Telomere dysfunction alters intestinal stem cell dynamics to promote cancer.

Telomere dynamics are linked to aging hallmarks, and age-associated telomere loss fuels the development of epithelial cancers. In Apc-mutant mice, the onset of DNA damage associated with telomere dysfunction has been shown to accelerate adenoma initiation via unknown mechanisms. Here, we observed that Apc-mutant mice engineered to experience telomere dysfunction show accelerated adenoma formation resulting from augmented cell competition and clonal expansion. Mechanistically, telomere dysfunction induces the repression of EZH2, resulting in the derepression of Wnt antagonists, which causes the differentiation of adjacent stem cells and a relative growth advantage to Apc-deficient telomere dysfunctional cells. Correspondingly, in this mouse model, GSK3ß inhibition countered the actions of Wnt antagonists on intestinal stem cells, resulting in impaired adenoma formation of telomere dysfunctional Apc-mutant cells. Thus, telomere dysfunction contributes to cancer initiation through altered stem cell dynamics, identifying an interception strategy for human APC-mutant cancers with shortened telomeres.

Multiobjectivization of Single-Objective Optimization in Evolutionary Computation: A Survey.

Multiobjectivization has emerged as a new promising paradigm to solve single-objective optimization problems (SOPs) in evolutionary computation, where an SOP is transformed into a multiobjective optimization problem (MOP) and solved by an evolutionary algorithm to find the optimal solutions of the original SOP. The transformation of an SOP into an MOP can be done by adding helper-objective(s) into the original objective, decomposing the original objective into multiple subobjectives, or aggregating subobjectives of the original objective into multiple scalar objectives. Multiobjectivization bridges the gap between SOPs and MOPs by transforming an SOP into the counterpart MOP, through which multiobjective optimization methods manage to attain superior solutions of the original SOP. Particularly, using multiobjectivization to solve SOPs can reduce the number of local optima, create new search paths from local optima to global optima, attain more incomparability solutions, and/or improve solution diversity. Since the term "multiobjectivization" was coined by Knowles et al. in 2001, this subject has accumulated plenty of works in the last two decades, yet there is a lack of systematic and comprehensive survey of these efforts. This article presents a comprehensive multifacet survey of the state-of-the-art multiobjectivization methods. Particularly, a new taxonomy of the methods is provided in this article and the advantages, limitations, challenges, theoretical analyses, benchmarks, applications, as well as future directions of the multiobjectivization methods are discussed.

EZH2 engages TGFß signaling to promote breast cancer bone metastasis via integrin ß1-FAK activation.

Bone metastases occur in 50-70% of patients with late-stage breast cancers and effective therapies are needed. The expression of enhancer of zeste homolog 2 (EZH2) is correlated with breast cancer metastasis, but its function in bone metastasis hasn't been well-explored. Here we report that EZH2 promotes osteolytic metastasis of breast cancer through regulating transforming growth factor beta (TGFß) signaling. EZH2 induces cancer cell proliferation and osteoclast maturation, whereas EZH2 knockdown decreases bone metastasis incidence and outgrowth in vivo. Mechanistically, EZH2 transcriptionally increases ITGB1, which encodes for integrin ß1. Integrin ß1 activates focal adhesion kinase (FAK), which phosphorylates TGFß receptor type I (TGFßRI) at tyrosine 182 to enhance its binding to TGFß receptor type II (TGFßRII), thereby activating TGFß signaling. Clinically applicable FAK inhibitors but not EZH2 methyltransferase inhibitors effectively inhibit breast cancer bone metastasis in vivo. Overall, we find that the EZH2-integrin ß1-FAK axis cooperates with the TGFß signaling pathway to promote bone metastasis of breast cancer.

Vitamin E Enhances Cancer Immunotherapy by Reinvigorating Dendritic Cells via Targeting Checkpoint SHP1.

Despite the popular use of dietary supplements during conventional cancer treatments, their impacts on the efficacies of prevalent immunotherapies, including immune-checkpoint therapy (ICT), are unknown. Surprisingly, our analyses of electronic health records revealed that ICT-treated patients with cancer who took vitamin E (VitE) had significantly improved survival. In mouse models, VitE increased ICT antitumor efficacy, which depended on dendritic cells (DC). VitE entered DCs via the SCARB1 receptor and restored tumor-associated DC functionality by directly binding to and inhibiting protein tyrosine phosphatase SHP1, a DC-intrinsic checkpoint. SHP1 inhibition, genetically or by VitE treatment, enhanced tumor antigen cross-presentation by DCs and DC-derived extracellular vesicles (DC-EV), triggering systemic antigen-specific T-cell antitumor immunity. Combining VitE with DC-recruiting cancer vaccines or immunogenic chemotherapies greatly boosted ICT efficacy in animals. Therefore, combining VitE supplement or SHP1-inhibited DCs/DC-EVs with DC-enrichment therapies could substantially augment T-cell antitumor immunity and enhance the efficacy of cancer immunotherapies. SIGNIFICANCE: The impacts of nutritional supplements on responses to immunotherapies remain unexplored. Our study revealed that dietary vitamin E binds to and inhibits DC checkpoint SHP1 to increase antigen presentation, prime antitumor T-cell immunity, and enhance immunotherapy efficacy. VitE-treated or SHP1-silenced DCs/DC-EVs could be developed as potent immunotherapies. This article is highlighted in the In This Issue feature, p. 1599.

Enhanced Multifactorial Evolutionary Algorithm With Meme Helper-Tasks.

Evolutionary multitasking (EMT) is an emerging research direction in the field of evolutionary computation. EMT solves multiple optimization tasks simultaneously using evolutionary algorithms with the aim to improve the solution for each task via intertask knowledge transfer. The effectiveness of intertask knowledge transfer is the key to the success of EMT. The multifactorial evolutionary algorithm (MFEA) represents one of the most widely used implementation paradigms of EMT. However, it tends to suffer from noneffective or even negative knowledge transfer. To address this issue and improve the performance of MFEA, we incorporate a prior-knowledge-based multiobjectivization via decomposition (MVD) into MFEA to construct strongly related meme helper-tasks. In the proposed method, MVD creates a related multiobjective optimization problem for each component task based on the corresponding problem structure or decision variable grouping to enhance positive intertask knowledge transfer. MVD can reduce the number of local optima and increase population diversity. Comparative experiments on the widely used test problems demonstrate that the constructed meme helper-tasks can utilize the prior knowledge of the target problems to improve the performance of MFEA.

Cloning of ground-state intestinal stem cells from endoscopic biopsy samples.

'Adult' or 'somatic' stem cells harbor an intrinsic ability to regenerate tissues. Heterogeneity of such stem cells along the gastrointestinal tract yields the known segmental specificity of this organ and may contribute to the pathology of certain enteric conditions. Here we detail technology for the generation of 'libraries' of clonogenic cells from 1-mm-diamter endoscopic biopsy samples from the human gastrointestinal tract. Each of the 150-300 independent clones in a typical stem cell library can be clonally expanded to billions of cells in a few weeks while maintaining genomic stability and the ability to undergo multipotent differentiation to the specific epithelia from which the sample originated. The key to this methodology is the intrinsic immortality of normal intestinal stem cells (ISCs) and culture systems that maintain them as highly immature, ground-state ISCs marked by a single-cell clonogenicity of 70% and a corresponding 250-fold proliferative advantage over spheroid technologies. Clonal approaches such as this enhance the resolution of molecular genetics, make genome editing easier, and may be useful in regenerative medicine, unravelling heterogeneity in disease, and facilitating drug discovery.

Unlimited expansion of intestinal stem cells from a wide range of ages.

The recent technical advance in cloning and culturing ground-state intestinal stem cells (ISC) provides us an opportunity of accurate assessment of age-related impact on the function of highly proliferative intestinal stem cells. Our ability of indefinitely and robustly expanding single-stem-cell derived pedigrees in vitro allows us to study intestinal stem cells at the clonal level. Interestingly, comparable number of ISC clones was yielded from 1mm endoscopic biopsy of all donors despite the age. They were passaged in vitro as pedigrees and expanded to 1 billion cells in approximately sixty days without changes in stemness demonstrated by clonogenicity and multipotency. Therefore, our study shows that ISCs from a wide range of ages can be cloned and expanded to unlimited number in vitro with similar efficiency and stability. These patient-derived ISCs harbor intrinsic immortality and are ideal for autologous transplantation, supporting the promise of adult-stem-cell based personalized medicine.

A Combinatorial Solution to Point Symbol Recognition.

Recent work has shown that recognizing point symbols is an essential task in the field of map digitization. For the identification of symbols, it is generally necessary to compare the symbols with a specific criterion and find the most similar one with each known symbol one by one. Most of the works can only identify a single symbol, a small number of works are to deal with multiple symbols simultaneously with a low recognition accuracy. Given the two deficiencies, this paper proposes a deep transfer learning architecture, where the task is to learn a symbol classifier with AlexNet. For the insufficient dataset, we develop a method for transfer learning that uses a MNIST dataset to pretrain the model, which makes up for the problem of small training dataset and enhances the generalization of the model. Before the recognition process, preprocessing the point symbols in the map to coarse screening out the areas suspected of point symbols. We show a significant improvement over using point symbol images to keep a high performance in being able to deal with many more categories of symbols simultaneously.

Metabolic interactions between dynamic bacterial subpopulations.

Individual microbial species are known to occupy distinct metabolic niches within multi-species communities. However, it has remained largely unclear whether metabolic specialization can similarly occur within a clonal bacterial population. More specifically, it is not clear what functions such specialization could provide and how specialization could be coordinated dynamically. Here, we show that exponentially growing Bacillus subtilis cultures divide into distinct interacting metabolic subpopulations, including one population that produces acetate, and another population that differentially expresses metabolic genes for the production of acetoin, a pH-neutral storage molecule. These subpopulations exhibit distinct growth rates and dynamic interconversion between states. Furthermore, acetate concentration influences the relative sizes of the different subpopulations. These results show that clonal populations can use metabolic specialization to control the environment through a process of dynamic, environmentally-sensitive state-switching.

Ground-State Stem Cells: A Novel Approach for Adult Stem Cell Research.

A robust and reliable culture system of adult stem cells is essential for applying the cutting-edge technologies of drug screening, gene editing, and genomics to stem cell research necessary for breakthroughs in this field. In addition, personalized regenerative medicine based on autologous transplantation requires our ability to clone and expand the numbers of these stem cells in vitro. In comparison to the 3D "organoid" culture system that shows limited ability to propagate stem cells as the majority of cells are differentiated or transit amplifying cells, ground-state stem cell culture system is a novel technology that permits long-lived adult stem cells to maintain immaturity, self-renewal capacity, multi-potency and genomic stability despite long-term culturing in a 2D system. The robustness, reliability and easy-to-use features of this new technology bypass the deficiencies of 3D organoid culture systems and provided unlimited stem cell sources for research, therapeutic use, and drug discovery.

Quantitative, time-resolved proteomic analysis by combining bioorthogonal noncanonical amino acid tagging and pulsed stable isotope labeling by amino acids in cell culture.

An approach to proteomic analysis that combines bioorthogonal noncanonical amino acid tagging (BONCAT) and pulsed stable isotope labeling with amino acids in cell culture (pSILAC) provides accurate quantitative information about rates of cellular protein synthesis on time scales of minutes. The method is capable of quantifying 1400 proteins produced by HeLa cells during a 30 min interval, a time scale that is inaccessible to isotope labeling techniques alone. Potential artifacts in protein quantification can be reduced to insignificant levels by limiting the extent of noncanonical amino acid tagging. We find no evidence for artifacts in protein identification in experiments that combine the BONCAT and pSILAC methods.

MOEA/D with adaptive weight adjustment.

Recently, MOEA/D (multi-objective evolutionary algorithm based on decomposition) has achieved great success in the field of evolutionary multi-objective optimization and has attracted a lot of attention. It decomposes a multi-objective optimization problem (MOP) into a set of scalar subproblems using uniformly distributed aggregation weight vectors and provides an excellent general algorithmic framework of evolutionary multi-objective optimization. Generally, the uniformity of weight vectors in MOEA/D can ensure the diversity of the Pareto optimal solutions, however, it cannot work as well when the target MOP has a complex Pareto front (PF; i.e., discontinuous PF or PF with sharp peak or low tail). To remedy this, we propose an improved MOEA/D with adaptive weight vector adjustment (MOEA/D-AWA). According to the analysis of the geometric relationship between the weight vectors and the optimal solutions under the Chebyshev decomposition scheme, a new weight vector initialization method and an adaptive weight vector adjustment strategy are introduced in MOEA/D-AWA. The weights are adjusted periodically so that the weights of subproblems can be redistributed adaptively to obtain better uniformity of solutions. Meanwhile, computing efforts devoted to subproblems with duplicate optimal solution can be saved. Moreover, an external elite population is introduced to help adding new subproblems into real sparse regions rather than pseudo sparse regions of the complex PF, that is, discontinuous regions of the PF. MOEA/D-AWA has been compared with four state of the art MOEAs, namely the original MOEA/D, Adaptive-MOEA/D, [Formula: see text]-MOEA/D, and NSGA-II on 10 widely used test problems, two newly constructed complex problems, and two many-objective problems. Experimental results indicate that MOEA/D-AWA outperforms the benchmark algorithms in terms of the IGD metric, particularly when the PF of the MOP is complex.

Structural dynamics of the MecA-ClpC complex: a type II AAA+ protein unfolding machine.

The MecA-ClpC complex is a bacterial type II AAA(+) molecular machine responsible for regulated unfolding of substrates, such as transcription factors ComK and ComS, and targeting them to ClpP for degradation. The six subunits of the MecA-ClpC complex form a closed barrel-like structure, featured with three stacked rings and a hollow passage, where substrates are threaded and translocated through successive pores. Although the general concepts of how polypeptides are unfolded and translocated by internal pore loops of AAA(+) proteins have long been conceived, the detailed mechanistic model remains elusive. With cryoelectron microscopy, we captured four different structures of the MecA-ClpC complexes. These complexes differ in the nucleotide binding states of the two AAA(+) rings and therefore might presumably reflect distinctive, representative snapshots from a dynamic unfolding cycle of this hexameric complex. Structural analysis reveals that nucleotide binding and hydrolysis modulate the hexameric complex in a number of ways, including the opening of the N-terminal ring, the axial and radial positions of pore loops, the compactness of the C-terminal ring, as well as the relative rotation between the two nucleotide-binding domain rings. More importantly, our structural and biochemical data indicate there is an active allosteric communication between the two AAA(+) rings and suggest that concerted actions of the two AAA(+) rings are required for the efficiency of the substrate unfolding and translocation. These findings provide important mechanistic insights into the dynamic cycle of the MecA-ClpC unfoldase and especially lay a foundation toward the complete understanding of the structural dynamics of the general type II AAA(+) hexamers.

Structure and mechanism of the hexameric MecA-ClpC molecular machine.

Regulated proteolysis by ATP-dependent proteases is universal in all living cells. Bacterial ClpC, a member of the Clp/Hsp100 family of AAA+ proteins (ATPases associated with diverse cellular activities) with two nucleotide-binding domains (D1 and D2), requires the adaptor protein MecA for activation and substrate targeting. The activated, hexameric MecA-ClpC molecular machine harnesses the energy of ATP binding and hydrolysis to unfold specific substrate proteins and translocate the unfolded polypeptide to the ClpP protease for degradation. Here we report three related crystal structures: a heterodimer between MecA and the amino domain of ClpC, a heterododecamer between MecA and D2-deleted ClpC, and a hexameric complex between MecA and full-length ClpC. In conjunction with biochemical analyses, these structures reveal the organizational principles behind the hexameric MecA-ClpC complex, explain the molecular mechanisms for MecA-mediated ClpC activation and provide mechanistic insights into the function of the MecA-ClpC molecular machine. These findings have implications for related Clp/Hsp100 molecular machines.

Crystal structure of the MecA degradation tag.

MecA is an adaptor protein that regulates the assembly and activity of the ATP-dependent ClpCP protease in Bacillus subtilis. MecA contains two domains. Although the amino-terminal domain of MecA recruits substrate proteins such as ComK and ComS, the carboxyl-terminal domain (residues 121-218) has dual roles in the regulation and function of ClpCP protease. MecA-(121-218) facilitates the assembly of ClpCP oligomer, which is required for the protease activity of ClpCP. This domain was identified to be a non-recycling degradation tag that targets heterologous fusion proteins to the ClpCP protease for degradation. To elucidate the mechanism of MecA, we determined the crystal structure of MecA-(121-218) at 2.2 A resolution, which reveals a previously uncharacterized alpha/beta fold. Structure-guided mutagenesis allows identification of surface residues that are essential for the function of MecA. We also solved the structure of a carboxyl-terminal domain of YpbH, a paralogue of MecA in B. subtilis, at 2.4 A resolution. Despite low sequence identity, the two structures share essentially the same fold. The presence of MecA homologues in other bacterial species suggests conservation of a large family of unique degradation tags.

Molecular determinants of MecA as a degradation tag for the ClpCP protease.

Regulated proteolysis by ATP-dependent proteases is universal in all living cells. In Bacillus subtilis, the degradation of the competence transcription factor ComK is mediated by a ternary complex involving the adaptor protein MecA and the ATP-dependent protease ClpCP. Here we demonstrate that a C-terminal, 98-amino acid domain of MecA (residues 121-218) serves as a non-recycling, degradation tag and targets a variety of fusion proteins to the ClpCP protease for degradation. MecA-(121-218) facilitates productive oligomerization of ClpC, stimulates the ATPase activity of ClpC, and allows the activated ClpC complex to stably associate with ClpP. Importantly, the ClpCP protease undergoes dynamic cycles of assembly and disassembly, which are triggered by association with MecA and the degradation of MecA, respectively.

Parallelisation of the blast algorithm.

Retrieving homologous DNA and protein sequences from existing databases is a fundamental routine in bioinformatics research. Programs of the NCBI BLAST family are widely used for this purpose. We evaluated paraBLAST, a parallelised version of the NCBI BLAST algorithm, using a Message Passing Interface (MPI) on a multi-node compute cluster. Here, we propose static and dynamic database-partitioning schemes based on the availability of the cluster. We evaluated the application of the algorithm in querying nucleotide sequences against a large-scale sequence database with different numbers of database partitions, and hence, different numbers of CPUs. Since the program's tasks are performed independently of each other, each available CPU can run its own copy of BLAST queries, resulting in reduced interference between processes and leading to a highly scalable solution.