Maternal Innate Immune Reprogramming After Complicated Pregnancy.

PROBLEM: Preeclampsia (PE) and fetal growth restriction (FGR) are often associated with maternal inflammation and an increased risk of cardiovascular and metabolic disease in the affected mothers. The mechanism responsible for this increased risk of subsequent disease may involve reprogramming of innate immune cells, characterized by epigenetic modifications. METHOD OF STUDY: Circulating monocytes from women with PE, FGR, or uncomplicated pregnancies (control) were isolated before labor. Cytokine release from monocytes following exposure to lipopolysaccharide (LPS) and the presence of lysine 4-trimethylated histone 3 (H3K4me3) within TNF promoter sequences were evaluated. Single-cell transcriptomic profiles of circulating monocytes from women with PE or uncomplicated pregnancies were assessed. RESULTS: Monocytes from women with PE or FGR exhibited increased IL-10 secretion and decreased IL-1ß and GM-CSF secretion in response to LPS. While TNFα secretion was not significantly different in cultures of control monocytes versus those from complicated pregnancies with or without LPS exposure, monocytes from complicated pregnancies had significantly decreased levels of H3K4me3 associated with TNF promoter sequences. Cluster quantification and pathway analysis of differentially expressed genes revealed an increased proportion of anti-inflammatory myeloid cells and a lower proportion of inflammatory non-classical monocytes among the circulating monocyte population in women with PE. CONCLUSIONS: Monocytes from women with PE and FGR exhibit an immune tolerance phenotype before initiation of labor. Further investigation is required to determine whether this tolerogenic phenotype persists after the affected pregnancy and contributes to increased risk of subsequent disease.

The genetic diversity and evolution analysis of the Hainan melioidosis outbreak strains.

Melioidosis is a zoonotic disease, with its outbreaks being rare and indicative of an unusual concurrence of extreme climate and natural environmental factors. An outbreak of melioidosis cases emerged in Hainan following Typhoon "Dianmu" from October to December 2021, presenting an opportunity to identify the environmental sources of infection for these cases due to its nature as a well-defined point-source cluster. To investigate the relationship between the occurrence of these melioidosis cases and the environment, we extracted the entire genome of 25 clinical strains and conducted MLST typing, followed by whole genome sequencing and analysis of molecular genetic information for four ST46 genotypes from these strains. Phylogenetic and evolutionary relationships between Hainan sequence types (STs) and those found in other endemic regions were analyzed using IslandPath-DIMO, PHASTER, e-BURST, PHYLOViZ, and the maximum likelihood method. Notably, a total of 25 clinical strains were identified, encompassing 12 STs (ST46, ST1105, ST1991, ST30, ST1992, ST50, ST164, ST55, ST70, ST1993, ST1545, and ST58), with ST1991, ST1992, and ST1993 being newly discovered subtypes. PHYLOViZ clustering analysis divided the strains into two groups (A and B), both closely related to the Asian region. Phylogenetic tree analysis further revealed that most of the strains in this study were closely related to those found in Australia and Thailand. Analysis of patient information and visits to their residences suggested that contaminated water sources might be the primary source of infection during this outbreak. Our findings underscore that extreme weather events, such as typhoons, significantly increase the infection rate of B. pseudomallei, along with its genetic diversity, necessitating additional prevention strategies to control these B. pseudomallei infections.

m5c-iDeep: 5-Methylcytosine sites identification through deep learning.

5-Methylcytosine (m5c) is a modified cytosine base which is formed as the result of addition of methyl group added at position 5 of carbon. This modification is one of the most common PTM that used to occur in almost all types of RNA. The conventional laboratory methods do not provide quick reliable identification of m5c sites. However, the sequence data readiness has made it feasible to develop computationally intelligent models that optimize the identification process for accuracy and robustness. The present research focused on the development of in-silico methods built using deep learning models. The encoded data was then fed into deep learning models, which included gated recurrent unit (GRU), long short-term memory (LSTM), and bi-directional LSTM (Bi-LSTM). After that, the models were subjected to a rigorous evaluation process that included both independent set testing and 10-fold cross validation. The results revealed that LSTM-based model, m5c-iDeep, outperformed revealing 99.9 % accuracy while comparing with existing m5c predictors. In order to facilitate researchers, m5c-iDeep was also deployed on a web-based server which is accessible at https://taseersuleman-m5c-ideep-m5c-ideep.streamlit.app/.

Protocol for analysis of single-cell sequencing data by Seqtometry.

Seqtometry (sequencing-to-measurement) is an analytical platform for single-cell analysis based on direct profiling of gene expression and accessibility achieved by advanced scoring with gene signatures. Here, we present a protocol for single-cell RNA sequencing (scRNA-seq) and single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) analysis using Seqtometry. We describe steps for preprocessing, imputation, scoring, and plotting, with extensions to large datasets and integration of multiple datasets. This protocol yields results in the form of biologically interpretable dimensions for direct identification and comprehensive characterization of specific cells. For complete details on the use and execution of this protocol, please refer to Kousnetsov et al.1.

Molecular characterisation of Histoplasma capsulatum sensu lato from Ethiopian horses reveals two distinct phylogenetic clades.

Epizootic lymphangitis (EL) is a highly prevalent and contagious infectious disease affecting horses in many parts of Ethiopia caused by Histoplasma capsulatum sensu lato ('var. farciminosum'). In this study, 12 suspected isolates of H. capsulatum sensu lato or yeasts unidentified by conventional biochemical tests isolated from Ethiopian horses with EL were characterised by ITS sequencing. Six of the 12 isolates were identified to be members of H. capsulatum sensu lato and the other six were Pichia kudriavzevii (synonym: Candida krusei) (n = 3), Trichosporon asahii (n = 1), Geotrichum silvicola (n = 1) and Moesziomyces aphidis (n = 1), respectively. The six H. capsulatum sensu lato isolates were further characterised by multilocus sequence analysis. Four distinct gene loci [arf (462 bases), H-anti (410 bases), ole1 (338 bases) and tub1 (272 bases)] of these six isolates as well as those of two H. capsulatum sensu lato ('var. farciminosum') reference strains (ATCC 58332 and ATCC 28798) were PCR-amplified and sequenced. Phylogenetic analyses of their concatenated nucleotide sequences showed that three of the isolates and the reference strain ATCC 58332 were identical and belonged to the Eurasia clade within Latin American (LAm) A (H. suramericanum), and those of the other three isolates and the reference strain ATCC 28798 were identical and belonged to the Africa clade. At least two distinct phylogenetic clades of Histoplasma capsulatum sensu lato were circulating in Ethiopian horses with EL. Advanced molecular technologies and bioinformatics tools are crucial for accurate identification and typing of pathogens as well as discovery of novel microorganisms in veterinary microbiology.

Using multilocus sequence analysis with four concatenated housekeeping gene loci, at least two distinct phylogenetic clades, namely Eurasia clade and Africa clade, of Histoplasma capsulatum sensu lato were confirmed to be circulating in Ethiopian horses with epizootic lymphangitis.

Francisella sciaenopsi sp. nov. isolated from diseased red drum Sciaenops ocellatus in Florida, USA.

Piscine francisellosis is one of the most important bacterial diseases affecting various fish species worldwide. Francisella orientalis, F. noatunensis, and F. salimarina (F. marina) have been reported as etiological agents of disease in fish. A Francisella sp. was isolated from several diseased red drum Sciaenops ocellatus experiencing morbidity in Florida, USA, in 2008. In this study, molecular and phenotypic characterization of the recovered isolate was conducted. Phenotypically, the isolate showed a biochemical reaction profile distinct from that of F. orientalis and F. salimarina. Although the 16S rRNA sequence of this isolate shared 99.61% identity to the type strain of F. philomiragia O#319LT, whole genome analysis (average nucleotide identity <95%; digital DNA-DNA hybridization <70%) and a multilocus sequence analysis of 8 concatenated housekeeping genes in comparison with other Francisella spp. indicated that this isolate was a novel Francisella species, more closely related to F. orientalis. Immersion, intracoelomic injection, and co-habitation challenges using a Nile tilapia Oreochromis niloticus fingerling model of infection were done to investigate virulence in a piscine model. Variably pigmented granulomas and pigmented macrophage aggregates were observed in the kidneys and spleens of the challenged fish, but no mortality was recorded during the 15 d challenge period, suggesting that this novel Francisella sp. might be an opportunistic pathogen of fish. Based on the phenotypic and genotypic differences from other Francisella spp. observed in this study, we propose the name Francisella sciaenopsi sp. nov. for this novel isolate.

Shared germline genomic variants in two patients with double primary gastrointestinal stromal tumours (GISTs).

BACKGROUND: Gastrointestinal stromal tumours (GISTs) are prevalent mesenchymal tumours of the gastrointestinal tract, commonly exhibiting structural variations in KIT and PDGFRA genes. While the mutational profiling of somatic tumours is well described, the genes behind the susceptibility to develop GIST are not yet fully discovered. This study explores the genomic landscape of two primary GIST cases, aiming to identify shared germline pathogenic variants and shed light on potential key players in tumourigenesis. METHODS: Two patients with distinct genotypically and phenotypically GISTs underwent germline whole genome sequencing. CNV and single nucleotide variant (SNV) analyses were performed. RESULTS: Both patients harbouring low-risk GISTs with different mutations (PDGFRA and KIT) shared homozygous germline pathogenic deletions in both CFHR1 and CFHR3 genes. CNV analysis revealed additional shared pathogenic deletions in other genes such as SLC25A24. No particular pathogenic SNV shared by both patients was detected. CONCLUSION: Our study provides new insights into germline variants that can be associated with the development of GISTs, namely, CFHR1 and CFHR3 deep deletions. Further functional validation is warranted to elucidate the precise contributions of identified germline mutations in GIST development.

Adult outpatients with long COVID infected with SARS-CoV-2 Omicron variant. Part 1: Oral microbiota alterations.

BACKGROUND: Many individuals experience long COVID after SARS-CoV-2 infection. As microbiota can influence health, it may change with COVID-19. This study investigated differences in oral microbiota between COVID-19 patients with and without long COVID. METHODS: Based on a prospective follow-up investigation, this nested case-control study evaluated the differences in oral microbiota in individuals with and without long COVID (Symptomatic and Asymptomatic groups), which were assessed by 16S rRNA sequencing on tongue coating samples. A predictive model was established using machine learning based on specific differential microbial communities. RESULTS: One-hundred-and-eight patients were included (n=54 Symptomatic group). The Symptomatic group had higher Alpha diversity indices (observed_otus, Chao1, Shannon, and Simpson indices), differences in microbial composition (Beta diversity), and microbial dysbiosis with increased diversity and relative abundance of pathogenic bacteria. Marker bacteria (c__Campylobacterota, o__Coriobacteriales, o__Pseudomonadales, and o__Campylobacterales) were associated with long COVID by linear discriminant analysis effect size and receiver operating characteristic curves (AUC 0.821). CONCLUSION: There were distinct variations in oral microbiota between COVID-19 patients with and without long COVID. Changes in oral microbiota may indicate long COVID.

Efficient prediction of anticancer peptides through deep learning.

Background: Cancer remains one of the leading causes of mortality globally, with conventional chemotherapy often resulting in severe side effects and limited effectiveness. Recent advancements in bioinformatics and machine learning, particularly deep learning, offer promising new avenues for cancer treatment through the prediction and identification of anticancer peptides. Objective: This study aimed to develop and evaluate a deep learning model utilizing a two-dimensional convolutional neural network (2D CNN) to enhance the prediction accuracy of anticancer peptides, addressing the complexities and limitations of current prediction methods. Methods: A diverse dataset of peptide sequences with annotated anticancer activity labels was compiled from various public databases and experimental studies. The sequences were preprocessed and encoded using one-hot encoding and additional physicochemical properties. The 2D CNN model was trained and optimized using this dataset, with performance evaluated through metrics such as accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC-ROC). Results: The proposed 2D CNN model achieved superior performance compared to existing methods, with an accuracy of 0.87, precision of 0.85, recall of 0.89, F1-score of 0.87, and an AUC-ROC value of 0.91. These results indicate the model's effectiveness in accurately predicting anticancer peptides and capturing intricate spatial patterns within peptide sequences. Conclusion: The findings demonstrate the potential of deep learning, specifically 2D CNNs, in advancing the prediction of anticancer peptides. The proposed model significantly improves prediction accuracy, offering a valuable tool for identifying effective peptide candidates for cancer treatment. Future Work: Further research should focus on expanding the dataset, exploring alternative deep learning architectures, and validating the model's predictions through experimental studies. Efforts should also aim at optimizing computational efficiency and translating these predictions into clinical applications.

Identification of macrophage differentiation related genes and subtypes linking atherosclerosis plaque processing and metabolic syndrome via integrated bulk and single-cell sequence analysis.

Metabolic syndrome(MS) is a separate risk factor for the advancement of atherosclerosis(AS) plaque but mechanism behind this remains unclear. There may be a significant role for the immune system in this process. This study aims to identify potential diagnostic genes in MS patients at a higher risk of developing and progressing to AS. Datasets were retrevied from gene expression omnibus(GEO) database and differentially expressed genes were identified. Hub genes, immune cell dysregulation and AS subtypes were identified using a conbination of muliple bioinformatic analysis, machine learning and consensus clustering. Diagnostic value of hub genes was estimated using a nomogram and ROC analysis. Finally, enrichment analysis, competing endogenous RNA(ceRNA) network, single-cell RNA(scRNA) sequencing analysis and drug-protein interaction prediction was constructed to identify the functional roles, potential regulators and distribution for hub genes. Four hub genes and two macrophage-related subtypes were identified. Their strong diagnostic value was validated and functional process were identified. ScRNA analysis identified the macrophage differentiation regulation function of F13A1. CeRNA network and drug-protein binding modes revealed the potential therapeutic method. Four immune-correlated hub genes(F13A1, MMRN1, SLCO2A1 and ZNF521) were identified with their diagnostic value being assesed, which F13A1 was found strong correlated with macrophage differentiation and could be potential diagnostic and therapeutic marker for AS progression in MS patients.

Work-Family Life Course Trajectories and Women's Mental Health: The Moderating Role of Defamilization Policies in 15 European Territories.

This study employs multichannel sequence analysis of data from the Survey of Health, Ageing, and Retirement in Europe to explore variations in the association between work-family life trajectories and women's mental health across European cohorts born between 1924 and 1965 within different policy contexts. It finds that trajectories characterized by prolonged employment and delayed familial commitments are generally associated with increased depressive symptoms. Notably, the strength of this association varies significantly across cohorts and is notably moderated by defamilization policies. These policies, which aim to reduce dependency on family for managing social risks, buffer mental health challenges in traditional family roles but are less effective for women in trajectories with delayed family formation. This investigation highlights the nuanced ways in which historical and cultural contexts alongside policy environments shape mental health outcomes at various life stages, offering valuable insights into our understanding of health disparities across the life course, with an emphasis on exposure to changing institutions.

Vaccine Therapy for Heart Failure Targeting the Inflammatory Cytokine Igfbp7.

BACKGROUND: The heart comprises many types of cells such as cardiomyocytes, endothelial cells (ECs), fibroblasts, smooth muscle cells, pericytes, and blood cells. Every cell type responds to various stressors (eg, hemodynamic overload and ischemia) and changes its properties and interrelationships among cells. To date, heart failure research has focused mainly on cardiomyocytes; however, other types of cells and their cell-to-cell interactions might also be important in the pathogenesis of heart failure. METHODS: Pressure overload was imposed on mice by transverse aortic constriction and the vascular structure of the heart was examined using a tissue transparency technique. Functional and molecular analyses including single-cell RNA sequencing were performed on the hearts of wild-type mice and EC-specific gene knockout mice. Metabolites in heart tissue were measured by capillary electrophoresis-time of flight-mass spectrometry system. The vaccine was prepared by conjugating the synthesized epitope peptides with keyhole limpet hemocyanin and administered to mice with aluminum hydroxide as an adjuvant. Tissue samples from heart failure patients were used for single-nucleus RNA sequencing to examine gene expression in ECs and perform pathway analysis in cardiomyocytes. RESULTS: Pressure overload induced the development of intricately entwined blood vessels in murine hearts, leading to the accumulation of replication stress and DNA damage in cardiac ECs. Inhibition of cell proliferation by a cyclin-dependent kinase inhibitor reduced DNA damage in ECs and ameliorated transverse aortic constriction-induced cardiac dysfunction. Single-cell RNA sequencing analysis revealed upregulation of Igfbp7 (insulin-like growth factor-binding protein 7) expression in the senescent ECs and downregulation of insulin signaling and oxidative phosphorylation in cardiomyocytes of murine and human failing hearts. Overexpression of Igfbp7 in the murine heart using AAV9 (adeno-associated virus serotype 9) exacerbated cardiac dysfunction, while EC-specific deletion of Igfbp7 and the vaccine targeting Igfbp7 ameliorated cardiac dysfunction with increased oxidative phosphorylation in cardiomyocytes under pressure overload. CONCLUSIONS: Igfbp7 produced by senescent ECs causes cardiac dysfunction and vaccine therapy targeting Igfbp7 may be useful to prevent the development of heart failure.

Quantifying periodontitis-associated oral dysbiosis in tongue and saliva microbiomes-An integrated data analysis.

BACKGROUND: Periodontitis is primarily driven by subgingival biofilm dysbiosis. However, the quantification and impact of this periodontal dysbiosis on other oral microbial niches remain unclear. This study seeks to quantify the dysbiotic changes in tongue and salivary microbiomes resulting from periodontitis by applying a clinically relevant dysbiosis index to an integrated data analysis. METHODS: The National Center for Biotechnology Information (NCBI) database was searched to identify BioProjects with published studies on salivary and tongue microbiomes of healthy and periodontitis subjects. Raw sequence datasets were processed using a standardized bioinformatic pipeline and categorized by their ecological niche and periodontal status. The subgingival microbial dysbiosis index (SMDI), a dysbiosis index originally developed using the subgingival microbiome, was computed at species and genus levels and customized for each niche. Its diagnostic accuracy for periodontitis was evaluated using receiver operating characteristic curves. RESULTS: Four studies, contributing 328 microbiome samples, were included. At both species and genus levels, periodontitis samples had a higher SMDI, but the differences were only significant for subgingival biofilm and saliva (p < 0.001). However, SMDI showed good diagnostic accuracy for periodontitis status for all three niches (area under curve ranging from 0.76 to 0.90, p < 0.05). The dysbiosis index of subgingival biofilm was positively correlated with saliva consistently (p < 0.001) and with the tongue at the genus level (p = 0.036). CONCLUSIONS: While the impact on the tongue microbiome requires further investigation, periodontitis-associated dysbiosis affects the salivary microbiome and is quantifiable using the dysbiosis index. The diagnostic potential of salivary microbial dysbiosis as a convenient periodontal biomarker for assessing periodontal status has potential public health and clinical applications. PLAIN LANGUAGE SUMMARY: Periodontitis, a severe inflammation of the gums which causes bone loss, is a disease caused by an imbalance of good and bad bacteria under the gums. However, it is unclear how this bacterial imbalance in the gums affects the bacterial balance of other distinct parts of the mouth, such as the saliva and tongue. This study uses bacteria datasets of four previously published studies, contributing a total of 328 bacterial samples. The data were processed using a uniform data analysis workflow, and a bacterial score, the subgingival microbial dysbiosis index (SMDI), previously shown to capture periodontitis-associated bacteria imbalance, was calculated separately for samples from under the gums, the saliva, and the tongue. The SMDI was able to distinguish between health and periodontitis within each oral location, and in general, the scores were higher for periodontitis samples, though this difference was significant only for bacteria under the gums and in saliva. Saliva scores were also consistently correlated with bacteria under the gums. This study shows that periodontitis-associated bacterial imbalances are observed in oral locations beyond just under the gums, particularly the saliva. Thus, saliva bacteria may be used as a convenient biomarker for assessing gum disease, allowing for potential public health and clinical applications.

Linked lives: intergenerational transmission of labour-market pathways between parent dyads and children.

While a vast number of studies confirm the transmission of labour-market disadvantages from one generation to the next, less is known about how parents' interconnected labour-market pathways co-evolve and shape the opportunities and obstacles for their children's future careers. This study uses a multidimensional view of intergenerational transmission by describing the most typical pathways of parents' occupational careers and assesses how these patterns are associated with their children's labour-market outcomes. Drawing on Swedish longitudinal register data, we used multichannel sequence analysis to follow a cohort of people born in 1985 (n = 72,409) and their parents across 26 years. We identified four parental earning models, differentiating between (1) dual earners with high wages, (2) dual earners with low-wage, (3) one-and-a-half-earners and (4) mother as the main breadwinner. Regression analysis shows strong intergenerational transmission among the most advantageous trajectories, with education as a key determinant for young people to become less dependent on family resources. This study stresses the importance of intra-couple perspectives in life course research to understand how inequalities are shaped and preserved across generations.

Near full-length genome analysis of HEV 4b subtype in pigs showed a similarity up to 99.944% compared to a patient in Guangdong province, China.

Hepatitis E virus (HEV) is a prevalent pathogen responsible for acute viral hepatitis, HEV genotypes 3 and 4 infections causing zoonotic infections. Currently, the nucleotide similarity analysis between humans and pigs for HEV genotype 4 is limited. In this study, stool samples from an HEV-infected patient who is a pig farmer and from pigs were collected to obtain the near full-length genome of HEV, phylogenetic trees were constructed for genotyping, and similarity of HEV sequences was analyzed. The results showed that HEV-RNA was detected in the stool samples from the patient and six pigs (6/30, 20.0%). Both HEV subtype in the patient and pigs was 4b. Additionally, similarity analysis showed that the range was 99.875%-99.944% between the patient and pigs at the nucleotide level. Four isolates of amino acid sequences (ORFs 1-3) from pigs were 100% identical to the patient. Phylogenetic tree and similarity analysis of an additional nine HEV sequences isolated from other patients in this region showed that the HEV sequence from the pig farmer had the closest relationship with the pigs from his farm rather than other sources of infection in this region. This study provides indirect evidences for HEV subtype 4b can be transmitted from pigs to humans at the nucleotide level. Further research is needed to explore the characteristics of different HEV subtypes.

Characterising the allergic fungal rhinosinusitis microenvironment using full-length 16S rRNA gene amplicon sequencing and fungal ITS sequencing.

INTRODUCTION: Allergic fungal rhinosinusitis (AFRS) is a severe phenotype of chronic rhinosinusitis with nasal polyposis (CRSwNP), characterised by localised and exaggerated type 2 inflammation. While fungal antigenic stimulation of unregulated Th2-mediated inflammation is the core pathophysiological mechanism, the direct and synergistic role of bacteria in disease modification is a pervasive hypothesis. We set out to define the microenvironment of AFRS to elucidate virulent organisms that may be implicated in the pathophysiology of AFRS. METHODOLOGY: We undertook a cross-sectional study of AFRS patients and non-fungal CRSwNP patients. Demographics, disease severity, culture and microbiome sequences were analysed. Multimodality microbiome sequencing included short-read next-generation sequencing (NGS) on the Illumina Miseq (16S rRNA and ITS) and full-length 16S rRNA sequencing on the Oxford Nanopore Technologies GridION (ONT). RESULTS: Thirty-two AFRS and 29 non-fungal CRSwNP patients (NF) were included in this study. Staphylococcus aureus was the dominant organism cultured and sequenced in both AFRS and NF groups (AFRS 27.54%; NF 18.04%; p = .07). Streptococcus pneumoniae (AFRS 12.31%; NF 0.98%; p = .03) and Haemophilus influenzae (AFRS 15.03%; NF 0.24%; p = .005) were significantly more abundant in AFRS. Bacterial diversity (Shannon's index) was considerably lower in AFRS relative to NF (AFRS 0.6; NF 1.0, p = .008). Aspergillus was the most cultured fungus in AFRS (10/32, 31.3%). The AFRS sequenced mycobiome was predominantly represented by Malassezia (43.6%), Curvularia (18.5%) and Aspergillus (16.8%), while the NF mycobiome was nearly exclusively Malassezia (84.2%) with an absence of Aspergillus or dematiaceous fungi. CONCLUSION: A low diversity, dysbiotic microenvironment dominated by Staphylococcus aureus, Streptococcus pneumoniae and Haemophilus influenzae characterised the bacterial microbiome of AFRS, with a mycobiome abundant in Malassezia, Aspergillus and Curvularia. While Staphylococcus aureus has been previously implicated in AFRS through enterotoxin superantigen potential, Streptococcus pneumoniae and Haemophilus influenzae are novel findings that may represent alternate cross-kingdom pathophysiological mechanisms.

Protocol for the construction and functional profiling of metagenome-assembled genomes for microbiome analyses.

Constructing metagenome-assembled genomes (MAGs) from complex metagenomic samples involves a series of bioinformatics operations, each requiring deep bioinformatics knowledge. Here, we present a protocol for constructing MAGs and conducting functional profiling to address biological questions. We describe steps for system configuration, data downloads, read processing, removal of human DNA contamination, metagenomic assembly, and statistical quality assessment of the final assembly. Additionally, we detail procedures for the construction and refinement of MAGs, as well as the functional profiling of MAGs.

Epidemiological, and molecular investigation of Canine parvovirus-2 infection in Egypt.

IMPORTANCE: Canine parvovirus enteritis (CPE) is a contagious viral disease of dogs caused by the canine parvovirus-2 (CPV-2) associated with high morbidity and mortality rates. CPV-2 has a high global evolutionary rate. Molecular characterization of CPV-2 and understanding its epidemiology are essential for controlling CPV-2 infections. OBJECTIVE: This study examined the risk factors and survival outcomes of dogs infected with CPV-2. Molecular characterization of CPV-2 genotypes circulating in Egypt was performed to determine the evolution of CPV-2 nationally and globally. METHODS: An age-matched case-control study was conducted on 47 control and 47 CPV-infected dogs. Conditional logistic regression analysis examined the association between the potential risk factors and CPE in dogs. Survival analysis was performed to determine the survival pattern of the infected dogs. Thirteen fecal samples from infected dogs were collected to confirm the CPV genotype by CPV-2 VP2 gene sequencing, assembly of nucleotide sequences, and phylogenic analysis. RESULTS: Unvaccinated and roamer dogs had eight and 2.3 times higher risks of CPV infection than vaccinated dogs and non-roamer dogs, respectively. The risk of death from CPE was high among dogs without routine visits to veterinary clinics and among non-roamer dogs. Molecular characterization of CPV-2 confirmed its genotype identity and relationship with the CPV-2 c and b clade types. CONCLUSIONS AND RELEVANCE: This study highlights the potential factors for CPE control, especially vaccination and preventing dogs from roaming freely outside houses. Isolated CPV genotypes are closely related to southern Asian genotypes, suggesting a substantial opportunity for global transmission.

SENSE-PPI reconstructs interactomes within, across, and between species at the genome scale.

Ab initio computational reconstructions of protein-protein interaction (PPI) networks will provide invaluable insights into cellular systems, enabling the discovery of novel molecular interactions and elucidating biological mechanisms within and between organisms. Leveraging the latest generation protein language models and recurrent neural networks, we present SENSE-PPI, a sequence-based deep learning model that efficiently reconstructs ab initio PPIs, distinguishing partners among tens of thousands of proteins and identifying specific interactions within functionally similar proteins. SENSE-PPI demonstrates high accuracy, limited training requirements, and versatility in cross-species predictions, even with non-model organisms and human-virus interactions. Its performance decreases for phylogenetically more distant model and non-model organisms, but signal alteration is very slow. In this regard, it demonstrates the important role of parameters in protein language models. SENSE-PPI is very fast and can test 10,000 proteins against themselves in a matter of hours, enabling the reconstruction of genome-wide proteomes.

Molecular Identification and Survey of Cyclospora spp. in Cattle in Shanxi Province, North China.

To date, more than 20 species in the genus Cyclospora have been reported. Among them, Cyclospora cayetanensis has been recognized as the causative agent of human cyclosporiasis, which is characterized by severe intestinal injury and prolonged diarrhea in patients with immune dysfunction. The presence of C. cayetanensis in cattle has been confirmed. To date, however, no surveillance data are available on the occurrence and prevalence of Cyclospora spp. in cattle in Shanxi Province, North China. In the present study, a total of 761 fecal samples collected from cattle in three representative counties (Qi, Jishan, and Shanyin) in this Province were examined for Cyclospora spp. by using a polymerase-chain-reaction-restriction-fragment-length polymorphism (PCR-RFLP) test based on the nuclear small subunit ribosomal RNA (SSU rRNA) gene. The prevalence of Cyclospora spp. in cattle was 2.1%, and region, age, sex, and breed were not identified to be risk factors. Molecular evolutionary analysis based on the SSU rRNA sequences revealed that all 12 of the isolates were relatively distant from the human pathogen C. cayetanensis; seven isolates were grouped with Cyclospora colobi, whereas the others were grouped with cattle Cyclospora spp. reported previously. Though C. cayetanensis was not detected in cattle in the present study, more investigations should be performed in human populations, other animal species, or cattle from other regions of Shanxi Province and other environmental sources from the One Health perspective.