Zoonotic transmission of Chlamydia felis from domestic cats; A case series of chronic follicular conjunctivitis in humans

We present three cases of chronic follicular conjunctivitis resulting from the zoonotic transmission of Chlamydia felis from domestic cats. Our objective is to raise awareness regarding the potential zoonotic transmission of Chlamydia felis from domestic cats and describe the methodology for definitive pathogen identification using Polymerase Chain Reaction (PCR) and subsequent sequence analysis, a useful tool in the identification of these rare pathogens. We discuss the factors that could be contributing to the potential under-diagnosis of zoonotic C. felis infections and propose a treatment regime for cases of C. felis-related conjunctivitis.

Aberrant sparganosis in cat caused by Spirometra mansoni (Cestoda: Diphyllobothriidae): a case report

BACKGROUND

Sparganosis is a rare zoonotic disease caused by plerocercoid larvae of the genera Spirometra or Sparganum (Cestoda: Diphyllobothriidae). The larvae of Spirometra generally do not undergo asexual reproduction, whereas those of Sparganum can induce proliferative lesions in infected tissues. This paper presents an unusual case of proliferative sparganosis due to infection with Spirometra mansoni in a cat, normally considered a definitive host of the species.

CASE PRESENTATION

A 9-year-old male domestic cat was presented with a mass on the right side of the face that underwent progressive enlargement for 1 month. The morphological and histopathological examinations revealed multiple asexual proliferative cestode larvae in the lesions, suggestive of proliferative sparganosis. Next-generation sequencing analysis of formalin-fixed and paraffin-embedded specimens of surgically excised tissue indicated that the worm was Spirometra mansoni.

CONCLUSION

Although S. mansoni a common tapeworm species found in the small intestine of domestic cats and dogs in Japan, proliferative sparganosis is extremely rare. This is the first confirmed case of proliferative sparganosis due to infection with S. mansoni in cat.

Protocol for constructing glycan biosynthetic networks using glycowork

Glycans, present across all domains of life, comprise a wide range of monosaccharides assembled into complex, branching structures. Here, we present an in silico protocol to construct biosynthetic networks from a list of observed glycans using the Python package glycowork. We describe steps for data preparation, network construction, feature analysis, and data export. This protocol is implemented in Python using example data and can be adapted for use with customized datasets. For complete details on the use and execution of this protocol, please refer to Thomès et al.1.

Trajectories of school refusal: sequence analysis using retrospective parent reports

School refusal (SR) is a form of school attendance problem (SAP) that requires specific mental health care. Despite improvements in the definition of SAPs, the course of SR is not well characterized. To explore three-year patterns of SR course in children, as reported by their parents, we deployed an anonymous web-based survey. We defined SR onset as the absence of ≥ 2 school weeks during one academic year, combined with emotional distress. We defined standard SR trajectories using sequence analysis of parents' recollection of three consecutive years of school attendance. We obtained 1970 responses, 1328 (67%) completed by a parent and meeting the definition of SR. Of these, 729 (55%) responses included three years of school attendance recollection. We identified five prototypical trajectories of SR: two profiles for children: beaded absences (n = 272), and rapid recovery (n = 132); and three for adolescents: prolonged recovery (n = 93), gradual decline (n = 89), and rapid decline (n = 143). We found five distinct trajectories of retrospective recall of SR course. Through pattern recognition, this typology could help with timely identification of SR and implementation of evidence-based interventions to optimize outcomes. Prospective replication of these findings and their field application is warranted.

Porcine respiratory coronavirus genome sequences; comparisons and relationships to transmissible gastroenteritis viruses

Porcine respiratory coronavirus (PRCV) was initially detected in Europe, and later in the United States of America (US), in the 1980s. In this study we obtained and compared PRCV sequences from Europe and the US, and investigated how these are related to transmissible gastroenteritis virus (TGEV) sequences. The whole genome sequences of Danish (1/90-DK), Italian (PRCV15087/12 III NPTV Parma), and Belgian PRCV (91V44) strains are presented. These sequences were aligned with nine other PRCV sequences from Europe and the US, and 43 TGEV sequences. Following alignment of the PRCV sequences, it was apparent that multiple amino acid variations in the structural proteins were distinct between the European and US strains. The alignments were used to build phylogenetic trees to infer the evolutionary relationships between the strains. In these trees, the European PRCV strains clustered as a separate group, whereas the US strains of PRCV all clustered with TGEVs.

A new species of Demodex (Acari: Demodecidae) from the skin of golden-handed tamarins, Saguinus midas (Primates: Cebidae)

Two captive-bred golden-handed tamarins, Saguinus midas L., 1758 (Primates: Cebidae), kept in households in Japan, presented with psoriasis-like plaques on their faces, along with scale, alopecia, and itching. Histopathological examination revealed numerous Demodex mites in the hair follicles, and the clinical symptoms in both cases improved after treatment with fluralaner. Based on the morphological and genetic characteristics of the mites collected from tamarins, we describe a new species of Demodex. This new species is the fifth valid Demodex species recorded from primates.

Effect of Lactobacillus Rhamnosus GG for Regulation of Inflammatory Response in Radiation-Induced Enteritis

The purpose of this study was to investigate the role of Lactobacillus rhamnosus GG (LGG) probiotics in radiation enteritis using in vivo mice. A total of 40 mice were randomly assigned to four groups: control, probiotics, radiotherapy (RT), and RT + probiotics. For the group of probiotics, 0.2 mL of solution that contained 1.0 × 108 colony-forming units (CFU) of LGG was used and orally administered daily until sacrifice. For RT, a single dose of 14 Gy was administered using a 6 mega-voltage photon beam to the abdominopelvic area. Mice were sacrifice at day 4 (S1) and day 7 (S2) after RT. Their jejunum, colon, and stool were collected. A multiplex cytokine assay and 16 s ribosomal RNA amplicon sequencing were then performed. Regarding cytokine concentrations in tissues, pro-inflammatory cytokines, such as tumor necrosis factor-α, interleukin-6 and monocyte chemotactic protein-1, showed significantly decreased protein levels in colon tissues of the RT + probiotics group than in the RT alone group (all p < 0.05). As for comparing microbial abundance through alpha-diversity and beta-diversity, no significant differences were observed between the RT + probiotics and RT alone groups, except for an increase in alpha-diversity in the stool of the RT + probiotics group. Upon analysis of differential microbes based on treatment, the dominance of anti-inflammatory-related microbes, such as Porphyromonadaceae, Bacteroides acidifaciens, and Ruminococcus, was observed in the jejunum, colon, and stool of the RT + probiotics group. With regard to predicted metabolic pathway abundances, the pathways associated with anti-inflammatory processes, such as biosynthesis of pyrimidine nucleotides, peptidoglycans, tryptophan, adenosylcobalamin, and propionate, were differentially identified in the RT + probiotics group compared to the RT alone group. Protective effects of probiotics on radiation enteritis were potentially derived from dominant anti-inflammation-related microbes and metabolites.

Becoming a parent: Trajectories of family division of labor in Germany and the United States

The transition to parenthood represents a turning point shaping couples' arrangements for paid work and housework. Previous studies often examined these changes in isolation, rather than as interrelated trajectories reflecting diverse models of family division of labor. Drawing on data from different-sex couples from the 1984-2019 Panel Study of Income Dynamics and the 1984-2020 German Socio-Economic Panel, this study uses multichannel sequence analysis to identify four and three patterned constellations of family division of labor in the United States and Germany, respectively. These constellations differ in women's and men's respective contributions to household earnings and their relative participation in housework, spanning from one year before to ten years after the birth of a first child. National differences are found in the identified constellations, their prevalence, and the role of couples' conjoint education in shaping these constellations. In both countries, couples in which the husband has an educational advantage are most likely to transition to a traditional arrangement. However, only in the U.S. do couples with both partners holding a college degree also tend to enter a traditional arrangement. Furthermore, among U.S. couples in which the wife has an educational advantage, they are most likely to adopt a partly egalitarian arrangement (equal earnings but not housework) upon becoming parents.

Sequencing hour-level temporal patterns of polysubstance use among persons who use cocaine, alcohol, and cannabis: A back-translational approach

BACKGROUND

Polysubstance use is highly prevalent among persons who use cocaine; however, little is known about how alcohol and cannabis are used with cocaine. We identified temporal patterns of cocaine+alcohol and cocaine+cannabis polysubstance use to inform more translationally relevant preclinical models.

METHODS

Participants who used cocaine plus alcohol and/or cannabis at least once in the past 30 days (n=148) were interviewed using the computerized Substance Abuse Module and the newer Polysubstance Use-Temporal Patterns Section. For each day in the past 30 days, participants reported whether they had used cocaine, alcohol, and cannabis; if any combinations of use were endorsed, participants described detailed hourly use of each substance on the most "typical day" for the combination. Sequence analysis and hierarchical clustering were applied to identify patterns of timing of drug intake on typical days of cocaine polysubstance use.

RESULTS

We identified five temporal patterns among the 180 sequences of reported cocaine polysubstance use: 1) limited cocaine/cocaine+alcohol use (53%); 2) extensive cannabis then cocaine+alcohol+cannabis use (22%); 3) limited alcohol/cannabis then cocaine+alcohol use (13%); 4) extensive cocaine+cannabis then cocaine+alcohol+cannabis use (4%); and 5) extensive cocaine then cocaine+alcohol use (8%). While drug intake patterns differed, prevalence of use disorders did not.

CONCLUSIONS

Patterns were characterized by cocaine, alcohol, and cannabis polysubstance use and by the timing, order, duration, and quantity of episode-level substance use. The identification of real-world patterns of cocaine polysubstance use represents an important step toward developing laboratory models that accurately reflect human behavior.

Protocol for the isolation and single-nuclei multiomic analyses of the human fetal epicardium

The characterization of cell populations that reside in the outer layer of the heart has been hindered by difficulties in their isolation. Here, we present a protocol for isolation and single-nuclei multiomic analyses of the human fetal epicardium. We describe steps for microdissection, isolation, and enrichment of epicardial cells by mechanical dissociations and direct lysis. We then detail procedures for integrating transcriptome and chromatin accessibility datasets. This approach allows the analysis of diverse cell populations, marked by unique cis-regulatory elements. For complete details on the use and execution of this protocol, please refer to Travisano et al.1.

Protocol for fast clonal family inference and analysis from large-scale B cell receptor repertoire sequencing data

The expeditious identification and comprehensive analysis of clonal families from extensive B cell receptor (BCR) repertoire sequencing data are imperative for elucidating the intricacies of B cell immune responses. Here, we introduce a computational pipeline designed to swiftly deduce clonal families from bulk BCR heavy-chain sequencing data, accompanied by a suite of functional modules tailored to streamline post-clustering analysis. The outlined methodology encompasses guidelines for software installation, meticulous data preparation, and the systematic inference and analysis of clonal families. For complete details on the use and execution of this protocol, please refer to Wang et al.1.

DandD: Efficient measurement of sequence growth and similarity

Genome assembly databases are growing rapidly. The redundancy of sequence content between a new assembly and previous ones is neither conceptually nor algorithmically easy to measure. We introduce pertinent methods and DandD, a tool addressing how much new sequence is gained when a sequence collection grows. DandD can describe how much structural variation is discovered in each new human genome assembly and when discoveries will level off in the future. DandD uses a measure called δ ("delta"), developed initially for data compression and chiefly dependent on k-mer counts. DandD rapidly estimates δ using genomic sketches. We propose δ as an alternative to k-mer-specific cardinalities when computing the Jaccard coefficient, thereby avoiding the pitfalls of a poor choice of k. We demonstrate the utility of DandD's functions for estimating δ, characterizing the rate of pangenome growth, and computing all-pairs similarities using k-independent Jaccard.

Protocol for the detection of defined T cell clones in a heterogeneous cell population

Identifying defined T cell clones within a polyclonal population is key to clarifying their phenotype and function. Here, we present a protocol for detecting specified T cell clones in a heterogeneous cell population. We describe steps for stimulating human CD4+ T cells isolated from blood with a protein antigen, sorting antigen-specific cells by fluorescence-activated cell sorting, and detecting among these the presence of predefined T cell clones, based on their T cell receptor (TCR). TCR cDNA is amplified through 5'-RACE (TCR-SMART) and detected by qPCR. For complete details on the use and execution of this protocol, please refer to Notarbartolo et al. (2021).1.

Establishing a 3D culture system for early organogenesis of monkey embryos ex vivo and single-cell transcriptome analysis of cultured embryos

Creating in vitro culture platforms for monkey embryos is crucial for understanding the initial 4 weeks of early primate embryogenesis. Here, we present a protocol to culture cynomolgus monkey embryos in vitro for 25 days post-fertilization and to delineate the key developmental events of gastrulation and early organogenesis. We describe steps for culturing with a 3D system, immunofluorescence analysis, single-cell RNA sequencing, and bioinformatic analysis. For complete details on the use and execution of this protocol, please refer to Gong et al. (2023).1.

Integrative detection of genome-wide translation using iRibo

Ribosome profiling is a sequencing technique that provides a global picture of translation across a genome. Here, we present iRibo, a software program for integrating any number of ribosome profiling samples to obtain sensitive inference of annotated or unannotated translated open reading frames. We describe the process of using iRibo to generate a species' translatome from a set of ribosome profiling samples using S. cerevisiae as an example. For complete details on the use and execution of this protocol, please refer to Wacholder et al. (2023).1.

A deep learning adversarial autoencoder with dynamic batching displays high performance in denoising and ordering scRNA-seq data

By providing high-resolution of cell-to-cell variation in gene expression, single-cell RNA sequencing (scRNA-seq) offers insights into cell heterogeneity, differentiating dynamics, and disease mechanisms. However, challenges such as low capture rates and dropout events can introduce noise in data analysis. Here, we propose a deep neural generative framework, the dynamic batching adversarial autoencoder (DB-AAE), which excels at denoising scRNA-seq datasets. DB-AAE directly captures optimal features from input data and enhances feature preservation, including cell type-specific gene expression patterns. Comprehensive evaluation on simulated and real datasets demonstrates that DB-AAE outperforms other methods in denoising accuracy and biological signal preservation. It also improves the accuracy of other algorithms in establishing pseudo-time inference. This study highlights DB-AAE's effectiveness and potential as a valuable tool for enhancing the quality and reliability of downstream analyses in scRNA-seq research.

High-throughput protocol for studying pri-miRNA processing using randomized sequences

The Microprocessor complex is crucial in microRNA (miRNA) biogenesis, as it processes primary miRNAs (pri-miRNAs) into precursor miRNAs. Here, we present a high-throughput, radioisotope-free protocol for studying pri-miRNA processing using randomized sequences. We describe steps for randomized substrate preparation, protein purification, processing assays, and DNA library construction for sequencing. This technique explores pri-miRNA processing, uncovers key RNA elements, and illuminates gene expression regulation. However, its efficacy may be constrained by data analysis complexity and the requirement for specialized equipment. For complete details on the use and execution of this protocol, please refer to Nguyen et al. (2023).1.

Protocol for identifying differentially expressed genes using the RumBall RNA-seq analysis platform

Here, we present a protocol for the identification of differentially expressed genes through RNA sequencing analysis. Starting with FASTQ files from public datasets, this protocol leverages RumBall within a self-contained Docker system. We describe the steps for software setup, obtaining data, read mapping, sample normalization, statistical modeling, and gene ontology enrichment. We then detail procedures for interpreting results with plots and tables. RumBall internally utilizes popular tools, ensuring a comprehensive understanding of the analysis process.

Guide for phenotype-specific profiling of DNA G-quadruplex-regulated genes

DNA G-quadruplex (G4) is a non-canonical four-stranded secondary structure that has been shown to play a role in epigenetic modulation of gene expression. Here, we present a primer on phenotype-specific profiling of DNA G-quadruplex-regulated genes. We provide guidance on in silico exploration of G4-related genes and phenotypes, and in vitro and in vivo validation of the relationship between G4 and phenotype. We describe commonly utilized techniques and detail critical steps involved in determining the phenotype-specific G4-regulated genes for subsequent investigations.

Identifying promoter and enhancer sequences by graph convolutional networks

Identification of promoters, enhancers, and their interactions helps understand genetic regulation. This study proposes a graph-based semi-supervised learning model (GCN4EPI) for the enhancer-promoter classification problem. We adopt a graph convolutional network (GCN) architecture to integrate interaction information with sequence features. Nodes of the constructed graph hold word embeddings of DNA sequences while edges hold the Enhancer-Promoter Interaction (EPI) information. By means of semi-supervised learning, much less data (16%) and time are needed in model training. Comparisons on a benchmark dataset of six human cell lines show that the proposed approach outperforms the state-of-the-art methods by a large margin (10% higher F1 score) and has the fastest training time (up to 3 times). Moreover, GCN4EPI's performance on cross-cell line data is also better than the baselines (3% higher F1 score). Our qualitative analyses with graph explainability models prove that GCN4EPI learns from both text and graph structure. The results suggest that integrating interaction information with sequence features improves predictive performance and compensates for the number of training instances.

Identification and Validation of Lipid Metabolism Gene FASN-Associated miRNA in Wilms Tumor

miRNA has been a research hotspot in recent years and its scope of action is very wide, involving the regulation of cell proliferation, differentiation, apoptosis, and other biological behaviors. This study intends to explore the role of miRNA in the lipid metabolism and development of Wilms tumor (WT) by detecting and analyzing the differences in the expression profiles of miRNAs between the tumor and adjacent normal tissue. Gene detection was performed in tumor tissues and adjacent normal tissues of three cases of WT to screen differentially expressed miRNAs (DEMs). According to our previous research, FASN, which participates in the lipid metabolism pathway, may be a target of WT. The starBase database was used to predict FASN-targeted miRNAs. The above two groups of miRNAs were intersected to obtain FASN-targeted DEMs and then GO Ontology (GO) functional enrichment analysis of FASN-targeted DEMs was performed. Finally, the FASN-targeted DEMs were compared and further verified by qRT‒PCR. Through gene sequencing and differential analysis, 287 DEMs were obtained, including 132 upregulated and 155 downregulated miRNAs. The top ten DEMs were all downregulated. Fourteen miRNAs targeted by the lipid metabolism-related gene FASN were predicted by starBase. After intersection with the DEMs, three miRNAs were finally obtained, namely, miR-107, miR-27a-3p, and miR-335-5p. GO enrichment analysis was mainly concentrated in the Parkin-FBXW7-Cul1 ubiquitin ligase complex and response to prostaglandin E. Further experimental verification showed that miR-27a-3p was significantly correlated with WT (P = 0.0018). Imbalanced expression of miRNAs may be involved in the occurrence and development of WT through lipid metabolism. The expression of miR-27a-3p is related to the malignant degree of WT, and it may become the target of diagnosis, prognosis, and treatment of WT in the later stage.

Stack-DHUpred: Advancing the accuracy of dihydrouridine modification sites detection via stacking approach

Dihydrouridine (DHU, D) is one of the most abundant post-transcriptional uridine modifications found in tRNA, mRNA, and snoRNA, closely associated with disease pathogenesis and various biological processes in eukaryotes. Identifying D sites is important for understanding the modification mechanisms and/or epigenetic regulation. However, biological experiments for detecting D sites are time-consuming and expensive. Given these challenges, computational methods have been developed for accurately identifying the D sites in genome-wide datasets. However, existing methods have some limitations, and their prediction performance needs to be improved. In this work, we have developed a new computational predictor for accurately identifying D sites called Stack-DHUpred. Briefly, we trained 66 baseline models or single-feature models by connecting six machine learning classifiers with eleven different feature encoding methods and stacked different baseline models to build stacked ensemble learning models. Subsequently, the optimal combination of the baseline models was identified for the construction of the final stacked model. Remarkably, the Stack-DHUpred outperformed the existing predictors on our new independent dataset, indicating that the stacking approach significantly improved the prediction performance. We have made Stack-DHUpred available to the public through a web server (http://kurata35.bio.kyutech.ac.jp/Stack-DHUpred) and a standalone program (https://github.com/kuratahiroyuki/Stack-DHUpred). We believe that Stack-DHUpred will be a valuable tool for accelerating the discovery of D modifications and understanding their role in post-transcriptional regulation.

Applying sequence analysis to uncover 'real-world' clinical pathways from routinely collected data: a systematic review

OBJECTIVES

This systematic review aims to elucidate the methodological practices and reporting standards associated with sequence analysis (SA) for the identification of clinical pathways in real-world scenarios, using routinely collected data.

STUDY DESIGN AND SETTING

We conducted a methodological systematic review, searching five medical and health databases: MEDLINE, PsycINFO, CINAHL, EMBASE and Web of Science. The search encompassed articles from the inception of these databases up to February 28, 2023. The search strategy comprised two distinctive sets of search terms, specifically focused on sequence analysis and clinical pathways.

RESULTS

19 studies met the eligibility criteria for this systematic review. Nearly 60% of the included studies were published in or after 2021, with a significant proportion originating from Canada (n = 7) and France (n = 5). 90% of the studies adhered to the fundamental SA steps. The optimal matching (OM) method emerged as the most frequently employed dissimilarity measure (63%), while agglomerative hierarchical clustering using Ward's linkage was the preferred clustering algorithm (53%). However, it is imperative to underline that a majority of the studies inadequately reported key methodological decisions pertaining to SA.

CONCLUSION

This review underscores the necessity for enhanced transparency in reporting both data management procedures and key methodological choices within SA processes. The development of reporting guidelines and a robust appraisal tool tailored to assess the quality of SA would be invaluable for researchers in this field.

Food environment trajectories: a sequence analysis from the CARTaGENE cohort

OBJECTIVE

The purpose of this study was to create a typology of longitudinal exposure to food environment based on socio-economic context.

DESIGN

Food environment trajectories were modelled using a sequence analysis method, followed by a logistic regression to describe those trajectories.

SETTING

The study took place in Quebec, Canada, using food environment data from 2009, 2011 and 2018 merged with participants' demographic and socio-economic characteristics.

PARTICIPANT

At recruitment, 38 627 participants between the ages of 40 and 69 years from six urban areas in Quebec were included in the CARTaGENE cohort study. The cohort was representative of the Quebec urban population within this age range.

RESULTS

Our study revealed five trajectories of food access over time: (1) limited access to food stores throughout the study period, (2) limited access improving, (3) good access diminishing, (4) good access throughout the period and (5) low access throughout the period. Logistic regression analysis showed that participants who were unable to work (OR = 1·42, CI = 1·08-1·86), lived in households with five or more persons (OR = 1·69, CI = 1·17-2·42) and those living in low-income households (OR = 1·32, CI = 1·03-1·71) had higher odds of experiencing a disadvantaged food environment trajectory. Additionally, the level of education and age of participants were associated with the odds of experiencing a disadvantaged food environment trajectory.

CONCLUSIONS

The study demonstrates that people facing socio-economic disadvantage are more likely to experience a disadvantaged food environment trajectory over time.

Characterizing adjuvants' effects at murine immunoglobulin repertoire level

Generating large-scale, high-fidelity sequencing data is challenging and, furthermore, not much has been done to characterize adjuvants' effects at the repertoire level. Thus, we introduced an IgSeq pipeline that standardized library prep protocols and data analysis functions for accurate repertoire profiling. We then studied systemically effects of CpG and Alum on the Ig heavy chain repertoire using the ovalbumin (OVA) murine model. Ig repertoires of different tissues (spleen and bone marrow) and isotypes (IgG and IgM) were examined and compared in IGHV mutation, gene usage, CDR3 length, clonal diversity, and clonal selection. We found Ig repertoires of different compartments exhibited distinguishable profiles at the non-immunized steady state, and distinctions became more pronounced upon adjuvanted immunizations. Notably, Alum and CpG effects exhibited different tissue- and isotype-preferences. The former led to increased diversity of abundant clones in bone marrow, and the latter promoted the selection of IgG clones in both tissues.

Psychosocial conditions during school-age as determinants of long-term labour market attachment: a study of the Northern Swedish Cohort from the 1980s to the 2020s

BACKGROUND

This study, conducted on a Swedish population cohort, explores how internalized (depressive and functional somatic) and externalized (smoking, drinking, truancy, vandalism, delinquency) mental health symptoms, as well as close interpersonal relations (family climate and school connectedness) reported during adolescence, influence the work-life course up to late midlife.

METHODS

We examined repeated measurements of labour market status from age 16 to 56 using sequence analyses. We identified five different labour market attachment (LMA16-56) trajectories, namely 'strong,' 'early intermediate,' 'early weak,' 'late weak,' and 'constantly weak.' Multinomial logistic regressions were employed to relate each of the nine determinants to the identified trajectories.

RESULTS

When compared to the risk of 'strong' LMA16-56, adversity in all conditions, except for vandalism, entailed a higher risk of the 'constantly weak' trajectory. Moreover, all conditions, except for functional somatic symptoms, entailed a higher risk of the 'late weak' LMA16-56. The risk of the 'early intermediate' LMA16-56 was non-significant across all the conditions.

CONCLUSIONS

This study contributes to existing knowledge through its novel exploration of labour market attachment and the revelation of the significance of proximal interpersonal relationships in attachment outcomes. Additionally, the study reaffirms the importance of externalizing behaviour, while suggesting that internalized symptoms in adolescence might have a less influential, though not negligible, role. These results underscore the importance of addressing acting out behaviour and nurturing human relationships during compulsory basic education, when the entire age group is still within reach. This approach aims not only to reduce frictions in the school-to-work transition but also to prevent midlife labour market attachment problems that may arise with delayed intervention.

Sequences of vocational rehabilitation services in Germany: a cohort study

BACKGROUND

This study aimed to describe sequences of vocational rehabilitation services among individuals with approved vocational rehabilitation in Germany and to identify typical service sequences.

METHODS

We used administrative data on vocational rehabilitation services and questionnaire data on health and work ability to describe frequencies and sequences of vocational rehabilitation services financed by the Federal German Pension Insurance. Through sequence analysis, we were able to map the service sequences. We did cluster analyses to identify typical different service sequences.

RESULTS

Our sample included 1,652 individuals with 2,584 services. Integration services and two-year vocational retraining were the most common services. We could identify three different service clusters around integration services: shorter ones, followed by employer benefits and without employer benefits. We found two different clusters around two-year vocational retraining: shorter and longer clusters. Two-year vocational retraining was more often initiated by preparatory services and followed by employer benefits than integration services. Longer services in both clusters were associated with better baseline data for physical health, work ability, risk of future work disability, and younger age than shorter services. People in two-year-vocational retraining reported at baseline better general health, better work ability, low risk of future work disability, and less mental illness compared to people in integration services.

CONCLUSIONS

Multiple services, that is, sequences of services, were more likely to occur among individuals with more complex services like two-year vocational retraining. Utilization of complex services and longer services was influenced by health, age, risk of future work disability, and education.

TRIAL REGISTRATION

German Clinical Trials Register DRKS00009910, registration 25/01/2016.

Disease suppression, growth promotion and colonization attributes of resident endophytic bacteria against white root rot (Dematophora necatrix Hartig) of apple

The inherent potential of apple plants was investigated to explore bacterial endophytes and their role in suppressing Dematophora necatrix, the causative pathogen of white root rot disease. Resultantly 34 endophytic bacteria isolated from healthy apple plants, and subsequently 6 most efficient isolates viz., Bacillus megaterium strain EA3, Enterobacter sp. strain EA7, Bacillus megaterium strain EK2, Stenotrophomonas maltophilia strain EK6, Acinetobacter nosocomialis strain ES2 and Pseudomonas aeruginosa strain ES8 depicting anti-pathogen interactions through preliminary screening were assessed further under in vitro, glasshouse and field conditions against white root rot pathogen/disease. Maximum mycelial growth inhibition (80.37%) was obtained with S. maltophilia strain EK6 encouraging for its seed treatment and soil application thereby providing significant effective control (87.91%) of white root rot under glasshouse conditions to other five bacterial endophytes evaluated simultaneously, followed by field efficacy of 83.70%. In addition, it has significantly enhanced the growth parameters of apple trees under both glasshouse and field conditions. The inoculated healthy plants were assessed for endophytic colonization which revealed maximum endosphere colonialism by S. maltophilia strain EK6. Additionally, confocal microscopic images of transverse sections of root cells colonized by bacterial endophytes as compared to untreated control implied their persistence and establishment in endosphere of apple seedlings. The study provides the first report on interaction between apple associated bacterial root endophytes and D. necatrix. The obtained endophytic strains could be employed as alternative for mitigating white root rot disease in future.

Nicotine destructs dental stem cell-based periodontal tissue regeneration

Background/purpose

Nicotine is a widely known addictive and toxic substance in cigarette that exacerbates periodontitis. However, its deleterious effects on dental stem cells and subsequent implications in tissue regeneration remain unclear. This study aimed to explore the effects of nicotine on the regenerative capacity of human periodontal ligament stem cells (hPDLSCs) based on transcriptomics and proteomics, and determined possible targeted genes associated with smoking-related periodontitis.

Materials and methods

hPDLSCs were treated with different concentrations of nicotine ranging from 10-3 to 10-8 M. Transcriptomics and proteomics were performed and confirmed employing Western blot, 5-ethynyl-2'-deoxyuridine (EdU), and alkaline phosphatase (ALP) staining. A ligature-induced periodontitis mouse model was established and administrated with nicotine (16.2 μg/10 μL) via gingival sulcus. The bone resorption was assessed by micro-computed tomography and histological staining. Key genes were identified using multi-omics analysis with verifications in hPDLSCs and human periodontal tissues.

Results

Based on enrichments analysis, nicotine-treated hPDLSCs exhibited decreased proliferation and differentiation abilities. Local administration of nicotine in mouse model significantly aggravated bone resorption and undermined periodontal tissue regeneration by inhibiting the endogenous dental stem cells regenerative ability. HMGCS1, GPNMB, and CHRNA7 were hub-genes according to the network analysis and corelated with proliferation and differentiation capabilities, which were also verified in both cells and tissues.

Conclusion

Our study investigated the destructive effects of nicotine on the regeneration of periodontal tissues from aspects of in vitro and in vivo with the supporting information from both transcriptome and proteome, providing novel targets into the molecular mechanisms of smoking-related periodontitis.

Application of life course trajectory methods to public health data: A comparison of sequence analysis and group-based multi-trajectory modeling for modelling childhood adversity trajectories

There is increasing awareness of the importance of modelling life course trajectories to unravel how social, economic and health factors relate to health over time. Different methods have been developed and applied in public health to classify individuals into groups based on characteristics of their life course. However, the application and results of different methods are rarely compared. We compared the application and results of two methods to classify life course trajectories of individuals, i.e. sequence analysis and group-based multi-trajectory modeling (GBTM), using public health data. We used high-resolution Danish nationwide register data on 926,160 individuals born between 1987 and 2001, including information on the yearly occurrence of 7 childhood adversities in 2 dimensions (i.e. family poverty and family dynamics). We constructed childhood adversity trajectories from 0 to 15 years by applying (1) sequence analysis using optimal matching and cluster analysis using Ward's method and (2) GBTM using logistic and zero-inflated Poisson regressions. We identified 2 to 8 cluster solutions using both methods and determined the optimal solution for both methods. Both methods generated a low adversity, a poverty, and a consistent or high adversity cluster. The 5-cluster solution using sequence analysis additionally included a household psychiatric illness and a late adversity cluster. The 4-group solution using GBTM additionally included a moderate adversity cluster. Compared with the solution obtained through sequence analysis, the solution obtained through GBTM contained fewer individuals in the low adversity cluster and more in the other clusters. We find that the two methods generate qualitatively similar solutions, but the quantitative distributions of children over the groups are different. The method of choice depends on the type of data available and the research question of interest. We provide a comprehensive overview of important considerations and benefits and drawbacks of both methods.

Identifying and Qualifying Deviant Cases in Clusters of Sequences: The Why and The How

Sequence analysis is employed in different fields-e.g., demography, sociology, and political sciences-to describe longitudinal processes represented as sequences of categorical states. In many applications, sequences are clustered to identify relevant types, which reflect the different empirical realisations of the temporal process under study. We explore criteria to inspect internal cluster composition and to detect deviant sequences, that is, cases characterised by rare patterns or outliers that might compromise cluster homogeneity. We also introduce tools to visualise and distinguish the features of regular and deviant cases. Our proposals offer a more accurate and granular description of the data structure, by identifying-besides the most typical types-peculiar sequences that might be interesting from a substantive and theoretical point of view. This analysis could be very useful in applications where-under the assumption of within homogeneity-clusters are used as outcome or explanatory variables in regressions. We demonstrate the added value of our proposal in a motivating application from life-course socio-demography, focusing on Italian women's employment trajectories and on their link with their mothers' participation in the labour market across geographical areas.

Chronic AMPK inactivation slows SHH medulloblastoma progression by inhibiting mTORC1 signaling and depleting tumor stem cells

We show that inactivating AMPK in a genetic medulloblastoma model depletes tumor stem cells and slows progression. In medulloblastoma, the most common malignant pediatric brain tumor, drug-resistant stem cells co-exist with transit-amplifying cells and terminally differentiated neuronal progeny. Prior studies show that Hk2-dependent glycolysis promotes medulloblastoma progression by suppressing neural differentiation. To determine how the metabolic regulator AMPK affects medulloblastoma growth and differentiation, we inactivated AMPK genetically in medulloblastomas. We bred conditional Prkaa1 and Prkaa2 deletions into medulloblastoma-prone SmoM2 mice and compared SmoM2-driven medulloblastomas with intact or inactivated AMPK. AMPK-inactivation increased event-free survival (EFS) and altered cellular heterogeneity, increasing differentiation and decreasing tumor stem cell populations. Surprisingly, AMPK-inactivation decreased mTORC1 activity and decreased Hk2 expression. Hk2 deletion similarly depleted medulloblastoma stem cells, implicating reduced glycolysis in the AMPK-inactivated phenotype. Our results show that AMPK inactivation disproportionately impairs medulloblastoma stem cell populations typically refractory to conventional therapies.

Protocol for generation of a time-resolved cellular interactome during tissue remodeling in adult mice

Efficient skeletal muscle regeneration necessitates fine-tuned coordination among multiple cell types through an intricate network of intercellular communication. We present a protocol for generation of a time-resolved cellular interactome during tissue remodeling. We describe steps for isolating distinct cell populations from skeletal muscle of adult mice after acute damage and extracting RNA from purified cells prior to the generation of RNA sequencing data. We then detail procedures for generating and deciphering a time- and lineage-resolved model of intercellular crosstalk. For complete details on the use and execution of this protocol, please refer to Groppa et al. (2023).1.

Protocol for generating high-quality genome-scale DNA methylation sequencing data from human cancer biospecimens

Aberrant DNA methylation is a universal feature of cancer. Here, we present a protocol for generating high-quality genome-scale DNA methylation sequencing data from a variety of human cancer biospecimens including immortalized cell lines, fresh-frozen surgical resections, and formalin-fixed paraffin-embedded tissues. We describe steps for DNA extraction considerations, reduced representation bisulfite sequencing, data processing and quality control, and downstream data analysis and integration. This protocol is also applicable for other human diseases and methylome profiling in other organisms. For complete details on the use and execution of this protocol, please refer to Rodger et al. (2023).1.

Protocol of CRISPR-Cas9 knockout screens for identifying ferroptosis regulators

Ferroptosis, an iron-dependent programmed cell death triggered by excessive lipid peroxidation, has shown promising therapeutic potentials in human diseases. Here, we describe a protocol of a CRISPR-Cas9 loss-of-function screen to identify regulators in response to different inducers of ferroptosis. We emphasize the steps of library amplification, drug treatment, high-throughput sequencing preparation, and bioinformatics analysis using model-based analysis of genome-wide CRISPR-Cas9 knockout (MAGeCK). We also present a method to discover the regulators of ferroptosis and verify the potential targets efficiently. For complete details on use and execution of this protocol, please refer to Yang et al. (2023).1.

Labour market participation after sickness absence due to cancer: a dynamic cohort study in Catalonia (Spain)

BACKGROUND

The consequences of cancer on working until retirement age remain unclear. This study aimed to analyse working life considering all possible labour market states in a sample of workers after sickness absence (SA) due to cancer and to compare their working life paths to those of a sample of workers without SA and with an SA due to other diseases.

METHODS

This was a retrospective dynamic cohort study among social security affiliates in Catalonia from 2012-2018. Cases consisted of workers with an SA due to cancer between 2012-2015 (N = 516) and were individually age- and sex-matched with those of affiliates with an SA due to other diagnoses and workers without an SA. All workers (N = 1,548, 56% women) were followed up from entry into the cohort until the end of 2018 to characterise nine possible weekly labour states. Sequence analysis, optimal matching, and multinomial logistic regression were used to identify and assess the probability of future labour market participation patterns (LMPPs). All analyses were stratified by sex.

RESULTS

Compared with workers with an SA due to cancer, male workers with no SA and SA due to other causes showed a lower probability of being in the LMPP of death (aRRR 0.02, 95% CI: 0.00‒0.16; aRRR 0.17, 95% CI: 0.06‒0.46, respectively) and, among women, a lower probability of permanent disability and death (aRRR 0.24, 95% CI: 0.10‒0.57; aRRR 0.39, 95% CI: 0.19‒0.83, respectively). Compared to workers with SA due to cancer, the risk of early retirement was lower among workers with no SA (women, aRRR 0.60, 95% CI: 0.22‒1.65; men, aRRR 0.64, 95% CI: 0.27‒1.52), although these results were not statistically significant.

CONCLUSIONS

Workplaces, many of which have policies common to all diagnoses, should be modified to the needs of cancer survivors to prevent an increasing frequency of early retirement and permanent disability when possible. Future studies should assess the impact of cancer on premature exit from the labour market among survivors, depending on cancer localisation and type of treatment.

Molecular detection of bornavirus in parrots imported to China in 2022

BACKGROUND

Avian bornavirus (ABV) is a neurotropic virus, it has been established as the primary causative agent of proventricular dilatation disease (PDD). However, substantial international trade and transnational trafficking of wild birds occur, potentially enabling these birds to harbor and transmit pathogens to domestic poultry, adversely affecting their well-being. Real-time RT-PCR was employed to detect the presence of PaBV-4 in parrots imported to China in 2022.

RESULTS

In 2022, a total of 47 cloacal swabs from 9 distinct species of parrots were collected at the Wildlife Rescue Monitoring Center in Guangdong, China. The purpose of this collection was to detect the presence of PaBV-4. Using real-time PCR techniques, it was determined that the positive rate of PaBV-4 was 2.12% (1 out of 47) in parrots. The PaBV-4 virus was detected in a Amazona aestiva that had been adopted for one month. Conversely, all other species tested negative for the virus. Subsequently, the whole genome of the PaBV-4 GD2207 strains was sequenced, and the homology and genetic evolution between these strains and previously published PaBV-4 strains on GenBank were analyzed using DNAStar and MEGA7.0 software. The findings revealed that the full-length genome of PaBV-4 consisted of 8915 nucleotides and encoded six proteins. Additionally, it exhibited the highest nucleotide similarity (99.9%) to the GZ2019 strain, which causes death and severe clinical symptoms in Aratinga solstitialis. Furthermore, when compared to other strains of PaBV-4, the GD2207 strain demonstrated the highest amino acid homology with GZ2019. The phylogenetic analysis demonstrated that the GD2207 strain clustered with various strains found in Japanese, American, and German parrots, indicating a close genetic relationship with PaBV-4, but it revealed a distant relationship with PaBV-5 Cockg5 from America. Notably, the GD2207 was closely associated with the GZ2019 strain from Aratinga solstitialis in China.

CONCLUSION

This study presents the preliminary identification of PaBV-4 in Amazona aestiva parrots, emphasizing its importance as the predominant viral genotype linked to parrot infections resulting from trade into China. Through genetic evolution analysis, it was determined that the GD2207 strain of PaBV-4 exhibits the closest genetic relationship with GZ 2019 (Aratinga solstitialis, China), M14 (Ara macao, USA), AG5 (Psittacus erithacus, USA) and 6758 (Ara ararauna, Germany) suggesting a shared ancestry.

Discovery of RNA secondary structural motifs using sequence-ordered thermodynamic stability and comparative sequence analysis

Major advances in RNA secondary structural motif prediction have been achieved in the last few years; however, few methods harness the predictive power of multiple approaches to deliver in-depth characterizations of local RNA motifs and their potential functionality. Additionally, most available methods do not predict RNA pseudoknots. This work combines complementary bioinformatic systems into one robust discovery pipeline where: •RNA sequences are folded to search for thermodynamically favorable motifs utilizing ScanFold.•Motifs are expanded and refolded into alternate pseudoknot conformations by Knotty/Iterative HFold.•All conformations are evaluated for covariance via the cm-builder pipeline (Infernal and R-scape).

Genotype analysis of rotaviruses isolated from children during a phase III clinical trial with the hexavalent rotavirus vaccine in China

The oral hexavalent live human-bovine reassortant rotavirus vaccine (RV6) developed by Wuhan Institute of Biological Products Co., Ltd (WIBP) has finished a randomized, placebo-controlled phase III clinical trial in four provinces of China in 2021. The trail demonstrated that RV6 has a high vaccine efficacy against the prevalent strains and is safe for use in infants. During the phase III clinical trial (2019-2021), 200 rotavirus-positive fecal samples from children with RV gastroenteritis (RVGE) were further studied. Using reverse transcription-polymerase chain reaction and high-throughput sequencing, VP7 and VP4 sequences were obtained and their genetic characteristics, as well as the differences in antigenic epitopes of VP7, were analyzed in detail. Seven rotavirus genotypes were identified. The predominant rotavirus genotype was G9P [8] (77.0%), followed by prevalent strains G8P [8] (8.0%), G3P [8] (3.5%), G3P [9] (1.5%), G1P [8] (1.0%), G2P [4] (1.0%), and G4P [6] (1.0%). The amino acid sequence identities of G1, G2, G3, G4, G8, and G9 genotypes of isolates compared to the vaccine strains were 98.8%, 98.2%-99.7%, 88.4%-99.4%, 98.2%, 94.2%-100%, and 93.9%-100%, respectively. Notably, the vaccine strains exhibited high similarity in amino acid sequence, with only minor differences in antigenic epitopes compared to the Chinese endemic strains. This supports the potential application of the vaccine in preventing diseases caused by rotaviruses.

Comparing methods for constructing and representing human pangenome graphs

BACKGROUND

As a single reference genome cannot possibly represent all the variation present across human individuals, pangenome graphs have been introduced to incorporate population diversity within a wide range of genomic analyses. Several data structures have been proposed for representing collections of genomes as pangenomes, in particular graphs.

RESULTS

In this work, we collect all publicly available high-quality human haplotypes and construct the largest human pangenome graphs to date, incorporating 52 individuals in addition to two synthetic references (CHM13 and GRCh38). We build variation graphs and de Bruijn graphs of this collection using five of the state-of-the-art tools: Bifrost, mdbg, Minigraph, Minigraph-Cactus and pggb. We examine differences in the way each of these tools represents variations between input sequences, both in terms of overall graph structure and representation of specific genetic loci.

CONCLUSION

This work sheds light on key differences between pangenome graph representations, informing end-users on how to select the most appropriate graph type for their application.

Deciphering tumor immune microenvironment differences between high-grade serous and endometrioid ovarian cancer to investigate their potential in indicating immunotherapy response

BACKGROUND

Ovarian cancer is a significant public health concern with a poor prognosis for epithelial ovarian cancer. To explore the potential of immunotherapy in treating epithelial ovarian cancer, we investigated the immune microenvironments of 52 patients with epithelial ovarian cancer, including 43 with high-grade serous ovarian cancer and 9 with endometrioid ovarian cancer.

RESULTS

Fresh tumor tissue was analyzed for genetic mutations and various parameters related to immune evasion and infiltration. The mean stromal score (stromal cell infiltration) in high-grade serous ovarian cancer was higher than in endometrioid ovarian cancer. The infiltration of CD8 T cells and exhausted CD8 T cells were found to be more extensive in high-grade serous ovarian cancer. Tumor Immune Dysfunction and Exclusion scores, T cell exclusion scores, and cancer-associated fibroblasts (CAF) scores were also higher in the high-grade serous ovarian cancer group, suggesting that the number of cytotoxic lymphocytes in the tumor microenvironment of high-grade serous ovarian cancer is likely lower compared to endometrioid ovarian cancer.

CONCLUSIONS

The high mean stromal score and more extensive infiltration and exhaustion of CD8 T cells in high-grade serous ovarian cancer indicate that high-grade serous ovarian cancer exhibits a higher level of cytotoxic T cell infiltration, yet these T cells tend to be in a dysfunctional state. Higher Tumor Immune Dysfunction and Exclusion scores, T cell exclusion scores, and CAF scores in high-grade serous ovarian cancers suggest that immune escape is more likely to occur in high-grade serous ovarian cancer, thus endometrioid ovarian cancer may be more conducive to immunotherapy. Therefore, it is crucial to design immunotherapy clinical trials for ovarian cancer to distinguish between high-grade serous and endometrioid ovarian cancer from the outset. This distinction will help optimize treatment strategies and improve outcomes for patients with different subtypes.

aKmerBroom: Ancient oral DNA decontamination using Bloom filters on k-mer sets

Dental calculus samples are modeled as a mixture of DNA coming from dental plaque and contaminants. Current computational decontamination methods such as Recentrifuge and DeconSeq require either a reference database or sequenced negative controls, and therefore have limited use cases. We present a reference-free decontamination tool tailored for the removal of contaminant DNA of ancient oral sample called aKmerBroom. Our tool builds a Bloom filter of known ancient and modern oral k-mers, then scans an input set of ancient metagenomic reads using multiple passes to iteratively retain reads likely to be of oral origin. On synthetic data, aKmerBroom achieves over 89.53 % sensitivity and 94.00 % specificity. On real datasets, aKmerBroom shows higher read retainment (+ 60 % on average) than other methods. We anticipate aKmerBroom will be a valuable tool for the processing of ancient oral samples as it will prevent contaminated datasets from being completely discarded in downstream analyses.

Noninvasive diagnosis of secondary infections in COVID-19 by sequencing of plasma microbial cell-free DNA

Secondary infection (SI) diagnosis in severe COVID-19 remains challenging. We correlated metagenomic sequencing of plasma microbial cell-free DNA (mcfDNA-Seq) with clinical SI assessment, immune response, and outcomes. We classified 42 COVID-19 inpatients as microbiologically confirmed-SI (Micro-SI, n = 8), clinically diagnosed-SI (Clinical-SI, n = 13, i.e., empiric antimicrobials), or no-clinical-suspicion-for-SI (No-Suspected-SI, n = 21). McfDNA-Seq was successful in 73% of samples. McfDNA detection was higher in Micro-SI (94%) compared to Clinical-SI (57%, p = 0.03), and unexpectedly high in No-Suspected-SI (83%), similar to Micro-SI. We detected culture-concordant mcfDNA species in 81% of Micro-SI samples. McfDNA correlated with LRT 16S rRNA bacterial burden (r = 0.74, p = 0.02), and biomarkers (white blood cell count, IL-6, IL-8, SPD, all p < 0.05). McfDNA levels were predictive of worse 90-day survival (hazard ratio 1.30 [1.02-1.64] for each log10 mcfDNA, p = 0.03). High mcfDNA levels in COVID-19 patients without clinical SI suspicion may suggest SI under-diagnosis. McfDNA-Seq offers a non-invasive diagnostic tool for pathogen identification, with prognostic value on clinical outcomes.

Detection and annotation of transposable element insertions and deletions on the human genome using nanopore sequencing

Repetitive sequences represent about 45% of the human genome. Some are transposable elements (TEs) with the ability to change their position in the genome, creating genetic variability both as insertions or deletions, with potential pathogenic consequences. We used long-read nanopore sequencing to identify TE variants in the genomes of 24 patients with antithrombin deficiency. We identified 7 344 TE insertions and 3 056 TE deletions, 2 926 were not previously described in publicly available databases. The insertions affected 3 955 genes, with 6 insertions located in exons, 3 929 in introns, and 147 in promoters. Potential functional impact was evaluated with gene annotation and enrichment analysis, which suggested a strong relationship with neuron-related functions and autism. We conclude that this study encourages the generation of a complete map of TEs in the human genome, which will be useful for identifying new TEs involved in genetic disorders.

Identification, molecular characterization, and antifungal susceptibility of Cyberlindnera fabianii strains isolated from urinary tract

OBJECTIVES

Cyberlindnera fabianii is an opportunistic pathogen isolated from clinical specimens. It can be incorrectly identified as Candida utulis by phenotypic methods. This study aimed to accurately identify Cy.fabianii strains isolated from the urinary tract, and to determine their molecular characterization and antifungal susceptibilities as well.

METHODS

Twenty-nine yeast strains isolated from urinary tract samples were studied. Strains were identified by phenotypically, sequence analysis and MALDI-TOF MS. Sequence analysis using different gene regions (ITS1-2,D1/D2,EF-1-alpha) in ribosomal DNA was performed for the molecular analysis. Phylogenetic analysis was done by the neighbor-joining method. Antifungal susceptibilities of strains were determined for nine antifungals by reference broth microdilution and the Sensititre YeastOne broth microdilution method (SensititreTMYeastOneTMAST Plate, Thermo Fisher Scientific™,USA) according to CLSI M60-Ed2 recommendations.

RESULTS

All strains were identified as C.utulis phenotypically by conventional methods, however all strains were identified as Cy.fabianii by sequence analysis and MALDI-TOF MS. It was observed that the gene regions examined in terms of determining evolutionary relatedness did not show intraspecies nucleotide variations. In all strains, the MIC50/MIC90 values for fluconazole were higher than the other antifungals tested.

CONCLUSION

Cy.fabianii should be considered in fluconazole-resistant urinary tract yeast infections. Although conventional phenotypical methods were insufficient to identify Cy.fabianii, it could be correctly identified with sequence analysis using different gene regions (ITS1-2,D1/D2,EF-1-alpha) in ribosomal DNA and MALDI-TOF MS.

PRMT1-mediated arginine methylation promotes postnatal calvaria bone formation through BMP-Smad signaling

PRMT1 deficiency leads to severely compromised craniofacial development in neural crest cells and profound abnormalities of the craniofacial tissues. Here, we show PRMT1 controls several key processes in calvarial development, including frontal and parietal bone growth rate and the boundary between sutural and osteogenic cells. Pharmacologic PRMT1 inhibition suppresses MC3T3-E1 cell viability and proliferation and impairs osteogenic differentiation. In this text, we investigate the cellular events behind the morphological changes and uncover an essential role of PRMT1 in simulating postnatal bone formation. Inhibition of PRMT1 alleviated BMP signaling through Smads phosphorylation and reduced the deposition of the H4R3me2a mark. Our study demonstrates a regulatory mechanism whereby PRMT1 regulates BMP signaling and the overall properties of the calvaria bone through Smads methylation, which may facilitate the development of an effective therapeutic strategy for craniosynostosis.

HIV-1 subtypes maintain distinctive physicochemical signatures in Nef domains associated with immunoregulation

BACKGROUND

HIV subtype is associated with varied rates of disease progression. The HIV accessory protein, Nef, continues to be present during antiretroviral therapy (ART) where it has numerous immunoregulatory effects. In this study, we analyzed Nef sequences from HIV subtypes A1, B, C, and D using a machine learning approach that integrates functional amino acid information to identify if unique physicochemical features are associated with Nef functional/structural domains in a subtype-specific manner.

METHODS

2253 sequences representing subtypes A1, B, C, and D were aligned and domains with known functional properties were scored based on amino acid physicochemical properties. Following feature generation, we used statistical pruning and evolved neural networks (ENNs) to determine if we could successfully classify subtypes. Next, we used ENNs to identify the top five key Nef physicochemical features applied to specific immunoregulatory domains that differentiated subtypes. A signature pattern analysis was performed to the assess amino acid diversity in sub-domains that differentiated each subtype.

RESULTS

In validation studies, ENNs successfully differentiated each subtype at A1 (87.2%), subtype B (89.5%), subtype C (91.7%), and subtype D (85.1%). Our feature-based domain scoring, followed by t-tests, and a similar ENN identified subtype-specific domain-associated features. Subtype A1 was associated with alterations in Nef CD4 binding domain; subtype B was associated with alterations with the AP-2 Binding domain; subtype C was associated with alterations in a structural Alpha Helix domain; and, subtype D was associated with alterations in a Beta-Sheet domain.

CONCLUSIONS

Recent studies have focused on HIV Nef as a driver of immunoregulatory disease in those HIV infected and on ART. Nef acts through a complex mixture of interactions that are directly linked to the key features of the subtype-specific domains we identified with the ENN. The study supports the hypothesis that varied Nef subtypes contribute to subtype-specific disease progression.

Adaptation to the High-Arctic island environment despite long-term reduced genetic variation in Svalbard reindeer

Typically much smaller in number than their mainland counterparts, island populations are ideal systems to investigate genetic threats to small populations. The Svalbard reindeer (Rangifer tarandus platyrhynchus) is an endemic subspecies that colonized the Svalbard archipelago ca. 6,000-8,000 years ago and now shows numerous physiological and morphological adaptations to its arctic habitat. Here, we report a de-novo chromosome-level assembly for Svalbard reindeer and analyze 133 reindeer genomes spanning Svalbard and most of the species' Holarctic range, to examine the genomic consequences of long-term isolation and small population size in this insular subspecies. Empirical data, demographic reconstructions, and forward simulations show that long-term isolation and high inbreeding levels may have facilitated the reduction of highly deleterious-and to a lesser extent, moderately deleterious-variation. Our study indicates that long-term reduced genetic diversity did not preclude local adaptation to the High Arctic, suggesting that even severely bottlenecked populations can retain evolutionary potential.

Umami-BERT: An interpretable BERT-based model for umami peptides prediction

Umami peptides have received extensive attention due to their ability to enhance flavors and provide nutritional benefits. The increasing demand for novel umami peptides and the vast number of peptides present in food call for more efficient methods to screen umami peptides, and further exploration is necessary. Therefore, the purpose of this study is to develop deep learning (DL) model to realize rapid screening of umami peptides. The Umami-BERT model was devised utilizing a novel two-stage training strategy with Bidirectional Encoder Representations from Transformers (BERT) and the inception network. In the pre-training stage, attention mechanisms were implemented on a large amount of bioactive peptides sequences to acquire high-dimensional generalized features. In the re-training stage, umami peptide prediction was carried out on UMP789 dataset, which is developed through the latest research. The model achieved the performance with an accuracy (ACC) of 93.23% and MCC of 0.78 on the balanced dataset, as well as an ACC of 95.00% and MCC of 0.85 on the unbalanced dataset. The results demonstrated that Umami-BERT could predict umami peptides directly from their amino acid sequences and exceeded the performance of other models. Furthermore, Umami-BERT enabled the analysis of attention pattern learned by Umami-BERT model. The amino acids Alanine (A), Cysteine (C), Aspartate (D), and Glutamicacid (E) were found to be the most significant contributors to umami peptides. Additionally, the patterns of summarized umami peptides involving A, C, D, and E were analyzed based on the learned attention weights. Consequently, Umami-BERT exhibited great potential in the large-scale screening of candidate peptides and offers novel insight for the further exploration of umami peptides.

ADP-Fuse: A novel two-layer machine learning predictor to identify antidiabetic peptides and diabetes types using multiview information

Diabetes mellitus has become a major public health concern associated with high mortality and reduced life expectancy and can cause blindness, heart attacks, kidney failure, lower limb amputations, and strokes. A new generation of antidiabetic peptides (ADPs) that act on β-cells or T-cells to regulate insulin production is being developed to alleviate the effects of diabetes. However, the lack of effective peptide-mining tools has hampered the discovery of these promising drugs. Hence, novel computational tools need to be developed urgently. In this study, we present ADP-Fuse, a novel two-layer prediction framework capable of accurately identifying ADPs or non-ADPs and categorizing them into type 1 and type 2 ADPs. First, we comprehensively evaluated 22 peptide sequence-derived features coupled with eight notable machine learning algorithms. Subsequently, the most suitable feature descriptors and classifiers for both layers were identified. The output of these single-feature models, embedded with multiview information, was trained with an appropriate classifier to provide the final prediction. Comprehensive cross-validation and independent tests substantiate that ADP-Fuse surpasses single-feature models and the feature fusion approach for the prediction of ADPs and their types. In addition, the SHapley Additive exPlanation method was used to elucidate the contributions of individual features to the prediction of ADPs and their types. Finally, a user-friendly web server for ADP-Fuse was developed and made publicly accessible (https://balalab-skku.org/ADP-Fuse), enabling the swift screening and identification of novel ADPs and their types. This framework is expected to contribute significantly to antidiabetic peptide identification.