UniProt: the universal protein knowledgebase

NeuroPred-ResSE: Predicting neuropeptides by integrating residual block and squeeze-excitation attention mechanism

Neuropeptides play crucial roles in regulating neurological function acting as signaling molecules, which provide new opportunity for developing drugs for the treatment of neurological diseases. Therefore, it is very necessary to develop a rapid and accurate prediction model for neuropeptides. Although a few prediction tools have been developed, there is room for improvement in prediction accuracy by using deep learning approach. In this paper, we establish the NeuroPred-ResSE model based on residual block and squeeze-excitation attention mechanism. Firstly, we extract multi-features by using one-hot coding based on the NT5CT5 sequence, dipeptide deviation from expected mean and natural vector. Then, we integrate residual block and squeeze-excitation attention mechanism, which can capture and identify the most relevant attribute features. Finally, the accuracies of the training set and test set are 97.16 % and 96.60 % based on the 5-fold cross-validation and independent test, respectively, and other evaluation metrics have also obtained satisfactory results. The experimental results show that the performance of the NeuroPred-ResSE model outperforms those of existing state-of-the-art models, and our model is an effective, intelligent and robust prediction tool. The datasets and source codes are available at https://github.com/yunyunliang88/NeuroPred-ResSE.

Exploring fatty acids from royal jelly as a source of histone deacetylase inhibitors: from the hive to applications in human well-being and health

A differential diet with royal jelly (RJ) during early larval development in honeybees shapes the phenotype, which is probably mediated by epigenetic regulation of gene expression. Evidence indicates that small molecules in RJ can modulate gene expression in mammalian cells, such as the fatty acid 10-hydroxy-2-decenoic acid (10-HDA), previously associated with the inhibition of histone deacetylase enzymes (HDACs). Therefore, we combined computational (molecular docking simulations) and experimental approaches for the screening of potential HDAC inhibitors (HDACi) among 32 RJ-derived fatty acids. Biochemical assays and gene expression analyses (Reverse Transcriptase - quantitative Polymerase Chain Reaction) were performed to evaluate the functional effects of the major RJ fatty acids, 10-HDA and 10-HDAA (10-hydroxy-decanoic acid), in two human cancer cell lines (HCT116 and MDA-MB-231). The molecular docking simulations indicate that these fatty acids might interact with class I HDACs, specifically with the catalytic domain of human HDAC2, likewise well-known HDAC inhibitors (HDACi) such as SAHA (suberoylanilide hydroxamic acid) and TSA (Trichostatin A). In addition, the combined treatment with 10-HDA and 10-HDAA inhibits the activity of human nuclear HDACs and leads to a slight increase in the expression of HDAC-coding genes in cancer cells. Our findings indicate that royal jelly fatty acids collectively contribute to HDAC inhibition and that 10-HDA and 10-HDAA are weak HDACi that facilitate the acetylation of lysine residues of chromatin, triggering an increase in gene expression levels in cancer cells.

Sequence-based identification of amyloidogenic β-hairpins reveals a prostatic acid phosphatase fragment promoting semen amyloid formation

β-Structure-rich amyloid fibrils are hallmarks of several diseases, including Alzheimer's (AD), Parkinson's (PD), and type 2 diabetes (T2D). While amyloid fibrils typically consist of parallel β-sheets, the anti-parallel β-hairpin is a structural motif accessible to amyloidogenic proteins in their monomeric and oligomeric states. Here, to investigate implications of β-hairpins in amyloid formation, potential β-hairpin-forming amyloidogenic segments in the human proteome were predicted based on sequence similarity with β-hairpins previously observed in Aβ, α-synuclein, and islet amyloid polypeptide, amyloidogenic proteins associated with AD, PD, and T2D, respectively. These three β-hairpins, established upon binding to the engineered binding protein β-wrapin AS10, are characterized by proximity of two sequence segments rich in hydrophobic and aromatic amino acids, with high β-aggregation scores according to the TANGO algorithm. Using these criteria, 2505 potential β-hairpin-forming amyloidogenic segments in 2098 human proteins were identified. Characterization of a test set of eight protein segments showed that seven assembled into Thioflavin T-positive aggregates and four formed β-hairpins in complex with AS10 according to NMR. One of those is a segment of prostatic acid phosphatase (PAP) comprising amino acids 185-208. PAP is naturally cleaved into fragments, including PAP(248-286) which forms functional amyloid in semen. We find that PAP(185-208) strongly decreases the protein concentrations required for fibril formation of PAP(248-286) and of another semen amyloid peptide, SEM1(86-107), indicating that it promotes nucleation of semen amyloids. In conclusion, β-hairpin-forming amyloidogenic protein segments could be identified in the human proteome with potential roles in functional or disease-related amyloid formation.

Genome-wide analysis reveals the contributors to fast molecular evolution of the Chinese hook snout carp (Opsariichthys bidens)

Variations in molecular evolutionary rate have been widely investigated among lineages and genes. However, it remains an open question whether fast rate of molecular evolution is driven by natural selection or random drift, and how the fast rate is linked to metabolic rate. Additionally, previous studies on fast molecular evolution have been largely restricted to concatenated matrix of genes or a few specifically selected genes, but less is known for individual genes at the genome-wide level. Here we addressed these questions using more than 5000 single-copy orthologous (SCO) genes through comparative genomic and phylogenetic analyses among fishes, with a special focus on a newly-sequenced clupeocephalan fish the Chinese hook snout carp Opsariichthys bidens. We showed O. bidens displays significantly higher mean substitution rate and more fast-evolving SCO genes (2172 genes) than most fishes studied here. The rapidly evolving genes are enriched in highly conserved and very basic functions such as translation and ribosome that are critical for biological fitness. We further revealed that ∼25 % of these fast-evolving genes exhibit a constant increase of substitution rate from the common ancestor down to the present, suggesting a neglected but important contribution from ancestral states. Model fitting showed that ∼85 % of fast-evolving genes exclusive to O. bidens and related species follow the adaptive evolutionary model rather than random-drift model, and 7.6 % of fast-evolving genes identified in O. bidens have experienced positive selection, both indicating the reflection of adaptive selection. Finally, metabolic rate was observed to be linked with substitution rate in a gene-specific manner. Overall, our findings reveal fast molecular evolution of SCO genes at genome-wide level in O. bidens, and uncover the evolutionary and ecological contributors to it.

Characterization of metabolic features and potential anti-osteoporosis mechanism of pinoresinol diglucoside using metabolite profiling and network pharmacology

RATIONALE

Eucommia cortex is the core herb in traditional Chinese medicine preparations for the treatment of osteoporosis. Pinoresinol diglucoside (PDG), the quality control marker and the key pharmacodynamic component in Eucommia cortex, has attracted global attention because of its definite effects on osteoporosis. However, the in vivo metabolic characteristics of PDG and its anti-osteoporotic mechanism are still unclear, restricting its development and application.

METHODS

Ultra-high-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry was used to analyze the metabolic characteristics of PDG in rats, and its anti-osteoporosis targets and mechanism were predicted using network pharmacology.

RESULTS

A total of 51 metabolites were identified or tentatively characterized in rats after oral administration of PDG (10 mg/kg/day), including 9 in plasma, 28 in urine, 13 in feces, 10 in liver, 4 in heart, 3 in spleen, 11 in kidneys, and 5 in lungs. Furan-ring opening, dimethoxylation, glucuronidation, and sulfation were the main metabolic characteristics of PDG in vivo. The potential mechanism of PDG against osteoporosis was predicted using network pharmacology. PDG and its metabolites could regulate BCL2, MARK3, ALB, and IL6, involving PI3K-Akt signaling pathway, estrogen signaling pathway, and so on.

CONCLUSIONS

This study was the first to demonstrate the metabolic characteristics of PDG in vivo and its potential anti-osteoporosis mechanism, providing the data for further pharmacological validation of PDG in the treatment of osteoporosis.

Chromosome genome assembly and annotation of Adzuki Bean (Vigna angularis)

Adzuki bean (Vigna angularis) is a significant dietary legume crop that is prevalent in East Asia. It also holds traditional medicinal importance in China. In this study, we report a high-quality, chromosome-level genome assembly of adzuki bean obtained by employing Illumina short-read sequencing, PacBio long-read sequencing, and Hi-C technology. The assembly spans 447.8 Mb, encompassing 96.32% of the estimated genome, with contig and scaffold N50 values of 16.5 and 41.0 Mb, respectively. More than 98.2% of the 1,614 BUSCO genes were fully identified, and 25,939 genes were annotated, with 98.23% of them being functionally identifiable. Vigna angularis was estimated to diverge successively from Vigna unguiculata and Vigna radiata about 15.3 and 8.7 million years ago (Ma), respectively. This chromosome-level reference genome of Vigna angularis provides a robust foundation for exploring the functional genomics and genome evolution of adzuki bean, thereby facilitating advancements in molecular breeding of adzuki bean.

Structure prediction analysis of human core TIM23 complex reveals conservation of the protein translocation mechanism

The majority of mitochondrial proteins are encoded in the nucleus, translated on cytosolic ribosomes, and subsequently targeted to the mitochondrial surface. Their further import into the organelle is facilitated by highly specialized protein translocases. Mitochondrial precursor proteins that are destined to the mitochondrial matrix and, to some extent, the inner membrane, utilize translocase of the inner membrane (TIM23). This indispensable import machinery has been extensively studied in yeast. The translocating unit of the TIM23 complex in yeast consists of two membrane proteins, Tim17 and Tim23. In contrast to previous findings, recent reports demonstrate the primary role of Tim17, rather than Tim23, in the translocation of newly synthesized proteins. Very little is known about human TIM23 translocase. Human cells have two orthologs of yeast Tim17, TIMM17A and TIMM17B. Here, using computational tools, we present the architecture of human core TIM23 variants with either TIMM17A or TIMM17B, forming two populations of highly similar complexes. The structures reveal high conservation of the core TIM23 complex between human and yeast. Interestingly, both TIMM17A and TIMM17B variants interact with TIMM23 and reactive oxygen species modulator 1 (ROMO1); a homolog of yeast Mgr2, a protein that can create a channel-like structure with Tim17. The high structural conservation of proteins that form the core TIM23 complex in yeast and humans raises an interesting question about mechanistic and functional differences that justify existence of the two variants of TIM23 in higher eukaryotes.

Identification of imidazole-based small molecules to combat cognitive disability caused by Alzheimer's disease: A molecular docking and MD simulations based approach

Alzheimer's disease (AD) is a chronic neurodegenerative disorder that is the primary cause of dementia. It is characterised by the gradual loss of brain cells, which results in memory loss and cognitive dysfunction. One of the hallmarks of AD is an abnormally upregulated glutaminyl-peptide cyclotransferase (QPCT or QC) enzyme. Not only AD, but QC has also been implicated with pathological conditions like Huntington's disease (HD), melanomas, carcinomas, atherosclerosis, and septic arthritis. Therefore, the inhibition of QC emerged as a potential strategy for preventing multiple pathological conditions. Considering this, we screened a library of 153,536 imidazole-based compounds against a doubly mutant (Y115E-Y117E) QC target. Molecular docking based virtual screening and absorption, distribution, metabolism, excretion/toxicity (ADME/T) predictions identified five compounds, namely 118981836, 136459842, 139388116, 139388226, and 139958725. Furthermore, molecular dynamics (MD) simulations of 500 ns were conducted to investigate the behaviour of the identified compounds with the target receptor. The results were compared to the co-ligand by analysing RMSD, RMSF, and SASA parameters. To our knowledge, this is the first computational study that employed a protein with double mutation to identify new imidazole-based QC-inhibitors.

Fe3O4@silica-thermolysin: A robust, advantageous, and reusable microbial nanobiocatalyst for proteolysis and milk-clotting

Thermolysin (TLN) is a microbial highly-priced thermostable metallo-endoprotease with complementary substrate specificity to those of proteases widely used in science and industry for protein digestion and milk-clotting. This study is the first to immobilize TLN on aminated superparamagnetic nanoparticles (Fe3O4@silica-NH2) aiming for higher stability, recoverability, reusability, and applicability in proteolysis and as a microbial rennet-like milk-clotting enzyme. The nanobiocatalyst developed (Fe3O4@silica-TLN) displays hydrolytic activity on a synthetic TLN substrate and, apparently, was fully recovered from reaction media by magnetic decantation. More importantly, Fe3O4@silica-TLN retains TLN catalytic properties in the presence of calcium ions even after exposure to 60 °C for 48 h, storage at 4 °C for 80 days and room temperature for 42 days, use in proteolyses, and in milk-clotting for up to 11 cycles. Its proteolytic activity on bovine milk casein in 24 h furnished 84 peptides, of which 29 are potentially bioactive. Also, Fe3O4@silica-TLN catalyzed the digestion of bovine serum albumin. In conclusion, Fe3O4@silica-TLN showed to be a new, less autolytic, thermostable, non-toxic, magnetically-separable, and reusable nanobiocatalyst with highly attractive properties for both science (peptide/protein chemistry and structure, proteomic studies, and the search for new bioactive peptides) and food industry (cheese manufacture).

Yeast9: a consensus genome-scale metabolic model for S. cerevisiae curated by the community

Genome-scale metabolic models (GEMs) can facilitate metabolism-focused multi-omics integrative analysis. Since Yeast8, the yeast-GEM of Saccharomyces cerevisiae, published in 2019, has been continuously updated by the community. This has increased the quality and scope of the model, culminating now in Yeast9. To evaluate its predictive performance, we generated 163 condition-specific GEMs constrained by single-cell transcriptomics from osmotic pressure or reference conditions. Comparative flux analysis showed that yeast adapting to high osmotic pressure benefits from upregulating fluxes through central carbon metabolism. Furthermore, combining Yeast9 with proteomics revealed metabolic rewiring underlying its preference for nitrogen sources. Lastly, we created strain-specific GEMs (ssGEMs) constrained by transcriptomics for 1229 mutant strains. Well able to predict the strains' growth rates, fluxomics from those large-scale ssGEMs outperformed transcriptomics in predicting functional categories for all studied genes in machine learning models. Based on those findings we anticipate that Yeast9 will continue to empower systems biology studies of yeast metabolism.

Preclinical alternative drug discovery programs for monogenic rare diseases. Should small molecules or gene therapy be used? The case of hereditary spastic paraplegias

Patients diagnosed with rare diseases and their and families search desperately to organize drug discovery campaigns. Alternative models that differ from default paradigms offer real opportunities. There are, however, no clear guidelines for the development of such models, which reduces success rates and raises costs. We address the main challenges in making the discovery of new preclinical treatments more accessible, using rare hereditary paraplegia as a paradigmatic case. First, we discuss the necessary expertise, and the patients' clinical and genetic data. Then, we revisit gene therapy, de novo drug development, and drug repurposing, discussing their applicability. Moreover, we explore a pool of recommended in silico tools for pathogenic variant and protein structure prediction, virtual screening, and experimental validation methods, discussing their strengths and weaknesses. Finally, we focus on successful case applications.

Equine Doping Controls of Thymosin β $$ \beta $$ 4: A Population Study and Strategy for Misuse Detection

Thymosinβ $$ \beta $$ 4 (TB4) is a ubiquitous, highly conserved and abundant peptide among mammals with a critical role in cytoskeleton organization. In spite of its yet non-authorized use as a medicine and being forbidden by the IFHA, the FEI, and the WADA, intelligence and doping control laboratories reported numerous products available online claiming to contain a synthetic acetylated fragment of TB4 or TB4 itself, promoted as a growth factor with regenerative properties. In this article, the first estimation of the endogenous TB4 concentration in racing horses' blood samples was performed through a population study. We reveal that this concentration does not significantly depend on gender, age, nor horse breed. We highlight that the TB4 concentration increases significantly and rapidly in plasma stored at 4°C when not separated from blood cells due to cell lysis. Finally, we also demonstrate that the detection of a non-natural synthesis impurity is possible in equine plasma after a single dose administration of a TB4 containing product to a horse.

Reliability of AlphaFold2 Models in Virtual Drug Screening: A Focus on Selected Class A GPCRs

Protein three-dimensional (3D) structure prediction is one of the most challenging issues in the field of computational biochemistry, which has overwhelmed scientists for almost half a century. A significant breakthrough in structural biology has been established by developing the artificial intelligence (AI) system AlphaFold2 (AF2). The AF2 system provides a state-of-the-art prediction of protein structures from nearly all known protein sequences with high accuracy. This study examined the reliability of AF2 models compared to the experimental structures in drug discovery, focusing on one of the most common protein drug-targeted classes known as G protein-coupled receptors (GPCRs) class A. A total of 32 representative protein targets were selected, including experimental structures of X-ray crystallographic and Cryo-EM structures and their corresponding AF2 models. The quality of AF2 models was assessed using different structure validation tools, including the pLDDT score, RMSD value, MolProbity score, percentage of Ramachandran favored, QMEAN Z-score, and QMEANDisCo Global. The molecular docking was performed using the Genetic Optimization for Ligand Docking (GOLD) software. The AF2 models' reliability in virtual drug screening was determined by their ability to predict the ligand binding poses closest to the native binding pose by assessing the Root Mean Square Deviation (RMSD) metric and docking scoring function. The quality of the docking and scoring function was evaluated using the enrichment factor (EF). Furthermore, the capability of using AF2 models in molecular docking to identify hits with key protein-ligand interactions was analyzed. The posing power results showed that the AF2 models successfully predicted ligand binding poses (RMSD < 2 Å). However, they exhibited lower screening power, with average EF values of 2.24, 2.42, and 1.82 for X-ray, Cryo-EM, and AF2 structures, respectively. Moreover, our study revealed that molecular docking using AF2 models can identify competitive inhibitors. In conclusion, this study found that AF2 models provided docking results comparable to experimental structures, particularly for certain GPCR targets, and could potentially significantly impact drug discovery.

Unlocking epitope similarity: A comparative analysis of the American manatee (Trichechus manatus) IgA and human IgA through an immuno-informatics approach

The American manatee (Trichechus manatus), experiencing population declines due to various threats, is the focus of conservation efforts that include the capture, rehabilitation, and release of orphaned calves when their mothers are unable to care for them. These efforts are compromised by the use of commercially available milk substitutes that lack essential components found in natural manatee breast milk, particularly immunoglobulin A (IgA). IgA plays a crucial role in nurturing the immune mucosal system and fostering a healthy microbiota. However, research on IgA in non-maternally fed manatees is limited due to the lack of species-specific reagents. To address this gap, our study employs immuno-informatics analysis to compare IgA sequences from manatees with those from other species, aiming to explore epitope similarity and sharing. We compared the protein sequence of manatee IgA with available IgA sequences, assessing similarity at the sequence, 3D structures, and epitope levels. Our findings reveal that human IgA exhibits the highest similarity in terms of sequence and 3D structure. Additionally, epitope analysis shows high conservation, identity, and similarity of predicted epitopes compared to human IgA. Future studies should focus on functional analysis using human IgA polyclonal reagents to detect manatee IgA in breast milk. Our findings highlight the potential of comparative analysis in advancing the understanding of immunology in non-human animals and overcoming challenges associated with the scarcity of species-specific reagents.

Identification and analysis of key immunity-related genes in experimental ischemic stroke

The regulation of the immune system and the occurrence of inflammation are vital factors in the pathophysiology of ischemic stroke. This study aims to screen target molecules which play key roles in alleviating the brain injury following ischemic stroke via regulating neuroinflammation. Several bioinformatics methods were used to identify immune-related genes in ischemic stroke. A total of 218 genes were identified as differentially expressed genes within the GSE97537 dataset. By performing GO, KEGG, and GSEA analyses, DEGs were mainly enriched in pathways related to immunity and inflammation. By utilizing the MCODE plugin in conjunction with Cytoscape software, a total of six crucial genes were identified, including C1qb, C1qc, Fcer1g, Fcgr3a, Tyrobp, and CD14. Based on the above crucial genes, 13 miRNAs were predicted. Furthermore, 71 potential drugs with therapeutic properties that target the crucial genes were screened, including lovastatin, ASPIRIN, and PREDNISOLONE. Moreover, the results of RT-qPCR showed that compared with Sham group, the expressions of C1qb, C1qc, Fcer1g, Fcgr3a, Tyrobp, and CD14 in MCAO group were significantly increased, which was consistent with the expression trend of validation dataset and training dataset. In conclusion, immune-related genes may play a key role in ischemic stroke. In addition, six crucial genes were identified as potential biomarkers and 71 promising drugs were screened to treat ischemic stroke patients.

In Silico Screening, Molecular Dynamics Simulation and Binding Free Energy Identify Single-Point Mutations That Destabilize p53 and Reduce Binding to DNA

Considering p53's pivotal role as a tumor suppressor protein, proactive identification and characterization of potentially harmful p53 mutations are crucial before they appear in the population. To address this, four computational prediction tools-SIFT, Polyphen-2, PhD-SNP, and MutPred2-utilizing sequence-based and machine-learning algorithms, were employed to identify potentially deleterious p53 nsSNPs (nonsynonymous single nucleotide polymorphisms) that may impact p53 structure, dynamics, and binding with DNA. These computational methods identified three variants, namely, C141Y, C238S, and L265P, as detrimental to p53 stability. Furthermore, molecular dynamics (MD) simulations revealed that all three variants exhibited heightened structural flexibility compared to the native protein, especially the C141Y and L265P mutations. Consequently, due to the altered structure of mutant p53, the DNA-binding affinity of all three variants decreased by approximately 1.8 to 9.7 times compared to wild-type p53 binding with DNA (14 μM). Notably, the L265P mutation exhibited an approximately ten-fold greater reduction in binding affinity. Consequently, the presence of the L265P mutation in p53 could pose a substantial risk to humans. Given that p53 regulates abnormal tumor growth, this research carries significant implications for surveillance efforts and the development of anticancer therapies.

Guidelines for mitochondrial RNA analysis

Mitochondria are the energy-producing organelles of mammalian cells with critical involvement in metabolism and signaling. Studying their regulation in pathological conditions may lead to the discovery of novel drugs to treat, for instance, cardiovascular or neurological diseases, which affect high-energy-consuming cells such as cardiomyocytes, hepatocytes, or neurons. Mitochondria possess both protein-coding and noncoding RNAs, such as microRNAs, long noncoding RNAs, circular RNAs, and piwi-interacting RNAs, encoded by the mitochondria or the nuclear genome. Mitochondrial RNAs are involved in anterograde-retrograde communication between the nucleus and mitochondria and play an important role in physiological and pathological conditions. Despite accumulating evidence on the presence and biogenesis of mitochondrial RNAs, their study continues to pose significant challenges. Currently, there are no standardized protocols and guidelines to conduct deep functional characterization and expression profiling of mitochondrial RNAs. To overcome major obstacles in this emerging field, the EU-CardioRNA and AtheroNET COST Action networks summarize currently available techniques and emphasize critical points that may constitute sources of variability and explain discrepancies between published results. Standardized methods and adherence to guidelines to quantify and study mitochondrial RNAs in normal and disease states will improve research outputs, their reproducibility, and translation potential to clinical application.

Investigating TNNC1 gene inheritance and clinical outcomes through a comprehensive familial study

Hypertrophic cardiomyopathy (HCM) and restrictive cardiomyopathy (RCM) have significant phenotypic overlap and a similar genetic background, both caused mainly by variants in sarcomeric genes. HCM is the most common cardiomyopathy, while RCM is a rare and often underdiagnosed heart condition, with a poor prognosis. This study focuses on a large family with four infants diagnosed with fatal RCM associated with biventricular hypertrophy. Affected infants were found to be homozygous for NM_003280.3(TNNC1):c.23C>T(p.Ala8Val) variant. Interestingly, this variant resulted in a low penetrance and mild form of hypertrophic cardiomyopathy (HCM) in relatives carrying a single copy of the variant. Overall, this study underscores the complex nature of genetic inheritance in cardiomyopathies and the wide range of clinical presentations they can exhibit. This emphasizes the vital role of genetic testing in providing essential insights crucial for diagnosis, prognosis, early intervention, and the development of potential treatment strategies.

Transforming Clinical Research: The Power of High-Throughput Omics Integration

High-throughput omics technologies have dramatically changed biological research, providing unprecedented insights into the complexity of living systems. This review presents a comprehensive examination of the current landscape of high-throughput omics pipelines, covering key technologies, data integration techniques and their diverse applications. It looks at advances in next-generation sequencing, mass spectrometry and microarray platforms and highlights their contribution to data volume and precision. In addition, this review looks at the critical role of bioinformatics tools and statistical methods in managing the large datasets generated by these technologies. By integrating multi-omics data, researchers can gain a holistic understanding of biological systems, leading to the identification of new biomarkers and therapeutic targets, particularly in complex diseases such as cancer. The review also looks at the integration of omics data into electronic health records (EHRs) and the potential for cloud computing and big data analytics to improve data storage, analysis and sharing. Despite significant advances, there are still challenges such as data complexity, technical limitations and ethical issues. Future directions include the development of more sophisticated computational tools and the application of advanced machine learning techniques, which are critical for addressing the complexity and heterogeneity of omics datasets. This review aims to serve as a valuable resource for researchers and practitioners, highlighting the transformative potential of high-throughput omics technologies in advancing personalized medicine and improving clinical outcomes.

Pontocerebellar Hypoplasia Type 9: A New Case with a Novel Mutation and Review of Literature

Pontocerebellar hypoplasia type 9 (PCH-9) is a very rare autosomal recessive neurodegenerative disorder. Affected infants present early with severe developmental delay, spasticity, with the unique magnetic resonance imaging picture of thin corpus callosum, atrophied pons, and cerebellum. It is caused by loss of function mutations in the AMPD2 gene, encoding for the adenosine monophosphate deaminase enzyme-paralog 2. This gene is expressed in different somatic tissues with high level of expression in cerebellum and its encoded enzyme catalyzes a critical step in de novo biosynthesis of purines and its deficiency in the developing neurons severely affects neuronal differentiation and cell viability. We clinically evaluated an Emirati patient presented with severe developmental and growth delay, as well as corpus callosum agenesis and atrophy of brainstem and cerebellum. We performed exome sequencing, Sanger sequencing, and segregation analysis to identify the genetic cause of the phenotype, followed by in silico and in vitro analysis. We identified the novel variant (NM_004037.9:c.1471G > A) in AMPD2 gene leading to a single amino acid substitution (p.Gly491Arg) in adenosine monophosphate deaminase-2 enzyme. This variant is predicted to be pathogenic using several in silico tools, and resulted in a decrease in the enzyme function in the patient's polymorphonuclear cells by 82% (95% confidence interval: 73.3-91.7%, p  = 0.029) compared with the control. This data establishes that the affected child is affected by PCH-9. Furthermore, we review all reported cases in literature to summarize the main clinical features of this rare disease.

Integrating functional metagenomics to decipher microbiome-immune interactions

Microbial metabolites can be viewed as the cytokines of the microbiome, transmitting information about the microbial and metabolic environment of the gut to orchestrate and modulate local and systemic immune responses. Still, many immunology studies focus solely on the taxonomy and community structure of the gut microbiota rather than its functions. Early sequencing-based microbiota profiling approaches relied on PCR amplification of small regions of bacterial and fungal genomes to facilitate identification of the microbes present. However, recent microbiome analysis methods, particularly shotgun metagenomic sequencing, now enable culture-independent profiling of microbiome functions and metabolites in addition to taxonomic characterization. In this review, we showcase recent advances in functional metagenomics methods and applications and discuss the current limitations and potential avenues for future development. Importantly, we highlight a few examples of key areas of opportunity in immunology research where integrating functional metagenomic analyses of the microbiome can substantially enhance a mechanistic understanding of microbiome-immune interactions and their contributions to health and disease states.

Cross-Species Insights into PR Proteins: A Comprehensive Study of Arabidopsis thaliana, Solanum lycopersicum, and Solanum tuberosum

This study provides a comprehensive analysis of pathogenesis-related (PR) proteins, focusing on PR1, PR5, and PR10, in three plant species: Arabidopsis thaliana (At), Solanum lycopersicum (Sl), and Solanum tuberosum (St). We investigated various physico-chemical properties, including protein length, molecular weight, isoelectric point (pI), hydrophobicity, and structural characteristics, such as RMSD, using state-of-the-art tools like AlphaFold and PyMOL. Our analysis found that the SlPR10-StPR10 protein pair had the highest sequence identity (80.00%), lowest RMSD value (0.307 Å), and a high number of overlapping residues (160) among all other protein pairs, indicating their remarkable similarity. Additionally, we used bioinformatics tools such as Cello, Euk-mPLoc 2.0, and Wolfpsort to predict subcellular localization, with AtPR1, AtPR5, and SlPR5 proteins predicted to be located in the extracellular space in both Arabidopsis and S. lycopersicum, while AtPR10 was predicted to be located in the cytoplasm. This comprehensive analysis, including the use of cutting-edge structural prediction and subcellular localization tools, enhances our understanding of the structural, functional, and localization aspects of PR proteins, shedding light on their roles in plant defense mechanisms across different plant species.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12088-024-01343-1.

Integrative approach using network pharmacology, bioinformatics, and experimental methods to explore the mechanism of cantharidin in treating colorectal cancer

Cantharidin, a terpenoid produced by blister beetles, has been used in traditional Chinese medicine to treat various ailments and cancers. However, its biological activity, impact, and anticancer mechanisms remain unclear. The Cantharidin chemical gene connections were identified using various databases. The GSE21815 dataset was used to collect the gene expression information. Differential gene analysis and gene ontology analyses were performed. Gene set enrichment analysis was used to assess the activation of disease pathways. Weighted gene co-expression network analysis and differential analysis were used to identify illness-associated genes, examine differential genes, and discover therapeutic targets via protein-protein interactions. MCODE analysis of major subgroup networks was used to identify critical genes influenced by Cantharidin, examine variations in the expression of key clustered genes in colorectal cancer vs. control samples, and describe the subject operators. Single-cell GSE188711 dataset was preprocessed to investigate Cantharidin's therapeutic targets and signaling pathways in colorectal cancer. Single-cell RNA sequencing was utilized to identify 22 cell clusters and marker genes for two different cell types in each cluster. The effects of different Cantharidin concentrations on colorectal cancer cells were studied in vitro. One hundred and ninety-seven Cantharidin-associated target genes and 480 critical genes implicated in the development of the illness were identified. Cantharidin significantly inhibited the proliferation and migration of HCT116 cells and promoted apoptosis at certain concentrations. Patients on current therapy develop inherent and acquired resistance. Our study suggests that Cantharidin may play an anti-CRC role by modulating immune function.

Mitochondrial related variants associated with cardiovascular traits

Introduction

Cardiovascular disease (CVD) is responsible for over 30% of mortality worldwide. CVD arises from the complex influence of molecular, clinical, social, and environmental factors. Despite the growing number of autosomal genetic variants contributing to CVD, the cause of most CVDs is still unclear. Mitochondria are crucial in the pathophysiology, development and progression of CVDs; the impact of mitochondrial DNA (mtDNA) variants and mitochondrial haplogroups in the context of CVD has recently been highlighted.

Aims

We investigated the role of genetic variants in both mtDNA and nuclear-encoded mitochondrial genes (NEMG) in CVD, including coronary artery disease (CAD), hypertension, and serum lipids in the UK Biobank, with sub-group analysis for diabetes.

Methods

We investigated 371,542 variants in 2,527 NEMG, along with 192 variants in 32 mitochondrial genes in 381,994 participants of the UK Biobank, stratifying by presence of diabetes.

Results

Mitochondrial variants showed associations with CVD, hypertension, and serum lipids. Mitochondrial haplogroup J was associated with CAD and serum lipids, whereas mitochondrial haplogroups T and U were associated with CVD. Among NEMG, variants within Nitric Oxide Synthase 3 (NOS3) showed associations with CVD, CAD, hypertension, as well as diastolic and systolic blood pressure. We also identified Translocase Of Outer Mitochondrial Membrane 40 (TOMM40) variants associated with CAD; Solute carrier family 22 member 2 (SLC22A2) variants associated with CAD and CVD; and HLA-DQA1 variants associated with hypertension. Variants within these three genes were also associated with serum lipids.

Conclusion

Our study demonstrates the relevance of mitochondrial related variants in the context of CVD. We have linked mitochondrial haplogroup U to CVD, confirmed association of mitochondrial haplogroups J and T with CVD and proposed new markers of hypertension and serum lipids in the context of diabetes. We have also evidenced connections between the etiological pathways underlying CVDs, blood pressure and serum lipids, placing NOS3, SLC22A2, TOMM40 and HLA-DQA1 genes as common nexuses.

Nine Human Leukocyte Antigen (HLA) Class I Alleles are Omnipotent Against 11 Antigens Expressed in Melanoma Tumors

Objective

Host immunogenetics (Human Leukocyte Antigen, HLA) play a critical role in the human immune response to melanoma, influencing both melanoma prevalence and immunotherapy outcomes. Beneficial outcomes hinge on the successful binding of epitopes of melanoma antigens to HLA Class I molecules for an effective engagement of cytotoxic CD8+ lymphocytes and subsequent elimination of the cancerous cell. This study evaluated the binding affinity and immunogenicity of HLA Class I to melanoma tumor antigens to identify alleles best suited to facilitate elimination of melanoma antigens.

Methods

In this study, we used freely available software tools to determine in silico the binding affinity and immunogenicity of 2462 reported HLA Class I alleles to all linear nonamer epitopes of 11 known antigens expressed in melanoma tumors (TRP2, S100, Tyrosinase, TRP1, PMEL(17), MAGE1, MAGE4, CTA, BAGE, GAGE/SSX2, Melan).

Results

We identified the following 9 HLA Class I alleles with very high immunogenicity and binding affinity against all 11 melanoma antigens: A*02:14, B*07:10, B*35:10, B*40:10, B*40:12, B*44:10, C*07:11, and C*07:13, and C*07:14.

Conclusion

These 9 HLA alleles possess the potential to aid in the elimination of melanoma both by themselves and by enhancing the beneficial effect of immune checkpoint inhibitors.

Seqrutinator: scrutiny of large protein superfamily sequence datasets for the identification and elimination of non-functional homologues

Seqrutinator is an objective, flexible pipeline that removes sequences with sequencing and/or gene model errors and sequences from pseudogenes from complex, eukaryotic protein superfamilies. Testing Seqrutinator on major superfamilies BAHD, CYP, and UGT removes only 1.94% of SwissProt entries, 14% of entries from the model plant Arabidopsis thaliana, but 80% of entries from Pinus taeda's recent complete proteome. Application of Seqrutinator on crude BAHDomes, CYPomes, and UGTomes obtained from 16 plant proteomes shows convergence of the numbers of paralogues. MSAs, phylogenies, and particularly functional clustering improve drastically upon Seqrutinator application, indicating good performance.

Investigation of the mechanism of baicalein in the treatment of periodontitis based on network pharmacology, molecular docking and experimental validation

PURPOSE

To verify the effect and mechanism of baicalein in the treatment of periodontitis through network pharmacology, molecular docking and in vitro experiments.

METHODS

Firstly, multiple databases were used to predict targets of baicalein and periodontitis. And the screened key target genes of baicalein for treating periodontitis were subjected to GO and KEGG analysis; then these targets were analyzed by molecular docking techniques. In vitro experiments including CCK-8, RT-qPCR, ELISA and Immunofluorescence were conducted to validate the efficacy of baicalein in treating periodontitis.

RESULTS

Seventeen key targets were screened from the databases, GO and KEGG analysis of these targets revealed that baicalein may exert therapeutic effects through regulating TNF, PI3K-Akt, HIF-1 and other signaling pathways. Molecular docking analysis showed that baicalein has good binding potential to several targets. In vitro cellular assays showed that baicalein inhibited the expression of TNF-α, MMP-9, IL-6 and MCP1 in P.g-LPS-induced macrophages at both the mRNA and protein level. And the immunofluorescence intensity of iNOS, a marker of M1 type macrophages, which mainly secretes inflammatory factors, was significantly reduced.

CONCLUSION

Baicalein has the characteristics and advantages of "multicomponent, multitarget, and multipathway" in the treatment of periodontitis. In vitro cellular assays further confirmed the inhibitory effect of baicalein on the secretion of inflammatory factors of macrophages in periodontitis models, providing a theoretical basis for further study of the material basis and molecular mechanism of baicalein in the treatment of periodontal diseases.

Molecular and Clinicopathologic Impact of GNAS Variants Across Solid Tumors

PURPOSE

The molecular drivers underlying mucinous tumor pathogenicity are poorly understood. GNAS mutations predict metastatic burden and treatment resistance in mucinous appendiceal adenocarcinoma. We investigated the pan-cancer clinicopathologic relevance of GNAS variants.

METHODS

We assessed 58,043 patients with Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets (IMPACT)-sequenced solid tumors to identify oncogenic variants, including GNAS, associated with mucinous tumor phenotype. We then performed comprehensive molecular analyses to compare GNAS-mutant (mut) and wild-type tumors across cancers. Gene expression patterns associated with GNAS-mut tumors were assessed in a The Cancer Genome Atlas cohort. Associations between GNAS variant status and peritoneal metastasis, first-line systemic therapy response, progression-free survival (PFS), and overall survival (OS) were determined using a propensity-matched subcohort of patients with metastatic disease.

RESULTS

Mucinous tumors were enriched for oncogenic GNAS variants. GNAS was mutated in >1% of small bowel, cervical, colorectal, pancreatic, esophagogastric, hepatobiliary, and GI neuroendocrine cancers. Across these cancers, GNAS-mut tumors exhibited a generally conserved C-to-T mutation-high, aneuploidy-low molecular profile with co-occurring prevalent KRAS variants (65% of GNAS-mut tumors) and fewer TP53 alterations. GNAS-mut tumors exhibited recurrently comutated alternative tumor suppressors (RBM10, INPPL1) and upregulation of MAPK and cell surface modulators. GNAS-mut tumors demonstrate an increased prevalence of peritoneal metastases (odds ratio [OR], 1.7 [95% CI, 1.1 to 2.5]; P = .006), worse response to first-line systemic therapy (OR, 2.2 [95% CI, 1.3 to 3.8]; P = .003), and shorter PFS (median, 5.6 v 7.0 months; P = .047). In a multivariable analysis, GNAS mutated status was independently prognostic of worse OS (hazard ratio, 1.25 [95% CI, 1.01 to 1.56]; adjusted P = .04).

CONCLUSION

Across the assessed cancers, GNAS-mut tumors exhibit a conserved molecular and clinical phenotype defined by mucinous tumor status, increased peritoneal metastasis, poor response to first-line systemic therapy, and worse survival.

Comprehensive EHMT1 variants analysis broadens genotype-phenotype associations and molecular mechanisms in Kleefstra syndrome

The shift to a genotype-first approach in genetic diagnostics has revolutionized our understanding of neurodevelopmental disorders, expanding both their molecular and phenotypic spectra. Kleefstra syndrome (KLEFS1) is caused by EHMT1 haploinsufficiency and exhibits broad clinical manifestations. EHMT1 encodes euchromatic histone methyltransferase-1-a pivotal component of the epigenetic machinery. We have recruited 209 individuals with a rare EHMT1 variant and performed comprehensive molecular in silico and in vitro testing alongside DNA methylation (DNAm) signature analysis for the identified variants. We (re)classified the variants as likely pathogenic/pathogenic (molecularly confirming Kleefstra syndrome) in 191 individuals. We provide an updated and broader clinical and molecular spectrum of Kleefstra syndrome, including individuals with normal intelligence and familial occurrence. Analysis of the EHMT1 variants reveals a broad range of molecular effects and their associated phenotypes, including distinct genotype-phenotype associations. Notably, we showed that disruption of the "reader" function of the ankyrin repeat domain by a protein altering variant (PAV) results in a KLEFS1-specific DNAm signature and milder phenotype, while disruption of only "writer" methyltransferase activity of the SET domain does not result in KLEFS1 DNAm signature or typical KLEFS1 phenotype. Similarly, N-terminal truncating variants result in a mild phenotype without the DNAm signature. We demonstrate how comprehensive variant analysis can provide insights into pathogenesis of the disorder and DNAm signature. In summary, this study presents a comprehensive overview of KLEFS1 and EHMT1, revealing its broader spectrum and deepening our understanding of its molecular mechanisms, thereby informing accurate variant interpretation, counseling, and clinical management.

Genome assembly of Hibiscus sabdariffa L. provides insights into metabolisms of medicinal natural products

Hibiscus sabdariffa L. is a widely cultivated herbaceous plant with diverse applications in food, tea, fiber, and medicine. In this study, we present a high-quality genome assembly of H. sabdariffa using more than 33 Gb of high-fidelity (HiFi) long-read sequencing data, corresponding to ∼20× depth of the genome. We obtained 3 genome assemblies of H. sabdariffa: 1 primary and 2 partially haplotype-resolved genome assemblies. These genome assemblies exhibit N50 contig lengths of 26.25, 11.96, and 14.50 Mb, with genome coverage of 141.3, 86.0, and 88.6%, respectively. We also utilized 26 Gb of total RNA sequencing data to predict 154k, 79k, and 87k genes in the respective assemblies. The completeness of the primary genome assembly and its predicted genes was confirmed by the benchmarking universal single-copy ortholog analysis with a completeness rate of 99.3%. Based on our high-quality genomic resources, we constructed genetic networks for phenylpropanoid and flavonoid metabolism and identified candidate biosynthetic genes, which are responsible for producing key intermediates of roselle-specific medicinal natural products. Our comprehensive genomic and functional analysis opens avenues for further exploration and application of valuable natural products in H. sabdariffa.

Machine Learning-Driven Biomarker Discovery for Skeletal Complications in Type 1 Gaucher Disease Patients

Type 1 Gaucher disease (GD1) is a rare, autosomal recessive disorder caused by glucocerebrosidase deficiency. Skeletal manifestations represent one of the most debilitating and potentially irreversible complications of GD1. Although imaging studies are the gold standard, early diagnostic/prognostic tools, such as molecular biomarkers, are needed for the rapid management of skeletal complications. This study aimed to identify potential protein biomarkers capable of predicting the early diagnosis of bone skeletal complications in GD1 patients using artificial intelligence. An in silico study was performed using the novel Therapeutic Performance Mapping System methodology to construct mathematical models of GD1-associated complications at the protein level. Pathophysiological characterization was performed before modeling, and a data science strategy was applied to the predicted protein activity for each protein in the models to identify classifiers. Statistical criteria were used to prioritize the most promising candidates, and 18 candidates were identified. Among them, PDGFB, IL1R2, PTH and CCL3 (MIP-1α) were highlighted due to their ease of measurement in blood. This study proposes a validated novel tool to discover new protein biomarkers to support clinician decision-making in an area where medical needs have not yet been met. However, confirming the results using in vitro and/or in vivo studies is necessary.

Genetic Signatures of Positive Selection in Human Populations Adapted to High Altitude in Papua New Guinea

Papua New Guinea (PNG) hosts distinct environments mainly represented by the ecoregions of the Highlands and Lowlands that display increased altitude and a predominance of pathogens, respectively. Since its initial peopling approximately 50,000 years ago, inhabitants of these ecoregions might have differentially adapted to the environmental pressures exerted by each of them. However, the genetic basis of adaptation in populations from these areas remains understudied. Here, we investigated signals of positive selection in 62 highlanders and 43 lowlanders across 14 locations in the main island of PNG using whole-genome genotype data from the Oceanian Genome Variation Project (OGVP) and searched for signals of positive selection through population differentiation and haplotype-based selection scans. Additionally, we performed archaic ancestry estimation to detect selection signals in highlanders within introgressed regions of the genome. Among highland populations we identified candidate genes representing known biomarkers for mountain sickness (SAA4, SAA1, PRDX1, LDHA) as well as candidate genes of the Notch signaling pathway (PSEN1, NUMB, RBPJ, MAML3), a novel proposed pathway for high altitude adaptation in multiple organisms. We also identified candidate genes involved in oxidative stress, inflammation, and angiogenesis, processes inducible by hypoxia, as well as in components of the eye lens and the immune response. In contrast, candidate genes in the lowlands are mainly related to the immune response (HLA-DQB1, HLA-DQA2, TAAR6, TAAR9, TAAR8, RNASE4, RNASE6, ANG). Moreover, we find two candidate regions to be also enriched with archaic introgressed segments, suggesting that archaic admixture has played a role in the local adaptation of PNG populations.

Exploring the pharmacological mechanism of Xianlingubao against diabetic osteoporosis based on network pharmacology and molecular docking: An observational study

Xianlinggubao formula (XLGB), is a traditional Chinese compound Medicine that has been extensively used in osteoarthritis and aseptic osteonecrosis, but its curative effect on diabetic osteoporosis (DOP) and its pharmacological mechanisms remains not clear. The aim of the present study was to investigate the possible mechanism of drug repurposing of XLGB in DOP therapy. We acquired XLGB active compounds from the traditional Chinese medicine systems pharmacology and traditional Chinese medicines integrated databases and discovered potential targets for these compounds by conducting target fishing using the traditional Chinese medicine systems pharmacology and Swiss Target Prediction databases. Gene Cards and Online Mendelian Inheritance in Man® database were used to identify the DOP targets. Overlapping related targets between XLGB and DOP was selected to build a protein-protein interaction network. Next, the Metascape database was utilized to enrich the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathways. In addition, Auto-Dock Vina software was used to verify drug and target binding. In total, 48 hub targets were obtained as the candidate targets responsible for DOP therapy. The anti-DOP effect mediated by XLGB was primarily centralized on the advanced glycation end products (AGEs)-receptor for AGE signaling pathway in diabetic complications and osteoclast differentiation. In addition, AKT serine/threonine kinase 1, tumor necrosis factor, Interleukin-6, vascular endothelial growth factor A and peroxisome proliferator activated receptor gamma, which were considered as potential therapeutic targets. Furthermore, molecular docking results confirm the credibility of the predicted therapeutic targets. This study elucidates that XLGB may through regulating AGEs formation and osteoclast differentiation as well as angiogenesis and adipogenesis against DOP. And this study provides new promising points to find the exact regulatory mechanisms of XLGB mediated anti-DOP effect.

An E3 ubiquitin ligase localization screen uncovers DTX2 as a novel ADP-ribosylation-dependent regulator of DNA double-strand break repair

DNA double-strand breaks (DSBs) elicit an elaborate response to signal damage and trigger repair via two major pathways: nonhomologous end-joining (NHEJ), which functions throughout the interphase, and homologous recombination (HR), restricted to S/G2 phases. The DNA damage response relies, on post-translational modifications of nuclear factors to coordinate the mending of breaks. Ubiquitylation of histones and chromatin-associated factors regulates DSB repair and numerous E3 ubiquitin ligases are involved in this process. Despite significant progress, our understanding of ubiquitin-mediated DNA damage response regulation remains incomplete. Here, we have performed a localization screen to identify RING/U-box E3 ligases involved in genome maintenance. Our approach uncovered 7 novel E3 ligases that are recruited to microirradiation stripes, suggesting potential roles in DNA damage signaling and repair. Among these factors, the DELTEX family E3 ligase DTX2 is rapidly mobilized to lesions in a poly ADP-ribosylation-dependent manner. DTX2 is recruited and retained at DSBs via its WWE and DELTEX conserved C-terminal domains. In cells, both domains are required for optimal binding to mono and poly ADP-ribosylated proteins with WWEs playing a prominent role in this process. Supporting its involvement in DSB repair, DTX2 depletion decreases HR efficiency and moderately enhances NHEJ. Furthermore, DTX2 depletion impeded BRCA1 foci formation and increased 53BP1 accumulation at DSBs, suggesting a fine-tuning role for this E3 ligase in repair pathway choice. Finally, DTX2 depletion sensitized cancer cells to X-rays and PARP inhibition and these susceptibilities could be rescued by DTX2 reexpression. Altogether, our work identifies DTX2 as a novel ADP-ribosylation-dependent regulator of HR-mediated DSB repair.

AI-Driven Deep Learning Techniques in Protein Structure Prediction

Protein structure prediction is important for understanding their function and behavior. This review study presents a comprehensive review of the computational models used in predicting protein structure. It covers the progression from established protein modeling to state-of-the-art artificial intelligence (AI) frameworks. The paper will start with a brief introduction to protein structures, protein modeling, and AI. The section on established protein modeling will discuss homology modeling, ab initio modeling, and threading. The next section is deep learning-based models. It introduces some state-of-the-art AI models, such as AlphaFold (AlphaFold, AlphaFold2, AlphaFold3), RoseTTAFold, ProteinBERT, etc. This section also discusses how AI techniques have been integrated into established frameworks like Swiss-Model, Rosetta, and I-TASSER. The model performance is compared using the rankings of CASP14 (Critical Assessment of Structure Prediction) and CASP15. CASP16 is ongoing, and its results are not included in this review. Continuous Automated Model EvaluatiOn (CAMEO) complements the biennial CASP experiment. Template modeling score (TM-score), global distance test total score (GDT_TS), and Local Distance Difference Test (lDDT) score are discussed too. This paper then acknowledges the ongoing difficulties in predicting protein structure and emphasizes the necessity of additional searches like dynamic protein behavior, conformational changes, and protein-protein interactions. In the application section, this paper introduces some applications in various fields like drug design, industry, education, and novel protein development. In summary, this paper provides a comprehensive overview of the latest advancements in established protein modeling and deep learning-based models for protein structure predictions. It emphasizes the significant advancements achieved by AI and identifies potential areas for further investigation.

Elucidating the therapeutic mechanism of betanin in Alzheimer's Disease treatment through network pharmacology and bioinformatics analysis

Betanin, a natural compound with anti-inflammatory and antioxidant properties, has shown promise in mitigating Alzheimer's disease (AD) by reducing amyloid plaque production. Employing network pharmacology, this study aimed to elucidate betanin's therapeutic mechanism in AD treatment. Through integrated analyses utilizing SwissTargetPrediction, STITCH, BindingDB, Therapeutic Target Database (TTD), and OMIM databases, potential protein targets of betanin in AD were predicted. Gene ontology analysis facilitated the identification of 49 putative AD targets. Subsequent gene enrichment and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway analysis revealed associations between these targets and AD. Network pharmacology techniques and molecular docking aided in prioritizing essential genes, with APP, CASP7, ITPR1, CASP8, CASP3, ITPR3, and NF-KB1 emerging as top candidates. The results provide novel insights into betanin's therapeutic efficacy, shedding light on its potential clinical application in AD treatment. By targeting key genes implicated in AD pathology, betanin demonstrates promise as a valuable addition to existing therapeutic strategies. This holistic approach emphasizes the relevance of network pharmacology and bioinformatics analysis in understanding natural chemical disease therapy processes.

Exploring oak processionary caterpillar induced lepidopterism (Part 1): unveiling molecular insights through transcriptomics and proteomics

Lepidopterism, a skin inflammation condition caused by direct or airborne exposure to irritating hairs (setae) from processionary caterpillars, is becoming a significant public health concern. Recent outbreaks of the oak processionary caterpillar (Thaumetopoea processionea) have caused noteworthy health and economic consequences, with a rising frequency expected in the future, exacerbated by global warming promoting the survival of the caterpillar. Current medical treatments focus on symptom relief due to the lack of an effective therapy. While the source is known, understanding the precise causes of symptoms remain incomplete understood. In this study, we employed an advanced method to extract venom from the setae and identify the venom components through high-quality de novo transcriptomics, venom proteomics, and bioinformatic analysis. A total of 171 venom components were identified, including allergens, odorant binding proteins, small peptides, enzymes, enzyme inhibitors, and chitin biosynthesis products, potentially responsible for inflammatory and allergic reactions. This work presents the first comprehensive proteotranscriptomic database of T. processionea, contributing to understanding the complexity of lepidopterism. Furthermore, these findings hold promise for advancing therapeutic approaches to mitigate the global health impact of T. processionea and related caterpillars.

Development of a logic regression-based approach for the discovery of host- and niche-informative biomarkers in Escherichia coli and their application for microbial source tracking

Microbial source tracking leverages a wide range of approaches designed to trace the origins of fecal contamination in aquatic environments. Although source tracking methods are typically employed within the laboratory setting, computational techniques can be leveraged to advance microbial source tracking methodology. Herein, we present a logic regression-based supervised learning approach for the discovery of source-informative genetic markers within intergenic regions across the Escherichia coli genome that can be used for source tracking. With just single intergenic loci, logic regression was able to identify highly source-specific (i.e., exceeding 97.00%) biomarkers for a wide range of host and niche sources, with sensitivities reaching as high as 30.00%-50.00% for certain source categories, including pig, sheep, mouse, and wastewater, depending on the specific intergenic locus analyzed. Restricting the source range to reflect the most prominent zoonotic sources of E. coli transmission (i.e., bovine, chicken, human, and pig) allowed for the generation of informative biomarkers for all host categories, with specificities of at least 90.00% and sensitivities between 12.50% and 70.00%, using the sequence data from key intergenic regions, including emrKY-evgAS, ibsB-(mdtABCD-baeSR), ompC-rcsDB, and yedS-yedR, that appear to be involved in antibiotic resistance. Remarkably, we were able to use this approach to classify 48 out of 113 river water E. coli isolates collected in Northwestern Sweden as either beaver, human, or reindeer in origin with a high degree of consensus-thus highlighting the potential of logic regression modeling as a novel approach for augmenting current source tracking efforts.IMPORTANCEThe presence of microbial contaminants, particularly from fecal sources, within water poses a serious risk to public health. The health and economic burden of waterborne pathogens can be substantial-as such, the ability to detect and identify the sources of fecal contamination in environmental waters is crucial for the control of waterborne diseases. This can be accomplished through microbial source tracking, which involves the use of various laboratory techniques to trace the origins of microbial pollution in the environment. Building on current source tracking methodology, we describe a novel workflow that uses logic regression, a supervised machine learning method, to discover genetic markers in Escherichia coli, a common fecal indicator bacterium, that can be used for source tracking efforts. Importantly, our research provides an example of how the rise in prominence of machine learning algorithms can be applied to improve upon current microbial source tracking methodology.

QAFI: a novel method for quantitative estimation of missense variant impact using protein-specific predictors and ensemble learning

Next-generation sequencing (NGS) has revolutionized genetic diagnostics, yet its application in precision medicine remains incomplete, despite significant advances in computational tools for variant annotation. Many variants remain unannotated, and existing tools often fail to accurately predict the range of impacts that variants have on protein function. This limitation restricts their utility in relevant applications such as predicting disease severity and onset age. In response to these challenges, a new generation of computational models is emerging, aimed at producing quantitative predictions of genetic variant impacts. However, the field is still in its early stages, and several issues need to be addressed, including improved performance and better interpretability. This study introduces QAFI, a novel methodology that integrates protein-specific regression models within an ensemble learning framework, utilizing conservation-based and structure-related features derived from AlphaFold models. Our findings indicate that QAFI significantly enhances the accuracy of quantitative predictions across various proteins. The approach has been rigorously validated through its application in the CAGI6 contest, focusing on ARSA protein variants, and further tested on a comprehensive set of clinically labeled variants, demonstrating its generalizability and robust predictive power. The straightforward nature of our models may also contribute to better interpretability of the results.

Microbial diversity, genomics, and phage-host interactions of cyanobacterial harmful algal blooms

The occurrence of cyanobacterial harmful algal blooms (cyanoHABs) is related to their physical and chemical environment. However, less is known about their associated microbial interactions and processes. In this study, cyanoHABs were analyzed as a microbial ecosystem, using 1 year of 16S rRNA sequencing and 70 metagenomes collected during the bloom season from Lake Okeechobee (Florida, USA). Biogeographical patterns observed in microbial community composition and function reflected ecological zones distinct in their physical and chemical parameters that resulted in bloom "hotspots" near major lake inflows. Changes in relative abundances of taxa within multiple phyla followed increasing bloom severity. Functional pathways that correlated with increasing bloom severity encoded organic nitrogen and phosphorus utilization, storage of nutrients, exchange of genetic material, phage defense, and protection against oxidative stress, suggesting that microbial interactions may promote cyanoHAB resilience. Cyanobacterial communities were highly diverse, with picocyanobacteria ubiquitous and oftentimes most abundant, especially in the absence of blooms. The identification of novel bloom-forming cyanobacteria and genomic comparisons indicated a functionally diverse cyanobacterial community with differences in its capability to store nitrogen using cyanophycin and to defend against phage using CRISPR and restriction-modification systems. Considering blooms in the context of a microbial ecosystem and their interactions in nature, physiologies and interactions supporting the proliferation and stability of cyanoHABs are proposed, including a role for phage infection of picocyanobacteria. This study displayed the power of "-omics" to reveal important biological processes that could support the effective management and prediction of cyanoHABs.

IMPORTANCE

Cyanobacterial harmful algal blooms pose a significant threat to aquatic ecosystems and human health. Although physical and chemical conditions in aquatic systems that facilitate bloom development are well studied, there are fundamental gaps in the biological understanding of the microbial ecosystem that makes a cyanobacterial bloom. High-throughput sequencing was used to determine the drivers of cyanobacteria blooms in nature. Multiple functions and interactions important to consider in cyanobacterial bloom ecology were identified. The microbial biodiversity of blooms revealed microbial functions, genomic characteristics, and interactions between cyanobacterial populations that could be involved in bloom stability and more coherently define cyanobacteria blooms. Our results highlight the importance of considering cyanobacterial blooms as a microbial ecosystem to predict, prevent, and mitigate them.

Reduced anti-viral IgG repertoire in HIV-exposed but uninfected infants compared to HIV-unexposed infants

Infants who are HIV exposed but uninfected (iHEU) have higher risk of viral infections compared to infants who are HIV unexposed (iHUU). We explored the effect of intrauterine HIV exposure on the infant antibody repertoire by quantifying plasma immunoglobulin (Ig) G against 206 eukaryote-infecting viruses using phage immunoprecipitation sequencing (PhiPSeq) in iHEU and iHUU at birth and 36 weeks of life. Maternal HIV infection altered the infant IgG repertoire against eukaryote-infecting viruses at birth, resulting in significantly lower antibody breadth and diversity among iHEU compared to iHUU. Neonatal anti-viral IgG repertoire was dominated by antibodies against viruses belonging to the Herpesviridae family, although, by 36 weeks, this had shifted toward antibodies against enteroviruses, likely due to waning of maternal-derived antibodies and polio vaccine-induced antibody responses as expected. The observed reduced anti-viral IgG repertoire breadth and diversity acquired at birth in iHEU might contribute to the increased rates of viral infections among iHEU during early life.

Benchmarking and integrating human B-cell receptor genomic and antibody proteomic profiling

Immunoglobulins (Ig), which exist either as B-cell receptors (BCR) on the surface of B cells or as antibodies when secreted, play a key role in the recognition and response to antigenic threats. The capability to jointly characterize the BCR and antibody repertoire is crucial for understanding human adaptive immunity. From peripheral blood, bulk BCR sequencing (bulkBCR-seq) currently provides the highest sampling depth, single-cell BCR sequencing (scBCR-seq) allows for paired chain characterization, and antibody peptide sequencing by tandem mass spectrometry (Ab-seq) provides information on the composition of secreted antibodies in the serum. Yet, it has not been benchmarked to what extent the datasets generated by these three technologies overlap and complement each other. To address this question, we isolated peripheral blood B cells from healthy human donors and sequenced BCRs at bulk and single-cell levels, in addition to utilizing publicly available sequencing data. Integrated analysis was performed on these datasets, resolved by replicates and across individuals. Simultaneously, serum antibodies were isolated, digested with multiple proteases, and analyzed with Ab-seq. Systems immunology analysis showed high concordance in repertoire features between bulk and scBCR-seq within individuals, especially when replicates were utilized. In addition, Ab-seq identified clonotype-specific peptides using both bulk and scBCR-seq library references, demonstrating the feasibility of combining scBCR-seq and Ab-seq for reconstructing paired-chain Ig sequences from the serum antibody repertoire. Collectively, our work serves as a proof-of-principle for combining bulk sequencing, single-cell sequencing, and mass spectrometry as complementary methods towards capturing humoral immunity in its entirety.

Proteome profiling of cutaneous leishmaniasis lesions due to dermotropic Leishmania donovani in Sri Lanka

BACKGROUND

Characterization of the host response in cutaneous leishmaniasis (CL) through proteome profiling has gained limited insights into leishmaniasis research compared to that of the parasite. The primary objective of this study was to comprehensively analyze the proteomic profile of the skin lesions tissues in patients with CL, by mass spectrometry, and subsequent validation of these findings through immunohistochemical methods.

METHODS

Eight lesion specimens from leishmaniasis-confirmed patients and eight control skin biopsies were processed for proteomic profiling by mass spectrometry. Formalin-fixed paraffin-embedded lesion specimens from thirty patients and six control skin specimens were used for Immunohistochemistry (IHC) staining. Statistical analyses were carried out using SPSS software. The chi-square test was used to assess the association between the degree of staining for each marker and the clinical and pathological features.

RESULTS

Sixty-seven proteins exhibited significant differential expression between tissues of CL lesions and healthy controls (p < 0.01), representing numerous enriched biological processes within the lesion tissue, as evident by both the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Reactome databases. Among these, the integrated endoplasmic reticulum stress response (IERSR) emerges as a pathway characterized by the up-regulated proteins in CL tissues compared to healthy skin. Expression of endoplasmic reticulum (ER) stress sensors, inositol-requiring enzyme-1 (IRE1), protein kinase RNA-like ER kinase (PERK) and activating transcription factor 6 (ATF6) in lesion tissue was validated by immunohistochemistry.

CONCLUSIONS

In conclusion, proteomic profiling of skin lesions carried out as a discovery phase study revealed a multitude of probable immunological and pathological mechanisms operating in patients with CL in Sri Lanka, which needs to be further elaborated using more in-depth and targeted investigations. Further research exploring the intricate interplay between ER stress and CL pathophysiology may offer promising avenues for the development of novel diagnostic tools and therapeutic strategies in combating this disease.

Drugst.One - a plug-and-play solution for online systems medicine and network-based drug repurposing

In recent decades, the development of new drugs has become increasingly expensive and inefficient, and the molecular mechanisms of most pharmaceuticals remain poorly understood. In response, computational systems and network medicine tools have emerged to identify potential drug repurposing candidates. However, these tools often require complex installation and lack intuitive visual network mining capabilities. To tackle these challenges, we introduce Drugst.One, a platform that assists specialized computational medicine tools in becoming user-friendly, web-based utilities for drug repurposing. With just three lines of code, Drugst.One turns any systems biology software into an interactive web tool for modeling and analyzing complex protein-drug-disease networks. Demonstrating its broad adaptability, Drugst.One has been successfully integrated with 21 computational systems medicine tools. Available at https://drugst.one, Drugst.One has significant potential for streamlining the drug discovery process, allowing researchers to focus on essential aspects of pharmaceutical treatment research.

CPIExtract: A software package to collect and harmonize small molecule and protein interactions

The binding interactions between small molecules and proteins are the basis of cellular functions. Yet, experimental data available regarding compound-protein interaction is not harmonized into a single entity but rather scattered across multiple institutions, each maintaining databases with different formats. Extracting information from these multiple sources remains challenging due to data heterogeneity. Here, we present CPIExtract (Compound-Protein Interaction Extract), a tool to interactively extract experimental binding interaction data from multiple databases, perform filtering, and harmonize the resulting information, thus providing a gain of compound-protein interaction data. When compared to a single source, DrugBank, we show that it can collect more than 10 times the amount of annotations. The end-user can apply custom filtering to the aggregated output data and save it in any generic tabular file suitable for further downstream tasks such as network medicine analyses for drug repurposing and cross-validation of deep learning models.

Quantitative mass spectrometry analysis of the injured proximal and distal human digital nerve ends

Introduction

Proteomic analysis of injured human peripheral nerves, particularly focusing on events occurring in the proximal and distal nerve ends, remains relatively underexplored. This study aimed to investigate the molecular patterns underlying a digital nerve injury, focusing on differences in protein expression between the proximal and distal nerve ends.

Methods

A total of 26 human injured digital nerve samples (24 men; 2 women; median age 47 [30-66] years), harvested during primary nerve repair within 48 h post-injury from proximal and distal nerve ends, were analyzed using mass spectrometry.

Results

A total of 3,914 proteins were identified, with 127 proteins showing significant differences in abundance between the proximal and the distal nerve ends. The downregulation of proteins in the distal nerve end was associated with synaptic transmission, autophagy, neurotransmitter regulation, cell adhesion and migration. Conversely, proteins upregulated in the distal nerve end were implicated in cellular stress response, neuromuscular junction stability and muscle contraction, neuronal excitability and neurotransmitter release, synaptic vesicle recycling and axon guidance and angiogenesis.

Discussion

Investigation of proteins, with functional annotations analysis, in proximal and the distal ends of human injured digital nerves, revealed dynamic cellular responses aimed at promoting tissue degeneration and restoration, while suppressing non-essential processes.

Understanding bacterial pathogen diversity: A proteogenomic analysis and use of an array of genome assemblies to identify novel virulence factors of the honey bee bacterial pathogen Paenibacillus larvae

Mass spectrometry proteomics data are typically evaluated against publicly available annotated sequences, but the proteogenomics approach is a useful alternative. A single genome is commonly utilized in custom proteomic and proteogenomic data analysis. We pose the question of whether utilizing numerous different genome assemblies in a search database would be beneficial. We reanalyzed raw data from the exoprotein fraction of four reference Enterobacterial Repetitive Intergenic Consensus (ERIC) I-IV genotypes of the honey bee bacterial pathogen Paenibacillus larvae and evaluated them against three reference databases (from NCBI-protein, RefSeq, and UniProt) together with an array of protein sequences generated by six-frame direct translation of 15 genome assemblies from GenBank. The wide search yielded 453 protein hits/groups, which UpSet analysis categorized into 50 groups based on the success of protein identification by the 18 database components. Nine hits that were not identified by a unique peptide were not considered for marker selection, which discarded the only protein that was not identified by the reference databases. We propose that the variability in successful identifications between genome assemblies is useful for marker mining. The results suggest that various strains of P. larvae can exhibit specific traits that set them apart from the established genotypes ERIC I-V.

Polysaccharide utilization loci encoded DUF1735 likely functions as membrane-bound spacer for carbohydrate active enzymes

Proteins featuring the Domain of Unknown Function 1735 are frequently found in Polysaccharide Utilization Loci, yet their role remains unknown. The domain and vicinity analyzer programs we developed mine the Kyoto Encyclopedia of Genes and Genomes and UniProt to enhance the functional prediction of DUF1735. Our datasets confirmed the exclusive presence of DUF1735 in Bacteroidota genomes, with Bacteroidetes thetaiotaomicron harboring 46 copies. Notably, 97.8% of DUF1735 are encoded in PULs, and 89% are N-termini of multimodular proteins featuring C-termini like Laminin_G_3, F5/8-typeC, and GH18 domains. Predominantly possessing a predicted lipoprotein signal peptide and sharing an immunoglobulin-like β-sandwich fold with the BACON domain and the N-termini of SusE/F, DUF1735 likely functions as N-terminal, membrane-bound spacer for diverse C-termini involved in PUL-mediated carbohydrate utilization.

Deep-time phylogenetic inference by paleoproteomic analysis of dental enamel

In temperate and subtropical regions, ancient proteins are reported to survive up to about 2 million years, far beyond the known limits of ancient DNA preservation in the same areas. Accordingly, their amino acid sequences currently represent the only source of genetic information available to pursue phylogenetic inference involving species that went extinct too long ago to be amenable for ancient DNA analysis. Here we present a complete workflow, including sample preparation, mass spectrometric data acquisition and computational analysis, to recover and interpret million-year-old dental enamel protein sequences. During sample preparation, the proteolytic digestion step, usually an integral part of conventional bottom-up proteomics, is omitted to increase the recovery of the randomly degraded peptides spontaneously generated by extensive diagenetic hydrolysis of ancient proteins over geological time. Similarly, we describe other solutions we have adopted to (1) authenticate the endogenous origin of the protein traces we identify, (2) detect and validate amino acid variation in the ancient protein sequences and (3) attempt phylogenetic inference. Sample preparation and data acquisition can be completed in 3-4 working days, while subsequent data analysis usually takes 2-5 days. The workflow described requires basic expertise in ancient biomolecules analysis, mass spectrometry-based proteomics and molecular phylogeny. Finally, we describe the limits of this approach and its potential for the reconstruction of evolutionary relationships in paleontology and paleoanthropology.

D'or: deep orienter of protein-protein interaction networks

MOTIVATION

Protein-protein interactions (PPIs) provide the skeleton for signal transduction in the cell. Current PPI measurement techniques do not provide information on their directionality which is critical for elucidating signaling pathways. To date, there are hundreds of thousands of known PPIs in public databases, yet only a small fraction of them have an assigned direction. This information gap calls for computational approaches for inferring the directionality of PPIs, aka network orientation.

RESULTS

In this work, we propose a novel deep learning approach for PPI network orientation. Our method first generates a set of proximity scores between a protein interaction and sets of cause and effect proteins using a network propagation procedure. Each of these score sets is fed, one at a time, to a deep set encoder whose outputs are used as features for predicting the interaction's orientation. On a comprehensive dataset of oriented PPIs taken from five different sources, we achieve an area under the precision-recall curve of 0.89-0.92, outperforming previous methods. We further demonstrate the utility of the oriented network in prioritizing cancer driver genes and disease genes.

AVAILABILITY AND IMPLEMENTATION

D'or is implemented in Python and is publicly available at https://github.com/pirakd/DeepOrienter.