Tob Control. 2024 Jun 15:tc-2024-058794. doi: 10.1136/tc-2024-058794. Online ahead of print.

NO ABSTRACT

PMID:38879183 | DOI:10.1136/tc-2024-058794

Comput Methods Programs Biomed. 2024 May 31;254:108260. doi: 10.1016/j.cmpb.2024.108260. Online ahead of print.

ABSTRACT

BACKGROUND AND OBJECTIVE: Proteome microarrays are one of the popular high-throughput screening methods for large-scale investigation of protein interactions in cells. These interactions can be measured on protein chips when coupled with fluorescence-labeled probes, helping indicate potential biomarkers or discover drugs. Several computational tools were developed to help analyze the protein chip results. However, existing tools fail to provide a user-friendly interface for biologists and present only one or two data analysis methods suitable for limited experimental designs, restricting the use cases.

METHODS: In order to facilitate the biomarker examination using protein chips, we implemented a user-friendly and comprehensive web tool called BAPCP (Biomarker Analysis tool for Protein Chip Platforms) in this research to deal with diverse chip data distributions.

RESULTS: BAPCP is well integrated with standard chip result files and includes 7 data normalization methods and 7 custom-designed quality control/differential analysis filters for biomarker extraction among experiment groups. Moreover, it can handle cost-efficient chip designs that repeat several blocks/samples within one single slide. Using experiments of the human coronavirus (HCoV) protein microarray and the E. coli proteome chip that helps study the immune response of Kawasaki disease as examples, we demonstrated that BAPCP can accelerate the time-consuming week-long manual biomarker identification process to merely 3 min.

CONCLUSIONS: The developed BAPCP tool provides substantial analysis support for protein interaction studies and conforms to the necessity of expanding computer usage and exchanging information in bioscience and medicine. The web service of BAPCP is available at https://cosbi.ee.ncku.edu.tw/BAPCP/.

PMID:38878357 | DOI:10.1016/j.cmpb.2024.108260

Cell Rep. 2024 Jun 14;43(6):114369. doi: 10.1016/j.celrep.2024.114369. Online ahead of print.

ABSTRACT

Epitranscriptomics represents a further layer of gene expression regulation. Specifically, N6-methyladenosine (m6A) regulates RNA maturation, stability, degradation, and translation. Regarding microRNAs (miRNAs), while it has been reported that m6A impacts their biogenesis, the functional effects on mature miRNAs remain unclear. Here, we show that m6A modification on specific miRNAs weakens their coupling to AGO2, impairs their function on target mRNAs, determines their delivery into extracellular vesicles (EVs), and provides functional information to receiving cells. Mechanistically, the intracellular functional impairment is caused by m6A-mediated inhibition of AGO2/miRNA interaction, the EV loading is favored by m6A-mediated recognition by the RNA-binding protein (RBP) hnRNPA2B1, and the EV-miRNA function in the receiving cell requires their FTO-mediated demethylation. Consequently, cells express specific miRNAs that do not impact endogenous transcripts but provide regulatory information for cell-to-cell communication. This highlights that a further level of complexity should be considered when relating cellular dynamics to specific miRNAs.

PMID:38878288 | DOI:10.1016/j.celrep.2024.114369

Insect Mol Biol. 2024 Jun 15. doi: 10.1111/imb.12931. Online ahead of print.

ABSTRACT

The digestive physiology of house dust mites (HDMs) is particularly relevant for their allergenicity since many of their allergens participate in digestion and are excreted into faecal pellets, a main source of exposure for allergic subjects. To gain insight into the mite dietary digestion, the genome of the HDM Dermatophagoides pteronyssinus was screened for genes encoding peptidases (n = 320), glycosylases (n = 77), lipases and esterases (n = 320), peptidase inhibitors (n = 65) and allergen-related proteins (n = 52). Basal gene expression and transcriptional responses of mites to dietary cystatin A, a cysteine endopeptidase inhibitor with previously shown antinutritional effect on mites, were analysed by RNAseq. The ingestion of cystatin A resulted in significant regulation of different cysteine endopeptidase and glycosylase genes. One Der p 1-like and two cathepsin B-like cysteine endopeptidase genes of high basal expression were induced, which suggests their prominent role in proteolytic digestion together with major allergen Der p 1. A number of genes putatively participating in the interaction of mites with their microbiota and acquired by horizontal gene transfer were repressed, including genes encoding the peptidase Der p 38, two 1,3-beta-glucanases, a lysozyme and a GH19 chitinase. Finally, the disruption of mite digestion resulted in the regulation of up to 17 allergen and isoallergen genes. Altogether, our results shed light on the putative role of specific genes in digestion and illustrate the connection between the digestive physiology of HDM and allergy.

PMID:38878274 | DOI:10.1111/imb.12931

Br J Haematol. 2024 Jun 14. doi: 10.1111/bjh.19587. Online ahead of print.

NO ABSTRACT

PMID:38877747 | DOI:10.1111/bjh.19587

Mol Plant. 2024 Jun 13:S1674-2052(24)00185-0. doi: 10.1016/j.molp.2024.06.005. Online ahead of print.

ABSTRACT

Doubled haploid (DH) technology and synthetic apomixis approaches can greatly shorten breeding cycles and thereby enhance breeding efficiency. Compared with traditional breeding methods, DH technology offers the advantage of being able to rapidly generate inbred lines, while synthetic apomixis can effectively fix hybrid vigor. In this review, we focus on (i) recent advances in identifying and characterizing genes responsible for haploid induction (HI), (ii) the molecular mechanisms of HI, (iii) spontaneous genome doubling, and (iv) crop synthetic apomixis. We also discuss the challenges and potential solutions for future crop breeding programs utilizing DH technology and synthetic apomixis. Finally, we offer a perspective on integrating DH and synthetic apomixis within precision breeding and de novo domestication.

PMID:38877700 | DOI:10.1016/j.molp.2024.06.005

Clin Transl Med. 2024 Jun;14(6):e1723. doi: 10.1002/ctm2.1723.

ABSTRACT

BACKGROUND: Cholangiocarcinoma (CCA) is a fatal cancer of the bile duct with a poor prognosis owing to limited therapeutic options. The incidence of intrahepatic CCA (iCCA) is increasing worldwide, and its molecular basis is emerging. Environmental factors may contribute to regional differences in the mutation spectrum of European patients with iCCA, which are underrepresented in systematic genomic and transcriptomic studies of the disease.

METHODS: We describe an integrated whole-exome sequencing and transcriptomic study of 37 iCCAs patients in Germany.

RESULTS: We observed as most frequently mutated genes ARID1A (14%), IDH1, BAP1, TP53, KRAS, and ATM in 8% of patients. We identified FGFR2::BICC1 fusions in two tumours, and FGFR2::KCTD1 and TMEM106B::ROS1 as novel fusions with potential therapeutic implications in iCCA and confirmed oncogenic properties of TMEM106B::ROS1 in vitro. Using a data integration framework, we identified PBX1 as a novel central regulatory gene in iCCA. We performed extended screening by targeted sequencing of an additional 40 CCAs. In the joint analysis, IDH1 (13%), BAP1 (10%), TP53 (9%), KRAS (7%), ARID1A (7%), NF1 (5%), and ATM (5%) were the most frequently mutated genes, and we found PBX1 to show copy gain in 20% of the tumours. According to other studies, amplifications of PBX1 tend to occur in European iCCAs in contrast to liver fluke-associated Asian iCCAs.

CONCLUSIONS: By analyzing an additional European cohort of iCCA patients, we found that PBX1 protein expression was a marker of poor prognosis. Overall, our findings provide insight into key molecular alterations in iCCA, reveal new targetable fusion genes, and suggest that PBX1 is a novel modulator of this disease.

PMID:38877653 | DOI:10.1002/ctm2.1723

BMC Genomics. 2024 Jun 15;25(1):600. doi: 10.1186/s12864-024-10512-x.

ABSTRACT

BACKGROUND: Splicing variants are a major class of pathogenic mutations, with their severity equivalent to nonsense mutations. However, redundant and degenerate splicing signals hinder functional assessments of sequence variations within introns, particularly at branch sites. We have established a massively parallel splicing assay to assess the impact on splicing of 11,191 disease-relevant variants. Based on the experimental results, we then applied regression-based methods to identify factors determining splicing decisions and their respective weights.

RESULTS: Our statistical modeling is highly sensitive, accurately annotating the splicing defects of near-exon intronic variants, outperforming state-of-the-art predictive tools. We have incorporated the algorithm and branchpoint information into a web-based tool, SpliceAPP, to provide an interactive application. This user-friendly website allows users to upload any genetic variants with genome coordinates (e.g., chr15 74,687,208 A G), and the tool will output predictions for splicing error scores and evaluate the impact on nearby splice sites. Additionally, users can query branch site information within the region of interest.

CONCLUSIONS: In summary, SpliceAPP represents a pioneering approach to screening pathogenic intronic variants, contributing to the development of precision medicine. It also facilitates the annotation of splicing motifs. SpliceAPP is freely accessible using the link https://bc.imb.sinica.edu.tw/SpliceAPP . Source code can be downloaded at https://github.com/hsinnan75/SpliceAPP .

PMID:38877417 | DOI:10.1186/s12864-024-10512-x

EMBO Rep. 2024 Jun 14. doi: 10.1038/s44319-024-00175-w. Online ahead of print.

ABSTRACT

Junctions between the endoplasmic reticulum (ER) and the outer membrane of the nuclear envelope (NE) physically connect both organelles. These ER-NE junctions are essential for supplying the NE with lipids and proteins synthesized in the ER. However, little is known about the structure of these ER-NE junctions. Here, we systematically study the ultrastructure of ER-NE junctions in cryo-fixed mammalian cells staged in anaphase, telophase, and interphase by correlating live cell imaging with three-dimensional electron microscopy. Our results show that ER-NE junctions in interphase cells have a pronounced hourglass shape with a constricted neck of 7-20 nm width. This morphology is significantly distinct from that of junctions within the ER network, and their morphology emerges as early as telophase. The highly constricted ER-NE junctions are seen in several mammalian cell types, but not in budding yeast. We speculate that the unique and highly constricted ER-NE junctions are regulated via novel mechanisms that contribute to ER-to-NE lipid and protein traffic in higher eukaryotes.

PMID:38877171 | DOI:10.1038/s44319-024-00175-w

Nat Commun. 2024 Jun 14;15(1):5102. doi: 10.1038/s41467-024-49425-4.

ABSTRACT

Tomato (Solanum lycopersicum) is one of the world's most important food crops, and as such, its production needs to be protected from infectious diseases that can significantly reduce yield and quality. Here, we survey the effector-triggered immunity (ETI) landscape of tomato against the bacterial pathogen Pseudomonas syringae. We perform comprehensive ETI screens in five cultivated tomato varieties and two wild relatives, as well as an immunodiversity screen on a collection of 149 tomato varieties that includes both wild and cultivated varieties. The screens reveal a tomato ETI landscape that is more limited than what was previously found in the model plant Arabidopsis thaliana. We also demonstrate that ETI eliciting effectors can protect tomato against P. syringae infection when the effector is delivered by a non-virulent strain either prior to or simultaneously with a virulent strain. Overall, our findings provide a snapshot of the ETI landscape of tomatoes and demonstrate that ETI can be used as a biocontrol treatment to protect crop plants.

PMID:38877009 | DOI:10.1038/s41467-024-49425-4

Sci Total Environ. 2024 Jun 12:173961. doi: 10.1016/j.scitotenv.2024.173961. Online ahead of print.

ABSTRACT

The sulfur (S) cycle is an important biogeochemical cycle with profound implications for both cellular- and ecosystem-level processes by diverse microorganisms. Mangrove sediments are a hotspot of biogeochemical cycling, especially for the S cycle with high concentrations of S compounds. Previous studies have mainly focused on some specific inorganic S cycling processes without paying specific attention to the overall S-cycling communities and processes as well as organic S metabolism. In this study, we comprehensively analyzed the distribution, ecological network and assembly mechanisms of S cycling microbial communities and their changes with sediment depths using metagenome sequencing data. The results showed that the abundance of gene families involved in sulfur oxidation, assimilatory sulfate reduction, and dimethylsulfoniopropionate (DMSP) cleavage and demethylation decreased with sediment depths, while those involved in S reduction and dimethyl sulfide (DMS) transformation showed an opposite trend. Specifically, glpE, responsible for converting S2O32- to SO32-, showed the highest abundance in the surface sediment and decreased with sediment depths; in contrast, high abundances of dmsA, responsible for converting dimethyl sulfoxide (DMSO) to DMS, were identified and increased with sediment depths. We identified Pseudomonas and Streptomyces as the main S-cycling microorganisms, while Thermococcus could play an import role in microbial network connections in the S-cycling microbial community. Our statistical analysis showed that both taxonomical and functional compositions were generally shaped by stochastic processes, while the functional composition of organic S metabolism showed a transition from stochastic to deterministic processes. This study provides a novel perspective of diversity distribution of S-cycling functions and taxa as well as their potential assembly mechanisms, which has important implications for maintaining mangrove ecosystem functions.

PMID:38876338 | DOI:10.1016/j.scitotenv.2024.173961

Cell. 2024 Jun 11:S0092-8674(24)00532-4. doi: 10.1016/j.cell.2024.05.023. Online ahead of print.

ABSTRACT

Vector-borne diseases are a leading cause of death worldwide and pose a substantial unmet medical need. Pathogens binding to host extracellular proteins (the "exoproteome") represents a crucial interface in the etiology of vector-borne disease. Here, we used bacterial selection to elucidate host-microbe interactions in high throughput (BASEHIT)-a technique enabling interrogation of microbial interactions with 3,324 human exoproteins-to profile the interactomes of 82 human-pathogen samples, including 30 strains of arthropod-borne pathogens and 8 strains of related non-vector-borne pathogens. The resulting atlas revealed 1,303 putative interactions, including hundreds of pairings with potential roles in pathogenesis, including cell invasion, tissue colonization, immune evasion, and host sensing. Subsequent functional investigations uncovered that Lyme disease spirochetes recognize epidermal growth factor as an environmental cue of transcriptional regulation and that conserved interactions between intracellular pathogens and thioredoxins facilitate cell invasion. In summary, this interactome atlas provides molecular-level insights into microbial pathogenesis and reveals potential host-directed targets for next-generation therapeutics.

PMID:38876107 | DOI:10.1016/j.cell.2024.05.023

Cancer Cell. 2024 Jun 6:S1535-6108(24)00190-9. doi: 10.1016/j.ccell.2024.05.022. Online ahead of print.

ABSTRACT

Bacteria exhibit key features of cancer metastasis, such as motility, invasion, and modulation of the tumor microenvironment. They migrate through lymphatic and blood systems, invade metastatic tissues, and alter local microenvironments to support metastatic growth. Bacteria also shape the tumor microenvironment, affecting immune responses and inflammation, which influence tumor progression and therapy response. While they hold therapeutic potential, challenges like contamination and complex characterization persist, necessitating advanced sequencing and research for clinical application.

PMID:38876104 | DOI:10.1016/j.ccell.2024.05.022

Biomed Pharmacother. 2024 Jun 13;176:116920. doi: 10.1016/j.biopha.2024.116920. Online ahead of print.

ABSTRACT

Sarcopenia is a major public health concern among older adults, leading to disabilities, falls, fractures, and mortality. This study aimed to elucidate the pathophysiological mechanisms of sarcopenia and identify potential therapeutic targets using systems biology approaches. RNA-seq data from muscle biopsies of 24 sarcopenic and 29 healthy individuals from a previous cohort were analysed. Differential expression, gene set enrichment, gene co-expression network, and topology analyses were conducted to identify target genes implicated in sarcopenia pathogenesis, resulting in the selection of 6 hub genes (PDHX, AGL, SEMA6C, CASQ1, MYORG, and CCDC69). A drug repurposing approach was then employed to identify new pharmacological treatment options for sarcopenia (clofibric-acid, troglitazone, withaferin-a, palbociclib, MG-132, bortezomib). Finally, validation experiments in muscle cell line (C2C12) revealed MG-132 and troglitazone as promising candidates for sarcopenia treatment. Our approach, based on systems biology and drug repositioning, provides insight into the molecular mechanisms of sarcopenia and offers potential new treatment options using existing drugs.

PMID:38876054 | DOI:10.1016/j.biopha.2024.116920

JMIR AI. 2024 Mar 29;3:e47652. doi: 10.2196/47652.

ABSTRACT

BACKGROUND: Central collection of distributed medical patient data is problematic due to strict privacy regulations. Especially in clinical environments, such as clinical time-to-event studies, large sample sizes are critical but usually not available at a single institution. It has been shown recently that federated learning, combined with privacy-enhancing technologies, is an excellent and privacy-preserving alternative to data sharing.

OBJECTIVE: This study aims to develop and validate a privacy-preserving, federated survival support vector machine (SVM) and make it accessible for researchers to perform cross-institutional time-to-event analyses.

METHODS: We extended the survival SVM algorithm to be applicable in federated environments. We further implemented it as a FeatureCloud app, enabling it to run in the federated infrastructure provided by the FeatureCloud platform. Finally, we evaluated our algorithm on 3 benchmark data sets, a large sample size synthetic data set, and a real-world microbiome data set and compared the results to the corresponding central method.

RESULTS: Our federated survival SVM produces highly similar results to the centralized model on all data sets. The maximal difference between the model weights of the central model and the federated model was only 0.001, and the mean difference over all data sets was 0.0002. We further show that by including more data in the analysis through federated learning, predictions are more accurate even in the presence of site-dependent batch effects.

CONCLUSIONS: The federated survival SVM extends the palette of federated time-to-event analysis methods by a robust machine learning approach. To our knowledge, the implemented FeatureCloud app is the first publicly available implementation of a federated survival SVM, is freely accessible for all kinds of researchers, and can be directly used within the FeatureCloud platform.

PMID:38875678 | DOI:10.2196/47652

JMIR AI. 2023 Jan 12;2:e40843. doi: 10.2196/40843.

ABSTRACT

BACKGROUND: Public health surveillance relies on the collection of data, often in near-real time. Recent advances in natural language processing make it possible to envisage an automated system for extracting information from electronic health records.

OBJECTIVE: To study the feasibility of setting up a national trauma observatory in France, we compared the performance of several automatic language processing methods in a multiclass classification task of unstructured clinical notes.

METHODS: A total of 69,110 free-text clinical notes related to visits to the emergency departments of the University Hospital of Bordeaux, France, between 2012 and 2019 were manually annotated. Among these clinical notes, 32.5% (22,481/69,110) were traumas. We trained 4 transformer models (deep learning models that encompass attention mechanism) and compared them with the term frequency-inverse document frequency associated with the support vector machine method.

RESULTS: The transformer models consistently performed better than the term frequency-inverse document frequency and a support vector machine. Among the transformers, the GPTanam model pretrained with a French corpus with an additional autosupervised learning step on 306,368 unlabeled clinical notes showed the best performance with a micro F1-score of 0.969.

CONCLUSIONS: The transformers proved efficient at the multiclass classification of narrative and medical data. Further steps for improvement should focus on the expansion of abbreviations and multioutput multiclass classification.

PMID:38875539 | DOI:10.2196/40843

Sci Immunol. 2024 Jun 14;9(96):eadd6774. doi: 10.1126/sciimmunol.add6774. Epub 2024 Jun 14.

ABSTRACT

Pro-inflammatory CD4+ T cells are major drivers of autoimmune diseases, yet therapies modulating T cell phenotypes to promote an anti-inflammatory state are lacking. Here, we identify T helper 17 (TH17) cell plasticity in the kidneys of patients with antineutrophil cytoplasmic antibody-associated glomerulonephritis on the basis of single-cell (sc) T cell receptor analysis and scRNA velocity. To uncover molecules driving T cell polarization and plasticity, we established an in vivo pooled scCRISPR droplet sequencing (iCROP-seq) screen and applied it to mouse models of glomerulonephritis and colitis. CRISPR-based gene targeting in TH17 cells could be ranked according to the resulting transcriptional perturbations, and polarization biases into T helper 1 (TH1) and regulatory T cells could be quantified. Furthermore, we show that iCROP-seq can facilitate the identification of therapeutic targets by efficient functional stratification of genes and pathways in a disease- and tissue-specific manner. These findings uncover TH17 to TH1 cell plasticity in the human kidney in the context of renal autoimmunity.

PMID:38875317 | DOI:10.1126/sciimmunol.add6774

PLoS One. 2024 Jun 14;19(6):e0304324. doi: 10.1371/journal.pone.0304324. eCollection 2024.

ABSTRACT

BACKGROUND: Anti-vascular endothelial growth factor (VEGF) monoclonal antibodies (mAbs) are widely used for tumor treatment, including metastatic colorectal cancer (mCRC). So far, there are no biomarkers that reliably predict resistance to anti-VEGF mAbs like bevacizumab. A biomarker-guided strategy for early and accurate assessment of resistance could avoid the use of non-effective treatment and improve patient outcomes. We hypothesized that repeated analysis of multiple cytokines and angiogenic growth factors (CAFs) before and during treatment using machine learning could provide an accurate and earlier, i.e., 100 days before conventional radiologic staging, prediction of resistance to first-line mCRC treatment with FOLFOX plus bevacizumab.

PATIENTS AND METHODS: 15 German and Austrian centers prospectively recruited 50 mCRC patients receiving FOLFOX plus bevacizumab as first-line treatment. Plasma samples were collected every two weeks until radiologic progression (RECIST 1.1) as determined by CT scans performed every 2 months. 102 pre-selected CAFs were centrally analyzed using a cytokine multiplex assay (Luminex, Myriad RBM).

RESULTS: Using random forests, we developed a predictive machine learning model that discriminated between the situations of "no progress within 100 days before radiological progress" and "progress within 100 days before radiological progress". We could further identify a combination of ten out of the 102 CAF markers, which fulfilled this task with 78.2% accuracy, 71.8% sensitivity, and 82.5% specificity.

CONCLUSIONS: We identified a CAF marker combination that indicates treatment resistance to FOLFOX plus bevacizumab in patients with mCRC within 100 days prior to radiologic progress.

PMID:38875244 | DOI:10.1371/journal.pone.0304324

Proc Natl Acad Sci U S A. 2024 Jun 18;121(25):e2323009121. doi: 10.1073/pnas.2323009121. Epub 2024 Jun 14.

ABSTRACT

Error correction is central to many biological systems and is critical for protein function and cell health. During mitosis, error correction is required for the faithful inheritance of genetic material. When functioning properly, the mitotic spindle segregates an equal number of chromosomes to daughter cells with high fidelity. Over the course of spindle assembly, many initially erroneous attachments between kinetochores and microtubules are fixed through the process of error correction. Despite the importance of chromosome segregation errors in cancer and other diseases, there is a lack of methods to characterize the dynamics of error correction and how it can go wrong. Here, we present an experimental method and analysis framework to quantify chromosome segregation error correction in human tissue culture cells with live cell confocal imaging, timed premature anaphase, and automated counting of kinetochores after cell division. We find that errors decrease exponentially over time during spindle assembly. A coarse-grained model, in which errors are corrected in a chromosome-autonomous manner at a constant rate, can quantitatively explain both the measured error correction dynamics and the distribution of anaphase onset times. We further validated our model using perturbations that destabilized microtubules and changed the initial configuration of chromosomal attachments. Taken together, this work provides a quantitative framework for understanding the dynamics of mitotic error correction.

PMID:38875144 | DOI:10.1073/pnas.2323009121

Elife. 2024 Jun 14;13:e100526. doi: 10.7554/eLife.100526.

ABSTRACT

PMID:38875009 | DOI:10.7554/eLife.100526