A large-scale systematic survey reveals recurring molecular features of public antibody responses to SARS-CoV-2.

Global research to combat the COVID-19 pandemic has led to the isolation and characterization of thousands of human antibodies to the SARS-CoV-2 spike protein, providing an unprecedented opportunity to study the antibody response to a single antigen. Using the information derived from 88 research publications and 13 patents, we assembled a dataset of ∼8,000 human antibodies to the SARS-CoV-2 spike protein from >200 donors. By analyzing immunoglobulin V and D gene usages, complementarity-determining region H3 sequences, and somatic hypermutations, we demonstrated that the common (public) responses to different domains of the spike protein were quite different. We further used these sequences to train a deep-learning model to accurately distinguish between the human antibodies to SARS-CoV-2 spike protein and those to influenza hemagglutinin protein. Overall, this study provides an informative resource for antibody research and enhances our molecular understanding of public antibody responses.

Regional-specific calibration enables application of computational evidence for clinical classification of 5' cis-regulatory variants in Mendelian disease.

To date, clinical genetic testing for Mendelian disease variants has focused heavily on exonic coding and intronic gene regions. This multi-step study was undertaken to provide an evidence base for selecting and applying computational approaches for use in clinical classification of 5' cis-regulatory region variants. Curated datasets of clinically reported disease-causing 5' cis-regulatory region variants and variants from matched genomic regions in population controls were used to calibrate six bioinformatic tools as predictors of variant pathogenicity. Likelihood ratio estimates were aligned to code weights following ClinGen recommendations for application of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP) classification scheme. Considering code assignment across all reference dataset variants, performance was best for CADD (81.2%) and REMM (81.5%). Optimized thresholds provided moderate evidence toward pathogenicity (CADD, REMM) and moderate (CADD) or supporting (REMM) evidence against pathogenicity. Both sensitivity and specificity of prediction were improved when further categorizing variants based on location in an EPDnew-defined promoter region. Combining predictions (CADD, REMM, and location in a promoter region) increased specificity at the expense of sensitivity. Importantly, the optimal CADD thresholds for assigning ACMG/AMP codes PP3 (≥10) and BP4 (≤8) were vastly different from recommendations for protein-coding variants (PP3 ≥25.3; BP4 ≤22.7); CADD <22.7 would incorrectly assign BP4 for >90% of reported disease-causing cis-regulatory region variants. Our results demonstrate the need to consider a tiered approach and tailored score thresholds to optimize bioinformatic impact prediction for clinical classification of 5' cis-regulatory region variants.

PB-LKS: a python package for predicting phage-bacteria interaction through local K-mer strategy.

Bacteriophages can help the treatment of bacterial infections yet require in-silico models to deal with the great genetic diversity between phages and bacteria. Despite the tolerable prediction performance, the application scope of current approaches is limited to the prediction at the species level, which cannot accurately predict the relationship of phages across strain mutants. This has hindered the development of phage therapeutics based on the prediction of phage-bacteria relationships. In this paper, we present, PB-LKS, to predict the phage-bacteria interaction based on local K-mer strategy with higher performance and wider applicability. The utility of PB-LKS is rigorously validated through (i) large-scale historical screening, (ii) case study at the class level and (iii) in vitro simulation of bacterial antiphage resistance at the strain mutant level. The PB-LKS approach could outperform the current state-of-the-art methods and illustrate potential clinical utility in pre-optimized phage therapy design.

A two-stage computational framework for identifying antiviral peptides and their functional types based on contrastive learning and multi-feature fusion strategy.

Antiviral peptides (AVPs) have shown potential in inhibiting viral attachment, preventing viral fusion with host cells and disrupting viral replication due to their unique action mechanisms. They have now become a broad-spectrum, promising antiviral therapy. However, identifying effective AVPs is traditionally slow and costly. This study proposed a new two-stage computational framework for AVP identification. The first stage identifies AVPs from a wide range of peptides, and the second stage recognizes AVPs targeting specific families or viruses. This method integrates contrastive learning and multi-feature fusion strategy, focusing on sequence information and peptide characteristics, significantly enhancing predictive ability and interpretability. The evaluation results of the model show excellent performance, with accuracy of 0.9240 and Matthews correlation coefficient (MCC) score of 0.8482 on the non-AVP independent dataset, and accuracy of 0.9934 and MCC score of 0.9869 on the non-AMP independent dataset. Furthermore, our model can predict antiviral activities of AVPs against six key viral families (Coronaviridae, Retroviridae, Herpesviridae, Paramyxoviridae, Orthomyxoviridae, Flaviviridae) and eight viruses (FIV, HCV, HIV, HPIV3, HSV1, INFVA, RSV, SARS-CoV). Finally, to facilitate user accessibility, we built a user-friendly web interface deployed at https://awi.cuhk.edu.cn/∼dbAMP/AVP/.

The current state of molecular testing in the treatment of patients with solid tumors, 2019.

The world of molecular profiling has undergone revolutionary changes over the last few years as knowledge, technology, and even standard clinical practice have evolved. Broad molecular profiling is now nearly essential for all patients with metastatic solid tumors. New agents have been approved based on molecular testing instead of tumor site of origin. Molecular profiling methodologies have likewise changed such that tests that were performed on patients a few years ago are no longer complete and possibly inaccurate today. As with all rapid change, medical providers can quickly fall behind or struggle to find up-to-date sources to ensure he or she provides optimum care. In this review, the authors provide the current state of the art for molecular profiling/precision medicine, practice standards, and a view into the future ahead.

Vaccine Therapy for Heart Failure Targeting the Inflammatory Cytokine Igfbp7.

BACKGROUND: The heart comprises many types of cells such as cardiomyocytes, endothelial cells (ECs), fibroblasts, smooth muscle cells, pericytes, and blood cells. Every cell type responds to various stressors (eg, hemodynamic overload and ischemia) and changes its properties and interrelationships among cells. To date, heart failure research has focused mainly on cardiomyocytes; however, other types of cells and their cell-to-cell interactions might also be important in the pathogenesis of heart failure. METHODS: Pressure overload was imposed on mice by transverse aortic constriction and the vascular structure of the heart was examined using a tissue transparency technique. Functional and molecular analyses including single-cell RNA sequencing were performed on the hearts of wild-type mice and EC-specific gene knockout mice. Metabolites in heart tissue were measured by capillary electrophoresis-time of flight-mass spectrometry system. The vaccine was prepared by conjugating the synthesized epitope peptides with keyhole limpet hemocyanin and administered to mice with aluminum hydroxide as an adjuvant. Tissue samples from heart failure patients were used for single-nucleus RNA sequencing to examine gene expression in ECs and perform pathway analysis in cardiomyocytes. RESULTS: Pressure overload induced the development of intricately entwined blood vessels in murine hearts, leading to the accumulation of replication stress and DNA damage in cardiac ECs. Inhibition of cell proliferation by a cyclin-dependent kinase inhibitor reduced DNA damage in ECs and ameliorated transverse aortic constriction-induced cardiac dysfunction. Single-cell RNA sequencing analysis revealed upregulation of Igfbp7 (insulin-like growth factor-binding protein 7) expression in the senescent ECs and downregulation of insulin signaling and oxidative phosphorylation in cardiomyocytes of murine and human failing hearts. Overexpression of Igfbp7 in the murine heart using AAV9 (adeno-associated virus serotype 9) exacerbated cardiac dysfunction, while EC-specific deletion of Igfbp7 and the vaccine targeting Igfbp7 ameliorated cardiac dysfunction with increased oxidative phosphorylation in cardiomyocytes under pressure overload. CONCLUSIONS: Igfbp7 produced by senescent ECs causes cardiac dysfunction and vaccine therapy targeting Igfbp7 may be useful to prevent the development of heart failure.

A Galectin-9-Driven CD11chigh Decidual Macrophage Subset Suppresses Uterine Vascular Remodeling in Preeclampsia.

BACKGROUND: Preeclampsia is a serious disease of pregnancy that lacks early diagnosis methods or effective treatment, except delivery. Dysregulated uterine immune cells and spiral arteries are implicated in preeclampsia, but the mechanistic link remains unclear. METHODS: Single-cell RNA sequencing and spatial transcriptomics were used to identify immune cell subsets associated with preeclampsia. Cell-based studies and animal models including conditional knockout mice and a new preeclampsia mouse model induced by recombinant mouse galectin-9 were applied to validate the pathogenic role of a CD11chigh subpopulation of decidual macrophages (dMφ) and to determine its underlying regulatory mechanisms in preeclampsia. A retrospective preeclampsia cohort study was performed to determine the value of circulating galectin-9 in predicting preeclampsia. RESULTS: We discovered a distinct CD11chigh dMφ subset that inhibits spiral artery remodeling in preeclampsia. The proinflammatory CD11chigh dMφ exhibits perivascular enrichment in the decidua from patients with preeclampsia. We also showed that trophoblast-derived galectin-9 activates CD11chigh dMφ by means of CD44 binding to suppress spiral artery remodeling. In 3 independent preeclampsia mouse models, placental and plasma galectin-9 levels were elevated. Galectin-9 administration in mice induces preeclampsia-like phenotypes with increased CD11chigh dMφ and defective spiral arteries, whereas galectin-9 blockade or macrophage-specific CD44 deletion prevents such phenotypes. In pregnant women, increased circulating galectin-9 levels in the first trimester and at 16 to 20 gestational weeks can predict subsequent preeclampsia onset. CONCLUSIONS: These findings highlight a key role of a distinct perivascular inflammatory CD11chigh dMφ subpopulation in the pathogenesis of preeclampsia. CD11chigh dMφ activated by increased galectin-9 from trophoblasts suppresses uterine spiral artery remodeling, contributing to preeclampsia. Increased circulating galectin-9 may be a biomarker for preeclampsia prediction and intervention.

Sars-escape network for escape prediction of SARS-COV-2.

MOTIVATION: Viruses have coevolved with their hosts for over millions of years and learned to escape the host's immune system. Although not all genetic changes in viruses are deleterious, some significant mutations lead to the escape of neutralizing antibodies and weaken the immune system, which increases infectivity and transmissibility, thereby impeding the development of antiviral drugs or vaccines. Accurate and reliable identification of viral escape mutational sequences could be a good indicator for therapeutic design. We developed a computational model that recognizes significant mutational sequences based on escape feature identification using natural language processing along with prior knowledge of experimentally validated escape mutants. RESULTS: Our machine learning-based computational approach can recognize the significant spike protein sequences of severe acute respiratory syndrome coronavirus 2 using sequence data alone. This modelling approach can be applied to other viruses, such as influenza, monkeypox and HIV using knowledge of escape mutants and relevant protein sequence datasets. AVAILABILITY: Complete source code and pre-trained models for escape prediction of severe acute respiratory syndrome coronavirus 2 protein sequences are available on Github at https://github.com/PremSinghBist/Sars-CoV-2-Escape-Model.git. The dataset is deposited to Zenodo at: doi: 10.5281/zenodo.7142638. The Python scripts are easy to run and customize as needed. CONTACT: premsing212@jbnu.ac.kr.

ResNetKhib: a novel cell type-specific tool for predicting lysine 2-hydroxyisobutylation sites via transfer learning.

Lysine 2-hydroxyisobutylation (Khib), which was first reported in 2014, has been shown to play vital roles in a myriad of biological processes including gene transcription, regulation of chromatin functions, purine metabolism, pentose phosphate pathway and glycolysis/gluconeogenesis. Identification of Khib sites in protein substrates represents an initial but crucial step in elucidating the molecular mechanisms underlying protein 2-hydroxyisobutylation. Experimental identification of Khib sites mainly depends on the combination of liquid chromatography and mass spectrometry. However, experimental approaches for identifying Khib sites are often time-consuming and expensive compared with computational approaches. Previous studies have shown that Khib sites may have distinct characteristics for different cell types of the same species. Several tools have been developed to identify Khib sites, which exhibit high diversity in their algorithms, encoding schemes and feature selection techniques. However, to date, there are no tools designed for predicting cell type-specific Khib sites. Therefore, it is highly desirable to develop an effective predictor for cell type-specific Khib site prediction. Inspired by the residual connection of ResNet, we develop a deep learning-based approach, termed ResNetKhib, which leverages both the one-dimensional convolution and transfer learning to enable and improve the prediction of cell type-specific 2-hydroxyisobutylation sites. ResNetKhib is capable of predicting Khib sites for four human cell types, mouse liver cell and three rice cell types. Its performance is benchmarked against the commonly used random forest (RF) predictor on both 10-fold cross-validation and independent tests. The results show that ResNetKhib achieves the area under the receiver operating characteristic curve values ranging from 0.807 to 0.901, depending on the cell type and species, which performs better than RF-based predictors and other currently available Khib site prediction tools. We also implement an online web server of the proposed ResNetKhib algorithm together with all the curated datasets and trained model for the wider research community to use, which is publicly accessible at https://resnetkhib.erc.monash.edu/.

iAMPCN: a deep-learning approach for identifying antimicrobial peptides and their functional activities.

Antimicrobial peptides (AMPs) are short peptides that play crucial roles in diverse biological processes and have various functional activities against target organisms. Due to the abuse of chemical antibiotics and microbial pathogens' increasing resistance to antibiotics, AMPs have the potential to be alternatives to antibiotics. As such, the identification of AMPs has become a widely discussed topic. A variety of computational approaches have been developed to identify AMPs based on machine learning algorithms. However, most of them are not capable of predicting the functional activities of AMPs, and those predictors that can specify activities only focus on a few of them. In this study, we first surveyed 10 predictors that can identify AMPs and their functional activities in terms of the features they employed and the algorithms they utilized. Then, we constructed comprehensive AMP datasets and proposed a new deep learning-based framework, iAMPCN (identification of AMPs based on CNNs), to identify AMPs and their related 22 functional activities. Our experiments demonstrate that iAMPCN significantly improved the prediction performance of AMPs and their corresponding functional activities based on four types of sequence features. Benchmarking experiments on the independent test datasets showed that iAMPCN outperformed a number of state-of-the-art approaches for predicting AMPs and their functional activities. Furthermore, we analyzed the amino acid preferences of different AMP activities and evaluated the model on datasets of varying sequence redundancy thresholds. To facilitate the community-wide identification of AMPs and their corresponding functional types, we have made the source codes of iAMPCN publicly available at https://github.com/joy50706/iAMPCN/tree/master. We anticipate that iAMPCN can be explored as a valuable tool for identifying potential AMPs with specific functional activities for further experimental validation.

Sequence Alignment/Map format: a comprehensive review of approaches and applications.

The Sequence Alignment/Map (SAM) format file is the text file used to record alignment information. Alignment is the core of sequencing analysis, and downstream tasks accept mapping results for further processing. Given the rapid development of the sequencing industry today, a comprehensive understanding of the SAM format and related tools is necessary to meet the challenges of data processing and analysis. This paper is devoted to retrieving knowledge in the broad field of SAM. First, the format of SAM is introduced to understand the overall process of the sequencing analysis. Then, existing work is systematically classified in accordance with generation, compression and application, and the involved SAM tools are specifically mined. Lastly, a summary and some thoughts on future directions are provided.

TMKit: a Python interface for computational analysis of transmembrane proteins.

Transmembrane proteins are receptors, enzymes, transporters and ion channels that are instrumental in regulating a variety of cellular activities, such as signal transduction and cell communication. Despite tremendous progress in computational capacities to support protein research, there is still a significant gap in the availability of specialized computational analysis toolkits for transmembrane protein research. Here, we introduce TMKit, an open-source Python programming interface that is modular, scalable and specifically designed for processing transmembrane protein data. TMKit is a one-stop computational analysis tool for transmembrane proteins, enabling users to perform database wrangling, engineer features at the mutational, domain and topological levels, and visualize protein-protein interaction interfaces. In addition, TMKit includes seqNetRR, a high-performance computing library that allows customized construction of a large number of residue connections. This library is particularly well suited for assigning correlation matrix-based features at a fast speed. TMKit should serve as a useful tool for researchers in assisting the study of transmembrane protein sequences and structures. TMKit is publicly available through https://github.com/2003100127/tmkit and https://tmkit-guide.herokuapp.com/doc/overview.

Loss of mitogen-activated protein kinase phosphate-5 aggravates islet dysfunction in mice with type 1 and type 2 diabetes.

Impaired functionality and loss of islet ß-cells are the primary abnormalities underlying the pathogenesis of both type 1 and 2 diabetes (T1DM and T2DM). However, specific therapeutic and preventive mechanisms underlying these conditions remain unclear. Mitogen-activated protein kinase phosphatase-5 (MKP-5) has been implicated in carcinogenesis, lipid metabolism regulation, and immune cell activation. In a previous study, we demonstrated the involvement of exogenous MKP-5 in the regulation of obesity-induced T2DM. However, the role of endogenous MKP-5 in the T1DM and T2DM processes is unclear. Thus, mice with MKP-5 knockout (KO) were generated and used to establish mouse models of both T1DM and T2DM. Our results showed that MKP-5 KO exacerbated diabetes-related symptoms in mice with both T1DM and T2DM. Given that most phenotypic studies on islet dysfunction have focused on mice with T2DM rather than T1DM, we specifically aimed to investigate the role of endoplasmic reticulum stress (ERS) and autophagy in T2DM KO islets. To accomplish this, we performed RNA sequence analysis to gain comprehensive insight into the molecular mechanisms associated with ERS and autophagy in T2DM KO islets. The results showed that the islets from mice with MKP-5 KO triggered 5' adenosine monophosphate-activated protein kinase (AMPK)-mediated autophagy inhibition and glucose-regulated protein 78 (GRP-78)-dominated ERS. Hence, we concluded that the autophagy impairment, resulting in islet dysfunction in mice with MKP-5 KO, is mediated through GRP-78 involvement. These findings provide valuable insights into the molecular pathogenesis of diabetes and highlight the significant role of MKP-5. Moreover, this knowledge holds promise for novel therapeutic strategies targeting MKP-5 for diabetes management.

Single-Cell RNA Sequencing Reveals an Immune Landscape of CD4+ T Cells in Coronary Culprit Plaques With Acute Coronary Syndrome in Humans-Brief Report.

BACKGROUND: Acute coronary syndrome (ACS) involves plaque-related thrombosis, causing primary ischemic cardiomyopathy or lethal arrhythmia. We previously demonstrated a unique immune landscape of myeloid cells in the culprit plaques causing ACS by using single-cell RNA sequencing. Here, we aimed to characterize T cells in a single-cell level, assess clonal expansion of T cells, and find a therapeutic target to prevent ACS. METHODS: We obtained the culprit lesion plaques from 4 patients with chronic coronary syndrome (chronic coronary syndrome plaques) and the culprit lesion plaques from 3 patients with ACS (ACS plaques) who were candidates for percutaneous coronary intervention with directional coronary atherectomy. Live CD45+ immune cells were sorted from each pooled plaque samples and applied to the 10× platform for single-cell RNA sequencing analysis. We also extracted RNA from other 3 ACS plaque samples and conducted unbiased TCR (T-cell receptor) repertoire analysis. RESULTS: CD4+ T cells were divided into 5 distinct clusters: effector, naive, cytotoxic, CCR7+ (C-C chemokine receptor type 7) central memory, and FOXP3 (forkhead box P3)+ regulatory CD4+ T cells. The proportion of central memory CD4+ T cells was higher in the ACS plaques. Correspondingly, dendritic cells also tended to express more HLAs (human leukocyte antigens) and costimulatory molecules in the ACS plaques. The velocity analysis suggested the differentiation flow from central memory CD4+ T cells into effector CD4+ T cells and that from naive CD4+ T cells into central memory CD4+ T cells in the ACS plaques, which were not observed in the chronic coronary syndrome plaques. The bulk repertoire analysis revealed clonal expansion of TCRs in each patient with ACS and suggested that several peptides in the ACS plaques work as antigens and induced clonal expansion of CD4+ T cells. CONCLUSIONS: For the first time, we revealed single cell-level characteristics of CD4+ T cells in patients with ACS. CD4+ T cells could be therapeutic targets of ACS. REGISTRATION: URL: https://upload.umin.ac.jp/cgi-open-bin/icdr_e/ctr_view.cgi?recptno=R000046521; Unique identifier: UMIN000040747.

PAI-1 Regulates the Cytoskeleton and Intrinsic Stiffness of Vascular Smooth Muscle Cells.

BACKGROUND: Plasma concentration of PAI-1 (plasminogen activator inhibitor-1) correlates with arterial stiffness. Vascular smooth muscle cells (SMCs) express PAI-1, and the intrinsic stiffness of SMCs is a major determinant of total arterial stiffness. We hypothesized that PAI-1 promotes SMC stiffness by regulating the cytoskeleton and that pharmacological inhibition of PAI-1 decreases SMC and aortic stiffness. METHODS: PAI-039, a specific inhibitor of PAI-1, and small interfering RNA were used to inhibit PAI-1 expression in cultured human SMCs. Effects of PAI-1 inhibition on SMC stiffness, F-actin (filamentous actin) content, and cytoskeleton-modulating enzymes were assessed. WT (wild-type) and PAI-1-deficient murine SMCs were used to determine PAI-039 specificity. RNA sequencing was performed to determine the effects of PAI-039 on SMC gene expression. In vivo effects of PAI-039 were assessed by aortic pulse wave velocity. RESULTS: PAI-039 significantly reduced intrinsic stiffness of human SMCs, which was accompanied by a significant decrease in cytoplasmic F-actin content. PAI-1 gene knockdown also decreased cytoplasmic F-actin. PAI-1 inhibition significantly increased the activity of cofilin, an F-actin depolymerase, in WT murine SMCs, but not in PAI-1-deficient SMCs. RNA-sequencing analysis suggested that PAI-039 upregulates AMPK (AMP-activated protein kinase) signaling in SMCs, which was confirmed by Western blotting. Inhibition of AMPK prevented activation of cofilin by PAI-039. In mice, PAI-039 significantly decreased aortic stiffness and tunica media F-actin content without altering the elastin or collagen content. CONCLUSIONS: PAI-039 decreases intrinsic SMC stiffness and cytoplasmic stress fiber content. These effects are mediated by AMPK-dependent activation of cofilin. PAI-039 also decreases aortic stiffness in vivo. These findings suggest that PAI-1 is an important regulator of the SMC cytoskeleton and that pharmacological inhibition of PAI-1 has the potential to prevent and treat cardiovascular diseases involving arterial stiffening.

mRNA-CLA: An interpretable deep learning approach for predicting mRNA subcellular localization.

Messenger RNA (mRNA) is vital for post-transcriptional gene regulation, acting as the direct template for protein synthesis. However, the methods available for predicting mRNA subcellular localization need to be improved and enhanced. Notably, few existing algorithms can annotate mRNA sequences with multiple localizations. In this work, we propose the mRNA-CLA, an innovative multi-label subcellular localization prediction framework for mRNA, leveraging a deep learning approach with a multi-head self-attention mechanism. The framework employs a multi-scale convolutional layer to extract sequence features across different regions and uses a self-attention mechanism explicitly designed for each sequence. Paired with Position Weight Matrices (PWMs) derived from the convolutional neural network layers, our model offers interpretability in the analysis. In particular, we perform a base-level analysis of mRNA sequences from diverse subcellular localizations to determine the nucleotide specificity corresponding to each site. Our evaluations demonstrate that the mRNA-CLA model substantially outperforms existing methods and tools.

Computational prediction of phosphorylation sites of SARS-CoV-2 infection using feature fusion and optimization strategies.

SARS-CoV-2's global spread has instigated a critical health and economic emergency, impacting countless individuals. Understanding the virus's phosphorylation sites is vital to unravel the molecular intricacies of the infection and subsequent changes in host cellular processes. Several computational methods have been proposed to identify phosphorylation sites, typically focusing on specific residue (S/T) or Y phosphorylation sites. Unfortunately, current predictive tools perform best on these specific residues and may not extend their efficacy to other residues, emphasizing the urgent need for enhanced methodologies. In this study, we developed a novel predictor that integrated all the residues (STY) phosphorylation sites information. We extracted ten different feature descriptors, primarily derived from composition, evolutionary, and position-specific information, and assessed their discriminative power through five classifiers. Our results indicated that Light Gradient Boosting (LGB) showed superior performance, and five descriptors displayed excellent discriminative capabilities. Subsequently, we identified the top two integrated features have high discriminative capability and trained with LGB to develop the final prediction model, LGB-IPs. The proposed approach shows an excellent performance on 10-fold cross-validation with an ACC, MCC, and AUC values of 0.831, 0.662, 0.907, respectively. Notably, these performances are replicated in the independent evaluation. Consequently, our approach may provide valuable insights into the phosphorylation mechanisms in SARS-CoV-2 infection for biomedical researchers.

m5c-iDeep: 5-Methylcytosine sites identification through deep learning.

5-Methylcytosine (m5c) is a modified cytosine base which is formed as the result of addition of methyl group added at position 5 of carbon. This modification is one of the most common PTM that used to occur in almost all types of RNA. The conventional laboratory methods do not provide quick reliable identification of m5c sites. However, the sequence data readiness has made it feasible to develop computationally intelligent models that optimize the identification process for accuracy and robustness. The present research focused on the development of in-silico methods built using deep learning models. The encoded data was then fed into deep learning models, which included gated recurrent unit (GRU), long short-term memory (LSTM), and bi-directional LSTM (Bi-LSTM). After that, the models were subjected to a rigorous evaluation process that included both independent set testing and 10-fold cross validation. The results revealed that LSTM-based model, m5c-iDeep, outperformed revealing 99.9 % accuracy while comparing with existing m5c predictors. In order to facilitate researchers, m5c-iDeep was also deployed on a web-based server which is accessible at https://taseersuleman-m5c-ideep-m5c-ideep.streamlit.app/.

MLm5C: A high-precision human RNA 5-methylcytosine sites predictor based on a combination of hybrid machine learning models.

RNA modification serves as a pivotal component in numerous biological processes. Among the prevalent modifications, 5-methylcytosine (m5C) significantly influences mRNA export, translation efficiency and cell differentiation and are also associated with human diseases, including Alzheimer's disease, autoimmune disease, cancer, and cardiovascular diseases. Identification of m5C is critically responsible for understanding the RNA modification mechanisms and the epigenetic regulation of associated diseases. However, the large-scale experimental identification of m5C present significant challenges due to labor intensity and time requirements. Several computational tools, using machine learning, have been developed to supplement experimental methods, but identifying these sites lack accuracy and efficiency. In this study, we introduce a new predictor, MLm5C, for precise prediction of m5C sites using sequence data. Briefly, we evaluated eleven RNA sequence-derived features with four basic machine learning algorithms to generate baseline models. From these 44 models, we ranked them based on their performance and subsequently stacked the Top 20 baseline models as the best model, named MLm5C. The MLm5C outperformed the-state-of-the-art predictors. Notably, the optimization of the sequence length surrounding the modification sites significantly improved the prediction performance. MLm5C is an invaluable tool in accelerating the detection of m5C sites within the human genome, thereby facilitating in the characterization of their roles in post-transcriptional regulation.

Long-read sequencing identifies an SVA_D retrotransposon insertion deep within the intron of ATP7A as a novel cause of occipital horn syndrome.

BACKGROUND: SINE-VNTR-Alu (SVA) retrotransposons move from one genomic location to another in a 'copy-and-paste' manner. They continue to move actively and cause monogenic diseases through various mechanisms. Currently, disease-causing SVA retrotransposons are classified into human-specific young SVA_E or SVA_F subfamilies. In this study, we identified an evolutionarily old SVA_D retrotransposon as a novel cause of occipital horn syndrome (OHS). OHS is an X-linked, copper metabolism disorder caused by dysfunction of the copper transporter, ATP7A. METHODS: We investigated a 16-year-old boy with OHS whose pathogenic variant could not be detected via routine molecular genetic analyses. RESULTS: A 2.8 kb insertion was detected deep within the intron of the patient's ATP7A gene. This insertion caused aberrant mRNA splicing activated by a new donor splice site located within it. Long-read circular consensus sequencing enabled us to accurately read the entire insertion sequence, which contained highly repetitive and GC-rich segments. Consequently, the insertion was identified as an SVA_D retrotransposon. Antisense oligonucleotides (AOs) targeting the new splice site restored the expression of normal transcripts and functional ATP7A proteins. AO treatment alleviated excessive accumulation of copper in patient fibroblasts in a dose-dependent manner. Pedigree analysis revealed that the retrotransposon had moved into the OHS-causing position two generations ago. CONCLUSION: This is the first report of a human monogenic disease caused by the SVA_D retrotransposon. The fact that the evolutionarily old SVA_D is still actively transposed, leading to increased copy numbers may make a notable impact on rare genetic disease research.