AdaPPI: identification of novel protein functional modules via adaptive graph convolution networks in a protein-protein interaction network.

Identifying unknown protein functional modules, such as protein complexes and biological pathways, from protein-protein interaction (PPI) networks, provides biologists with an opportunity to efficiently understand cellular function and organization. Finding complex nonlinear relationships in underlying functional modules may involve a long-chain of PPI and pose great challenges in a PPI network with an unevenly sparse and dense node distribution. To overcome these challenges, we propose AdaPPI, an adaptive convolution graph network in PPI networks to predict protein functional modules. We first suggest an attributed graph node presentation algorithm. It can effectively integrate protein gene ontology attributes and network topology, and adaptively aggregates low- or high-order graph structural information according to the node distribution by considering graph node smoothness. Based on the obtained node representations, core cliques and expansion algorithms are applied to find functional modules in PPI networks. Comprehensive performance evaluations and case studies indicate that the framework significantly outperforms state-of-the-art methods. We also presented potential functional modules based on their confidence.

Exploring the Causal Role of Immune Cells in Cerebral Aneurysm Through Single-Cell Transcriptomics and Mendelian Randomization Analysis.

Cerebral aneurysm (CA) represent a significant clinical challenge, characterized by pathological dilation of cerebral arteries. Recent evidence underscores the crucial involvement of immune cells in CA pathogenesis. This study aims to explore the complex interplay between immune cells and CA formation. We analyzed single-cell RNA sequencing data from the GSE193533 dataset, focusing on unruptured CA and their controls. Comprehensive cell-type identification and pseudotime trajectory analyses were conducted to delineate the dynamic shifts in immune cell populations. Additionally, a two-sample Mendelian Randomization (MR) approach was employed to investigate the causal influence of various immunophenotypes on CA susceptibility and the reciprocal effect of CA formation on immune phenotypes. Single-cell transcriptomic analysis revealed a progressive loss of vascular smooth muscle cells (VSMCs) and an increase in monocytes/macrophages (Mo/MΦ) and other immune cells, signifying a shift from a structural to an inflammatory milieu in CA evolution. MR analysis identified some vital immunophenotypes, such as CD64 on CD14+ CD16+ monocytes (OR: 1.236, 95% CI: 1.064 to 1.435, p=0.006), as potential risk factors for CA development, while others, like CD28- CD8br %CD8br (OR: 0.883, 95% CI: 0.789 to 0.988, p=0.030), appeared protective. Reverse MR analysis demonstrated that CA formation could modulate specific immunophenotypic expressions, highlighting a complex bidirectional interaction between CA pathology and immune response. This study underscores the pivotal role of immune cells in this process through the integration of single-cell transcriptomics with MR analysis, offering a comprehensive perspective on CA pathogenesis, potentially guiding future therapeutic strategies targeting specific immune pathways.

An Albumin-Holliday Junction Biomolecular Modular Design for Programmable Multifunctionality and Prolonged Circulation.

Combinatorial properties such as long-circulation and site- and cell-specific engagement need to be built into the design of advanced drug delivery systems to maximize drug payload efficacy. This work introduces a four-stranded oligonucleotide Holliday Junction (HJ) motif bearing functional moieties covalently conjugated to recombinant human albumin (rHA) to give a "plug-and-play" rHA-HJ multifunctional biomolecular assembly with extended circulation. Electrophoretic gel-shift assays show successful functionalization and purity of the individual high-performance liquid chromatography-purified modules as well as efficient assembly of the rHA-HJ construct. Inclusion of an epidermal growth factor receptor (EGFR)-targeting nanobody module facilitates specific binding to EGFR-expressing cells resulting in approximately 150-fold increased fluorescence intensity determined by flow cytometric analysis compared to assemblies absent of nanobody inclusion. A cellular recycling assay demonstrated retained albumin-neonatal Fc receptor (FcRn) binding affinity and accompanying FcRn-driven cellular recycling. This translated to a 4-fold circulatory half-life extension (2.2 and 0.55 h, for the rHA-HJ and HJ, respectively) in a double transgenic humanized FcRn/albumin mouse. This work introduces a novel biomolecular albumin-nucleic acid construct with extended circulatory half-life and programmable multifunctionality due to its modular design.

LncRNA SNHG15 regulates autophagy and prevents cerebral ischaemia-reperfusion injury through mediating miR-153-3p/ATG5 axis.

Ischaemic stroke is a common cerebrovascular disease. Long non-coding RNA (lncRNA) of small nucleolar RNA host gene (SNHG15) has been supposedly performed a regulatory role in many diseases. Nonetheless, the function of SNHG15 in cerebral ischaemia-reperfusion injury has not been clarified. The OGD/R of Neuro2A cells simulated the ischaemic and reperfused states of the brain. Neuro2a cell line with stable transfection of plasmid with silent expression of SNHG15 was constructed. Neuro2a cell lines transfected with miR-153-3p mimic (miR-153-3p-mimics) and miR-153-3p inhibitor (miR-153-3p-inhibition) were constructed. Expression of SNHG15, mi R-200a, FOXO3 and ATG7 in mouse brain tissue and N2a cells was identified by qRT-PCR. Western blot (WB) analysis of mouse brain tissue and Neuro2a cells revealed the presence of the proteins ATG5, Cle-caspase-3, Bax, Bcl-2, LC3 II/I and P62 (WB). The representation and distribution of LC3B were observed by immunofluorescence. The death of cells was measured using a technique called flow cytometry (FACS). SNHG15 was highly expressed in cerebral ischaemia-reperfusion injury model. Down-regulation of SNHG15 lead to lower apoptosis rate and decreased autophagy. Dual luciferase assay and co-immunoprecipitation (CoIP) found lncRNA SNHG15/miR-153-3p/ATG5. Compared to cells transfected with NC suppression, cells transfected with miR-153-3p-inhibition had substantially greater overexpression of LC 3 II/I, ATG5, cle-Caspase-3, and Bax, as determined by a recovery experiment, the apoptosis rate was elevated, yet both P62 and Bcl-2 were significantly lower and LC3+ puncta per cells were significantly increased. Co-transfection of miR-153-3p-inhibition and sh-SNHG15 could reverse these results. LncRNA SNHG15 regulated autophagy and prevented cerebral ischaemia-reperfusion injury through mediating the miR-153-3p/ATG5 axis.

Identification of epileptic networks with graph convolutional network incorporating oscillatory activities and evoked synaptic responses.

Stereoelectroencephalography (SEEG) offers unique neural data from in-depth brain structures with fine temporal resolutions to better investigate the origin of epileptic brain activities. Although oscillatory patterns from different frequency bands and functional connectivity computed from the SEEG datasets are employed to study the epileptic zones, direct electrical stimulation-evoked electrophysiological recordings of synaptic responses, namely cortical-cortical evoked potentials (CCEPs), from the same SEEG electrodes are not explored for the localization of epileptic zones. Here we proposed a two-stream model with unsupervised learning and graph convolutional network tailored to the SEEG and CCEP datasets in individual patients to perform localization of epileptic zones. We compared our localization results with the clinically marked electrode sites determined for surgical resections. Our model had good classification capability when compared to other state-of-the-art methods. Furthermore, based on our prediction results we performed group-level brain-area mapping analysis for temporal, frontal and parietal epilepsy patients and found that epileptic and non-epileptic brain networks were distinct in patients with different types of focal epilepsy. Our unsupervised data-driven model provides personalized localization analysis for the epileptic zones. The epileptic and non-epileptic brain areas disclosed by the prediction model provide novel insights into the network-level pathological characteristics of epilepsy.

Identification and Validation of Key Gene Modules and Pathways in Coronary Artery Disease Development and Progression.

The development and progression of atherosclerosis represent a chronic process involving complex molecular interactions. Therefore, identifying the potential hub genes and pathways contributing to coronary artery disease (CAD) development is essential for understanding its underlying molecular mechanisms. To this end, we performed transcriptome analysis of peripheral venous blood collected from 100 patients who were divided into four groups according to disease severity, including 27 patients in the atherosclerosis group, 22 patients in the stable angina group, 35 patients in the acute myocardial infarction group, and 16 controls. Weighted gene co-expression network analysis was performed using R programming. Significant module-trait correlations were identified according to module membership and genetic significance. Metascape was used for the functional enrichment of differentially expressed genes between groups, and the hub genes were identified via protein-protein interaction network analysis. The hub genes were further validated by analyzing Gene Expression Omnibus (GSE48060 and GSE141512) datasets. A total of 9,633 messenger ribonucleic acids were detected in three modules, among which the blue module was highly correlated with the Gensini score. The hub genes were significantly enriched in the myeloid leukocyte activation pathway, suggesting its important role in the progression of atherosclerosis. Among these genes, the Mediterranean fever gene (MEFV) may play a key role in the progression of atherosclerosis and CAD severity.

Advanced Stretchable Photodetectors: Strategies, Materials and Devices.

Past decades have witnessed the generation of new stretchable photodetectors in electronic eyes, health sensing, wearable devices, intelligent monitoring and other fields. Stretchable devices require not only outstanding performance but also excellent flexibility, adaptability and stability. Innovative strategies have been proposed to realize the stretchability of devices. In addition, novel functional materials including zero-dimensional nanomaterials, one-dimensional inorganic nanomaterials, two-dimensional layered materials, organic materials, and organic-inorganic composite materials with excellent properties are emerging to continuously improve the performance of devices. Here, the recent research progress of stretchable photodetectors in terms of both various design methods and functional materials is outlined. The optical performance and stretchable properties are also comprehensively reviewed. Finally, a summary and the challenges associated with the application of stretchable photodetectors are presented.

Traffic Vibration Signal Analysis of DAS Fiber Optic Cables with Different Coupling Based on an Improved Wavelet Thresholding Method.

Distributed Acoustic Sensing (DAS) is a novel technology that uses fiber optics to sense and monitor vibrations. It has demonstrated immense potential for various applications, including seismology research, traffic vibration detection, structural health inspection, and lifeline engineering. DAS technology transforms long sections of fiber optic cables into a high-density array of vibration sensors, providing exceptional spatial and temporal resolution for real-time monitoring of vibrations. Obtaining high-quality vibration data using DAS requires a robust coupling between the fiber optic cable and the ground layer. The study utilized the DAS system to detect vibration signals generated by vehicles operating on the campus road of Beijing Jiaotong University. Three distinct deployment methods were employed: the uncoupled fiber on the road, the underground communication fiber optic cable ducts, and the cement-bonded fixed fiber optic cable on the road shoulder, and compared for their outcomes. Vehicle vibration signals under the three deployment methods were analyzed using an improved wavelet threshold algorithm, which was verified to be effective. The results indicate that for practical applications, the most effective deployment method is the cement-bonded fixed fiber optic cable on the road shoulder, followed by the uncoupled fiber on the road, and the underground communication fiber optic cable ducts are the least effective. This has important implications for the future development of DAS as a tool for various fields.

Aircraft Detection Using Phase-Sensitive Optical-Fiber OTDR.

Aircraft detection plays a vital role in aviation management and safe operation in the aviation system. Phase-Sensitive Optical Time Domain Reflectometry (Φ-OTDR) technology is a prevailing sensing method in geophysics research, structure inspection, transportation detection, etc. Compared with existing video- or radio-based detection methods, Φ-OTDR is cost-effective, suitable for long-distance detection, and resistant to severe weather conditions. We present a detection system using Φ-OTDR technology and analyze the character of the acoustic signal of aircraft. Instead of runway monitoring in the airport or noise detection in the air, this study focuses on the detection of seismic vibration signal excited by the sound of aircraft. The Chebyshev filter is adopted to eliminate the impact of background noise and random noise from the original vibration signal; the short-time Fourier transform is used for time-frequency analysis. The experimental results showed that the seismic vibration signal excited by the aircraft sound is mainly low-frequency, which is under 5 Hz. Time delay of aircraft vibration signal in different locations of the optic fiber is recorded by the sensing system. The Doppler effect is also revealed by the time-domain analysis: the frequency increases when the aircraft is approaching and decreases when the aircraft moves away.

A parallel computational framework for ultra-large-scale sequence clustering analysis.

Motivation: The rapid development of sequencing technology has led to an explosive accumulation of genomic data. Clustering is often the first step to be performed in sequence analysis. However, existing methods scale poorly with respect to the unprecedented growth of input data size. As high-performance computing systems are becoming widely accessible, it is highly desired that a clustering method can easily scale to handle large-scale sequence datasets by leveraging the power of parallel computing. Results: In this paper, we introduce SLAD (Separation via Landmark-based Active Divisive clustering), a generic computational framework that can be used to parallelize various de novo operational taxonomic unit (OTU) picking methods and comes with theoretical guarantees on both accuracy and efficiency. The proposed framework was implemented on Apache Spark, which allows for easy and efficient utilization of parallel computing resources. Experiments performed on various datasets demonstrated that SLAD can significantly speed up a number of popular de novo OTU picking methods and meanwhile maintains the same level of accuracy. In particular, the experiment on the Earth Microbiome Project dataset (∼2.2B reads, 437 GB) demonstrated the excellent scalability of the proposed method. Availability and implementation: Open-source software for the proposed method is freely available at https://www.acsu.buffalo.edu/~yijunsun/lab/SLAD.html. Supplementary information: Supplementary data are available at Bioinformatics online.

Independent Validation of a Diagnostic Noninvasive 3-MicroRNA Ratio Model (uCaP) for Prostate Cancer in Cell-Free Urine.

BACKGROUND: Detection of prostate cancer (PC) based on serum prostate-specific antigen (PSA) testing leads to many unnecessary prostate biopsies, overdiagnosis, and overtreatment of clinically insignificant tumors. Thus, novel and more accurate molecular biomarkers are required. METHODS: Using reverse transcription quantitative PCR, we measured the concentrations of 45 preselected microRNAs (miRNAs) in extracellular vesicle-enriched cell-free urine samples from 4 independent patient cohorts from Spain and Denmark, including 758 patients with clinically localized PC, 289 noncancer controls with benign prostatic hyperplasia (BPH), and 233 patients undergoing initial transrectal ultrasound (TRUS)-guided prostate biopsy owing to PC suspicion (101 with benign and 132 with malignant outcome). Diagnostic potential was assessed by ROC and decision curve analysis. RESULTS: We identified and successfully validated 8 upregulated and 21 downregulated miRNAs in urine from PC patients. Furthermore, we validated a previously identified 3-miRNA diagnostic ratio model, uCaP (miR-222-3p*miR-24-3p/miR-30c-5p). High uCaP scores were distinctive of PC in urine samples from BPH vs PC patients in 3 independent cohorts [area under the curve (AUC) = 0.84, 0.71, 0.72]. Additionally, uCaP predicted TRUS biopsy results with greater accuracy than PSA (AUC uCaP = 0.644; AUC PSA = 0.527) for patients within the diagnostic gray zone (PSA ≤ 10 ng/mL). CONCLUSIONS: We successfully validated a urine-based diagnostic 3-miRNA signature for PC (uCaP) in 3 independent patient cohorts from 2 countries. In the future, the simple and noninvasive uCaP test may be used to help more accurately select patients for prostate biopsy. Prospective clinical validation is warranted.

A benchmark study of sequence alignment methods for protein clustering.

BACKGROUND: Protein sequence alignment analyses have become a crucial step for many bioinformatics studies during the past decades. Multiple sequence alignment (MSA) and pair-wise sequence alignment (PSA) are two major approaches in sequence alignment. Former benchmark studies revealed drawbacks of MSA methods on nucleotide sequence alignments. To test whether similar drawbacks also influence protein sequence alignment analyses, we propose a new benchmark framework for protein clustering based on cluster validity. This new framework directly reflects the biological ground truth of the application scenarios that adopt sequence alignments, and evaluates the alignment quality according to the achievement of the biological goal, rather than the comparison on sequence level only, which averts the biases introduced by alignment scores or manual alignment templates. Compared with former studies, we calculate the cluster validity score based on sequence distances instead of clustering results. This strategy could avoid the influence brought by different clustering methods thus make results more dependable. RESULTS: Results showed that PSA methods performed better than MSA methods on most of the BAliBASE benchmark datasets. Analyses on the 80 re-sampled benchmark datasets constructed by randomly choosing 90% of each dataset 10 times showed similar results. CONCLUSIONS: These results validated that the drawbacks of MSA methods revealed in nucleotide level also existed in protein sequence alignment analyses and affect the accuracy of results.

Palmitoylated phosphodiester gapmer designs with albumin binding capacity and maintained in vitro gene silencing activity.

BACKGROUND: Antisense gapmer oligonucleotide drugs require delivery and biodistribution enabling technologies to increase in vivo efficacy. An attractive approach is their binding and consequent transport by the endogenous human serum albumin pool as mediated by fatty acid incorporation into the gapmer design. METHODS: The present study investigated the effect of palmitoyl modification and position on albumin-binding, cellular uptake and in vitro gene silencing of gapmers with either a phosphorothioate (PS) or phosphodiester (PO) backbone. RESULTS: Two palmitoyls positioned exclusively at the 5' end, or a single palmitoyl at both the 3' and 5' positions, showed similar binding to human serum albumin as demonstrated by a gel-shift assay. Decreased cellular uptake determined by flow cytometry (27% compared to nonpalmitoyl gapmers) was observed for palmitoylated Cy5.5 labelled gapmers. However, HER3 (human epidermal growth factor receptor 3) gene silencing was exhibited by the palmitoylated gapmers with transfection agent in PC-3 and Caco-2 cells (68% and 62%, respectively), which was comparable to nonpalmitoyl gapmers (68% and 82%, respectively). Importantly, PO gapmers with a single palmitoyl positioned at both the 3' and 5' positions showed high silencing efficiencies (68% and 66% in PC-3 and Caco-2 cells, respectively) similar to those of PS nonpalmitoylated gapmers (67% and 66% in PC-3 and Caco-2 cells, respectively) in the absence of a transfection agent. CONCLUSIONS: The present study defines phosphodiester gapmer design criteria exhibiting high gene silencing activity and albumin binding that may be utilized with potentially less in vivo toxicity that can be associated with phosphorothioate gapmer designs.

ESPRIT-Forest: Parallel clustering of massive amplicon sequence data in subquadratic time.

The rapid development of sequencing technology has led to an explosive accumulation of genomic sequence data. Clustering is often the first step to perform in sequence analysis, and hierarchical clustering is one of the most commonly used approaches for this purpose. However, it is currently computationally expensive to perform hierarchical clustering of extremely large sequence datasets due to its quadratic time and space complexities. In this paper we developed a new algorithm called ESPRIT-Forest for parallel hierarchical clustering of sequences. The algorithm achieves subquadratic time and space complexity and maintains a high clustering accuracy comparable to the standard method. The basic idea is to organize sequences into a pseudo-metric based partitioning tree for sub-linear time searching of nearest neighbors, and then use a new multiple-pair merging criterion to construct clusters in parallel using multiple threads. The new algorithm was tested on the human microbiome project (HMP) dataset, currently one of the largest published microbial 16S rRNA sequence dataset. Our experiment demonstrated that with the power of parallel computing it is now compu- tationally feasible to perform hierarchical clustering analysis of tens of millions of sequences. The software is available at http://www.acsu.buffalo.edu/∼yijunsun/lab/ESPRIT-Forest.html.

Mobile GPU-based implementation of automatic analysis method for long-term ECG.

BACKGROUND: Long-term electrocardiogram (ECG) is one of the important diagnostic assistant approaches in capturing intermittent cardiac arrhythmias. Combination of miniaturized wearable holters and healthcare platforms enable people to have their cardiac condition monitored at home. The high computational burden created by concurrent processing of numerous holter data poses a serious challenge to the healthcare platform. An alternative solution is to shift the analysis tasks from healthcare platforms to the mobile computing devices. However, long-term ECG data processing is quite time consuming due to the limited computation power of the mobile central unit processor (CPU). METHODS: This paper aimed to propose a novel parallel automatic ECG analysis algorithm which exploited the mobile graphics processing unit (GPU) to reduce the response time for processing long-term ECG data. By studying the architecture of the sequential automatic ECG analysis algorithm, we parallelized the time-consuming parts and reorganized the entire pipeline in the parallel algorithm to fully utilize the heterogeneous computing resources of CPU and GPU. RESULTS: The experimental results showed that the average executing time of the proposed algorithm on a clinical long-term ECG dataset (duration 23.0 ± 1.0 h per signal) is 1.215 ± 0.140 s, which achieved an average speedup of 5.81 ± 0.39× without compromising analysis accuracy, comparing with the sequential algorithm. Meanwhile, the battery energy consumption of the automatic ECG analysis algorithm was reduced by 64.16%. Excluding energy consumption from data loading, 79.44% of the energy consumption could be saved, which alleviated the problem of limited battery working hours for mobile devices. CONCLUSION: The reduction of response time and battery energy consumption in ECG analysis not only bring better quality of experience to holter users, but also make it possible to use mobile devices as ECG terminals for healthcare professions such as physicians and health advisers, enabling them to inspect patient ECG recordings onsite efficiently without the need of a high-quality wide-area network environment.

Fatty Acid-Modified Gapmer Antisense Oligonucleotide and Serum Albumin Constructs for Pharmacokinetic Modulation.

Delivery technologies are required for realizing the clinical potential of molecular medicines. This work presents an alternative technology to preformulated delivery systems by harnessing the natural transport properties of serum albumin using endogenous binding of gapmer antisense oligonucleotides (ASOs)/albumin constructs. We show by an electrophoretic mobility assay that fatty acid-modified gapmer and human serum albumin (HSA) can self-assemble into constructs that offer favorable pharmacokinetics. The interaction was dependent on fatty acid type (either palmitic or myristic acid), number, and position within the gapmer ASO sequence, as well as phosphorothioate (PS) backbone modifications. Binding correlated with increased blood circulation in mice (t1/2 increased from 23 to 49 min for phosphodiester [PO] gapmer ASOs and from 28 to 66 min for PS gapmer ASOs with 2× palmitic acid modification). Furthermore, a shift toward a broader biodistribution was detected for PS compared with PO gapmer ASOs. Inclusion of 2× palmitoyl to the ASOs shifted the biodistribution to resemble that of natural albumin. This work, therefore, presents a novel strategy based on the proposed endogenous assembly of gapmer ASOs/albumin constructs for increased circulatory half-life and modulation of the biodistribution of gapmer ASOs that offers tunable pharmacokinetics based on the gapmer modification design.

A single subcutaneous administration of a sustained-release ivermectin suspension eliminates Psoroptes cuniculi infection in a rabbit farm.

BACKGROUND: Psoroptes cuniculi mites are the most common ear parasites infesting breeding female rabbits. The suffering rabbits show cutaneous signs of the infestation in the ears and are prone to secondary infections. OBJECTIVES: This trial was conducted to eliminate P. cuniculi in farm rabbits with a sustained-release ivermectin-loaded solid dispersion suspension (IVM-SD) suspension, and studied the stability of the formulation. ANIMALS: There were 986 breeding female Hyplus rabbits naturally infected with P. cuniculi. METHODS: All rabbits infected with P. cuniculi were subcutaneously administered with a single dose of IVM-SD suspension at 2 mg/kg body weight. Twenty-seven rabbits with severe infections were observed daily and examined on days 0 and 14 to score the lesions and count mites in crusts. RESULTS: Fourteen days after the treatment no live mites were detected, demonstrating 100% therapeutic efficacy. The mean lesion scores decreased from 4.33 to 0.11 in the left ears and from 4.22 to 0.22 in the right ears. No reinfection occurred within 60 days of treatment. CONCLUSIONS: A single subcutaneous administration of the IVM-SD suspension at 2 mg/kg was effective in eliminating P. cuniculi infection in the rabbit farm.

Methylation-Based Classification of Cervical Squamous Cell Carcinoma into Two New Subclasses Differing in Immune-Related Gene Expression.

Cervical cancer is traditionally classified into two major histological subtypes, cervical squamous cell carcinoma (CSCC) and cervical adenocarcinoma (CA). However, heterogeneity exists among patients, comprising possible subpopulations with distinct molecular profiles. We applied consensus clustering to 307 methylation samples with cervical cancer from The Cancer Genome Atlas (TCGA). Fisher's exact test was used to perform transcription factors (TFs) and genomic region enrichment. Gene expression profiles were downloaded from TCGA to assess expression differences. Immune cell fraction was calculated to quantify the immune cells infiltration. Putative neo-epitopes were predicted from somatic mutations. Three subclasses were identified: Class 1 correlating with the CA subtype and Classes 2 and 3 dividing the CSCC subtype into two subclasses. We found the hypomethylated probes in Class 3 exhibited strong enrichment in promoter region as compared with Class 2. Five TFs significantly enriched in the hypomethylated promoters and their highly expressed target genes in Class 3 functionally involved in the immune pathway. Gene function analysis revealed that immune-related genes were significantly increased in Class 3, and a higher level of immune cell infiltration was estimated. High expression of 24 immune genes exhibited a better overall survival and correlated with neo-epitope burden. Additionally, we found only two immune-related driver genes, CARD11 and JAK3, to be significantly increased in Class 3. Our analyses provide a classification of the largest CSCC subtype into two new subclasses, revealing they harbored differences in immune-related gene expression.

Hyaluronic Acid Molecular Weight-Dependent Modulation of Mucin Nanostructure for Potential Mucosal Therapeutic Applications.

This study investigates the effects of different molecular weight hyaluronic acids (HAs) on the mucosal nanostructure using a pig stomach mucin hydrogel as a mucosal barrier model. Microparticles (1.0 µm) and nanoparticles (200 nm) were used as probes, and their movement in mucin was studied by a three-dimensional confocal microscopy-based particle tracking technique and by Nanoparticle Tracking Analysis (NTA) after addition of high-molecular weight (900 kDa) and low-molecular weight (33 kDa) HA. This demonstrated a molecular weight-dependent HA modulation of the mucin nanostructure with a 2.5-fold decrease in the mobility of 200 nm nanoparticles. To further investigate these mechanisms and to verify that the natural viscoelastic properties of mucus are not undesirably altered, rheological measurements were performed on mucin hydrogels with or without HA. This suggested the observed particle mobility restriction was not attributed to alterations of the natural mucin cohesive and viscoelastic properties but, instead, indicates that the added high-molecular weight HA primarily modulates the mucin nanostructure and mesh size. This study, hereby, demonstrates how mucus nanostructure can be modulated by the addition of high-molecular weight HA that offers an opportunity to control mucosal pathogenesis and drug delivery.

A large-scale benchmark study of existing algorithms for taxonomy-independent microbial community analysis.

Recent advances in massively parallel sequencing technology have created new opportunities to probe the hidden world of microbes. Taxonomy-independent clustering of the 16S rRNA gene is usually the first step in analyzing microbial communities. Dozens of algorithms have been developed in the last decade, but a comprehensive benchmark study is lacking. Here, we survey algorithms currently used by microbiologists, and compare seven representative methods in a large-scale benchmark study that addresses several issues of concern. A new experimental protocol was developed that allows different algorithms to be compared using the same platform, and several criteria were introduced to facilitate a quantitative evaluation of the clustering performance of each algorithm. We found that existing methods vary widely in their outputs, and that inappropriate use of distance levels for taxonomic assignments likely resulted in substantial overestimates of biodiversity in many studies. The benchmark study identified our recently developed ESPRIT-Tree, a fast implementation of the average linkage-based hierarchical clustering algorithm, as one of the best algorithms available in terms of computational efficiency and clustering accuracy.