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The improvement and decreasing costs of third‐generation sequencing technologies has widened the scope of biological questions researchers can address with de novo genome assemblies. With the increasing number of reference genomes, validating their integrity with minimal overhead is vital for establishing confident results in their applications. Here, we present Klumpy, a tool for detecting and visualizing both misassembled regions in a genome assembly and genetic elements (e.g. genes) of interest in a set of sequences. By leveraging the initial raw reads in combination with their respective genome assembly, we illustrate Klumpy's utility by investigating antifreeze glycoprotein (afgp) loci across two icefishes, by searching for a reported absent gene in the northern snakehead fish, and by scanning the reference genomes of a mudskipper and bumblebee for misassembled regions. In the two former cases, we were able to provide support for the noncanonical placement of an afgp locus in the icefishes and locate the missing snakehead gene. Furthermore, our genome scans were able identify an unmappable locus in the mudskipper reference genome and identify a putative repetitive element shared among several species of bees. [ABSTRACT FROM AUTHOR]

Evidence supporting non‐DNA sequence‐based inheritance in animals has increasingly been described in recent years, often under intergenerational inheritance or transgenerational epigenetic inheritance (TEI). Existence of the latter, a stronger indicator of germline transmission, has been demonstrated in invertebrates and mammals alike. The mechanisms and physiological implications of TEI, however, remain unclear. Here, in an unbiased approach, we compared existing transcriptomic data associated with so far available Drosophila models of inter‐ and trans‐, and rodent models of inter‐generational inheritance; observed phenotypic cross‐species conservation and cross‐generation directionality shift therein; and confirmed these observations experimentally in flies. Specifically, previous models of cold and diet‐induced inheritance in both flies and mice were commonly associated with altered regulation of proteolysis genes. Besides, fly TEI models were in general characterized by opposite phenotypic regulation in transgenerational offsprings, compared to the ancestors. As insulin‐producing cell (IPC) ablation was also associated with proteolysis gene dysregulation in one of the mouse models, we opted to use genetic ablation of IPCs in flies for the experimental validation. Remarkably, the ablation led to transcriptomic alterations across multiple generations, with dysregulated genes showing proteolysis enrichment. Similarly, phenotypic directionality changed in the opposite direction in transgenerational offsprings, in comparison of the ancestors. These results support evolutionary conservation, and both physiologically adaptive and maladaptive consequences of germline mediated epigenetic inheritance. [ABSTRACT FROM AUTHOR]

Abstract Background Pulpitis is a common disease mainly caused by bacteria. Conventional approaches of diagnosing the state of dental pulp are mainly based on clinical symptoms, thereby harbor deficiencies. The accurate and rapid diagnosis of pulpitis is important for choosing the suitable therapy. The study aimed to identify pulpits related key genes by integrating micro-array data analysis and systems biology network-based methods such as weighted gene co-expression network analysis (WGCNA). Methods The micro-array data of 13 inflamed pulp and 11 normal pulp were acquired from Gene Expression Omnibus (GEO). WGCNA was utilized to establish a genetic network and categorize genes into diverse modules. Hub genes in the most associated module to pulpitis were screened out using high module group members (MM) methods. Pulpitis model in rat was constructed and iRoot BP plus was applied to cap pulp. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was used for validation of hub genes. Results WGCNA was established and genes were categorized into 22 modules. The darkgrey module had the highest correlation with pulpitis among them. A total of 5 hub genes (HMOX1, LOX, ACTG1, STAT3, GNB5) were identified. RT-qPCR proved the differences in expression levels of HMOX1, LOX, ACTG1, STAT3, GNB5 in inflamed dental pulp. Pulp capping reversed the expression level of HMOX1, LOX, ACTG1. Conclusion The study was the first to produce a holistic view of pulpitis, screen out and validate hub genes involved in pulpitis using WGCNA method. Pulp capping using iRoot BP plus could reverse partial hub genes.

Objective: This study aims to evaluate the diagnostic value of human serum cysteine protease inhibitors (cystatin 4 [CST4]) in colorectal cancer (CRC) patients. Methods: A total of 291 patients who were admitted to Zhuzhou Central Hospital for colonoscopy from January 2020 to December 2021 and met the inclusion criteria were selected. Serum samples of the patients were collected, and CST4 was detected by double-antibody sandwich enzyme-linked immunosorbent assay. Simultaneously, CEA and CA19-9 were detected, and the patients were divided into the CRC group, benign lesion group, and healthy control group. An attempt was made to construct a CRC prediction model including CST4 and draw a subject working characteristic curve as a diagnostic threshold for CRC prediction, and evaluate the diagnostic efficacy of the above indicators. At the same time, the expression analysis of CST4, CEA, and CA19-9 was verified by combining the data of CRC in the Tumor Genome Atlas (TCGA). Results: In this study, the levels of serum CST4, CEA, and CA19-9 in the CRC group were higher than those in the colorectal benign lesion group and healthy control group, with statistical significance (P <.001). The analysis results of the receiver operating characteristic curve showed that the area under the receiver operator characteristic curve (AUC) of CST4 was 0.7739, which was obviously larger than the AUC of CA19-9 and CEA. CRC data from the TCGA expression database showed that CST4 expression and CEA expression were higher in CRC patients than in normal samples. The combined model based on CST4 was successfully constructed, and the AUC for predicting the occurrence of CRC was 0.7851. Conclusion: CST4 is a novel and improved diagnostic marker for CRC. The combined model based on CST4 has a certain potential value in terms of predicting the occurrence of intestinal cancer. [ABSTRACT FROM AUTHOR]

Background: N6-methyladenosine (m6A) modification is the most common RNA modification, but its potential role in the development of esophageal cancer and its specific mechanisms still need to be further investigated. Methods: Bulk RNA-seq of 174 patients with esophageal squamous carcinoma from the TCGA-ESCC cohort, GSE53625, and single-cell sequencing data from patients with esophageal squamous carcinoma from GSE188900 were included in this study. Single-cell analysis of scRNA-seq data from GSE188900 of 4 esophageal squamous carcinoma samples and calculation of PROGENy scores. Demonstrate the scoring of tumor-associated pathways for different cell populations. Cell Chat was calculated for cell populations. thereafter, m6Arelated differential genes were sought and risk models were constructed to analyze the relevant biological functions and impact pathways of potential m6A genes and their impact on immune infiltration and tumor treatment sensitivity in ESCC was investigated. Results: By umap downscaling analysis, ESCC single-cell data were labelled into clusters of seven immune cell classes. Cellchat analysis showed that the network interactions of four signaling pathways, MIF, AFF, FN1 and CD99, all showed different cell type interactions. The prognostic risk model constructed by screening for m6A-related differential genes was of significant value in the prognostic stratification of ESCC patients and had a significant impact on immune infiltration and chemotherapy sensitivity in ESCC patients. Conclusion: In our study, we explored a blueprint for the distribution of single cells in ESCC based on m6A methylation and constructed a risk model for immune infiltration analysis and tumor efficacy stratification in ESCC on this basis. This may provide important potential guidance for revealing the role of m6A in immune escape and treatment resistance in esophageal cancer. [ABSTRACT FROM AUTHOR]

Transcriptional profiling continues to produce phenotypical data essential for understanding of basic cardiac biology and required to improve efficiency of cardiac regenerative and therapeutic approaches after injury. Accurate interpretation of cardiac transcriptional data comes with the unique challenges of heart biology including cardiomyocyte morphology, cryopreservation of limited samples and adequate selection of transcriptional platform at a single-cell resolution. Consequently, development and implementation of novel transcriptional platforms and creative bioinformatic analysis are essential to resolve standing questions in the field of cardiac regenerative medicine. Targeted bioinformatic approaches, advancing technological access, increase technical availability and fostering communication between interdisciplinary groups is critical to improve therapeutic approaches and to overcome challenges inherent to transcriptomic cardiac research.

Background: Pulpitis is a common disease mainly caused by bacteria. Conventional approaches of diagnosing the state of dental pulp are mainly based on clinical symptoms, thereby harbor deficiencies. The accurate and rapid diagnosis of pulpitis is important for choosing the suitable therapy. The study aimed to identify pulpits related key genes by integrating micro-array data analysis and systems biology network-based methods such as weighted gene co-expression network analysis (WGCNA). Methods: The micro-array data of 13 inflamed pulp and 11 normal pulp were acquired from Gene Expression Omnibus (GEO). WGCNA was utilized to establish a genetic network and categorize genes into diverse modules. Hub genes in the most associated module to pulpitis were screened out using high module group members (MM) methods. Pulpitis model in rat was constructed and iRoot BP plus was applied to cap pulp. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was used for validation of hub genes. Results: WGCNA was established and genes were categorized into 22 modules. The darkgrey module had the highest correlation with pulpitis among them. A total of 5 hub genes (HMOX1, LOX, ACTG1, STAT3, GNB5) were identified. RT-qPCR proved the differences in expression levels of HMOX1, LOX, ACTG1, STAT3, GNB5 in inflamed dental pulp. Pulp capping reversed the expression level of HMOX1, LOX, ACTG1. Conclusion: The study was the first to produce a holistic view of pulpitis, screen out and validate hub genes involved in pulpitis using WGCNA method. Pulp capping using iRoot BP plus could reverse partial hub genes. [ABSTRACT FROM AUTHOR]

This document is an editorial titled "Bioinformatics for modern neuroscience" published in the journal Frontiers in Computational Neuroscience. The editorial discusses the use of bioinformatics in neuroscience research, particularly in understanding the functioning of the nervous system. It highlights the advancements in computing power that have made bioinformatics more accessible to researchers. The editorial also summarizes several articles published in a research topic on bioinformatics for neuroscience, including topics such as analyzing transcriptomics data, bibliometric analysis of glioma research, personalized treatment for post-traumatic stress disorder (PTSD), protein misfolding in neurodegenerative diseases, and the role of the endocannabinoid system in epilepsy. The editorial concludes by emphasizing the importance of integrating in silico experiments with traditional lab-based research in neuroscience. [Extracted from the article]

The molecular characterization of complex behaviours is a challenging task as a range of different factors are often involved to produce the observed phenotype. An established approach is to look at the overall levels of expression of brain genes—or 'neurogenomics'—to select the best candidates that associate with patterns of interest. However, traditional neurogenomic analyses have some well‐known limitations: above all, the usually limited number of biological replicates compared to the number of genes tested—known as the "curse of dimensionality." In this study we implemented a machine learning (ML) approach that can be used as a complement to more established methods of transcriptomic analyses. We tested three supervised learning algorithms (Random Forests, Lasso and Elastic net Regularized Generalized Linear Model, and Support Vector Machine) for their performance in the characterization of transcriptomic patterns and identification of genes associated with honeybee waggle dance. We then matched the results of these analyses with traditional outputs of differential gene expression analyses and identified two promising candidates for the neural regulation of the waggle dance: boss and hnRNP A1. Overall, our study demonstrates the application of ML to analyse transcriptomics data and identify candidate genes underlying social behaviour. This approach has great potential for application to a wide range of different scenarios in evolutionary ecology, when investigating the genomic basis for complex phenotypic traits, and can present some clear advantages compared to the established tools of gene expression analysis, making it a valuable complement for future studies. [ABSTRACT FROM AUTHOR]

Physical activity (PA) is globally recognized as a pillar of general health. Step count, as one measure of PA, is a well known predictor of long-term morbidity and mortality. Despite its popularity in consumer devices, a lack of methodological standards and clinical validation remains a major impediment to step count being accepted as a valid clinical endpoint. Previous works have mainly focused on device-specific step-count algorithms and often employ sensor modalities that may not be widely available. This may limit step-count suitability in clinical scenarios. In this paper, we trained neural network models on publicly available data and tested on an independent cohort using two approaches: generalization and personalization. Specifically, we trained neural networks on accelerometer signals from one device and either directly applied them or adapted them individually to accelerometer data obtained from a separate subject cohort wearing multiple distinct devices. The best models exhibited highly accurate step-count estimates for both the generalization (96–99%) and personalization (98–99%) approaches. The results demonstrate that it is possible to develop device-agnostic, accelerometer-only algorithms that provide highly accurate step counts, positioning step count as a reliable mobility endpoint and a strong candidate for clinical validation. [ABSTRACT FROM AUTHOR]

The most common intraocular malignancy in adults remains uveal melanoma (UVM), and those with metastatic disease have a poor outlook. Proliferation, angiogenesis, and metastasis of tumor cells can be triggered by cuproptosis, affecting the survival of cancer patients. Nonetheless, cuproptosis-related genes (CRGs) have not been identified in UVM. In this study, we analyzed 10 CRGs in 80 patients with UVM in the Cancer Genome Atlas (TCGA) database regarding the alterations of the genes including copy number variation and methylation. We further constructed a prognostic gene model using these CRGs and built the risk score formula. Univariate and multivariate Cox regression was applied to validate the risk score as an independent prognostic factor. The prognostic model was validated using 63 UVM samples from the GSE22138 cohort, an independent validation data set. Based on the risk scores for 80 patients with UVM from TCGA, we categorized the patients into high- and low-risk groups. Differentially expressed genes (DEGs) between groups were enriched in allograft rejection, hypoxia, glycolysis, TNFα signaling via NF-κB, and interferon-γ responses via Gene set enrichment analysis (GSEA). CD8 T cells and exhausted T cells were notably enriched in the high-risk group. In conclusion, the alteration of CRGs is related to patients with UVM, and the constructed CRG-related model may be helpful to predict the prognosis of such patients.

Background: MicroRNA (miRNA) has been reported to play a critical regulatory role in papillary thyroid carcinomas (PTC). However, the role of miR-221/222 in PTC remains unclear. Here, we performed this study to explore the diagnostic potentials and mechanisms of miR-221/222 in PTC. Methods: First, we systematically analyzed the diagnostic value of miR-221/222 in the diagnosis PTC by pooling the published studies. Afterwards, we performed comprehensive bioinformatics analysis including gene ontology analysis, pathway enrichment analysis and protein-protein interaction analysis to explore the potential mechanisms of miR-221/222 involved in PTC. Results: The overall sensitivity and specificity of miR-221/222 for PTC were 0.75 (95% CI: 0.70–0.80) and 0.80 (95% CI: 0.76–0.84) respectively with the AUC of 0.85 (95% CI: 0.81-0.88). The diagnostic performance varied among different subgroups including geographical locations, sample sources and sample sizes. Meanwhile, we found that a combination of miR-221/222 and other miRNAs when used in a diagnostic panel could improve the diagnostic accuracy than individual miR-221/222. Moreover, through the bioinformatics analysis, we confirmed that miR-221/222 targets were highly related to the molecular pathogenesis of PTC. The results revealed that miR-221/222 may exert important functions in PTC through thyroid hormone signaling pathway and some other key pathways by regulating some key genes. Conclusion: These findings indicated that miR-221/222 have the potential to serve as auxiliary tools for diagnosing PTC. Further prospective clinical trials should be performed to assess the accuracy of these findings in a larger cohort and determine the clinical uses. [ABSTRACT FROM AUTHOR]

BackgroundMicroRNA (miRNA) has been reported to play a critical regulatory role in papillary thyroid carcinomas (PTC). However, the role of miR-221/222 in PTC remains unclear. Here, we performed this study to explore the diagnostic potentials and mechanisms of miR-221/222 in PTC.MethodsFirst, we systematically analyzed the diagnostic value of miR-221/222 in the diagnosis PTC by pooling the published studies. Afterwards, we performed comprehensive bioinformatics analysis including gene ontology analysis, pathway enrichment analysis and protein-protein interaction analysis to explore the potential mechanisms of miR-221/222 involved in PTC.ResultsThe overall sensitivity and specificity of miR-221/222 for PTC were 0.75 (95% CI: 0.70–0.80) and 0.80 (95% CI: 0.76–0.84) respectively with the AUC of 0.85 (95% CI: 0.81-0.88). The diagnostic performance varied among different subgroups including geographical locations, sample sources and sample sizes. Meanwhile, we found that a combination of miR-221/222 and other miRNAs when used in a diagnostic panel could improve the diagnostic accuracy than individual miR-221/222. Moreover, through the bioinformatics analysis, we confirmed that miR-221/222 targets were highly related to the molecular pathogenesis of PTC. The results revealed that miR-221/222 may exert important functions in PTC through thyroid hormone signaling pathway and some other key pathways by regulating some key genes.ConclusionThese findings indicated that miR-221/222 have the potential to serve as auxiliary tools for diagnosing PTC. Further prospective clinical trials should be performed to assess the accuracy of these findings in a larger cohort and determine the clinical uses.

Background: The primary non-response (PNR) rate of infliximab (IFX) varies from 20 to 46% for the treatment of Crohn’s disease (CD). Detected PNR reduces the improper use of specific treatments. To date, there is hardly any knowledge regarding early markers of PNR. The aim of this study was to evaluate the role of Interleukin-6 (IL-6) as an early predictor of PNR of IFX for the treatment of CD.Methods: We enrolled 322 bio-naïve patients diagnosed with CD from January 2016 to May 2020. Primary response was determined at week 14. Multivariable logistic regression was used to construct prediction models. Area under the curve (AUC), calibration and decision curve analyses (DCA) were assessed in the validation cohort. GEO data were analyzed to identify potential mechanisms of IL-6 in IFX therapy for CD.Results: PNR occurred in 31.06% (100 of 322) patients who were assessable at week 14. IL-6 levels significantly decreased after IFX therapy (p < 0.001). The validation model containing IL-6 presented enhanced discrimination with an AUC of 0.908 and high calibration. Decision curve analysis (DCA) indicated that the model added extra predictive value. GEO data confirmed the IL-6 levels were increased in the PNR group and IL-6-related differentially expressed genes (DEGs) were enriched in the inflammatory response.Conclusions: We concluded that IL-6 may be used as a predictive factor to assess the risk of PNR to IFX therapy.

Gastric cancer (GC) is a complex and heterogeneous disease, making it difficult to ascertain the optimal therapeutic approach for individual GC patients. Stromal and immune cell infiltration in GC has a strong correlation with clinical outcomes; however, the underlying mechanisms that drive immunosuppression remain vastly undiscovered. Recent studies validated that anthrax toxin receptor 1 (ANTXR1) is aberrantly expressed in several cancers and holds promise as a new therapeutic target for cancer. However, its immunological roles in GC are still unclear. Here, we show that we identify the distinct stromal and immune cell infiltration in GC between the high and low ANTXR1 expression group by analyzing genomic data. Clinically, ANTXR1 is highly expressed in GC and correlates with adverse clinicopathological characteristics. Additionally, high ANTXR1 expression is linked to markedly poor clinical outcomes and resistance to chemotherapy, whereas the low ANTXR1 expression group is correlated with better outcomes and response to chemotherapy in GC patients. We further revealed the differential landscape of somatic tumor mutation burden (TMB) between the two groups and observed that patients with high ANTXR1 expression suffered from a lower TMB, potentially leading to less sensitivity to checkpoint therapy. Molecularly, results displayed that ANTXR1 is an immunosuppressive element, which may perform its function via promoting the secretion of immunosuppressive factors that play a significant role in modulating tumor-associated fibroblast transformation, M2 macrophage polarization, and T cell exhaustion. Gene set enrichment analysis revealed that cancer-related pathways including epithelial-to-mesenchymal transition, focal adhesion, and transforming growth factor-β (TGF-β) signaling pathways were enriched in high ANTXR1 expression tumors. Our work suggests that ANTXR1 could not only serve as a valuable prognostic biomarker in GC but also be deemed as a potential immunotherapeutic target and useful biomarker of sensitivity to chemotherapy.

A decade ago, de novo transcriptome assembly evolved as a versatile and powerful approach to make evolutionary assumptions, analyse gene expression, and annotate novel transcripts, in particular, for non‐model organisms lacking an appropriate reference genome. Various tools have been developed to generate a transcriptome assembly, and even more computational methods depend on the results of these tools for further downstream analyses. In this issue of Molecular Ecology Resources, Freedman et al. (Mol Ecol Resourc 2020) present a comprehensive analysis of errors in de novo transcriptome assemblies across public data sets and different assembly methods. They focus on two implicit assumptions that are often violated: First, the assembly presents an unbiased view of the transcriptome. Second, the expression estimates derived from the assembly are reasonable, albeit noisy, approximations of the relative frequency of expressed transcripts. They show that appropriate filtering can reduce this bias but can also lead to the loss of a reasonable number of highly expressed transcripts. Thus, to partly alleviate the noise in expression estimates, they propose a new normalization method called length‐rescaled CPM. Remarkably, the authors found considerable distortions at the nucleotide level, which leads to an underestimation of diversity in transcriptome assemblies. The study by Freedman et al. (Mol Ecol Resourc 2020) clearly shows that we have not yet reached "high‐quality" in the field of transcriptome assembly. Above all, it helps researchers be aware of these problems and filter and interpret their transcriptome assembly data appropriately and with caution. [ABSTRACT FROM AUTHOR]

Background: N6-methyladenosine (m6A) modification is the most common RNA modification, but its potential role in the development of esophageal cancer and its specific mechanisms still need to be further investigated., Methods: Bulk RNA-seq of 174 patients with esophageal squamous carcinoma from the TCGA-ESCC cohort, GSE53625, and single-cell sequencing data from patients with esophageal squamous carcinoma from GSE188900 were included in this study. Single-cell analysis of scRNA-seq data from GSE188900 of 4 esophageal squamous carcinoma samples and calculation of PROGENy scores. Demonstrate the scoring of tumor-associated pathways for different cell populations. Cell Chat was calculated for cell populations. thereafter, m6A-related differential genes were sought and risk models were constructed to analyze the relevant biological functions and impact pathways of potential m6A genes and their impact on immune infiltration and tumor treatment sensitivity in ESCC was investigated., Results: By umap downscaling analysis, ESCC single-cell data were labelled into clusters of seven immune cell classes. Cellchat analysis showed that the network interactions of four signaling pathways, MIF, AFF, FN1 and CD99, all showed different cell type interactions. The prognostic risk model constructed by screening for m6A-related differential genes was of significant value in the prognostic stratification of ESCC patients and had a significant impact on immune infiltration and chemotherapy sensitivity in ESCC patients., Conclusion: In our study, we explored a blueprint for the distribution of single cells in ESCC based on m6A methylation and constructed a risk model for immune infiltration analysis and tumor efficacy stratification in ESCC on this basis. This may provide important potential guidance for revealing the role of m6A in immune escape and treatment resistance in esophageal cancer., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Nie, Yao, Xu, Liu, Yin, Lai, Li, Zhou and Yang.)

Understanding the types and functions of genes that are able to cross species boundaries-and those that are not-is an important step in understanding the forces maintaining species as largely independent lineages across the remainder of the genome. With large next-generation sequencing data sets we are now able to ask whether introgression has occurred across the genome, and multiple methods have been proposed to detect the signature of such events. Here, we introduce a new summary statistic that can be used to test for introgression, RNDmin, that makes use of the minimum pairwise sequence distance between two population samples relative to divergence to an outgroup. We find that our method offers a modest increase in power over other, related tests, but that all such tests have high power to detect introgressed loci when migration is recent and strong. RNDmin is robust to variation in the mutation rate, and remains reliable even when estimates of the divergence time between sister species are inaccurate. We apply RNDmin to population genomic data from the African mosquitoes Anopheles quadriannulatus and A. arabiensis, identifying three novel candidate regions for introgression. Interestingly, one of the introgressed loci is on the X chromosome, but outside of an inversion separating these two species. Our results suggest that significant, but rare, sharing of alleles is occurring between species that diverged more than 1 million years ago, and that application of these methods to additional systems are likely to reveal similar results. [ABSTRACT FROM AUTHOR]

As scholars have predicted and researchers have now shown, we are entering an age of global artificial intelligence (AI) convergence. Health care is just one area in which AI is gaining a foothold, as evidenced by two parallel conferences held in March 2018 in Las Vegas, Nevada. The first of these was the annual meeting of the Hospital Information and Management Systems Society, attended by nearly 45,000 participants; the second was a more focused, engineering-oriented conference on biomedical health informatics and biosensor networks, attended by about 450. Straddling those two meetings, IEEE Pulse held its second IEEE Pulse on Stage event, focusing (not surprisingly) on the use of AI in health care (Figure 1). [ABSTRACT FROM AUTHOR]

Protein modifications such as oxidations have a strong impact on protein function and activity in various organisms. High-resolution mass spectrometric techniques in combination with various sample preparation methodologies allow for the in-detail characterization of protein structures and strongly contribute to a greater understanding of the impact of protein modifications in nature. This paper outlines the general workflows for the characterization of oxidation sites in proteins by mass spectrometry (MS). Different types of oxidations are taken into consideration; both qualitative and quantitative aspects of MS-based approaches are presented with respect to oxidized proteins. Both bottom-up and top-down MS approaches are described and evaluated; a wide range of the particular applications corresponding to these techniques is also presented. Furthermore, the common advantages and downsides of these techniques are assessed. The approaches for enrichment of low-abundance oxidized proteins are extensively presented for different cysteine oxidations and protein carbonylations. A short description about databases and bioinformatic software solutions for oxidative protein prediction, identification, and biological interpretation is also given in this review. [ABSTRACT FROM AUTHOR]

The most common intraocular malignancy in adults remains uveal melanoma (UVM), and those with metastatic disease have a poor outlook. Proliferation, angiogenesis, and metastasis of tumor cells can be triggered by cuproptosis, affecting the survival of cancer patients. Nonetheless, cuproptosis-related genes (CRGs) have not been identified in UVM. In this study, we analyzed 10 CRGs in 80 patients with UVM in the Cancer Genome Atlas (TCGA) database regarding the alterations of the genes including copy number variation and methylation. We further constructed a prognostic gene model using these CRGs and built the risk score formula. Univariate and multivariate Cox regression was applied to validate the risk score as an independent prognostic factor. The prognostic model was validated using 63 UVM samples from the GSE22138 cohort, an independent validation data set. Based on the risk scores for 80 patients with UVM from TCGA, we categorized the patients into high- and low-risk groups. Differentially expressed genes (DEGs) between groups were enriched in allograft rejection, hypoxia, glycolysis, TNFα signaling via NF-κB, and interferon-γ responses via Gene set enrichment analysis (GSEA). CD8 T cells and exhausted T cells were notably enriched in the high-risk group. In conclusion, the alteration of CRGs is related to patients with UVM, and the constructed CRG-related model may be helpful to predict the prognosis of such patients., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Chen, Chen and Wang.)

Shrimps are islands of periodicity within a chaotic sea in phase and parameter spaces of dimensions larger than one. Islands of different periodicities have recently been shown to be connected by spirals that emanate from a joint focal point, paving ways to wander around in parameter space without ever crossing the chaotic sea. We discuss the shrimp building and scaling principles as well as the influence of individual system properties. While the emergence of shrimps has abundantly been demonstrated for artificial systems, we discuss here in detail evidence of rich hierarchies of shrimps in experimental systems. We finally pinpoint the importance of shrimps in the field of bioinformatics. [ABSTRACT FROM AUTHOR]

The North-Western Mediterranean basin is well known for its high number of relictual endemic taxa, and has been indicated as one of the world’s major biodiversity hotspots at the species level. A possible contributing factor may be long-term persistence of populations and their prolonged stability. This study was designed to investigate the phylogeographic structure of three common species of the genus Lepidocyrtus (Hexapoda: Collembola), soil-dwelling arthropods characterized by limited dispersal capabilities and generally associated with forest habitats. We observed a remarkable geographic structure, with numerous deeply divergent genetic lineages occupying islands as well as mainland sites with no apparent gene flow among most sites, even across distances of only tens of kilometres. The reconstructed time frame for the evolution of these lineages suggests divergence between 5 and 15 Ma. This indicates a remarkably ancient origin and long-term persistence of individual lineages over a fine geographic scale despite the occurrence of abrupt sea level and climatic fluctuations in the area. This further suggests that currently recognized morphological species might be a serious underestimation of the true springtail biodiversity within this region. [ABSTRACT FROM AUTHOR]

Background Recent outbreaks of highly pathogenic avian influenza and multiple occurrences of zoonotic infection and deaths in humans have sparked a dramatic increase in influenza research. In order to rapidly identify and help prevent future influenza outbreaks, numerous laboratories around the world are working to develop new nucleotide-based diagnostics for identifying and subtyping influenza viruses. While there are several databases that have been developed for manipulating the vast amount of influenza genetic data that have been produced, significant progress can still be made in developing tools for translating the genetic data into effective diagnostics. Description The Influenza Primer Design Resource (IPDR) is the combination of a comprehensive database of influenza nucleotide sequences and a web interface that provides several important tools that aid in the development of oligonucleotides that may be used to develop better diagnostics. IPDR’s database can be searched using a variety of criteria, allowing the user to align the subset of influenza sequences that they are interested in. In addition, IPDR reports a consensus sequence for the alignment along with sequence polymorphism information, a summary of most published primers and probes that match the consensus sequence, and a Primer3 analysis of potential primers and probes that could be used for amplifying the sequence subset. Conclusions The IPDR is a unique combination of bioinformatics tools that will greatly aid researchers in translating influenza genetic data into diagnostics, which can effectively identify and subtype influenza strains. The website is freely available at . [ABSTRACT FROM AUTHOR]

A correlation analysis among 20 amino acids is performed for four protein structural classes (α, β, α/β, and α+β) in a total of 204 proteins. The correlation relationships among amino acids can be classified into the following four types: (1) strong positive correlation, (2) strong negative correlation, (3) weak correlation, and (4) no correlation. The correlation relationships are different for different proteins and are correlated with the features of their structural classes. The amino acids with the weak correlation relationship can be treated as the independent basis functions for the space where proteins are defined. The amino acids with large correlation coefficients are linear correlative with each other and they are not independent. The strong correlation among amino acids reflects their mutual constrained relationship, as exhibited by their relevant structural features. The information obtained through the correlation analysis is used for predicting protein structural classes and a better prediction quality is obtained than that by the simple geometry distance methods without taking into account the correlation effects. [Copyright &y& Elsevier]

Background: The primary non-response (PNR) rate of infliximab (IFX) varies from 20 to 46% for the treatment of Crohn's disease (CD). Detected PNR reduces the improper use of specific treatments. To date, there is hardly any knowledge regarding early markers of PNR. The aim of this study was to evaluate the role of Interleukin-6 (IL-6) as an early predictor of PNR of IFX for the treatment of CD. Methods: We enrolled 322 bio-naïve patients diagnosed with CD from January 2016 to May 2020. Primary response was determined at week 14. Multivariable logistic regression was used to construct prediction models. Area under the curve (AUC), calibration and decision curve analyses (DCA) were assessed in the validation cohort. GEO data were analyzed to identify potential mechanisms of IL-6 in IFX therapy for CD. Results: PNR occurred in 31.06% (100 of 322) patients who were assessable at week 14. IL-6 levels significantly decreased after IFX therapy (p < 0.001). The validation model containing IL-6 presented enhanced discrimination with an AUC of 0.908 and high calibration. Decision curve analysis (DCA) indicated that the model added extra predictive value. GEO data confirmed the IL-6 levels were increased in the PNR group and IL-6-related differentially expressed genes (DEGs) were enriched in the inflammatory response. Conclusions: We concluded that IL-6 may be used as a predictive factor to assess the risk of PNR to IFX therapy. [ABSTRACT FROM AUTHOR]