Nanoscale probing of electron-regulated structural transitions in silk proteins by near-field IR imaging and nano-spectroscopy

Silk protein fibres produced by silkworms and spiders are renowned for their unparalleled mechanical strength and extensibility arising from their high-β-sheet crystal contents as natural materials. Investigation of β-sheet-oriented conformational transitions in silk proteins at the nanoscale remains a challenge using conventional imaging techniques given their limitations in chemical sensitivity or limited spatial resolution. Here, we report on electron-regulated nanoscale polymorphic transitions in silk proteins revealed by near-field infrared imaging and nano-spectroscopy at resolutions approaching the molecular level. The ability to locally probe nanoscale protein structural transitions combined with nanometre-precision electron-beam lithography offers us the capability to finely control the structure of silk proteins in two and three dimensions. Our work paves the way for unlocking essential nanoscopic protein structures and critical conditions for electron-induced conformational transitions, offering new rules to design protein-based nanoarchitectures.National Science Foundation (U.S.) (1563422)National Science Foundation (U.S.) (1562915

Unraveling the immunological landscape in acute pancreatitis progression to sepsis: insights from a Mendelian randomization study on immune cell traits

BackgroundAcute pancreatitis (AP) is a severe digestive system disorder with a significant risk of progressing to sepsis, a major cause of mortality. Unraveling the immunological pathways in AP is essential for developing effective treatments, particularly understanding the role of specific immune cell traits in this progression.MethodsEmploying a bidirectional two-sample Mendelian Randomization (MR) approach, this study first examined the causal relationship between AP and 731 immune cell traits to identify those significantly associated with AP. Subsequently, we explored the causal associations between 731 immune cell traits and sepsis. The analysis utilized extensive genome-wide association studies (GWAS) summary datasets, with a focus on identifying common immune cell traits with statistically significant causal associations between AP and sepsis.ResultsOur investigation identified 44 immune cell traits unidirectionally associated with AP and 36 traits unidirectionally associated with sepsis. Among these, CD127 on CD28+ CD45RA- CD8+ T cells emerged as a common mediator, accounting for 5.296% of the increased risk of sepsis in AP patients. This finding highlights the significant role of specific memory CD8+ T cells in the pathophysiology of AP and its progression to sepsis.ConclusionThis study elucidates the critical role of specific immune cell traits, particularly CD127hi memory CD8+ T cells, in the progression of AP to sepsis. Our findings provide a foundation for future research into targeted immune-modulatory therapies, potentially improving patient outcomes in AP-related sepsis and offering new insights into the complex immunological dynamics of this condition

Interpreting Deep Learning-Based Networking Systems

While many deep learning (DL)-based networking systems have demonstrated superior performance, the underlying Deep Neural Networks (DNNs) remain blackboxes and stay uninterpretable for network operators. The lack of interpretability makes DL-based networking systems prohibitive to deploy in practice. In this paper, we propose Metis, a framework that provides interpretability for two general categories of networking problems spanning local and global control. Accordingly, Metis introduces two different interpretation methods based on decision tree and hypergraph, where it converts DNN policies to interpretable rule-based controllers and highlight critical components based on analysis over hypergraph. We evaluate Metis over several state-of-the-art DL-based networking systems and show that Metis provides human-readable interpretations while preserving nearly no degradation in performance. We further present four concrete use cases of Metis, showcasing how Metis helps network operators to design, debug, deploy, and ad-hoc adjust DL-based networking systems.Comment: To appear at ACM SIGCOMM 202

Characterization and Evolution of microRNA Genes Derived from Repetitive Elements and Duplication Events in Plants

MicroRNAs (miRNAs) are a major class of small non-coding RNAs that act as negative regulators at the post-transcriptional level in animals and plants. In this study, all known miRNAs in four plant species (Arabidopsis thaliana, Populus trichocarpa, Oryza sativa and Sorghum bicolor) have been analyzed, using a combination of computational and comparative genomic approaches, to systematically identify and characterize the miRNAs that were derived from repetitive elements and duplication events. The study provides a complete mapping, at the genome scale, of all the miRNAs found on repetitive elements in the four test plant species. Significant differences between repetitive element-related miRNAs and non-repeat-derived miRNAs were observed for many characteristics, including their location in protein-coding and intergenic regions in genomes, their conservation in plant species, sequence length of their hairpin precursors, base composition of their hairpin precursors and the minimum free energy of their hairpin structures. Further analysis showed that a considerable number of miRNA families in the four test plant species arose from either tandem duplication events, segmental duplication events or a combination of the two. However, comparative analysis suggested that the contribution made by these two duplication events differed greatly between the perennial tree species tested and the other three annual species. The expansion of miRNA families in A. thaliana, O. sativa and S. bicolor are more likely to occur as a result of tandem duplication events than from segmental duplications. In contrast, genomic segmental duplications contributed significantly more to the expansion of miRNA families in P. trichocarpa than did tandem duplication events. Taken together, this study has successfully characterized miRNAs derived from repetitive elements and duplication events at the genome scale and provides comprehensive knowledge and deeper insight into the origins and evolution of miRNAs in plants

ECMpy, a Simplified Workflow for Constructing Enzymatic Constrained Metabolic Network Model

Genome-scale metabolic models (GEMs) have been widely used for the phenotypic prediction of microorganisms. However, the lack of other constraints in the stoichiometric model often leads to a large metabolic solution space being inaccessible. Inspired by previous studies that take an allocation of macromolecule resources into account, we developed a simplified Python-based workflow for constructing enzymatic constrained metabolic network model (ECMpy) and constructed an enzyme-constrained model for Escherichia coli (eciML1515) by directly adding a total enzyme amount constraint in the latest version of GEM for E. coli (iML1515), considering the protein subunit composition in the reaction, and automated calibration of enzyme kinetic parameters. Using eciML1515, we predicted the overflow metabolism of E. coli and revealed that redox balance was the key reason for the difference between E. coli and Saccharomyces cerevisiae in overflow metabolism. The growth rate predictions on 24 single-carbon sources were improved significantly when compared with other enzyme-constrained models of E. coli. Finally, we revealed the tradeoff between enzyme usage efficiency and biomass yield by exploring the metabolic behaviours under different substrate consumption rates. Enzyme-constrained models can improve simulation accuracy and thus can predict cellular phenotypes under various genetic perturbations more precisely, providing reliable guidance for metabolic engineering

The number of segmental duplications and flanking conserved protein-coding genes within each block.

a<p>The number of observed duplicated blocks, containing miRNAs within the same family or within the different family, that have at least two flanking protein-coding gene surrounding miRNAs.</p>b<p>The percentage of observed duplicated blocks in each test species with respect to the total number of duplicated blocks.</p><p>ath: <i>A. thaliana</i>; ptc: <i>P. trichocarpa</i>; osa: <i>O. sativa</i>; sbi: <i>S. Bicolor.</i></p

Characterization and variation between RrmiRNAs and NRrmiRNAs.

<p>(A) The distribution of miRNA hairpin precursor sequence lengths in RrmiRNAs and NRrmiRNAs. (B) The G-C content in miRNA hairpin precursor sequences in RrmiRNAs and NRrmiRNAs (C) The MFEs for miRNA hairpin precursors in RrmiRNAs and NRrmiRNAs.</p

The overview for origins and expansion of miRNAs derived from duplicated events in the four test plant species.

<p>ath: <i>A. thaliana</i>; ptc: <i>P. trichocarpa</i>; osa: <i>O. sativa</i>; sbi: <i>S. bicolor</i>.</p

The results of conservation analysis for miRNA duplicated blocks in the four test plant species.

<p>ath: <i>A. thaliana</i>; ptc: <i>P. trichocarpa</i>; osa: <i>O. sativa</i>; sbi: <i>S. bicolor</i>.</p

The overall percentage and copy number data for tandemly duplicated miRNAs in the four test plant species.

<p>(A) The percentage of miRNA families and miRNAs arising by tandem duplication with respect to the total number of miRNA families containing at least one miRNA, and the number of members of the corresponding miRNA family, respectively, for each species tested. (B) The average miRNA copy number in tandemly duplicated regions. (C) The percentage distribution of tandemly duplicated miRNAs on the same or opposite strands. (D) The percentage of conserved or species-specific tandemly duplicated miRNAs with respect to the total number of observed tandemly duplicated miRNAs for each species tested.</p