Biology subjects

Hendrix, D. A.

Publications and source records attributed to Hendrix, D. A..

12 recordsLinked to original sources

bioRxiv2025

The Drosophila PDGF/VEGF signaling pathway regulates host immunometabolism in response to parasitoid infection

Mounting an immune response requires energy, but how that energy is reallocated at the organismal level remains poorly understood. In Drosophila melanogaster, infection by a parasitoid wasp triggers a systemic metabolic switch known as immunometabolism which is characterized by a shift in metabolic activity and the redistribution of resources away from organismal development and toward the production of a cellular immune response. We identify the PDGF/VEGF (PVF) signaling pathway as a key initiator of this immunometabolic switch. Genetic manipulation of PVF signaling alters infection outcomes, modulates systemic metabolite profiles, and reveals a direct trade-off between immune function and development. Our findings establish the Drosophila-parasitoid wasp system as a genetically tractable model for understanding the molecular basis of immunometabolism.

biochemistry↗

bioRxiv2024

Analysis of natural structures and chemical mapping data reveals local stability compensation in RNA

RNA molecules adopt complex structures that perform essential biological functions across all forms of life, making them promising candidates for therapeutic applications. However, our ability to design new RNA structures remains limited by an incomplete understanding of their folding principles. While global metrics such as the minimum free energy are widely used, they are at odds with naturally occurring structures and incompatible with established design rules. Here, we introduce local stability compensation (LSC), a principle that RNA folding is governed by the local balance between destabilizing loops and their stabilizing adjacent stems, challenging the focus on global energetic optimization. Analysis of over 100,000 RNA structures revealed that LSC signatures are particularly pronounced in bulges and their adjacent stems, with distinct patterns across different RNA families that align with their biological functions. To validate LSC experimentally, we systematically analyzed thousands of RNA variants using DMS chemical mapping. Our results demonstrate that stem folding, as measured by reactivity, correlates with LSC (R2 = 0.458 for hairpin loops) and that instabilities show no significant effect on folding for distal stems. These findings demonstrate that LSC can be a guiding principle for understanding RNA function and for the rational design of custom RNAs. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=74 SRC="FIGDIR/small/627843v2_ufig1.gif" ALT="Figure 1"> View larger version (21K): org.highwire.dtl.DTLVardef@a493bdorg.highwire.dtl.DTLVardef@df7a58org.highwire.dtl.DTLVardef@1d946a0org.highwire.dtl.DTLVardef@1abedcb_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2023

Unraveling Unbreakable Hairpins: Characterizing RNA secondary structures that are persistent after dinucleotide shuffling

The sequence of nucleotides that make up an RNA determines its structure, which determines its function. The RNA hairpin, also known as a stem-loop, is a ubiquitous and fundamental feature of RNA secondary structure. A common method of randomizing an RNA sequence is dinucleotide shuffling with the Altschul-Erickson algorithm, which preserves the dinucleotide content of the sequence. This algorithm generates randomized sequences by sampling Eulerian paths through the de Bruijn graph representation of the original sequence. We identified a subset of RNA hairpins in the bpRNA-1m meta-database that always form hairpins after repeated application of dinucleotide shuffling. We investigated these "unbreakable hairpins" and found several common properties. First, we found that unbreakable hairpins had on average similar folding energies compared to other hairpins of similar lengths, although they frequently contained ultra-stable hairpin loops. We found that they tend to be split by purines and pyrimidines on opposite strands of the stem. Furthermore, we found that this specific sequence feature restricts the number of distinct Eulerian paths through their de Bruijn graph representation, resulting in a small number of distinguishable dinucleotide-shuffled sequences. Beyond this algorithmic means of identification, these distinct sequences may have biological significance because we found that a significant percentage occur in a specific location of 16S ribosomal RNAs. Finally, we present a formula to calculate the number of possible unique dinucleotide shuffled sequences for an input RNA sequence, which has utility for the general application of the Altschul-Erickson algorithm.

bioinformatics↗

bioRxiv2023

RNA structure and multiple weak interactions balance the interplay between RNA binding and phase separation of SARS-CoV-2 nucleocapsid

The nucleocapsid (N) protein of SARS-CoV-2 binds viral RNA, condensing it inside the virion, and phase separating with RNA to form liquid-liquid condensates. There is little consensus on what differentiates sequence-independent N-RNA interactions in the virion or in liquid droplets from those with specific genomic RNA motifs necessary for viral function inside infected cells. To identify the RNA structures and the N domains responsible for specific interactions and phase separation, we use the first 1000nt of viral RNA and short RNA segments designed as models for single-stranded and paired RNA. Binding affinities estimated from fluorescence anisotropy of these RNAs to the two folded domains of N (the NTD and CTD) and comparison to full-length N demonstrate that the NTD binds preferentially to single-stranded RNA, and while it is the primary RNA binding site, it is not essential to phase separation. Nuclear magnetic resonance spectroscopy identifies two RNA binding sites on the NTD: a previously characterized site and an additional although weaker RNA-binding face that becomes prominent when binding to the primary site is weak, such as with dsRNA or a binding-impaired mutant. Phase separation assays of nucleocapsid domains with different RNA structures support a model where multiple weak interactions, such as with the CTD or the NTDs secondary face promote phase separation, while strong, specific interactions do not. These studies indicate that both strong and multivalent weak N-RNA interactions underlie the multifunctional abilities of N. SignificanceThe nucleocapsid protein of the SARS-CoV-2 coronavirus binds to viral RNA, both to protect and condense it inside the viral particle and to facilitate viral transcription inside infected host cells. Evidence suggests that variations in RNA structure impact how and where it binds to the nucleocapsid, but these differences are not well understood at a structural level. Using nuclear magnetic resonance spectroscopy, we examine the interactions between each folded domain of the nucleocapsid and different RNA structures. Binding affinities and NMR chemical shift profiles demonstrate that binding between the N-terminal domain and single stranded RNA is driven by strong interactions at a specific site, while multiple weak nonspecific interactions at newly discovered sites lead to phase separation and RNA condensation.

biophysics↗

bioRxiv2023

Improving deep models of protein-coding potential with a Fourier-transform architecture and machine translation task

Ribosomes are information-processing macromolecular machines that integrate complex sequence patterns in messenger RNA (mRNA) transcripts to synthesize proteins. Studies of the sequence features that distinguish mRNAs from long noncoding RNAs (lncRNAs) may yield insight into the information that directs and regulates translation. Computational methods for calculating protein-coding potential are important for distinguishing mRNAs from lncRNAs during genome annotation, but most machine learning methods for this task rely on previously known rules to define features. Sequence-to-sequence (seq2seq) models, particularly ones using transformer networks, have proven capable of learning complex grammatical relationships between words to perform natural language translation. Seeking to leverage these advancements in the biological domain, we present a seq2seq formulation for predicting protein-coding potential with deep neural networks and demonstrate that simultaneously learning translation from RNA to protein improves classification performance relative to a classification-only training objective. Inspired by classical signal processing methods for gene discovery and Fourier-based image-processing neural networks, we introduce LocalFilterNet (LFNet). LFNet is a network architecture with an inductive bias for modeling the three-nucleotide periodicity apparent in coding sequences. We incorporate LFNet within an encoder-decoder framework to test whether the translation task improves the classification of transcripts and the interpretation of their sequence features. We use the resulting model to compute nucleotide-resolution importance scores, revealing sequence patterns that could assist the cellular machinery in distinguishing mRNAs and lncRNAs. Finally, we develop a novel approach for estimating mutation effects from Integrated Gradients, a backpropagation-based feature attribution, and characterize the difficulty of efficient approximations in this setting.

bioinformatics↗

bioRxiv2022

Discovery and Visualization of Age-dependent Patterns in the Diurnal Transcriptome of Drosophila

Many critical life processes are regulated by input from 24-hour external light/dark cycles, such as metabolism, cellular homeostasis, and detoxification. The circadian clock, which helps coordinate the response to these diurnal light/dark cycles, remains rhythmic across lifespan; however, rhythmic transcript expression is altered during normal aging. To better understand how aging impacts diurnal expression, we present an improved Fourier-based method for detecting and visualizing rhythmicity that is based on the relative power of the 24-hour period compared to other periods (RP24). We apply RP24 to transcript-level expression profiles from the heads of young (5-day) and old (55-day) Drosophila melanogaster, and reveal novel age-dependent rhythmicity changes that may be masked at the gene level. We show that core clock transcripts phase advance during aging, while most rhythmic transcripts phase delay. Transcripts rhythmic only in young flies tend to peak before lights on, while transcripts only rhythmic in old peak after lights on. We show that several pathways including glutathione metabolism, gain or lose coordinated rhythmic expression with age providing insight into possible mechanisms of age-onset neurodegeneration. Remarkably, we find that many pathways show very robust coordinated rhythms across lifespan, highlighting their putative roles in promoting neural health. We investigate statistically enriched transcription factor binding site motifs that may be involved in these rhythmicity changes.

systems biology↗

bioRxiv2022

bpRNA-align: Improved RNA Secondary Structure Global Alignment for Comparing and Clustering RNA Structures

Ribonucleic acid (RNA) is a polymeric molecule that is fundamental to biological processes, with structure being more highly conserved than primary sequence and often key to its function. Advances in RNA structure characterization have resulted in an increase in the number of accurate secondary structures. The task of uncovering common RNA structural motifs with a collective function through structural comparison remains challenging and could be used to discover new RNA families. In this work, we present a novel secondary structure alignment and clustering method, bpRNA-align. bpRNA-align is a customized global structural alignment method, utilizing an inverted (gap extend costs more than gap open) context-specific affine gap penalty and a structural, feature-specific substitution matrix to provide similarity scores. We evaluate our similarity scores in comparison to other methods, using affinity propagation clustering, applied to a benchmarking data set of known structure types.

bioinformatics↗

bioRxiv2022

A chromosome-level assembly of the "Cascade" hop (Humulus lupulus) genome provides insights into Cannabaceae evolution

O_LIWe present a chromosome-level assembly of the Cascade hop (Humulus lupulus L. var. lupulus) genome. The hop genome is large (2.8 Gb) and complex, and early attempts at assembly resulted in fragmented assemblies. Recent advances have made assembly of the hop genome more tractable, transforming the extent of investigation that can occur. C_LIO_LIThe chromosome-level assembly of Cascade was developed by scaffolding the previously-reported Cascade assembly generated with PacBio long-read sequencing, and polishing with Illumina short-read DNA sequencing. We developed gene models and repeat annotations, and used a controlled bi-parental mapping population to identify significant sex-associated markers. We assess molecular evolution in gene sequences, gene family expansion and contraction, and time divergence using Bayesian inference. C_LIO_LIWe identified the putative sex chromosome in the female genome based on significant sex-associated markers from the bi-parental mapping population. While the estimate of repeat content (~64%) is similar to the hemp genome, syntenic blocks in hop contain a greater percentage of LTRs. Hop is enriched for disease resistance-associated genes in syntenic gene blocks and expanded gene families. C_LIO_LIThe Cascade chromosome-level assembly will inform cultivation strategies and serve to deepen our understanding of the hop genomic landscape, benefiting hop researchers and the Cannabaceae genomics community. C_LI

genomics↗

bioRxiv2021

Position-dependent Codon Usage Bias in the Human Transcriptome

All life depends on the reliable translation of RNA to protein according to complex interactions between translation machinery and RNA sequence features. While ribosomal occupancy and codon frequencies vary across coding regions, well-established metrics for computing coding potential of RNA do not capture such positional dependence. Here, we investigate positiondependent codon usage bias (PDCUB), which dynamically accounts for the position of proteincoding signals embedded within coding regions. We demonstrate the existence of PDCUB in the human transcriptome, and show that it can be used to predict translation-initiating codons with greater accuracy than other models. We further show that observed PDCUB is not accounted for by other common metrics, including position-dependent GC content, consensus sequences, and the presence of signal peptides in the translation product. More importantly, PDCUB defines a spectrum of translational efficiency supported by ribosomal occupancy and tRNA adaptation index (tAI). High PDCUB scores correspond to a tAI-defined translational ramp and low ribosomal occupancy, while low PDCUB scores exhibit a translational valley and the highest ribosomal occupancy. Finally, we examine the relationship between PDCUB intensity and functional enrichment. We find that transcripts with start codons showing the highest PDCUB are enriched for functions relating to neuropeptide signaling and nucleosome assembly, as well as development of musculoskeletal, cardiovascular, neurological, gastrointestinal, sensory, and other body systems. Furthermore, transcripts with high PDCUB are depleted for functions related to immune response and detection of chemical stimulus. These findings lay important groundwork for advances in our understanding of the regulation of translation, the calculation of coding potential, and the classification of RNA transcripts.

bioinformatics↗

bioRxiv2019

A phased, diploid assembly of the Cascade hop (Humulus lupulus) genome reveals patterns of selection and haplotype variation

Hop (Humulus lupulus L. var Lupulus) is a diploid, dioecious plant with a history of cultivation spanning more than one thousand years. Hop cones are valued for their use in brewing, and around the world, hop has been used in traditional medicine to treat a variety of ailments. Efforts to determine how biochemical pathways responsible for desirable traits are regulated have been challenged by the large, repetitive, and heterozygous genome of hop. We present the first report of a haplotype-phased assembly of a large plant genome. Our assembly and annotation of the Cascade cultivar genome is the most extensive to date. PacBio long-read sequences from hop were assembled with FALCON and phased with FALCON-Unzip. Using the diploid assembly to assess haplotype variation, we discovered genes under positive selection enriched for stress-response, growth, and flowering functions. Comparative analysis of haplotypes provides insight into large-scale structural variation and the selective pressures that have driven hop evolution. Previous studies estimated repeat content at around 60%. With improved resolution of long terminal retrotransposons (LTRs) due to long-read sequencing, we found that hop is nearly 78% repetitive. Our quantification of repeat content provides context for the size of the hop genome, and supports the hypothesis of whole genome duplication (WGD), rather than expansion due to LTRs. With our more complete assembly, we have identified a homolog of cannabidiolic acid synthase (CBDAS) that is expressed in multiple tissues. The approaches we developed to analyze a phased, diploid assembly serve to deepen our understanding of the genomic landscape of hop and may have broader applicability to the study of other large, complex genomes.

genomics↗

bioRxiv2018

bpRNA: Large-scale Automated Annotation and Analysis of RNA Secondary Structure

While RNA secondary structure prediction from sequence data has made remarkable progress, there is a need for improved strategies for annotating the features of RNA secondary structures. Here we present bpRNA, a novel annotation tool capable of parsing RNA structures, including complex pseudoknot-containing RNAs, to yield an objective, precise, compact, unambiguous, easily-interpretable description of all loops, stems, and pseudoknots, along with the positions, sequence, and flanking base pairs of each such structural feature. We also introduce several new informative representations of RNA structure types to improve structure visualization and interpretation. We have further used bpRNA to generate a web-accessible meta-database, \"bpRNA-1m\", of over 100,000 single-molecule, known secondary structures; this is both more fully and accurately annotated and over 20-times larger than existing databases. We use a subset of the database with highly similar ([≥]90% identical) sequences filtered out to report on statistical trends in sequence, flanking base pairs, and length. Both the bpRNA method and the bpRNA-1m database will be valuable resources both for specific analysis of individual RNA molecules and large-scale analyses such as are useful for updating RNA energy parameters for computational thermodynamic predictions, improving machine learning models for structure prediction, and for benchmarking structure-prediction algorithms.

bioinformatics↗

bioRxiv2017

A Deep Recurrent Neural Network Discovers Complex Biological Rules to Decipher RNA Protein-Coding Potential

The current deluge of newly identified RNA transcripts presents a singular opportunity for improved assessment of coding potential, a cornerstone of genome annotation, and for machine-driven discovery of biological knowledge. While traditional, feature-based methods for RNA classification are limited by current scientific knowledge, deep learning methods can independently discover complex biological rules in the data de novo. We trained a gated recurrent neural network (RNN) on human messenger RNA (mRNA) and long noncoding RNA (lncRNA) sequences. Our model, mRNA RNN (mRNN), surpasses state-of-the-art methods at predicting protein-coding potential. To understand what mRNN learned, we probed the network and uncovered several context-sensitive codons highly predictive of coding potential. Our results suggest that gated RNNs can learn complex and long-range patterns in full-length human transcripts, making them ideal for performing a wide range of difficult classification tasks and, most importantly, for harvesting new biological insights from the rising flood of sequencing data.

bioinformatics↗