Biology subjects

Emrich, S.

Publications and source records attributed to Emrich, S..

9 recordsLinked to original sources

bioRxiv2025

Functional genomic signatures predict microbial culturability across the tree of life

Most microbial taxa on Earth remain uncultivated, limiting our ability to study their physiology, ecology, and roles in environmental processes. Although metagenome-assembled genomes (MAGs) have expanded access to uncultured phylogenetic diversity, the functional basis for culturability remains poorly understood. Here, we analyze the 52,515 MAGs from the Genomes from Earths Microbiomes (GEM) catalog to test two hypotheses: 1) genomes from uncultured microbes encode more functionally novel genes than those from cultured taxa, and 2) specific genomic features are systematically associated with culturability across phyla. To assess functional novelty, we aligned predicted proteins to SwissProt and measured sequence dissimilarity to the nearest curated homolog. We find that uncultured MAGs, particularly among Archaea, harbor substantially more divergent proteins. To identify genomic traits predictive of culturability, we combined pathway-level enrichment with LASSO regression and permutation-based feature importance. Cultured MAGs were consistently enriched in Clusters of Orthologous Groups (COG) pathways related to vitamin and cofactor biosynthesis (e.g., thiamine, folate, B12), energy metabolism (e.g., TCA cycle), and CRISPR-Cas systems--functions often depleted in uncultured counterparts. LASSO models identified a subset of these pathways as strong predictors of cultured status even in poorly sampled phyla, suggesting conserved genomic signatures of culturability. In contrast, pathways such as purine biosynthesis and NADH dehydrogenase were associated with uncultured lineages, highlighting potential barriers to cultivation. These results 1) demonstrate the great functional novelty of uncultured microbes, potentially offering unprecedented opportunities for discoveries of novel function, and 2) identify metabolic traits associated with culturability to inform future cultivation strategies. ImportanceThe vast majority of microbes are uncultured, which means they have never been characterized under laboratory conditions. We showed that genomic sequences of uncultured microbes have less similarity to characterized proteins compared to cultured microbes, revealing that there may be fundamental biological reasons why they are not cultured. We also showed that certain metabolic pathways, such as those related to vitamin and cofactor biosynthesis, can predict the ability of microbes to grow under laboratory conditions, and these pathways are abundant in highly cultured phyla, indicating how metabolic pathways can influence cultivation strategies.

microbiology↗

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↗

bioRxiv2019

Surprising variation in the outcome of two malaria genetic crosses using humanized mice: implications for genetic mapping and malaria biology

Genetic crosses are most powerful for linkage analysis when progeny numbers are high, when parental alleles segregate evenly and, for hermaphroditic organisms, when numbers of inbred progeny are minimized. We previously developed a novel genetic crossing platform for the human malaria parasite Plasmodium falciparum, an obligately sexual, hermaphroditic protozoan, using mice carrying human hepatocytes (the human liver-chimeric FRG NOD huHep mouse) as the vertebrate host. Here we examine the statistical power of two different genetic crosses - (1) between a laboratory parasite (NF54) of African origin and a patient-derived Asian parasite, and (2) between two sympatric patient-derived Asian parasites. We generated >140 unique recombinant clones over a 12-month period from the four parental genotypes, doubling the number of unique recombinant progeny generated in the previous 30 years. Both crosses show bi-parental inheritance of plastid markers amongst recombinant progeny, in contrast to previous crosses (conducted using chimpanzee hosts) which carried single dominant plastid genotypes. Both crosses show distinctive segregation patterns. The allopatric African/Asian cross has minimal levels of inbreeding (2% of clonal progeny are inbred) and extreme skews in marker segregation, while in the sympatric Asian cross, inbred progeny predominate (66% of clonal progeny are inbred) and parental alleles segregate evenly. Using simulations, we demonstrate that these progeny arrays (particularly the sympatric Asian cross) have excellent power to map large-effect mutations to a 31 kb interval and can capture complex, epistatic interactions that were far beyond the capacity of previous malaria crosses to detect. The extreme segregation distortion in the allopatric African/Asian cross erodes power to detect linkage in several genome regions, but the repeatable distortions observed offer promising alternative approaches to identifying genes underlying traits of interest. These crosses show surprising variation in marker segregation, nevertheless, the increased progeny numbers improve our ability to rapidly map biomedically important parasite traits. Author SummaryUnderstanding how genome mutations contribute to newly emerging drug resistance in parasites like Plasmodium falciparum is important to monitor the spread of drug resistance. This scenario has been playing out in Southeast Asia with the emergence and spread of artemisinin resistance. Here we show that new P. falciparum genetic crosses, using mice carrying human liver cells and infused with human red blood cells (the human liver-chimeric FRG NOD huHep/huRBC mouse), provide an important new tool for understanding complex interactions underlying drug resistance phenotypes. We report two new genetic maps with 84 and 60 unique recombinant progeny, which doubles the number of progeny available from 4 previous P. falciparum genetic crosses. Through extensive simulations we show that with 84 progeny we can find association for a gene that controls only 20% of the variation in a phenotype. We also show that a cross generated from Southeast Asian parasites collected from the same geographic region have unique characteristics not previously observed in P. falciparum genetic crosses. This Southeast Asian cross exhibits even segregation across the genome, unbiased inheritance of mitochondria and apicoplast and higher levels of inbreeding than previously observed.

genetics↗

bioRxiv2019

Phlebotomus papatasi sand fly salivary protein diversity and immune response potential in Egypt and Jordan populations

Phlebotomus papatasi sand flies inject their hosts with a myriad of pharmacologically active salivary proteins to assist with blood feeding and to modulate host defenses. These salivary proteins have been studied for their role in cutaneous leishmaniasis disease outcome with different salivary proteins attenuating or exacerbating lesion size. Studies have shown that while co-administered sand fly saliva exacerbates Leishmania major infections in naive mice, animals pre-exposed to saliva are protected, with the infection attenuated via a delayed-type hypersensitivity immune reaction. These studies highlight the potential of the salivary components to be used as a vaccine. One protein in particular, P. papatasi salivary protein 15 (PpSP15) has been intensively studied because of its ability to protect mice against Le. major challenge. The number of antigenic molecules included in vaccines is restricted thus emphasizing the role of population genetics to identify molecules, like PpSP15, that are functionally significant, conserved across populations and do not experience selection. Three distinct ecotope study sites, one in Egypt (Aswan) and two in Jordan (Swaimeh and Malka), were chosen based on their elevation, rainfall, vegetation, differing reservoir species, and the presence or absence of Le. major. The objective of this work was to analyze the genetic variability of nine of the most abundantly expressed salivary proteins including PpSP12, PpSP14, PpSP28, PpSP29, PpSP30, PpSP32, PpSP36, PpSP42, and PpSP44 and to predict their ability to elicit an immune response. Two proteins, PpSP12 and PpSP14, demonstrated low genetic variability across the three sand fly populations represented in this study, with multiple predicted MHCII epitope binding sites, identified by alleles present in the human populations from the study sites. The other seven salivary proteins revealed greater allelic variation across the same sand fly populations indicating that their use as vaccine targets may prove to be challenging.

genetics↗

bioRxiv2019

Genetic mapping of fitness determinants across the malaria parasite Plasmodium falciparum life cycle

Malaria is transmitted through female Anopheline mosquitoes where gamete fusion and meiosis occurs, and humans where parasites proliferate asexually. We describe a powerful approach to identify the genetic determinants of parasite fitness across both invertebrate and vertebrate life-cycle stages in human malaria parasite Plasmodium falciparum using bulk segregant analysis (BSA). We combined experimental genetic crosses using humanized mice, with selective whole genome amplification and BSA at multiple developmental stages in both mosquito and vertebrate host to examine parasite competition and identify genomic regions under selection. We generated crosses between artemisinin resistant (ART-R, kelch13-C580Y) and ART-sensitive (ART-S, kelch13-WT) parasite clones recently isolated from Southeast Asian patients. We then quantified genome-wide changes in allele frequency in the parasite progeny population from infected midgut and salivary glands of Anopheles stephensi mosquitoes, infected livers, emerging merozoites and aliquots of in vitro cultured progeny parasites at intervals over 30 days. Three striking results emerge: we observed (i) a strong skew (>80%) towards alleles from the ART-R parent in the mosquito stage, that dropped to [~]50% in the blood stage as selfed ART-R parasites were selected against; (ii) highly repeatable skews in allele frequencies across the genome in blood stage parasites; (iii) particularly strong selection (selection coefficient (s) [≤] 0.18/asexual cycle) against alleles from the ART-R parent at loci on chromosome 12 containing MRP2 and chromosome 14 containing ARPS10. This approach robustly identifies selected loci and has strong potential for identifying parasite genes that interact with the mosquito vector or compensatory loci involved in drug resistance.

evolutionary biology↗

bioRxiv2018

The Effects of Normalization, Transformation, and Rarefaction on Clustering of OTU Abundance

Introduction Introduction Methods Discussion Data and Code Availability References Metagenomic clustering presents a unique opportunity to associate and understand communities. Working with Operational Taxonomic Units (OTUs), however, often requires a strategy for handling OTUs that may be over or under represented in a given sample, which is thought of as \"erroneous\". PCR amplification, for example, is known to sometimes non-linearly over-represent more common species Gonzalez et al. 2012. Strategies dealing with this bias include Normalization, Rarefaction, and Log Transformation. Here, we examine how methods to handle potential outlier observations affect de novo estimation of groups using both clustering and matrix factorization methods.\n\nWhile log transformations may affect parametric tests ...

ecology↗

bioRxiv2017

Methods in Description and Validation of Local Metagenetic Microbial Communities

1. We propose MinHash (as implemented by MASH) and NMF as alternative methods to estimate similarity between metagenetic samples. We further describe these results with cluster analysis and correlations with independent ecological metadata.\n\n2. Using sample to sample similarities based on MinHash similarities we use hierarchal clustering to generate clusters, simultaneously we generate groups based on NMF, and we compare groups generated from the MinHash similarity derived clusters and from NMF to those determined by the environment, looking to Silhouette Width for an assessment of the quality of the cluster.\n\n3. We analyze existing data from the Atacama Desert to determine the relationship between ecological factors and group membership, and using the generated groups from MASH and NMF we run an ANOVA to uncover links between metagenetic samples and known environmental variables such as pH and Soil Conductivity.

bioinformatics↗

bioRxiv2017

HECIL: A Hybrid Error Correction Algorithm for Long Reads with Iterative Learning

Second-generation sequencing techniques generate short reads that can result in fragmented genome assemblies. Third-generation sequencing platforms mitigate this limitation by producing longer reads that span across complex and repetitive regions. Currently, the usefulness of such long reads is limited, however, because of high sequencing error rates. To exploit the full potential of these longer reads, it is imperative to correct the underlying errors. We propose HECIL--Hybrid Error Correction with Iterative Learning--a hybrid error correction framework that determines a correction policy for erroneous long reads, based on optimal combinations of decision weights obtained from short read alignments. We demonstrate that HECIL outperforms state-of-the-art error correction algorithms for an overwhelming majority of evaluation metrics on diverse real data sets including E. coli, S. cerevisiae, and the malaria vector mosquito A. funestus. We further improve the performance of HECIL by introducing an iterative learning paradigm that improves the correction policy at each iteration by incorporating knowledge gathered from previous iterations via confidence metrics assigned to prior corrections.\n\nAvailability and Implementationhttps://github.com/NDBL/HECIL\n\nContactsemrich@nd.edu

bioinformatics↗