DeTox: a pipeline for the detection of toxins in venomous organisms.

Venomous organisms have independently evolved the ability to produce toxins 101 times during their evolutionary history, resulting in over 200 000 venomous species. Collectively, these species produce millions of toxins, making them a valuable resource for bioprospecting and understanding the evolutionary mechanisms underlying genetic diversification. RNA-seq is the preferred method for characterizing toxin repertoires, but the analysis of the resulting data remains challenging. While early approaches relied on similarity-based mapping to known toxin databases, recent studies have highlighted the importance of structural features for toxin detection. The few existing pipelines lack an integration between these complementary approaches, and tend to be difficult to run for non-experienced users. To address these issues, we developed DeTox, a comprehensive and user-friendly tool for toxin research. It combines fast execution, parallelization and customization of parameters. DeTox was tested on published transcriptomes from gastropod mollusks, cnidarians and snakes, retrieving most putative toxins from the original articles and identifying additional peptides as potential toxins to be confirmed through manual annotation and eventually proteomic analysis. By integrating a structure-based search with similarity-based approaches, DeTox allows the comprehensive characterization of toxin repertoire in poorly-known taxa. The effect of the taxonomic bias in existing databases is minimized in DeTox, as mirrored in the detection of unique and divergent toxins that would have been overlooked by similarity-based methods. DeTox streamlines toxin annotation, providing a valuable tool for efficient identification of venom components that will enhance venom research in neglected taxa.

From tradition to innovation: conventional and deep learning frameworks in genome annotation.

Following the milestone success of the Human Genome Project, the 'Encyclopedia of DNA Elements (ENCODE)' initiative was launched in 2003 to unearth information about the numerous functional elements within the genome. This endeavor coincided with the emergence of numerous novel technologies, accompanied by the provision of vast amounts of whole-genome sequences, high-throughput data such as ChIP-Seq and RNA-Seq. Extracting biologically meaningful information from this massive dataset has become a critical aspect of many recent studies, particularly in annotating and predicting the functions of unknown genes. The core idea behind genome annotation is to identify genes and various functional elements within the genome sequence and infer their biological functions. Traditional wet-lab experimental methods still rely on extensive efforts for functional verification. However, early bioinformatics algorithms and software primarily employed shallow learning techniques; thus, the ability to characterize data and features learning was limited. With the widespread adoption of RNA-Seq technology, scientists from the biological community began to harness the potential of machine learning and deep learning approaches for gene structure prediction and functional annotation. In this context, we reviewed both conventional methods and contemporary deep learning frameworks, and highlighted novel perspectives on the challenges arising during annotation underscoring the dynamic nature of this evolving scientific landscape.

Transposable Element Insertions Are Associated with Batesian Mimicry in the Pantropical Butterfly Hypolimnas misippus.

Hypolimnas misippus is a Batesian mimic of the toxic African Queen butterfly (Danaus chrysippus). Female H. misippus butterflies use two major wing patterning loci (M and A) to imitate three color morphs of D. chrysippus found in different regions of Africa. In this study, we examine the evolution of the M locus and identify it as an example of adaptive atavism. This phenomenon involves a morphological reversion to an ancestral character that results in an adaptive phenotype. We show that H. misippus has re-evolved an ancestral wing pattern present in other Hypolimnas species, repurposing it for Batesian mimicry of a D. chrysippus morph. Using haplotagging, a linked-read sequencing technology, and our new analytical tool, Wrath, we discover two large transposable element insertions located at the M locus and establish that these insertions are present in the dominant allele responsible for producing mimetic phenotype. By conducting a comparative analysis involving additional Hypolimnas species, we demonstrate that the dominant allele is derived. This suggests that, in the derived allele, the transposable elements disrupt a cis-regulatory element, leading to the reversion to an ancestral phenotype that is then utilized for Batesian mimicry of a distinct model, a different morph of D. chrysippus. Our findings present a compelling instance of convergent evolution and adaptive atavism, in which the same pattern element has independently evolved multiple times in Hypolimnas butterflies, repeatedly playing a role in Batesian mimicry of diverse model species.

Personalized immune subtypes based on machine learning predict response to checkpoint blockade in gastric cancer.

Immune checkpoint inhibitors (ICI) show high efficiency in a small fraction of advanced gastric cancer (GC). However, personalized immune subtypes have not been developed for the prediction of ICI efficiency in GC. Herein, we identified Pan-Immune Activation Module (PIAM), a curated gene expression profile (GEP) representing the co-infiltration of multiple immune cell types in tumor microenvironment of GC, which was associated with high expression of immunosuppressive molecules such as PD-1 and CTLA-4. We also identified Pan-Immune Dysfunction Genes (PIDG), a conservative PIAM-derivated GEP indicating the dysfunction of immune cell cooperation, which was associated with upregulation of metastatic programs (extracellular matrix receptor interaction, TGF-ß signaling, epithelial-mesenchymal transition and calcium signaling) but downregulation of proliferative signalings (MYC targets, E2F targets, mTORC1 signaling, and DNA replication and repair). Moreover, we developed 'GSClassifier', an ensemble toolkit based on top scoring pairs and extreme gradient boosting, for population-based modeling and personalized identification of GEP subtypes. With PIAM and PIDG, we developed four Pan-immune Activation and Dysfunction (PAD) subtypes and a GSClassifier model 'PAD for individual' with high accuracy in predicting response to pembrolizumab (anti-PD-1) in advance GC (AUC = 0.833). Intriguingly, PAD-II (PIAMhighPIDGlow) displayed the highest objective response rate (60.0%) compared with other subtypes (PAD-I, PIAMhighPIDGhigh, 0%; PAD-III, PIAMlowPIDGhigh, 0%; PAD-IV, PIAMlowPIDGlow, 17.6%; P = 0.003), which was further validated in the metastatic urothelial cancer cohort treated with atezolizumab (anti-PD-L1) (P = 0.018). In all, we provided 'GSClassifier' as a refined computational framework for GEP-based stratification and PAD subtypes as a promising strategy for exploring ICI responders in GC. Metastatic pathways could be potential targets for GC patients with high immune infiltration but resistance to ICI therapy.

Human-virus protein-protein interactions maps assist in revealing the pathogenesis of viral infection.

Many significant viral infections have been recorded in human history, which have caused enormous negative impacts worldwide. Human-virus protein-protein interactions (PPIs) mediate viral infection and immune processes in the host. The identification, quantification, localization, and construction of human-virus PPIs maps are critical prerequisites for understanding the biophysical basis of the viral invasion process and characterising the framework for all protein functions. With the technological revolution and the introduction of artificial intelligence, the human-virus PPIs maps have been expanded rapidly in the past decade and shed light on solving complicated biomedical problems. However, there is still a lack of prospective insight into the field. In this work, we comprehensively review and compare the effectiveness, potential, and limitations of diverse approaches for constructing large-scale PPIs maps in human-virus, including experimental methods based on biophysics and biochemistry, databases of human-virus PPIs, computational methods based on artificial intelligence, and tools for visualising PPIs maps. The work aims to provide a toolbox for researchers, hoping to better assist in deciphering the relationship between humans and viruses.

Screening of novel disease genes of sepsis-induced myocardial Disfunction by RNA sequencing and bioinformatics analysis.

BACKGROUND: There is still a lack of effective treatment for sepsis-induced myocardial dysfunction (SIMD), while the pathogenesis of SIMD still remains largely unexplained. METHODS: RNA sequencing results (GSE267388 and GSE79962) were used for cross-species integrative analysis. Bioinformatic analyses were used to delve into function, tissue- and cell- specificity, and interactions of genes. External datasets and qRT-PCR experiments were used for validation. L1000 FWD was used to predict targeted drugs, and 3D structure files were used for molecular docking. RESULTS: Based on bioinformatic analyses, ten differentially expressed genes were selected as genes of interest, seven of which were verified to be significantly differential expression. Bucladesine was considered as a potential targeted drug for SIMD, which banded to seven target proteins primarily by forming hydrogen bonds. CONCLUSION: It was considered that Cebpd, Timp1, Pnp, Osmr, Tgm2, Cp, and Asb2 were novel disease genes, while bucladesine was a potential therapeutic drug, of SIMD.

Risk factor prediction and immune correlation analysis of cuproptosis-related gene in osteoarthritis.

Osteoarthritis (OA) is a widespread inflammatory joint disease with significant global disability burden. Cuproptosis, a newly identified mode of cell death, has emerged as a crucial factor in various pathological conditions, including OA. In this context, our study aims to investigate the intrinsic relationship between cuproptosis-related genes (CRGs) and OA, and assess their potential as biomarkers for OA diagnosis and treatment. Datasets from the GEO databases were analysed the differential expression of CRGs, leading to the identification of 10 key CRGs (CDKN2A, DLD, FDX1, GLS, LIAS, LIPT1, MTF1, PDHA1, DLAT and PDHB). A logistic regression analysis and calibration curves were used to show excellent diagnostic accuracy. Consensus clustering revealed two CRG patterns, with Cluster 1 indicating a closer association with OA progression. RT-PCR confirmed a significant increase in the expression levels of these nine key genes in IL-1ß-induced C28/i2 cells, and the expression of CDKN2A and FDX1 were also elevated in conditioned monocytes, while the expression of GLS and MTF1 were significantly decreased. In vitro experiments demonstrated that the expression levels of these 7/10 CRGs were significantly increased in chondrocytes induced by IL-1ß, and upon stimulation with cuproptosis inducers, chondrocyte apoptosis was exacerbated, accompanied by an increase in the expression of cuproptosis-related proteins. These further substantiated our research findings and indicated that the nine selected cuproptosis genes have high potential for application in the diagnosis of OA.

In silico prediction of polyketide biosynthetic gene clusters in the genomes of Hypericum-borne endophytic fungi.

BACKGROUND: The search for new bioactive natural compounds with anticancer activity is still of great importance. Even though their potential for diagnostics and treatment of cancer has already been proved, the availability is still limited. Hypericin, a naphthodianthrone isolated essentially from plant source Hypericum perforatum L. along with other related anthraquinones and bisanthraquinones belongs to this group of compounds. Although it has been proven that hypericin is synthesized by the polyketide pathway in plants, none of the candidate genes coding for key enzymes has been experimentally validated yet. Despite the rare occurrence of anthraquinones in plants, their presence in microorganisms, including endophytic fungi, is quite common. Unlike plants, several biosynthetic genes grouped into clusters (BGCs) in fungal endophytes have already been characterized. RESULTS: The aim of this work was to predict, identify and characterize the anthraquinone BGCs in de novo assembled and functionally annotated genomes of selected endophytic fungal isolates (Fusarium oxysporum, Plectosphaerella cucumerina, Scedosporium apiospermum, Diaporthe eres, Canariomyces subthermophilus) obtained from different tissues of Hypericum spp. The number of predicted type I polyketide synthase (PKS) BGCs in the studied genomes varied. The non-reducing type I PKS lacking thioesterase domain and adjacent discrete gene encoding protein with product release function were identified only in the genomes of C. subthermophilus and D. eres. A candidate bisanthraquinone BGC was predicted in C. subthermophilus genome and comprised genes coding the enzymes that catalyze formation of the basic anthraquinone skeleton (PKS, metallo-beta-lactamase, decarboxylase, anthrone oxygenase), putative dimerization enzyme (cytochrome P450 monooxygenase), other tailoring enzymes (oxidoreductase, dehydrogenase/reductase), and non-catalytic proteins (fungal transcription factor, transporter protein). CONCLUSIONS: The results provide an insight into genetic background of anthraquinone biosynthesis in Hypericum-borne endophytes. The predicted bisanthraquinone gene cluster represents a basis for functional validation of the candidate biosynthetic genes in a simple eukaryotic system as a prospective biotechnological alternative for production of hypericin and related bioactive anthraquinones.

New investigation of encoding secondary metabolites gene by genome mining of a marine bacterium, Pseudoalteromonas viridis BBR56.

Pseudoalteromonas viridis strain BBR56 was isolated from seawater at Dutungan Island, South Sulawesi, Indonesia. Bacterial DNA was isolated using Promega Genomic DNA TM050. DNA purity and quantity were assessed using NanoDrop spectrophotometers and Qubit fluorometers. The DNA library and sequencing were prepared using Oxford Nanopore Technology GridION MinKNOW 20.06.9 with long read, direct, and comprehensive analysis. High accuracy base calling was assessed with Guppy version 4.0.11. Filtlong and NanoPlot were used for filtering and visualizing the FASTQ data. Flye (2.8.1) was used for de novo assembly analysis. Variant calls and consensus sequences were created using Medaka. The annotation of the genome was elaborated by DFAST. The assembled genome and annotation were tested using Busco and CheckM. Herein, we found that the highest similarity of the BBR56 isolate was 98.37% with the 16 S rRNA gene sequence of P. viridis G-1387. The genome size was 5.5 Mb and included chromosome 1 (4.2 Mbp) and chromosome 2 (1.3 Mbp), which encoded 61 pseudogenes, 4 noncoding RNAs, 113 tRNAs, 31 rRNAs, 4,505 coding DNA sequences, 4 clustered regularly interspaced short palindromic repeats, 4,444 coding genes, and a GC content of 49.5%. The sequence of the whole genome of P. viridis BBR56 was uploaded to GenBank under the accession numbers CP072425-CP072426, biosample number SAMN18435505, and bioproject number PRJNA716373. The sequence read archive (SRR14179986) was successfully obtained from NCBI for BBR56 raw sequencing reads. Digital DNA-DNA hybridization results showed that the genome of BBR56 had the potential to be a new species because no other bacterial genomes were similar to the sample. Biosynthetic gene clusters (BGCs) were assessed using BAGEL4 and the antiSMASH bacterial version. The genome harbored diverse BGCs, including genes that encoded polyketide synthase, nonribosomal peptide synthase, RiPP-like, NRP-metallophore, hydrogen cyanide, betalactone, thioamide-NRP, Lant class I, sactipeptide, and prodigiosin. Thus, BBR56 has considerable potential for further exploration regarding the use of its secondary metabolite products in the human and fisheries sectors.

Pseudogenomic insights into the evolution of Mycobacterium ulcerans.

BACKGROUND: Buruli ulcer (BU) disease, caused by Mycobacterium ulcerans (MU), and characterized by necrotic ulcers is still a health problem in Africa and Australia. The genome of the bacterium has several pseudogenes due to recent evolutionary events and environmental pressures. Pseudogenes are genetic elements regarded as nonessential in bacteria, however, they are less studied due to limited available tools to provide understanding of their evolution and roles in MU pathogenicity. RESULTS: This study developed a bioinformatic pipeline to profile the pseudogenomes of sequenced MU clinical isolates from different countries. One hundred and seventy-two MU genomes analyzed revealed that pseudogenomes of African strains corresponded to the two African lineages 1 and 2. Pseudogenomes were lineage and location specific and African lineage 1 was further divided into A and B. Lineage 2 had less relaxation in positive selection than lineage 1 which may signify different evolutionary points. Based on the Gil-Latorre model, African MU strains may be in the latter stages of evolutionary adaption and are adapting to an environment rich in metabolic resources with a lower temperature and decreased UV radiation. The environment fosters oxidative metabolism and MU may be less reliant on some secondary metabolites. In-house pseudogenomes from Ghana and Cote d'Ivoire were different from other African strains, however, they were identified as African strains. CONCLUSION: Our bioinformatic pipeline provides pseudogenomic insights to complement other whole genome analyses, providing a better view of the evolution of the genome of MU and suggest an adaptation model which is important in understanding transmission. MU pseudogene profiles vary based on lineage and country, and an apparent reduction in insertion sequences used for the detection of MU which may adversely affect the sensitivity of diagnosis.

SIGNIFICANCE: Prevention and treatment of Buruli ulcer is still a problem but large whole genome datasets on M. ulcerans are readily available. However, genomic studies fail to thoroughly investigate pseudogenes to probe evolutionary changes in the bacteria, and this can be attributed to the lack of bioinformatic tools. This work studied pseudogenes in Mycobacterium ulcerans (MU) to understand its adapted niche and evolutionary differences across African strains. Our results posit an MU niche-adapted model important in understanding transmission. Also, MU pseudogene profiles vary based on lineage and country, suggesting their influence on pseudogenization patterns in the genome. We further identify a reduction in insertion sequences that are used for the detection of the bacteria which may affect the sensitivity of diagnosis.

LncRNA and mRNA expression characteristic and bioinformatic analysis in myocardium of diabetic cardiomyopathy mice.

BACKGROUND: Diabetic cardiomyopathy (DCM) is becoming a very well-known clinical entity and leads to increased heart failure in diabetic patients. Long non-coding RNAs (LncRNAs) play an important role in the pathogenesis of DCM. In the present study, the expression profiles of lncRNAs and mRNAs were illuminated in myocardium from DCM mice, with purpose of exploring probable pathological processes of DCM involved by differentially expressed genes in order to provide a new direction for the future researches of DCM. RESULTS: The results showed that a total of 93 differentially expressed lncRNA transcripts and 881 mRNA transcripts were aberrantly expressed in db/db mice compared with the controls. The top 6 differentially expressed lncRNAs like up-regulated Hmga1b, Gm8909, Gm50252 and down-regulated Msantd4, 4933413J09Rik, Gm41414 have not yet been reported in DCM. The lncRNAs-mRNAs co-expression network analysis showed that LncRNA 2610507I01Rik, 2310015A16Rik, Gm10503, A930015D03Rik and Gm48483 were the most relevant to differentially expressed mRNAs. CONCLUSION: Our results showed that db/db DCM mice exist differentially expressed lncRNAs and mRNAs in hearts. These differentially expressed lncRNAs may be involved in the pathological process of cardiomyocyte apoptosis and fibrosis in DCM.

Linc-ROR inhibits NK cell-killing activity by promoting RXRA ubiquitination and reducing MICB expression in gastric cancer patients.

Linc-ROR plays an important role in gastric cancer (GC) development and progression. This study sought to determine how the aberrant expression of Linc-ROR impacts GC progression and immune evasion, and to identify new targets for GC therapy. GC cells overexpressing Linc-ROR and GSAGS cells were cocultured with NK-92 cells, respectively, and Linc-ROR expression was determined using reverse transcription polymerase chain reaction. Linc-ROR overexpression experiments were used to measure the expression of MICB, a tumor protein that is recognized by natural killer (NK) cells. Bioinformatics analysis identified retinoid X receptor α (RXRA) and YY1 as MICB-specific transcription factors. Cotransfection and ubiquitinated drug experiments found that Linc-ROR promoted the ubiquitination and degradation of RXRA. Linc-ROR was upregulated in GC tissue and high expression was associated with tumor escape from NK-92 cell-mediated immunity. Linc-ROR overexpression inhibited the expression of MICB on the cell surface by degrading RXRA. These findings indicate that Linc-ROR promotes the binding of RXRA and E3 ligase UBE4B, reducing RXRA and MICB expression, and limiting NK cell-killing activity. Linc-ROR is a critical long noncoding RNA with a tumor-promoting function in GC and thus may serve as a potential therapeutic target.

Analysis and experimental validation of necroptosis-related molecular classification, immune signature and feature genes in Alzheimer's disease.

Necroptosis, a programmed cell death pathway, has been demonstrated to be activated in Alzheimer's disease (AD). However, the precise role of necroptosis and its correlation with immune cell infiltration in AD remains unclear. In this study, we conducted non-negative matrix factorization clustering analysis to identify three subtypes of AD based on necroptosis-relevant genes. Notably, these subtypes exhibited varying necroptosis scores, clinical characteristics and immune infiltration signatures. Cluster B, characterized by high necroptosis scores, showed higher immune cell infiltration and was associated with a more severe pathology, potentially representing a high-risk subgroup. To identify potential biomarkers for AD within cluster B, we employed two machine learning algorithms: the least absolute shrinkage and selection operator regression and Random Forest. Subsequently, we identified eight feature genes (CARTPT, KLHL35, NRN1, NT5DC3, PCYOX1L, RHOQ, SLC6A12, and SLC38A2) that were utilized to develop a diagnosis model with remarkable predictive capacity for AD. Moreover, we conducted validation using bulk RNA-seq, single-nucleus RNA-seq, and in vivo experiments to confirm the expression of these feature genes. In summary, our study identified a novel necroptosis-related subtype of AD and eight diagnostic biomarkers, explored the roles of necroptosis in AD progression and shed new light for the clinical diagnosis and treatment of this disease.

TNF-α and RPLP0 drive the apoptosis of endothelial cells and increase susceptibility to high-altitude pulmonary edema.

High-altitude pulmonary edema (HAPE) is a fatal threat for sojourners who ascend rapidly without sufficient acclimatization. Acclimatized sojourners and adapted natives are both insensitive to HAPE but have different physiological traits and molecular bases. In this study, based on GSE52209, the gene expression profiles of HAPE patients were compared with those of acclimatized sojourners and adapted natives, with the common and divergent differentially expressed genes (DEGs) and their hub genes identified, respectively. Bioinformatic methodologies for functional enrichment analysis, immune infiltration, diagnostic model construction, competing endogenous RNA (ceRNA) analysis and drug prediction were performed to detect potential biological functions and molecular mechanisms. Next, an array of in vivo experiments in a HAPE rat model and in vitro experiments in HUVECs were conducted to verify the results of the bioinformatic analysis. The enriched pathways of DEGs and immune landscapes for HAPE were significantly different between sojourners and natives, and the common DEGs were enriched mainly in the pathways of development and immunity. Nomograms revealed that the upregulation of TNF-α and downregulation of RPLP0 exhibited high diagnostic efficiency for HAPE in both sojourners and natives, which was further validated in the HAPE rat model. The addition of TNF-α and RPLP0 knockdown activated apoptosis signaling in endothelial cells (ECs) and enhanced endothelial permeability. In conclusion, TNF-α and RPLP0 are shared biomarkers and molecular bases for HAPE susceptibility during the acclimatization/adaptation/maladaptation processes in sojourners and natives, inspiring new ideas for predicting and treating HAPE.

Tissue gene expression profiles and communication networks inform candidate blood biomarker identification in psoriasis and atopic dermatitis.

Overlapping clinical and pathomechanistic features can complicate the diagnosis and treatment of inflammatory skin diseases, including psoriasis and atopic dermatitis (AD). Spatial transcriptomics allows the identification of disease- and cell-specific molecular signatures that may advance biomarker development and future treatments. This study identified transcriptional signatures in keratinocytes and sub-basal CD4+ and CD8+ T lymphocytes from patients with psoriasis and AD. In silico prediction of ligand:receptor interactions delivered key signalling pathways (interferon, effector T cells, stroma cell and matrix biology, neuronal development, etc.). Targeted validation of selected transcripts, including CCL22, RELB, and JUND, in peripheral blood T cells suggests the chosen approach as a promising tool also in other inflammatory diseases. Psoriasis and AD are characterized by transcriptional dysregulation in T cells and keratinocytes that may be targeted therapeutically. Spatial transcriptomics is a valuable tool in the search for molecular signatures that can be used as biomarkers and/or therapeutic targets.

Assisting the analysis of insertions and deletions using regional allele frequencies.

Accurate estimation of population allele frequency (AF) is crucial for gene discovery and genetic diagnostics. However, determining AF for frameshift-inducing small insertions and deletions (indels) faces challenges due to discrepancies in mapping and variant calling methods. Here, we propose an innovative approach to assess indel AF. We developed CRAFTS-indels (Calculating Regional Allele Frequency Targeting Small indels), an algorithm that combines AF of distinct indels within a given region and provides "regional AF" (rAF). We tested and validated CRAFTS-indels using three independent datasets: gnomAD v2 (n=125,748 samples), an internal dataset (IGM; n=39,367), and the UK BioBank (UKBB; n=469,835). By comparing rAF against standard AF, we identified rare indels with rAF exceeding standard AF (sAF≤10-4 and rAF>10-4) as "rAF-hi" indels. Notably, a high percentage of rare indels were "rAF-hi", with a higher proportion in gnomAD v2 (11-20%) and IGM (11-22%) compared to the UKBB (5-9% depending on the CRAFTS-indels' parameters). Analysis of the overlap of regions based on their rAF with low complexity regions and with ClinVar classification supported the pertinence of rAF. Using the internal dataset, we illustrated the utility of CRAFTS-indel in the analysis of de novo variants and the potential negative impact of rAF-hi indels in gene discovery. In summary, annotation of indels with cohort specific rAF can be used to handle some of the limitations of current annotation pipelines and facilitate detection of novel gene disease associations. CRAFTS-indels offers a user-friendly approach to providing rAF annotation. It can be integrated into public databases such as gnomAD, UKBB and used by ClinVar to revise indel classifications.

Dissection of pleiotropic effects of variants in and adjacent to F8 exon 19 and rescue of mRNA splicing and protein function.

The pathogenic significance of nucleotide variants commonly relies on nucleotide position within the gene, with exonic changes generally attributed to quantitative or qualitative alteration of protein biosynthesis, secretion, activity, or clearance. However, these changes may exert pleiotropic effects on both protein biology and mRNA splicing due to the overlapping of the amino acid and splicing codes, thus shaping the disease phenotypes. Here, we focused on hemophilia A, in which the definition of F8 variants' causative role and association to bleeding phenotypes is crucial for proper classification, genetic counseling, and management of affected individuals. We extensively characterized a large panel of hemophilia A-causing variants (n = 30) within F8 exon 19 by combining and comparing in silico and recombinant expression analyses. We identified exonic variants with pleiotropic effects and dissected the altered protein features of all missense changes. Importantly, results from multiple prediction algorithms provided qualitative results, while recombinant assays allowed us to correctly infer the likely phenotype severity for 90% of variants. Molecular characterization of pathogenic variants was also instrumental for the development of tailored correction approaches to rescue splicing affecting variants or missense changes impairing protein folding. A single engineered U1snRNA rescued mRNA splicing of nine different variants and the use of a chaperone-like drug resulted in improved factor VIII protein secretion for four missense variants. Overall, dissection of the molecular mechanisms of a large panel of HA variants allowed precise classification of HA-affected individuals and favored the development of personalized therapeutic approaches.

Inhibition of RAC attenuates Adriamycin-induced podocyte injury.

Minimal Change Disease (MCD), which is associated with podocyte injury, is the leading cause of nephrotic syndrome in children. A considerable number of patients experience relapses and require prolonged use of prednisone and immunosuppressants. Multi-drug resistance and frequent relapses can lead to disease progression to focal and segmental glomerulosclerosis (FSGS). To identify potential targets for therapy of podocyte injury, we examined microarray data of mRNAs in glomerular samples from both MCD patients and healthy donors, obtained from the GEO database. Differentially expressed genes (DEGs) were used to construct the protein-protein interactions (PPI) network through the application of the search tool for the retrieval of interacting genes (STRING) tool. The most connected genes in the network were ranked using cytoHubba. 16 hub genes were selected and validated by qRT-PCR. RAC2 was identified as a potential therapeutic target for further investigation. By downregulating RAC2, Adriamycin (ADR)-induced human podocytes (HPCs) injury was attenuated. EHT-1864, a small molecule inhibitor that targets the RAC (RAC1, RAC2, RAC3) family, proved to be more effective than RAC2 silencing in reducing HPCs injury. In conclusion, our research suggests that EHT-1864 may be a promising new molecular drug candidate for patients with MCD and FSGS.

Genome-wide analysis of the switchgrass YABBY family and functional characterization of PvYABBY14 in response to ABA and GA stress in Arabidopsis.

BACKGROUND: The small YABBY plant-specific transcription factor has a prominent role in regulating plant growth progress and responding to abiotic stress. RESULTS: Here, a total of 16 PvYABBYs from switchgrass (Panicum virgatum L.) were identified and classified into four distinct subgroups. Proteins within the same subgroup exhibited similar conserved motifs and gene structures. Synteny analyses indicated that segmental duplication contributed to the expansion of the YABBY gene family in switchgrass and that complex duplication events occurred in rice, maize, soybean, and sorghum. Promoter regions of PvYABBY genes contained numerous cis-elements related to stress responsiveness and plant hormones. Expression profile analysis indicated higher expression levels of many PvYABBY genes during inflorescence development and seed maturation, with lower expression levels during root growth. Real-time quantitative PCR analysis demonstrated the sensitivity of multiple YABBY genes to PEG, NaCl, ABA, and GA treatments. The overexpression of PvYABBY14 in Arabidopsis resulted in increased root length after treatment with GA and ABA compared to wild-type plants. CONCLUSIONS: Taken together, our study provides the first genome-wide overview of the YABBY transcription factor family, laying the groundwork for understanding the molecular basis and regulatory mechanisms of PvYABBY14 in response to ABA and GA responses in switchgrass.

Construction of a pathological model of skin lesions in acute herpes zoster virus infection and its molecular mechanism.

Varicella-zoster virus (VZV), a common pathogen with humans as the sole host, causes primary infection and undergoes a latent period in sensory ganglia. The recurrence of VZV is often accompanied by severe neuralgia in skin tissue, which has a serious impact on the life of patients. During the acute infection of VZV, there are few related studies on the pathophysiological mechanism of skin tissue. In this study, transcriptome sequencing data from the acute response period within 2 days of VZV antigen stimulation of the skin were used to explore a model of the trajectory of skin tissue changes during VZV infection. It was found that early VZV antigen stimulation caused activation of mainly natural immune-related signaling pathways, while in the late phase activation of mainly active immune-related signaling pathways. JAK-STAT, NFκB, and TNFα signaling pathways are gradually activated with the progression of infection, while Hypoxia is progressively inhibited. In addition, we found that dendritic cell-mediated immune responses play a dominant role in the lesion damage caused by VZV antigen stimulation of the skin. This study provides a theoretical basis for the study of the molecular mechanisms of skin lesions during acute VZV infection.