Glia-to-Neuron Conversion by CRISPR-CasRx Alleviates Symptoms of Neurological Disease in Mice.

Conversion of glial cells into functional neurons represents a potential therapeutic approach for replenishing neuronal loss associated with neurodegenerative diseases and brain injury. Previous attempts in this area using expression of transcription factors were hindered by the low conversion efficiency and failure of generating desired neuronal types in vivo. Here, we report that downregulation of a single RNA-binding protein, polypyrimidine tract-binding protein 1 (Ptbp1), using in vivo viral delivery of a recently developed RNA-targeting CRISPR system CasRx, resulted in the conversion of Müller glia into retinal ganglion cells (RGCs) with a high efficiency, leading to the alleviation of disease symptoms associated with RGC loss. Furthermore, this approach also induced neurons with dopaminergic features in the striatum and alleviated motor defects in a Parkinson's disease mouse model. Thus, glia-to-neuron conversion by CasRx-mediated Ptbp1 knockdown represents a promising in vivo genetic approach for treating a variety of disorders due to neuronal loss.

A Mettl16/m6A/mybl2b/Igf2bp1 axis ensures cell cycle progression of embryonic hematopoietic stem and progenitor cells.

Prenatal lethality associated with mouse knockout of Mettl16, a recently identified RNA N6-methyladenosine (m6A) methyltransferase, has hampered characterization of the essential role of METTL16-mediated RNA m6A modification in early embryonic development. Here, using cross-species single-cell RNA sequencing analysis, we found that during early embryonic development, METTL16 is more highly expressed in vertebrate hematopoietic stem and progenitor cells (HSPCs) than other methyltransferases. In Mettl16-deficient zebrafish, proliferation capacity of embryonic HSPCs is compromised due to G1/S cell cycle arrest, an effect whose rescue requires Mettl16 with intact methyltransferase activity. We further identify the cell-cycle transcription factor mybl2b as a directly regulated by Mettl16-mediated m6A modification. Mettl16 deficiency resulted in the destabilization of mybl2b mRNA, likely due to lost binding by the m6A reader Igf2bp1 in vivo. Moreover, we found that the METTL16-m6A-MYBL2-IGF2BP1 axis controlling G1/S progression is conserved in humans. Collectively, our findings elucidate the critical function of METTL16-mediated m6A modification in HSPC cell cycle progression during early embryonic development.

Arabidopsis protein S-acyl transferases positively mediate BR signaling through S-acylation of BSK1.

Protein S-acyl transferases (PATs) catalyze S-acylation, a reversible post-translational modification critical for membrane association, trafficking, and stability of substrate proteins. Many plant proteins are potentially S-acylated but few have corresponding PATs identified. By using genomic editing, confocal imaging, pharmacological, genetic, and biochemical assays, we demonstrate that three Arabidopsis class C PATs positively regulate BR signaling through S-acylation of BRASSINOSTEROID-SIGNALING KINASE1 (BSK1). PAT19, PAT20, and PAT22 associate with the plasma membrane (PM) and the trans-Golgi network/early endosome (TGN/EE). Functional loss of all three genes results in a plethora of defects, indicative of reduced BR signaling and rescued by enhanced BR signaling. PAT19, PAT20, and PAT22 interact with BSK1 and are critical for the S-acylation of BSK1, and for BR signaling. The PM abundance of BSK1 was reduced by functional loss of PAT19, PAT20, and PAT22 whereas abolished by its S-acylation-deficient point mutations, suggesting a key role of S-acylation in its PM targeting. Finally, an active BR analog induces vacuolar trafficking and degradation of PAT19, PAT20, or PAT22, suggesting that the S-acylation of BSK1 by the three PATs serves as a negative feedback module in BR signaling.

Omitting Radiotherapy after Breast-Conserving Surgery in Luminal A Breast Cancer.

BACKGROUND: Adjuvant radiotherapy is prescribed after breast-conserving surgery to reduce the risk of local recurrence. However, radiotherapy is inconvenient, costly, and associated with both short-term and long-term side effects. Clinicopathologic factors alone are of limited use in the identification of women at low risk for local recurrence in whom radiotherapy can be omitted. Molecularly defined intrinsic subtypes of breast cancer can provide additional prognostic information. METHODS: We performed a prospective cohort study involving women who were at least 55 years of age, had undergone breast-conserving surgery for T1N0 (tumor size <2 cm and node negative), grade 1 or 2, luminal A-subtype breast cancer (defined as estrogen receptor positivity of ≥1%, progesterone receptor positivity of >20%, negative human epidermal growth factor receptor 2, and Ki67 index of ≤13.25%), and had received adjuvant endocrine therapy. Patients who met the clinical eligibility criteria were registered, and Ki67 immunohistochemical analysis was performed centrally. Patients with a Ki67 index of 13.25% or less were enrolled and did not receive radiotherapy. The primary outcome was local recurrence in the ipsilateral breast. In consultation with radiation oncologists and patients with breast cancer, we determined that if the upper boundary of the two-sided 90% confidence interval for the cumulative incidence at 5 years was less than 5%, this would represent an acceptable risk of local recurrence at 5 years. RESULTS: Of 740 registered patients, 500 eligible patients were enrolled. At 5 years after enrollment, recurrence was reported in 2.3% of the patients (90% confidence interval [CI], 1.3 to 3.8; 95% CI, 1.2 to 4.1), a result that met the prespecified boundary. Breast cancer occurred in the contralateral breast in 1.9% of the patients (90% CI, 1.1 to 3.2), and recurrence of any type was observed in 2.7% (90% CI, 1.6 to 4.1). CONCLUSIONS: Among women who were at least 55 years of age and had T1N0, grade 1 or 2, luminal A breast cancer that were treated with breast-conserving surgery and endocrine therapy alone, the incidence of local recurrence at 5 years was low with the omission of radiotherapy. (Funded by the Canadian Cancer Society and the Canadian Breast Cancer Foundation; LUMINA ClinicalTrials.gov number, NCT01791829.).

Design patterns for the construction of computational biological models.

Computational biological models have proven to be an invaluable tool for understanding and predicting the behaviour of many biological systems. While it may not be too challenging for experienced researchers to construct such models from scratch, it is not a straightforward task for early stage researchers. Design patterns are well-known techniques widely applied in software engineering as they provide a set of typical solutions to common problems in software design. In this paper, we collect and discuss common patterns that are usually used during the construction and execution of computational biological models. We adopt Petri nets as a modelling language to provide a visual illustration of each pattern; however, the ideas presented in this paper can also be implemented using other modelling formalisms. We provide two case studies for illustration purposes and show how these models can be built up from the presented smaller modules. We hope that the ideas discussed in this paper will help many researchers in building their own future models.

BERT-TFBS: a novel BERT-based model for predicting transcription factor binding sites by transfer learning.

Transcription factors (TFs) are proteins essential for regulating genetic transcriptions by binding to transcription factor binding sites (TFBSs) in DNA sequences. Accurate predictions of TFBSs can contribute to the design and construction of metabolic regulatory systems based on TFs. Although various deep-learning algorithms have been developed for predicting TFBSs, the prediction performance needs to be improved. This paper proposes a bidirectional encoder representations from transformers (BERT)-based model, called BERT-TFBS, to predict TFBSs solely based on DNA sequences. The model consists of a pre-trained BERT module (DNABERT-2), a convolutional neural network (CNN) module, a convolutional block attention module (CBAM) and an output module. The BERT-TFBS model utilizes the pre-trained DNABERT-2 module to acquire the complex long-term dependencies in DNA sequences through a transfer learning approach, and applies the CNN module and the CBAM to extract high-order local features. The proposed model is trained and tested based on 165 ENCODE ChIP-seq datasets. We conducted experiments with model variants, cross-cell-line validations and comparisons with other models. The experimental results demonstrate the effectiveness and generalization capability of BERT-TFBS in predicting TFBSs, and they show that the proposed model outperforms other deep-learning models. The source code for BERT-TFBS is available at https://github.com/ZX1998-12/BERT-TFBS.

pathMap: a path-based mapping tool for long noisy reads with high sensitivity.

With the rapid development of single-molecule sequencing (SMS) technologies, the output read length is continuously increasing. Mapping such reads onto a reference genome is one of the most fundamental tasks in sequence analysis. Mapping sensitivity is becoming a major concern since high sensitivity can detect more aligned regions on the reference and obtain more aligned bases, which are useful for downstream analysis. In this study, we present pathMap, a novel k-mer graph-based mapper that is specifically designed for mapping SMS reads with high sensitivity. By viewing the alignment chain as a path containing as many anchors as possible in the matched k-mer graph, pathMap treats chaining as a path selection problem in the directed graph. pathMap iteratively searches the longest path in the remaining nodes; more candidate chains with high quality can be effectively detected and aligned. Compared to other state-of-the-art mapping methods such as minimap2 and Winnowmap2, experiment results on simulated and real-life datasets demonstrate that pathMap obtains the number of mapped chains at least 11.50% more than its closest competitor and increases the mapping sensitivity by 17.28% and 13.84% of bases over the next-best mapper for Pacific Biosciences and Oxford Nanopore sequencing data, respectively. In addition, pathMap is more robust to sequence errors and more sensitive to species- and strain-specific identification of pathogens using MinION reads.

Phosphorylation and ubiquitination of OsWRKY31 are integral to OsMKK10-2-mediated defense responses in rice.

Mitogen-activated protein kinase (MPK) cascades play vital roles in plant innate immunity, growth, and development. Here, we report that the rice (Oryza sativa) transcription factor gene OsWRKY31 is a key component in a MPK signaling pathway involved in plant disease resistance in rice. We found that the activation of OsMKK10-2 enhances resistance against the rice blast pathogen Magnaporthe oryzae and suppresses growth through an increase in jasmonic acid and salicylic acid accumulation and a decrease of indole-3-acetic acid levels. Knockout of OsWRKY31 compromises the defense responses mediated by OsMKK10-2. OsMKK10-2 and OsWRKY31 physically interact, and OsWRKY31 is phosphorylated by OsMPK3, OsMPK4, and OsMPK6. Phosphomimetic OsWRKY31 has elevated DNA-binding activity and confers enhanced resistance to M. oryzae. In addition, OsWRKY31 stability is regulated by phosphorylation and ubiquitination via RING-finger E3 ubiquitin ligases interacting with WRKY 1 (OsREIW1). Taken together, our findings indicate that modification of OsWRKY31 by phosphorylation and ubiquitination functions in the OsMKK10-2-mediated defense signaling pathway.

The RNA helicase Mtr4p modulates polyadenylation in the TRAMP complex.

Many steps in nuclear RNA processing, surveillance, and degradation require TRAMP, a complex containing the poly(A) polymerase Trf4p, the Zn-knuckle protein Air2p, and the RNA helicase Mtr4p. TRAMP polyadenylates RNAs designated for decay or trimming by the nuclear exosome. It has been unclear how polyadenylation by TRAMP differs from polyadenylation by conventional poly(A) polymerase, which produces poly(A) tails that stabilize RNAs. Using reconstituted S. cerevisiae TRAMP, we show that TRAMP inherently suppresses poly(A) addition after only 3-4 adenosines. This poly(A) tail length restriction is controlled by Mtr4p. The helicase detects the number of 3'-terminal adenosines and, over several adenylation steps, elicits precisely tuned adjustments of ATP affinities and rate constants for adenylation and TRAMP dissociation. Our data establish Mtr4p as a critical regulator of polyadenylation by TRAMP and reveal that an RNA helicase can control the activity of another enzyme in a highly complex fashion and in response to features in RNA.

Self-powered, light-controlled, bioresorbable platforms for programmed drug delivery.

Degradable polymer matrices and porous scaffolds provide powerful mechanisms for passive, sustained release of drugs relevant to the treatment of a broad range of diseases and conditions. Growing interest is in active control of pharmacokinetics tailored to the needs of the patient via programmable engineering platforms that include power sources, delivery mechanisms, communication hardware, and associated electronics, most typically in forms that require surgical extraction after a period of use. Here we report a light-controlled, self-powered technology that bypasses key disadvantages of these systems, in an overall design that is bioresorbable. Programmability relies on the use of an external light source to illuminate an implanted, wavelength-sensitive phototransistor to trigger a short circuit in an electrochemical cell structure that includes a metal gate valve as its anode. Consequent electrochemical corrosion eliminates the gate, thereby opening an underlying reservoir to release a dose of drugs by passive diffusion into surrounding tissue. A wavelength-division multiplexing strategy allows release to be programmed from any one or any arbitrary combination of a collection of reservoirs built into an integrated device. Studies of various bioresorbable electrode materials define the key considerations and guide optimized choices in designs. In vivo demonstrations of programmed release of lidocaine adjacent the sciatic nerves in rat models illustrate the functionality in the context of pain management, an essential aspect of patient care that could benefit from the results presented here.

ARR1 and ARR12 modulate arsenite toxicity responses in Arabidopsis roots by transcriptionally controlling the actions of NIP1;1 and NIP6;1.

Cytokinin is central to coordinating plant adaptation to environmental stresses. Here, we first demonstrated the involvement of cytokinin in Arabidopsis responses to arsenite [As(III)] stress. As(III) treatment reduced cytokinin contents, while cytokinin treatment repressed further primary root growth in Arabidopsis plants under As(III) stress. Subsequently, we revealed that the cytokinin signaling members ARR1 and ARR12, the type-B ARABIDOPSIS RESPONSE REGULATORs, participate in cytokinin signaling-mediated As(III) responses in plants as negative regulators. A comprehensive transcriptome analysis of the arr1 and arr12 single and arr1,12 double mutants was then performed to decipher the cytokinin signaling-mediated mechanisms underlying plant As(III) stress adaptation. Results revealed important roles for ARR1 and ARR12 in ion transport, nutrient responses, and secondary metabolite accumulation. Furthermore, using hierarchical clustering and regulatory network analyses, we identified two NODULIN 26-LIKE INTRINSIC PROTEIN (NIP)-encoding genes, NIP1;1 and NIP6;1, potentially involved in ARR1/12-mediated As(III) uptake and transport in Arabidopsis. By analyzing various combinations of arr and nip mutants, including high-order triple and quadruple mutants, we demonstrated that ARR1 and ARR12 redundantly function as negative regulators of As(III) tolerance by acting upstream of NIP1;1 and NIP6;1 to modulate their function in arsenic accumulation. ChIP-qPCR, EMSA, and transient dual-LUC reporter assays revealed that ARR1 and ARR12 transcriptionally activate the expression of NIP1;1 and NIP6;1 by directly binding to their promoters and upregulating their expression, leading to increased arsenic accumulation under As(III) stress. These findings collectively provide insights into cytokinin signaling-mediated plant adaptation to excessive As(III), contributing to the development of crops with low arsenic accumulation.

HNSPPI: a hybrid computational model combing network and sequence information for predicting protein-protein interaction.

Most life activities in organisms are regulated through protein complexes, which are mainly controlled via Protein-Protein Interactions (PPIs). Discovering new interactions between proteins and revealing their biological functions are of great significance for understanding the molecular mechanisms of biological processes and identifying the potential targets in drug discovery. Current experimental methods only capture stable protein interactions, which lead to limited coverage. In addition, expensive cost and time consuming are also the obvious shortcomings. In recent years, various computational methods have been successfully developed for predicting PPIs based only on protein homology, primary sequences of protein or gene ontology information. Computational efficiency and data complexity are still the main bottlenecks for the algorithm generalization. In this study, we proposed a novel computational framework, HNSPPI, to predict PPIs. As a hybrid supervised learning model, HNSPPI comprehensively characterizes the intrinsic relationship between two proteins by integrating amino acid sequence information and connection properties of PPI network. The experimental results show that HNSPPI works very well on six benchmark datasets. Moreover, the comparison analysis proved that our model significantly outperforms other five existing algorithms. Finally, we used the HNSPPI model to explore the SARS-CoV-2-Human interaction system and found several potential regulations. In summary, HNSPPI is a promising model for predicting new protein interactions from known PPI data.

Artificial intelligence-assisted quantification and assessment of whole slide images for pediatric kidney disease diagnosis.

MOTIVATION: Pediatric kidney disease is a widespread, progressive condition that severely impacts growth and development of children. Chronic kidney disease is often more insidious in children than in adults, usually requiring a renal biopsy for diagnosis. Biopsy evaluation requires copious examination by trained pathologists, which can be tedious and prone to human error. In this study, we propose an artificial intelligence (AI) method to assist pathologists in accurate segmentation and classification of pediatric kidney structures, named as AI-based Pediatric Kidney Diagnosis (APKD). RESULTS: We collected 2935 pediatric patients diagnosed with kidney disease for the development of APKD. The dataset comprised 93 932 histological structures annotated manually by three skilled nephropathologists. APKD scored an average accuracy of 94% for each kidney structure category, including 99% in the glomerulus. We found strong correlation between the model and manual detection in detected glomeruli (Spearman correlation coefficient r = 0.98, P < .001; intraclass correlation coefficient ICC = 0.98, 95% CI = 0.96-0.98). Compared to manual detection, APKD was approximately 5.5 times faster in segmenting glomeruli. Finally, we show how the pathological features extracted by APKD can identify focal abnormalities of the glomerular capillary wall to aid in the early diagnosis of pediatric kidney disease. AVAILABILITY AND IMPLEMENTATION: https://github.com/ChunyueFeng/Kidney-DataSet.

Selective Inhibition of mTORC1 Signaling Supports the Development and Maintenance of Pluripotency.

Insight into the molecular mechanisms governing the development and maintenance of pluripotency is important for understanding early development and the use of stem cells in regenerative medicine. We demonstrate the selective inhibition of mTORC1 signaling is important for developing the inner cell mass (ICM) and the self-renewal of human embryonic stem cells. S6K suppressed the expression and function of pluripotency-related transcription factors (PTFs) OCT4, SOX2, and KLF4 through phosphorylation and ubiquitin proteasome-mediated protein degradation, indicating that S6K inhibition is required for pluripotency. PTFs inhibited mTOR signaling. The phosphorylation of S6 was decreased in PTF-positive cells of the ICM in embryos. Activation of mTORC1 signaling blocked ICM formation and the selective inhibition of S6K by rapamycin increased the ICM size in mouse blastocysts. Thus, selective inhibition of mTORC1 signaling supports the development and maintenance of pluripotency.

Cardiac forces regulate zebrafish heart valve delamination by modulating Nfat signaling.

In the clinic, most cases of congenital heart valve defects are thought to arise through errors that occur after the endothelial-mesenchymal transition (EndoMT) stage of valve development. Although mechanical forces caused by heartbeat are essential modulators of cardiovascular development, their role in these later developmental events is poorly understood. To address this question, we used the zebrafish superior atrioventricular valve (AV) as a model. We found that cellularized cushions of the superior atrioventricular canal (AVC) morph into valve leaflets via mesenchymal-endothelial transition (MEndoT) and tissue sheet delamination. Defects in delamination result in thickened, hyperplastic valves, and reduced heart function. Mechanical, chemical, and genetic perturbation of cardiac forces showed that mechanical stimuli are important regulators of valve delamination. Mechanistically, we show that forces modulate Nfatc activity to control delamination. Together, our results establish the cellular and molecular signature of cardiac valve delamination in vivo and demonstrate the continuous regulatory role of mechanical forces and blood flow during valve formation.

C3d-Targeted factor H inhibits tissue complement in disease models and reduces glomerular injury without affecting circulating complement.

Complement-mediated diseases can be treated using systemic inhibitors. However, complement components are abundant in circulation, affecting systemic inhibitors' exposure and efficacy. Furthermore, because of complement's essential role in immunity, systemic treatments raise infection risk in patients. To address these challenges, we developed antibody fusion proteins combining the alternative-pathway complement inhibitor factor H (fH1-5) with an anti-C3d monoclonal antibody (C3d-mAb-2fH). Because C3d is deposited at sites of complement activity, this molecule localizes to tissue complement while minimizing circulating complement engagement. These fusion proteins bind to deposited complement in diseased human skin sections and localize to activated complement in a primate skin injury model. We further explored the pharmacology of C3d-mAb-2fH proteins in rodent models with robust tissue complement activation. Doses of C3d-mAb-2fH >1 mg/kg achieved >75% tissue complement inhibition in mouse and rat injury models while avoiding circulating complement blockade. Glomerular-specific complement inhibition reduced proteinuria and preserved podocyte foot-process architecture in rat membranous nephropathy, indicating disease-modifying efficacy. These data indicate that targeting local tissue complement results in durable and efficacious complement blockade in skin and kidney while avoiding systemic inhibition, suggesting broad applicability of this approach in treating a range of complement-mediated diseases.

Rod genesis driven by mafba in an nrl knockout zebrafish model with altered photoreceptor composition and progressive retinal degeneration.

Neural retina leucine zipper (NRL) is an essential gene for the fate determination and differentiation of the precursor cells into rod photoreceptors in mammals. Mutations in NRL are associated with the autosomal recessive enhanced S-cone syndrome and autosomal dominant retinitis pigmentosa. However, the exact role of Nrl in regulating the development and maintenance of photoreceptors in the zebrafish (Danio rerio), a popular animal model used for retinal degeneration and regeneration studies, has not been fully determined. In this study, we generated an nrl knockout zebrafish model via the CRISPR-Cas9 technology and observed a surprising phenotype characterized by a reduced number, but not the total loss, of rods and over-growth of green cones. We discovered two waves of rod genesis, nrl-dependent and -independent at the embryonic and post-embryonic stages, respectively, in zebrafish by monitoring the rod development. Through bulk and single-cell RNA sequencing, we characterized the gene expression profiles of the whole retina and each retinal cell type from the wild type and nrl knockout zebrafish. The over-growth of green cones and mis-expression of green-cone-specific genes in rods in nrl mutants suggested that there are rod/green-cone bipotent precursors, whose fate choice between rod versus green-cone is controlled by nrl. Besides, we identified the mafba gene as a novel regulator of the nrl-independent rod development, based on the cell-type-specific expression patterns and the retinal phenotype of nrl/mafba double-knockout zebrafish. Gene collinearity analysis revealed the evolutionary origin of mafba and suggested that the function of mafba in rod development is specific to modern fishes. Furthermore, the altered photoreceptor composition and abnormal gene expression in nrl mutants caused progressive retinal degeneration and subsequent regeneration. Accordingly, this study revealed a novel function of the mafba gene in rod development and established a working model for the developmental and regulatory mechanisms regarding the rod and green-cone photoreceptors in zebrafish.

Biobased Polymers Enabling the Synthesis of Ultralong Silver Nanowires and Other Nanostructures.

Conventional polyol synthesis of silver nanowires has exclusively relied on polyvinylpyrrolidone (PVP), a nonbiodegradable polymer with no viable alternatives. The underlying reaction mechanism remains unclear. Herein, we discovered a new sustainable solution by employing biobased cellulose derivatives, including hydroxyethyl cellulose (HEC), as effective substitutes for PVP. Under mild reaction conditions (125 °C, ambient pressure), HEC facilitates the growth of ultralong silver nanowires (>100 µm) from penta-twinned silver seeds through a four-stage kinetic process. Theoretical calculations further reveal that HEC is physiosorbed onto the silver surfaces, while the presence of bromide ions (Br-) facilitates the evolution of seeds into nanowires. By varying halide ion concentrations and substitution in different cellulose derivatives, we successfully synthesized silver nanostructures with additional intriguing morphologies, including quasi-spherical nanoparticles, bipyramids, and nanocubes. Furthermore, transparent conductive films fabricated from ultralong silver nanowires synthesized with HEC demonstrated superior performance compared to those made with PVP-synthesized nanowires.

Liquid Biopsy Proteomics in Ophthalmology.

Minimally invasive liquid biopsies from the eye capture locally enriched fluids that contain thousands of proteins from highly specialized ocular cell types, presenting a promising alternative to solid tissue biopsies. The advantages of liquid biopsies include sampling the eye without causing irreversible functional damage, potentially better reflecting tissue heterogeneity, collecting samples in an outpatient setting, monitoring therapeutic response with sequential sampling, and even allowing examination of disease mechanisms at the cell level in living humans, an approach that we refer to as TEMPO (Tracing Expression of Multiple Protein Origins). Liquid biopsy proteomics has the potential to transform molecular diagnostics and prognostics and to assess disease mechanisms and personalized therapeutic strategies in individual patients. This review addresses opportunities, challenges, and future directions of high-resolution liquid biopsy proteomics in ophthalmology, with particular emphasis on the large-scale collection of high-quality samples, cutting edge proteomics technology, and artificial intelligence-supported data analysis.