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New Jersey coastal areas are experiencing eutrophication due to human-induced nutrient overloading. Algal blooms occur frequently in New Jersey coastal waters, and excessive blooms shift water quality. However, phytoplankton–bacteria interactions mediated through dissolved organic matter (DOM) have not been extensively studied in New Jersey coastal waters, especially near overburdened communities. We targeted a traditionally underrepresented township area, Keyport Harbor, as a model site to investigate seasonal variabilities of phytoplankton biomass, DOM, and bacteria biomass. Chlorophyll- a concentrations were significantly higher in spring–summer (bloom) than in fall–winter (nonbloom). Nitrate + nitrite and ammonium were negatively correlated with chlorophyll- a , and the water was nitrogen-limited during bloom time while phosphorus-limited during nonbloom time, implying that regulating nitrogen loading was key to controlling algal blooms, especially during bloom seasons. Phytoplankton–bacteria interactions were assessed by monitoring dissolved organic carbon (DOC) and bacterial abundance between bloom and nonbloom time from field and incubation studies. A significantly higher DOC, but not dissolved organic nitrogen, occurred in the bloom than nonbloom period, suggesting that phytoplankton contributed to the production of more carbon-rich than nitrogen-rich compounds. DOC fueled threefold bacterial growth in the bloom period, exceeding the temperature effect and indicating strong phytoplankton–DOM–bacteria connections. Microbial remineralization incubations showed rapid phytoplankton–DOC drawdown, and more ambient DOC drawdown and bacterial growth in the bloom than nonbloom time, further supporting the important role of phytoplankton–DOC in shaping bacteria. With water quality monitoring via chemical and biological indicators, the study aimed to understand carbon cycling better, assess anthropogenic impacts on coastal environments, and help facilitate coastal management.

Abstract The copackaging of mRNAs into biomolecular condensates called germ granules is a conserved strategy to posttranscriptionally regulate germline mRNAs. In Drosophila melanogaster, mRNAs accumulate in germ granules by forming homotypic clusters, aggregates containing multiple transcripts from the same gene. Nucleated by Oskar (Osk), homotypic clusters are generated through a stochastic seeding and self-recruitment process that requires the 3′ untranslated region (UTR) of germ granule mRNAs. Interestingly, the 3′ UTR belonging to germ granule mRNAs, such as nanos (nos), have considerable sequence variations among Drosophila species and we hypothesized that this diversity influences homotypic clustering. To test our hypothesis, we investigated the homotypic clustering of nos and polar granule component (pgc) in four Drosophila species and concluded that clustering is a conserved process used to enrich germ granule mRNAs. However, we discovered germ granule phenotypes that included significant changes in the abundance of transcripts present in species’ homotypic clusters, which also reflected diversity in the number of coalesced primordial germ cells within their embryonic gonads. By integrating biological data with computational modeling, we found that multiple mechanisms underlie naturally occurring germ granule diversity, including changes in nos, pgc, osk levels and/or homotypic clustering efficacy. Furthermore, we demonstrated how the nos 3′ UTR from different species influences nos clustering, causing granules to have ∼70% less nos and increasing the presence of defective primordial germ cells. Our results highlight the impact that evolution has on germ granules, which should provide broader insight into processes that modify compositions and activities of other classes of biomolecular condensate.

The co-packaging of mRNAs into biomolecular condensates called germ granules is a conserved strategy to post-transcriptionally regulate mRNAs that function in germline development and maintenance. In D. melanogaster, mRNAs accumulate in germ granules by forming homotypic clusters, aggregates that contain multiple transcripts from a specific gene. Nucleated by Oskar (Osk), homotypic clusters in D. melanogaster are generated through a stochastic seeding and self-recruitment process that requires the 3'UTR of germ granule mRNAs. Interestingly, the 3'UTR belonging to germ granule mRNAs, such as nanos (nos), have considerable sequence variations among Drosophila species. Thus, we hypothesized that evolutionary changes in the 3'UTR influences germ granule development. To test our hypothesis, we investigated the homotypic clustering of nos and polar granule component (pgc) in four Drosophila species and concluded that homotypic clustering is a conserved developmental process used to enrich germ granule mRNAs. Additionally, we discovered that the number of transcripts found in nos and/or pgc clusters could vary significantly among species. By integrating biological data with computational modeling, we determined that multiple mechanisms underlie naturally occurring germ granule diversity, including changes in nos, pgc, osk levels, and/or homotypic clustering efficacy. Finally, we found that the nos 3'UTR from different species can alter the efficacy of nos homotypic clustering, resulting in germ granules with reduced nos accumulation. Our findings highlight the impact that evolution has on the development of germ granules and may provide insight into processes that modify the content of other classes of biomolecular condensates.Competing Interest StatementThe authors have declared no competing interest.

The integration of multimodal sensory information into a common neural code is a critical function of all complex nervous systems. This process is required for adaptive responding to incoming stimuli as well as the formation of a cognitive map of the external sensory environment. The underlying neural mechanisms of multimodal integration are poorly understood due, in part, to the technical difficulties of manipulating multimodal sensory information in combination with simultaneous in-vivo electrophysiological recording in awake behaving animals. We therefore developed a non-invasive multimodal virtual reality system that is conducive to wired electrophysiological recording techniques. This system allows for the dynamic presentation of highly immersive audiovisual virtual environments to rats maintained in a body fixed position on top of a quiet spherical treadmill. Notably, this allows the rats to remain at the same spatial location in the real world without the need for head fixation. This method opens the door for a wide array of future studies aimed at elucidating the underlying neural mechanisms of multimodal integration.

Steroidal glycoalkaloids (SGAs) are specialized metabolites produced by hundreds of Solanum species including food crops, such as tomato, potato and eggplant. Unlike true alkaloids, nitrogen is introduced at a late stage of SGA biosynthesis through an unknown transamination reaction. Here, we reveal the mechanism by which GLYCOALKALOID METABOLISM12 (GAME12) directs the biosynthesis of nitrogen-containing steroidal alkaloid aglycone in Solanum . We report that GAME12, a neofunctionalized γ -aminobutyric acid (GABA) transaminase, undergoes changes in both active site specificity and subcellular localization to switch from its renown and generic activity in core metabolism to function in a specialized metabolic pathway. Moreover, overexpression of GAME12 alone in engineered S.   nigrum leaves is sufficient for de novo production of nitrogen-containing SGAs. Our results highlight how hijacking a core metabolism GABA shunt enzyme is crucial in numerous Solanum species for incorporating a nitrogen to a steroidal-specialized metabolite backbone and form defensive alkaloids. The incorporation of nitrogen in steroidal glycoalkaloids is hypothesized to occur through a transamination reaction. Here, the authors show that GLYCOALKALOID METABOLISM12 appears to evolve from the canonical γ -aminobutyric acid transaminases and directs the biosynthesis of nitrogen-containing steroidal metabolites in Solanum plants.

A significant challenge in cell biology is to uncover the function of uncharacterized proteins. Surprisingly a quarter of the proteome is still poorly understood even in the most well studied model organisms. Systematic methodologies, including the use of tagged protein collections, have emerged as a powerful approach to address this gap. Despite the availability of proteome-wide collections featuring various fused proteins, the impact of tag size on protein function highlighted the need for using minimally disruptive tags for functional genomic studies. To rise to this challenge, we have created a proteome-wide collection of yeast strains in which proteins are N-terminally tagged with the Hemagglutinin (HA) epitope. The library leverages the compact size of the HA tag to minimize drawbacks associated with larger tags while enabling efficient functional analysis. We showcase the potential uses of our library for systematically evaluating protein size, abundance and localization using an in vivo labeling approach. Our characterization underscores the potential utility of a proteome-wide HA-tagged library in revealing novel aspects of cell biology, providing an additional powerful tool for functional genomics.

Trustworthy answer content is abundant in many high-resource languages and is instantly accessible through question answering systems, yet this content can be hard to access for those that do not speak these languages. The leap forward in cross-lingual modeling quality offered by generative language models offers much promise, yet their raw generations often fall short in factuality. To improve trustworthiness in these systems, a promising direction is to attribute the answer to a retrieved source, possibly in a content-rich language different from the query. Our work is the first to study attribution for cross-lingual question answering. First, we collect data in 5 languages to assess the attribution level of a state-of-the-art cross-lingual QA system. To our surprise, we find that a substantial portion of the answers is not attributable to any retrieved passages (up to 50% of answers exactly matching a gold reference) despite the system being able to attend directly to the retrieved text. Second, to address this poor attribution level, we experiment with a wide range of attribution detection techniques. We find that Natural Language Inference models and PaLM 2 fine-tuned on a very small amount of attribution data can accurately detect attribution. Based on these models, we improve the attribution level of a cross-lingual question-answering system. Overall, we show that current academic generative cross-lingual QA systems have substantial shortcomings in attribution and we build tooling to mitigate these issues.