TMED10 mediates the trafficking of insulin-like growth factor 2 along the secretory pathway for myoblast differentiation.

The insulin-like growth factor 2 (IGF2) plays critical roles in cell proliferation, migration, differentiation, and survival. Despite its importance, the molecular mechanisms mediating the trafficking of IGF2 along the secretory pathway remain unclear. Here, we utilized a Retention Using Selective Hook system to analyze molecular mechanisms that regulate the secretion of IGF2. We found that a type I transmembrane protein, TMED10, is essential for the secretion of IGF2 and for differentiation of mouse myoblast C2C12 cells. Further analyses indicate that the residues 112-140 in IGF2 are important for the secretion of IGF2 and these residues directly interact with the GOLD domain of TMED10. We then reconstituted the release of IGF2 into COPII vesicles. This assay suggests that TMED10 mediates the packaging of IGF2 into COPII vesicles to be efficiently delivered to the Golgi. Moreover, TMED10 also mediates ER export of TGN-localized cargo receptor, sortilin, which subsequently mediates TGN export of IGF2. These analyses indicate that TMED10 is critical for IGF2 secretion by directly regulating ER export and indirectly regulating TGN export of IGF2, providing insights into trafficking of IGF2 for myoblast differentiation.

Identifications of novel host cell factors that interact with the receptor-binding domain of the SARS-CoV-2 spike protein.

SARS-CoV-2 entry into host cells is facilitated by the interaction between the receptor-binding domain of its spike protein (CoV2-RBD) and host cell receptor, ACE2, promoting viral membrane fusion. The virus also uses endocytic pathways for entry, but the mediating host factors remain largely unknown. It is also unknown whether mutations in the RBD of SARS-CoV-2 variants promote interactions with additional host factors to promote viral entry. Here, we used the GST pull-down approach to identify novel surface-located host factors that bind to CoV2-RBD. One of these factors, SH3BP4, regulates internalization of CoV2-RBD in an ACE2-independent but integrin- and clathrin-dependent manner and mediates SARS-CoV-2 pseudovirus entry, suggesting that SH3BP4 promotes viral entry via the endocytic route. Many of the identified factors, including SH3BP4, ADAM9, and TMEM2, show stronger affinity to CoV2-RBD than to RBD of the less infective SARS-CoV, suggesting SARS-CoV-2-specific utilization. We also found factors preferentially binding to the RBD of the SARS-CoV-2 Delta variant, potentially enhancing its entry. These data identify the repertoire of host cell surface factors that function in the events leading to the entry of SARS-CoV-2.

Zero-shot-capable identification of phage-host relationships with whole-genome sequence representation by contrastive learning.

Accurately identifying phage-host relationships from their genome sequences is still challenging, especially for those phages and hosts with less homologous sequences. In this work, focusing on identifying the phage-host relationships at the species and genus level, we propose a contrastive learning based approach to learn whole-genome sequence embeddings that can take account of phage-host interactions (PHIs). Contrastive learning is used to make phages infecting the same hosts close to each other in the new representation space. Specifically, we rephrase whole-genome sequences with frequency chaos game representation (FCGR) and learn latent embeddings that 'encapsulate' phages and host relationships through contrastive learning. The contrastive learning method works well on the imbalanced dataset. Based on the learned embeddings, a proposed pipeline named CL4PHI can predict known hosts and unseen hosts in training. We compare our method with two recently proposed state-of-the-art learning-based methods on their benchmark datasets. The experiment results demonstrate that the proposed method using contrastive learning improves the prediction accuracy on known hosts and demonstrates a zero-shot prediction capability on unseen hosts. In terms of potential applications, the rapid pace of genome sequencing across different species has resulted in a vast amount of whole-genome sequencing data that require efficient computational methods for identifying phage-host interactions. The proposed approach is expected to address this need by efficiently processing whole-genome sequences of phages and prokaryotic hosts and capturing features related to phage-host relationships for genome sequence representation. This approach can be used to accelerate the discovery of phage-host interactions and aid in the development of phage-based therapies for infectious diseases.

biotextgraph: graphical summarization of functional similarities from textual information.

SUMMARY: Functional interpretation of biological entities such as differentially expressed genes is one of the fundamental analyses in bioinformatics. The task can be addressed by using biological pathway databases with enrichment analysis (EA). However, textual description of biological entities in public databases is less explored and integrated in existing tools and it has a potential to reveal new mechanisms. Here, we present a new R package biotextgraph for graphical summarization of omics' textual description data which enables assessment of functional similarities of the lists of biological entities. We illustrate application examples of annotating gene identifiers in addition to EA. The results suggest that the visualization based on words and inspection of biological entities with text can reveal a set of biologically meaningful terms that could not be obtained by using biological pathway databases alone. The results suggest the usefulness of the package in the routine analysis of omics-related data. The package also offers a web-based application for convenient querying. AVAILABILITY AND IMPLEMENTATION: The package, documentation, and web server are available at: https://github.com/noriakis/biotextgraph.

A Global Analysis of the Receptor Tyrosine Kinase-Protein Phosphatase Interactome.

Receptor tyrosine kinases (RTKs) and protein phosphatases comprise protein families that play crucial roles in cell signaling. We used two protein-protein interaction (PPI) approaches, the membrane yeast two-hybrid (MYTH) and the mammalian membrane two-hybrid (MaMTH), to map the PPIs between human RTKs and phosphatases. The resulting RTK-phosphatase interactome reveals a considerable number of previously unidentified interactions and suggests specific roles for different phosphatase families. Additionally, the differential PPIs of some protein tyrosine phosphatases (PTPs) and their mutants suggest diverse mechanisms of these PTPs in the regulation of RTK signaling. We further found that PTPRH and PTPRB directly dephosphorylate EGFR and repress its downstream signaling. By contrast, PTPRA plays a dual role in EGFR signaling: besides facilitating EGFR dephosphorylation, it enhances downstream ERK signaling by activating SRC. This comprehensive RTK-phosphatase interactome study provides a broad and deep view of RTK signaling.

Targeted Quantification of Glutathione/Arginine Redox Metabolism Based on a Novel Paired Mass Spectrometry Probe Approach for the Functional Assessment of Redox Status.

Glutathione (GSH) redox control and arginine metabolism are critical in regulating the physiological response to injury and oxidative stress. Quantification assessment of the GSH/arginine redox metabolism supports monitoring metabolic pathway shifts during pathological processes and their linkages to redox regulation. However, assessing the redox status of organisms with complex matrices is challenging, and single redox molecule analysis may not be accurate for interrogating the redox status in cells and in vivo. Herein, guided by a paired derivatization strategy, we present a new ultraperformance liquid chromatography tandem mass spectrometry (UPLC-MS/MS)-based approach for the functional assessment of biological redox status. Two structurally analogous probes, 6-aminoquinolyl-N-hydroxysuccinimidyl carbamate (AQC) and newly synthesized 2-methyl-6-aminoquinolyl-N-hydroxysuccinimidyl carbamate (MeAQC), were set for paired derivatization. The developed approach was successfully applied to LPS-stimulated RAW 264.7 cells and HDM-induced asthma mice to obtain quantitative information on GSH/arginine redox metabolism. The results suggest that the redox status was remarkably altered upon LPS and HDM stimulation. We expect that this approach will be of good use in a clinical biomarker assay and potential drug screening associated with redox metabolism, oxidative damage, and redox signaling.

MTD: a unique pipeline for host and meta-transcriptome joint and integrative analyses of RNA-seq data.

Ribonucleic acid (RNA)-seq data contain not only host transcriptomes but also nonhost information that comprises transcripts from active microbiota in the host cells. Therefore, joint and integrative analyses of both host and meta-transcriptome can reveal gene expression of the microbial community in a given sample as well as the correlative and interactive dynamics of the host response to the microbiome. However, there are no convenient tools that can systemically analyze host-microbiota interactions through simultaneously quantifying the host and meta-transcriptome in the same sample at the tissue and the single-cell level. This poses a challenge for interested researchers with limited expertise in bioinformatics. Here, we developed a software pipeline that can comprehensively and synergistically analyze and correlate the host and meta-transcriptome in a single sample using bulk and single-cell RNA-seq data. This pipeline, named meta-transcriptome detector (MTD), can extensively identify and quantify microbiome, including viruses, bacteria, protozoa, fungi, plasmids and vectors, in the host cells and correlate the microbiome with the host transcriptome. MTD is easy to install and run, involving only a few lines of simple commands. It offers researchers with unique genomics insights into host responses to microorganisms.

Investigation of the BERT model on nucleotide sequences with non-standard pre-training and evaluation of different k-mer embeddings.

MOTIVATION: In recent years, pre-training with the transformer architecture has gained significant attention. While this approach has led to notable performance improvements across a variety of downstream tasks, the underlying mechanisms by which pre-training models influence these tasks, particularly in the context of biological data, are not yet fully elucidated. RESULTS: In this study, focusing on the pre-training on nucleotide sequences, we decompose a pre-training model of Bidirectional Encoder Representations from Transformers (BERT) into its embedding and encoding modules to analyze what a pre-trained model learns from nucleotide sequences. Through a comparative study of non-standard pre-training at both the data and model levels, we find that a typical BERT model learns to capture overlapping-consistent k-mer embeddings for its token representation within its embedding module. Interestingly, using the k-mer embeddings pre-trained on random data can yield similar performance in downstream tasks, when compared with those using the k-mer embeddings pre-trained on real biological sequences. We further compare the learned k-mer embeddings with other established k-mer representations in downstream tasks of sequence-based functional prediction. Our experimental results demonstrate that the dense representation of k-mers learned from pre-training can be used as a viable alternative to one-hot encoding for representing nucleotide sequences. Furthermore, integrating the pre-trained k-mer embeddings with simpler models can achieve competitive performance in two typical downstream tasks. AVAILABILITY AND IMPLEMENTATION: The source code and associated data can be accessed at https://github.com/yaozhong/bert_investigation.

Stability and integrity of self-assembled bovine parvovirus virus­like particles (BPV­VLPs) of VP2 and combination of VP1VP2 assisted by baculovirus-insect cell expression: a potential logistical platform for vaccine deployment.

BACKGROUND: Bovine parvovirus (BPV) is an autonomous DNA virus with a smaller molecular size and subtle differences in its structural proteins, unlike other animal parvoviruses. More importantly, this virus has the potential to produce visible to silent economic catastrophes in the livestock business, despite receiving very little attention. Parvoviral virus-like particles (VLPs) as vaccines and as logistical platforms for vaccine deployment are well studied. However, no single experimental report on the role of VP1 in the assembly and stability of BPV-VLPs is available. Furthermore, the self-assembly, integrity and stability of the VLPs of recombinant BPV VP2 in comparison to VP1 VP2 Cap proteins using any expression method has not been studied previously. In this study, we experimentally evaluated the self-assembling ability with which BPV virus-like particles (VLPs) could be synthesized from a single structural protein (VP2) and by integrating both VP2 and VP1 amino acid sequences. METHODS: In silico and experimental cloning methods were carried out. His-tagged and without-His-tag VP2 and V1VP2-encoding amino acid sequences were cloned and inserted into pFastbacdual, and insect cell-generated recombinant protein was evaluated by SDS‒PAGE and western blot. Period of infectivity and expression level were determined by IFA. The integrity and stability of the BPV VLPs were evaluated by transmission electron microscopy. The secondary structure of the BPV VLPs from both VP2 and V1VP2 was analyzed by circular dichroism. RESULTS: Our findings show that VP2 alone was equally expressed and purified into detectable proteins, and the stability at different temperatures and pH values was not appreciably different between the two kinds of VLPs. Furthermore, BPV-VP2 VLPs were praised for their greater purity and integrity than BPV-VP1VP2 VLPs, as indicated by SDS‒PAGE. Therefore, our research demonstrates that the function of VP1 has no bearing on the stability or integrity of BPV-VLPs. CONCLUSIONS: In summary, incredible physiochemically stable BPV VP2-derived VLPs have been found to be promising candidates for the development of multivalent vaccines and immunodiagnostic kits against enteric viruses and to carry heterogeneous epitopes for various economically important livestock diseases.

Phosphorylation of PP2Ac by PKC is a key regulatory step in the PP2A-switch-dependent AKT dephosphorylation that leads to apoptosis.

BACKGROUND: Although GqPCR activation often leads to cell survival by activating the PI3K/AKT pathway, it was previously shown that in several cell types AKT activity is reduced and leads to JNK activation and apoptosis. The mechanism of AKT inactivation in these cells involves an IGBP1-coupled PP2Ac switch that induces the dephosphorylation and inactivation of both PI3K and AKT. However, the machinery involved in the initiation of PP2A switch is not known. METHODS: We used phospho-mass spectrometry to identify the phosphorylation site of PP2Ac, and raised specific antibodies to follow the regulation of this phosphorylation. Other phosphorylations were monitored by commercial antibodies. In addition, we used coimmunoprecipitation and proximity ligation assays to follow protein-protein interactions. Apoptosis was detected by a TUNEL assay as well as PARP1 cleavage using SDS-PAGE and Western blotting. RESULTS: We identified Ser24 as a phosphorylation site in PP2Ac. The phosphorylation is mediated mainly by classical PKCs (PKCα and PKCß) but not by novel PKCs (PKCδ and PKCε). By replacing the phosphorylated residue with either unphosphorylatable or phosphomimetic residues (S24A and S24E), we found that this phosphorylation event is necessary and sufficient to mediate the PP2A switch, which ultimately induces AKT inactivation, and a robust JNK-dependent apoptosis. CONCLUSION: Our results show that the PP2A switch is induced by PKC-mediated phosphorylation of Ser24-PP2Ac and that this phosphorylation leads to apoptosis upon GqPCR induction of various cells. We propose that this mechanism may provide an unexpected way to treat some cancer types or problems in the endocrine machinery.

An in vitro vesicle formation assay reveals cargo clients and factors that mediate vesicular trafficking.

The fidelity of protein transport in the secretory pathway relies on the accurate sorting of proteins to their correct destinations. To deepen our understanding of the underlying molecular mechanisms, it is important to develop a robust approach to systematically reveal cargo proteins that depend on specific sorting machinery to be enriched into transport vesicles. Here, we used an in vitro assay that reconstitutes packaging of human cargo proteins into vesicles to quantify cargo capture. Quantitative mass spectrometry (MS) analyses of the isolated vesicles revealed cytosolic proteins that are associated with vesicle membranes in a GTP-dependent manner. We found that two of them, FAM84B (also known as LRAT domain containing 2 or LRATD2) and PRRC1, contain proline-rich domains and regulate anterograde trafficking. Further analyses revealed that PRRC1 is recruited to endoplasmic reticulum (ER) exit sites, interacts with the inner COPII coat, and its absence increases membrane association of COPII. In addition, we uncovered cargo proteins that depend on GTP hydrolysis to be captured into vesicles. Comparing control cells with cells depleted of the cargo receptors, SURF4 or ERGIC53, we revealed specific clients of each of these two export adaptors. Our results indicate that the vesicle formation assay in combination with quantitative MS analysis is a robust and powerful tool to uncover novel factors that mediate vesicular trafficking and to uncover cargo clients of specific cellular factors.

Fe(III)/peroxymonosulfate oxidation system for the degradation of rhein, a toxic component abundance in rhubarb residue.

Rhubarb is widely used in health care, but causing a great amount of rhein-containing herbal residue. Rhein with several toxicities might pollute environment, damage ecology and even hazard human health if left untreated. In this study, the degradation effects of bisulfite- (BS) and peroxymonosulfate- (PMS) based oxidation systems on rhein in rhubarb residue were compared and investigated. The effects of BS and PMS with two valence states of ferric ion (Fe) on the degradation of rhein in rhubarb residue were optimized for the selection of optimal oxidation system. The influences of reaction temperature, reaction time and initial pH on the removal of rhein under the optimal oxidation system were evaluated. The chemical profiles of rhubarb residue with and without oxidation process were compared by UPLC-QTOF-MS/MS, and the degradation effects were investigated by PLS-DA and S plot/OPLS-DA analysis. The results manifested that PMS showed relative higher efficiency than BS on the degradation of rhein. Moreover, Fe(III) promoted the degradation effect of PMS, demonstrated that Fe(III)/PMS is the optimal oxidation system to degrade rhein in rhubarb residue. Further studies indicated that the degradation of rhein by the Fe(III)/PMS oxidation system was accelerated with the prolong of reaction time and the elevation of reaction temperature, and also affected by the initial pH. More importantly, Fe(III)/PMS oxidation system could degrade rhein in rhubarb residue completely under the optimal conditions. In conclusion, Fe(III)/PMS oxidation system is a feasible method to treat rhein in rhubarb residue.

Genome-Wide Association Analysis of Protein-Coding Variants in IgA Nephropathy.

SIGNIFICANCE STATEMENT: Genome-wide association studies have identified nearly 20 IgA nephropathy susceptibility loci. However, most nonsynonymous coding variants, particularly ones that occur rarely or at a low frequency, have not been well investigated. The authors performed a chip-based association study of IgA nephropathy in 8529 patients with the disorder and 23,224 controls. They identified a rare variant in the gene encoding vascular endothelial growth factor A (VEGFA) that was significantly associated with a two-fold increased risk of IgA nephropathy, which was further confirmed by sequencing analysis. They also identified a novel common variant in PKD1L3 that was significantly associated with lower haptoglobin protein levels. This study, which was well-powered to detect low-frequency variants with moderate to large effect sizes, helps expand our understanding of the genetic basis of IgA nephropathy susceptibility. BACKGROUND: Genome-wide association studies have identified nearly 20 susceptibility loci for IgA nephropathy. However, most nonsynonymous coding variants, particularly those occurring rarely or at a low frequency, have not been well investigated. METHODS: We performed a three-stage exome chip-based association study of coding variants in 8529 patients with IgA nephropathy and 23,224 controls, all of Han Chinese ancestry. Sequencing analysis was conducted to investigate rare coding variants that were not covered by the exome chip. We used molecular dynamic simulation to characterize the effects of mutations of VEGFA on the protein's structure and function. We also explored the relationship between the identified variants and the risk of disease progression. RESULTS: We discovered a novel rare nonsynonymous risk variant in VEGFA (odds ratio, 1.97; 95% confidence interval [95% CI], 1.61 to 2.41; P = 3.61×10 -11 ). Further sequencing of VEGFA revealed twice as many carriers of other rare variants in 2148 cases compared with 2732 controls. We also identified a common nonsynonymous risk variant in PKD1L3 (odds ratio, 1.16; 95% CI, 1.11 to 1.21; P = 1.43×10 -11 ), which was associated with lower haptoglobin protein levels. The rare VEGFA mutation could cause a conformational change and increase the binding affinity of VEGFA to its receptors. Furthermore, this variant was associated with the increased risk of kidney disease progression in IgA nephropathy (hazard ratio, 2.99; 95% CI, 1.09 to 8.21; P = 0.03). CONCLUSIONS: Our study identified two novel risk variants for IgA nephropathy in VEGFA and PKD1L3 and helps expand our understanding of the genetic basis of IgA nephropathy susceptibility.

Biochemical characterization of a new ulvan lyase and its applicability in utilization of ulvan and preparation of ulva oligosaccharides.

Ulva is globally distributed specie and has a high economic value. Ulvan is one of the main active substances in Ulva, which has a variety of biological properties. Ulvan lyase degrades ulvan through a ß-elimination mechanism which cleaves the ß-glycosidic bond between Rha3S and GlcA or IdoA. The complex monosaccharide composition of ulvan makes it promising for use in food and pharmaceutical applications. This thesis explores a putative ulvan lyase from Alteromonas sp. KUL_42. We expressed and purified the protein, performed a series of characterizations and signal peptide had been removed. The results showed that the protein molecular weight of ULA-2 was 53.97 kDa, and it had the highest catalytic activity at 45 °C and pH 8.0 in Tris-HCl buffer. The Km and Vmax values were 2.24 mg · mL-1 and 2.048 µmol · min-1 · mL-1, respectively. The activity of ULA-2 was able to maintain more than 80% at 20 ~ 30 °C. ESI-MS analysis showed that the primary end-products were mainly disaccharides to tetrasaccharides. The study of ULA-2 enriches the ulvan lyase library, promotes the development and high-value utilization of Ulva resources, and facilitates further research applications of ulvan lyase in ulva oligosaccharides.

Identification of bacteriophage genome sequences with representation learning.

MOTIVATION: Bacteriophages/phages are the viruses that infect and replicate within bacteria and archaea, and rich in human body. To investigate the relationship between phages and microbial communities, the identification of phages from metagenome sequences is the first step. Currently, there are two main methods for identifying phages: database-based (alignment-based) methods and alignment-free methods. Database-based methods typically use a large number of sequences as references; alignment-free methods usually learn the features of the sequences with machine learning and deep learning models. RESULTS: We propose INHERIT which uses a deep representation learning model to integrate both database-based and alignment-free methods, combining the strengths of both. Pre-training is used as an alternative way of acquiring knowledge representations from existing databases, while the BERT-style deep learning framework retains the advantage of alignment-free methods. We compare INHERIT with four existing methods on a third-party benchmark dataset. Our experiments show that INHERIT achieves a better performance with the F1-score of 0.9932. In addition, we find that pre-training two species separately helps the non-alignment deep learning model make more accurate predictions. AVAILABILITY AND IMPLEMENTATION: The codes of INHERIT are now available in: https://github.com/Celestial-Bai/INHERIT. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

CFTR interactome mapping using the mammalian membrane two-hybrid high-throughput screening system.

Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) is a chloride and bicarbonate channel in secretory epithelia with a critical role in maintaining fluid homeostasis. Mutations in CFTR are associated with Cystic Fibrosis (CF), the most common lethal autosomal recessive disorder in Caucasians. While remarkable treatment advances have been made recently in the form of modulator drugs directly rescuing CFTR dysfunction, there is still considerable scope for improvement of therapeutic effectiveness. Here, we report the application of a high-throughput screening variant of the Mammalian Membrane Two-Hybrid (MaMTH-HTS) to map the protein-protein interactions of wild-type (wt) and mutant CFTR (F508del), in an effort to better understand CF cellular effects and identify new drug targets for patient-specific treatments. Combined with functional validation in multiple disease models, we have uncovered candidate proteins with potential roles in CFTR function/CF pathophysiology, including Fibrinogen Like 2 (FGL2), which we demonstrate in patient-derived intestinal organoids has a significant effect on CFTR functional expression.

A novel approach to quantify calcifications of thyroid nodules in US images based on deep learning: predicting the risk of cervical lymph node metastasis in papillary thyroid cancer patients.

OBJECTIVE: Based on ultrasound (US) images, this study aimed to detect and quantify calcifications of thyroid nodules, which are regarded as one of the most important features in US diagnosis of thyroid cancer, and to further investigate the value of US calcifications in predicting the risk of lymph node metastasis (LNM) in papillary thyroid cancer (PTC). METHODS: Based on the DeepLabv3+ networks, 2992 thyroid nodules in US images were used to train a model to detect thyroid nodules, of which 998 were used to train a model to detect and quantify calcifications. A total of 225 and 146 thyroid nodules obtained from two centers, respectively, were used to test the performance of these models. A logistic regression method was used to construct the predictive models for LNM in PTCs. RESULTS: Calcifications detected by the network model and experienced radiologists had an agreement degree of above 90%. The novel quantitative parameters of US calcification defined in this study showed a significant difference between PTC patients with and without cervical LNM (p < 0.05). The calcification parameters were beneficial to predicting the LNM risk in PTC patients. The LNM prediction model using these calcification parameters combined with patient age and other US nodular features showed a higher specificity and accuracy than the calcification parameters alone. CONCLUSIONS: Our models not only detect the calcifications automatically, but also have value in predicting cervical LNM risk of PTC patients, thereby making it possible to investigate the relationship between calcifications and highly invasive PTC in detail. CLINICAL RELEVANCE STATEMENT: Due to the high association of US microcalcifications with thyroid cancers, our model will contribute to the differential diagnosis of thyroid nodules in daily practice. KEY POINTS: • We developed an ML-based network model for automatically detecting and quantifying calcifications within thyroid nodules in US images. • Three novel parameters for quantifying US calcifications were defined and verified. • These US calcification parameters showed value in predicting the risk of cervical LNM in PTC patients.

Sleep Deprivation Impairs Learning-Induced Increase in Hippocampal Sharp Wave Ripples and Associated Spike Dynamics during Recovery Sleep.

Sleep deprivation (SD) causes deficits in off-line memory consolidation, but the underlying network oscillation mechanisms remain unclear. Hippocampal sharp wave ripple (SWR) oscillations play a critical role in off-line memory consolidation. Therefore, we trained mice to learn a hippocampus-dependent trace eyeblink conditioning (tEBC) task and explored the influence of 1.5-h postlearning SD on hippocampal SWRs and related spike dynamics during recovery sleep. We found an increase in hippocampal SWRs during postlearning sleep, which predicted the consolidation of tEBC in conditioned mice. In contrast, sleep-deprived mice showed a loss of tEBC learning-induced increase in hippocampal SWRs during recovery sleep. Moreover, the sleep-deprived mice exhibited weaker reactivation of tEBC learning-associated pyramidal cells in hippocampal SWRs during recovery sleep. In line with these findings, tEBC consolidation was impaired in sleep-deprived mice. Furthermore, sleep-deprived mice showed augmented fast excitation from pyramidal cells to interneurons and enhanced participation of interneurons in hippocampal SWRs during recovery sleep. Among various interneurons, parvalbumin-expressing interneurons specifically exhibited overexcitation during hippocampal SWRs. Our findings suggest that altered hippocampal SWRs and associated spike dynamics during recovery sleep may be candidate network oscillation mechanisms underlying SD-induced memory deficits.

A novel alginate lyase and its domain functions for the preparation of unsaturated monosaccharides.

Brown algae are considered promising crops for the production of sustainable biofuels. However, the commercial application has been limited by lack of efficient methods for converting alginate into fermentable sugars. Herein, we cloned and characterized a novel alginate lyase AlyPL17 from Pedobacter hainanensis NJ-02. It possessed outstanding catalytic efficiency toward polymannuronic acid (polyM), polyguluronic acid (polyG), and alginate sodium, with kcat of 39.42 ± 1.9 s-1, 32.53 ± 0.88 s-1, and 38.30 ± 2.12 s-1, respectively. AlyPL17 showed maximum activity at 45 °C and pH 9.0. The domain truncation did not change the optimal temperature and optimal pH, but greatly reduced the activity. In addition, AlyPL17 degrades alginate through the cooperative action of two structural domains in an exolytic mode. The minimal degradation substrate of AlyPL17 is a disaccharide. Furthermore, AlyPL17 and AlyPL6 can synergistically degrade alginate to prepare unsaturated monosaccharides that can be converted to 4-deoxy-L-erythron-5-hexoseuloseuronate acid (DEH). DEH is reduced to KDG by DEH reductase (Sdr), which enters the Entner-Doudoroff (ED) pathway as a common metabolite and is converted to bioethanol. KEY POINTS: • Biochemical characterization of alginate lyase from Pedobacter hainanensis NJ-02 and its truncated form. • Degradation patterns of AlyPL17 and the role of its domains in product distribution and mode of action. • Potential of synergistic degradation system for efficient preparation of unsaturated monosaccharides.

Met-HER3 crosstalk supports proliferation via MPZL3 in MET-amplified cancer cells.

Receptor tyrosine kinases (RTKs) are recognized as targets of precision medicine in human cancer upon their gene amplification or constitutive activation, resulting in increased downstream signal complexity including heterotypic crosstalk with other RTKs. The Met RTK exhibits such reciprocal crosstalk with several members of the human EGFR (HER) family of RTKs when amplified in cancer cells. We show that Met signaling converges on HER3-tyrosine phosphorylation across a panel of seven MET-amplified cancer cell lines and that HER3 is required for cancer cell expansion and oncogenic capacity in vitro and in vivo. Gene expression analysis of HER3-depleted cells identified MPZL3, encoding a single-pass transmembrane protein, as HER3-dependent effector in multiple MET-amplified cancer cell lines. MPZL3 interacts with HER3 and MPZL3 loss phenocopies HER3 loss in MET-amplified cells, while MPZL3 overexpression can partially rescue proliferation upon HER3 depletion. Together, these data support an oncogenic role for a HER3-MPZL3 axis in MET-amplified cancers.