SNP-based and haplotype-based genome-wide association on drug dependence in Han Chinese.

BACKGROUND: Drug addiction is a serious problem worldwide and is influenced by genetic factors. The present study aimed to investigate the association between genetics and drug addiction among Han Chinese. METHODS: A total of 1000 Chinese users of illicit drugs and 9693 healthy controls were enrolled and underwent single nucleotide polymorphism (SNP)-based and haplotype-based association analyses via whole-genome genotyping. RESULTS: Both single-SNP and haplotype tests revealed associations between illicit drug use and several immune-related genes in the major histocompatibility complex (MHC) region (SNP association: log10BF = 15.135, p = 1.054e-18; haplotype association: log10BF = 20.925, p = 2.065e-24). These genes may affect the risk of drug addiction via modulation of the neuroimmune system. The single-SNP test exclusively reported genome-wide significant associations between rs3782886 (SNP association: log10BF = 8.726, p = 4.842e-11) in BRAP and rs671 (SNP association: log10BF = 7.406, p = 9.333e-10) in ALDH2 and drug addiction. The haplotype test exclusively reported a genome-wide significant association (haplotype association: log10BF = 7.607, p = 3.342e-11) between a region with allelic heterogeneity on chromosome 22 and drug addiction, which may be involved in the pathway of vitamin B12 transport and metabolism, indicating a causal link between lower vitamin B12 levels and methamphetamine addiction. CONCLUSIONS: These findings provide new insights into risk-modeling and the prevention and treatment of methamphetamine and heroin dependence, which may further contribute to potential novel therapeutic approaches.

Automated detection of MRI-negative temporal lobe epilepsy with ROI-based morphometric features and machine learning.

Objective: Temporal lobe epilepsy (TLE) predominantly originates from the anteromedial basal region of the temporal lobe, and its prognosis is generally favorable following surgical intervention. However, TLE often appears negative in magnetic resonance imaging (MRI), making it difficult to quantitatively diagnose the condition solely based on clinical symptoms. There is a pressing need for a quantitative, automated method for detecting TLE. Methods: This study employed MRI scans and clinical data from 51 retrospective epilepsy cases, dividing them into two groups: 34 patients in TLE group and 17 patients in non-TLE group. The criteria for defining the TLE group were successful surgical removal of the epileptogenic zone in the temporal lobe and a favorable postoperative prognosis. A standard procedure was used for normalization, brain extraction, tissue segmentation, regional brain partitioning, and cortical reconstruction of T1 structural MRI images. Morphometric features such as gray matter volume, cortical thickness, and surface area were extracted from a total of 20 temporal lobe regions in both hemispheres. Support vector machine (SVM), extreme learning machine (ELM), and cmcRVFL+ classifiers were employed for model training and validated using 10-fold cross-validation. Results: The results demonstrated that employing ELM classifiers in conjunction with specific temporal lobe gray matter volume features led to a better identification of TLE. The classification accuracy was 92.79%, with an area under the curve (AUC) value of 0.8019. Conclusion: The method proposed in this study can significantly assist in the preoperative identification of TLE patients. By employing this method, TLE can be included in surgical criteria, which could alleviate patient symptoms and improve prognosis, thereby bearing substantial clinical significance.

Deciphering temporal gene expression dynamics in multiple coral species exposed to heat stress: Implications for predicting resilience.

Coral reefs are facing unprecedented threats due to global climate change, particularly elevated sea surface temperatures causing coral bleaching. Understanding coral responses at the molecular level is crucial for predicting their resilience and developing effective conservation strategies. In this study, we conducted a comprehensive gene expression analysis of four coral species to investigate their long-term molecular response to heat stress. We identified distinct gene expression patterns among the coral species, with laminar corals exhibiting a stronger response compared to branching corals. Heat shock proteins (HSPs) showed an overall decreasing expression trend, indicating the high energy cost associated with sustaining elevated HSP levels during prolonged heat stress. Peroxidases and oxidoreductases involved in oxidative stress response demonstrated significant upregulation, highlighting their role in maintaining cellular redox balance. Differential expression of genes related to calcium homeostasis and bioluminescence suggested distinct mechanisms for coping with heat stress among the coral species. Furthermore, the impact of heat stress on coral biomineralization varied, with downregulation of carbonic anhydrase and skeletal organic matrix proteins indicating reduced capacity for biomineralization in the later stages of heat stress. Our findings provide insights into the molecular mechanisms underlying coral responses to heat stress and highlight the importance of considering species-specific responses in assessing coral resilience. The identified biomarkers may serve as indicators of heat stress and contribute to early detection of coral bleaching events. These findings contribute to our understanding of coral resilience and provide a basis for future research aimed at enhancing coral survival in the face of climate change.

Comparative transcriptome analysis reveals deep molecular landscapes in stony coral Montipora clade.

Introduction: Coral reefs, among the most invaluable ecosystems in the world, face escalating threats from climate change and anthropogenic activities. To decipher the genetic underpinnings of coral adaptation and resilience, we undertook comprehensive transcriptome profiling of two emblematic coral species, Montipora foliosa and Montipora capricornis, leveraging PacBio Iso-Seq technology. These species were strategically selected for their ecological significance and their taxonomic proximity within the Anthozoa class. Methods: Our study encompassed the generation of pristine transcriptomes, followed by thorough functional annotation via diverse databases. Subsequently, we quantified transcript abundance and scrutinized gene expression patterns, revealing notable distinctions between the two species. Results: Intriguingly, shared orthologous genes were identified across a spectrum of coral species, highlighting a substantial genetic conservation within scleractinian corals. Importantly, a subset of genes, integral to biomineralization processes, emerged as exclusive to scleractinian corals, shedding light on their intricate evolutionary history. Furthermore, we discerned pronounced upregulation of genes linked to immunity, stress response, and oxidative-reduction processes in M. foliosa relative to M. capricornis. These findings hint at the presence of more robust mechanisms in M. foliosa for maintaining internal equilibrium and effectively navigating external challenges, underpinning its potential ecological advantage. Beyond elucidating genetic adaptation in corals, our research underscores the urgency of preserving genetic diversity within coral populations. Discussion: These insights hold promise for informed conservation strategies aimed at safeguarding these imperiled ecosystems, bearing ecological and economic significance. In synthesis, our study seamlessly integrates genomic inquiry with ecological relevance, bridging the gap between molecular insights and the imperative to conserve coral reefs in the face of mounting threats.

Spatial Transcriptomic Analysis Reveals Regional Transcript Changes in Early and Late Stages of rd1 Model Mice with Retinitis Pigmentosa.

Retinitis pigmentosa (RP) is the leading cause of inherited blindness with a genetically heterogeneous disorder. Currently, there is no effective treatment that can protect vision for those with RP. In recent decades, the rd1 mouse has been used to study the pathological mechanisms of RP. Molecular biological studies using rd1 mice have clarified the mechanism of the apoptosis of photoreceptor cells in the early stage of RP. However, the pathological changes in RP over time remain unclear. The unknown pathology mechanism of RP over time and the difficulty of clinical treatment make it urgent to perform more refined and spatially informed molecular biology studies of RP. In this study, spatial transcriptomic analysis is used to study the changes in different retinal layers of rd1 mice at different ages. The results demonstrate the pattern of photoreceptor apoptosis between rd1 mice and the control group. Not only was oxidative stress enhanced in the late stage of RP, but it was accompanied by an up-regulation of the VEGF pathway. Analysis of temporal kinetic trends has further identified patterns of changes in the key pathways of the early and late stages, to help understand the important pathogenesis of RP. Overall, the application of spatial transcriptomics to rd1 mice can help to elucidate the important pathogenesis of RP involving photoreceptor apoptosis and retinal remodeling.

Deep-Cloud: A Deep Neural Network-Based Approach for Analyzing Differentially Expressed Genes of RNA-seq Data.

Presently, the field of analyzing differentially expressed genes (DEGs) of RNA-seq data is still in its infancy, with new approaches constantly being proposed. Taking advantage of deep neural networks to explore gene expression information on RNA-seq data can provide a novel possibility in the biomedical field. In this study, a novel approach based on a deep learning algorithm and cloud model was developed, named Deep-Cloud. Its main advantage is not only using a convolutional neural network and long short-term memory to extract original data features and estimate gene expression of RNA-seq data but also combining the statistical method of the cloud model to quantify the uncertainty and carry out in-depth analysis of the DEGs between the disease groups and the control groups. Compared with traditional analysis software of DEGs, the Deep-cloud model further improves the sensitivity and accuracy of obtaining DEGs from RNA-seq data. Overall, the proposed new approach Deep-cloud paves a new pathway for mining RNA-seq data in the biomedical field.

Exploration of whole genome amplification generated chimeric sequences in long-read sequencing data.

MOTIVATION: Multiple displacement amplification (MDA) has become the most commonly used method of whole genome amplification, generating a vast amount of DNA with higher molecular weight and greater genome coverage. Coupling with long-read sequencing, it is possible to sequence the amplicons of over 20 kb in length. However, the formation of chimeric sequences (chimeras, expressed as structural errors in sequencing data) in MDA seriously interferes with the bioinformatics analysis but its influence on long-read sequencing data is unknown. RESULTS: We sequenced the phi29 DNA polymerase-mediated MDA amplicons on the PacBio platform and analyzed chimeras within the generated data. The 3rd-ChimeraMiner has been constructed as a pipeline for recognizing and restoring chimeras into the original structures in long-read sequencing data, improving the efficiency of using TGS data. Five long-read datasets and one high-fidelity long-read dataset with various amplification folds were analyzed. The result reveals that the mis-priming events in amplification are more frequently occurring than widely perceived, and the propor tion gradually accumulates from 42% to over 78% as the amplification continues. In total, 99.92% of recognized chimeric sequences were demonstrated to be artifacts, whose structures were wrongly formed in MDA instead of existing in original genomes. By restoring chimeras to their original structures, the vast majority of supplementary alignments that introduce false-positive structural variants are recycled, removing 97% of inversions on average and contributing to the analysis of structural variation in MDA-amplified samples. The impact of chimeras in long-read sequencing data analysis should be emphasized, and the 3rd-ChimeraMiner can help to quantify and reduce the influence of chimeras. AVAILABILITY AND IMPLEMENTATION: The 3rd-ChimeraMiner is available on GitHub, https://github.com/dulunar/3rdChimeraMiner.

MineProt: a stand-alone server for structural proteome curation.

AlphaFold-like systems are rapidly expanding the scale of proteome structuring, and MineProt provides an effective solution for custom curation of these novel high-throughput data. It enables researchers to build their own server in simple steps, run almost out-of-the-box scripts to annotate and curate their proteins, analyze their data via a user-friendly online interface, and utilize plugins to extend the functionality of server. It is expected to support researcher productivity and facilitate data sharing in the new era of structural proteomics. Database URL MineProt is open-source software available at https://github.com/huiwenke/MineProt.

Magnetic Bead Spherical Nucleic Acid Microstructure for Reliable DNA Preservation and Repeated DNA Reading.

DNA is an attractive medium for long-term data storage because of its density, ease of copying, sustainability, and longevity. Recent advances have focused on the development of new encoding algorithms, automation, and sequencing technologies. Despite progress in these subareas, the most challenging hurdle in the deployment of DNA storage remains the reliability of preservation and the repeatability of reading. Herein, we report the construction of a magnetic bead spherical nucleic acid (MB-SNA) composite microstructure and its use as a cost-effective platform for reliable DNA preservation and repeated reading. MB-SNA has an inner core of silica@γ-Fe2O3@silica microbeads and an outer spherical shell of double-stranded DNA (dsDNA) with a density as high as 34 pmol/cm2. For MB-SNA, each strand of dsDNA stored a piece of data, and the high-density packing of dsDNA achieved high-capacity storage. MB-SNA was advantageous in terms of reliable preservation over free DNA. By accelerated aging tests, the data of MB-SNA is demonstrated to be readable after 0.23 million years of preservation at -18 °C and 50% relative humidity. Moreover, MB-SNA facilitated repeated reading by facile PCR-magnetic separation. After 10 cycles of PCR access, the retention rate of dsDNA for MB-SNA is demonstrated to be as high as 93%, and the accuracy of sequencing is more than 98%. In addition, MB-SNA makes cost-effective DNA storage feasible. By serial dilution, the physical limit for MB-SNA to achieve accurate reading is probed to be as low as two microstructures.

Polyp-Canal Reconstruction Reveals Evolution Toward Complexity in Corals.

Modern scleractinian corals are classified into robust, complex, and basal clades through comparative molecular studies. However, only few morphological or biological criteria can systematically determine the evolutionary trajectories of these major scleractinian coral clades. Here, we obtained the structural information of 21 scleractinian coral species representing robust and complex clades: High-resolution micro-computed tomography was used to reconstruct the polyp-canal systems in their colonies and to visualize the dynamic polyp growth processes. We found that the emergence of mesh-like canals may distinguish representatives of complex and robust clades. The differences in polyp-canal connections suggest distinct evolutionary trajectories among coral species: The formation of the canal network promoted the development of more complex coral structures, and coral polyps within this network formed calices of very similar volume, following precise axial growth directions. The influence of individual polyps on the coral colony becomes less significant as coral structures become more complex, and coral species with more complicated polyp-canal systems occupied niches more efficiently. This work supplements current evolutionary studies on reef-building corals, providing insight for further studies on coral growth patterns.

Nanopore sensors for single molecular protein detection: Research progress based on computer simulations.

As biological macromolecules, proteins are involved in important cellular functions ranging from DNA replication and biosynthesis to metabolic signalling and environmental sensing. Protein sequencing can help understand the relationship between protein function and structure, and provide key information for disease diagnosis and new drug design. Nanopore sensors are a novel technology to achieve the goal of label-free and high-throughput protein sequencing. In recent years, nanopore-based biosensors have been widely used in the detection and analysis of biomolecules such as DNA, RNA, and proteins. At the same time, computer simulations can describe the transport of proteins through nanopores at the atomic level. This paper reviews the applications of nanopore sensors in protein sequencing over the past decade and the solutions to key problems from a computer simulation perspective, with the aim of pointing the way to the future of nanopore protein sequencing.

Chimera: The spoiler in multiple displacement amplification.

Multiple displacement amplification (MDA) based on isothermal random priming and high fidelity phi29 DNA polymerase-mediated processive extension has revolutionized the field of whole genome amplification by enabling the amplification of minute amounts of DNA, such as from a single cell, generating vast amounts of DNA with high genome coverage. Despite its advantages, MDA has its own challenges, one of the grandest being the formation of chimeric sequences (chimeras), which presents in all MDA products and seriously disturbs the downstream analysis. In this review, we provide a comprehensive overview of current research on MDA chimeras. We first reviewed the mechanisms of chimera formation and chimera detection methods. We then systematically summarized the characteristics of chimeras, including overlap, chimeric distance, chimeric density, and chimeric rate, as found in independently published sequencing data. Finally, we reviewed the methods used to process chimeric sequences and their impacts on the improvement of data utilization efficiency. The information presented in this review will be useful for those interested in understanding the challenges with MDA and in improving its performance.

Analysis of mutational genotyping using correctable decoding sequencing with superior specificity.

The ability to accurately identify SNPs or low-abundance mutations is important for early clinical diagnosis of diseases, but the existing high-throughput sequencing platforms are limited in terms of their accuracy. Here, we propose a correctable decoding sequencing strategy that may be used for high-throughput sequencing platforms. This strategy is based on adding a mixture of two types of mononucleotides, natural nucleotide and cyclic reversible termination (CRT), for cyclic sequencing. Using the synthetic characteristic of CRTs, about 75% of the calls are unambiguous for a single sequencing run, and the remaining ambiguous sequence can be accurately deduced by two parallel sequencing runs. We demonstrate the feasibility of this strategy, and its cycle efficiency can reach approximately 99.3%. This strategy is proved to be effective for correcting errors and identifying whether the sequencing information is correct or not. And its conservative theoretical error rate was determined to be 0.0009%, which is lower than that of Sanger sequencing. In addition, we establish that the information of only a single sequencing run can be used to detect samples with known mutation sites. We apply this strategy to accurately identify a mutation site in mitochondrial DNA from human cells.

A molecular dynamics investigation of Taq DNA polymerase and its complex with a DNA substrate using a solid-state nanopore biosensor.

Proteins have a small volume difference by the diversity of amino acids, which make protein detection and identification a great challenge. Solid-state nanopore as label-free biosensors has attracted attention with high sensitivity. In this work, we investigated the Taq DNA polymerase before and after combining it with a DNA substrate on a solid-state nanopore through molecular dynamics. In simulation, we analyzed the contribution source of nanopore current blockage. In addition to considering the traditional physical exclusion volume model, the non-covalent interaction between the protein molecules and the pore wall also showed to affect the current blockage in the nanopore. When choosing pores of comparable size to protein molecules, the two states of Taq DNA polymerase produce differentiated non-covalent interactions with the pore wall, which enhanced the amplitude difference in current blockage. As a result, the two DNA polymerases can be distinguished through the distinct current blockage. However, when applying additional pulling force or increasing the pore size of the nanopore, the differences between the current blockages are not significant enough to distinguish. The introduction of the non-covalent interaction makes it clear to understand the current blockage differences, which guide the mechanism between molecules with similar structures or volumes.

The Mechanism of Overflow Amplitude in Nanopore Experiments and Its Application in Molecule Detection.

The investigation of abnormal experimental phenomena observed in nanopore research improves our understanding of nanopores. In this article, we report and explore the unusual phenomenon that the amplitude of current blockage decreases beyond zero baseline (overflow amplitudes), which was observed in the translocation behavior of 100 bp double-stranded DNA molecules through SiNx nanopores. In our experiments, the overflow amplitude decreases with the increase of salt concentration and also decreases when the dwell time is shortened as the normalized amplitude of the overflow current showed a reduction with the increase of voltage. Upon analyzing the electric double layer meanwhile, the overflow amplitudes were shown to be positively correlated with the depth of the electric double layer and the duration of interaction between biological molecules. The formation of overflow amplitude can be attributed to the double electric layer ionic perturbation and reconfiguration, which are the results of the interaction between the biomolecule and the electric bilayer. The validation of the assumption using biomolecules containing different charges demonstrated that the overflow amplitude increased with the increase of the charge. It was concluded that proteins that pass through the nanopore with different orientation were differentiated based on their different overflow amplitude patterns. The investigation of overflow amplitude helps to enhance the understanding and the performance of nanopores.

Utilizing an artificial intelligence system to build the digital structural proteome of reef-building corals.

BACKGROUND: Reef-building corals play an important role in the marine ecosystem, and analyzing their proteomes from a structural perspective will exert positive effects on exploring their biology. Here we integrated mass spectrometry with newly published ColabFold to obtain digital structural proteomes of dominant reef-building corals. RESULTS: Of the 8,382 homologous proteins in Acropora muricata, Montipora foliosa, and Pocillopora verrucosa identified, 8,166 received predicted structures after about 4,060 GPU hours of computation. The resulting dataset covers 83.6% of residues with a confident prediction, while 25.9% have very high confidence. CONCLUSIONS: Our work provides insight-worthy predictions for coral research, confirms the reliability of ColabFold in practice, and is expected to be a reference case in the impending high-throughput era of structural proteomics.

Full-Length Transcriptome Maps of Reef-Building Coral Illuminate the Molecular Basis of Calcification, Symbiosis, and Circadian Genes.

Coral transcriptomic data largely rely on short-read sequencing, which severely limits the understanding of coral molecular mechanisms and leaves many important biological questions unresolved. Here, we sequence the full-length transcriptomes of four common and frequently dominant reef-building corals using the PacBio Sequel II platform. We obtain information on reported gene functions, structures, and expression profiles. Among them, a comparative analysis of biomineralization-related genes provides insights into the molecular basis of coral skeletal density. The gene expression profiles of the symbiont Symbiodiniaceae are also isolated and annotated from the holobiont sequence data. Finally, a phylogenetic analysis of key circadian clock genes among 40 evolutionarily representative species indicates that there are four key members in early metazoans, including cry genes; Clock or Npas2; cyc or Arntl; and tim, while per, as the fifth member, occurs in Bilateria. In summary, this work provides a foundation for further work on the manipulation of skeleton production or symbiosis to promote the survival of these important organisms.

Constructing the boundary between potent and ineffective siRNAs by MG-algorithm with C-features.

BACKGROUND: In siRNA based antiviral therapeutics, selection of potent siRNAs is an indispensable step, but these commonly used features are unable to construct the boundary between potent and ineffective siRNAs. RESULTS: Here, we select potent siRNAs by removing ineffective ones, where these conditions for removals are constructed by C-features of siRNAs, C-features are generated by MG-algorithm, Icc-cluster and the different combinations of some commonly used features, MG-algorithm and Icc-cluster are two different algorithms to search the nearest siRNA neighbors. For the ineffective siRNAs in test data, they are removed from test data by I-iteration, where I-iteration continually updates training data by adding these successively removed siRNAs. Furthermore, the efficacy of siRNAs of test data is predicted by their nearest neighbors of training data. CONCLUSIONS: By siRNAs of Hencken dataset, results show that our algorithm removes almost ineffective siRNAs from test data, gives the clear boundary between potent and ineffective siRNAs, and accurately predicts the efficacy of siRNAs also. We suggest that our algorithm can provide new insights for selecting the potent siRNAs.

Prediction of Time-Series Transcriptomic Gene Expression Based on Long Short-Term Memory with Empirical Mode Decomposition.

RNA degradation can significantly affect the results of gene expression profiling, with subsequent analysis failing to faithfully represent the initial gene expression level. It is urgent to have an artificial intelligence approach to better utilize the limited data to obtain meaningful and reliable analysis results in the case of data with missing destination time. In this study, we propose a method based on the signal decomposition technique and deep learning, named Multi-LSTM. It is divided into two main modules: One decomposes the collected gene expression data by an empirical mode decomposition (EMD) algorithm to obtain a series of sub-modules with different frequencies to improve data stability and reduce modeling complexity. The other is based on long short-term memory (LSTM) as the core predictor, aiming to deeply explore the temporal nonlinear relationships embedded in the sub-modules. Finally, the prediction results of sub-modules are reconstructed to obtain the final prediction results of time-series transcriptomic gene expression. The results show that EMD can efficiently reduce the nonlinearity of the original data, which provides reliable theoretical support to reduce the complexity and improve the robustness of LSTM models. Overall, the decomposition-combination prediction framework can effectively predict gene expression levels at unknown time points.

Solid-state nanopore analysis on the conformation change of DNA polymerase I induced by a DNA substrate.

Proteins with a changeable conformation, such as polymerases, play a very important role in various life activities. Their conformational changes can be reflected in their structural size and flexibility, which may influence their transport kinetics. Recently, solid-state nanopore sensors have been widely applied to characterize the conformation of proteins and other complex structures as sensitive and high throughput single-molecule detectors. In this work, we used a SiN nanopore sensor to study the conformational changes between the Klenow fragment (KF) and its monomer complex with a DNA substrate (KF-DNA). By calculating their hydrodynamic radii, pore volume, the duration of translocation events, drift velocity, and molecular dynamics simulations, we found that the KF-DNA monomer complex has a tighter structure and transports slower. The study performed here can be potentially used to identify single polymerases in real time and may ultimately reveal conformation changes and the interaction between polymerases and their substrates.