Human retinoic acid-regulated CD161+ regulatory T cells support wound repair in intestinal mucosa.

Repair of tissue damaged during inflammatory processes is key to the return of local homeostasis and restoration of epithelial integrity. Here we describe CD161+ regulatory T (Treg) cells as a distinct, highly suppressive population of Treg cells that mediate wound healing. These Treg cells were enriched in intestinal lamina propria, particularly in Crohn's disease. CD161+ Treg cells had an all-trans retinoic acid (ATRA)-regulated gene signature, and CD161 expression on Treg cells was induced by ATRA, which directly regulated the CD161 gene. CD161 was co-stimulatory, and ligation with the T cell antigen receptor induced cytokines that accelerated the wound healing of intestinal epithelial cells. We identified a transcription-factor network, including BACH2, RORγt, FOSL2, AP-1 and RUNX1, that controlled expression of the wound-healing program, and found a CD161+ Treg cell signature in Crohn's disease mucosa associated with reduced inflammation. These findings identify CD161+ Treg cells as a population involved in controlling the balance between inflammation and epithelial barrier healing in the gut.

Natural product myricetin is a pan-KDM4 inhibitor which with poly lactic-co-glycolic acid formulation effectively targets castration-resistant prostate cancer.

BACKGROUND: Castration-resistant prostate cancer (CRPC) with sustained androgen receptor (AR) signaling remains a critical clinical challenge, despite androgen depletion therapy. The Jumonji C-containing histone lysine demethylase family 4 (KDM4) members, KDM4A‒KDM4C, serve as critical coactivators of AR to promote tumor growth in prostate cancer and are candidate therapeutic targets to overcome AR mutations/alterations-mediated resistance in CRPC. METHODS: In this study, using a structure-based approach, we identified a natural product, myricetin, able to block the demethylation of histone 3 lysine 9 trimethylation by KDM4 members and evaluated its effects on CRPC. A structure-based screening was employed to search for a natural product that inhibited KDM4B. Inhibition kinetics of myricetin was determined. The cytotoxic effect of myricetin on various prostate cancer cells was evaluated. The combined effect of myricetin with enzalutamide, a second-generation AR inhibitor toward C4-2B, a CRPC cell line, was assessed. To improve bioavailability, myricetin encapsulated by poly lactic-co-glycolic acid (PLGA), the US food and drug administration (FDA)-approved material as drug carriers, was synthesized and its antitumor activity alone or with enzalutamide was evaluated using in vivo C4-2B xenografts. RESULTS: Myricetin was identified as a potent α-ketoglutarate-type inhibitor that blocks the demethylation activity by KDM4s and significantly reduced the proliferation of both androgen-dependent (LNCaP) and androgen-independent CRPC (CWR22Rv1 and C4-2B). A synergistic cytotoxic effect toward C4-2B was detected for the combination of myricetin and enzalutamide. PLGA-myricetin, enzalutamide, and the combined treatment showed significantly greater antitumor activity than that of the control group in the C4-2B xenograft model. Tumor growth was significantly lower for the combination treatment than for enzalutamide or myricetin treatment alone. CONCLUSIONS: These results suggest that myricetin is a pan-KDM4 inhibitor and exhibited potent cell cytotoxicity toward CRPC cells. Importantly, the combination of PLGA-encapsulated myricetin with enzalutamide is potentially effective for CRPC.

Aberrant DNA methylation defines isoform usage in cancer, with functional implications.

Alternative transcript isoforms are common in tumors and act as potential drivers of cancer. Mechanisms determining altered isoform expression include somatic mutations in splice regulatory sites or altered splicing factors. However, since DNA methylation is known to regulate transcriptional isoform activity in normal cells, we predicted the highly dysregulated patterns of DNA methylation present in cancer also affect isoform activity. We analyzed DNA methylation and RNA-seq isoform data from 18 human cancer types and found frequent correlations specifically within 11 cancer types. Examining the top 25% of variable methylation sites revealed that the location of the methylated CpG site in a gene determined which isoform was used. In addition, the correlated methylation-isoform patterns classified tumors into known subtypes and predicted distinct protein functions between tumor subtypes. Finally, methylation-correlated isoforms were enriched for oncogenes, tumor suppressors, and cancer-related pathways. These findings provide new insights into the functional impact of dysregulated DNA methylation in cancer and highlight the relationship between the epigenome and transcriptome.

Significant associations between driver gene mutations and DNA methylation alterations across many cancer types.

Recent evidence shows that mutations in several driver genes can cause aberrant methylation patterns, a hallmark of cancer. In light of these findings, we hypothesized that the landscapes of tumor genomes and epigenomes are tightly interconnected. We measured this relationship using principal component analyses and methylation-mutation associations applied at the nucleotide level and with respect to genome-wide trends. We found that a few mutated driver genes were associated with genome-wide patterns of aberrant hypomethylation or CpG island hypermethylation in specific cancer types. In addition, we identified associations between 737 mutated driver genes and site-specific methylation changes. Moreover, using these mutation-methylation associations, we were able to distinguish between two uterine and two thyroid cancer subtypes. The driver gene mutation-associated methylation differences between the thyroid cancer subtypes were linked to differential gene expression in JAK-STAT signaling, NADPH oxidation, and other cancer-related pathways. These results establish that driver gene mutations are associated with methylation alterations capable of shaping regulatory network functions. In addition, the methodology presented here can be used to subdivide tumors into more homogeneous subsets corresponding to underlying molecular characteristics, which could improve treatment efficacy.

Matching phenotypes to whole genomes: Lessons learned from four iterations of the personal genome project community challenges.

The advent of next-generation sequencing has dramatically decreased the cost for whole-genome sequencing and increased the viability for its application in research and clinical care. The Personal Genome Project (PGP) provides unrestricted access to genomes of individuals and their associated phenotypes. This resource enabled the Critical Assessment of Genome Interpretation (CAGI) to create a community challenge to assess the bioinformatics community's ability to predict traits from whole genomes. In the CAGI PGP challenge, researchers were asked to predict whether an individual had a particular trait or profile based on their whole genome. Several approaches were used to assess submissions, including ROC AUC (area under receiver operating characteristic curve), probability rankings, the number of correct predictions, and statistical significance simulations. Overall, we found that prediction of individual traits is difficult, relying on a strong knowledge of trait frequency within the general population, whereas matching genomes to trait profiles relies heavily upon a small number of common traits including ancestry, blood type, and eye color. When a rare genetic disorder is present, profiles can be matched when one or more pathogenic variants are identified. Prediction accuracy has improved substantially over the last 6 years due to improved methodology and a better understanding of features.

A hybrid likelihood model for sequence-based disease association studies.

In the past few years, case-control studies of common diseases have shifted their focus from single genes to whole exomes. New sequencing technologies now routinely detect hundreds of thousands of sequence variants in a single study, many of which are rare or even novel. The limitation of classical single-marker association analysis for rare variants has been a challenge in such studies. A new generation of statistical methods for case-control association studies has been developed to meet this challenge. A common approach to association analysis of rare variants is the burden-style collapsing methods to combine rare variant data within individuals across or within genes. Here, we propose a new hybrid likelihood model that combines a burden test with a test of the position distribution of variants. In extensive simulations and on empirical data from the Dallas Heart Study, the new model demonstrates consistently good power, in particular when applied to a gene set (e.g., multiple candidate genes with shared biological function or pathway), when rare variants cluster in key functional regions of a gene, and when protective variants are present. When applied to data from an ongoing sequencing study of bipolar disorder (191 cases, 107 controls), the model identifies seven gene sets with nominal p-values < 0.05, of which one MAPK signaling pathway (KEGG) reaches trend-level significance after correcting for multiple testing.

A probabilistic model to predict clinical phenotypic traits from genome sequencing.

Genetic screening is becoming possible on an unprecedented scale. However, its utility remains controversial. Although most variant genotypes cannot be easily interpreted, many individuals nevertheless attempt to interpret their genetic information. Initiatives such as the Personal Genome Project (PGP) and Illumina's Understand Your Genome are sequencing thousands of adults, collecting phenotypic information and developing computational pipelines to identify the most important variant genotypes harbored by each individual. These pipelines consider database and allele frequency annotations and bioinformatics classifications. We propose that the next step will be to integrate these different sources of information to estimate the probability that a given individual has specific phenotypes of clinical interest. To this end, we have designed a Bayesian probabilistic model to predict the probability of dichotomous phenotypes. When applied to a cohort from PGP, predictions of Gilbert syndrome, Graves' disease, non-Hodgkin lymphoma, and various blood groups were accurate, as individuals manifesting the phenotype in question exhibited the highest, or among the highest, predicted probabilities. Thirty-eight PGP phenotypes (26%) were predicted with area-under-the-ROC curve (AUC)>0.7, and 23 (15.8%) of these were statistically significant, based on permutation tests. Moreover, in a Critical Assessment of Genome Interpretation (CAGI) blinded prediction experiment, the models were used to match 77 PGP genomes to phenotypic profiles, generating the most accurate prediction of 16 submissions, according to an independent assessor. Although the models are currently insufficiently accurate for diagnostic utility, we expect their performance to improve with growth of publicly available genomics data and model refinement by domain experts.

Helicobacter pylori PldA modulates TNFR1-mediated p38 signaling pathways to regulate macrophage responses for its survival.

Helicobacter pylori, a dominant member of the gastric microbiota was associated with various gastrointestinal diseases and presents a significant challenge due to increasing antibiotic resistance. This study identifies H. pylori's phospholipase A (PldA) as a critical factor in modulating host macrophage responses, facilitating H. pylori 's evasion of the immune system and persistence. PldA alters membrane lipids through reversible acylation and deacylation, affecting their structure and function. We found that PldA incorporates lysophosphatidylethanolamine into macrophage membranes, disrupting their bilayer structure and impairing TNFR1-mediated p38-MK2 signaling. This disruption results in reduced macrophage autophagy and elevated RIP1-dependent apoptosis, thereby enhancing H. pylori survival, a mechanism also observed in multidrug-resistant strains. Pharmacological inhibition of PldA significantly decreases H. pylori viability and increases macrophage survival. In vivo studies corroborate PldA's essential role in H. pylori persistence and immune cell recruitment. Our findings position PldA as a pivotal element in H. pylori pathogenesis through TNFR1-mediated membrane modulation, offering a promising therapeutic target to counteract bacterial resistance.

The immune microenvironment shapes transcriptional and genetic heterogeneity in chronic lymphocytic leukemia.

In chronic lymphocytic leukemia (CLL), B-cell receptor signaling, tumor-microenvironment interactions, and somatic mutations drive disease progression. To better understand the intersection between the microenvironment and molecular events in CLL pathogenesis, we integrated bulk transcriptome profiling of paired peripheral blood (PB) and lymph node (LN) samples from 34 patients. Oncogenic processes were upregulated in LN compared with PB and in immunoglobulin heavy-chain variable (IGHV) region unmutated compared with mutated cases. Single-cell RNA sequencing (scRNA-seq) distinguished 3 major cell states: quiescent, activated, and proliferating. The activated subpopulation comprised only 2.2% to 4.3% of the total tumor bulk in LN samples. RNA velocity analysis found that CLL cell fate in LN is unidirectional, starts in the proliferating state, transitions to the activated state, and ends in the quiescent state. A 10-gene signature derived from activated tumor cells was associated with inferior treatment-free survival (TFS) and positively correlated with the proportion of activated CD4+ memory T cells and M2 macrophages in LN. Whole exome sequencing (WES) of paired PB and LN samples showed subclonal expansion in LN in approximately half of the patients. Since mouse models have implicated activation-induced cytidine deaminase in mutagenesis, we compared AICDA expression between cases with and without clonal evolution but did not find a difference. In contrast, the presence of a T-cell inflamed microenvironment in LN was associated with clonal stability. In summary, a distinct minor tumor subpopulation underlies CLL pathogenesis and drives the clinical outcome. Clonal trajectories are shaped by the LN milieu, where T-cell immunity may contribute to suppressing clonal outgrowth. The clinical study is registered at clinicaltrials.gov as NCT00923507.

Molecular mimicry of human endothelial cell antigen by autoantibodies to nonstructural protein 1 of dengue virus.

The pathogenesis of dengue hemorrhagic fever and dengue shock syndrome (DHF/DSS), both serious complications of dengue virus (DV) infection, remains unclear. In this study, we found that anti-DV NS1 (nonstructural protein 1) polyclonal antibodies cross-reacted with human umbilical vein endothelial cells (HUVECs). We further identified a complex-specific mAb, DB16-1, which could recognize DV NS1 and cross-react with HUVECs and human blood vessels. The target protein of DB16-1 was further purified by immunoaffinity chromatography. LC-MS/MS analysis and co-immunoprecipitation revealed that the target protein of DB16-1 was human LYRIC (lysine-rich CEACAM1 co-isolated). Our newly generated anti-LYRIC mAbs bound to HUVECs in a pattern similar to that of DB16-1. The B-cell epitope of DB16-1 displayed a consensus motif, Lys-X-Trp-Gly (KXWG), which corresponded to amino acid residues 116-119 of DV NS1 and mimicked amino acid residues 334-337 in LYRIC. Moreover, the binding activity of DB16-1 in NS1 of DV-2 and in LYRIC disappeared after the KXWG epitope was deleted in each. In conclusion, DB16-1 targeted the same epitope in DV NS1 and LYRIC protein on human endothelial cells, suggesting that it might play a role in the pathogenesis of DHF/DSS. Future studies on the role of the anti-NS1 antibody in causing vascular permeability will undoubtedly be performed on sera collected from individuals before, during, and after the endothelial cell malfunction phase of a dengue illness.

Network Analysis and Transcriptome Profiling Identify Autophagic and Mitochondrial Dysfunctions in SARS-CoV-2 Infection.

Analyzing host cells' transcriptional response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection will help delineate biological processes underlying viral pathogenesis. First, analysis of expression profiles of lung cell lines A549 and Calu3 revealed upregulation of antiviral interferon signaling genes in response to all three SARS-CoV-2, MERS-CoV, or influenza A virus (IAV) infections. However, perturbations in expression of genes involved in inflammatory, mitochondrial, and autophagy processes were specifically observed in SARS-CoV-2-infected cells. Next, a validation study in infected human nasopharyngeal samples also revealed perturbations in autophagy and mitochondrial processes. Specifically, mTOR expression, mitochondrial ribosomal, mitochondrial complex I, lysosome acidification, and mitochondrial fission promoting genes were concurrently downregulated in both infected cell lines and human samples. SARS-CoV-2 infection impeded autophagic flux either by upregulating GSK3B in lung cell lines or by downregulating autophagy genes, SNAP29, and lysosome acidification genes in human samples, contributing to increased viral replication. Therefore, drugs targeting lysosome acidification or autophagic flux could be tested as intervention strategies. Finally, age-stratified SARS-CoV-2-positive human data revealed impaired upregulation of chemokines, interferon-stimulated genes, and tripartite motif genes that are critical for antiviral signaling. Together, this analysis has revealed specific aspects of autophagic and mitochondrial function that are uniquely perturbed in SARS-CoV-2-infected host cells.

Ultra-Small Lung Cysts Impair Diffusion Without Obstructing Air Flow in Lymphangioleiomyomatosis.

BACKGROUND: Lymphangioleiomyomatosis (LAM) is a rare lung disease found primarily in women of childbearing age, characterized by the formation of air-filled cysts, which may be associated with reductions in lung function. An experimental, regional ultra-high resolution CT scan identified an additional volume of cysts relative to standard chest CT imaging, which consisted primarily of ultra-small cysts. RESEARCH QUESTION: What is the impact of these ultra-small cysts on the pulmonary function of patients with LAM? STUDY DESIGN AND METHODS: A group of 103 patients with LAM received pulmonary function tests and a CT examination in the same visit. Cyst score, the percentage lung volume occupied by cysts, was measured by using commercial software approved by the US Food and Drug Administration. The association between cyst scores and pulmonary function tests of diffusing capacity of the lungs for carbon monoxide (Dlco) (% predicted), FEV1 (% predicted), and FEV1/FVC (% predicted) was assessed with statistical analysis adjusted for demographic variables. The distributions of average cyst size and ultra-small cyst fraction among the patients were evaluated. RESULTS: The additional cyst volume identified by the experimental, higher resolution scan consisted of cysts of 2.2 ± 0.8 mm diameter on average and are thus labeled the "ultra-small cyst fraction." It accounted for 27.9 ± 19.0% of the total cyst volume among the patients. The resulting adjusted, whole-lung cyst scores better explained the variance of Dlco (P < .001 adjusted for multiple comparisons) but not FEV1 and FEV1/FVC (P = 1.00). The ultra-small cyst fraction contributed to the reduction in Dlco (P < .001) but not to FEV1 and FEV1/FVC (P = .760 and .575, respectively). The ultra-small cyst fraction and average cyst size were correlated with cyst burden, FEV1, and FEV1/FVC but less with Dlco. INTERPRETATION: The ultra-small cysts primarily contributed to the reduction in Dlco, with minimal effects on FEV1 and FEV1/FVC. Patients with lower cyst burden and better FEV1 and FEV1/FVC tended to have smaller average cyst size and higher ultra-small cyst fraction. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov; No.: NCT00001465; URL: www.clinicaltrials.gov.

A verified genomic reference sample for assessing performance of cancer panels detecting small variants of low allele frequency.

BACKGROUND: Oncopanel genomic testing, which identifies important somatic variants, is increasingly common in medical practice and especially in clinical trials. Currently, there is a paucity of reliable genomic reference samples having a suitably large number of pre-identified variants for properly assessing oncopanel assay analytical quality and performance. The FDA-led Sequencing and Quality Control Phase 2 (SEQC2) consortium analyze ten diverse cancer cell lines individually and their pool, termed Sample A, to develop a reference sample with suitably large numbers of coding positions with known (variant) positives and negatives for properly evaluating oncopanel analytical performance. RESULTS: In reference Sample A, we identify more than 40,000 variants down to 1% allele frequency with more than 25,000 variants having less than 20% allele frequency with 1653 variants in COSMIC-related genes. This is 5-100× more than existing commercially available samples. We also identify an unprecedented number of negative positions in coding regions, allowing statistical rigor in assessing limit-of-detection, sensitivity, and precision. Over 300 loci are randomly selected and independently verified via droplet digital PCR with 100% concordance. Agilent normal reference Sample B can be admixed with Sample A to create new samples with a similar number of known variants at much lower allele frequency than what exists in Sample A natively, including known variants having allele frequency of 0.02%, a range suitable for assessing liquid biopsy panels. CONCLUSION: These new reference samples and their admixtures provide superior capability for performing oncopanel quality control, analytical accuracy, and validation for small to large oncopanels and liquid biopsy assays.

Network Analysis and Transcriptome Profiling Identify Autophagic and Mitochondrial Dysfunctions in SARS-CoV-2 Infection.

Analyzing host transcriptional changes in response to SARS-CoV-2 infection will help delineate biological processes underlying viral pathogenesis. Comparison of expression profiles of lung cell lines A549 (infected with either SARS-CoV-2 (with ACE2 expression)) or Influenza A virus (IAV)) and Calu3 (infected with SARS-CoV-2 or MERS-CoV) revealed upregulation of the antiviral interferon signaling in all three viral infections. However, perturbations in inflammatory, mitochondrial, and autophagy processes were specifically observed in SARS-CoV-2 infected cells. Validation of findings from cell line data revealed perturbations in autophagy and mitochondrial processes in the infected human nasopharyngeal samples. Specifically, downregulation of mTOR expression, mitochondrial ribosomal, mitochondrial complex I, and lysosome acidification genes were concurrently observed in both infected cell lines and human datasets. Furthermore, SARS-CoV-2 infection impedes autophagic flux by upregulating GSK3B in lung cell lines, or by downregulating autophagy genes, SNAP29 and lysosome acidification genes in human samples, contributing to increased viral replication. Therefore, drugs targeting lysosome acidification or autophagic flux could be tested as intervention strategies. Additionally, downregulation of MTFP1 (in cell lines) or SOCS6 (in human samples) results in hyperfused mitochondria and impede proper interferon response. Coexpression networks analysis identifies correlated clusters of genes annotated to inflammation and mitochondrial processes that are misregulated in SARS-CoV-2 infected cells. Finally, comparison of age stratified human gene expression data revealed impaired upregulation of chemokines, interferon stimulated and tripartite motif genes that are critical for antiviral signaling. Together, this analysis has revealed specific aspects of autophagic and mitochondrial function that are uniquely perturbed in SARS-CoV-2 infection.

Misregulation of ELK1, AP1, and E12 Transcription Factor Networks Is Associated with Melanoma Progression.

Melanoma is among the most malignant cutaneous cancers and when metastasized results in dramatically high mortality. Despite advances in high-throughput gene expression profiling in cancer transcriptomic studies, our understanding of mechanisms driving melanoma progression is still limited. We present here an in-depth bioinformatic analysis of the melanoma RNAseq, chromatin immunoprecipitation (ChIP)seq, and single-cell (sc)RNA seq data to understand cancer progression. Specifically, we have performed a consensus network analysis of RNA-seq data from clinically re-grouped melanoma samples to identify gene co-expression networks that are conserved in early (stage 1) and late (stage 4/invasive) stage melanoma. Overlaying the fold-change information on co-expression networks revealed several coordinately up or down-regulated subnetworks that may play a critical role in melanoma progression. Furthermore, by incorporating histone lysine-27 acetylation information and highly expressed genes identified from the single-cell RNA data from melanoma patient samples, we present a comprehensive list of pathways, putative protein-protein interactions (PPIs) and transcription factor (TF) networks that are driving cancer progression. From this analysis, we have identified Elk1, AP1 and E12 TF networks that coordinately change expression in late melanoma when compared to early melanoma, implicating these TFs in melanoma progression. Additionally, the sumoylation-associated interactome is upregulated in invasive melanoma. Together, this bioinformatic analysis potentially implicates a combination of TF networks and PPIs in melanoma progression, which if confirmed in the experimental systems, could be used as targets for drug intervention in melanoma.

Genome-Wide Analysis of Off-Target CRISPR/Cas9 Activity in Single-Cell-Derived Human Hematopoietic Stem and Progenitor Cell Clones.

CRISPR/Cas9 (clustered regularly interspaced short palindromic repeats/CRISPR-associated protein 9)-mediated genome editing holds remarkable promise for the treatment of human genetic diseases. However, the possibility of off-target Cas9 activity remains a concern. To address this issue using clinically relevant target cells, we electroporated Cas9 ribonucleoprotein (RNP) complexes (independently targeted to two different genomic loci, the CXCR4 locus on chromosome 2 and the AAVS1 locus on chromosome 19) into human mobilized peripheral blood-derived hematopoietic stem and progenitor cells (HSPCs) and assessed the acquisition of somatic mutations in an unbiased, genome-wide manner via whole genome sequencing (WGS) of single-cell-derived HSPC clones. Bioinformatic analysis identified >20,000 total somatic variants (indels, single nucleotide variants, and structural variants) distributed among Cas9-treated and non-Cas9-treated control HSPC clones. Statistical analysis revealed no significant difference in the number of novel non-targeted indels among the samples. Moreover, data analysis showed no evidence of Cas9-mediated indel formation at 623 predicted off-target sites. The median number of novel single nucleotide variants was slightly elevated in Cas9 RNP-recipient sample groups compared to baseline, but did not reach statistical significance. Structural variants were rare and demonstrated no clear causal connection to Cas9-mediated gene editing procedures. We find that the collective somatic mutational burden observed within Cas9 RNP-edited human HSPC clones is indistinguishable from naturally occurring levels of background genetic heterogeneity.

lncRNAKB, a knowledgebase of tissue-specific functional annotation and trait association of long noncoding RNA.

Long non-coding RNA Knowledgebase (lncRNAKB) is an integrated resource for exploring lncRNA biology in the context of tissue-specificity and disease association. A systematic integration of annotations from six independent databases resulted in 77,199 human lncRNA (224,286 transcripts). The user-friendly knowledgebase covers a comprehensive breadth and depth of lncRNA annotation. lncRNAKB is a compendium of expression patterns, derived from analysis of RNA-seq data in thousands of samples across 31 solid human normal tissues (GTEx). Thousands of co-expression modules identified via network analysis and pathway enrichment to delineate lncRNA function are also accessible. Millions of expression quantitative trait loci (cis-eQTL) computed using whole genome sequence genotype data (GTEx) can be downloaded at lncRNAKB that also includes tissue-specificity, phylogenetic conservation and coding potential scores. Tissue-specific lncRNA-trait associations encompassing 323 GWAS (UK Biobank) are also provided. LncRNAKB is accessible at http://www.lncrnakb.org/ , and the data are freely available through Open Science Framework ( https://doi.org/10.17605/OSF.IO/RU4D2 ).

Tumor-targeted delivery of siRNA by self-assembled nanoparticles.

We have developed a self-assembled nanoparticle (NP) that efficiently delivers small interfering RNA (siRNA) to the tumor by intravenous (IV) administration. The NP was obtained by mixing carrier DNA, siRNA, protamine, and lipids, followed by post-modification with polyethylene glycol and a ligand, anisamide. Four hours after IV injection of the formulation into a xenograft model, 70-80% of injected siRNA/g accumulated in the tumor, approximately 10% was detected in the liver and approximately 20% recovered in the lung. Confocal microscopy showed that fluorescent-labeled siRNA was efficiently delivered into the cytoplasm of the sigma receptor expressing NCI-H460 xenograft tumor by the targeted NPs, whereas free siRNA and non-targeted NPs showed little uptake. Three daily injections (1.2 mg/kg) of siRNA formulated in the targeted NPs silenced the epidermal growth factor receptor (EGFR) in the tumor and induced approximately 15% tumor cell apoptosis. Forty percent tumor growth inhibition was achieved by treatment with targeted NPs, while complete inhibition lasted for 1 week when combined with cisplatin. The serum level of liver enzymes and body weight monitoring during the treatment indicated a low level of toxicity of the formulation. The carrier itself also showed little immunotoxicity (IMT).

IKAP-Identifying K mAjor cell Population groups in single-cell RNA-sequencing analysis.

BACKGROUND: In single-cell RNA-sequencing analysis, clustering cells into groups and differentiating cell groups by differentially expressed (DE) genes are 2 separate steps for investigating cell identity. However, the ability to differentiate between cell groups could be affected by clustering. This interdependency often creates a bottleneck in the analysis pipeline, requiring researchers to repeat these 2 steps multiple times by setting different clustering parameters to identify a set of cell groups that are more differentiated and biologically relevant. FINDINGS: To accelerate this process, we have developed IKAP-an algorithm to identify major cell groups and improve differentiating cell groups by systematically tuning parameters for clustering. We demonstrate that, with default parameters, IKAP successfully identifies major cell types such as T cells, B cells, natural killer cells, and monocytes in 2 peripheral blood mononuclear cell datasets and recovers major cell types in a previously published mouse cortex dataset. These major cell groups identified by IKAP present more distinguishing DE genes compared with cell groups generated by different combinations of clustering parameters. We further show that cell subtypes can be identified by recursively applying IKAP within identified major cell types, thereby delineating cell identities in a multi-layered ontology. CONCLUSIONS: By tuning the clustering parameters to identify major cell groups, IKAP greatly improves the automation of single-cell RNA-sequencing analysis to produce distinguishing DE genes and refine cell ontology using single-cell RNA-sequencing data.