Activating transcription factor 5 (ATF5) promotes tumorigenic capability and activates the Wnt/b-catenin pathway in bladder cancer.

BACKGROUND: In bladder cancer, up to 70% of patients will relapse after resection within 5 years, in which the mechanism underlying the recurrence remains largely unclear. METHODS: Quantitative real-time PCR, western blot and immunohistochemistry were conducted. The assays of tumor sphere formation and tumor xenograft were further performed to assess the potential biological roles of ATF5 (activating transcription factor 5). Chromatin immunoprecipitation-qPCR and luciferase activity assays were carried out to explore the potential molecular mechanism. A two-tailed paired Student's t-test, χ2 test, Kaplan Meier and Cox regression analyses, and Spearman's rank correlation coefficients were used for statistical analyses. RESULTS: ATF5 is elevated in bladder urothelial cancer (BLCA) tissues, especially in recurrent BLCA, which confers a poor prognosis. Overexpressing ATF5 significantly enhanced, whereas silencing ATF5 inhibited, the capability of tumor sphere formation in bladder cancer cells. Mechanically, ATF5 could directly bind to and stimulate the promoter of DVL1 gene, resulting in activation of Wnt/ß-catenin pathway. CONCLUSIONS: This study provides a novel insight into a portion of the mechanism underlying high recurrence potential of BLCA, presenting ATF5 as a prognostic factor or potential therapeutic target for preventing recurrence in BLCA.

Overexpression of SPHK1 associated with targeted therapy resistance in predicting poor prognosis in renal cell carcinoma.

Background: Sphingosine kinase 1 (SPHK1) is a key enzyme that catalyzes the phosphorylation of sphingosine. Recent studies reported SPHK1 to be associated with renal cell carcinoma (RCC) progression by inducing targeted therapy resistance. However, the expression and the clinical significance of SPHK1 on RCC in those having received targeted therapy have not been elucidated. The present study explored the expression of SPHK1 in RCC tissues from targeted therapy recipients, the correlation of SPHK1 with clinicopathological parameters, and the effect of SPHK1 on RCC patient prognosis. Methods: Differential gene expression analysis of RCC treated with and without targeted therapy was performed. The correlations of SPHK1 expression with clinical parameters of RCC were examined. Gene set enrichment analysis (GSEA) was performed to clarify the potential role of SPHK1 associated with targeted therapy resistance. The value of SPHK1 as a diagnostic marker for RCC was also evaluated. The Kaplan-Meier method was applied to analyze the correlation between SPHK1 expression and patient survival rate by using the clinical data from patients with RCC. Results: Significant overexpression of SPHK1 was detected in RCC treated with targeted therapy. SPHK1 expression was closely correlated with RCC progression-related clinicopathological parameters. Therefore, elevated SPHK1 could effectively diagnose RCC and distinguish RCC with an advanced clinical stage and a high pathological grade. SPHK1 was associated with the stemness of RCC cells via the activation of the Wnt, Hedgehog, or Notch signaling pathways in targeted drug-treated or untreated RCC. Survival analysis of a large cohort of RCC samples indicated overexpression of SPHK1 to be inversely correlated with the overall and disease-free survival of patients with RCC. Conclusions: Our study indicated that SPHK1 associated with targeted therapy resistance could serve as a potential prognostic marker and a valuable biomarker of response to angiogenic agents in RCC.

ACO1 and IREB2 downregulation confer poor prognosis and correlate with autophagy-related ferroptosis and immune infiltration in KIRC.

Background: ACO1 and IREB2 are two homologous cytosolic regulatory proteins, which sense iron levels and change iron metabolism-linked molecules. These two genes were noticeably decreased in kidney renal clear cell carcinoma (KIRC), which confer poor survival. Meanwhile, there is a paucity of information about the mechanisms and clinical significance of ACO1 and IREB2 downregulation in renal cancers. Methods: The expression profiles of ACO1 and IREB2 were assessed using multiple public data sets via several bioinformatics platforms. Clinical and pathological information was utilized to stratify cohorts for comparison. Patient survival outcomes were evaluated using the Kaplan-Meier plotter, a meta-analysis tool. The correlations of ACO1 and IREB2 with ferroptosis were further evaluated in The Cancer Genome Atlas (TCGA)-KIRC database. Tumor immune infiltration was analyzed using the CIBERSORT, TIMER, and GEPIA data resources. ACO1 antagonist sodium oxalomalate (OMA) and IREB2 inhibitor sodium nitroprusside (SNP) was used to treat renal cancer ACHN cells together with sorafenib. Results: KIRC patients with low ACO1 or IREB2 contents exhibited a remarkably worse survival rate in contrast with those with high expression in Kaplan-Meier survival analyses. Meanwhile, ACO1 and IREB2 regulate autophagy-linked ferroptosis along with immune cell invasion in the tumor microenvironment in KIRC patients. Blocking the activation of these two genes by their inhibitors OMA and SNP ameliorated sorafenib-triggered cell death, supporting that ACO1 and IREB2 could be participated in its cytotoxic influence on renal cancer cells. Conclusion: ACO1 and IREB2 downregulation in renal cancers were correlated with cancer aggressiveness, cellular iron homeostasis, cytotoxic immune cell infiltration, and patient survival outcomes. Our research is integral to verify the possible significance of ACO1 and IREB2 contents as a powerful signature for targeted treatment or novel immunotherapy in clinical settings.

Identifying misbonded atoms in the 2019 CoRE metal-organic framework database.

Databases of experimentally-derived metal-organic framework (MOF) crystal structures are useful for large-scale computational screening to identify which MOFs are best-suited for particular applications. However, these crystal structures must be cleaned to identify and/or correct various artifacts. The recently published 2019 CoRE MOF database (Chung et al., J. Chem. Eng. Data, 2019, 64, 5985-5998) reported thousands of experimentally-derived crystal structures that were partially cleaned to remove solvent molecules, to identify hundreds of disordered structures (approximately thirty of those were corrected), and to manually correct approximately 100 structures (e.g., adding missing hydrogen atoms). Herein, further cleaning of the 2019 CoRE MOF database is performed to identify structures with misbonded or isolated atoms: (i) structures containing an isolated atom, (ii) structures containing atoms too close together (i.e., overlapping atoms), (iii) structures containing a misplaced hydrogen atom, (iv) structures containing an under-bonded carbon atom (which might be caused by missing hydrogen atoms), and (v) structures containing an over-bonded carbon atom. This study should not be viewed as the final cleaning of this database, but rather as progress along the way towards the goal of someday achieving a completely cleaned set of experimentally-derived MOF crystal structures. We performed atom typing for all of the accepted structures to identify those structures that can be parameterized by previously reported forcefield precursors (Chen and Manz, RSC Adv., 2019, 9, 36492-36507). We report several forcefield precursors (e.g., net atomic charges, atom-in-material polarizabilities, atom-in-material dispersion coefficients, electron cloud parameters, etc.) for more than five thousand MOFs in the 2019 CoRE MOF database.

Bond orders of the diatomic molecules.

Bond order quantifies the number of electrons dressed-exchanged between two atoms in a material and is important for understanding many chemical properties. Diatomic molecules are the smallest molecules possessing chemical bonds and play key roles in atmospheric chemistry, biochemistry, lab chemistry, and chemical manufacturing. Here we quantum-mechanically calculate bond orders for 288 diatomic molecules and ions. For homodiatomics, we show bond orders correlate to bond energies for elements within the same chemical group. We quantify and discuss how semicore electrons weaken bond orders for elements having diffuse semicore electrons. Lots of chemistry is effected by this. We introduce a first-principles method to represent orbital-independent bond order as a sum of orbital-dependent bond order components. This bond order component analysis (BOCA) applies to any spin-orbitals that are unitary transformations of the natural spin-orbitals, with or without periodic boundary conditions, and to non-magnetic and (collinear or non-collinear) magnetic materials. We use this BOCA to study all period 2 homodiatomics plus Mo2, Cr2, ClO, ClO-, and Mo2(acetate)4. Using Manz's bond order equation with DDEC6 partitioning, the Mo-Mo bond order was 4.12 in Mo2 and 1.46 in Mo2(acetate)4 with a sum of bond orders for each Mo atom of ∼4. Our study informs both chemistry research and education. As a learning aid, we introduce an analogy between bond orders in materials and message transmission in computer networks. We also introduce the first working quantitative heuristic model for all period 2 homodiatomic bond orders. This heuristic model incorporates s-p mixing to give heuristic bond orders of ¾ (Be2), 1¾ (B2), 2¾ (C2), and whole number bond orders for the remaining period 2 homodiatomics.

New scaling relations to compute atom-in-material polarizabilities and dispersion coefficients: part 2. Linear-scaling computational algorithms and parallelization.

We present two algorithms to compute system-specific polarizabilities and dispersion coefficients such that required memory and computational time scale linearly with increasing number of atoms in the unit cell for large systems. The first algorithm computes the atom-in-material (AIM) static polarizability tensors, force-field polarizabilities, and C 6, C 8, C 9, C 10 dispersion coefficients using the MCLF method. The second algorithm computes the AIM polarizability tensors and C 6 coefficients using the TS-SCS method. Linear-scaling computational cost is achieved using a dipole interaction cutoff length function combined with iterative methods that avoid large dense matrix multiplies and large matrix inversions. For MCLF, Richardson extrapolation of the screening increments is used. For TS-SCS, a failproof conjugate residual (FCR) algorithm is introduced that solves any linear equation system having Hermitian coefficients matrix. These algorithms have mathematically provable stable convergence that resists round-off errors. We parallelized these methods to provide rapid computation on multi-core computers. Excellent parallelization efficiencies were obtained, and adding parallel processors does not significantly increase memory requirements. This enables system-specific polarizabilities and dispersion coefficients to be readily computed for materials containing millions of atoms in the unit cell. The largest example studied herein is an ice crystal containing >2 million atoms in the unit cell. For this material, the FCR algorithm solved a linear equation system containing >6 million rows, 7.57 billion interacting atom pairs, 45.4 billion stored non-negligible matrix components used in each large matrix-vector multiplication, and ∼19 million unknowns per frequency point (>300 million total unknowns).

A collection of forcefield precursors for metal-organic frameworks.

A host of important performance properties for metal-organic frameworks (MOFs) and other complex materials can be calculated by modeling statistical ensembles. The principle challenge is to develop accurate and computationally efficient interaction models for these simulations. Two major approaches are (i) ab initio molecular dynamics in which the interaction model is provided by an exchange-correlation theory (e.g., DFT + dispersion functional) and (ii) molecular mechanics in which the interaction model is a parameterized classical force field. The first approach requires further development to improve computational speed. The second approach requires further development to automate accurate forcefield parameterization. Because of the extreme chemical diversity across thousands of MOF structures, this problem is still mostly unsolved today. For example, here we show structures in the 2014 CoRE MOF database contain more than 8 thousand different atom types based on first and second neighbors. Our results showed that atom types based on both first and second neighbors adequately capture the chemical environment, but atom types based on only first neighbors do not. For 3056 MOFs, we used density functional theory (DFT) followed by DDEC6 atomic population analysis to extract a host of important forcefield precursors: partial atomic charges; atom-in-material (AIM) C6, C8, and C10 dispersion coefficients; AIM dipole and quadrupole moments; various AIM polarizabilities; quantum Drude oscillator parameters; AIM electron cloud parameters; etc. Electrostatic parameters were validated through comparisons to the DFT-computed electrostatic potential. These forcefield precursors should find widespread applications to developing MOF force fields.

New scaling relations to compute atom-in-material polarizabilities and dispersion coefficients: part 1. Theory and accuracy.

Polarizabilities and London dispersion forces are important to many chemical processes. Force fields for classical atomistic simulations can be constructed using atom-in-material polarizabilities and C n (n = 6, 8, 9, 10…) dispersion coefficients. This article addresses the key question of how to efficiently assign these parameters to constituent atoms in a material so that properties of the whole material are better reproduced. We develop a new set of scaling laws and computational algorithms (called MCLF) to do this in an accurate and computationally efficient manner across diverse material types. We introduce a conduction limit upper bound and m-scaling to describe the different behaviors of surface and buried atoms. We validate MCLF by comparing results to high-level benchmarks for isolated neutral and charged atoms, diverse diatomic molecules, various polyatomic molecules (e.g., polyacenes, fullerenes, and small organic and inorganic molecules), and dense solids (including metallic, covalent, and ionic). We also present results for the HIV reverse transcriptase enzyme complexed with an inhibitor molecule. MCLF provides the non-directionally screened polarizabilities required to construct force fields, the directionally-screened static polarizability tensor components and eigenvalues, and environmentally screened C6 coefficients. Overall, MCLF has improved accuracy compared to the TS-SCS method. For TS-SCS, we compared charge partitioning methods and show DDEC6 partitioning yields more accurate results than Hirshfeld partitioning. MCLF also gives approximations for C8, C9, and C10 dispersion coefficients and quantum Drude oscillator parameters. This method should find widespread applications to parameterize classical force fields and density functional theory (DFT) + dispersion methods.

Motion correction for cellular-resolution multi-photon fluorescence microscopy imaging of awake head-restrained mice using speed embedded HMM.

Multi-photon fluorescence microscopy (MFM) captures high-resolution fluorescence image sequences and can be used for the intact brain imaging of small animals. Recently, it has been extended from anesthetized and head-stabilized mice to awake and head-restrained ones for in vivo neurological study. In these applications, motion correction is an important pre-processing step since brain pulsation and body movement can cause motion artifact and prevent stable serial image acquisition at such high spatial resolution. This paper proposes a speed embedded Hidden Markov model (SEHMM) for motion correction in MFM imaging of awake head-restrained mice. The algorithm extends the traditional Hidden Markov model (HMM) method by embedding a motion prediction model to better estimate the state transition probability. The novelty of the method lies in using adaptive probability to estimate the linear motion, while the state-of-the-art method assumes that the highest probability is assigned to the case with no motion. In experiments we demonstrated that SEHMM is more accurate than the traditional HMM using both simulated and real MFM image sequences.

Motion artifact correction of multi-photon imaging of awake mice models using speed embedded HMM.

Multi-photon fluorescence microscopy (MFM) captures high-resolution anatomical and functional fluorescence image sequences and can be used for the intact brain imaging of small animals. Recently, it has been extended from imaging anesthetized and head-stabilized animals to awake and head-restrained ones for in vivo neurological study. In these applications, motion correction is an important pre-processing step since brain pulsation and tiny body movement can cause motion artifacts and prevent stable serial image acquisition at such a high spatial resolution. This paper proposes a speed embedded hidden Markov model (SEHMM) for motion correction in MFM imaging of awake head-restrained mice. The algorithm extends the traditional HMM method by embedding a motion prediction model to better estimate the state transition probability. SEHMM is a line-by-line motion correction algorithm, which is implemented within the in-focal-plane 2-D videos and can operate directly on the motion-distorted imaging data without external signal measurements such as the movement, heartbeat, respiration, or muscular tension. In experiments, we demonstrat that SEHMM is more accurate than traditional HMM using both simulated and real MFM image sequences.