The Roots of Genetic Coding in Aminoacyl-tRNA Synthetase Duality.

Codon-dependent translation underlies genetics and phylogenetic inferences, but its origins pose two challenges. Prevailing narratives cannot account for the fact that aminoacyl-tRNA synthetases (aaRSs), which translate the genetic code, must collectively enforce the rules used to assemble themselves. Nor can they explain how specific assignments arose from rudimentary differentiation between ancestral aaRSs and corresponding transfer RNAs (tRNAs). Experimental deconstruction of the two aaRS superfamilies created new experimental tools with which to analyze the emergence of the code. Amino acid and tRNA substrate recognition are linked to phase transfer free energies of amino acids and arise largely from aaRS class-specific differences in secondary structure. Sensitivity to protein folding rules endowed ancestral aaRS-tRNA pairs with the feedback necessary to rapidly compare alternative genetic codes and coding sequences. These and other experimental data suggest that the aaRS bidirectional genetic ancestry stabilized the differentiation and interdependence required to initiate and elaborate the genetic coding table.

The Ground State and Evolution of Promoter Region Directionality.

Eukaryotic promoter regions are frequently divergently transcribed in vivo, but it is unknown whether the resultant antisense RNAs are a mechanistic by-product of RNA polymerase II (Pol II) transcription or biologically meaningful. Here, we use a functional evolutionary approach that involves nascent transcript mapping in S. cerevisiae strains containing foreign yeast DNA. Promoter regions in foreign environments lose the directionality they have in their native species. Strikingly, fortuitous promoter regions arising in foreign DNA produce equal transcription in both directions, indicating that divergent transcription is a mechanistic feature that does not imply a function for these transcripts. Fortuitous promoter regions arising during evolution promote bidirectional transcription and over time are purged through mutation or retained to enable new functionality. Similarly, human transcription is more bidirectional at newly evolved enhancers and promoter regions. Thus, promoter regions are intrinsically bidirectional and are shaped by evolution to bias transcription toward coding versus non-coding RNAs.

Head-on and co-directional RNA polymerase collisions orchestrate bidirectional transcription termination.

Genomic DNA is a crowded track where motor proteins frequently collide. It remains underexplored whether these collisions carry physiological function. In this work, we develop a single-molecule assay to visualize the trafficking of individual E. coli RNA polymerases (RNAPs) on DNA. Based on transcriptomic data, we hypothesize that RNAP collisions drive bidirectional transcription termination of convergent gene pairs. Single-molecule results show that the head-on collision between two converging RNAPs is necessary to prevent transcriptional readthrough but insufficient to release the RNAPs from the DNA. Remarkably, co-directional collision of a trailing RNAP into the head-on collided complex dramatically increases the termination efficiency. Furthermore, stem-loop structures formed in the nascent RNA are required for collisions to occur at well-defined positions between convergent genes. These findings suggest that physical collisions between RNAPs furnish a mechanism for transcription termination and that programmed genomic conflicts can be exploited to co-regulate the expression of multiple genes.

Identification and correction for collider bias in a genome-wide association study of diabetes-related heart failure.

Type 2 diabetes (T2D) is a major risk factor for heart failure (HF) and has elevated incidence among individuals with HF. Since genetics and HF can independently influence T2D, collider bias may occur when T2D (i.e., collider) is controlled for by design or analysis. Thus, we conducted a genome-wide association study (GWAS) of diabetes-related HF with correction for collider bias. We first performed a GWAS of HF to identify genetic instrumental variables (GIVs) for HF and to enable bidirectional Mendelian randomization (MR) analysis between T2D and HF. We identified 61 genomic loci, significantly associated with all-cause HF in 114,275 individuals with HF and over 1.5 million controls of European ancestry. Using a two-sample bidirectional MR approach with 59 and 82 GIVs for HF and T2D, respectively, we estimated that T2D increased HF risk (odds ratio [OR] 1.07, 95% confidence interval [CI] 1.04-1.10), while HF also increased T2D risk (OR 1.60, 95% CI 1.36-1.88). Then we performed a GWAS of diabetes-related HF corrected for collider bias due to the study design of index cases. After removing the spurious association of TCF7L2 locus due to collider bias, we identified two genome-wide significant loci close to PITX2 (chromosome 4) and CDKN2B-AS1 (chromosome 9) associated with diabetes-related HF in the Million Veteran Program and replicated the associations in the UK Biobank. Our MR findings provide strong evidence that HF increases T2D risk. As a result, collider bias leads to spurious genetic associations of diabetes-related HF, which can be effectively corrected to identify true positive loci.

A silicon diode-based optoelectronic interface for bidirectional neural modulation.

The development of advanced neural modulation techniques is crucial to neuroscience research and neuroengineering applications. Recently, optical-based, nongenetic modulation approaches have been actively investigated to remotely interrogate the nervous system with high precision. Here, we show that a thin-film, silicon (Si)-based diode device is capable to bidirectionally regulate in vitro and in vivo neural activities upon adjusted illumination. When exposed to high-power and short-pulsed light, the Si diode generates photothermal effects, evoking neuron depolarization and enhancing intracellular calcium dynamics. Conversely, low-power and long-pulsed light on the Si diode hyperpolarizes neurons and reduces calcium activities. Furthermore, the Si diode film mounted on the brain of living mice can activate or suppress cortical activities under varied irradiation conditions. The presented material and device strategies reveal an innovated optoelectronic interface for precise neural modulations.

Effective Regulation of Auditory Processing by Parvalbumin Interneurons in the Tail of the Striatum.

Parvalbumin (PV) interneurons in the auditory cortex (AC) play a crucial role in shaping auditory processing, including receptive field formation, temporal precision enhancement, and gain regulation. PV interneurons are also the primary inhibitory neurons in the tail of the striatum (TS), which is one of the major descending brain regions in the auditory nervous system. However, the specific roles of TS-PV interneurons in auditory processing remain elusive. In this study, morphological and slice recording experiments in both male and female mice revealed that TS-PV interneurons, compared with AC-PV interneurons, were present in fewer numbers but exhibited longer projection distances, which enabled them to provide sufficient inhibitory inputs to spiny projection neurons (SPNs). Furthermore, TS-PV interneurons received dense auditory input from both the AC and medial geniculate body (MGB), particularly from the MGB, which rendered their auditory responses comparable to those of AC-PV interneurons. Optogenetic manipulation experiments demonstrated that TS-PV interneurons were capable of bidirectionally regulating the auditory responses of SPNs. Our findings suggest that PV interneurons can effectively modulate auditory processing in the TS and may play a critical role in auditory-related behaviors.

The kinesin-5 protein Cut7 moves bidirectionally on fission yeast spindles with activity that increases in anaphase.

Kinesin-5 motors are essential to separate mitotic spindle poles and assemble a bipolar spindle in many organisms. These motors crosslink and slide apart antiparallel microtubules via microtubule plus-end-directed motility. However, kinesin-5 localization is enhanced away from antiparallel overlaps. Increasing evidence suggests this localization occurs due to bidirectional motility or trafficking. The purified fission-yeast kinesin-5 protein Cut7 moves bidirectionally, but bidirectionality has not been shown in cells, and the function of the minus-end-directed movement is unknown. Here, we characterized the motility of Cut7 on bipolar and monopolar spindles and observed movement toward both plus- and minus-ends of microtubules. Notably, the activity of the motor increased at anaphase B onset. Perturbations to microtubule dynamics only modestly changed Cut7 movement, whereas Cut7 mutation reduced movement. These results suggest that the directed motility of Cut7 contributes to the movement of the motor. Comparison of the Cut7 mutant and human Eg5 (also known as KIF11) localization suggest a new hypothesis for the function of minus-end-directed motility and spindle-pole localization of kinesin-5s.

Transport of synaptic vesicles is modulated by vesicular reversals and stationary cargo clusters.

Stationary clusters of vesicles are a prominent feature of axonal transport, but little is known about their physiological and functional relevance to axonal transport. Here, we investigated the role of vesicle motility characteristics in modulating the formation and lifetimes of such stationary clusters, and their effect on cargo flow. We developed a simulation model describing key features of axonal cargo transport, benchmarking the model against experiments in the posterior lateral mechanosensory neurons of Caenorhabditis elegans. Our simulations included multiple microtubule tracks and varied cargo motion states, and account for dynamic cargo-cargo interactions. Our model also incorporates static obstacles to vesicle transport in the form of microtubule ends, stalled vesicles and stationary mitochondria. We demonstrate, both in simulations and in an experimental system, that a reduction in reversal rates is associated with a higher proportion of long-lived stationary vesicle clusters and reduced net anterograde transport. Our simulations support the view that stationary clusters function as dynamic reservoirs of cargo vesicles, and reversals aid cargo in navigating obstacles and regulate cargo transport by modulating the proportion of stationary vesicle clusters along the neuronal process.

Short human eccDNAs are predictable from sequences.

BACKGROUND: Ubiquitous presence of short extrachromosomal circular DNAs (eccDNAs) in eukaryotic cells has perplexed generations of biologists. Their widespread origins in the genome lacking apparent specificity led some studies to conclude their formation as random or near-random. Despite this, the search for specific formation of short eccDNA continues with a recent surge of interest in biomarker development. RESULTS: To shed new light on the conflicting views on short eccDNAs' randomness, here we present DeepCircle, a bioinformatics framework incorporating convolution- and attention-based neural networks to assess their predictability. Short human eccDNAs from different datasets indeed have low similarity in genomic locations, but DeepCircle successfully learned shared DNA sequence features to make accurate cross-datasets predictions (accuracy: convolution-based models: 79.65 ± 4.7%, attention-based models: 83.31 ± 4.18%). CONCLUSIONS: The excellent performance of our models shows that the intrinsic predictability of eccDNAs is encoded in the sequences across tissue origins. Our work demonstrates how the perceived lack of specificity in genomics data can be re-assessed by deep learning models to uncover unexpected similarity.

Phen2Disease: a phenotype-driven model for disease and gene prioritization by bidirectional maximum matching semantic similarities.

Human Phenotype Ontology (HPO)-based approaches have gained popularity in recent times as a tool for genomic diagnostics of rare diseases. However, these approaches do not make full use of the available information on disease and patient phenotypes. We present a new method called Phen2Disease, which utilizes the bidirectional maximum matching semantic similarity between two phenotype sets of patients and diseases to prioritize diseases and genes. Our comprehensive experiments have been conducted on six real data cohorts with 2051 cases (Cohort 1, n = 384; Cohort 2, n = 281; Cohort 3, n = 185; Cohort 4, n = 784; Cohort 5, n = 208; and Cohort 6, n = 209) and two simulated data cohorts with 1000 cases. The results of the experiments showed that Phen2Disease outperforms the three state-of-the-art methods when only phenotype information and HPO knowledge base are used, particularly in cohorts with fewer average numbers of HPO terms. We also observed that patients with higher information content scores have more specific information, leading to more accurate predictions. Moreover, Phen2Disease provides high interpretability with ranked diseases and patient HPO terms presented. Our method provides a novel approach to utilizing phenotype data for genomic diagnostics of rare diseases, with potential for clinical impact. Phen2Disease is freely available on GitHub at https://github.com/ZhuLab-Fudan/Phen2Disease.

Predicting lncRNA-disease associations based on combining selective similarity matrix fusion and bidirectional linear neighborhood label propagation.

Recent studies have revealed that long noncoding RNAs (lncRNAs) are closely linked to several human diseases, providing new opportunities for their use in detection and therapy. Many graph propagation and similarity fusion approaches can be used for predicting potential lncRNA-disease associations. However, existing similarity fusion approaches suffer from noise and self-similarity loss in the fusion process. To address these problems, a new prediction approach, termed SSMF-BLNP, based on organically combining selective similarity matrix fusion (SSMF) and bidirectional linear neighborhood label propagation (BLNP), is proposed in this paper to predict lncRNA-disease associations. In SSMF, self-similarity networks of lncRNAs and diseases are obtained by selective preprocessing and nonlinear iterative fusion. The fusion process assigns weights to each initial similarity network and introduces a unit matrix that can reduce noise and compensate for the loss of self-similarity. In BLNP, the initial lncRNA-disease associations are employed in both lncRNA and disease directions as label information for linear neighborhood label propagation. The propagation was then performed on the self-similarity network obtained from SSMF to derive the scoring matrix for predicting the relationships between lncRNAs and diseases. Experimental results showed that SSMF-BLNP performed better than seven other state of-the-art approaches. Furthermore, a case study demonstrated up to 100% and 80% accuracy in 10 lncRNAs associated with hepatocellular carcinoma and 10 lncRNAs associated with renal cell carcinoma, respectively. The source code and datasets used in this paper are available at: https://github.com/RuiBingo/SSMF-BLNP.

Grain protein function prediction based on self-attention mechanism and bidirectional LSTM.

With the development of genome sequencing technology, using computing technology to predict grain protein function has become one of the important tasks of bioinformatics. The protein data of four grains, soybean, maize, indica and japonica are selected in this experimental dataset. In this paper, a novel neural network algorithm Chemical-SA-BiLSTM is proposed for grain protein function prediction. The Chemical-SA-BiLSTM algorithm fuses the chemical properties of proteins on the basis of amino acid sequences, and combines the self-attention mechanism with the bidirectional Long Short-Term Memory network. The experimental results show that the Chemical-SA-BiLSTM algorithm is superior to other classical neural network algorithms, and can more accurately predict the protein function, which proves the effectiveness of the Chemical-SA-BiLSTM algorithm in the prediction of grain protein function. The source code of our method is available at https://github.com/HwaTong/Chemical-SA-BiLSTM.

DeepSTF: predicting transcription factor binding sites by interpretable deep neural networks combining sequence and shape.

Precise targeting of transcription factor binding sites (TFBSs) is essential to comprehending transcriptional regulatory processes and investigating cellular function. Although several deep learning algorithms have been created to predict TFBSs, the models' intrinsic mechanisms and prediction results are difficult to explain. There is still room for improvement in prediction performance. We present DeepSTF, a unique deep-learning architecture for predicting TFBSs by integrating DNA sequence and shape profiles. We use the improved transformer encoder structure for the first time in the TFBSs prediction approach. DeepSTF extracts DNA higher-order sequence features using stacked convolutional neural networks (CNNs), whereas rich DNA shape profiles are extracted by combining improved transformer encoder structure and bidirectional long short-term memory (Bi-LSTM), and, finally, the derived higher-order sequence features and representative shape profiles are integrated into the channel dimension to achieve accurate TFBSs prediction. Experiments on 165 ENCODE chromatin immunoprecipitation sequencing (ChIP-seq) datasets show that DeepSTF considerably outperforms several state-of-the-art algorithms in predicting TFBSs, and we explain the usefulness of the transformer encoder structure and the combined strategy using sequence features and shape profiles in capturing multiple dependencies and learning essential features. In addition, this paper examines the significance of DNA shape features predicting TFBSs. The source code of DeepSTF is available at https://github.com/YuBinLab-QUST/DeepSTF/.

The Chd1 Chromatin Remodeler Shifts Nucleosomal DNA Bidirectionally as a Monomer.

Chromatin remodelers catalyze dynamic packaging of the genome by carrying out nucleosome assembly/disassembly, histone exchange, and nucleosome repositioning. Remodeling results in evenly spaced nucleosomes, which requires probing both sides of the nucleosome, yet the way remodelers organize sliding activity to achieve this task is not understood. Here, we show that the monomeric Chd1 remodeler shifts DNA back and forth by dynamically alternating between different segments of the nucleosome. During sliding, Chd1 generates unstable remodeling intermediates that spontaneously relax to a pre-remodeled position. We demonstrate that nucleosome sliding is tightly controlled by two regulatory domains: the DNA-binding domain, which interferes with sliding when its range is limited by a truncated linking segment, and the chromodomains, which play a key role in substrate discrimination. We propose that active interplay of the ATPase motor with the regulatory domains may promote dynamic nucleosome structures uniquely suited for histone exchange and chromatin reorganization during transcription.

RNA Pol II Dynamics Modulate Co-transcriptional Chromatin Modification, CTD Phosphorylation, and Transcriptional Direction.

Eukaryotic genes are marked by conserved post-translational modifications on the RNA pol II C-terminal domain (CTD) and the chromatin template. How the 5'-3' profiles of these marks are established is poorly understood. Using pol II mutants in human cells, we found that slow transcription repositioned specific co-transcriptionally deposited chromatin modifications; histone H3 lysine 36 trimethyl (H3K36me3) shifted within genes toward 5' ends, and histone H3 lysine 4 dimethyl (H3K4me2) extended farther upstream of start sites. Slow transcription also evoked a hyperphosphorylation of CTD Ser2 residues at 5' ends of genes that is conserved in yeast. We propose a "dwell time in the target zone" model to explain the effects of transcriptional dynamics on the establishment of co-transcriptionally deposited protein modifications. Promoter-proximal Ser2 phosphorylation is associated with a longer pol II dwell time at start sites and reduced transcriptional polarity because of strongly enhanced divergent antisense transcription at promoters. These results demonstrate that pol II dynamics help govern the decision between sense and divergent antisense transcription.

Offcut Substrate-Induced Defect Trapping at Step Edges.

We report step edge-induced localized defects suppressing subsequent antiphase boundary formation in the bulk structure of a trilayer oxide heterostructure. The heterostructure encompasses a layer of La0.66Sr0.34MnO3 sandwiched between a superconducting La1.84Sr0.16CuO4 bottom layer and an insulating La2CuO4 top layer. The combination of a minor a-axis mismatch (0.11 Å) and a pronounced c-axis mismatch (2.73 Å) at the step edges leads to the emergence of localized defects exclusively forming at the step edge. Employing atomically resolved electron energy-loss spectroscopy maps, we discern the electronic state of those structures in the second La0.66Sr0.34MnO3 unit cell near the step edge. In particular, a reduction in the pre-edge region of the O-K edge indicates the formation of oxygen vacancies induced by the strained step edge. This study underscores our capability to control defects at the nanoscale.

Renaissance of Allostery to Disrupt Protein Kinase Interactions.

Protein-protein interactions often regulate the activity of protein kinases by allosterically modulating the conformation of the ATP-binding site. Bidirectional allostery implies that reverse modulation (i.e., from the ATP-binding site to the interaction and regulatory sites) must also be possible. Here, we review both the allosteric regulation of protein kinases and recent work describing how compounds binding at the ATP-binding site can promote or inhibit protein kinase interactions at regulatory sites via the reverse mechanism. Notably, the pharmaceutical industry has been developing compounds that bind to the ATP-binding site of protein kinases and potently disrupt protein-protein interactions between target protein kinases and their regulatory interacting partners. Learning to modulate allosteric processes will facilitate the development of protein-protein interaction modulators.

Reversible and bidirectional signaling of notch ligands.

The Notch signaling pathway is a direct cell-cell communication system involved in a wide variety of biological processes, and its disruption is observed in several pathologies. The pathway is comprised of a ligand-expressing (sender) cell and a receptor-expressing (receiver) cell. The canonical ligands are members of the Delta/Serrate/Lag-1 (DSL) family of proteins. Their binding to a Notch receptor in a neighboring cell induces a conformational change in the receptor, which will undergo regulated intramembrane proteolysis (RIP), liberating the Notch intracellular domain (NICD). The NICD is translocated to the nucleus and promotes gene transcription. It has been demonstrated that the ligands can also undergo RIP and nuclear translocation, suggesting a function for the ligands in the sender cell and possible bidirectionality of the Notch pathway. Although the complete mechanism of ligand processing is not entirely understood, and its dependence on Notch receptors has not been ruled out. Also, ligands have autonomous functions beyond Notch activation. Here we review the concepts of reverse and bidirectional signalization of DSL proteins and discuss the characteristics that make them more than just ligands of the Notch pathway.

O-GlcNAcPRED-DL: Prediction of Protein O-GlcNAcylation Sites Based on an Ensemble Model of Deep Learning.

O-linked ß-N-acetylglucosamine (O-GlcNAc) is a post-translational modification (i.e., O-GlcNAcylation) on serine/threonine residues of proteins, regulating a plethora of physiological and pathological events. As a dynamic process, O-GlcNAc functions in a site-specific manner. However, the experimental identification of the O-GlcNAc sites remains challenging in many scenarios. Herein, by leveraging the recent progress in cataloguing experimentally identified O-GlcNAc sites and advanced deep learning approaches, we establish an ensemble model, O-GlcNAcPRED-DL, a deep learning-based tool, for the prediction of O-GlcNAc sites. In brief, to make a benchmark O-GlcNAc data set, we extracted the information on O-GlcNAc from the recently constructed database O-GlcNAcAtlas, which contains thousands of experimentally identified and curated O-GlcNAc sites on proteins from multiple species. To overcome the imbalance between positive and negative data sets, we selected five groups of negative data sets in humans and mice to construct an ensemble predictor based on connection of a convolutional neural network and bidirectional long short-term memory. By taking into account three types of sequence information, we constructed four network frameworks, with the systematically optimized parameters used for the models. The thorough comparison analysis on two independent data sets of humans and mice and six independent data sets from other species demonstrated remarkably increased sensitivity and accuracy of the O-GlcNAcPRED-DL models, outperforming other existing tools. Moreover, a user-friendly Web server for O-GlcNAcPRED-DL has been constructed, which is freely available at http://oglcnac.org/pred_dl.

The Sec1-Munc18 protein VPS33B forms a uniquely bidirectional complex with VPS16B.

Loss-of-function variants of vacuolar protein sorting proteins VPS33B and VPS16B (VIPAS39) are causative for arthrogryposis, renal dysfunction, and cholestasis syndrome, where early lethality of patients indicates that VPS33B and VPS16B play essential cellular roles. VPS33B is a member of the Sec1-Munc18 protein family and thought to facilitate vesicular fusion via interaction with soluble N-ethylmaleimide-sensitive factor attachment protein receptor (SNARE) complexes, like its paralog VPS33A in the homotypic fusion and vacuole sorting complex. VPS33B and VPS16B are known to associate, but little is known about the composition, structure, or function of the VPS33B-VPS16B complex. We show here that human VPS33B-VPS16B is a high molecular weight complex, which we expressed in yeast to perform structural, composition, and stability analysis. Circular dichroism data indicate VPS33B-VPS16B has a well-folded α-helical secondary structure, and size-exclusion chromatography-multiangle light scattering revealed a molecular weight of ∼315 kDa. Quantitative immunoblotting indicated a VPS33B:VPS16B ratio of 2:3. Expression of arthrogryposis, renal dysfunction, and cholestasis syndrome-causing VPS33B missense variants showed L30P disrupts complex formation but not S243F or H344D. Truncated VPS16B (amino acids 143 to 316) was sufficient to form a complex with VPS33B. Small-angle X-ray scattering and negative-staining EM revealed a two-lobed shape for VPS33B-VPS16B. Avidin tagging indicated that each lobe contains a VPS33B molecule, and they are oriented in opposite directions. We propose a structure for VPS33B-VPS16B that allows the VPS33B at each end to interact with separate SNARE bundles and/or SNAREpins, plus associated membrane components. These observations reveal the only known potentially bidirectional Sec1-Munc18 protein complex.