Design of Organic Structure-Directing Agents for the Controlled Synthesis of Zeolites for Use in Carbon Dioxide/Methane Membrane Separations.

One strategy to mitigate global warming is carbon capture and sequestration. Membrane separation is one promising approach to separation of CO2 from feed streams. Here we report the investigation of four zeolites that have been predicted to be effective at separating CO2 from methane, but which have not to date been synthesized experimentally as membranes. Using an in silico de novo design procedure, we identify organic structure-directing agents (OSDAs) that are predicted to aid the synthesis of these zeolites. Using a genetic algorithm approach, we designed OSDAs for zeolites for which no purely siliceous form is known, and we also designed OSDAs for predicted zeolites. Stabilization energies of the best OSDAs designed for the zeolites GIS, ABW, and predicted zeolite 8198030 lie within -8 to -12 kJ/(mol Si), in the range of values for other known OSDAs. Stabilization energies of the OSDAs designed for predicted zeolite 8186909 are -16 kJ/(mol Si), comparable to the best known OSDAs for any zeolite. The OSDAs reported here may lead to zeolites that could enable a practical separation of CO2 from methane.

Nanoporous materials with predicted zeolite topologies.

An increasing number of newly synthesized materials have been found to be previously present in databases of predicted porous materials. This has been observed not only for zeolites, but also for other inorganic materials and for MOFs. We here quantify the number of synthesized zeolites that are present in a large database of predicted zeolite structures as well as the number of other inorganic crystals and MOFs present in this same database. We find a significant number of real materials are in this predicted database of zeolite-like structures. These results suggest that many other predicted structures in this database may be suitable targets for designer materials synthesis.

Design of organic structure directing agents to guide the synthesis of zeolites for the separation of ethylene-ethane mixtures.

Industrial production of ethylene entails a costly separation from the ethane by-product, and this separation is the dominant consumer of energy in the process. Zeolites have been proposed as a next generation material for this separation process, and a molecular screen of all known zeolites has revealed several promising candidate materials. None of the identified materials has yet been synthesized in the all-silica form evaluated in the screen. We here design organic structure directing agents (OSDAs) for four of the zeolites with the best predicted separation performance, two that are ethylene selective and two that are ethane selective. The designed OSDAs may enable the synthesis of these zeolites for more energy efficient separation of ethylene and ethane.

Modular epitope binding predicts influenza quasispecies dominance and vaccine effectiveness: Application to 2018/19 season.

The modular binding sites on the influenza A(H3N2) hemagglutinin protein are under significant pressure to acquire mutations in order to evade human antibody recognition. Analysis of these hemagglutinin epitopes in the strains circulating during 2017/18 and early 2018/19 identified the emergence of a new antigenic cluster that has grown from 4% of circulating strains to 11%. We regressed our module-based antigenic distance, pepitope, with A(H3N2) vaccine effectiveness from recent studies conducted by the US Centers for Disease Control and Prevention (r2 = 0.92), and we used this to estimate that the 2018/19 vaccines will protect against most circulating A(H3N2) strains. The pEpitope model is useful for A(H3N2) influenza vaccine virus selection and development, and it has the potential to aid national or regional regulatory authorities in making geographically localized decisions.

Machine-learning approach to the design of OSDAs for zeolite beta.

We report a machine-learning strategy for design of organic structure directing agents (OSDAs) for zeolite beta. We use machine learning to replace a computationally expensive molecular dynamics evaluation of the stabilization energy of the OSDA inside zeolite beta with a neural network prediction. We train the neural network on 4,781 candidate OSDAs, spanning a range of stabilization energies. We find that the stabilization energies predicted by the neural network are highly correlated with the molecular dynamics computations. We further find that the evolutionary design algorithm samples the space of chemically feasible OSDAs thoroughly. In total, we find 469 OSDAs with verified stabilization energies below -17 kJ/(mol Si), comparable to or better than known OSDAs for zeolite beta, and greatly expanding our previous list of 152 such predicted OSDAs. We expect that these OSDAs will lead to syntheses of zeolite beta.

Design of organic structure directing agents to control the synthesis of zeolites for carbon capture and storage.

We have de novo designed organic structure directing agents (OSDAs) for zeolites that have been predicted to be effective materials for carbon capture and storage. The zeolites were selected for their reduced parasitic energy when used as CO2 adsorbants in a pressure-temperature swing process in coal- or natural gas-fired power plants. Synthetically accessible OSDAs were designed for five known and two theoretical frameworks.

Corrigendum: Static and Dynamic Measures of Human Brain Connectivity Predict Complementary Aspects of Human Cognitive Performance.

[This corrects the article DOI: 10.3389/fnhum.2017.00420.].

Analysis of Hierarchical Organization in Gene Expression Networks Reveals Underlying Principles of Collective Tumor Cell Dissemination and Metastatic Aggressiveness of Inflammatory Breast Cancer.

Clusters of circulating tumor cells (CTCs), despite being rare, may account for more than 90% of metastases. Cells in these clusters do not undergo a complete epithelial-to-mesenchymal transition (EMT), but retain some epithelial traits as compared to individually disseminating tumor cells. Determinants of single cell dissemination versus collective dissemination remain elusive. Inflammatory breast cancer (IBC), a highly aggressive breast cancer subtype that chiefly metastasizes via CTC clusters, is a promising model for studying mechanisms of collective tumor cell dissemination. Previous studies, motivated by a theory that suggests physical systems with hierarchical organization tend to be more adaptable, have found that the expression of metastasis-associated genes is more hierarchically organized in cases of successful metastases. Here, we used the cophenetic correlation coefficient (CCC) to quantify the hierarchical organization in the expression of two distinct gene sets, collective dissemination-associated genes and IBC-associated genes, in cancer cell lines and in tumor samples from breast cancer patients. Hypothesizing that a higher CCC for collective dissemination-associated genes and for IBC-associated genes would be associated with retention of epithelial traits enabling collective dissemination and with worse disease progression in breast cancer patients, we evaluated the correlation of CCC with different phenotypic groups. The CCC of both the abovementioned gene sets, the collective dissemination-associated genes and the IBC-associated genes, was higher in (a) epithelial cell lines as compared to mesenchymal cell lines and (b) tumor samples from IBC patients as compared to samples from non-IBC breast cancer patients. A higher CCC of both gene sets was also correlated with a higher rate of metastatic relapse in breast cancer patients. In contrast, neither the levels of CDH1 gene expression nor gene set enrichment analysis (GSEA) of the abovementioned gene sets could provide similar insights. These results suggest that retention of some epithelial traits in disseminating tumor cells as IBC progresses promotes successful breast cancer metastasis. The CCC provides additional information regarding the organizational complexity of gene expression in comparison to GSEA. We have shown that the CCC may be a useful metric for investigating the collective dissemination phenotype and a prognostic factor for IBC.

The Standard Genetic Code Facilitates Exploration of the Space of Functional Nucleotide Sequences.

The standard genetic code is well known to be optimized for minimizing the phenotypic effects of single-nucleotide substitutions, a property that was likely selected for during the emergence of a universal code. Given the fitness advantage afforded by high standing genetic diversity in a population in a dynamic environment, it is possible that selection to explore a large fraction of the space of functional proteins also occurred. To determine whether selection for such a property played a role during the emergence of the nearly universal standard genetic code, we investigated the number of functional variants of the Escherichia coli PhoQ protein explored at different time scales under translation using different genetic codes. We found that the standard genetic code is highly optimal for exploring a large fraction of the space of functional PhoQ variants at intermediate time scales as compared to random codes. Environmental changes, in response to which genetic diversity in a population provides a fitness advantage, are likely to have occurred at these intermediate time scales. Our results indicate that the ability of the standard code to explore a large fraction of the space of functional sequence variants arises from a balance between robustness and flexibility and is largely independent of the property of the standard code to minimize the phenotypic effects of mutations. We propose that selection to explore a large fraction of the functional sequence space while minimizing the phenotypic effects of mutations contributed toward the emergence of the standard code as the universal genetic code.

Predicting Influenza H3N2 Vaccine Efficacy From Evolution of the Dominant Epitope.

We predict vaccine efficacy with a measure of antigenic distance between influenza A(H3N2) and vaccine viruses based on amino acid substitutions in the dominant epitope. In 2016-2017, our model predicts 19% efficacy compared with 20% observed. This tool assists candidate vaccine selection by predicting human protection against circulating strains.

The physicist's guide to one of biotechnology's hottest new topics: CRISPR-Cas.

Clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated proteins (Cas) constitute a multi-functional, constantly evolving immune system in bacteria and archaea cells. A heritable, molecular memory is generated of phage, plasmids, or other mobile genetic elements that attempt to attack the cell. This memory is used to recognize and interfere with subsequent invasions from the same genetic elements. This versatile prokaryotic tool has also been used to advance applications in biotechnology. Here we review a large body of CRISPR-Cas research to explore themes of evolution and selection, population dynamics, horizontal gene transfer, specific and cross-reactive interactions, cost and regulation, non-immunological CRISPR functions that boost host cell robustness, as well as applicable mechanisms for efficient and specific genetic engineering. We offer future directions that can be addressed by the physics community. Physical understanding of the CRISPR-Cas system will advance uses in biotechnology, such as developing cell lines and animal models, cell labeling and information storage, combatting antibiotic resistance, and human therapeutics.

Modularity of the metabolic gene network as a prognostic biomarker for hepatocellular carcinoma.

Abnormal metabolism is an emerging hallmark of cancer. Cancer cells utilize both aerobic glycolysis and oxidative phosphorylation (OXPHOS) for energy production and biomass synthesis. Understanding the metabolic reprogramming in cancer can help design therapies to target metabolism and thereby to improve prognosis. We have previously argued that more malignant tumors are usually characterized by a more modular expression pattern of cancer-associated genes. In this work, we analyzed the expression patterns of metabolism genes in terms of modularity for 371 hepatocellular carcinoma (HCC) samples from the Cancer Genome Atlas (TCGA). We found that higher modularity significantly correlated with glycolytic phenotype, later tumor stages, higher metastatic potential, and cancer recurrence, all of which contributed to poorer prognosis. Among patients with recurred tumors, we found the correlation of higher modularity with worse prognosis during early to mid-progression. Furthermore, we developed metrics to calculate individual modularity, which was shown to be predictive of cancer recurrence and patients' survival and therefore may serve as a prognostic biomarker. Our overall conclusion is that more aggressive HCC tumors, as judged by decreased host survival probability, had more modular expression patterns of metabolic genes. These results may be used to identify cancer driver genes and for drug design.

Single molecule sequencing of the M13 virus genome without amplification.

Next generation sequencing (NGS) has revolutionized life sciences research. However, GC bias and costly, time-intensive library preparation make NGS an ill fit for increasing sequencing demands in the clinic. A new class of third-generation sequencing platforms has arrived to meet this need, capable of directly measuring DNA and RNA sequences at the single-molecule level without amplification. Here, we use the new GenoCare single-molecule sequencing platform from Direct Genomics to sequence the genome of the M13 virus. Our platform detects single-molecule fluorescence by total internal reflection microscopy, with sequencing-by-synthesis chemistry. We sequenced the genome of M13 to a depth of 316x, with 100% coverage. We determined a consensus sequence accuracy of 100%. In contrast to GC bias inherent to NGS results, we demonstrated that our single-molecule sequencing method yields minimal GC bias.

Static and Dynamic Measures of Human Brain Connectivity Predict Complementary Aspects of Human Cognitive Performance.

In cognitive network neuroscience, the connectivity and community structure of the brain network is related to measures of cognitive performance, like attention and memory. Research in this emerging discipline has largely focused on two measures of connectivity-modularity and flexibility-which, for the most part, have been examined in isolation. The current project investigates the relationship between these two measures of connectivity and how they make separable contribution to predicting individual differences in performance on cognitive tasks. Using resting state fMRI data from 52 young adults, we show that flexibility and modularity are highly negatively correlated. We use a Brodmann parcellation of the fMRI data and a sliding window approach for calculation of the flexibility. We also demonstrate that flexibility and modularity make unique contributions to explain task performance, with a clear result showing that modularity, not flexibility, predicts performance for simple tasks and that flexibility plays a greater role in predicting performance on complex tasks that require cognitive control and executive functioning. The theory and results presented here allow for stronger links between measures of brain network connectivity and cognitive processes.

Brain Modularity Mediates the Relation between Task Complexity and Performance.

Recent work in cognitive neuroscience has focused on analyzing the brain as a network, rather than as a collection of independent regions. Prior studies taking this approach have found that individual differences in the degree of modularity of the brain network relate to performance on cognitive tasks. However, inconsistent results concerning the direction of this relationship have been obtained, with some tasks showing better performance as modularity increases and other tasks showing worse performance. A recent theoretical model [Chen, M., & Deem, M. W. 2015. Development of modularity in the neural activity of children's brains. Physical Biology, 12, 016009] suggests that these inconsistencies may be explained on the grounds that high-modularity networks favor performance on simple tasks whereas low-modularity networks favor performance on more complex tasks. The current study tests these predictions by relating modularity from resting-state fMRI to performance on a set of simple and complex behavioral tasks. Complex and simple tasks were defined on the basis of whether they did or did not draw on executive attention. Consistent with predictions, we found a negative correlation between individuals' modularity and their performance on a composite measure combining scores from the complex tasks but a positive correlation with performance on a composite measure combining scores from the simple tasks. These results and theory presented here provide a framework for linking measures of whole-brain organization from network neuroscience to cognitive processing.

Enantiomerically enriched, polycrystalline molecular sieves.

Zeolite and zeolite-like molecular sieves are being used in a large number of applications such as adsorption and catalysis. Achievement of the long-standing goal of creating a chiral, polycrystalline molecular sieve with bulk enantioenrichment would enable these materials to perform enantioselective functions. Here, we report the synthesis of enantiomerically enriched samples of a molecular sieve. Enantiopure organic structure directing agents are designed with the assistance of computational methods and used to synthesize enantioenriched, polycrystalline molecular sieve samples of either enantiomer. Computational results correctly predicted which enantiomer is obtained, and enantiomeric enrichment is proven by high-resolution transmission electron microscopy. The enantioenriched and racemic samples of the molecular sieves are tested as adsorbents and heterogeneous catalysts. The enantioenriched molecular sieves show enantioselectivity for the ring opening reaction of epoxides and enantioselective adsorption of 2-butanol (the R enantiomer of the molecular sieve shows opposite and approximately equal enantioselectivity compared with the S enantiomer of the molecular sieve, whereas the racemic sample of the molecular sieve shows no enantioselectivity).

Non-classical phase diagram for virus bacterial coevolution mediated by clustered regularly interspaced short palindromic repeats.

CRISPR is a newly discovered prokaryotic immune system. Bacteria and archaea with this system incorporate genetic material from invading viruses into their genomes, providing protection against future infection by similar viruses. The condition for coexistence of prokaryots and viruses is an interesting problem in evolutionary biology. In this work, we show an intriguing phase diagram of the virus extinction probability, which is more complex than that of the classical predator-prey model. As the CRISPR incorporates genetic material, viruses are under pressure to evolve to escape recognition by CRISPR. When bacteria have a small rate of deleting spacers, a new parameter region in which bacteria and viruses can coexist arises, and it leads to a more complex coexistence patten for bacteria and viruses. For example, when the virus mutation rate is low, the virus extinction probability changes non-montonically with the bacterial exposure rate. The virus and bacteria coevolution not only alters the virus extinction probability, but also changes the bacterial population structure. Additionally, we show that recombination is a successful strategy for viruses to escape from CRISPR recognition when viruses have multiple proto-spacers, providing support for a recombination-mediated escape mechanism suggested experimentally. Finally, we suggest that the re-entrant phase diagram, in which phages can progress through three phases of extinction and two phases of abundance at low spacer deletion rates as a function of exposure rate to bacteria, is an experimentally testable phenomenon.

T Cell Repertoire Diversity Is Decreased in Type 1 Diabetes Patients.

Type 1 diabetes mellitus (T1D) is an immune-mediated disease. The autoreactive T cells in T1D patients attack and destroy their own pancreatic cells. In order to systematically investigate the potential autoreactive T cell receptors (TCRs), we used a high-throughput immune repertoire sequencing technique to profile the spectrum of TCRs in individual T1D patients and controls. We sequenced the T cell repertoire of nine T1D patients, four type 2 diabetes (T2D) patients, and six nondiabetic controls. The diversity of the T cell repertoire in T1D patients was significantly decreased in comparison with T2D patients (P=7.0E-08 for CD4+ T cells, P=1.4E-04 for CD8+ T cells) and nondiabetic controls (P=2.7E-09 for CD4+ T cells, P=7.6E-06 for CD8+ T cells). Moreover, T1D patients had significantly more highly-expanded T cell clones than T2D patients (P=5.2E-06 for CD4+ T cells, P=1.9E-07 for CD8+ T cells) and nondiabetic controls (P=1.7E-07 for CD4+ T cells, P=3.3E-03 for CD8+ T cells). Furthermore, we identified a group of highly-expanded T cell receptor clones that are shared by more than two T1D patients. Although further validation in larger cohorts is needed, our data suggest that T cell receptor diversity measurements may become a valuable tool in investigating diabetes, such as using the diversity as an index to distinguish different types of diabetes.