Subclones of bone marrow CD34+ cells in acute myeloid leukemia at diagnosis confer responses of patients to induction chemotherapy.

BACKGROUND: Acute myeloid leukemia (AML) is a hematopoietic malignancy with a prognosis that varies with genetic heterogeneity of hematopoietic stem/progenitor cells (HSPCs). Induction chemotherapy with cytarabine and anthracycline has been the standard care for newly diagnosed AML, but about 30% of patients have no response to this regimen. The resistance mechanisms require deeper understanding. METHODS: In our study, using single-cell RNA sequencing, we analyzed the heterogeneity of bone marrow CD34+ cells from newly diagnosed patients with AML who were then divided into sensitive and resistant groups according to their responses to induction chemotherapy with cytarabine and anthracycline. We verified our findings by TCGA database, GEO datasets, and multiparameter flow cytometry. RESULTS: We established a landscape for AML CD34+ cells and identified HSPC types based on the lineage signature genes. Interestingly, we found a cell population with CRIP1high LGALS1high S100Ashigh showing features of granulocyte-monocyte progenitors was associated with poor prognosis of AML. And two cell populations marked by CD34+ CD52+ or CD34+ CD74+ DAP12+ were related to good response to induction therapy, showing characteristics of hematopoietic stem cells. CONCLUSION: Our study indicates the subclones of CD34+ cells confers for outcomes of AML and provides biomarkers to predict the response of patients with AML to induction chemotherapy.

An approach to high-throughput growth of submillimeter transition metal dichalcogenide single crystals.

High-throughput growth of large size transition metal dichalcogenide (TMD) single crystals is an important challenge for their applications in the next generation electronic and optoelectronic integration devices. Here we report the high-throughput growth of submillimeter monolayer TMD single crystals by two-stage space confined chemical vapor deposition, where the nucleation density of TMD crystals is significantly decreased for the growth of large size monolayer crystals by the space confinement effect. Moreover, high-throughput growth of submillimeter TMD crystals is also achieved by stacking the substrates along the perpendicular direction to the flow of the reaction gases. The mobilities of the TMD materials produced in this way are up to 1.2, 17.0 and 25.0 cm2 (V s)-1 for monolayer WS2, WSe2 and MoS2 single crystals, respectively. The results demonstrate that two-stage space confined growth is a highly promising method for high-throughput fabrication of high-quality submillimeter monolayer TMD single crystals, which will pave a new pathway to large-scale production of TMD-based electronic and optoelectronic devices.

Metal Induced Growth of Transition Metal Dichalcogenides at Controlled Locations.

Metal induced nucleation is adopted to achieve the growth of transition metal dichalcogenides at controlled locations. Ordered arrays of MoS2 and WS2 have successfully been fabricated on SiO2 substrates by using the patterned Pt/Ti dots as the nucleation sites. Uniform MoS2 monolayers with the adjustable size up to 50 µm are grown surrounding these metal patterns and the mobility of such layer is about 0.86 cm2/V·s. The crystalline flakes of WS2 are also fabricated extending from the metal patterns and the electron mobility of these flakes is up to 11.36 cm2/V·s.

iNuc-PseKNC: a sequence-based predictor for predicting nucleosome positioning in genomes with pseudo k-tuple nucleotide composition.

MOTIVATION: Nucleosome positioning participates in many cellular activities and plays significant roles in regulating cellular processes. With the avalanche of genome sequences generated in the post-genomic age, it is highly desired to develop automated methods for rapidly and effectively identifying nucleosome positioning. Although some computational methods were proposed, most of them were species specific and neglected the intrinsic local structural properties that might play important roles in determining the nucleosome positioning on a DNA sequence. RESULTS: Here a predictor called 'iNuc-PseKNC' was developed for predicting nucleosome positioning in Homo sapiens, Caenorhabditis elegans and Drosophila melanogaster genomes, respectively. In the new predictor, the samples of DNA sequences were formulated by a novel feature-vector called 'pseudo k-tuple nucleotide composition', into which six DNA local structural properties were incorporated. It was observed by the rigorous cross-validation tests on the three stringent benchmark datasets that the overall success rates achieved by iNuc-PseKNC in predicting the nucleosome positioning of the aforementioned three genomes were 86.27%, 86.90% and 79.97%, respectively. Meanwhile, the results obtained by iNuc-PseKNC on various benchmark datasets used by the previous investigators for different genomes also indicated that the current predictor remarkably outperformed its counterparts. AVAILABILITY: A user-friendly web-server, iNuc-PseKNC is freely accessible at http://lin.uestc.edu.cn/server/iNuc-PseKNC.

Prediction of the types of ion channel-targeted conotoxins based on radial basis function network.

Conotoxins are small disulfide-rich peptide toxins, which have the exceptional diversity of sequences. Because conotoxins are able to specifically bind to ion channels and interfere with neurotransmission, they are considered as the excellent pharmacological candidates in drug design. Appropriate type assignment of newly sequenced mature ion channel-targeted conotoxins with computational method is conducive to explore the biological and pharmacological functions of conotoxins. In this paper, we developed a novel method based on binomial distribution and radial basis function network to predict the types of ion-channel targeted conotoxins. We achieved the overall accuracy of 89.3% with average accuracy of 89.7% in the prediction of three types of ion channel-targeted conotoxins in jackknife cross-validation test, indicating that the method is superior to other state-of-the-art methods. In addition, we evaluated the proposed model with an independent dataset including 77 conotoxins. The overall accuracy of 85.7% was achieved, validating that our model is reliable. Moreover, we used the proposed method to annotate 336 function-undefined mature conotoxins in the UniProt Database. The model provides the valuable instructions for theoretical and experimental research on conotoxins.

Identification of mycobacterial membrane proteins and their types using over-represented tripeptide compositions.

Mycobacterium can cause many serious diseases, such as tuberculosis and leprosy. Its membrane proteins play a critical role for multidrug-resistance and its tenacious survival ability. Knowing the types of membrane proteins will provide novel insights into understanding their functions and facilitate drug target discovery. In this study, a novel method was developed for predicting mycobacterial membrane protein and their types by using over-represented tripeptides. A total of 295 non-membrane proteins and 274 membrane proteins were collected to evaluate the performance of proposed method. The results of jackknife cross-validation test show that our method achieves an overall accuracy of 93.0% in discriminating between mycobacterial membrane proteins and mycobacterial non-membrane proteins and an overall accuracy of 93.1% in classifying mycobacterial membrane protein types. By comparing with other methods, the proposed method showed excellent predictive performance. Based on the proposed method, we built a predictor, called MycoMemSVM, which is freely available at http://lin.uestc.edu.cn/server/MycoMemSVM. It is anticipated that MycoMemSVM will become a useful tool for the annotation of mycobacterial membrane proteins and the development of anti-mycobacterium drug design.