Biology subjects

Liu, T.-Y.

Publications and source records attributed to Liu, T.-Y..

At least 19 recordsLinked to original sources

bioRxiv2026

Phosphorylation-dependent regulation of Hsp70 chaperones stimulates client recruitment

Molecular chaperones of the Hsp70 family play essential roles in maintaining proteostasis, particularly under conditions of cellular stress. Posttranslational modifications of Hsp70, collectively termed the chaperone code, are emerging as critical regulators of chaperone function, yet their mechanistic contributions remain incompletely understood. Here, we investigate the functional significance of a conserved phosphorylation site in Hsp70, corresponding to serine 326 in yeast Ssa1 and serine 329 in human HSPA8. We demonstrate that DNA double-strand break stress increases cellular reliance on Hsp70 activity in yeast, highlighting a role for chaperones in the DNA damage response. Loss of Ssa1 serine 326 phosphorylation impairs multiple Hsp70-dependent functions, including prion sequestration and glucocorticoid receptor maturation, indicating that this modification is required for optimal chaperone activity. Extending these findings to human cells, we show that the homologous HSPA8 serine 329 residue is necessary for efficient clearance of polyglutamine aggregates. Mechanistically, a phospho-deficient HSPA8-S329A mutant exhibits reduced client-binding capacity. Together, our findings identify a conserved phosphorylation event that enhances Hsp70 function by promoting client engagement, providing new insight into how the chaperone code regulates proteostasis across species.

molecular biology↗

bioRxiv2026

De Novo Regeneration of Rete Ridges during Cetacean skin wound healing

Humans are tight-skinned mammals who typically fail to regenerate large full-thickness skin wounds, instead healing with substantial scarring and concomitant loss of function. Mechanical context is a major determinant of this outcome: elevated tissue tension or stiffness promotes fibrotic repair associated with hypertrophic or keloid scarring. Accordingly, regenerative medicine research has relied on diverse animal models to understand scar development and skin regeneration. Loose-skinned mammals exhibit greater regeneration ability. Furthermore, spiny mouse skin is significantly less stiff and associated with enhanced regenerative ability. Interestingly, this skin wound stiffness can be modulated to shift healing toward more regenerative or more fibrotic trajectories. Despite of this progress, the restoration of normal skin architecture after large-full thickness injury has not been elucidated in tight-skinned mammals. Can large full-thickness wounds regenerate with minimal scarring in tight-skinned mammals? Here we show the tight-skinned mammal Frasers Dolphin regenerates de novo a complex rete ridge architecture with associated vasculature and minimal scar following large full-thickness wound healing. Counterintuitively, this skin regeneration occurs in an aqueous, high-shear stress and high-tension environment. Complete rete ridge regeneration in tight-skinned mammals has not been documented and not observed in humans except in utero. This unique ability to rebuild elaborate rete ridges under tension is an opportunity to uncover molecular, cellular, and tissue-level mechanisms that enable regenerative wound healing in a mechanical regime typically associated with fibrosis.

pathology↗

bioRxiv2025

Epigenetic Coalitions Couple Tissue Growth to Generate Periodic Colour Patterns in Birds

Periodic patterning is fundamental to biological organization. In birds, colour stripes and spots on growing embryonic surface and elongating feather filaments provide a unique system to study continuous periodic patterning in expanding domains. Agouti-signaling protein (ASIP) directly reports epigenomic activity during colour patterning. Using a neural-network-like architecture, we show that ASIPs cis-regulatory landscape functions as hidden layers, integrating morphogen feedback, chromatin topology, and tissue geometry to generate discrete colour outputs. Single-nucleus multiome and Micro-C uncover stage- and context-specific enhancer-silencer coalitions. Epidermal Wnt ligands activate ASIP while inducing Wnt inhibitors in fibroblasts, forming a negative-feedback loop coupling periodic patterning to domain expansion. Comparative cross-tissue and cross-species analyses define cis-regulatory modules comprising an epigenetic grammar; functional assays highlight retrotransposon co-option expanding ASIPs cis-regulatory repertoire, potentially contributing to colour pattern evolution. Together, these findings motivate a Turing-principle-based communication model: ASIP reflects epigenetic coalitions shifting to drive diverse, environmentally tunable colour motifs for adaptation.

developmental biology↗

bioRxiv2025

Novel Tissue Mechanics-Guided Cellular Flows Enable the Evolution of Feather Follicles

Complex tissue architecture is achieved through multiple rounds of morphological transitions. Here, we analyzed cellular flows and tissue mechanics during avian skin development. We showed how novel cellular flows initiate chemo-mechanical circuits that guide epithelial protrusion, folding, invagination, and spatial cell fate specification. In the initial feather bud formation, stiff dermal condensates protrude vertically out of the locally softened epithelial sheet. As the bud elongates, it stretches the epithelial cells at the base, which mechanically activates YAP and causes the epithelial sheet to fold downward and form a stiff cylindrical wall that invaginates into the skin. This stiff epithelial tongue is essential to the compaction and formation of the tightly packed dermal papillae. These topological transformational events are mechanically interconnected, and the completion of the previous circuit initializes the next one. On the contrary, during scale development, its rigid epithelial sheet restricts dermal cell flows, preventing other further topological transformation. We generated a topological transformation model to show how the process enables the novel evolution of feather follicles.

developmental biology↗

bioRxiv2025

Illuminating the Virosphere's Dark Matter using Hierarchical Deep Learning

Systematic discovery of novel viruses is essential for pandemic preparedness, understanding tumor-associated viruses, developing viral delivery systems, and advancing biomedical applications. Yet, the majority of sequences in metagenomic datasets lack close relatives in existing references, representing a vast viral "dark matter" whose biology and evolution remain largely unknown. The central task is threefold: 1) to determine whether a genome is viral or non-viral, 2) to correctly assign viral genomes to known lineages when possible, and, critically, 3) to recognize when no existing lineage applies and thereby identify candidates for entirely novel viral groups. Existing approaches, which depend on sequence homology or narrow markers, struggle to capture this uncharted viral space. Here we present DeepVirus, a hierarchical transformer-based framework that models viral genomes as structured sequences of protein-coding genes. By combining protein-level embeddings from a foundation model with genome-aware representations, DeepVirus not only achieves accurate classification across deep taxonomic hierarchies, but also extends beyond conventional classification to detect and organize candidate novel viral lineages through open-set recognition. Applied to large-scale metagenomic resources, DeepVirus uncovered extensive viral diversity, including previously uncharacterized RNA-dependent RNA polymerases (RdRps), thereby expanding the known evolutionary space of RNA viruses. DeepVirus integrates deep learning with genome-aware open-set discovery to illuminate viral dark matter, providing a foundation for systematic viral taxonomy and advancing exploration of the global virosphere, with broad implications for safeguarding human health.

bioinformatics↗

bioRxiv2025

Mouse Bio-behavioral Phenotyping Using a Digital Homecage Framework for Long-timescale, High-resolution, and Multi-factor Data Collection and Analytics

MOTIVATION Long-term monitoring of behavioral and physiological processes is critical for understanding complex brain-based phenomena and disorders that develop over extended periods, such as chronic stress, circadian disorders, and metabolic conditions. While digital phenotyping is available in humans using smart devices, there remains a deficit in rodent models. To address this lack of long-term rodent phenotyping, we introduce the "Digital Homecage" (DHC) system. Our system uses accessible components and is designed for seamless integration with brain recording technologies. The Digital Homecage (DHC) allows uninterrupted, long-timescale recording of more than 20 behavioral metrics in single-housed mice, captured at sub-second resolution via video, operant interactions, and wheel-running data. This report demonstrates the DHCs capacity to enable continuous, automated tracking of behaviors like actigraphy, sleep, grooming, and food choice options over weeks, thereby opening up new avenues for longitudinal analyses of chronic conditions. Data collected reveal circadian patterns in multiple spontaneous behaviors, aligning with known nocturnal tendencies. The systems potential to facilitate groundbreaking observations on the long-term behavioral correlates of various neuropsychiatric syndromes is aided by open-source software and relatively low cost. The DHC sets the stage for community-driven innovation, potentially transforming our approach to studying complex traits of brain function and behavior in laboratory settings.

animal behavior and cognition↗

bioRxiv2025

Involvement of PHOSPHATE TRANSPORTER TRAFFIC FACILITATOR1 in COPII assembly by interacting with SAR1 GTPase

Inorganic phosphate (Pi) uptake and translocation are crucial for plant growth and development, relying on plasma membrane targeting of PHOSPHATE TRANSPORTER1 (PHT1) transporters. The plant-specific endoplasmic reticulum (ER)-resident PHOSPHATE TRANSPORTER TRAFFIC FACILITATOR1 (PHF1) is structurally related to SEC12, which initiates the coat protein complex II (COPII) assembly as a guanine nucleotide exchange factor (GEF) by activating the small GTPase SAR1. In contrast, PHF1 loses the conserved catalytic residues critical for GEF activity and specifically assists the ER exit of the PHT1 transporters. However, the underlying molecular mechanism remains unknown. In this study, we showed that overexpression of Arabidopsis thaliana PHT1;1 (AtPHT1;1) in the tobacco transient expression system caused a portion of AtPHF1 distribution into AtSAR1b- and AtSEC24a-labeled ER exit sites. We demonstrated that AtPHF1 interacts with AtSAR1b and AtSAR1c based on the tripartite split-GFP association in agro-infiltrated tobacco leaves and verified this interaction using miniTurbo-based proximity labeling. We also confirmed its physiological relevance by co-immunoprecipitation of the endogenous AtPHF1 with AtSAR1c-GFP in Arabidopsis transgenic lines. Importantly, AtPHF1 preferentially interacts with the GDP-locked AtSAR1. Therefore, we propose that AtPHF1 or the AtPHT1;1-AtPHF1 complex interacts with the SAR1 GTPase to participate in the early step of COPII recruitment for the ER export of PHT1 transporters.

plant biology↗

bioRxiv2024

CORNICHON HOMOLOG 5-dependent ER export of membrane cargoes in phosphate-starved Arabidopsis root as revealed by membrane proteomic analysis

Developing plants tolerant of low phosphate (Pi) availability is essential to reduce reliance on fertilizers and achieve agricultural sustainability. One strategy is to enhance the endoplasmic reticulum (ER) export of cargoes associated with Pi starvation and their trafficking to final destinations. However, the mechanisms underlying this process are underexplored. We recently discovered that Arabidopsis thaliana CORNICHON HOMOLOG 5 (AtCNIH5) encodes a Pi deficiency-induced ER cargo receptor that regulates Pi homeostasis. To find potential membrane cargoes of AtCNIH5, we applied the UV-cleavable 4-hexylphenylazosulfonate (Azo)-solubilized microsomal protein extraction for iTRAQ-based proteomic analysis. We identified 4,317 proteins in Pi-limited Arabidopsis roots, with 372 upregulated and 106 downregulated proteins in cnih5. Besides PHOSPHATE TRANSPORTER 1 proteins (PHT1s), downregulation of the biosynthetic or modifying enzymes for cell wall polysaccharides, very long-chain fatty acids, and their derivative extracellular aliphatic compounds is over-represented. Using the yeast split-ubiquitin and the in-planta tripartite split-GFP assays, we verified the interaction of AtCNIH5 with various downregulated transporters in cnih5, including AtPHT1s, AtOCT1, AtURGT6, AtDTX21, and AtDTX35. In addition, we demonstrated that the C-terminal acidic residue of AtCNIH5 is required for interaction with AtOCT1 but not with AtPHT1;1 or AtDTX21, indicating distinct cargo selection mechanisms. More importantly, enhancing in-situ AtCNIH5 expression/activity enhances plant growth. By analogy with transcriptional factors that govern gene expression, we propose that AtCNIH5 acts as a low Pi-responsive hub to facilitate ER export of specific membrane cargoes, providing a potential engineering strategy to improve plant fitness under suboptimal Pi supply.

plant biology↗

bioRxiv2024

NKX2.2 and KLF4 cooperate to regulate alpha cell identity

Transcription factors (TFs) are indispensable for maintaining cell identity through regulating cell-specific gene expression. Distinct cell identities derived from a common progenitor are frequently perpetuated by shared TFs; yet the mechanisms that enable these TFs to regulate cell-specific targets are poorly characterized. We report that the TF NKX2.2 is critical for the identity of pancreatic islet cells by directly activating cell genes and repressing alternate islet cell fate genes. When compared to the known role of NKX2.2 in islet {beta} cells, we demonstrate that NKX2.2 regulates cell genes, facilitated in part by cell specific DNA binding at gene promoters. Furthermore, we have identified the reprogramming factor KLF4 as having enriched expression in cells, where it co-occupies NKX2.2-bound cell promoters, is necessary for NKX2.2 promoter occupancy in cells and co-regulates many NKX2.2 cell transcriptional targets. Misexpression of Klf4 in {beta} cells is sufficient to manipulate chromatin accessibility, increase binding of NKX2.2 at cell specific promoter sites, and alter expression of NKX2.2-regulated cell-specific targets. This study identifies KLF4 is a novel cell factor that cooperates with NKX2.2 to regulate cell identity.

developmental biology↗

bioRxiv2024

Phosphate Starvation-Induced CORNICHON HOMOLOG 5 as Endoplasmic Reticulum Cargo Receptor for PHT1 Transporters in Arabidopsis

Inorganic phosphate (Pi) is essential for plant growth and is acquired and distributed by the plasma membrane PHOSPHATE TRANSPORTER 1 proteins (PHT1s). Enhancing the abundance of PHT1s at the cell surface thus ensures plant productivity and sustainable agriculture. CORNICHON HOMOLOG proteins (CNIHs) are conserved eukaryotic cargo receptors that mediate the selective endoplasmic reticulum (ER) export of membrane proteins. In this study, we identified the Arabidopsis thaliana CNIH5 (AtCNIH5) as a Pi starvation-inducible gene, preferentially expressed in vascular tissues and outer root cell layers above the meristem. AtCNIH5 co-localizes to the AtSAR1A/AtSEC16A/AtSEC24A-labeled ER exit sites and interacts with AtPHT1;1. Loss of AtCNIH5 confers reduced shoot Pi levels under Pi sufficiency due to the reduced translocation of Pi from roots to shoots, as well as decreased Pi uptake under Pi deficiency. The cnih5 mutant exhibits decreased abundance of AtPHT1s but increased PHOSPHATE TRANSPORTER TRAFFIC FACILITATOR1 (AtPHF1), which enables the ER exit of PHT1s. The cnih5 mutant also shows a lower plasma membrane targeting efficiency of split-GFP tagged-AtPHT1;1 in the root hair and the epidermis within the root transition/elongation zone. Consistently, dysfunctional AtCNIH5 exerts a suppressive effect on the growth of phf1 and alleviates Pi toxicity in the Pi overaccumulator pho2. However, the in vivo protein-protein interaction and degradation assays indicated that AtCNIH5 is not a direct target of AtPHO2. Our findings unveil that AtCNIH5 is a low Pi-responsive ER cargo receptor that interplays with AtPHF1 to promote the plasma membrane targeting of AtPHT1s in a cell-type-dependent manner.

plant biology↗

bioRxiv2024

The tumor-maintaining function of UTX/KDM6A in DNA replication and the PARP1-dependent repair pathway

Histone H3K27 demethylase UTX (aka KDM6A) is mutated in many human cancers, suggesting its tumor suppressive role during cancer development. However, most tumors still express wild-type UTX/KDM6A and its function is not always linked to tumor suppression. Here, we present evidence of UTX/KDM6As role in sustaining tumor growth, revealing its function in tumor maintenance. We find that UTX/KDM6A sustains tumor cell cycling and survival via regulating DNA replication-associated transcriptional programs in a demethylase-independent manner. UTX/KDM6A can also interact with PARP1 and facilitate its recruitment to DNA lesions. Therefore, UTX/KDM6A depletion disrupts DNA replication and repair pathways, activating ATM-CHK2 and ATR-CHK1 signaling pathways and triggering S and G2/M checkpoints, leading to a pronounced defect in tumor growth. Analysis of human cancer xenograft models further demonstrates that knockdown of UTX/KDM6A by RNA-interference, rather than inhibition of its enzymatic activity via GSK-J4, shows potent anticancer effects. Dual inhibition of UTX/KDM6A and ATR further demonstrates synergistic anticancer activities. Our work highlights UTX/KDM6A as a potential therapeutic target for cancer treatment, especially when combined with ATR inhibition. HighlightsO_LIUTX/KDM6A contributes to tumor maintenance by promoting the growth and survival of tumor cells C_LIO_LITumor cells rely on UTX/KDM6A to maintain DNA replication, cell cycling, and DNA damage repair C_LIO_LIUTX/KDM6A depletion triggers S and G2/M checkpoints via activating ATM-CHK2 and ATR-CHK1 signaling pathways C_LIO_LITargeting UTX/KDM6A may prove to be an innovative strategy for cancer therapy, whether employed independently or in conjunction with ATR inhibitors. C_LI The Paper ExplainedO_ST_ABSProblemC_ST_ABSThe aggressive growth of tumors relies significantly on heightened proliferation rates and genomic instability, which necessitate robust DNA replication machinery and efficient DNA damage repair mechanisms for tumor cell survival and proliferation. UTX/KDM6A, a histone demethylase central to chromatin and epigenetic regulation, is commonly mutated in various human cancers. However, its role as a tumor suppressor or promoter remains unclear across different cancer contexts. This study delves into the potential tumor-maintaining role of UTX/KDM6A in cancer progression and tumorigenesis, establishing the mechanistic foundation for its tumor-promoting function. ResultsWe uncover UTX/KDM6As crucial role in tumor maintenance via its participation in DNA replication and repair pathways. Surprisingly, we find that its histone demethylase activity is dispensable for these functions, implying an alternative role as a scaffold protein. Consequently, our findings suggest that targeting the entire UTX/KDM6A gene or protein, rather than inhibiting its enzymatic activity, holds promise as a therapeutic strategy for tumors dependent on its tumor-maintaining function. ImpactThis study unveils UTX/KDM6As multifaceted role in cancer progression, shedding light on its diverse contributions to tumorigenesis. Our findings suggest promising therapeutic strategies for cancer treatment, highlighting the importance of targeting UTX/KDM6A and its impact on DNA replication and repair pathways. These discoveries set the stage for further exploration of UTX/KDM6A-mediated mechanisms in clinical settings, indicating potential applications in future clinical trials and combination therapy strategies.

cancer biology↗

bioRxiv2024

Target-aware Molecule Generation for Drug Design Using a Chemical Language Model

Generative drug design facilitates the creation of compounds effective against pathogenic target proteins. This opens up the potential to discover novel compounds within the vast chemical space and fosters the development of innovative therapeutic strategies. However, the practicality of generated molecules is often limited, as many designs focus on a narrow set of drug-related properties, failing to improve the success rate of subsequent drug discovery process. To overcome these challenges, we develop TamGen, a method that employs a GPT-like chemical language model and enables target-aware molecule generation and compound refinement. We demonstrate that the compounds generated by TamGen have improved molecular quality and viability. Additionally, we have integrated TamGen into a drug discovery pipeline and identified 7 compounds showing compelling inhibitory activity against the Tuberculosis ClpP protease, with the most effective compound exhibiting a half maximal inhibitory concentration (IC50) of 1.9 M. Our findings underscore the practical potential and real-world applicability of generative drug design approaches, paving the way for future advancements in the field.

biochemistry↗

bioRxiv2023

Accelerating protein engineering with fitness landscape modeling and reinforcement learning

Protein engineering holds significant promise for designing proteins with customized functions, yet the vast landscape of potential mutations versus limited lab capacity constrains the discovery of optimal sequences. To address this, we present the {micro}Protein framework, which accelerates protein engineering by combining {micro}Former, a deep learning model for accurate mutational effect prediction, with {micro}Search, a reinforcement learning algorithm designed to efficiently navigate the protein fitness landscape using {micro}Former as an oracle. {micro}Protein leverages single mutation data to predict optimal sequences with complex, multi-amino acid mutations through its modeling of epistatic interactions and a multi-step search strategy. Except from state-of-the-art performance on benchmark datasets, {micro}Protein identified high-gain-of-function multi-point mutants for the enzyme {beta}-lactamase, surpassing the highest known activity level, in wet-lab, trained solely on single mutation data. These results demonstrate {micro}Proteins capability to discover impactful mutations across vast protein sequence space, offering a robust, efficient approach for protein optimization.

synthetic biology↗

bioRxiv2023

AI2BMD: efficient characterization of protein dynamics with ab initio accuracy

Biomolecular dynamics simulation is a fundamental technology for life sciences research, and its usefulness depends on its accuracy and efficiency. Classical molecular dynamics simulation is fast but lacks chemical accuracy. Quantum chemistry methods like density functional theory (DFT) can reach chemical accuracy but cannot scale to support large biomolecules. We introduce an AI-based ab initio biomolecular dynamics system (AI2BMD) that can efficiently simulate large biomolecules with ab initio accuracy. AI2BMD uses a protein fragmentation scheme and machine learning force field to achieve generalizable ab initio accuracy for energy and force calculations for various proteins comprising over 10,000 atoms. Compared to DFT, it reduces computational time by several orders of magnitude. With several hundred nanoseconds of dynamics simulations, AI2BMD demonstrated its capability of efficiently exploring the conformational space of peptides and proteins, deriving accurate 3J-couplings that match NMR experiments, and showing protein folding and unfolding tendencies. Furthermore, AI2BMD enables precise free energy calculations for protein folding, and the estimated melting temperatures are well aligned with experiments. AI2BMD could potentially complement wet-lab experiments, detect the dynamic processes of bioactivities, and enable biomedical research that is currently impossible to conduct.

bioinformatics↗

bioRxiv2022

Incorporating Pre-training Paradigm for Antibody Sequence-Structure Co-design

Antibodies are versatile proteins that can bind to pathogens and provide effective protection for human body. Recently, deep learning-based computational antibody design has attracted popular attention since it automatically mines the antibody patterns from data that could be complementary to human experiences. However, the computational methods heavily rely on the high-quality antibody structure data, which is quite limited. Besides, the complementarity-determining region (CDR), which is the key component of an antibody that determines the specificity and binding affinity, is highly variable and hard to predict. Therefore, data limitation issue further raises the difficulty of CDR generation for antibodies. Fortunately, there exists a large amount of sequence data of antibodies that can help model the CDR and alleviate the reliance on structured data. By witnessing the success of pre-training models for protein modeling, in this paper, we develop an antibody pre-trained language model and incorporate it into the (antigen-specific) antibody design model in a systemic way. Specifically, we first pre-train an antibody language model based on the sequence data, then propose a one-shot way for sequence and structure generation of CDR to avoid the heavy cost and error propagation from an autoregressive manner, and finally leverage the pre-trained antibody model for the antigen-specific antibody generation model with some carefully designed modules. Through various experiments, we show that our method achieves superior performance over previous baselines on different tasks, such as sequence and structure generation, antigen-binding CDR-H3 design.

molecular biology↗

bioRxiv2022

Structural and computational insights into the SARS-CoV-2 Omicron RBD-ACE2 interaction

Since SARS-CoV-2 Omicron variant (B.1.1.529) was reported in November 2021, it has quickly spread to many countries and outcompeted the globally dominant Delta variant in several countries. The Omicron variant contains the largest number of mutations to date, with 32 mutations located at spike (S) glycoprotein, which raised great concern for its enhanced viral fitness and immune escape[1-4]. In this study, we reported the crystal structure of the receptor binding domain (RBD) of Omicron variant S glycoprotein bound to human ACE2 at a resolution of 2.6 [A]. Structural comparison, molecular dynamics simulation and binding free energy calculation collectively identified four key mutations (S477N, G496S, Q498R and N501Y) for the enhanced binding of ACE2 by the Omicron RBD compared to the WT RBD. Representative states of the WT and Omicron RBD-ACE2 systems were identified by Markov State Model, which provides a dynamic explanation for the enhanced binding of Omicron RBD. The effects of the mutations in the RBD for antibody recognition were analyzed, especially for the S371L/S373P/S375F substitutions significantly changing the local conformation of the residing loop to deactivate several class IV neutralizing antibodies.

biophysics↗

bioRxiv2020

CopulaNet: Learning residue co-evolution directly from multiple sequence alignment for protein structure prediction

Protein functions are largely determined by the final details of their tertiary structures, and the structures could be accurately reconstructed based on inter-residue distances. Residue co-evolution has become the primary principle for estimating inter-residue distances since the residues in close spatial proximity tend to co-evolve. The widely-used approaches infer residue co-evolution using an indirect strategy, i.e., they first extract from the multiple sequence alignment (MSA) of query protein some handcrafted features, say, co-variance matrix, and then infer residue co-evolution using these features rather than the raw information carried by MSA. This indirect strategy always leads to considerable information loss and inaccurate estimation of inter-residue distances. Here, we report a deep neural network framework (called CopulaNet) to learn residue co-evolution directly from MSA without any handcrafted features. The CopulaNet consists of two key elements: i) an encoder to model context-specific mutation for each residue, and ii) an aggregator to model correlations among residues and thereafter infer residue co-evolutions. Using the CASP13 (the 13th Critical Assessment of Protein Structure Prediction) target proteins as representatives, we demonstrated the successful application of CopulaNet for estimating inter-residue distances and further predicting protein tertiary structure with improved accuracy and efficiency. Head-to-head comparison suggested that for 24 out of the 31 free modeling CASP13 domains, ProFOLD outperformed AlphaFold, one of the state-of-the-art prediction approaches.

bioinformatics↗

bioRxiv2019

Cross-Sectional and Longitudinal Validation of Serum Neurofilament Light Chain (NfL) as a Biomarker of Parkinson’s Disease Progression

ObjectiveTo assess neurofilament light chain (NfL), as a biomarker for Parkinsons disease (PD). MethodsWe quantified NfL in (1) longitudinal CSF samples from PD, other cognate/neurodegenerative disorders (OND), and healthy controls (HC); (2) a cross-sectional cohort with paired CSF and serum samples from participants with PD, OND, and HC, and (3) a large longitudinal validation cohort with serum samples from PD, OND, HC, prodromal conditions, and mutation carriers. ResultsIn the longitudinal discovery cohort (1) NfL in CSF was highest in OND and higher in PD vs. HC across all visits (p<0.05) but did not change longitudinally. In the cross-sectional cohort (2) paired CSF and serum NfL samples were highly correlated (Spearmans rank [Formula]; p<10^-6). In the large validation cohort (3) mean baseline serum NfL was higher in PD (13{+/-}7.2pg/ml) vs. HC (12{+/-}6.7pg/ml; p=0.0336) and was highest in OND (18{+/-}7pg/ml; p=0.0351). Serum NfL increased longitudinally in PD vs. HC (p<0.01). Longitudinal motor scores were positively longitudinally associated with NfL, whereas some cognitive scores showed a negative longitudinal association with NfL. ConclusionsNfL levels in serum samples are increased in PD vs. HC, increase significantly over time, and correlate with clinical measures of PD severity. Although the specificity of NfL in PD is low and more specific biomarkers are needed, serum NfL is the first blood-based biomarker candidate that could support disease stratification (PD vs. OND), track clinical progression, and possibly assess responsiveness to neuroprotective treatments. NfL as a biomarker of response to neuroprotective interventions remains to be determined. Funding sources for studyPPMI is sponsored by the Michael J. Fox Foundation for Parkinsons Research (MJFF) and is co-funded by MJFF, Abbvie, Avid Radiopharmaceuticals, Biogen Idec, Bristol-Myers Squibb, Covance, Eli Lilly & Co., F. Hoffman-La Roche, Ltd., GE Healthcare, Genentech, GlaxoSmithKline, Lundbeck, Merck, MesoScale, Piramal, Pfizer and UCB. The funders had no role in the design and conduct of the study, in the collection, management, analysis, and interpretation of the data, in the preparation, review, or approval of the manuscript or in the decision to submit the manuscript for publication. Financial Disclosure/Conflict of Interest concerning the research related to the manuscriptBrit Mollenhauer, Douglas Galasko, Tatiana Foroud, Lana M. Chahine, Christopher S. Coffey, Andrew B. Singleton, Tanya Simuni, Daniel Weintraub, John Seibyl, Arthur W. Toga, and Caroline M. Tanner received funding from The Michael J. Fox Foundation for Parkinsons Research. Mohammed Dakna, Tzu-Ying Liu, Henrik Zetterberg, Sebastian Schade, Roland G. Gera, Wenting Wang, Feng Gao, Niels Kruse, Mark Frasier, Jesse M. Cedarbaum, Samantha J. Hutten, Claudia Trenkwalder, and Danielle Graham report no disclosures.

neuroscience↗