Biology subjects

Song, Z.

Publications and source records attributed to Song, Z..

At least 73 records · Page 4Linked to original sources

bioRxiv2024

RNAi-mediated HBV antigen shutdown enhances the antiviral immune effects of PEGIFNalpha via altering T and B cell crosstalk

PEGylated interferon- (PEGIFN) demonstrates promising therapeutic outcomes against chronic hepatitis B (CHB), whereas patient response to PEGIFN therapy remains unsatisfied. Shutdown of hepatitis B virus (HBV) antigens by RNA interference (RNAi) could enhance PEGIFN efficacy in CHB patients, whereas the underlying immunological mechanisms remain obscure. We performed studies by utilizing our newly established extracellular humanized IFNAR (IFNAR-hEC) mice. An in-house constructed small interfering RNAs (GalNac-siHBV) was administrated to mice either alone or in combination with PEGIFN. The phenotypic and functional characteristics of peripheral and organ-specific immune cells were assessed by flow cytometry, ELISpot, RNA sequencing (RNA-seq), and single-cell RNA-seq (scRNA-seq) analysis. Our results demonstrated that combined treatment with PEGIFN and RNAi exerted a synergistic and prolonged inhibition of HBsAg ([~]4log10 IU/mL, vs PBS) and induced a higher incidence of HBsAg seroconversion ([~]30%), comparing with either monotreatment. Mechanistically, combined therapy improved the functionality of global T and B cells, triggered increased anti-HBs producing B cells, and enhanced IFN{gamma}-producing T cells. scRNA-seq analysis revealed that the combined therapy reduced inhibitory B cell-B cell interaction, enhanced MHC-I signaling mediated T cell-T cell communication, and improved T cell-B cell crosstalk, thus improving the functionality of T and B cells. Enhanced MHC-II signaling networks across B cells and hepatocytes/Cd8+ T cells further promoted HBsAg seroconversion in the combined treatment groups. These results together provided scientific rationale and lessons for the combination of the two towards better therapeutic efficacy. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=133 SRC="FIGDIR/small/626539v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@84785aorg.highwire.dtl.DTLVardef@f984fborg.highwire.dtl.DTLVardef@189a966org.highwire.dtl.DTLVardef@d081f9_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIShutting down HBsAg through RNA interference augmented the antiviral immune effects of PEGIFN in chronic HBV-carrier IFNAR-hEC mice. C_LIO_LICombined RNAi plus PEGIFN augmented the functionality of T cells, promoted B cell activation and class switch, but also exerted some suppressive effects on B cells. C_LIO_LIReduced inhibitory B cell-B cell interaction, enhanced MHC-I signaling between T cells and T cells, and improved T cell-B cell crosstalk, improved the functionality of T cells and B cells. C_LIO_LIEnhanced MHC-II signaling networks across B cells and hepatocytes/ Cd8+ T cells further promoted HBsAg seroconversion in RNAi plus PEGIFN combined treatment groups. C_LI

microbiology↗

bioRxiv2024

Female budgerigars prefer males with foraging skills that differ from their own

Foraging skills influence food intake and may therefore also play a role in mate choice decision. Previous empirical work has shown that individuals benefit from being in groups that include individuals with a variety of foraging skills as this increases foraging success. This idea, formalized in the skill-pool hypothesis, may extend to mate choice. Diverse foraging skills can expand the foraging niche of a pair and benefit offspring through enhanced parental provisioning, and exposure to a broader foraging skillset. To test this idea, we trained captive female and male budgerigars to solve one of two different novel foraging puzzle boxes. Then, females simultaneously observed two males that could solve either the same or the other box, and assessed female preferences in a binary mate choice apparatus. Females preferred males with foraging skills that differed from their own, independent of the skill type and the number of times males solved the foraging puzzle. These findings show that foraging skills can influence social preferences, including in a mate choice context, and support intraspecific diversity in foraging skills.

animal behavior and cognition↗

bioRxiv2024

Accurate Detection of Tandem Repeats from Error-Prone Sequences with EquiRep

A tandem repeat is a sequence of nucleotides that occurs as multiple contiguous and near-identical copies positioned next to each other. These repeats play critical roles in genetic diversity, gene regulation, and are strongly linked to various neurological and developmental disorders. While several methods exist for detecting tandem repeats, they often exhibit low accuracy when the repeat unit length increases or the number of copies is low. Furthermore, methods capable of handling highly mutated sequences remain scarce, highlighting a significant opportunity for improvement. We introduce EquiRep, a tool for accurate detection of tandem repeats from erroneous sequences. EquiRep estimates the likelihood of positions originating from the same position in the unit by self-alignment followed by a novel approach that refines the estimation. The built equivalent classes and the consecutive position information will be then used to build a weighted graph, and the cycle in this graph with maximum bottleneck weight while covering most nucleotide positions will be identified to reconstruct the repeat unit. We test EquiRep on simulated and real HOR and RCA datasets where it consistently outperforms or is comparable to state-of-the-art methods. EquiRep is robust to sequencing errors, and is able to make better predictions for long units and low frequencies which underscores its broad usability for studying tandem repeats.

bioinformatics↗

bioRxiv2024

Mapping AAV capsid sequences to functions through function-guided in silico evolution

Artificial intelligence (AI) offers significant potential to accelerate the functional engineering of adeno-associated virus (AAV) capsids in a time- and cost-efficient manner. However, existing approaches lack the ability to systematically map capsid sequences to multifunctional properties. To address this challenge, we developed ALICE, a knowledge-driven platform for in silico AAV capsid engineering that directly maps sequence to multiple functions. Our method incorporating a heuristic algorithm with contrastive learning principles, termed function-guided evolution (FE), to iteratively optimize high-performing capsid sequences generated by a naive language model toward desired functions. We elucidate FEs evolutionary mechanism, demonstrating its ability to navigate a function-guided landscape and generate capsids with tailored properties. After validating ALICEs ability to design murine-specific AAV capsids targeting Ly6a/Ly6c1 with limited training data, we developed ALICE-X by integrating a curriculum learning strategy. This upgrade facilitated exploration of sequence space beyond existing wet-lab datasets, enabling successful targeting of the human transferrin receptor 1 (hTfR1). The resulting engineered AAV variant, AAV.ALICE-H3, demonstrated improved viability, specific receptor targeting, and [~] 251-fold enhanced CNS tropism in human TFRC knock-in (hTFRC KI) mice compared to wildtype controls. This interpretable, knowledge-driven framework advances in silico AAV capsid design, offering broad applicability for translational gene therapy.

bioengineering↗

bioRxiv2024

Mapping snoRNA-target RNA interactions in an RNA binding protein-dependent manner with chimeric eCLIP

Small nucleolar RNAs (snoRNAs) are non-coding RNAs that function in ribosome and spliceosome biogenesis, primarily by guiding modifying enzymes to specific sites on ribosomal RNA (rRNA) and spliceosomal RNA (snRNA). However, many orphan snoRNAs remain uncharacterized, with unidentified or unvalidated targets, and studies on additional snoRNA-associated proteins are limited. We adapted an enhanced chimeric eCLIP approach to comprehensively profile snoRNA-target RNA interactions using both core and accessory snoRNA binding proteins as baits. Using core snoRNA binding proteins, we confirmed most annotated snoRNA-rRNA and snoRNA-snRNA interactions in mouse and human cell lines and called novel, high-confidence interactions for orphan snoRNAs. While some of these interactions result in chemical modification, others may have modification-independent functions. We then showed that snoRNA ribonucleoprotein complexes containing certain accessory proteins, like WDR43 and NOLC1, enriched for specific subsets of snoRNA-target RNA interactions with distinct roles in ribosome and spliceosome biogenesis. Notably, we discovered that SNORD89 guides 2-O-methylation at two neighboring sites in U2 snRNA that are important for activating splicing, but also appear to ensure imperfect splicing for a subset of near-constitutive exons. Thus, chimeric eCLIP of snoRNA-associating proteins enables a comprehensive framework for studying snoRNA-target interactions in an RNA binding protein-dependent manner, revealing novel interactions and regulatory roles in RNA biogenesis.

genomics↗

bioRxiv2024

Protocol for Simulating the Effect of THz Electromagnetic Field on Ion Channels

Terahertz (THz) electromagnetic fields are increasingly recognized for their crucial roles in various aspects of medical research and treatment. Recent computational studies have demon-strated that THz waves can modulate ion channel function by interacting with either the channel proteins or the bound ions through distinct mechanisms. Here we outline a universal simulation protocol to identify the THz frequencies that may affect ion channels, which consists of frequency spectrum analysis and ion conductance analysis. Following this protocol, we studied the effect of THz field on a CaV channel and found a broad frequency band in 1 to 20 THz range. We believe that this protocol, along with the identified characteristic frequencies, will provide a theoretical foundation for future terahertz experimental studies.

biophysics↗

bioRxiv2024

The Effect of THz Electromagnetic Field on the Conductance of Potassium and Sodium Channels

Ion channels are essential to various physiological processes and their defects are associated with many diseases. Previous research has revealed that Terahertz electromagnetic field can alter the channel conductance by affecting the motion of chemical groups of ion channels, and hence regulate the electric signals of neurons. In this study, we conducted molecular dynamics simulations to systematically investigate the effects of terahertz electromagnetic fields on the conductance of voltage-gated potassium and sodium channels, particularly focusing on the bound ions in the selectivity filters that have not been studied previously. Our results identified multiple new characteristic frequencies and showed that 1.4, 2.2, or 2.9 THz field increases the conductance of Kv1.2, and 2.5 or 48.6 THz field increases the conductance of Nav1.5. The conductance-enhancing effects are specific to the frequencies and directions of the electric field, which are determined by the intrinsic oscillation motions of the permeating ions in the selectivity filter or certain chemical groups of the ion channels. The amplitude of the THz field positively correlates with the change in ion conductance. Therefore, this study demonstrates that THz fields can specifically regulate ion channel conductances, which may carry great potential in biomedical applications.

biophysics↗

bioRxiv2024

A single-molecule nanopore sequencing platform

Nanopore sequencing, a third-generation sequencing technology, has revolutionized the gene sequencing industry with its advantages of long reads, fast speed, real-time sequencing and analysis, and potential in detecting base modifications. This technology allows researchers to sequence longer DNA fragments in a single read, providing more comprehensive genomic information compared to previous methods. Nanopore sequencing operates on electrical signals generated by a nanopore embedded in a membrane separating two electrolyte-filled chambers. When single-stranded DNA (ssDNA) passes through the nanopore, it creates variations in the current that correspond to different DNA bases. By analyzing these current fluctuations with machine learning algorithms, the DNA sequence can be determined. In this study, we introduced several improvements to nanopore sequencing, including nanopore local chemistry sequencing, novel motor and pore proteins, chip design, and basecalling algorithms. Our new nanopore sequencing platform, CycloneSEQ, demonstrated long-duration sequencing (107 hours) on a single chip with high yield (>50 Gb). In human genomic DNA sequencing, CycloneSEQ was able to produce long reads with N50 33.6 kb and modal identity 97.0%. Preliminary findings on human whole-genome de novo assembly, variant calling, metagenomics sequencing, and single-cell RNA sequencing have further highlighted CycloneSEQs potential across different areas of genomics.

genomics↗

bioRxiv2024

The Identification Markers of activated myofibroblast subsets in the Human Lung Fibrosis Ecosystem via integrated omics Analysis

BackgroundThe aberrant remodeling of the extracellular matrix (ECM) is closely associated with lung fibrosis. However, the mechanisms underlying ECM remodeling in pulmonary fibrosis (PF) remain unclear. The advent of single-cell RNA sequencing (scRNA-seq) has provided valuable insights into the diverse phenotypic and functional characteristics of human PF. Nevertheless, the dynamic of ECM remodeling in terms of ECM synthesizing and the potential activating markers of myofibroblasts in the human PF microenvironment still needs to be investigated. MethodsWe performed integrative scRNA-seq analyses on high-fidelity PF data from a public platform by filtering out the low-quality counts and doublets using two doublet prediction methods. Next, we investigated the dynamic of the ECM signature in diverse cells in PF and screened the potential markers of myofibroblasts via fitting a successful polynomial regression model. Finally, the markers of activated myofibroblasts were identified using bulk RNA-seq of pulmonary tissue. ResultsFirst, we depicted the pathogenic landscape and demonstrated the heterogeneity of ECM in PF by integratively analyzing single-cell RNA-seq data, and we hypothesized that myofibroblasts played a significant role in ECM formation. Second, our results successfully displayed the biological dynamic changes of ECM and investigated the 73 positive correlated genes of myofibroblasts in PF via a polynomial regression model. Then, the bulk RNA-seq results further identified eight new activating markers of myofibroblasts, such as MFAP2, MXRA5, and LRRC17 via transcriptomic signature, correlation and ROC scores. Finally, the results of cell-cell interaction indicated that myeloid cells may be involved in regulating ECM remodeling through proliferation mediated by myofibroblasts that secrete POSTN, suggesting that ECM remodeling in PF is a complex and multi-participated process. ConclusionsIn summary, we provided insights into the contributions of ECM in human PF by integrative analysis and highlighted potential clinical utilities of myofibroblast subsets as therapeutic targets.

bioinformatics↗

bioRxiv2024

Bird brains fit the bill: morphological diversification and the evolution of avian brain size

Brain size varies greatly across and even within lineages. Attempts to explain this variation have mostly focused on the role of specific cognitive demands in the social or ecological domain. However, their predictive power is modest, whereas the effects of additional functions, especially sensory information processing and motor control, on brain size remain underexplored. Here, using phylogenetic comparative models, we show that the socio-cognitive and eco-cognitive demands do not have direct links to relative brain size (that is the residual from a regression against body mass) once morphological features are taken into account. Thus, specific cognitive abilities linked to social life or ecology play a much smaller role in brain size evolution than generally assumed. Instead, parental provisioning, generation length, and especially eye size and beak and leg morphology have a strong direct link to relative brain size. Phylogenetic lability analyses suggest that morphological diversification preceded changes in the rate of brain size evolution and greater visual input, and thus that morphological diversification opened up specialized niches where efficient foraging could produce energy surpluses. Increases in brain size provided general behavioural flexibility, which improved survival by reducing interspecific competition and predation, and was made possible by intense parental provisioning. Indeed, comparative analyses in a subset of species show that thicker beaks are associated with larger size of brain regions involved in behavioural flexibility (telencephalon, pallium). Thus, morphological evolution had a key role in niche diversification, which subsequently may have facilitated the evolution of general cognitive flexibility.

evolutionary biology↗

bioRxiv2024

H2A.Z deposition at meiotic prophase I underlies homologous recombination and pachytene genome activation during male meiosis

Accurate meiotic progression is important for gamete formation and the generation of genetic diversity. However, little is known about the identity of chromatin regulators that underlie mammalian meiosis in vivo. Here, we identify the multifaceted functions of the chromatin remodeler ZNHIT1 in governing meiosis. The expression of Znhit1 gradually increases during the meiotic prophase, and Znhit1 knockout in spermatocytes results in arrested pachytene development, impaired DNA double-strand break repair, and defective homologous recombination. Single-cell RNA sequencing and transcriptome analysis reveal that Znhit1 loss dysregulates meiotic transcriptional programs at the pachytene stage. Chromatin immunoprecipitation data show that ZNHIT1 is needed for the incorporation of the histone variant H2A.Z into pachytene chromatin. Moreover, we found that H2A.Z cooperates with the transcription factor A-MYB to co-bind DNA elements and control gene activity. Our findings provide insights into the regulatory mechanisms governing meiotic progression and highlight ZNHIT1 as a critical regulator of meiotic progression.

developmental biology↗

bioRxiv2024

Parallel Analyses by Mass Spectrometry (MS) and Reverse Phase Protein Array (RPPA) Reveal Complementary Proteomic Profiles in Triple-Negative Breast Cancer (TNBC) Patient Tissues and Cell Cultures

High-plex proteomic technologies have made substantial contributions to mechanism studies and biomarker discovery in complex diseases, particularly cancer. Despite technological advancements, inherent limitations in individual proteomic approaches persist, impeding the achievement of comprehensive quantitative insights into the proteome. In this study, we employed two widely used proteomic technologies, Mass Spectrometry (MS) and Reverse Phase Protein Array (RPPA) to analyze identical samples, aiming to systematically assess the outcomes and performance of the different technologies. Additionally, we sought to establish an integrated workflow by combining these two proteomic approaches to augment the coverage of protein targets for discovery purposes. We used fresh frozen tissue samples from triple-negative breast cancer (TNBC) and cell line samples to evaluate both technologies and implement this dual-proteomic strategy. Using a single-step protein denaturation and extraction protocol, protein samples were subjected to reverse phase chromatography (LC) followed by electrospray ionization (ESI)-mediated MS/MS for proteomic profiling. Concurrently, identical sample aliquots were analyzed by RPPA for profiling of over 300 proteins and phosphoproteins that are in key signaling pathways or druggable targets in cancer. Both proteomic methods demonstrated the expected ability to differentiate samples by groups, revealing distinct proteomic patterns under various experimental conditions, albeit with minimal overlap in identified targets. Mechanism-based analysis uncovered divergent biological processes identified with the two proteomic technologies, capitalizing on their complementary exploratory potential.

cancer biology↗

bioRxiv2024

Evaluation of enzyme activity predictions for variants of unknown significance in Arylsulfatase A

Continued advances in variant effect prediction are necessary to demonstrate the ability of machine learning methods to accurately determine the clinical impact of variants of unknown significance (VUS). Towards this goal, the ARSA Critical Assessment of Genome Interpretation (CAGI) challenge was designed to characterize progress by utilizing 219 experimentally assayed missense VUS in the Arylsulfa-tase A (ARSA) gene to assess the performance of community-submitted predictions of variant functional effects. The challenge involved 15 teams, and evaluated additional predictions from established and recently released models. Notably, a model developed by participants of a genetics and coding bootcamp, trained with standard machine-learning tools in Python, demonstrated superior performance among sub-missions. Furthermore, the study observed that state-of-the-art deep learning methods provided small but statistically significant improvement in predictive performance compared to less elaborate techniques. These findings underscore the utility of variant effect prediction, and the potential for models trained with modest resources to accurately classify VUS in genetic and clinical research.

genetics↗

bioRxiv2024

Learning Gaussian Graphical Models from Correlated Data

Gaussian Graphical Models (GGM) have been widely used in biomedical research to explore complex relationships between many variables. There are well established procedures to build GGMs from a sample of independent and identical distributed observations. However, many studies include clustered and longitudinal data that result in correlated observations and ignoring this correlation among observations can lead to inflated Type I error. In this paper, we propose a Bootstrap algorithm to infer GGM from correlated data. We use extensive simulations of correlated data from family-based studies to show that the Bootstrap method does not inflate the Type I error while retaining statistical power compared to alternative solutions. We apply our method to learn the GGM that represents complex relations between 47 Polygenic Risk Scores generated using genome-wide genotype data from a family-based study known as the Long Life Family Study. By comparing it to the conventional methods that ignore within-cluster correlation, we show that our method controls the Type I error well in this real example.

bioinformatics↗

bioRxiv2024

Whole-genome resequencing revealed the Origin and Domestication of Chinese Domestic Rabbits

The evolutionary and genetic origins of Chinese indigenous rabbits (Oryctolagus cuniculus) remain largely unclear, despite being among the most recently domesticated animals. We sequenced the whole genomes of 142 individual rabbits and collected 25 resequencing accessions from the NCBI, representing six Chinese indigenous breeds, three other domesticated breeds (serving as a bridge between European wild-type and Chinese domestic populations), and two wild rabbit populations from the Iberian Peninsula and Southern France. Population and demographic analyses suggest that Chinese domestic rabbits are most likely descendants of O. c. cuniculus, native to France 800-1500 years ago. These rabbits likely first arrived in the southeast coastal areas of China through trade before spreading to inland regions. Additionally, there may be other origins for Chinese domestic rabbits. We observed considerable variation in the genetic makeup of maternal ancestry between Chinese domestic rabbits and European wild populations, with Chinese rabbits possessing unique mitochondrial haplotypes. Our analysis also highlights selective sweeps on genes affecting brain and neuronal development, which may have been under strong positive selection during domestication; genes related to starch digestion and fat metabolism, suggesting an evolutionary adaptation to digest high-starch diets; and the white coat phenotype in rabbits, resulting from selection at the melanogenesis-associated transcription factor locus. Overall, our data provide comprehensive insights into the origin and domestication of rabbits and lay the foundation for genome-based breeding.

evolutionary biology↗

bioRxiv2024

Short-term memory, attentional control and brain size in primates

Brain size variability in primates has been attributed to various domain-specific socio-ecological factors. A recently published large-scale study of short-term memory abilities in 41 primate species [1] did not find any correlations with 11 different proxies of external cognitive demands. Here we found that the interspecific variation in test performance shows correlated evolution with total brain size, with the relationship becoming tighter as species with small sample sizes were successively removed, whereas it was not predicted by the often-used encephalization quotient (EQ). In a subsample, we also found that the sizes of brain region thought to be involved in short-term memory did not predict performance better than did overall brain size. The dependence on brain size suggests that domain-general cognitive processes underlie short-term memory as tested in [1]. These results support the emerging notion that comparative studies of brain size do not generally identify domain-specific cognitive adaptations, but rather reveal varying selection on domain-general cognitive abilities. Finally, because attentional processes beyond short- term memory also affected test performance, we suggest that the delayed response test can be refined.

animal behavior and cognition↗

bioRxiv2024

Non-Invasive dsRNA Delivery via Feeding for Effective Gene Silencing in Teleost Fish: A Novel Approach in the Study of Gene Function Analysis

RNA interference is a powerful technique for gene silencing, involving post-transcriptional regulation of gene expression. Successful application of RNAi has been demonstrated in various organisms like nematodes, insects, and oysters by ingesting dsRNA-expressing bacteria. In this study, we attempted a non-invasive feeding method in the marine economy teleost fish, Sebastes schlegelii. To validate the feasibility of this approach in S. schlegelii, we utilized rotifers (Brachionus plicatilis), Artemia nauplii, and commercial pellet feed as vectors to deliver Escherichia coli strain HT115 engineered to express double-stranded RNA (dsRNA), targeting dead end (dnd) gene, known to be important for primordial germ cells (PGCs) migration and proliferation in fish. Our experimental results consistently showed that knockdown of the dnd gene effectively inhibited the development of PGCs in S. schlegelii, resulting in gonadal dysplasia and even sex reversal. This study marks a significant advancement in understanding gene function in teleost fish, laying the groundwork for future large-scale research in this field.

genetics↗

bioRxiv2024

yQTL Pipeline: a structured computational workflow for large scale quantitative trait loci discovery and downstream visualization

1Quantitative trait loci (QTL) denote regions of DNA whose variation is associated with variations in quantitative traits. QTL discovery is a powerful approach to understand how changes in molecular and clinical phenotypes may be related to DNA sequence changes. However, QTL discovery analysis encompasses multiple analytical steps and the processing of multiple input files, which can be laborious, error prone, and hard to reproduce if performed manually. In order to facilitate and automate large-scale QTL analysis, we developed the yQTL Pipeline, where the y indicates the dependent quantitative variable being modeled. Prior to genome-wide association test, the pipeline supports the calculation or the direct input of pre-defined genome-wide principal components and genetic relationship matrix when applicable. User-specified covariates can also be provided. Depending on whether familial relatedness exists among the subjects, genome-wide association tests will be performed using either a linear mixed-effect model or a linear model. Using the workflow management tool Nextflow, the pipeline parallelizes the analysis steps to optimize run-time and ensure results reproducibility. In addition, a user-friendly R Shiny App is developed to facilitate result visualization. Upon uploading the result file, it can generate Manhattan plots of user-selected phenotype traits and trait-QTL connection networks based on user-specified p-value thresholds. We applied the yQTL Pipeline to analyze metabolomics profiles of blood serum from the New England Centenarians Study (NECS) participants. A total of 9.1M SNPs and 1,052 metabolites across 194 participants were analyzed. Using a p-value cutoff 5e-8, we found 14,983 mQTLs cumulatively associated with 312 metabolites. The built-in parallelization of our pipeline reduced the run time from [~]90 min to [~]26 min. Visualization using the R Shiny App revealed multiple mQTLs shared across multiple metabolites. The yQTL Pipeline is available with documentation on GitHub at https://github.com/montilab/yQTL-Pipeline.

bioinformatics↗