Viral escape mutations do not account for non-protection from SIVmac239 challenge in RhCMV/SIV vaccinated rhesus macaques.

Simian immunodeficiency virus (SIV) vaccines based upon 68-1 Rhesus Cytomegalovirus (RhCMV) vectors show remarkable protection against pathogenic SIVmac239 challenge. Across multiple independent rhesus macaque (RM) challenge studies, nearly 60% of vaccinated RM show early, complete arrest of SIVmac239 replication after effective challenge, whereas the remainder show progressive infection similar to controls. Here, we performed viral sequencing to determine whether the failure to control viral replication in non-protected RMs is associated with the acquisition of viral escape mutations. While low level viral mutations accumulated in all animals by 28 days-post-challenge, which is after the establishment of viral control in protected animals, the dominant circulating virus in virtually all unprotected RMs was nearly identical to the challenge stock, and there was no difference in mutation patterns between this cohort and unvaccinated controls. These data definitively demonstrate that viral mutation does not explain lack of viral control in RMs not protected by RhCMV/SIV vaccination. We further demonstrate that during chronic infection RhCMV/SIV vaccinated RMs do not acquire escape mutation in epitopes targeted by RhCMV/SIV, but instead display mutation in canonical MHC-Ia epitopes similar to unvaccinated RMs. This suggests that after the initial failure of viral control, unconventional T cell responses induced by 68-1 RhCMV/SIV vaccination do not exert strong selective pressure on systemically replicating SIV.

Enhancing Familial Hypercholesterolemia Detection in South Korea: A Targeted Screening Approach Integrating National Program and Genetic Cascade Screening.

BACKGROUND AND OBJECTIVES: Familial hypercholesterolemia (FH) increases the risk of premature cardiovascular disease through disrupted low-density lipoprotein cholesterol (LDL-C) metabolism. Although FH is a severe condition, it remains widely underdiagnosed, which can be attributed to barriers in genetic testing and a lack of awareness. This study aims to propose and evaluate a targeted screening program for FH in South Korea by integrating the General Health Screening Program (GHSP) with cascade genetic screening. METHODS: The study included individuals with LDL-C levels ≥190 mg/dL identified during the 2021 GHSP (primary participants). Data on demographics, lifestyle, medical history, and family history were collected through questionnaires. Targeted next-generation sequencing was used to identify pathogenic mutations in the PCSK9, APOB, LDLRAP1, and LDLR genes associated with FH. Pathogenic mutations found in primary participants were confirmed in their relatives (secondary participants) using Sanger sequencing. Participant characteristics were analyzed based on the presence of pathogenic mutations. RESULTS: Among 83 individuals with severe hypercholesterolemia identified through the GHSP, 7 primary participants (8.4%) carried pathogenic mutations in the LDLR and PCSK9 genes. In secondary participants, pathogenic mutations were identified in 61.1% of the relatives of 4 patients with pathogenic mutations. The prevalence of pathogenic mutations was significantly higher in primary participants compared to secondary participants. CONCLUSIONS: Integrating community resources with FH screening can enhance the early detection and treatment of FH. By utilizing GHSP data and adding genetic screening, the proposed model provides a strategy to reduce the cardiovascular risks associated with FH, supporting its wider adoption at the national level.

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.

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.

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.

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.

Associations Between Divorce Histories and Unhealthy Alcohol Use Among Middle Aged and Older Adults.

Background: Unhealthy alcohol use has been considered a coping strategy related to stressful and traumatic life events such as relationship loss. Yet, the effects of marital status on health behaviors are generally studied cross-sectionally or over one transition. We explored associations between the frequency and quantity of alcohol use with the number of episodes and duration of separation/divorce events across adulthood among English adults in mid to later life. Methods: This study used life history data from wave 3 (2006/07) of the English Longitudinal Study of Aging to compute marital sequences based on marital status at each year of age from 18 years of 6,355 adults aged 50-80 years. These sequences were used to compute the portion of adulthood spent separated/divorced and the number of episodes of divorce. These variables were used as predictors in logistic regressions predicting unhealthy alcohol use, while also controlling for current marital status. Results: We found that the number of episodes of separation/divorce increased the odds of drinking ≥5 days/week and binge drinking (≥6 drinks/occasion for women; ≥8 drinks/occasion for men), whereas the portion of adulthood spent divorced was not associated with drinking frequency or binge drinking. Some nuances by gender were also noted. Conclusions: Recurrent transitions into separation/divorce over adulthood appears to increase risk of unhealthy alcohol use in mid to later life beyond the risks associated with current marital status.

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/.

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.

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.

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.

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.

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.

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.

Pathways from school to work: A sequence analysis of non-engaged youth.

INTRODUCTION: Research on heterogeneous pathways in school-to-work transitions (SWT), particularly longitudinal research, has been limited, as have empirical studies examining effective interventions for facilitating multiple SWT pathways among non-engaged youth (NEY), who are generally at risk of being not in education, employment, or training (NEET). METHODS: To develop a typology of SWT pathways, we conducted sequence analysis with longitudinal data from a sample of 630 NEY aged 14-29 (M = 19.78; 63.65% males) in Hong Kong during a 22-month period beginning in September 2020. We also performed multinomial logistic regressions to assess the impact of career and life development (CLD) interventions on SWT outcomes. RESULTS: Our analysis yielded a fivefold typology of SWT pathways: the Employment/Entrepreneurship cluster (31.27%), the Vocational Education and Training cluster (13.49%), the Generic Education cluster (16.83%), the Serious Leisure Development cluster (15.24%), and the long-term NEET cluster (23.17%). NEY in the intervention group receiving CLD services, inspired by the expanded notion of work (ENOW) and youth development and intervention framework (YDIF), demonstrated significantly higher likelihoods of being in the Employment/Entrepreneurship (OR = 34.5, 95% CI [10.53, 105.08]), Generic Education (OR = 3.74, 95% CI [1.81, 7.74]), Vocational Education and Training (OR = 1.55, 95% CI [1.05, 6.26]), and Serious Leisure Development (OR = 1.77, 95% CI [1.04, 4.46]) clusters than the long-term NEET cluster. CONCLUSIONS: Our findings highlight the dynamic, heterogeneous nature of NEY's CLD journeys, including that CLD interventions based on ENOW-YDIF have had a beneficial effect on NEY's multiple SWT pathways.

Long-read sequencing identifies an SVA_D retrotransposon insertion deep within the intron of ATP7A as a novel cause of occipital horn syndrome.

BACKGROUND: SINE-VNTR-Alu (SVA) retrotransposons move from one genomic location to another in a 'copy-and-paste' manner. They continue to move actively and cause monogenic diseases through various mechanisms. Currently, disease-causing SVA retrotransposons are classified into human-specific young SVA_E or SVA_F subfamilies. In this study, we identified an evolutionarily old SVA_D retrotransposon as a novel cause of occipital horn syndrome (OHS). OHS is an X-linked, copper metabolism disorder caused by dysfunction of the copper transporter, ATP7A. METHODS: We investigated a 16-year-old boy with OHS whose pathogenic variant could not be detected via routine molecular genetic analyses. RESULTS: A 2.8 kb insertion was detected deep within the intron of the patient's ATP7A gene. This insertion caused aberrant mRNA splicing activated by a new donor splice site located within it. Long-read circular consensus sequencing enabled us to accurately read the entire insertion sequence, which contained highly repetitive and GC-rich segments. Consequently, the insertion was identified as an SVA_D retrotransposon. Antisense oligonucleotides (AOs) targeting the new splice site restored the expression of normal transcripts and functional ATP7A proteins. AO treatment alleviated excessive accumulation of copper in patient fibroblasts in a dose-dependent manner. Pedigree analysis revealed that the retrotransposon had moved into the OHS-causing position two generations ago. CONCLUSION: This is the first report of a human monogenic disease caused by the SVA_D retrotransposon. The fact that the evolutionarily old SVA_D is still actively transposed, leading to increased copy numbers may make a notable impact on rare genetic disease research.

Impact of therapeutic inhibition of oncogenic cell signaling tyrosine kinase on cell metabolism: in vivo-detectable metabolic biomarkers of inhibition.

BACKGROUND: Inhibition of kinases is the ever-expanding therapeutic approach to various types of cancer. Typically, assessment of the treatment response is accomplished by standard, volumetric imaging procedures, performed weeks to months after the onset of treatment, given the predominantly cytostatic nature of the kinase inhibitors, at least when used as single agents. Therefore, there is a great clinical need to develop new monitoring approaches to detect the response to kinase inhibition much more promptly. Noninvasive 1H magnetic resonance spectroscopy (MRS) can measure in vitro and in vivo concentration of key metabolites which may potentially serve as biomarkers of response to kinase inhibition. METHODS: We employed mantle cell lymphoma (MCL) cell lines demonstrating markedly diverse sensitivity of inhibition of Bruton's tyrosine kinase (BTK) regarding their growth and studied in-depth effects of the inhibition on various aspects of cell metabolism including metabolite synthesis using metabolomics, glucose and oxidative metabolism by Seahorse XF technology, and concentration of index metabolites lactate, alanine, total choline and taurine by 1H MRS. RESULTS: Effective BTK inhibition profoundly suppressed key cell metabolic pathways, foremost pyrimidine and purine synthesis, the citrate (TCA) cycle, glycolysis, and pyruvate and glutamine/alanine metabolism. It also inhibited glycolysis and amino acid-related oxidative metabolism. Finally, it profoundly and quickly decreased concentration of lactate (a product of mainly glycolysis) and alanine (an indicator of amino acid metabolism) and, less universally total choline both in vitro and in vivo, in the MCL xenotransplant model. The decrease correlated directly with the degree of inhibition of lymphoma cell expansion and tumor growth. CONCLUSIONS: Our results indicate that BTK inhibition exerts a broad and profound suppressive effect on cell metabolism and that the affected index metabolites such as lactate, alanine may serve as early, sensitive, and reliable biomarkers of inhibition in lymphoma patients detectable by noninvasive MRS-based imaging method. This kind of imaging-based detection may also be applicable to other kinase inhibitors, as well as diverse lymphoid and non-lymphoid malignancies.

TP53 mutations in myeloid neoplasms: implications for accurate laboratory detection, diagnosis, and treatment.

Genetic alterations that affect the function of p53 tumor suppressor have been extensively investigated in myeloid neoplasms, revealing their significant impact on disease progression, treatment response, and patient outcomes. The identification and characterization of TP53 mutations play pivotal roles in subclassifying myeloid neoplasms and guiding treatment decisions. Starting with the presentation of a typical case, this review highlights the complicated nature of genetic alterations involving TP53 and provides a comprehensive analysis of TP53 mutations and other alterations in myeloid neoplasms. Currently available methods used in clinical laboratories to identify TP53 mutations are discussed, focusing on the importance of establishing a robust testing protocol within clinical laboratories to ensure the delivery of accurate and reliable results. The treatment implications of TP53 mutations in myeloid neoplasms and clinical trial options are reviewed. Ultimately, we hope that this review provides valuable insights into the patterns of TP53 alterations in myeloid neoplasms and offers guidance to establish practical laboratory testing protocols to support the best practices of precision oncology.

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.

Protocol for the characterization of the pancreatic tumor microenvironment using organoid-derived mouse models and single-nuclei RNA sequencing.

Single-nuclei RNA sequencing (snRNA-seq) allows for obtaining gene expression profiles from frozen or hard-to-dissociate tissues at the single-nuclei level. Here, we describe a protocol to obtain snRNA-seq data of pancreatic tumors from orthotopically grafted organoid-derived mouse models. We provide details on the establishment of these mouse models, the isolation of single nuclei from pancreatic tumors, and the analysis of the snRNA-seq datasets. For complete details on the use and execution of this protocol, please refer to Mucciolo et al.1.