Patients diagnosed with metastatic breast cancer have a dismal 5-year survival rate of only 24%. The RNA-binding protein Hu antigen R (HuR) is upregulated in breast cancer, and elevated cytoplasmic HuR correlates with high-grade tumors and poor clinical outcome of breast cancer. HuR promotes tumorigenesis by regulating numerous proto-oncogenes, growth factors, and cytokines that support major tumor hallmarks including invasion and metastasis. Here, we report a HuR inhibitor KH-3, which potently suppresses breast cancer cell growth and invasion. Furthermore, KH-3 inhibits breast cancer experimental lung metastasis, improves mouse survival, and reduces orthotopic tumor growth. Mechanistically, we identify FOXQ1 as a direct target of HuR. KH-3 disrupts HuR–FOXQ1 mRNA interaction, leading to inhibition of breast cancer invasion. Our study suggests that inhibiting HuR is a promising therapeutic strategy for lethal metastatic breast cancer.

Extracellular vesicles (EVs), particularly nano-sized small EV exosomes, are emerging biomarker sources. However, due to heterogeneous populations secreted from diverse cell types, mapping exosome multi-omic molecular information specifically to their pathogenesis origin for cancer biomarker identification is still extraordinarily challenging. Herein, we introduced a novel 3D-structured nanographene immunomagnetic particles (NanoPoms) with unique flower pom-poms morphology and photo-click chemistry for specific marker-defined capture and release of intact exosome. This specific exosome isolation approach leads to the expanded identification of targetable cancer biomarkers with enhanced specificity and sensitivity, as demonstrated by multi-omic exosome analysis of bladder cancer patient tissue fluids using the next generation sequencing of somatic DNA mutations, miRNAs, and the global proteome (Data are available via ProteomeXchange with identifier PXD034454). The NanoPoms prepared exosomes also exhibit distinctive in vivo biodistribution patterns, highlighting the highly viable and integral quality. The developed method is simple and straightforward, which is applicable to nearly all types of biological fluids and amenable for enrichment, scale up, and high-throughput exosome isolation.

We performed various analyses on the taxonomic and functional features of the gut microbiome from NSCLC patients treated with immunotherapy to establish a model that may predict whether a patient will benefit from immunotherapy. We collected 65 published whole metagenome shotgun sequencing samples along with 14 samples from our previous study. We systematically studied the taxonomical characteristics of the dataset and used both the random forest (RF) and the multilayer perceptron (MLP) neural network models to predict patients with progression-free survival (PFS) above 6 months versus those below 3 months. Our results showed that the RF classifier achieved the highest F-score (85.2%) and the area under the receiver operating characteristic curve (AUC) (95%) using the protein families (Pfam) profile, and the MLP neural network classifier achieved a 99.9% F-score and 100% AUC using the same Pfam profile. When applying the model trained in the Pfam profile directly to predict the treatment response, we found that both trained RF and MLP classifiers significantly outperformed the stochastic predictor in F-score. Our results suggested that such a predictive model based on functional (e.g., Pfam) rather than taxonomic profile might be clinically useful to predict whether an NSCLC patient will benefit from immunotherapy, as both the F-score and AUC of functional profile outperform that of taxonomic profile. In addition, our model suggested that interactive biological processes such as methanogenesis, one-carbon, and amino acid metabolism might be important in regulating the immunotherapy response that warrants further investigation.

Specific language impairment (SLI) is a common neurodevelopmental disorder (NDD) that displays high heritability estimates. Genetic studies have identified several loci, but the molecular basis of SLI remains unclear. With the aim to better understand the genetic architecture of SLI, we performed whole-exome sequencing (WES) in a single family (ID: 489; n = 11). We identified co-segregating rare variants in three new genes: BUD13, APLP2, and NDRG2. To determine the significance of these genes in SLI, we Sanger sequenced all coding regions of each gene in unrelated individuals with SLI (n = 175). We observed 13 additional rare variants in 18 unrelated individuals. Variants in BUD13 reached genome-wide significance (p-value < 0.01) upon comparison with similar variants in the 1000 Genomes Project, providing gene level evidence that BUD13 is involved in SLI. Additionally, five BUD13 variants showed cohesive variant level evidence of likely pathogenicity. Bud13 is a component of the retention and splicing (RES) complex. Additional supportive evidence from studies of an animal model (loss-of-function mutations in BUD13 caused a profound neural phenotype) and individuals with an NDD phenotype (carrying a CNV spanning BUD13), indicates BUD13 could be a target for investigation of the neural basis of language.

MicroRNA (miRNA) trans-regulates the stability of many mRNAs and controls their expression levels. Reconstruction of the miRNA-mRNA interactome is key to the understanding of the miRNA regulatory network and related biological processes. However, existing miRNA target prediction methods are limited to canonical miRNA-mRNA interactions and have high false prediction rates. Other experimental methods are low throughput and cannot be used to probe genome-wide interactions. To address this challenge, the Cross-linking Ligation and Sequencing of Hybrids (CLASH) technology was developed for high-throughput probing of transcriptome-wide microRNA-mRNA interactions in vivo. The mapping of duplex reads, chimeras of two ultra-short RNA strands, poses computational challenges to current mapping and alignment methods. To address this issue, we developed CLAN (CrossLinked reads ANalysis toolkit). CLAN generated a comparable mapping of singular reads to other tools, and significantly outperformed in mapping simulated and real CLASH duplex reads, offering a potential application to other next-generation sequencing-based duplex-read-generating technologies.

A crucial task in metagenomic analysis is to annotate the function and taxonomy of the sequencing reads generated from a microbiome sample. In general, the reads can either be assembled into contigs and searched against reference databases, or individually searched without assembly. The first approach may suffer from fragmentary and incomplete assembly, while the second is hampered by the reduced functional signal contained in the short reads. To tackle these issues, we have previously developed GRASP (Guided Reference-based Assembly of Short Peptides), which accepts a reference protein sequence as input and aims to assemble its homologs from a database containing fragmentary protein sequences. In addition to a gene-centric assembly tool, GRASP also serves as a homolog search tool when using the assembled protein sequences as templates to recruit reads. GRASP has significantly improved recall rate (60–80% vs. 30–40%) compared to other homolog search tools such as BLAST. However, GRASP is both time- and space-consuming. Subsequently, we developed GRASPx, which is 30X faster than GRASP. Here, we present a completely redesigned algorithm, GRASP2, for this computational problem. GRASP2 utilizes Burrows-Wheeler Transformation (BWT) and FM-index to perform assembly graph generation, and reduces the search space by employing a fast ungapped alignment strategy as a filter. GRASP2 also explicitly generates candidate paths prior to alignment, which effectively uncouples the iterative access of the assembly graph and alignment matrix. This strategy makes the execution of the program more efficient under current computer architecture, and contributes to GRASP2’s speedup. GRASP2 is 8-fold faster than GRASPx (and 250-fold faster than GRASP) and uses 8-fold less memory while maintaining the original high recall rate of GRASP. GRASP2 reaches ~ 80% recall rate compared to that of ~ 40% generated by BLAST, both at a high precision level (> 95%). With such a high performance, GRASP2 is only ~3X slower than BLASTP. GRASP2 is a high-performance gene-centric and homolog search tool with significant speedup compared to its predecessors, which makes GRASP2 a useful tool for metagenomics data analysis, GRASP2 is implemented in C++ and is freely available from http://www.sourceforge.net/projects/grasp2 .