Infiltrating lipid-rich macrophage subpopulations identified as a regulator of increasing prostate size in human benign prostatic hyperplasia.

Macrophages exhibit marked phenotypic heterogeneity within and across disease states, with lipid metabolic reprogramming contributing to macrophage activation and heterogeneity. Chronic inflammation has been observed in human benign prostatic hyperplasia (BPH) tissues, however macrophage activation states and their contributions to this hyperplastic disease have not been defined. We postulated that a shift in macrophage phenotypes with increasing prostate size could involve metabolic alterations resulting in prostatic epithelial or stromal hyperplasia. Single-cell RNA-seq of CD45+ transition zone leukocytes from 10 large (>90 grams) and 10 small (<40 grams) human prostates was conducted. Macrophage subpopulations were defined using marker genes. BPH macrophages do not distinctly categorize into M1 and M2 phenotypes. Instead, macrophages with neither polarization signature preferentially accumulate in large versus small prostates. Specifically, macrophage subpopulations with altered lipid metabolism pathways, demarcated by TREM2 and MARCO expression, significantly accumulate with increased prostate volume. TREM2 + and MARCO + macrophage abundance positively correlates with patient body mass index and urinary symptom scores. TREM2+ macrophages have significantly higher neutral lipid than TREM2- macrophages from BPH tissues. Lipid-rich macrophages were observed to localize within the stroma in BPH tissues. In vitro studies indicate that lipid-loaded macrophages increase prostate epithelial and stromal cell proliferation compared to control macrophages. These data define two new BPH immune subpopulations, TREM2+ and MARCO+ macrophages, and suggest that lipid-rich macrophages may exacerbate lower urinary tract symptoms in patients with large prostates. Further investigation is needed to evaluate the therapeutic benefit of targeting these cells in BPH.

KRAS-mediated upregulation of CIP2A promotes suppression of PP2A-B56α to initiate pancreatic cancer development.

Oncogenic mutations in KRAS are present in approximately 95% of patients diagnosed with pancreatic ductal adenocarcinoma (PDAC) and are considered the initiating event of pancreatic intraepithelial neoplasia (PanIN) precursor lesions. While it is well established that KRAS mutations drive the activation of oncogenic kinase cascades during pancreatic oncogenesis, the effects of oncogenic KRAS signaling on regulation of phosphatases during this process is not fully appreciated. Protein Phosphatase 2A (PP2A) has been implicated in suppressing KRAS-driven cellular transformation. However, low PP2A activity is observed in PDAC cells compared to non-transformed cells, suggesting that suppression of PP2A activity is an important step in the overall development of PDAC. In the current study, we demonstrate that KRASG12D induces the expression of both an endogenous inhibitor of PP2A activity, Cancerous Inhibitor of PP2A (CIP2A), and the PP2A substrate, c-MYC. Consistent with these findings, KRASG12D sequestered the specific PP2A subunit responsible for c-MYC degradation, B56α, away from the active PP2A holoenzyme in a CIP2A-dependent manner. During PDAC initiation in vivo, knockout of B56α promoted KRASG12D tumorigenesis by accelerating acinar-to-ductal metaplasia (ADM) and the formation of PanIN lesions. The process of ADM was attenuated ex vivo in response to pharmacological re-activation of PP2A utilizing direct small molecule activators of PP2A (SMAPs). Together, our results suggest that suppression of PP2A-B56α through KRAS signaling can promote the MYC-driven initiation of pancreatic tumorigenesis.

Understanding the Natural Language of DNA using Encoder-Decoder Foundation Models with Byte-level Precision.

This paper presents the Ensemble Nucleotide Byte-level Encoder-Decoder (ENBED) foundation model, analyzing DNA sequences at byte-level precision with an encoder-decoder Transformer architecture. ENBED uses a sub-quadratic implementation of attention to develop an efficient model capable of sequence-to-sequence transformations, generalizing previous genomic models with encoder-only or decoder-only architectures. We use Masked Language Modeling to pre-train the foundation model using reference genome sequences and apply it in the following downstream tasks: (1) identification of enhancers, promotors and splice sites, (2) recognition of sequences containing base call mismatches and insertion/deletion errors, an advantage over tokenization schemes involving multiple base pairs, which lose the ability to analyze with byte-level precision, (3) identification of biological function annotations of genomic sequences, and (4) generating mutations of the Influenza virus using the encoder-decoder architecture and validating them against real-world observations. In each of these tasks, we demonstrate significant improvement as compared to the existing state-of-the-art results.

A first-in-class fully modified version of miR-34a with outstanding stability, activity, and anti-tumor efficacy.

Altered by defects in p53, epigenetic silencing, and genomic loss, the microRNA miR-34a represents one of the most clinically relevant tumor-suppressive microRNAs. Without question, a striking number of patients with cancer would benefit from miR-34a replacement, if poor miR-34a stability, non-specific delivery, and delivery-associated toxicity could be overcome. Here, we highlight a fully modified version of miR-34a (FM-miR-34a) that overcomes these hurdles when conjugated to a synthetically simplistic ligand. FM-miR-34a is orders of magnitude more stable than a partially modified version, without compromising its activity, leading to stronger repression of a greater number of miR-34a targets. FM-miR-34a potently inhibited proliferation and invasion, and induced sustained downregulation of endogenous target genes for >120 h following in vivo delivery. In vivo targeting was achieved through conjugating FM-miR-34a to folate (FM-FolamiR-34a), which inhibited tumor growth leading to complete cures in some mice. These results have the ability to revitalize miR-34a as an anti-cancer agent, providing a strong rationale for clinical testing.

cTULIP: application of a human-based RNA-seq primary tumor classification tool for cross-species primary tumor classification in canine.

Introduction: The domestic dog, Canis familiaris, is quickly gaining traction as an advantageous model for use in the study of cancer, one of the leading causes of death worldwide. Naturally occurring canine cancers share clinical, histological, and molecular characteristics with the corresponding human diseases. Methods: In this study, we take a deep-learning approach to test how similar the gene expression profile of canine glioma and bladder cancer (BLCA) tumors are to the corresponding human tumors. We likewise develop a tool for identifying misclassified or outlier samples in large canine oncological datasets, analogous to that which was developed for human datasets. Results: We test a number of machine learning algorithms and found that a convolutional neural network outperformed logistic regression and random forest approaches. We use a recently developed RNA-seq-based convolutional neural network, TULIP, to test the robustness of a human-data-trained primary tumor classification tool on cross-species primary tumor prediction. Our study ultimately highlights the molecular similarities between canine and human BLCA and glioma tumors, showing that protein-coding one-to-one homologs shared between humans and canines, are sufficient to distinguish between BLCA and gliomas. Discussion: The results of this study indicate that using protein-coding one-to-one homologs as the features in the input layer of TULIP performs good primary tumor prediction in both humans and canines. Furthermore, our analysis shows that our selected features also contain the majority of features with known clinical relevance in BLCA and gliomas. Our success in using a human-data-trained model for cross-species primary tumor prediction also sheds light on the conservation of oncological pathways in humans and canines, further underscoring the importance of the canine model system in the study of human disease.

Comparative transcriptome profiling of pomegranate genotypes having resistance and susceptible reaction to Xanthomonas axonopodis pv. punicae.

Pomegranate (Punica granatum L.) is an important fruit crop, rich in fiber, vitamins, antioxidants, minerals and source of different biologically active compounds. The bacterial blight caused by Xanthomonas axonopodispv. punicae is a serious threat to the crop leading to 60-80% yield loss under epiphytotic conditions. In this work, we have generated comparative transcriptome profile to mark the gene expression signatures during resistance and susceptible interactions. We analyzed leaf and fruits samples of moderately resistant genotype (IC 524207) and susceptible variety (Bhagawa) of pomegranate at three progressive infection stages upon inoculation with the pathogen. RNA-Seq with the Illumina HiSeq 2500 platform revealed 1,88,337 non-redundant (nr) transcript sequences from raw sequencing data, for a total of 34,626 unigenes with size >2 kb. Moreover, 85.3% unigenes were annotated in at least one of the seven databases examined. Comparative analysis of gene-expression signatures in resistant and susceptible varieties showed that the genes known to be involved in defense mechanism in plants were up-regulated in resistant variety. Gene Ontology (GO) analysis successfully annotated 90,485 pomegranate unigenes, of which 68,464 were assigned to biological, 78,107 unigenes molecular function and 44,414 to cellular components. Significantly enriched GO terms in DEGs were related to oxidations reduction biological process, protein binding and oxidoreductase activity. This transcriptome data on pomegranate could help in understanding resistance and susceptibility nature of cultivars and further detailed fine mapping and functional validation of identified candidate gene would provide scope for resistance breeding programme in pomegranate.