The Virtual Institute for Microbial Stress and Survival (VIMSS, http://vimss.lbl.gov/) funded by the Dept. of Energy's Genomics:GTL Program, is dedicated to using integrated environmental, functional genomic, and comparative sequence and phylogeny data to understand mechanisms by which microbes and microbial communities survive in uncertain environments while carrying out processes of interest for bioremediation and energy generation. To support this work, VIMSS has developed a Web portal, along with computational analysis and an underlying database, for comparative functional genomics of bacteria and archaea. MicrobesOnline (http://www.microbesonline.org) has been enabling comparative genome analysis since 2003. The portal currently includes 702 complete microbial genomes (662 bacterial, 40 archaeal, 1770 viruses, 268 plasmids) and offers a suite of analysis and tools including: comprehensive gene family and domain annotations, information on three dimensional structure representatives and similarities, an interactive gene expression heatmap browser along with functionality for gene expression profile searches, a multi-species genome browser, operon and regulon predictions, a combined gene and species phylogeny browser, a gene ontology browser, a metabolic pathway browser, a workbench for sequence analysis (including sequence motif detection, motif searches, sequence alignment and phylogeny reconstruction), and capabilities for community annotation of genomes. VIMSS integrates functional genomic data and provides novel web-based viewing and analysis tools for gene expression microarray, proteomic, metabolomic, and phenotype microarray data. Currently, these data are mostly project generated for wild-type and mutants of Desulfovibrio vulgaris and Shewanella oneidensis exposed to stress conditions found at DOE field sites. However, the organism scope is being expanded, and recently the E. coli gene expression data compendium from the Many Microbes Microarray Database (M3D) has been imported into MicrobesOnline. Additionally VIMSS has developed capabilities to analyze microarray experiments performed on multiple species simultaneously. Selecting an organism or gene of interest in MicrobesOnline leads to information about and data viewers for VIMSS experiments conducted on that organism and involving that gene or gene product. It is possible to view microarray data from multiple stress conditions as an interactive heatmap and to analyze correlations between gene expression results from different experiments. Among the major new features is the ability to search the microarray data compendium for genes with gene expression profiles similar to a query expression profile (either based on a gene or set of genes). Such new compendium-wide functionalities allow to observe patterns in gene expression changes across multiple conditions and genes, and to search for similarities to these patterns. The information integration and analysis performed by VIMSS serves not only to generate insights into the stress responses and their regulation in these microorganisms, but also to document VIMSS experiments, allow contextual access to experimental data, and facilitate the planning of future experiments. VIMSS also is incorporating into MicrobesOnline publicly available functional genomics data from published research, so as to centralize and synergize data on and analysis of microbial physiology and ecology in a unified comparative functional genomic framework.

A wide variety of symptoms is associated with Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection, and these symptoms can overlap with other conditions and diseases. Knowing the distribution of symptoms across diseases and individuals can support clinical actions on timelines shorter than those for drug and vaccine development. Here, we focus on zinc deficiency symptoms, symptom overlap with other conditions, as well as zinc effects on immune health and mechanistic zinc deficiency risk groups. There are well-studied beneficial effects of zinc on the immune system including a decreased susceptibility to and improved clinical outcomes for infectious pathogens including multiple viruses. Zinc is also an anti-inflammatory and anti-oxidative stress agent, relevant to some severe Coronavirus Disease 2019 (COVID-19) symptoms. Unfortunately, zinc deficiency is common worldwide and not exclusive to the developing world. Lifestyle choices and preexisting conditions alone can result in zinc deficiency, and we compile zinc risk groups based on a review of the literature. It is also important to distinguish chronic zinc deficiency from deficiency acquired upon viral infection and immune response and their different supplementation strategies. Zinc is being considered as prophylactic or adjunct therapy for COVID-19, with 12 clinical trials underway, highlighting the relevance of this trace element for global pandemics. Using the example of zinc, we show that there is a critical need for a deeper understanding of essential trace elements in human health, and the resulting deficiency symptoms and their overlap with other conditions. This knowledge will directly support human immune health for decreasing susceptibility, shortening illness duration, and preventing progression to severe cases in the current and future pandemics.

Background

The worldwide persistence of drug-resistant Plasmodium falciparum, the most lethal variety of human malaria, is a global health concern. The P. falciparum sequencing project has brought new opportunities for identifying molecular targets for antimalarial drug and vaccine development.

Results

We developed a software package, ArrayOligoSelector, to design an open reading frame (ORF)-specific DNA microarray using the publicly available P. falciparum genome sequence. Each gene was represented by one or more long 70 mer oligonucleotides selected on the basis of uniqueness within the genome, exclusion of low-complexity sequence, balanced base composition and proximity to the 3' end. A first-generation microarray representing approximately 6,000 ORFs of the P. falciparum genome was constructed. Array performance was evaluated through the use of control oligonucleotide sets with increasing levels of introduced mutations, as well as traditional northern blotting. Using this array, we extensively characterized the gene-expression profile of the intraerythrocytic trophozoite and schizont stages of P. falciparum. The results revealed extensive transcriptional regulation of genes specialized for processes specific to these two stages.

Conclusions

DNA microarrays based on long oligonucleotides are powerful tools for the functional annotation and exploration of the P. falciparum genome. Expression profiling of trophozoites and schizonts revealed genes associated with stage-specific processes and may serve as the basis for future drug targets and vaccine development.

Molecular biologists frequently interpret gene lists derived from high-throughput experiments and computational analysis. This is typically done as a statistical enrichment analysis that measures the over- or under-representation of biological function terms associated with genes or their properties, based on curated assertions from a knowledge base (KB) such as the Gene Ontology (GO). Interpreting gene lists can also be framed as a textual summarization task, enabling Large Language Models (LLMs) to use scientific texts directly and avoid reliance on a KB. TALISMAN (Terminological ArtificiaL Intelligence SuMmarization of Annotation and Narratives) uses generative AI to perform gene set function summarization as a complement to standard enrichment analysis. This method can use different sources of gene functional information: (1) structured text derived from curated ontological KB annotations, (2) ontology-free narrative gene summaries, or (3) direct retrieval from the model. We demonstrate that these methods are able to generate plausible and biologically valid summary GO term lists for an input gene set. However, LLM-based approaches are unable to deliver reliable scores or p-values and often return terms that are not statistically significant. Crucially, in our experiments these methods were rarely able to recapitulate the most precise and informative term from standard enrichment analysis. We also observe minor differences depending on prompt input information, with GO term descriptions leading to higher recall but lower precision. However, newer LLM models perform statistically significantly better than the oldest model across all performance metrics, suggesting that future models may lead to further improvements. Overall, the results are nondeterministic, with minor variations in prompt resulting in radically different term lists, true to the stochastic nature of LLMs. Our results show that at this point, LLM-based methods are unsuitable as a replacement for standard term enrichment analysis, however they may provide summarization benefits for implicit knowledge integration across extant but unstandardized knowledge, for large sets of features, and where the amount of information is difficult for humans to process.

Splicing factor 1 (SF1) binds to the branch point sequence (BPS) of mammalian introns and is believed to be important for the splicing of some, but not all, introns. To help identify BPSs, particularly those that depend on SF1, we generated a BPS profile model in which SF1 binding affinity data, validated by branch point mapping, were iteratively incorporated into computational models. We searched a data set of 117,499 human introns for best matches to the SF1 Affinity Model above a threshold, and counted the number of matches at each intronic position. After subtracting a background value, we found that 87.9% of remaining high-scoring matches identified were located in a region upstream of 3'-splice sites where BPSs are typically found. Since U2AF65 recognizes the polypyrimidine tract (PPT) and forms a cooperative RNA complex with SF1, we combined the SF1 model with a PPT model computed from high affinity binding sequences for U2AF65. The combined model, together with binding site location constraints, accurately identified introns bound by SF1 that are candidates for SF1-dependent splicing.

Background

Desulfovibrio vulgaris Hildenborough is a sulfate-reducing bacterium (SRB) that is intensively studied in the context of metal corrosion and heavy-metal bioremediation, and SRB populations are commonly observed in pipe and subsurface environments as surface-associated populations. In order to elucidate physiological changes associated with biofilm growth at both the transcript and protein level, transcriptomic and proteomic analyses were done on mature biofilm cells and compared to both batch and reactor planktonic populations. The biofilms were cultivated with lactate and sulfate in a continuously fed biofilm reactor, and compared to both batch and reactor planktonic populations.

Results

The functional genomic analysis demonstrated that biofilm cells were different compared to planktonic cells, and the majority of altered abundances for genes and proteins were annotated as hypothetical (unknown function), energy conservation, amino acid metabolism, and signal transduction. Genes and proteins that showed similar trends in detected levels were particularly involved in energy conservation such as increases in an annotated ech hydrogenase, formate dehydrogenase, pyruvate:ferredoxin oxidoreductase, and rnf oxidoreductase, and the biofilm cells had elevated formate dehydrogenase activity. Several other hydrogenases and formate dehydrogenases also showed an increased protein level, while decreased transcript and protein levels were observed for putative coo hydrogenase as well as a lactate permease and hyp hydrogenases for biofilm cells. Genes annotated for amino acid synthesis and nitrogen utilization were also predominant changers within the biofilm state. Ribosomal transcripts and proteins were notably decreased within the biofilm cells compared to exponential-phase cells but were not as low as levels observed in planktonic, stationary-phase cells. Several putative, extracellular proteins (DVU1012, 1545) were also detected in the extracellular fraction from biofilm cells.

Conclusions

Even though both the planktonic and biofilm cells were oxidizing lactate and reducing sulfate, the biofilm cells were physiologically distinct compared to planktonic growth states due to altered abundances of genes/proteins involved in carbon/energy flow and extracellular structures. In addition, average expression values for multiple rRNA transcripts and respiratory activity measurements indicated that biofilm cells were metabolically more similar to exponential-phase cells although biofilm cells are structured differently. The characterization of physiological advantages and constraints of the biofilm growth state for sulfate-reducing bacteria will provide insight into bioremediation applications as well as microbially-induced metal corrosion.