Biology subjects

Milenkovic, T.

Publications and source records attributed to Milenkovic, T..

3 recordsLinked to original sources

bioRxiv2023

Enhancing gene co-expression network inference for the malaria parasite Plasmodium falciparum

BackgroundMalaria results in more than 550,000 deaths each year due to drug resistance in the most lethal Plasmodium (P.) species P. falciparum. A full P. falciparum genome was published in 2002, yet 44.6% of its genes have unknown functions. Improving functional annotation of genes is important for identifying drug targets and understanding the evolution of drug resistance. ResultsGenes function by interacting with one another. So, analyzing gene co-expression networks can enhance functional annotations and prioritize genes for wet lab validation. Earlier efforts to build gene co-expression networks in P. falciparum have been limited to a single network inference method or gaining biological understanding for only a single gene and its interacting partners. Here, we explore multiple inference methods and aim to systematically predict functional annotations for all P. falciparum genes. We evaluate each inferred network based on how well it predicts existing gene-Gene Ontology (GO) term annotations using network clustering and leave-one-out cross-validation. We assess overlaps of the different networks edges (gene co-expression relationships) as well as predicted functional knowledge. The networks edges are overall complementary: 47%-85% of all edges are unique to each network. In terms of accuracy of predicting gene functional annotations, all networks yield relatively high precision (as high as 87% for the network inferred using mutual information), but the highest recall reached is below 15%. All networks having low recall means that none of them capture a large amount of all existing gene-GO term annotations. In fact, their annotation predictions are highly complementary, with the largest pairwise overlap of only 27%. We provide ranked lists of inferred gene-gene interactions and predicted gene-GO term annotations for future use and wet lab validation by the malaria community. ConclusionsThe different networks seem to capture different aspects of the P. falciparum biology in terms of both inferred interactions and predicted gene functional annotations. Thus, relying on a single network inference method should be avoided when possible. Supplementary dataAttached. Availability and implementationAll data and code are available at https://nd.edu/~cone/pfalGCEN/. Contacttmilenko@nd.edu

systems biology↗

bioRxiv2022

Synonymous codon substitutions regulate transcription and translation of an upstream gene

Synonymous codons were originally viewed as interchangeable with no phenotypic consequences. However, over the years a substantial body of evidence has demonstrated that some synonymous substitutions can perturb a variety of gene expression and protein homeostasis mechanisms, including translational efficiency, translational fidelity and co-translational folding of the encoded protein. To date, synonymous codon-derived perturbations have largely focused on effects within a single gene. Here we show that synonymous codon substitutions made far within an E. coli plasmid-encoded protein coding sequence frequently led to significant upregulation of a neighboring, upstream gene. Notably, in four out of nine synonymously recoded sequences, significant upregulation of the upstream gene arose due to cryptic transcription of the anti-sense strand. Surprisingly, cryptic transcription of the upstream gene readily bypassed its native transcriptional repression mechanism. Even more surprisingly, translation of this upstream gene correlates closely with the subset of its mRNA transcribed from the cryptic internal promoter, rather than its total mRNA level. These results suggest that synonymous codons in bacteria may be under selection to both preserve the amino acid sequence of the encoded gene while also avoiding internal sequence elements that significantly perturb transcriptional and translational regulation of neighboring genes.

molecular biology↗

bioRxiv2020

Analysis of computational codon usage models and their association with translationally slow codons

Improved computational modeling of protein translation rates, including better prediction of where translational slowdowns along an mRNA sequence may occur, is critical for understanding co-translational folding. Because codons within a synonymous codon group are translated at different rates, many computational translation models rely on analyzing synonymous codons. Some models rely on genome-wide codon usage bias (CUB), believing that globally rare and common codons are the most informative of slow and fast translation, respectively. Others use the CUB observed only in highly expressed genes, which should be under selective pressure to be translated efficiently (and whose CUB may therefore be more indicative of translation rates). No prior work has analyzed these models for their ability to predict translational slowdowns. Here, we evaluate five models for their association with slowly translated positions as denoted by two independent ribosome footprint (RFP) count experiments from S. cerevisiae, because RFP data is often considered as a "ground truth" for translation rates across mRNA sequences. We show that all five considered models strongly associate with the RFP data and therefore have potential for estimating translational slowdowns. However, we also show that there is a weak correlation between RFP counts for the same genes originating from independent experiments, even when their experimental conditions are similar. This raises concerns about the efficacy of using current RFP experimental data for estimating translation rates and highlights a potential advantage of using computational models to understand translation rates instead.

bioinformatics↗