Bioinformatics Tools Research Articles

The synergy between the PscC subunits for electron transfer to the P840 special pair in Chlorobaculum tepidum.

The primary photochemical reaction of photosynthesis in green sulfur bacteria occurs in the homodimer PscA core proteins by a special chlorophyll pair. The light induced excited state of the special pair producing P840+ is rapidly reduced by electron transfer from one of the two PscC subunits. Molecular dynamics (MD) simulations are combined with bioinformatic tools herein to provide structural and dynamic insight into the complex between the two PscA core proteins and the two PscC subunits. The microscopic dynamic model involves extensive sampling at atomic resolution and at a cumulative time-scale of 22µs and reveals well defined protein-protein interactions. The membrane complex is composed of the two PscA and the two PscC subunits and macroscopic connections are revealed within a putative electron transfer pathway from the PscC subunit to the special pair P840 located within the PscA subunits. Our results provide a structural basis for understanding the electron transport to the homodimer RC of the green sulfur bacteria. The MD based approach can provide the basis to further probe the PscA-PscC complex dynamics and observe electron transfer therein at the quantum level. Furthermore, the transmembrane helices of the different PscC subunits exert distinct dynamics in the complex.

Genome-wide identification of polyamine metabolism and ethylene synthesis genes in Chenopodium quinoa Willd. and their responses to low-temperature stress

Abstract Background Quinoa (Chenopodium quinoa Willd.) is valued for its nutritional richness. However, pre-harvest sprouting poses a significant threat to yield and grain quality. This study aims to enhance our understanding of pre-harvest sprouting mitigation strategies, specifically through delayed sowing and avoiding rainy seasons during quinoa maturation. The overarching goal is to identify cold-resistant varieties and unravel the molecular mechanisms behind the low-temperature response of quinoa. We employed bioinformatics and genomics tools for a comprehensive genome-wide analysis of polyamines (PAs) and ethylene synthesis gene families in quinoa under low-temperature stress. Results This involved the identification of 37 PA biosynthesis and 30 PA catabolism genes, alongside 227 ethylene synthesis. Structural and phylogenetic analyses showcased conserved patterns, and subcellular localization predictions indicated diverse cellular distributions. The results indicate that the PA metabolism of quinoa is closely linked to ethylene synthesis, with multiple genes showing an upregulation in response to cold stress. However, differential expression within gene families suggests a nuanced regulatory network. Conclusions Overall, this study contributes valuable insights for the functional characterization of the PA metabolism and ethylene synthesis of quinoa, which emphasize their roles in plant low-temperature tolerance and providing a foundation for future research in this domain.

Benchmarking bioinformatic virus identification tools using real-world metagenomic data across biomes.

As most viruses remain uncultivated, metagenomics is currently the main method for virus discovery. Detecting viruses in metagenomic data is not trivial. In the past few years, many bioinformatic virus identification tools have been developed for this task, making it challenging to choose the right tools, parameters, and cutoffs. As all these tools measure different biological signals, and use different algorithms and training and reference databases, it is imperative to conduct an independent benchmarking to give users objective guidance. We compare the performance of nine state-of-the-art virus identification tools in thirteen modes on eight paired viral and microbial datasets from three distinct biomes, including a new complex dataset from Antarctic coastal waters. The tools have highly variable true positive rates (0-97%) and false positive rates (0-30%). PPR-Meta best distinguishes viral from microbial contigs, followed by DeepVirFinder, VirSorter2, and VIBRANT. Different tools identify different subsets of the benchmarking data and all tools, except for Sourmash, find unique viral contigs. Performance of tools improved with adjusted parameter cutoffs, indicating that adjustment of parameter cutoffs before usage should be considered. Together, our independent benchmarking facilitates selecting choices of bioinformatic virus identification tools and gives suggestions for parameter adjustments to viromics researchers.

Upregulated lncRNA PRNT promotes progression and oxaliplatin resistance of colorectal cancer cells by regulating HIPK2 transcription

BACKGROUND Colorectal cancer (CRC) is the third most common cancer and a significant cause of cancer-related mortality globally. Resistance to chemotherapy, especially during CRC treatment, leads to reduced effectiveness of drugs and poor patient outcomes. Long noncoding RNAs (lncRNAs) have been implicated in various pathophysiological processes of tumor cells, including chemotherapy resistance, yet the roles of many lncRNAs in CRC remain unclear. AIM To identify and analyze the lncRNAs involved in oxaliplatin resistance in CRC and to understand the underlying molecular mechanisms influencing this resistance. METHODS Gene Expression Omnibus datasets GSE42387 and GSE30011 were reanalyzed to identify lncRNAs and mRNAs associated with oxaliplatin resistance. Various bioinformatics tools were employed to elucidate molecular mechanisms. The expression levels of lncRNAs and mRNAs were assessed via quantitative reverse transcription-polymerase chain reaction. Functional assays, including MTT, wound healing, and Transwell, were conducted to investigate the functional implications of lncRNA alterations. Interactions between lncRNAs and transcription factors were examined using RIP and luciferase reporter assays, while Western blotting was used to confirm downstream pathways. Additionally, a xenograft mouse model was utilized to study the in vivo effects of lncRNAs on chemotherapy resistance. RESULTS LncRNA prion protein testis specific (PRNT) was found to be upregulated in oxaliplatin-resistant CRC cell lines and negatively correlated with homeodomain interacting protein kinase 2 (HIPK2) expression. PRNT was demonstrated to sponge transcription factor zinc finger protein 184 (ZNF184), which in turn could regulate HIPK2 expression. Altered expression of PRNT influenced CRC cell sensitivity to oxaliplatin, with overexpression leading to decreased sensitivity and decreased expression reducing resistance. Both RIP and luciferase reporter assays indicated that ZNF184 and HIPK2 are targets of PRNT. The PRNT/ZNF184/HIPK2 axis was implicated in promoting CRC progression and oxaliplatin resistance both in vitro and in vivo . CONCLUSION The study concludes that PRNT is upregulated in oxaliplatin-resistant CRC cells and modulates the expression of HIPK2 by sponging ZNF184. This regulatory mechanism enhances CRC progression and resistance to oxaliplatin, positioning PRNT as a promising therapeutic target for CRC patients undergoing oxaliplatin-based chemotherapy.

16S rRNA sequencing reveals microbiota differences in orthopedic implants between aseptic loosening and prosthetic joint infection cases.

Prosthetic joint infection (PJI) and aseptic loosening (AL) are common complications of total joint arthroplasty. An accumulation of evidence indicates the presence of microbial communities on prosthetic implants, but the overall microbial profile is unclear. In this study, we aimed to investigate the differences in the microbial composition of prosthetic implants obtained from PJI and AL patients using the 16S rRNA sequencing method. Patients who underwent revision hip, knee, or shoulder arthroplasty caused by PJI (n = 20) or AL (n = 10) were enrolled in the study. 16S rRNA sequencing targeting the V3-V4 region was performed on the microbial specimens collected from synovial fluid, periprosthetic deep-tissue, and biofilm during the revision surgery. The sequenced raw data were analysed for microbial composition and ecological and differential abundance analyses using bioinformatics tools. The AL group had relatively balanced and higher diversity, with Staphylococcus, Streptococcus, and Veillonella being prominent. In the PJI group, Staphylococcus and Pseudomonas were predominant, especially in deep-tissue samples and biofilm samples, respectively. The differential abundance analysis identified 15 and 2 distinctive taxa in the AL and PJI groups, respectively. Our findings provided preliminary insights supporting the existence of periprosthetic microbiota in orthopedic implants and explaining the differences in microbial composition between the AL and PJI groups.

Genome mining of Mycobacterium tuberculosis: targeting SufD as a novel drug candidate through in silico characterization and inhibitor screening

Tuberculosis (TB) stands as the second most fatal infectious disease globally, causing 1.3 million deaths in 2022. The resurgence of TB and the alarming rise of antibiotic resistance demand urgent call to develop novel antituberculosis drugs. Despite concerted efforts to control TB, the disease persists and spreads rapidly on a global scale. Targeting stress response pathways in Mycobacterium tuberculosis (Mtb) has become imperative to achieve complete eradication. This study employs subtractive genomics to identify and prioritize potential drug targets among the hypothetical proteins of Mtb, focusing on indispensable pathways. Amongst 177 essential hypothetical proteins, 152 were nonhomologous to human. These proteins participated in 34 pathways, and a 20-fold enrichment of SUF pathway genes led to its selection as a target pathway. Fe–S clusters are fundamental, widely distributed protein cofactors involved in vital cellular processes. The survival of Mtb in a hypoxic environment relies on the iron–sulfur (Fe–S) cluster biogenesis pathway for the repair of damaged Fe–S clusters. It also protects pathogen against drugs, ensuring controlled iron utilization and contributing to drug resistance. In Mtb, six proteins of Fe–S cluster assembly pathway are encoded by the suf operon. The present study was focused on SufD because of its role in iron acquisition and prevention of Fenton reaction. The research further delves into the in silico characterization of SufD, utilizing bioinformatics tools for sequence and structure based analysis. The protein’s structural features, including the identification of conserved regions, motifs, and 3D structure prediction enhanced functional annotation. Target based virtual screening of compounds from the ChEMBL database resulted in 12 inhibitors with best binding affinities. Drug likeness and ADMET profiling of potential inhibitors identified promising compounds with favorable drug-like properties. The study also involved cloning in SUMO-pRSF-Duet1 expression vector, overexpression, and purification of recombinant SufD from E. coli BL21 (DE3) cells. Optimization of expression conditions resulted in soluble production, and subsequent purification highlighting the efficacy of the SUMO fusion system for challenging Mtb proteins in E. coli. These findings provide valuable insights into pharmacological targets for future experimental studies, holding promise for the development of targeted therapy against Mtb.

Identification of potential biomarkers of gout through weighted gene correlation network analysis

BackgroundAlthough hyperuricemia is not always associated with acute gouty arthritis, uric acid is a significant risk factor for gout. Therefore, we investigated the specific mechanism of uric acid activity.MethodsUsing the gout-associated transcriptome dataset GSE160170, we conducted differential expression analysis to identify differentially expressed genes (DEGs). Moreover, we discovered highly linked gene modules using weighted gene coexpression network analysis (WGCNA) and evaluated their intersection. Subsequently, we screened for relevant biomarkers using the cytoHubba and Mcode algorithms in the STRING database, investigated their connection to immune cells and constructed a competitive endogenous RNA (ceRNA) network to identify upstream miRNAs and lncRNAs. We also collected PBMCs from acute gouty arthritis patients and healthy individuals and constructed a THP-1 cell gout inflammatory model, RT−qPCR and western blotting (WB) were used to detect the expression of C-X-C motif ligand 8 (CXCL8), C-X-C motif ligand 2 (CXCL2), and C-X-C motif ligand 1 (CXCL1). Finally, we predicted relevant drug targets through hub genes, hoping to find better treatments.ResultsAccording to differential expression analysis, there were 76 upregulated and 28 downregulated mRNAs in GSE160170. Additionally, WGCNA showed that the turquoise module was most strongly correlated with primary gout; 86 hub genes were eventually obtained upon intersection. IL1β, IL6, CXCL8, CXCL1, and CXCL2 are the principal hub genes of the protein–protein interaction (PPI) network. Using RT−qPCR and WB, we found that there were significant differences in the expression levels of CXCL8, CXCL1, and CXCL2 between the gouty group and the healthy group, and we also predicted 10 chemicals related to these proteins.ConclusionIn this study, we screened and validated essential genes using a variety of bioinformatics tools to generate novel ideas for the diagnosis and treatment of gout.

CircDiaph3 aggravates H/R-induced cardiomyocyte apoptosis and inflammation through miR-338-3p/SRSF1 axis.

Acute myocardial infarction (AMI) is one of the most prevalent cardiovascular diseases, accounting for a high incidence rate and high mortality worldwide. Hypoxia/reoxygenation (H/R)-induced myocardial cell injury is the main cause of AMI. Several studies have shown that circular RNA contributes significantly to the pathogenesis of AMI. Here, we established an AMI mouse model to investigate the effect of circDiaph3 in cardiac function and explore the functional role of circDiaph3 in H/R-induced cardiomyocyte injury and its molecular mechanism. Bioinformatics tool and RT-qPCR techniques were applied to detect circDiaph3 expression in human patient samples, heart tissues of AMI mice, and H/R-induced H9C2 cells. CCK-8 was used to examine cell viability, while annexin-V/PI staining was used to assess cell apoptosis. Myocardial reactive oxygen species (ROS) levels were detected by immunofluorescence. Western blot was used to detect the protein expression of anti-apoptotic Bcl-2 while pro-apoptotic Bax and cleaved-Caspase-3. Furthermore, ELISA was used to detect inflammatory cytokines production. While bioinformatics tool and RNA pull-down assay were used to verify the interaction between circDiaph3 and miR-338-3p. We found that circDiaph3 expression was high in AMI patients and mice, as well as in H/R-treated H9C2 cells. CircDiaph3 silencing ameliorated apoptosis and inflammatory response of cardiomyocytes in vivo. Moreover, the knockdown of cirDiaph3 mitigated H/R-induced apoptosis and the release of inflammatory mediators like IL-1β, IL-6, and TNF-α in H9C2 cells. Mechanistically, circDiaph3 induced cell apoptosis and inflammatory responses in H/R-treated H9C2 cells by sponging miR-338-3p. Overexpressing miR-338-3p in H/R-treated cells prominently reversed circDiaph3-induced effects. Notably, miR-338-3p inhibited SRSF1 expression in H/R-treated H9C2 cells. While overexpressing SRSF1 abrogated miR-338-3p-mediated alleviation of apoptosis and inflammation after H/R treatment. To summarize, circDiaph3 aggravates H/R-induced cardiomyocyte apoptosis and inflammation through the miR-338-3p/SRSF1 axis. These findings suggest that the circDiaph3/miR-338-3pp/SRSF1 axis could be a potential therapeutic target for treating H/R-induced myocardial injury.

CAPN2 correlates with insulin resistance states in PCOS as evidenced by multi-dataset analysis

ObjectiveIR emerges as a feature in the pathophysiology of PCOS, precipitating ovulatory anomalies and endometrial dysfunctions that contribute to the infertility challenges characteristic of this condition. Despite its clinical significance, a consensus on the precise mechanisms by which IR exacerbates PCOS is still lacking. This study aims to harness bioinformatics tools to unearth key IR-associated genes in PCOS patients, providing a platform for future therapeutic research and potential intervention strategies.MethodsWe retrieved 4 datasets detailing PCOS from the GEO, and sourced IRGs from the MSigDB. We applied WGCNA to identify gene modules linked to insulin resistance, utilizing IR scores as a phenotypic marker. Gene refinement was executed through the LASSO, SVM, and Boruta feature selection algorithms. qPCR was carried out on selected samples to confirm findings. We predicted both miRNA and lncRNA targets using the ENCORI database, which facilitated the construction of a ceRNA network. Lastly, a drug-target network was derived from the CTD.ResultsThirteen genes related to insulin resistance in PCOS were identified via WGCNA analysis. LASSO, SVM, and Boruta algorithms further isolated CAPN2 as a notably upregulated gene, corroborated by biological verification. The ceRNA network involving lncRNA XIST and hsa-miR-433-3p indicated a possible regulatory link with CAPN2, supported by ENCORI database. Drug prediction analysis uncovered seven pharmacological agents, most being significant regulators of the endocrine system, as potential candidates for addressing insulin resistance in PCOS.ConclusionsThis study highlights the pivotal role of CAPN2 in insulin resistance within the context of PCOS, emphasizing its importance as both a critical biomarker and a potential therapeutic target. By identifying CAPN2, our research contributes to the expanding evidence surrounding the CAPN family, particularly CAPN10, in insulin resistance studies beyond PCOS. This work enriches our understanding of the mechanisms underlying insulin resistance, offering insights that bridge gaps in the current scientific landscape.

Systematic identification and characterization of exon-intron circRNAs.

Exon-intron circRNAs (EIciRNAs) are a circRNA subclass with retained introns. Global features of EIciRNAs remain largely unexplored, mainly owing to the lack of bioinformatic tools. The regulation of intron retention (IR) in EIciRNAs and the associated functionality also require further investigation. We developed a framework, FEICP, which efficiently detected EIciRNAs from high-throughput sequencing (HTS) data. EIciRNAs are distinct from exonic circRNAs (EcircRNAs) in aspects such as with larger length, localization in the nucleus, high tissue specificity, and enrichment mostly in the brain. Deep learning analyses revealed that compared with regular introns, the retained introns of circRNAs (CIRs) are shorter in length, have weaker splice site strength, and have higher GC content. Compared with retained introns in linear RNAs (LIRs), CIRs are more likely to form secondary structures and show greater sequence conservation. CIRs are closer to the 5'-end, whereas LIRs are closer to the 3'-end of transcripts. EIciRNA-generating genes are more actively transcribed and associated with epigenetic marks of gene activation. Computational analyses and genome-wide CRISPR screening revealed that SRSF1 binds to CIRs and inhibits the biogenesis of most EIciRNAs. SRSF1 regulates the biogenesis of EIciLIMK1, which enhances the expression of LIMK1 in cis to boost neuronal differentiation, exemplifying EIciRNA physiological function. Overall, our study has developed the FEICP pipeline to identify EIciRNAs from HTS data, and reveals multiple features of CIRs and EIciRNAs. SRSF1 has been identified to regulate EIciRNA biogenesis. EIciRNAs and the regulation of EIciRNA biogenesis play critical roles in neuronal differentiation.

Neuroretinal degeneration in a mouse model of systemic chronic immune activation observed by proteomics

Blindness or vision loss due to neuroretinal and photoreceptor degeneration affects millions of individuals worldwide. In numerous neurodegenerative diseases, including age-related macular degeneration, dysregulated immune response-mediated retinal degeneration has been found to play a critical role in the disease pathogenesis. To better understand the pathogenic mechanisms underlying the retinal degeneration, we used a mouse model of systemic immune activation where we infected mice with lymphocytic choriomeningitis virus (LCMV) clone 13. Here, we evaluated the effects of LCMV infection and present a comprehensive discovery-based proteomic investigation using tandem mass tag (TMT) labeling and high-resolution liquid chromatography–tandem mass spectrometry (LC-MS/MS). Changes in protein regulation in the posterior part of the eye, neuroretina, and RPE/choroid were compared to those in the spleen as a secondary lymphoid organ and to the kidney as a non-lymphoid but encapsulated organ at 1, 8, and 28 weeks of infection. Using bioinformatic tools, we found several proteins responsible for maintaining normal tissue homeostasis to be differentially regulated in the neuroretina and the RPE/choroid during the degenerative process. Additionally, in the organs we observed, several important protein pathways contributing to cellular homeostasis and tissue development were perturbed and associated with LCMV-mediated inflammation, promoting disease progression. Our findings suggest that the response to a systemic chronic infection differs between the neuroretina and the RPE/choroid, and the processes induced by chronic systemic infection in the RPE/choroid are not unlike those induced in non-immune-privileged organs such as the kidney and spleen. Overall, our data provide detailed insight into several molecular mechanisms of neuroretinal degeneration and highlight various novel protein pathways that further suggest that the posterior part of the eye is not an isolated immunological entity despite the existence of neuroretinal immune privilege.

Identification of differentially expressed genes and splicing events in early-onset colorectal cancer

BackgroundThe incidence of colorectal cancer (CRC) has been steadily increasing in younger individuals over the past several decades for reasons that are incompletely defined. Identifying differences in gene expression profiles, or transcriptomes, in early-onset colorectal cancer (EOCRC, < 50 years old) patients versus later-onset colorectal cancer (LOCRC, > 50 years old) patients is one approach to understanding molecular and genetic features that distinguish EOCRC.MethodsWe performed RNA-sequencing (RNA-seq) to characterize the transcriptomes of patient-matched tumors and adjacent, uninvolved (normal) colonic segments from EOCRC (n=21) and LOCRC (n=22) patients. The EOCRC and LOCRC cohorts were matched for demographic and clinical characteristics. We used The Cancer Genome Atlas Colon Adenocarcinoma (TCGA-COAD) database for validation. We used a series of computational and bioinformatic tools to identify EOCRC-specific differentially expressed genes, molecular pathways, predicted cell populations, differential gene splicing events, and predicted neoantigens.ResultsWe identified an eight-gene signature in EOCRC comprised of ALDOB, FBXL16, IL1RN, MSLN, RAC3, SLC38A11, WBSCR27 and WNT11, from which we developed a score predictive of overall CRC patient survival. On the entire set of genes identified in normal tissues and tumors, cell type deconvolution analysis predicted a differential abundance of immune and non-immune populations in EOCRC versus LOCRC. Gene set enrichment analysis identified increased expression of splicing machinery in EOCRC. We further found differences in alternative splicing (AS) events, including one within the long non-coding RNA, HOTAIRM1. Additional analysis of AS found seven events specific to EOCRC that encode potential neoantigens.ConclusionOur transcriptome analyses identified genetic and molecular features specific to EOCRC which may inform future screening, development of prognostic indicators, and novel drug targets.

A MiR181/Sirtuin1 regulatory circuit modulates drug response in biliary cancers

Despite recent advances, biliary tract cancer (BTC) remains one of the most lethal tumor worldwide due to late diagnosis, limited therapeutic strategies and resistance to conventional therapies. In recent years, high-throughput technologies have enabled extensive genome, and transcriptome sequencing unveiling, among others, the regulatory potential of microRNAs (miRNAs). Compelling evidence shown that miRNA are attractive therapeutic targets and promising candidates as biomarkers for various therapy-resistant tumors. The analysis of miRNA profile successfully identified miR-181c and -181d as significantly downregulated in BTC patients. Low miR-181c and -181d expression levels were correlated with worse prognosis and poor treatment efficacy. In fact, progression-free survival analysis indicated poor survival rates in miR-181c and -181d low expressing patients. The expression profile of miR-181c and -181d in BTC cell lines revealed that both miRNAs were dysregulated. Functional in vitro experiments in BTC cell lines showed that overexpression of miR-181c and -181d affected cell viability and increased sensitivity to chemotherapy compared to controls. In addition, by using bioinformatic tools we showed that the miR-181c/d functional role is determined by binding to their target SIRT1 (Sirtuin 1). Moreover, BTC patients expressing high levels of miR-181 and low SIRT1 shown an improved survival and treatment response. An integrative network analysis demonstrated that, miR-181/SIRT1 circuit had a regulatory effect on several important metabolic tumor-related processes. Our study demonstrated that miR-181c and -181d act as tumor suppressor miRNA in BTC, suggesting the potential use as therapeutic strategy in resistant cancers and as predictive biomarker in the precision medicine of BTC.

Compound heterozygous mutation of the SNX14 gene causes autosomal recessive spinocerebellar ataxia 20

Objective: The article aims to provide genetic counseling to a family with two children who were experiencing growth and developmental delays.Methods: Clinical information of the proband was collected. Peripheral blood was collected from core family members to identify the initial reason for growth and developmental delays by whole exome sequencing (WES) and Sanger sequencing. To ascertain the consequences of the newly discovered variants, details of the variants detected were analyzed by bioinformatic tools. Furthermore, we performed in vitro experimentation targeting SNX14 gene expression to confirm whether the variants could alter the expression of SNX14.Results: The proband had prenatal ultrasound findings that included flattened frontal bones, increased interocular distance, widened bilateral cerebral sulci, and shortened long bones, which resulted in subsequent postnatal developmental delays. The older sister also displayed growth developmental delays and poor muscle tone. WES identified compound heterozygous variants of c.712A>T (p.Arg238Ter) and .2744A>T (p.Gln915Leu) in the SNX14 gene in these two children. Both are novel missense variant that originates from the father and mother, respectively. Sanger sequencing confirmed this result. Following the guideline of the American College of Medical Genetics and Genomics (ACMG), the SNX14 c.712A>T (p.Arg238Ter) variant was predicted to be pathogenic (P), while the SNX14 c.2744A>T (p.Gln915Leu) variant was predicted to be a variant of uncertain significance (VUS). The structural analysis revealed that the c.2744A>T (p.Gln915Leu) variant may impact the stability of the SNX14 protein. In vitro experiments demonstrated that both variants reduced SNX14 expression.Conclusion: The SNX14 gene c.712A>T (p.Arg238Ter) and c.2744A>T (p.Gln915Leu) were identified as the genetic causes of growth and developmental delay in two affected children. This conclusion was based on the clinical presentations of the children, structural analysis of the mutant protein, and in vitro experimental validation. This discovery expands the range of SNX14 gene variants and provides a foundation for genetic counseling and guidance for future pregnancies in the affected children’s families.

Multi-targets of antimicrobial photodynamic therapy mediated by erythrosine against Staphylococcus aureus identified by proteomic approach.

Staphylococcus aureus is a global challenge to the clinical field and food industry. Therefore, the development of antimicrobial photodynamic therapy (aPDT) has become one of the valuable methods to control this pathogen. The antibacterial activity of photoinactivation by erythrosine (Ery) against S. aureus has been reported, but its modes of action are unclear. This study aimed to employ a proteomic approach to analyze modes of action of Ery-aPDT against S. aureus. We determined the antibacterial effect by Ery-aPDT assays, quantified reactive oxygen species (ROS) and injury to the cell membrane, and determined protein expression using a proteomic approach combined with bioinformatic tools. Ery-aPDT was effective in reducing S. aureus to undetectable levels. In addition, the increment of ROS accompanied the increase in the reduction of cell viability, and damage to cellular membranes was shown by sublethal injury. In proteomic analysis, we found 17 differentially expressed proteins. These proteins revealed changes mainly associated with defense to oxidative stress, energy metabolism, translation, and protein biosynthesis. Thus, these results suggest that the effectiveness of Ery-aPDT is due to multi-targets in the bacterial cell that cause the death of S. aureus.

Multiple approaches in the study of ancient human microbiome

Abstract Research related to the analysis of microbial communities in ancient human samples began two decades ago. Since then, new technologies as well as new bioinformatic tools have been developed for the manipulation of massive data. All this together, with the theoretical knowledge generated from microbial ecology has expanded our understanding of the human microbiome. Despite the challenge of studying ancient samples and their microbiome, some approaches applied to the contemporary human microbiome have been successfully applied to ancient samples. Most studies in ancient samples have focused on taxonomic profiling at the community level with metagenomic or high-throughput 16S rRNA gene amplicon sequencing data. However, other approaches have emerged in order to get deeper understanding of the ancient human microbiome. Therefore, the aim of this review is to discuss all the approaches and methodologies successfully applied to ancient samples and ancient microbiome as well as the information that can be generated using them, in order to understand the evolution of human microbiome interactions throughout history.

Leucoma salicis nucleopolyhedrovirus (LesaNPV) genome sequence shed new light on the origin of the Alphabaculovirus orpseudotsugatae species.

LesaNPV (Leucoma salicis nucleopolyhedrovirus) is an alphabaculovirus group Ib. Potentially, it can be an eco-friendly agent to control the white satin moth Leucoma salicis population. In this study, we have established the relationship between LesaNPV and other closely related alphabaculoviruses. Environmental samples of late instar of white satin moth collected in Poland infected with baculovirus have been homogenized, polyhedra were purified and subjected to scanning and transmission electron microscopy. Viral DNA was sequenced using the Illumina platform and the whole-genome sequence was established by de novo assembly of paired reads. Genome annotation and phylogenetic analyses were performed with the use of bioinformatics tools. The genome of LesaNPV is 132549bp long with 154 ORFs and 54.9% GC content. Whole-genome sequencing revealed deletion of dUTPase as well as ribonucleoside reductases small and large subunits region in LesaNPV genome compared to Dasychira pudibunda nucleopolyhedrovirus (DapuNPV) and Orgyia pseudotsugata multiple nucleopolyhedrovirus (OpMNPV) where this region is complete. Phylogenetic analysis of Baculoviridae family members showed that LesaNPV is less divergent from a common ancestor than closely related species DapuNPV and OpMNPV. This is interesting because their hosts do not occur in the same area. The baculoviruses described in this manuscript are probably isolates of one species and could be assigned to recently denominated species Alphabaculovirus orpseudotsugatae, historically originating from OpMNPV. This finding could have significant implications for the classification and understanding of the phylogeographical spread of baculoviruses.

Mass spectrometry based proteomics: Changing the impact of protein analysis in forensic science

AbstractMass spectrometry‐based proteomics, born from the invention of soft ionization, has revolutionized protein analysis. In‐depth characterization of complex and variable mixtures of proteins, such as that found at crime‐scenes, is now possible. This capability continues to expand with advancements in mass spectral instrumentation and computational power, allowing for the development of dynamic data acquisition approaches and sophisticated bioinformatics tools for data interpretation. Proteins are the major component of all biological material, as a result they have been of interest to forensic practitioners for centuries. While DNA is the gold standard for identification, proteins are able to provide contextual information about biological material not available through genomic approaches alone. Mass spectrometry‐based proteomics has expanded the breadth and ease of forensic protein analysis with several emerging applications that promise to become common place as investigative tools, particularly in forensic serology and toxicology. This includes the targeted analysis of protein biomarkers for the classification of biological tissue and fluids, the detection of protein toxins such as ricin and identification of peptide hormones such as insulin. The ability of protein analysis to leverage information from the genome has been demonstrated to achieve individual identification from biological material, an emerging technology that can be used in the absence of extractable DNA. In recent years, there has been considerable scrutiny of forensic techniques that lack objectivity and a statistical basis, an alternative molecular approach for forensic analysis that utilizes the high specificity of mass spectrometry, is a welcome addition with many potential future applications.This article is categorized under: Forensic Chemistry and Trace Evidence > Trace Evidence

Studying Venom Toxin Variation Using Accurate Masses from Liquid Chromatography–Mass Spectrometry Coupled with Bioinformatic Tools

This study provides a new methodology for the rapid analysis of numerous venom samples in an automated fashion. Here, we use LC-MS (Liquid Chromatography–Mass Spectrometry) for venom separation and toxin analysis at the accurate mass level combined with new in-house written bioinformatic scripts to obtain high-throughput results. This analytical methodology was validated using 31 venoms from all members of a monophyletic clade of Australian elapids: brown snakes (Pseudonaja spp.) and taipans (Oxyuranus spp.). In a previous study, we revealed extensive venom variation within this clade, but the data was manually processed and MS peaks were integrated into a time-consuming and labour-intensive approach. By comparing the manual approach to our new automated approach, we now present a faster and more efficient pipeline for analysing venom variation. Pooled venom separations with post-column toxin fractionations were performed for subsequent high-throughput venomics to obtain toxin IDs correlating to accurate masses for all fractionated toxins. This workflow adds another dimension to the field of venom analysis by providing opportunities to rapidly perform in-depth studies on venom variation. Our pipeline opens new possibilities for studying animal venoms as evolutionary model systems and investigating venom variation to aid in the development of better antivenoms.

Understanding the Molecular Landscape of Endometriosis: A Bioinformatics Approach to Uncover Signaling Pathways and Hub Genes

Background: Endometriosis is a chronic gynecological disorder characterized by the ectopic growth of endometrial tissue outside the uterus, leading to debilitating pain and infertility in affected women. Despite its prevalence and clinical significance, the molecular mechanisms underlying the progression of endometriosis remain poorly understood. This study employs bioinformatics tools and molecular docking simulations to unravel the intricate genetic and molecular networks associated with endometriosis progression. Objectives: The primary objectives of this research are to identify differentially expressed genes (DEGs) linked to endometriosis, elucidate associated biological pathways using the Database for Annotation, Visualization, and Integrated Discovery (DAVID), construct a Protein-Protein Interaction (PPI) network to identify hub genes, and perform molecular docking simulations to explore potential ligand-protein interactions associated with endometriosis. Methods: Microarray data from Homo sapiens, specifically Accession: GDS3092 Series = GSE5108 (Platform: GPL2895), were retrieved from the NCBI Gene Expression Omnibus (GEO). The data underwent rigorous preprocessing and DEG analysis using NCBI GEO2. Database for Annotation, Visualization, and Integrated Discovery analysis was employed for functional annotation, and a PPI network was constructed using the STITCH database and Cytoscape 3.8.2. Molecular docking simulations against target proteins associated with endometriosis were conducted using MVD 7.0. Results: A total of 1 911 unique elements were identified as DEGs associated with endometriosis from the microarray data. Database for Annotation, Visualization, and Integrated Discovery analysis revealed pathways and biological characteristics positively and negatively correlated with endometriosis. Hub genes, including BCL2, CCNA2, CDK7, EGF, GAS6, MAP3K7, and TAB2, were identified through PPI network analysis. Molecular docking simulations highlighted potential ligands, such as Quercetin-3-o-galactopyranoside and Kushenol E, exhibiting favorable interactions with target proteins associated with endometriosis. Conclusions: This study provides insights into the molecular signatures, pathways, and hub genes associated with endometriosis. Utilizing DAVID in this study clarifies biological pathways associated with endometriosis, revealing insights into intricate genetic networks. Molecular docking simulations identified ligands for further exploration in therapeutic interventions. The consistent efficacy of these ligands across diverse targets suggests broad-spectrum effectiveness, encouraging further exploration for potential therapeutic interventions. The study contributes to a deeper understanding of endometriosis pathogenesis, paving the way for targeted therapies and precision medicine approaches to improve patient outcomes. These findings advance our understanding of the molecular mechanisms in endometriosis (EMS), offering promising avenues for future research and therapeutic development in addressing this complex condition.