A Stem-like Patient-Derived Ovarian Cancer Model of Platinum Resistance Reveals Dissociation of Stemness and Resistance.

To understand chemoresistance in the context of cancer stem cells (CSC), a cisplatin resistance model was developed using a high-grade serous ovarian cancer patient-derived, cisplatin-sensitive sample, PDX4. As a molecular subtype-specific stem-like cell line, PDX4 was selected for its representative features, including its histopathological and BRCA2 mutation status, and exposed to cisplatin in vitro. In the cisplatin-resistant cells, transcriptomics were carried out, and cell morphology, protein expression, and functional status were characterized. Additionally, potential signaling pathways involved in cisplatin resistance were explored. Our findings reveal the presence of distinct molecular signatures and phenotypic changes in cisplatin-resistant PDX4 compared to their sensitive counterparts. Surprisingly, we observed that chemoresistance was not inherently linked with increased stemness. In fact, although resistant cells expressed a combination of EMT and stemness markers, functional assays revealed that they were less proliferative, migratory, and clonogenic-features indicative of an underlying complex mechanism for cell survival. Furthermore, DNA damage tolerance and cellular stress management pathways were enriched. This novel, syngeneic model provides a valuable platform for investigating the underlying mechanisms of cisplatin resistance in a clinically relevant context, contributing to the development of targeted therapies tailored to combat resistance in stem-like ovarian cancer.

Zinc and Chronic Kidney Disease: A Review.

Chronic kidney disease (CKD) poses a major global public health challenge. The World Health Organization's data shows that CKD affects about 10% of the world's population, particularly in low- and middle-income countries. Due to limited access to diagnosis and treatment, CKD has become the 12th leading cause of death worldwide. The advanced stage of CKD can lead to kidney failure, which is clinically referred to as end-stage renal disease (ESRD). In such cases, patients can only sustain life through dialysis or kidney transplantation. However, the long-term affordability of these treatments remains low. Moreover, the effectiveness of kidney transplantation is modest, posing a significant treatment barrier in resource-limited settings, and significantly impacting patient survival. To address this issue, we suggest using dietary supplementation of the trace element zinc to impede CKD development and prolong patient survival.

Continuous Detection of Cobalt Ions and Pyrophosphates by NAC-CdTe Quantum Dots Fluorescence Probes.

In this work, a simple and sensitive N-acetyl-L-cysteine (NAC)-covered CdTe quantum dots (NAC-CdTe QDs) fluorescence probe for continuous detection of Co2+ and pyrophosphate ions (PPi, P2O74-) was synthesized. The fluorescence of the quantum dots was significantly quenched by Co2+ through the coordination of Co2+ and the carboxyl groups on the NAC-CdTe quantum dots. Interestingly, the combination of NAC-CdTe quantum dots with Co2+ yields a new fluorescence probe of Co2+-modified NAC-CdTe QDs (Co2+@NAC-CdTe). The addition of PPi restored the fluorescence due to the competition between PPi and carboxyl groups with Co2+ causing Co2+ to detach from the surface of Co2+@NAC-CdTe quantum dots. Thus, a sensitive and reversible detection of Co2+ and PPi had been successfully established. The Co2+@NAC-CdTe quantum dots fluorescence probe exhibits excellent selectivity and high sensitivity toward PPi detection with low detection limit of 0.28 µM. In addition, the novel fluorescence probe was successfully applied to detect the concentration of PPi in environmental water samples and in-vitro cells imaging.

Dysregulated miRNAs modulate tumor microenvironment associated signaling networks in pancreatic ductal adenocarcinoma.

The desmoplastic and complex tumor microenvironment of pancreatic ductal adenocarcinoma (PDAC) has presented tremendous challenges for developing effective therapeutic strategies. Strategies targeting tumor stroma, albeit with great potential, have met with limited success due to the lack of knowledge on the molecular dynamics within the tumor microenvironment (TME). In pursuit of a better understanding of the influence of miRNAs on TME reprogramming and to explore circulating miRNAs as diagnostic and prognostic biomarkers for PDAC, using RNA-seq, miRNA-seq, and single-cell RNA-seq (scRNA-seq), we investigated the dysregulated signaling pathways in PDAC TME modulated by miRNAs from plasma and tumor tissue. Our bulk RNA-seq in PDAC tumor tissue identified 1445 significantly differentially expressed genes with extracellular matrix and structure organization as the top enriched pathways. Our miRNA-seq identified 322 and 49 abnormally expressed miRNAs in PDAC patient plasma and tumor tissue, respectively. We found many of the TME signaling pathways were targeted by those dysregulated miRNAs in PDAC plasma. Combined with scRNA-seq from patient PDAC tumor, our results revealed that these dysregulated miRNAs were closely associated with extracellular matrix (ECM) remodeling, cell-ECM communication, epithelial-mesenchymal transition, as well as immunosuppression orchestrated by different cellular components of TME. The findings of this study could assist the development of miRNA-based stromal targeting biomarkers or therapy for PDAC patients.

Reprogramming of human peripheral blood mononuclear cells into induced mesenchymal stromal cells using non-integrating vectors.

Mesenchymal stromal cells (MSCs) have great value in cell therapies. The MSC therapies have many challenges due to its inconsistent potency and limited quantity. Here, we report a strategy to generate induced MSCs (iMSCs) by directly reprogramming human peripheral blood mononuclear cells (PBMCs) with OCT4, SOX9, MYC, KLF4, and BCL-XL using a nonintegrating episomal vector system. While OCT4 was not required to reprogram PBMCs into iMSCs, omission of OCT4 significantly impaired iMSC functionality. The omission of OCT4 resulted in significantly downregulating MSC lineage specific and mesoderm-regulating genes, including SRPX, COL5A1, SOX4, SALL4, TWIST1. When reprogramming PBMCs in the absence of OCT4, 67 genes were significantly hypermethylated with reduced transcriptional expression. These data indicate that transient expression of OCT4 may serve as a universal reprogramming factor by increasing chromatin accessibility and promoting demethylation. Our findings represent an approach to produce functional MSCs, and aid in identifying putative function associated MSC markers.

Effect of extremely low frequency electromagnetic field on the pathogenicity of Magnaporthe oryzae.

Numerous works have reported that extremely low frequency electromagnetic fields (ELF-EMFs) were associated with human health; however, little is known about their effects on the occurrence of agricultural diseases. In this study, Magnaporthe oryzae was used as a model organism, and its pathogenicity under 50 Hz, 3 mT ELF-EMF was studied. Our results showed that the pathogenicity, growth rate, and conidia generation of M. oryzae were enhanced under ELF-EMF exposure. In addition, M. oryzae exposed to ELF-EMF showed enhanced tolerance to cell wall-perturbing agents sodium lauryl sulphate, and increased expression of cell wall integrity-related genes, including RAC1, CDC42, RHO2, and NOX2. In addition, the level of reactive oxygen species (ROS) and the expression level of ROS scavenger system-related gene MoAP1 increased in ELF-EMF-exposed samples, whereas the total antioxidant capacity and the activities of superoxide dismutase and catalase did not change. Results of our study demonstrated that exposure to 50 Hz, 3 mT ELF-EMF enhanced the infection ability of M. oryzae, which present new important challenges for understanding the effect of ELF-EMF exposure on farmland ecology, especially on agricultural diseases.

Structural variant analysis of a cancer reference cell line sample using multiple sequencing technologies.

BACKGROUND: The cancer genome is commonly altered with thousands of structural rearrangements including insertions, deletions, translocation, inversions, duplications, and copy number variations. Thus, structural variant (SV) characterization plays a paramount role in cancer target identification, oncology diagnostics, and personalized medicine. As part of the SEQC2 Consortium effort, the present study established and evaluated a consensus SV call set using a breast cancer reference cell line and matched normal control derived from the same donor, which were used in our companion benchmarking studies as reference samples. RESULTS: We systematically investigated somatic SVs in the reference cancer cell line by comparing to a matched normal cell line using multiple NGS platforms including Illumina short-read, 10X Genomics linked reads, PacBio long reads, Oxford Nanopore long reads, and high-throughput chromosome conformation capture (Hi-C). We established a consensus SV call set of a total of 1788 SVs including 717 deletions, 230 duplications, 551 insertions, 133 inversions, 146 translocations, and 11 breakends for the reference cancer cell line. To independently evaluate and cross-validate the accuracy of our consensus SV call set, we used orthogonal methods including PCR-based validation, Affymetrix arrays, Bionano optical mapping, and identification of fusion genes detected from RNA-seq. We evaluated the strengths and weaknesses of each NGS technology for SV determination, and our findings provide an actionable guide to improve cancer genome SV detection sensitivity and accuracy. CONCLUSIONS: A high-confidence consensus SV call set was established for the reference cancer cell line. A large subset of the variants identified was validated by multiple orthogonal methods.

Personalized genome assembly for accurate cancer somatic mutation discovery using tumor-normal paired reference samples.

BACKGROUND: The use of a personalized haplotype-specific genome assembly, rather than an unrelated, mosaic genome like GRCh38, as a reference for detecting the full spectrum of somatic events from cancers has long been advocated but has never been explored in tumor-normal paired samples. Here, we provide the first demonstrated use of de novo assembled personalized genome as a reference for cancer mutation detection and quantifying the effects of the reference genomes on the accuracy of somatic mutation detection. RESULTS: We generate de novo assemblies of the first tumor-normal paired genomes, both nuclear and mitochondrial, derived from the same individual with triple negative breast cancer. The personalized genome was chromosomal scale, haplotype phased, and annotated. We demonstrate that it provides individual specific haplotypes for complex regions and medically relevant genes. We illustrate that the personalized genome reference not only improves read alignments for both short-read and long-read sequencing data but also ameliorates the detection accuracy of somatic SNVs and SVs. We identify the equivalent somatic mutation calls between two genome references and uncover novel somatic mutations only when personalized genome assembly is used as a reference. CONCLUSIONS: Our findings demonstrate that use of a personalized genome with individual-specific haplotypes is essential for accurate detection of the full spectrum of somatic mutations in the paired tumor-normal samples. The unique resource and methodology established in this study will be beneficial to the development of precision oncology medicine not only for breast cancer, but also for other cancers.

Single-nucleus chromatin accessibility and RNA sequencing reveal impaired brain development in prenatally e-cigarette exposed neonatal rats.

Although emerging evidence reveals that vaping alters the function of the central nervous system, the effects of maternal vaping on offspring brain development remain elusive. Using a well-established in utero exposure model, we performed single-nucleus ATAC-seq (snATAC-seq) and RNA sequencing (snRNA-seq) on prenatally e-cigarette-exposed rat brains. We found that maternal vaping distorted neuronal lineage differentiation in the neonatal brain by promoting excitatory neurons and inhibiting lateral ganglionic eminence-derived inhibitory neuronal differentiation. Moreover, maternal vaping disrupted calcium homeostasis, induced microglia cell death, and elevated susceptibility to cerebral ischemic injury in the developing brain of offspring. Our results suggest that the aberrant calcium signaling, diminished microglial population, and impaired microglia-neuron interaction may all contribute to the underlying mechanisms by which prenatal e-cigarette exposure impairs neonatal rat brain development. Our findings raise the concern that maternal vaping may cause adverse long-term brain damage to the offspring.

Whole genome and exome sequencing reference datasets from a multi-center and cross-platform benchmark study.

With the rapid advancement of sequencing technologies, next generation sequencing (NGS) analysis has been widely applied in cancer genomics research. More recently, NGS has been adopted in clinical oncology to advance personalized medicine. Clinical applications of precision oncology require accurate tests that can distinguish tumor-specific mutations from artifacts introduced during NGS processes or data analysis. Therefore, there is an urgent need to develop best practices in cancer mutation detection using NGS and the need for standard reference data sets for systematically measuring accuracy and reproducibility across platforms and methods. Within the SEQC2 consortium context, we established paired tumor-normal reference samples and generated whole-genome (WGS) and whole-exome sequencing (WES) data using sixteen library protocols, seven sequencing platforms at six different centers. We systematically interrogated somatic mutations in the reference samples to identify factors affecting detection reproducibility and accuracy in cancer genomes. These large cross-platform/site WGS and WES datasets using well-characterized reference samples will represent a powerful resource for benchmarking NGS technologies, bioinformatics pipelines, and for the cancer genomics studies.

Toward best practice in cancer mutation detection with whole-genome and whole-exome sequencing.

Clinical applications of precision oncology require accurate tests that can distinguish true cancer-specific mutations from errors introduced at each step of next-generation sequencing (NGS). To date, no bulk sequencing study has addressed the effects of cross-site reproducibility, nor the biological, technical and computational factors that influence variant identification. Here we report a systematic interrogation of somatic mutations in paired tumor-normal cell lines to identify factors affecting detection reproducibility and accuracy at six different centers. Using whole-genome sequencing (WGS) and whole-exome sequencing (WES), we evaluated the reproducibility of different sample types with varying input amount and tumor purity, and multiple library construction protocols, followed by processing with nine bioinformatics pipelines. We found that read coverage and callers affected both WGS and WES reproducibility, but WES performance was influenced by insert fragment size, genomic copy content and the global imbalance score (GIV; G > T/C > A). Finally, taking into account library preparation protocol, tumor content, read coverage and bioinformatics processes concomitantly, we recommend actionable practices to improve the reproducibility and accuracy of NGS experiments for cancer mutation detection.

Establishing community reference samples, data and call sets for benchmarking cancer mutation detection using whole-genome sequencing.

The lack of samples for generating standardized DNA datasets for setting up a sequencing pipeline or benchmarking the performance of different algorithms limits the implementation and uptake of cancer genomics. Here, we describe reference call sets obtained from paired tumor-normal genomic DNA (gDNA) samples derived from a breast cancer cell line-which is highly heterogeneous, with an aneuploid genome, and enriched in somatic alterations-and a matched lymphoblastoid cell line. We partially validated both somatic mutations and germline variants in these call sets via whole-exome sequencing (WES) with different sequencing platforms and targeted sequencing with >2,000-fold coverage, spanning 82% of genomic regions with high confidence. Although the gDNA reference samples are not representative of primary cancer cells from a clinical sample, when setting up a sequencing pipeline, they not only minimize potential biases from technologies, assays and informatics but also provide a unique resource for benchmarking 'tumor-only' or 'matched tumor-normal' analyses.

A benchmarking study of SARS-CoV-2 whole-genome sequencing protocols using COVID-19 patient samples.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an emerging new type of coronavirus that is responsible for the COVID-19 pandemic and the unprecedented global health emergency. Whole-genome sequencing (WGS) of SARS-CoV-2 plays a critical role in understanding the disease. Performance variation exists across SARS-CoV-2 viral WGS technologies, but there is currently no benchmarking study comparing different WGS sequencing protocols. We compared seven different SARS-CoV-2 WGS library protocols using RNA from patient nasopharyngeal swab samples under two storage conditions with low and high viral inputs. We found large differences in mappability and genome coverage, and variations in sensitivity, reproducibility, and precision of single-nucleotide variant calling across different protocols. For certain amplicon-based protocols, an appropriate primer trimming step is critical for accurate single-nucleotide variant calling. We ranked the performance of protocols based on six different metrics. Our findings offer guidance in choosing appropriate WGS protocols to characterize SARS-CoV-2 and its evolution.

Computational molecular docking and virtual screening revealed promising SARS-CoV-2 drugs.

The pandemic of novel coronavirus disease 2019 (COVID-19) has rampaged the world, with more than 58.4 million confirmed cases and over 1.38 million deaths across the world by 23 November 2020. There is an urgent need to identify effective drugs and vaccines to fight against the virus. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) belongs to the family of coronaviruses consisting of four structural and 16 non-structural proteins (NSP). Three non-structural proteins, main protease (Mpro), papain-like protease (PLpro), and RNA-dependent RNA polymerase (RdRp), are believed to have a crucial role in replication of the virus. We applied computational ligand-receptor binding modeling and performed comprehensive virtual screening on FDA-approved drugs against these three SARS-CoV-2 proteins using AutoDock Vina, Glide, and rDock. Our computational studies identified six novel ligands as potential inhibitors against SARS-CoV-2, including antiemetics rolapitant and ondansetron for Mpro; labetalol and levomefolic acid for PLpro; and leucal and antifungal natamycin for RdRp. Molecular dynamics simulation confirmed the stability of the ligand-protein complexes. The results of our analysis with some other suggested drugs indicated that chloroquine and hydroxychloroquine had high binding energy (low inhibitory effect) with all three proteins-Mpro, PLpro, and RdRp. In summary, our computational molecular docking approach and virtual screening identified some promising candidate SARS-CoV-2 inhibitors that may be considered for further clinical studies.

4-Methylcytosine distribution follows the power function in Geobacter sulfurreducens genome.

Single-molecule real-time DNA sequencing revealed that 4-methylcytosine (m4C) commonly exists in bacterial genomes. In this work, samples with different m4C methylation patterns were studied. Results reveal that m4C modification is a biochemical reaction with distance effect, and its distribution follows the power function in the positive, negative, and double strands of genomic DNA sequences of Geobacter sulfurreducens. Furthermore, the value of regression coefficient in the fitting formula for double strands was the sum of those in the fitting formulae for positive and negative strands. Meanwhile, the value of exponent coefficient was the average, implicating an interesting mathematical phenomenon about power function. Considering the potent role of m4C in gene expression and the present results being obtained from the same genomic DNA sequence, this work suggests that the patterns of m4C distribution may be served as a signal for G. sulfurreducens to rapidly identify the genes to respond to environmental stresses or signals. This study opens a new avenue to extend our knowledge about the modification mechanisms and the epigenetic information of m4C modification in prokaryotes.

A multi-center cross-platform single-cell RNA sequencing reference dataset.

Single-cell RNA sequencing (scRNA-seq) is developing rapidly, and investigators seeking to use this technology are left with a variety of options for both experimental platform and bioinformatics methods. There is an urgent need for scRNA-seq reference datasets for benchmarking of different scRNA-seq platforms and bioinformatics methods. To be broadly applicable, these should be generated from renewable, well characterized reference samples and processed in multiple centers across different platforms. Here we present a benchmark scRNA-seq dataset that includes 20 scRNA-seq datasets acquired either as mixtures or as individual samples from two biologically distinct cell lines for which a large amount of multi-platform whole genome sequencing data are also available. These scRNA-seq datasets were generated from multiple popular platforms across four sequencing centers. We believe the datasets we describe here will provide a resource that meets this need by allowing evaluation of various bioinformatics methods for scRNA-seq analyses, including but not limited to data preprocessing, imputation, normalization, clustering, batch correction, and differential analysis.

HDAC inhibitors improve CRISPR-mediated HDR editing efficiency in iPSCs.

Genome-edited human induced pluripotent stem cells (iPSCs) hold great promise for therapeutic applications. However, low editing efficiency has hampered the applications of CRISPR-Cas9 technology in creating knockout and homology-directed repair (HDR)-edited iPSC lines, particularly for silent genes. This is partially due to chromatin compaction, inevitably limiting Cas9 access to the target DNA. Among the six HDAC inhibitors we examined, vorinostat, or suberoylanilide hydroxamic acid (SAHA), led to the highest HDR efficiency at both open and closed loci, with acceptable toxicity. HDAC inhibitors equally increased non-homologous end joining (NHEJ) editing efficiencies (∼50%) at both open and closed loci, due to the considerable HDAC inhibitor-mediated increase in Cas9 and sgRNA expression. However, we observed more substantial HDR efficiency improvement at closed loci relative to open chromatin (2.8 vs. 1.7-fold change). These studies provide a new strategy for HDR-editing of silent genes in iPSCs.

A multicenter study benchmarking single-cell RNA sequencing technologies using reference samples.

Comparing diverse single-cell RNA sequencing (scRNA-seq) datasets generated by different technologies and in different laboratories remains a major challenge. Here we address the need for guidance in choosing algorithms leading to accurate biological interpretations of varied data types acquired with different platforms. Using two well-characterized cellular reference samples (breast cancer cells and B cells), captured either separately or in mixtures, we compared different scRNA-seq platforms and several preprocessing, normalization and batch-effect correction methods at multiple centers. Although preprocessing and normalization contributed to variability in gene detection and cell classification, batch-effect correction was by far the most important factor in correctly classifying the cells. Moreover, scRNA-seq dataset characteristics (for example, sample and cellular heterogeneity and platform used) were critical in determining the optimal bioinformatic method. However, reproducibility across centers and platforms was high when appropriate bioinformatic methods were applied. Our findings offer practical guidance for optimizing platform and software selection when designing an scRNA-seq study.

Correction: PDGFB-expressing mesenchymal stem cells improve human hematopoietic stem cell engraftment in immunodeficient mice.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

PDGFB-expressing mesenchymal stem cells improve human hematopoietic stem cell engraftment in immunodeficient mice.

The bone marrow (BM) niche regulates multiple hematopoietic stem cell (HSC) processes. Clinical treatment for hematological malignancies by HSC transplantation often requires preconditioning via total body irradiation, which severely and irreversibly impairs the BM niche and HSC regeneration. Novel strategies are needed to enhance HSC regeneration in irradiated BM. We compared the effects of EGF, FGF2, and PDGFB on HSC regeneration using human mesenchymal stem cells (MSCs) that were transduced with these factors via lentiviral vectors. Among the above niche factors tested, MSCs transduced with PDGFB (PDGFB-MSCs) most significantly improved human HSC engraftment in immunodeficient mice. PDGFB-MSC-treated BM enhanced transplanted human HSC self-renewal in secondary transplantations more efficiently than GFP-transduced MSCs (GFP-MSCs). Gene set enrichment analysis showed increased antiapoptotic signaling in PDGFB-MSCs compared with GFP-MSCs. PDGFB-MSCs exhibited enhanced survival and expansion after transplantation, resulting in an enlarged humanized niche cell pool that provide a better humanized microenvironment to facilitate superior engraftment and proliferation of human hematopoietic cells. Our studies demonstrate the efficacy of PDGFB-MSCs in supporting human HSC engraftment.