Mitochondrial DNA Copy Number Variation in Asthma Risk, Severity, and Exacerbations.

Rationale: Although airway oxidative stress and inflammation are central to asthma pathogenesis, there is limited knowledge of the relationship of asthma risk, severity, or exacerbations to mitochondrial dysfunction, which is pivotal to oxidant generation and inflammation. Objectives: We investigated whether mitochondrial DNA copy number (mtDNA-CN) as a measure of mitochondrial function is associated with asthma diagnosis, severity, oxidative stress, and exacerbations. Methods: We measured mtDNA-CN in blood in two cohorts. In the UK Biobank (UKB), we compared mtDNA-CN in mild and moderate-severe asthmatics to non-asthmatics. In the Severe Asthma Research Program (SARP), we evaluated mtDNA-CN in relation to asthma severity, biomarkers of oxidative stress and inflammation, and exacerbations. Measures and Main Results: In UK Biobank, asthmatics (n = 29,768) have lower mtDNA-CN compared to non-asthmatics (n = 239,158) (beta, -0.026 [95% CI, -0.038 to -0.014], P = 2.46×10-5). While lower mtDNA-CN is associated with asthma, mtDNA-CN did not differ by asthma severity in either UKB or SARP. Biomarkers of inflammation show that asthmatics have higher white blood cells (WBC), neutrophils, eosinophils, fraction exhaled nitric oxide (FENO), and lower superoxide dismutase (SOD) than non-asthmatics, confirming greater oxidative stress in asthma. In one year follow-up in SARP, higher mtDNA-CN is associated with reduced risk of three or more exacerbations in the subsequent year (OR 0.352 [95% CI, 0.164 to 0.753], P = 0.007). Conclusions: Asthma is characterized by mitochondrial dysfunction. Higher mtDNA-CN identifies an exacerbation-resistant asthma phenotype, suggesting mitochondrial function is important in exacerbation risk.

Comparison of whole genome sequencing and targeted sequencing for mitochondrial DNA.

Mitochondrial dysfunction has emerged to be associated with a broad spectrum of diseases, and there is an increasing demand for accurate detection of mitochondrial DNA (mtDNA) variants. Whole genome sequencing (WGS) has been the dominant sequencing approach to identify genetic variants in recent decades, but most studies focus on variants on the nuclear genome. Whole genome sequencing is also costly and time consuming. Sequencing specifically targeted for mtDNA is commonly used in the diagnostic settings and has lower costs. However, there is a lack of pairwise comparisons between these two sequencing approaches for calling mtDNA variants on a population basis. In this study, we compared WGS and mtDNA-targeted sequencing (targeted-seq) in analyzing mitochondrial DNA from 1499 participants recruited into the Severe Asthma Research Program (SARP). Our study reveals that targeted-sequencing and WGS have comparable capacity to determine genotypes and to call haplogroups and homoplasmies on mtDNA. However, there exists a large variability in calling heteroplasmies, especially for low-frequency heteroplasmies, which indicates that investigators should be cautious about heteroplasmies acquired from different sequencing methods. Further research is highly desired to improve variant detection methods for mitochondrial DNA.

Inhibition of cyclin-dependent kinase 9 synergistically enhances venetoclax activity in mantle cell lymphoma.

Mantle cell lymphoma (MCL) is an aggressive and largely incurable subtype of non-Hodgkin's lymphoma. Venetoclax has demonstrated efficacy in MCL patients with relapsed or refractory disease, however response is variable and less durable than CLL. This may be the result of co-expression of other anti-apoptotic proteins such as MCL-1, which is associated with both intrinsic and acquired resistance to venetoclax in B-cell malignancies. One strategy for neutralizing MCL-1 and other short-lived survival factors is to inhibit CDK9, which plays a key role in transcription. Here, we report the response of MCL cell lines and primary patient samples to the combination of venetoclax and novel CDK9 inhibitors. Primary samples represented de novo patients and relapsed disease, including relapse after ibrutinib failure. Despite the diverse responses to each single agent, possibly due to variable expression of the BCL-2 family members, venetoclax plus CDK9 inhibitors synergistically induced apoptosis in MCL cells. The synergistic effect was also confirmed via venetoclax plus a direct MCL-1 inhibitor. Murine xenograft studies demonstrated potent in vivo efficacy of venetoclax plus CDK9 inhibitor that was superior to each agent alone. Together, this study supports clinical investigation of this combination in MCL, including in patients who have progressed on ibrutinib.

Integrative proteomics and phosphoproteomics in pulmonary arterial hypertension.

Pulmonary arterial endothelial cells (PAEC) are mechanistically linked to origins of pulmonary arterial hypertension (PAH). Here, global proteomics and phosphoproteomics of PAEC from PAH (n = 4) and healthy lungs (n = 5) were performed using LC-MS/MS to confirm known pathways and identify new areas of investigation in PAH. Among PAH and control cells, 170 proteins and 240 phosphopeptides were differentially expressed; of these, 45 proteins and 18 phosphopeptides were located in the mitochondria. Pathologic pathways were identified with integrative bioinformatics and human protein-protein interactome network analyses, then confirmed with targeted proteomics in PAH PAEC and non-targeted metabolomics and targeted high-performance liquid chromatography of metabolites in plasma from PAH patients (n = 30) and healthy controls (n = 12). Dysregulated pathways in PAH include accelerated one carbon metabolism, abnormal tricarboxylic acid (TCA) cycle flux and glutamate metabolism, dysfunctional arginine and nitric oxide pathways, and increased oxidative stress. Functional studies in cells confirmed abnormalities in glucose metabolism, mitochondrial oxygen consumption, and production of reactive oxygen species in PAH. Altogether, the findings indicate that PAH is typified by changes in metabolic pathways that are primarily found in mitochondria.

CLT030, a leukemic stem cell-targeting CLL1 antibody-drug conjugate for treatment of acute myeloid leukemia.

The current standard of care for acute myeloid leukemia (AML) is largely ineffective with very high relapse rates and low survival rates, mostly due to the inability to eliminate a rare population of leukemic stem cells (LSCs) that initiate tumor growth and are resistant to standard chemotherapy. RNA-sequencing analysis on isolated LSCs confirmed C-type lectin domain family 12 member A (CLL1, also known as CLEC12A) to be highly expressed on LSCs but not on normal hematopoietic stem cells (HSCs) or other healthy organ tissues. Expression of CLL1 was consistent across different types of AML. We developed CLT030 (CLL1-ADC), an antibody-drug conjugate (ADC) based on a humanized anti-CLL1 antibody with 2 engineered cysteine residues linked covalently via a cleavable linker to a highly potent DNA-binding payload, thus resulting in a site-specific and homogenous ADC product. The ADC is designed to be stable in the bloodstream and to release its DNA-binding payload only after the ADC binds to CLL1-expressing tumor cells, is internalized, and the linker is cleaved in the lysosomal compartment. CLL1-ADC inhibits in vitro LSC colony formation and demonstrates robust in vivo efficacy in AML cell tumor models and tumor growth inhibition in the AML patient-derived xenograft model. CLL1-ADC demonstrated a reduced effect on differentiation of healthy normal human CD34+ cells to various lineages as observed in an in vitro colony formation assay and in an in vivo xenotransplantation model as compared with CD33-ADC. These results demonstrate that CLL1-ADC could be an effective ADC therapeutic for the treatment of AML.

A systems biology approach to studying the molecular mechanisms of osteoblastic differentiation under cytokine combination treatment.

Recent studies revealed that sequential release of bone morphogenetic protein 2 and insulin-like growth factor 1 plays an important role in osteogenic process, suggesting that cytokines bone morphogenetic protein 2 and insulin-like growth factor 1 function in a time-dependent manner. However, the specific molecular mechanisms underlying these observations remained elusive, impeding the elaborate manipulation of cytokine sequential delivery in tissue repair. The aim of this study was to identify the key relevant pathways and processes regulating bone morphogenetic protein 2/insulin-like growth factor 1-mediated osteoblastic differentiation. Based on the microarray and proteomics data, and differentiation/growth status of mouse bone marrow stromal cells, we constructed a multiscale systems model to simulate the bone marrow stromal cells lineage commitment and bone morphogenetic protein 2 and insulin-like growth factor 1-regulated signaling dynamics. The accuracy of our model was validated using a set of independent experimental data. Our study reveals that, treatment of bone marrow stromal cells with bone morphogenetic protein 2 prior to insulin-like growth factor 1 led to the activation of transcription factor Runx2 through TAK1-p38 MAPK and SMAD1/5 signaling pathways and initiated the lineage commitment of bone marrow stromal cells. Delivery of insulin-like growth factor 1 four days after bone morphogenetic protein 2 treatment optimally activated transcription factors osterix and ß-catenin through ERK and AKT pathways, which are critical to preosteoblast maturity. Our systems biology approach is expected to provide technical and scientific support in optimizing therapeutic scheme to improve osteogenesis/bone regeneration and other essential biological processes.

The role of SH3GL3 in myeloma cell migration/invasion, stemness and chemo-resistance.

Multiple myeloma (MM) is an incurable cancer characterized by clonal expansion of malignant plasma cells in the bone marrow and their egress into peripheral blood. The mechanisms of myeloma cells migration/invasion have remained unclear. Herein, we found SH3GL3 was highly expressed in the CD138-negative (CD138-) myeloma cells. The migration/invasion capability of CD138- cells was significantly higher than that in the CD138-positive (CD138+) cells. Silencing SH3GL3 using shRNA reduced myeloma cells migration/invasion. Conversely, overexpression of SH3GL3 increased myeloma cells migration/invasion. Moreover, SH3GL3 is also associated with the stemness and chemo-resistance of CD138- myeloma cells. Elevated expression of stem cell and multi-drug resistant markers were seen in the myeloma cells with overexpressed SH3GL3; while knocking-down SH3GL3 reduced the expression of these markers. A marked increase in p-PI3K and p-FAK was observed in the cells with overexpressed SH3GL3. To test if FAK/PI3K signaling pathway was involved in the SH3GL3-mediated myeloma cells migration, the cells transfected w/wo SH3GL3 cDNA were treated with FAK inhibitor 14 and PI3K inhibitor LY294002. Inhibition of FAK and PI3K attenuated SH3GL3-mediated migration /invasion. Our findings indicate that SH3GL3 plays an important role in myeloma cell migration/invasion, stemness and chemo-resistance. The SH3GL3-mediated myeloma cell migration/invasion is mediated by FAK/PI3K signaling pathway.

CD138-negative myeloma cells regulate mechanical properties of bone marrow stromal cells through SDF-1/CXCR4/AKT signaling pathway.

As the second most prevalent hematologic malignancy, multiple myeloma (MM) remains incurable and relapses due to intrinsic or acquired drug resistance. Therefore, new therapeutic strategies that target molecular mechanisms responsible for drug resistance are attractive. Interactions of tumor cells with their surrounding microenvironment impact tumor initiation, progression and metastasis, as well as patient prognosis. This cross-talk is bidirectional. Tumor cells can also attract or activate tumor-associated stromal cells by releasing cytokines to facilitate their growth, invasion and metastasis. The effect of myeloma cells on bone marrow stromal cells (BMSCs) has not been well studied. In our study, we found that higher stiffness of BMSCs was not a unique characteristic of BMSCs from MM patients (M-BMSCs). BMSCs from MGUS (monoclonal gammopathy of undetermined significance) patients were also stiffer than the BMSCs from healthy volunteers (N-BMSCs). The stiffness of M-BMSCs was enhanced when cocultured with myeloma cells. In contrast, no changes were seen in myeloma cell-primed MGUS- and N-BMSCs. Interestingly, our data indicated that CD138⁻ myeloma cells, but not CD138⁺ cells, regulated M-BMSC stiffness. SDF-1 was highly expressed in the CD138⁻ myeloma subpopulation compared with that in CD138⁺ cells. Inhibition of SDF-1 using AMD3100 or knocking-down CXCR4 in M-BMSCs blocked CD138⁻ myeloma cells-induced increase in M-BMSC stiffness, suggesting a crucial role of SDF-1/CXCR4. AKT inhibition attenuated SDF-1-induced increases in M-BMSC stiffness. These findings demonstrate, for the first time, CD138⁻ myeloma cell-directed cross-talk with BMSCs and reveal that CD138⁻ myeloma cells regulate M-BMSC stiffness through SDF-1/CXCR4/AKT signaling.

Rigorous assessment and integration of the sequence and structure based features to predict hot spots.

BACKGROUND: Systematic mutagenesis studies have shown that only a few interface residues termed hot spots contribute significantly to the binding free energy of protein-protein interactions. Therefore, hot spots prediction becomes increasingly important for well understanding the essence of proteins interactions and helping narrow down the search space for drug design. Currently many computational methods have been developed by proposing different features. However comparative assessment of these features and furthermore effective and accurate methods are still in pressing need. RESULTS: In this study, we first comprehensively collect the features to discriminate hot spots and non-hot spots and analyze their distributions. We find that hot spots have lower relASA and larger relative change in ASA, suggesting hot spots tend to be protected from bulk solvent. In addition, hot spots have more contacts including hydrogen bonds, salt bridges, and atomic contacts, which favor complexes formation. Interestingly, we find that conservation score and sequence entropy are not significantly different between hot spots and non-hot spots in Ab+ dataset (all complexes). While in Ab- dataset (antigen-antibody complexes are excluded), there are significant differences in two features between hot pots and non-hot spots. Secondly, we explore the predictive ability for each feature and the combinations of features by support vector machines (SVMs). The results indicate that sequence-based feature outperforms other combinations of features with reasonable accuracy, with a precision of 0.69, a recall of 0.68, an F1 score of 0.68, and an AUC of 0.68 on independent test set. Compared with other machine learning methods and two energy-based approaches, our approach achieves the best performance. Moreover, we demonstrate the applicability of our method to predict hot spots of two protein complexes. CONCLUSION: Experimental results show that support vector machine classifiers are quite effective in predicting hot spots based on sequence features. Hot spots cannot be fully predicted through simple analysis based on physicochemical characteristics, but there is reason to believe that integration of features and machine learning methods can remarkably improve the predictive performance for hot spots.

Prediction of protein interaction hot spots using rough set-based multiple criteria linear programming.

Protein-protein interactions are fundamentally important in many biological processes and it is in pressing need to understand the principles of protein-protein interactions. Mutagenesis studies have found that only a small fraction of surface residues, known as hot spots, are responsible for the physical binding in protein complexes. However, revealing hot spots by mutagenesis experiments are usually time consuming and expensive. In order to complement the experimental efforts, we propose a new computational approach in this paper to predict hot spots. Our method, Rough Set-based Multiple Criteria Linear Programming (RS-MCLP), integrates rough sets theory and multiple criteria linear programming to choose dominant features and computationally predict hot spots. Our approach is benchmarked by a dataset of 904 alanine-mutated residues and the results show that our RS-MCLP method performs better than other methods, e.g., MCLP, Decision Tree, Bayes Net, and the existing HotSprint database. In addition, we reveal several biological insights based on our analysis. We find that four features (the change of accessible surface area, percentage of the change of accessible surface area, size of a residue, and atomic contacts) are critical in predicting hot spots. Furthermore, we find that three residues (Tyr, Trp, and Phe) are abundant in hot spots through analyzing the distribution of amino acids.