We observed that heat shock of Caenorhabditis elegans leads to the formation of nuclear double-stranded RNA (dsRNA) foci, detectable with a dsRNA-specific monoclonal antibody. These foci significantly overlap with nuclear HSF-1 granules. To investigate the molecular mechanism(s) underlying dsRNA foci formation, we used RNA-seq to globally characterize total RNA and immunoprecipitated dsRNA from control and heat shocked worms. We find a subset of both sense and antisense transcripts enriched in the dsRNA pool by heat shock overlap with dsRNA transcripts enriched by deletion of tdp-1, which encodes the C. elegans ortholog of TDP-43. Interestingly, transcripts involved in translation are over-represented in the dsRNAs induced by either heat shock or deletion of tdp-1. Also enriched in the dsRNA transcripts are sequences downstream of annotated genes (DoGs), which we globally quantified with a new algorithm. To validate these observations, we used fluorescence in situ hybridization (FISH) to confirm both antisense and downstream of gene transcription for eif-3.B, one of the affected loci we identified.

Numerous reports have suggested that infectious agents could play a role in neurodegenerative diseases, but specific etiological agents have not been convincingly demonstrated. To search for candidate agents in an unbiased fashion, we have developed a bioinformatic pipeline that identifies microbial sequences in mammalian RNA-seq data, including sequences with no significant nucleotide similarity hits in GenBank. Effectiveness of the pipeline was tested using publicly available RNA-seq data and in a reconstruction experiment using synthetic data. We then applied this pipeline to a novel RNA-seq dataset generated from a cohort of 120 samples from amyotrophic lateral sclerosis patients and controls, and identified sequences corresponding to known bacteria and viruses, as well as novel virus-like sequences. The presence of these novel virus-like sequences, which were identified in subsets of both patients and controls, were confirmed by quantitative RT-PCR. We believe this pipeline will be a useful tool for the identification of potential etiological agents in the many RNA-seq datasets currently being generated.

Numerous reports have suggested that infectious agents could play a role in neurodegenerative diseases, but specific etiological agents have not been convincingly demonstrated. To search for candidate agents in an unbiased fashion, we have developed a bioinformatic pipeline that identifies microbial sequences in mammalian RNA-seq data, including sequences with no significant nucleotide similarity hits in GenBank. Effectiveness of the pipeline was tested using publicly available RNA-seq data. We then applied this pipeline to a novel RNA-seq dataset generated from a cohort of 120 samples from amyotrophic lateral sclerosis (ALS) patients and controls, and identified sequences corresponding to known bacteria and viruses, as well as novel virus-like sequences. The presence of these novel virus-like sequences, which were identified in subsets of both patients and controls, were confirmed by quantitative RT-PCR. We believe this pipeline will be a useful tool for the identification of potential etiological agents in the many RNA-seq data sets currently being generated.

We observed that heat shock of Caenorhabditis elegans leads to the formation of nuclear double-stranded RNA (dsRNA) foci, detectable with a dsRNA-specific monoclonal antibody. These foci significantly overlap with nuclear HSF-1 granules. To investigate the molecular mechanism(s) underlying dsRNA foci formation, we used RNA-seq to globally characterize total RNA and immunoprecipitated dsRNA from control and heat shocked worms. We find antisense transcripts are generally increased after heat shock, and a subset of both sense and antisense transcripts enriched in the dsRNA pool by heat shock overlap with dsRNA transcripts enriched by deletion of tdp-1, which encodes the C. elegans ortholog of TDP-43. Interestingly, transcripts involved in translation are over-represented in the dsRNAs induced by either heat shock or deletion of tdp-1. Also enriched in the dsRNA transcripts are sequences downstream of annotated genes (DoGs), which we globally quantified with a new algorithm. To validate these observations, we used fluorescence in situ hybridization (FISH) to confirm both antisense and downstream of gene transcription for eif-3.B, one of the affected loci we identified.

Background/Objectives: Coupled with the already-problematic background rates of traditional cigarette consumption during pregnancy, the surging epidemic of electronic cigarette usage among pregnant women redoubles the importance of understanding the impacts of nicotine exposure during critical periods of development. To date, a burgeoning body of human epidemiological and animal model research indicates that not only the children but also the grandchildren of maternal smokers are at higher risk for neurodevelopmental disorders such as ADHD, autism, and schizophrenia and are predisposed to neurodevelopmental abnormalities which transcend these diagnoses. However, the roles of discrete cellular sub-populations in these and other intergenerational consequences of smoking during pregnancy remain indeterminate. Methods: Toward the resolution of this void in the literature, the present study characterized alterations in the gene expression profiles of dopamine receptor D1-expressing striatal cells from the first- and second-generation male progeny of female mice that were continuously exposed to nicotine beginning prior to conception, continuing throughout pregnancy, and concluding upon weaning of offspring. Results: Dopamine receptor D1-expressing striatal cells from our mouse models of the children and grandchildren of maternal smokers exhibit differential expression patterns for a multitude of genes that are (1) individually associated with neurodevelopmental disorders, (2) collectively overrepresented in gene set annotations related to brain, behavioral, neurobiological, and epigenomic phenotypes shared among neurodevelopmental disorders, and (3) orthologous to human genes that exhibit differential DNA methylation signatures in the newborns of maternal smokers. Conclusions: Together with our and others’ previous findings, the results of this study support the emerging theory that, by inducing extensive alterations in gene expression that in turn elicit cascading neurobiological changes which ultimately confer widespread neurobehavioral abnormalities, nicotine-induced epigenomic dysregulation may be a primary driver of neurodevelopmental deficits and disorders in the children and grandchildren of maternal smokers.

1. Passive acoustic monitoring (PAM) is scaling rapidly, but automated recognition for insects lags far behind birds. The dominant recogniser (BirdNET) classifies only ~35 insect species and building bespoke insect classifiers requires labelled training data that does not exist for most taxa. 2. We present EarthChirp, a training-free recogniser for singing insects (Orthoptera and Cicadidae) built on a frozen bioacoustic foundation model (Perch 2.0). EarthChirp pairs a global reference library of 2,012 species (assembled from GBIF, Xeno-canto, SINA and InsectSingers) with a single confidence-gated recogniser that returns a named species where the match is strong and an unnamed morphospecies placed phylogenetically against the reference library (reliably only for the dominant group) where it is not. 3. We find detection and discovery generalise zero-shot to independent data, and show EarthChirp separates insects from vertebrate biophony (AUROC 95%) and recovers morphospecies on an independent 104-species European corpus. Fine species naming does not transfer zero-shot across recording conditions (~26% open-set), but saturates near 58% with ten local reference clips per species (recording-disjoint). The residual false-positive risk is anthropogenic mechanical noise, not other animals. Using an outgroup prototype we are able to lift insect-versus-noise discrimination from 74% to 93%. 4. EarthChirp is released as a ~29 MB library and classifier head usable with the public Perch model and requires no GPU and no retraining. It detects and discovers insects in any soundscape and adapts to local faunas with a handful of reference clips, which is a deployment recipe rather than a per-site retraining cost. The same frozen-embedding approach reaches beyond the model's 16 kHz ceiling by time-expansion, recovering ultrasonic insects and bats. On the limited open bat audio currently available, we show EarthChirp generalizes to this unrelated ultrasonic taxon (bats) and provisionally places species absent from its reference library. We map the method's capabilities and limits to guide its use in conservation monitoring.