Host-virus interactions in hepatitis B and hepatitis C infection

Humanin and Its Pathophysiological Roles in Aging: A Systematic Review

BACKGROUND

Senescence is a cellular aging process in all multicellular organisms. It is characterized by a decline in cellular functions and proliferation, resulting in increased cellular damage and death. These conditions play an essential role in aging and significantly contribute to the development of age-related complications. Humanin is a mitochondrial-derived peptide (MDP), encoded by mitochondrial DNA, playing a cytoprotective role to preserve mitochondrial function and cell viability under stressful and senescence conditions. For these reasons, humanin can be exploited in strategies aiming to counteract several processes involved in aging, including cardiovascular disease, neurodegeneration, and cancer. Relevance of these conditions to aging and disease: Senescence appears to be involved in the decay in organ and tissue function, it has also been related to the development of age-related diseases, such as cardiovascular conditions, cancer, and diabetes. In particular, senescent cells produce inflammatory cytokines and other pro-inflammatory molecules that can participate to the development of such diseases. Humanin, on the other hand, seems to contrast the development of such conditions, and it is also known to play a role in these diseases by promoting the death of damaged or malfunctioning cells and contributing to the inflammation often associated with them. Both senescence and humanin-related mechanisms are complex processes that have not been fully clarified yet. Further research is needed to thoroughly understand the role of such processes in aging and disease and identify potential interventions to target them in order to prevent or treat age-related conditions.

OBJECTIVES

This systematic review aims to assess the potential mechanisms underlying the link connecting senescence, humanin, aging, and disease.

Integrated enzymes activity and transcriptome reveal the effect of exogenous melatonin on the strain degeneration of Cordyceps militaris

As a valuable medicinal and edible fungus, Cordyceps militaris has been industrialized with broad development prospects. It contains a lot of bioactive compounds that are beneficial to our health. However, during artificial cultivation, strain degeneration is a challenge that inhibits the industrialization utility of C. militaris. Exogenous melatonin (MT) can scavenge for reactive oxygen species (ROS) in fungus and can alleviate strain degeneration. To establish the significance and molecular mechanisms of MT on strain degeneration, we investigated the third-generation strain (W5-3) of C. militaris via morphological, biochemical, and transcriptomic approaches under MT treatment. Morphological analyses revealed that colony angulation of C. militaris was significantly weakened, and the aerial hypha was reduced by 60 μmol L-1 MT treatment. Biochemical analyses showed low levels of ROS and malondialdehyde (MDA), as well as increasing endogenous MT levels as exogenous MT increased. RNA-Seq revealed that compared with the control, several antioxidant enzyme-related genes were up-regulated under 60 μmol L-1 MT treatment. Among them, glutathione s-transferase genes were up-regulated by a factor of 11.04. In addition, genes that are potentially involved in cordycepin, adenosine and active compound biosynthesis for the growth and development of mycelium were up-regulated. Collectively, these findings provide the basis for further elucidation of the molecular mechanisms involved in C. militaris strain degeneration.

Dysregulation of metalloproteins in ischemic heart disease patients with systolic dysfunction

Ischemic heart disease (IHD) is the leading cause of mortality worldwide. Metalloproteins have been linked to human health and diseases. The molecular functions of metalloproteins in IHD is not well understood and require further exploration. The objective of this study was to find out the role of metalloproteins in the pericardial fluid of IHD patients having normal (EF > 45) and impaired (EF < 45) left ventricular ejection fraction (LVEF). IHD patients were grouped into two categories: LVEF<45 (n = 12) and LVEF >45 (n = 33). Pooled samples of pericardial fluid were fractionated by using ZOOM-isoelectric focusing (IEF) followed by further processing using one-dimensional gel electrophoresis (1D SDS-PAGE) and filter-aided sample preparation (FASP). Tryptic peptides of each fraction and differential bands were then analyzed by nano-LC-ESI-MS/MS. Protein identification was performed through a Mascot search engine using NCBI-Prot and SwissProt databases. A total of 1082 proteins including 154 metalloproteins were identified. In the differential bands, 60 metalloproteins were identified, while 115 metalloproteins were identified in all ZOOM-IEF fractions. Twelve differentially expressed metalloproteins were selected in the intense bands according to their molecular weight (MW) and isoelectric point (pI). The 12 differentially expressed metalloprotein includes ceruloplasmin, Prothrombin, Vitamin K-dependent protein, Fibulin-1, Ribosomal protein S6 kinase alpha-6, nidogen, partial, Serum albumin, Hemopexin, C-reactive protein, Serum amyloid P-component, and Intelectin-1 protein which were all up-regulated while serotransferrin is the only metalloprotein that was down-regulated in impaired (LVEF<45) group. Among the metalloproteins, Zn-binding proteins are 36.5 % followed by Ca-binging 32.2 %, and Fe-binging 12.2 %. KEGG, pathway analysis revealed the association of ceruloplasmin and serotransferrin with the ferroptosis pathway. In conclusion, 154 metalloproteins were identified of them the Zn-binding protein followed by Ca-binding and Fe-binding proteins were the most abundant metalloproteins. The two metalloproteins, the Cu-binding protein ceruloplasmin, and Fe-binding protein serotransferrin are involved in the ferroptosis pathway, an iron-dependent form of regulated cell death that has been linked to cardiac pathology, especially in IHD patients having impaired systolic (LVEF<45) dysfunction. However, further research is required to validate these findings.

Nano-DESI Mass Spectrometry Imaging of Proteoforms in Biological Tissues with High Spatial Resolution

Mass spectrometry imaging (MSI) is a powerful tool for label-free mapping of the spatial distribution of proteins in biological tissues. We have previously demonstrated imaging of individual proteoforms in biological tissues using nanospray desorption electrospray ionization (nano-DESI), an ambient liquid extraction-based MSI technique. Nano-DESI MSI generates multiply charged protein ions, which is advantageous for their identification using top-down proteomics analysis. In this study, we demonstrate proteoform mapping in biological tissues with a spatial resolution down to 7 μm using nano-DESI MSI. A substantial decrease in protein signals observed in high-spatial-resolution MSI makes these experiments challenging. We have enhanced the sensitivity of nano-DESI MSI experiments by optimizing the design of the capillary-based probe and the thickness of the tissue section. In addition, we demonstrate that oversampling may be used to further improve spatial resolution at little or no expense to sensitivity. These developments represent a new step in MSI-based spatial proteomics, which complements targeted imaging modalities widely used for studying biological systems.

Comparison of SPEED, S-Trap, and In-Solution-Based Sample Preparation Methods for Mass Spectrometry in Kidney Tissue and Plasma

Mass spectrometry is a powerful technique for investigating renal pathologies and identifying biomarkers, and efficient protein extraction from kidney tissue is essential for bottom-up proteomic analyses. Detergent-based strategies aid cell lysis and protein solubilization but are poorly compatible with downstream protein digestion and liquid chromatography-coupled mass spectrometry, requiring additional purification and buffer-exchange steps. This study compares two well-established detergent-based methods for protein extraction (in-solution sodium deoxycholate (SDC); suspension trapping (S-Trap)) with the recently developed sample preparation by easy extraction and digestion (SPEED) method, which uses strong acid for denaturation. We compared the quantitative performance of each method using label-free mass spectrometry in both sheep kidney cortical tissue and plasma. In kidney tissue, SPEED quantified the most unique proteins (SPEED 1250; S-Trap 1202; SDC 1197). In plasma, S-Trap produced the most unique protein quantifications (S-Trap 150; SDC 148; SPEED 137). Protein quantifications were reproducible across biological replicates in both tissue (R2 = 0.85–0.90) and plasma (SPEED R2 = 0.84; SDC R2 = 0.76, S-Trap R2 = 0.65). Our data suggest SPEED as the optimal method for proteomic preparation in kidney tissue and S-Trap or SPEED as the optimal method for plasma, depending on whether a higher number of protein quantifications or greater reproducibility is desired.

Novel Aryl Sulfonamide Derivatives as NLRP3 Inflammasome Inhibitors for the Potential Treatment of Cancer

The NLRP3 inflammasome is a critical component of innate immunity that senses diverse pathogen- and host-derived molecules. However, its aberrant activation has been associated with the pathogenesis of multiple diseases, including cancer. In this study, we designed and synthesized a series of aryl sulfonamide derivatives (ASDs) to inhibit the NLRP3 inflammasome. Among these, compounds 6c, 7n, and 10 specifically inhibited NLRP3 activation at nanomolar concentrations without affecting the activation of the NLRC4 and AIM2 inflammasomes. Furthermore, we demonstrated that these compounds reduce interleukin-1β (IL-1β) production in vivo and attenuate melanoma tumor growth. Moreover, metabolic stability in liver microsomes of 6c, 7n, and 10 was studied along with plasma exposure in mice of the most interesting compound 6c. Therefore, we generated potent NLRP3 inflammasome inhibitors, which can be considered in future medicinal chemistry and pharmacological studies aimed at developing a new therapeutic approach for NLRP3 inflammasome-driven cancer.

In Silico-Based Structural Evaluation to Categorize the Pathogenicity of Mutations Identified in the RAD Class of Proteins

RAD genes, known as double-strand break repair proteins, play a major role in maintaining the genomic integrity of a cell by carrying out essential DNA repair functions via double-strand break repair pathways. Mutations in the RAD class of proteins show high susceptibility to breast and ovarian cancers; however, adequate research on the mutations identified in these genes has not been extensively reported for their deleterious effects. Changes in the folding pattern of RAD proteins play an important role in protein-protein interactions and also functions. Missense mutations identified from four cancer databases, cBioPortal, COSMIC, ClinVar, and gnomAD, cause aberrant conformations, which may lead to faulty DNA repair mechanisms. It is therefore necessary to evaluate the effects of pathogenic mutations of RAD proteins and their subsequent role in breast and ovarian cancers. In this study, we have used eight computational prediction servers to analyze pathogenic mutations and understand their effects on the protein structure and function. A total of 5122 missense mutations were identified from four different cancer databases, of which 1165 were predicted to be pathogenic using at least five pathogenicity prediction servers. These mutations were characterized as high-risk mutations based on their location in the conserved domains and subsequently subjected to structural stability characterization. The mutations included in the present study were selected from clinically relevant mutants in breast cancer pedigrees. Comparative folding patterns and intra-atomic interaction results showed alterations in the structural behavior of RAD proteins, specifically RAD51C triggered by mutations G125V and L138F and RAD51D triggered by mutations S207L and E233G.

Transcriptome and Metabolome Profiling Provide Insights into Flavonoid Synthesis in Acanthus ilicifolius Linn

Acanthus ilicifolius is an important medicinal plant in mangrove forests, which is rich in secondary metabolites with various biological activities. In this study, we used transcriptomic analysis to obtain differentially expressed genes in the flavonoid metabolic pathway and metabolomic methods to detect changes in the types and content in the flavonoid metabolic synthesis pathway. The results showed that DEGs were identified in the mature roots vs. leaves comparison (9001 up-regulated and 8910 down-regulated), mature roots vs. stems comparison (5861 up-regulated and 7374 down-regulated), and mature stems vs. leaves comparison (10,837 up-regulated and 11,903 down-regulated). Furthermore, two AiCHS genes and four AiCHI genes were up-regulated in the mature roots vs. stems of mature A. ilicifolius, and were down-regulated in mature stems vs. leaves, which were highly expressed in the A. ilicifolius stems. A total of 215 differential metabolites were found in the roots vs. leaves of mature A. ilicifolius, 173 differential metabolites in the roots vs. stems, and 228 differential metabolites in the stems vs. leaves. The metabolomic results showed that some flavonoids in A. ilicifolius stems were higher than in the roots. A total of 18 flavonoid differential metabolites were detected in the roots, stems, and leaves of mature A. ilicifolius. In mature leaves, quercetin-3-O-glucoside-7-O-rhamnoside, gossypitrin, isoquercitrin, quercetin 3,7-bis-O-β-D-glucoside, and isorhamnetin 3-O-β-(2″-O-acetyl-β-D-glucuronide) were found in a high content, while in mature roots, di-O-methylquercetin and isorhamnetin were the major compounds. The combined analysis of the metabolome and transcriptome revealed that DEGs and differential metabolites were related to flavonoid biosynthesis. This study provides a theoretical basis for analyzing the molecular mechanism of flavonoid synthesis in A. ilicifolius and provides a reference for further research and exploitation of its medicinal value.

Comparative proteome profiles of Polygonatum cyrtonema Hua rhizomes (Rhizoma Ploygonati) in response to different levels of cadmium stress

BACKGROUND

The Polygonatum cyrtonema Hua rhizomes (also known as Rhizoma Polygonati, RP) are consumed for their health benefits. The main source of the RP is wild P. cyrtonema populations in the Hunan province of China. However, the soil Cadmium (Cd) content in Huanan is increasing, thus increasing the risks of Cd accumulation in RP which may end up in the human food chain. To understand the mechanism of Cd accumulation and resistance in P. cyrtonema, we subjected P. cyrtonema plants to four levels of Cd stress [(D2) 1, (D3) 2, (D4) 4, and (D5) 8 mg/kg)] compared to (D1) 0.5 mg/kg.

RESULTS

The increase in soil Cd content up to 4 mg/kg resulted in a significant increase in tissue (root hair, rhizome, stem, and leaf) Cd content. The increase in Cd concentration variably affected the antioxidant enzyme activities. We could identify 14,171 and 12,115 protein groups and peptides, respectively. There were 193, 227, 260, and 163 differentially expressed proteins (DEPs) in D2, D3, D4, and D5, respectively, compared to D1. The number of downregulated DEPs increased with an increase in Cd content up to 4 mg/kg. These downregulated proteins belonged to sugar biosynthesis, amino acid biosynthesis-related pathways, and secondary metabolism-related pathways. Our results indicate that Cd stress increases ROS generation, against which, different ROS scavenging proteins are upregulated in P. cyrtonema. Moreover, Cd stress affected the expression of lipid transport and assembly, glycolysis/gluconeogenesis, sugar biosynthesis, and ATP generation.

CONCLUSION

These results suggest that an increase in soil Cd content may end up in Huangjing. Cadmium stress initiates expression changes in multiple pathways related to energy metabolism, sugar biosynthesis, and secondary metabolite biosynthesis. The proteins involved in these pathways are potential candidates for manipulation and development of Cd stress-tolerant genotypes.

The MicroRNA Ame-Bantam-3p Controls Larval Pupal Development by Targeting the Multiple Epidermal Growth Factor-like Domains 8 Gene (megf8) in the Honeybee, Apis mellifera

20-Hydroxyecdysone (20E) plays an essential role in coordinating developmental transitions in insects through responsive protein-coding genes and microRNAs (miRNAs). However, the interplay between 20E and miRNAs during insect metamorphosis is unknown. In this study, using small RNA sequencing, a comparative miRNA transcriptomic analysis in different development stages, and 20E treatment, we identified ame-bantam-3p as a key candidate miRNA involved in honeybee metamorphosis. Target prediction and in vitro dual-luciferase assays confirmed that ame-bantam-3p interacts with the coding region of the megf8 gene and promotes its expression. Meanwhile, temporal expression analysis revealed that the expression of ame-bantam-3p is higher in the larval stage than in prepupal and pupal stages, and that this expression pattern is similar to that of megf8. In vivo, we found that the mRNA level of megf8 was significantly increased after the injection of ame-bantam-3p agomir. A 20E feeding assay showed that 20E downregulated the expression of both ame-bantam-3p and its target gene megf8 on larval days five, six, and seven. Meanwhile, the injection of ame-bantam-3p agomir also reduced the 20E titer, as well as the transcript levels of essential ecdysteroid synthesis genes, including Dib, Phm, Sad, and Nvd. The transcript levels of 20E cascade genes, including EcRA, ECRB1, USP, E75, E93, and Br-c, were also significantly decreased after ame-bantam-3p agomir injection. However, ame-bantam-3p antagomir injection and dsmegf8 injection showed the opposite effect to ame-bantam-3p agomir injection. Ame-bantam-3p agomir treatment ultimately led to mortality and the failure of larval pupation by inhibiting ecdysteroid synthesis and the 20E signaling pathway. However, the expression of 20E signaling-related genes was significantly increased after megf8 knockdown, and larvae injected with dsmegf8 showed early pupation. Combined, our results indicate that ame-bantam-3p is involved in the 20E signaling pathway through positively regulating its target gene megf8 and is indispensable for larval–pupal development in the honeybee. These findings may enhance our understanding of the relationship between 20E signaling and small RNAs during honeybee development.

Comparative Analyses of Human Exosome Proteomes

Exosomes are responsible for cell-to-cell communication and serves as a valuable drug delivery vehicle. However, exosome heterogeneity, non-standardized isolation methods and proteomics/bioinformatics approaches limit its clinical applications. To better understand exosome heterogeneity, biological function and molecular mechanism of its biogenesis, secretion and uptake, techniques in proteomics or bioinformatics were applied to investigate human embryonic kidney cell (293T cell line)-derived exosome proteome and enable an integrative comparison of exosomal proteins and protein-protein interaction (PPI) networks of eleven exosome proteomes extracted from diverse human samples, including 293T (two datasets), dermal fibroblast, mesenchymal stem cell, thymic epithelial primary cell, breast cancer cell line (MDA-MB-231), patient neuroblastoma cell, plasma, saliva, serum and urine. Mapping of exosome biogenesis/secretion/uptake-related proteins onto exosome proteomes highlights exosomal origin-specific routes of exosome biogenesis/secretion/uptake and exosome-dependent intercellular communication. The finding provides insight into comparative exosome proteomes and its biogenesis, secretion and uptake, and potentially contributes to clinical applications.

Genome Analysis Using Whole-Exome Sequencing of Non-Syndromic Cleft Lip and/or Palate from Malagasy Trios Identifies Variants Associated with Cilium-Related Pathways and Asian Genetic Ancestry

Background: Orofacial clefts (OFCs) are common congenital disabilities that can occur as isolated non-syndromic events or as part of Mendelian syndromes. OFC risk factors vary due to differences in regional environmental exposures, genetic variants, and ethnicities. In recent years, significant progress has been made in understanding OFCs, due to advances in sequencing and genotyping technologies. Despite these advances, very little is known about the genetic interplay in the Malagasy population. Methods: Here, we performed high-resolution whole-exome sequencing (WES) on non-syndromic cleft lip with or without palate (nCL/P) trios in the Malagasy population (78 individuals from 26 families (trios)). To integrate the impact of genetic ancestry admixture, we computed both global and local ancestries. Results: Participants demonstrated a high percentage of both African and Asian admixture. We identified damaging variants in primary cilium-mediated pathway genes WNT5B (one family), GPC4 (one family), co-occurrence in MSX1 (five families), WDR11 (one family), and tubulin stabilizer SEPTIN9 (one family). Furthermore, we identified an autosomal homozygous damaging variant in PHGDH (one family) gene that may impact metabiotic activity. Lastly, all variants were predicted to reside on local Asian genetic ancestry admixed alleles. Conclusion: Our results from examining the Malagasy genome provide limited support for the hypothesis that germline variants in primary cilia may be risk factors for nCL/P, and outline the importance of integrating local ancestry components better to understand the multi-ethnic impact on nCL/P.

Trophic level and proteobacteria abundance drive antibiotic resistance levels in fish from coastal New England

BACKGROUND

The natural marine environment represents a vast reservoir of antimicrobial resistant bacteria. The wildlife that inhabits this environment plays an important role as the host to these bacteria and in the dissemination of resistance. The relationship between host diet, phylogeny, and trophic level and the microbiome/resistome in marine fish is not fully understood. To further explore this relationship, we utilize shotgun metagenomic sequencing to define the gastrointestinal tract microbiomes of seven different marine vertebrates collected in coastal New England waters.

RESULTS

We identify inter and intraspecies differences in the gut microbiota of these wild marine fish populations. Furthermore, we find an association between antibiotic resistance genes and host dietary guild, which suggests that higher trophic level organisms have a greater abundance of resistance genes. Additionally, we demonstrate that antibiotic resistance gene burden is positively correlated with Proteobacteria abundance in the microbiome. Lastly, we identify dietary signatures within the gut of these fish and find evidence of possible dietary selection for bacteria with specific carbohydrate utilization potential.

CONCLUSIONS

This work establishes a link between host lifestyle/dietary guild, and microbiome composition and the abundance of antibiotic resistance genes within the gastrointestinal tract of marine organisms. We expand the current understanding of marine organism-associated microbial communities and their role as reservoirs of antimicrobial resistance genes.

Local Ancestry-Informed Candidate Pathway Analysis of Warfarin Stable Dose in Latino Populations

Accuracy of warfarin dose prediction algorithms may be improved by including data from diverse populations in genetic studies of dose variability. Here, we surveyed single nucleotide polymorphisms in vitamin K-related genetic pathways for association with warfarin dose requirements in two admixed Latino populations in standard-principal component adjusted and contemporary-local ancestry adjusted regression models. A total of five variants from vitamin K-related genes/pathways were associated with warfarin dose in both cohorts (P < 0.0125) in standard models. Local ancestry-adjusted analysis unveiled 35 associated variants with absolute effects ranging from β = 9.04 ( ±2.23) to 39.18 ( ±10.89) per ancestral allele in the discovery cohort and β = 6.47 (± 2.02) to 17.82 (± 6.83) in the replication cohort. Importantly, we demonstrate the technical validity of the Tractor model in cohorts with admixed ancestry from three founder populations and bring attention to the technical hurdles obstructing the inclusion of diverse, especially admixed, populations in pharmacogenomic research.

A bivalent remipede toxin promotes calcium release via ryanodine receptor activation

Multivalent ligands of ion channels have proven to be both very rare and highly valuable in yielding unique insights into channel structure and pharmacology. Here, we describe a bivalent peptide from the venom of Xibalbanus tulumensis, a troglobitic arthropod from the enigmatic class Remipedia, that causes persistent calcium release by activation of ion channels involved in muscle contraction. The high-resolution solution structure of φ-Xibalbin3-Xt3a reveals a tandem repeat arrangement of inhibitor-cysteine knot (ICK) domains previously only found in spider venoms. The individual repeats of Xt3a share sequence similarity with a family of scorpion toxins that target ryanodine receptors (RyR). Single-channel electrophysiology and quantification of released Ca²⁺ stores within skinned muscle fibers confirm Xt3a as a bivalent RyR modulator. Our results reveal convergent evolution of RyR targeting toxins in remipede and scorpion venoms, while the tandem-ICK repeat architecture is an evolutionary innovation that is convergent with toxins from spider venoms.

Computational Chemistry for the Identification of Lead Compounds for Radiotracer Development

The use of computer-aided drug design (CADD) for the identification of lead compounds in radiotracer development is steadily increasing. Traditional CADD methods, such as structure-based and ligand-based virtual screening and optimization, have been successfully utilized in many drug discovery programs and are highlighted throughout this review. First, we discuss the use of virtual screening for hit identification at the beginning of drug discovery programs. This is followed by an analysis of how the hits derived from virtual screening can be filtered and culled to highly probable candidates to test in in vitro assays. We then illustrate how CADD can be used to optimize the potency of experimentally validated hit compounds from virtual screening for use in positron emission tomography (PET). Finally, we conclude with a survey of the newest techniques in CADD employing machine learning (ML).

Binary Colloidal Crystals Promote Cardiac Differentiation of Human Pluripotent Stem Cells via Nuclear Accumulation of SETDB1

Biophysical cues can facilitate the cardiac differentiation of human pluripotent stem cells (hPSCs), yet the mechanism is far from established. One of the binary colloidal crystals, composed of 5 μm Si and 400 nm poly(methyl methacrylate) particles named 5PM, has been applied as a substrate for hPSCs cultivation and cardiac differentiation. In this study, cell nucleus, cytoskeleton, and epigenetic states of human induced pluripotent stem cells on the 5PM were analyzed using atomic force microscopy, molecular biology assays, and the assay for transposase-accessible chromatin sequencing (ATAC-seq). Cells were more spherical with stiffer cell nuclei on the 5PM compared to the flat control. ATAC-seq revealed that chromatin accessibility decreased on the 5PM, caused by the increased entry of histone lysine methyltransferase SETDB1 into the cell nuclei and the amplified level of histone H3K9me3 modification. Reducing cytoskeleton tension using a ROCK inhibitor attenuated the nuclear accumulation of SETDB1 on the 5PM, indicating that the effect is cytoskeleton-dependent. In addition, the knockdown of SETDB1 reversed the promotive effects of the 5PM on cardiac differentiation, demonstrating that biophysical cue-induced cytoskeletal tension, cell nucleus deformation, and then SETDB1 accumulation are critical outside-in signal transformations in cardiac differentiation. Human embryonic stem cells showed similar results, indicating that the biophysical impact of the 5PM surfaces on cardiac differentiation could be universal. These findings contribute to our understanding of material-assistant hPSC differentiation, which benefits materiobiology and stem cell bioengineering.

Bioinformatics Tools and Knowledgebases to Assist Generating Targeted Assays for Plasma Proteomics

In targeted proteomics experiments, selecting the appropriate proteotypic peptides as surrogate for the target protein is a crucial pre-acquisition step. This step is largely a bioinformatics exercise that involves integrating information on the peptides and proteins and using various software tools and knowledgebases. We present here a few resources that automate and simplify the selection process to a great degree. These tools and knowledgebases were developed primarily to streamline targeted proteomics assay development and include PeptidePicker, PeptidePickerDB, MRMAssayDB, MouseQuaPro, and PeptideTracker. We have used these tools to develop and document thousands of targeted proteomics assays, many of them for plasma proteins with focus on human and mouse. An important aspect in all these resources is the integrative approach on which they are based. Using these tools in the first steps of designing a singleplexed or multiplexed targeted proteomic experiment can reduce the necessary experimental steps tremendously. All the tools and knowledgebases we describe here are Web-based and freely accessible so scientists can query the information conveniently from the browser. This chapter provides an overview of these software tools and knowledgebases, their content, and how to use them for targeted plasma proteomics. We further demonstrate how to use them with the results of the HUPO Human Plasma Proteome Project to produce a new database of 3.8 k targeted assays for known human plasma proteins. Upon experimental validation, these assays should help in the further quantitative characterizing of the plasma proteome.

HyperVR: a hybrid deep ensemble learning approach for simultaneously predicting virulence factors and antibiotic resistance genes

Infectious diseases emerge unprecedentedly, posing serious challenges to public health and the global economy. Virulence factors (VFs) enable pathogens to adhere, reproduce and cause damage to host cells, and antibiotic resistance genes (ARGs) allow pathogens to evade otherwise curable treatments. Simultaneous identification of VFs and ARGs can save pathogen surveillance time, especially in situ epidemic pathogen detection. However, most tools can only predict either VFs or ARGs. Few tools that predict VFs and ARGs simultaneously usually have high false-negative rates, are sensitive to the cutoff thresholds and can only identify conserved genes. For better simultaneous prediction of VFs and ARGs, we propose a hybrid deep ensemble learning approach called HyperVR. By considering both best hit scores and statistical gene sequence patterns, HyperVR combines classical machine learning and deep learning to simultaneously and accurately predict VFs, ARGs and negative genes (neither VFs nor ARGs). For the prediction of individual VFs and ARGs, in silico spike-in experiment (the VFs and ARGs in real metagenomic data), and pseudo-VFs and -ARGs (gene fragments), HyperVR outperforms the current state-of-the-art prediction tools. HyperVR uses only gene sequence information without strict cutoff thresholds, hence making prediction straightforward and reliable.

Combined analysis of chromatin accessibility and gene expression profiles provide insight into Fucoxanthin biosynthesis in Isochrysis galbana under green light

Isochrysis galbana, as a potential accumulator of fucoxanthin, has become a valuable material to develop functional foods for humans. Our previous research revealed that green light effectively promotes the accumulation of fucoxanthin in I. galbana, but there is little research on chromatin accessibility in the process of transcriptional regulation. This study was conducted to reveal the mechanism of fucoxanthin biosynthesis in I. galbana under green light by analyzing promoter accessibility and gene expression profiles. Differentially accessible chromatin regions (DARs)-associated genes were enriched in carotenoid biosynthesis and photosynthesis-antenna protein formation, including IgLHCA1, IgLHCA4, IgPDS, IgZ-ISO, IglcyB, IgZEP, and IgVDE. The motifs for the MYB family were also identified as candidates controlling metabolic regulation responses to green light culture of I. galbana, including IgMYB1, IgMYB2, IgMYB33, IgMYB42, IgMYB98, IgMYB118, and IgMYB119. The results of differential expression analysis and WGCNA showed that several genes or transcription factors (TFs) related to carotenoid metabolism and photosynthesis exhibited a higher expression level and were significantly upregulated in A-G5d compared with A-0d and A-W5d, including IgMYB98, IgLHCA1, IgLHCX2, IgLHCB4, and IgLHCB5. This suggests that upregulation of these genes by green light may be the key factor leading to fucoxanthin accumulation by regulating the photosynthesis-antenna protein pathway. An integrated analysis of ATAC-seq and RNA-seq showed that 3 (IgphoA, IgPKN1, IgOTC) of 34 DARs-associated genes displayed obvious changes in their chromatin regions in ATAC-seq data, suggesting that these genes specific for green light may play a key role in fucoxanthin biosynthesis in I. galbana through a complex regulatory network of multiple metabolic pathways interacting with each other. These findings will facilitate in-depth understanding the molecular regulation mechanisms of fucoxanthin in I. galbana and its role in response to green light regulation, providing technical support for the construction of high fucoxanthin content strains.

A Knowledge-Graph-Based Multimodal Deep Learning Framework for Identifying Drug-Drug Interactions

The identification of drug-drug interactions (DDIs) plays a crucial role in various areas of drug development. In this study, a deep learning framework (KGCN_NFM) is presented to recognize DDIs using coupling knowledge graph convolutional networks (KGCNs) with neural factorization machines (NFMs). A KGCN is used to learn the embedding representation containing high-order structural information and semantic information in the knowledge graph (KG). The embedding and the Morgan molecular fingerprint of drugs are then used as input of NFMs to predict DDIs. The performance and effectiveness of the current method have been evaluated and confirmed based on the two real-world datasets with different sizes, and the results demonstrate that KGCN_NFM outperforms the state-of-the-art algorithms. Moreover, the identified interactions between topotecan and dantron by KGCN_NFM were validated through MTT assays, apoptosis experiments, cell cycle analysis, and molecular docking. Our study shows that the combination therapy of the two drugs exerts a synergistic anticancer effect, which provides an effective treatment strategy against lung carcinoma. These results reveal that KGCN_NFM is a valuable tool for integrating heterogeneous information to identify potential DDIs.

The complete genome sequence of Pseudomonas chengduensis BC1815 for genome mining of PET degrading enzymes

Pseudomonas chengduensis BC1815, isolated from a marine sediment sample of the Pacific Ocean, can grow in mineral medium with PET plastic as sole carbon source. Here, we present the complete genome of Pseudomonas chengduensis BC1815, which will facilitate the genome mining of PET degrading enzymes. The total length of the sequenced genome consists of 5,578,440 bases, with mean G + C content of 62.65%. A total of 5150 coding genes including 65 tRNAs and 12 rRNAs were predicted in the genome. Thirteen proteins of esterase, lipase and α/β hydrolase were taken as candidates for PET degrading enzymes, in which eight were membrane bounded and the others were secretory.

In silico identification and characterization of small-molecule inhibitors specific to RhoG/Rac1 signaling pathway

Rho family GTPases serve as molecular switches in numerous cellular processes, and their overexpression is involved in disease conditions. RhoG is one of the less explored Rho GTPases with significant sequential and structural homology with Rac1. Experimental mutations in RhoG (i.e., RhoGG12V and RhoGQ61L) are shown to dysregulate cell migration. Thus, targeting upstream activators of RhoG, such as guanine nucleotide exchange factors (GEFs), maybe an important strategy for inhibiting RhoG activation. In the current study, we have modelled the 3D structure of RhoG with greater accuracy as confirmed through PROCHECK, ProSA, and Verify3D. Our results indicate that 90.4% of residues are in the Ramachandran plots favoured region, with the Z-score of -6.46, and 87.96% of residues had an averaged 3D-1D score ≥0.2. Further, we have evaluated and binding dynamics of ten Rac1 inhibitors to investigate their potential to inhibit RhoG by targeting GEFs binding grooves. To this end, the binding energy of the docked complexes of the wild-type (WT) RhoG and its mutant proteins with inhibitor molecules was calculated using the MM/PBSA method. Our results from docking studies showed that macrolide1 binds efficiently with the GEF site of WT RhoG and the mutants mentioned above. However, an extensive analysis using MD simulations (200 ns) showed that the Rac1 based inhibitor, EHop-016, and NSC23766 might bind with greater affinity to GEF sites of mutants and WT RhoG. Thus, the results from the study indicate that Rac1 inhibitors have the potential for use as therapeutics in conditions involving dysregulation of RhoG.Communicated by Ramaswamy H. Sarma.

Plant Growth Promotion, Phytohormone Production and Genomics of the Rhizosphere-Associated Microalga, Micractinium rhizosphaerae sp. nov

Microalgae are important members of the soil and plant microbiomes, playing key roles in the maintenance of soil and plant health as well as in the promotion of plant growth. However, not much is understood regarding the potential of different microalgae strains in augmenting plant growth, or the mechanisms involved in such activities. In this work, the functional and genomic characterization of strain NFX-FRZ, a eukaryotic microalga belonging to the Micractinium genus that was isolated from the rhizosphere of a plant growing in a natural environment in Portugal, is presented and analyzed. The results obtained demonstrate that strain NFX-FRZ (i) belongs to a novel species, termed Micractinium rhizosphaerae sp. nov.; (ii) can effectively bind to tomato plant tissues and promote its growth; (iii) can synthesize a wide range of plant growth-promoting compounds, including phytohormones such as indole-3-acetic acid, salicylic acid, jasmonic acid and abscisic acid; and (iv) contains multiple genes involved in phytohormone biosynthesis and signaling. This study provides new insights regarding the relevance of eukaryotic microalgae as plant growth-promoting agents and helps to build a foundation for future studies regarding the origin and evolution of phytohormone biosynthesis and signaling, as well as other plant colonization and plant growth-promoting mechanisms in soil/plant-associated Micractinium.

Genome-wide identification of drought-responsive microRNAs and their target genes in Chinese jujube by deep sequencing

BACKGROUND

MicroRNAs (miRNAs) are about 21 snucleotide (nt) long, non-coding RNAs that play an important role in plant abiotic stress responses. Chinese jujube is a native fruit tree in China, which is also an admittedly drought-resistant plant. But the drought-related miRNAs have little been reported in jujube.

OBJECTIVE

To identify possibly drought-responsive microRNAs and their target genes in Chinese Jujube.

METHODS

Twelve small RNA libraries were constructed from two jujube genotypes both drought treated and control samples with three replicates to identify known and novel miRNAs in Chinese Jujube, DESeq2 was used to identify expression pattern of miRNAs between drought treatment and control samples, TargetFinder program was used to predict potential target genes of conserved and novel miRNAs, RT-qPCR were used to analysis the expression levels of drought-related miRNAs and their potential targets. The RNA ligase-mediated RLM-5' RACE experiments were performed to validate predicted target genes of drought-related miRNAs.

RESULTS

43 known miRNAs and 431 novel miRNAs were identified in Chinese jujube. Expression analysis showed that 28 miRNAs were differential expressed under drought stress in jujube variety "Dongzao", including 21 up-regulated miRNAs and 7 down-regulated miRNAs, 61 miRNAs were differential expressed under drought stress in Chinese jujube variety "Zanhuangdazao", including 23 up-regulated miRNAs and 37 down-regulated miRNAs. Depend on miRNAs target prediction, functional annotation and expression analysis, we identified 9 drought-related miRNAs, and 7 target genes of 6 miRNAs were confirmed using the modified 5'-RACE method. Also, RT-qPCR analyses revealed that relative expression of those miRNAs and their targets have negative tendency.

CONCLUSION

We identified 6 drought-related miRNAs by high-throughout sequencing and target gene annotation from Chinese jujube, and targets of those miRNAs were confirmed by the modified 5'-RACE method. These findings provide molecular evidence for enhancing drought tolerance in Chinese jujube and other plants.

Transcriptome analysis of hepatopancreas of Chinese grass shrimp, Palaemonetes sinensis, infected by Enterocytospora artemiae

The emergence of the microsporidian, Enterocytospora artemiae, has caused serious economic losses to the aquaculture industry of Palaemonetes sinensis. The hepatopancreas is the main digestive and immune organ of P. sinensis, and the main site of E. artemiae infection. We used next-generation sequencing to determine the effects of E. artemiae parasitism on the hepatopancreas of P. sinensis at the transcriptome level. The hepatopancreas of P. sinensis was parasitized by E. artemiae, and 881 differentially expressed genes (DEGs) were obtained, of which 643 were upregulated and 238 were downregulated. These DEGs are mainly involved in DNA replication, transcription, translation, immunity, and metabolism. Among them, the cellular processes of DNA replication, transcription and translation are significantly strengthened, which may be related to the use of host ATP and nucleic acid by E. artemiae to achieve proliferation and damage to host cells to enhance DNA replication and repair. Moreover, to defend against E. artemiae, some immune genes related to antioxidation, such as glutathione metabolism, seleno compound metabolism, and cytochrome p450 2L1, were significantly upregulated, but simultaneously, tumor necrosis factor, NF-κB inhibitor α, and other immune-related genes were significantly down regulated, indicating that the parasitism of E. artemiae led to a significant decline in the immune defense ability of P. sinensis. From the perspective of metabolism, the metabolism-related DEGs of retinol, glycine, serine, and threonine metabolism, were significantly downregulated, resulting in insufficient nutrient absorption and decreased energy supply of the P. sinensis, which in turn affected their growth. The differential genes and pathways identified in this study can provide a reference basis to further elucidate the pathogenic mechanism of P. sinensis infected with E. artemiae and the prevention and control of microsporidia disease.

MYB pathways that regulate UV-B-induced anthocyanin biosynthesis in blueberry (Vaccinium corymbosum)

Ultraviolet-B (UV-B) promotes anthocyanin accumulation and improves fruit quality in plants. To explore the underlying network of MYB transcription factors that regulates UV-B-induced anthocyanin biosynthesis in blueberry (Vaccinium corymbosum), we analyzed the response of MYB transcription factor genes to UV-B treatment. Transcriptome sequencing analysis revealed that VcMYBA2 and VcMYB114 expression were upregulated and were positively correlated with the expression of anthocyanin structural genes under UV-B radiation according to weighted gene co-expression network analysis (WGCNA) data. The VcUVR8-VcCOP1-VcHY5 pathway perceives UV-B signals and promotes the expression of anthocyanin structural genes by upregulating VcMYBA2 and VcMYB114 or by regulating the VcBBXs-VcMYB pathway, ultimately promoting anthocyanin accumulation. By contrast, VcMYB4a and VcUSP1 were downregulated under UV-B treatment, and VcMYB4a expression was negatively correlated with that of anthocyanin biosynthesis genes in response to UV-B. Analysis of VcMYB4a-overexpressing and wild-type blueberry calli exposed to UV-B radiation revealed that VcMYB4a represses UV-B-induced anthocyanin accumulation. Yeast one-hybrid and dual luciferase assays showed that the universal stress protein VcUSP1 directly bound to the promoter of VcMYB4a. These results suggest that the VcUSP1-VcMYB4a pathway negatively regulates UV-B-induced anthocyanin biosynthesis and provide insight into UV-B-induced anthocyanin biosynthesis.

DeepMPF: deep learning framework for predicting drug-target interactions based on multi-modal representation with meta-path semantic analysis

BACKGROUND

Drug-target interaction (DTI) prediction has become a crucial prerequisite in drug design and drug discovery. However, the traditional biological experiment is time-consuming and expensive, as there are abundant complex interactions present in the large size of genomic and chemical spaces. For alleviating this phenomenon, plenty of computational methods are conducted to effectively complement biological experiments and narrow the search spaces into a preferred candidate domain. Whereas, most of the previous approaches cannot fully consider association behavior semantic information based on several schemas to represent complex the structure of heterogeneous biological networks. Additionally, the prediction of DTI based on single modalities cannot satisfy the demand for prediction accuracy.

METHODS

We propose a multi-modal representation framework of 'DeepMPF' based on meta-path semantic analysis, which effectively utilizes heterogeneous information to predict DTI. Specifically, we first construct protein-drug-disease heterogeneous networks composed of three entities. Then the feature information is obtained under three views, containing sequence modality, heterogeneous structure modality and similarity modality. We proposed six representative schemas of meta-path to preserve the high-order nonlinear structure and catch hidden structural information of the heterogeneous network. Finally, DeepMPF generates highly representative comprehensive feature descriptors and calculates the probability of interaction through joint learning.

RESULTS

To evaluate the predictive performance of DeepMPF, comparison experiments are conducted on four gold datasets. Our method can obtain competitive performance in all datasets. We also explore the influence of the different feature embedding dimensions, learning strategies and classification methods. Meaningfully, the drug repositioning experiments on COVID-19 and HIV demonstrate DeepMPF can be applied to solve problems in reality and help drug discovery. The further analysis of molecular docking experiments enhances the credibility of the drug candidates predicted by DeepMPF.

CONCLUSIONS

All the results demonstrate the effectively predictive capability of DeepMPF for drug-target interactions. It can be utilized as a useful tool to prescreen the most potential drug candidates for the protein. The web server of the DeepMPF predictor is freely available at http://120.77.11.78/DeepMPF/ , which can help relevant researchers to further study.

Data-driven enzyme engineering to identify function-enhancing enzymes

Identifying function-enhancing enzyme variants is a 'holy grail' challenge in protein science because it will allow researchers to expand the biocatalytic toolbox for late-stage functionalization of drug-like molecules, environmental degradation of plastics and other pollutants, and medical treatment of food allergies. Data-driven strategies, including statistical modeling, machine learning, and deep learning, have largely advanced the understanding of the sequence-structure-function relationships for enzymes. They have also enhanced the capability of predicting and designing new enzymes and enzyme variants for catalyzing the transformation of new-to-nature reactions. Here, we reviewed the recent progresses of data-driven models that were applied in identifying efficiency-enhancing mutants for catalytic reactions. We also discussed existing challenges and obstacles faced by the community. Although the review is by no means comprehensive, we hope that the discussion can inform the readers about the state-of-the-art in data-driven enzyme engineering, inspiring more joint experimental-computational efforts to develop and apply data-driven modeling to innovate biocatalysts for synthetic and pharmaceutical applications.

Deep-learning based approach to identify substrates of human E3 ubiquitin ligases and deubiquitinases

E3 ubiquitin ligases (E3s) and deubiquitinating enzymes (DUBs) play key roles in protein degradation. However, a large number of E3 substrate interactions (ESIs) and DUB substrate interactions (DSIs) remain elusive. Here, we present DeepUSI, a deep learning-based framework to identify ESIs and DSIs using the rich information present in protein sequences. Utilizing the collected golden standard dataset, key hyperparameters in the process of model training, including the ones relevant to data sampling and number of epochs, have been systematically assessed. The performance of DeepUSI was thoroughly evaluated by multiple metrics, based on internal and external validation. Application of DeepUSI to cancer-associated E3 and DUB genes identified a list of druggable substrates with functional implications, warranting further investigation. Together, DeepUSI presents a new framework for predicting substrates of E3 ubiquitin ligases and deubiquitinates.

Anti-Vimentin Nanobody Decreases Glioblastoma Cell Invasion In Vitro and In Vivo

PURPOSE

Glioblastoma (GBM) is the most common primary brain tumour and one of the deadliest cancers. In addition to late diagnosis and inadequate treatment, the extremely low survival rate is also due to the lack of appropriate therapeutic biomarkers and corresponding therapeutic agents. One of the potential therapeutic biomarkers is the intermediate filament vimentin, which is associated with epithelial-mesenchymal transition (EMT). The purpose of this study was to analyse the effect of the anti-vimentin nanobody Nb79 on cell invasion in vitro and in vivo. To further our understanding of the mechanism of action, we investigated the association between Nb79 and EMT in GBM and GBM stem cells by analysing the expression levels of key EMT-related proteins.

METHODS

The expression of vimentin in glioma tissues and cells was determined by RT-qPCR. An invasion assay was performed on differentiated glioblastoma cell line U-87 MG and stem cell line NCH421k in vitro as well as in vivo in zebrafish embryos. The effect of Nb79 on expression of EMT biomarkers beta-catenin, vimentin, ZEB-1 and ZO1 was determined by Western blot and immunocytochemistry.

RESULTS

Our study shows that vimentin is upregulated in glioblastoma tissue compared to lower grade glioma and non-tumour brain tissue. We demonstrated that treatment with Nb79 reduced glioblastoma cell invasion by up to 64% in vitro and up to 21% in vivo. In addition, we found that the tight junction protein ZO-1 had higher expression on the cell membrane, when treated with inhibitory anti-vimentin Nb79 compared to control.

CONCLUSION

In conclusion, our results suggest that anti-vimentin nanobody Nb79 is a promising tool to target glioblastoma cell invasion.

Evolutionary Landscape of SOX Genes to Inform Genotype-to-Phenotype Relationships

The SOX transcription factor family is pivotal in controlling aspects of development. To identify genotype-phenotype relationships of SOX proteins, we performed a non-biased study of SOX using 1890 open-reading frame and 6667 amino acid sequences in combination with structural dynamics to interpret 3999 gnomAD, 485 ClinVar, 1174 Geno2MP, and 4313 COSMIC human variants. We identified, within the HMG (High Mobility Group)- box, twenty-seven amino acids with changes in multiple SOX proteins annotated to clinical pathologies. These sites were screened through Geno2MP medical phenotypes, revealing novel SOX15 R104G associated with musculature abnormality and SOX8 R159G with intellectual disability. Within gnomAD, SOX18 E137K (rs201931544), found within the HMG box of ~0.8% of Latinx individuals, is associated with seizures and neurological complications, potentially through blood-brain barrier alterations. A total of 56 highly conserved variants were found at sites outside the HMG-box, including several within the SOX2 HMG-box-flanking region with neurological associations, several in the SOX9 dimerization region associated with Campomelic Dysplasia, SOX14 K88R (rs199932938) flanking the HMG box associated with cardiovascular complications within European populations, and SOX7 A379V (rs143587868) within an SOXF conserved far C-terminal domain heterozygous in 0.716% of African individuals with associated eye phenotypes. This SOX data compilation builds a robust genotype-to-phenotype association for a gene family through more robust ortholog data integration.

Comprehensive genome-scale metabolic model of the human pathogen Cryptococcus neoformans: A platform for understanding pathogen metabolism and identifying new drug targets

Introduction: The fungal priority pathogen Cryptococcus neoformans causes cryptococcal meningoencephalitis in immunocompromised individuals and leads to hundreds of thousands of deaths per year. The undesirable side effects of existing treatments, the need for long application times to prevent the disease from recurring, the lack of resources for these treatment methods to spread over all continents necessitate the search for new treatment methods. Methods: Genome-scale models have been shown to be valuable in studying the metabolism of many organisms. Here we present the first genome-scale metabolic model for C. neoformans, iCryptococcus. This comprehensive model consists of 1,270 reactions, 1,143 metabolites, 649 genes, and eight compartments. The model was validated, proving accurate when predicting the capability of utilizing different carbon and nitrogen sources and growth rate in comparison to experimental data. Results and Discussion: The compatibility of the in silico Cryptococcus metabolism under infection conditions was assessed. The steroid and amino acid metabolisms found in the essentiality analyses have the potential to be drug targets for the therapeutic strategies to be developed against Cryptococcus species. iCryptococcus model can be applied to explore new targets for antifungal drugs along with essential gene, metabolite and reaction analyses and provides a promising platform for elucidation of pathogen metabolism.

psnGPCRdb: The Structure-network Database of G Protein Coupled Receptors

G protein coupled receptors (GPCRs) are critical eukaryotic signal transduction gatekeepers and represent the largest protein superfamily in the human proteome, with more than 800 members. They share seven transmembrane helices organized in an up-down bundle architecture. GPCR-mediated signaling pathways have been linked to numerous human diseases, and GPCRs are the targets of approximately 35% of all drugs currently on the market. Structure network analysis, a graph theory-based approach, represents a cutting-edge tool to deeply understand GPCR function, which strongly relies on communication between the extracellular and intracellular poles of their structure. psnGPCRdb stores the structure networks (i.e., linked nodes, hubs, communities and communication pathways) computed on all updated GPCR structures in the Protein Data Bank, in their isolated states or in complex with extracellular and/or intracellular molecules. The structure communication signatures of a sub-family or family of GPCRs as well as of their small-molecule activators or inhibitors are stored as consensus networks. The database stores also all meaningful structure network-based comparisons (i.e., difference networks) of functionally different states (i.e., inactive or active) of a given receptor sub-type, or of consensus networks representative of a receptor sub-type, type, sub-family or family. Single or consensus GPCR networks hold also information on amino acid conservation. The database allows to graphically analyze 3D structure networks together with interactive data-tables. Ligand-centric networks can be analyzed as well. psnGPCRdb is unique and represents a powerful resource to unravel GPCR function with important implications in cell signaling and drug design. psnGPCRdb is freely available at: http://webpsn.hpc.unimo.it/psngpcr.php.

Transcriptome analysis and gene expression analysis related to salinity-alkalinity and low temperature adaptation of Triplophysa yarkandensis

T. yarkandensis is a common species of Triplophysa, and it is distributed in Shule river of Hexi Corridor, of Gansu province in China. In order to enrich gene database resources and explore the environment adaptation of T. yarkandensis, fifteen tissues were collected from three adult T. yarkandensis for transcriptome sequencing and de novo assembly. Nine major international gene annotation databases (NR, COG, egg_NOG, TrEMBL, Pfam, KOG, Swiss prot, KEGG and Gene Ontology) were utilized to annotate unigenes. A detailed study was conducted to explore the gene expression and the differentially expressed genes among five tissues (brain, heart, kidney, liver and spleen). In addition, the current study showed that candidate genes involved in salinity-alkalinity and low temperature adaptation were differentially expressed in tissues of T. yarkandensis. Precisely, mapk1, abcc1, gpx1, gpx4, cat and aqp1 genes participated in the regulation process of salinity-alkalinity adaptation, and elovl4, acaca, fasn, acaa2, acox1 and acox3 genes were involved in fatty acid metabolism and closely associated with low temperature adaptation. On the one hand, it was found that the expression of these genes varied among different tissues, and the important pathways involved in these genes were mapped. Furthermore, we analyzed mapk1 and acox1 genes in depth to obtain the predicted gene structure and important amino acid sites. The transcriptome information in this study will be conducive to provide further understanding for the molecular level research and exploration of the environmental adaptation of T. yarkandensis.

New approaches in developing medicinal herbs databases

Medicinal herbs databases have become a crucial part of organizing new scientific literature generated in medicinal herbs field, as well as new drug discoveries in the information era. The aim of this review was to track the current status of medicinal herbs databases. Search for finding medicinal herbs databases was carried out via Google and PubMed. PubMed was searched for papers introducing medicinal herbs databases by the recruited search strategy. Papers with an active database on the web were included in the review. Google was also searched for medicinal herbs databases. Both retrieved papers and databases were reviewed by the authors. In this review, the current status of 25 medicinal herbs databases was reviewed, and the important characteristics of databases were mentioned. The reviewed databases had a great variety in terms of characteristics and functions. Finally, some recommendations for the efficient development of medicinal herbs databases were suggested. Although contemporary medicinal herbs databases represent much useful information, adding some features to these databases could assist them to have better functionality. This work may not cover all the necessary information, but we hope that our review can provide readers with fundamental concepts, perspectives and suggestions for constructing more useful databases.

Highly Accessible Computational Prediction and In Vivo/In Vitro Experimental Validation: Novel Synthetic Phenyl Ketone Derivatives as Promising Agents against NAFLD via Modulating Oxidoreductase Activity

Nonalcoholic fatty liver disease (NAFLD) has reached epidemic proportions with no pharmacological treatment approved. Several highly accessible computational tools were employed to predict the activities of twelve novel compounds prior to actual chemical synthesis. We began our work by designing two or three hydroxyl groups appended to the phenyl ketone core, followed by prediction of drug-likeness and targets. Most predicted targets for each compound overlapped with NAFLD targets (≥80%). Enrichment analysis showed that these compounds might regulate oxidoreductase activity. Then, these compounds were synthesized and confirmed by IR, MS, ¹H, and ¹³C NMR. Their cell viability demonstrated that twelve compounds exhibited appreciable potencies against NAFLD (EC₅₀ values ≤ 13.5 μM). Furthermore, the most potent compound 5f effectively prevented NAFLD progression as evidenced by the change in histological features. 5f significantly reduced total cholesterol and triglyceride levels in vitro/in vivo, and the effects of 5f were significantly stronger than those of the control drug. The proteomic data showed that oxidoreductase activity was the most significantly enriched, and this finding was consistent with docking results. In summary, this validated presynthesis prediction approach was cost-saving and worthy of popularization. The novel synthetic phenyl ketone derivative 5f holds great therapeutic potential by modulating oxidoreductase activity to counter NAFLD.

LGBMDF: A cascade forest framework with LightGBM for predicting drug-target interactions

Prediction of drug-target interactions (DTIs) plays an important role in drug development. However, traditional laboratory methods to determine DTIs require a lot of time and capital costs. In recent years, many studies have shown that using machine learning methods to predict DTIs can speed up the drug development process and reduce capital costs. An excellent DTI prediction method should have both high prediction accuracy and low computational cost. In this study, we noticed that the previous research based on deep forests used XGBoost as the estimator in the cascade, we applied LightGBM instead of XGBoost to the cascade forest as the estimator, then the estimator group was determined experimentally as three LightGBMs and three ExtraTrees, this new model is called LGBMDF. We conducted 5-fold cross-validation on LGBMDF and other state-of-the-art methods using the same dataset, and compared their Sn, Sp, MCC, AUC and AUPR. Finally, we found that our method has better performance and faster calculation speed.

Isolation and Characterization of Bacteria from Natural Hot Spring and Insights into the Thermophilic Cellulase Production

A thermophilic cellulase-producing bacterium, Bacillus velezensis strain MRC 5958, from Bakra natural hot Springs, India was characterized through genome sequencing. It has a genome size of 4,467,129 bp and a GC content of 45.7%. A cellulase purified from its fermentation broth has a molecular weight of about 18 kDa. The optimum temperature and pH for carboxymethyl cellulase activity were at 55 °C and pH ~ 7.0. The enzyme is stable over a wide range of temperatures from 30 °C to 70 °C with maximum activity observed at 48 h of incubation. The strain produces cellulase on alkali-treated sugarcane bagasse, rice straw, rice husk, rice bran, and sawdust. The sugarcane bagasse exhibited the most effective carbon source for cellulase production at (85 U/ml) followed by rice bran (68 U/ml), rice husk (60 U/ml), rice straw (48 U/ml), and sawdust (39 U/ml). Therefore, this strain can be a potential thermostable cellulase-producing candidate for converting the waste biomass into biofuel and other industrial enzymes.

Pan-genome association study of Mycobacterium tuberculosis lineage-4 revealed specific genes related to the high and low prevalence of the disease in patients from the North-Eastern area of Medellín, Colombia

Mycobacterium tuberculosis (Mtb) lineage 4 is responsible for the highest burden of tuberculosis (TB) worldwide. This lineage has been the most prevalent lineage in Colombia, especially in the North-Eastern (NE) area of Medellin, where it has been shown to have a high prevalence of LAM9 SIT42 and Haarlem1 SIT62 sublineages. There is evidence that regardless of environmental factors and host genetics, differences among sublineages of Mtb strains play an important role in the course of infection and disease. Nevertheless, the genetic basis of the success of a sublineage in a specific geographic area remains uncertain. We used a pan-genome-wide association study (pan-GWAS) of 47 Mtb strains isolated from NE Medellin between 2005 and 2008 to identify the genes responsible for the phenotypic differences among high and low prevalence sublineages. Our results allowed the identification of 12 variants in 11 genes, of which 4 genes showed the strongest association to low prevalence (mmpL12, PPE29, Rv1419, and Rv1762c). The first three have been described as necessary for invasion and intracellular survival. Polymorphisms identified in low prevalence isolates may suggest related to a fitness cost of Mtb, which might reflect a decrease in their capacity to be transmitted or to cause an active infection. These results contribute to understanding the success of some sublineages of lineage-4 in a specific geographical area.

Regulation of secondary hair follicle cycle in cashmere goats by miR-877-3p targeting IGFBP5 gene

Cashmere, a highly valuable animal product derived from cashmere goats, holds significant economic importance. MiRNAs serve as crucial regulators in the developmental processes of mammalian hair follicles. Understanding the regulation of miRNAs during the hair follicle cycle is essential for enhancing cashmere quality. In this investigation, we employed high-throughput sequencing technology to analyze the expression profiles of miRNAs in the secondary hair follicles of Jiangnan cashmere goats at different stages. Through bioinformatics analysis, we identified differentially expressed miRNAs (DE miRNAs). The regulatory relationships between miRNAs and their target genes were verified using multiple techniques, including RT-qPCR, western blot, Dual-Luciferase Reporter, and CKK-8 assays. Our findings revealed the presence of 193 DE miRNAs during various stages of the hair follicle cycle in Jiangnan cashmere goats. Based on the previously obtained mRNA data, the target genes of DE miRNA were predicted, and 1,472 negative regulatory relationships between DE miRNAs and target genes were obtained. Notably, the expression of chi-miR-877-3p was down-regulated during the telogen (Tn) phase compared to the anagen (An) and catagen (Cn) phases, while the IGFBP5 gene exhibited up-regulation. Further validation experiments confirmed that overexpression of chi-miR-877-3p in dermal papilla cells suppressed IGFBP5 gene expression and facilitated cell proliferation. The results of this study provide novel insights for analyzing the hair follicle cycle.

Cross-species cell-type assignment from single-cell RNA-seq data by a heterogeneous graph neural network

Cross-species comparative analyses of single-cell RNA sequencing (scRNA-seq) data allow us to explore, at single-cell resolution, the origins of the cellular diversity and evolutionary mechanisms that shape cellular form and function. Cell-type assignment is a crucial step to achieve that. However, the poorly annotated genome and limited known biomarkers hinder us from assigning cell identities for nonmodel species. Here, we design a heterogeneous graph neural network model, CAME, to learn aligned and interpretable cell and gene embeddings for cross-species cell-type assignment and gene module extraction from scRNA-seq data. CAME achieves significant improvements in cell-type characterization across distant species owing to the utilization of non-one-to-one homologous gene mapping ignored by early methods. Our large-scale benchmarking study shows that CAME significantly outperforms five classical methods in terms of cell-type assignment and model robustness to insufficiency and inconsistency of sequencing depths. CAME can transfer the major cell types and interneuron subtypes of human brains to mouse and discover shared cell-type-specific functions in homologous gene modules. CAME can align the trajectories of human and macaque spermatogenesis and reveal their conservative expression dynamics. In short, CAME can make accurate cross-species cell-type assignments even for nonmodel species and uncover shared and divergent characteristics between two species from scRNA-seq data.

Transcriptome analysis and exploration of genes involved in the biosynthesis of secoiridoids in Gentiana rhodantha

Gentiana rhodantha is a medicinally important perennial herb used as traditional Chinese and ethnic medicines. Secoiridoids are one of the major bioactive compounds in G. rhodantha. To better understand the secoiridoid biosynthesis pathway, we generated transcriptome sequences from four organs (root, leaf, stem and flower), followed by the de novo sequence assembly. We verified 8-HGO (8-hydroxygeraniol oxidoreductase), which may encode key enzymes of the secoiridoid biosynthesis by qRT-PCR. The mangiferin, swertiamarin and loganic acid contents in root, stem, leaf, and flower were determined by HPLC. The results showed that there were 47,871 unigenes with an average length of 1,107.38 bp. Among them, 1,422 unigenes were involved in 25 standard secondary metabolism-related pathways in the KEGG database. Furthermore, we found that 1,005 unigenes can be divided into 66 transcription factor (TF) families, with no family members exhibiting significant organ-specificity. There were 54 unigenes in G. rhodantha that encoded 17 key enzymes of the secoiridoid biosynthetic pathway. The qRT-PCR of the 8-HGO and HPLC results showed that the relative expression and the mangiferin, swertiamarin, and loganic acid contents of the aerial parts were higher than in the root. Six types of SSR were identified by SSR analysis of unigenes: mono-nucleoside repeat SSR, di-nucleoside repeat SSR, tri-nucleoside repeat SSR, tetra-nucleoside repeat SSR, penta-nucleoside repeat SSR, and hexa-nucleoside repeat SSR. This report not only enriches the Gentiana transcriptome database but helps further study the function and regulation of active component biosynthesis of G. rhodantha.

PEATmoss: A Gene Expression Atlas for Bryophytes

PEATmoss is an interactive gene expression atlas for bryophytes, which originally unified Physcomitrium patens RNA-seq and microarray expression data from multiple gene annotation versions. This atlas includes more than 100 experiments of P. patens, is expanding to host Anthoceros agrestis and Marchantia polymorpha, and aims to host data from more species in the future. PEATmoss has multiple visualization methods and tools for data downloading and is connected to the Physcomitrium patens Gene Model Lookup DB (PpGML DB), which links P. patens genes to annotations and resources from several databases and contains tools for gene version lookup and sequence and annotation extraction. Among the new features available in PEATmoss are dataset privacy control, multispecies menu, interactive color scale, co-expression network visualization, and replicate data downloading.

The Interactome of Protein, DNA, and RNA

Proteins participate in many processes of the organism and are very important for maintaining the health of the organism. However, proteins cannot function independently in the body. They must interact with proteins, DNA, RNA, and other substances to perform biological functions and maintain the body's health. At present, there are many experimental methods and software tools that can detect and predict the interaction between proteins and other substances. There are also many databases that record the interaction between proteins and other substances. This article mainly describes protein-protein, protein-DNA, and protein-RNA interactions in detail by introducing some commonly used experimental methods, the software tools produced with the accumulation of experimental data and the rapid development of machine learning, and the related databases that record the relationship between proteins and some substances. By this review, we hope that through the analysis and summary of various aspects, it will be convenient for researchers to conduct further research on protein interactions.

Quorum sensing-based interactions among drugs, microbes, and diseases

Many diseases and health conditions are closely related to various microbes, which participate in complex interactions with diverse drugs; nonetheless, the detailed targets of such drugs remain to be elucidated. Many existing studies have reported causal associations among drugs, gut microbes, or diseases, calling for a workflow to reveal their intricate interactions. In this study, we developed a systematic workflow comprising three modules to construct a Quorum Sensing-based Drug-Microbe-Disease (QS-DMD) database ( http://www.qsdmd.lbci.net/ ), which includes diverse interactions for more than 8,000 drugs, 163 microbes, and 42 common diseases. Potential interactions between microbes and more than 8,000 drugs have been systematically studied by targeting microbial QS receptors combined with a docking-based virtual screening technique and in vitro experimental validations. Furthermore, we have constructed a QS-based drug-receptor interaction network, proposed a systematic framework including various drug-receptor-microbe-disease connections, and mapped a paradigmatic circular interaction network based on the QS-DMD, which can provide the underlying QS-based mechanisms for the reported causal associations. The QS-DMD will promote an understanding of personalized medicine and the development of potential therapies for diverse diseases. This work contributes to a paradigm for the construction of a molecule-receptor-microbe-disease interaction network for human health that may form one of the key knowledge maps of precision medicine in the future.

Understanding the Molecular Basis for Enhanced Glutenase Activity of Actinidin using Structural Bioinformatics

BACKGROUND

Management of gluten intolerance is currently possible only by consumption of a gluten-free diet (GFD) for a lifetime. The scientific community has been searching for alternatives to GFD, like the inclusion of natural proteases with meals or pre-treatment of gluten-containing foods with glutenases. Actinidin from kiwifruit has shown considerable promise in digesting immunogenic gliadin peptides compared to other plant-derived cysteine proteases.

METHODS

In this study, we aimed to understand the structural basis for the elevated protease action of actinidin against gliadin peptides by using an in silico approach.

RESULTS

Docking experiments revealed key differences between the binding of gliadin peptide to actinidin and papain, which may be responsible for their differential digestive action.

CONCLUSION

Sequence comparison of different plant cysteine proteases highlights amino acid residues surrounding the active site pocket of actinidin that are unique to this molecule and hence likely to contribute to its digestive properties.