Biology subjects

Yang, Y.

Publications and source records attributed to Yang, Y..

At least 919 records · Page 51Linked to original sources

bioRxiv2022

Chemoenzymatic Measurement of Cell-surface Glycan in Single-cell Multiomics: LacNAc as an Example

Despite the rich information of a cells physiological state encoded in the dynamic changes of cell-surface glycans, methods of capturing glycosylation states at the single-cell level are quite limited. Here we report a chemoenzymatic single-cell N-acetyllactosamine (LacNAc) detection method via tagging the LacNAc with a specific DNA barcode. Compared to the lectin-based glycan detection, the chemoenzymatic labeling does not change the transcriptional status of immune cells and is more compatible with scRNA-seq. Integrated analysis of LacNAc and transcriptome of T cells at a single-cell level reveals that the quantity of cell-surface LacNAc is significantly upregulated in activated CD8+ T cells but maintained at the basal level in quiescent CD8+ T cells (i.e., naive and central memory T cells). Further analysis confirms that the LacNAc level is positively correlated to the glycolytic activity of CD8+ T cells at all statues. Taken together, our study demonstrates the feasibility of chemoenzymatic detection of cell-surface glycan in single-cell RNA sequencing-based multiomics with information of TCR sequence and cell-surface epitopes (i.e., scTCR and CITE-seq) and offers a new way to characterize the biological role of glycan in diversified physiological states.

immunology↗

bioRxiv2022

The tea plant (Camellia sinensis) FLOWERING LOCUS C-like gene CsFLC1 is correlated with bud dormancy and triggers early flowering in Arabidopsis

Flowering and bud dormancy are crucial stages in the life cycle of perennial angiosperms in temperate climates. MADS-box family genes are involved in many plant growth and development processes. Here, we identified 3 MADS-box genes in tea plant belonging to the FLOWERING LOCUS C (CsFLC) family. We monitored CsFLC1 transcription throughout the year and found that CsFLC1 was expressed at a higher level during the winter bud dormancy and flowering phases. To clarify the function of CsFLC1, we developed transgenic Arabidopsis thaliana plants heterologously expressing 35S::CsFLC1. These lines bolted and bloomed earlier than the WT (Col-0), and the seed germination rate was inversely proportional to the increased CsFLC1 expression level. RNA-seq of 35S::CsFLC1 transgenic Arabidopsis showed that many genes responding to ageing, flower development and leaf senescence were affected, and phytohormone-related pathways were especially enriched. According to the results of hormone content detection and RNA transcript level analysis, CsFLC1 controls flowering time possibly by regulating SOC1, AGL42, SEP3 and AP3 and hormone signalling, accumulation and metabolism. Our results suggest that CsFLC1 might play dual roles in flowering and winter bud dormancy and provide new insight into the molecular mechanisms of FLC in tea plants as well as other plant species. HighlightThree FLOWERING LOCUS C-like genes were identified in tea plants, among them CsFLC1 played dual roles in flowering and winter bud dormancy.

plant biology↗

bioRxiv2022

Magnetic Resonance Spectroscopy Spectral Registration Using Deep Learning

A novel convolutional neural network based spectral registration (CNN-SR) approach is introduced to achieve efficient and accurate simultaneous frequency-and-phase correction (FPC) of single-voxel MEGA-PRESS magnetic resonance spectroscopy (MRS) data. For this approach, one neural network was trained and validated using a published simulated and in vivo MEGA-PRESS MRS dataset with a wide-range of artificial frequency (0-20 Hz) and phase (0-90{degrees}) offsets applied. The proposed CNN-SR approach was subsequently tested and compared to the sequential FPC deep learning approaches and demonstrated more effective and accurate performance. Furthermore, a large random Gaussian signal-to-noise ratio (SNR 20 and SNR 2.5) and line broadening (0-20 ms) was introduced to the original simulated dataset to investigate our model performance compared to the other deep learning models. The testing showed that CNN-SR was a more accurate quantification tool and resulted in a lower SNR when compared with the other deep learning methods, due to having smaller mean absolute errors in both frequency and phase offset predictions. For Off spectra, the CNN-SR model was capable of correcting frequency offsets with 0.014 {+/-} 0.010 Hz and phase offsets with 0.104 {+/-} 0.076{degrees} absolute errors on average for unseen simulated data with SNR 20 and correcting frequency offsets with 0.678 {+/-} 0.883 Hz and phase offsets with 2.367 {+/-} 2.616{degrees} absolute errors on average at very low SNR (2.5) and line broadening (0-20 ms) introduced. In addition, we tested the simulated dataset with additional SNR and line broadening on a more refined model (CNN-SR+) where the pre-trained CNN-SR was further optimized by minimizing the difference between individual spectra and a common template with unsupervised learning. The performance on Off spectra was improved to 0.058 {+/-} 0.050 Hz for correcting frequency offsets and to 0.416 {+/-} 0.317{degrees} for correcting phase offsets. We further evaluated the ability of our model to process the published Big GABA in vivo dataset and CNN-SR+ achieved the best performance. Moreover, additional frequency and phase offsets (i.e., small, moderate, large) were applied to the in vivo dataset, and CNN-SR+ also demonstrated better performance for FPC when compared to the other deep learning models. These results indicate the utility of using deep learning for spectral registration and demonstrate the application of unsupervised learning in further improving the model to achieve state-of-the-art performance.

neuroscience↗

bioRxiv2022

Deep Neural Networks Predict MHC-I Epitope Presentation and Transfer Learn Neoepitope Immunogenicity

Identifying neoepitopes that elicit an adaptive immune response is a major bottleneck to developing personalized cancer vaccines. Experimental validation of candidate neoepitopes is extremely resource intensive, and the vast majority of candidates are non-immunogenic, making their identification a needle-in-a-haystack problem. To address this challenge, we present computational methods for predicting MHC-I epitopes and identifying immunogenic neoepitopes with improved precision. The BigMHC method comprises an ensemble of seven pan-allelic deep neural networks trained on peptide-MHC eluted ligand data from mass spectrometry assays and transfer learned on data from assays of antigen-specific immune response. Compared with four state-of-the-art classifiers, BigMHC significantly improves the prediction of epitope presentation on a test set of 45,409 MHC ligands among 900,592 random negatives (AUROC=0.9733, AUPRC=0.8779). After transfer learning on immunogenicity data, BigMHC yields significantly higher precision than seven state-of-the-art models in identifying immunogenic neoepitopes, making BigMHC effective in clinical settings. All data and code are freely available at https://github.com/KarchinLab/bigmhc.

bioinformatics↗

bioRxiv2022

An evolutionally conserved archaeal KEOPS complex functions in DNA repair

KEOPS is a multi-subunit protein complex conserved in eukaryotes and archaea. It is composed of Pcc1, Kae1, Bud32, Cgi121, and Gon7 in eukaryotes and is primarily involved in N6-threonylcarbamoyl adenosine (t6A) modification of tRNAs. Recently, KEOPS is reported to participate in homologous recombination repair in yeast. To characterize the KEOPS in archaea (aKEOPS), we conducted genetic and biochemical analyses of its encoding genes in the hyperthermophilic archaeon Saccharolobus islandicus. We show that aKEOPS also possesses five subunits, Pcc1, Kae1, Bud32, Cgi121, and Pcc1-like (or Gon7-like), just as eukaryotic KEOPS. Pcc1-like has physical interactions with Kae1 and Pcc1 and can mediate the monomerization of the dimeric subcomplex (Kae1-Pcc1-Pcc1-Kae1), suggesting that Pcc1-like is a functional homolog of the eukaryotic Gon7 subunit. Strikingly, none of the genes encoding aKEOPS subunits, including Pcc1 and Pcc1-like, can be deleted in the wild type and in a t6A modification complementary strain named TsaKI, implying that aKEOPS complex is essential for an additional cellular process in this archaeon. Knock-down of the Cgi121 subunit leads to severe growth retardance in the wild type which is partially rescued in TsaKI. These results suggest that aKEOPS plays an essential role independent of cellular t6A modification level. In addition, archaeal Cgi121 possesses dsDNA-binding activity which relies on its tRNA 3 CCA tail binding module. Our study clarifies the subunit organization of archaeal KEOPS and suggests of an origin of eukaryotic Gon7. The study also reveals a possible link between the function in t6A modification and the additional function presumably homologous recombination.

microbiology↗

bioRxiv2022

An uncertainty-based interpretable deep learning framework for breast cancer outcomes prediction

Accurate prediction of breast cancer outcomes is important for selecting appropriate treatment, which can prolong the survival period of the patients and improve the life quality. Recently, different deep learning-based methods are carefully designed for cancer outcomes prediction. However, the applications of these methods are still challenging due to the model interpretability. In this study, we proposed a novel multi-task deep neural network UISNet to interpret the feature importance of the prediction model by an uncertainty-based integrated gradients algorithm. Additionally, UISNet improves the prediction accuracy by introducing the prior biological pathway knowledge and utilizing the patients heterogeneity information. By applications to seven breast cancer public datasets, the method was shown to outperform state-of-the-art methods by achieving a 5.79% higher C-index value on average. For the identified genes based on the interpretable model, 11 out of the top 20 genes have been proved to be associated with breast cancer by literature review. The comprehensive tests indicated that our proposed method is accurate and robust to predict breast cancer outcomes, and is an effective way to identify the prognosis-related genes. The method codes are available at: https://github.com/chh171/UISNet.

bioinformatics↗

bioRxiv2022

Extracellular heparan 6-O-endosulfatases SULF1 and SULF2 in HNSC and other malignancies

SULF1 and SULF2 are oncogenic in a number of human malignancies, including head and neck squamous cell carcinoma (HNSC). The function of these two heparan sulfate editing enzymes was previously considered largely redundant but the biology of cancer suggests differences that we explore in our RNAseq and RNAScope studies of HNSC and in a pan cancer analysis using the TCGA and CPTAC (proteomics) data. Our studies document a consistent upregulation of SULF1 and SULF2 in HNSC which is associated with poor survival outcomes. SULF2 expression increases in multiple malignancies but less consistently than SULF1, which uniformly increases in the tumor tissues and negatively impacts survival in several types of cancer. Meanwhile, SULF1 showed low expression in cancer cell lines and a scRNAseq study of HNSC shows that SULF1 is not supplied by epithelial tumor cells, like SULF2, but is secreted by cancer associated fibroblasts. Our RNAScope and PDX analysis of the HNSC tissues fully confirm the stromal source of SULF1 and explain the uniform impact of this enzyme on the biology of multiple malignancies. In summary, the SULF1 enzyme, supplied by a subset of cancer associated fibroblasts, is upregulated and negatively impacts HNSC survival at an early stage of the disease progression while the SULF2 enzyme, supplied by tumor cells, impacts survival at later stages of HNSC. This paradigm is common to multiple malignancies and suggests a potential for diagnostic and therapeutic targeting of the heparan sulfatases in cancer diseases.

cancer biology↗

bioRxiv2022

Phosphorylation of the Smooth Muscle Master Splicing Regulator RBPMS Regulates its Splicing Activity

We previously identified RBPMS as a master regulator of alternative splicing in differentiated smooth muscle cells (SMCs). RBPMS is transcriptionally downregulated during SMC dedifferentiation, but we hypothesized that RBPMS protein activity might be acutely downregulated by post-translational modifications. Publicly available phosphoproteomic datasets reveal that Thr113 and Thr118 immediately adjacent to the RRM domain are commonly both phosphorylated. An RBPMS T113/118 phosphomimetic T/E mutant showed decreased splicing regulatory activity both in transfected cells and in a cell-free in vitro assay, while a non-phosphorylatable T/A mutant retained full activity. Loss of splicing activity was associated with a modest reduction in RNA affinity but significantly reduced RNA binding in nuclear extract. A lower degree of oligomerization of the T/E mutant might cause lower avidity of multivalent RNA binding. However, NMR analysis also revealed that the T113/118E peptide acts as an RNA mimic which can loop back and antagonize RNA-binding by the RRM domain. Finally, we identified ERK2 as the most likely kinase responsible for phosphorylation at Thr113 and Thr118. Collectively, our data identify a potential mechanism for rapid modulation of the SMC splicing program in response to external signals during the vascular injury response and atherogenesis.

molecular biology↗

bioRxiv2022

YAP antagonizes TEAD-mediated AR signaling and prostate cancer growth

Hippo signaling restricts tumor growth by inhibiting the oncogenic potential of YAP/TAZ-TEAD transcriptional complex. Here we uncover a context-dependent tumor suppressor function of YAP in androgen receptor (AR) positive prostate cancer (PCa) and show that YAP impedes AR+ PCa growth by antagonizing TEAD-mediated AR signaling. TEAD forms a complex with AR to enhance its promoter/enhancer occupancy and transcriptional activity. YAP and AR compete for TEAD binding and consequently, elevated YAP in the nucleus disrupts AR-TEAD interaction and prevents TEAD from promoting AR signaling. Pharmacological inhibition of Hippo/MST1/2 kinase or transgenic activation of YAP suppressed the growth of PCa expressing therapy resistant AR splicing variants. Our study uncovers an unanticipated crosstalk between Hippo and AR signaling pathways, reveals an antagonistic relationship between YAP and TEAD in AR+ PCa, and suggests that targeting the Hippo signaling pathway may provide a therapeutical opportunity to treat PCa driven by therapy resistant AR variants. O_LIYAP acts as a context-dependent tumor suppressor in AR+ PCa C_LIO_LITEAD interacts with AR to enhance its promoter/enhancer occupancy C_LIO_LIYAP inhibits AR activity by competing for TEAD binding C_LIO_LISmall molecule Hippo pathway inhibitor impedes AR+ PCa growth C_LI

cancer biology↗

bioRxiv2022

Coronaviruses using different strategies to antagonize antiviral responses and pyroptosis

Viral infection triggers inflammasome-mediated caspase-1 activation. Nevertheless, limited understanding exists regarding how viruses use the active caspase-1 to evade host immune response. Here, we use porcine epidemic diarrhea virus (PEDV) as a model of coronaviruses (CoVs) to illustrate the intricate regulation of CoVs to combat IFN-I signaling and pyroptosis. Our findings demonstrate that PEDV infection stabilizes caspase-1 expression via papain-like protease PLP2s deubiquitinase activity and the enhanced stabilization of caspase-1 disrupts IFN-I signaling by cleaving RIG-I at D189 residue. 6-Thioguanine (6TG), a PLP2 inhibitor, plays a critical role in reversing the inhibitory effect of IFN-I via PLP2 and also inhibiting PEDV replication. Meanwhile, PLP2 can degrade GSDMD-p30 by removing its K27-linked ubiquitin chain at K275 to restrain pyroptosis. Papain-like proteases from other genera of CoVs (PDCoV and SARS-CoV-2) have the similar activity to degrade GSDMD-p30. We further demonstrate that SARS-CoV-2 N protein induced NLRP3 inflammasome activation also uses the active caspase-1 to counter IFN-I signaling by cleaving RIG-I. Therefore, our work unravels a novel antagonistic mechanism employed by CoVs to evade host antiviral response.

immunology↗

bioRxiv2022

Fat Extract Modulates Calcium Signaling and Protects Against Hyperactive Osteoclastogenesis in Bone Remodeling with Antioxidant Capacity

Osteoclasts are cells which are primarily involved in bone remodeling and osteolytic bone diseases. Hyperactive osteoclastogenesis leads to pathological bone loss and microarchitectural deterioration, particularly in postmenopausal osteoporosis. However, given the limitations of current first-line osteoclast inhibitors, there is an urgent need for a novel antiresorptive agent with higher efficiency and fewer side effects. Cell-free fat extract (CEFFE) is the liquid fraction obtained from human adipose tissues, which are enriched with a variety of cytokines and growth factors. This study aims to explore its pharmaceutical effect on hyperactive osteoclast formation in vivo and in vitro. CEFFE exhibits excellent potentials to attenuate osteoclast-associated bone loss in an ovariectomy (OVX) mouse model and to inhibit RANKL-induced osteoclastogenesis in primary bone marrow-derived monocytes. Furthermore, the cationic protein fraction of CEFFE (CEFFE-Cation) is identified as the main inhibitory component in osteoclast formation assay. Excessive reactive oxygen species (ROS) production is the main cause of osteoclast overactivation. According to LC-MS/MS analysis, the CEFFE-Cation fraction mainly consists of various antioxidant enzymes, extracellular matrix, and secreted cytokines, which endow it with a superior antioxidant capacity. Ca2+ signaling contributes to osteoclast maturation. In addition to scavenging ROS, CEFFE-Cation is also capable of mitigating Ca2+ oscillation, calcineurin activation, and subsequent NFATc1 nuclear translocation during osteoclastogenesis. Overall, this study elucidates the promising translational potential of CEFFE as a next-generation personalized antiresorptive agent for osteolytic bone disease treatment.

pathology↗

bioRxiv2022

Type 17 Follicular Helper T (Tfh17) Cells are Superior for Memory Maintenance

A defining feature of successful vaccination is the ability to induce long-lived antigen- specific memory cells. Follicular helper T (Tfh) cells specialize in providing help to B cells in mounting protective humoral immunity in infection and after vaccination. Memory Tfh cells that retain the CXCR5 expression can confer protection through enhancing humoral response upon antigen re-exposure but how they are maintained is poorly understood. CXCR5+ memory Tfh cells in human blood are divided into Tfh1, Tfh2 and Tfh17 cells by the expression of chemokine receptors CXCR3 and CCR6 associated with Th1 and Th17 respectively. Here, we developed a new method to induce Tfh1, Tfh2 and Tfh17-like (iTfh1, iTfh2 and iTfh17) cells in vitro. Although all three iTfh subsets efficiently support antibody responses in recipient mice with immediate immunization, iTfh17 cells are superior to iTfh1 and iTfh2 cells in supporting antibody response to a later immunization after extended resting in vivo to mimic memory maintenance. Notably, the counterpart human Tfh17 cells are selectively enriched in CCR7+ central memory Tfh (TfhCM) with survival and proliferative advantages. Furthermore, the analysis of multiple human cohorts that received different vaccines for HBV, influenza virus, tetanus toxin or measles revealed that vaccine-specific Tfh17 cells outcompete Tfh1 or Tfh2 cells for the persistence in memory phase. Therefore, the complementary mouse and human results showing the advantage of Tfh17 cells in maintenance and memory function supports the notion that Tfh17-induced immunization might be preferable in vaccine development to confer long-term protection.

immunology↗

bioRxiv2022

Bioinformatics analysis of immune-related prognostic genes and immunotherapy in renal clear cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is an immunogenic tumor and investigating the immunorelated genes is of great significance. To investigate the immunoprognostic genes of ccRCC, we analyzed the data assimilated from a public database (The Cancer Genome Atlas (TCGA) database and the gene expression omnibus (GEO) database) using bioinformatics. Then, a immunoprognosis model was constructed to identify 4 hub genes with moderate predictive values for the prognosis of ccRCC patients. Interestingly, these 4 genes were associated with the prognosis of ccRCC patients based on Oncomine and Gena Expression Profiling Interactive Analysis (GEPIA) databases. The correlation analysis between the immune infiltrate, immune checkpoints, and immunotherapy and this immunoprognosis model showed that immune infiltrate and could predict the immunotherapy effects. We also conducted a quantitative real-time polymerase chain reaction analysis and found that the expressions of 3 hub genes were associated with tumor progression (P<0.1). In conclusion, 4 genes that may serve as potential biomarkers in ccRCC were identified with respect to prognosis.

cancer biology↗

bioRxiv2022

MINE is a method for detecting spatial density of regulatory chromatin interactions based on a MultI-modal NEtwork

Chromatin interactions play essential roles in chromatin conformation and gene expression. However, few tools exist to analyze the spatial density of regulatory chromatin interactions. Here, we present the MultI-modal NEtwork (MINE) toolkit, including MINE-Loop, MINE-Density, and MINE-Viewer. MINE-Loop network modeling integrates Hi-C, ATAC-seq, and histone ChIP-seq data to enhance the detection of regulatory chromatin interactions (RCIs, i.e., chromatin interactions that are anchoring regulatory elements to chromatin); MINE-Density quantifies the spatial density of regulatory chromatin interactions identified by MINE-Loop within different chromatin conformations; and MINE-Viewer facilitates 3D visualization of the density of chromatin interactions and participating regulatory factors, such as transcription factors. We applied MINE to investigate the relationship between the spatial density of regulatory chromatin interactions (SD-RCI) and chromatin volume change in HeLa cells before and after liquid-liquid phase separation. Changes in SD-RCI before and after treating the HeLa cells with 1,6-hexanediol showed that the change in chromatin volume was related to the degree of activation or repression of genes in chromatin interactions. The MINE toolkit provides a new method for quantitative study of chromatin conformation. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=187 HEIGHT=200 SRC="FIGDIR/small/499656v1_ufig1.gif" ALT="Figure 1"> View larger version (51K): org.highwire.dtl.DTLVardef@1705cc8org.highwire.dtl.DTLVardef@1db89adorg.highwire.dtl.DTLVardef@2be8e4org.highwire.dtl.DTLVardef@c6f1a6_HPS_FORMAT_FIGEXP M_FIG C_FIG In BriefGong et al. provide a toolkit MINE to explore the relationship between spatial density of regulatory chromatin interactions, gene expression and chromatin structure change. HighlightsO_LIMINE toolkit was provided to detect more regulatory chromatin interactions (RCI), count the spatial density of regulatory chromatin interactions and visualize the density of chromatin interactions with transcription factors in three-dimension. C_LIO_LIChromatin regions were divided into developed active hub, developing active hub, developed repressive hub, and developing repressive hub according to the density of active or repressive regulatory chromatin interactions. C_LIO_LIThe change of chromatin structure before and after liquid-liquid phase separation is quantitively described by using the MINE toolkit. C_LI

bioinformatics↗

bioRxiv2022

Cryo-EM structures of α-synuclein filaments from Parkinson's disease and dementia with Lewy bodies

Parkinsons disease (PD) is the most common movement disorder, with resting tremor, rigidity, bradykinesia and postural instability being major symptoms (1). Neuropathologically, it is characterised by the presence of abundant filamentous inclusions of -synuclein in the form of Lewy bodies and Lewy neurites in some brain cells, including dopaminergic nerve cells of the substantia nigra (2). PD is increasingly recognised as a multisystem disorder, with cognitive decline being one of its most common non-motor symptoms. Many patients with PD develop dementia more than 10 years after diagnosis (3). PD dementia (PDD) is clinically and neuropathologically similar to dementia with Lewy bodies (DLB), which is diagnosed when cognitive impairment precedes parkinsonian motor signs or begins within one year from their onset (4). In PDD, cognitive impairment develops in the setting of well-established PD. Besides PD and DLB, multiple system atrophy (MSA) is the third major synucleinopathy (5). It is characterised by the presence of abundant filamentous -synuclein inclusions in brain cells, especially oligodendrocytes (Papp-Lantos bodies). We previously reported the electron cryo-microscopy (cryo-EM) structures of two types of -synuclein filaments extracted from the brains of individuals with MSA (6). Each filament type is made of two different protofilaments. Here we report that the cryo-EM structures of -synuclein filaments from the brains of individuals with PD, PDD and DLB are made of a single protofilament (Lewy fold) that is markedly different from the protofilaments of MSA. These findings establish the existence of distinct molecular conformers of assembled -synuclein in neurodegenerative disease.

neuroscience↗

bioRxiv2022

The specificity of different-distance connections in human structural connectomes

Brain structural connectomes underpin complex cognitive processes. To date, abundant organizational features have been distilled by network-based tools, including hubs, modules, and small-worldness. However, these features are often devoid of spatial characteristics which directly shape connection formation. By considering the spatial embedding of brain networks, we reveal the connection specificity, that is, the similarity of similar-distance connections and the dissimilarity of different-distance connections. It is induced by the whole-brain connection length distribution, allowing areas to send and receive diverse signals through different-distance connections. Based on it, areas functional repertoires are associated with their connection length profiles, and meanwhile, length dispersion and clustering coefficients can be integrated into a hierarchy whose age-related degeneration may be related to cognitive decline. These results construct a putative bridge between brain spatial, topological, and functional features, expanding our understanding of how different architectures complement and reinforce each other to achieve complicated brain functions.

neuroscience↗

bioRxiv2022

Enhanced brain structure-function tethering in transmodal cortex revealed by high-frequency eigenmodes

The brains structural connectome supports signal propagation between neuronal elements, shaping diverse coactivation patterns that can be captured as functional connectivity. While the link between structure and function remains an ongoing challenge, the prevailing hypothesis is that the structure-function relationship may itself be gradually decoupled along a macroscale functional gradient spanning unimodal to transmodal regions. However, this hypothesis is strongly constrained by the underlying models which may neglect requisite signaling mechanisms. Here, we transform the structural connectome into a set of orthogonal eigenmodes governing frequency-specific diffusion patterns and show that regional structure-function relationships vary markedly under different signaling mechanisms. Specifically, low-frequency eigenmodes, which are considered sufficient to capture the essence of the functional network, contribute little to functional connectivity reconstruction in transmodal regions, resulting in structurefunction decoupling along the unimodal-transmodal gradient. In contrast, high-frequency eigenmodes, which are usually on the periphery of attention due to their association with noisy and random dynamical patterns, contribute significantly to functional connectivity prediction in transmodal regions, inducing gradually convergent structure-function relationships from unimodal to transmodal regions. Although the information in high-frequency eigenmodes is weak and scattered, it effectively enhances the structure-function correspondence by 35% in unimodal regions and 56% in transmodal regions. Altogether, our findings suggest that the structure-function divergence in transmodal areas may not be an intrinsic property of brain organization, but can be narrowed through multiplexed and regionally specialized signaling mechanisms.

neuroscience↗

bioRxiv2022

A novel RHH family transcription factor aCcr1 and its viral homologs dictate cell cycle progression in archaea

Cell cycle regulation is of paramount importance for all forms of life. Here we report that a conserved and essential cell cycle-specific transcription factor (designated as aCcr1) and its viral homologs control cell division in Sulfolobales. We show that the transcription level of accr1 reaches peak during active cell division (D-phase) subsequent to the expression of CdvA, an archaea-specific cell division protein. Cells over-expressing the 58-aa-long RHH (ribbon-helix-helix) family cellular transcription factor as well as the homologs encoded by large spindle-shaped viruses Acidianus two-tailed virus (ATV) and Sulfolobus monocaudavirus 3 (SMV3) display significant growth retardation and cell division failure, manifested as enlarged cells with multiple chromosomes. aCcr1 over-expression results in downregulation of 17 genes (>4-folds) including cdvA. A conserved motif, aCcr1-box, located between the TATA-binding box and the translation initiation site in the promoters of 13 out of the 17 highly repressed genes, is critical for aCcr1 binding. The aCcr1-box is present in the promoters of cdvA genes across Sulfolobales, suggesting that aCcr1-mediated cdvA repression is an evolutionarily conserved mechanism by which archaeal cells dictate cytokinesis progression, whereas their viruses take advantage of this mechanism to manipulate the host cell cycle.

microbiology↗