Biology subjects

Zhou, J.

Publications and source records attributed to Zhou, J..

At least 271 records · Page 15Linked to original sources

bioRxiv2024

A foundation model for generalizable cancer diagnosis and survival prediction from histopathological images

Computational pathology, utilizing whole slide image (WSI) for pathological diagnosis, has advanced the development of intelligent healthcare. However, the scarcity of annotated data and histological differences hinder the general application of existing methods. Extensive histopathological data and the robustness of self-supervised models in small-scale data demonstrate promising prospects for developing foundation pathology models. Due to the need for deployment, lightweight foundation models also need to be developed. In this work, we propose the BEPH (BEiT-based model Pre-training on Histopathological images), a general lightweight foundation model that leverages self-supervised learning to learn meaningful representations from 11 million unlabeled histopathological images. These representations are then efficiently adapted to various tasks, including 2 cancer patch-level recognition tasks, 3 cancer WSI-level classification tasks, and 6 cancer subtypes survival prediction tasks. Experimental results demonstrate that our model consistently outperforms several comparative models with similar parameters, even with limited training data reduced to 50%. Especially when the downstream structure is the same, the model can improve ResNet and DINO by up to a maximum increase of 8.8% and 7.2% (WSI level classification), and 6.44% and 3.28% on average (survival prediction), respectively. Therefore, BEPH offers a universal solution to enhance model performance, reduce the burden of expert annotations, and enable widespread clinical applications of artificial intelligence. The code and models can be obtained at https://github.com/Zhcyoung/BEPH. And currently, online fine-tuning of WSI classification tasks is available for use on http://yulab-sjtu.natapp1.cc/BEPH.

pathology↗

bioRxiv2024

Cell invasive amyloid assemblies from SARS-CoV-2 peptides can form multiple polymorphs with varying neurotoxicity

The neurological symptoms of COVID-19, such as memory loss, cognitive and sensory disruption (neuro-COVID) are well reported. These neurological symptoms frequently persist for months (post-acute sequalae of COVID-19 or PASC). The molecular origins of neuro-COVID and how it contributes to PASC are unknown, however a growing body of research highlights that the self-assembly of protein fragments from SARS-CoV-2 into amyloid nanofibrils may play a causative role. Previously, we identified two fragments from the proteins Open Reading Frame 6 (ORF6) and ORF10 that self-assemble into neurotoxic amyloid assemblies. Here we further our understanding of the self-assembly mechanisms and nano-architectures formed by these fragments as well as performing a more in-depth study of the biological responses of co-cultured neurons. By solubilising the peptides in a fluorinated solvent we eliminate insoluble aggregates in the starting materials (seeds) that change the polymorphic landscape of the assemblies. The resultant assemblies are dominated by structures with higher free energies (e.g. ribbons and amorphous aggregates) that are less toxic to cultured neurons. We also show the first direct evidence of cellular uptake by viral amyloids. This work highlights the importance of understanding the polymorphic behaviour of amyloids particularly in the context of neuro-COVID and PASC. Graphical Abstract for ToCThe neurological symptoms of COVID-19 are likely to be, in part, caused by the aggregation of viral proteins into neurotoxic amyloid nanofibrils. Changes in aggregation conditions alters the balance of fibril structures formed (polymorphism), influencing their toxicity to a neuronal cell line. These findings increase our understanding of viral amyloids and highlight the importance of careful choice of experimental protocol when studying these systems. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=161 SRC="FIGDIR/small/594465v1_ufig1.gif" ALT="Figure 1"> View larger version (50K): org.highwire.dtl.DTLVardef@68f505org.highwire.dtl.DTLVardef@ae3624org.highwire.dtl.DTLVardef@164a1f1org.highwire.dtl.DTLVardef@1c3f9d5_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2024

Deconvolution of polygenic risk score in single cells unravels cellular and molecular heterogeneity of complex human diseases

Polygenic risk scores (PRSs) are commonly used for predicting an individuals genetic risk of complex diseases. Yet, their implication for disease pathogenesis remains largely limited. Here, we introduce scPRS, a geometric deep learning model that constructs single-cell-resolved PRS leveraging reference single-cell chromatin accessibility profiling data to enhance biological discovery as well as disease prediction. Real-world applications across multiple complex diseases, including type 2 diabetes (T2D), hypertrophic cardiomyopathy (HCM), and Alzheimers disease (AD), showcase the superior prediction power of scPRS compared to traditional PRS methods. Importantly, scPRS not only predicts disease risk but also uncovers disease-relevant cells, such as hormone-high alpha and beta cells for T2D, cardiomyocytes and pericytes for HCM, and astrocytes, microglia and oligodendrocyte progenitor cells for AD. Facilitated by a layered multi-omic analysis, scPRS further identifies cell-type-specific genetic underpinnings, linking disease-associated genetic variants to gene regulation within corresponding cell types. We substantiate the disease relevance of scPRS-prioritized HCM genes and demonstrate that the suppression of these genes in HCM cardiomyocytes is rescued by Mavacamten treatment. Additionally, we establish a novel microglia-specific regulatory relationship between the AD risk variant rs7922621 and its target genes ANXA11 and TSPAN14. We further illustrate the detrimental effects of suppressing these two genes on microglia phagocytosis. Our work provides a multi-tasking, interpretable framework for precise disease prediction and systematic investigation of the genetic, cellular, and molecular basis of complex diseases, laying the methodological foundation for single-cell genetics.

genetics↗

bioRxiv2024

Gauge fixing for sequence-function relationships

Quantitative models of sequence-function relationships are ubiquitous in computational biology, e.g., for modeling the DNA binding of transcription factors or the fitness landscapes of proteins. Interpreting these models, however, is complicated by the fact that the values of model parameters can often be changed without affecting model predictions. Before the values of model parameters can be meaningfully interpreted, one must remove these degrees of freedom (called "gauge freedoms" in physics) by imposing additional constraints (a process called "fixing the gauge"). However, strategies for fixing the gauge of sequence-function relationships have received little attention. Here we derive an analytically tractable family of gauges for a large class of sequence-function relationships. These gauges are derived in the context of models with all-order interactions, but an important subset of these gauges can be applied to diverse types of models, including additive models, pairwise-interaction models, and models with higher-order interactions. Many commonly used gauges are special cases of gauges within this family. We demonstrate the utility of this family of gauges by showing how different choices of gauge can be used both to explore complex activity landscapes and to reveal simplified models that are approximately correct within localized regions of sequence space. The results provide practical gauge-fixing strategies and demonstrate the utility of gauge-fixing for model exploration and interpretation. Significance StatementComputational biology relies heavily on mathematical models that predict biological activities from DNA, RNA, or protein sequences. Interpreting the parameters of these models, however, remains difficult. Here we address a core challenge for model interpretation-the presence of gauge freedoms, i.e., ways of changing model parameters without affecting model predictions. The results unify commonly used methods for eliminating gauge freedoms and show how these methods can be used to simplify complex models in localized regions of sequence space. This work thus overcomes a major obstacle in the interpretation of quantitative sequence-function relationships.

biophysics↗

bioRxiv2024

Direct inference of the distribution of fitness effects of spontaneous mutations from recombinant inbred C. elegans mutation accumulation lines

The distribution of fitness effects (DFE) of new mutations plays a central role in evolutionary biology. Estimates of the DFE from experimental Mutation Accumulation (MA) lines are compromised by the complete linkage disequilibrium (LD) between mutations in different lines. To reduce LD, we constructed two sets of recombinant inbred lines from a cross of two C. elegans MA lines. One set of lines ("RIAILs") was intercrossed for ten generations prior to ten generations of selfing; the second set of lines ("RILs") omitted the intercrossing. Residual LD in the RIAILs is much less than in the RILs, which affects the inferred DFE when the sets of lines are analyzed separately. The best-fit model estimated from all lines (RIAILs + RILs) infers a large fraction of mutations with positive effects ([~]40%); models that constrain mutations to have negative effects fit much worse. The conclusion is the same using only the RILs. For the RIAILs, however, models that constrain mutations to have negative effects fit nearly as well as models that allow positive effects. When mutations in high LD are pooled into haplotypes, the inferred DFE becomes increasingly negative-skewed and leptokurtic. We conclude that the conventional wisdom - most mutations have effects near zero, a handful of mutations have effects that are substantially negative and mutations with positive effects are very rare - is likely correct, and that unless it can be shown otherwise, estimates of the DFE that infer a substantial fraction of mutations with positive effects are likely confounded by LD.

evolutionary biology↗

bioRxiv2024

An ingestible, battery-free, tissue-adhering robotic interface for non-invasive and chronic electrostimulation of the gut

Ingestible electronics have the capacity to transform our ability to effectively diagnose and potentially treat a broad set of conditions. Current applications could be significantly enhanced by addressing poor electrode-tissue contact, lack of navigation, short dwell time, and limited battery life. Here we report the development of an ingestible, battery-free, and tissue-adhering robotic interface (IngRI) for non-invasive and chronic electrostimulation of the gut, which addresses challenges associated with contact, navigation, retention, and powering (C-N-R-P) faced by existing ingestibles. We show that near-field inductive coupling operating near 13.56 MHz was sufficient to power and modulate the IngRI to deliver therapeutically relevant electrostimulation, which can be further enhanced by a bio-inspired, hydrogel-enabled adhesive interface. In swine models, we demonstrated the electrical interaction of IngRI with the gastric mucosa by recording conductive signaling from the subcutaneous space. We further observed changes in plasma ghrelin levels, the "hunger hormone," while IngRI was activated in vivo, demonstrating its clinical potential in regulating appetite and treating other endocrine conditions. The results of this study suggest that concepts inspired by soft and wireless skin-interfacing electronic devices can be applied to ingestible electronics with potential clinical applications for evaluating and treating gastrointestinal conditions.

bioengineering↗

bioRxiv2024

Disentangling signal and noise in neural responses through generative modeling

Measurements of neural responses to identically repeated experimental events often exhibit large amounts of variability. This noise is distinct from signal, operationally defined as the average expected response across repeated trials for each given event. Accurately distinguishing signal from noise is important, as each is a target that is worthy of study (many believe noise reflects important aspects of brain function) and it is important not to confuse one for the other. Here, we describe a principled modeling approach in which response measurements are explicitly modeled as the sum of samples from multivariate signal and noise distributions. In our proposed method--termed Generative Modeling of Signal and Noise (GSN)--the signal distribution is estimated by subtracting the estimated noise distribution from the estimated data distribution. Importantly, GSN improves estimates of the signal distribution, but does not provide improved estimates of responses to individual events. We validate GSN using ground-truth simulations and show that it compares favorably with related methods. We also demonstrate the application of GSN to empirical fMRI data to illustrate a simple consequence of GSN: by disentangling signal and noise components in neural responses, GSN denoises principal components analysis and improves estimates of dimensionality. We end by discussing other situations that may benefit from GSNs characterization of signal and noise, such as estimation of noise ceilings for computational models of neural activity. A code toolbox for GSN is provided with both MATLAB and Python implementations.

neuroscience↗

bioRxiv2024

Exploring differences between depression and bipolar disorder through the urinary proteome

How to differentiate the diagnosis of depression and bipolar disorder has always been an important problem that needs to be solved urgently in clinical practice. In this study, from the perspective of urine proteomics, urine samples of similar age were collected from two hospitals to investigate the candidate biomarkers for differentiating the diagnosis of depression and bipolar disorder using both group analysis and one-to-many analysis(1 patient: many control samples). The experimental results of the paired group analysis showed that 108 differential proteins were identified in the depressed group compared to the bipolar group under strict screening conditions with screening criteria of FC [≥] 2 or [≤] 0.5 and a two-tailed unpaired t-test of P < 0.01, with an average of 3.7 randomly generated differential proteins, and a confidence level of 96.6 % for the correlation between these proteins and the disease difference. In the one-to-many analysis, 24 differential proteins were co-identified by the samples of 13 depressed patients, 16 of which showed a completely consistent trend of expression changes in all depressed patients studied, and 6 of which were associated with immunoglobulins; 41 differential proteins were co-identified by the samples of 12 depressed patients out of 13, and 19 of which showed a completely consistent trend of expression change in the These results reflect the strong consistency of differential proteins between the two groups of patients. 12 or more samples from depressed patients were enriched for differential proteins related to multiple biological processes and signaling pathways associated with the immune system, which is consistent with previous studies: immune mechanisms may be one of the pathogenetic mechanisms of major depression and that drugs with major immune targets can improve depressive symptoms. In the future, it may be possible to observe the immune status of patients with depression to provide direction and basis for the precise treatment of depression. The results of this paper show that urine proteomics can differentiate between depression and bipolar disorder, suggest possible mechanisms and potential targets for the treatment of depression and bipolar disorder, and provide a tool for future differential diagnosis and precision treatment of the diseases.

biochemistry↗

bioRxiv2024

Transcriptome-wide identification of 5-methylcytosine by deaminase and reader protein-assisted sequencing

5-Methylcytosine (m5C) is one of the post-transcriptional modifications in mRNA and is involved in the pathogenesis of various diseases. However, the capacity of existing assays for accurately and comprehensively transcriptome-wide m5C mapping still needs improvement. Here, we develop a detection method named DRAM (deaminase and reader protein assisted RNA methylation analysis), in which deaminases (APOBEC1 and TadA-8e) are fused with m5C reader proteins (ALYREF and YBX1) to identify the m5C sites through deamination events neighboring the methylation sites. This antibody-free and bisulfite-free approach provides transcriptome-wide editing regions which are highly overlapped with the publicly available BS-seq datasets and allows for a more stable and comprehensive identification of the m5C loci. In addition, DRAM system even supports ultra-low input RNA (10ng). We anticipate that the DRAM system could pave the way for uncovering further biological functions of m5C modifications.

biochemistry↗

bioRxiv2024

Functionally selective dopamine D1 receptor endocytosis and signaling by catechol and non-catechol agonists

The dopamine D1 receptor (D1R) has fundamental roles in voluntary movement and memory and is a validated drug target for neurodegenerative and neuropsychiatric disorders. However, previously developed D1R selective agonists possess a catechol moiety which displays poor pharmacokinetic properties. The first selective non-catechol D1R agonists were recently discovered and unexpectedly many of these ligands showed G protein biased signaling. Here, we investigate both catechol and non-catechol D1R agonists to validate potential biased signaling and examine if this impacts agonist-induced D1R endocytosis. We determined that most, but not all, non-catechol agonists display G protein biased signaling at the D1R and have reduced or absent {beta}-arrestin recruitment. A notable exception was compound (Cmpd) 19, a non-catechol agonist with full efficacy at both D1R-G protein or D1R-{beta}-arrestin pathways. In addition, the catechol ligand A-77636 was a highly potent, super agonist for D1R-{beta}-arrestin activity. When examined for agonist-induced D1R endocytosis, balanced agonists SKF-81297 and Cmpd 19 induced robust D1R endocytosis while the G protein biased agonists did not. The {beta}-arrestin super agonist, A-77636, showed significantly increased D1R endocytosis. Moreover, {beta}-arrestin recruitment efficacy of tested agonists strongly correlated with total D1R endocytosis. Taken together, these results indicate the degree of D1R signaling functional selectivity profoundly impacts D1R endocytosis regardless of pharmacophore. The range of functional selectivity of these D1R agonists will provide valuable tools to further investigate D1R signaling, trafficking and therapeutic potential. Significance StatementThe D1R is a validated therapeutic target and the recently discovered non-catechol D1R agonists have translational potential. We have systematically characterized several structurally distinct D1R agonists including non-catechols with balanced or G protein biased activity. When examined for agonist-induced D1R endocytosis, balanced agonists induced robust D1R endocytosis while G protein biased agonists did not. These results indicate the degree of D1R signaling functional selectivity profoundly impacts receptor endocytosis. This work also independently validates agonist tools to further investigate D1R activation in basic and translational research.

pharmacology and toxicology↗

bioRxiv2024

African Swine Fever Virus Protein B263R Inhibits Autophagy by Promoting Proteasomal Degradation of Beclin1

African swine fever (ASF) is a highly contagious viral disease that affects both domestic and wild pigs, leading to substantial economic losses in the swine industry. However,the precise role of ASFV-encoded viral protein in viral replication and disease has remained elusive. In our study, we elucidated that B263R inhibited autophagy by interacting with Beclin1. Mechanistically, the binding of B263R to Beclin1 promotes its degradation through the mediation of K48-linked ubiquitination at site K31. Notably, the knockdown of Beclin1 mitigated the effect of B263R on reducing the levels of LC3-II, thus further affirming the pivotal role of Beclin1 in B263R-mediated autophagy inhibition. The proliferation of recombinant adenovirus inherently expressing B263R significantly decreased, underscoring the inherent role of B263R-mediated autophagy in virus replication. These findings shed light on the molecular mechanisms employed by ASFV to evade host cell autophagic defenses and offered potential targets for therapeutic interventions against ASFV infection. IMPORTANCEUnderstanding the mechanisms underlying the evasion of host cellular defenses by the African swine fever virus (ASFV) is of pivotal significance owing to its profound impact on the global swine industry. The precise functional roles of ASFV proteins in virus replication and disease remain incompletely characterized. In this study, it is demonstrated that a specific ASFV protein, B263R, acts to inhibit autophagy by interacting with Beclin1, thereby facilitating its degradation. The elucidation of this molecular pathway not only advances our comprehension of ASFV pathogenesis but also furnishes potential targets for therapeutic interventions against ASFV infection. By unraveling the intricate interplay between ASFV and host cell autophagy, this scholarly endeavor contributes invaluable insights to the development of strategies aimed at mitigating the spread of ASFV and minimizing its adverse repercussions on the swine industry.

microbiology↗

bioRxiv2024

Quinoline-based compounds can inhibit diverse enzymes that act on DNA

DNA methylation, as exemplified by cytosine-C5 methylation in mammals and adenine-N6 methylation in bacteria, is a crucial epigenetic mechanism driving numerous vital biological processes. Developing non-nucleoside inhibitors to cause DNA hypomethylation is a high priority, in order to treat a variety of significant medical conditions without the toxicities associated with existing cytidine-based hypomethylating agents. In this study, we have characterized fifteen quinoline-based analogs. Notably, compounds with additions like a methylamine (9) or methylpiperazine (11) demonstrate similar low micromolar inhibitory potency against both human DNMT1 (which generates C5-methylcytosine) and Clostridioides difficile CamA (which generates N6-methyladenine). Structurally, compounds 9 and 11 specifically intercalate into CamA-bound DNA via the minor groove, adjacent to the target adenine, leading to a substantial conformational shift that moves the catalytic domain away from the DNA. This study adds to the limited examples of DNA methyltransferases being inhibited by non-nucleotide compounds through DNA intercalation, following the discovery of dicyanopyridine-based inhibitors for DNMT1. Furthermore, our study shows that some of these quinoline-based analogs inhibit other enzymes that act on DNA, such as polymerases and base excision repair glycosylases. Finally, in cancer cells compound 11 elicits DNA damage response via p53 activation. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=108 SRC="FIGDIR/small/587980v1_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@da2e94org.highwire.dtl.DTLVardef@27f909org.highwire.dtl.DTLVardef@4ec628org.highwire.dtl.DTLVardef@1b384df_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LISix of fifteen quinoline-based derivatives demonstrated comparable low micromolar inhibitory effects on human cytosine methyltransferase DNMT1, and the bacterial adenine methyltransferases Clostridioides difficile CamA and Caulobacter crescentus CcrM. C_LIO_LICompounds 9 and 11 were found to intercalate into a DNA substrate bound by CamA. C_LIO_LIThese quinoline-based derivatives also showed inhibitory activity against various base excision repair DNA glycosylases, and DNA and RNA polymerases. C_LIO_LICompound 11 provokes DNA damage response via p53 activation in cancer cells. C_LI

molecular biology↗

bioRxiv2024

Systematic Evaluation of Cell Type Deconvolution Methods for Plasma Cell-free DNA

Plasma cell-free DNA (cfDNA) is derived from cellular death in various tissues. Investigating the origin of cfDNA through tissue/cell type deconvolution allows us to detect changes in tissue homeostasis that occur during disease progression or in response to treatment. Consequently, cfDNA has emerged as a valuable noninvasive biomarker for disease detection and treatment monitoring. Although there are numerous methylation-based methods of cfDNA cell type deconvolution available, a comprehensive and systematic evaluation of these methods has yet to be conducted. In this study, we thoroughly benchmarked five previously published methods: MethAtlas, cfNOMe, CelFiE, CelFEER, and UXM. Utilizing deep whole-genome bisulfite sequencing data from 35 human cell types, we generated cfDNA mixtures with known ground truth to assess the deconvolution performance under various scenarios. Our findings indicate that different factors, including sequencing depth, reference marker selection, and reference completeness, influence cell type deconvolution performance. Notably, omitting cell types present in a mixture from the reference leads to suboptimal results. Despite each method exhibited distinct performances under various scenarios, CelFEER and UXM exhibit overall superior performance compared to the others. In summary, we comprehensively evaluated factors influencing methylation-based cfDNA cell type deconvolution and proposed general guidelines to maximize the performance.

bioinformatics↗

bioRxiv2024

A Systems Biology Analysis of Chronic Lymphocytic Leukemia

Whole-genome sequencing has revealed that TP53, NOTCH1, ATM, SF3B1, BIRC3, ABL, NXF1, BCR, ZAP70 are often mutated in CLL, but not consistently across all CLL patients. This paper employs a statistical thermo-dynamics approach in combination with the systems biology of the CLL protein-protein interaction networks to identify the most significant participant proteins in the cancerous transformation. Betti number (a topology of complexity) estimates highlight a protein hierarchy, primarily in the Wnt pathway known for aberrant CLL activation. These individually identified proteins suggest a network-targeted strategy over single-target drug development. The findings advocate for a multi-target inhibition approach, limited to several key proteins to minimize side effects, thereby providing a foundation for designing therapies. This study emphasizes a shift towards a comprehensive, multi-scale analysis to enhance personalized treatment strategies for CLL, which could be experimentally validated using siRNA or small molecule inhibitors. The result is not just the identification of these proteins but their rank-order, offering a potent signal amplification in the context of the 20,000 proteins produced by the human body, thus providing a strategic basis for therapeutic intervention in CLL, underscoring the necessity for a more holistic, cellular, chromosomal, and genome-wide study to develop tailored treatments for CLL patients. Author SummaryChronic Lymphocytic Leukemia (CLL) is a unique and slowly progressing cancer affecting white blood cells, and research on CLL has highlighted the inconsistency of gene mutations across patients. Using a novel approach that merges statistical thermodynamics and systems biology, this research examines the CLL protein-protein interaction networks to pinpoint proteins integral to the onset of the disease. Betti number (a topology of complexity) estimates, which measure the importance of individual proteins when removed from the network, helped identify numerous potential therapeutic targets, notably within the Wnt signaling pathway, a pathway implicated in various cellular processes and known for its defective expression in CLL. The finding advocates for a multi-target inhibition approach, focusing on several key proteins to minimize side effects, thereby laying a foundation for designing more effective therapies for CLL. This paper emphasizes the potential benefits of a comprehensive study, spanning cellular to genome-wide scales, to design personalized treatments for CLL patients.

systems biology↗

bioRxiv2024

High-fidelity enhanced AsCas12a knock-in mice for efficient multiplexed gene editing, disease modeling and orthogonal immunogenetics

The advancement of CRISPR gene editing technology, especially the development of Cas9 knock-in mice, has significantly boosted the functional discovery of various genetics factors in diverse fields including genetics, genomics, immunology, and the biology of cancer. However, the pleiotropic effects on human disease and the complex nature of gene interaction networks require a knock-in mouse model capable of simultaneous multiplexed gene perturbation. Here, we present the generation and applications of Cre-dependent conditional and constitutive high-fidelity, enhanced AsCas12a (enAsCas12a-HF1) Rosa26-knock-in mice in the C57BL/6 background. With these mouse strains, we demonstrate highly efficient and multiplexed in vivo and ex vivo genome engineering as applied to lipid nanoparticle (LNP)-RNA-based liver protein targeting, AAV-based tumor modeling, and retrovirus-based immune cell engineering. By integrating with a dCas9-SPH CRISPR activation transgenic strain, we establish a simultaneous dual gene activation and knockout (DAKO) system that showcases the modular potential of these enAsCas12a-HF1 mice. Importantly, constitutive expression of enAsCas12a-HF1 does not lead to any discernable pathological differences as compared to the C57BL/6 background strain. These knock-in mice and the accompanying delivery methods would empower the deconvolution of complex gene interaction networks in broad areas of research.

bioengineering↗

bioRxiv2024

Protein disulfide isomerase disassembles stress granules and blocks cytoplasmic aggregation of TDP-43 in ALS

Cytoplasmic aggregation of the transactive response DNA-binding protein-43 (TDP-43) in neurons, a pathological feature common to amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration, has been found in some Alzheimers patients. Protein disulfide isomerase (PDI) functions as both an enzyme and a molecular chaperone to correct or eliminate misfolded proteins under pathological conditions. Here, we report that TDP-43 is mislocalized to the cytoplasm and colocalizes with PDI in the brain and spinal cord of two ALS patients and the brain of six Alzheimers patients compared to controls. TDP-43 selectively recruits wild-type PDI into its phase-separated condensate, which in turn slows down in vitro liquid-liquid phase separation of TDP-43, shifting the equilibrium phase boundary to higher protein concentrations. Importantly, wild-type PDI decreases oxidative stress-induced interaction between TDP-43 and G3BP1 to disassemble stress granules containing TDP-43 in neuronal cells. Wild-type PDI blocks the oxidative stress-induced mislocalization of TDP-43 to the cytoplasm, and blocks subsequent pathological phosphorylation and aggregation of TDP-43. We demonstrate that under pathological stress conditions, wild-type PDI disassembles stress granules, blocks cytoplasmic mislocalization and aggregation of TDP-43, and suppresses mitochondrial damage and TDP-43 toxicity. In the presence of abnormal forms of PDI, however, PDI loses its activity, and stress granules containing TDP-43 are assembled into amyloid fibrils, resulting in mitochondrial impairment and neuronal cell death in ALS patients and some Alzheimers patients. TeaserPDI disassembles SGs, blocks cytoplasmic mislocalization and aggregation of TDP-43, and suppress TDP-43 toxicity in ALS.

biochemistry↗

bioRxiv2024

Unicellular cyanobacteria rely on sodium energetics to fix N2

Diazotrophic cyanobacteria can thrive in combined nitrogen (N)-limited environments due to their ability to fix nitrogen gas (N2) from the atmosphere. Despite this, they occur in low abundance in N-limited coastal waters, which represents an ecological paradox1-3. One hypothesis is that this is partly due to elevated salinity (> 10 g/L NaCl), which inhibits cyanobacterial N2 fixation2,3. Here we show that N2 fixation in a unicellular coastal cyanobacterium is not inhibited but rather exclusively dependent on sodium (Na+) ions. In N-deficient environments, both N2 fixation and population growth were significantly inhibited at low NaCl concentrations (< 4 g/L). Additional experiments indicated that sodium energetics, rather than proton energetics, is necessary for N2 fixation, as Na+ deficiency resulted in insufficient ATP supply for N2 fixation. We show that this is due to the non-functioning Na+-coupled ATP synthase, which we found to be likely coupled to anaerobic rather than aerobic respiration. Sequence alignment analysis of the ion-coupling site of the ATP synthase revealed a high prevalence of Na+ energetics in cyanobacteria, with all unicellular N2 fixers capable of Na+ energetics. This suggests a critical role for sodium energetics in cyanobacteria. It also raises the possibility that sodium energetics is not as rare as thought, but that we may have underestimated the prevalence and importance of sodium energetics in other organisms. Finally, the low abundance of diazotrophic unicellular cyanobacteria in coastal waters may be due to insufficient NaCl levels to support N2-fixation during periods of growth-supporting high temperatures. This provides another perspective on the regulation of the oceanic N cycle that needs to be considered in times of global climate change. Changes in current patterns could lead to an overlap of periods optimal for N2 fixation and population growth, likely resulting in dense cyanobacterial blooms.

microbiology↗

bioRxiv2024

ATG6 interacting with NPR1 increases Arabidopsis thaliana resistance to Pst DC3000/avrRps4 by increasing its nuclear accumulation and stability

Autophagy-related gene 6 (ATG6) plays a crucial role in plant immunity. Nonexpressor of pathogenesis-related genes1 (NPR1) acts as a signaling hub of plant immunity. However, the relationship between ATG6 and NPR1 is unclear. Here, we find that ATG6 directly interacts with NPR1. ATG6 overexpression significantly increased nuclear accumulation of NPR1. Furthermore, we demonstrate that ATG6 increases NPR1 protein levels and improves its stability. Interestingly, ATG6 promotes the formation of SINCs (SA-induced NPR1 condensates)-like condensates. Additionally, ATG6 and NPR1 synergistically promote the expression of pathogenesis-related genes. Further results showed that silencing ATG6 in NPR1-GFP exacerbates Pst DC3000/avrRps4 infection, while double overexpression of ATG6 and NPR1 synergistically inhibits Pst DC3000/avrRps4 infection. In summary, our findings unveil an interplay of NPR1 with ATG6 and elucidate important molecular mechanisms for enhancing plant immunity. HighlightWe unveil a novel relationship in which ATG6 positively regulates NPR1 in plant immunity.

plant biology↗