BioMart and Bioconductor: a powerful link between biological databases and microarray data analysis

Evolution of Size-Fecundity Relationship in Medaka Fish From Different Latitudes

In most fishes, the number of offspring increases with maternal body size. Although this size-fecundity relationship often varies among species as a result of the coevolution of life-history traits, the genetic basis of such size-fecundity relationships remains unclear. We explored the genetic basis underlying this size-fecundity relationship in two small medaka species, Oryzias latipes and O. sakaizumii. Our findings showed that O. sakaizumii has a higher fecundity than O. latipes, and quantitative trait locus analysis using interspecific F2 hybrids showed that chromosome 23 is linked to the size-fecundity relationship. In particular, the genes igf1 and lep-b in this region are known to be associated with life-history traits, including somatic growth, gonad maturation, and progeny numbers in various taxa. Because O. sakaizumii is distributed at higher latitudes and has a shorter spawning season than O. latipes in the wild, we propose that the relatively high fecundity observed in O. sakaizumii is an adaptation to high latitudes. We also discuss the potential ecological ramifications associated with the evolution of increased fecundity in this species.

Interferon epsilon is produced in the testis and protects the male reproductive tract against virus infection, inflammation and damage

The testis is a reservoir for viruses that can cause persistent infection and adversely affect male reproductive health, an observation commonly attributed to deficiencies in inducible antiviral defence mechanisms. In this study, we demonstrate that interferon-epsilon (IFNε), a type I interferon initially discovered in female reproductive epithelia, is constitutively expressed by meiotic and post-meiotic spermatogenic cells, Leydig cells and macrophages in mouse testes. A similar distribution pattern was observed in human testes. Mice lacking IFNɛ were more susceptible to Zika virus-induced inflammation and damage of the testis and epididymis compared to wild-type mice. Exogenous IFNε treatment reduced the viral infection burden in cultured human testicular cells by inducing interferon-stimulated gene expression, and reducing inflammatory gene expression and cell damage. Treatment was more effective when administered prior to infection. These data indicate a critical role for constitutively-expressed IFNɛ in limiting viral infection and inflammatory damage in the male reproductive tract.

Expanded detection and impact of BAP1 alterations in cancer

Aberrant expression of the BAP1 (BRCA associated protein 1) tumor suppressor gene is a prominent risk factor for several tumor types and is important in tumor evolution and progression. Here we performed integrated multi-omics analyses using data from The Cancer Genome Atlas for 33 cancer types and over 10 000 individuals to identify alterations leading to BAP1 disruption. We combined existing variant calls and new calls derived from a de novo local realignment pipeline across multiple independent variant callers, increasing somatic variant detection by 41% from 182 to 257, including 11 indels ≥40 bp. The expanded detection of mutations highlights the power of new tools to uncover longer indels and impactful mutations. We developed an expression-based BAP1 activity score and identified a transcriptional profile associated with BAP1 disruption in cancer. BAP1 has been proposed to play a critical role in controlling tumor plasticity and normal cell fate. Leveraging human and mouse liver datasets, BAP1 loss in normal cells resulted in lower BAP1 activity scores and lower scores were associated with a less-differentiated phenotype in embryonic cells. Together, our expanded BAP1 mutant samples revealed a transcriptional signature in cancer cells, supporting BAP1's influences on cellular plasticity and cell identity maintenance.

Molecular characterisation of influenza B virus from the 2017/18 season in primary models of the human lung reveals improved adaptation to the lower respiratory tract

The 2017/18 influenza season was characterized by unusual high numbers of severe infections and hospitalizations. Instead of influenza A viruses, this season was dominated by infections with influenza B viruses of the Yamagata lineage. While this IBV/Yam dominance was associated with a vaccine mismatch, a contribution of virus intrinsic features to the clinical severity of the infections was speculated. Here, we performed a molecular and phenotypic characterization of three IBV isolates from patients with severe flu symptoms in 2018 and compared it to an IBV/Yam isolate from 2016 using experimental models of increasing complexity, including human lung explants, lung organoids, and alveolar macrophages. Viral genome sequencing revealed the presence of clade but also isolate specific mutations in all viral genes, except NP, M1, and NEP. Comparative replication kinetics in different cell lines provided further evidence for improved replication fitness, tolerance towards higher temperatures, and the development of immune evasion mechanisms by the 2018 IBV isolates. Most importantly, immunohistochemistry of infected human lung explants revealed an impressively altered cell tropism, extending from AT2 to AT1 cells and macrophages. Finally, transcriptomics of infected human lung explants demonstrated significantly reduced amounts of type I and type III IFNs by the 2018 IBV isolate, supporting the existence of additional immune evasion mechanisms. Our results show that the severeness of the 2017/18 Flu season was not only the result of a vaccine mismatch but was also facilitated by improved adaptation of the circulating IBV strains to the environment of the human lower respiratory tract.

Identification of proteins associated with type 2 diabetes risk in diverse racial and ethnic populations

AIMS/HYPOTHESIS

Several studies have reported associations between specific proteins and type 2 diabetes risk in European populations. To better understand the role played by proteins in type 2 diabetes aetiology across diverse populations, we conducted a large proteome-wide association study using genetic instruments across four racial and ethnic groups: African; Asian; Hispanic/Latino; and European.

METHODS

Genome and plasma proteome data from the Multi-Ethnic Study of Atherosclerosis (MESA) study involving 182 African, 69 Asian, 284 Hispanic/Latino and 409 European individuals residing in the USA were used to establish protein prediction models by using potentially associated cis- and trans-SNPs. The models were applied to genome-wide association study summary statistics of 250,127 type 2 diabetes cases and 1,222,941 controls from different racial and ethnic populations.

RESULTS

We identified three, 44 and one protein associated with type 2 diabetes risk in Asian, European and Hispanic/Latino populations, respectively. Meta-analysis identified 40 proteins associated with type 2 diabetes risk across the populations, including well-established as well as novel proteins not yet implicated in type 2 diabetes development.

CONCLUSIONS/INTERPRETATION

Our study improves our understanding of the aetiology of type 2 diabetes in diverse populations.

DATA AVAILABILITY

The summary statistics of multi-ethnic type 2 diabetes GWAS of MVP, DIAMANTE, Biobank Japan and other studies are available from The database of Genotypes and Phenotypes (dbGaP) under accession number phs001672.v3.p1. MESA genetic, proteome and covariate data can be accessed through dbGaP under phs000209.v13.p3. All code is available on GitHub ( https://github.com/Arthur1021/MESA-1K-PWAS ).

SARS-CoV-2 human challenge reveals biomarkers that discriminate early and late phases of respiratory viral infections

Blood transcriptional biomarkers of acute viral infections typically reflect type 1 interferon (IFN) signalling, but it is not known whether there are biological differences in their regulation that can be leveraged for distinct translational applications. We use high frequency sampling in the SARS-CoV-2 human challenge model to show induction of IFN-stimulated gene (ISG) expression with different temporal and cellular profiles. MX1 gene expression correlates with a rapid and transient wave of ISG expression across all cell types, which may precede PCR detection of replicative infection. Another ISG, IFI27, shows a delayed but sustained response restricted to myeloid cells, attributable to gene and cell-specific epigenetic regulation. These findings are reproducible in experimental and naturally acquired infections with influenza, respiratory syncytial virus and rhinovirus. Blood MX1 expression is superior to IFI27 expression for diagnosis of early infection, as a correlate of viral load and for discrimination of virus culture positivity. Therefore, MX1 expression offers potential to stratify patients for antiviral therapy or infection control interventions. Blood IFI27 expression is superior to MX1 expression for diagnostic accuracy across the time course of symptomatic infection and thereby, offers higher diagnostic yield for respiratory virus infections that incur a delay between transmission and testing.

OrthologAL: A Shiny application for quality-aware humanization of non-human pre-clinical high-dimensional gene expression data

Single-cell and spatial transcriptomics provide unprecedented insight into the inner workings of disease. Pharmacotranscriptomic approaches are powerful tools that leverage gene expression data for drug repurposing and treatment discovery in many diseases. Multiple databases attempt to connect human cellular transcriptional responses to small molecules for use in transcriptome-based drug discovery efforts. However, pre-clinical research often requires in vivo experiments in non-human species, which makes capitalizing on such valuable resources difficult. To facilitate the application of pharmacotranscriptomic databases to pre-clinical research models and to facilitate human orthologous conversion of non-human transcriptomes, we introduce OrthologAL. OrthologAL leverages the BioMart database to access different gene sets from Ensembl, facilitating the interaction between these servers without needing user-generated code. Researchers can input their single-cell or other high-dimensional gene expression data from any species, and OrthologAL will output a human ortholog-converted dataset for download and use. To demonstrate the utility of this application, we characterized orthologous conversion in single-cell, single-nuclei, and spatial transcriptomic data derived from common pre-clinical models, including patient-derived orthotopic xenografts of medulloblastoma, and mouse and rat models of spinal cord injury. We show that OrthologAL can convert these data types efficiently to that of corresponding orthologs while preserving the dimensional architecture of the original non-human expression data. OrthologAL will be broadly useful for applying pre-clinical, high-dimensional transcriptomics data in functional small molecule predictions using existing human-annotated databases.

Transcriptomic Point of Departure (tPOD) of androstenedione in zebrafish embryos as a potential surrogate for chronic endpoints

The transcriptomic Point of Departure (tPOD) is increasingly used in ecotoxicology to derive quantitative endpoints from RNA sequencing studies. Utilizing transcriptomic data in zebrafish embryos as a New Approach Methodology (NAM) is beneficial due to its acknowledgment as an alternative to animal testing under EU Directive 2010/63/EU. Transcriptomic profiles are available in zebrafish for various modes of action (MoA). The limited literature available suggest that tPOD values from Fish Embryo Toxicity (FET) tests align with, but are generally lower than, No Observed Effect Concentrations (NOEC) from long-term chronic fish toxicity tests. In studies with the androgenic hormone androstenedione in a Fish Sexual Development Test (FSDT), a significant shift in the sex ratio towards males was noted at all test concentrations, making it impossible to determine a NOEC (NOEC <4.34 μg/L). To avoid additional animal testing in a repetition of the FSDT and adhere to the 3Rs principle (replacement, reduction, and refinement), a modified zebrafish FET (zFET) was conducted aiming to determine a regulatory acceptable effect threshold. This involved lower concentration ranges (20 to 6105 ng/L), overlapping with the masculinization-observed concentrations in the FSDT. The tPOD analysis in zFET showed consistent results with previous FSDT findings, observing strong expression changes in androgen-dependent genes at higher concentrations but not at lower ones, demonstrating a concentration-response relationship. The tPOD values for androstenedione were determined as 24 ng/L (10th percentile), 60 ng/L (20th gene), and 69 ng/L (1st peak). The 10th percentile tPOD value in zFET was 200 times lower than the lowest concentration in the FSDT. Comparing the tPOD values to literature suggests their potential to inform on the NOEC range in FSDT tests.

Assessment of genome complementarity in three beef-on-dairy crossbreds reveals sire-specific effects on production traits with comparable rates of genomic inbreeding reduction

BACKGROUND

Crossbreeding beef bulls with dairy cows can improve the economic value and fitness of calves not entering dairy production owing to increased meat yield and heterosis. However, outcrossing might reduce the dosage of alleles that confer local adaptation or result in a higher risk of dystocia due to increased calf size. Given the clear phenotypic differences between beef breeds, the varying phylogenetic distances between beef and dairy breeds, and the genomic variations within breeds, the attainable economic and fitness gains of calves will strongly depend on the selection of sires for crossing. Thus, the aim of this study was to assess genome complementarity between Angus (AAN), Limousin (LIM), or Simmental (SIM) beef bulls and Brown Swiss (BSW) dairy cows by quantifying genomic inbreeding reduction in F1 crosses and identifying genes potentially under BSW-specific selection that might be affected by outcrossing.

RESULTS

Low-pass sequencing data from 181 cows, 34 bulls, and 301 of their F1 progeny, and body weight and carcass composition measurements of 248 F1s were obtained. The high genomic inbreeding levels detected in the BSW cows were substantially reduced in the crossbreds, with only minor differences between the sire breeds. In the BSW cows, 585 candidate genes under selection were identified, overrepresenting genes associated with milk, meat and carcass, and production traits. Only a few genes were strongly differentiated at nonsynonymous variants between the BSW and beef breeds, including four tightly clustered genes (FAM184B, NCAPG, DCAF16, and LCORL) nearly fixed for alternate alleles in the BSW cows but mostly heterozygous or homozygous for the reference alleles in the AAN and LIM bulls. The alternate allele dosage at these genes significantly correlated with reduced carcass weight and protein mass in F1s.

CONCLUSION

Some of the few genes that were highly divergent between the BSW and beef breeds at nonsynonymous variants were likely under strong selection for reduced carcass weight in the BSW breed, potentially due to trade-offs between beef and dairy productions. As alleles with opposing effects still segregate in beef cattle, marker-assisted selection of mating pairs may be used to modulate the desired phenotypes and simultaneously decrease genomic inbreeding.

Analysis of genes differentially expressed in the cortex of mice with the Tbl1xr1Y446C/Y446C variant

Transducin β-like 1 X-linked receptor 1 (mouse Tbl1xr1) or TBL1X/Y related 1 (human TBL1XR1), part of the NCoR/SMRT corepressor complex, is involved in nuclear receptor signaling. Variants in TBL1XR1 cause a variety of neurodevelopmental disorders including Pierpont syndrome caused by the p.Tyr446Cys variant. We recently reported a mouse model carrying the Tbl1xr1Y446C/Y446C variant as a model for Pierpont syndrome. To obtain insight into mechanisms involved in altered brain development we studied gene expression patterns in the cortex of mutant and wild type (WT) mice, using RNA-sequencing, differentially expressed gene (DEG) analysis, gene set enrichment analysis (GSEA), weighted gene correlation network analysis (WGCNA) and hub gene analysis. We validated results in mutated mouse cortex, as well as in BV2 and SK-N-AS cell lines, in both of which Tbl1xr1 was knocked down by siRNA. Two DEGs (adj.P. Val < 0.05) were found in the cortex, Mpeg1 (downregulated in mutant mice) and 2900052N01Rik (upregulated in mutant mice). GSEA, WGCNA and hub gene analysis demonstrated changes in genes involved in ion channel function and neuroinflammation in the cortex of the Tbl1xr1Y446C/Y446C mice. The lowered expression of ion channel genes Kcnh3 and Kcnj4 mRNA was validated in the mutant mouse cortex, and increased expression of TRIM9, associated with neuroinflammation, was confirmed in the SK-N-AS cell line. Conclusively, our results show altered expression of genes involved in ion channel function and neuroinflammation in the cortex of the Tbl1xr1Y446C/Y446C mice. These may partly explain the impaired neurodevelopment observed in individuals with Pierpont syndrome and related TBL1XR1-related disorders.

Peripheral immune cell abundance differences link blood mitochondrial DNA copy number and Parkinson's disease

Mitochondrial dysfunction plays an important role in Parkinson's disease (PD), with mitochondrial DNA copy number (mtDNA-CN) emerging as a potential marker for mitochondrial health. We investigated the links between blood mtDNA-CN and PD severity and risk using the Accelerating Medicines Partnership program for Parkinson's Disease dataset, replicating our results in the UK Biobank. Our findings reveal that reduced blood mtDNA-CN levels are associated with heightened PD risk and increased severity of motor symptoms and olfactory dysfunction. We estimated blood cell composition using complete blood cell profile when available or RNA-sequencing data as a surrogate. After adjusting for blood cell composition, the associations between mtDNA-CN and PD risk and clinical symptoms became non-significant. Bidirectional Mendelian randomization analysis also found no evidence of a direct causal relationship between blood mtDNA-CN and PD susceptibility. Hence peripheral inflammatory immune responses rather than mitochondrial dysfunction underpin these previously identified associations in PD.

Single-Sample Networks Reveal Intra-Cytoband Co-Expression Hotspots in Breast Cancer Subtypes

Breast cancer is a heterogeneous disease comprising various subtypes with distinct molecular characteristics, clinical outcomes, and therapeutic responses. This heterogeneity evidences significant challenges for diagnosis, prognosis, and treatment. Traditional genomic co-expression network analyses often overlook individual-specific interactions critical for personalized medicine. In this study, we employed single-sample gene co-expression network analysis to investigate the structural and functional genomic alterations across breast cancer subtypes (Luminal A, Luminal B, Her2-enriched, and Basal-like) and compared them with normal breast tissue. We utilized RNA-Seq gene expression data to infer gene co-expression networks. The LIONESS algorithm allowed us to construct individual networks for each patient, capturing unique co-expression patterns. We focused on the top 10,000 gene interactions to ensure consistency and robustness in our analysis. Network metrics were calculated to characterize the topological properties of both aggregated and single-sample networks. Our findings reveal significant fragmentation in the co-expression networks of breast cancer subtypes, marked by a change from interchromosomal (TRANS) to intrachromosomal (CIS) interactions. This transition indicates disrupted long-range genomic communication, leading to localized genomic regulation and increased genomic instability. Single-sample analyses confirmed that these patterns are consistent at the individual level, highlighting the molecular heterogeneity of breast cancer. Despite these pronounced alterations, the proportion of CIS interactions did not significantly correlate with patient survival outcomes across subtypes, suggesting limited prognostic value. Furthermore, we identified high-degree genes and critical cytobands specific to each subtype, providing insights into subtype-specific regulatory networks and potential therapeutic targets. These genes play pivotal roles in oncogenic processes and may represent important keys for targeted interventions. The application of single-sample co-expression network analysis proves to be a powerful tool for uncovering individual-specific genomic interactions.

Effect of genetically predicted sclerostin on cardiovascular biomarkers, risk factors, and disease outcomes

Sclerostin inhibitors protect against osteoporotic fractures, but their cardiovascular safety remains unclear. We conducted a cis-Mendelian randomisation analysis to estimate the causal effect of sclerostin levels on cardiovascular risk factors. We meta-analysed three GWAS of sclerostin levels including 49,568 Europeans and selected 2 SNPs to be used as instruments. We included heel bone mineral density and hip fracture risk as positive control outcomes. Public GWAS and UK Biobank patient-level data were used for the study outcomes, which include cardiovascular events, risk factors, and biomarkers. Lower sclerostin levels were associated with higher bone mineral density and 85% reduction in hip fracture risk. However, genetically predicted lower sclerostin levels led to 25-85% excess coronary artery disease risk, 40% to 60% increased risk of type 2 diabetes, and worse cardiovascular biomarkers values, including higher triglycerides, and decreased HDL cholesterol levels. Results also suggest a potential (but borderline) association with increased risk of myocardial infarction. Our study provides genetic evidence of a causal relationship between reduced levels of sclerostin and improved bone health and fracture protection, but increased risk of cardiovascular events and risk factors.

Oxidative Stress and Inflammation-Related mRNAs Are Elevated in Serum of a Finnish Wet AMD Cohort

Purpose

Localized diseases can be affected by and affect the systemic environment via blood circulation. In this study, we explored the differences in circulating serum mRNAs between patients with wet AMD (wAMD) and controls.

Methods

Blood samples were obtained from 60 Finnish patients with wAMD and 64 controls. After serum preparation and RNA sequencing, the count data was examined for differentially expressed genes (DEGs) and further checked for enriched molecular pathways and ontology terms as well as links to clinical data.

Results

We found many DEGs and some enriched pathways, including the inflammation and cell survival-associated pathway tumour necrosis factor alpha (TNF-α) signaling via nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB). The related DEGs were oxidized low-density lipoprotein receptor 1 (OLR1), salt inducible kinase 1 (SIK1), and coagulation factor III (F3). DEGs from degradative macular and retinal processes were also examined, many of which were also related to cardiovascular disease and maintenance. Additionally, DEG counts were inspected in relation to clinical and anti-VEGF treatment parameters, and glutamine amidotransferase-like class 1 domain-containing 3A (GATD3A) levels were found to be significantly lower in patients with wAMD treated with anti-VEGF.

Conclusions

Differentially expressed systemic mRNAs that are linked to mitochondrial function, oxidative stress, and inflammation may have a role in the pathology of wAMD. Our observations provide new data for the understanding of the progression of wAMD.

A concise guide to essential R packages for analyses of DNA, RNA, and proteins

R is widely regarded as unrivaled by other high-level programming languages for its statistical functions. The popularity of R as a statistical language has led many to overlook its applications outside the statistical realm. In this brief review, we present a list of R packages for supporting projects that entail analyses of DNA, RNA, and proteins. These R packages span the gamut of important molecular techniques, from routine quantitative polymerase chain reaction (qPCR) and Western blotting to high-throughput sequencing and proteomics generating very large datasets. The text-mining power of R can also be harnessed to facilitate literature reviews and predict future research trends and avenues. We encourage researchers to make full use of R in their work, given the versatility of the language, as well as its straightforward syntax which eases the initial learning curve.

A genome-wide investigation into the underlying genetic architecture of personality traits and overlap with psychopathology

Personality is influenced by both genetic and environmental factors and is associated with other psychiatric traits such as anxiety and depression. The 'big five' personality traits, which include neuroticism, extraversion, agreeableness, conscientiousness and openness, are a widely accepted and influential framework for understanding and describing human personality. Of the big five personality traits, neuroticism has most often been the focus of genetic studies and is linked to various mental illnesses, including depression, anxiety and schizophrenia. Our knowledge of the genetic architecture of the other four personality traits is more limited. Here, utilizing the Million Veteran Program cohort, we conducted a genome-wide association study in individuals of European and African ancestry. Adding other published data, we performed genome-wide association study meta-analysis for each of the five personality traits with sample sizes ranging from 237,390 to 682,688. We identified 208, 14, 3, 2 and 7 independent genome-wide significant loci associated with neuroticism, extraversion, agreeableness, conscientiousness and openness, respectively. These findings represent 62 novel loci for neuroticism, as well as the first genome-wide significant loci discovered for agreeableness. Gene-based association testing revealed 254 genes showing significant association with at least one of the five personality traits. Transcriptome-wide and proteome-wide analysis identified altered expression of genes and proteins such as CRHR1, SLC12A5, MAPT and STX4. Pathway enrichment and drug perturbation analyses identified complex biology underlying human personality traits. We also studied the inter-relationship of personality traits with 1,437 other traits in a phenome-wide genetic correlation analysis, identifying new associations. Mendelian randomization showed positive bidirectional effects between neuroticism and depression and anxiety, while a negative bidirectional effect was observed for agreeableness and these psychiatric traits. This study improves our comprehensive understanding of the genetic architecture underlying personality traits and their relationship to other complex human traits.

Blood transcriptome profiling reveals distinct gene networks induced by mRNA vaccination against COVID-19

Messenger RNA (mRNA) vaccines represent a new class of vaccines that has been shown to be highly effective during the COVID-19 pandemic and that holds great potential for other preventative and therapeutic applications. While it is known that the transcriptional activity of various genes is altered following mRNA vaccination, identifying and studying gene networks could reveal important scientific insights that might inform future vaccine designs. In this study, we conducted an in-depth weighted gene correlation network analysis of the blood transcriptome before and 24 h after the second and third vaccination with licensed mRNA vaccines against COVID-19 in humans, following a prime vaccination with either mRNA or ChAdOx1 vaccines. Utilizing this unsupervised gene network analysis approach, we identified distinct modular networks of co-varying genes characterized by either an expressional up- or downregulation in response to vaccination. Downregulated networks were associated with cell metabolic processes and regulation of transcription factors, while upregulated networks were associated with myeloid differentiation, antigen presentation, and antiviral, interferon-driven pathways. Within this interferon-associated network, we identified highly connected hub genes such as STAT2 and RIGI and associated upstream transcription factors, potentially playing important regulatory roles in the vaccine-induced immune response. The expression profile of this network significantly correlated with S1-specific IgG levels at the follow-up visit in vaccinated individuals. Those findings could be corroborated in a second, independent cohort of mRNA vaccine recipients. Collectively, results from this modular gene network analysis enhance the understanding of mRNA vaccines from a systems immunology perspective. Influencing specific gene networks could lead to optimized vaccines that elicit augmented vaccine responses.

Machine learning approach to predict blood-secretory proteins and potential biomarkers for liver cancer using omics data

Identifying non-invasive blood-based biomarkers is crucial for early detection and monitoring of liver cancer (LC), thereby improving patient outcomes. This study leveraged computational approaches to predict potential blood-based biomarkers for LC. Machine learning (ML) models were developed using selected features from blood-secretory proteins collected from the curated databases. The logistic regression (LR) model demonstrated the optimal performance. Transcriptome analysis across 7 LC cohorts revealed 231 common differentially expressed genes (DEGs). The encoded proteins of these DEGs were compared with the ML dataset, revealing 29 proteins overlapping with the blood-secretory dataset. The LR model also predicted 29 additional proteins as blood-secretory with the remaining protein-coding genes. As a result, 58 potential blood-secretory proteins were obtained. Among the top 20 genes, 13 common hub genes were identified. Further, area under the receiver operating characteristic curve (ROC AUC) analysis was performed to assess the genes as potential diagnostic blood biomarkers. Six genes, ESM1, FCN2, MDK, GPC3, CTHRC1 and COL6A6, exhibited an AUC value higher than 0.85 and were predicted as blood-secretory. This study highlights the potential of an integrative computational approach for discovering non-invasive blood-based biomarkers in LC, facilitating for further validation and clinical translation. SIGNIFICANCE: Liver cancer is one of the leading causes of premature death worldwide, with its prevalence and mortality rates projected to increase. Although current diagnostic methods are highly sensitive, they are invasive and unsuitable for repeated testing. Blood biomarkers offer a promising non-invasive alternative, but their wide dynamic range of protein concentration poses experimental challenges. Therefore, utilizing available omics data to develop a diagnostic model could provide a potential solution for accurate diagnosis. This study developed a computational method integrating machine learning and bioinformatics analysis to identify potential blood biomarkers. As a result, ESM1, FCN2, MDK, GPC3, CTHRC1 and COL6A6 biomarkers were identified, holding significant promise for improving diagnosis and understanding of liver cancer. The integrated method can be applied to other cancers, offering a possible solution for early detection and improved patient outcomes.

Defining the optimal setting for transcriptomic analyses on blood samples for response prediction in immunotherapy-treated NSCLC patients

Transcriptomic profiling of blood immune cells offers a promising alternative to invasive, sampling bias-prone tissue-based biomarker assays for predicting immune checkpoint inhibitor (ICI) therapy response in non-small cell lung cancer (NSCLC) patients. However, the optimal analytical approach to identify systemic correlates of response still needs to be explored. We collected peripheral blood mononuclear cells and whole blood (WB) samples from 33 ICI-treated NSCLC patients before ICI treatment and at the first response evaluation. After bulk polyadenylated RNA-sequencing, we assessed differences in gene expression profiles between non-responders and responders using differential expression analysis, single sample gene set enrichment analysis (ssGSEA), and cell type deconvolution. We evaluated gene expression values, ssGSEA scores, and deconvolved cell type proportions to distinguish non-responders from responders via ROC curve (AUC) analysis, training a logistic regression classification model. Gene expression values and deconvolved proportions yielded the best results with WB samples after treatment (AUC = 0.87 and 0.85, respectively). Overall, ssGSEA scores showed superior classification performance across all sample types and timepoints (AUC > 0.7). In conclusion, transcriptomic analysis through ssGSEA demonstrated the best performance as a non-invasive biomarker for predicting clinical benefit in ICI-treated NSCLC patients, with gene expression and deconvolution on post-treatment WB samples also showing promising results.

Chemoproteogenomic stratification of the missense variant cysteinome

Cancer genomes are rife with genetic variants; one key outcome of this variation is widespread gain-of-cysteine mutations. These acquired cysteines can be both driver mutations and sites targeted by precision therapies. However, despite their ubiquity, nearly all acquired cysteines remain unidentified via chemoproteomics; identification is a critical step to enable functional analysis, including assessment of potential druggability and susceptibility to oxidation. Here, we pair cysteine chemoproteomics-a technique that enables proteome-wide pinpointing of functional, redox sensitive, and potentially druggable residues-with genomics to reveal the hidden landscape of cysteine genetic variation. Our chemoproteogenomics platform integrates chemoproteomic, whole exome, and RNA-seq data, with a customized two-stage false discovery rate (FDR) error controlled proteomic search, which is further enhanced with a user-friendly FragPipe interface. Chemoproteogenomics analysis reveals that cysteine acquisition is a ubiquitous feature of both healthy and cancer genomes that is further elevated in the context of decreased DNA repair. Reference cysteines proximal to missense variants are also found to be pervasive, supporting heretofore untapped opportunities for variant-specific chemical probe development campaigns. As chemoproteogenomics is further distinguished by sample-matched combinatorial variant databases and is compatible with redox proteomics and small molecule screening, we expect widespread utility in guiding proteoform-specific biology and therapeutic discovery.

The landscape of RNA binding proteins in mammalian spermatogenesis

Despite continuous expansion of the RNA binding protein (RBP) world, there is a lack of systematic understanding of RBPs in the mammalian testis, which harbors one of the most complex tissue transcriptomes. We adapted RNA interactome capture to mouse male germ cells, building an RBP atlas characterized by multiple layers of dynamics along spermatogenesis. Trapping of RNA-cross-linked peptides showed that the glutamic acid-arginine (ER) patch, a residue-coevolved polyampholytic element present in coiled coils, enhances RNA binding of its host RBPs. Deletion of this element in NONO (non-POU domain-containing octamer-binding protein) led to a defective mitosis-to-meiosis transition due to compromised NONO-RNA interactions. Whole-exome sequencing of over 1000 infertile men revealed a prominent role of RBPs in the human genetic architecture of male infertility and identified risk ER patch variants.

RNA polymerase II transcription initiation in holo-TFIID-depleted mouse embryonic stem cells

The recognition of core promoter sequences by TFIID is the first step in RNA polymerase II (Pol II) transcription initiation. Metazoan holo-TFIID is a trilobular complex, composed of the TATA binding protein (TBP) and 13 TBP-associated factors (TAFs). Why and how TAFs are necessary for the formation of TFIID domains and how they contribute to transcription initiation remain unclear. Inducible TAF7 or TAF10 depletion, followed by comprehensive analysis of TFIID subcomplex formation, chromatin binding, and nascent transcription in mouse embryonic stem cells, result in the formation of a TAF7-lacking TFIID or a minimal core-TFIID complex, respectively. These partial complexes support TBP recruitment at promoters and nascent Pol II transcription at most genes early after depletion, but importantly, TAF10 is necessary for efficient Pol II pausing. We show that partially assembled TFIID complexes can sustain Pol II transcription initiation but cannot replace holo-TFIID over several cell divisions and/or development.

Predicting 'pain genes': multi-modal data integration using probabilistic classifiers and interaction networks

Motivation

Accurate identification of pain-related genes remains challenging due to the complex nature of pain pathophysiology and the subjective nature of pain reporting in humans. Here, we use machine learning to identify possible 'pain genes'. Labelling was based on a gold-standard list with validated involvement across pain conditions, and was trained on a selection of -omics, protein-protein interaction network features, and biological function readouts for each gene.

Results

The top-performing model was selected to predict a 'pain score' per gene. The top-ranked genes were then validated against pain-related human SNPs. Functional analysis revealed JAK2/STAT3 signal, ErbB, and Rap1 signalling pathways as promising targets for further exploration, while network topological features contribute significantly to the identification of 'pain' genes. As such, a network based on top-ranked genes was constructed to reveal previously uncharacterized pain-related genes. Together, these novel insights into pain pathogenesis can indicate promising directions for future experimental research.

Availability and implementation

These analyses can be further explored using the linked open-source database at https://livedataoxford.shinyapps.io/drg-directory/, which is accompanied by a freely accessible code template and user guide for wider adoption across disciplines.

Genetic ancestry in population pharmacogenomics unravels distinct geographical patterns related to drug toxicity

Genetic ancestry plays a major role in pharmacogenomics, and a deeper understanding of the genetic diversity among individuals holds immerse promise for reshaping personalized medicine. In this pivotal study, we have conducted a large-scale genomic analysis of 1,136 pharmacogenomic variants employing machine learning algorithms on 3,714 individuals from publicly available datasets to assess the risk proximity of experiencing drug-related adverse events. Our findings indicate that Admixed Americans and Europeans have demonstrated a higher risk of experiencing drug toxicity, whereas individuals with East Asian ancestry and, to a lesser extent, Oceanians displayed a lower risk proximity. Polygenic risk scores for drug-gene interactions did not necessarily follow similar assumptions, reflecting distinct genetic patterns and population-specific differences that vary depending on the drug class. Overall, our results provide evidence that genetic ancestry is a pivotal factor in population pharmacogenomics and should be further exploited to strengthen even more personalized drug therapy.

Single-cell somatic copy number variants in brain using different amplification methods and reference genomes

The presence of somatic mutations, including copy number variants (CNVs), in the brain is well recognized. Comprehensive study requires single-cell whole genome amplification, with several methods available, prior to sequencing. Here we compare PicoPLEX with two recent adaptations of multiple displacement amplification (MDA): primary template-directed amplification (PTA) and droplet MDA, across 93 human brain cortical nuclei. We demonstrate different properties for each, with PTA providing the broadest amplification, PicoPLEX the most even, and distinct chimeric profiles. Furthermore, we perform CNV calling on two brains with multiple system atrophy and one control brain using different reference genomes. We find that 20.6% of brain cells have at least one Mb-scale CNV, with some supported by bulk sequencing or single-cells from other brain regions. Our study highlights the importance of selecting whole genome amplification method and reference genome for CNV calling, while supporting the existence of somatic CNVs in healthy and diseased human brain.

Multidimensional profiling of human T cells reveals high CD38 expression, marking recent thymic emigrants and age-related naive T cell remodeling

Thymic involution is a key factor in human immune aging, leading to reduced thymic output and a decline in recent thymic emigrant (RTE) naive T cells in circulation. Currently, the precise definition of human RTEs and their corresponding cell surface markers lacks clarity. Analysis of single-cell RNA-seq/ATAC-seq data distinguished RTEs by the expression of SOX4, IKZF2, and TOX and CD38 protein, whereby surface CD38hi expression universally identified CD8+ and CD4+ RTEs. We further determined the dynamics of RTEs and mature cells in a cohort of 158 individuals, including age-associated transcriptional reprogramming and shifts in cytokine production. Spectral cytometry profiling revealed two axes of aging common to naive CD8+ and CD4+ T cells: (1) a decrease in CD38++ cells (RTEs) and (2) an increase in CXCR3hi cells. Identification of RTEs enables direct assessment of thymic health. Furthermore, resolving the dynamics of naive T cell remodeling yields insight into vaccination and infection responsiveness throughout aging.

Pesticide-induced transgenerational alterations of genome-wide DNA methylation patterns in the pancreas of Xenopus tropicalis correlate with metabolic phenotypes

The unsustainable use of manmade chemicals poses significant threats to biodiversity and human health. Emerging evidence highlights the potential of certain chemicals to cause transgenerational impacts on metabolic health. Here, we investigate male transmitted epigenetic transgenerational effects of the anti-androgenic herbicide linuron in the pancreas of Xenopus tropicalis frogs, and their association with metabolic phenotypes. Reduced representation bisulfite sequencing (RRBS) was used to assess genome-wide DNA methylation patterns in the pancreas of adult male F2 generation ancestrally exposed to environmentally relevant linuron levels (44 ± 4.7 μg/L). We identified 1117 differentially methylated regions (DMRs) distributed across the X. tropicalis genome, revealing potential regulatory mechanisms underlying metabolic disturbances. DMRs were identified in genes crucial for pancreatic function, including calcium signalling (clstn2, cacna1d and cadps2), genes associated with type 2 diabetes (tcf7l2 and adcy5) and a biomarker for pancreatic ductal adenocarcinoma (plec). Correlation analysis revealed associations between DNA methylation levels in these genes and metabolic phenotypes, indicating epigenetic regulation of glucose metabolism. Moreover, differential methylation in genes related to histone modifications suggests alterations in the epigenetic machinery. These findings underscore the long-term consequences of environmental contamination on pancreatic function and raise concerns about the health risks associated with transgenerational effects of pesticides.

tidysbml: R/Bioconductor package for SBML extraction into dataframes

Summary

We present tidysbml, an R package able to perform compartments, species, and reactions data extraction from Systems Biology Markup Language (SBML) documents (up to Level 3) in tabular data structures (i.e. R dataframes) to easily access and handle the richness of the biological information. Thanks to its output format, the package facilitates data manipulation, enabling manageable construction, and therefore analysis, of custom networks, as well as data retrieval, by means of R packages such as igraph, RCy3, and biomaRt. Exemplar data (i.e. SBML files) are extracted from Reactome.

Availability and implementation

The tidysbml R package is distributed under CC BY 4.0 License and can be found publicly available in Bioconductor (https://bioconductor.org/packages/tidysbml) and on GitHub (https://github.com/veronicapaparozzi/tidysbml).

An anciently diverged family of RNA binding proteins maintain correct splicing of a class of ultra-long exons through cryptic splice site repression

Previously, we showed that the germ cell-specific nuclear protein RBMXL2 represses cryptic splicing patterns during meiosis and is required for male fertility (Ehrmann et al., 2019). Here, we show that in somatic cells the similar yet ubiquitously expressed RBMX protein has similar functions. RBMX regulates a distinct class of exons that exceed the median human exon size. RBMX protein-RNA interactions are enriched within ultra-long exons, particularly within genes involved in genome stability, and repress the selection of cryptic splice sites that would compromise gene function. The RBMX gene is silenced during male meiosis due to sex chromosome inactivation. To test whether RBMXL2 might replace the function of RBMX during meiosis we induced expression of RBMXL2 and the more distantly related RBMY protein in somatic cells, finding each could rescue aberrant patterns of RNA processing caused by RBMX depletion. The C-terminal disordered domain of RBMXL2 is sufficient to rescue proper splicing control after RBMX depletion. Our data indicate that RBMX and RBMXL2 have parallel roles in somatic tissues and the germline that must have been conserved for at least 200 million years of mammalian evolution. We propose RBMX family proteins are particularly important for the splicing inclusion of some ultra-long exons with increased intrinsic susceptibility to cryptic splice site selection.

An optimized instrument variable selection approach to improve causality estimation in association studies

Mendelian randomization (MR) is an emerging tool for inferring causality in genetic epidemiology. MR studies suffer bias from weak genetic instrument variables (IVs) and horizontal pleiotropy. We introduce a robust integrative framework strictly adhering with STROBE-MR guidelines to improve causality inference through MR studies. We implemented novel t-statistics-based criteria to improve the reliability of selected IVs followed by various MR methods. Further, we include sensitivity analyses to remove horizontal-pleiotropy bias. For functional validation, we perform enrichment analysis of identified causal SNPs. We demonstrate effectiveness of our proposed approach on 5 different MR datasets selected from diverse populations. Our pipeline outperforms its counterpart MR analyses using default parameters on these datasets. Notably, we found a significant association between total cholesterol and coronary artery disease (P = 1.16 × 10-71) in a single-sample dataset using our pipeline. Contrarily, this same association was deemed ambiguous while using default parameters. Moreover, in a two-sample dataset, we uncover 13 new causal SNPs with enhanced statistical significance (P = 1.06 × 10-11) for liver-iron-content and liver-cell-carcinoma. Likewise, these SNPs remained undetected using the default parameters (P = 7.58 × 10-4). Furthermore, our analysis confirmed previously known pathways, such as hyperlipidemia in heart diseases and gene ME1 in liver cancer. In conclusion, we propose a robust and powerful framework to infer causality across diverse populations and easily adaptable to different diseases.

DAVID Ortholog: an integrative tool to enhance functional analysis through orthologs

MOTIVATION

The Database for Annotation, Visualization, and Integrated Discovery (DAVID) is a web-based bioinformatics system for the functional interpretation of large lists of genes/proteins generated from high-throughput assays. It has been cited in 72 287 papers since its debut in 2003 as of 23 July 2024. The analysis is usually limited to the species of study. However, the knowledge of genes may be incomplete or unavailable for some species. Model organisms have been studied more extensively and analyzing gene lists in the context of these species can offer valuable insights, helping users better understand the genes and biological themes in their species of interest.

RESULTS

We developed DAVID Ortholog for the conversion of gene lists between species. We utilized the ortholog data downloaded from Orthologous MAtrix (OMA) and Ensembl Compara as the base for the conversion. The OMA ortholog IDs and Ensembl gene IDs were converted to DAVID gene IDs and the pairing information of these IDs from these two sources was integrated into the DAVID Knowledgebase. DAVID Ortholog can convert the user's source gene list to an ortholog list of a desired species and the downstream DAVID analysis, in the context of that species, can be continued seamlessly, allowing users to further understand the biological meaning of their gene list based on the functional annotation found for the orthologs.

AVAILABILITY AND IMPLEMENTATION

https://davidbioinformatics.nih.gov/ortholog.jsp.

Behavioral and transcriptomic analyses of mecp2 function in zebrafish

Rett syndrome (RTT), a human neurodevelopmental disorder characterized by severe cognitive and motor impairments, is caused by dysfunction of the conserved transcriptional regulator Methyl-CpG-binding protein 2 (MECP2). Genetic analyses in mouse Mecp2 mutants, which exhibit key features of human RTT, have been essential for deciphering the mechanisms of MeCP2 function; nonetheless, our understanding of these complex mechanisms is incomplete. Zebrafish mecp2 mutants exhibit mild behavioral deficits but have not been analyzed in depth. Here, we combine transcriptomic and behavioral assays to assess baseline and stimulus-evoked motor responses and sensory filtering in zebrafish mecp2 mutants from 5 to 7 days post-fertilization (dpf). We show that zebrafish mecp2 function is required for normal thigmotaxis but is dispensable for gross movement, acoustic startle response, and sensory filtering (habituation and sensorimotor gating), and reveal a previously unknown role for mecp2 in behavioral responses to visual stimuli. RNA-seq analysis identified a large gene set that requires mecp2 function for correct transcription at 4 dpf, and pathway analysis revealed several pathways that require MeCP2 function in both zebrafish and mammals. These findings show that MeCP2's function as a transcriptional regulator is conserved across vertebrates and supports using zebrafish to complement mouse modeling in elucidating these conserved mechanisms.

A lentiviral toolkit to monitor airway epithelial cell differentiation using bioluminescence

Basal cells are adult stem cells in the airway epithelium and regenerate differentiated cell populations, including the mucosecretory and ciliated cells that enact mucociliary clearance. Human basal cells can proliferate and produce differentiated epithelium in vitro. However, studies of airway epithelial differentiation mostly rely on immunohistochemical or immunofluorescence-based staining approaches, meaning that a dynamic approach is lacking, and quantitative data are limited. Here, we use a lentiviral reporter gene approach to transduce primary human basal cells with bioluminescence reporter constructs to monitor airway epithelial differentiation longitudinally. We generated three constructs driven by promoter sequences from the TP63, MUC5AC, and FOXJ1 genes to quantitatively assess basal cell, mucosecretory cell, and ciliated cell abundance, respectively. We validated these constructs by tracking differentiation of basal cells in air-liquid interface and organoid ("bronchosphere") cultures. Transduced cells also responded appropriately to stimulation with interleukin 13 (IL-13; to increase mucosecretory differentiation and mucus production) and IL-6 (to increase ciliated cell differentiation). These constructs represent a new tool for monitoring airway epithelial cell differentiation in primary epithelial and/or induced pluripotent stem cell (iPSC)-derived cell cultures.NEW & NOTEWORTHY Orr et al. generated and validated new lentiviral vectors to monitor the differentiation of airway basal cells, goblet cells, or multiciliated cells using bioluminescence.

GEDI: An R Package for Integration of Transcriptomic Data from Multiple Platforms for Bioinformatics Applications

Transcriptomic data is often expensive and difficult to generate in large cohorts relative to genomic data; therefore, it is often important to integrate multiple transcriptomic datasets from both microarray- and next generation sequencing (NGS)-based transcriptomic data across similar experiments or clinical trials to improve analytical power and discovery of novel transcripts and genes. However, transcriptomic data integration presents a few challenges including reannotation and batch effect removal. We developed the Gene Expression Data Integration (GEDI) R package to enable transcriptomic data integration by combining existing R packages. With just four functions, the GEDI R package makes constructing a transcriptomic data integration pipeline straightforward. Together, the functions overcome the complications in transcriptomic data integration by automatically reannotating the data and removing the batch effect. The removal of the batch effect is verified with principal component analysis and the data integration is verified using a logistic regression model with forward stepwise feature selection. To demonstrate the functionalities of the GEDI package, we integrated five bovine endometrial transcriptomic datasets from the NCBI Gene Expression Omnibus. These transcriptomic datasets were from multiple high-throughput platforms, namely, array-based Affymetrix and Agilent platforms, and NGS-based Illumina paired-end RNA-seq platform. Furthermore, we compared the GEDI package to existing tools and found that GEDI is the only tool that provides a full transcriptomic data integration pipeline including verification of both batch effect removal and data integration for downstream genomic and bioinformatics applications. © 2024 The Author(s). Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: ReadGE, a function to import gene expression datasets Basic Protocol 2: GEDI, a function to reannotate and merge gene expression datasets Basic Protocol 3: BatchCorrection, a function to remove batch effects from gene expression data Basic Protocol 4: VerifyGEDI, a function to confirm successful integration of gene expression data.

Maintaining mitochondrial DNA copy number mitigates ROS-induced oocyte decline and female reproductive aging

Oocytes play a crucial role in transmitting maternal mitochondrial DNA (mtDNA), essential for the continuation of species. However, the effects of mitochondrial reactive oxygen species (ROS) on mammalian oocyte maturation and mtDNA maintenance remain unclear. We investigated this by conditionally knocking out the Sod2 gene in primordial follicles, elevating mitochondrial matrix ROS levels from early oocyte stages. Our data indicates that reproductive aging in Sod2 conditional knockout females begins at 6 months, with oxidative stress impairing oocyte quality, particularly affecting OXPHOS complex II and mtDNA-encoded mRNA levels. Despite unchanged mtDNA mutation load, mtDNA copy numbers exhibited significant variations. Strikingly, reducing mtDNA copy numbers by reducing mtSSB protein, crucial for mtDNA replication, accelerated reproductive aging onset to three months, underscoring the critical role of mtDNA copy number maintenance under oxidative stress conditions. This research provides new insights into the relationship among mitochondrial ROS, mtDNA, and reproductive aging, offering potential strategies for delaying aging-related fertility decline.

Deficiency of metabolic regulator PKM2 activates the pentose phosphate pathway and generates TCF1+ progenitor CD8+ T cells to improve immunotherapy

TCF1high progenitor CD8+ T cells mediate the efficacy of immunotherapy; however, the mechanisms that govern their generation and maintenance are poorly understood. Here, we show that targeting glycolysis through deletion of pyruvate kinase muscle 2 (PKM2) results in elevated pentose phosphate pathway (PPP) activity, leading to enrichment of a TCF1high progenitor-exhausted-like phenotype and increased responsiveness to PD-1 blockade in vivo. PKM2KO CD8+ T cells showed reduced glycolytic flux, accumulation of glycolytic intermediates and PPP metabolites and increased PPP cycling as determined by 1,2-13C glucose carbon tracing. Small molecule agonism of the PPP without acute glycolytic impairment skewed CD8+ T cells toward a TCF1high population, generated a unique transcriptional landscape and adoptive transfer of agonist-treated CD8+ T cells enhanced tumor control in mice in combination with PD-1 blockade and promoted tumor killing in patient-derived tumor organoids. Our study demonstrates a new metabolic reprogramming that contributes to a progenitor-like T cell state promoting immunotherapy efficacy.

Post-Transcriptional Modifications to miRNAs Undergo Widespread Alterations, Creating a Unique Lung Adenocarcinoma IsomiRome

BACKGROUND

MicroRNAs (miRNAs) modulate the expression of oncogenes and tumor suppressor genes, functioning as significant epigenetic regulators in cancer. IsomiRs are miRNA molecules that have undergone small modifications during miRNA processing. These modifications can alter an isomiR's binding stability with mRNA targets, and certain isomiRs have been implicated in the development of specific cancers. Still, the isomiRomes of many tissues, including the lung, have not been characterized; Methods: In this study, we analyzed small RNA sequencing data for three cohorts of lung adenocarcinoma (LUAD) and adult non-malignant lung (ANL) samples.

RESULTS

We quantified isomiR expression and found 16 A-to-I edited isomiRs expressed in multiple cohorts, as well as 213 5' isomiRs, 128 3' adenylated isomiRs, and 100 3' uridylated isomiRs. Rates of A-to-I editing at editing hotspots correlated with mRNA expression of the editing enzymes ADAR and ADARB1, which were both observed to be deregulated in LUAD. LUAD samples displayed lower overall rates of A-to-I editing and 3' adenylation than ANL samples. Support vector machines and random forest models were trained on one cohort to distinguish ANL and stage I/II LUAD samples using reads per million (RPM) and frequency data for different types of isomiRs. Models trained on A-to-I editing rates at editing hotspots displayed high accuracy when tested on the other two cohorts and compared favorably to classifiers trained on miRNA expression alone; Conclusions: We have identified isomiRs in the human lung and found that their expression differs between non-malignant and tumor tissues, suggesting they hold potential as cancer biomarkers.

Transcriptomic landscape of mammalian ventral pallidum at single-cell resolution

The ventral pallidum (VP) is critical for motivated behaviors. While contemporary work has begun to elucidate the functional diversity of VP neurons, the molecular heterogeneity underlying this functional diversity remains incompletely understood. We used snRNA-seq and in situ hybridization to define the transcriptional taxonomy of VP cell types in mice, macaques, and baboons. We found transcriptional conservation between all three species, within the broader neurochemical cell types. Unique dopaminoceptive and cholinergic subclusters were identified and conserved across both primate species but had no homolog in mice. This harmonized consensus VP cellular atlas will pave the way for understanding the structure and function of the VP and identified key neuropeptides, neurotransmitters, and neuro receptors that could be targeted within specific VP cell types for functional investigations.

Teaser

Genetic identity of ventral pallidum cell types is conserved across rodents and primates at the transcriptional level.

Multi-omic analysis of longitudinal acute myeloid leukemia patient samples reveals potential prognostic markers linked to disease progression

Relapse remains a determinant of treatment failure and contributes significantly to mortality in acute myeloid leukemia (AML) patients. Despite efforts to understand AML progression and relapse mechanisms, findings on acquired gene mutations in relapse vary, suggesting inherent genetic heterogeneity and emphasizing the role of epigenetic modifications. We conducted a multi-omic analysis using Omni-C, ATAC-seq, and RNA-seq on longitudinal samples from two adult AML patients at diagnosis and relapse. Herein, we characterized genetic and epigenetic changes in AML progression to elucidate the underlying mechanisms of relapse. Differential interaction analysis showed significant 3D chromatin landscape reorganization between relapse and diagnosis samples. Comparing global open chromatin profiles revealed that relapse samples had significantly fewer accessible chromatin regions than diagnosis samples. In addition, we discovered that relapse-related upregulation was achieved either by forming new active enhancer contacts or by losing interactions with poised enhancers/potential silencers. Altogether, our study highlights the impact of genetic and epigenetic changes on AML progression, underlining the importance of multi-omic approaches in understanding disease relapse mechanisms and guiding potential therapeutic interventions.

3D genome contributes to MHC-II neoantigen prediction

Reliable and ultra-fast DNA and RNA sequencing have been achieved with the emergence of high-throughput sequencing technology. When combining the results of DNA and RNA sequencing for tumor cells of cancer patients, neoantigens that potentially stimulate the immune response of either CD4+ or CD8+ T cells can be identified. However, due to the abundance of somatic mutations and the high polymorphic nature of human leukocyte antigen (HLA) it is challenging to accurately predict the neoantigens. Moreover, comparing to HLA-I presented peptides, the HLA-II presented peptides are more variable in length, making the prediction of HLA-II loaded neoantigens even harder. A number of computational approaches have been proposed to address this issue but none of them considers the DNA origin of the neoantigens from the perspective of 3D genome. Here we investigate the DNA origins of the immune-positive and non-negative HLA-II neoantigens in the context of 3D genome and discovered that the chromatin 3D architecture plays an important role in more effective HLA-II neoantigen prediction. We believe that the 3D genome information will help to increase the precision of HLA-II neoantigen discovery and eventually benefit precision and personalized medicine in cancer immunotherapy.

Unraveling the phenotypic states of human innate-like T cells: Comparative insights with conventional T cells and mouse models

The "innate-like" T cell compartment, known as Tinn, represents a diverse group of T cells that straddle the boundary between innate and adaptive immunity. We explore the transcriptional landscape of Tinn compared to conventional T cells (Tconv) in the human thymus and blood using single-cell RNA sequencing (scRNA-seq) and flow cytometry. In human blood, the majority of Tinn cells share an effector program driven by specific transcription factors, distinct from those governing Tconv cells. Conversely, only a fraction of thymic Tinn cells displays an effector phenotype, while others share transcriptional features with developing Tconv cells, indicating potential divergent developmental pathways. Unlike the mouse, human Tinn cells do not differentiate into multiple effector subsets but develop a mixed type 1/type 17 effector potential. Cross-species analysis uncovers species-specific distinctions, including the absence of type 2 Tinn cells in humans, which implies distinct immune regulatory mechanisms across species.

Semen sEV tRF-Based Models Increase Non-Invasive Prediction Accuracy of Clinically Significant Prostate Cancer among Patients with Moderately Altered PSA Levels

PSA screening has led to an over-diagnosis of prostate cancer (PCa) and unnecessary biopsies of benign conditions due to its low cancer specificity. Consequently, more accurate, preferentially non-invasive, tests are needed. We aim to evaluate the potential of semen sEV (small extracellular vesicles) tsRNAs (tRNA-derived small RNAs) as PCa indicators. Initially, following a literature review in the OncotRF database and high-throughput small RNA-sequencing studies in PCa tissue together with the sncRNA profile in semen sEVs, we selected four candidate 5'tRF tsRNAs for validation as PCa biomarkers. RT-qPCR analysis in semen sEVs from men with moderately elevated serum PSA levels successfully shows that the differential expression of the four tRFs between PCa and healthy control groups can be detected in a non-invasive manner. The combined model incorporating PSA and specific tRFs (5'-tRNA-Glu-TTC-9-1_L30 and 5'-tRNA-Val-CAC-3-1_L30) achieved high predictive accuracy in identifying samples with a Gleason score ≥ 7 and staging disease beyond IIA, supporting that the 5'tRF fingerprint in semen sEV can improve the PSA predictive value to discriminate between malignant and indolent prostate conditions. The in silico study allowed us to map target genes for the four 5'tRFs possibly involved in PCa. Our findings highlight the synergistic use of multiple biomarkers as an efficient approach to improve PCa screening and prognosis.

Genome-Wide Association Study for Milk Somatic Cell Score in Holstein Friesian Cows in Slovenia

Mastitis is a serious challenge for the dairy industry, leading to economic losses and affecting milk quality. The aim of this study is to identify genetic factors associated with mastitis resistance by conducting a genome-wide association study (GWAS) for the somatic cell score (SCS). Phenotypic records of 350 Holstein Friesian cows were obtained from the Slovenian Cattle Recording Scheme Database and consisted of around 1500 lactation data from 2012 to 2023 collected on a single farm in Slovenia. Corresponding genotypic data were also retrieved from the same database and genotyped using the Illumina BovineSNP50 BeadChip (Illumina, Inc., San Diego, CA, USA). For the association study, three SCS parameters were considered, including lactation mean somatic cell score (LM_SCS), maximum SCS value (SCSMAX), and top three mean value of SCS (TOP3). After performing a GWAS using FarmCPU and BLINK models, five significant SNPs associated with the TOP3 trait were found on BTA 14, 15, 22, and 29. The identified SNP markers were closely linked to six known candidate genes (DNASE1L3, SLC36A4, ARMC1, PDE7A, MMP13, CD44). These results indicate potential genetic markers associated with SCS in the Slovenian Holstein Friesian population.

Characterization of the angiomodulatory effects of Interleukin 11 cis- and trans-signaling in the retina

BACKGROUND

The IL-6 cytokine family, with its crucial and pleiotropic intracellular signaling pathway STAT3, is a promising target for treating vasoproliferative retinal diseases. Previous research has shown that IL-6 cis-signaling (via membrane-bound receptors) and trans-signaling (via soluble receptors) can have distinct effects on target cells, leading to their application in various disease treatments. While IL-6 has been extensively studied, less is known about the angiogenic effects of IL-11, another member of the IL-6 family, in the retina. Therefore, the aim of this study was to characterize the effects of IL-11 on retinal angiogenesis.

MAIN TEXT

In vitreous samples from proliferative diabetic retinopathy (PDR) patients, elevated levels of IL-11Rα, but not IL-11, were detected. In vitro studies using vascular endothelial cells revealed distinct effects of cis- and trans-signaling: cis-signaling (IL-11 alone) had antiangiogenic effects, while trans-signaling (IL-11 + sIL-11Rα) had proangiogenic and pro-migratory effects. These differences can be attributed to their individual signaling responses and associated transcriptomic changes. Notably, no differences in cis- and trans-signaling were detected in primary mouse Müller cell cultures. STAT3 and STAT1 siRNA knockdown experiments revealed opposing effects on IL-11 signaling, with STAT3 functioning as an antiproliferative and proapoptotic player while STAT1 acts in opposition to STAT3. In vivo, both IL-11 and IL-11 + sIL-11Rα led to a reduction in retinal neovascularization. Immunohistochemical staining revealed Müller cell activation in response to treatment, suggesting that IL-11 affects multiple retinal cell types in vivo beyond vascular endothelial cells.

CONCLUSIONS

Cis- and trans-signaling by IL-11 have contrasting angiomodulatory effects on endothelial cells in vitro. In vivo, cis- and trans-signaling also influence Müller cells, ultimately determining the overall angiomodulatory impact on the retina, highlighting the intricate interplay between vascular and glial cells in the retina.

Multiplex, single-cell CRISPRa screening for cell type specific regulatory elements

CRISPR-based gene activation (CRISPRa) is a strategy for upregulating gene expression by targeting promoters or enhancers in a tissue/cell-type specific manner. Here, we describe an experimental framework that combines highly multiplexed perturbations with single-cell RNA sequencing (sc-RNA-seq) to identify cell-type-specific, CRISPRa-responsive cis-regulatory elements and the gene(s) they regulate. Random combinations of many gRNAs are introduced to each of many cells, which are then profiled and partitioned into test and control groups to test for effect(s) of CRISPRa perturbations of both enhancers and promoters on the expression of neighboring genes. Applying this method to a library of 493 gRNAs targeting candidate cis-regulatory elements in both K562 cells and iPSC-derived excitatory neurons, we identify gRNAs capable of specifically upregulating intended target genes and no other neighboring genes within 1 Mb, including gRNAs yielding upregulation of six autism spectrum disorder (ASD) and neurodevelopmental disorder (NDD) risk genes in neurons. A consistent pattern is that the responsiveness of individual enhancers to CRISPRa is restricted by cell type, implying a dependency on either chromatin landscape and/or additional trans-acting factors for successful gene activation. The approach outlined here may facilitate large-scale screens for gRNAs that activate genes in a cell type-specific manner.

Comparison of B-Cell Lupus and Lymphoma Using a Novel Immune Imbalance Transcriptomics Algorithm Reveals Potential Therapeutic Targets

BACKGROUND/OBJECTIVES

Systemic lupus erythematosus (lupus) and B-cell lymphoma (lymphoma) co-occur at higher-than-expected rates and primarily depend on B cells for their pathology. These observations implicate shared inflammation-related B cell molecular mechanisms as a potential cause of co-occurrence.

METHODS

We consequently implemented a novel Immune Imbalance Transcriptomics (IIT) algorithm and applied IIT to lupus, lymphoma, and healthy B cell RNA-sequencing (RNA-seq) data to find shared and contrasting mechanisms that are potential therapeutic targets.

RESULTS

We observed 7143 significantly dysregulated genes in both lupus and lymphoma. Of those genes, we found 5137 to have a significant immune imbalance, defined as a significant dysregulation by both diseases, as analyzed by IIT. Gene Ontology (GO) term and pathway enrichment of the IIT genes yielded immune-related "Neutrophil Degranulation" and "Adaptive Immune System", which validates that the IIT algorithm isolates biologically relevant genes in immunity and inflammation. We found that 344 IIT gene products are known targets for established and/or repurposed drugs. Among our results, we found 48 known and 296 novel lupus targets, along with 151 known and 193 novel lymphoma targets. Known disease drug targets in our IIT results further validate that IIT isolates genes with disease-relevant mechanisms.

CONCLUSIONS

We anticipate the IIT algorithm, together with the shared and contrasting gene mechanisms uncovered here, will contribute to the development of immune-related therapeutic options for lupus and lymphoma patients.

gsQTL: Associating genetic risk variants with gene sets by exploiting their shared variability

To investigate the functional significance of genetic risk loci identified through genome-wide association studies (GWASs), genetic loci are linked to genes based on their capacity to account for variation in gene expression, resulting in expression quantitative trait loci (eQTL). Following this, gene set analyses are commonly used to gain insights into functionality. However, the efficacy of this approach is hampered by small effect sizes and the burden of multiple testing. We propose an alternative approach: instead of examining the cumulative associations of individual genes within a gene set, we consider the collective variation of the entire gene set. We introduce the concept of gene set QTL (gsQTL), and show it to be more adept at identifying links between genetic risk variants and specific gene sets. Notably, gsQTL experiences less susceptibility to inflation or deflation of significant enrichments compared with conventional methods. Furthermore, we demonstrate the broader applicability of shared variability within gene sets. This is evident in scenarios such as the coordinated regulation of genes by a transcription factor or coordinated differential expression.

SMIM1 absence is associated with reduced energy expenditure and excess weight

BACKGROUND

Obesity rates have nearly tripled in the past 50 years, and by 2030 more than 1 billion individuals worldwide are projected to be obese. This creates a significant economic strain due to the associated non-communicable diseases. The root cause is an energy expenditure imbalance, owing to an interplay of lifestyle, environmental, and genetic factors. Obesity has a polygenic genetic architecture; however, single genetic variants with large effect size are etiological in a minority of cases. These variants allowed the discovery of novel genes and biology relevant to weight regulation and ultimately led to the development of novel specific treatments.

METHODS

We used a case-control approach to determine metabolic differences between individuals homozygous for a loss-of-function genetic variant in the small integral membrane protein 1 (SMIM1) and the general population, leveraging data from five cohorts. Metabolic characterization of SMIM1-/- individuals was performed using plasma biochemistry, calorimetric chamber, and DXA scan.

FINDINGS

We found that individuals homozygous for a loss-of-function genetic variant in SMIM1 gene, underlying the blood group Vel, display excess body weight, dyslipidemia, altered leptin to adiponectin ratio, increased liver enzymes, and lower thyroid hormone levels. This was accompanied by a reduction in resting energy expenditure.

CONCLUSION

This research identified a novel genetic predisposition to being overweight or obese. It highlights the need to investigate the genetic causes of obesity to select the most appropriate treatment given the large cost disparity between them.

FUNDING

This work was funded by the National Institute of Health Research, British Heart Foundation, and NHS Blood and Transplant.

Identification of SNPs and Candidate Genes Associated with Monocyte/Lymphocyte Ratio and Neutrophil/Lymphocyte Ratio in Duroc × Erhualian F2 Population

Understanding the pig immune function is crucial for disease-resistant breeding and potentially for human health research due to shared immune system features. Immune cell ratios, like monocyte/lymphocyte ratio (MLR) and neutrophil/lymphocyte ratio (NLR), offer a more comprehensive view of immune status compared to individual cell counts. However, research on pig immune cell ratios remains limited. This study investigated MLR and NLR in a Duroc × Erhualian F2 resource population. Heritability analysis revealed high values (0.649 and 0.688 for MLR and NLR, respectively), suggesting a strong genetic component. Furthermore, we employed an ensemble-like GWAS (E-GWAS) strategy and functional annotation analysis to identify 11 MLR-associated and 6 NLR-associated candidate genes. These genes were significantly enriched in immune-related biological processes. These findings provide novel genetic markers and candidate genes associated with porcine immunity, thereby providing valuable insights for addressing biosecurity and animal welfare concerns in the pig industry.

Liver eQTL meta-analysis illuminates potential molecular mechanisms of cardiometabolic traits

Understanding the molecular mechanisms of complex traits is essential for developing targeted interventions. We analyzed liver expression quantitative-trait locus (eQTL) meta-analysis data on 1,183 participants to identify conditionally distinct signals. We found 9,013 eQTL signals for 6,564 genes; 23% of eGenes had two signals, and 6% had three or more signals. We then integrated the eQTL results with data from 29 cardiometabolic genome-wide association study (GWAS) traits and identified 1,582 GWAS-eQTL colocalizations for 747 eGenes. Non-primary eQTL signals accounted for 17% of all colocalizations. Isolating signals by conditional analysis prior to coloc resulted in 37% more colocalizations than using marginal eQTL and GWAS data, highlighting the importance of signal isolation. Isolating signals also led to stronger evidence of colocalization: among 343 eQTL-GWAS signal pairs in multi-signal regions, analyses that isolated the signals of interest resulted in higher posterior probability of colocalization for 41% of tests. Leveraging allelic heterogeneity, we predicted causal effects of gene expression on liver traits for four genes. To predict functional variants and regulatory elements, we colocalized eQTL with liver chromatin accessibility QTL (caQTL) and found 391 colocalizations, including 73 with non-primary eQTL signals and 60 eQTL signals that colocalized with both a caQTL and a GWAS signal. Finally, we used publicly available massively parallel reporter assays in HepG2 to highlight 14 eQTL signals that include at least one expression-modulating variant. This multi-faceted approach to unraveling the genetic underpinnings of liver-related traits could lead to therapeutic development.