Extracellular pectin-RALF phase separation mediates FERONIA global signaling function.

The FERONIA (FER)-LLG1 co-receptor and its peptide ligand RALF regulate myriad processes for plant growth and survival. Focusing on signal-induced cell surface responses, we discovered that intrinsically disordered RALF triggers clustering and endocytosis of its cognate receptors and FER- and LLG1-dependent endocytosis of non-cognate regulators of diverse processes, thus capable of broadly impacting downstream responses. RALF, however, remains extracellular. We demonstrate that RALF binds the cell wall polysaccharide pectin. They phase separate and recruit FER and LLG1 into pectin-RALF-FER-LLG1 condensates to initiate RALF-triggered cell surface responses. We show further that two frequently encountered environmental challenges, elevated salt and temperature, trigger RALF-pectin phase separation, promiscuous receptor clustering and massive endocytosis, and that this process is crucial for recovery from stress-induced growth attenuation. Our results support that RALF-pectin phase separation mediates an exoskeletal mechanism to broadly activate FER-LLG1-dependent cell surface responses to mediate the global role of FER in plant growth and survival.

A Map-like Micro-Organization of Grid Cells in the Medial Entorhinal Cortex.

How the topography of neural circuits relates to their function remains unclear. Although topographic maps exist for sensory and motor variables, they are rarely observed for cognitive variables. Using calcium imaging during virtual navigation, we investigated the relationship between the anatomical organization and functional properties of grid cells, which represent a cognitive code for location during navigation. We found a substantial degree of grid cell micro-organization in mouse medial entorhinal cortex: grid cells and modules all clustered anatomically. Within a module, the layout of grid cells was a noisy two-dimensional lattice in which the anatomical distribution of grid cells largely matched their spatial tuning phases. This micro-arrangement of phases demonstrates the existence of a topographical map encoding a cognitive variable in rodents. It contributes to a foundation for evaluating circuit models of the grid cell network and is consistent with continuous attractor models as the mechanism of grid formation.

An IDR-dependent mechanism for nuclear receptor control of Mediator interaction with RNA polymerase II.

The essential Mediator (MED) coactivator complex plays a well-understood role in regulation of basal transcription in all eukaryotes, but the mechanism underlying its role in activator-dependent transcription remains unknown. We investigated modulation of metazoan MED interaction with RNA polymerase II (RNA Pol II) by antagonistic effects of the MED26 subunit and the CDK8 kinase module (CKM). Biochemical analysis of CKM-MED showed that the CKM blocks binding of the RNA Pol II carboxy-terminal domain (CTD), preventing RNA Pol II interaction. This restriction is eliminated by nuclear receptor (NR) binding to CKM-MED, which enables CTD binding in a MED26-dependent manner. Cryoelectron microscopy (cryo-EM) and crosslinking-mass spectrometry (XL-MS) revealed that the structural basis for modulation of CTD interaction with MED relates to a large intrinsically disordered region (IDR) in CKM subunit MED13 that blocks MED26 and CTD interaction with MED but is repositioned upon NR binding. Hence, NRs can control transcription initiation by priming CKM-MED for MED26-dependent RNA Pol II interaction.

IgE Effector Mechanisms, in Concert with Mast Cells, Contribute to Acquired Host Defense against Staphylococcusaureus.

Allergies are considered to represent mal-directed type 2 immune responses against mostly innocuous exogenous compounds. Immunoglobulin E (IgE) antibodies are a characteristic feature of allergies and mediate hypersensitivity against allergens through activation of effector cells, particularly mast cells (MCs). Although the physiological functions of this dangerous branch of immunity have remained enigmatic, recent evidence shows that allergic immune reactions can help to protect against the toxicity of venoms. Because bacteria are a potent alternative source of toxins, we assessed the possible role of allergy-like type 2 immunity in antibacterial host defense. We discovered that the adaptive immune response against Staphylococcus aureus (SA) skin infection substantially improved systemic host defense against secondary SA infections in mice. Moreover, this acquired protection depended on IgE effector mechanisms and MCs. Importantly, our results reveal a previously unknown physiological function of allergic immune responses, IgE antibodies, and MCs in host defense against a pathogenic bacterium.

The Mediator kinase module enhances polymerase activity to regulate transcriptional memory after heat stress in Arabidopsis.

Plants are often exposed to recurring adverse environmental conditions in the wild. Acclimation to high temperatures entails transcriptional responses, which prime plants to better withstand subsequent stress events. Heat stress (HS)-induced transcriptional memory results in more efficient re-induction of transcription upon recurrence of heat stress. Here, we identified CDK8 and MED12, two subunits of the kinase module of the transcription co-regulator complex, Mediator, as promoters of heat stress memory and associated histone modifications in Arabidopsis. CDK8 is recruited to heat-stress memory genes by HEAT SHOCK TRANSCRIPTION FACTOR A2 (HSFA2). Like HSFA2, CDK8 is largely dispensable for the initial gene induction upon HS, and its function in transcriptional memory is thus independent of primary gene activation. In addition to the promoter and transcriptional start region of target genes, CDK8 also binds their 3'-region, where it may promote elongation, termination, or rapid re-initiation of RNA polymerase II (Pol II) complexes during transcriptional memory bursts. Our work presents a complex role for the Mediator kinase module during transcriptional memory in multicellular eukaryotes, through interactions with transcription factors, chromatin modifications, and promotion of Pol II efficiency.

Kinase Interaction Network Expands Functional and Disease Roles of Human Kinases.

Protein kinases are essential for signal transduction and control of most cellular processes, including metabolism, membrane transport, motility, and cell cycle. Despite the critical role of kinases in cells and their strong association with diseases, good coverage of their interactions is available for only a fraction of the 535 human kinases. Here, we present a comprehensive mass-spectrometry-based analysis of a human kinase interaction network covering more than 300 kinases. The interaction dataset is a high-quality resource with more than 5,000 previously unreported interactions. We extensively characterized the obtained network and were able to identify previously described, as well as predict new, kinase functional associations, including those of the less well-studied kinases PIM3 and protein O-mannose kinase (POMK). Importantly, the presented interaction map is a valuable resource for assisting biomedical studies. We uncover dozens of kinase-disease associations spanning from genetic disorders to complex diseases, including cancer.

Twist-dependent ratchet functioning downstream from Dorsal revealed using a light-inducible degron.

Graded transcription factors are pivotal regulators of embryonic patterning, but whether their role changes over time is unclear. A light-regulated protein degradation system was used to assay temporal dependence of the transcription factor Dorsal in dorsal-ventral axis patterning of Drosophila embryos. Surprisingly, the high-threshold target gene snail only requires Dorsal input early but not late when Dorsal levels peak. Instead, late snail expression can be supported by action of the Twist transcription factor, specifically, through one enhancer, sna.distal This study demonstrates that continuous input is not required for some Dorsal targets and downstream responses, such as twist, function as molecular ratchets.

HokB Monomerization and Membrane Repolarization Control Persister Awakening.

Every bacterial population harbors a small subpopulation of so-called persisters that are transiently antibiotic tolerant. These persisters are associated with the recalcitrance of chronic infections because they can recolonize the host after antibiotic removal. Although several effectors have been described to induce persistence, persister cell awakening is poorly understood. We previously reported that the toxin HokB induces persistence via pore formation, resulting in membrane depolarization and ATP leakage. We now delineate mechanisms responsible for the awakening of HokB-induced persisters. We show that HokB dimerization by the oxidoreductase DsbA is essential for pore formation and peptide stability. Pores are disassembled via DsbC-mediated monomerization, which targets HokB for DegQ-mediated degradation. Finally, pore disassembly allows membrane repolarization by the electron transport chain, supporting cells to resume growth. These results provide a detailed view of both the formation and awakening of HokB-induced persister cells.

Congruence modules in higher codimension and zeta lines in Galois cohomology.

This article builds on recent work of the first three authors where a notion of congruence modules in higher codimension is introduced. The main results are a criterion for detecting regularity of local rings in terms of congruence modules, and a more refined version of a result tracking the change of congruence modules under deformation. Number theoretic applications include the construction of canonical lines in certain Galois cohomology groups arising from adjoint motives of Hilbert modular forms.

All-back-contact neutral-colored transparent crystalline silicon solar cells enabling seamless modularization.

Transparent solar cells (TSCs) hold substantial potential as continuous energy generators, enabling their use in situations where conventional devices may not be feasible. However, research aimed at modularizing TSCs for the purpose of regulating the overall voltage and current they produce, a critical step toward practical application, is still in its nascent stages. In this study, we explored a custom-designed, all-back-contact (ABC) configuration, which situates all electrical contacts on the rear side, to create glass-like transparent crystalline silicon (c-Si) solar cells and seamless modules. The ABC design not only demonstrates high power conversion efficiency (PCE) in solar cells but also ensures unobstructed visibility through transparent solar modules. Notably, ABC-transparent c-Si solar cells achieved a peak PCE of 15.8% while maintaining an average visible transmittance of 20%. Through seamlessly interconnecting the unit cells, the output voltage and power were systematically tuned from 0.64 V and 15.8 mW (for a 1 cm2-sized unit cell) to 10.0 V and 235 mW (for a 16 cm2-sized module). Furthermore, we successfully demonstrated the photocharging of a smartphone using a transparent ABC solar module.

LARP4 is an RNA-binding protein that binds nuclear-encoded mitochondrial mRNAs to promote mitochondrial function.

Mitochondria-associated RNA-binding proteins (RBPs) have emerged as key contributors to mitochondrial biogenesis and homeostasis. With few examples known, we set out to identify RBPs that regulate nuclear-encoded mitochondrial mRNAs (NEMmRNAs). Our systematic analysis of RNA targets of 150 RBPs identified RBPs with a preference for binding NEMmRNAs, including LARP4, a La RBP family member. We show that LARP4's targets are particularly enriched in mRNAs that encode respiratory chain complex proteins (RCCPs) and mitochondrial ribosome proteins (MRPs) across multiple human cell lines. Through quantitative proteomics, we demonstrate that depletion of LARP4 leads to a significant reduction in RCCP and MRP protein levels. Furthermore, we show that LARP4 depletion reduces mitochondrial function, and that LARP4 re-expression rescues this phenotype. Our findings shed light on a novel function for LARP4 as an RBP that binds to and positively regulates NEMmRNAs to promote mitochondrial respiratory function.

A cloud-based training module for efficient de novo transcriptome assembly using Nextflow and Google cloud.

This study describes the development of a resource module that is part of a learning platform named "NIGMS Sandbox for Cloud-based Learning" (https://github.com/NIGMS/NIGMS-Sandbox). The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox at the beginning of this Supplement. This module delivers learning materials on de novo transcriptome assembly using Nextflow in an interactive format that uses appropriate cloud resources for data access and analysis. Cloud computing is a powerful new means by which biomedical researchers can access resources and capacity that were previously either unattainable or prohibitively expensive. To take advantage of these resources, however, the biomedical research community needs new skills and knowledge. We present here a cloud-based training module, developed in conjunction with Google Cloud, Deloitte Consulting, and the NIH STRIDES Program, that uses the biological problem of de novo transcriptome assembly to demonstrate and teach the concepts of computational workflows (using Nextflow) and cost- and resource-efficient use of Cloud services (using Google Cloud Platform). Our work highlights the reduced necessity of on-site computing resources and the accessibility of cloud-based infrastructure for bioinformatics applications.

CCPA: cloud-based, self-learning modules for consensus pathway analysis using GO, KEGG and Reactome.

This manuscript describes the development of a resource module that is part of a learning platform named 'NIGMS Sandbox for Cloud-based Learning' (https://github.com/NIGMS/NIGMS-Sandbox). The module delivers learning materials on Cloud-based Consensus Pathway Analysis in an interactive format that uses appropriate cloud resources for data access and analyses. Pathway analysis is important because it allows us to gain insights into biological mechanisms underlying conditions. But the availability of many pathway analysis methods, the requirement of coding skills, and the focus of current tools on only a few species all make it very difficult for biomedical researchers to self-learn and perform pathway analysis efficiently. Furthermore, there is a lack of tools that allow researchers to compare analysis results obtained from different experiments and different analysis methods to find consensus results. To address these challenges, we have designed a cloud-based, self-learning module that provides consensus results among established, state-of-the-art pathway analysis techniques to provide students and researchers with necessary training and example materials. The training module consists of five Jupyter Notebooks that provide complete tutorials for the following tasks: (i) process expression data, (ii) perform differential analysis, visualize and compare the results obtained from four differential analysis methods (limma, t-test, edgeR, DESeq2), (iii) process three pathway databases (GO, KEGG and Reactome), (iv) perform pathway analysis using eight methods (ORA, CAMERA, KS test, Wilcoxon test, FGSEA, GSA, SAFE and PADOG) and (v) combine results of multiple analyses. We also provide examples, source code, explanations and instructional videos for trainees to complete each Jupyter Notebook. The module supports the analysis for many model (e.g. human, mouse, fruit fly, zebra fish) and non-model species. The module is publicly available at https://github.com/NIGMS/Consensus-Pathway-Analysis-in-the-Cloud. This manuscript describes the development of a resource module that is part of a learning platform named ``NIGMS Sandbox for Cloud-based Learning'' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [1] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses.

Heterogeneous biomedical entity representation learning for gene-disease association prediction.

Understanding the genetic basis of disease is a fundamental aspect of medical research, as genes are the classic units of heredity and play a crucial role in biological function. Identifying associations between genes and diseases is critical for diagnosis, prevention, prognosis, and drug development. Genes that encode proteins with similar sequences are often implicated in related diseases, as proteins causing identical or similar diseases tend to show limited variation in their sequences. Predicting gene-disease association (GDA) requires time-consuming and expensive experiments on a large number of potential candidate genes. Although methods have been proposed to predict associations between genes and diseases using traditional machine learning algorithms and graph neural networks, these approaches struggle to capture the deep semantic information within the genes and diseases and are dependent on training data. To alleviate this issue, we propose a novel GDA prediction model named FusionGDA, which utilizes a pre-training phase with a fusion module to enrich the gene and disease semantic representations encoded by pre-trained language models. Multi-modal representations are generated by the fusion module, which includes rich semantic information about two heterogeneous biomedical entities: protein sequences and disease descriptions. Subsequently, the pooling aggregation strategy is adopted to compress the dimensions of the multi-modal representation. In addition, FusionGDA employs a pre-training phase leveraging a contrastive learning loss to extract potential gene and disease features by training on a large public GDA dataset. To rigorously evaluate the effectiveness of the FusionGDA model, we conduct comprehensive experiments on five datasets and compare our proposed model with five competitive baseline models on the DisGeNet-Eval dataset. Notably, our case study further demonstrates the ability of FusionGDA to discover hidden associations effectively. The complete code and datasets of our experiments are available at https://github.com/ZhaohanM/FusionGDA.

The Volitional Control of Individual Motor Units Is Constrained within Low-Dimensional Neural Manifolds by Common Inputs.

The implementation of low-dimensional movement control by the central nervous system has been debated for decades. In this study, we investigated the dimensionality of the control signals received by spinal motor neurons when controlling either the ankle or knee joint torque. We first identified the low-dimensional latent factors underlying motor unit activity during torque-matched isometric contractions in male participants. Subsequently, we evaluated the extent to which motor units could be independently controlled. To this aim, we used an online control paradigm in which participants received the corresponding motor unit firing rates as visual feedback. We identified two main latent factors, regardless of the muscle group (vastus lateralis-medialis and gastrocnemius lateralis-medialis). The motor units of the gastrocnemius lateralis could be controlled largely independently from those of the gastrocnemius medialis during ankle plantarflexion. This dissociation of motor unit activity imposed similar behavior to the motor units that were not displayed in the feedback. Conversely, it was not possible to dissociate the activity of the motor units between the vastus lateralis and medialis muscles during the knee extension tasks. These results demonstrate that the number of latent factors estimated from linear dimensionality reduction algorithms does not necessarily reflect the dimensionality of volitional control of motor units. Overall, individual motor units were never controlled independently of all others but rather belonged to synergistic groups. Together, these findings provide evidence for a low-dimensional control of motor units constrained by common inputs, with notable differences between muscle groups.

Functional role of carbohydrate-binding modules in multi-modular chitinase OfChtII.

The primary distinction between insect and bacterial chitin degradation systems lies in the presence of a multi-modular endo-acting chitinase ChtII, in contrast to a processive exo-acting chitinase. Although the essential role of ChtII during insect development and its synergistic action with processive chitinase during chitin degradation has been established, the mechanistic understanding of how it deconstructs chitin remains largely elusive. Here OfChtII from the insect Ostrinia furnacalis was investigated employing comprehensive approaches encompassing biochemical and microscopic analyses. The results demonstrated that OfChtII truncations with more carbohydrate-binding modules (CBMs) exhibited enhanced hydrolysis activity, effectively yielding a greater proportion of fibrillary fractions from the compacted chitin substrate. At the single-molecule level, the CBMs in these OfChtII truncations have been shown to primarily facilitate chitin substrate association rather than dissociation. Furthermore, a greater number of CBMs was demonstrated to be essential for the enzyme to effectively bind to chitin substrates with high crystallinity. Through real-time imaging by high-speed atomic force microscopy, the OfChtII-B4C1 truncation with three CBMs was observed to shear chitin fibers, thereby generating fibrillary fragments and deconstructing the compacted chitin structure. This work pioneers in revealing the nanoscale mechanism of endo-acting multi-modular chitinase involved in chitin degradation, which provides an important reference for the rational design of chitinases or other glycoside hydrolases.

Carbohydrate-binding modules enhance H2O2 tolerance by promoting lytic polysaccharide monooxygenase active site H2O2 consumption.

Lytic polysaccharide monooxygenases (LPMOs) oxidatively depolymerize recalcitrant polysaccharides, which is important for biomass conversion. The catalytic domains of many LPMOs are linked to carbohydrate-binding modules (CBMs) through flexible linkers, but the function of these CBMs in LPMO catalysis is not well understood. In this study, we utilized MtLPMO9L and MtLPMO9G derived from Myceliophthora thermophila to investigate the impact of CBMs on LPMO activity, with particular emphasis on their influence on H2O2 tolerance. Using truncated forms of MtLPMO9G generated by removing the CBM, we found reduced substrate binding affinity and enzymatic activity. Conversely, when the CBM was fused to the C terminus of the single-domain MtLPMO9L to create MtLPMO9L-CBM, we observed a substantial improvement in substrate binding affinity, enzymatic activity, and notably, H2O2 tolerance. Furthermore, molecular dynamics simulations confirmed that the CBM fusion enhances the proximity of the active site to the substrate, thereby promoting multilocal cleavage and impacting the exposure of the copper active site to H2O2. Importantly, the fusion of CBM resulted in more efficient consumption of H2O2 by LPMO, leading to improved enzymatic activity and reduced auto-oxidative damage of the copper active center.

miR156-PvSPL2 controls culm development by transcriptional repression of switchgrass CYTOKININ OXIDASE/DEHYDROGENASE4.

Culm development in grasses can be controlled by both miR156 and cytokinin. However, the crosstalk between the miR156-SPL module and the cytokinin metabolic pathway remains largely unknown. Here, we found CYTOKININ OXIDASE/DEHYDROGENASE4 (PvCKX4) plays a negative regulatory role in culm development of the bioenergy grass Panicum virgatum (switchgrass). Overexpression of PvCKX4 in switchgrass reduced the internode diameter and length without affecting tiller number. Interestingly, we also found that PvCKX4 was always upregulated in miR156 overexpressing (miR156OE) transgenic switchgrass lines. Additionally, upregulation of either miR156 or PvCKX4 in switchgrass reduced the content of isopentenyl adenine (iP) without affecting trans-zeatin (tZ) accumulation. It is consistent with the evidence that the recombinant PvCKX4 protein exhibited much higher catalytic activity against iP than tZ in vitro. Furthermore, our results showed that miR156-targeted SPL2 bound directly to the promoter of PvCKX4 to repress its expression. Thus, alleviating the SPL2-mediated transcriptional repression of PvCKX4 through miR156 overexpression resulted in a significant increase in cytokinin degradation and impaired culm development in switchgrass. On the contrary, suppressing PvCKX4 in miR156OE transgenic plants restored iP content, internode diameter, and length to wild-type levels. Most strikingly, the double transgenic lines retained the same increased tiller numbers as the miR156OE transgenic line, which yielded more biomass than the wild type. These findings indicate that the miR156-SPL module can control culm development through transcriptional repression of PvCKX4 in switchgrass, which provides a promising target for precise design of shoot architecture to yield more biomass from grasses.

Deciphering the adsorption machinery of Deep-Blue and Vp4, two myophages targeting members of the Bacillus cereus group.

In tailed phages, the baseplate is the macromolecular structure located at the tail distal part, which is directly implicated in host recognition and cell wall penetration. In myophages (i.e., with contractile tails), the baseplate is complex and comprises a central puncturing device and baseplate wedges connecting the hub to the receptor-binding proteins (RBPs). In this work, we investigated the structures and functions of adsorption-associated tail proteins of Deep-Blue and Vp4, two Herelleviridae phages infecting members of the Bacillus cereus group. Their interest resides in their different host spectrum despite a high degree of similarity. Analysis of their tail module revealed that the gene order is similar to that of the Listeria phage A511. Among their tail proteins, Gp185 (Deep-Blue) and Gp112 (Vp4) had no structural homolog, but the C-terminal variable parts of these proteins were able to bind B. cereus strains, confirming their implication in the phage adsorption. Interestingly, Vp4 and Deep-Blue adsorption to their hosts was also shown to require polysaccharides, which are likely to be bound by the arsenal of carbohydrate-binding modules (CBMs) of these phages' baseplates, suggesting that the adsorption does not rely solely on the RBPs. In particular, the BW Gp119 (Vp4), harboring a CBM fold, was shown to effectively bind to bacterial cells. Finally, we also showed that the putative baseplate hub proteins (i.e., Deep-Blue Gp189 and Vp4 Gp110) have a bacteriolytic activity against B. cereus strains, which supports their role as ectolysins locally degrading the peptidoglycan to facilitate genome injection. IMPORTANCE: The Bacillus cereus group comprises closely related species, including some with pathogenic potential (e.g., Bacillus anthracis and Bacillus cytotoxicus). Their toxins represent the most frequently reported cause of food poisoning outbreaks at the European level. Bacteriophage research is undergoing a remarkable renaissance for its potential in the biocontrol and detection of such pathogens. As the primary site of phage-bacteria interactions and a prerequisite for successful phage infection, adsorption is a crucial process that needs further investigation. The current knowledge about B. cereus phage adsorption is currently limited to siphoviruses and tectiviruses. Here, we present the first insights into the adsorption process of Herelleviridae Vp4 and Deep-Blue myophages preying on B. cereus hosts, highlighting the importance of polysaccharide moieties in this process and confirming the binding to the host surface of Deep-Blue Gp185 and Vp4 Gp112 receptor-binding proteins and Gp119 baseplate wedge.

MATTE: a pipeline of transcriptome module alignment for anti-noise phenotype-gene-related analysis.

A phenotype may be associated with multiple genes that interact with each other in the form of a gene module or network. How to identify these relationships is one important aspect of comparative transcriptomics. However, it is still a challenge to align gene modules associated with different phenotypes. Although several studies attempted to address this issue in different aspects, a general framework is still needed. In this study, we introduce Module Alignment of TranscripTomE (MATTE), a novel approach to analyze transcriptomics data and identify differences in a modular manner. MATTE assumes that gene interactions modulate a phenotype and models phenotype differences as gene location changes. Specifically, we first represented genes by a relative differential expression to reduce the influence of noise in omics data. Meanwhile, clustering and aligning are combined to depict gene differences in a modular way robustly. The results show that MATTE outperformed state-of-the-art methods in identifying differentially expressed genes under noise in gene expression. In particular, MATTE could also deal with single-cell ribonucleic acid-seq data to extract the best cell-type marker genes compared to other methods. Additionally, we demonstrate how MATTE supports the discovery of biologically significant genes and modules, and facilitates downstream analyses to gain insight into breast cancer. The source code of MATTE and case analysis are available at https://github.com/zjupgx/MATTE.