Paenibacillus dendrobii sp. nov., an indole-3-acetic acid-producing endophytic bacterium isolated from Dendrobium nobile.

A novel rod-shaped, Gram-stain-positive, spore-forming and motile by peritrichous flagella strain, designated HJL G12T, was isolated from the root of Chinese herb Dendrobium nobile. Strain HJL G12T grew optimally at pH 7.0, 30 °C and in the presence of 1.0 % NaCl (w/v). Phylogenetic analysis based on 16S rRNA gene and genomic sequences showed that HJL G12T clustered with Paenibacillus chibensis NBRC 15958T and Paenibacillus dokdonensis YH-JAE5T with 98.3 and 98.2 % sequence similarity. The DNA-DNA hybridization values between strain HJL G12T and the two reference strains were 23.6 % and 24.9 %, respectively. Menaquinone-7 was the only respiratory quinone and meso-diaminopimelic acid was present in the cell-wall peptidoglycan. Antesio-C15 : 0 and iso-C16 : 0 were detected to be the major cellular fatty acids. The cellular polar lipid profile contained diphosphatidyglycerol, phosphatidylglycerol, phosphatidylethanolamine, lysyl-phospatidylglycerol and three unidentified aminophospholipids. Based on these results, strain HJL G12T is considered to represent a novel species within the genus Paenibacillus, for which the name Paenibacillus dendrobii sp. nov. is proposed, with HJL G12T (=NBRC 115617T=CGMCC 1.18520T) as the type strain.

Site-specific covalent labeling of large RNAs with nanoparticles empowered by expanded genetic alphabet transcription.

Conjugation of RNAs with nanoparticles (NPs) is of significant importance because of numerous applications in biology and medicine, which, however, remains challenging especially for large ones. So far, the majority of RNA labeling relies on solid-phase chemical synthesis, which is generally limited to RNAs smaller than 100 nucleotides (nts). We, here, present an efficient and generally applicable labeling strategy for site-specific covalent conjugation of large RNAs with a gold nanoparticle (Nanogold) empowered by transcription of an expanded genetic alphabet containing the A-T/U and G-C natural base pairs (bps) and the TPT3-NaM unnatural base pair (UBP). We synthesize an amine-derivatized TPT3 (TPT3A), which is site specifically incorporated into a 97-nt 3'SL RNA and a 719-nt minigenomic RNA (DENV-mini) from Dengue virus serotype 2 (DENV2) by in vitro T7 transcription. The TPT3A-modified RNAs are covalently conjugated with mono-Sulfo-N-hydroxysuccinimidyl (NHS)-Nanogold NPs via an amine and NHS ester reaction and further purified under nondenaturing conditions. TPT3 modification and Nanogold labeling cause minimal structural perturbations to the RNAs by circular dichroism, small angle X-ray scattering (SAXS), and binding activity assay. We demonstrate the application of the Nanogold-RNA conjugates in large RNA structural biology by an emerging molecular ruler, X-ray scattering interferometry (XSI). The internanoparticle distance distributions in the 3'SL and DENV-mini RNAs derived from XSI measurements support the hypothetical model of flavivirus genome circularization, thus, validate the applicability of this labeling strategy. The presented strategy overcomes the size constraints in conventional RNA labeling strategies and is expected to have wide applications in large RNA structural biology and RNA nanotechnology.

Machine learning implicit solvation for molecular dynamics.

Accurate modeling of the solvent environment for biological molecules is crucial for computational biology and drug design. A popular approach to achieve long simulation time scales for large system sizes is to incorporate the effect of the solvent in a mean-field fashion with implicit solvent models. However, a challenge with existing implicit solvent models is that they often lack accuracy or certain physical properties compared to explicit solvent models as the many-body effects of the neglected solvent molecules are difficult to model as a mean field. Here, we leverage machine learning (ML) and multi-scale coarse graining (CG) in order to learn implicit solvent models that can approximate the energetic and thermodynamic properties of a given explicit solvent model with arbitrary accuracy, given enough training data. Following the previous ML-CG models CGnet and CGSchnet, we introduce ISSNet, a graph neural network, to model the implicit solvent potential of mean force. ISSNet can learn from explicit solvent simulation data and be readily applied to molecular dynamics simulations. We compare the solute conformational distributions under different solvation treatments for two peptide systems. The results indicate that ISSNet models can outperform widely used generalized Born and surface area models in reproducing the thermodynamics of small protein systems with respect to explicit solvent. The success of this novel method demonstrates the potential benefit of applying machine learning methods in accurate modeling of solvent effects for in silico research and biomedical applications.

Coarse graining molecular dynamics with graph neural networks.

Coarse graining enables the investigation of molecular dynamics for larger systems and at longer timescales than is possible at an atomic resolution. However, a coarse graining model must be formulated such that the conclusions we draw from it are consistent with the conclusions we would draw from a model at a finer level of detail. It has been proved that a force matching scheme defines a thermodynamically consistent coarse-grained model for an atomistic system in the variational limit. Wang et al. [ACS Cent. Sci. 5, 755 (2019)] demonstrated that the existence of such a variational limit enables the use of a supervised machine learning framework to generate a coarse-grained force field, which can then be used for simulation in the coarse-grained space. Their framework, however, requires the manual input of molecular features to machine learn the force field. In the present contribution, we build upon the advance of Wang et al. and introduce a hybrid architecture for the machine learning of coarse-grained force fields that learn their own features via a subnetwork that leverages continuous filter convolutions on a graph neural network architecture. We demonstrate that this framework succeeds at reproducing the thermodynamics for small biomolecular systems. Since the learned molecular representations are inherently transferable, the architecture presented here sets the stage for the development of machine-learned, coarse-grained force fields that are transferable across molecular systems.

Chronic intermittent alcohol disrupts the GluN2B-associated proteome and specifically regulates group I mGlu receptor-dependent long-term depression.

N-Methyl-d-aspartate receptors (NMDARs) are major targets of both acute and chronic alcohol, as well as regulators of plasticity in a number of brain regions. Aberrant plasticity may contribute to the treatment resistance and high relapse rates observed in alcoholics. Recent work suggests that chronic alcohol treatment preferentially modulates both the expression and subcellular localization of NMDARs containing the GluN2B subunit. Signaling through synaptic and extrasynaptic GluN2B-NMDARs has already been implicated in the pathophysiology of various other neurological disorders. NMDARs interact with a large number of proteins at the glutamate synapse, and a better understanding of how alcohol modulates this proteome is needed. We employed a discovery-based proteomic approach in subcellular fractions of hippocampal tissue from chronic intermittent alcohol (CIE)-exposed C57Bl/6J mice to gain insight into alcohol-induced changes in GluN2B signaling complexes. Protein enrichment analyses revealed changes in the association of post-synaptic proteins, including scaffolding, glutamate receptor and PDZ-domain binding proteins with GluN2B. In particular, GluN2B interaction with metabotropic glutamate (mGlu)1/5 receptor-dependent long-term depression (LTD)-associated proteins such as Arc and Homer 1 was increased, while GluA2 was decreased. Accordingly, we found a lack of mGlu1/5 -induced LTD while α1 -adrenergic receptor-induced LTD remained intact in hippocampal CA1 following CIE. These data suggest that CIE specifically disrupts mGlu1/5 -LTD, representing a possible connection between NMDAR and mGlu receptor signaling. These studies not only demonstrate a new way in which alcohol can modulate plasticity in the hippocampus but also emphasize the utility of this discovery-based proteomic approach to generate new hypotheses regarding alcohol-related mechanisms.

Identification of proteins at active, stalled, and collapsed replication forks using isolation of proteins on nascent DNA (iPOND) coupled with mass spectrometry.

Both DNA and chromatin need to be duplicated during each cell division cycle. Replication happens in the context of defects in the DNA template and other forms of replication stress that present challenges to both genetic and epigenetic inheritance. The replication machinery is highly regulated by replication stress responses to accomplish this goal. To identify important replication and stress response proteins, we combined isolation of proteins on nascent DNA (iPOND) with quantitative mass spectrometry. We identified 290 proteins enriched on newly replicated DNA at active, stalled, and collapsed replication forks. Approximately 16% of these proteins are known replication or DNA damage response proteins. Genetic analysis indicates that several of the newly identified proteins are needed to facilitate DNA replication, especially under stressed conditions. Our data provide a useful resource for investigators studying DNA replication and the replication stress response and validate the use of iPOND combined with mass spectrometry as a discovery tool.

Transition metal-mediated C═O and C═C bond-forming reactions: a regioselective strategy for the synthesis of imidazo[1,2-a]pyridines and imidazo[1,2-a]pyrazines.

A novel and convenient transformation for the regiospecific synthesis of functionalized imidazo[1,2-a]pyridine aldehydes/ketones and 3-vinyl imidazo[1,2-a]pyridines has been developed via copper(I)- and palladium(II)-catalyzed cyclization. The one-pot reaction proceeds smoothly with commercially available catalysts and affords the products in moderate to good yields. It represents an efficient approach for the formation of C-N, C═O, and C═C bonds under mild conditions.

Segregation of morphogenetic regulatory function of Shox2 from its cell fate guardian role in sinoatrial node development.

Shox2 plays a vital role in the morphogenesis and physiological function of the sinoatrial node (SAN), the primary cardiac pacemaker, manifested by the formation of a hypoplastic SAN and failed differentiation of pacemaker cells in Shox2 mutants. Shox2 and Nkx2-5 are co-expressed in the developing SAN and regulate the fate of the pacemaker cells through a Shox2-Nkx2-5 antagonistic mechanism. Here we show that simultaneous inactivation of Nkx2-5 in the SAN of Shox2 mutants (dKO) rescued the pacemaking cell fate but not the hypoplastic defects, indicating uncoupling of SAN cell fate determination and morphogenesis. Single-cell RNA-seq revealed that the presumptive SAN cells of Shox2-/- mutants failed to activate pacemaking program but remained in a progenitor state preceding working myocardium, while both wildtype and dKO SAN cells displayed normal pacemaking cell fate with similar cellular state. Shox2 thus acts as a safeguard but not a determinant to ensure the pacemaking cell fate through the Shox2-Nkx2-5 antagonistic mechanism, which is segregated from its morphogenetic regulatory function in SAN development.

Associations of evening-type and insomnia symptoms with depressive symptoms among youths.

BACKGROUND: Evening-type and insomnia symptoms are significantly related to each other and independently associated with depressive symptoms, yet few studies have examined the potential interaction between these two conditions. Therefore, we aimed to examine the associations of evening-type and insomnia symptoms with depressive symptoms among Chinese youths, with a specific focus on the joint effects of the two conditions on depressive symptoms. METHODS: Participants aged between 12 and 25 were invited to participate in an online survey from December 15, 2022, to May 26, 2023. Multivariate logistic regression models and additive interaction models were used to examine the independent and joint effects of chronotypes and insomnia symptoms on depressive symptoms, respectively. RESULTS: Of the 6145 eligible youths, the prevalence of evening-type and insomnia symptoms were 24.9 % and 29.6 %, respectively. Both evening-type (adjusted OR, [AdjOR]: 3.21, 95 % CI: 2.80-3.67) and insomnia symptoms (AdjOR: 10.53, 95 % CI: 9.14-12.12) were associated with an increased risk of depressive symptoms. In addition, the additive interaction models showed that there is an enhanced risk of depression related to interaction between evening-type and insomnia symptoms (relative excess risk due to interaction, [RERI]: 11.66, 95 % CI: 7.21-16.11). CONCLUSIONS: The present study provided additional evidence demonstrating the presence of interaction between evening-type and insomnia symptoms, which can lead to a higher risk of depressive symptoms. Our findings argue the need for addressing both sleep and circadian factors in the management of depressive symptoms in young people.

IDPQuantify: combining precursor intensity with spectral counts for protein and peptide quantification.

Differentiating and quantifying protein differences in complex samples produces significant challenges in sensitivity and specificity. Label-free quantification can draw from two different information sources: precursor intensities and spectral counts. Intensities are accurate for calculating protein relative abundance, but values are often missing due to peptides that are identified sporadically. Spectral counting can reliably reproduce difference lists, but differentiating peptides or quantifying all but the most concentrated protein changes is usually beyond its abilities. Here we developed new software, IDPQuantify, to align multiple replicates using principal component analysis, extract accurate precursor intensities from MS data, and combine intensities with spectral counts for significant gains in differentiation and quantification. We have applied IDPQuantify to three comparative proteomic data sets featuring gold standard protein differences spiked in complicated backgrounds. The software is able to associate peptides with peaks that are otherwise left unidentified to increase the efficiency of protein quantification, especially for low-abundance proteins. By combing intensities with spectral counts from IDPicker, it gains an average of 30% more true positive differences among top differential proteins. IDPQuantify quantifies protein relative abundance accurately in these test data sets to produce good correlations between known and measured concentrations.

Flow-Matching: Efficient Coarse-Graining of Molecular Dynamics without Forces.

Coarse-grained (CG) molecular simulations have become a standard tool to study molecular processes on time and length scales inaccessible to all-atom simulations. Parametrizing CG force fields to match all-atom simulations has mainly relied on force-matching or relative entropy minimization, which require many samples from costly simulations with all-atom or CG resolutions, respectively. Here we present flow-matching, a new training method for CG force fields that combines the advantages of both methods by leveraging normalizing flows, a generative deep learning method. Flow-matching first trains a normalizing flow to represent the CG probability density, which is equivalent to minimizing the relative entropy without requiring iterative CG simulations. Subsequently, the flow generates samples and forces according to the learned distribution in order to train the desired CG free energy model via force-matching. Even without requiring forces from the all-atom simulations, flow-matching outperforms classical force-matching by an order of magnitude in terms of data efficiency and produces CG models that can capture the folding and unfolding transitions of small proteins.

Statistically Optimal Force Aggregation for Coarse-Graining Molecular Dynamics.

Machine-learned coarse-grained (CG) models have the potential for simulating large molecular complexes beyond what is possible with atomistic molecular dynamics. However, training accurate CG models remains a challenge. A widely used methodology for learning bottom-up CG force fields maps forces from all-atom molecular dynamics to the CG representation and matches them with a CG force field on average. We show that there is flexibility in how to map all-atom forces to the CG representation and that the most commonly used mapping methods are statistically inefficient and potentially even incorrect in the presence of constraints in the all-atom simulation. We define an optimization statement for force mappings and demonstrate that substantially improved CG force fields can be learned from the same simulation data when using optimized force maps. The method is demonstrated on the miniproteins chignolin and tryptophan cage and published as open-source code.

Machine learned coarse-grained protein force-fields: Are we there yet?

The successful recent application of machine learning methods to scientific problems includes the learning of flexible and accurate atomic-level force-fields for materials and biomolecules from quantum chemical data. In parallel, the machine learning of force-fields at coarser resolutions is rapidly gaining relevance as an efficient way to represent the higher-body interactions needed in coarse-grained force-fields to compensate for the omitted degrees of freedom. Coarse-grained models are important for the study of systems at time and length scales exceeding those of atomistic simulations. However, the development of transferable coarse-grained models via machine learning still presents significant challenges. Here, we discuss recent developments in this field and current efforts to address the remaining challenges.

Refining comparative proteomics by spectral counting to account for shared peptides and multiple search engines.

Spectral counting has become a widely used approach for measuring and comparing protein abundance in label-free shotgun proteomics. However, when analyzing complex samples, the ambiguity of matching between peptides and proteins greatly affects the assessment of peptide and protein inventories, differentiation, and quantification. Meanwhile, the configuration of database searching algorithms that assign peptides to MS/MS spectra may produce different results in comparative proteomic analysis. Here, we present three strategies to improve comparative proteomics through spectral counting. We show that comparing spectral counts for peptide groups rather than for protein groups forestalls problems introduced by shared peptides. We demonstrate the advantage and flexibility of this new method in two datasets. We present four models to combine four popular search engines that lead to significant gains in spectral counting differentiation. Among these models, we demonstrate a powerful vote counting model that scales well for multiple search engines. We also show that semi-tryptic searching outperforms tryptic searching for comparative proteomics. Overall, these techniques considerably improve protein differentiation on the basis of spectral count tables.

Posttranscriptional site-directed spin labeling of large RNAs with an unnatural base pair system under non-denaturing conditions.

Site-directed spin labeling (SDSL) of large RNAs for electron paramagnetic resonance (EPR) spectroscopy has remained challenging to date. We here demonstrate an efficient and generally applicable posttranscriptional SDSL method for large RNAs using an expanded genetic alphabet containing the NaM-TPT3 unnatural base pair (UBP). An alkyne-modified TPT3 ribonucleotide triphosphate (rTPT3COTP) is synthesized and site-specifically incorporated into large RNAs by in vitro transcription, which allows attachment of the azide-containing nitroxide through click chemistry. We validate this strategy by SDSL of a 419-nucleotide ribonuclease P (RNase P) RNA from Bacillus stearothermophilus under non-denaturing conditions. The effects of site-directed UBP incorporation and subsequent spin labeling on the global structure and function of RNase P are marginal as evaluated by Circular Dichroism spectroscopy, Small Angle X-ray Scattering, Sedimentation Velocity Analytical Ultracentrifugation and enzymatic assay. Continuous-Wave EPR analyses reveal that the labeling reaction is efficient and specific, and Pulsed Electron-Electron Double Resonance measurements yield an inter-spin distance distribution that agrees with the crystal structure. The labeling strategy as presented overcomes the size constraint of RNA labeling, opening new avenues of spin labeling and EPR spectroscopy for investigating the structure and dynamics of large RNAs.