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.

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.

The impact of mechanical stress on anatomy, morphology, and gene expression in Urtica dioica L

MAIN CONCLUSION

Mechanical stress induces distinct anatomical, molecular, and morphological changes in Urtica dioica, affecting trichome development, gene expression, and leaf morphology under controlled conditions The experiments were performed on common nettle, a widely known plant characterized by high variability of leaf morphology and responsiveness to mechanical touch. A specially constructed experimental device was used to study the impact of mechanical stress on Urtica dioica plants under strictly controlled parameters of the mechanical stimulus (touching) and environment in the growth chamber. The general anatomical structure of the plants that were touched was similar to that of control plants, but the shape of the internodes' cross section was different. Stress-treated plants showed a distinct four-ribbed structure. However, as the internodes progressed, the shape gradually approached a rectangular form. The epidermis of control plants included stinging, glandular and simple setulose trichomes, but plants that were touched had no stinging trichomes, and setulose trichomes accumulated more callose. Cell wall lignification occurred in the older internodes of the control plants compared to stress-treated ones. Gene analysis revealed upregulation of the expression of the UdTCH1 gene in touched plants compared to control plants. Conversely, the expression of UdERF4 and UdTCH4 was downregulated in stressed plants. These data indicate that the nettle's response to mechanical stress reaches the level of regulatory networks of gene expression. Image analysis revealed reduced leaf area, increased asymmetry and altered contours in touched leaves, especially in advanced growth stages, compared to control plants. Our results indicate that mechanical stress triggers various anatomical, molecular, and morphological changes in nettle; however, further interdisciplinary research is needed to better understand the underlying physiological mechanisms.

Integration of chromosome locations and functional aspects of enhancers and topologically associating domains in knowledge graphs enables versatile queries about gene regulation

Knowledge about transcription factor binding and regulation, target genes, cis-regulatory modules and topologically associating domains is not only defined by functional associations like biological processes or diseases but also has a determinative genome location aspect. Here, we exploit these location and functional aspects together to develop new strategies to enable advanced data querying. Many databases have been developed to provide information about enhancers, but a schema that allows the standardized representation of data, securing interoperability between resources, has been lacking. In this work, we use knowledge graphs for the standardized representation of enhancers and topologically associating domains, together with data about their target genes, transcription factors, location on the human genome, and functional data about diseases and gene ontology annotations. We used this schema to integrate twenty-five enhancer datasets and two domain datasets, creating the most powerful integrative resource in this field to date. The knowledge graphs have been implemented using the Resource Description Framework and integrated within the open-access BioGateway knowledge network, generating a resource that contains an interoperable set of knowledge graphs (enhancers, TADs, genes, proteins, diseases, GO terms, and interactions between domains). We show how advanced queries, which combine functional and location restrictions, can be used to develop new hypotheses about functional aspects of gene expression regulation.

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.

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.

Quality assessment and Q-markers discovery in Citri Sarcodactylis Fructus by integrating serum pharmacochemistry and network pharmacology

INTRODUCTION

Citri Sarcodactylis Fructus (CSF), a common fruit and traditional Chinese medicine (TCM), has been hindered in its further development and research owing to the lack of comprehensive and specific quality evaluation standards.

OBJECTIVE

This study aimed to establish clear TCM quality standards related to the therapeutic mechanisms of CSF and to provide a basis for subsequent research and development.

METHODS

Ultra-high performance liquid chromatography coupled with hybrid quadrupole-orbitrap high-resolution mass spectrometry (UPLC-Q-orbitrap HRMS) technology was used to comprehensively identify CSF components and explore their absorbance levels in rat serum. Network pharmacology research methods were employed to investigate the potential mechanisms of action of the identified components in the treatment of major clinical diseases. Subsequently, a combination of HPLC chromatographic fingerprinting for qualitative analysis and multi-index content determination was used to evaluate the detectability of the identified quality markers (Q-markers).

RESULTS

Twenty-six prototype components were tentatively characterized in rat serum. Network pharmacology analysis showed six effective components, namely 7-hydroxycoumarin, isoscopoletin, diosmin, hesperidin, 5,7-dimethoxycoumarin, and bergapten, which played important roles in the treatment of chronic gastritis, functional dyspepsia, peptic ulcer, and depression and were preliminarily identified as Q-markers. The results of content determination in 15 batches of CSF indicated significant differences in the content of medicinal materials from different origins. However, compared with the preliminarily determined Q-markers, all six components could be measured and were determined as Q-markers of CSF.

CONCLUSION

The chemical Q-markers obtained in this study could be used for effective quality control of CSF.

Comparative Phenotypic and Transcriptomic Analyses Provide Novel Insights into the Molecular Mechanism of Seed Germination in Response to Low Temperature Stress in Alfalfa

Low temperature is the most common abiotic factor that usually occurs during the seed germination of alfalfa (Medicago sativa L.). However, the potential regulatory mechanisms involved in alfalfa seed germination under low temperature stress are still ambiguous. Therefore, to determine the relevant key genes and pathways, the phenotypic and transcriptomic analyses of low-temperature sensitive (Instict) and low-temperature tolerant (Sardi10) alfalfa were conducted at 6 and 15 h of seed germination under normal (20 °C) and low (10 °C) temperature conditions. Germination phenotypic results showed that Sardi10 had the strongest germination ability under low temperatures, which was manifested by the higher germination-related indicators. Further transcriptome analysis indicated that differentially expressed genes were mainly enriched in galactose metabolism and carbon metabolism pathways, which were the most commonly enriched in two alfalfa genotypes. Additionally, fatty acid metabolism and glutathione metabolism pathways were preferably enriched in Sardi10 alfalfa. The Weighted Gene Co-Expression Network Analysis (WGCNA) suggested that genes were closely related to galactose metabolism, fatty acid metabolism, and glutathione metabolism in Sardi10 alfalfa at the module with the highest correlation (6 h of germination under low temperature). Finally, qRT-PCR analysis further validated the related genes involved in the above pathways, which might play crucial roles in regulating seed germination of alfalfa under low temperature conditions. These findings provide new insights into the molecular mechanisms of seed germination underlying the low temperature stress in alfalfa.

Drug-target interaction prediction using knowledge graph embedding

The prediction of drug-target interactions (DTIs) is a critical phase in the sustainable drug development process, especially when the research focus is to capitalize on the repositioning of existing drugs. Computational approaches to predicting DTIs can provide important insights into drug mechanisms of action. However, current methods for predicting DTIs based on the structural information of the knowledge graph may suffer from the sparseness and incompleteness of the knowledge graph and neglect the latent type information of the knowledge graph. In this paper, we propose TTModel, a knowledge graph embedding model for DTI prediction. By exploiting biomedical text and type information, TTModel can learn latent text semantics and type information to improve the performance of representation learning. Comprehensive experiments on two public datasets demonstrate that our model outperforms the state-of-the-art methods significantly on the task of DTI prediction.

Autophagic signaling promotes systems-wide remodeling in skeletal muscle upon oncometabolic stress by D2-HG

OBJECTIVES

Cachexia is a metabolic disorder and comorbidity with cancer and heart failure. The syndrome impacts more than thirty million people worldwide, accounting for 20% of all cancer deaths. In acute myeloid leukemia, somatic mutations of the metabolic enzyme isocitrate dehydrogenase 1 and 2 cause the production of the oncometabolite D2-hydroxyglutarate (D2-HG). Increased production of D2-HG is associated with heart and skeletal muscle atrophy, but the mechanistic links between metabolic and proteomic remodeling remain poorly understood. Therefore, we assessed how oncometabolic stress by D2-HG activates autophagy and drives skeletal muscle loss.

METHODS

We quantified genomic, metabolomic, and proteomic changes in cultured skeletal muscle cells and mouse models of IDH-mutant leukemia using RNA sequencing, mass spectrometry, and computational modeling.

RESULTS

D2-HG impairs NADH redox homeostasis in myotubes. Increased NAD+ levels drive activation of nuclear deacetylase Sirt1, which causes deacetylation and activation of LC3, a key regulator of autophagy. Using LC3 mutants, we confirm that deacetylation of LC3 by Sirt1 shifts its distribution from the nucleus into the cytosol, where it can undergo lipidation at pre-autophagic membranes. Sirt1 silencing or p300 overexpression attenuated autophagy activation in myotubes. In vivo, we identified increased muscle atrophy and reduced grip strength in response to D2-HG in male vs. female mice. In male mice, glycolytic intermediates accumulated, and protein expression of oxidative phosphorylation machinery was reduced. In contrast, female animals upregulated the same proteins, attenuating the phenotype in vivo. Network modeling and machine learning algorithms allowed us to identify candidate proteins essential for regulating oncometabolic adaptation in mouse skeletal muscle.

CONCLUSIONS

Our multi-omics approach exposes new metabolic vulnerabilities in response to D2-HG in skeletal muscle and provides a conceptual framework for identifying therapeutic targets in cachexia.

Predicting human and viral protein variants affecting COVID-19 susceptibility and repurposing therapeutics

The COVID-19 disease is an ongoing global health concern. Although vaccination provides some protection, people are still susceptible to re-infection. Ostensibly, certain populations or clinical groups may be more vulnerable. Factors causing these differences are unclear and whilst socioeconomic and cultural differences are likely to be important, human genetic factors could influence susceptibility. Experimental studies indicate SARS-CoV-2 uses innate immune suppression as a strategy to speed-up entry and replication into the host cell. Therefore, it is necessary to understand the impact of variants in immunity-associated human proteins on susceptibility to COVID-19. In this work, we analysed missense coding variants in several SARS-CoV-2 proteins and their human protein interactors that could enhance binding affinity to SARS-CoV-2. We curated a dataset of 19 SARS-CoV-2: human protein 3D-complexes, from the experimentally determined structures in the Protein Data Bank and models built using AlphaFold2-multimer, and analysed the impact of missense variants occurring in the protein-protein interface region. We analysed 468 missense variants from human proteins and 212 variants from SARS-CoV-2 proteins and computationally predicted their impacts on binding affinities for the human viral protein complexes. We predicted a total of 26 affinity-enhancing variants from 13 human proteins implicated in increased binding affinity to SARS-CoV-2. These include key-immunity associated genes (TOMM70, ISG15, IFIH1, IFIT2, RPS3, PALS1, NUP98, AXL, ARF6, TRIMM, TRIM25) as well as important spike receptors (KREMEN1, AXL and ACE2). We report both common (e.g., Y13N in IFIH1) and rare variants in these proteins and discuss their likely structural and functional impact, using information on known and predicted functional sites. Potential mechanisms associated with immune suppression implicated by these variants are discussed. Occurrence of certain predicted affinity-enhancing variants should be monitored as they could lead to increased susceptibility and reduced immune response to SARS-CoV-2 infection in individuals/populations carrying them. Our analyses aid in understanding the potential impact of genetic variation in immunity-associated proteins on COVID-19 susceptibility and help guide drug-repurposing strategies.

Combined transcriptomic and metabolomic analysis revealed that pH changes affected the expression of carbohydrate and ribosome biogenesis-related genes in Aspergillus niger SICU-33

The process of carbohydrate metabolism and genetic information transfer is an important part of the study on the effects of the external environment on microbial growth and development. As one of the most significant environmental parameters, pH has an important effect on mycelial growth. In this study, the effects of environmental pH on the growth and nutrient composition of Aspergillus niger (A. niger) filaments were determined. The pH values of the medium were 5, 7, and 9, respectively, and the molecular mechanism was further investigated by transcriptomics and metabolomics methods. The results showed that pH 5 and 9 significantly inhibited filament growth and polysaccharide accumulation of A. niger. Further, the mycelium biomass of A. niger and the crude polysaccharide content was higher when the medium's pH was 7. The DEGs related to ribosome biogenesis were the most abundant, and the downregulated expression of genes encoding XRN1, RRM, and RIO1 affected protein translation, modification, and carbohydrate metabolism in fungi. The dynamic changes of pargyline and choline were in response to the oxidative metabolism of A. niger SICU-33. The ribophorin_I enzymes and DL-lactate may be important substances related to pH changes during carbohydrate metabolism of A.niger SICU-33. The results of this study provide useful transcriptomic and metabolomic information for further analyzing the bioinformatic characteristics of A. niger and improving the application in ecological agricultural fermentation.

Characterisation of the Cinnamomumparthenoxylon (Jack) Meisn (Lauraceae) transcriptome using Illumina paired-end sequencing and EST-SSR markers development for population genetics

Cinnamomumparthenoxylon is an endemic and endangered species with significant economic and ecological value in Vietnam. A better understanding of the genetic architecture of the species will be useful when planning management and conservation. We aimed to characterize the transcriptome of C.parthenoxylon, develop novel molecular markers, and assess the genetic variability of the species. First, transcriptome sequencing of five trees (C.parthenoxylon) based on root, leaf, and stem tissues was performed for functional annotation analysis and development of novel molecular markers. The transcriptomes of C.parthenoxylon were analyzed via an Illumina HiSeqTM 4000 sequencing system. A total of 27,363,199 bases were generated for C.parthenoxylon. De novo assembly indicated that a total of 160,435 unigenes were generated (average length = 548.954 bp). The 51,691 unigenes were compared against different databases, i.e. COG, GO, KEGG, KOG, Pfam, Swiss-Prot, and NR for functional annotation. Furthermore, a total of 12,849 EST-SSRs were identified. Of the 134 primer pairs, 54 were randomly selected for testing, with 15 successfully amplified across nine populations of C.parthenoxylon. We uncovered medium levels of genetic diversity (PIC = 0.52, Na = 3.29, Ne = 2.18, P = 94.07%, Ho = 0.56 and He = 0.47) within the studied populations. The molecular variance was 10% among populations and low genetic differentiation (Fst = 0.06) indicated low gene flow (Nm = 2.16). A reduction in the population size of C.parthenoxylon was detected using BOTTLENECK (VP population). The structure analysis suggested two optimal genetic clusters related to gene flow among the populations. Analysis of molecular variance (AMOVA) revealed higher genetic variation within populations (90%) than among populations (10%). The UPGMA approach and DAPC divided the nine populations into three main clusters. Our findings revealed a significant fraction of the transcriptome sequences and these newlydeveloped novel EST-SSR markers are a very efficient tool for germplasm evaluation, genetic diversity and molecular marker-assisted selection in C.parthenoxylon. This study provides comprehensive genetic resources for the breeding and conservation of different varieties of C.parthenoxylon.

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.

Sensitive and error-tolerant annotation of protein-coding DNA with BATH

Summary

We present BATH, a tool for highly sensitive annotation of protein-coding DNA based on direct alignment of that DNA to a database of protein sequences or profile hidden Markov models (pHMMs). BATH is built on top of the HMMER3 code base, and simplifies the annotation workflow for pHMM-based translated sequence annotation by providing a straightforward input interface and easy-to-interpret output. BATH also introduces novel frameshift-aware algorithms to detect frameshift-inducing nucleotide insertions and deletions (indels). BATH matches the accuracy of HMMER3 for annotation of sequences containing no errors, and produces superior accuracy to all tested tools for annotation of sequences containing nucleotide indels. These results suggest that BATH should be used when high annotation sensitivity is required, particularly when frameshift errors are expected to interrupt protein-coding regions, as is true with long-read sequencing data and in the context of pseudogenes.

Availability and implementation

The software is available at https://github.com/TravisWheelerLab/BATH.

Identification of high-risk non-synonymous SNPs (nsSNPs) in DNAH1 and DNAH17 genes associated with male infertility: a bioinformatics analysis

Male infertility is a significant reproductive issue affecting a considerable number of couples worldwide. While there are various causes of male infertility, genetic factors play a crucial role in its development. We focused on identifying and analyzing the high-risk nsSNPs in DNAH1 and DNAH17 genes, which encode proteins involved in sperm motility. A total of 20 nsSNPs for DNAH1 and 10 nsSNPs for DNAH17 were analyzed using various bioinformatics tools including SIFT, PolyPhen-2, CADD, PhD-SNPg, VEST-4, and MutPred2. As a result, V1287G, L2071R, R2356W, R3169C, R3229C, E3284K, R4096L, R4133C, and A4174T in DNAH1 gene and C1803Y, C1829Y, R1903C, and L3595P in DNAH17 gene were identified as high-risk nsSNPs. These nsSNPs were predicted to decrease protein stability, and almost all were found in highly conserved amino acid positions. Additionally, 4 nsSNPs were observed to alter post-translational modification status. Furthermore, the interaction network analysis revealed that DNAH1 and DNAH17 interact with DNAH2, DNAH3, DNAH5, DNAH7, DNAH8, DNAI2, DNAL1, CFAP70, DNAI3, DNAI4, ODAD1, and DNAI7, demonstrating the importance of DNAH1 and DNAH17 proteins in the overall functioning of the sperm motility machinery. Taken together, these findings revealed the detrimental effects of identified high-risk nsSNPs on protein structure and function and highlighted their potential relevance to male infertility. Further studies are warranted to validate these findings and to elucidate the underlying mechanisms.

Defining the KRAS- and ERK-dependent transcriptome in KRAS-mutant cancers

How the KRAS oncogene drives cancer growth remains poorly understood. Therefore, we established a systemwide portrait of KRAS- and extracellular signal-regulated kinase (ERK)-dependent gene transcription in KRAS-mutant cancer to delineate the molecular mechanisms of growth and of inhibitor resistance. Unexpectedly, our KRAS-dependent gene signature diverges substantially from the frequently cited Hallmark KRAS signaling gene signature, is driven predominantly through the ERK mitogen-activated protein kinase (MAPK) cascade, and accurately reflects KRAS- and ERK-regulated gene transcription in KRAS-mutant cancer patients. Integration with our ERK-regulated phospho- and total proteome highlights ERK deregulation of the anaphase promoting complex/cyclosome (APC/C) and other components of the cell cycle machinery as key processes that drive pancreatic ductal adenocarcinoma (PDAC) growth. Our findings elucidate mechanistically the critical role of ERK in driving KRAS-mutant tumor growth and in resistance to KRAS-ERK MAPK targeted therapies.

IMMUNOTAR - Integrative prioritization of cell surface targets for cancer immunotherapy

Cancer remains a leading cause of mortality globally. Recent improvements in survival have been facilitated by the development of less toxic immunotherapies; however, identifying targets for immunotherapies remains a challenge in the field. To address this challenge, we developed IMMUNOTAR, a computational tool that systematically prioritizes and identifies candidate immunotherapeutic targets. IMMUNOTAR integrates user-provided RNA-sequencing or proteomics data with quantitative features extracted from publicly available databases based on predefined optimal immunotherapeutic target criteria and quantitatively prioritizes potential surface protein targets. We demonstrate the utility and flexibility of IMMUNOTAR using three distinct datasets, validating its effectiveness in identifying both known and new potential immunotherapeutic targets within the analyzed cancer phenotypes. Overall, IMMUNOTAR enables the compilation of data from multiple sources into a unified platform, allowing users to simultaneously evaluate surface proteins across diverse criteria. By streamlining target identification, IMMUNOTAR empowers researchers to efficiently allocate resources and accelerate immunotherapy development.

Are Next-Generation Pathogenicity Predictors Applicable to Cancer?

Next-generation pathogenicity predictors are designed to identify pathogenic mutations in genetic disorders but are increasingly used to detect driver mutations in cancer. Despite this, their suitability for cancer is not fully established. Here we have assessed the effectiveness of next-generation pathogenicity predictors when applied to cancer by using a comprehensive experimental benchmark of cancer driver and neutral mutations. Our findings indicate that state-of-the-art methods AlphaMissense and VARITY demonstrate commendable performance despite generally underperforming compared to cancer-specific methods. This is notable considering that these methods do not explicitly incorporate cancer-related data in their training and have made concerted efforts to prevent data leakage from the human-curated training and test sets. Nevertheless, it should be mentioned that a significant limitation of using pathogenicity predictors for cancer arises from their inability to detect cancer potential driver mutations specific for a particular cancer type.

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.

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.

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.

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.

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.

A survey of generative AI for de novo drug design: new frontiers in molecule and protein generation

Artificial intelligence (AI)-driven methods can vastly improve the historically costly drug design process, with various generative models already in widespread use. Generative models for de novo drug design, in particular, focus on the creation of novel biological compounds entirely from scratch, representing a promising future direction. Rapid development in the field, combined with the inherent complexity of the drug design process, creates a difficult landscape for new researchers to enter. In this survey, we organize de novo drug design into two overarching themes: small molecule and protein generation. Within each theme, we identify a variety of subtasks and applications, highlighting important datasets, benchmarks, and model architectures and comparing the performance of top models. We take a broad approach to AI-driven drug design, allowing for both micro-level comparisons of various methods within each subtask and macro-level observations across different fields. We discuss parallel challenges and approaches between the two applications and highlight future directions for AI-driven de novo drug design as a whole. An organized repository of all covered sources is available at https://github.com/gersteinlab/GenAI4Drug.

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.

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.

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.

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.

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.

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.