Nipple-areolar complex (NAC) or skin flap ischemia necrosis post nipple-sparing mastectomy (NSM)-analysis of clinicopathologic factors and breast magnetic resonance imaging (MRI) features.

BACKGROUND: The purpose of this study is to identify clinicopathologic factors and/or preoperative MRI vascular patterns in the prediction of ischemia necrosis of the nipple-areola complex (NAC) or skin flap post nipple-sparing mastectomy (NSM). METHODS: We performed a retrospective analysis of 441 NSM procedures from January 2011 to September 2021 from the breast cancer database at our institution. The ischemia necrosis of NAC or skin flap was evaluated in correlation with clinicopathologic factors and types of skin incision. Patients who received NSM with preoperative MRI evaluation were further evaluated for the relationship between vascular pattern and the impact on ischemia necrosis of NAC or skin flap. RESULTS: A total of 441 cases with NSM were enrolled in the current study, and the mean age of the cases was 49.1 ± 9.8 years old. A total of 41 (9.3%) NSM procedures were found to have NAC ischemia/necrosis. Risk factors were evaluated of which old age, large mastectomy specimen weight (> 450 g), and peri-areola incision were identified as predictors of NAC necrosis. Two-hundred seventy NSM procedures also received preoperative MRI, and the blood supply pattern was 18% single-vessel type and 82% double-vessel pattern. There were no correlations between MRI blood supply patterns or types of skin flap incisions with ischemia necrosis of NAC. There were also no correlations between blood loss and the pattern or size of the blood vessel. CONCLUSION: Factors such as the type of skin incision, age, and size of mastectomy weight played an important role in determining ischemia necrosis of NAC; however, MRI vascular (single or dual vessel supply) pattern was not a significant predictive factor.

A Nucleolar Protein, Ribosomal RNA Processing 1 Homolog B (RRP1B), Enhances the Recruitment of Cellular mRNA in Influenza Virus Transcription.

UNLABELLED: Influenza A virus (IAV) undergoes RNA transcription by a unique capped-mRNA-dependent transcription, which is carried out by the viral RNA-dependent RNA polymerase (RdRp), consisting of the viral PA, PB1, and PB2 proteins. However, how the viral RdRp utilizes cellular factors for virus transcription is not clear. Previously, we conducted a genome-wide pooled short hairpin RNA (shRNA) screen to identify host factors important for influenza A virus replication. Ribosomal RNA processing 1 homolog B (RRP1B) was identified as one of the candidates. RRP1B is a nucleolar protein involved in ribosomal biogenesis. Upon IAV infection, part of RRP1B was translocated from the nucleolus to the nucleoplasm, where viral RNA synthesis likely takes place. The depletion of RRP1B significantly reduced IAV mRNA transcription in a minireplicon assay and in virus-infected cells. Furthermore, we showed that RRP1B interacted with PB1 and PB2 of the RdRp and formed a coimmunoprecipitable complex with RdRp. The depletion of RRP1B reduced the amount of capped mRNA in the RdRp complex. Taken together, these findings indicate that RRP1B is a host factor essential for IAV transcription and provide a target for new antivirals. IMPORTANCE: Influenza virus is an important human pathogen that causes significant morbidity and mortality and threatens the human population with epidemics and pandemics every year. Due to the high mutation rate of the virus, antiviral drugs targeting viral proteins might ultimately lose their effectiveness. An alternative strategy that explores the genetic stability of host factors indispensable for influenza virus replication would thus be desirable. Here, we characterized the rRNA processing 1 homolog B (RRP1B) protein as an important cellular factor for influenza A virus transcription. We showed that silencing RRP1B hampered viral RNA-dependent RNA polymerase (RdRp) activity, which is responsible for virus transcription and replication. Furthermore, we reported that RRP1B is crucial for RdRp binding to cellular capped mRNA, which is a critical step of virus transcription. Our study not only provides a deeper understanding of influenza virus-host interplay, but also suggests a potential target for antiviral drug development.

HypoRiPPAtlas as an Atlas of hypothetical natural products for mass spectrometry database search.

Recent analyses of public microbial genomes have found over a million biosynthetic gene clusters, the natural products of the majority of which remain unknown. Additionally, GNPS harbors billions of mass spectra of natural products without known structures and biosynthetic genes. We bridge the gap between large-scale genome mining and mass spectral datasets for natural product discovery by developing HypoRiPPAtlas, an Atlas of hypothetical natural product structures, which is ready-to-use for in silico database search of tandem mass spectra. HypoRiPPAtlas is constructed by mining genomes using seq2ripp, a machine-learning tool for the prediction of ribosomally synthesized and post-translationally modified peptides (RiPPs). In HypoRiPPAtlas, we identify RiPPs in microbes and plants. HypoRiPPAtlas could be extended to other natural product classes in the future by implementing corresponding biosynthetic logic. This study paves the way for large-scale explorations of biosynthetic pathways and chemical structures of microbial and plant RiPP classes.

Phosphoproteomics Reveals the Role of Constitutive KAP1 Phosphorylation by B-cell Receptor Signaling in Chronic Lymphocytic Leukemia.

Application of B-cell receptor (BCR) pathway inhibitor ibrutinib for chronic lymphocytic leukemia (CLL) is a major breakthrough, yet the downstream effects following inhibition of BCR signaling and during relapse await further clarification. By comparative phosphoproteomic profiling of B cells from patients with CLL and healthy donors, as well as CLL B cells collected at multiple time points during the course of ibrutinib treatment, we provided the landscape of dysregulated phosphoproteome in CLL and its dynamic alterations associated with ibrutinib treatment. Particularly, differential phosphorylation events associated with several signaling pathways, including BCR pathway, were enriched in patient CLL cells. A constitutively elevated phosphorylation level of KAP1 at serine 473 (S473) was found in the majority of CLL samples prior to treatment. Further verification showed that BCR activation promoted KAP1 S473 phosphorylation, whereas ibrutinib treatment abolished it. Depletion of KAP1 in primary CLL cells decelerated cell-cycle progression and ectopic expression of a KAP1 S473 phospho-mimicking mutant accelerated G2-M cell-cycle transition of CLL cells. Moreover, temporal phosphoproteomic profiles using a series of CLL cells isolated from one patient during the ibrutinib treatment revealed the dynamic changes of several molecules associated with BCR signaling in the ibrutinib responsive and recurrent stages. IMPLICATIONS: This phosphoproteomic analysis and functional validation illuminated the phosphorylation of KAP1 at S473 as an important downstream BCR signaling event and a potential indicator for the success of ibrutinib treatment in CLL.

MolDiscovery: learning mass spectrometry fragmentation of small molecules.

Identification of small molecules is a critical task in various areas of life science. Recent advances in mass spectrometry have enabled the collection of tandem mass spectra of small molecules from hundreds of thousands of environments. To identify which molecules are present in a sample, one can search mass spectra collected from the sample against millions of molecular structures in small molecule databases. The existing approaches are based on chemistry domain knowledge, and they fail to explain many of the peaks in mass spectra of small molecules. Here, we present molDiscovery, a mass spectral database search method that improves both efficiency and accuracy of small molecule identification by learning a probabilistic model to match small molecules with their mass spectra. A search of over 8 million spectra from the Global Natural Product Social molecular networking infrastructure shows that molDiscovery correctly identify six times more unique small molecules than previous methods.