Functional Clustering Analysis Research Articles

Systematic analysis of olfactory protein-protein interactions network of fruitfly, Drosophila melanogaster.

Olfaction is one of the physiological traits of insect behavior. Insects have evolved a sophisticated olfactory system and use a combined coding strategy to process general odor. Drosophila melanogaster is a powerful model to reveal the molecular and cellular mechanisms of odor detection. Identifying new olfactory targets through complex interactions will contribute to a better understanding of the functions, interactions, and signaling pathways of olfactory proteins. However, the mechanism of D. melanogaster olfaction is still unclear, and more olfactory proteins are required to be discovered. In this study, we tried to explore essential proteins in the olfactory system of D. melanogaster and conduct protein-protein interactions (PPIs) analysis. We constructed the PPIs network of the olfactory system of D. melanogaster, consisting of 863 proteins and 18,959 interactions. Various methods were used to perform functional enrichment analysis, topological analysis and cluster analysis. Our results confirmed that Class B scavenger receptors (SR-Bs), glutathione S-transferases (GSTs), and UDP-glycosyltransferases (UGTs) play an essential role in olfaction of D. melanogaster. The proteins obtained in this study can be used for subsequent functional identification in D. melanogaster olfactory.

Influence of environmental variables on macroinvertebrate community structure in Lianhuan Lake.

The structural characteristics of the macroinvertebrate community can effectively reflect the health status of lake ecosystems and the quality of the lake ecological environment. It is therefore important to identify the limiting factors of macroinvertebrate community structure for the maintenance of lake ecosystem health. In this study, the community composition of macroinvertebrate assemblages and their relationships with environmental variables were investigated in 13 small lakes within Lianhuan Lake in northern China. A self‐organizing map and K‐means clustering analysis grouped the macroinvertebrate communities into five groups, and the indicator species reflected the environmental characteristics of each group. Principal component analysis indicated that the classification of the macroinvertebrate communities was affected by environmental variables. The Kruskal–Wallis test results showed that environmental variables (pH, total phosphorus, nitrate, water temperature, dissolved oxygen, conductivity, permanganate index, and ammonium) had a significant effect on the classification of the macroinvertebrate communities. Redundancy analysis showed that mollusks were significantly negatively correlated with pH and chlorophyll a, while annelids and aquatic insects were significantly positively correlated with chlorophyll a and dissolved oxygen. Spearman correlation analysis showed that the species richness and Shannon's diversity of macroinvertebrates were significantly negatively correlated with total phosphorus, while the biomass of macroinvertebrates was significantly negatively correlated with pH. High alkalinity and lake eutrophication have a serious impact on the macroinvertebrate community. Human disturbances, such as industrial and agricultural runoff, negatively impact the ecological environment and affect macroinvertebrate community structure. Thus, macroinvertebrate community structure should be improved by enhancing the ecological environment and controlling environmental pollution at a watershed scale.

The Novel microRNA Rno-miR-686-3p Is Associated with the Ischaemic Penumbra

Introduction: We explored microRNA (miRNA) profiles correlated with the penumbra in three different phases of ischaemic stroke, using a permanent middle cerebral artery occlusion (p-MCAO) rat model. Materials and Methods: A 2-mm coronal section was cut from the optic chiasma in the caudal direction, and the penumbra was located in the area between a longitudinal line approximately 2 mm from the midline and a transverse diagonal line at the “2-o’clock” position. Total RNA was extracted from tissue specimens and peripheral blood samples, followed by deep sequencing analysis. Results: We identified nine novel miRNA candidates in tissues and evaluated their expression levels using real-time quantitative polymerase chain reaction. In situ hybridization was conducted to assess miRNA localization in the brain. Of these nine candidates, we identified and characterized a novel miRNA, rno-miR-686-3p, which was localized in cell nuclei of the cortex, and associated with the penumbra. rno-miR-686-3p was downregulated at 1 (p = 0.042), 3 (p = 0.032), and 4 h (p = 0.007) post-p-MCAO in the penumbra. A total of 297 potential target genes were predicted. Moreover, functional annotation clustering and pathway enrichment analysis predicted that rno-miR-686-3p participates in transcriptional regulation and the Wnt and cyclic adenosine monophosphate (cAMP) signalling pathways. Conclusion: rno-miR-686-3p is a novel miRNA associated with the ischaemic penumbra that is implicated in transcriptional regulation and modulation of the Wnt and cAMP signalling pathways. Furthermore, it may serve as a possible new biomarker with potential value for detecting the existence of the penumbra.

Detection of Multidecadal Changes in Vegetation Dynamics and Association with Intra-Annual Climate Variability in the Columbia River Basin

Remotely-sensed Leaf Area Index (LAI) is a useful metric for assessing changes in vegetation cover and greeness over time and space. Satellite-derived LAI measurements can be used to assess these intra- and inter-annual vegetation dynamics and how they correlate with changing regional and local climate conditions. The detection of such changes at local and regional levels is challenged by the underlying continuity and extensive missing values of high-resolution spatio-temporal vegetation data. Here, the feasibility of functional data analysis methods was evaluated to improve the exploration of such data. In this paper, an investigation of multidecadal variation in LAI is conducted in the Columbia River Watershed, as detected by NOAA Advanced Very High-Resolution Radiometer (AVHRR) satellite imaging. The inter- and intra-annual correlation of LAI with temperature and precipitation were then investigated using data from the European Centre for Medium-Range Weather Forecasts global atmospheric re-analysis (ERA-Interim) in the period 1996–2017. A functional cluster analysis model was implemented to identify regions in the Columbia River Watershed that exhibit similar long-term greening trends. Across this region, a multidecadal trend toward earlier and higher annual LAI peaks was detected, and strong correlations were found between earlier and higher LAI peaks and warmer temperatures in late winter and early spring. Although strongly correlated to LAI, maximum temperature and precipitation do not demonstrate a similar strong multidecadal trend over the studied time period. The modeling approach is proficient for analyzing tens or hundreds of thousands of sampled sites without parallel processing or high-performance computing (HPC).

Dominant spatial patterns of interannual variability in summer precipitation across northern Eurasia from Coupled Model Intercomparison Project Phase 5 models

There is a lack of studies elucidating the characteristics of interannual variability in different models, especially for northern Eurasia. Therefore, spatial patterns of interannual variability in summer precipitation over northern Eurasia across 15 models of Coupled Model Intercomparison Project Phase 5 (CMIP5) were investigated using empirical orthogonal function (EOF) analysis and cluster analysis. The spatial patterns of EOF1-3 obtained from the models exhibited similar features across the models. The spatial pattern characteristics of the interannual variability of models were similar to those of the actual observations. However, their contribution rates and the order of magnitude of the EOFs in each model differ among models and from observations. Despite the similarity between the patterns of the EOF in the models and the observations, the atmospheric circulations causing the pattern differed in the models and actual observations, resulting in uncertainty in the models. This indicates that in the analysis of extreme events and their impact, the reproducibility and uncertainty of climate models with regard to interannual variability need to be adequately evaluated. In addition, similar to many climate models, the spatial pattern of interannual variability, which is different from any of EOF1-3 for the observations, has been more strongly represented than the actual observations. Clarification of the factors governing the common but unsuitable variability across models may be expected to decrease the uncertainty of the models in terms of interannual variability.

Integrated Proteome and Phosphoproteome Analyses Reveal Early- and Late-Stage Protein Networks of Traumatic Brain Injury

Traumatic brain injury (TBI) is a major public health concern all around the world. Accumulating evidence suggests that pathological processes after brain injury continuously evolve. Here, we identified the differentially expressed proteins (DEPs) and differentially expressed phosphoproteins (DEPPs) in the early and late stages of TBI in mice using TMT labeling, enrichment of Phos affinity followed, and high-resolution LC-MS/MS analysis. Subsequently, integrative analyses, including functional enrichment-based clustering analysis, motif analysis, cross-talk pathway/process enrichment analysis, and protein-protein interaction enrichment analysis were performed to further identify the different and similar pathophysiologic mechanisms in the early and late stage. Our work reveals a map of early and late-stage protein networks in TBI, which shed light on useful biomarkers and the underlying mechanisms in TBI and its sequelae.

The Effect of Sleep Deprivation and Subsequent Recovery Period on the Synaptic Proteome of Rat Cerebral Cortex

Sleep deprivation (SD) is commonplace in the modern way of life and has a substantial social, medical, and human cost. Sleep deprivation induces cognitive impairment such as loss of executive attention, working memory decline, poor emotion regulation, increased reaction times, and higher cognitive functions are particularly vulnerable to sleep loss. Furthermore, SD is associated with obesity, diabetes, cardiovascular diseases, cancer, and a vast majority of psychiatric and neurodegenerative disorders are accompanied by sleep disturbances. Despite the widespread scientific interest in the effect of sleep loss on synaptic function, there is a lack of investigation focusing on synaptic transmission on the proteome level. In the present study, we report the effects of SD and recovery period (RP) on the cortical synaptic proteome in rats. Synaptosomes were isolated after 8 h of SD performed by gentle handling and after 16 h of RP. The purity of synaptosome fraction was validated with western blot and electron microscopy, and the protein abundance alterations were analyzed by mass spectrometry. We observed that SD and RP have a wide impact on neurotransmitter-related proteins at both the presynaptic and postsynaptic membranes. The abundance of synaptic proteins has changed to a greater extent in consequence of SD than during RP: we identified 78 proteins with altered abundance after SD and 39 proteins after the course of RP. Levels of most of the altered proteins were upregulated during SD, while RP showed the opposite tendency, and three proteins (Gabbr1, Anks1b, and Decr1) showed abundance changes with opposite direction after SD and RP. The functional cluster analysis revealed that a majority of the altered proteins is related to signal transduction and regulation, synaptic transmission and synaptic assembly, protein and ion transport, and lipid and fatty acid metabolism, while the interaction network analysis revealed several connections between the significantly altered proteins and the molecular processes of synaptic plasticity or sleep. Our proteomic data implies suppression of SNARE-mediated synaptic vesicle exocytosis and impaired endocytic processes after sleep deprivation. Both SD and RP altered GABA neurotransmission and affected protein synthesis, several regulatory processes and signaling pathways, energy homeostatic processes, and metabolic pathways.

Impact of the Glycemic Level on the Salivary Proteome of Middle-Aged and Elderly People With Type 2 Diabetes Mellitus: An Observational Study.

Type 2 diabetes mellitus (T2DM) is an increasing global public health concern, but its impact on the salivary proteome is still unclear. To evaluate the effect of glycemic levels in middle-aged and elderly individuals with T2DM on salivary proteomics, we compared the differences by liquid chromatography tandem mass spectrometry (LC–MS/MS). Unstimulated whole saliva samples from 8 T2DM patients with good glycemic control (G group, HbA1c <6.5%) and 16 patients with poor control (P group, HbA1c ≥6.5%) were analyzed by LC–MS/MS in the data-independent acquisition mode (Clinical register number: ChiCTR1900023582.). After functional annotation, cluster analysis and receiver operating characteristic (ROC) curve analysis were carried out to screen and evaluate candidate proteins. A total of 5,721 proteins were quantified, while 40 proteins differed significantly. In the P group, proteins involved in oxidative stress-related processes were upregulated, whereas proteins related to salivary secretion were downregulated. The combination of thioredoxin domain-containing protein 17, zymogen granule protein 16B, and FAM3 metabolism regulating signaling molecule D yielded an area under the curve of 0.917 which showed a robust ability to distinguish the P and G groups. In conclusion, poorly controlled hyperglycemia may affect salivary proteins through various pathways, including oxidative stress and glandular secretion. Furthermore, the differentially expressed proteins, especially the three proteins with the best differentiation, might serve as an anchor point for the further study of hyperglycemia and oral diseases.

Functional clustering analysis identifies specific subtypes of aldehyde dehydrogenase associated with glioma immunity.

BackgroundAs a novel cancer stem cell marker, the biological functions of aldehyde dehydrogenases (ALDH enzymes) are a hot topic in current cancer research. However, there is a lack of systematic understanding of ALDH enzymes, which has hindered the translation of targeted therapies from bench work into the clinic.MethodsBased on transcriptome data from 999 glioma patients, functional clustering analysis was performed to reveal the functional phenotypes of ALDH isoforms. Subsequently, ALDH subgroups closely related to gliomas were identified by Cox survival analysis. Finally, gene set variation analysis (GSVA) and Pearson correlation analysis were used to explore the biological functions of ALDH enzymes.ResultsOur study found that ALDH enzymes could be classified into 5 subgroups, among which 3 groups were closely related to malignant progression and the prognosis of gliomas. We found that ALDH enzymes were closely related to gene mutations, which were most likely caused by changes in DNA repair functions. Further studies revealed that ALDH enzymes affect tumor immune functions, especially the expression of immune checkpoints. The effectiveness of immune checkpoint inhibitors in treating glioma might be improved by altering the expression of ALDH enzymes in specific subgroups.ConclusionsThis study comprehensively revealed the biological functions of ALDH enzymes in glioma and provided details about the potential clinical application of targeted therapy for ALDH enzymes.

Exploring digital interactivity: a multimodal and social semiotic approach to intersemiotic construction in COVID China website

Abstract This paper explores intersemiotic construction and digital interactivity through a multimodal social semiotic lens examining the semiotic instantiations of interactive signs on the homepage of “Fighting COVID-19 the Chinese Way” (covid-19.chinadaily.com.cn), a website created to disseminate information on news and development of COVID prevention and control practices in China to contribute to global efforts to fight the pandemic. The study focuses on how digital interactivity is afforded by the COVID China website, where the interactive signs are ideationally and compositionally constructed for meaning representation and interpersonally for text-viewer relation construal. From the multimodal social semiotic perspective, systemic functional model-based cluster and intersemiotic analyses are applied to explore the visual, spatial, and linguistic features that contribute to the design and construction of interactive semiotic signs on the COVID China website and afford digital interactivity for viewers’ action potentials. This study extends the analytical focus to the semiotic instantiations of interactive signs and their intersemiotic construction process that stimulate the enabling of interactivity, instead of the interactivity per se, and demonstrates how different semiotic instantiations of interactive signs are featured and interact to afford digital interactivity. It argues for an integrated lens in analysis to look at the interactive signs not only as signs of action with action-enabling forms but also as signs of meanings that afford user-page interactivity.

Sperm flagellar 2 (SPEF2) is essential for sperm flagellar assembly in humans.

Spermiogenesis is a complex and tightly regulated process, consisting of acrosomal biogenesis, condensation of chromatin, flagellar assembly, and disposal of extra cytoplasm. Previous studies have reported that sperm flagellar 2 (SPEF2) deficiency causes severe asthenoteratozoospermia owing to spermiogenesis failure, but the underlying molecular mechanism in humans remains unclear. Here, we performed proteomic analysis on spermatozoa from three SPEF2 mutant patients to study the functional role of SPEF2 during sperm tail development. A total of 1262 differentially expressed proteins were detected, including 486 upregulated and 776 downregulated. The constructed heat map of the differentially expressed proteins showed similar trends. Among these, the expression of proteins related to flagellar assembly, including SPEF2, sperm associated antigen 6 (SPAG6), dynein light chain tctex-type 1 (DYNLT1), radial spoke head component 1 (RSPH1), translocase of outer mitochondrial membrane 20 (TOM20), EF-hand domain containing 1 (EFHC1), meiosis-specific nuclear structural 1 (MNS1) and intraflagellar transport 20 (IFT20), was verified by western blot. Functional clustering analysis indicated that these differentially expressed proteins were specifically enriched for terms such as spermatid development and flagellar assembly. Furthermore, we showed that SPEF2 interacts with radial spoke head component 9 (RSPH9) and IFT20 in vitro, which are well-studied components of radial spokes or intra-flagellar transport and are essential for flagellar assembly. These results provide a rich resource for further investigation into the molecular mechanism underlying the role that SPEF2 plays in sperm tail development and could provide a theoretical basis for gene therapy in SPEF2 mutant patients in the future.

Characteristic profiling of Aconiti Lateralis Radix for distinguishing it from compatible herbal pair using UPLC-Q-TOF-MS coupled with chemometrics.

A method combining ultra-high-performance liquid chromatograph/quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS) and chemometrics was established to evaluate the differences in chemical composition between Aconiti Lateralis Radix (Fuzi in Chinese) before and after combination with Glycyrrhizae Radix et Rhizoma (Gancao in Chinese). UPLC-Q-TOF-MS was used to characterize the chemical components before and after the combination of Fuzi with Gancao, and genetic algorithm selection variables were applied to extract important variables. Partial least square discriminant analysis was used to verify the reliability of the variables obtained by genetic algorithm selection in differentiating Fuzi and combinations with Gancao, and nine potential chemical markers were obtained. The changes in content of chemical markers in Fuzi before and after combination were visualized using a heat map and hierarchical cluster analysis. Based on the chemical markers, characteristic profiling of UPLC-Q-TOF-MS data was developed, then unsupervised principal components analysis and a supervised counter-propagation artificial neural network were used to validate the characteristic profiling approach and showed that it performed well in differentiating between Fuzi and combinations with Gancao.

Early prediction of developing spontaneous activity in cultured neuronal networks

Synchronization and bursting activity are intrinsic electrophysiological properties of in vivo and in vitro neural networks. During early development, cortical cultures exhibit a wide repertoire of synchronous bursting dynamics whose characterization may help to understand the parameters governing the transition from immature to mature networks. Here we used machine learning techniques to characterize and predict the developing spontaneous activity in mouse cortical neurons on microelectrode arrays (MEAs) during the first three weeks in vitro. Network activity at three stages of early development was defined by 18 electrophysiological features of spikes, bursts, synchrony, and connectivity. The variability of neuronal network activity during early development was investigated by applying k-means and self-organizing map (SOM) clustering analysis to features of bursts and synchrony. These electrophysiological features were predicted at the third week in vitro with high accuracy from those at earlier times using three machine learning models: Multivariate Adaptive Regression Splines, Support Vector Machines, and Random Forest. Our results indicate that initial patterns of electrical activity during the first week in vitro may already predetermine the final development of the neuronal network activity. The methodological approach used here may be applied to explore the biological mechanisms underlying the complex dynamics of spontaneous activity in developing neuronal cultures.

Investigation of the Geographical Environment Impact on the Chemical Components of Peganum harmala L. through a Combined Analytical Method.

By implementing NMR, inductively coupled plasma-atomic emission spectrometry (ICP-AES), and the UV–vis spectroscopic techniques, metabolites, mineral elements, and antioxidant activities (DPPH) of Peganum harmala L. samples from Manasi and Fuhai of Xinjiang were studied in this research to investigate the geographical environment impact at the molecular level. First of all, partial least squares discriminant analysis was conducted to explore differential endogenous metabolites. A total of 18 metabolites were identified, and 14 mineral element contents were calculated quantitatively, which displayed diverse changing trends from these two origins. Valine, succinic acid, betaine, sucrose, and vasicine exhibited significant differences between these two groups as well as mineral nutrient profiles (Mg, Cu, N, K, Na, P, Zn, C) and DPPH antioxidant activities (EC50). The obvious different characteristics of chemical components and antioxidant activities in these two groups were further verified by heat map cluster analysis. Pearson correlation analysis also revealed the remarkable relationship of chemical components and antioxidant activities, which are strongly associated with the regional environment. This study showed that the combination of methodologies proposed will be highly useful in evaluating the environmental variation and diversity in terrestrial ecosystems.

Comparison of the transcriptome in circulating leukocytes in early lactation between primiparous and multiparous cows provides evidence for age-related changes

BackgroundPrevious studies have identified many immune pathways which are consistently altered in humans and model organisms as they age. Dairy cows are often culled at quite young ages due to an inability to cope adequately with metabolic and infectious diseases, resulting in reduced milk production and infertility. Improved longevity is therefore a desirable trait which would benefit both farmers and their cows. This study analysed the transcriptome derived from RNA-seq data of leukocytes obtained from Holstein cows in early lactation with respect to lactation number.ResultsSamples were divided into three lactation groups for analysis: i) primiparous (PP, n = 53), ii) multiparous in lactations 2–3 (MP 2–3, n = 121), and iii) MP in lactations 4–7 (MP > 3, n = 55). Leukocyte expression was compared between PP vs MP > 3 cows with MP 2–3 as background using DESeq2 followed by weighted gene co-expression network analysis (WGCNA). Seven modules were significantly correlated (r ≥ 0.25) to the trait lactation number. Genes from the modules which were more highly expressed in either the PP or MP > 3 cows were pooled, and the gene lists subjected to David functional annotation cluster analysis. The top three clusters from modules more highly expressed in the PP cows all involved regulation of gene transcription, particularly zinc fingers. Another cluster included genes encoding enzymes in the mitochondrial beta-oxidation pathway. Top clusters up-regulated in MP > 3 cows included the terms Glycolysis/Gluconeogenesis, C-type lectin, and Immunity. Differentially expressed candidate genes for ageing previously identified in the human blood transcriptome up-regulated in PP cows were mainly associated with T-cell function (CCR7, CD27, IL7R, CAMK4, CD28), mitochondrial ribosomal proteins (MRPS27, MRPS9, MRPS31), and DNA replication and repair (WRN). Those up-regulated in MP > 3 cows encoded immune defence proteins (LYZ, CTSZ, SREBF1, GRN, ANXA5, ADARB1).ConclusionsGenes and pathways associated with lactation number in cows were identified for the first time to date, and we found that many were comparable to those known to be associated with ageing in humans and model organisms. We also detected changes in energy utilization and immune responses in leukocytes from older cows.

Integrated Analysis of lncRNA-Associated ceRNA Network Identifies Two lncRNA Signatures as a Prognostic Biomarker in Gastric Cancer.

Background Gastric cancer (GC) is a malignant tumour that originates in the gastric mucosal epithelium and is associated with high mortality rates worldwide. Long noncoding RNAs (lncRNAs) have been identified to play an important role in the development of various tumours, including GC. Yet, lncRNA biomarkers in a competing endogenous RNA network (ceRNA network) that are used to predict survival prognosis remain lacking. The aim of this study was to construct a ceRNA network and identify the lncRNA signature as prognostic factors for survival prediction. Methods The lncRNAs with overall survival significance were used to construct the ceRNA network. Function enrichment, protein-protein interaction, and cluster analysis were performed for dysregulated mRNAs. Multivariate Cox proportional hazards regression was performed to screen the potential prognostic lncRNAs. RT-qPCR was used to measure the relative expression levels of lncRNAs in cell lines. CCK8 assay was used to assess the proliferation of GC cells transfected with sh-lncRNAs. Results Differentially expressed genes were identified including 585 lncRNAs, 144 miRNAs, and 2794 mRNAs. The ceRNA network was constructed using 35 DElncRNAs associated with overall survival of GC patients. Functional analysis revealed that these dysregulated mRNAs were enriched in cancer-related pathways, including TGF-beta, Rap 1, calcium, and the cGMP-PKG signalling pathway. A multivariate Cox regression analysis and cumulative risk score suggested that two of those lncRNAs (LINC01644 and LINC01697) had significant prognostic value. Furthermore, the results indicate that LINC01644 and LINC01697 were upregulated in GC cells. Knockdown of LINC01644 or LINC01697 suppressed the proliferation of GC cells. Conclusions The authors identified 2-lncRNA signature in ceRNA regulatory network as prognostic biomarkers for the prediction of GC patient survival and revealed that silencing LINC01644 or LINC01697 inhibited the proliferation of GC cells.

Optical characterisation and classification of water types in the southern South China Sea and Straits of Malacca

We describe the characteristics of surface water spectral reflectance variability and identify reflectance-based optical water types that are present in the southern South China Sea (SSCS) and the Straits of Malacca (SoM). Simultaneous measurements of in-water hyperspectral remote sensing reflectance (Rrs), particulate backscattering coefficient (bbp) and in-water constituents were conducted at 473 stations from May 2009 to March 2019 during different monsoon seasons at both coastal and oceanic waters. Empirical orthogonal function (EOF) and hierarchical cluster analysis (HCA) were performed to determine the spectral variability of Rrs and group the homogenous optical water type regimes. The results indicate that monsoons and regional hydrodynamic conditions have great influence on the optical properties. The SSCS exhibited strong seasonal patterns with maximum values of all the bio-optical parameters during the northeast monsoon and minimum values during the spring inter-monsoon. In contrast, the SoM waters are subjected to strong influence of freshwater discharges, thus resulting in optically complex water regimes. The EOF analyses clearly revealed two dominant modes of optical variability, with each mode corresponding to the optical conditions of SSCS and SoM, respectively. The correlation between EOF amplitude factors and optical parameters indicated that the Rrs variability in SoM was primarily driven by the particulate backscattering and secondarily by light-absorbing components, while it was only minimally influenced by the absorption coefficients in SSCS. Hierarchical clustering revealed the presence of five distinct optical water types, which varied considerably according to the concentration of biogeochemical and optical properties. Classes 1 and 2, which were only found in SSCS waters, were defined by high magnitudes within the blue spectral region with very low concentrations of in-water constituents, indicating clear waters. Class 3 represents the optically transitional waters between coastal and open oceans, characterizing by a well-defined Rrs peak at the blue-end of the spectrum with no single dominant constituent. Classes 4 and 5 are most representative of waters influenced by riverine plumes, particularly in the SoM, with the highest Rrs magnitudes in the green and a well-defined absorption peak in the blue spectral region. Our findings confirmed that the performance of global Chl-a algorithms varied with the optical conditions present in the study area with the retrieval uncertainties increasing substantially from Class 1 to Class 5 water types. This finding will contribute significantly to the development of regional class-specific bio-optical models.

TMT-Based proteomics analysis of LPS-induced acute lung injury

Purpose: The proteome during lipopolysaccharide (LPS)-induced acute lung injury (ALI) in mice is unclear. Materials and Methods: In this study, eight-week-old male C57BL/6 mice were intraperitoneally injected with LPS and sacrificed 18 hours after LPS administration to identify protein expression levels in lung tissue using tandem mass tag (TMT) analysis for relative quantification. Hematoxylin-eosin (HE) staining was used to evaluate lung injury in mice. Immunohistochemical staining was used to calculate the production of myeloperoxidase (MPO) and TUNEL staining was performed to detect apoptosis. GO functional clustering and KEGG pathway enrichment analyses were performed to determine functions of differentially expressed proteins (DEPs) and transduction pathways. Domain annotation and subcellular localization analysis of the DEPs were also performed. Furthermore, parallel reaction monitoring (PRM) analysis was used to verify the top 30 DEPs. Results: A total of 5188 proteins were found to be expressed in lung tissues from LPS- and saline-treated mice. Among these proteins, 293 were differentially expressed between the two groups; 255 proteins were upregulated in the LPS-treated ALI mice, while 38 were downregulated. GO analysis showed that the DEPs are mainly extracellular, and KEGG analysis suggested that the DEPs are mainly enriched in the NOD-like receptor signaling pathway, complement and coagulation cascades and natural killer cell-mediated cytotoxicity. Enrichment of the DEPs is mainly peptidase S1A, serine proteases, peptidase S1, and the serpin domain. 26.6% of the DEPs are in the nucleus, 24.6% are in the cytosol, 19.1% are in the extracellular space, and 18.8% are in the plasma membrane. PRM validation showed that the trend of 30 DEPs was same with TMT analysis. Among these, Cytochrome b-245 heavy chain (Cybb), Monocyte differentiation antigen CD14 (Cd14) and Neutrophil gelatinase-associated lipocalin (NGAL) were the most obvious change. Conclusions: Our results may help to identify markers and therapeutic targets for LPS-induced ALI.

Comparison of the Intestinal Microbiota During the Different Growth Stages of Red Swamp Crayfish (Procambarus clarkii).

This study aimed to determine the effect of the growth stage of Procambarus clarkii on their intestinal microbiota. Intestinal samples of five different growth stages of P. clarkii (first instar, second instar, third instar, juvenile, and adult) from laboratory culture were analyzed through the Illumina MiSeq high-throughput sequencing platform to determine the intestinal microbiome of crayfish. The alpha diversity decreased along with the growth of the crayfish, with the relative abundance of the microbiota changing among stages; crayfish at closer development stages had a more comparable intestinal microbiota composition. A comparative analysis by principal component analysis and principal coordinate analysis showed that there were significant differences in the intestinal microbiota of crayfish among the different growth stages, except for the first two stages of larval crayfish, and the intestinal microbiota showed a consistent progression pattern from the larval stage to the juvenile stage. Some microbiota showed stage specificity, which might be the characteristic microbiota of different stages of growth. According to FAPROTAX functional clustering analysis, the three stages of larvae were clustered together, while the juvenile and adult stages were clustered separately according to the growth stage, indicating that, in the early stages of larval development, the function of the intestinal flora was similar; as the body grew and developed, the composition and function of the intestinal microbiota also changed.

The association between expression of lncRNAs in patients with GDM.

ObjectiveGestational diabetes mellitus (GDM) is common worldwide and seriously threatens maternal and infant health. The expression of non-coding (ncRNA) is tissue-specific and highly stable in eukaryotic cells and the circulatory system, which can act as an early molecular marker of GDM.MethodsThe differential expression of lncRNA and mRNA in the peripheral blood of patients with GDM (experimental group) and healthy pregnant women (control group) was analysed via lncRNA gene chip. Employing biological function clustering and KEGG signalling pathway analysis, we selected the mRNAs and lncRNAs closely related to the insulin signalling pathway of GDM to analyse the possible regulatory mechanism in the pathogenesis of GDM. The sequencing results were further verified via quantitative PCR (Q-PCR).ResultsLncRNA microarray analysis revealed 7498 genes (3592 upregulated, 3906 downregulated) differentially expressed in the GDM group and healthy pregnant women control group, including 1098 differentially expressed lncRNAs (609 upregulated, 489 downregulated). According to the regulatory pathway of the lncRNA mRNA network, 6 lncRNAs and 4 mRNAs were found to play a significant role in insulin resistance.ConclusionsThe lncRNAs ERMP1, TSPAN32 and MRPL38 form a co-expression network with TPH1, which is mainly involved in the tryptophan metabolism pathway and in the development of GDM. Moreover, lncRNA RPL13P5 forms a co-expression network with the TSC2 gene via the PI3K-AKT and insulin signalling pathways, which are involved in the process of insulin resistance in GDM.