Core promoterome of barley embryo

Precise localization and dissection of gene promoters are key to understanding transcriptional gene regulation and to successful bioengineering applications. The core RNA polymerase II initiation machinery is highly conserved among eukaryotes, leading to a general expectation of equivalent underlying mechanisms. Still, less is known about promoters in the plant kingdom. In this study, we employed cap analysis of gene expression (CAGE) at three embryonic developmental stages in barley to accurately map, annotate, and quantify transcription initiation events. Unsupervised discovery of de novo sequence clusters grouped promoters based on characteristic initiator and position-specific core-promoter motifs. This grouping was complemented by the annotation of transcription factor binding site (TFBS) motifs. Integration with genome-wide epigenomic data sets and gene ontology (GO) enrichment analysis further delineated the chromatin environments and functional roles of genes associated with distinct promoter categories. The TATA-box presence governs all features explored, supporting the general model of two separate genomic regulatory environments. We describe the extent and implications of alternative transcription initiation events, including those that are specific to developmental stages, which can affect the protein sequence or the presence of regions that regulate translation. The generated promoterome dataset provides a valuable genomic resource for enhancing the functional annotation of the barley genome. It also offers insights into the transcriptional regulation of individual genes and presents opportunities for the informed manipulation of promoter architecture, with the aim of enhancing traits of agronomic importance.

Causal association and shared genetics between telomere length and COVID-19 outcomes: New evidence from the latest large-scale summary statistics

Background

Observational studies suggested that leukocyte telomere length (LTL) is shortened in COVID-19 patients. However, the genetic association and causality remained unknown.

Methods

Based on the genome-wide association of LTL (N = 472,174) and COVID-19 phenotypes (N = 1086,211-2597,856), LDSC and SUPERGNOVA were used to estimate the genetic correlation. Cross-trait GWAS meta-analysis, colocalization, fine-mapping analysis, and transcriptome-wide association study were conducted to explore the shared genetic etiology. Mendelian randomization (MR) was utilized to infer the causality. Upstream and downstream two-step MR was performed to investigate the potential mediating effects.

Results

LDSC identified a significant genetic association between LTL and all COVID-19 phenotypes (rG < 0, p < 0.05). Six significant regions were observed for LTL and COVID-19 susceptibility and hospitalization, respectively. Colocalization analysis found rs144204502, rs34517439, and rs56255908 were shared causal variants between LTL and COVID-19 phenotypes. Numerous biological pathways associated with LTL and COVID-19 outcomes were identified, mainly involved in -immune-related pathways. MR showed that longer LTL was significantly associated with a lower risk of COVID-19 severity (OR [95% CI] = 0.81 [0.71-0.92], p = 1.24 ×10-3) and suggestively associated with lower risks of COVID-19 susceptibility (OR [95% CI] = 0.96 [0.92-1.00], p = 3.44 ×10-2) and COVID-19 hospitalization (OR [95% CI] = 0.89 [0.80-0.98], p = 1.89 ×10-2). LTL partially mediated the effects of BMI, smoking, and education on COVID-19 outcomes. Furthermore, six proteins partially mediated the causality of LTL on COVID-19 outcomes, including BNDF, QPCT, FAS, MPO, SFTPB, and APOF.

Conclusions

Our findings suggested that shorter LTL was genetically associated with a higher risk of COVID-19 phenotypes, with shared genetic etiology and potential causality.

Screening biomarkers for spinal cord injury using weighted gene co-expression network analysis and machine learning

JOURNAL/nrgr/04.03/01300535-202412000-00028/figure1/v/2024-04-08T165401Z/r/image-tiff Immune changes and inflammatory responses have been identified as central events in the pathological process of spinal cord injury. They can greatly affect nerve regeneration and functional recovery. However, there is still limited understanding of the peripheral immune inflammatory response in spinal cord injury. In this study, we obtained microRNA expression profiles from the peripheral blood of patients with spinal cord injury using high-throughput sequencing. We also obtained the mRNA expression profile of spinal cord injury patients from the Gene Expression Omnibus (GEO) database (GSE151371). We identified 54 differentially expressed microRNAs and 1656 differentially expressed genes using bioinformatics approaches. Functional enrichment analysis revealed that various common immune and inflammation-related signaling pathways, such as neutrophil extracellular trap formation pathway, T cell receptor signaling pathway, and nuclear factor-κB signal pathway, were abnormally activated or inhibited in spinal cord injury patient samples. We applied an integrated strategy that combines weighted gene co-expression network analysis, LASSO logistic regression, and SVM-RFE algorithm and identified three biomarkers associated with spinal cord injury: ANO10, BST1, and ZFP36L2. We verified the expression levels and diagnostic performance of these three genes in the original training dataset and clinical samples through the receiver operating characteristic curve. Quantitative polymerase chain reaction results showed that ANO10 and BST1 mRNA levels were increased and ZFP36L2 mRNA was decreased in the peripheral blood of spinal cord injury patients. We also constructed a small RNA-mRNA interaction network using Cytoscape. Additionally, we evaluated the proportion of 22 types of immune cells in the peripheral blood of spinal cord injury patients using the CIBERSORT tool. The proportions of naïve B cells, plasma cells, monocytes, and neutrophils were increased while the proportions of memory B cells, CD8+ T cells, resting natural killer cells, resting dendritic cells, and eosinophils were markedly decreased in spinal cord injury patients increased compared with healthy subjects, and ANO10, BST1 and ZFP26L2 were closely related to the proportion of certain immune cell types. The findings from this study provide new directions for the development of treatment strategies related to immune inflammation in spinal cord injury and suggest that ANO10, BST1, and ZFP36L2 are potential biomarkers for spinal cord injury. The study was registered in the Chinese Clinical Trial Registry (registration No. ChiCTR2200066985, December 12, 2022).

Treponema pallidum protein Tp0136 promotes angiogenesis to facilitate the dissemination of Treponema pallidum

The incompletely eliminated Treponema pallidum (T. pallidum) during primary syphilis chancre infection can result in the progression of secondary, tertiary, or latent syphilis in individuals, suggesting that T. pallidum has successfully evaded the immune response and spread to distant sites. The mechanism underlying the dissemination of T. pallidum is unclear. Here, a syphilitic rabbit model dorsal-injected with recombinant Tp0136 protein or Tp0136 antibody subcutaneously was used to demonstrate the role of Tp0136 protein in promoting the dissemination of T. pallidum to the testis and angiogenesis in vivo; vascular endothelial cell line HMEC-1 was employed to display that Tp0136 protein enhances the angiogenesis. Furthermore, the three-dimensional microfluidic angiogenesis system showed that the angiogenesis would heighten vascular permeability. Then transcriptome sequencing analysis, in conjunction with cell-level validation, elucidated the critical role of the PI3K-AKT signaling pathway in the promotion of angiogenesis by Tp0136 protein, resulting in heightened permeability. These findings elucidate the strategy employed by T. pallidum in evading immune clearance.

Widespread genomic de novo DNA methylation occurs following CD8+ T cell activation and proliferation

This research investigates the intricate dynamics of DNA methylation in the hours following CD8+ T cell activation, during a critical yet understudied temporal window. DNA methylation is an epigenetic modification central to regulation of gene expression and directing immune responses. Our investigation spanned 96-h post-activation and unveils a nuanced tapestry of global and site-specific methylation changes. We identified 15,626 significant differentially methylated CpGs spread across the genome, with the most significant changes occurring within the genes ADAM10, ICA1, and LAPTM5. While many changes had modest effect sizes, approximately 120 CpGs exhibited a log2FC above 1.5, with cell activation and proliferation pathways the most affected. Relatively few of the differentially methylated CpGs occurred along adjacent gene regions. The exceptions were seven differentially methylated gene regions, with the Human T cell Receptor Alpha Joining Genes demonstrating consistent methylation change over a 3kb window. We also investigated whether an inflammatory environment could alter DNA methylation during activation, with proliferating cells exposed to the oxidant glycine chloramine. No substantial differential methylation was observed in this context. The temporal perspective of early activation adds depth to the evolving field of epigenetic immunology, offering insights with implications for therapeutic innovation and expanding our understanding of epigenetic modulation in immune function.

The implications for urological malignancies of non-coding RNAs in the the tumor microenvironment

Urological malignancies are a major global health issue because of their complexity and the wide range of ways they affect patients. There's a growing need for in-depth research into these cancers, especially at the molecular level. Recent studies have highlighted the importance of non-coding RNAs (ncRNAs) – these don't code for proteins but are crucial in controlling genes – and the tumor microenvironment (TME), which is no longer seen as just a background factor but as an active player in cancer progression. Understanding how ncRNAs and the TME interact is key for finding new ways to diagnose and predict outcomes in urological cancers, and for developing new treatments. This article reviews the basic features of ncRNAs and goes into detail about their various roles in the TME, focusing specifically on how different ncRNAs function and act in urological malignancies.

Association of changes in expression of HDAC and SIRT genes after drug treatment with cancer cell line sensitivity to kinase inhibitors

Histone deacetylases (HDACs) and sirtuins (SIRTs) are important epigenetic regulators of cancer pathways. There is a limited understanding of how transcriptional regulation of their genes is affected by chemotherapeutic agents, and how such transcriptional changes affect tumour sensitivity to drug treatment. We investigated the concerted transcriptional response of HDAC and SIRT genes to 15 approved antitumor agents in the NCI-60 cancer cell line panel. Antitumor agents with diverse mechanisms of action induced upregulation or downregulation of multiple HDAC and SIRT genes. HDAC5 was upregulated by dasatinib and erlotinib in the majority of the cell lines. Tumour cell line sensitivity to kinase inhibitors was associated with upregulation of HDAC5, HDAC1, and several SIRT genes. We confirmed changes in HDAC and SIRT expression in independent datasets. We also experimentally validated the upregulation of HDAC5 mRNA and protein expression by dasatinib in the highly sensitive IGROV1 cell line. HDAC5 was not upregulated in the UACC-257 cell line resistant to dasatinib. The effects of cancer drug treatment on expression of HDAC and SIRT genes may influence chemosensitivity and may need to be considered during chemotherapy.

A highly efficient method for genomic deletion across diverse lengths in thermophilic Parageobacillus thermoglucosidasius

Parageobacillus thermoglucosidasius is emerging as a highly promising thermophilic organism for metabolic engineering. The utilization of CRISPR-Cas technologies has facilitated programmable genetic manipulation in P. thermoglucosidasius. However, the absence of thermostable NHEJ enzymes limited the capability of the endogenous type I CRISPR-Cas system to generate a variety of extensive genomic deletions. Here, two thermophilic NHEJ enzymes were identified and combined with the endogenous type I CRISPR-Cas system to develop a genetic manipulation tool that can achieve long-range genomic deletion across various lengths. By optimizing this tool-through adjusting the expression level of NHEJ enzymes and leveraging our discovery of a negative correlation between GC content of the guide RNA (gRNA) and deletion efficacy-we streamlined a comprehensive gRNA selection manual for whole-genome editing, achieving a 100 % success rate in randomly selecting gRNAs. Notably, using just one gRNA, we achieved genomic deletions spanning diverse length, exceeding 200 kilobases. This tool will facilitate the genomic manipulation of P. thermoglucosidasius for both fundamental research and applied engineering studies, further unlocking its potential as a thermophilic cell factory.

Retracted article: Functional analysis of ceRNA network of lncRNA TSIX/miR-34a-5p/RBP2 in acute myocardial infarction based on GEO database

Jiezhong Lin, Jianyi Zhou, Guiting Xie, Xiongwei Xie, Yanfang Luo and Jinguang Liu. Functional analysis of ceRNA network of lncRNA TSIX/miR-34a-5p/RBP2 in acute myocardial infarction based on GEO database. 2021 Oct. doi: 10.1080/21655979.2021.2006865.Since publication, significant concerns have been raised about the compliance with ethical policies for human research and the integrity of the data reported in the article.When approached for an explanation, the authors provided some original data but were not able to provide all the necessary supporting information. As verifying the validity of published work is core to the scholarly record's integrity, we are retracting the article. All authors listed in this publication have been informed.We have been informed in our decision-making by our editorial policies and the COPE guidelines.The retracted article will remain online to maintain the scholarly record, but it will be digitally watermarked on each page as 'Retracted.'

Transcriptome analyses reveal key features of mouse seminal vesicle during aging

Despite the significant morphological changes that occur in the seminal vesicles with aging, the transcriptomic characteristics remain largely unexplored. To address this, we performed bulk RNA sequencing on seminal vesicle samples from mice aged 3, 13, and 21 months to uncover transcriptomic alterations. Our findings reveal that aged seminal vesicles display cystic dilatation, epithelial hypoplasia, disordered muscle layers, fibrosis, and reduced proliferation capability. A comparison between 3-month-old and 21-month-old mice indicated that leukocyte-mediated immunity and leukocyte migration were the most significantly upregulated biological processes among differentially expressed genes (DEGs). Notably, several DEGs associated with "leukocyte migration," such as Vcam1, Cxcl13, and Ccl8, exhibited an increasing trend in transcriptomic and protein expression at three different time points in the seminal vesicles of mice. Additionally, we identified multiple aging-associated DEGs, including P21 and Tnfrsf1b. Two genes (Cd209f and Ccl8) were consistently upregulated across all six regions of the male reproductive glands (testis, epididymis, and seminal vesicle) in the comparison of bulk RNA datasets from 3-month-old and 21-month-old mice. These analyses highlight an enhanced state of immune and inflammatory response in aged seminal vesicles. This study represents the first exploration of the overall transcriptome landscape of seminal vesicles in a murine model of natural aging, offering new insights into the mechanisms underlying aging-related seminal vesicle dysfunction.

A cyanobacterial sigma factor F controls biofilm-promoting genes through intra- and intercellular pathways

Cyanobacteria frequently constitute integral components of microbial communities known as phototrophic biofilms, which are widespread in various environments. Moreover, assemblages of these organisms, which serve as an expression platform, simplify harvesting the biomass, thereby holding significant industrial relevance. Previous studies of the model cyanobacterium Synechococcus elongatus PCC 7942 revealed that its planktonic growth habit results from a biofilm-suppression mechanism that depends on an extracellular inhibitor, an observation that opens the door to investigating cyanobacterial intercellular communication. Here, we demonstrate that the RNA polymerase sigma factor SigF1, is required for this biofilm-suppression mechanism whereas the S. elongatus paralog SigF2 is not involved in biofilm regulation. Comprehensive transcriptome analyses identified distinct regulons under the control of each of these sigma factors. sigF1 inactivation substantially lowers transcription of genes that code for the primary pilus subunit and consequently prevents pilus assembly. Moreover, additional data demonstrate absence of the biofilm inhibitor from conditioned medium of the sigF1 mutant, further validating involvement of the pilus assembly complex in secretion of the biofilm inhibitor. Consequently, expression is significantly upregulated for the ebfG-operon that encodes matrix components and the genes that encode the corresponding secretion system, which are repressed by the biofilm inhibitor in the wild type. Thus, this study uncovers a basic regulatory component of cyanobacterial intercellular communication, a field that is in its infancy. Elevated expression of biofilm-promoting genes in a sigF1 mutant supports an additional layer of regulation by SigF1 that operates via an intracellular mechanism.

Insight into the complexity of male infertility: a multi-omics review

Male infertility is a reproductive disorder, accounting for 40-50% of infertility. Currently, in about 70% of infertile men, the cause remains unknown. With the introduction of novel omics and advancement in high-throughput technology, potential biomarkers are emerging. The main purpose of our work was to overview different aspects of omics approaches in association with idiopathic male infertility and highlight potential genes, transcripts, non-coding RNA, proteins, and metabolites worth further exploring. Using the Gene Ontology (GO) analysis, we aimed to compare enriched GO terms from each omics approach and determine their overlapping. A PubMed database screening for the literature published between February 2014 and June 2022 was performed using the keywords: male infertility in association with different omics approaches: genomics, epigenomics, transcriptomics, ncRNAomics, proteomics, and metabolomics. A GO enrichment analysis was performed using the Enrichr tool. We retrieved 281 global studies: 171 genomics (DNA level), 21 epigenomics (19 of methylation and two histone residue modifications), 15 transcriptomics, 31 non-coding RNA, 29 proteomics, two protein posttranslational modification, and 19 metabolomics studies. Gene ontology comparison showed that different omics approaches lead to the identification of different molecular factors and that the corresponding GO terms, obtained from different omics approaches, do not overlap to a larger extent. With the integration of novel omics levels into the research of idiopathic causes of male infertility, using multi-omic systems biology approaches, we will be closer to finding the potential biomarkers and consequently becoming aware of the entire spectrum of male infertility, their cause, prognosis, and potential treatment.

Effects of age on differential resistance to duck hepatitis A virus genotype 3 in Pekin ducks by 16 S and transcriptomics

Duck hepatitis A virus genotype 3 (DHAV-3) is the major cause of viral hepatitis in ducks in Asia. Previous studies have shown that ducklings younger than 21 days are more susceptible to DHAV-3. To elucidate the mechanism by which age affects the differential susceptibility of Pekin ducks to DHAV-3, intestinal (n = 520), liver (n = 40) and blood (n = 260) samples were collected from control and DHAV-3-infected ducks at 7, 10, 14, and 21 days of age. Comparisons of plasma markers, mortality rates, and intestinal histopathological data showed that the resistance of Pekin ducks to DHAV-3 varied with age. 16 S sequencing revealed that the ileal microbial composition was influenced by age, and this correlation was greater than that recorded for caecal microbes. Candidatus Arthromitus, Bacteroides, Corynebacterium, Enterococcus, Romboutsia, and Streptococcus were the differntially abundant microbes in the ileum at the genus level after DHAV-3 infection and were significantly correlated with 7 differentially expressed genes (DEGs) in 7- and 21-day-old ducklings. 3 immunity-related pathways were significantly different between 7- and 21-day-old ducklings, especially for IFIH1-mediated induction of the interferon-alpha/beta pathway, which induces differential production of CD8(+) T cells and was influenced by a combination of differentially abundant microbiota and DEGs. We found that microbes in the ileum changed regularly with age. The intestinal microbiota was associated with the expression of genes in the liver through IFIH1-mediated induction of the interferon-alpha/beta pathway, which may partially explain why younger ducklings were more susceptible to DHAV-3 infection.

Challenges and opportunities in processing NanoString nCounter data

NanoString nCounter is a medium-throughput technology used in mRNA and miRNA differential expression studies. It offers several advantages, including the absence of an amplification step and the ability to analyze low-grade samples. Despite its considerable strengths, the popularity of the nCounter platform in experimental research stabilized in 2022 and 2023, and this trend may continue in the upcoming years. Such stagnation could potentially be attributed to the absence of a standardized analytical pipeline or the indication of optimal processing methods for nCounter data analysis. To standardize the description of the nCounter data analysis workflow, we divided it into five distinct steps: data pre-processing, quality control, background correction, normalization and differential expression analysis. Next, we evaluated eleven R packages dedicated to nCounter data processing to point out functionalities belonging to these steps and provide comments on their applications in studies of mRNA and miRNA samples.

Visualizing metagenomic and metatranscriptomic data: A comprehensive review

The fields of Metagenomics and Metatranscriptomics involve the examination of complete nucleotide sequences, gene identification, and analysis of potential biological functions within diverse organisms or environmental samples. Despite the vast opportunities for discovery in metagenomics, the sheer volume and complexity of sequence data often present challenges in processing analysis and visualization. This article highlights the critical role of advanced visualization tools in enabling effective exploration, querying, and analysis of these complex datasets. Emphasizing the importance of accessibility, the article categorizes various visualizers based on their intended applications and highlights their utility in empowering bioinformaticians and non-bioinformaticians to interpret and derive insights from meta-omics data effectively.

Granzyme B May Act as an Effector Molecule to Control the Inflammatory Process in COPD

Chronic obstructive pulmonary disease (COPD) is caused by smoking, but only a small proportion of smokers have disease severe enough to develop COPD. COPD is not always progressive. The question then arises as to what explains the different trajectories of COPD. The role of autoimmunity and regulatory T (Treg) cells in the pathogenesis of COPD is increasingly being recognized. Nine published studies on Treg cells in the lung tissue or bronchoalveolar lavage fluid have shown that smokers with COPD have fewer Treg cells than smokers without COPD or nonsmokers. Three studies showed a positive correlation between Treg cell count and FEV1%, suggesting an important role for Treg cells in COPD progression. Treg cells can regulate immunological responses via the granzyme B (GzmB) pathway. Immunohistochemical staining for GzmB in surgically resected lungs with centrilobular emphysema showed that the relationship between the amount of GzmB+ cells and FEV1% was comparable to that between Treg cell count and FEV1% in the COPD lung, suggesting that GzmB could be a functional marker for Treg cells. The volume fraction of GzmB+  cells in the small airways, the number of alveolar GzmB+ cells, and GzmB expression measured by enzyme-linked immunosorbent assay in the lung tissue of smokers were significantly correlated with FEV1%. These results suggest that the GzmB content in lung tissue may determine the progression of COPD by acting as an effector molecule to control inflammatory process. Interventions to augment GzmB-producing immunosuppressive cells in the early stages of COPD could help prevent or delay COPD progression.

A novel framework for human leukocyte antigen (HLA) genotyping using probe capture-based targeted next-generation sequencing and computational analysis

Human leukocyte antigen (HLA) genes play pivotal roles in numerous immunological applications. Given the immense number of polymorphisms, achieving accurate high-throughput HLA typing remains challenging. This study aimed to harness the human pan-genome reference consortium (HPRC) resources as a potential benchmark for HLA reference materials. We meticulously annotated specific four field-resolution alleles for 11 HLA genes (HLA-A, -B, -C, -DPA1, -DPB1, -DQA1, -DQB1, -DRB1, -DRB3, -DRB4 and -DRB5) from 44 high-quality HPRC personal genome assemblies. For sequencing, we crafted HLA-specific probes and conducted capture-based targeted sequencing of the genomic DNA of the HPRC cohort, ensuring focused and comprehensive coverage of the HLA region of interest. We used publicly available short-read whole-genome sequencing (WGS) data from identical samples to offer a comparative perspective. To decipher the vast amount of sequencing data, we employed seven distinct software tools: OptiType, HLA-VBseq, HISAT genotype, SpecHLA, T1K, QzType, and DRAGEN. Each tool offers unique capabilities and algorithms for HLA genotyping, allowing comprehensive analysis and validation of the results. We then compared these results with benchmarks derived from personal genome assemblies. Our findings present a comprehensive four-field-resolution HLA allele annotation for 44 HPRC samples. Significantly, our innovative targeted next-generation sequencing (NGS) approach for HLA genes showed superior accuracy compared with conventional short-read WGS. An integrated analysis involving QzType, T1K, and DRAGEN was developed, achieving 100% accuracy for all 11 HLA genes. In conclusion, our study highlighted the combination of targeted short-read sequencing and astute computational analysis as a robust approach for HLA genotyping. Furthermore, the HPRC cohort has emerged as a valuable assembly-based reference in this realm.

Non-coding 886 (nc886/vtRNA2-1), the epigenetic odd duck - implications for future studies

Non-coding 886 (nc886, vtRNA2-1) is the only human polymorphically imprinted gene, in which the methylation status is not determined by genetics. Existing literature regarding the establishment, stability and consequences of the methylation pattern, as well as the nature and function of the nc886 RNAs transcribed from the locus, are contradictory. For example, the methylation status of the locus has been reported to be stable through life and across somatic tissues, but also susceptible to environmental effects. The nature of the produced nc886 RNA(s) has been redefined multiple times, and in carcinogenesis, these RNAs have been reported to have conflicting roles. In addition, due to the bimodal methylation pattern of the nc886 locus, traditional genome-wide methylation analyses can lead to false-positive results, especially in smaller datasets. Herein, we aim to summarize the existing literature regarding nc886, discuss how the characteristics of nc886 give rise to contradictory results, as well as to reinterpret, reanalyse and, where possible, replicate the results presented in the current literature. We also introduce novel findings on how the distribution of the nc886 methylation pattern is associated with the geographical origins of the population and describe the methylation changes in a large variety of human tumours. Through the example of this one peculiar genetic locus and RNA, we aim to highlight issues in the analysis of DNA methylation and non-coding RNAs in general and offer our suggestions for what should be taken into consideration in future analyses.

WGBS of embryonic gonads revealed that long non-coding RNAs in the MHM region might be involved in cell autonomous sex identity and female gonadal development in chickens

DNA methylation plays a key role in sex determination and differentiation in vertebrates. However, there are few studies on DNA methylation involved in chicken gonad development, and most focused on male hypermethylated regions (MHM). It is unclear whether there are specific differentially methylated regions (DMRs) in chicken embryonic gonads regulating sex determination and differentiation. Here, the DNA methylation maps showed that the difference of DNA methylation level between sexes was much higher at embryonic day 10 (E10) than that at embryonic day 6 (E6), and the significant differentially methylated regions at both stages were mainly distributed on the Z chromosome, including MHM1 and MHM2. The results of bisulphite sequencing PCR (BSP) and qRT-PCR showed hypomethylation of female MHM and upregulation of long non-coding RNAs (lncRNAs) whose promoter in the MHM region was consistent with the sequencing results, and similar results were in brain and muscle. In female sex-reversed gonads, the methylation pattern of MHM remained unchanged, and the expression levels of the three candidate lncRNAs were significantly decreased compared with those in females, but were significantly increased compared to males. The fluorescence in situ hybridization (FISH) results also showed that these lncRNAs were highly expressed in female embryonic gonads. The results of methyltransferase inhibitor and dual-luciferase reporter assay suggest that lncRNA expression may be regulated by DNA methylation within their promoters. Therefore, we speculated that MHM may be involved in cell-autonomous sex identity in chickens, and that lncRNAs regulated by MHM may be involved in female sexual differentiation.

Detailed immune profiling in pediatric Crohn's disease using methylation cytometry

DNA methylation has been extensively utilized to study epigenetic patterns across many diseases as well as to deconvolve blood cell type proportions. This study builds upon previous studies examining methylation patterns in paediatric patients with varying stages of Crohn's disease to extend the immune profiling of these patients using a novel deconvolution approach. Compared with control subjects, we observed significantly decreased levels of CD4 memory and naive, CD8 naive, and natural killer cells and elevated neutrophil levels in Crohn's disease. In addition, Crohn's patients had a significantly elevated neutrophil-to-lymphocyte ratio. Using an epigenome-wide association approach and adjusting for potential confounders, including cell type, we observed 397 differentially methylated CpG (DMC) sites associated with Crohn's disease. The top genetic pathway associated with the DMCs was the regulation of arginine metabolic processes which are involved in the regulation of T cells.

Characterization of Tau95 led to the identification of a four-subunit TFIIIC complex in trypanosomatid parasites

RNA polymerase III (RNAP III) synthetizes small essential non-coding RNA molecules such as tRNAs and 5S rRNA. In yeast and vertebrates, RNAP III needs general transcription factors TFIIIA, TFIIIB, and TFIIIC to initiate transcription. TFIIIC, composed of six subunits, binds to internal promoter elements in RNAP III-dependent genes. Limited information is available about RNAP III transcription in the trypanosomatid protozoa Trypanosoma brucei and Leishmania major, which diverged early from the eukaryotic lineage. Analyses of the first published draft of the trypanosomatid genome sequences failed to recognize orthologs of any of the TFIIIC subunits, suggesting that this transcription factor is absent in these parasites. However, a putative TFIIIC subunit was recently annotated in the databases. Here we characterize this subunit in T. brucei and L. major and demonstrate that it corresponds to Tau95. In silico analyses showed that both proteins possess the typical Tau95 sequences: the DNA binding region and the dimerization domain. As anticipated for a transcription factor, Tau95 localized to the nucleus in insect forms of both parasites. Chromatin immunoprecipitation (ChIP) assays demonstrated that Tau95 binds to tRNA and U2 snRNA genes in T. brucei. Remarkably, by performing tandem affinity purifications we identified orthologs of TFIIIC subunits Tau55, Tau131, and Tau138 in T. brucei and L. major. Thus, contrary to what was assumed, trypanosomatid parasites do possess a TFIIIC complex. Other putative interacting partners of Tau95 were identified in T. brucei and L. major. KEY POINTS: • A four-subunit TFIIIC complex is present in T. brucei and L. major • TbTau95 associates with tRNA and U2 snRNA genes • Putative interacting partners of Tau95 might include some RNAP II regulators.

Global omics study of Tetraselmis chuii reveals time-related metabolic adaptations upon oxidative stress

Microalgae species encounter oxidative stress in their natural environments, prompting the development of species-specific adaptation mechanisms. Understanding these mechanisms can offer valuable insights for biotechnological applications in microalgal metabolic manipulation. In this study, we investigated the response of Tetraselmis chuii, an industrially important microalga, to H2O2-induced oxidative stress. Exposure to 0.5-mM H2O2 resulted in reduced cell viability, and higher concentrations led to a drastic decline. After 1 h of exposure to H2O2, photosynthetic capacity (Qy) was negatively impacted, and this reduction intensified after 6 h of continuous stress. Global multi-omics analysis revealed that T. chuii rapidly responded to H2O2-induced oxidative stress within the first hour, causing significant changes in both transcriptomic and metabolomic profiles. Among the cellular functions negatively affected were carbon and energy flow, with photosynthesis-related PSBQ having a 2.4-fold downregulation, pyruvate kinase decreased by 1.5-fold, and urea content reduced by threefold. Prolonged exposure to H2O2 incurred a high energy cost, leading to unsuccessful attempts to enhance carbon metabolism, as depicted, for example, by the upregulation of photosystems-related PETC and PETJ by more than twofold. These findings indicate that T. chuii quickly responds to oxidative stress, but extended exposure can have detrimental effects on its cellular functions. KEY POINTS: • 0.5-mM H2O2-induced oxidative stress strongly affects T. chuii • Distinct short- and long-term adaptation mechanisms are induced • Major metabolic adaptations occur within the first hour of exposure.

Genetic confounds of transgenerational epigenetic inheritance in mice

Transgenerational epigenetic inheritance in mammals remains a controversial phenomenon. A recent study by Takahashi et al. provides evidence for this mode of inheritance in mice by using a CRISPR/Cas9-based epigenetic editing technique to modify DNA methylation levels at specific promoters and then demonstrating the inheritance of the gain in methylation in offspring. In this technical commentary, we argue that the method used in the original study inherently amplifies the likelihood of genetic changes that thereafter lead to the heritability of epigenetic changes. We provide evidence that genetic changes from multiple sources do indeed occur in these experiments and explore several avenues by which these changes could be causal to the apparent inheritance of epigenetic changes. We conclude a genetic basis of inheritance cannot be ruled out and thus transgenerational epigenetic inheritance has not been adequately established by the original study.

Identification of potential therapeutic target SPP1 and related RNA regulatory pathway in keloid based on bioinformatics analysis

OBJECTIVE

To explore the complex mechanisms of keloid, new approaches have been developed by different strategies. However, conventional treatment did not significantly reduce the recurrence rate. This study aimed to identify new biomarkers and mechanisms for keloid progression through bioinformatics analyses.

METHODS

In our study, microarray datasets for keloid were downloaded from the GEO database. Differentially expressed genes (DEGs) were identified by R software. Multiple bioinformatics tools were used to identify hub genes, and reverse predict upstream miRNAs and lncRNA molecules of target hub genes. Finally, the total RNA-sequencing technique and miRNA microarray were combined to validate the identified genes.

RESULTS

Thirty-one DEGs were screened out and the upregulated hub gene SPP1 was finally identified, which was consistent with our RNA-sequencing analysis results and validation dataset. In addition, a ceRNA network of mRNA (SPP1)-miRNA (miR-181a-5p)-lncRNA (NEAT1, MALAT1, LINC00667, NORAD, XIST and MIR4458HG) was identified by the bioinformatics databases. The results of our miRNA microarray showed that miR-181a-5p was upregulated in keloid, also we found that the lncRNA NEAT1 could affect keloid progression by retrieving the relevant literature.

CONCLUSIONS

We speculate that SPP1 is a potential candidate biomarker and therapeutic target for patients with keloid, and NEAT1/miR-181a-5p/SPP1 might be the RNA regulatory pathway that regulates keloid formation.

Development of a multi-targeted chemotherapeutic approach based on G-quadruplex stabilisation and carbonic anhydrase inhibition

A novel class of compounds designed to hit two anti-tumour targets, G-quadruplex structures and human carbonic anhydrases (hCAs) IX and XII is proposed. The induction/stabilisation of G-quadruplex structures by small molecules has emerged as an anticancer strategy, disrupting telomere maintenance and reducing oncogene expression. hCAs IX and XII are well-established anti-tumour targets, upregulated in many hypoxic tumours and contributing to metastasis. The ligands reported feature a berberine G-quadruplex stabiliser scaffold connected to a moiety inhibiting hCAs IX and XII. In vitro experiments showed that our compounds selectively stabilise G-quadruplex structures and inhibit hCAs IX and XII. The crystal structure of a telomeric G-quadruplex in complex with one of these ligands was obtained, shedding light on the ligand/target interaction mode. The most promising ligands showed significant cytotoxicity against CA IX-positive HeLa cancer cells in hypoxia, and the ability to stabilise G-quadruplexes within tumour cells.

NagZ modulates the virulence of E. cloacae by acting through the gene of unknown function, ECL_03795

β-N-acetylglucosaminidase (NagZ), a cytosolic glucosaminidase, plays a pivotal role in peptidoglycan recycling. Previous research demonstrated that NagZ knockout significantly eradicated AmpC-dependent β-lactam resistance in Enterobacter cloacae. However, NagZ's role in the virulence of E. cloacae remains unclear. Our study, incorporating data on mouse and Galleria mellonella larval mortality rates, inflammation markers, and histopathological examinations, revealed a substantial reduction in the virulence of E. cloacae following NagZ knockout. Transcriptome sequencing uncovered differential gene expression between NagZ knockout and wild-type strains, particularly in nucleotide metabolism pathways. Further investigation demonstrated that NagZ deletion led to a significant increase in cyclic diguanosine monophosphate (c-di-GMP) levels. Additionally, transcriptome sequencing and RT-qPCR confirmed significant differences in the expression of ECL_03795, a gene with an unknown function but speculated to be involved in c-di-GMP metabolism due to its EAL domain known for phosphodiesterase activity. Interestingly, in ECL_03795 knockout strains, a notable reduction in the virulence was observed, and virulence was rescued upon complementation with ECL_03795. Consequently, our study suggests that NagZ's function on virulence is partially mediated through the ECL_03795→c-di-GMP pathway, providing insight into the development of novel therapies and strongly supporting the interest in creating highly efficient NagZ inhibitors.

Optimal variable identification for accurate detection of causal expression Quantitative Trait Loci with applications in heart-related diseases

Gene expression plays a pivotal role in various diseases, contributing significantly to their mechanisms. Most GWAS risk loci are in non-coding regions, potentially affecting disease risk by altering gene expression in specific tissues. This expression is notably tissue-specific, with genetic variants substantially influencing it. However, accurately detecting the expression Quantitative Trait Loci (eQTL) is challenging due to limited heritability in gene expression, extensive linkage disequilibrium (LD), and multiple causal variants. The single variant association approach in eQTL analysis is limited by its susceptibility to capture the combined effects of multiple variants, and a bias towards common variants, underscoring the need for a more robust method to accurately identify causal eQTL variants. To address this, we developed an algorithm, CausalEQTL, which integrates L 0 +L 1 penalized regression with an ensemble approach to localize eQTL, thereby enhancing prediction performance precisely. Our results demonstrate that CausalEQTL outperforms traditional models, including LASSO, Elastic Net, Ridge, in terms of power and overall performance. Furthermore, analysis of heart tissue data from the GTEx project revealed that eQTL sites identified by our algorithm provide deeper insights into heart-related tissue eQTL detection. This advancement in eQTL mapping promises to improve our understanding of the genetic basis of tissue-specific gene expression and its implications in disease. The source code and identified causal eQTLs for CausalEQTL are available on GitHub: https://github.com/zhc-moushang/CausalEQTL.

PRIMITI: A computational approach for accurate prediction of miRNA-target mRNA interaction

Current medical research has been demonstrating the roles of miRNAs in a variety of cellular mechanisms, lending credence to the association between miRNA dysregulation and multiple diseases. Understanding the mechanisms of miRNA is critical for developing effective diagnostic and therapeutic strategies. miRNA-mRNA interactions emerge as the most important mechanism to be understood despite their experimental validation constraints. Accordingly, several computational models have been developed to predict miRNA-mRNA interactions, albeit presenting limited predictive capabilities, poor characterisation of miRNA-mRNA interactions, and low usability. To address these drawbacks, we developed PRIMITI, a PRedictive model for the Identification of novel miRNA-Target mRNA Interactions. PRIMITI is a novel machine learning model that utilises CLIP-seq and expression data to characterise functional target sites in 3'-untranslated regions (3'-UTRs) and predict miRNA-target mRNA repression activity. The model was trained using a reliable negative sample selection approach and the robust extreme gradient boosting (XGBoost) model, which was coupled with newly introduced features, including sequence and genetic variation information. PRIMITI achieved an area under the receiver operating characteristic (ROC) curve (AUC) up to 0.96 for a prediction of functional miRNA-target site binding and 0.96 for a prediction of miRNA-target mRNA repression activity on cross-validation and an independent blind test. Additionally, the model outperformed state-of-the-art methods in recovering miRNA-target repressions in an unseen microarray dataset and in a collection of validated miRNA-mRNA interactions, highlighting its utility for preliminary screening. PRIMITI is available on a reliable, scalable, and user-friendly web server at https://biosig.lab.uq.edu.au/primiti.

Animal and plant protein intake during infancy and childhood DNA methylation: a meta-analysis in the NutriPROGRAM consortium

BACKGROUND

Higher early-life animal protein intake is associated with a higher childhood obesity risk compared to plant protein intake. Differential DNA methylation may represent an underlying mechanism.

METHODS

We analysed associations of infant animal and plant protein intakes with DNA methylation in early (2-6 years, N = 579) and late (7̄-12 years, N = 604) childhood in two studies. Study-specific robust linear regression models adjusted for relevant confounders were run, and then meta-analysed using a fixed-effects model. We also performed sex-stratified meta-analyses. Follow-up analyses included pathway analysis and eQTM look-up.

RESULTS

Infant animal protein intake was not associated with DNA methylation in early childhood, but was associated with late-childhood DNA methylation at cg21300373 (P = 4.27 × 10¯8, MARCHF1) and cg10633363 (P = 1.09 × 10¯7, HOXB9) after FDR correction. Infant plant protein intake was associated with early-childhood DNA methylation at cg25973293 (P = 2.26 × 10-7, C1orf159) and cg15407373 (P = 2.13 × 10-7, MBP) after FDR correction. There was no overlap between the findings from the animal and plant protein analyses. We did not find enriched functional pathways at either time point using CpGs associated with animal and plant protein. These CpGs were not previously associated with childhood gene expression. Sex-stratified meta-analyses showed sex-specific DNA methylation associations for both animal and plant protein intake.

CONCLUSION

Infant animal protein intake was associated with DNA methylation at two CpGs in late childhood. Infant plant protein intake was associated with DNA methylation in early childhood at two CpGs. A potential mediating role of DNA methylation at these CpGs between infant protein intake and health outcomes requires further investigation.

scSID: A lightweight algorithm for identifying rare cell types by capturing differential expression from single-cell sequencing data

Single-cell RNA sequencing (scRNA-seq) is currently an important technology for identifying cell types and studying diseases at the genetic level. Identifying rare cell types is biologically important as one of the downstream data analyses of single-cell RNA sequencing. Although rare cell identification methods have been developed, most of these suffer from insufficient mining of intercellular similarities, low scalability, and being time-consuming. In this paper, we propose a single-cell similarity division algorithm (scSID) for identifying rare cells. It takes cell-to-cell similarity into consideration by analyzing both inter-cluster and intra-cluster similarities, and discovers rare cell types based on the similarity differences. We show that scSID outperforms other existing methods by benchmarking it on different experimental datasets. Application of scSID to multiple datasets, including 68K PBMC and intestine, highlights its exceptional scalability and remarkable ability to identify rare cell populations.

The genome in space and time comes of age

DNA sequencing is not enough to grasp the complexity of genome organization and function. The four-dimensional (three in space, one in time) configuration of the eukaryotic nucleus varies with cell types, during development and in diseased tissues, and has to be taken into account to decipher genome function. To study, discuss, and advance in such direction, the International Nucleome Consortium COST Action, funded by the European Union, held its concluding symposium 'The Genome in Space and Time' at the Ionian University in Corfu, Greece, on September 10-13, 2023.

Using methylation data to improve transcription factor binding prediction

Modelling the regulatory mechanisms that determine cell fate, response to external perturbation, and disease state depends on measuring many factors, a task made more difficult by the plasticity of the epigenome. Scanning the genome for the sequence patterns defined by Position Weight Matrices (PWM) can be used to estimate transcription factor (TF) binding locations. However, this approach does not incorporate information regarding the epigenetic context necessary for TF binding. CpG methylation is an epigenetic mark influenced by environmental factors that is commonly assayed in human cohort studies. We developed a framework to score inferred TF binding locations using methylation data. We intersected motif locations identified using PWMs with methylation information captured in both whole-genome bisulfite sequencing and Illumina EPIC array data for six cell lines, scored motif locations based on these data, and compared with experimental data characterizing TF binding (ChIP-seq). We found that for most TFs, binding prediction improves using methylation-based scoring compared to standard PWM-scores. We also illustrate that our approach can be generalized to infer TF binding when methylation information is only proximally available, i.e. measured for nearby CpGs that do not directly overlap with a motif location. Overall, our approach provides a framework for inferring context-specific TF binding using methylation data. Importantly, the availability of DNA methylation data in existing patient populations provides an opportunity to use our approach to understand the impact of methylation on gene regulatory processes in the context of human disease.

Distribution of antibiotic resistance genes and their pathogen hosts in duck farm environments in south-east coastal China

Livestock farms are major reservoirs of antibiotic resistance genes (ARGs) that are discharged into the environment. However, the abundance, diversity, and transmission of ARGs in duck farms and its impact on surrounding environments remain to be further explored. Therefore, the characteristics of ARGs and their bacterial hosts from duck farms and surrounding environment were investigated by using metagenomic sequencing. Eighteen ARG types which consist of 823 subtypes were identified and the majority conferred resistance to multidrug, tetracyclines, aminoglycosides, chloramphenicols, MLS, and sulfonamides. The floR gene was the most abundant subtype, followed by sul1, tetM, sul2, and tetL. ARG abundance in fecal sample was significantly higher than soil and water sample. Our results also lead to a hypothesis that Shandong province have been the most contaminated by ARGs from duck farm compared with other four provinces. PcoA results showed that the composition of ARG subtypes in water and soil samples was similar, but there were significant differences between water and feces samples. However, the composition of ARG subtypes were similar between samples from five provinces. Bacterial hosts of ARG subtypes were taxonomically assigned to eight phyla that were dominated by the Proteobacteria, Firmicutes, Bacteroidetes, and Actinobacteria. In addition, some human bacterial pathogens could be enriched in duck feces, including Enterococcus faecium, Acinetobacter baumannii, and Staphylococcus aureus, and even serve as the carrier of ARGs. The combined results indicate that a comprehensive overview of the diversity and abundance of ARGs, and strong association between ARGs and bacterial community shift proposed, and benefit effective measures to improve safety of antibiotics use in livestock and poultry farming. KEY POINTS: • ARG distribution was widespread in the duck farms and surroundings environment • ARG abundance on the duck farms was significantly higher than in soil and water • Human bacterial pathogens may serve as the vectors for ARGs.

Role of folic acid in regulating gut microbiota and short-chain fatty acids based on an in vitro fermentation model

Folic acid deficiency is common worldwide and is linked to an imbalance in gut microbiota. However, based on model animals used to study the utilization of folic acid by gut microbes, there are challenges of reproducibility and individual differences. In this study, an in vitro fecal slurry culture model of folic acid deficiency was established to investigate the effects of supplementation with 5-methyltetrahydrofolate (MTHF) and non-reduced folic acid (FA) on the modulation of gut microbiota. 16S rRNA sequencing results revealed that both FA (29.7%) and MTHF (27.9%) supplementation significantly reduced the relative abundance of Bacteroidetes compared with control case (34.3%). MTHF supplementation significantly improved the relative abundance of Firmicutes by 4.49%. Notably, compared with the control case, FA and MTHF supplementation promoted an increase in fecal levels of Lactobacillus, Bifidobacterium, and Pediococcus. Short-chain fatty acid (SCFA) analysis showed that folic acid supplementation decreased acetate levels and increased fermentative production of isobutyric acid. The in vitro fecal slurry culture model developed in this study can be utilized as a model of folic acid deficiency in humans to study the gut microbiota and demonstrate that exogenous folic acid affects the composition of the gut microbiota and the level of SCFAs. KEY POINTS: • Establishment of folic acid deficiency in an in vitro culture model. • Folic acid supplementation regulates intestinal microbes and SCFAs. • Connections between microbes and SCFAs after adding folic acid are built.

Selecting a preculture strategy for improving biomass and astaxanthin productivity of Chromochloris zofingiensis

Chromochloris zofingiensis is a potential source of natural astaxanthin; however, its rapid growth and astaxanthin enrichment cannot be achieved simultaneously. This study established autotrophic, mixotrophic, and heterotrophic preculture patterns to assess their ameliorative effect on the C. zofingiensis heterotrophic growth state. In comparison, mixotrophic preculture (MP) exhibited the best improving effect on heterotrophic biomass concentration of C. zofingiensis (up to 121.5 g L-1) in a 20 L fermenter, reaching the global leading level. The astaxanthin productivity achieved 111 mg L-1 day-1, 7.4-fold higher than the best record. The transcriptome and 13C tracer-based metabolic flux analysis were used for mechanism inquiry. The results revealed that MP promoted carotenoid and lipid synthesis, and supported synthesis preference of low unsaturated fatty acids represented by C18:1 and C16:0. The MP group maintained the best astaxanthin productivity via mastering the balance between increasing glucose metabolism and inhibition of carotenoid synthesis. The MP strategy optimized the physiological state of C. zofingiensis and realized its heterotrophic high-density growth for an excellent astaxanthin yield on a pilot scale. This strategy exhibits great application potential in the microalgae-related industry. KEY POINTS: • Preculture strategies changed carbon flux and gene expression in C. zofingiensis • C. zofingiensis realized a high-density culture with MP and fed-batch culture (FBC) • Astaxanthin productivity achieved 0.111 g L-1 day-1 with MP and FBC.

Transcriptional response of Saccharomyces cerevisiae to lactic acid enantiomers

The model yeast, Saccharomyces cerevisiae, is a popular object for both fundamental and applied research, including the development of biosensors and industrial production of pharmaceutical compounds. However, despite multiple studies exploring S. cerevisiae transcriptional response to various substances, this response is unknown for some substances produced in yeast, such as D-lactic acid (DLA). Here, we explore the transcriptional response of the BY4742 strain to a wide range of DLA concentrations (from 0.05 to 45 mM), and compare it to the response to 45 mM L-lactic acid (LLA). We recorded a response to 5 and 45 mM DLA (125 and 113 differentially expressed genes (DEGs), respectively; > 50% shared) and a less pronounced response to 45 mM LLA (63 DEGs; > 30% shared with at least one DLA treatment). Our data did not reveal natural yeast promoters quantitatively sensing DLA but provide the first description of the transcriptome-wide response to DLA and enrich our understanding of the LLA response. Some DLA-activated genes were indeed related to lactate metabolism, as well as iron uptake and cell wall structure. Additional analyses showed that at least some of these genes were activated only by acidic form of DLA but not its salt, revealing the role of pH. The list of LLA-responsive genes was similar to those published previously and also included iron uptake and cell wall genes, as well as genes responding to other weak acids. These data might be instrumental for optimization of lactate production in yeast and yeast co-cultivation with lactic acid bacteria. KEY POINTS: • We present the first dataset on yeast transcriptional response to DLA. • Differential gene expression was correlated with yeast growth inhibition. • The transcriptome response to DLA was richer in comparison to LLA.

scQA: A dual-perspective cell type identification model for single cell transcriptome data

Single-cell RNA sequencing technologies have been pivotal in advancing the development of algorithms for clustering heterogeneous cell populations. Existing methods for utilizing scRNA-seq data to identify cell types tend to neglect the beneficial impact of dropout events and perform clustering focusing solely on quantitative perspective. Here, we introduce a novel method named scQA, notable for its ability to concurrently identify cell types and cell type-specific key genes from both qualitative and quantitative perspectives. In contrast to other methods, scQA not only identifies cell types but also extracts key genes associated with these cell types, enabling bidirectional clustering for scRNA-seq data. Through an iterative process, our approach aims to minimize the number of landmarks to approximately a dozen while maximizing the inclusion of quasi-trend-preserved genes with dropouts both qualitatively and quantitatively. It then clusters cells by employing an ingenious label propagation strategy, obviating the requirement for a predetermined number of cell types. Validated on 20 publicly available scRNA-seq datasets, scQA consistently outperforms other salient tools. Furthermore, we confirm the effectiveness and potential biological significance of the identified key genes through both external and internal validation. In conclusion, scQA emerges as a valuable tool for investigating cell heterogeneity due to its distinctive fusion of qualitative and quantitative facets, along with bidirectional clustering capabilities. Furthermore, it can be seamlessly integrated into border scRNA-seq analyses. The source codes are publicly available at https://github.com/LD-Lyndee/scQA.

Running the gauntlet: challenges to genome integrity in spermiogenesis

Species' continuity depends on gametogenesis to produce the only cell types that can transmit genetic information across generations. Spermiogenesis, which encompasses post-meiotic, haploid stages of male gametogenesis, is a process that leads to the formation of sperm cells well-known for their motility. Spermiogenesis faces three major challenges. First, after two rounds of meiotic divisions, the genome lacks repair templates (no sister chromatids, no homologous chromosomes), making it incredibly vulnerable to any genomic insults over an extended time (typically days-weeks). Second, the sperm genome becomes transcriptionally silent, making it difficult to respond to new perturbations as spermiogenesis progresses. Third, the histone-to-protamine transition, which is essential to package the sperm genome, counterintuitively involves DNA break formation. How spermiogenesis handles these challenges remains poorly understood. In this review, we discuss each challenge and their intersection with the biology of protamines. Finally, we discuss the implication of protamines in the process of evolution.

Transmission of blaNDM in Enterobacteriaceae among animals, food and human

Despite carbapenems not being used in animals, carbapenem-resistant Enterobacterales (CRE), particularly New Delhi metallo-β-lactamase-producing CRE (NDM-CRE), are prevalent in livestock. Concurrently, the incidence of human infections caused by NDM-CRE is rising, particularly in children. Although a positive association between livestock production and human NDM-CRE infections at the national level was identified, the evidence of direct transmission of NDM originating from livestock to humans remains largely unknown. Here, we conducted a cross-sectional study in Chengdu, Sichuan Province, to examine the prevalence of NDM-CRE in chickens and pigs along the breeding-slaughtering-retail chains, in pork in cafeterias of schools, and in colonizations and infections from children's hospital and examined the correlation of NDM-CRE among animals, foods and humans. Overall, the blaNDM increases gradually along the chicken and pig breeding (4.70%/2.0%) -slaughtering (7.60%/22.40%) -retail (65.56%/34.26%) chains. The slaughterhouse has become a hotspot for cross-contamination and amplifier of blaNDM. Notably, 63.11% of pork from the school cafeteria was positive for blaNDM. The prevalence of blaNDM in intestinal and infection samples from children's hospitals was 21.68% and 19.80%, respectively. whole genome sequencing (WGS) analysis revealed the sporadic, not large-scale, clonal spread of NDM-CRE along the chicken and pig breeding-slaughtering-retail chain, with further spreading via IncX3-blaNDM plasmid within each stage of whole chains. Clonal transmission of NDM-CRE is predominant in children's hospitals. The IncX3-blaNDM plasmid was highly prevalent among animals and humans and accounted for 57.7% of Escherichia coli and 91.3% of Klebsiella pneumoniae. Attention should be directed towards the IncX3 plasmid to control the transmission of blaNDM between animals and humans.

Alternative polyadenylation regulates the translation of metabolic and inflammation-related proteins in adipose tissue of gestational diabetes mellitus

In gestational diabetes mellitus (GDM), adipose tissue undergoes metabolic disturbances and chronic low-grade inflammation. Alternative polyadenylation (APA) is a post-transcriptional modification mechanism that generates mRNA with variable lengths of 3' untranslated regions (3'UTR), and it is associated with inflammation and metabolism. However, the role of APA in GDM adipose tissue has not been well characterized. In this study, we conducted transcriptomic and proteomic sequencing on subcutaneous and omental adipose tissues from both control and GDM patients. Using Dapars, a novel APA quantitative algorithm, we delineated the APA landscape of adipose tissue, revealing significant 3'UTR elongation of mRNAs in the GDM group. Omental adipose tissue exhibited a significant correlation between elongated 3'UTRs and reduced translation levels of genes related to metabolism and inflammation. Validation experiments in THP-1 derived macrophages (TDMs) demonstrated the impact of APA on translation levels by overexpressing long and short 3'UTR isoforms of a representative gene LRRC25. Additionally, LRRC25 was validated to suppress proinflammatory polarization in TDMs. Further exploration revealed two underexpressed APA trans-acting factors, CSTF3 and PPP1CB, in GDM omental adipose tissue. In conclusion, this study provides preliminary insights into the APA landscape of GDM adipose tissue. Reduced APA regulation in GDM omental adipose tissue may contribute to metabolic disorders and inflammation by downregulating gene translation levels. These findings advance our understanding of the molecular mechanisms underlying GDM-associated adipose tissue changes.

Benchmarking feature selection and feature extraction methods to improve the performances of machine-learning algorithms for patient classification using metabolomics biomedical data

Objective

Classification tasks are an open challenge in the field of biomedicine. While several machine-learning techniques exist to accomplish this objective, several peculiarities associated with biomedical data, especially when it comes to omics measurements, prevent their use or good performance achievements. Omics approaches aim to understand a complex biological system through systematic analysis of its content at the molecular level. On the other hand, omics data are heterogeneous, sparse and affected by the classical "curse of dimensionality" problem, i.e. having much fewer observation, samples (n) than omics features (p). Furthermore, a major problem with multi-omics data is the imbalance either at the class or feature level. The objective of this work is to study whether feature extraction and/or feature selection techniques can improve the performances of classification machine-learning algorithms on omics measurements.

Methods

Among all omics, metabolomics has emerged as a powerful tool in cancer research, facilitating a deeper understanding of the complex metabolic landscape associated with tumorigenesis and tumor progression. Thus, we selected three publicly available metabolomics datasets, and we applied several feature extraction techniques both linear and non-linear, coupled or not with feature selection methods, and evaluated the performances regarding patient classification in the different configurations for the three datasets.

Results

We provide general workflow and guidelines on when to use those techniques depending on the characteristics of the data available. To further test the extension of our approach to other omics data, we have included a transcriptomics and a proteomics data. Overall, for all datasets, we showed that applying supervised feature selection improves the performances of feature extraction methods for classification purposes. Scripts used to perform all analyses are available at: https://github.com/Plant-Net/Metabolomic_project/.

Resource: A curated database of brain-related functional gene sets (Brain.GMT)

Transcriptional profiling has become a common tool for investigating the nervous system. During analysis, differential expression results are often compared to functional ontology databases, which contain curated gene sets representing well-studied pathways. This dependence can cause neuroscience studies to be interpreted in terms of functional pathways documented in better studied tissues (e.g., liver) and topics (e.g., cancer), and systematically emphasizes well-studied genes, leaving other findings in the obscurity of the brain "ignorome". To address this issue, we compiled a curated database of 918 gene sets related to nervous system function, tissue, and cell types ("Brain.GMT") that can be used within common analysis pipelines (GSEA, limma, edgeR) to interpret results from three species (rat, mouse, human). Brain.GMT includes brain-related gene sets curated from the Molecular Signatures Database (MSigDB) and extracted from public databases (GeneWeaver, Gemma, DropViz, BrainInABlender, HippoSeq) and published studies containing differential expression results. Although Brain.GMT is still undergoing development and currently only represents a fraction of available brain gene sets, "brain ignorome" genes are already better represented than in traditional Gene Ontology databases. Moreover, Brain.GMT substantially improves the quantity and quality of gene sets identified as enriched with differential expression in neuroscience studies, enhancing interpretation. •We compiled a curated database of 918 gene sets related to nervous system function, tissue, and cell types ("Brain.GMT").•Brain.GMT can be used within common analysis pipelines (GSEA, limma, edgeR) to interpret neuroscience transcriptional profiling results from three species (rat, mouse, human).•Although Brain.GMT is still undergoing development, it substantially improved the interpretation of differential expression results within our initial use cases.

Elucidating molecular mechanism and chemical space of chalcones through biological networks and machine learning approaches

We developed a bio-cheminformatics method, exploring disease inhibition mechanisms using machine learning-enhanced quantitative structure-activity relationship (ML-QSAR) models and knowledge-driven neural networks. ML-QSAR models were developed using molecular fingerprint descriptors and the Random Forest algorithm to explore the chemical spaces of Chalcones inhibitors against diverse disease properties, including antifungal, anti-inflammatory, anticancer, antimicrobial, and antiviral effects. We generated and validated robust machine learning-based bioactivity prediction models (https://github.com/RatulChemoinformatics/QSAR) for the top genes. These models underwent ROC and applicability domain analysis, followed by molecular docking studies to elucidate the molecular mechanisms of the molecules. Through comprehensive neural network analysis, crucial genes such as AKT1, HSP90AA1, SRC, and STAT3 were identified. The PubChem fingerprint-based model revealed key descriptors: PubchemFP521 for AKT1, PubchemFP180 for SRC, PubchemFP633 for HSP90AA1, and PubchemFP145 and PubchemFP338 for STAT3, consistently contributing to bioactivity across targets. Notably, chalcone derivatives demonstrated significant bioactivity against target genes, with compound RA1 displaying a predictive pIC50 value of 5.76 against HSP90AA1 and strong binding affinities across other targets. Compounds RA5 to RA7 also exhibited high binding affinity scores comparable to or exceeding existing drugs. These findings emphasize the importance of knowledge-based neural network-based research for developing effective drugs against diverse disease properties. These interactions warrant further in vitro and in vivo investigations to elucidate their potential in rational drug design. The presented models provide valuable insights for inhibitor design and hold promise for drug development. Future research will prioritize investigating these molecules for mycobacterium tuberculosis, enhancing the comprehension of effectiveness in addressing infectious diseases.

CST2 promotes cell proliferation and regulates cell cycle by activating Wnt-β-catenin signalling pathway in serous ovarian cancer

BACKGROUND

Cystatin SA (CST2) plays multiple roles in different types of malignant tumours; however, its role in serous ovarian cancer (SOC) remains unclear. Therefore, we aimed to investigate the expression levels, survival outcomes, immune cell infiltration, proliferation, cell cycle, and underlying molecular mechanisms associated with the CST2 signature in SOC.

METHODS

The Cancer Genome Atlas database was used to acquire clinical information and CST2 expression profiles from patients with SOC. Wilcoxon rank-sum tests were used to compare CST2 expression levels between SOC and normal ovarian tissues. A prognostic assessment of CST2 was conducted using Cox regression analysis and the Kaplan-Meier method. Differentially expressed genes were identified using functional enrichment analysis. Immune cell infiltration was examined using a single-sample gene set enrichment analysis. Cell cycle characteristics and proliferation were assessed using a colony formation assay, flow cytometry, and a cell counting kit-8 assay. Western blots and quantitative reverse transcription PCR analyses were employed to examine CST2 expressions and related genes involved in the cell cycle and the Wnt-β-catenin signalling pathway.

RESULTS

Our findings revealed significant upregulation of CST2 in SOC, and elevated CST2 expression was correlated with advanced clinicopathological characteristics and unfavourable prognoses. Pathway enrichment analysis highlighted the association between the cell cycle and the Wnt signalling pathway. Moreover, increased CST2 levels were positively correlated with immune cell infiltration. Functionally, CST2 played vital roles in promoting cell proliferation, orchestrating the G1-to-S phase transition, and driving malignant SOC progression through activating the Wnt-β-catenin signalling pathway.

CONCLUSIONS

The elevated expression of CST2 may be related to the occurrence and progression of SOC by activating the Wnt-β-catenin pathway. Additionally, our findings suggest that CST2 is a promising novel biomarker with potential applications in therapeutic, prognostic, and diagnostic strategies for SOC.

Stress-induced epigenetic effects driven by maternal lactation in dairy cattle: a comethylation network approach

Epigenetic marks do not follow the Mendelian laws of inheritance. The environment can alter the epigenotype of an individual when exposed to different external stressors. In lactating cows, the first stages of gestation overlap with the lactation peak, creating a negative energy balance that is difficult to overcome with diet. This negative energy balance could affect early embryo development that must compete with the mammary tissue for nutrients. We hypothesize that the methylation profiles of calves born to nonlactating heifers are different from those of calves born to lactating cows. We found 50,277 differentially methylated cytosines and 2,281 differentially methylated regions between these two groups of animals. A comethylation network was constructed to study the correlation between the phenotypes of the mothers and the epigenome of the calves, revealing 265 regions associated with the phenotypes. Our study revealed the presence of DMCs and DMRs in calves gestated by heifers and lactating cows, which were linked to the dam's lactation and the calves' ICAP and milk EBV. Gene-specific analysis highlighted associations with vasculature and organ morphogenesis and cell communication and signalling. These finding support the hypothesis that calves gestated by nonlactating mothers have a different methylation profile than those gestated by lactating cows.

From molecular mechanisms of prostate cancer to translational applications: based on multi-omics fusion analysis and intelligent medicine

Prostate cancer is the most common cancer in men worldwide and has a high mortality rate. The complex and heterogeneous development of prostate cancer has become a core obstacle in the treatment of prostate cancer. Simultaneously, the issues of overtreatment in early-stage diagnosis, oligometastasis and dormant tumor recognition, as well as personalized drug utilization, are also specific concerns that require attention in the clinical management of prostate cancer. Some typical genetic mutations have been proved to be associated with prostate cancer's initiation and progression. However, single-omic studies usually are not able to explain the causal relationship between molecular alterations and clinical phenotypes. Exploration from a systems genetics perspective is also lacking in this field, that is, the impact of gene network, the environmental factors, and even lifestyle behaviors on disease progression. At the meantime, current trend emphasizes the utilization of artificial intelligence (AI) and machine learning techniques to process extensive multidimensional data, including multi-omics. These technologies unveil the potential patterns, correlations, and insights related to diseases, thereby aiding the interpretable clinical decision making and applications, namely intelligent medicine. Therefore, there is a pressing need to integrate multidimensional data for identification of molecular subtypes, prediction of cancer progression and aggressiveness, along with perosonalized treatment performing. In this review, we systematically elaborated the landscape from molecular mechanism discovery of prostate cancer to clinical translational applications. We discussed the molecular profiles and clinical manifestations of prostate cancer heterogeneity, the identification of different states of prostate cancer, as well as corresponding precision medicine practices. Taking multi-omics fusion, systems genetics, and intelligence medicine as the main perspectives, the current research results and knowledge-driven research path of prostate cancer were summarized.

Interplay between JAK/STAT pathway and non-coding RNAs in different cancers

Progress in the identification of core multi-protein modules within JAK/STAT pathway has enabled researchers to develop a better understanding of the linchpin role of deregulated signaling cascade in carcinogenesis and metastasis. More excitingly, complex interplay between JAK/STAT pathway and non-coding RNAs has been shown to reprogramme the outcome of signaling cascade and modulate immunological responses within tumor microenvironment. Wealth of information has comprehensively illustrated that most of this complexity regulates the re-shaping of the immunological responses. Increasingly sophisticated mechanistic insights have illuminated fundamental role of STAT-signaling in polarization of macrophages to M2 phenotype that promotes disease aggressiveness. Overall, JAK/STAT signaling drives different stages of cancer ranging from cancer metastasis to the reshaping of the tumor microenvironment. JAK/STAT signaling has also been found to play role in the regulation of infiltration and activity of natural killer cells and CD4/CD8 cells by PD-L1/PD-1 signaling. In this review, we have attempted to set spotlight on regulation of JAK/STAT pathway by microRNAs, long non-coding RNAs and circular RNAs in primary tumors and metastasizing tumors. Therefore, existing knowledge gaps need to be addressed to propel this fledgling field of research to the forefront and bring lncRNAs and circRNAs to the frontline of clinical practice. Leveraging the growing momentum will enable interdisciplinary researchers to gain transition from segmented view to a fairly detailed conceptual continuum.

Gene expression in the dorsal root ganglion and the cerebrospinal fluid metabolome in polyneuropathy and opioid tolerance in rats

First-line pharmacotherapy for peripheral neuropathic pain (NP) of diverse pathophysiology consists of antidepressants and gabapentinoids, but only a minority achieve sufficient analgesia with these drugs. Opioids are considered third-line analgesics in NP due to potential severe and unpredictable adverse effects in long-term use. Also, opioid tolerance and NP may have shared mechanisms, raising further concerns about opioid use in NP. We set out to further elucidate possible shared and separate mechanisms after chronic morphine treatment and oxaliplatin-induced and diabetic polyneuropathies, and to identify potential diagnostic markers and therapeutic targets. We analysed thermal nociceptive behaviour, the transcriptome of dorsal root ganglia (DRG) and the metabolome of cerebrospinal fluid (CSF) in these three conditions, in rats. Several genes were differentially expressed, most following oxaliplatin and least after chronic morphine treatment, compared with saline-treated rats. A few genes were differentially expressed in the DRGs in all three models (e.g. Csf3r and Fkbp5). Some, e.g. Alox15 and Slc12a5, were differentially expressed in both diabetic and oxaliplatin models. Other differentially expressed genes were associated with nociception, inflammation, and glial cells. The CSF metabolome was most significantly affected in the diabetic rats. Interestingly, we saw changes in nicotinamide metabolism, which has been associated with opioid addiction and withdrawal, in the CSF of morphine-tolerant rats. Our results offer new hypotheses for the pathophysiology and treatment of NP and opioid tolerance. In particular, the role of nicotinamide metabolism in opioid addiction deserves further study.

piRNA expression patterns in high vs. low fertility bovine sperm

PIWI-interacting RNAs (piRNAs) are 24-32 nucleotide RNA sequences primarily expressed in germ cells and developing embryos that suppress transposable element expression to protect genomic integrity during epigenetic reprogramming events. We characterized the expression of piRNA sequences and their encoding clusters in sperm samples from an idiopathic fertility model of Holstein bulls with high and low Sire Conception Rates. The piRNA populations were determined to be mostly similar between fertility conditions when investigated by principal component and differential expression analysis, suggesting that a high degree of conservation in the piRNA system is likely necessary for the production of viable sperm. Both fertility conditions demonstrated evidence of 'ping-pong' activity - a secondary biogenesis pathway associated with active transposable element targeting and suppression. Most sperm-borne piRNAs were between 29-30 nucleotides in length and originated from 226 clusters across the genome, with the exception of chromosome 20. Mapping analysis revealed abundant targeting of several transposable element families, suggesting a suppressive function of sperm piRNAs consistent with their established roles. Expression of genes targeted by sperm-borne piRNAs is significantly reduced throughout early embryogenesis compared to the mRNA population. Limited transposable element expression is known to be essential for spermatogenesis, thus epigenetic regulation of this pathway is likely to influence sperm quality and fertilizing capacity.

3D clustering of gene expression data from systemic autoinflammatory diseases using self-organizing maps (Clust3D)

Background and objective

Systemic autoinflammatory diseases (SAIDs) are characterized by widespread inflammation, but for most of them there is a lack of specific biomarkers for accurate diagnosis. Although a number of machine learning algorithms have been used to analyze SAID datasets, aiding in the discovery of novel biomarkers, there is a growing recognition of the importance of SAID timeseries clustering, as it can capture the temporal dynamics of gene expression patterns.

Methodology

This paper proposes a novel clustering methodology to efficiently associate three-dimensional data. The algorithm utilizes competitive learning to create a self-organizing neural network and adjust neuron positions in time-dependent and high dimensional feature space in order to assign them as clustering centers. The quantitative evaluation of the clustering was based on well-known clustering indices. Furthermore, a differential expression analysis and classification pipeline was employed to assess the capability of the proposed methodology to extract more accurate pathway-specific genes from its clusters. For that, a comparative analysis was also conducted against a heuristic timeseries clustering method.

Results

The proposed methodology achieved better overall clustering indices scores and classification metrics using genes derived from its clusters. Notable cases include a threefold increase in the Calinski-Harabasz clustering index, a twofold improvement in the Davies-Bouldin clustering index and a ∼ 60 % increase in the classification specificity score.

Conclusion

A novel clustering methodology was developed and applied on several gene expression timeseries datasets from systemic autoinflammatory diseases, and its ability to efficiently produce well separated clusters compared to existing heuristic methods was demonstrated.