Gene Set Analysis Research Articles

Abstract P391: Genetic Analysis of Blood Pressure Traits in African Populations

High blood pressure is a heritable risk factor for cardiovascular disease, stroke and other chronic conditions. There is an increasing prevalence of high blood pressure in sub-Saharan Africa. We aimed to identify genetic variants underlying blood pressure traits in Sub-Saharan Africa. We conducted a GWAS, meta-analyses and gene-set analyses of blood pressure traits (systolic, diastolic, pulse pressure), in African populations. We used the African Collaborative Center for Microbiome and Genomics Research data (ACCME, n=~15,000) for GWAS discovery and the Africa America Diabetes Mellitus study data (AADM, n=~5,200), for GWAS replication. Multiple SNPs were identified reaching genome-wide significance. The lead SNP from the elevated blood pressure group was on chromosome 16 (rs148403740, minor allelic frequency (MAF)=0.0176, p= 9.28x10 -9 , odds ratio(OR)= 2.58) nearest to the MGRN1 gene. The lead SNP from the hypertension stage 1 group was on chromosome 7 (rs4236415, MAF= 0.4912, p= 1.33x10 -8 , OR= 1.2). The index SNP within the pulse pressure group was on chromosome 10 (rs61836106, MAF= 0.1828, p= 7.4x10 -9 , OR= 5.7). The findings provide promising candidate SNPs for developing risk stratification models in African populations.

Genome-Wide Gene-Set Analysis Identifies Molecular Mechanisms Associated with ALS.

Amyotrophic lateral sclerosis (ALS) is a fatal late-onset motor neuron disease characterized by the loss of the upper and lower motor neurons. Our understanding of the molecular basis of ALS pathology remains elusive, complicating the development of efficient treatment. Gene-set analyses of genome-wide data have offered insight into the biological processes and pathways of complex diseases and can suggest new hypotheses regarding causal mechanisms. Our aim in this study was to identify and explore biological pathways and other gene sets having genomic association to ALS. Two cohorts of genomic data from the dbGaP repository were combined: (a) the largest available ALS individual-level genotype dataset (N = 12,319), and (b) a similarly sized control cohort (N = 13,210). Following comprehensive quality control pipelines, imputation and meta-analysis, we assembled a large European descent ALS-control cohort of 9244 ALS cases and 12,795 healthy controls represented by genetic variants of 19,242 genes. Multi-marker analysis of genomic annotation (MAGMA) gene-set analysis was applied to an extensive collection of 31,454 gene sets from the molecular signatures database (MSigDB). Statistically significant associations were observed for gene sets related to immune response, apoptosis, lipid metabolism, neuron differentiation, muscle cell function, synaptic plasticity and development. We also report novel interactions between gene sets, suggestive of mechanistic overlaps. A manual meta-categorization and enrichment mapping approach is used to explore the overlap of gene membership between significant gene sets, revealing a number of shared mechanisms.

Network-medicine approach for the identification of genetic association of parathyroid adenoma with cardiovascular disease and type-2 diabetes.

Primary hyperparathyroidism is caused by solitary parathyroid adenomas (PTAs) in most cases (⁓85%), and it has been previously reported that PTAs are associated with cardiovascular disease (CVD) and type-2 diabetes (T2D). To understand the molecular basis of PTAs, we have investigated the genetic association amongst PTAs, CVD and T2D through an integrative network-based approach and observed a remarkable resemblance. The current study proposed to compare the PTAs-associated proteins with the overlapping proteins of CVD and T2D to determine the disease relationship. We constructed the protein-protein interaction network by integrating curated and experimentally validated interactions in humans. We found the $11$ highly clustered modules in the network, which contain a total of $13$ hub proteins (TP53, ESR1, EGFR, POTEF, MEN1, FLNA, CDKN2B, ACTB, CTNNB1, CAV1, MAPK1, G6PD and CCND1) that commonly co-exist in PTAs, CDV and T2D and reached to network's hierarchically modular organization. Additionally, we implemented a gene-set over-representation analysis over biological processes and pathways that helped to identify disease-associated pathways and prioritize target disease proteins. Moreover, we identified the respective drugs of these hub proteins. We built a bipartite network that helps decipher the drug-target interaction, highlighting the influential roles of these drugs on apparently unrelated targets and pathways. Targeting these hub proteins by using drug combinations or drug-repurposing approaches will improve the clinical conditions in comorbidity, enhance the potency of a few drugs and give a synergistic effect with better outcomes. This network-based analysis opens a new horizon for more personalized treatment and drug-repurposing opportunities to investigate new targets and multi-drug treatment and may be helpful in further analysis of the mechanisms underlying PTA and associated diseases.

Clinical and Genetic Characteristics of CKD Patients with High-Risk APOL1 Genotypes.

APOL1 high-risk genotypes confer a significant risk of kidney disease, but variability in patient outcomes suggests the presence of modifiers of the APOL1 effect. We show that a diverse population of CKD patients with high-risk APOL1 genotypes have an increased lifetime risk of kidney failure and higher eGFR decline rates, with a graded risk among specific high-risk genotypes. CKD patients with high-risk APOL1 genotypes have a lower diagnostic yield for monogenic kidney disease. Exome sequencing revealed enrichment of rare missense variants within the inflammasome pathway modifying the effect of APOL1 risk genotypes, which may explain some clinical heterogeneity. APOL1 genotype has significant effects on kidney disease development and progression that vary among specific causes of kidney disease, suggesting the presence of effect modifiers. We assessed the risk of kidney failure and the eGFR decline rate in patients with CKD carrying high-risk ( N =239) and genetically matched low-risk ( N =1187) APOL1 genotypes. Exome sequencing revealed monogenic kidney diseases. Exome-wide association studies and gene-based and gene set-based collapsing analyses evaluated genetic modifiers of the effect of APOL1 genotype on CKD. Compared with genetic ancestry-matched patients with CKD with low-risk APOL1 genotypes, those with high-risk APOL1 genotypes had a higher risk of kidney failure (Hazard Ratio [HR]=1.58), a higher decline in eGFR (6.55 versus 3.63 ml/min/1.73 m 2 /yr), and were younger at time of kidney failure (45.1 versus 53.6 years), with the G1/G1 genotype demonstrating the highest risk. The rate for monogenic kidney disorders was lower among patients with CKD with high-risk APOL1 genotypes (2.5%) compared with those with low-risk genotypes (6.7%). Gene set analysis identified an enrichment of rare missense variants in the inflammasome pathway in individuals with high-risk APOL1 genotypes and CKD (odds ratio=1.90). In this genetically matched cohort, high-risk APOL1 genotypes were associated with an increased risk of kidney failure and eGFR decline rate, with a graded risk between specific high-risk genotypes and a lower rate of monogenic kidney disease. Rare missense variants in the inflammasome pathway may act as genetic modifiers of APOL1 effect on kidney disease.

Identification of novel genomic risk loci shared between common epilepsies and psychiatric disorders.

Psychiatric disorders and common epilepsies are heritable disorders with a high comorbidity and overlapping symptoms. However, the causative mechanisms underlying this relationship are poorly understood. Here we aimed to identify overlapping genetic loci between epilepsy and psychiatric disorders to gain a better understanding of their comorbidity and shared clinical features. We analysed genome-wide association study data for all epilepsies (n = 44 889), genetic generalized epilepsy (n = 33 446), focal epilepsy (n = 39 348), schizophrenia (n = 77 096), bipolar disorder (n = 406 405), depression (n = 500 199), attention deficit hyperactivity disorder (n = 53 293) and autism spectrum disorder (n = 46 350). First, we applied the MiXeR tool to estimate the total number of causal variants influencing the disorders. Next, we used the conjunctional false discovery rate statistical framework to improve power to discover shared genomic loci. Additionally, we assessed the validity of the findings in independent cohorts, and functionally characterized the identified loci. The epilepsy phenotypes were considerably less polygenic (1.0 K to 3.4 K causal variants) than the psychiatric disorders (5.6 K to 13.9 K causal variants), with focal epilepsy being the least polygenic (1.0 K variants), and depression having the highest polygenicity (13.9 K variants). We observed cross-trait genetic enrichment between genetic generalized epilepsy and all psychiatric disorders and between all epilepsies and schizophrenia and depression. Using conjunctional false discovery rate analysis, we identified 40 distinct loci jointly associated with epilepsies and psychiatric disorders at conjunctional false discovery rate <0.05, four of which were associated with all epilepsies and 39 with genetic generalized epilepsy. Most epilepsy risk loci were shared with schizophrenia (n = 31). Among the identified loci, 32 were novel for genetic generalized epilepsy, and two were novel for all epilepsies. There was a mixture of concordant and discordant allelic effects in the shared loci. The sign concordance of the identified variants was highly consistent between the discovery and independent datasets for all disorders, supporting the validity of the findings. Gene-set analysis for the shared loci between schizophrenia and genetic generalized epilepsy implicated biological processes related to cell cycle regulation, protein phosphatase activity, and membrane and vesicle function; the gene-set analyses for the other loci were underpowered. The extensive genetic overlap with mixed effect directions between psychiatric disorders and common epilepsies demonstrates a complex genetic relationship between these disorders, in line with their bi-directional relationship, and indicates that overlapping genetic risk may contribute to shared pathophysiological and clinical features between epilepsy and psychiatric disorders.

The Genetics of Primary Biliary Cholangitis: A GWAS and Post-GWAS Update.

Primary biliary cholangitis (PBC) is a chronic, progressive cholestatic liver disease in which the small intrahepatic bile ducts are destroyed by autoimmune reactions. Among autoimmune diseases, which are polygenic complex traits caused by the combined contribution of genetic and environmental factors, PBC exhibits the strongest involvement of genetic heritability in disease development. As at December 2022, genome-wide association studies (GWASs) and associated meta-analyses identified approximately 70 PBC susceptibility gene loci in various populations, including those of European and East Asian descent. However, the molecular mechanisms through which these susceptibility loci affect the pathogenesis of PBC are not fully understood. This study provides an overview of current data regarding the genetic factors of PBC as well as post-GWAS approaches to identifying primary functional variants and effector genes in disease-susceptibility loci. Possible mechanisms of these genetic factors in the development of PBC are also discussed, focusing on four major disease pathways identified by in silico gene set analyses, namely, (1) antigen presentation by human leukocyte antigens, (2) interleukin-12-related pathways, (3) cellular responses to tumor necrosis factor, and (4) B cell activation, maturation, and differentiation pathways.

Development and validation of an immune-related gene signature for prognosis in Lung adenocarcinoma.

The most common type of lung cancer tissue is lung adenocarcinoma. The TCGA-LUAD cohort retrieved from the TCGA dataset was considered the internal training cohort, while GSE68465 and GSE13213 datasets from the GEO database were used as the external test cohort. The TCGA-LUAD cohort was classified into two immune subtypes using single-sample gene set enrichment analysis of the immune gene set and unsupervised clustering analysis. The ESTIMATE algorithm, the CIBERSORT algorithm, and HLA family expression levels again validated the reliability of this typing. We performed Venn analysis using immune-related genes from the immport dataset and differentially expressed genes from the subtypes to retrieve differentially expressed immune genes (DEIGs). In addition, DEIGs were used to construct a prognostic model with the least absolute shrinkage and selection operator regression analysis. A reliable risk model consisting of 11 DEIGs, including S100P, INHA, SEMA7A, INSL4, CD40LG, AGER, SERPIND1, CD1D, CX3CR1, SFTPD, and CD79A, was then built, and its reliability was further confirmed by ROC curve and calibration plot analysis. The high-risk score subgroup had a poor prognosis and a lower tumour immune dysfunction and exclusion score, indicating a greater likelihood of anti-PD-1/cytotoxic T lymphocyte antigen 4 benefit.

Genome-wide association study of age at menarche in the Taiwan Biobank suggests NOL4 as a novel associated gene.

Sexual maturation is a complex physiological process that involves multiple variables, such as genetic and environmental factors. Among females, age at menarche (AM) is a critical milestone for sexual maturation. This study aimed to identify genetic markers of AM using nationwide population cohort data in Taiwan. Females with self-reported AM between 10 and 16 years (N = 39,827) were eligible for the final analysis. To identify genetic signals related to AM, we conducted a genome-wide association study using a linear regression model and split-half meta-analysis method to verify our findings. The Functional Mapping and Annotation web-based platform was used for positional mapping and gene-based and gene-set analyses. The meta-analysis identified four significant loci, i.e., LIN28B (pooled P = 1.39 × 10-21), NOL4 (pooled P = 8.94 × 10-9), GPR45 (pooled P = 4.19 × 10-11), and LOC105373831 (pooled P = 4.37 × 10-8), that were associated with AM. MAGMA gene-based analysis revealed that LIN28B (P = 1.13 × 10-8), NOL4 (P = 2.27 × 10-7), RXRG (P = 4.34 × 10-7), ETV5 (P = 1.75 × 10-6), and HACE1 (P = 1.82 × 10-6) were significantly associated with AM, while the gene-set analysis identified a significantly enriched pathway involving mTOR signaling complex (FDR corrected P = 1.28 × 10-2). The results replicated evidence for several genetic markers associated with AM in the Taiwanese female population. Our analysis identified a novel locus (rs7239368) in NOL4 associated with AM (β = 0.051 ± 0.009 years, pooled P = 8.94 × 10-9), whereas additional research is needed to validate its molecular role in sexual maturation.

Evaluating the Genetic Role of Circadian Clock Genes in Parkinson's Disease.

Increasing evidence suggests that circadian dysfunction is related to Parkinson's disease (PD). However, the role of circadian clock genes in PD is still poorly understood. This study aimed to illustrate the association between genetic variants of circadian clock genes and PD in a large Chinese population cohort. Ten circadian clock genes were included in this study. Whole-exome sequencing (WES) was conducted in 1997 early-onset or familial PD patients and 1652 controls (WES cohort), and whole-genome sequencing (WGS) was conducted in 1962 sporadic late-onset PD patients and 1279 controls (WGS cohort). Analyses were completed using the optimized sequence kernel association test and regression analyses. In the burden analysis of the circadian clock gene set, we found suggestive significant associations between the circadian clock genes and PD in the WES cohort when considering missense, damaging missense (Dmis), and deleterious variants. Moreover, the burden analysis of single genes revealed suggestive significant associations between PD and the loss-of-function variants of the CRY1 gene, missense, Dmis, and deleterious variants of the PER1 gene, and Dmis and deleterious variants of the PER2 gene in the WES cohort.Rare variants in the WGS cohort and all common variants in the WGS and WES cohorts were unrelated to PD. Phenotypic analysis indicated that deleterious variants of the PER1 gene were associated with dyskinesia in the WES cohort. Our study provides evidence of a potential link between circadian clock genes and PD from a genetic perspective.

Hierarchical cell-type identifier accurately distinguishes immune-cell subtypes enabling precise profiling of tissue microenvironment with single-cell RNA-sequencing.

Single-cell RNA-seq enabled in-depth study on tissue micro-environment and immune-profiling, where a crucial step is to annotate cell identity. Immune cells play key roles in many diseases, whereas their activities are hard to track due to their diverse and highly variable nature. Existing cell-type identifiers had limited performance for this purpose. We present HiCAT, a hierarchical, marker-based cell-type identifier utilising gene set analysis for statistical scoring for given markers. It features successive identification of major-type, minor-type and subsets utilising subset markers structured in a three-level taxonomy tree. Comparison with manual annotation and pairwise match test showed HiCAT outperforms others in major- and minor-type identification. For subsets, we qualitatively evaluated the marker expression profile demonstrating that HiCAT provide the clearest immune-cell landscape. HiCAT was also used for immune-cell profiling in ulcerative colitis and discovered distinct features of the disease in macrophage and T-cell subsets that could not be identified previously.

Excitatory/inhibitory imbalance in autism: the role of glutamate and GABA gene-sets in symptoms and cortical brain structure

The excitatory/inhibitory (E/I) imbalance hypothesis posits that imbalance between excitatory (glutamatergic) and inhibitory (GABAergic) mechanisms underlies the behavioral characteristics of autism. However, how E/I imbalance arises and how it may differ across autism symptomatology and brain regions is not well understood. We used innovative analysis methods—combining competitive gene-set analysis and gene-expression profiles in relation to cortical thickness (CT) to investigate relationships between genetic variance, brain structure and autism symptomatology of participants from the AIMS-2-TRIALS LEAP cohort (autism = 359, male/female = 258/101; neurotypical control participants = 279, male/female = 178/101) aged 6–30 years. Using competitive gene-set analyses, we investigated whether aggregated genetic variation in glutamate and GABA gene-sets could be associated with behavioral measures of autism symptoms and brain structural variation. Further, using the same gene-sets, we corelated expression profiles throughout the cortex with differences in CT between autistic and neurotypical control participants, as well as in separate sensory subgroups. The glutamate gene-set was associated with all autism symptom severity scores on the Autism Diagnostic Observation Schedule-2 (ADOS-2) and the Autism Diagnostic Interview-Revised (ADI-R) within the autistic group. In adolescents and adults, brain regions with greater gene-expression of glutamate and GABA genes showed greater differences in CT between autistic and neurotypical control participants although in opposing directions. Additionally, the gene expression profiles were associated with CT profiles in separate sensory subgroups. Our results suggest complex relationships between E/I related genetics and autism symptom profiles as well as brain structure alterations, where there may be differential roles for glutamate and GABA.

Disruption to the FOXO-PRDM1 axis resulting from deletions of chromosome 6 in acute lymphoblastic leukaemia

A common problem in the study of human malignancy is the elucidation of cancer driver mechanisms associated with recurrent deletion of regions containing multiple genes. Taking B-cell acute lymphoblastic leukaemia (B-ALL) and large deletions of 6q [del(6q)] as a model, we integrated analysis of functional cDNA clone tracking assays with patient genomic and transcriptomic data, to identify the transcription factors FOXO3 and PRDM1 as candidate tumour suppressor genes (TSG). Analysis of cell cycle and transcriptomic changes following overexpression of FOXO3 or PRDM1 indicated that they co-operate to promote cell cycle exit at the pre-B cell stage. FOXO1 abnormalities are absent in B-ALL, but like FOXO3, FOXO1 expression suppressed growth of TCF3::PBX1 and ETV6::RUNX1 B-ALL in-vitro. While both FOXOs induced PRDM1 and other genes contributing to late pre-B cell development, FOXO1 alone induced the key transcription factor, IRF4, and chemokine, CXCR4. CRISPR-Cas9 screening identified FOXO3 as a TSG, while FOXO1 emerged as essential for B-ALL growth. We relate this FOXO3-specific leukaemia-protective role to suppression of glycolysis based on integrated analysis of CRISPR-data and gene sets induced or suppressed by FOXO1 and FOXO3. Pan-FOXO agonist Selinexor induced the glycolysis inhibitor TXNIP and suppressed B-ALL growth at low dose (ID50 < 50 nM).

ABI3BP is a prognosis biomarker related with clinicopathological features and immunity infiltration of lung tumor.

Background: The primary factor of cancer mortality is lung tumor. ABI3BP gene encodes an extracellular matrix bind protein associated to multiplication and derivation. However, the prognosis score of ABI3BP for lung tumor and its relation with immunity cellular infiltration for lung tumor have not been reported. Methods: Public repository systems (Timer, GEPIA, TCGA, HPA) were utilized to explore expression of ABI3BP for lung tumor, and explored the relation of ABI3BP and clinicopathological parameters. TCGA information set was utilized for cox analysis for data with one or more variables of ABI3BP for lung tumor. STRING was utilized to explore ABI3BP regulatory networks. GO/KEGG enrichment analysis as well as enrichment analysis of gene sets were carried out for ABI3BP co-expression via R package. And finally we explored the relation of expression of ABI3BP and lung tumor immunity invasion, exploring the influence of ABI3BP level of expression on immunotreatment and whether immunity invasion would affect the prognosis of patients with lung tumor. Results: ABI3BP is downregulated in LUAD and LUSC, and associated to lung tumor phase and prognosis. Univariate and multivariate cox regression showed that ABI3BP was an independent prognostic factor in patients with lung tumors. The extracellular matrix protein-coding gene and the ABI3BP-related gene were intersected to obtain 10 hub genes. On the basis of GO/KEGG enrichment analysis, hub genes are closely associated to immunity-associated pathways including T cell receptor signaling pathway, immune response-activating cell surface receptor signaling pathway. Finally, the expression of ABI3BP is closely related to immune cell infiltration and immune cell marker set, and the expression of ABI3BP can help predict the therapeutic effect of immune checkpoint inhibitors and improve the prognosis of patients. Conclusion: ABI3BP could be a new target for lung tumor that could be utilized as a diagnostic and therapeutic tool.

Unraveling the genetics underlying micronutrient signatures of diversity panel present in brown rice through genome-ionome linkages.

Rice (Oryza sativa) is an important staple crop to address the Hidden Hunger problem not only in Asia but also in Africa where rice is fast becoming an important source of calories. The brown rice (whole grain with bran) is known to be more nutritious due to elevated mineral composition. The genetics underlying brown rice ionome (sum total of such mineral composition) remains largely unexplored. Hence, we conducted a comprehensive study to dissect the genetic architecture of the brown rice ionome. We used genome-wide association studies, gene set analysis, and targeted association analysis for 12 micronutrients in the brown rice grains. A diverse panel of 300 resequenced indica accessions, with more than 1.02million single nucleotide polymorphisms, was used. We identified 109 candidate genes with 5-20% phenotypic variation explained for the 12 micronutrients and identified epistatic interactions with multiple micronutrients. Pooling all candidate genes per micronutrient exhibited phenotypic variation explained values ranging from 11% to almost 40%. The key donor lines with larger concentrations for most of the micronutrients possessed superior alleles, which were absent in the breeding lines. Through gene regulatory networks we identified enriched functional pathways for central regulators that were detected as key candidate genes through genome-wide association studies. This study provided important insights on the ionome variations in rice, on the genetic basis of the genome-ionome relationships and on the molecular mechanisms underlying micronutrient signatures.

Long-Read cDNA Sequencing Revealed Novel Expressed Genes and Dynamic Transcriptome Landscape of Triticale (x Triticosecale Wittmack) Seed at Different Developing Stages

Developing seed is a unique stage of plant development with highly dynamic changes in transcriptome. Here, we aimed to detect the novel previously unannotated, genes of the triticale (x Triticosecale Wittmack, AABBRR genome constitution) genome that are expressed during different stages and at different parts of the developing seed. For this, we carried out the Oxford Nanopore sequencing of cDNA obtained for middle (15 days post-anthesis, dpa) and late (20 dpa) stages of seed development. The obtained data together with our previous direct RNA sequencing of early stage (10 dpa) of seed development revealed 39,914 expressed genes including 7128 (17.6%) genes that were not previously annotated in A, B, and R genomes. The bioinformatic analysis showed that the identified genes belonged to long non-coding RNAs (lncRNAs), protein-coding RNAs, and TE-derived RNAs. The gene set analysis revealed the transcriptome dynamics during seed development with distinct patterns of over-represented gene functions in early and middle/late stages. We performed analysis of the lncRNA genes polymorphism and showed that the genes of some of the tested lncRNAs are indeed polymorphic in the triticale collection. Altogether, our results provide information on thousands of novel loci expressed during seed development that can be used as new targets for GWAS analysis, the marker-assisted breeding of triticale, and functional elucidation.

The hitchhikers' guide to RNA sequencing and functional analysis.

DNA and RNA sequencing technologies have revolutionized biology and biomedical sciences, sequencing full genomes and transcriptomes at very high speeds and reasonably low costs. RNA sequencing (RNA-Seq) enables transcript identification and quantification, but once sequencing has concluded researchers can be easily overwhelmed with questions such as how to go from raw data to differential expression (DE), pathway analysis and interpretation. Several pipelines and procedures have been developed to this effect. Even though there is no unique way to perform RNA-Seq analysis, it usually follows these steps: 1) raw reads quality check, 2) alignment of reads to a reference genome, 3) aligned reads' summarization according to an annotation file, 4) DE analysis and 5) gene set analysis and/or functional enrichment analysis. Each step requires researchers to make decisions, and the wide variety of options and resulting large volumes of data often lead to interpretation challenges. There also seems to be insufficient guidance on how best to obtain relevant information and derive actionable knowledge from transcription experiments. In this paper, we explain RNA-Seq steps in detail and outline differences and similarities of different popular options, as well as advantages and disadvantages. We also discuss non-coding RNA analysis, multi-omics, meta-transcriptomics and the use of artificial intelligence methods complementing the arsenal of tools available to researchers. Lastly, we perform a complete analysis from raw reads to DE and functional enrichment analysis, visually illustrating how results are not absolute truths and how algorithmic decisions can greatly impact results and interpretation.

Transcriptomic Analysis of Glycosylation and Neuroregulatory Pathways in Rodent Models in Response to Psychedelic Molecules.

The potential for psychedelic molecules in impacting cognitive flexibility has long been supported and acknowledged across scientific reports. In the current study, an approach leveraging knowledge-based gene-set information analysis has been adopted to explore the potential impact of psychedelic molecules on both glycosylation, (a post-translational modifications (PTM)) and on neuro-regulatory pathways. Though limitations and restrictions rise from the scarcity of publicly available 'omics' data, targeted analysis enabled us to identify a number of key glycogenes (Hexb, Hs6st2, Col9a2, B3gat2, Mgat5, Bgn) involved the structural organization of extracellular matrix and neuroprotective factors (Kl, Pomc, Oxt, Gal, Avp, Cartpt) which play vital roles in neuron protection, development as well as synaptic stability. In response to psychedelic molecules, we found that these genes and associated pathways are transcriptional altered in rodent models. The approach used indicates the potential to exploit existing datasets for hypothesis generation and testing for the molecular processes which play a role in the physiological response to psychedelic molecule effects. These reported findings, which focused on alterations in glycogenes and neuro-regulatory factors may provide a novel range of biomarkers to track the beneficial, as well as potential toxicological effects of psychedelic molecules.

EXPANSION: a webserver to explore the functional consequences of protein-coding alternative splice variants in cancer genomics.

EXPANSION (https://expansion.bioinfolab.sns.it/) is an integrated web-server to explore the functional consequences of protein-coding alternative splice variants. We combined information from Differentially Expressed (DE) protein-coding transcripts from cancer genomics, together with domain architecture, protein interaction network, and gene enrichment analysis to provide an easy-to-interpret view of the effects of protein-coding splice variants. We retrieved all the protein-coding Ensembl transcripts and mapped Interpro domains and post-translational modifications on canonical sequences to identify functionally relevant splicing events. We also retrieved isoform-specific protein-protein interactions and binding regions from IntAct to uncover isoform-specific functions via gene-set over-representation analysis. Through EXPANSION, users can analyze precalculated or user-inputted DE transcript datasets, to easily gain functional insights on any protein spliceform of interest. EXPANSION is freely available at http://expansion.bioinfolab.sns.it/. The code of the scripts used for EXPASION is available at: https://github.com/raimondilab/expansion. Datasets associated to this resource are available at the following URL: https://doi.org/10.5281/zenodo.8229120. The web-server was developed using Apache2 (https://https.apache.org/) and Flask (v2.0.2) (http://flask.pocoo.org/) for the web frontend and for the internal pipeline to handle back-end processes. We additionally used the following Python and JavaScript libraries at both back- and front-ends: D3 (v4), jQuery (v3.2.1), DataTables (v2.3.2), biopython (v1.79), gprofiler-officia l(v1.0.0), Mysql-connector-python (v8.0.31). To construct the API, Fast API library (v0.95.1) was used.

Unlocking molecular mechanisms and identifying druggable targets in matched-paired brain metastasis of breast and lung cancers.

The incidence of brain metastases in cancer patients is increasing, with lung and breast cancer being the most common sources. Despite advancements in targeted therapies, the prognosis remains poor, highlighting the importance to investigate the underlying mechanisms in brain metastases. The aim of this study was to investigate the differences in the molecular mechanisms involved in brain metastasis of breast and lung cancers. In addition, we aimed to identify cancer lineage-specific druggable targets in the brain metastasis. To that aim, a cohort of 44 FFPE tissue samples, including 22 breast cancer and 22 lung adenocarcinoma (LUAD) and their matched-paired brain metastases were collected. Targeted gene expression profiles of primary tumors were compared to their matched-paired brain metastases samples using nCounter PanCancer IO 360™ Panel of NanoString technologies. Pathway analysis was performed using gene set analysis (GSA) and gene set enrichment analysis (GSEA). The validation was performed by using Immunohistochemistry (IHC) to confirm the expression of immune checkpoint inhibitors. Our results revealed the significant upregulation of cancer-related genes in primary tumors compared to their matched-paired brain metastases (adj. p ≤ 0.05). We found that upregulated differentially expressed genes in breast cancer brain metastasis (BM-BC) and brain metastasis from lung adenocarcinoma (BM-LUAD) were associated with the metabolic stress pathway, particularly related to the glycolysis. Additionally, we found that the upregulated genes in BM-BC and BM-LUAD played roles in immune response regulation, tumor growth, and proliferation. Importantly, we identified high expression of the immune checkpoint VTCN1 in BM-BC, and VISTA, IDO1, NT5E, and HDAC3 in BM-LUAD. Validation using immunohistochemistry further supported these findings. In conclusion, the findings highlight the significance of using matched-paired samples to identify cancer lineage-specific therapies that may improve brain metastasis patients outcomes.

Multi-omics Pathways Workflow (MOPAW): An Automated Multi-omics Workflow on the Cancer Genomics Cloud.

In the era of big data, gene-set pathway analyses derived from multi-omics are exceptionally powerful. When preparing and analyzing high-dimensional multi-omics data, the installation process and programing skills required to use existing tools can be challenging. This is especially the case for those who are not familiar with coding. In addition, implementation with high performance computing solutions is required to run these tools efficiently. We introduce an automatic multi-omics pathway workflow, a point and click graphical user interface to Multivariate Single Sample Gene Set Analysis (MOGSA), hosted on the Cancer Genomics Cloud by Seven Bridges Genomics. This workflow leverages the combination of different tools to perform data preparation for each given data types, dimensionality reduction, and MOGSA pathway analysis. The Omics data includes copy number alteration, transcriptomics data, proteomics and phosphoproteomics data. We have also provided an additional workflow to help with downloading data from The Cancer Genome Atlas and Clinical Proteomic Tumor Analysis Consortium and preprocessing these data to be used for this multi-omics pathway workflow. The main outputs of this workflow are the distinct pathways for subgroups of interest provided by users, which are displayed in heatmaps if identified. In addition to this, graphs and tables are provided to users for reviewing. Multi-omics Pathway Workflow requires no coding experience. Users can bring their own data or download and preprocess public datasets from The Cancer Genome Atlas and Clinical Proteomic Tumor Analysis Consortium using our additional workflow based on the samples of interest. Distinct overactivated or deactivated pathways for groups of interest can be found. This useful information is important in effective therapeutic targeting.