Background

Post translational modifications (PTMs) are covalent modifications of proteins that can range from small chemical changes to addition of entire proteins. They regulate folding, localization, interactions, degradation and activity of proteins and can also play a role in signal transmission. Aberrant PTMs can drive disease and disorders by altering protein folding and dysregulation of cell signalling. Thus, they could represent new targets for therapeutic interventions and accurate identification and quantification of PTMs is important to facilitate differential analysis across conditions.

Analysis and Results

Shotgun mass spectrometry is a powerful tool for unraveling the diversity and complexity of the proteome at a granular level. We have developed an analysis pipeline that can identify, localize and annotate PTMs from tandem MS/MS data as well as conduct differential analysis after MaxQuant quantification for common PTMs. Identification of the modifications is accomplished through an “error-tolerant” method Comet-PTM that allows matching of fragment spectra to peptides with modifications. This method provides shifts in mass as well as the most likely localization of the modification along the peptide sequence based on comparison with peptides in the uniprot database. This information is subsequently utilized by an annotation algorithm that compares this to unimod database to identify the most likely change for the modified peptides.

The MaxQuant program was utilized to quantify both modified and unmodified peptides. Differential analyses are subsequently carried based on the LIMMA framework as well as 2-sample t tests from the proteus and perseus tools respectively. We used 2 different metrics to normalize the peptide intensities prior to identifying statistically significant hits from differential analysis. These metrics test for differential PTM abundance as well as differential PTM relative abundance (correcting for overall abundance of peptide). Lastly, we conduct pathway over-representation analyses among the significant hits.

We showcase application of the pipeline on a cancer dataset as well as data from human heart chambers obtained through cardiac biopsy. We identified PTMs and peptides that demonstrated statistically significant differences across conditions in these datasets. Pathway analyses identified muscle contraction, cardiomyopathy and cancer pathways as overrepresented among the statistically significant hits.

Background

Multiple sclerosis (MS) is a chronic, recurrent, inflammatory, demyelinating disease of the central nervous system, affecting over 2 million people worldwide. The impact of comorbidities in MS is a significant and growing area of interest. Comorbidities in MS patients can delay MS diagnosis, increase disability progression, reduce quality of life, and increase mortality. While most studies focus on unhealthy individuals, we evaluated the effects of comorbidities in a healthy subset of the UK Biobank cohort. Additionally, we aimed to identify potential biomarkers through multi-omics data analysis to improve MS disease prognosis.

Objective

To identify potential biomarkers that may elucidate the underlying mechanisms or prognosis of MS and to evaluate the association of metabolic comorbidities, such as hypertension (HT) and hyperlipidemia (HL), within a healthy sub-cohort of individuals diagnosed with MS.

Methods

Relevant data and metadata for the evaluated cohorts were extracted from the UK Biobank. A MS case-control cohort of individuals following a healthy lifestyle was created, comprising individuals with no smoking history or who had quit, a BMI <30kg/m² and who reported regular physical activity. Logistic regression analysis was used to evaluate the impact of HL and HT comorbidities in this cohort. Additionally, MS and non-MS individuals were compared using genomics and proteomics analyses, including: (1) GWAS to identify shared genetic risk loci; (2) protein quantitative trait loci (pQTL) mapping; and (3) differential protein expression profiling to correlate the SNPs with a potential impact on proteins and phenotypes in MS individuals.

Results and Conclusion

Our findings demonstrate a significant association between HL and MS, even among individuals adhering to a healthy lifestyle (calculated Odds Ratio = 2.2, p value = 0.007). HT did not show any substantial association with MS, nor a synergistic interaction with HL. These results support the hypothesis that lipid dysregulation plays a role in MS disease progression, implying the need for effective lipid management in MS. Omics analysis revealed immune cell and neural signaling pathway markers associated with MS, suggesting candidate biomarkers for further evaluation. This study highlights the value of large-scale biobank data in evaluating associations between lifestyle, metabolic comorbidities, and chronic diseases like MS, also enabling discovery of clinically relevant biomarkers.

Background

An accurate diagnosis of rare disorders is essential to design appropriate treatment and management strategies. However, establishing a diagnosis for many of these rare disorders is a complex, lengthy and expensive process, which starts with recognition of specific phenotypic features and may involve multiple tests followed by consultation with multiple medical specialists.

Methods

We sequenced an Indian cohort with suspected rare disorders, using whole exome panel. Genetic variations were identified using the Strand NGS software and interpretation was done by using in-house tool, FestiVAR (Fast estimation of variants for automated reporting). FestiVAR prioritizes the variant based on HPO terms assigned to a case based on the clinical indication. The genes and variants are ranked according to their HPO matches and the variant label as per ACMG (The American College of Medical Genetics and Genomics) guidelines, which take into account the predicted impact of the variant on the gene/protein function, mode of inheritance/zygosity, presence in the database (ClinVar, gnomad, dbSNP etc.) and literature. The short-listed are reviewed and relevant variant/s are selected for reporting in the StrandOmics platform.

Results

Conclusion

Need

Liquid biopsy (LB) assays offer the ability to detect and monitor non-small-cell lung cancer (NSCLC). LBs capture tumor heterogeneity and provide insights into tumor genomics without requiring tissue samples, making them invaluable for early detection, stratification, and monitoring of cancers. However, manual data analysis is time- and resource-intensive, with a high risk of error, emphasizing the need for automated bioinformatics pathways to process LB biopsy assays.

Objective

We aimed to automate the bioinformatics workflow and validate the analytical sensitivity and specificity of a combined DNA/RNA-based LB assay for detecting a broad spectrum of somatic alterations relevant to NSCLC, including SNVs, INDELs, CNVs, RNA gene fusions, and quantification of TMB and MSI, with scalability to larger user bases.

Methods and Results

Conclusion