Biology subjects

Lane, L.

Publications and source records attributed to Lane, L..

7 recordsLinked to original sources

bioRxiv2025

Cell-death induced immune response and coagulopathy promote cachexia in Drosophila

Tumors can exert a far-reaching influence on the body, triggering systemic responses that contribute to debilitating conditions like cancer cachexia. To characterize the mechanisms underlying tumor-host interactions, we utilized a BioID-based proximity labeling method to identify proteins secreted by Ykiact adult Drosophila gut tumors into the bloodstream/hemolymph. Among the major proteins identified are coagulation and immune-responsive factors that contribute to the systemic wasting phenotypes associated with Ykiact tumors. The effect of innate immunity factors is mediated by NF{kappa}B transcription factors Relish, dorsal, and Dif, which in turn upregulate the expression of the cachectic factors Pvf1, Impl2, and Upd3. In addition, Ykiact tumors secrete Eiger, a TNF-alpha homolog, which activates the JNK signaling pathway in neighboring non-tumor cells, leading to cell death. The release of damage-associated molecular patterns (DAMPs) from these dying cells presumably amplifies the inflammatory response, exacerbating systemic wasting. Targeting the inflammatory response, the JNK pathway, or the production of cachectic factors could potentially alleviate the debilitating effects of cancer cachexia.

genetics↗

bioRxiv2023

DeepGO-SE: Protein function prediction as Approximate Semantic Entailment

The Gene Ontology (GO) is one of the most successful ontologies in the biological domain. GO is a formal theory with over 100,000 axioms that describe the molecular functions, biological processes, and cellular locations of proteins in three sub-ontologies. Many methods have been developed to automatically predict protein functions. However, only few of them use the background knowledge provided in the axioms of GO for knowledge-enhanced machine learning, or adjust and evaluate the model for the differences between the sub-ontologies. We have developed DeepGO-SE, a novel method which predicts GO functions from protein sequences using a pretrained large language model combined with a neuro-symbolic model that exploits GO axioms and performs protein function prediction as a form of approximate semantic entailment. We specifically evaluate DeepGO-SE on proteins that have no significant similarity with training proteins and demonstrate that DeepGO-SE can improve function prediction for those proteins.

bioinformatics↗

bioRxiv2022

Time-Dependent Increase in Susceptibility and Severity of Secondary Bacterial Infection during SARS-CoV-2 Infection

Secondary bacterial infections can exacerbate SARS-CoV-2 infection, but their prevalence and impact remain poorly understood. Here, we established that a mild to moderate SARS-CoV-2 infection increased the risk of pneumococcal coinfection in a time-dependent, but sexindependent, manner in the transgenic K18-hACE mouse model of COVID-19. Bacterial coinfection was not established at 3 d post-virus, but increased lethality was observed when the bacteria was initiated at 5 or 7 d post-virus infection (pvi). Bacterial outgrowth was accompanied by neutrophilia in the groups coinfected at 7 d pvi and reductions in B cells, T cells, IL-6, IL-15, IL-18, and LIF were present in groups coinfected at 5 d pvi. However, viral burden, lung pathology, cytokines, chemokines, and immune cell activation were largely unchanged after bacterial coinfection. Examining surviving animals more than a week after infection resolution suggested that immune cell activation remained high and was exacerbated in the lungs of coinfected animals compared with SARS-CoV-2 infection alone. These data suggest that SARS-CoV-2 increases susceptibility and pathogenicity to bacterial coinfection, and further studies are needed to understand and combat disease associated with bacterial pneumonia in COVID-19 patients.

microbiology↗

bioRxiv2020

BRAWNIN: A sORF-encoded Peptide Essential for Vertebrate Mitochondrial Complex III Assembly

The emergence of small open reading frame (sORF)-encoded peptides (SEPs) is rapidly expanding the known proteome at the lower end of the size distribution1,2. Here, we show that the mitochondria proteome is enriched for proteins smaller than 100 a.a. (defined as SEPs). Using a mitochondrial prediction and validation pipeline for small open-reading-frame (sORF)-encoded peptides (SEPs), we report the discovery of 16 endogenous mitochondrial SEPs (mito-SEPs) associated with oxidative phosphorylation (OXPHOS). Through functional prediction, proteomics, metabolomics and metabolic flux modeling, we demonstrate that BRAWNIN (BR), a 71 amino acid peptide encoded by the C12orf73 gene, is essential for respiratory chain complex III (CIII) assembly. In human cells, BR is induced by the energy-sensing AMPK pathway, and its depletion impairs mitochondrial ATP production. In vivo, BR is enriched in muscle tissues and its maternal zygotic deletion in zebrafish causes complete CIII loss, resulting in severe growth retardation, lactic acidosis and early death. Our findings demonstrate that BR is essential for oxidative phosphorylation across vertebrate species. We propose that mito-SEPs are an untapped resource for essential regulators of oxidative metabolism.

cell biology↗

bioRxiv2019

Human Proteome Project Mass Spectrometry Data Interpretation Guidelines 3.0

The Human Proteome Organizations (HUPO) Human Proteome Project (HPP) developed Mass Spectrometry (MS) Data Interpretation Guidelines that have been applied since 2016. These guidelines have helped ensure that the emerging draft of the complete human proteome is highly accurate and with low numbers of false-positive protein identifications. Here, we describe an update to these guidelines based on consensus-reaching discussions with the wider HPP community over the past year. The revised 3.0 guidelines address several major and minor identified gaps. We have added guidelines for emerging data independent acquisition (DIA) MS workflows and for use of the new Universal Spectrum Identifier (USI) system being developed by the HUPO Proteomics Standards Initiative (PSI). In addition, we discuss updates to the standard HPP pipeline for collecting MS evidence for all proteins in the HPP, including refinements to minimum evidence. We present a new plan for incorporating MassIVE-KB into the HPP pipeline for the next (HPP 2020) cycle in order to obtain more comprehensive coverage of public MS data sets. The main checklist has been reorganized under headings and subitems and related guidelines have been grouped. In sum, Version 2.1 of the HPP MS Data Interpretation Guidelines has served well and this timely update to version 3.0 will aid the HPP as it approaches its goal of collecting and curating MS evidence of translation and expression for all predicted [~]20,000 human proteins encoded by the human genome.\n\n\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=134 SRC=\"FIGDIR/small/733576v2_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (18K):\norg.highwire.dtl.DTLVardef@8765f2org.highwire.dtl.DTLVardef@136235aorg.highwire.dtl.DTLVardef@14aef76org.highwire.dtl.DTLVardef@af33d2_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2019

Dynamic pneumococcal genetic adaptations support bacterial growth and inflammation during coinfection with influenza

Streptococcus pneumoniae (pneumococcus) is one of the primary bacterial pathogens that complicates influenza virus infections. These bacterial coinfections increase influenza-associated morbidity and mortality through a number of immunological and viral-mediated mechanisms, but the specific bacterial genes that contribute to post-influenza pathogenicity are not known. Here, we used genome-wide transposon mutagenesis (Tn-Seq) to reveal bacterial genes that confer improved fitness in influenza-infected hosts. The majority of the 32 identified genes are involved in bacterial metabolism, including nucleotide biosynthesis, amino acid biosynthesis, protein translation, and membrane transport. We generated single-gene deletion (SGD) mutants of five identified genes: SPD1414, SPD2047 (cbiO1), SPD0058 (purD), SPD1098, and SPD0822 (proB), to investigate their effect on in vivo fitness, disease severity, and host immune responses. Growth of SGD mutants was slightly attenuated in vitro and in vivo, but each still grew to high titers in the lungs of mock- and influenza-infected hosts. Despite high bacterial loads, mortality was significantly reduced or delayed with all SGD mutants. Time-dependent reductions in pulmonary neutrophils, inflammatory macrophages, and select proinflammatory cytokines and chemokines were also observed. Immunohistochemical staining further revealed that neutrophil phenotype and distribution was altered in the lungs of influenza-SGD coinfected animals. These studies demonstrate a critical role for specific bacterial genes and for bacterial metabolism in driving virulence and modulating immune function during influenza-associated bacterial pneumonia.

microbiology↗

bioRxiv2019

Proteomics Standards Initiative Extended FASTA Format (PEFF)

Mass spectrometry-based proteomics enables the high-throughput identification and quantification of proteins, including sequence variants and post-translational modifications (PTMs), in biological samples. However, most workflows require that such variations be included in the search space used to analyze the data, and doing so remains challenging with most analysis tools. In order to facilitate the search for known sequence variants and PTMs, the Proteomics Standards Initiative (PSI) has designed and implemented the PSI Extended FASTA Format (PEFF). PEFF is based on the very popular FASTA format but adds a uniform mechanism for encoding substantially more metadata about the sequence collection as well as individual entries, including support for encoding known sequence variants, PTMs, and proteoforms. The format is very nearly backwards compatible, and as such, existing FASTA parsers will require little or no changes to be able to read PEFF files as FASTA files, although without supporting any of the extra capabilities of PEFF. PEFF is defined by a full specification document, controlled vocabulary terms, a set of example files, software libraries, and a file validator. Popular software and resources are starting to support PEFF, including the sequence search engine Comet and the knowledge bases neXtProt and UniProtKB. Widespread implementation of PEFF is expected to further enable proteogenomics and top-down proteomics applications by providing a standardized mechanism for encoding protein sequences and their known variations. All the related documentation, including the detailed file format specification and example files, are available at http://www.psidev.info/peff.

bioinformatics↗