Biology subjects

Lahens, N. F.

Publications and source records attributed to Lahens, N. F..

7 recordsLinked to original sources

bioRxiv2025

Sources of non-uniform coverage in short-read RNA-Seq data

The origin of several normal cellular functions and related abnormalities can be traced back to RNA splicing. As such, RNA splicing is currently the focus of a vast array of studies. To quantify the transcriptome, short-read RNA-Seq remains the standard assay. The primary technical artifact of RNA-Seq library prep, which severely interferes with analysis, is extreme non-uniformity in coverage across transcripts. This non-uniformity is present in both bulk and single-cell RNA-Seq and is observed even when the sample contains only full-length transcripts. This issue dramatically affects the accuracy of isoform-level quantification of multi-isoform genes. Understanding the sources of this non-uniformity is critical to developing improved protocols and analysis methods. Here, we explore eight potential sources of non-uniformity. We demonstrate that it cannot be explained by one factor alone. We performed targeted experiments to investigate the effect of fragment length, PCR ramp rate, and ribosomal depletion. We assessed existing data sets with varying sample quality, PCR cycle number, reverse transcriptase, and technical or biological replicates. We found evidence that interference of reverse transcription by secondary structure is unlikely to be the major contributing factor, that rRNA pull-down methods do not cause non-uniformity, that PCR ramp rate does not substantially impact non-uniformity, and that shorter fragments do not reduce non-uniformity. All these findings contradict prior publications or recommendations.

bioinformatics↗

bioRxiv2023

Evidence for a role of human blood-borne factors in mediating age-associated changes in molecular circadian rhythms

Aging is associated with a number of physiologic changes including perturbed circadian rhythms; however, mechanisms by which rhythms are altered remain unknown. To test the idea that circulating factors mediate age-dependent changes in peripheral rhythms, we compared the ability of human serum from young and old individuals to synchronize circadian rhythms in culture. We collected blood from apparently healthy young (age 25-30) and old (age 70-76) individuals at 14:00 and used the serum to synchronize cultured fibroblasts. We found that young and old sera are equally competent at initiating robust [~]24h oscillations of a luciferase reporter driven by clock gene promoter. However, cyclic gene expression is affected, such that young and old sera promote cycling of different sets of genes. Genes that lose rhythmicity with old serum entrainment are associated with oxidative phosphorylation and Alzheimers Disease as identified by STRING and IPA analyses. Conversely, the expression of cycling genes associated with cholesterol biosynthesis increased in the cells entrained with old serum. Genes involved in the cell cycle and transcription/translation remain rhythmic in both conditions. We did not observe a global difference in the distribution of phase between groups, but found that peak expression of several clock-controlled genes (PER3, NR1D1, NR1D2, CRY1, CRY2, and TEF) lagged in the cells synchronized ex vivo with old serum. Taken together, these findings demonstrate that age-dependent blood-borne factors affect circadian rhythms in peripheral cells and have the potential to impact health and disease via maintaining or disrupting rhythms respectively.

molecular biology↗

bioRxiv2023

BEERS2: RNA-Seq simulation through high fidelity in silico modeling

Simulation of RNA-seq reads is critical in the assessment, comparison, benchmarking, and development of bioinformatics tools. Yet the field of RNA-seq simulators has progressed little in the last decade. To address this need we have developed BEERS2, which combines a flexible and highly configurable design with detailed simulation of the entire library preparation and sequencing pipeline. BEERS2 takes input transcripts (typically fully-length mRNA transcripts with polyA tails) from either customizable input or from CAMPAREE simulated RNA samples. It produces realistic reads of these transcripts as FASTQ, SAM, or BAM formats with the SAM or BAM formats containing the true alignment to the reference genome. It also produces true transcript-level quantification values. BEERS2 combines a flexible and highly configurable design with detailed simulation of the entire library preparation and sequencing pipeline and is designed to include the effects of polyA selection and RiboZero for ribosomal depletion, hexamer priming sequence biases, GC-content biases in PCR amplification, barcode read errors, and errors during PCR amplification. These characteristics combine to make BEERS2 the most complete simulation of RNA-seq to date. Finally, we demonstrate the use of BEERS2 by measuring the effect of several settings on the popular Salmon pseudoalignment algorithm.

bioinformatics↗

bioRxiv2021

RNA splicing analysis using heterogeneous and large RNA-seq1datasets

The ubiquity of RNA-seq has led to many methods that use RNA-seq data to analyze variations in RNA splicing. However, available methods are not well suited for handling heterogeneous and large datasets. Such datasets scale to thousands of samples across dozens of experimental conditions, exhibit increased variability compared to biological replicates, and involve thousands of unannotated splice variants resulting in increased transcriptome complexity. We describe here a suite of algorithms and tools implemented in the MAJIQ v2 package to address challenges in detection, quantification, and visualization of splicing variations from such datasets. Using both large scale synthetic data and GTEx v8 as benchmark datasets, we demonstrate that the approaches in MAJIQ v2 outperform existing methods. We then apply MAJIQ v2 package to analyze differential splicing across 2,335 samples from 13 brain subregions, demonstrating its ability to offer new insights into brain subregion-specific splicing regulation.

bioinformatics↗

bioRxiv2019

Comparative evaluation of full-length isoform quantification from RNA-Seq

Full-length isoform quantification from RNA-Seq is a key goal in transcriptomics analyses and has been an area of active development since the beginning. The fundamental difficulty stems from the fact that RNA transcripts are long, while RNA-Seq reads are short. Here we use simulated benchmarking data that reflects many properties of real data, including polymorphisms, intron signal and non-uniform coverage, allowing for systematic comparative analyses of isoform quantification accuracy and its impact on differential expression analysis. Genome, transcriptome and pseudo alignment-based methods are included; and a simple approach is included as a baseline control. Salmon, kallisto, RSEM, and Cufflinks exhibit the highest accuracy on idealized data, while on more realistic data they do not perform dramatically better than the simple approach. We determine the structural parameters with the greatest impact on quantification accuracy to be length and sequence compression complexity and not so much the number of isoforms. The effect of incomplete annotation on performance is also investigated. Overall, the tested methods show sufficient divergence from the truth to suggest that full-length isoform quantification and isoform level DE should still be employed selectively.

bioinformatics↗

bioRxiv2018

Variability in the Analgesic Response to Ibuprofen Following Third-Molar Extraction is Associated with Differences in Activation of the Cyclooxygenase Pathway

The analgesic efficacy of non-steroidal anti-inflammatory drugs (NSAIDs) has long been recognized to be limited by substantial interindividual variability in pain relief, but the underlying mechanisms are not well understood. We performed pain phenotyping, functional neuroimaging, pharmacokinetic/pharmacodynamic assessments, inflammation biomarkers, and gene expression profiling in healthy subjects who underwent surgical extraction of bony impacted third molars, in order to characterize factors associated with heterogeneity in response to ibuprofen.\n\nSubjects were treated with rapid-acting ibuprofen (400 mg; N=19) or placebo (N=10) in a randomized, double-blind design. Compared to placebo, ibuprofen-treated subjects exhibited greater reduction in pain scores, alterations in regional cerebral blood flow in brain regions associated with pain processing, and inhibition of ex vivo cyclooxygenase activity and urinary prostaglandin metabolite excretion as indices of biochemical drug action (p<0.05). As expected, ibuprofen-treated subjects could be stratified into partial responders (N=9, required rescue medication within the dosing interval) and complete responders (N=10, no rescue medication). This was also reflected by differences in pain scores (p<0.01) as early as 30 minutes following drug administration (p<0.05). Variability in analgesic efficacy was not associated with demographic or clinical characteristics, ibuprofen pharmacokinetics, metabolizing enzyme genotype, or the degree of cyclooxygenase inhibition by ibuprofen. However, complete responders had higher concentrations of inflammatory biomarkers in urine and serum, than partial responders. Specifically, a stable urinary prostaglandin E2 metabolite, serum TNF and IL-8 were higher in patients who did not require rescue medication compared those who did (p < 0.05). RNAseq gene expression analysis in PBMCs collected after surgery and ibuprofen administration showed enrichment of inflammation related pathways among genes differentially expressed (q < 0.2) between complete and partial responders\n\nThese findings suggest that patients who receive substantial pain relief from ibuprofen have a more pronounced activation of the prostanoid biosynthetic pathway and regulation of the inflammatory pain phenotype differs from those patients who are insufficiently treated with ibuprofen alone and may require an opioid or other therapeutic intervention.

genomics↗

bioRxiv2017

Transcriptional profiling reveals extraordinary diversity among skeletal muscle tissues

Skeletal muscle comprises a family of diverse tissues with highly specialized morphology, function, and metabolism. Many acquired diseases - including HIV, COPD, cancer cachexia, critical illness myopathy, and sepsis - affect specific muscles while sparing others. Even monogenic muscular dystrophies tend to selectively affect certain muscle groups, despite their causative genetic mutations being present in all tissues. These observations suggest that factors intrinsic to muscle tissues influence their susceptibility to various disease mechanisms. Nevertheless, most studies have not addressed transcriptional diversity among skeletal muscles. Here we use RNA sequencing (RNA-seq) to profile global mRNA expression in a wide array of skeletal, smooth, and cardiac muscle tissues from mice and rats. Our data set, MuscleDB, reveals extensive transcriptional diversity, with greater than 50% of transcripts differentially expressed among skeletal muscle tissues. This diversity is only partly explained by fiber type composition and developmental history, suggesting that specialized transcriptional profiles establish the functional identity of muscle tissues. We find conservation in the transcriptional profiles across species as well as between males and females, indicating that these data may be useful in predicting gene expression in related species. Notably, thousands of differentially expressed genes in skeletal muscle are associated with human disease, and hundreds of these genes encode targets of drugs on the market today. We detect mRNA expression of hundreds of putative myokines that may underlie the endocrine functions of skeletal muscle. In addition to demonstrating the intrinsic diversity of skeletal muscles, these data provide a resource for generating testable hypotheses regarding the mechanisms that establish differential disease susceptibility in muscle.\n\nSignificance StatementSkeletal muscles are a diverse family of tissues with a common contractile function but divergent morphology, development, and metabolism. One need only reflect on the different functions of limb muscles and the diaphragm to realize the highly specialized nature of these tissues. Nevertheless, every study of global gene expression has analyzed at most one representative skeletal muscle. Here we measure gene expression from 11 different skeletal muscles in mice and rats. We show that there is no such thing as a representative skeletal muscle, as gene expression profiles vary widely among the tissues analyzed. These data are an important resource for pharmacologists, tissue engineers, and investigators studying the mechanisms of cellular specialization.

systems biology↗