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Zhan, J.

Publications and source records attributed to Zhan, J..

At least 37 records · Page 2Linked to original sources
bioRxiv2024

Comparative transcriptome profiling identifies stage-specific phasiRNAs and co-expressed Argonaute genes in Bambusoideae and Pooideae species

PhasiRNAs (phased, small interfering RNAs) play a crucial role in supporting male fertility in grasses. Earlier work in maize (Zea mays) and rice (Oryza sativa) - and subsequently many other plant species - identified premeiotic 21-nt and meiotic 24-nt phasiRNAs. More recently, a group of premeiotic 24-nt phasiRNAs were discovered in the anthers of two Pooideae species, barley (Hordeum vulgare) and bread wheat (Triticum aestivum). The conservation of premeiotic 24-nt phasiRNAs and other classes of reproductive phasiRNAs across Pooideae species remains unclear. We conducted a comparative RNA profiling of three anther stages in six Pooideae species and one Bambusoideae species. We observed complex temporal accumulation patterns of 21-nt and 24-nt phasiRNAs in Pooideae and Bambusoideae grasses. In Bambusoideae, 21-nt phasiRNAs accumulated during meiosis, whereas 24-nt phasiRNAs were present in both premeiotic and postmeiotic stages. We identified premeiotic 24-nt phasiRNAs in all seven species examined. These phasiRNAs exhibit distinct biogenesis mechanisms and potential Argonaute effectors compared to meiotic 24-nt phasiRNAs. We show that specific Argonaute genes co-expressed with stage-specific phasiRNAs are conserved across Bambusoideae and Pooideae species. Our degradome analysis identified a set of conserved miRNA target genes across species, while 21-nt phasiRNAs targets were species-specific. Cleavage of few targets was observed for 24-nt phasiRNAs.

plant biology↗
bioRxiv2024

On the completeness of existing RNA fragment structures

Success in protein structure prediction by the deep learning method AlphaFold 2 naturally gives arise the question if we can do the same for RNA structure prediction. One reason for the success in protein structure prediction is that the structural space of proteins at the fragment level has been nearly complete for many years. Here, we examined the completeness of RNA fragment structural space at dimeric, trimeric, tetrameric, and pentameric levels. We showed that the RNA structural space is not even complete at the di-nucleotide level, whereas the exponential increase of new structural fragments is observed at tetrameric and pentameric levels. Moreover, the number of backbone fragments found in RNA (2510) is far smaller than the number of backbone fragments found in proteins (6652). This further suggests that a structural space currently observed in RNA is far from complete, considering that the RNA backbone (6 torsion angles) has more degrees of freedom than the protein backbone (3 torsion angles with one nearly fixed). In addition, we found that the three-atom representation (one backbone atom C4 and two sidechain atoms C1 and N1) has the lowest number of structural fragments, suggesting it as the most "stable" structural frame for building up the entire RNA structure.

bioinformatics↗
bioRxiv2024

The Brain Computes Dynamic Facial Movements for Emotion Categorization Using a Third Pathway

Recent theories suggest a new brain pathway dedicated to processing social movement is involved in understanding emotions from biological motion, beyond the well-known ventral and dorsal pathways. However, how this social pathway functions as a network that computes dynamic biological motion signals for perceptual behavior is unchartered. Here, we used a generative model of important facial movements that participants (N = 10) categorized as "happy," "surprise," "fear," "anger," "disgust," "sad" while we recorded their MEG brain responses. Using new representational interaction measures (between facial features, MEGt source, and behavioral responses), we reveal per participant a functional social pathway extending from occipital cortex to superior temporal gyrus. Its MEG sources selectively represent, communicate and compose facial movements to disambiguate emotion categorization behavior, while occipital cortex swiftly filters out task-irrelevant identity-defining face shape features. Our findings reveal how social pathway selectively computes complex dynamic social signals to categorize emotions in individual participants.

neuroscience↗
bioRxiv2024

An experimental approach to study multi-species diseases interactions in wheat using machine learning-aided image analysis

Fusarium Head Blight (FHB) is a major disease of cereal crops present everywhere where wheat is produced. A complex of several, distinctly different, species from the genus Fusarium are responsible for causing the disease, with important taxa being F. graminearum, F. culmorum, and F. avenaceum. FHB is an adult stage, late season disease affecting wheat at anthesis. However, recent discoveries are now challenging the basic assumption that anthers and spikes are the only ecological niches relevant for resistance to FHB. Most prominently, it has been established that pathogens typically occurring earlier in the season, such as Zymoseptoria tritici, induce strong systemic changes in the plant immune system, that expand way beyond their primary ecological niche. Furthermore, taxa implicated in FHB can be isolated from virtually all wheat tissues, including typical blotch symptoms on wheat leaves. Strikingly, such important biological observations about the complex lifestyle of FHB causing taxa are overlooked as potential targets in resistance breeding. In this work we present a simplified approach allowing the dissection of the intricate lifestyles of the FHB causing Fusarium taxa. In doing so, we provide an experimental proof-of-concept that systemic signals from Z. tritici can affect the basic response of wheat to F. graminearum. We further demonstrate that differences in Fusarium species composition affect the outcome of the interaction in a genotype-specific manner. We argue that efforts to study hidden layers of the biology of FHB associated taxa are crucial to developing new resistance breeding strategies, based on a better understanding of the genetic drivers of the disease.

plant biology↗
bioRxiv2024

Premeiotic 24-nt phasiRNAs are present in the Zea genus and unique in biogenesis mechanism and molecular function

Reproductive phasiRNAs are broadly present in angiosperms and play crucial roles in sustaining male fertility. While the premeiotic 21-nt phasiRNAs and meiotic 24-nt phasiRNA pathways have been extensively studied in maize (Zea mays) and rice (Oryza sativa), a third putative category of reproductive phasiRNAs-named premeiotic 24-nt phasiRNAs-have recently been reported in barley (Hordeum vulgare) and wheat (Triticum aestivum). To determine whether premeiotic 24-nt phasiRNAs are also present in maize and related species and begin to characterize their biogenesis and function, we performed a comparative transcriptome and degradome analysis of premeiotic and meiotic anthers from five maize inbred lines and three teosinte species/subspecies. Our data indicate that a substantial subset of the 24-nt phasiRNA loci in maize and teosinte are already highly expressed at premeiotic phase. The premeiotic 24-nt phasiRNAs are similar to meiotic 24-nt phasiRNAs in genomic origin and dependence on DCL5 for biogenesis, however, premeiotic 24-nt phasiRNAs are unique in that they are likely (i) not triggered by microRNAs, (ii) not loaded by AGO18 proteins, and (iii) not capable of mediating cis-cleavage. In addition, we also observed a group of premeiotic 24-nt phasiRNAs in rice using previously published data. Together, our results indicate that the premeiotic 24-nt phasiRNAs constitute a unique class of reproductive phasiRNAs and are present more broadly in the grass family (Poaceae) than previously known. SIGNIFICANCEWe previously reported two classes of reproductive phasiRNAs in maize, the premeiotic 21-nt phasiRNAs and the meiotic 24-nt phasiRNAs. Here we report a third class of reproductive phasiRNAs - premeiotic 24-nt phasiRNAs - that are present in the Zea genus, including all five maize inbred lines and three teosinte species/subspecies that we examined, plus rice. We show that in the Zea genus the premeiotic 24-nt phasiRNAs are distinct from the meiotic 24-nt phasiRNAs in triggering mechanism, effector protein, and molecular function.

plant biology↗
bioRxiv2024

Assembly of mTORC3 involves binding of ETV7 to two separate sequences in the mTOR kinase domain.

mTOR plays a crucial role in cell growth by controlling ribosome biogenesis, metabolism, autophagy, mRNA translation, and cytoskeleton organization. It is a serine/threonine kinase that is part of two distinct extensively described protein complexes, mTORC1 and mTORC2. We have identified a rapamycin resistant mTOR complex, called mTORC3, which is different from the canonical mTORC1 and mTORC2 complexes in that it does not contain the Raptor, Rictor, or mLST8 mTORC1 / 2 components. mTORC3 phosphorylates mTORC1 and mTORC2 targets and contains the ETS transcription factor ETV7, which binds to mTOR and is essential for mTORC3 assembly in the cytoplasm. Tumor cells that assemble mTORC3 have a proliferative advantage and become resistant to rapamycin, indicating that inhibiting mTORC3 may have a therapeutic impact on cancer. Here, we investigate which domains or amino acid residues of ETV7 and mTOR are involved in their mutual binding. We found that the mTOR FRB and LBE sequences in the kinase domain interact with the pointed (PNT) and ETS domains of ETV7, respectively. We also found that forced expression of the mTOR FRB domain in the mTORC3 expressing, rapamycin-resistant cell line Karpas-299 out competes mTOR for ETV7 binding and renders these cells rapamycin-sensitive in vivo. Our data provide useful information for the development of molecules that prevent the assembly of mTORC3, which may have therapeutic value in the treatment of mTORC3 positive cancer.

cancer biology↗
bioRxiv2024

RNA-MobiSeq: Deep mutational scanning and mobility-based selection for RNA structure inference

Tertiary structures of RNAs are increasingly found to be essential for their functions and yet are notoriously challenging to be determined by experimental techniques or predicted by energy-based and AlphaFold2-like deep-learning-based computational methods. Here, we coupled deep mutational scanning with mobility-based selection to separate structurally stable from nonstable RNA variants. The subsequent high-throughput sequencing allows the detection of the covariational signals of key secondary and tertiary base pairs with significantly improved, RNA-specific, unsupervised analysis of covariation-induced deviation of activity (CODA2) and amplification of these signals by Monte-Carlo simulated annealing. The resulting base-pairing structures can serve as the restraints for final energy-based structure predictions. This MobiSeq technique was tested on four structurally different RNAs. The results show that the detection of tertiary base pairs by MobiSeq allows reasonably accurate prediction of RNA tertiary structures. The proposed method should be useful for structural inference and improved mechanistic understanding of all RNAs with detectable changes in mobility due to deleterious mutations.

molecular biology↗
bioRxiv2024

mTORC3 activity is regulated by GSK3β through the activation of PKCδ that phosphorylates and activates STAT1.

The mechanistic target of rapamycin serine/threonine kinase (mTOR), a critical regulator of cell proliferation, is implicated in many human diseases, including cancer. Our laboratory recently discovered a new mTOR complex, called mTORC3, distinct from the two canonical mTOR complexes, mTORC1, and mTORC2, which causes accelerated proliferation and increased malignancy of cancer cells. Understanding how mTORC3 is controlled is an important approach to designing cancer therapies targeting mTORC3. Having investigated how mTORC3 activity was affected by glycogen synthase kinase-3{beta} (GSK3{beta}), a known regulator of mTOR activity, we found that cells do not assemble mTORC3 upon GSK3{beta} knockout, due to extinguished expression of ETV7, an essential component of the mTORC3 complex. We discovered that the lack of ETV7 expression resulted from reduced phosphorylation of serine 727 (S727) of the Signal Transducer and Activator of Transcription 1 (STAT1), which is required for transcriptional activation of ETV7. Finally, we identified that GSK3{beta} through PKC{delta} phosphorylates STAT1-S727. Together, we identified a novel GSK3{beta}/PKC{delta}/ STAT1 pathway in Karpas cells that regulates ETV7 transcription, thus positively regulating mTORC3 activity. The identification of this signaling pathway could benefit the therapeutic design for numerous diseases including cancer.

cell biology↗
bioRxiv2024

Shrimp Hemocyanin elicits a potent humoral response in mammals and is favorable to hapten conjugation

Conjugation to a carrier protein is essential to give rise to the antigenicity of hapten, which is a small molecule and cannot induce an immune response by itself alone. Three carrier proteins e.g. KLH (Keyhole Limpet Hemocyanin), BSA (Bovine Serum Albumin), and OVA (Ovalbumin) were used mostly. KLH is advantageous to the others, majorly owing to its strong immunogenicity and limited usage in other biological assays. However, the solubility of KLH is not as well as the others, especially after hapten conjugation. Besides, the high market price of KLH results in high costs in vaccine and antibody development. Here, we extracted the shrimp hemocyanin (SHC) from Penaeus vannamei (P. vannamei) with a production yield of > 1 g proteins (98 % pure) per 1 kg shrimp. Compared to KLH, the peptide-SHC conjugates exhibit higher solubility after hapten conjugation. Furthermore, compared with KLH, SHC induces comparable antibody production efficiency in mammals, with or without conjugation. Finally, rabbit polyclonal antibodies or mouse monoclonal antibodies were generated by immunizing SHC-peptide conjugates, and their applications in western blot, immunofluorescence and immunohistochemistry were confirmed. Therefore, we demonstrated that SHC may be used as a substitute for KLH in future antibody and vaccine development.

bioengineering↗
bioRxiv2024

Survey of the wheat mycobiome associated with leaf blotch and head blight diseases in Sweden

Increasing the durability of crop resistance to plant pathogens has become a major concern in plant pathology. There are 32 diseases of agronomical importance in wheat, which makes disease management in this crop of worldwide importance extremely challenging. Two important groups of wheat pathogens driving the consumption of chemical fungicides in Sweden and worldwide are those causing head blight diseases and leaf blotch diseases. Swedish fields are regularly infested with head blight diseases typically driven by a complex of Fusarium and non-fusarium taxa including F. graminearum, F. culmorum, while leaf blotches are typically caused by Zymoseptoria tritici, Pyrenophora tritici-repentis, and Parastagonospora nodorum. In this work, we present a survey of the larger diversity of taxa associated with head blight and leaf blotch diseases in Sweden, with the primary aim to assess the usefulness of such an approach to develop new understanding and tools for disease diagnostics and management sensu lato. Here, we analyze nearly a thousand isolates collected from wheat fields across eight counties in Sweden and report the identification of pathogenic and non-pathogenic taxa as well as candidate biological control agents associated with head blight and leaf blotch diseases. We argue that such surveys represent an important tool for improving disease diagnostics, disease forecasting, and integrated management in wheat.

plant biology↗
bioRxiv2023

Temporal Transcriptome Analysis Uncovers Regulatory Modules Programming Embryo Development from Embryonic Morphogenesis to Post-Germination

We profiled the soybean seed embryo transcriptome across embryonic development to post-germinative development to understand gene activities and regulatory networks promoting these processes. Transcriptomic landscapes feature highly prevalent transcripts which are categorized into early and late groups with major functions of reserve accumulation and energy generation, respectively, and both functions are dominant during late reserve accumulation as the transitioning stage. During the mid-reserve accumulation, regulatory events simultaneously dominate at the transcriptional and chromatin levels, followed by the emergence of distinct mRNA populations during late reserve accumulation throughout germination. We identified diverse functions conducted by sequentially activated genes across developmental stages. Gene coexpression network analysis reveals modules associated with developmental stages, some of which are enriched in genes with functions involved in specific developmental processes. We identified an early-desiccation-associated gene module, containing most transcription factors responsive to abiotic stress, within which one transcription factor is functionally validated to demonstrate increased drought tolerance in Arabidopsis overexpression lines. Finally, we found that a subset of genes is under purifying selection, surpasses the number of their Arabidopsis germination-specific homologs and most are active before germination from embryonic morphogenesis through dormancy, suggesting a potential role in governing physical dormancy in soybean compared to physiological dormancy in Arabidopsis. Our data represent a step toward identifying genes and regulatory networks in the soybean genome facilitating developmental programs across transition phases to bridge embryonic and germinative development.

plant biology↗
bioRxiv2023

Effective High-Accuracy Prediction of Protein Structures from Easily Obtainable Artificial Homologous Sequences by Structure-Stability-Based Selection

High-resolution protein structure determination by experimental techniques is notoriously costly and labor intensive. This problem is mostly solved with arrival of deep-learning-based computational prediction by AlphaFold2 but only for those proteins with enough naturally occurring homologous sequences. Here, we attempt to close the remaining gap by employing artificially generated, structure-stability-selected homologous sequences as an input for AlphaFold2. We showed that only one round of selection of deeply mutated sequences of a few mutations is sufficient to bring the accuracy of predicted structures to better than 2 [A] RMSD from their respective native structures for four of the five proteins experimented. The performance for three out of five proteins is even better than AlphaFold2 with naturally occurring sequences. The only protein with predicted structure of >2 [A] (at 2.92 [A]) RMSD is due to a fully exposed (i.e., likely flexible) {beta}-hairpin. The result supports a future of determining protein structures at low cost and fast turnaround by integrating simple molecular biology experiments (deep mutational scanning and in vivo or in vitro selection) with high-throughput sequencing. The technique proposed here can be further extended to predict structures of protein complexes as well as proteins with posttranslational modifications.

molecular biology↗
bioRxiv2023

Deep learning models of RNA base-pairing structures generalize to unseen folds and make accurate zero-shot predictions of base-base interactions of RNA complexes

The intricate network of RNA-RNA interactions, crucial for orchestrating essential cellular processes like transcriptional and translational regulation, has been unveiling through high-throughput techniques and computational predictions. With the emergence of deep learning methodologies, the question arises: how do these cutting-edge techniques for base-pairing prediction compare to traditional free-energy-based approaches, particularly when applied to the challenging domain of interaction prediction via chain concatenation? In this study, we employ base pairs derived from three-dimensional RNA complex structures as the gold standard benchmark to assess the performance of 23 different methods, including recently developed deep learning models. Our results demonstrate that the deep-learning-based methods, SPOT-RNA can be generalized to make accurate zero-shot predictions of RNA-RNA interactions not only between previously unseen RNA structures but also between RNAs without monomeric structures. The finding underscores the potential of deep learning as a robust tool for advancing our understanding of these complex molecular interactions.

bioinformatics↗
bioRxiv2023

Replication stress inducing ELF3 upregulation promotes BRCA1-deficient breast tumorigenesis in luminal progenitors

BRCA1 is a critical tumor suppressor, mutations in which greatly increase risks for many tumors in carriers, most notably breast cancer. Luminal progenitor cells (LPs) are the currently recognized cells origin of BRCA1-deficient breast cancers. However, the reason why LPs are prone to transform with BRCA1 deficiency has not been elucidated. Here, using single-cell sequencing of human BRCA1 mutant breast cancers and RNA sequencing (RNA-seq) of BRCA1-deficient normal mammary cells, we reveal that replication stress is a feature of LPs and a driving factor during BRCA1-associated tumorigenesis. Mechanistically, replication stress and BRCA1 deficiency lead to significant upregulation of ELF3 expression. ELF3 can help suppress excessive genomic instability and promote LP transformation with BRCA1 deficiency. Moreover, ELF3 emerged as a core transcription factor regulating LP genes, leading to LP expansion. Our findings suggest that replication stress is a driving factor during BRCA1-associated tumorigenesis in luminal progenitor cells and elucidates the key role of ELF3 during this process.

cancer biology↗
bioRxiv2023

SPIN-CGNN: Improved fixed backbone protein design with contact map-based graph construction and contact graph neural network

Recent advances in deep learning have significantly improved the ability to infer protein sequences directly from protein structures for the fix-backbone design. The methods have evolved from the early use of multi-layer perceptrons to convolutional neural networks, transformer, and graph neural networks (GNN). However, the conventional approach of constructing K-nearest-neighbors (KNN) graph for GNN has limited the utilization of edge information, which plays a critical role in network performance. Here we introduced SPIN-CGNN based on protein contact maps for nearest neighbors. Together with auxiliary edge updates and selective kernels, we found that SPIN-CGNN provided a comparable performance in refolding ability by AlphaFold2 to the current state-of-the-art techniques but a significant improvement over them in term of sequence recovery, perplexity, deviation from amino-acid compositions of native sequences, conservation of hydrophobic positions, and low complexity regions, according to the test by unseen structures and "hallucinated" structures. Results suggest that low complexity regions in the sequences designed by deep learning techniques remain to be improved, when compared to the native sequences.

bioinformatics↗
bioRxiv2023

Mechanisms underlying the therapeutic effects of Semen Cuscutae in treating Recurrent Spontaneous Abortion based on Network Pharmacology and Molecular Docking

BackgroundThis paper aims to analyze the active components of SC by network pharmacology and screen the most stable compounds with TNF-a by molecular docking, to explore the mechanism of SC treatment of RSA and provide theoretical basis for drug development. MethodsActive compounds of SC and the potential inflammatory targets of RSA were obtained from the Traditional Chinese Medicine Systematic Pharmacology Database and GeneCard, respectively. The interaction network of RSA-SC-target gene was accomplished and Visualizing by STRING database and Cytoscape software.GO and KEGG pathway enrichment analyses were obtained from DAVID to further explore the RSA mechanism and therapeutic effects of SC. Interactions between TNF- and active compounds were analyzed by molecular docking. Results10 active ingredients and 128 target genes were screened from SC, and 79 of them intersected with RSA target inflammatory genes,which were considered to be potential therapeutic targets. Network pharmacological analysis showed that Sesamin, matrine, matrol and other SC active components had good correlation with the inflammatory target genes of RSA.Related genes include PGR, PTGS1, PTGS2, TGFB1 and CHRNA7. Multiple signaling pathways are involved in RSA pathogenesis,sunh as TNF- signaling pathway, HIF-1 signaling pathway, estrogen signaling pathway, proteoglycans in cancer, FoxO signaling pathway, etc. Molecular docking results suggested that sesamin was the most appropriate natural TNFis. ConclusionOur findings provide an important role and basis for further research on the molecular mechanism of SC treatment of RSA and drug development of TNFis.

bioinformatics↗
bioRxiv2023

Neural representation strength of predicted category features biases decision behavior

Theories of prediction-for-perception propose that the brain predicts the information contents of upcoming stimuli to facilitate their perceptual categorization. A mechanistic understanding should therefore address where, when, and how the brain predicts the stimulus features that change behavior. However, typical approaches do not address these predicted stimulus features. Instead, multivariate classifiers are trained to contrast the bottom-up patterns of neural activity between two stimulus categories. These classifiers then quantify top-down predictions as reactivations of the category contrast. However, a category-contrast cannot quantify the features reactivated for each category-which might be from either category, or both. To study the predicted category-features, we randomly sampled features of stimuli that afford two categorical perceptions and trained multivariate classifiers to discriminate the features specific to each. In a cueing design, we show where, when and how trial-by-trial category-feature reactivation strength directly biases decision behavior, transforming our conceptual and mechanistic understanding of prediction-for-perception.

neuroscience↗