Large-scale integration of omics and electronic health records to identify potential risk protein biomarkers and therapeutic drugs for cancer prevention and intervention.

Identifying risk protein targets and their therapeutic drugs is crucial for effective cancer prevention. Here, we conduct integrative and fine-mapping analyses of large genome-wide association studies data for breast, colorectal, lung, ovarian, pancreatic, and prostate cancers, and characterize 710 lead variants independently associated with cancer risk. Through mapping protein quantitative trait loci (pQTL) for these variants using plasma proteomics data from over 75,000 participants, we identify 365 proteins associated with cancer risk. Subsequent colocalization analysis identifies 101 proteins, including 74 not reported in previous studies. We further characterize 36 potential druggable proteins for cancers or other disease indications. Analyzing >3.5 million electronic health records, we uncover five drugs (Haloperidol, Trazodone, Tranexamic Acid, Haloperidol, and Captopril) associated with increased cancer risk and two drugs (Caffeine and Acetazolamide) linked to reduced colorectal cancer risk. This study offers novel insights into therapeutic drugs targeting risk proteins for cancer prevention and intervention.

Innate lymphoid cell-based immunomodulatory hydrogel microspheres containing Cutibacterium acnes extracellular vesicles for the treatment of psoriasis.

Psoriasis is a chronic skin inflammation influenced by dysregulated skin microbiota, with the role of microbiota in psoriasis gaining increasing prominence. Bacterial extracellular vesicles (bEVs) serve as crucial regulators in the interaction between hosts and microbiota. However, the mechanism underlying the therapeutic potential of bEVs from commensal bacteria in psoriasis remains unclear. Here, we investigated the therapeutic role of Cutibacterium acnes (C. acnes)-derived extracellular vesicles (CA-EVs) in psoriasis treatment. To prolong the active duration of CA-EVs, we encapsulated them in gelatin methacrylate (GelMA) to fabricate hydrogel microspheres (CA-EVs@GHM) with sustained release properties. As GelMA degraded, CA-EVs were gradually released, maintaining a high concentration in mouse skin even 96 hours post-treatment. In human keratinocyte cells (HaCaT), CA-EVs@GHM enhanced resistance to Staphylococcus aureus (S. aureus), promoted proliferation and migration of HaCaT cells exposed to S. aureus, and significantly reduced the expression of inflammatory genes such as interleukin (IL)-6 and C-X-C motif chemokine ligand 8 (CXCL8). In vivo, CA-EVs@GHM, more potent than CA-EVs alone, markedly attenuated proinflammatory gene expression, including tumor necrosis factor (TNF), Il6, Il17a, Il22 and Il23a in imiquimod (IMQ)-induced psoriasis-like mice, and restored skin barrier function. 16S rRNA sequencing revealed that CA-EVs@GHM might provide therapeutic effects against psoriasis by restoring microbiota diversity on the back skin of mice, reducing Staphylococcus colonization, and augmenting lipid metabolism. Furthermore, flow cytometry analysis showed that CA-EVs@GHM prevented the conversion of type 2 innate lymphoid cells (ILC2) to type 3 innate lymphoid cells (ILC3) in psoriasis-like mouse skin, reducing the pathogenic ILC3 population and suppressing the secretion of IL-17 and IL-22. In summary, our findings demonstrate that the long-term sustained release of CA-EVs alleviated psoriasis symptoms by controlling the transformation of innate lymphoid cells (ILCs) subgroups and restoring skin microbiota homeostasis, thus offering a promising therapy for psoriasis treatment. STATEMENT OF SIGNIFICANCE: Cutibacterium acnes, which is reduced in psoriasis skin, has been reported to promote skin homeostasis by regulating immune balance. Compared to live bacteria, bacterial extracellular vesicles (bEVs) are less prone to toxicity and safety concerns. bEVs play a pivotal role in maintaining bacterial homeostasis and modulating the immune system. However, bEVs without sustained release materials are unable to function continuously in chronic diseases. Therefore, we utilized hydrogel microspheres to encapsulate Cutibacterium acnes (C. acnes)-derived extracellular vesicles (CA-EVs), enabling long term sustained release. Our findings indicate that, CA-EVs loaded gelatin methacrylate hydrogel microspheres (CA-EVs@GHM) showed superior therapeutic effects in treating psoriasis compared to CA-EVs. CA-EVs@GHM exhibited a more significant regulation of pathological type 3 innate lymphoid cells (ILC3) and skin microbiota, providing a promising approach for microbiota-derived extracellular vesicle therapy in the treatment of skin inflammation.

Moiré Engineering of Spin-Orbit Torque by Twisted WS2 Homobilayers.

Artificial moiré superlattices created by stacking two-dimensional crystals have emerged as a powerful platform with unprecedented material-engineering capabilities. While moiré superlattices have been reported to host a number of novel quantum states, their potential for spintronic applications remains largely unexplored. Here, we demonstrated the effective manipulation of spin-orbit torque (SOT) using moiré superlattices in ferromagnetic devices comprised of twisted WS2/WS2 homobilayer (t-WS2) and CoFe/Pt thin films by altering twisting angle (θ) and gate voltage. Notably, we observed a substantial enhancement of up to 44.5% in SOT conductivity at θ approximately 8.3°. Furthermore, compared to the WS2 monolayer and untwisted WS2/WS2 bilayers, the moiré superlattices in t-WS2 enable a greater gate-voltage tunability of SOT conductivity. We related these results to the generation of interfacial moiré magnetic field by real-space Berry phase in moiré superlattices, which modulates the absorption of the spin-Hall current arising from Pt through magnetic proximity effect. This study highlights the moiré physics as a new building block for designing enhanced spintronic devices. This article is protected by copyright. All rights reserved.

Evaluating performance and applications of sample-wise cell deconvolution methods on human brain transcriptomic data.

Sample-wise deconvolution methods estimate cell-type proportions and gene expressions in bulk tissue samples, yet their performance and biological applications remain unexplored, particularly in human brain transcriptomic data. Here, nine deconvolution methods were evaluated with sample-matched data from bulk tissue RNA sequencing (RNA-seq), single-cell/nuclei (sc/sn) RNA-seq, and immunohistochemistry. A total of 1,130,767 nuclei per cells from 149 adult postmortem brains and 72 organoid samples were used. The results showed the best performance of dtangle for estimating cell proportions and bMIND for estimating sample-wise cell-type gene expressions. For eight brain cell types, 25,273 cell-type eQTLs were identified with deconvoluted expressions (decon-eQTLs). The results showed that decon-eQTLs explained more schizophrenia GWAS heritability than bulk tissue or single-cell eQTLs did alone. Differential gene expressions associated with Alzheimer's disease, schizophrenia, and brain development were also examined using the deconvoluted data. Our findings, which were replicated in bulk tissue and single-cell data, provided insights into the biological applications of deconvoluted data in multiple brain disorders.

Evaluation of Protein Identification and Quantification by the diaPASEF Method on timsTOF SCP.

Accurate and precise quantification is crucial in modern proteomics, particularly in the context of exploring low-amount samples. While the innovative 4D-data-independent acquisition (DIA) quantitative proteomics facilitated by timsTOF mass spectrometers gives enhanced sensitivity and selectivity for protein identification, the diaPASEF (parallel accumulation-serial fragmentation combined with data-independent acquisition) parameters have not been systematically optimized, and a comprehensive evaluation of the quantification is currently lacking. In this study, we conducted a thorough optimization of key parameters on a timsTOF SCP instrument, including sample loading amount (50 ng), ramp/accumulation time (140 ms), isolation window width (20 m/z), and gradient time (60 min). To further improve the identification of proteins in low-amount samples, we utilized different column settings and introduced 0.02% n-dodecyl-ß-d-maltoside (DDM) in the sample reconstitution solution, resulting in a remarkable 19-fold increase in protein identification at the single-cell-equivalent level. Moreover, a comprehensive comparison of protein quantification using a tandem mass tag reporter (TMT-reporter), complement TMT ions (TMTc), and diaPASEF revealed a strong correlation between these methods. Both diaPASEF and TMTc have effectively addressed the issue of ratio compression, highlighting the diaPASEF method's effectiveness in achieving accurate quantification data compared to TMT reporter quantification. Additionally, an in-depth analysis of in-group variation positioned diaPASEF between the TMT-reporter and TMTc methods. Therefore, diaPASEF quantification on the timsTOF SCP instrument emerges as a precise and accurate methodology for quantitative proteomics, especially for samples with small amounts.

Developmental isoform diversity in the human neocortex informs neuropsychiatric risk mechanisms.

RNA splicing is highly prevalent in the brain and has strong links to neuropsychiatric disorders; yet, the role of cell type-specific splicing and transcript-isoform diversity during human brain development has not been systematically investigated. In this work, we leveraged single-molecule long-read sequencing to deeply profile the full-length transcriptome of the germinal zone and cortical plate regions of the developing human neocortex at tissue and single-cell resolution. We identified 214,516 distinct isoforms, of which 72.6% were novel (not previously annotated in Gencode version 33), and uncovered a substantial contribution of transcript-isoform diversity-regulated by RNA binding proteins-in defining cellular identity in the developing neocortex. We leveraged this comprehensive isoform-centric gene annotation to reprioritize thousands of rare de novo risk variants and elucidate genetic risk mechanisms for neuropsychiatric disorders.

Linear ubiquitin chains remodel the proteome and influence the levels of hundreds of regulators in Drosophila.

Ubiquitin controls many cellular processes via its post-translational conjugation onto substrates. Its use is highly variable due to its ability to form poly-ubiquitin with various topologies. Among them, linear chains have emerged as important regulators of immune responses and protein degradation. Previous studies in Drosophila melanogaster found that expression of linear poly-ubiquitin that cannot be dismantled into single moieties leads to their own ubiquitination and degradation or, alternatively, to their conjugation onto proteins. However, it remains largely unknown which proteins are sensitive to linear poly-ubiquitin. To address this question, here we expanded the toolkit to modulate linear chains and conducted ultra-deep coverage proteomics from flies that express non-cleavable, linear chains comprising 2, 4, or 6 moieties. We found that these chains regulate shared and distinct cellular processes in Drosophila by impacting hundreds of proteins. Our results provide key insight into the proteome subsets and cellular pathways that are influenced by linear poly-ubiquitin with distinct lengths and suggest that the ubiquitin system is exceedingly pliable.

Genetic regulation of human brain proteome reveals proteins implicated in psychiatric disorders.

Psychiatric disorders are highly heritable yet polygenic, potentially involving hundreds of risk genes. Genome-wide association studies have identified hundreds of genomic susceptibility loci with susceptibility to psychiatric disorders; however, the contribution of these loci to the underlying psychopathology and etiology remains elusive. Here we generated deep human brain proteomics data by quantifying 11,608 proteins across 268 subjects using 11-plex tandem mass tag coupled with two-dimensional liquid chromatography-tandem mass spectrometry. Our analysis revealed 788 cis-acting protein quantitative trait loci associated with the expression of 883 proteins at a genome-wide false discovery rate <5%. In contrast to expression at the transcript level and complex diseases that are found to be mainly influenced by noncoding variants, we found protein expression level tends to be regulated by non-synonymous variants. We also provided evidence of 76 shared regulatory signals between gene expression and protein abundance. Mediation analysis revealed that for most (88%) of the colocalized genes, the expression levels of their corresponding proteins are regulated by cis-pQTLs via gene transcription. Using summary data-based Mendelian randomization analysis, we identified 4 proteins and 19 genes that are causally associated with schizophrenia. We further integrated multiple omics data with network analysis to prioritize candidate genes for schizophrenia risk loci. Collectively, our findings underscore the potential of proteome-wide linkage analysis in gaining mechanistic insights into the pathogenesis of psychiatric disorders.

A Continuous Non-Invasive Blood Pressure Prediction Method Based on Deep Sparse Residual U-Net Combined with Improved Squeeze and Excitation Skip Connections.

Arterial blood pressure (ABP) serves as a pivotal clinical metric in cardiovascular health assessments, with the precise forecasting of continuous blood pressure assuming a critical role in both preventing and treating cardiovascular diseases. This study proposes a novel continuous non-invasive blood pressure prediction model, DSRUnet, based on deep sparse residual U-net combined with improved SE skip connections, which aim to enhance the accuracy of using photoplethysmography (PPG) signals for continuous blood pressure prediction. The model first introduces a sparse residual connection approach for path contraction and expansion, facilitating richer information fusion and feature expansion to better capture subtle variations in the original PPG signals, thereby enhancing the network's representational capacity and predictive performance and mitigating potential degradation in the network performance. Furthermore, an enhanced SE-GRU module was embedded in the skip connections to model and weight global information using an attention mechanism, capturing the temporal features of the PPG pulse signals through GRU layers to improve the quality of the transferred feature information and reduce redundant feature learning. Finally, a deep supervision mechanism was incorporated into the decoder module to guide the lower-level network to learn effective feature representations, alleviating the problem of gradient vanishing and facilitating effective training of the network. The proposed DSRUnet model was trained and tested on the publicly available UCI-BP dataset, with the average absolute errors for predicting systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean blood pressure (MBP) being 3.36 ± 6.61 mmHg, 2.35 ± 4.54 mmHg, and 2.21 ± 4.36 mmHg, respectively, meeting the standards set by the Association for the Advancement of Medical Instrumentation (AAMI), and achieving Grade A according to the British Hypertension Society (BHS) Standard for SBP and DBP predictions. Through ablation experiments and comparisons with other state-of-the-art methods, the effectiveness of DSRUnet in blood pressure prediction tasks, particularly for SBP, which generally yields poor prediction results, was significantly higher. The experimental results demonstrate that the DSRUnet model can accurately utilize PPG signals for real-time continuous blood pressure prediction and obtain high-quality and high-precision blood pressure prediction waveforms. Due to its non-invasiveness, continuity, and clinical relevance, the model may have significant implications for clinical applications in hospitals and research on wearable devices in daily life.

Aged polystyrene microplastics exacerbate alopecia associated with tight junction injuries and apoptosis via oxidative stress pathway in skin.

Microplastics (MPs) are pervasive pollutants in the natural environment and contribute to increased levels of illness in both animals and humans. However, thespecific impacts of MPs on skin damage and alopeciaare not yet well understood. In this study, we have examined the effects of two types of polystyrene MPs (pristine and aged) on skin and hair follicle damage in mice. UV irradiation changed the chemical and physical properties of the aged MPs, including functional groups, surface roughness, and contact angles. In both in vivo and in vitro experiments, skin and cell injuries related to oxidative stress, apoptosis, tight junctions (TJs), alopecia, mitochondrial dysfunction, and other damages were observed. Mechanistically, MPs and aged MPs can induce TJs damage via the oxidative stress pathway and inhibition of antioxidant-related proteins, and this can lead to alopecia. The regulation of cell apoptosis was also observed, and this is involved in the ROS-mediated mitochondrial signaling pathway. Importantly, aged MPs showed exacerbated toxicity, which may be due to their elevated surface irregularities and altered chemical compositions. Collectively, this study suggests a potential therapeutic approach for alopecia and hair follicle damage caused by MPs pollution.

Multimodal joint deconvolution and integrative signature selection in proteomics.

Deconvolution is an efficient approach for detecting cell-type-specific (cs) transcriptomic signals without cellular segmentation. However, this type of methods may require a reference profile from the same molecular source and tissue type. Here, we present a method to dissect bulk proteome by leveraging tissue-matched transcriptome and proteome without using a proteomics reference panel. Our method also selects the proteins contributing to the cellular heterogeneity shared between bulk transcriptome and proteome. The deconvoluted result enables downstream analyses such as cs-protein Quantitative Trait Loci (cspQTL) mapping. We benchmarked the performance of this multimodal deconvolution approach through CITE-seq pseudo bulk data, a simulation study, and the bulk multi-omics data from human brain normal tissues and breast cancer tumors, individually, showing robust and accurate cell abundance quantification across different datasets. This algorithm is implemented in a tool MICSQTL that also provides cspQTL and multi-omics integrative visualization, available at https://bioconductor.org/packages/MICSQTL .

Single-cell transcriptome profiling reveals cell type-specific variation and development in HLA expression of human skin.

Skin, the largest organ of body, is a highly immunogenic tissue with a diverse collection of immune cells. Highly polymorphic human leukocyte antigen (HLA) molecules have a central role in coordinating immune responses as recognition molecules. Nevertheless, HLA gene expression patterns among diverse cell types within a specific organ, like the skin, have yet to be thoroughly investigated, with stromal cells attracting much less attention than immune cells. To illustrate HLA expression profiles across different cell types in the skin, we performed single-cell RNA sequencing (scRNA-seq) analyses on skin datasets, covering adult and fetal skin, and hair follicles as the skin appendages. We revealed the variation in HLA expression between different skin populations by examining normal adult skin datasets. Moreover, we evaluated the potential immunogenicity of multiple skin populations based on the expression of classical HLA class I genes, which were well represented in all cell types. Furthermore, we generated scRNA-seq data of developing skin from fetuses of 15 post conception weeks (PCW), 17 PCW, and 22 PCW, delineating the dynamic expression of HLA genes with cell type-dependent variation among various cell types during development. Notably, the pseudotime trajectory analysis unraveled the significant variance in HLA genes during the evolution of vascular endothelial cells. Moreover, we uncovered the immune-privileged properties of hair follicles at single-cell resolution. Our study presents a comprehensive single-cell transcriptomic landscape of HLA genes in the skin, which provides new insights into variation in HLA molecules and offers a clue for allogeneic skin transplantation.

Modulating macrophage phenotype for accelerated wound healing with chlorogenic acid-loaded nanocomposite hydrogel.

Wound healing involves distinct phases, including hemostasis, inflammation, proliferation, and remodeling, which is a complex and dynamic process. Conventional preparations often fail to meet multiple demands and provide prompt information about wound status. Here, a pH/ROS dual-responsive hydrogel (OHA-PP@Z-CA@EGF) was constructed based on oxidized hyaluronic acid (OHA), phenylboronic acid-grafted ε-polylysine (PP), chlorogenic acid (CA)-loaded ZIF-8 (Z-CA), and epidermal growth factor (EGF), which possesses intrinsic antibacterial, antioxidant, and angiogenic capacities. Due to the Schiff base and Phenylboronate ester bonds, the hydrogel exhibited excellent mechanical properties, strong adhesion, good biodegradability, high biocompatibility, stable rheological properties, and self-healing ability. Moreover, introducing Z-CA as an initiator and nanofiller led to the additional cross-linking of hydrogel through coordination bonds, which further improved the mechanical properties and antioxidant capabilities. Bleeding models of liver and tail amputations demonstrated rapid hemostatic properties of the hydrogel. Besides, the hydrogel regulated macrophage phenotypes via the NF-κB/JAK-STAT pathways, relieved oxidative stress, promoted cell migration and angiogenesis, and accelerated diabetic wound healing. The hydrogel also enabled real-time monitoring of the wound healing stages by colorimetric detection. This multifunctional hydrogel opens new avenues for the treatment and management of full-thickness diabetic wounds.

Precision and Accuracy of Single-Cell/Nuclei RNA Sequencing Data.

Single-cell/nuclei RNA sequencing (sc/snRNA-Seq) is widely used for profiling cell-type gene expressions in biomedical research. An important but underappreciated issue is the quality of sc/snRNA-Seq data that would impact the reliability of downstream analyses. Here we evaluated the precision and accuracy in 18 sc/snRNA-Seq datasets. The precision was assessed on data from human brain studies with a total of 3,483,905 cells from 297 individuals, by utilizing technical replicates. The accuracy was evaluated with sample-matched scRNA-Seq and pooled-cell RNA-Seq data of cultured mononuclear phagocytes from four species. The results revealed low precision and accuracy at the single-cell level across all evaluated data. Cell number and RNA quality were highlighted as two key factors determining the expression precision, accuracy, and reproducibility of differential expression analysis in sc/snRNA-Seq. This study underscores the necessity of sequencing enough high-quality cells per cell type per individual, preferably in the hundreds, to mitigate noise in expression quantification.

Tetraspanin CD82 Correlates with and May Regulate S100A7 Expression in Oral Cancer.

Many metastatic cancers with poor prognoses correlate to downregulated CD82, but exceptions exist. Understanding the context of this correlation is essential to CD82 as a prognostic biomarker and therapeutic target. Oral squamous cell carcinoma (OSCC) constitutes over 90% of oral cancer. We aimed to uncover the function and mechanism of CD82 in OSCC. We investigated CD82 in human OSCC cell lines, tissues, and healthy controls using the CRISPR-Cas9 gene knockout, transcriptomics, proteomics, etc. CD82 expression is elevated in CAL 27 cells. Knockout CD82 altered over 300 genes and proteins and inhibited cell migration. Furthermore, CD82 expression correlates with S100 proteins in CAL 27, CD82KO, SCC-25, and S-G cells and some OSCC tissues. The 37-50 kDa CD82 protein in CAL 27 cells is upregulated, glycosylated, and truncated. CD82 correlates with S100 proteins and may regulate their expression and cell migration. The truncated CD82 explains the invasive metastasis and poor outcome of the CAL 27 donor. OSCC with upregulated truncated CD82 and S100A7 may represent a distinct subtype with a poor prognosis. Differing alternatives from wild-type CD82 may elucidate the contradictory functions and pave the way for CD82 as a prognostic biomarker and therapeutic target.

Cycloplatinated (II) Complex Based on Isoquinoline Alkaloid Elicits Ferritinophagy-Dependent Ferroptosis in Triple-Negative Breast Cancer Cells.

The development and optimization of metal-based anticancer drugs with novel cytotoxic mechanisms have emerged as key strategies to overcome chemotherapeutic resistance and side effects. Agents that simultaneously induce ferroptosis and autophagic death have received extensive attention as potential modalities for cancer therapy. However, only a limited set of drugs or treatment modalities can synergistically induce ferroptosis and autophagic tumor cell death. In this work, we designed and synthesized four new cycloplatinated (II) complexes harboring an isoquinoline alkaloid C∧N ligand. On screening the in vitro activity of these agents, we found that Pt-3 exhibited greater selectivity of cytotoxicity, decreased resistance factors, and improved anticancer activity compared to cisplatin. Furthermore, Pt-3, which we demonstrate can initiate potent ferritinophagy-dependent ferroptosis, exhibits less toxic and better therapeutic activity than cisplatin in vivo. Our results identify Pt-3 as a promising candidate or paradigm for further drug development in cancer treatment.

A revamped rat reference genome improves the discovery of genetic diversity in laboratory rats.

The seventh iteration of the reference genome assembly for Rattus norvegicus-mRatBN7.2-corrects numerous misplaced segments and reduces base-level errors by approximately 9-fold and increases contiguity by 290-fold compared with its predecessor. Gene annotations are now more complete, improving the mapping precision of genomic, transcriptomic, and proteomics datasets. We jointly analyzed 163 short-read whole-genome sequencing datasets representing 120 laboratory rat strains and substrains using mRatBN7.2. We defined ∼20.0 million sequence variations, of which 18,700 are predicted to potentially impact the function of 6,677 genes. We also generated a new rat genetic map from 1,893 heterogeneous stock rats and annotated transcription start sites and alternative polyadenylation sites. The mRatBN7.2 assembly, along with the extensive analysis of genomic variations among rat strains, enhances our understanding of the rat genome, providing researchers with an expanded resource for studies involving rats.

Precisely Controlled Polymerization of Two-Dimensional Conducting Polymers in Quasi-Liquid Layer Enables Ultrahigh Sensing Performance.

Gas sensors based on conducting polymers offer great potential for high-performance room temperature applications due to their cost-effectiveness, high-sensitivity, and operational advantage. However, their current performance is limited by the deficiency of control in conventional polymerization methods, leading to poor crystallinity and inconsistent material properties. Here, the quasi-liquid layer (QLL) on the ice surface acts as a self-regulating nano-reactor for precise control of thermodynamics and kinetics in the polymerization, resulting in a 7.62 nm thick two-dimensional (2D) polyaniline (PANI) film matching the QLL thickness. The ultra-thin film optimizes the exposure of active sites, enhancing the detection of analyte gases at low concentrations. It is validated by fabricating a chemiresistive gas sensor with the 2D PANI film, demonstrating stable room-temperature detection of ammonia down to 10 ppt in ambient air with an impressive 10% response. This achievement represents the highest sensitivity among sensors of this kind while maintaining excellent selectivity and repeatability. Moreover, the QLL-controlled polymerization strategy offers an alternative route for precise control of the polymerization process for conducting polymers, enabling the creation of advanced materials with enhanced properties.

Research on aquatic microcosm: Bibliometric analysis, toxicity comparison and model prediction.

Recently, aquatic microcosms have attracted considerable attention because they can be used to simulate natural aquatic ecosystems. First, to evaluate the development of trends, hotspots, and national cooperation networks in the field, bibliometric analysis was performed based on 1841 articles on aquatic microcosm (1962-2022). The results of the bibliometric analysis can be categorized as follows: (1) Aquatic microcosm research can be summarized in two sections, with the first part focusing on the ecological processes and services of aquatic ecosystems, and the second focusing on the toxicity and degradation of pollutants. (2) The United States (number of publications: 541, proportion: 29.5%) and China (248, 13.5%) are the two most active countries. Second, to determine whether there is a difference between single-species and microcosm tests, that is, to perform different-tier assessments, the recommended aquatic safety thresholds in risk assessment [i.e., the community-level no effect concentration (NOECcommunity), hazardous concentrations for 5% of species (HC5) and predicted no effect concentration (PNEC)] were compared based on these tests. There was a significant difference between the NOECcommunity and HC5 (P < 0.05). Moreover, regression models predicting microcosm toxicity values were constructed to provide a reference for ecological systemic risk assessments based on aquatic microcosms.

Profiling Protein-Protein Interactions in the Human Brain by Refined Cofractionation Mass Spectrometry.

Proteins usually execute their biological functions through interactions with other proteins and by forming macromolecular complexes, but global profiling of protein complexes directly from human tissue samples has been limited. In this study, we utilized cofractionation mass spectrometry (CF-MS) to map protein complexes within the postmortem human brain with experimental replicates. First, we used concatenated anion and cation Ion Exchange Chromatography (IEX) to separate native protein complexes in 192 fractions and then proceeded with Data-Independent Acquisition (DIA) mass spectrometry to analyze the proteins in each fraction, quantifying a total of 4,804 proteins with 3,260 overlapping in both replicates. We improved the DIA's quantitative accuracy by implementing a constant amount of bovine serum albumin (BSA) in each fraction as an internal standard. Next, advanced computational pipelines, which integrate both a database-based complex analysis and an unbiased protein-protein interaction (PPI) search, were applied to identify protein complexes and construct protein-protein interaction networks in the human brain. Our study led to the identification of 486 protein complexes and 10054 binary protein-protein interactions, which represents the first global profiling of human brain PPIs using CF-MS. Overall, this study offers a resource and tool for a wide range of human brain research, including the identification of disease-specific protein complexes in the future.