Search

IntroductionThe influenza virus is recognized as the primary cause of human respiratory diseases, with the current influenza vaccine primarily offering strain-specific immunity and limited protection against drifting strains. Considering this, the development of a broad-spectrum influenza vaccine capable of inducing effective immunity is considered the future direction in combating influenza.MethodsThe present study proposes a novel mRNA-based multi-epitope influenza vaccine, which combines three conserved antigens derived from the influenza A virus. The antigens consist of M2 ion channel’s extracellular domain (M2e), the conserved epitope of located in HA2 of hemagglutinin (H1, H3, B), and HA1 of hemagglutinin. At the same time, trimeric sequences and ferritin were conjugated separately to investigate the immune effects of antigen multivalent presentation.ResultsImmunization studies conducted on C57BL/6 mice with these vaccines revealed that they can elicit both humoral immunity and CD4+ and CD8+ T cell responses, which collectively contribute to enhancing cross-protective effects. The virus challenge results showed that vaccinated groups had significantly reduced lung damage, lower viral loads in the lungs, nasal turbinates, and trachea, as well as decreased levels of pro-inflammatory cytokines.ConclusionThese findings clearly demonstrate the wide range of protective effects provided by these vaccines against H1N1 and B influenza viruses. The present finding highlights the potential of mRNA-based influenza vaccines encoding conserved proteins as a promising strategy for eliciting broad-spectrum protective humoral and cellular immunity against H1N1 and B influenza viruses.

The primary mechanism of secondary injury after cerebral ischemia may be the brain inflammation that emerges after an ischemic stroke, which promotes neuronal death and inhibits nerve tissue regeneration. As the first immune cells to be activated after an ischemic stroke, microglia play an important immunomodulatory role in the progression of the condition. After an ischemic stroke, peripheral blood immune cells (mainly T cells) are recruited to the central nervous system by chemokines secreted by immune cells in the brain, where they interact with central nervous system cells (mainly microglia) to trigger a secondary neuroimmune response. This review summarizes the interactions between T cells and microglia in the immune-inflammatory processes of ischemic stroke. We found that, during ischemic stroke, T cells and microglia demonstrate a more pronounced synergistic effect. Th1, Th17, and M1 microglia can co-secrete pro-inflammatory factors, such as interferon-γ, tumor necrosis factor-α, and interleukin-1β, to promote neuroinflammation and exacerbate brain injury. Th2, Treg, and M2 microglia jointly secrete anti-inflammatory factors, such as interleukin-4, interleukin-10, and transforming growth factor-β, to inhibit the progression of neuroinflammation, as well as growth factors such as brain-derived neurotrophic factor to promote nerve regeneration and repair brain injury. Immune interactions between microglia and T cells influence the direction of the subsequent neuroinflammation, which in turn determines the prognosis of ischemic stroke patients. Clinical trials have been conducted on the ways to modulate the interactions between T cells and microglia toward anti-inflammatory communication using the immunosuppressant fingolimod or overdosing with Treg cells to promote neural tissue repair and reduce the damage caused by ischemic stroke. However, such studies have been relatively infrequent, and clinical experience is still insufficient. In summary, in ischemic stroke, T cell subsets and activated microglia act synergistically to regulate inflammatory progression, mainly by secreting inflammatory factors. In the future, a key research direction for ischemic stroke treatment could be rooted in the enhancement of anti-inflammatory factor secretion by promoting the generation of Th2 and Treg cells, along with the activation of M2-type microglia. These approaches may alleviate neuroinflammation and facilitate the repair of neural tissues.

N-terminal acetylation is a specific protein modification that occurs only at the N-terminus but plays a significant role in protein stability, folding, subcellular localization and protein-protein interactions. Computational methods enable finding N-terminal acetylated sites from large-scale proteins efficiently. However, limited by the number of the labeled proteins, existing tools only focus on certain subtypes of N-terminal acetylated sites on frequently detected amino acids. For example, NetAcet focuses on alanine, glycine, serine and threonine only, and N-Ace predicts on alanine, glycine, methionine, serine and threonine. With the growth of experimental N-terminal acetylated site data, it is observed that N-terminal protein acetylation occurs on nearly ten types of amino acids. To facilitate comprehensive analysis, we have developed MTNA (Multiple Types of N-terminal Acetylation), a deep learning network capable of accurately predicting N-terminal protein acetylation sites for various amino acids at the N-terminus. MTNA not only outperforms existing tools but also has the capability to identify rare types of N-terminal protein acetylated sites occurring on less studied amino acids.

The goal of precision oncology is to select more effective treatments or beneficial drugs for patients. The transcription of ‘‘hidden responders’’ which precision oncology often fails to identify for patients is important for revealing responsive molecular states. Recently, a RAS pathway activation detection method based on machine learning and a nature-inspired deep RAS activation pan-cancer has been proposed. However, we note that the activating gene variations found in KRAS, HRAS and NRAS vary substantially across cancers. Besides, the ability of a machine learning classifier to detect which KRAS, HRAS and NRAS gain of function mutations or copy number alterations causes the RAS pathway activation is not clear. Here, we proposed a deep neural network framework for deciphering and identifying pan-cancer RAS pathway activation (DIPRAS). DIPRAS brings a new insight into deciphering and identifying the pan-cancer RAS pathway activation from a deeper perspective. In addition, we further revealed the identification and characterization of RAS aberrant pathway activity through gene ontological enrichment and pathological analysis. The source code is available by the URL https://github.com/zhaoyw456/DIPRAS.

Abstract Background It remains an important challenge to predict the functional consequences or clinical impacts of genetic variants in human diseases, such as cancer. An increasing number of genetic variants in cancer have been discovered and documented in public databases such as COSMIC, but the vast majority of them have no functional or clinical annotations. Some databases, such as CiVIC are available with manual annotation of functional mutations, but the size of the database is small due to the use of human annotation. Since the unlabeled data (millions of variants) typically outnumber labeled data (thousands of variants), computational tools that take advantage of unlabeled data may improve prediction accuracy. Result To leverage unlabeled data to predict functional importance of genetic variants, we introduced a method using semi-supervised generative adversarial networks (SGAN), incorporating features from both labeled and unlabeled data. Our SGAN model incorporated features from clinical guidelines and predictive scores from other computational tools. We also performed comparative analysis to study factors that influence prediction accuracy, such as using different algorithms, types of features, and training sample size, to provide more insights into variant prioritization. We found that SGAN can achieve competitive performances with small labeled training samples by incorporating unlabeled samples, which is a unique advantage compared to traditional machine learning methods. We also found that manually curated samples can achieve a more stable predictive performance than publicly available datasets. Conclusions By incorporating much larger samples of unlabeled data, the SGAN method can improve the ability to detect novel oncogenic variants, compared to other machine-learning algorithms that use only labeled datasets. SGAN can be potentially used to predict the pathogenicity of more complex variants such as structural variants or non-coding variants, with the availability of more training samples and informative features.

ABSTRACT Widespread in public databases, foreign contaminant sequences pose a substantial obstacle in genomic analyses. Such contamination in viral genome databases is also notorious but more complicated and often causes questionable results in various applications, particularly in virome-based virus detection. Here, we conducted comprehensive screening and identification of the foreign sequences hidden in the largest eukaryotic viral genome collections of GenBank and UniProt using a scrutiny pipeline, which enables us to rigorously detect those problematic viral sequences (PVSs) with origins in hosts, vectors, and laboratory components. As a result, a total of 766 nucleotide PVSs and 276 amino acid PVSs with lengths up to 6,605 bp were determined, which were widely distributed in 39 families with many involving highly public health-concerning viruses, such as hepatitis C virus, Crimean-Congo hemorrhagic fever virus, and filovirus. The majority of these PVSs are genomic fragments of hosts including humans and bacteria. However, they cannot simply be regarded as foreign contaminants, since parts of them are results of natural occurrence or artificial engineering of viruses. Nevertheless, they severely disturb such sequence-based analyses as genome annotation, taxonomic assignment, and virome profiling. Therefore, we provide a clean version of the eukaryotic viral reference data set by the removal of these PVSs, which allows more accurate virome analysis with less time consumed than with other comprehensive databases. IMPORTANCE High-throughput sequencing-based viromics highly depends on reference databases, but foreign contamination is widespread in public databases and often leads to confusing and even wrong conclusions in genomic analysis and viromic profiling. To address this issue, we systematically detected and identified the contamination in the largest viral sequence collections of GenBank and UniProt based on a stringent scrutiny pipeline. We found hundreds of PVSs that are related to hosts, vectors, and laboratory components. By the removal of them, the resulting data set greatly improves the accuracy and efficiency of eukaryotic virome profiling. These results refresh our knowledge of the type and origin of PVSs and also have warning implications for viromic analysis. Viromic practitioners should be aware of these problems caused by PVSs and need to realize that a careful review of bioinformatic results is necessary for a reliable conclusion.

Three new aspochracin-type cyclic tripeptides, sclerotiotides M–O (1–3), together with three known analogues, sclerotiotide L (4), sclerotiotide F (5), and sclerotiotide B (6), were obtained from the ethyl acetate extract of the fungus Aspergillus insulicola HDN151418, which was isolated from an unidentified Antarctica sponge. Spectroscopic and chemical approaches were used to elucidate their structures. The absolute configuration of the side chain in compound 4 was elucidated for the first time. Compounds 1 and 2 showed broad antimicrobial activity against a panel of pathogenic strains, including Bacillus cereus, Proteus species, Mycobacterium phlei, Bacillus subtilis, Vibrio parahemolyticus, Edwardsiella tarda, MRCNS, and MRSA, with MIC values ranging from 1.56 to 25.0 µM.