Host-virus interactions in hepatitis B and hepatitis C infection

Gene regulatory network analysis identifies MYL1, MDH2, GLS, and TRIM28 as the principal proteins in the response of mesenchymal stem cells to Mg2+ ions

Magnesium (Mg)-based implants have emerged as a promising alternative for orthopedic applications, owing to their bioactive properties and biodegradability. As the implants degrade, Mg2+ ions are released, influencing all surrounding cell types, especially mesenchymal stem cells (MSCs). MSCs are vital for bone tissue regeneration, therefore, it is essential to understand their molecular response to Mg2+ ions in order to maximize the potential of Mg-based biomaterials. In this study, we conducted a gene regulatory network (GRN) analysis to examine the molecular responses of MSCs to Mg2+ ions. We used time-series proteomics data collected at 11 time points across a 21-day period for the GRN construction. We studied the impact of Mg2+ ions on the resulting networks and identified the key proteins and protein interactions affected by the application of Mg2+ ions. Our analysis highlights MYL1, MDH2, GLS, and TRIM28 as the primary targets of Mg2+ ions in the response of MSCs during 1-21 days phase. Our results also identify MDH2-MYL1, MDH2-RPS26, TRIM28-AK1, TRIM28-SOD2, and GLS-AK1 as the critical protein relationships affected by Mg2+ ions. By offering a comprehensive understanding of the regulatory role of Mg2+ ions on MSCs, our study contributes valuable insights into the molecular response of MSCs to Mg-based materials, thereby facilitating the development of innovative therapeutic strategies for orthopedic applications.

An immuno-informatics approach for annotation of hypothetical proteins and multi-epitope vaccine designed against the Mpox virus

A worrying new outbreak of Monkeypox (Mpox) in humans is caused by the Mpox virus (MpoxV). The pathogen has roughly 28 hypothetical proteins of unknown structure, function, and pathogenicity. Using reliable bioinformatics tools, we attempted to analyze the MpoxV genome, identify the role of hypothetical proteins (HPs), and design a potential candidate vaccine. Out of 28, we identified seven hypothetical proteins using multi-server validation with high confidence for the occurrence of conserved domains. Their physical, chemical, and functional characterizations, including molecular weight, theoretical isoelectric point, 3D structures, GRAVY value, subcellular localization, functional motifs, antigenicity, and virulence factors, were performed. We predicted possible cytotoxic T cell (CTL), helper T cell (HTL) and linear and conformational B cell epitopes, which were combined in a 219 amino acid multiepitope vaccine with human β defensin as a linker. This multi-epitopic vaccine was structurally modelled and docked with toll-like receptor-3 (TLR-3). The dynamical stability of the vaccine-TLR-3 docked complexes exhibited stable interactions based on RMSD and RMSF tests. Additionally, the modelled vaccine was cloned in-silico in an E. coli host to check the appropriate expression of the final vaccine built. Our results might conform to an immunogenic and safe vaccine, which would require further experimental validation.Communicated by Ramaswamy H. Sarma.

A novel proteomic approach for the identification and relative quantification of disulfide-bridges in the human milk proteome

This study explores the disulfide bridges present in the human milk proteome by a novel approach permitting both positional identification and relative quantification of the disulfide bridges. Human milk from six donors was subjected to trypsin digestion without reduction. The digested human milk proteins were analyzed by nanoLC-timsTOF Pro combined with data analysis using xiSEARCH. A total of 85 unique disulfide bridges were identified in 25 different human milk proteins. The total relative abundance of disulfide bridge-containing peptides constituted approximately 5% of the total amount of tryptic-peptides. Seven inter-molecular disulfide bridges were identified between either α-lactalbumin and lactotransferrin (5) or αS1-casein and κ-casein (2). All cysteines involved in the observed disulfide bridges of α-lactalbumin and lactotransferrin were mapped onto protein models using AlphaFold2 Multimer to estimate the length of the observed disulfide bridges. The lengths of the disulfide bridges of lactotransferrin indicate a potential for multi- or poly-merization of lactotransferrin. The high number of intramolecular lactotransferrin disulfide bridges identified, suggests that these are more heterogeneous than previously presumed. SIGNIFICANCE: Disulfide-bridges in the human milk proteome are an often overseen post-transaltional modification. Thus, mapping the disulfide-bridges, their positions and relative abundance, are valuable new knowledge needed for an improved understanding of human milk protein behaviour. Although glycosylation and phosphorylation have been described, even less information is available on the disulfide bridges and the disulfide-bridge derived protein complexes. This is important for future work in precision fermentation for recombinant production of human milk proteins, as this will highlight which disulfide-bridges are naturally occouring in human milk proteins. Further, this knowledge would be of value for the infant formula industry as it provides more information on how to humanize bovine-milk based infant formula. The novel method developed here can be broadly applied in other biological systems as the disulfid-brigdes are important for the structure and functionality of proteins.

Spiro-Acridine Compound as a Pteridine Reductase 1 Inhibitor: in silico Target Fishing and in vitro Studies

Among the many neglected tropical diseases, leishmaniasis ranks second in mortality rate and prevalence. In a previous study, acridine derivatives were synthesized and tested for their antileishmanial activity against L. chagasi. The most active compound identified in that study (1) showed a single digit IC50 value against the parasite (1.10 μg/mL), but its macromolecular target remained unknown. Aiming to overcome this limitation, this work exploited inverse virtual screening to identify compound 1's putative molecular mechanism of action. In vitro assays confirmed that compound 1 binds to Leishmania chagasi pteridine reductase 1 (LcPTR1), with moderate affinity (Kd=33,1 μM), according to differential scanning fluorimetry assay. Molecular dynamics simulations confirm the stability of LcPTR1-compound 1 complex, supporting a competitive mechanism of action. Therefore, the workflow presented in this work successfully identified PTR1 as a macromolecular target for compound 1, allowing the designing of novel potent antileishmanial compounds.

An upstream signaling gene calmodulin regulates the synthesis of insect wax via activating fatty acid biosynthesis pathway

Insect wax accumulates on the surface of insect cuticle, which acts as an important protective barrier against rain, ultraviolet light radiation, pathogens, etc. The waxing behavior, wax composition and molecular mechanism underling wax biosynthesis are unclear in dustywings. Herein, the current study determined the vital developmental stage for waxing behavior in dustywings, examined the components of waxy secretions, and identified key regulatory genes for wax biosynthesis. The wax glands were mainly located on the thorax and abdomen of dustywing adults. The adults spread the waxy secretions over their entire body surface. The metabolomics analysis identified 32 lipids and lipid-like molecules, 15 organic acids and derivatives, 7 benzenoids, etc. as the main components of waxy secretions. The fatty acids represented the largest proportion of the category of lipid and lipid-like molecules. The conjoint analysis of metabolomics and transcriptomics identified two crucial genes fatty acyl-CoA reductase (CsFAR) and calmodulin (CsCaM) for wax biosynthesis. The down-regulation of these genes via nanocarrier-mediated RNA interference technology significantly reduced the amount of wax particles. Notably, the RNAi of CsCaM apparently suppressed the expression of most genes in fatty acid biosynthesis pathway, indicating the CsCaM might act as a main upstream regulator of fatty acid biosynthesis pathway.

MulStack: An ensemble learning prediction model of multilabel mRNA subcellular localization

Subcellular localization of mRNA is related to protein synthesis, cell polarity, cell movement and other biological regulation mechanisms. The distribution of mRNAs in subcellulars is similar to that of proteins, and most mRNAs are distributed in multiple subcellulars. Recently, some computational methods have been designed to predict the subcellular localization of mRNA. However, these methods only employed a sin-gle level of mRNA features and did not employ the position encoding of nucleotides in mRNA. In this paper, an ensemble learning prediction model is proposed, named MulStack, which is based on random forest and deep learning for multilabel mRNA subcellular localization. The proposed method employs two levels of mRNA features, including sequence-level and residue-level features, and position encoding is employed for the first time in the field of subcellular localization of mRNA. Random forest is employed to learn mRNA sequence-level feature, deep learning is employed to learn mRNA sequence-level feature and mRNA residue-level combined with position encoding. And the outputs of random forest and deep learning model will be weighted sum as the prediction probability. Compared with existing methods, the results show that MulStack is the best in the localization of the nucleus, cytosol and exosome. In addition, position weight matrices (PWMs) are extracted by convolutional neural networks (CNNs) that can be matched with known RNA binding protein motifs. Gene ontology (GO) enrichment analysis shows biological processes, molecular functions and cellular components of mRNA genes. The prediction web server of MulStack is freely accessible at http://bliulab.net/MulStack.

DeepPLM_mCNN: An approach for enhancing ion channel and ion transporter recognition by multi-window CNN based on features from pre-trained language models

Accurate classification of membrane proteins like ion channels and transporters is critical for elucidating cellular processes and drug development. We present DeepPLM_mCNN, a novel framework combining Pretrained Language Models (PLMs) and multi-window convolutional neural networks (mCNNs) for effective classification of membrane proteins into ion channels and ion transporters. Our approach extracts informative features from protein sequences by utilizing various PLMs, including TAPE, ProtT5_XL_U50, ESM-1b, ESM-2_480, and ESM-2_1280. These PLM-derived features are then input into a mCNN architecture to learn conserved motifs important for classification. When evaluated on ion transporters, our best performing model utilizing ProtT5 achieved 90% sensitivity, 95.8% specificity, and 95.4% overall accuracy. For ion channels, we obtained 88.3% sensitivity, 95.7% specificity, and 95.2% overall accuracy using ESM-1b features. Our proposed DeepPLM_mCNN framework demonstrates significant improvements over previous methods on unseen test data. This study illustrates the potential of combining PLMs and deep learning for accurate computational identification of membrane proteins from sequence data alone. Our findings have important implications for membrane protein research and drug development targeting ion channels and transporters. The data and source codes in this study are publicly available at the following link: https://github.com/s1129108/DeepPLM_mCNN.

Potential immune-related therapeutic mechanisms of multiple traditional Chinese medicines on type 2 diabetic nephropathy based on bioinformatics, network pharmacology and molecular docking

BACKGROUND

The prevalence of type 2 diabetic nephropathy (T2DN) ranges from 20 % to 40 % among individuals with type 2 diabetes. Multiple immune pathways play a pivotal role in the pathogenesis of T2DN. This study aimed to investigate the immunomodulatory effects of active ingredients derived from 14 traditional Chinese medicines (TCMs) on T2DN.

METHODS

By removing batch effect on the GSE30528 and GSE96804 datasets, we employed a combination of weighted gene co-expression network analysis, least absolute shrinkage and selection operator analysis, protein-protein interaction network analysis, and the CIBERSORT algorithm to identify the active ingredients of TCMs as well as potential hub biomarkers associated with immune cells. Functional analysis was conducted using Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO), and gene set variation analysis (GSVA). Additionally, molecular docking was employed to evaluate interactions between active ingredients and potential immunotherapy targets.

RESULTS

A total of 638 differentially expressed genes (DEGs) were identified in this study, comprising 5 hub genes along with 4 potential biomarkers. Notably, CXCR1, CXCR2, and FOS exhibit significant associations with immune cells while displaying robust or favorable affinities towards the active ingredients kaempferol, quercetin, and luteolin. Furthermore, functional analysis unveiled intricate involvement of DEGs, hub genes and potential biomarkers in pathways closely linked to immunity and diabetes.

CONCLUSION

The potential hub biomarkers and immunotherapy targets associated with immune cells of T2DN comprise CXCR1, CXCR2, and FOS. Furthermore, kaempferol, quercetin, and luteolin demonstrate potential immunomodulatory effects in modulating T2DN through the regulation of CXCR1, CXCR2, and FOS expression.

Systematic characterization of multi-omics landscape between gut microbial metabolites and GPCRome in Alzheimer's disease

Shifts in the magnitude and nature of gut microbial metabolites have been implicated in Alzheimer's disease (AD), but the host receptors that sense and respond to these metabolites are largely unknown. Here, we develop a systems biology framework that integrates machine learning and multi-omics to identify molecular relationships of gut microbial metabolites with non-olfactory G-protein-coupled receptors (termed the "GPCRome"). We evaluate 1.09 million metabolite-protein pairs connecting 408 human GPCRs and 335 gut microbial metabolites. Using genetics-derived Mendelian randomization and integrative analyses of human brain transcriptomic and proteomic profiles, we identify orphan GPCRs (i.e., GPR84) as potential drug targets in AD and that triacanthine experimentally activates GPR84. We demonstrate that phenethylamine and agmatine significantly reduce tau hyperphosphorylation (p-tau181 and p-tau205) in AD patient induced pluripotent stem cell-derived neurons. This study demonstrates a systems biology framework to uncover the GPCR targets of human gut microbiota in AD and other complex diseases if broadly applied.

Function of MYB8 in larch under PEG simulated drought stress

Larch, a prominent afforestation, and timber species in northeastern China, faces growth limitations due to drought. To further investigate the mechanism of larch's drought resistance, we conducted full-length sequencing on embryonic callus subjected to PEG-simulated drought stress. The sequencing results revealed that the differentially expressed genes (DEGs) primarily played roles in cellular activities and cell components, with molecular functions such as binding, catalytic activity, and transport activity. Furthermore, the DEGs showed significant enrichment in pathways related to protein processing, starch and sucrose metabolism, benzose-glucuronic acid interconversion, phenylpropyl biology, flavonoid biosynthesis, as well as nitrogen metabolism and alanine, aspartic acid, and glutamic acid metabolism. Consequently, the transcription factor T_transcript_77027, which is involved in multiple pathways, was selected as a candidate gene for subsequent drought stress resistance tests. Under PEG-simulated drought stress, the LoMYB8 gene was induced and showed significantly upregulated expression compared to the control. Physiological indices demonstrated an improved drought resistance in the transgenic plants. After 48 h of PEG stress, the transcriptome sequencing results of the transiently transformed LoMYB8 plants and control plants exhibited that genes were significantly enriched in biological process, cellular component and molecular function. Function analyses indicated for the enrichment of multiple KEGG pathways, including energy synthesis, metabolic pathways, antioxidant pathways, and other relevant processes. The pathways annotated by the differential metabolites mainly encompassed signal transduction, carbohydrate metabolism, amino acid metabolism, and flavonoid metabolism.

Complete characterization of the yak testicular development using accurate full-length transcriptome sequencing

Alternative splicing is a prevalent phenomenon in testicular tissues. Due to the low assembly accuracy of short-read RNA sequencing technology in analyzing post-transcriptional regulatory events, full-length (FL) transcript sequencing is highly demanded to accurately determine FL splicing variants. In this study, we performed FL transcriptome sequencing of testicular tissues from 0.5, 1.5, 2.5, and 4-year-old yaks and 4-year-old cattle-yaks using Oxford Nanopore Technologies. The obtained sequencing data were predicted to have 47,185 open reading frames (ORFs), including 26,630 complete ORFs, detected 7645 fusion transcripts, 15,355 alternative splicing events, 25,798 simple sequence repeats, 7628 transcription factors, and 35,503 long non-coding RNAs. A total of 40,038 novel transcripts were obtained from the sequencing data, and the proportion was almost close to the number of known transcripts identified. Structural analysis and functional annotation of these novel transcripts resulted in the successful annotation of 9568 transcripts, with the highest and lowest annotation numbers in the Nr and KOG databases, respectively. Weighted gene co-expression network analysis revealed the key regulatory pathways and hub genes at various stages of yak testicular development. Our findings enhance our comprehension of transcriptome complexity, contribute to genome annotation refinement, and provide foundational data for further investigations into male sterility in cattle-yaks.

Proteomic analysis of Neobenedenia sp. and Rhabdosynochus viridisi (Monogenea, Monopisthocotylea): Insights into potential vaccine targets and diagnostic markers for finfish aquaculture

Monogeneans are parasitic flatworms that represent a significant threat to the aquaculture industry. Species like Neobenedenia melleni (Capsalidae) and Rhabdosynochus viridisi (Diplectanidae) have been identified as causing diseases in farmed fish. In the past years, molecular research on monogeneans of the subclass Monopisthocotylea has focused on the generation of genomic and transcriptomic information and the identification in silico of some protein families of veterinary interest. Proteomic analysis has been suggested as a powerful tool to investigate proteins in parasites and identify potential targets for vaccine development and diagnosis. To date, the proteomic dataset for monogeneans has been restricted to a species of the subclass Polyopisthocotylea, while in monopisthocotyleans there is no proteomic data. In this study, we present the first proteomic data on two monopisthocotylean species, Neobenedenia sp. and R. viridisi, obtained from three distinct sample types: tissue, excretory-secretory products (ESPs), and eggs. A total of 1691 and 1846 expressed proteins were identified in Neobenedenia sp. and R. viridisi, respectively. The actin family was the largest protein family, followed by the tubulin family and the heat shock protein 70 (HSP70) family. We focused mainly on ESPs because they are important to modulate the host immune system. We identified proteins of the actin, tubulin, HSP70 and HSP90 families in both tissue and ESPs, which have been recognized for their antigenic activities in parasitic flatworms. Furthermore, our study uncovered the presence of proteins within ESPs, such as annexin, calcium-binding protein, fructose bisphosphate aldolase, glutamate dehydrogenase, myoferlin, and paramyosin, that are targets for immunodiagnostic and vaccine development and hold paramount relevance in veterinary medicine. This study expands our knowledge of monogeneans and identified proteins that, in other platyhelminths are potential targets for vaccines and drug discovery.

Arginine methylation of caspase-8 controls life/death decisions in extrinsic apoptotic networks

Procaspase-8 is a key mediator of death receptor (DR)-mediated pathways. Recently, the role of post-translational modifications (PTMs) of procaspase-8 in controlling cell death has received increasing attention. Here, using mass spectrometry screening, pharmacological inhibition and biochemical assays, we show that procaspase-8 can be targeted by the PRMT5/RIOK1/WD45 methylosome complex. Furthermore, two potential methylation sites of PRMT5 on procaspase-8, R233 and R435, were identified in silico. R233 and R435 are highly conserved in mammals and their point mutations are among the most common mutations of caspase-8 in cancer. The introduction of mutations at these positions resulted in inhibitory effects on CD95L-induced caspase-8 activity, effector caspase activation and apoptosis. In addition, we show that procaspase-8 can undergo symmetric di-methylation. Finally, the pharmacological inhibition of PRMT5 resulted in the inhibitory effects on caspase activity and apoptotic cell death. Taken together, we have unraveled the additional control checkpoint in procaspase-8 activation and the arginine methylation network in the extrinsic apoptosis pathway.

Transcriptome dynamics of Gossypium purpurascens in response to abiotic stresses by Iso-seq and RNA-seq data

Gossypium purpurascens is a member of the Malvaceae family, holds immense economic significance as a fiber crop worldwide. Abiotic stresses harm cotton crops, reduce yields, and cause economic losses. Generating high-quality reference genomes and large-scale transcriptomic datasets across diverse conditions can offer valuable insights into identifying preferred agronomic traits for crop breeding. The present research used leaf tissues to conduct PacBio Iso-seq and RNA-seq analysis. We carried out an in-depth analysis of DEGs using both correlations with cluster analysis and principal component analysis. Additionally, the study also involved the identification of both lncRNAs and CDS. We have prepared RNA-seq libraries from 75 RNA samples to study the effects of drought, salinity, alkali, and saline-alkali stress, as well as control conditions. A total of 454.06 Gigabytes of transcriptome data were effectively validated through the identification of differentially expressed genes and KEGG and GO analysis. Overwhelmingly, gene expression profiles and full-length transcripts from cotton tissues will aid in understanding the genetic mechanism of abiotic stress tolerance in G. purpurascens.

Cellular and transcriptomic response to pathogenic and non-pathogenic Vibrio parahaemolyticus strains causing acute hepatopancreatic necrosis disease (AHPND) in Litopenaeus vannamei

The shrimp industry has historically been affected by viral and bacterial diseases. One of the most recent emerging diseases is Acute Hepatopancreatic Necrosis Disease (AHPND), which causes severe mortality. Despite its significance to sanitation and economics, little is known about the molecular response of shrimp to this disease. Here, we present the cellular and transcriptomic responses of Litopenaeus vannamei exposed to two Vibrio parahaemolyticus strains for 98 h, wherein one is non-pathogenic (VpN) and the other causes AHPND (VpP). Exposure to the VpN strain resulted in minor alterations in hepatopancreas morphology, including reductions in the size of R and B cells and detachments of small epithelial cells from 72 h onwards. On the other hand, exposure to the VpP strain is characterized by acute detachment of epithelial cells from the hepatopancreatic tubules and infiltration of hemocytes in the inter-tubular spaces. At the end of exposure, RNA-Seq analysis revealed functional enrichment in biological processes, such as the toll3 receptor signaling pathway, apoptotic processes, and production of molecular mediators involved in the inflammatory response of shrimp exposed to VpN treatment. The biological processes identified in the VpP treatment include superoxide anion metabolism, innate immune response, antimicrobial humoral response, and toll3 receptor signaling pathway. Furthermore, KEGG enrichment analysis revealed metabolic pathways associated with survival, cell adhesion, and reactive oxygen species, among others, for shrimp exposed to VpP. Our study proves the differential immune responses to two strains of V. parahaemolyticus, one pathogenic and the other nonpathogenic, enlarges our knowledge on the evolution of AHPND in L. vannamei, and uncovers unique perspectives on establishing genomic resources that may function as a groundwork for detecting probable molecular markers linked to the immune system in shrimp.

MFTrans: A multi-feature transformer network for protein secondary structure prediction

In the rapidly evolving field of computational biology, accurate prediction of protein secondary structures is crucial for understanding protein functions, facilitating drug discovery, and advancing disease diagnostics. In this paper, we propose MFTrans, a deep learning-based multi-feature fusion network aimed at enhancing the precision and efficiency of Protein Secondary Structure Prediction (PSSP). This model employs a Multiple Sequence Alignment (MSA) Transformer in combination with a multi-view deep learning architecture to effectively capture both global and local features of protein sequences. MFTrans integrates diverse features generated by protein sequences, including MSA, sequence information, evolutionary information, and hidden state information, using a multi-feature fusion strategy. The MSA Transformer is utilized to interleave row and column attention across the input MSA, while a Transformer encoder and decoder are introduced to enhance the extracted high-level features. A hybrid network architecture, combining a convolutional neural network with a bidirectional Gated Recurrent Unit (BiGRU) network, is used to further extract high-level features after feature fusion. In independent tests, our experimental results show that MFTrans has superior generalization ability, outperforming other state-of-the-art PSSP models by 3 % on average on public benchmarks including CASP12, CASP13, CASP14, TEST2016, TEST2018, and CB513. Case studies further highlight its advanced performance in predicting mutation sites. MFTrans contributes significantly to the protein science field, opening new avenues for drug discovery, disease diagnosis, and protein.

A new horizon in the phosphorylated sites of AGA: the structural impact of C163S mutation in aspartylglucosaminuria through molecular dynamics simulation

Aspartylglucosaminuria (AGU) is a lysosomal storage disorder caused by insufficient aspartylglucosaminidase (AGA) activity leading to chronic neurodegeneration. We utilized the PhosphoSitePlus tool to identify the AGA protein's phosphorylation sites. The phosphorylation was induced on the specific residue of the three-dimensional AGA protein, and the structural changes upon phosphorylation were studied via molecular dynamics simulation. Furthermore, the structural behaviour of C163S mutation and C163S mutation with adjacent phosphorylation was investigated. We have examined the structural impact of phosphorylated forms and C163S mutation in AGA. Molecular dynamics simulations (200 ns) exposed patterns of deviation, fluctuation, and change in compactness of Y178 phosphorylated AGA protein (Y178-p), T215 phosphorylated AGA protein (T215-p), T324 phosphorylated AGA protein (T324-p), C163S mutant AGA protein (C163S), and C163S mutation with Y178 phosphorylated AGA protein (C163S-Y178-p). Y178-p, T215-p, and C163S mutation demonstrated an increase in intramolecular hydrogen bonds, leading to greater compactness of the AGA forms. Principle component analysis (PCA) and Gibbs free energy of the phosphorylated/C163S mutation structures exhibit transition in motion/orientation than Wild type (WT). T215-p may be more dominant among these than the other studied phosphorylated forms. It might contribute to hydrolyzing L-asparagine functioning as an asparaginase, thereby regulating neurotransmitter activity. This study revealed structural insights into the phosphorylation of Y178, T215, and T324 in AGA protein. Additionally, it exposed the structural changes of the C163S mutation and C163S-Y178-p of AGA protein. This research will shed light on a better understanding of AGA's phosphorylated mechanism.Communicated by Ramaswamy H. Sarma.

Metabolomic and transcriptomic reveal flavonoid biosynthesis and regulation mechanism in Phlomoides rotata from different habitats

Phlomoides rotata is a traditional medical plant at 3100-5200 m altitude in the Tibet Plateau. In this study, flavonoid metabolites were investigated in P. rotata from Henan County (HN), Guoluo County (GL), Yushu County (YS), and Chengduo County (CD) habitats in Qinghai. The level of kaempferol 3-neohesperidoside, sakuranetin, and biochanin A was high in HN. The content of limocitrin and isoquercetin was high in YS. The levels of ikarisoside A and chrysosplenol D in GL were high. Schaftoside, miquelianin, malvidin chloride, and glabrene in CD exhibited high levels. The results showed a significant correlation between 59 flavonoids and 29 DEGs. Eleven flavonoids increased with altitude. PAL2, UFGT6, COMT1, HCT2, 4CL4, and HCT3 genes were crucial in regulating flavonoid biosynthesis. Three enzymes CHS, 4CL, and UFGT, were crucial in regulating flavonoid biosynthesis. This study provided biological and chemical evidence for the different uses of various regional plants of P. rotata.

An Active Site Tyr Residue Guides the Regioselectivity of Lysine Hydroxylation by Nonheme Iron Lysine-4-hydroxylase Enzymes through Proton-Coupled Electron Transfer

Lysine dioxygenase (KDO) is an important enzyme in human physiology involved in bioprocesses that trigger collagen cross-linking and blood pressure control. There are several KDOs in nature; however, little is known about the factors that govern the regio- and stereoselectivity of these enzymes. To understand how KDOs can selectively hydroxylate their substrate, we did a comprehensive computational study into the mechanisms and features of 4-lysine dioxygenase. In particular, we selected a snapshot from the MD simulation on KDO5 and created large QM cluster models (A, B, and C) containing 297, 312, and 407 atoms, respectively. The largest model predicts regioselectivity that matches experimental observation with rate-determining hydrogen atom abstraction from the C4-H position, followed by fast OH rebound to form 4-hydroxylysine products. The calculations show that in model C, the dipole moment is positioned along the C4-H bond of the substrate and, therefore, the electrostatic and electric field perturbations of the protein assist the enzyme in creating C4-H hydroxylation selectivity. Furthermore, an active site Tyr233 residue is identified that reacts through proton-coupled electron transfer akin to the axial Trp residue in cytochrome c peroxidase. Thus, upon formation of the iron(IV)-oxo species in the catalytic cycle, the Tyr233 phenol loses a proton to the nearby Asp179 residue, while at the same time, an electron is transferred to the iron to create an iron(III)-oxo active species. This charged tyrosyl residue directs the dipole moment along the C4-H bond of the substrate and guides the selectivity to the C4-hydroxylation of the substrate.

Haplotype-resolved T2T reference genomes for wild and domesticated accessions shed new insights into the domestication of jujube

Chinese jujube (Ziziphus jujuba Mill.) is one of the most important deciduous tree fruits in China, with substantial economic and nutritional value. Jujube was domesticated from its wild progenitor, wild jujube (Z. jujuba var. spinosa), and both have high medicinal value. Here we report the 767.81- and 759.24-Mb haplotype-resolved assemblies of a dry-eating 'Junzao' jujube (JZ) and a wild jujube accession (SZ), using a combination of multiple sequencing strategies. Each assembly yielded two complete haplotype-resolved genomes at the telomere-to-telomere (T2T) level, and ~81.60 and 69.07 Mb of structural variations were found between the two haplotypes within JZ and SZ, respectively. Comparative genomic analysis revealed a large inversion on each of chromosomes 3 and 4 between JZ and SZ, and numerous genes were affected by structural variations, some of which were associated with starch and sucrose metabolism. A large-scale population analysis of 672 accessions revealed that wild jujube originated from the lower reaches of the Yellow River and was initially domesticated at local sites. It spread widely and was then independently domesticated at the Shanxi-Shaanxi Gorge of the middle Yellow River. In addition, we identified some new selection signals regions on genomes, which are involved in the tissue development, pollination, and other aspects of jujube tree morphology and fertilization domestication. In conclusion, our study provides high-quality reference genomes of jujube and wild jujube and new insights into the domestication history of jujube.

Mycobacterium abscessus extracellular vesicles increase mycobacterial resistance to clarithromycin in vitro

Nontuberculous Mycobacteria (NTM) are a group of emerging bacterial pathogens that have been identified in cystic fibrosis (CF) patients with microbial lung infections. The treatment of NTM infection in CF patients is challenging due to the natural resistance of NTM species to many antibiotics. Mycobacterium abscessus is one of the most common NTM species found in the airways of CF patients. In this study, we characterized the extracellular vesicles (EVs) released by drug-sensitive M. abscessus untreated or treated with clarithromycin (CLR), one of the frontline anti-NTM drugs. Our data show that exposure to CLR increases mycobacterial protein trafficking into EVs as well as the secretion of EVs in culture. Additionally, EVs released by CLR-treated M. abscessus increase M. abscessus resistance to CLR when compared to EVs from untreated M. abscessus. Proteomic analysis further indicates that EVs released by CLR-treated M. abscessus carry an increased level of 50S ribosomal subunits, the target of CLR. Taken together, our results suggest that EVs play an important role in M. abscessus resistance to CLR treatment.

Establishing comprehensive quaternary structural proteomes from genome sequence

A critical body of knowledge has developed through advances in protein microscopy, protein-fold modeling, structural biology software, availability of sequenced bacterial genomes, large-scale mutation databases, and genome-scale models. Based on these recent advances, we develop a computational framework that; i) identifies the oligomeric structural proteome encoded by an organism's genome from available structural resources; ii) maps multi-strain alleleomic variation, resulting in the structural proteome for a species; and iii) calculates the 3D orientation of proteins across subcellular compartments with residue-level precision. Using the platform, we; iv) compute the quaternary E. coli K-12 MG1655 structural proteome; v) use a dataset of 12,000 mutations to build Random Forest classifiers that can predict the severity of mutations; and, in combination with a genome-scale model that computes proteome allocation, vi) obtain the spatial allocation of the E. coli proteome. Thus, in conjunction with relevant datasets and increasingly accurate computational models, we can now annotate quaternary structural proteomes, at genome-scale, to obtain a molecular-level understanding of whole-cell functions.

Significance

Advancements in experimental and computational methods have revealed the shapes of multi-subunit proteins. The absence of a unified platform that maps actionable datatypes onto these increasingly accurate structures creates a barrier to structural analyses, especially at the genome-scale. Here, we describe QSPACE, a computational annotation platform that evaluates existing resources to identify the best-available structure for each protein in a user's query, maps the 3D location of actionable datatypes ( e.g. , active sites, published mutations) onto the selected structures, and uses third-party APIs to determine the subcellular compartment of all amino acids of a protein. As proof-of-concept, we deployed QSPACE to generate the quaternary structural proteome of E. coli MG1655 and demonstrate two use-cases involving large-scale mutant analysis and genome-scale modelling.

WGCNA Reveals Hub Genes and Key Gene Regulatory Pathways of the Response of Soybean to Infection by Soybean mosaic virus

Soybean mosaic virus (SMV) is one of the main pathogens that can negatively affect soybean production and quality. To study the gene regulatory network of soybeans in response to SMV SC15, the resistant line X149 and susceptible line X97 were subjected to transcriptome analysis at 0, 2, 8, 12, 24, and 48 h post-inoculation (hpi). Differential expression analysis revealed that 10,190 differentially expressed genes (DEGs) responded to SC15 infection. Weighted gene co-expression network analysis (WGCNA) was performed to identify highly related resistance gene modules; in total, eight modules, including 2256 DEGs, were identified. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of 2256 DEGs revealed that the genes significantly clustered into resistance-related pathways, such as the plant-pathogen interaction pathway, mitogen-activated protein kinases (MAPK) signaling pathway, and plant hormone signal transduction pathway. Among these pathways, we found that the flg22, Ca2+, hydrogen peroxide (H2O2), and abscisic acid (ABA) regulatory pathways were fully covered by 36 DEGs. Among the 36 DEGs, the gene Glyma.01G225100 (protein phosphatase 2C, PP2C) in the ABA regulatory pathway, the gene Glyma.16G031900 (WRKY transcription factor 22, WRKY22) in Ca2+ and H2O2 regulatory pathways, and the gene Glyma.04G175300 (calcium-dependent protein kinase, CDPK) in Ca2+ regulatory pathways were highly connected hub genes. These results indicate that the resistance of X149 to SC15 may depend on the positive regulation of flg22, Ca2+, H2O2, and ABA regulatory pathways. Our study further showed that superoxide dismutase (SOD) activity, H2O2 content, and catalase (CAT) and peroxidase (POD) activities were significantly up-regulated in the resistant line X149 compared with those in 0 hpi. This finding indicates that the H2O2 regulatory pathway might be dependent on flg22- and Ca2+-pathway-induced ROS generation. In addition, two hub genes, Glyma.07G190100 (encoding F-box protein) and Glyma.12G185400 (encoding calmodulin-like proteins, CMLs), were also identified and they could positively regulate X149 resistance. This study provides pathways for further investigation of SMV resistance mechanisms in soybean.

Full-length transcriptome analysis of a bloom-forming dinoflagellate Prorocentrum shikokuense (Dinophyceae)

Prorocentrum shikokuense (formerly P. donghaiense) is a pivotal dinoflagellate species associating with the HABs in the East China Sea. The complexity of its large nuclear genome hindered us from understanding its genomic characteristics. Full-length transcriptome sequencing offers a practical solution to decipher the physiological mechanisms of a species without the reference genome. In this study, we employed single-molecule real-time (SMRT) sequencing technology to sequence the full-length transcriptome of Prorocentrum shikokuense. We successfully generated 41.73 Gb of clean SMRT sequencing reads and isolated 105,249 non-redundant full-length non-chimeric reads. Our trial has led to the identification of 11,917 long non-coding RNA transcripts, 514 alternative splicing events, 437 putative transcription factor genes from 17 TF gene families, and 34,723 simple sequence repeats. Additionally, a total of 78,265 open reading frames were identified, of them 15,501 were the protein coding sequences. This dataset is valuable for annotating P. shikokuense genome, and will contribute significantly to the in-depth studies on the molecular mechanisms underlining the dinoflagellate bloom formation.

Exploiting protein language models for the precise classification of ion channels and ion transporters

This study introduces TooT-PLM-ionCT, a comprehensive framework that consolidates three distinct systems, each meticulously tailored for one of the following tasks: distinguishing ion channels (ICs) from membrane proteins (MPs), segregating ion transporters (ITs) from MPs, and differentiating ICs from ITs. Drawing upon the strengths of six Protein Language Models (PLMs)-ProtBERT, ProtBERT-BFD, ESM-1b, ESM-2 (650M parameters), and ESM-2 (15B parameters), TooT-PLM-ionCT employs a combination of traditional classifiers and deep learning models for nuanced protein classification. Originally validated on an existing dataset by previous researchers, our systems demonstrated superior performance in identifying ITs from MPs and distinguishing ICs from ITs, with the IC-MP discrimination achieving state-of-the-art results. In light of recommendations for additional validation, we introduced a new dataset, significantly enhancing the robustness and generalization of our models across bioinformatics challenges. This new evaluation underscored the effectiveness of TooT-PLM-ionCT in adapting to novel data while maintaining high classification accuracy. Furthermore, this study explores critical factors affecting classification accuracy, such as dataset balancing, the impact of using frozen versus fine-tuned PLM representations, and the variance between half and full precision in floating-point computations. To facilitate broader application and accessibility, a web server (https://tootsuite.encs.concordia.ca/service/TooT-PLM-ionCT) has been developed, allowing users to evaluate unknown protein sequences through our specialized systems for IC-MP, IT-MP, and IC-IT classification tasks.

Characterization of a New Variant in ARHGAP31 Probably Involved in Adams-Oliver Syndrome in a Family with a Variable Phenotypic Spectrum

Adams-Oliver syndrome is a rare inherited condition characterized by scalp defects and limb abnormalities. It is caused by variants in different genes such as ARHGAP31. Here, we used an interdisciplinary approach to study a family with lower limb anomalies. We identified a novel variant in the ARHGAP31 gene that is predicted to result in a truncated protein with a constitutively activated catalytic site due to the loss of 688 amino acids involved in the C-terminal domain, essential for protein auto-inhibition. Pathogenic variants in ARHGAP31 exon 12, leading to a premature protein termination, are associated with Adams-Oliver syndrome. Bioinformatic analysis was useful to elucidate the impact of the identified genetic variant on protein structure. To better understand the impact of the identified variant, 3D protein models were predicted for the ARHGAP31 wild type, the newly discovered variant, and other pathogenetic alterations already reported. Our study identified a novel variant probably involved in Adams-Oliver syndrome and increased the evidence on the phenotypic variability in patients affected by this syndrome, underlining the importance of translational research, including experimental and bioinformatics analyses. This strategy represents a successful model to investigate molecular mechanisms involved in syndrome occurrence.

Orosomucoid 2 as a biomarker of carotid artery atherosclerosis plaque vulnerability through its generation of reactive oxygen species and lipid accumulation in vascular smooth muscle cells

BACKGROUND

Orosomucoid (ORM) has been reported as a biomarker of carotid atherosclerosis, but the role of ORM 2, a subtype of ORM, in carotid atherosclerotic plaque formation and the underlying mechanism have not been established.

METHODS

Plasma was collected from patients with carotid artery stenosis (CAS) and healthy participants and assessed using mass spectrometry coupled with isobaric tags for relative and absolute quantification (iTRAQ) technology to identify differentially expressed proteins. The key proteins and related pathways were identified via western blotting, immunohistochemistry, and polymerase chain reaction of carotid artery plaque tissues and in vitro experiments involving vascular smooth muscle cells (VSMCs).

RESULTS

We screened 33 differentially expressed proteins out of 535 proteins in the plasma. Seventeen proteins showed increased expressions in the CAS groups relative to the healthy groups, while 16 proteins showed decreased expressions during iTRAQ and bioinformatic analysis. The reactive oxygen species metabolic process was the most common enrichment pathway identified by Gene Ontology analysis, while ORM2, PRDX2, GPX3, HP, HBB, ANXA5, PFN1, CFL1, and S100A11 were key proteins identified by STRING and MCODE analysis. ORM2 showed increased expression in patients with CAS plaques, and ORM2 was accumulated in smooth muscle cells. Oleic acid increased the lipid accumulation and ORM2 and PRDX6 expressions in the VSMCs. The recombinant-ORM2 also increased the lipid accumulation and reactive oxygen species (ROS) in the VSMCs. The expressions of ORM2 and PRDX-6 were correlated, and MJ33 (an inhibitor of PRDX6-PLA2) decreased ROS production and lipid accumulation in VSMCs.

CONCLUSION

ORM2 may be a biomarker for CAS; it induced lipid accumulation and ROS production in VSMCs during atherosclerosis plaque formation. However, the relationships between ORM2 and PRDX-6 underlying lipid accumulation-induced plaque vulnerability require further research.

DeepReg: a deep learning hybrid model for predicting transcription factors in eukaryotic and prokaryotic genomes

Deep learning models (DLMs) have gained importance in predicting, detecting, translating, and classifying a diversity of inputs. In bioinformatics, DLMs have been used to predict protein structures, transcription factor-binding sites, and promoters. In this work, we propose a hybrid model to identify transcription factors (TFs) among prokaryotic and eukaryotic protein sequences, named Deep Regulation (DeepReg) model. Two architectures were used in the DL model: a convolutional neural network (CNN), and a bidirectional long-short-term memory (BiLSTM). DeepReg reached a precision of 0.99, a recall of 0.97, and an F1-score of 0.98. The quality of our predictions, the bias-variance trade-off approach, and the characterization of new TF predictions were evaluated and compared against those produced by DeepTFactor, as well as against experimental data from three model organisms. Predictions based on our DLM tended to exhibit less variance and bias than those from DeepTFactor, thus increasing reliability and decreasing overfitting.

Revealing Molecular Mechanisms of the Bioactive Saponins from Edible Root of Platycodon grandiflorum in Combating Obesity

Obesity has emerged as a significant health concern, as it is a disease linked to metabolic disorders in the body and is characterized by the excessive accumulation of lipids. As a plant-derived food, Platycodon grandiflorum (PG) was reported by many studies, indicating that the saponins from PG can improve obesity effectively. However, the anti-obesity saponins from PG and its anti-obesity mechanisms have not been fully identified. This study identified the active saponins and their molecular targets for treating obesity. The TCMSP database was used to obtain information on 18 saponins in PG. The anti-obesity target of the PG saponins was 115 targets and 44 core targets. GO and KEGG analyses using 44 core anti-obesity genes and targets of PG-active saponins screened from GeneCards, OMIM, Drugbank, and DisGeNet showed that the PI3K-Akt pathway, the JAK-STAT pathway, and the MAPK pathway were the major pathways involved in the anti-obesity effects of PG saponins. BIOVIA Discovery Studio Visualizer and AutoDock Vina were used to perform molecular docking and process the molecular docking results. The molecular docking results showed that the active saponins of PG could bind to the major therapeutic obesity targets to play an obesity-inhibitory role. The results of this study laid the foundation for further research on the anti-obesity saponins in PG and their anti-obesity mechanism and provided a new direction for the development of functional plant-derived food. This research studied the molecular mechanism of PG saponins combating obesity through various signaling pathways, and prosapogenin D can be used to develop as a new potential anti-obesity drug.

Differential Mitochondrial Genome Expression of Three Sympatric Lizards in Response to Low-Temperature Stress

Ecological factors related to climate extremes have a significant influence on the adaptability of organisms, especially for ectotherms such as reptiles that are sensitive to temperature change. Climate extremes can seriously affect the survival and internal physiology of lizards, sometimes even resulting in the loss of local populations or even complete extinction. Indeed, studies have shown that the expression levels of the nuclear genes and mitochondrial genomes of reptiles change under low-temperature stress. At present, the temperature adaptability of reptiles has rarely been studied at the mitochondrial genome level. In the present study, the mitochondrial genomes of three species of lizards, Calotes versicolor, Ateuchosaurus chinensis, and Hemidactylus bowringii, which live in regions of sympatry, were sequenced. We used RT-qPCR to explore the level of mitochondrial gene expression under low-temperature stress, as compared to a control temperature. Among the 13 protein-coding genes (PCGs), the steady-state transcript levels of ND4L, ND1, ATP6, and COII were reduced to levels of 0.61 ± 0.06, 0.50 ± 0.08, 0.44 ± 0.16, and 0.41 ± 0.09 in C. versicolor, respectively, compared with controls. The transcript levels of the ND3 and ND6 genes fell to levels of just 0.72 ± 0.05 and 0.67 ± 0.05 in H. bowringii, compared with controls. However, the transcript levels of ND3, ND5, ND6, ATP6, ATP8, Cytb, and COIII genes increased to 1.97 ± 0.15, 2.94 ± 0.43, 1.66 ± 0.07, 1.59 ± 0.17, 1.46 ± 0.04, 1.70 ± 0.16, and 1.83 ± 0.07 in A. chinensis. Therefore, the differences in mitochondrial gene expression may be internally related to the adaptative strategy of the three species under low-temperature stress, indicating that low-temperature environments have a greater impact on A. chinensis, with a small distribution area. In extreme environments, the regulatory trend of mitochondrial gene expression in reptiles is associated with their ability to adapt to extreme climates, which means differential mitochondrial genome expression can be used to explore the response of different lizards in the same region to low temperatures. Our experiment aims to provide one new research method to evaluate the potential extinction of reptile species in warm winter climates.

BigBind: Learning from Nonstructural Data for Structure-Based Virtual Screening

Deep learning methods that predict protein-ligand binding have recently been used for structure-based virtual screening. Many such models have been trained using protein-ligand complexes with known crystal structures and activities from the PDBBind data set. However, because PDBbind only includes 20K complexes, models typically fail to generalize to new targets, and model performance is on par with models trained with only ligand information. Conversely, the ChEMBL database contains a wealth of chemical activity information but includes no information about binding poses. We introduce BigBind, a data set that maps ChEMBL activity data to proteins from the CrossDocked data set. BigBind comprises 583 K ligand activities and includes 3D structures of the protein binding pockets. Additionally, we augmented the data by adding an equal number of putative inactives for each target. Using this data, we developed Banana (basic neural network for binding affinity), a neural network-based model to classify active from inactive compounds, defined by a 10 μM cutoff. Our model achieved an AUC of 0.72 on BigBind's test set, while a ligand-only model achieved an AUC of 0.59. Furthermore, Banana achieved competitive performance on the LIT-PCBA benchmark (median EF1% 1.81) while running 16,000 times faster than molecular docking with Gnina. We suggest that Banana, as well as other models trained on this data set, will significantly improve the outcomes of prospective virtual screening tasks.

Swertiamarin mitigates nephropathy in high-fat diet/streptozotocin-induced diabetic rats by inhibiting the formation of advanced glycation end products

CONTEXT

The molecular mechanism by which Swertiamarin (SM) prevents advanced glycation end products (AGEs) induced diabetic nephropathy (DN) has never been explored.

OBJECTIVE

To evaluate the effect of SM in preventing the progression of DN in high fat diet-streptozotocin-induced diabetic rats.

MATERIALS AND METHODS

After 1 week of acclimatisation, the rats were divided randomly into five groups as follows: (1) Control group, which received normal chow diet; (2) High-fat diet (HFD) group which was fed diet comprising of 58.7% fat, 27.5% carbohydrate and 14.4% protein); (3) Aminoguanidine (AG) group which received HFD + 100 mg/k.b.w.AG (intraperitoneal); (4) Metformin (Met) group which received HFD + 70 mg/k.b.w. the oral dose of Met and (5) SM group which was supplemented orally with 50 mg/k.b.w.SM along with HFD. After 12 weeks all HFD fed animals were given a single 35 mg/k.b.w. dose of streptozotocin with continuous HFD feeding for additional 18 weeks. Later, various biochemical assays, urine analyses, histopathological analysis of kidneys, levels of AGEs, expression of various makers, and in-silico analysis were performed.

RESULTS

The diabetic group demonstrated oxidative stress, increased levels of AGEs, decreased renal function, fibrosis in the renal tissue, higher expression of the receptor for advanced glycation end products (RAGE), which were ameliorated in the SM treated group. In-silico analysis suggests that SM can prevent the binding of AGEs with RAGE.

CONCLUSIONS

SM ameliorated DN by inhibiting the oxidative stress induced by AGEs.HighlightsSM reduces the levels of hyperglycaemia-induced advanced glycation end products in serum and renal tissue.SM prevents renal fibrosis by inhibiting the EMT in the kidney tissue.The in-silico analysis proves that SM can inhibit the binding of various AGEs with RAGE, thereby inhibiting the AGE-RAGE axis.

Proteomic scrutiny of nasal microbiomes: implications for the clinic

INTRODUCTION

The nasal cavity is the initial site of the human respiratory tract and is one of the habitats where microorganisms colonize. The findings from a growing number of studies have shown that the nasal microbiome is an important factor for human disease and health. 16S rRNA sequencing and metagenomic next-generation sequencing (mNGS) are the most commonly used means of microbiome evaluation. Among them, 16S rRNA sequencing is the primary method used in previous studies of nasal microbiomes. However, neither 16S rRNA sequencing nor mNGS can be used to analyze the genes specifically expressed by nasal microorganisms and their functions. This problem can be addressed by proteomic analysis of the nasal microbiome.

AREAS COVERED

In this review, we summarize current advances in research on the nasal microbiome, introduce the methods for proteomic evaluation of the nasal microbiome, and focus on the important roles of proteomic evaluation of the nasal microbiome in the diagnosis and treatment of related diseases.

EXPERT OPINION

The detection method for microbiome-expressed proteins is known as metaproteomics. Metaproteomic analysis can help us dig deeper into the nasal microbiomes and provide new targets and ideas for clinical diagnosis and treatment of many nasal dysbiosis-related diseases.

Existence and importance of Na+K+-ATPase in the plasma membrane of boar spermatozoa

Sodium-potassium-ATPase (Na+K+-ATPase), a target to treat congestive heart failure, is the only known receptor for cardiac glycosides implicated in intracellular signaling and additionally functions enzymatically in ion transport. Spermatozoa need transmembrane ion transport and signaling to fertilize, and Na+K+-ATPase is identified here for the first time in boar spermatozoa. Head plasma membrane (HPM) isolated from boar spermatozoa was confirmed pure by marker enzymes acid and alkaline phosphatase (218 ± 23% and 245 ± 38% enrichment, respectively, versus whole spermatozoa). Western immunoblotting detected α and β subunits (isoforms α1, α3, β1, β2, and β3) in different concentrations in whole spermatozoa and HPM. Immunofluorescence of intact sperm only detected α3 on the post-equatorial exterior membrane; methanol-permeabilized sperm also had α3 post-equatorially and other isoforms on the acrosomal ridge and cap. Mass spectrometry confirmed the presence of all isoforms in HPM. Incubating boar sperm in capacitating media to induce the physiological changes preceding fertilization significantly increased the percentage of capacitated sperm compared to 0 h control (33.0 ± 2.6% vs. 19.2 ± 2.6% capacitated sperm, respectively; p = 0.014) and altered the β2 immunofluorescence pattern. These results demonstrate the presence of Na+K+-ATPase in boar sperm HPM and that it changes during capacitation.

Machine learning for microbiologists

Machine learning is increasingly important in microbiology where it is used for tasks such as predicting antibiotic resistance and associating human microbiome features with complex host diseases. The applications in microbiology are quickly expanding and the machine learning tools frequently used in basic and clinical research range from classification and regression to clustering and dimensionality reduction. In this Review, we examine the main machine learning concepts, tasks and applications that are relevant for experimental and clinical microbiologists. We provide the minimal toolbox for a microbiologist to be able to understand, interpret and use machine learning in their experimental and translational activities.

Multi-omics analysis provides new insights into the changes of important nutrients and fructose metabolism in loquat bud sport mutant

Bud sport is a common and stable somatic variation in perennial fruit trees, and often leads to significant modification of fruit traits and affects the breeding value. To investigate the impact of bud sport on the main metabolites in the fruit of white-fleshed loquat, we conducted a multi-omics analysis of loquat fruits at different developmental stages of a white-fleshed bud sport mutant of Dongting loquat (TBW) and its wild type (TBY). The findings from the detection of main fruit quality indices and metabolites suggested that bud sport resulted in a reduction in the accumulation of carotenoids, fructose, titratable acid and terpenoids at the mature stage of TBW, while leading to the accumulation of flavonoids, phenolic acids, amino acids and lipids. The comparably low content of titratable acid further enhances the balanced and pleasent taste profile of TBW. Expression patterns of differentially expressed genes involved in fructose metabolism exhibited a significant increase in the expression level of S6PDH (EVM0006243, EVM0044405) prior to fruit maturation. The comparison of protein sequences and promoter region of S6PDH between TBY and TBW revealed no structural variations that would impact gene function or expression, indicating that transcription factors may be responsible for the rapid up-regulation of S6PDH before maturation. Furthermore, correlation analysis helped to construct a comprehensive regulatory network of fructose metabolism in loquat, including 23 transcription factors, six structural genes, and nine saccharides. Based on the regulatory network and existing studies, it could be inferred that transcription factors such as ERF, NAC, MYB, GRAS, and bZIP may promote fructose accumulation in loquat flesh by positively regulating S6PDH. These findings improve our understanding of the nutritional value and breeding potential of white-fleshed loquat bud sport mutant, as well as serve as a foundation for exploring the genes and transcription factors that regulate fructose metabolism in loquat.

Congenital fibrinogen disorders: a retrospective clinical and genetic analysis of the Prospective Rare Bleeding Disorders Database

ABSTRACT

Congenital fibrinogen deficiency (CFD) is a rare bleeding disorder caused by mutations in FGA, FGB, and FGG. We sought to comprehensively characterize patients with CFD using PRO-RBDD (Prospective Rare Bleeding Disorders Database). Clinical phenotypes, laboratory, and genetic features were investigated using retrospective data from the PRO-RBDD. Patients were classified from asymptomatic to grade 3 based on their bleeding severity. In addition, FGA, FGB, and FGG were sequenced to find causative variants. A total of 166 CFD cases from 16 countries were included, of whom 123 (30 afibrinogenemia, 33 hypofibrinogenemia, 55 dysfibrinogenemia, and 5 hypodysfibrinogenemia) were well characterized. Considering the previously established factor activity and antigen level thresholds, bleeding severity was correctly identified in 58% of the cases. The rates of thrombotic events among afibrinogenemic and hypofibrinogenemic patients were relatively similar (11% and 10%, respectively) and surprisingly higher than in dysfibrinogenemic cases. The rate of spontaneous abortions among 68 pregnancies was 31%, including 86% in dysfibrinogenemic women and 14% with hypofibrinogenemia. Eighty-six patients received treatment (69 on-demand and/or 17 on prophylaxis), with fibrinogen concentrates being the most frequently used product. Genetic analysis was available for 91 cases and 41 distinct variants were identified. Hotspot variants (FGG, p.Arg301Cys/His and FGA, p.Arg35Cys/His) were present in 51% of dysfibrinogenemia. Obstetric complications were commonly observed in dysfibrinogenemia. This large multicenter study provided a comprehensive insight into the clinical, laboratory, and genetic history of patients with CFDs. We conclude that bleeding severity grades were in agreement with the established factor activity threshold in nearly half of the cases with quantitative defects.

An exploration of mechanisms underlying Desemzia incerta colonization resistance to methicillin-resistant Staphylococcus aureus on the skin

Colonization of human skin and nares by methicillin-resistant Staphylococcus aureus (MRSA) leads to the community spread of MRSA. This spread is exacerbated by the transfer of MRSA between humans and livestock, particularly swine. Here, we capitalized on the shared features between human and porcine skin, including shared MRSA colonization, to study novel bacterial mediators of MRSA colonization resistance. We focused on the poorly studied bacterial species Desemzia incerta, which we found to exert antimicrobial activity through a secreted product and exhibited colonization resistance against MRSA in an in vivo murine skin model. Using parallel genomic and biochemical investigation, we discovered that D. incerta secretes an antimicrobial protein. Sequential protein purification and proteomics analysis identified 24 candidate inhibitory proteins, including a promising peptidoglycan hydrolase candidate. Aided by transcriptional analysis of D. incerta and MRSA cocultures, we found that exposure to D. incerta leads to decreased MRSA biofilm production. These results emphasize the value of exploring microbial communities across a spectrum of hosts, which can lead to novel therapeutic agents as well as an increased understanding of microbial competition.IMPORTANCEMethicillin-resistant Staphylococcus aureus (MRSA) causes a significant healthcare burden and can be spread to the human population via livestock transmission. Members of the skin microbiome can prevent MRSA colonization via a poorly understood phenomenon known as colonization resistance. Here, we studied the colonization resistance of S. aureus by bacterial inhibitors previously identified from a porcine skin model. We identify a pig skin commensal, Desemzia incerta, that reduced MRSA colonization in a murine model. We employ a combination of genomic, proteomic, and transcriptomic analyses to explore the mechanisms of inhibition between D. incerta and S. aureus. We identify 24 candidate antimicrobial proteins secreted by D. incerta that could be responsible for its antimicrobial activity. We also find that exposure to D. incerta leads to decreased S. aureus biofilm formation. These findings show that the livestock transmission of MRSA can be exploited to uncover novel mechanisms of MRSA colonization resistance.

Exploring host epigenetic enzymes as targeted therapies for visceral leishmaniasis: in silico design and in vitro efficacy of KDM6B and ASH1L inhibitors

In order to combat various infectious diseases, the utilization of host-directed therapies as an alternative to chemotherapy has gained a lot of attention in the recent past, since it bypasses the existing limitations of conventional therapies. The use of host epigenetic enzymes like histone lysine methyltransferases and lysine demethylases as potential drug targets has successfully been employed for controlling various inflammatory diseases like rheumatoid arthritis and acute leukemia. In our earlier study, we have already shown that the functional knockdown of KDM6B and ASH1L in the experimental model of visceral leishmaniasis has resulted in a significant reduction of organ parasite burden. Herein, we performed a high throughput virtual screening against KDM6B and ASH1L using > 53,000 compounds that were obtained from the Maybridge library and PubChem Database, followed by molecular docking to evaluate their docking score/Glide Gscore. Based on their docking scores, the selected inhibitors were later assessed for their in vitro anti-leishmanial efficacy. Out of all inhibitors designed against KDM6B and ASH1L, HTS09796, GSK-J4 and AS-99 particularly showed promising in vitro activity with IC50 < 5 µM against both extracellular promastigote and intracellular amastigote forms of L. donovani. In vitro drug interaction studies of these inhibitors further demonstrated their synergistic interaction with amphotericin-B and miltefosine. However, GSK-J4 makes an exception by displaying an in different mode of interaction with miltefosine. Collectively, our in silico and in vitro studies acted as a platform to identify the applicability of these inhibitors targeted against KDM6B and ASH1L for anti-leishmanial therapy.

An integrated network pharmacology approach to discover therapeutic mechanisms of Commiphora wightii for the treatment of Bell's palsy

Bell's palsy (BP) can result in facial paralysis. Inflammation or injury to the cranial nerves that regulate the facial muscles is primarily responsible for that disease. Commiphora wightii remains recognized as a cure for a few human ailments. This study focused on therapeutic phenomena of C. wightii for the treatment of Bell's palsy, utilizing the network drug discovery and molecular docking techniques. Active biological constituents of C. wightii were retrieved from literature and independent databases. Potential therapeutic targets (431) of 13 bioactive phytochemicals were fetched via SwissTargetPrediction tool. Putative intersecting targets (855) of Bell's palsy were computed through the DisGeNET and GeneCards datasets. Subsequently, by the analysis of potential shared targets (87) of C. wightii and Bell's palsy, a Venn diagram was drawn. DAVID database was used to evaluate gene functional annotations and enriched pathways that are involved in Bell's palsy. STRING database was used for generating the protein-protein relationship complex. Visual presentations of the interactions of potential targets to active chemical constituents were done by the Cytoscape. Whereas, the conformational research sorted out 10 key targets through the protein-protein interactions network. Moreover, the capacity of therapeutic ingredients to interact with a target inhibiting Bell's palsy was confirmed by molecular docking, which might ratify the findings of network pharmacology. In the molecular complex of AKT1-cholesterol, a 100-ns simulation unveiled a graceful stability, with a minimal 0.167 Å ligand shift and resilient hydrogen bonds (ASN54 and SER205). The final 20 ns showcased a P1 motif pirouette, gracefully forming aromatic bonds with H165 and W186, underscoring the complex's dynamic finesse. This study evaluated compound-target interactions and their impact on disease-related genes. It revealed that five genes (AKT1, TNF, MAPK3, EGFR and SRC) of C. wightii might be useful therapeutic targets for the treatment of Bell's palsy, as well as helping in lowering down the blood pressure.Communicated by Ramaswamy H. Sarma.

Transcriptomic profiling of Poa pratensis L. under treatment of various phytohormones

Poa pratensis L. (Poaceae) is a valuable grass across the north hemisphere, inhabiting diverse environments with wide altitudinal span, where ubiquitous various kinds of stresses. Phytohormones would be helpful to improve tolerance to abiotic and biotic stresses, but the responses of transcriptome regulation of P. pratensis to exogenous phytohormones application remain unclear. In this study, we explored the alteration of plant physiological responses by the application of phytohormones. Aiming to achieve this knowledge, we got full-length transcriptome data 42.76 Gb, of which 74.9% of transcripts were completed. Then used 27 samples representing four treatments conducted at two time points (1 h and 6 h after application) to generate RNA-seq data. 371 and 907 common DEGs were identified in response to four phytohormones application, respectively, these DEGs were involved in "plant hormone signal transduction", "carbon metabolism" and "plant-pathogen interaction". Finally, P. pratensis basic research can gain valuable information regarding the responses to exogenous application of phytohormones in physiological indicators and transcriptional regulations in order to facilitate the development of new cultivars.

Prediction of virus-host interactions and identification of hot spot residues of DENV-2 and SH3 domain interactions

Dengue virus, particularly serotype 2 (DENV-2), poses a significant global health threat, and understanding the molecular basis of its interactions with host cell proteins is imperative for developing targeted therapeutic strategies. This study elucidated the interactions between proline-enriched motifs and Src homology 3 (SH3) domain. The SH3 domain is pivotal in mediating protein-protein interactions, particularly by recognizing and binding to proline-rich regions in partner proteins. Through a computational pipeline, we analyzed the interactions and binding modes of proline-enriched motifs with SH3 domains, identified new potential DENV-2 interactions with the SH3 domain, and revealed potential hot spot residues, underscoring their significance in the viral life cycle. This comprehensive analysis provides crucial insights into the molecular basis of DENV-2 infection, highlighting conserved and serotype-specific interactions. The identified hot spot residues offer potential targets for therapeutic intervention, laying the foundation for developing antiviral strategies against Dengue virus infection. These findings contribute to the broader understanding of viral-host interactions and provide a roadmap for future research on Dengue virus pathogenesis and treatment.

Evaluation of novel biomarkers for early pregnancy outcome prediction†

OBJECTIVE

To assess performance and discriminatory capacity of commercially available enzyme-linked immunosorbent assays of biomarkers for predicting first trimester pregnancy outcome in a multi-center cohort.

DESIGN

In a case-control study at three academic centers of women with pain and bleeding in early pregnancy, enzyme-linked immunosorbent assays of biomarkers were screened for assay performance. Performance was assessed via functional sensitivity, assay reportable range, recovery/linearity, and intra-assay precision (%Coefficient of Variation). Top candidates were analyzed for discriminatory capacity for viability and location among 210 women with tubal ectopic pregnancy, viable intrauterine pregnancy, or miscarriage. Assay discrimination was assessed by visual plots, area under the curve with 95% confidence intervals, and measures of central tendency with two-sample t-tests.

RESULTS

Of 25 biomarkers evaluated, 22 demonstrated good or acceptable assay performance. Transgelin-2, oviductal glycoprotein, and integrin-linked kinase were rejected due to poor performance. The best biomarkers for discrimination of pregnancy location were pregnancy-specific beta-1-glycoprotein 9, pregnancy-specific beta-1-glycoprotein 1, insulin-like growth factor binding protein 1, kisspeptin (KISS1), pregnancy-specific beta-1-glycoprotein 3, and beta parvin (PARVB). The best biomarkers for discrimination of pregnancy viability were pregnancy-specific beta-1-glycoprotein 9, pregnancy-specific beta-1-glycoprotein 3, EH domain-containing protein 3, KISS1, WAP four-disulfide core domain protein 2 (HE4), quiescin sulfhydryl oxidase 2, and pregnancy-specific beta-1-glycoprotein 1.

CONCLUSION

Performance of commercially available enzyme-linked immunosorbent assays was acceptable for a panel of novel biomarkers to predict early pregnancy outcome. Of these, six and seven candidates demonstrated good discriminatory capacity of pregnancy location and viability, respectively, when validated in a distinct external population. Four markers demonstrated good discrimination for both location and viability.

Characterization of PYL gene family and identification of HaPYL genes response to drought and salt stress in sunflower

In the context of global climate change, drought and soil salinity are some of the most devastating abiotic stresses affecting agriculture today. PYL proteins are essential components of abscisic acid (ABA) signaling and play critical roles in responding to abiotic stressors, including drought and salt stress. Although PYL genes have been studied in many species, their roles in responding to abiotic stress are still unclear in the sunflower. In this study, 19 HaPYL genes, distributed on 15 of 17 chromosomes, were identified in the sunflower. Fragment duplication is the main cause of the expansion of PYL genes in the sunflower genome. Based on phylogenetic analysis, HaPYL genes were divided into three subfamilies. Members in the same subfamily share similar protein motifs and gene exon-intron structures, except for the second subfamily. Tissue expression patterns suggested that HaPYLs serve different functions when responding to developmental and environmental signals in the sunflower. Exogenous ABA treatment showed that most HaPYLs respond to an increase in the ABA level. Among these HaPYLs, HaPYL2a, HaPYL4d, HaPYL4g, HaPYL8a, HaPYL8b, HaPYL8c, HaPYL9b, and HaPYL9c were up-regulated with PEG6000 treatment and NaCl treatment. This indicates that they may play a role in resisting drought and salt stress in the sunflower by mediating ABA signaling. Our findings provide some clues to further explore the functions of PYL genes in the sunflower, especially with regards to drought and salt stress resistance.

An integrated transcriptome-microbiome host relationship associated with paraben toxicity in the brackish water flea Diaphanosoma celebensis

Parabens, a group of alkyl esters of p-hydroxybenzoic acid, have been found in aquatic systems in particular, leading to concerns about their potential impact on ecosystems. This study investigated the effects of three commonly used parabens, methylparaben (MeP), ethylparaben (EtP), and propylparaben (PrP), on the brackish water flea Diaphanosoma celebensis. The results showed that PrP had the most adverse impact on survival rates, followed by EtP and MeP, while MeP and EtP induced significant adverse effects on reproductive performance. A transcriptome analysis revealed significant differential gene expression patterns in response to paraben exposure, with MeP associated with the most significant effects. MeP and EtP exposure produced greater disruption in the microbiota of D. celebensis than did PrP compared with control groups, and we identified eight key microbiota, including Ruegeria and Roseovarius. Correlation analysis between transcriptome and microbiome data revealed key interactions between specific microbiota and host gene expression. Certain microbial taxa were associated with specific genes (e.g. cuticle related genes) and toxicological pathways, shedding light on the complex molecular response and in vivo toxicity effects of parabens. These findings contribute to a deeper understanding of the molecular mechanisms underlying paraben toxicity and highlight the importance of considering the ecological impact of chemical contaminants in aquatic ecosystems.

Data integration through canonical correlation analysis and its application to OMICs research

The subject of the paper is a review of multidimensional data analysis methods, which is the canonical analysis with its various variants and its use in omics data research. The dynamic development of high-throughput methods, and with them the availability of large and constantly growing data resources, forces the development of new analytical approaches that allow the review of the analyzed processes, taking into account data from various levels of the organization of living organisms. The multidimensional perspective allows for the assessment of the analyzed phenomenon in a more realistic way, as it generally takes into account much more data (including OMICs data). Without omitting the complexity of an organism, the method simplifies the multidimensional view, finally giving the result so that the researcher can draw practical conclusions. This is particularly important in medical sciences, where the study of pathological processes is usually aimed at developing treatment regimens. One of the primary methods for studying biomedical processes in a multidimensional approach is the canonical correlation analysis (CCA) with various variants. The use of CCA unique methodologies for simultaneous analysis of multiset biomolecular data opens up new avenues for studying previously undiscovered processes and interdependencies such as e.g. in the tumor microenvironment (TME) connected to intercellular communication. Because of the huge and still untapped potential of canonical correlation, in this review available implementations of CCA techniques are presented. In particular, the possibility of using the technique of canonical correlation analysis for OMICs data is emphasized.

Serum CCDC25 Levels as a Potential Marker for Metabolic Syndrome

BACKGROUND/AIM

Metabolic syndrome (MetS) stands as a significant risk for developing various severe health problems. Therefore, the discovery of biomarkers capable of predicting the progression of metabolic conditions is crucial for improving overall health outcomes. Recently, we reported that coiled-coil domain containing 25 (CCDC25) might be associated with key proteins involved in metabolic pathways, by bioinformatics analysis. Thus, we assumed that serum CCDC25 levels might have an association with MetS status.

PATIENTS AND METHODS

In this study, based on the modified National Cholesterol Education Program-Adult Treatment Panel III (modified NCEP-ATP III) criteria, the participants who had three or more of abnormal criteria were defined as MetS, and those who had 1 or 2 abnormal criteria as pre-MetS groups; those who had no abnormal criteria were classified as the healthy control (HC) group. Serum CCDC25 levels were measured using the dot blot assay.

RESULTS

The results showed that serum CCDC25 levels of the MetS group (0.072±0.026 ng/μl) were significantly higher (p<0.001) than that of pre-MetS (0.031±0.011 ng/μl) or HC groups (0.018±0.007 ng/μl). We can discern a consistent trend indicating that serum CCDC25 level is well correlated with the number of abnormal criteria of MetS of each participant. Although serum CCDC25 levels correlated with the distribution of all 5 MetS criteria, the highest correlation was seen in serum CCDC25 levels and triglyceride (TG) levels, with r=0.563, followed by systolic blood pressure (SBP) levels (r=0.557) and high-density lipoprotein-cholesterol (HDL-C) levels (r=-0.545).

CONCLUSION

CCDC25 showed correlations with all MetS parameters, particularly with TG, SBP, and HDL-C. This prompts speculation that heightened CCDC25 levels may indicate the development and/or progression of those MetS-associated diseases.

Multi-ancestry tandem repeat association study of hair colour using exome-wide sequencing

Hair colour variation is influenced by hundreds of positions across the human genome but this genetic contribution has only been narrowly explored. Genome-wide association studies identified single nucleotide polymorphisms (SNPs) influencing hair colour but the biology underlying these associations is challenging to interpret. We report 16 tandem repeats (TRs) with effects on different models of hair colour plus two TRs associated with hair colour in diverse ancestry groups. Several of these TRs expand or contract amino acid coding regions of their localized protein such that structure, and by extension function, may be altered. We also demonstrate that independent of SNP variation, these TRs can be used to great an additive polygenic score that predicts darker hair colour. This work adds to the growing body of evidence regarding TR influence on human traits with relatively large and independent effects relative to surrounding SNP variation.

Understanding a point mutation signature D54K in the caspase activation recruitment domain of NOD1 capitulating concerted immunity via atomistic simulation

Point mutation D54K in the human N-terminal caspase recruitment domain (CARD) of nucleotide-binding oligomerization domain -1 (NOD1) abrogates an imperative downstream interaction with receptor-interacting protein kinase (RIPK2) that entails combating bacterial infections and inflammatory dysfunction. Here, we addressed the molecular details concerning conformational changes and interaction patterns (monomeric-dimeric states) of D54K by signature-based molecular dynamics simulation. Initially, the sequence analysis prioritized D54K as a pathogenic mutation, among other variants, based on a sequence signature. Since the mutation is highly conserved, we derived the distant ortholog to predict the sequence and structural similarity between native and mutant. This analysis showed the utility of 33 communal core residues associated with structural-functional preservation and variations, concurrently served to infer the cryptic hotspots Cys39, Glu53, Asp54, Glu56, Ile57, Leu74, and Lys78 determining the inter helical fold forming homodimers for putative receptor interaction. Subsequently, the atomistic simulations with free energy (MM/PB(GB)SA) calculations predicted structural alteration that takes place in the N-terminal mutant CARD where coils changed to helices (45 α3- L4-α4-L6- α683) in contrast to native (45T2-L4-α4-L6-T483). Likewise, the C-terminal helices 93T1-α7105 connected to the loops distorted compared to native 93α6-L7105 may result in conformational misfolding that promotes functional regulation and activation. These structural perturbations of D54K possibly destabilize the flexible adaptation of critical homotypic NOD1CARD-CARDRIPK2 interactions (α4Asp42-Arg488α5 and α6Phe86-Lys471α4) is consistent with earlier experimental reports. Altogether, our findings unveil the conformational plasticity of mutation-dependent immunomodulatory response and may aid in functional validation exploring clinical investigation on CARD-regulated immunotherapies to prevent systemic infection and inflammation.Communicated by Ramaswamy H. Sarma.

Comparative transcriptome and metabolome analysis revealed diversity in the response of resistant and susceptible rose (Rosa hybrida) varieties to Marssonina rosae

Rose black spot disease caused by Marssonina rosae is among the most destructive diseases that affects the outdoor cultivation and production of roses; however, the molecular mechanisms underlying the defensive response of roses to M. rosae have not been clarified. To investigate the diversity of response to M. rosae in resistant and susceptible rose varieties, we performed transcriptome and metabolome analyses of resistant (KT) and susceptible (FG) rose varieties and identified differentially expressed genes (DEGs) and differentially accumulated metabolites (DAMs) in response to M. rosae at different time points. In response to M. rosae, DEGs and DAMs were mainly upregulated compared to the control and transcription factors were concentrated in the WRKY and AP2/ERF families. Gene Ontology analysis showed that the DEGs of FG were mainly enriched in biological processes, such as the abscisic acid-activated signaling pathway, cell wall, and defense response, whereas the DEGs of KT were mainly enriched in Golgi-mediated vesicle transport processes. Kyoto Encyclopedia of Genes and Genomes analysis showed that the DEGs of both varieties were concentrated in plant-pathogen interactions, plant hormone signal transduction, and mitogen-activated protein kinase signaling pathways, with the greatest number of DEGs associated with brassinosteroid (BR) in the plant hormone signal transduction pathway. The reliability of the transcriptome results was verified by qRT-PCR. DAMs of KT were significantly enriched in the butanoate metabolism pathway, whereas DAMs of FG were significantly enriched in BR biosynthesis, glucosinolate biosynthesis, and tryptophan metabolism. Moreover, the DAMs in these pathways were significantly positively correlated with the DEGs. Disease symptoms were aggravated when FG leaves were inoculated with M. rosae after 24-epibrassinolide treatment, indicating that the response of FG to M. rosae involves the BR signaling pathway. Our results provide new insights into the molecular mechanisms underlying rose response to M. rosae and lay a theoretical foundation for formulating rose black spot prevention and control strategies and cultivating resistant varieties.