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The mechanistic target of rapamycin complex 1 (mTORC1) controls cell growth and metabolism in response to nutrients, energy levels, and growth factors. It contains the atypical kinase mTOR and the RAPTOR subunit that binds to the Tor signalling sequence (TOS) motif of substrates and regulators. mTORC1 is activated by the small GTPase RHEB (Ras homologue enriched in brain) and inhibited by PRAS40. Here we present the 3.0 ångström cryo-electron microscopy structure of mTORC1 and the 3.4 ångström structure of activated RHEB–mTORC1. RHEB binds to mTOR distally from the kinase active site, yet causes a global conformational change that allosterically realigns active-site residues, accelerating catalysis. Cancer-associated hyperactivating mutations map to structural elements that maintain the inactive state, and we provide biochemical evidence that they mimic RHEB relieving auto-inhibition. We also present crystal structures of RAPTOR–TOS motif complexes that define the determinants of TOS recognition, of an mTOR FKBP12–rapamycin-binding (FRB) domain–substrate complex that establishes a second substrate-recruitment mechanism, and of a truncated mTOR–PRAS40 complex that reveals PRAS40 inhibits both substrate-recruitment sites. These findings help explain how mTORC1 selects its substrates, how its kinase activity is controlled, and how it is activated by cancer-associated mutations. The cryo-electron microscopy and crystal structures of several mTORC1 complexes, and accompanying biochemical analyses, shed light on how mTORC1 is regulated and how cancer mutations lead to its hyperactivation. mTORC1 structures shed light on function Mechanistic target of rapamycin complex 1 (mTORC1) is a protein complex that is important for regulating cell growth and homeostasis and is aberrantly regulated in many diseases such as cancer, diabetes and neurodegeneration. Here, Nikola Pavletich and colleagues use cryo-electron microscopy and crystallography to determine the structures of several mTORC1 complexes. The structures and accompanying biochemical analysis provide mechanistic insights into how mTORC1 is allosterically activated by the GTPase RHEB, how it is inhibited by PRAS40, and how it recognizes substrates via the TOS motif. The findings also shed light on how cancer mutations lead to hyperactivation of mTORC1.

The strand-exchange reaction is central to homologous recombination. It is catalysed by the RecA family of ATPases, which form a helical filament with single-stranded DNA (ssDNA) and ATP. This filament binds to a donor double-stranded DNA (dsDNA) to form synaptic filaments, which search for homology and then catalyse the exchange of the complementary strand, forming either a new heteroduplex or—if homology is limited—a D-loop 1 , 2 . How synaptic filaments form, search for homology and catalyse strand exchange is poorly understood. Here we report the cryo-electron microscopy analysis of synaptic mini-filaments with both non-complementary and partially complementary dsDNA, and structures of RecA–D-loop complexes containing a 10- or a 12-base-pair heteroduplex. The C-terminal domain of RecA binds to dsDNA and directs it to the RecA L2 loop, which inserts into and opens up the duplex. The opening propagates through RecA sequestering the homologous strand at a secondary DNA-binding site, which frees the complementary strand to sample pairing with the ssDNA. At each RecA step, there is a roughly 20% probability that duplex opening will terminate and the as-yet-unopened dsDNA portion will bind to another C-terminal domain. Homology suppresses this process, through the cooperation of heteroduplex pairing with the binding of ssDNA to the secondary site, to extend dsDNA opening. This mechanism locally limits the length of ssDNA sampled for pairing if homology is not encountered, and could allow for the formation of multiple, widely separated synapses on the donor dsDNA, which would increase the likelihood of encountering homology. These findings provide key mechanistic insights into homologous recombination. Cryo-electron microscopy structures of the bacterial recombination protein RecA with DNA, and of RecA–D-loop complexes, provide insights into the double-stranded DNA opening, homology search and strand-exchange processes of homologous recombination.

The RecA family of ATPases mediates homologous recombination, a reaction essential for maintaining genomic integrity and for generating genetic diversity. RecA, ATP and single-stranded DNA (ssDNA) form a helical filament that binds to double-stranded DNA (dsDNA), searches for homology, and then catalyses the exchange of the complementary strand, producing a new heteroduplex. Here we have solved the crystal structures of the Escherichia coli RecA–ssDNA and RecA–heteroduplex filaments. They show that ssDNA and ATP bind to RecA–RecA interfaces cooperatively, explaining the ATP dependency of DNA binding. The ATP γ-phosphate is sensed across the RecA–RecA interface by two lysine residues that also stimulate ATP hydrolysis, providing a mechanism for DNA release. The DNA is underwound and stretched globally, but locally it adopts a B-DNA-like conformation that restricts the homology search to Watson–Crick-type base pairing. The complementary strand interacts primarily through base pairing, making heteroduplex formation strictly dependent on complementarity. The underwound, stretched filament conformation probably evolved to destabilize the donor duplex, freeing the complementary strand for homology sampling. DNA repair: Fair exchange One way of reversing DNA damage involves homologous pairing of an undamaged DNA with a damaged DNA, a process mediated by a class of proteins known as strand-exchange proteins. Chen et al . now present a 'holy grail' of the DNA repair field: the structure of the E. coli strand-exchange protein, RecA, bound to one and two DNA molecules. More than a dozen crystal structures of bacterial, archaebacterial and eukaryotic RecA-family members have been determined previously, but because RecA forms a filament on DNA, no crystal structures of RecA–DNA complexes were available. The new study avoids the problem of crystallizing a polymer by engineering RecA–DNA complexes that represent finite segments of the filament. DNA damage can be reversed by the homologous pairing of an undamaged DNA with a damaged DNA. Pavletich and colleagues report the structure of the E. coli strand-exchange protein, RecA, bound to DNA, offering new insight into the process by which homologous DNAs are paired.

Photocatalyst-loaded hydrogels show great potential for effective degradation of pollutants. In this work, a novel composite photocatalyst preparation method is proposed to prepare a gel composite photocatalyst using hydrothermal reaction and in situ synthesis method for rapid degradation and removal of heavy metal Cr present in water bodies. Sodium alginate was chosen as the hydrogel carrier of photocatalytic material to improve the homogeneity and stability of the composite photocatalyst. The H2O2 generated from the hydrolysis of aluminum chloride during the reaction process can accelerate the photoreaction, so this experiment has high efficiency of autocatalysis. The experimental results showed that the degradation and removal of heavy metal Cr could reach 75% under the conditions of dosage of 0.08 g, pH 4, temperature 35 °C, initial concentration of 40 mg/L, and light intensity of 15 W. This work provides a low-cost and convenient method for constructing novel hydrogel carriers with high photocatalytic stability and efficiency.

Community detection in dynamic networks is one of the most challenging tasks in the field of network analysis. In general, networks often evolve smoothly between successive snapshots. Therefore, the community structure detected in each snapshot should not only be of high quality but also reflect the smoothness of the variations compared with the previous snapshot. In this paper, we propose a novel incremental community-detection method named Spiderweb, which detects the community structure in each snapshot by simulating the evolution of spiderwebs. We categorize the evolutionary events of the network into three types, and then address the changed nodes and edges according to three corresponding evolution rules. In this procedure, some nodes are assigned to proper communities. Then, we construct a new subgraph for the unclassified changed nodes, and detect its communities efficiently. Finally, we merge some communities to obtain the resulting community structure. We conduct extensive experiments on both artificial networks and real-world networks to test the proposed method, and the experimental results show the superiority of the proposed method over some state-of-the-art algorithms in terms of both the quality and the temporal smoothness of the detected community structures. The proposed method provides us with a stable and promising solution for the problem of community detection in dynamic networks.

PurposeThis study aims to examine the impact of migration growth on environmental outcomes and local governance and assess how well the existing local municipal governance has responded to the environmental impact of increased migration influx in Ontario, Canada using the annual data during 2012–2021.Design/methodology/approachThis study used the grey relational analysis (GRA) to examine the correlation degree between migrant growth, environmental outcomes and local governance, used coupling coordination degree model (CCDM) to access to what extent the existing local governance systems have responded to the environmental impact of immigrant growth.FindingsResults show that higher immigrant populations are associated with worse environmental outcomes and the need for more municipal environmental investment and service. The present local municipal environmental service in Ontario lags behind in response to the environmental impacts of increased migration. Good local governance practices and environmental services are required to improve the environmental adaptation capacity of host countries to migrant influx.Originality/valueClimate change has been regarded as an important driver of internal and international human migration. The mass influxes of migrants will threaten cities’ environmental quality and put considerable pressure on municipal services. This study provides empirical evidence for Ontario’s municipal environmental governance and relevant authorities on how to deal with the environmental impact of increased migration and contributes to call the attention of other countries to the urban environmental pressure caused by migration influx due to the changing climate world wide.

Peritoneal dialysis is a widely used method for treating kidney failure. However, over time, the peritoneal structure and function can deteriorate, leading to the failure of this therapy. This deterioration is primarily caused by infectious and sterile inflammation. Sterile inflammation, which is inflammation without infection, is particularly concerning as it can be subtle and often goes unnoticed. The onset of sterile inflammation involves various pathological processes. Peritoneal cells detect signals that promote inflammation and release substances that attract immune cells from the bloodstream. These immune cells contribute to the initiation and escalation of the inflammatory response. The existing literature extensively covers the involvement of different cell types in the sterile inflammation, including mesothelial cells, fibroblasts, endothelial cells, and adipocytes, as well as immune cells such as macrophages, lymphocytes, and mast cells. These cells work together to promote the occurrence and progression of sterile inflammation, although the exact mechanisms are not fully understood. This review aims to provide a comprehensive overview of the signals from both stromal cells and components of immune system, as well as the reciprocal interactions between cellular components, during the initiation of sterile inflammation. By understanding the cellular and molecular mechanisms underlying sterile inflammation, we may potentially develop therapeutic interventions to counteract peritoneal membrane damage and restore normal function.

Community structure is one of the common characteristics of complex networks. In the practical work, we have noted that every node and its most similar node tend to be assigned to the same community and that two communities are often merged together if there exist relatively more edges between them. Inspired by these observations, we present a community-detection method named NSCLS in this paper. Firstly, we calculate the similarities between any node and its first- and second-order neighbors in a novel way and then extract the initial communities from the network by allocating every node and its most similar node to the same community. In this procedure, some nodes located at the community boundaries might be classified in the incorrect communities. To make a redemption, we adjust their community affiliations by reclassifying each of them into the community in which most of its neighbors have been. After that, there might exist relatively larger number of edges between some communities. Therefore, we consider to merge such communities to improve the quality of the final community structure further. To this end, we calculate the link strength between communities and merge some densely connected communities based on this index. We evaluate NSCLS on both some synthetic networks and some real-world networks and show that it can detect high-quality community structures from various networks, and its results are much better than the counterparts of comparison algorithms.

BackgroundAmong patients with peritoneal dialysis-associated peritonitis (PDAP), It has been regarded as an indicator of deterioration of clinical condition that peritoneal dialysis effluent leukocyte count (PDELC) cannot be restored to normal after initial antibiotic therapy. However, the precise relationship between PDELC on day 5 and the clinical outcomes of PDAP episodes remains uncertain.AimsTo explore the association between PDELC on day 5 and clinical outcomes of PDAP episodes.MethodsThis retrospective study was based on the medical chart database of the Affiliated Hospital of Guangdong Medical University. Multivariable regressions were used to evaluate the association between PDELC on day 5 and 60-day mortality, half-year mortality, treatment failure, and the length of stay in hospital with adjustment for confounding factors.ResultsA total of 549 PDAP episodes in 309 patients were enrolled. The total 60-day mortality, half-year mortality, and rate of treatment failure was 6.0%, 9.8%, and 14.2%, respectively. Compared with patients with normal PDELC, those with PDELC ≥2000 × 106/L on day 5 had significantly higher 60-day mortality (31.1% vs 2.7%), half-year mortality (35.6% vs 5.6%), and treatment failure (46.7% vs 5.7%). In multivariate adjusted regression, the ORs (95%CI) were 6.99 (2.33, 20.92; p = 0.001), 4.97(1.93, 12.77; p = 0.001), and 5.77 (2.07, 16.11; p = 0.001), respectively. Patients with PDELC were 100–2000 × 106/L on day 5 had a higher rate of treatment failure than those with normal PDELC (26.9% vs 5.7%) (OR = 3.03, 95%CI 1.42, 6.46; p = 0.004). After sensitivity analysis, the results remained robust.ConclusionsAmong patients with PDAP, increased PDELC on day 5 was associated with a greater risk of 60-day mortality, half-year mortality, and treatment failure.

Community structures can reveal organizations and functional properties of complex networks; hence, detecting communities from networks is of great importance. With the surge of large networks in recent years, the efficiency of community detection is demanded critically. Therefore, many local methods have emerged. In this paper, we propose a node similarity based community detection method, which is also a local one consisted of two phases. In the first phase, we first take out the node with the largest degree from the network to take it as an exemplar of the first community and insert its most similar neighbor node into the community as well. Then, the one with the largest degree in the remainder nodes is selected; if its most similar neighbor has not been classified into any community yet, we create a new community for the selected node and its most similar neighbor. Otherwise, if its most similar neighbor has been classified into a certain community, we insert the selected node into the community to which its most similar neighbor belongs. This procedure is repeated until every node in the network is assigned to a community; at that time, we obtain a series of preliminary communities. However, some of them might be too small or too sparse; edges connecting to outside of them might go beyond the ones inside them. Keeping them as the final ones will lead to a low-quality community structure. Therefore, we merge some of them in an efficient approach in the second phase to improve the quality of the resulting community structure. To testify the performance of our proposed method, extensive experiments are performed on both some artificial networks and some real-world networks. The results show that the proposed method can detect high-quality community structures from networks steadily and efficiently and outperform the comparison algorithms significantly.