm based on comprehensive multimodal health assessments. We collected 1,385 data features from diverse modalities, including metabolome, microbiome, genetics and advanced imaging, from 1,253 individuals and from a longitudinal validation cohort of 1,083 individuals. We utilized an ensemble of unsupervised machine learning techniques to identify multimodal biomarker signatures of health and disease risk. In particular, our method identified a set of cardiometabolic biomarkers that goes beyond standard clinical biomarkers, which were used to cluster individuals into distinct health profiles. Cluster membership was a better predictor for diabetes than established clinical biomarkers such as glucose, insulin resistance, and BMI. The novel biomarkers in the diabetes signature included 1-stearoyl-2-dihomo-linolenoyl-GPC and 1-(1-enyl-palmitoyl)-2-oleoyl-GPC. Another metabolite, cinnamoylglycine, was identified as a potential biomarker for both gut microbiome health and lean mass percentage. We also identified an early disease signature for hypertension, and individuals at-risk for a poor metabolic health outcome. We found novel associations between an uremic toxin, p-cresol sulfate, and the abundance of the microbiome genera Intestinimonas and an unclassified genus in the Erysipelotrichaceae family. Our methodology and results demonstrate the potential of multimodal data integration, from the identification of novel biomarker signatures to a data-driven stratification of individuals into disease subtypes and stages -- an essential step towards personalized, preventative health risk assessment.

bioinformatics↗

bioRxiv2018

Cross-linking/Mass Spectrometry: A Community-Wide, Comparative Study Towards Establishing Best Practice Guidelines

The number of publications in the field of chemical cross-linking combined with mass spectrometry (XL-MS) to derive constraints for protein three-dimensional structure modeling and to probe protein-protein interactions has largely increased during the last years. As the technique is now becoming routine for in vitro and in vivo applications in proteomics and structural biology there is a pressing need to define protocols as well as data analysis and reporting formats that are generally accepted in the field and that have shown to lead to high-quality results. This first, community-based harmonization study on XL-MS is based on the results of 32 groups participating worldwide. The aim of this paper is to summarize the status quo of XL-MS and to compare and evaluate existing cross-linking strategies. From the results obtained, common protocols will be established. Our study serves as basis for establishing best practice guidelines in the field for conducting cross-linking experiments, performing data analysis, and reporting formats with the ultimate goal of assisting scientists to generate accurate and reproducible XL-MS results.

biochemistry↗

bioRxiv2018

High-throughput mapping of mesoscale connectomes in individual mice

Comprehensive analysis of neuronal networks requires brain-wide measurement of connectivity, activity, and gene expression. Although high-throughput methods are available for mapping brain-wide activity and transcriptomes, comparable methods for mapping region-to-region connectivity remain slow and expensive because they require averaging across hundreds of brains. Here we describe BRICseq, which leverages DNA barcoding and sequencing to map connectivity from single individuals in a few weeks and at low cost. Applying BRICseq to the mouse neocortex, we find that region-to-region connectivity provides a simple bridge relating transcriptome to activity: The spatial expression patterns of a few genes predict region-to-region connectivity, and connectivity predicts activity correlations. We also exploited BRICseq to map the mutant BTBR mouse brain, which lacks a corpus callosum, and recapitulated its known connectopathies. BRICseq allows individual laboratories to compare how age, sex, environment, genetics and species affect neuronal wiring, and to integrate these with functional activity and gene expression.

neuroscience↗

bioRxiv2018

Single-axon-resolution intravital imaging reveals a rapid onset form of Wallerian degeneration in the adult neocortex

Despite the widespread occurrence of axon degeneration in the injured and diseased nervous system, the mechanisms of the degenerative process remain incompletely understood. In particular, the factors that regulate how individual axons degenerate within their native environment in the mammalian brain are unknown. Longitudinal imaging of >120 individually injured cortical axons revealed a threshold length below which injured axons undergo a rapid-onset form of Wallerian degeneration (ROWD). ROWD consistently starts 10 times earlier and is executed 4 times slower than classic Wallerian degeneration (WD). ROWD is dependent on synaptic density, unlike WD, but is independent of axon complexity. Finally, we provide both pharmacological and genetic evidence that a Nicotinamide Adenine Dinucleotide (NAD+)-dependent pathway controls cortical axon ROWD independent of transcription in the damaged neurons. Thus, our data redefine the therapeutic window for intervention to maintain neurological function in injured cortical neurons, and support the use of in vivo optical imaging to gain unique insights into the mechanisms of axon degeneration in the brain.

neuroscience↗

bioRxiv2018

A large-scale, standardized physiological survey reveals higher order coding throughout the mouse visual cortex

To understand how the brain processes sensory information to guide behavior, we must know how stimulus representations are transformed throughout the visual cortex. Here we report an open, large-scale physiological survey of neural activity in the awake mouse visual cortex: the Allen Brain Observatory Visual Coding dataset. This publicly available dataset includes cortical activity from nearly 60,000 neurons collected from 6 visual areas, 4 layers, and 12 transgenic mouse lines from 221 adult mice, in response to a systematic set of visual stimuli. Using this dataset, we reveal functional differences across these dimensions and show that visual cortical responses are sparse but correlated. Surprisingly, responses to different stimuli are largely independent, e.g. whether a neuron responds to natural scenes provides no information about whether it responds to natural movies or to gratings. We show that these phenomena cannot be explained by standard local filter-based models, but are consistent with multi-layer hierarchical computation, as found in deeper layers of standard convolutional neural networks.

neuroscience↗

bioRxiv2018

Transcription initiation defines kinetoplast RNA boundaries

Mitochondrial genomes are often transcribed into polycistronic primary RNAs punctuated by tRNAs whose excision defines mature RNA boundaries. Although kinetoplast DNA lacks tRNA genes, it is commonly held that monophosphorylated 5'-ends of functional molecules typify precursor partitioning by an unknown endonuclease. To the contrary, we demonstrate that in Trypanosoma brucei individual mRNAs and rRNAs are independently synthesized as 3' extended precursors. The transcription-defined 5' terminus is converted into monophosphorylated state by the 5' pyrophosphohydrolase complex, termed PPsome, which is activated by RNA editing substrate binding complex (RESC). Most guide RNAs lack PPsome recognition sites and, therefore, remain triphosphorylated. We provide evidence that both 5' pyrophosphate removal and 3' adenylation are essential for mRNA stabilization. Furthermore, we uncover a mechanism by which antisense RNA-controlled 3'-5' exonucleolytic trimming defines mRNA 3'-end. We conclude that mitochondrial mRNAs and rRNAs are transcribed and processed as insulated units irrespective of their genomic location.\n\nSignificanceIt is commonly held that in trypanosomes both mitochondrial DNA strands are transcribed into polycistronic precursors. These primary RNAs are presumably partitioned into individual pre-mRNAs by a \"cryptic\" endonuclease. We challenged the polycistronic transcription/ endonuclease model after revealing precursor processing by 3'-5' degradation. This work demonstrates individual transcription of each gene and mRNA 5'-end definition by the first incorporated nucleotide triphosphate. We have uncovered the stabilizing role of 5' triphosphate to monophosphate conversion and identified a protein complex responsible for this reaction. We have discovered antisense noncoding RNA originating near mRNA 3' end and showed that a duplex formation modulates exonuclease activity to delimit the mature 3' end. Collectively, our findings reveal mechanisms by which transcription defines both mRNA termini.

biochemistry↗

bioRxiv2018

ALV-J and REV synergistically activate a new oncogene of KIAA1199 via NF-κB and EGFR signaling regulated by miR-147

The tumorigenesis is the result of the accumulation of multiple oncogenes and tumor suppressor genes changes. Co-infection of avian leucosis virus subgroup J (ALV-J) and reticuloendotheliosis virus (REV), as two oncogenic retroviruses, showed synergistic pathogenic effects characterized by enhanced tumor initiation and progression. The molecular mechanism underlying synergistic effects of ALV-J and REV on the neoplasia remains unclear. Here, we found co-infection of ALV-J and REV enhanced the ability of virus infection, increased viral life cycle, maintained cell survival and enhanced tumor formation. We combined the high-throughput proteomic readout with a large-scale miRNA screening to identify which molecules are involved in the synergism. Our results revealed co-infection of ALV-J and REV activated a latent oncogene of KIAA1199 and inhibited the expression of tumor suppressor miR-147. Further, enhanced KIAA1199, down-regulated miR-147, activated NF-{kappa}B and EGFR were demonstrated in co-infected tissues and tumor. Mechanistically, we showed ALV-J and REV synergistically enhanced KIAA1199 by activation of NF-{kappa}B and EGFR signalling pathway, and the suppression of tumor suppressor miR-147 was contributed to maintain the NF-{kappa}B/KIAA1199/EGFR pathway crosstalk by targeting the 3UTR region sequences of NF-{kappa}B p50 and KIAA1199. Our results contributed to the understanding of the molecular mechanisms of viral synergistic tumorgenesis, which provided the evidence that suggested the synergistic actions of two retroviruses could result in activation of latent pro-oncogenes.\n\nAuthor summaryThe tumorigenesis is the result of the accumulation of multiple oncogenes and tumor suppressor genes changes. Co-infection with ALV-J and REV showed synergistic pathogenic effects characterized by enhanced tumor progression, however, the molecular mechanism on the neoplasia remains unclear. Our results revealed co-infection of ALV-J and REV promotes tumorigenesis by both induction of a latent oncogene of KIAA1199 and suppression of the expression of tumor suppressor miR-147. Mechanistic studies revealed that ALV-J and REV synergistically enhance KIAA1199 by activation of NF-{kappa}B and EGFR signalling pathway, and the suppression of tumor suppressor miR-147 was contributed to maintain the NF-{kappa}B/KIAA1199/EGFR pathway crosstalk by targeting the 3UTR region sequences of NF-{kappa}B p50 and KIAA1199. These results provided the evidence that suggested the synergistic actions of two retroviruses could result in activation of latent pro-oncogenes, indicating the potential preventive target and predictive factor for ALV-J and REV induced tumorigenesis.

molecular biology↗

bioRxiv2018

Post-reactivation new learning impairs and updates human episodic memory through dissociable processes

Learning of competing information after reactivation has the potential to disrupt memory reconsolidation and thus impair a consolidated memory. Yet this effect has rarely been detected in episodic memory. By introducing an additional retrieving cue to the target memory, the current study detected significant impairment on the reactivated episodic memory, in addition to an integration of new information to the old memory. However, while the integration effect followed the time window of reconsolidation disruption, the impairment effect did not. MEG measurements further revealed alpha power change during reactivation and post-reactivation learning which showed different correlation patterns with the integration and impairment effects, confirming that the two effects relied on different processes. Therefore, post-reactivation new learning disrupts episodic memory but not through reconsolidation disruption. Further findings that the impairment effect was correlated with participants voluntary inhibition ability suggest an inhibition-based memory updating process underlying post-reactivation new learning.

neuroscience↗

bioRxiv2018

Profound perturbation of the human metabolome by obesity

Obesity is a heterogeneous phenotype that is crudely measured by body mass index (BMI). More precise phenotyping and categorization of risk in large numbers of people with obesity is needed to advance clinical care and drug development. Here, we used non-targeted metabolome analysis and whole genome sequencing to identify metabolic and genetic signatures of obesity. We collected anthropomorphic and metabolic measurements at three timepoints over a median of 13 years in 1,969 adult twins of European ancestry and at a single timepoint in 427 unrelated volunteers. We observe that obesity results in a profound perturbation of the metabolome; nearly a third of the assayed metabolites are associated with changes in BMI. A metabolome signature identifies the healthy obese and also identifies lean individuals with abnormal metabolomes - these groups differ in health outcomes and underlying genetic risk. Because metabolome profiling identifies clinically meaningful heterogeneity in obesity, this approach could help select patients for clinical trials.

genetics↗

bioRxiv2018

Mettl14 is required for mouse post-implantation development by facilitating epiblast maturation

N6-methyladenosine (m6A) is the most prevalent and reversible internal modification of mammalian messenger and noncoding RNAs mediated by specific m6A writer, reader, and eraser proteins. As an m6A writer, the METTL3-METTL14-WTAP complex dynamically regulates m6A modification and plays important roles in diverse biological processes. However, our knowledge about the complete functions of this RNA methyltransferase complex, the contributions of each component to the methylation and their impacts on different biological pathways, are still very limited. Here, by employing both in vivo and in vitro models, we report that METTL14 was indispensable for post-implantation embryonic development by facilitating the conversion from naive to primed state of the epiblast. Depletion of Mettl14 lead to conspicuous embryonic growth retardation from E6.5 mainly as a result of resistance to differentiation, which further lead to embryonic lethality early in gestation. Our data highlight the critical function of METTL14, as an m6A modification regulator, in orchestrating early mouse embryogenesis.

developmental biology↗

bioRxiv2018

Biallelic Mutations in LRRC56 encoding a protein associated with intraflagellar transport, cause mucociliary clearance and laterality defects

Defective motile cilia are responsible for a group of heterogeneous genetic conditions characterised by dysfunction of the apparatus responsible for generating fluid flows. Primary ciliary dyskinesia (PCD) is the prototype for such disorders and presents with impaired pulmonary mucus clearance, susceptibility to chronic recurrent respiratory infections, male infertility and laterality defects in about 50 % of patients. Here we report biallelic variants in LRRC56 (also known as ODA8), identified in two unrelated consanguineous families. The phenotype comprises laterality defects and chronic pulmonary infections. High speed video microscopy of cultured patient epithelial cells showed severely dyskinetic cilia, but no obvious ultra-structural abnormalities on routine transmission electron microscopy (TEM). Further investigation revealed that LRRC56 interacts with the intraflagellar transport (IFT) protein IFT88. The link to IFT was interrogated in Trypanosoma brucei. In this protist, LRRC56 is recruited to the cilium during axoneme construction, where it co-localises with IFT trains and facilitates the addition of dynein arms to the distal end of the flagellum. In T. brucei carrying LRRC56 null mutations, or a mutation (p.Leu259Pro) corresponding to the p.Leu140Pro variant seen in one of the affected families, we observed abnormal ciliary beat patterns and an absence of outer dynein arms restricted to the distal portion of the axoneme. Together, our findings confirm that deleterious variants in LRRC56 result in a human disease, and suggest this protein has a likely role in dynein transport during cilia assembly that is evolutionarily important for cilia motility.

genetics↗

bioRxiv2018

bpRNA: Large-scale Automated Annotation and Analysis of RNA Secondary Structure

While RNA secondary structure prediction from sequence data has made remarkable progress, there is a need for improved strategies for annotating the features of RNA secondary structures. Here we present bpRNA, a novel annotation tool capable of parsing RNA structures, including complex pseudoknot-containing RNAs, to yield an objective, precise, compact, unambiguous, easily-interpretable description of all loops, stems, and pseudoknots, along with the positions, sequence, and flanking base pairs of each such structural feature. We also introduce several new informative representations of RNA structure types to improve structure visualization and interpretation. We have further used bpRNA to generate a web-accessible meta-database, \"bpRNA-1m\", of over 100,000 single-molecule, known secondary structures; this is both more fully and accurately annotated and over 20-times larger than existing databases. We use a subset of the database with highly similar ([≥]90% identical) sequences filtered out to report on statistical trends in sequence, flanking base pairs, and length. Both the bpRNA method and the bpRNA-1m database will be valuable resources both for specific analysis of individual RNA molecules and large-scale analyses such as are useful for updating RNA energy parameters for computational thermodynamic predictions, improving machine learning models for structure prediction, and for benchmarking structure-prediction algorithms.

bioinformatics↗

bioRxiv2018

LinearFold: Linear-Time Prediction of RNA Secondary Structures

Predicting the secondary structure of an RNA sequence with speed and accuracy is useful in many applications such as drug design. The state-of-the-art predictors have a fundamental limitation: they have a run time that scales cubically with the length of the input sequence, which is slow for longer RNAs and limits the use of secondary structure prediction in genome-wide applications. To address this bottleneck, we designed the first linear-time algorithm for this problem. which can be used with both thermodynamic and machine-learned scoring functions. Our algorithm, like previous work, is based on dynamic programming (DP), but with two crucial differences: (a) we incrementally process the sequence in a left-to-right rather than in a bottom-up fashion, and (b) because of this incremental processing, we can further employ beam search pruning to ensure linear run time in practice (with the cost of exact search). Even though our search is approximate, surprisingly, it results in even higher overall accuracy on a diverse database of sequences with known structures. More interestingly, it leads to significantly more accurate predictions on the longest sequence families in that database (16S and 23S Ribosomal RNAs), as well as improved accuracies for long-range base pairs (500+ nucleotides apart).

bioinformatics↗

bioRxiv2017

Brain and psychological determinants of placebo pill response in chronic pain patients

Placebo response is universally observed in randomized controlled trials (RCTs), yet these effects are commonly dismissed as consequences of uncontrollable confounds. In this prospective neuroimaging-based RCT performed in chronic back pain patients, we demonstrate that the intensity, but not quality, of pain is diminished with placebo pill ingestion. The response to placebo pills depended on brain: subcortical limbic volume asymmetry, sensorimotor cortical thickness, and functional coupling of the dorsolateral prefrontal cortex (DLPFC) with the periaqueductal grey (PAG), the rostral anterior cingulate cortex (rACC), and the precentral gyrus (PreCG); and psychological factors. All features were present before exposure to the pill; most remained stable across treatment and washout periods, although specific functional coupling between DLPFC and PAG dissipated with repeated exposure. These brain properties and specific psychological factors, such as interoceptive awareness and openness, were also predictive of the magnitude of response (continuous variable). We used machine learning in a fully cross-validated procedure and demonstrated that psychological factors were sufficient for classifying and predicting response magnitude; and response magnitude could also be predicted from a functional network (nodes mainly located in the limbic community, the DLPFC, the orbitofrontal cortex, and the temporo-parietal junction); the combined model explained 36% of the variance. Together, our results demonstrate that placebo pill analgesia observed in clinical trials depends on a combination of brain properties and specific psychological factors.

neuroscience↗

bioRxiv2017

A suite of transgenic driver and reporter mouse lines with enhanced brain cell type targeting and functionality

Modern genetic approaches are powerful in providing access to diverse types of neurons within the mammalian brain and greatly facilitating the study of their function. We here report a large set of driver and reporter transgenic mouse lines, including 23 new driver lines targeting a variety of cortical and subcortical cell populations and 26 new reporter lines expressing an array of molecular tools. In particular, we describe the TIGRE2.0 transgenic platform and introduce Cre-dependent reporter lines that enable optical physiology, optogenetics, and sparse labeling of genetically-defined cell populations. TIGRE2.0 reporters broke the barrier in transgene expression level of single-copy targeted-insertion transgenesis in a wide range of neuronal types, along with additional advantage of a simplified breeding strategy compared to our first-generation TIGRE lines. These novel transgenic lines greatly expand the repertoire of high-precision genetic tools available to effectively identify, monitor, and manipulate distinct cell types in the mouse brain.

neuroscience↗

bioRxiv2017

A New Big-Data Paradigm For Target Identification And Drug Discovery

Drug target identification is one of the most important aspects of pre-clinical development yet it is also among the most complex, labor-intensive, and costly. This represents a major issue, as lack of proper target identification can be detrimental in determining the clinical application of a bioactive small molecule. To improve target identification, we developed BANDIT, a novel paradigm that integrates multiple data types within a Bayesian machine-learning framework to predict the targets and mechanisms for small molecules with unprecedented accuracy and versatility. Using only public data BANDIT achieved an accuracy of approximately 90% over 2000 different small molecules - substantially better than any other published target identification platform. We applied BANDIT to a library of small molecules with no known targets and generated [~]4,000 novel molecule-target predictions. From this set we identified and experimentally validated a set of novel microtubule inhibitors, including three with activity on cancer cells resistant to clinically used anti-microtubule therapies. We next applied BANDIT to ONC201 - an active anti- cancer small molecule in clinical development - whose target has remained elusive since its discovery in 2009. BANDIT identified dopamine receptor 2 as the unexpected target of ONC201, a prediction that we experimentally validated. Not only does this open the door for clinical trials focused on target-based selection of patient populations, but it also represents a novel way to target GPCRs in cancer. Additionally, BANDIT identified previously undocumented connections between approved drugs with disparate indications, shedding light onto previously unexplained clinical observations and suggesting new uses of marketed drugs. Overall, BANDIT represents an efficient and highly accurate platform that can be used as a resource to accelerate drug discovery and direct the clinical application of small molecule therapeutics with improved precision.

pharmacology and toxicology↗