Influence of Surface Pattern Characteristics on Electrolyte Separations Using Charge-Patterned Mosaic Membranes

Distinct transport mechanisms emerge when nanostructured substrates are patterned with multiple chemistries. For example, charge-patterned mosaic membranes possess surfaces functionalized with discrete domains of both positive and negative charge. These oppositely-charged domains provide pathways for both the cation and anion from a dissolved salt to permeate through the membrane without violating the macroscopic constraint of electroneutrality. Here, by systematically varying the geometry and size of the charge pattern, we elucidate the molecular interactions that promote the transport of salts under the action of pressure-driven flow. For patterns that consist of equivalent areal coverages of positively-charged and negatively-charged domains, the effects of the geometric parameters were encapsulated in a single variable, the interfacial packing density, that quantified the fraction of the membrane surface covered by junctions between oppositely-charged domains. Experimentally, the transport of symmetric electrolytes (i.e., KCl and MgSO4) increased with the value of the interfacial packing density, while the interfacial packing density did not significantly affect the transport of asymmetric electrolytes (i.e., K2SO4 and MgCl2). Simulations of the electrical potential near the membrane surface demonstrate that for symmetric electrolytes, the structural charge heterogeneity reduces the barrier to ion partitioning thereby promoting salt transport through the membranes. For asymmetric electrolytes, the charge heterogeneity skews the local availability of ions from the stoichiometric ratio of the salt thus hindering salt transport. These findings demonstrate the promise of accessing transport mechanisms which could find utility in a diverse range of chemical separations and sensing applications through chemical-patterning of membranes.</p

Novel Omicron Variants Enhance Anchored Recognition of TMEM106B: A New Pathway for SARS-CoV‑2 Cellular Invasion

The recent discovery that TMEM106B serves as a receptor mediating ACE2-independent SARS-CoV-2 entry into cells deserves attention, especially in the background of the frequent emergence of mutant strains. Here, the structure–dynamic features of this novel pathway are dissected deeply. Our investigation revealed that the large loop (RBD@471–491) could anchor TMEM106B, which was then firmly locked by another loop (RBD@444–451). The novel and widely disseminated Omicron variants (BA.2.86/EG.5.1) affect the anchoring recognition of proteins, with BA.2.86 being more likely to impact cells with limited or undetectable ACE2 expression. The large loop of the EG.5.1 variant captures TMEM106B poorly due to impaired electrostatic complementarity. Furthermore, we emphasize that antibody design against these two loops could enhance the protection of ACE2 low-expressing cells according to the alanine scanning mutagenesis of multiple antibodies. We hope this study will provide a novel perspective for the prevention and treatment against this new viral invasion pathway

The box-plot of ZCC indexes in different phyla.

Small rings represent outliers with extreme ZCC indexes. The genomes tending to have large absolute values of ZCC indexes indicate the correlation between AT and GC disparities are widely and obviously exist.</p

Quantitative analysis of correlation between AT and GC biases among bacterial genomes - Fig 4

<p><b>(A) Mean values of GC contents of genomes in each phylum. (B) Average percentages of genes in the leading strand grouped by genomes with the positive and negative ZCC indexes in each phylum.</b> In the histogram (A), mean values of GC content in N-ZCC phyla are entirely larger than those in P-ZCC phyla. The histogram (B) shows that genes are preferred to located in leading strands. Besides, the degree of strand-biased gene distribution (SGD) is generally stronger among genomes with positive ZCC indexes than those with negative ZCC indexes.</p

Percentages of bacterial genomes with the positive and negative ZCC indexes in 11 phyla.

The abbreviated phylum names of each histogram represent the full names of Proteobacteria, Actinobacteria, Bacteroidetes, Cyanobacteria, Spirochaetes, Chlamydiae, Deinococcus-Thermus, Chloroflexi, Firmicutes, Tenericutes and Thermotogae, respectively. We classified phyla Proteobacteria, Actinobacteria, Bacteroidetes, Cyanobacteria, Spirochaetes, Chlamydiae, Deinococcus-Thermus and Chloroflexi as the Negative ZCC phylum group (N-ZCC group), while phyla Firmicutes, Tenericutes and Thermotogae are classified as the Positive ZCC phylum group (P-ZCC group), according to the predominant signs of genomes in the corresponding phylum.</p

Data_Sheet_1_Comprehensive Analysis of Replication Origins in Saccharomyces cerevisiae Genomes.docx

DNA replication initiates from multiple replication origins (ORIs) in eukaryotes. Discovery and characterization of replication origins are essential for a better understanding of the molecular mechanism of DNA replication. In this study, the features of autonomously replicating sequences (ARSs) in Saccharomyces cerevisiae have been comprehensively analyzed as follows. Firstly, we carried out the analysis of the ARSs available in S. cerevisiae S288C. By evaluating the sequence similarity of experimentally established ARSs, we found that 94.32% of ARSs are unique across the whole genome of S. cerevisiae S288C and those with high sequence similarity are prone to locate in subtelomeres. Subsequently, we built a non-redundant dataset with a total of 520 ARSs, which are based on ARSs annotation of S. cerevisiae S288C from SGD and then supplemented with those from OriDB and DeOri databases. We conducted a large-scale comparison of ORIs among the diverse budding yeast strains from a population genomics perspective. We found that 82.7% of ARSs are not only conserved in genomic sequence but also relatively conserved in chromosomal position. The non-conserved ARSs tend to distribute in the subtelomeric regions. We also conducted a pan-genome analysis of ARSs among the S. cerevisiae strains, and a total of 183 core ARSs existing in all yeast strains were determined. We extracted the genes adjacent to replication origins among the 104 yeast strains to examine whether there are differences in their gene functions. The result showed that the genes involved in the initiation of DNA replication, such as orc3, mcm2, mcm4, mcm6, and cdc45, are conservatively located adjacent to the replication origins. Furthermore, we found the genes adjacent to conserved ARSs are significantly enriched in DNA binding, enzyme activity, transportation, and energy, whereas for the genes adjacent to non-conserved ARSs are significantly enriched in response to environmental stress, metabolites biosynthetic process and biosynthesis of antibiotics. In general, we characterized the replication origins from the genome-wide and population genomics perspectives, which would provide new insights into the replication mechanism of S. cerevisiae and facilitate the design of algorithms to identify genome-wide replication origins in yeast.</p

Genome distributions to DE and PC groups in different phyla.

<p>Genome distributions to DE and PC groups in different phyla.</p

The Z-curve disparity figures.

<p>Among different genomes, GC disparity curves always show inverted-V curves, while the shapes of AT disparity curves vary from phyla, ZCC index signs and numerical values.</p

Summary information of ZCC indexes in different phyla.

<p>Summary information of ZCC indexes in different phyla.</p

Ori-Finder: A web-based system for finding s in unannotated bacterial genomes-0

Egrated plot as a PNG figure for the original sequence and (C) the integrated plot as a PNG figure for the rotated sequence, displaying the obtained results, such as general genome information, four disparity curves, distribution of DnaA boxes, locations of putative indicator genes and the predicted region. Note that the coordinate origin of the rotated sequence begins and ends in the maximum of the GC disparity curve.<p><b>Copyright information:</b></p><p>Taken from "Ori-Finder: A web-based system for finding s in unannotated bacterial genomes"</p><p>http://www.biomedcentral.com/1471-2105/9/79</p><p>BMC Bioinformatics 2008;9():79-79.</p><p>Published online 1 Feb 2008</p><p>PMCID:PMC2275245.</p><p></p