Biology subjects

Si, Y.

Publications and source records attributed to Si, Y..

51 records · Page 3Linked to original sources

bioRxiv2021

Protein Complex Structure Prediction Powered by Multiple Sequence Alignment of Interologs from Multiple Taxonomic Ranks and AlphaFold2

AlphaFold2 is expected to be able to predict protein complex structures as long as a multiple sequence alignment (MSA) of the interologs of the target protein-protein interaction (PPI) can be provided. In this study, a simplified phylogeny-based approach was applied to generate the MSA of interologs, which was then used as the input to AlphaFold2 for protein complex structure prediction. Extensively benchmarked this protocol on non-redundant PPI dataset including 107 bacterial PPIs and 442 eukaryotic PPIs, we show complex structures of 79.5% of the bacterial PPIs and 49.8% of the eukaryotic PPIs can be successfully predicted, which yielded significantly better performance than the application of MSA of interologs prepared by two existing approaches. Considering PPIs may not be conserved in species with long evolutionary distances, we further restricted interologs in the MSA to different taxonomic ranks of the species of the target PPI in protein complex structure prediction. We found the success rates can be increased to 87.9% for the bacterial PPIs and 56.3% for the eukaryotic PPIs if interologs in the MSA are restricted to a specific taxonomic rank of the species of each target PPI. Finally, we show the optimal taxonomic ranks for protein complex structure prediction can be selected with the application of the predicted TM-scores of the output models.

bioinformatics↗

bioRxiv2021

The different brain areas occupied for integrating information of hierarchical linguistic units: a study based on EEG and TMS

Human linguistic units are hierarchical, and our brain responds differently when processing linguistic units during sentence comprehension, especially when the modality of the received signal is different (auditory, visual, or audio-visual). However, it is unclear how the brain processes and integrates language information at different linguistic units (words, phrases, and sentences) provided simultaneously in audio and visual modalities. To address the issue, we presented participants with sequences of short Chinese sentences through auditory or visual or combined audio- visual modalities, while electroencephalographic responses were recorded. With a frequency tagging approach, we analyzed the neural representations of basic linguistic units (i.e., characters/monosyllabic words) and higher-level linguistic structures (i.e., phrases and sentences) across the three modalities separately. We found that audio-visual integration occurs at all linguistic units, and the brain areas involved in the integration varied across different linguistic levels. In particular, the integration of sentences activated the local left prefrontal area. Therefore, we used continuous theta-burst stimulation (cTBS) to verify that the left prefrontal cortex plays a vital role in the audio-visual integration of sentence information. Our findings suggest the advantage of bimodal language comprehension at hierarchical stages in language-related information processing and provide evidence for the causal role of the left prefrontal regions in processing information of audio-visual sentences.

neuroscience↗

bioRxiv2021

High-frequency hearing is required for generating a topographic map of auditory space in the mouse superior colliculus

A topographic map of auditory space is a feature found in the superior colliculus (SC) of many species, including CBA/CaJ mice. In this genetic background, high-frequency monaural spectral cues and interaural level differences are used to generate spatial receptive fields (RFs) that form a topographic map along the azimuth. Unfortunately, C57BL/6 mice, a strain widely used for transgenic manipulation, display age-related hearing loss (AHL) due to an inbred mutation in the Cadherin 23 gene (Cdh23) that affects hair cell mechanotransduction. To overcome this problem, researchers have used young C57BL/6 mice in their studies, as they have been shown to have normal hearing thresholds. However, important details of the auditory response characteristics of the SC such as spectral responses and spatial localization, have not been characterized in young C57BL/6 mice. Here we show that 2-4-month C57BL/6 mice lack neurons with frontal auditory RFs and therefore lack a topographic representation of auditory space in the SC. Analysis of the spectrotemporal receptive fields (STRFs) of the SC auditory neurons shows that C57BL/6 mouse SC neurons lack the ability to detect the high-frequency (>40kHz) spectral cues that are needed to compute frontal RFs. We also show that crossing C57BL/6 mice with CBA/CaJ mice or introducing one copy of the wild-type Cdh23 to C57BL/6 mice rescues the high-frequency hearing deficit and improves the topographic map of auditory space. Taken together, these results demonstrate the importance of high-frequency hearing in computing a topographic map of auditory space. Significance StatementDespite the strains age-dependent hearing loss, C57BL/6 mice are widely used in auditory studies because of the development of transgenic reporter and Cre lines in this genetic background. Here we examined the topographic map of auditory space and spectrotemporal properties of neurons in the SC of C57BL/6 mice. We found an early-onset high-frequency hearing deficit that results in the loss of SC neurons with frontal RFs and, consequently, an absence of a topographic map of auditory space. These findings stress the importance of high-frequency hearing in generating spatially restricted receptive fields and serve as a caution to researchers that doing auditory-related research using the C57BL/6 genetic background may not be representative of true wild-type mice.

neuroscience↗

bioRxiv2021

Improved protein contact prediction using dimensional hybrid residual networks and singularity enhanced loss function

Deep residual learning has shown great success in protein contact prediction. In this study, a new deep residual learning-based protein contact prediction model was developed. Comparing with previous models, a new type of residual block hybridizing 1D and 2D convolutions was designed to increase the effective receptive field of the residual network, and a new loss function emphasizing the easily misclassified residue pairs was proposed to enhance the model training. The developed protein contact prediction model referred to as DRN-1D2D was first evaluated on 105 CASP11 targets, 76 CAMEO hard targets and 398 membrane proteins together with two in house-developed reference models based on either the standard 2D residual block or the traditional BCE loss function, from which we confirmed that both the dimensional hybrid residual block and the singularity enhanced loss function can be employed to improve the model performance for protein contact prediction. DRN-1D2D was further evaluated on 39 CASP13 and CASP14 free modeling targets together with the two reference models and six state-of-the-art protein contact prediction models including DeepCov, DeepCon, DeepConPred2, SPOT-Contact, RaptorX-Contact and TripleRes. The result shows that DRN-1D2D consistently achieved the best performance among all these models.

bioinformatics↗

bioRxiv2021

Longitudinal single-cell epitope and RNA-sequencing reveals the immunological impact of type 1 interferon autoantibodies in critical COVID-19

Type I interferon (IFN-I) neutralizing autoantibodies have been found in some critical COVID-19 patients; however, their prevalence and longitudinal dynamics across the disease severity scale, and functional effects on circulating leukocytes remain unknown. Here, in 284 COVID-19 patients, we found IFN-I autoantibodies in 19% of critical, 6% of severe and none of the moderate cases. Longitudinal profiling of over 600,000 peripheral blood mononuclear cells using multiplexed single-cell epitope and transcriptome sequencing from 54 COVID-19 patients, 15 non-COVID-19 patients and 11 non-hospitalized healthy controls, revealed a lack of IFN-I stimulated gene (ISG-I) response in myeloid cells from critical cases, including those producing anti-IFN-I autoantibodies. Moreover, surface protein analysis showed an inverse correlation of the inhibitory receptor LAIR-1 with ISG-I expression response early in the disease course. This aberrant ISG-I response in critical patients with and without IFN-I autoantibodies, supports a unifying model for disease pathogenesis involving ISG-I suppression via convergent mechanisms.

immunology↗

bioRxiv2021

REPROGRAMMING CBX8-PRC1 FUNCTION WITH A POSITIVE ALLOSTERIC MODULATOR

ABSTRACTCanonical targeting of Polycomb Repressive Complex 1 (PRC1) to repress developmental genes is mediated by cell type-specific, paralogous chromobox (CBX) proteins (CBX2, 4, 6, 7 and 8). Based on their central role in silencing and their misregulation associated with human disease including cancer, CBX proteins are attractive targets for small molecule chemical probe development. Here, we have used a quantitative and target-specific cellular assay to discover a potent positive allosteric modulator (PAM) of CBX8. The PAM activity of UNC7040 antagonizes H3K27me3 binding by CBX8 while increasing interactions with nucleic acids and participation in variant PRC1. We show that treatment with UNC7040 leads to efficient PRC1 chromatin eviction, loss of silencing and reduced proliferation across different cancer cell lines. Our discovery and characterization of UNC7040 not only revealed the most cellularly potent CBX8-specific chemical probe to date, but also corroborates a mechanism of polycomb regulation by non-histone lysine methylated interaction partners.

synthetic biology↗

bioRxiv2021

A Reproducibility Analysis-based Statistical Framework for Residue-Residue Evolutionary Coupling Detection

Direct coupling analysis (DCA) has been widely used to infer evolutionary coupled residue pairs from the multiple sequence alignment (MSA) of homologous sequences. However, effectively selecting residue pairs with significant evolutionary couplings according to the result of DCA is a non-trivial task. In this study, we developed a general statistical framework for significant evolutionary coupling detection, referred to as IDR-DCA, which is based on reproducibility analysis of the coupling scores obtained from DCA on manually created MSA replicates. IDR-DCA was applied to select residue pairs for contact prediction for monomeric proteins, protein-protein interactions and monomeric RNAs, in which three different versions of DCA were applied. We demonstrated that with the application of IDR-DCA, the residue pairs selected using a universal threshold always yielded stable performance for contact prediction. Comparing with the application of carefully tuned coupling score cutoffs, IDR-DCA always showed better performance. The robustness of IDR-DCA was also supported through the MSA down-sampling analysis. We further demonstrated the effectiveness of applying constraints obtained from residue pairs selected by IDR-DCA to assist RNA secondary structure prediction.

bioinformatics↗

bioRxiv2021

Nonlinear visuoauditory integration in the mouse superior colliculus

Sensory information from different modalities is processed in parallel, and then integrated in associative brain areas to improve object identification and the interpretation of sensory experiences. The Superior Colliculus (SC) is a midbrain structure that plays a critical role in integrating visual, auditory, and somatosensory input to assess saliency and promote action. Although the response properties of the individual SC neurons to visuoauditory stimuli have been characterized, little is known about the spatial and temporal dynamics of the integration at the population level. Here we recorded the response properties of SC neurons to spatially restricted visual and auditory stimuli using large-scale electrophysiology. We then created a general, population-level model that explains the spatial, temporal, and intensity requirements of stimuli needed for sensory integration. We found that the mouse SC contains topographically organized visual and auditory neurons that exhibit nonlinear multisensory integration. We show that nonlinear integration depends on properties of auditory but not visual stimuli. We also find that a heuristically derived nonlinear modulation function reveals conditions required for sensory integration that are consistent with previously proposed models of sensory integration such as spatial matching and the principle of inverse effectiveness.

neuroscience↗

bioRxiv2020

Cell-Free Biosynthesis to Evaluate Lasso Peptide Formation and Enzyme-Substrate Tolerance

Lasso peptides are ribosomally synthesized and post-translationally modified peptide (RiPP) natural products that display a unique lariat-like structure. Owing to a rigid topology, lasso peptides are unusually stable towards heat and proteolytic degradation. Some lasso peptides have been shown to bind human cell-surface receptors and exhibit anticancer properties, while others display antibacterial or antiviral activities. Known lasso peptides are produced by bacteria and genome-mining studies indicate that lasso peptides are a relatively prevalent RiPP class; however, the discovery, isolation, and characterization of lasso peptides are constrained by the lack of an efficient production system. In this study, we employ a cell-free biosynthesis (CFB) strategy to address the longstanding challenges associated with lasso peptide production. We report the successful formation of a diverse array of lasso peptides that include known examples as well as a new predicted lasso peptide from Thermobifida halotolerans. We further demonstrate the utility of CFB to rapidly generate and characterize multisite precursor peptide variants in order to evaluate the substrate tolerance of the biosynthetic pathway. We show that the lasso-forming cyclase from the fusilassin pathway can produce millions of sequence-diverse lasso peptides via CFB with an extraordinary level of sequence permissiveness within the ring region of the lasso peptide. These data lay a firm foundation for the creation of large lasso peptide libraries using CFB to identify new variants with unique properties.

synthetic biology↗

bioRxiv2020

Why are rare variants hard to impute? Coalescent models reveal theoretical limits in existing algorithms.

Genotype imputation is an indispensable step in human genetic studies. Large reference panels with deeply sequenced genomes now allow interrogating variants with minor allele frequency < 1% without sequencing. While it is critical to consider limits of this approach, imputation methods for rare variants have only done so empirically; the theoretical basis of their imputation accuracy has not been explored. To provide theoretical consideration of imputation accuracy under the current imputation framework, we develop a coalescent model of imputing rare variants, leveraging the joint genealogy of the sample to be imputed and reference individuals. We show that broadly used imputation algorithms includes model misspecifications about this joint genealogy that limit the ability to correctly impute rare variants. We develop closed-form solutions for the probability distribution of this joint genealogy and quantify the inevitable error rate resulting from the model misspecification across a range of allele frequencies and reference sample sizes. We show that the probability of a falsely imputed minor allele decreases with reference sample size, but the proportion of falsely imputed minor alleles mostly depends on the allele count in the reference sample. We summarize the impact of this error on genotype imputation on association tests by calculating the r2 between imputed and true genotype and show that even when modeling other sources of error, the impact of the model misspecification have a significant impact on the r2 of rare variants. To evaluate these predictions in practice, we compare the imputation of the same dataset across imputation panels of different sizes. While this empirical imputation accuracy is substantially lower than our theoretical prediction, modeling misspecification seems to further decrease imputation accuracy for variants with low allele counts in the reference. These results provide a framework for developing new imputation algorithms and for interpreting rare variant association analyses.

genetics↗

bioRxiv2020

Morphologic Classification and Automatic Nugent Scoring of Bacterial Vaginosis by Deep Neural Networks

BackgroundBacterial vaginosis (BV) was the most common condition for womens health caused by the disruption of normal vaginal flora and an overgrowth of certain disease-causing bacteria, affecting 30-50% of women at some time in their lives. Gram stain followed by Nugent scoring (NS) based on bacterial morphotypes under the microscope was long considered golden standard for BV diagnosis. This conventional manual method was often considered labor intensive, time consuming, and variable results from person to person. MethodsWe developed four convolutional neural networks (CNN) models, and evaluated their ability to automatic identify vaginal bacteria and classify Nugent scores from microscope images. All the CNN models were first trained with 23280 microscopic images labeled with Nugent scores from top experts. A separate set of 5815 images were evaluated by the CNN models. The best CNN model was selected to generalize its application on an independent sets of 1082 images collecting from three teaching hospitals. Different hardwares were used to take images in hospitals. ResultsOur model could classify three Nugent Scores from images with high three classification accuracy of 89.3% (with 82.4% sensitivity and 96.6% specificity) on the 5815 test images, which was better diagnostic yield than the top-level technologists and obstetricians in China. The ability of generalization for our model was strong that it obtained 75.1%, which was 6.6% higher than the average of technologists. ConclusionThe CNN model over performed human healthcare practitioners on accuracy, efficiency and stability for BV diagnosis using microscopic image-based Nugent scores. The deep learning model may offer translational application in automating diagnosis of bacterial vaginosis with proper supporting hardware.

microbiology↗

bioRxiv2020

Biallelic variants in the RNA exosome gene EXOSC5 are associated with developmental delays, short stature, cerebellar hypoplasia and motor weakness

The RNA exosome is an essential ribonuclease complex involved in the processing and degradation of both coding and noncoding RNAs. We present three patients with biallelic variants in EXOSC5, which encodes a structural subunit of the RNA exosome. The common clinical features of these patients comprise failure to thrive, short stature, feeding difficulties, developmental delays that affect motor skills, hypotonia and esotropia. Brain MRI revealed cerebellar hypoplasia and ventriculomegaly. The first patient had a deletion involving exons 5-6 of EXOSC5 and a missense variant, p.Thr114Ile, that were inherited in trans, the second patient was homozygous for p.Leu206His, and the third patient had paternal isodisomy for chromosome 19 and was homozygous for p.Met148Thr. We employed three complementary approaches to explore the requirement for EXOSC5 in brain development and assess the functional consequences of pathogenic variants in EXOSC5. Loss of function for the zebrafish ortholog results in shortened and curved tails and bodies, reduced eye and head size and edema. We modeled pathogenic EXOSC5 variants in both budding yeast and mammalian cells. Some of these variants show defects in RNA exosome function as well as altered interactions with other RNA exosome subunits. Overall, these findings expand the number of genes encoding RNA exosome components that have been implicated in human disease, while also suggesting that disease mechanism varies depending on the specific pathogenic variant.

genetics↗

bioRxiv2019

Spectral cues are necessary for encoding the azimuthal map of auditory space in the mouse superior colliculus

Sound localization plays a critical role in animal survival. Three cues can be used to compute sound direction: interaural timing differences (ITDs), interaural level differences (ILDs) and the direction-dependent spectral filtering by the head and pinnae (spectral cues). Little is known about how spectral cues contribute to the neural encoding of auditory space. Here we report on auditory space encoding in the mouse superior colliculus (SC). We show that the mouse SC contains neurons with spatially-restricted receptive fields (RFs) that form an azimuthal topographic map. We found that frontal RFs require spectral cues and lateral RFs require ILDs. The neurons with frontal RFs have frequency tunings that match the spectral structure of the specific head and pinna filter for sound coming from the front. These results demonstrate that patterned spectral cues in combination with ILDs give rise to the topographic map of azimuthal auditory space.

neuroscience↗

bioRxiv2019

Novel anti-somatostatin receptor 2 antibody-drug conjugate for neuroendocrine cancer therapy

Neuroendocrine (NE) cancers include a diverse spectrum of hormone-secreting neoplasms that arise from the endocrine and nervous systems. Current chemo- and radio- therapies have marginal curative benefits. This study aimed to develop an innovative antibody-drug conjugate (ADC) to effectively treat NE tumors (NETs). We first confirmed that somatostatin receptor 2 (SSTR2) is an ideal surface target by analyzing 38 patient-derived NET tissues, 33 normal organs, and 3 NET cell lines. We then developed a new monoclonal antibody (mAb, IgG1 and kappa) to target two extracellular domains of SSTR2, which showed strong and specific surface binding to NETs. The ADC was constructed by conjugating the anti-SSTR2 mAb and antimitotic monomethyl auristatin E. In vitro evaluations indicated that the ADC can effectively bind, internalize, release payload, and kill NET cells effectively. Finally, the ADC was evaluated in vivo using a NET xenografted mouse model to determine cancer targeting, maximal tolerated dosage, pharmacokinetics, and anti-cancer efficacy. The anti-SSTR2 ADC was able to exclusively target and kill NETs with minimal toxicity and high stability in vivo. This study demonstrates that the anti-SSTR2 mAb-based ADC has high therapeutic values for NET therapy.

bioengineering↗

bioRxiv2019

Scaffold subunits support associated subunit assembly in the Chlamydomonas ciliary nexin-dynein regulatory complex

The nexin-dynein regulatory complex (N-DRC) in motile cilia and flagella functions as a linker between neighboring doublet microtubules, acts to stabilize the axonemal core structure, and serves as a central hub for the regulation of ciliary motility. Although the N-DRC has been studied extensively using genetic, biochemical, and structural approaches, the precise arrangement of the eleven (or more) N-DRC subunits remains unknown. Here, using cryo-electron tomography, we have compared the structure of Chlamydomonas wild-type flagella to that of strains with specific DRC subunit deletions or rescued strains with tagged DRC subunits. Our results show that DRC7 is a central linker subunit that helps connect the N-DRC to the outer dynein arms. DRC11 is required for the assembly of DRC8, and DRC8/11 form a sub-complex in the proximal lobe of the linker domain that is required to form stable contacts to the neighboring B-tubule. Gold labeling of tagged subunits determines the precise locations of the previously ambiguous N-terminus of DRC4 which is now shown to contribute to the core scaffold of the N-DRC and C-terminus of DRC5. Our results reveal the overall architecture of N-DRC, with the three subunits, DRC1/2/4 forming a core complex that serves as the scaffold for the assembly of the \"functional subunits\" associate, namely DRC3/5-8/11. These findings shed light on N-DRC assembly and its role in regulating flagellar beating.\n\nSignificance StatementCilia and flagella are small hair-like appendages in eukaryotic cells that play essential roles in cell sensing, signaling, and motility. The highly conserved nexin-dynein regulatory complex (N-DRC) is one of the key regulators for ciliary motility. At least 11 proteins (DRC1-11) have been assigned to the N-DRC, but their precise arrangement within the large N-DRC structure is not yet known. Here, using cryo-electron tomography combined with genetic approaches, we have localized DRC7, the sub-complex DRC8/DRC11, the N-terminus of DRC4, and the C-terminus of DRC5. Our results provide insights into the N-DRC structure, its function in the regulation of dynein activity, and the mechanism by which n-drc mutations can lead to defects in ciliary motility that cause disease.

cell biology↗