Citations

Elucidating the structure and function of enteropeptidase (EP) is essential for advancing our understanding of its biological significance, particularly in regulating trypsinogen activation. Using cryo-EM and enzymatic activity, we uncovered the significance of the CUB2 domain in mediating the cleavage of macromolecular substrates. We identified crucial binding loops and key residues for EP’s proteolytic function. The mutation E574A enhanced the proteolytic activity of EP, whereas the mutation N619A diminished its cleavage efficiency, highlighting the importance of surface-charged interactions in modulating EP’s activity. A proteolytic cycle was proposed to deepen our understanding of the trypsinogen activation by EP. This work offers valuable insights into the molecular mechanisms underlying EP’s interaction with its substrate, and opens up avenues for therapeutically modulating EP-mediated proteolysis.

Abstract CDK12 is frequently deleted and/or mutated in human cancers, and CDK12 inactivation is associated with aggressive phenotypes, including the tendency to metastasis. In this study, we showed that CDK12 deficiency induced intronic polyadenylation (IPA) usage in BRD4 to upregulate expression of the bromodomain-containing protein 4 (BRD4) short isoform (BRD4S). BRD4S demonstrated an enhanced capacity to form phase separation despite lacking the intrinsically disordered region located in the C-terminal of full-length BRD4. Oligomerization of BRD4S mediated phase separation through a base-interacting structural domain. CUT&Tag and RNA sequencing analyses revealed that BRD4S preferentially bound to the promoters of TGFβ signaling genes, including TGFB2 and LTBP1, driving their expression. Consistently, BRD4S drove cancer cell dissemination in vitro and metastasis in vivo, which could be blocked by BET or TGFβ signaling pathway inhibitors. Overall, this study identifies IPA usage at the BRD4 gene locus induced by CDK12 deficiency as a mechanism responsible for BRD4S genesis, which represents an important mechanism driving cancer metastasis and a potential target for therapeutic intervention in CDK12-deficient cancers. Significance: The CDK12–BRD4S–TGFβ signaling axis represents a key regulatory module that promotes metastatic progression and can be targeted by BET or TGFβ pathway inhibitors for treating CDK12-deficient cancers.

Prostate cancer (PCa) progression is driven by intricate molecular mechanisms involving dysregulated signaling networks and posttranslational modifications of key regulatory proteins. In this study, we identify a novel oncogenic pathway wherein cyclin-dependent kinase 12 (CDK12) physically interacts with and phosphorylates forkhead box A1 (FOXA1) at serine 234 (S234). Phosphorylation at this residue markedly enhances FOXA1 transcriptional activity, leading to up-regulation of downstream targets including murine double minute 2 (MDM2), a critical negative regulator of the p53 tumor suppressor. Mechanistically, this CDK12–FOXA1–MDM2 axis destabilizes p53, attenuates apoptotic signaling, and promotes PCa cell survival and proliferation. Therapeutic targeting of CDK12 using the small-molecule inhibitor THZ531 or RNA interference effectively abrogates FOXA1 phosphorylation, restores p53 stability, reactivates apoptotic pathways, and suppresses tumor growth. Notably, the identification of S234 as a functional phosphorylation site in FOXA1 reveals a previously uncharacterized posttranslational regulatory mechanism in PCa biology. These findings establish the CDK12–FOXA1–MDM2 axis as a pivotal driver of PCa progression and underscore the therapeutic potential of targeting FOXA1 phosphorylation to restore tumor suppressor function and induce apoptosis in PCa. Our work provides a mechanistic framework for developing precision therapies aimed at disrupting this oncogenic cascade in PCa.

Abstract The roles of nucleoplasm-residing nucleoporins (NUP) in solid tumors, including prostate cancer, remain unknown. In this study, we reveal the clinical significance and mechanistic role of the off-pore NUP, soluble POM121 (sPOM121), as a crucial transcriptional regulator that enhances the aggressiveness of metastatic prostate cancer. Using orthogonal methodologies in human samples, sPOM121 was identified as the predominantly expressed nucleoplasmic NUP in prostate cancer. Unbiased proteomic and epigenomic studies demonstrate that sPOM121, through its C-terminus, interacts with the chromatin remodeler SMARCA5 at gene promoter sites and localizes at nuclear condensates, reprogramming gene expression. Indeed, sPOM121 regulates a distinct oncogenic gene network, including β-catenin, leading to prostate cancer progression and immune evasion. Importantly, targeting the sPOM121/β-catenin axis in patient-derived preclinical and syngeneic mouse models halts prostate cancer aggressiveness and enhances antitumor immunity. Taken together, these findings reveal previously unknown actionable reprogramming functions of off-pore NUPs in solid tumors. Significance: This study uncovers how oncogenic signaling programs are transcriptionally heightened by the NUP sPOM121 in metastatic prostate cancer. Localization of sPOM121 at active transcriptional nuclear condensates propels disease progression and immune evasion, offering novel anticancer therapeutic opportunities.

CDK12 primarily functions as a transcription regulatory cyclin-dependent kinase (CDK) that controls mRNA elongation, splicing, and polyadenylation. The CDK12 gene is implicated in human cancers since it is frequently mutated and/or deleted in prostate and ovarian cancer but paradoxically amplified in breast cancer. Here, we demonstrate that CDK12 promotes serine-933 phosphorylation of DNA2, a nuclease/helicase critical for replication fork stress regulation, and the phosphorylation subsequently facilitates DNA2 polyubiquitination and degradation mediated by the APC/C CDC20 E3 ubiquitin ligase. CDK12 inactivation induces but amplification suppresses genome-wide expression of interferon response and antigen processing and presentation machinery genes in ovarian and breast cancer cells, respectively. Besides causing aberrant DNA2 stabilization, replication stress, genomic instability, and cytosolic double-stranded DNA (dsDNA) accumulation, CDK12 loss also triggers cGAS–STING activation and innate immune response, which can be reversed by forced expression of replication protein A (RPA) subunits or DNA2 depletion. Our findings identify DNA2 as a phosphorylation substrate of CDK12, connecting CDK12 to cell cycle regulation. These data also reveal DNA2 protein destruction as a critical mechanism that dictates genomic instability, cGAS–STING signaling activation, and innate immune response in CDK12-deregulated cancers.

AbstractAutoimmune pancreatitis (AIP) is identified as a severe chronic immune‐related disorder in pancreas, including two subtypes. In this study, pancreatic lesions in patients diagnosed as either type 1 AIP or type 2 AIP are examined, and these patients’ peripheral blood at single‐cell level. Furthermore, flow cytometry, immunofluorescence, and functional assays are performed to verify the identified cell subtypes. In type 1 AIP, there is a notable increase in the amount of B cells and plasma cells, and IgG4+ plasma cells are key pathogenic cells of AIP. The differentiation path of naïve‐stage B cells into IgG4+ produced plasma cells is observed, and an increased amount of T helper cells and T follicular helper (Tfh) cells. This study also reveals that HIF‐1α, an activated transcriptional factor, can directly bind to promoter site of NAMPT, promoting higher levels of visfatin production in HIF1A+ classical monocytes. Pancreatic stellate cells can be activated by extracellular visfatin and promote the development of fibrotic response in pancreatic lesions across both AIP subtypes. The current findings shed light on the exploration of dynamic alterations in peripheral blood cells and cell subgroups in pancreatic lesions of AIP, while elucidating a pathogenic cell subset and potential fibrosis mechanism of AIP.

Abstract Approximately 50% of prostate cancer (PCa) patients harbor fusions involving the TMPRSS2 and ERG genes. Despite this, tailored therapies targeting the fused gene, tERG , remain undeveloped. Our study analyzed biopsy samples from two clinical trials assessing the efficacies of androgen receptor (AR) signaling inhibitors (ARSIs). The results revealed that tERG promotes resistance to ARSIs and is associated with elevated levels of the glucocorticoid receptor (GR). Subsequent assays showed that GR directly interacts with tERG, alleviates allosteric autoinhibition and prevents chemotherapy-induced tERG degradation. In PCa models, either inhibiting GR or lowering cortisol levels suppressed tumor growth in tERG-positive models, but not in fusion-negative models. In addition, patient-derived fusion-positive xenografts displayed enhanced sensitivity to combined GR and AR inhibitors. Collectively, these findings highlight TMPRSS2-ERG as a new biomarker and propose that simultaneous inhibition of GR and AR may specifically benefit tERG -positine patients. However, GR stimulatory corticosteroid therapies may not be advisable for this patient subgroup.

(1) Background: Prostate cancer treatment efficacy is significantly influenced by androgen receptor (AR) signaling pathways. SLC22A3, a membrane transporter, has been linked to SNP rs9364554 risk loci for drug efficacy in prostate cancer. (2) Methods: We examined the location of SNP rs9364554 in the genome and utilized TCGA and other publicly available datasets to analyze the association of this SNP with SLC22A3 transcription levels. We verified onco-mining findings in prostate cancer cell lines using quantitative PCR and Western blots. Additionally, we employed electrophoretic mobility shift assay (EMSA) to detect the binding affinity of transcription factors to this SNP. The ChIP-Seq was used to analyze the enrichment of H3K27ac on the SLC22A3 promoter. (3) Results: In this study, we revealed that SNP rs9364554 resides in the SLC22A3 gene and affects its transcription. The downregulation of SLC22A3 is associated with drug resistance. More importantly, we found that this SNP has different binding affinities with transcription factors, specifically FOXA1 and AR, which significantly affects their regulation of SLC22A3 transcription. (4) Conclusions: Our findings highlight the potential of using this SNP as a biomarker for predicting chemotherapeutic outcomes and uncover possible mechanisms underlying drug resistance in advanced prostate cancers. More importantly, it provides a clinical foundation for targeting FOXA1 to enhance drug efficacy in prostate cancer patients.

Abstract Generative artificial intelligence (AI) holds immense potential for medical applications, but the lack of a comprehensive evaluation framework and methodological deficiencies in existing studies hinder its effective implementation. Standardized assessment guidelines are crucial for ensuring reliable and consistent evaluation of generative AI in healthcare. Our objective is to develop robust, standardized guidelines tailored for evaluating generative AI performance in medical contexts. Through a rigorous literature review utilizing the Web of Sciences, Cochrane Library, PubMed, and Google Scholar, we focused on research testing generative AI capabilities in medicine. Our multidisciplinary team of experts conducted discussion sessions to develop a comprehensive 32‐item checklist. This checklist encompasses critical evaluation aspects of generative AI in medical applications, addressing key dimensions such as question collection, querying methodologies, and assessment techniques. The checklist and its broader assessment framework provide a holistic evaluation of AI systems, delineating a clear pathway from question gathering to result assessment. It guides researchers through potential challenges and pitfalls, enhancing research quality and reporting and aiding the evolution of generative AI in medicine and life sciences. Our framework furnishes a standardized, systematic approach for testing generative AI's applicability in medicine. For a concise checklist, please refer to Table S or visit GenAIMed.org .