The mRNA technology represents a paradigm shift for the biopharmaceutical industry because in vivo delivery of antibody-based therapeutics can expand their clinical applications. Moreover, the mRNA delivery shifts the burden of assuring CMC (chemistry, manufacturing, and control) to the mRNA molecule. However, in vivo developability issues such as intracellular expression, proper protein folding, secretion into extracellular milieu, degradation in physiological conditions, poly-specificity, immunogenicity, safety, efficacy, and pharmacology can still pose significant barriers to the success of these products. Here, we have leveraged insights from the sequence-structural properties of the variable regions (Fvs) from the marketed biotherapeutics to build a medicine-likeness profile for the mRNA-delivered biotherapeutics. Our dataset consists of 122 unique Fvs from 117 marketed biotherapeutics (as of February 2024) and was systematically evaluated using 9 nonredundant sequence and structural parameters to capture a holistic view of developability attributes relevant to in vivo performance of mRNA-delivered antibody therapeutics. To ensure robustness, 25 antibodies that failed to reach approval due to developability issues, as well as those withdrawn from the market post approval, were included as controls. Our findings highlight a complementary relationship between sequence- and structure-based characteristics, leading us to develop a combined scoring system. This integrated "medicine-likeness" profile enables early-stage in silico assessments of mRNA-delivered therapeutic antibody candidates, offering a valuable tool for researchers to predict and optimize their in vivo developability.

SUMMARY: Phenome-wide association studies (PheWASs) are known to be a powerful tool in discovery and replication of genetic association studies. To reduce the computational burden of PheWAS in the large cohorts, such as the UK Biobank, the SAIGE method has been proposed to control for case-control imbalance and sample relatedness in a tractable manner. However, SAIGE is still computationally intensive when deployed in analyzing the associations of thousands of ICD10-coded phenotypes with whole-genome imputed genotype data. Here, we present a new high-performance statistical R package (SAIGEgds) for large-scale PheWAS using generalized linear mixed models. The package implements the SAIGE method in optimized C++ codes, taking advantage of sparse genotype dosages and integrating the efficient genomic data structure file format. Benchmarks using the UK Biobank White British genotype data (N ≈ 430 K) with coronary heart disease and simulated cases show that the implementation in SAIGEgds is 5-6 times faster than the SAIGE R package. When used in conjunction with high-performance computing clusters, SAIGEgds provides an efficient analysis pipeline for biobank-scale PheWAS. AVAILABILITY AND IMPLEMENTATION: https://bioconductor.org/packages/SAIGEgds; vignettes included. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Endometriosis is a complex gynecological disorder. The diagnostic process of endometriosis involves an invasive procedure thus delaying the diagnosis for about 10 years on average. Both DNA-methylation data and RNA-seq data has the potential to uncover molecular mechanisms of diseases. The objective of this project is to identify diagnostic molecular mechanisms of endometriosis using a multi-omics approach that will lead to noninvasive diagnostic procedure.

Heterogeneous genetic and epigenetic alterations are commonly found in human non-Hodgkin's lymphomas (NHL). One such epigenetic alteration is aberrant methylation of gene promoter-related CpG islands, where hypermethylation frequently results in transcriptional inactivation of target genes, while a decrease or loss of promoter methylation (hypomethylation) is frequently associated with transcriptional activation. Discovering genes with these relationships in NHL or other types of cancers could lead to a better understanding of the pathobiology of these diseases. The simultaneous analysis of promoter methylation using Differential Methylation Hybridization (DMH) and its associated gene expression using Expressed CpG Island Sequence Tag (ECIST) microarrays generates a large volume of methylation-expression relational data. To analyze this data, we propose a set of algorithms based on fuzzy sets theory, in particular Possibilistic c-Means (PCM) and cluster fuzzy density. For each gene, these algorithms calculate measures of confidence of various methylation-expression relationships in each NHL subclass. Thus, these tools can be used as a means of high volume data exploration to better guide biological confirmation using independent molecular biology methods.