Biology subjects

Song, J.

Publications and source records attributed to Song, J..

At least 127 records · Page 7Linked to original sources

bioRxiv2024

Unusual traits shape the architecture of the Ig ancestor molecule

Understanding the ancestral Ig domains molecular structure and tracing the evolution of Ig-like proteins are fundamental components missing from our comprehension of their evolutionary trajectory and function. We have elucidated the molecular structures of two Ig-like proteins from the evolutionary most ancestral phylum, Porifera, revealing previously unidentified Ig-domain features that highlight the concomitant presence of a novel Ig "Early Variable" (EV)-set in tandem with a C1-set domain. The latter, to our knowledge, has never been reported before in non-vertebrates. The IgV and IgC1 sets and their combination into functional Ig-like receptors are part of the adaptive immune system in higher vertebrates, which allows for highly specific immune responses. These observations in the ancient Porifera phylum could indicate the presence of primitive forms of adaptive immunity or a foundational immune strategy that has been conserved through evolution. By unveiling important clues into the molecular configuration of ancestral Ig domains, these findings challenge and expand our understanding of how immunity has evolved within its current landscape.

evolutionary biology↗

bioRxiv2024

The asymmetric expression of HSPA2 in blastomeres governs the first embryonic cell-fate decision

The first cell-fate decision is the process by which cells of an embryo take on distinct lineage identities for the first time, thus representing the beginning of developmental patterning. Here, we demonstrate that the molecular chaperone heat shock protein A2 (HSPA2), a member of the 70 kDa heat shock protein (HSP70) family, is asymmetrically expressed in the late 2-cell stage of mouse embryos. The knockdown of Hspa2 in one of the 2-cell blastomeres prevented its progeny predominantly toward the inner cell mass (ICM) fate. In contrast, the overexpression of Hspa2 in one of the two-cell blastomeres did not induce blastomeres to differentiate towards the ICM fate. Furthermore, we demonstrated that HSPA2 interacts with CARM1 and its levels correlate with ICM-associated genes and that it interacts with the CARM1. Collectively, our results identify HSPA2 as a critical early regulator of the first cell-fate decision in mammalian 2-cell embryos.

developmental biology↗

bioRxiv2024

Substrate specificity and protein stability drive the divergence of plant-specific DNA methyltransferases

DNA methylation is an important epigenetic mechanism essential for transposon silencing and genome integrity. Across evolution, the substrates of DNA methylation have diversified between kingdoms to account for genome complexity. In plants, Chromomethylase3 (CMT3) and CMT2 are the major methyltransferases mediating CHG and CHH methylation, respectively. However, how these two enzymes diverge on substrate specificities during evolution remains unknown. Here, we reveal that CMT2 originates from a duplication of the evolutionarily more ancient CMT3 in flowering plants. Lacking a key arginine residue recognizing CHG in CMT2 impairs its CHG methylation activity in most flowering plants. An engineered V1200R mutation empowers CMT2 to restore both CHG and CHH methylation in Arabidopsis cmt2cmt3 mutant, testifying a loss-of-function effect for CMT2 after [~]200 million years of evolution. Interestingly, CMT2 has evolved a long and unstructured N-terminus critical for balancing protein stability, especially under heat stress. Furthermore, CMT2 N-terminus is plastic and can be tolerant to various natural mutations. Together, this study reveals the mechanism of chromomethylase divergence for context-specific DNA methylation in plants and sheds important lights on DNA methylation evolution and function.

plant biology↗

bioRxiv2024

SCREEN: a graph-based contrastive learning tool to infer catalytic residues and assess mutation tolerance in enzymes

The accurate identification of catalytic residues contributes to our understanding of enzyme functions in biological processes and pathways. The increasing number of protein sequences necessitates computational tools for the automated prediction of catalytic residues in enzymes. Here, we introduce SCREEN, a graph neural network for the high-throughput prediction of catalytic residues via the integration of enzyme functional and structural information. SCREEN constructs residue representations based on spatial arrangements and incorporates enzyme function priors into such representations through contrastive learning. We demonstrate that SCREEN (i) consistently outperforms currently-available predictors; (ii) provides accurate results when applied to inferred enzyme structures; and (iii) generalizes well to enzymes dissimilar from those in the training set. We also show that the putative catalytic residues predicted by SCREEN mimic key structural and biophysical characteristics of native catalytic residues. Moreover, using experimental data sets, we show that SCREENs predictions can be used to distinguish residues with a high mutation tolerance from those likely to cause functional loss when mutated, indicating that this tool might be used to infer disease-associated mutations.

bioinformatics↗

bioRxiv2024

Functional dissection of the conserved C. elegans LEM-3/ANKLE1 nuclease reveals a crucial requirement for the LEM-like and GIY-YIG domains for DNA bridges processing

Faithful chromosome segregation requires the removal of all DNA bridges physically linking chromatids before the completion of cell division. While several redundant safeguard mechanisms to process these DNA bridges exist from S-phase to late anaphase, the conserved LEM-3/ANKLE1 nuclease has been proposed to be part of a last chance mechanism that acts at the midbody to eliminate DNA bridges that persist until late cytokinesis. We show that LEM-3 can cleave a wide range of branched DNA substrates, including flaps, forks, nicked and intact Holliday Junctions. AlphaFold modeling data suggest that the catalytic mechanism of LEM-3/ANKLE1 is conserved, mirroring the mechanism observed in bacterial GIY-YIG nucleases. We also present evidence that LEM-3 may form a homodimeric complex on the Holliday Junction DNA. LEM-3 DNA binding capacity requires both the LEM-like and the GIY-YIG nuclease domains; both are also essential for LEM-3 recruitment to the midbody and its nuclease activity. Finally, we show that preventing LEM-3 nuclear access is important to avoid toxicity, likely caused by branched DNAs cleavage during normal DNA metabolism. Our data suggest that C. elegans LEM-3 acts as a last chance catch-all enzyme that processes DNA bridges caused by various perturbations of DNA metabolism just before cells divide.

biochemistry↗

bioRxiv2024

A universal residue numbering scheme for the Immunoglobulin-fold (Ig-fold) to study Ig-Proteomes and Ig-Interactomes

The Immunoglobulin fold (Ig-fold) is found in proteins from all domains of life and represents the most populous fold in the human genome, with current estimates ranging from 2 to 3% of protein coding regions. That proportion is much higher in the surfaceome where Ig and Ig-like domains orchestrate cell-cell recognition, adhesion and signaling. The ability of Ig-domains to reliably fold and self-assemble through highly specific interfaces represents a remarkable property of these domains, making them key elements of molecular interaction systems: the immune system, the nervous system, the vascular system and the muscular system. We define a universal residue numbering scheme, common to all domains sharing the Ig-fold in order to study the wide spectrum of Ig-domain variants constituting the Ig-proteome and Ig-Ig interactomes at the heart of these systems. The "IgStrand numbering scheme" enables the identification of Ig structural proteomes and interactomes in and between any species, and comparative structural, functional, and evolutionary analyses. We review how Ig-domains are classified today as topological and structural variants and highlight the "Ig-fold irreducible structural signature" shared by all of them. The IgStrand numbering scheme lays the foundation for the systematic annotation of structural proteomes by detecting and accurately labeling Ig-, Ig-like and Ig-extended domains in proteins, which are poorly annotated in current databases and opens the door to accurate machine learning. Importantly, it sheds light on the robust Ig protein folding algorithm used by nature to form beta sandwich supersecondary structures. The numbering scheme powers an algorithm implemented in the interactive structural analysis software iCn3D to systematically recognize Ig-domains, annotate them and perform detailed analyses comparing any domain sharing the Ig-fold in sequence, topology and structure, regardless of their diverse topologies or origin. The scheme provides a robust fold detection and labeling mechanism that reveals unsuspected structural homologies among protein structures beyond currently identified Ig- and Ig-like domain variants. Indeed, multiple folds classified independently contain a common structural signature, in particular jelly-rolls. Examples of folds that harbor an "Ig-extended" architecture are given. Applications in protein engineering around the Ig-architecture are straightforward based on the universal numbering. AUTHOR SUMMARYThe Immunoglobulin fold (Ig-fold) is a highly conserved protein architecture that has diversified extensively throughout evolution to provide a significant number of Ig-domain types with variable topologies in all life forms. Primarily known for its role in the vertebrate immune system and in particular for its presence in the structure of antibodies, the Ig-domains are found in a myriad of proteins involved in multiple diverse biological/structural functions within the immune, nervous, vascular, and muscular systems, where they mediate neural development, cell-cell recognition, cell adhesion and signaling. Structurally, the Ig-fold consists of 70-110 amino acids that form two beta sheets stabilized by hydrogen bonds facing each other in a sandwich configuration. This architecture provides stability and tremendous adaptability, making the Ig-fold a fundamental building block in many protein families across species, from bacteria to vertebrates. The Ig-fold is most abundant in the human genome, accounting for at least 2-3% of protein-coding regions, which is an underestimate as no reliable method exists to date that can identify all Ig domain variants with precision. To better identify and to study Ig-domain variants and most importantly their specific interactions behind biological functions, the "IgStrand universal residue numbering scheme" was developed. It enables the annotation of the diverse Ig-domain topological variants down to the residue level and their interactions, allowing detailed comparative structural, functional, and evolutionary analyses. This deconstruction of all known Ig-domain topological variant tertiary and quaternary structures will assist in the rational design from scratch of Ig-based therapeutics from single-domain antibodies to cellular therapies against any antigen of interest.

bioinformatics↗

bioRxiv2024

Molecular effects of indoor tanning

BackgroundTanning bed users have a significantly increased risk of melanoma, but it remains unclear how indoor tanning drives melanomagenesis. Tanning bed radiation is often thought of as a substitute for natural UV radiation despite differences in the maximum doses, UV content, body sites exposed, and patterns of melanoma that arise. MethodsTo better understand the epidemiologic trends and etiology of melanoma associated with tanning bed use, we described the patterns of melanoma in patients with quantifiable tanning bed usage and performed exome sequencing of 182 melanocytes from normal skin of a subset of these patients. ResultsTanning bed users were more likely than non-users to have melanoma on body sites with low cumulative levels of sun damage and were more likely to have multiple melanomas. The melanocytes in normal appearing skin from tanning bed users had higher mutation burdens, a higher proportion of melanocytes with pathogenic mutations, and distinct mutational signatures. These differences were most prominent over body sites that experience comparatively less exposure to natural sunlight. ConclusionsWe conclude that tanning bed radiation induces melanoma by increasing the mutation burden of melanocytes and by mutagenizing a broader field of melanocytes than are typically exposed to natural sunlight. The unique signatures of mutations in skin cells of tanning users may be attributable to the distinct spectra of radiation emitted from solariums.

genomics↗

bioRxiv2024

Prominin-1 null Xenopus laevis develop subretinal drusenoid-like deposits, cone-rod dystrophy, and dysmorphic retinal pigment epithelium.

Mutations in the PROMININ-1 (PROM1) gene are associated with inherited, non-syndromic vision loss. Here, we used CRISPR/Cas9 to induce truncating prom1-null mutations in Xenopus laevis to create a disease model. We then tracked progression of retinal degeneration in these animals from the ages of 6 weeks to 3 years old. We found that retinal degeneration caused by prom1-null is age-dependent and likely involves death or damage to the retinal pigment epithelium (RPE) that precedes photoreceptor degeneration. As prom1-null frogs age, they develop large cellular debris deposits in the subretinal space and outer segment layer which resemble subretinal drusenoid deposits (SDD) in their location, histology, and representation in color fundus photography and optical coherence tomography (OCT). In older frogs, these SDD-like deposits accumulate in size and number, and they are present before retinal degeneration occurs. Evidence for an RPE origin of these deposits includes infiltration of pigment granules into the deposits, thinning of RPE as measured by OCT, and RPE disorganization as measured by histology and OCT. The appearance and accumulation of SDD-like deposits and RPE thinning and disorganization in our animal model suggests an underlying disease mechanism for prom1-null mediated blindness of death and dysfunction of the RPE preceding photoreceptor degeneration, instead of direct effects upon photoreceptor outer segment morphogenesis, as was previously hypothesized.

cell biology↗

bioRxiv2024

Dynamic Modeling of Antibody Repertoire Reshaping in Response to Viral Infections

For many years, researchers have emphasized the production of high-affinity specific antibodies by hosts during viral infections. However, this has made it challenging for immunologists to systematically evaluate the initiation mechanisms of humoral immunity in specific immune responses. Employing mathematical modeling, we have systematically investigated the dynamic changes of the entire antibody atlas in response to exogenous antigenic stimuli, including viral infections. Our study reveals that the hosts antibody atlas is reshaped during viral infection, not through the proliferation of individual antibody types, but rather through the proliferation of antibody pools with strong binding activity. Moreover, we observe a contraction in pools of antibodies with low binding activity. We have identified the crucial role of self-antigens in maintaining antibody persistence, which can effectively explain the organisms lifelong protection against pathogens that are less prone to mutation. Using this model, we further explore the mechanisms underlying original antigenic sin and elucidate the specific practical applications of this model. This research transcends the limitations of mere mathematical parameter fitting, as we endeavor to elucidate the complex humoral immune processes using physical mechanisms as a foundation. Our work contributes to a renewed understanding of the antibody elicitation process in specific immune responses.

immunology↗

bioRxiv2024

Effects of Drought and Salinity, etc. on Root Pressure of Dracaena sanderiana

AimRoot pressure plays a crucial role in maintaining tissue hydration and ensuring the functionality of the plants hydraulic system while facilitating cellular and tissue growth. The study focuses on investigating the impact of drought, salinity, etc. on root pressure of Dracaena sanderiana. MethodsPressure sensors (PX26-100GV) were used to measure the diurnal variations in root pressure of soil-cultured and hydroponic D. sanderiana with heights ranging from 60 to 80 cm. The study also examined various influencing factors such as drought stress, salinity stress, temperatures, mineral concentration, change in leaves and roots that affect root pressure. This approach provides valuable insights into the role of root pressure in mediating water upward movement in xylem. Essential findingsThe research showed: (1) Both soil-cultured and hydroponic D. sanderiana exhibited positive root pressure throughout the day, demonstrating apparent diurnal pattern of high pressures at the day and low pressures at night. The maximum root pressure measured in the experiments was 96 kPa; (2) For soil-cultured D. sanderiana, root pressure decreased under drought and salinization (NaCl), while the diurnal rhythm remained unchanged; (3) For hydroponic D. sanderiana, under simulated drought (PEG 6000) and salt stress (NaCl), the rate of decrease in root pressure accelerated with increasing concentrations of PEG 6000 and NaCl, displaying diurnal rhythm at low concentrations but losing this pattern at high concentrations; (4) Under room temperature, root pressure of hydroponic D. sanderiana decreased with lower water temperature and increased with higher temperatures, but did not increase at 40; low concentrations of nitrate (KNO3) generally increased root pressure, however, root pressure decreased when con-centration reached 4 g{middle dot}L-1, and the diurnal rhythm remained unaffected; (5) For hydroponic D. sanderiana, the presence of leaves on the stem below the root pressure measurement point resulted in significantly lower root pressure compared to plants without leaves on the stem, while still maintaining a regular diurnal rhythm. Removal of all root hairs and the entire root system led to a rapid decline in root pressure to negative values, followed by a slow recovery to around 0 kPa. In conclusion, relative to its height, the root pressure of D. sanderiana is sufficient to transport water required for transpiration to the top of the plant, effectively countering the impact and function of transpirational pull. Moreover, root pressure shows a stable diurnal rhythm of higher values during the day and lower at night. Environmental factors such as drought, salinity stress, temperatures, mineral con-centration, and changes in leaves and roots significantly affect root pressure.

ecology↗

bioRxiv2024

Unveiling Tissue Structure and Tumor Microenvironment from Spatially Resolved Transcriptomics by Hypergraph Learning

Spatial omics technologies have revolutionized life sciences by enabling the simultaneous acquisition of biomolecular and spatial information. Identifying spatial patterns is crucial for understanding organ development and tumor microenvironments. However, the emergence of diverse spatial omics resolutions in these technologies has made it challenging to accurately characterize spatial domains at finer resolutions. To address this, we propose HyperSTAR, a hypergraph-based method designed to precisely identify spatial domains across varying resolutions by leveraging higher-order relationships among spatially adjacent tissue programs. Specifically, a gene expression-guided hyperedge decomposition module is introduced to refine the hypergraph structure to accurately delineate spatial domains boundaries. Additionally, a hypergraph attention convolutional neural network is designed to adaptively learn the importance of each hyperedge, enhancing the models ability to capture complex higher-order relationships within spatially neighboring multi-spots and/or single cells. HyperSTAR outperforms existing graph neural network models in tasks such as uncovering tissue substructures, inferring spatiotemporal patterns, and denoising spatially resolved gene expressions. It effectively handles diverse spatial omics data types and scales seamlessly to large datasets. The method successfully reveals spatial heterogeneity in breast cancer sections, with findings validated through functional and survival analyses of independent clinical data. HyperSTAR represents a significant advancement in spatial omics analysis, representing a robust tool for exploring complex spatial patterns across varying resolutions and data types. Its ability to capture intricate higher-order relationships among spatially neighboring spots/cells makes it an invaluable tool for advancing research in life sciences, particularly in cancer and developmental biology.

bioinformatics↗

bioRxiv2024

Nerve injury converts Schwann cells in a persisting repair state in human neuroma tissue

Peripheral nerve injury (PNI) induces neuroma formation at the severed nerve stump resulting in impaired nerve regeneration and functional recovery in patients. So far, molecular mechanisms and cell types present in the neuroma impeding on regeneration have only sparsely been analyzed. Herein we compare resected human neuroma tissue with intact donor nerves from the same patient. Neuroma from several post-injury timepoints (1-13 months) were included, thereby allowing for temporal correlation with molecular and cellular processes. We observed reduced axonal area and percentage of myelin producing Schwann cells (SCs) compared to intact nerves. However, total SOX10 positive SC numbers were comparable. Notably, markers for SCs in a repair mode including c-JUN and SHH (sonic hedgehog) and SC proliferation (pH3) were upregulated in neuroma, suggesting presence of SCs in repair rather than differentiated status. In agreement, in neuroma, pro-regenerative markers such as phosphorylated i.e. activated CREB (pCREB), ATF3, GAP43 and SCG10 were upregulated. Neuroma tissue was infiltrated by several types of macrophages. Finally, when taken in culture, neuroma SCs were indistinguishable from controls SCs with regard to proliferation and morphology. However, cultured neuroma SCs retained a different molecular signature from control SCs including increased inflammation and reduced gene expression for differentiation markers such as myelin genes. In summary, human neuroma tissue consists of SCs with a repair status and is infiltrated strongly by several types of macrophages.

neuroscience↗

bioRxiv2024

A deep learning-based model integrating base-pairing patterns improves microRNA target site prediction

Micro RNAs (miRNAs) are short non-coding RNAs involved in various cellular processes, playing a crucial role in gene regulation. Identifying miRNA targets remains a central challenge and is pivotal for elucidating the complex gene regulatory networks. Traditional computational approaches have predominantly focused on identifying miRNA targets through perfect Watson-Crick base pairings within the seed region, referred to as canonical sites. However, emerging evidence suggests that perfect seed matches are not a prerequisite for miRNA-mediated regulation, underscoring the importance of also recognizing imperfect, or non-canonical, sites. To address this challenge, we propose Mimosa, a new computational approach that employs the Transformer framework to enhance the prediction of miRNA targets. Mimosa distinguishes itself by integrating contextual, positional, and base-pairing information to capture in-depth attributes, thereby improving its predictive capabilities. Its unique ability to identify non-canonical base-pairing patterns makes Mimosa a standout model, reducing the reliance on pre-selecting candidate targets. Mimosa achieves superior performance in gene-level predictions and also shows impressive performance in site-level predictions across various non-human species through extensive benchmarking tests. To facilitate research efforts in miRNA targeting, we have developed an easy-to-use web server for comprehensive end-to-end predictions, which is publicly available at http://monash.bioweb.cloud.edu.au/Mimosa/.

bioinformatics↗

bioRxiv2024

scRCA: a Siamese network-based pipeline for the annotation of cell types using imperfect single-cell RNA-seq reference data

A critical step in the analysis of single-cell transcriptomic (scRNA-seq) data is the accurate identification and annotation of cell types. Such annotation is usually conducted by comparative analysis with known (reference) data sets - which assumes an accurate representation of cell types within the reference sample. However, this assumption is often incorrect, because factors, such as human errors in the laboratory or in silico, and methodological limitations, can ultimately lead to annotation errors in a reference dataset. As current pipelines for single-cell transcriptomic analysis do not adequately consider this challenge, there is a major demand for a computational pipeline that achieves high-quality cell type annotation using imperfect reference datasets that contain inherent errors (often referred to as "noise"). Here, we built a Siamese network-based pipeline, termed scRCA, that achieves an accurate annotation of cell types employing imperfect reference data. For researchers to decide whether to trust the scRCA annotations, an interpreter was developed to explore the factors on which the scRCA model makes its predictions. We also implemented 3 noise-robust losses-based cell type methods to improve the accuracy using imperfect dataset. Benchmarking experiments showed that scRCA outperforms the proposed noise-robust loss-based methods and methods commonly in use for cell type annotation using imperfect reference data. Importantly, we demonstrate that scRCA can overcome batch effects induced by distinctive single cell RNA-seq techniques. We anticipate that scRCA (https://github.com/LMC0705/scRCA) will serve as a practical tool for the annotation of cell types, employing a reference dataset-based approach.

bioinformatics↗

bioRxiv2024

Epitope-anchored contrastive transfer learning for paired CD8+ T cell receptor-antigen recognition

Understanding the mechanisms of T-cell antigen recognition that underpin adaptive immune responses is critical for the development of vaccines, immunotherapies, and treatments against autoimmune diseases. Despite extensive research efforts, the accurate identification of T cell receptor (TCR)-antigen binding pairs remains a significant challenge due to the vast diversity and cross-reactivity of TCRs. Here, we propose a deep-learning framework termed Epitope-anchored Contrastive Transfer Learning (EPACT) tailored to paired human CD8+ TCRs from single-cell sequencing data. Harnessing the pre-trained representations and the contrastive co-embedding space, EPACT demonstrates state-of-the-art model generalizability in predicting TCR binding specificity for unseen epitopes and distinct TCR repertoires, offering potential values for practical outcomes in real-world scenarios. The contrastive learning paradigm achieves highly precise predictions for immunodominant epitopes and facilitates interpretable analysis of epitope-specific T cells. The TCR binding strength predicted by EPACT aligns well with the surge in spike-specific immune responses targeting SARS-CoV-2 epitopes after vaccination. We further fine-tune EPACT on TCR-epitope structural data to decipher the residue-level interactions involved in T-cell antigen recognition. EPACT not only exhibits superior capabilities in quantifying inter-chain distance matrices and identifying contact residue pairs but also corroborates the presence of molecular mimicry across multiple tumor-associated antigens. Together, EPACT can serve as a useful AI approach with significant potential in practical applications and contribute toward the development of TCR-based diagnostics and immunotherapies.

bioinformatics↗

bioRxiv2024

Applying multi-state modeling using AlphaFold2 for kinases and its application for ensemble screening

Structure-based virtual screening (SBVS) is a pivotal computational approach in drug discovery, enabling the identification of potential drug candidates within vast chemical libraries by predicting their interactions with target proteins. The SBVS relies on the receptor protein structures, making it sensitive to structural variations. Kinase, one of the major drug targets, is known as one of the typical examples of an active site conformation change caused by the type of binding inhibitors. Examination of human kinase structures shows that the majority of conformations have the DFGin state. Thus, SBVS using the structures might cause a favor of type of ligand type I inhibitors, bind to the DFGin state, rather than finding the diverse scaffolds. Recent advances in protein structure prediction, such as AlphaFold2 (AF2), offer promising solutions but may still be possibly influenced by the structural bias in existing templates. To address these challenges, we introduce a multi-state modeling (MSM) protocol for kinase structures. We apply MSM to AF2 by providing state-specific templates, allowing us to overcome structural biases and thus apply them to kinase SBVS. We benchmarked our MSM models in three categories: quality of predicted models, reproducibility of ligand binding poses, and identification of hit compounds by ensemble SBVS. The results demonstrate that MSM-generated models exhibit comparable or improved structural accuracy compared to standard AF2 models. We also show that MSM models enhance the accuracy of cognate docking, effectively capturing the interactions between kinases and their ligands. In virtual screening experiments using DUD-E compound libraries, our MSM approach consistently outperforms standard AF2 modeling. Notably, MSM-based ensemble screening excels in identifying diverse hit compounds for kinases with structurally diverse active sites, surpassing standard AF2 models. We highlight the potential of MSM in broadening the scope of kinase inhibitor discovery by facilitating the identification of chemically diverse inhibitors. Author SummaryOne of the main problems with structure-based virtual screening is structural flexibility. Ensemble screening is one of the conventional approaches to solving the issue. Gathering experimental structures or molecular simulations could be used to compile the receptor structures. Recent developments in algorithms for predicting protein structures, like AlphaFold2, suggest that different receptor conformations could be produced. However, the prediction approaches produce biased structures because of the bias in the structure database. In order to solve the problem, we developed a protocol called multi-state modeling for kinases. Rather than supplying multiple sequence alignments as an input, we gave the AlphaFold2 a specific template structure and the sequence alignment between the template and query. Our findings imply that our technique can yield a particular structural state of interest with an enhanced or comparable structural quality to AlphaFold2 and predict highly accurate protein-ligand complex structures. Lastly, compared to the typical AlphaFold2 models, ensemble screening using the multi-state modeling approach improves the structure-based virtual screening performance, particularly for diverse active molecular scaffolds.

bioinformatics↗

bioRxiv2024

Investigation of the Fasciola Cinereum, Absent in BTBR mice, and Comparison with the Hippocampal Area CA2

The arginine vasopressin 1b receptor (Avpr1b) plays an important role in social behaviors including social learning, memory, and aggression, and is known to be a specific marker for the cornu ammonis area 2 (CA2) regions of the hippocampus. The fasciola cinereum (FC) is an anatomical region in which Avpr1b expressing neurons are prominent, but the functional roles of the FC have yet to be investigated. Surprisingly, the FC is absent in the inbred BTBR T+tf/J (BTBR) mouse strain used to study core behavioral deficits of autism. Here, we characterized and compared transcriptomic expression profiles using single nucleus RNA sequencing and identified 7 different subpopulations and heterogeneity within the dorsal CA2 (dCA2) and FC. Mef2c, involved in autism spectrum disorder, is more highly expressed in the FC. Using Hiplex in situ hybridization, we examined the neuroanatomical locations of these subpopulations in the proximal and distal regions of the hippocampus. Anterograde tracing of Avpr1b neurons specific for the FC showed projections to the IG, dCA2, lacunosum molecular layer of CA1, dorsal fornix, septofibrial nuclei, and intermediate lateral septum (iLS). In contrast to the dCA2, inhibition of Avpr1b neurons in the FC by the inhibitory DREADD system during behavioral testing did not impair social memory. We performed single nucleus RNA sequencing in the dCA2 region and compared between wildtype (WT) and BTBR mice. We found that transcriptomic profiles of dCA2 neurons between BTBR and WT mice are very similar as they did not form any unique clusters; yet, we found there were differentially expressed genes between the dCA2s of BTBR and WT mice. Overall, this is a comprehensive study of the comparison of Avpr1b neuronal subpopulations between the FC and dCA2. The fact that FC is absent in BTBR mice, a mouse model for autism spectrum disorder, suggests that the FC may play a role in understanding neuropsychiatric disease.

neuroscience↗

bioRxiv2024

Targeting G9a translational mechanism of SARS-CoV-2 pathogenesis for multifaceted therapeutics of COVID-19 and its sequalae

By largely unknown mechanism(s), SARS-CoV-2 hijacks the host translation apparatus to promote COVID-19 pathogenesis. We report that the histone methyltransferase G9a noncanonically regulates viral hijacking of the translation machinery to bring about COVID-19 symptoms of hyperinflammation, lymphopenia, and blood coagulation. Chemoproteomic analysis of COVID-19 patient peripheral mononuclear blood cells (PBMC) identified enhanced interactions between SARS-CoV-2-upregulated G9a and distinct translation regulators, particularly the N6-methyladenosine (m6A) RNA methylase METTL3. These interactions with translation regulators implicated G9a in translational regulation of COVID-19. Inhibition of G9a activity suppressed SARS-CoV-2 replication in human alveolar epithelial cells. Accordingly, multi-omics analysis of the same alveolar cells identified SARS-CoV-2-induced changes at the transcriptional, m6A-epitranscriptional, translational, and post-translational (phosphorylation or secretion) levels that were reversed by inhibitor treatment. As suggested by the aforesaid chemoproteomic analysis, these multi-omics-correlated changes revealed a G9a-regulated translational mechanism of COVID-19 pathogenesis in which G9a directs translation of viral and host proteins associated with SARS-CoV-2 replication and with dysregulation of host response. Comparison of proteomic analyses of G9a inhibitor-treated, SARS-CoV-2 infected cells, or ex vivo culture of patient PBMCs, with COVID-19 patient data revealed that G9a inhibition reversed the patient proteomic landscape that correlated with COVID-19 pathology/symptoms. These data also indicated that the G9a-regulated, inhibitor-reversed, translational mechanism outperformed G9a-transcriptional suppression to ultimately determine COVID-19 pathogenesis and to define the inhibitor action, from which biomarkers of serve symptom vulnerability were mechanistically derived. This cell line-to-patient conservation of G9a-translated, COVID-19 proteome suggests that G9a inhibitors can be used to treat patients with COVID-19, particularly patients with long-lasting COVID-19 sequelae.

immunology↗