BMC Genomics. 2024 Jun 28;25(1):646. doi: 10.1186/s12864-024-10552-3.

ABSTRACT

BACKGROUND: Ménière's disease (MD) is a disorder of the inner ear that causes episodic bouts of severe dizziness, roaring tinnitus, and fluctuating hearing loss. To date, no targeted therapy exists. As such, we have undertaken a large whole genome sequencing study on carefully phenotyped unilateral MD patients with the goal of gene/pathway discovery and a move towards targeted intervention. This study was a retrospective review of patients with a history of Ménière's disease. Genomic DNA, acquired from saliva samples, was purified and subjected to whole genome sequencing.

RESULTS: Stringent variant calling, performed on 511 samples passing quality checks, followed by gene-based filtering by recurrence and proximity in molecular interaction networks, led to 481 high priority MD genes. These high priority genes, including MPHOSPH8, MYO18A, TRIOBP, OTOGL, TNC, and MYO6, were previously implicated in hearing loss, balance, and cochlear function, and were significantly enriched in common variant studies of hearing loss. Validation in an independent MD cohort confirmed 82 recurrent genes. Pathway analysis pointed to cell-cell adhesion, extracellular matrix, and cellular energy maintenance as key mediators of MD. Furthermore, the MD-prioritized genes were highly expressed in human inner ear hair cells and dark/vestibular cells, and were differentially expressed in a mouse model of hearing loss.

CONCLUSION: By enabling the development of model systems that may lead to targeted therapies and MD screening panels, the genes and variants identified in this study will inform diagnosis and treatment of MD.

PMID:38943082 | DOI:10.1186/s12864-024-10552-3

Prog Mol Biol Transl Sci. 2024;207:193-206. doi: 10.1016/bs.pmbts.2024.01.006. Epub 2024 May 11.

ABSTRACT

Designing and predicting novel drug targets to accelerate drug discovery for treating metabolic dysfunction-associated steatohepatitis (MASH)-cirrhosis is a challenging task. The presence of superimposed (nested) and co-occurring clinical and histological phenotypes, namely MASH and cirrhosis, may partly explain this. Thus, in this scenario, each sub-phenotype has its own set of pathophysiological mechanisms, triggers, and processes. Here, we used gene/protein and set enrichment analysis to predict druggable pathways for the treatment of MASH-cirrhosis. Our findings indicate that the pathogenesis of MASH-cirrhosis can be explained by perturbations in multiple, simultaneous, and overlapping molecular processes. In this scenario, each sub-phenotype has its own set of pathophysiological mechanisms, triggers, and processes. Therefore, we used systems biology modeling to provide evidence that MASH and cirrhosis paradoxically present unique and distinct as well as common disease mechanisms, including a network of molecular targets. More importantly, pathway analysis revealed straightforward results consistent with modulation of the immune response, cell cycle control, and epigenetic regulation. In conclusion, the selection of potential therapies for MASH-cirrhosis should be guided by a better understanding of the underlying biological processes and molecular perturbations that progressively damage liver tissue and its underlying structure. Therapeutic options for patients with MASH may not necessarily be of choice for MASH cirrhosis. Therefore, the biology of the disease and the processes associated with its natural history must be at the forefront of the decision-making process.

PMID:38942537 | DOI:10.1016/bs.pmbts.2024.01.006

Microb Pathog. 2024 Jun 26:106766. doi: 10.1016/j.micpath.2024.106766. Online ahead of print.

ABSTRACT

Campylobacter jejuni is one of the major causes of bacterial gastrointestinal disease in humans worldwide. This foodborne pathogen colonizes the intestinal tracts of chickens, and consumption of chicken and poultry products is identified as a common route of transmission. We analyzed two C. jejuni strains after oral challenge with 105 CFU/ml of C. jejuni per chick; one strain was a robust colonizer (A74/C) and the other a poor colonizer (A74/O). We also found extensive phenotypic differences in growth rate, biofilm production, and in vitro adherence, invasion, intracellular survival, and transcytosis. Strains A74/C and A74/O were genotypically similar with respect to their whole genome alignment, core genome, and ribosomal MLST, MLST, flaA, porA, and PFGE typing. The global proteomes of the two congenic strains were quantitatively analyzed by ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) and 618 and 453 proteins were identified from A74/C and A74/O isolates, respectively. Cluster of Orthologous Groups (COG) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses showed that carbon metabolism and motility proteins were distinctively overexpressed in strain A74/C. The robust colonizer also exhibited a unique proteome profile characterized by significantly increased expression of proteins linked to adhesion, invasion, chemotaxis, energy, protein synthesis, heat shock proteins, iron regulation, two-component regulatory systems, and multidrug efflux pump. Our study underlines phenotypic, genotypic, and proteomic variations of the poor and robust colonizing C. jejuni strains, suggesting that several factors may contribute to mediating the different colonization potentials of the isogenic isolates.

PMID:38942248 | DOI:10.1016/j.micpath.2024.106766

Cell Genom. 2024 Jun 19:100586. doi: 10.1016/j.xgen.2024.100586. Online ahead of print.

ABSTRACT

Mycena s.s. is a ubiquitous mushroom genus whose members degrade multiple dead plant substrates and opportunistically invade living plant roots. Having sequenced the nuclear genomes of 24 Mycena species, we find them to defy the expected patterns for fungi based on both their traditionally perceived saprotrophic ecology and substrate specializations. Mycena displayed massive genome expansions overall affecting all gene families, driven by novel gene family emergence, gene duplications, enlarged secretomes encoding polysaccharide degradation enzymes, transposable element (TE) proliferation, and horizontal gene transfers. Mainly due to TE proliferation, Arctic Mycena species display genomes of up to 502 Mbp (2-8× the temperate Mycena), the largest among mushroom-forming Agaricomycetes, indicating a possible evolutionary convergence to genomic expansions sometimes seen in Arctic plants. Overall, Mycena show highly unusual, varied mosaic-like genomic structures adaptable to multiple lifestyles, providing genomic illustration for the growing realization that fungal niche adaptations can be far more fluid than traditionally believed.

PMID:38942024 | DOI:10.1016/j.xgen.2024.100586

Nat Immunol. 2024 Jun 28. doi: 10.1038/s41590-024-01882-1. Online ahead of print.

ABSTRACT

The immunological mechanisms underlying chronic colitis are poorly understood. T follicular helper (TFH) cells are critical in helping B cells during germinal center reactions. In a T cell transfer colitis model, a lymphoid structure composed of mature dendritic cells (DCs) and TFH cells was found within T cell zones of colonic lymphoid follicles. TFH cells were required for mature DC accumulation, the formation of DC-T cell clusters and colitis development. Moreover, DCs promoted TFH cell differentiation, contributing to colitis development. A lineage-tracing analysis showed that, following migration to the lamina propria, TFH cells transdifferentiated into long-lived pathogenic TH1 cells, promoting colitis development. Our findings have therefore demonstrated the reciprocal regulation of TFH cells and DCs in colonic lymphoid follicles, which is critical in chronic colitis pathogenesis.

PMID:38942990 | DOI:10.1038/s41590-024-01882-1

Bioinformatics. 2024 Jun 28:btae424. doi: 10.1093/bioinformatics/btae424. Online ahead of print.

ABSTRACT

MOTIVATION: Errors in the processing of genetic information during protein synthesis can lead to phenotypic mutations, such as amino acid substitutions, e.g. by transcription or translation errors. While genetic mutations can be readily identified using DNA sequencing, and mutations due to transcription errors by RNA sequencing, translation errors can only be identified proteome-wide using mass spectrometry.

RESULTS: Here, we provide a Python package implementation of a high-throughput pipeline to detect amino acid substitutions in mass spectrometry datasets. Our tools enable users to process hundreds of mass spectrometry datasets in batch mode to detect amino acid substitutions and calculate codon-specific and site-specific translation error rates. deTELpy will facilitate the systematic understanding of amino acid misincorporation rates (translation error rates), and the inference of error models across organisms and under stress conditions, such as drug treatment or disease conditions.

AVAILABILITY: deTELpy is implemented in Python 3 and is freely available with detailed documentation and practical examples at https://git.mpi-cbg.de/tothpetroczylab/detelpy and https://pypi.org/project/deTELpy/ and can be easily installed via pip install deTELpy.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID:38941503 | DOI:10.1093/bioinformatics/btae424

Sci Adv. 2024 Jun 28;10(26):eadf3411. doi: 10.1126/sciadv.adf3411. Epub 2024 Jun 28.

ABSTRACT

Gene regulation is essential to placental function and fetal development. We built a genome-scale transcriptional regulatory network (TRN) of the human placenta using digital genomic footprinting and transcriptomic data. We integrated 475 transcriptomes and 12 DNase hypersensitivity datasets from placental samples to globally and quantitatively map transcription factor (TF)-target gene interactions. In an independent dataset, the TRN model predicted target gene expression with an out-of-sample R2 greater than 0.25 for 73% of target genes. We performed siRNA knockdowns of four TFs and achieved concordance between the predicted gene targets in our TRN and differences in expression of knockdowns with an accuracy of >0.7 for three of the four TFs. Our final model contained 113,158 interactions across 391 TFs and 7712 target genes and is publicly available. We identified 29 TFs which were significantly enriched as regulators for genes previously associated with preterm birth, and eight of these TFs were decreased in preterm placentas.

PMID:38941464 | DOI:10.1126/sciadv.adf3411

Small. 2024 Jun 28:e2401848. doi: 10.1002/smll.202401848. Online ahead of print.

ABSTRACT

For every epidemic outbreak, the prevention and treatments in resource-limited areas are always out of reach. Critical to this is that high accuracy, stability, and more comprehensive analytical techniques always rely on expensive and bulky instruments and large laboratories. Here, a fully integrated and high-throughput microfluidic system is proposed for ultra-multiple point-of-care immunoassay, termed Dac system. Specifically, the Dac system only requires a handheld portable device to automatically recycle repetitive multi-step reactions including on-demand liquid releasing, dispensing, metering, collecting, oscillatory mixing, and discharging. The Dac system performs high-precision enzyme-linked immunosorbent assays for up to 17 samples or targets simultaneously on a single chip. Furthermore, reagent consumption is only 2% compared to conventional ELISA, and microbubble-accelerated reactions shorten the assay time by more than half. As a proof of concept, the multiplexed detections are achieved by detecting at least four infection targets for two samples simultaneously on a singular chip. Furthermore, the barcode-based multi-target results can rapidly distinguish between five similar cases, allowing for accurate therapeutic interventions. Compared to bulky clinical instruments, the accuracy of clinical inflammation classification is 92.38% (n = 105), with a quantitative correlation coefficient of R2 = 0.9838, while the clinical specificity is 100% and the sensitivity is 98.93%.

PMID:38940626 | DOI:10.1002/smll.202401848

mSystems. 2024 Jun 28:e0071724. doi: 10.1128/msystems.00717-24. Online ahead of print.

ABSTRACT

Upon nutrient starvation, Chlamydia trachomatis serovar L2 (CTL) shifts from its normal growth to a non-replicating form, termed persistence. It is unclear if persistence reflects an adaptive response or a lack thereof. To understand this, transcriptomics data were collected for CTL grown under nutrient-replete and nutrient-starved conditions. Applying K-means clustering on transcriptomics data revealed a global transcriptomic rewiring of CTL under stress conditions in the absence of any canonical global stress regulator. This is consistent with previous data that suggested that CTL's stress response is due to a lack of an adaptive response mechanism. To investigate the impact of this on CTL metabolism, we reconstructed a genome-scale metabolic model of CTL (iCTL278) and contextualized it with the collected transcriptomics data. Using the metabolic bottleneck analysis on contextualized iCTL278, we observed that phosphoglycerate mutase (pgm) regulates the entry of CTL to the persistence state. Our data indicate that pgm has the highest thermodynamics driving force and lowest enzymatic cost. Furthermore, CRISPRi-driven knockdown of pgm in the presence or absence of tryptophan revealed the importance of this gene in modulating persistence. Hence, this work, for the first time, introduces thermodynamics and enzyme cost as tools to gain a deeper understanding on CTL persistence.

IMPORTANCE: This study uses a metabolic model to investigate factors that contribute to the persistence of Chlamydia trachomatis serovar L2 (CTL) under tryptophan and iron starvation conditions. As CTL lacks many canonical transcriptional regulators, the model was used to assess two prevailing hypotheses on persistence-that the chlamydial response to nutrient starvation represents a passive response due to the lack of regulators or that it is an active response by the bacterium. K-means clustering of stress-induced transcriptomics data revealed striking evidence in favor of the lack of adaptive (i.e., a passive) response. To find the metabolic signature of this, metabolic modeling pin-pointed pgm as a potential regulator of persistence. Thermodynamic driving force, enzyme cost, and CRISPRi knockdown of pgm supported this finding. Overall, this work introduces thermodynamic driving force and enzyme cost as a tool to understand chlamydial persistence, demonstrating how systems biology-guided CRISPRi can unravel complex bacterial phenomena.

PMID:38940523 | DOI:10.1128/msystems.00717-24

Biointerphases. 2024 May 1;19(3):030801. doi: 10.1116/6.0003672.

ABSTRACT

The Mollusca comprises a diverse range of organisms, with the class Gastropoda alone boasting approximately 80 000 extant species. Their adaptability across various habitats is facilitated by the evolution of the radula, a key structure for food acquisition. The radula's composition and mechanical properties, including its chitinous membrane, teeth, and supporting structures, enable efficient food gathering and processing. Through adaptive tooth morphology and composition, an interplay between radular components is facilitated, which results in collective effects to withstand forces encountered during feeding and reduce structural failure, with the broad range of variations reflecting ecological niches. Furthermore, teeth consist of composite materials with sometimes high contents of iron, calcium, or silicon to reduce wear. During interaction with the food, the radula performs complex three-dimensional motions, challenging to document. Here, we provide a review on the morphology, the mechanical properties, the composition, and various other parameters that contribute to radular performance. Due to, e.g., the smallness of these structures, there are, however, limitations to radular research. However, numerical simulations and physical models tested on substrates offer avenues for further understanding radular function and performance during feeding. These studies not only advance our knowledge of molluscan biology and ecology but also provide inspirations for biomimetic design and further advances in materials engineering.

PMID:38940493 | DOI:10.1116/6.0003672

J Med Virol. 2024 Jul;96(7):e29773. doi: 10.1002/jmv.29773.

ABSTRACT

The dynamics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission are influenced by a variety of factors, including social restrictions and the emergence of distinct variants. In this study, we delve into the origins and dissemination of the Alpha, Delta, and Omicron-BA.1 variants of concern in Galicia, northwest Spain. For this, we leveraged genomic data collected by the EPICOVIGAL Consortium and from the GISAID database, along with mobility information from other Spanish regions and foreign countries. Our analysis indicates that initial introductions during the Alpha phase were predominantly from other Spanish regions and France. However, as the pandemic progressed, introductions from Portugal and the United States became increasingly significant. The number of detected introductions varied from 96 and 101 for Alpha and Delta to 39 for Omicron-BA.1. Most of these introductions left a low number of descendants (<10), suggesting a limited impact on the evolution of the pandemic in Galicia. Notably, Galicia's major coastal cities emerged as critical hubs for viral transmission, highlighting their role in sustaining and spreading the virus. This research emphasizes the critical role of regional connectivity in the spread of SARS-CoV-2 and offers essential insights for enhancing public health strategies and surveillance measures.

PMID:38940448 | DOI:10.1002/jmv.29773

Adv Sci (Weinh). 2024 Jun 28:e2402457. doi: 10.1002/advs.202402457. Online ahead of print.

ABSTRACT

Transmembrane protein 52B (TMEM52B), a newly identified tumor-related gene, has been reported to regulate various tumors, yet its role in nasopharyngeal carcinoma (NPC) remains unclear. Transcriptomic analysis of NPC cell lines reveals frequent overexpression of TMEM52B, and immunohistochemical results show that TMEM52B is associated with advanced tumor stage, recurrence, and decreased survival time. Depleting TMEM52B inhibits the proliferation, migration, invasion, and oncogenesis of NPC cells in vivo. TMEM52B encodes two isoforms, TMEM52B-P18 and TMEM52B-P20, differing in their N-terminals. While both isoforms exhibit similar pro-oncogenic roles and contribute to drug resistance in NPC, TMEM52B-P20 differentially promotes metastasis. This functional discrepancy may be attributed to their distinct subcellular localization; TMEM52B-P18 is confined to the cytoplasm, while TMEM52B-P20 is found both at the cell membrane and in the cytoplasm. Mechanistically, cytoplasmic TMEM52B enhances AKT phosphorylation by interacting with phosphoglycerate kinase 1 (PGK1), fostering NPC growth and metastasis. Meanwhile, membrane-localized TMEM52B-P20 promotes E-cadherin ubiquitination and degradation by facilitating its interaction with the E3 ubiquitin ligase NEDD4, further driving NPC metastasis. In conclusion, the TMEM52B-P18 and TMEM52B-P20 isoforms promote the metastasis of NPC cells through different mechanisms. Drugs targeting these TMEM52B isoforms may offer therapeutic benefits to cancer patients with varying degrees of metastasis.

PMID:38940427 | DOI:10.1002/advs.202402457

Bioinformatics. 2024 Jun 28;40(Supplement_1):i381-i389. doi: 10.1093/bioinformatics/btae262.

ABSTRACT

SUMMARY: Cis-acting mRNA elements play a key role in the regulation of mRNA stability and translation efficiency. Revealing the interactions of these elements and their impact plays a crucial role in understanding the regulation of the mRNA translation process, which supports the development of mRNA-based medicine or vaccines. Deep neural networks (DNN) can learn complex cis-regulatory codes from RNA sequences. However, extracting these cis-regulatory codes efficiently from DNN remains a significant challenge. Here, we propose a method based on our toolkit NeuronMotif and motif mutagenesis, which not only enables the discovery of diverse and high-quality motifs but also efficiently reveals motif interactions. By interpreting deep-learning models, we have discovered several crucial motifs that impact mRNA translation efficiency and stability, as well as some unknown motifs or motif syntax, offering novel insights for biologists. Furthermore, we note that it is challenging to enrich motif syntax in datasets composed of randomly generated sequences, and they may not contain sufficient biological signals.

AVAILABILITY AND IMPLEMENTATION: The source code and data used to produce the results and analyses presented in this manuscript are available from GitHub (https://github.com/WangLabTHU/combmotif).

PMID:38940172 | DOI:10.1093/bioinformatics/btae262

Bioinformatics. 2024 Jun 28;40(Supplement_1):i247-i256. doi: 10.1093/bioinformatics/btae212.

ABSTRACT

MOTIVATION: Acute kidney injury (AKI) is a syndrome that affects a large fraction of all critically ill patients, and early diagnosis to receive adequate treatment is as imperative as it is challenging to make early. Consequently, machine learning approaches have been developed to predict AKI ahead of time. However, the prevalence of AKI is often underestimated in state-of-the-art approaches, as they rely on an AKI event annotation solely based on creatinine, ignoring urine output.

We construct and evaluate early warning systems for AKI in a multi-disciplinary ICU setting, using the complete KDIGO definition of AKI. We propose several variants of gradient-boosted decision tree (GBDT)-based models, including a novel time-stacking based approach. A state-of-the-art LSTM-based model previously proposed for AKI prediction is used as a comparison, which was not specifically evaluated in ICU settings yet.

RESULTS: We find that optimal performance is achieved by using GBDT with the time-based stacking technique (AUPRC = 65.7%, compared with the LSTM-based model's AUPRC = 62.6%), which is motivated by the high relevance of time since ICU admission for this task. Both models show mildly reduced performance in the limited training data setting, perform fairly across different subcohorts, and exhibit no issues in gender transfer.

Following the official KDIGO definition substantially increases the number of annotated AKI events. In our study GBDTs outperform LSTM models for AKI prediction. Generally, we find that both model types are robust in a variety of challenging settings arising for ICU data.

AVAILABILITY AND IMPLEMENTATION: The code to reproduce the findings of our manuscript can be found at: https://github.com/ratschlab/AKI-EWS.

PMID:38940165 | DOI:10.1093/bioinformatics/btae212

Bioinformatics. 2024 Jun 28;40(Supplement_1):i501-i510. doi: 10.1093/bioinformatics/btae251.

ABSTRACT

MOTIVATION: In many biomedical applications, we are confronted with paired groups of samples, such as treated versus control. The aim is to detect discriminating features, i.e. biomarkers, based on high-dimensional (omics-) data. This problem can be phrased more generally as a two-sample problem requiring statistical significance testing to establish differences, and interpretations to identify distinguishing features. The multivariate maximum mean discrepancy (MMD) test quantifies group-level differences, whereas statistically significantly associated features are usually found by univariate feature selection. Currently, few general-purpose methods simultaneously perform multivariate feature selection and two-sample testing.

RESULTS: We introduce a sparse, interpretable, and optimized MMD test (SpInOpt-MMD) that enables two-sample testing and feature selection in the same experiment. SpInOpt-MMD is a versatile method and we demonstrate its application to a variety of synthetic and real-world data types including images, gene expression measurements, and text data. SpInOpt-MMD is effective in identifying relevant features in small sample sizes and outperforms other feature selection methods such as SHapley Additive exPlanations and univariate association analysis in several experiments.

AVAILABILITY AND IMPLEMENTATION: The code and links to our public data are available at https://github.com/BorgwardtLab/spinoptmmd.

PMID:38940158 | DOI:10.1093/bioinformatics/btae251

Bioinformatics. 2024 Jun 28;40(Supplement_1):i567-i575. doi: 10.1093/bioinformatics/btae234.

ABSTRACT

MOTIVATION: Profiling of gene expression and chromatin accessibility by single-cell multi-omics approaches can help to systematically decipher how transcription factors (TFs) regulate target gene expression via cis-region interactions. However, integrating information from different modalities to discover regulatory associations is challenging, in part because motif scanning approaches miss many likely TF binding sites.

RESULTS: We develop REUNION, a framework for predicting genome-wide TF binding and cis-region-TF-gene "triplet" regulatory associations using single-cell multi-omics data. The first component of REUNION, Unify, utilizes information theory-inspired complementary score functions that incorporate TF expression, chromatin accessibility, and target gene expression to identify regulatory associations. The second component, Rediscover, takes Unify estimates as input for pseudo semi-supervised learning to predict TF binding in accessible genomic regions that may or may not include detected TF motifs. Rediscover leverages latent chromatin accessibility and sequence feature spaces of the genomic regions, without requiring chromatin immunoprecipitation data for model training. Applied to peripheral blood mononuclear cell data, REUNION outperforms alternative methods in TF binding prediction on average performance. In particular, it recovers missing region-TF associations from regions lacking detected motifs, which circumvents the reliance on motif scanning and facilitates discovery of novel associations involving potential co-binding transcriptional regulators. Newly identified region-TF associations, even in regions lacking a detected motif, improve the prediction of target gene expression in regulatory triplets, and are thus likely to genuinely participate in the regulation.

AVAILABILITY AND IMPLEMENTATION: All source code is available at https://github.com/yangymargaret/REUNION.

PMID:38940155 | DOI:10.1093/bioinformatics/btae234

Front Mol Biosci. 2024 Jun 13;11:1404319. doi: 10.3389/fmolb.2024.1404319. eCollection 2024.

ABSTRACT

The search for new therapeutic strategies against cancer has favored the emergence of rationally designed treatments. These treatments have focused on attacking cell plasticity mechanisms to block the transformation of epithelial cells into cancerous cells. The aim of these approaches was to control particularly lethal cancers such as hepatocellular carcinoma. However, they have not been able to control the progression of cancer for unknown reasons. Facing this scenario, emerging areas such as systems biology propose using engineering principles to design and optimize cancer treatments. Beyond the possibilities that this approach might offer, it is necessary to know whether its implementation at a clinical level is viable or not. Therefore, in this paper, we will review the engineering principles that could be applied to rationally design strategies against hepatocellular carcinoma, and discuss whether the necessary elements exist to implement them. In particular, we will emphasize whether these engineering principles could be applied to fight hepatocellular carcinoma.

PMID:38939509 | PMC:PMC11208463 | DOI:10.3389/fmolb.2024.1404319

Gynecol Oncol Rep. 2024 Jun 4;54:101424. doi: 10.1016/j.gore.2024.101424. eCollection 2024 Aug.

ABSTRACT

Studies suggest a need for new diagnostic approaches for cervical cancer including microRNA technology. In this review, we assessed the diagnostic accuracy of microRNAs in detecting cervical cancer and Cervical Intraepithelial Neoplasia (CIN). We performed a systematic review following the Preferred Reporting Items for Systematic Review and Meta-Analysis guideline for protocols (PRISMA-P). We searched for all articles in online databases and grey literature from 01st January 2012 to 16th August 2022. We used the quality assessment of diagnostic accuracy studies tool (QUADAS-2) to assess the risk of bias of included studies and then conducted a Random Effects Meta-analysis. We identified 297 articles and eventually extracted data from 24 studies. Serum/plasma concentration miR-205, miR-21, miR-192, and miR-9 showed highest diagnostic accuracy (AUC of 0.750, 0.689, 0.980, and 0.900, respectively) for detecting CIN from healthy controls. MicroRNA panels (miR-21, miR-125b and miR-370) and (miR-9, miR-10a, miR-20a and miR-196a and miR-16-2) had AUC values of 0.897 and 0.886 respectively for detecting CIN from healthy controls. For detection of cervical cancer from healthy controls, the most promising microRNAs were miR-21, miR-205, miR-192 and miR-9 (AUC values of 0.723, 0.960, 1.00, and 0.99 respectively). We report higher diagnostic accuracy of upregulated microRNAs, especially miR-205, miR-9, miR-192, and miR-21. This highlights their potential as stand-alone screening or diagnostic tests, either with others, in a new algorithm, or together with other biomarkers for purposes of detecting cervical lesions. Future studies could standardize quantification methods, and also study microRNAs in higher prevalence populations like in sub-Saharan Africa and South Asia. Our review protocol was registered in PROSPERO (CRD42022313275).

PMID:38939506 | PMC:PMC11208915 | DOI:10.1016/j.gore.2024.101424

J Extracell Biol. 2022 Sep 27;1(9):e62. doi: 10.1002/jex2.62. eCollection 2022 Sep.

ABSTRACT

Small extracellular vesicles (sEVs) are nano-sized vesicles secreted from various cells that contain bioactive metabolites and function as key regulators for intercellular communication. sEVs modulate diverse biological and pathological processes in the body, and the amount of circulating sEVs has been reported to correlate with certain disease progression. Therefore, the identification of small molecular compounds that can control sEV production may become a novel therapeutic strategy. In this study, a rapid, highly sensitive sEV quantification method utilizing fusion proteins consisting of Gaussia luciferase (gLuc) reporter protein and sEV markers (CD63 and CD82) was developed. A total of 480 compounds were screened to identify potent inducers and inhibitors of gLuc activity. Two novel compounds, KPYC08425 and KPYC12163, showed significant and dose-dependent changes in gLuc activity with minimal cytotoxicity based on the LDH assay. The efficacy of these two compounds was further evaluated by protein quantification of the isolated sEVs. Further evaluation of KPYC12163 suggested that the autolysosomal pathway may be involved in its inhibitory effect on sEV production.

PMID:38938770 | PMC:PMC11080720 | DOI:10.1002/jex2.62

J Extracell Biol. 2023 Dec 27;3(1):e128. doi: 10.1002/jex2.128. eCollection 2024 Jan.

ABSTRACT

Human milk extracellular vesicles (HM EVs) are proposed to protect against disease development in infants. This protection could in part be facilitated by the bioactive EV cargo of proteins and RNA. Notably, mothers birth infants of different gestational ages with unique needs, wherein the EV cargo of HM may diverge. We collected HM from lactating mothers within two weeks of a term or preterm birth. Following purification of EVs, proteins and mRNA were extracted for proteomics and sequencing analyses, respectively. Over 2000 protein groups were identified, and over 8000 genes were quantified. The total number of proteins and mRNA did not differ significantly between the two conditions, while functional bioinformatics of differentially expressed cargo indicated enrichment in immunoregulatory cargo for preterm HM EVs. In term HM EVs, significantly upregulated cargo was enriched in metabolism-related functions. Based on gene expression signatures from HM-contained single cell sequencing data, we proposed that a larger portion of preterm HM EVs are secreted by immune cells, whereas term HM EVs contain more signatures of lactocyte epithelial cells. Proposed differences in EV cargo could indicate variation in mother's milk based on infants' gestational age and provide basis for further functional characterisation.

PMID:38938674 | PMC:PMC11080785 | DOI:10.1002/jex2.128