Sulfate-reducing bacteria (SRB) are terminal members of any anaerobic food chain. For example, they critically influence the biogeochemical cycling of carbon, nitrogen, sulfur, and metals (natural ...environment) as well as the corrosion of civil infrastructure (built environment). The United States alone spends nearly $4 billion to address the biocorrosion challenges of SRB. It is important to analyze the genetic mechanisms of these organisms under environmental stresses. The current study uses complementary methodologies, viz., transcriptome-wide marker gene panel mapping and gene clustering analysis to decipher the stress mechanisms in four SRB. Here, the accessible RNA-sequencing data from the public domains were mined to identify the key transcriptional signatures. Crucial transcriptional candidate genes of Desulfovibrio spp. were accomplished and validated the gene cluster prediction. In addition, the unique transcriptional signatures of Oleidesulfovibrio alaskensis (OA-G20) at graphene and copper interfaces were discussed using in-house RNA-sequencing data. Furthermore, the comparative genomic analysis revealed 12,821 genes with translation, among which 10,178 genes were in homolog families and 2643 genes were in singleton families were observed among the 4 genomes studied. The current study paves a path for developing predictive deep learning tools for interpretable and mechanistic learning analysis of the SRB gene regulation.
Sulfate-reducing bacteria (SRB) have a unique ability to respire under anaerobic conditions using sulfate as a terminal electron acceptor, reducing it to hydrogen sulfide. SRB thrives in many natural ...environments (freshwater sediments and salty marshes), deep subsurface environments (oil wells and hydrothermal vents), and processing facilities in an industrial setting. Owing to their ability to alter the physicochemical properties of underlying metals, SRB can induce fouling, corrosion, and pipeline clogging challenges. Indigenous SRB causes oil souring and associated product loss and, subsequently, the abandonment of impacted oil wells. The sessile cells in biofilms are 1,000 times more resistant to biocides and induce 100-fold greater corrosion than their planktonic counterparts. To effectively combat the challenges posed by SRB, it is essential to understand their molecular mechanisms of biofilm formation and corrosion. Here, we examine the critical genes involved in biofilm formation and microbiologically influenced corrosion and categorize them into various functional categories. The current effort also discusses chemical and biological methods for controlling the SRB biofilms. Finally, we highlight the importance of surface engineering approaches for controlling biofilm formation on underlying metal surfaces.
•Cerebellar EEG was analyzed during interval timing cognitive and lower-limb pedaling motor tasks in PD.•PD patients showed altered cognitive and motor behavior compared to controls.•PD patients ...exhibited attenuated mid-cerebellar Cbz theta-band (4–7 Hz) power during cognitive and motor tasks.•Mid-cerebellar (Cbz) and mid-frontal (Cz) connectivity showed theta-band differences only during pedaling motor task.
Structural and functional abnormalities in the cerebellar region have been shown in patients with Parkinson’s disease (PD). Since the cerebellar region has been associated with cognitive and lower-limb motor functions, it is imperative to study cerebellar oscillations in PD. Here, we evaluated cerebellar electroencephalography (EEG) during cognitive processing and lower-limb motor performances in PD. Cortical and cerebellar EEG were collected from 74 PD patients and 37 healthy control subjects during a 7-second interval timing task, 26 PD patients and 13 controls during a lower-limb pedaling task, and 23 PD patients during eyes-open/closed resting conditions. Analyses were focused on the mid-cerebellar Cbz electrode and further compared to the mid-occipital Oz and mid-frontal Cz electrodes. Increased alpha-band power was observed during the eyes-closed resting-state condition over Oz, but no change in alpha power was observed over Cbz. PD patients showed higher dispersion when performing the 7-second interval timing cognitive task and executed the pedaling motor task with reduced speed compared to controls. PD patients exhibited attenuated cue-triggered theta-band power over Cbz during both the interval timing and pedaling motor tasks. Connectivity measures between Cbz and Cz showed theta-band differences, but only during the pedaling motor task. Cbz oscillatory activity also differed from Oz across multiple frequency bands in both groups during both tasks. Our cerebellar EEG data along with previous magnetoencephalography and animal model studies clearly show alterations in cerebellar oscillations during cognitive and motor processing in PD.
In two qualitative evaluation studies, we explored the
construct validity of two diagrammatic measures of attachment network structure,
including the Bull's Eye diagrammatic technique and a ...significantly
modified version, the Web-based Hierarchical Mapping Technique (WHMT). In the
first study, 20 young adults completed a Bull's Eye diagram followed by a
semi-structured interview in which participants explained their placement of
support figures in their diagrams. Interview transcripts were analyzed using
theory-driven thematic analysis to determine the presence of attachment-related
themes, including safe haven, secure base effect, and proximity maintenance.
Findings indicated mixed support for the Bull's Eye as a measure of
attachment networks. Although attachment themes were dominant among participants
who identified a parent as their primary attachment figure, this was not the
case among participants who identified a peer as a primary attachment figure.
Participant justifications for peer attachments relied on non-attachment themes,
including identity exploration and companionship. In the second study, we
applied the same qualitative method to investigate the WHMT. Saturation was
reached sooner, and attachment themes were dominant for both parent and peered
primary attachment. Findings support the construct validity of the WHMT as a new
measure of attachment network composition and strength.
The growth and survival of an organism in a particular environment is highly depends on the certain indispensable genes, termed as essential genes. Sulfate-reducing bacteria (SRB) are obligate ...anaerobes which thrives on sulfate reduction for its energy requirements. The present study used
G20 (OA G20) as a model SRB to categorize the essential genes based on their key metabolic pathways. Herein, we reported a feedback loop framework for gene of interest discovery, from bio-problem to gene set of interest, leveraging expert annotation with computational prediction. Defined bio-problem was applied to retrieve the genes of SRB from literature databases (PubMed, and PubMed Central) and annotated them to the genome of OA G20. Retrieved gene list was further used to enrich protein-protein interaction and was corroborated to the pangenome analysis, to categorize the enriched gene sets and the respective pathways under essential and non-essential. Interestingly, the
gene (dde_2265) from the sulfur metabolism was the bridging gene between all the enriched pathways. Gene clusters involved in essential pathways were linked with the genes from seleno-compound metabolism, amino acid metabolism, secondary metabolite synthesis, and cofactor biosynthesis. Furthermore, pangenome analysis demonstrated the gene distribution, where 69.83% of the 116 enriched genes were mapped under "persistent," inferring the essentiality of these genes. Likewise, 21.55% of the enriched genes, which involves specially the formate dehydrogenases and metallic hydrogenases, appeared under "shell." Our methodology suggested that semi-automated text mining and network analysis may play a crucial role in deciphering the previously unexplored genes and key mechanisms which can help to generate a baseline prior to perform any experimental studies.
Sensing surface topography, an upsurge of signaling biomolecules, and upholding cellular homeostasis are the rate-limiting spatio-temporal events in microbial attachment and biofilm formation. ...Initially, a set of highly specialized proteins,
. conditioning protein, directs the irreversible attachment of the microbes. Later signaling molecules,
. autoinducer, take over the cellular communication phenomenon, resulting in a mature microbial biofilm. The mandatory release of conditioning proteins and autoinducers corroborated the existence of two independent mechanisms operating sequentially for biofilm development. However, both these mechanisms are significantly affected by the availability of the cofactor, e.g., Copper (Cu). Generally, the Cu concentration beyond threshold levels is detrimental to the anaerobes except for a few species of sulfate-reducing bacteria (SRB). Remarkably SRB has developed intricate ways to resist and thrive in the presence of Cu by activating numerous genes responsible for modifying the presence of more toxic Cu(I) to Cu(II) within the periplasm, followed by their export through the outer membrane. Therefore, the determinants of Cu toxicity, sequestration, and transportation are reconnoitered for their contribution towards microbial adaptations and biofilm formation. The mechanistic details revealing Cu as a quorum quencher (QQ) are provided in addition to the three pathways involved in the dissolution of cellular communications. This review articulates the Machine Learning based data curing and data processing for designing novel anti-biofilm peptides and for an in-depth understanding of QQ mechanisms. A pioneering data set has been mined and presented on the functional properties of the QQ homolog in
G20 and residues regulating the multicopper oxidase properties in SRB.
Detecting pathogenic bacteria and their phenotypes including microbial resistance is crucial for preventing infection, ensuring food safety, and promoting environmental protection. Raman spectroscopy ...offers rapid, seamless, and label-free identification, rendering it superior to gold-standard detection techniques such as culture-based assays and polymerase chain reactions. However, its practical adoption is hindered by issues related to weak signals, complex spectra, limited datasets, and a lack of adaptability for detection and characterization of bacterial pathogens. This review focuses on addressing these issues with recent Raman spectroscopy breakthroughs enabled by machine learning (ML), particularly deep learning methods. Given the regulatory requirements, consumer demand for safe food products, and growing awareness of risks with environmental pathogens, this study emphasizes addressing pathogen detection in clinical, food safety, and environmental settings. Here, we highlight the use of convolutional neural networks for analyzing complex clinical data and surface enhanced Raman spectroscopy for sensitizing early and rapid detection of pathogens and analyzing food safety and potential environmental risks. Deep learning methods can tackle issues with the lack of adequate Raman datasets and adaptability across diverse bacterial samples. We highlight pending issues and future research directions needed for accelerating real-world impacts of ML-enabled Raman diagnostics for rapid and accurate diagnosis and surveillance of pathogens across critical fields.
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•Development of BASIN for statistical analysis of micrograph object data.•BASIN provides an analysis report following FAIR guiding principles.•Users can retrieve analysis results, ...plots, and tables for immediate or future use.•BASIN is available online for small datasets and locally for large image datasets.•BASIN is an easy-to-use workflow for two-condition analysis of bio-images.
Micrograph comparison remains useful in bioscience. This technology provides researchers with a quick snapshot of experimental conditions. But sometimes a two- condition comparison relies on researchers’ eyes to draw conclusions. Our Bioimage Analysis, Statistic, and Comparison (BASIN) software provides an objective and reproducible comparison leveraging inferential statistics to bridge image data with other modalities. Users have access to machine learning-based object segmentation. BASIN provides several data points such as images’ object counts, intensities, and areas. Hypothesis testing may also be performed. To improve BASIN’s accessibility, we implemented it using R Shiny and provided both an online and offline version. We used BASIN to process 498 image pairs involving five bioscience topics. Our framework supported either direct claims or extrapolations 57% of the time. Analysis results were manually curated to determine BASIN’s accuracy which was shown to be 78%. Additionally, each BASIN version’s initial release shows an average 82% FAIR compliance score.
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Infectious diseases, including vector-borne diseases transmitted by arthropods, are a leading cause of morbidity and mortality worldwide. In the era of big data, addressing ...broad-scale, fundamental questions regarding the complex dynamics of these diseases will increasingly require the integration of diverse datasets to produce new biological knowledge. This review provides a current snapshot of the systematic assessment of the relationships between microbial pathogens, arthropod vectors and mammalian hosts using data mining and machine learning. We employ PRISMA to identify 32 key papers relevant to this topic. Our analysis shows an increasing use of data mining and machine learning tasks and techniques, including prediction, classification, clustering, association rules mining, and deep learning, over the last decade. However, it also reveals a number of critical challenges in applying these to the study of vector-host-pathogen interactions at various systems biology levels. Here, relevant studies, current limitations and future directions are discussed. Furthermore, the quality of data in relevant papers was assessed using the FAIR (Findable, Accessible, Interoperable, Reusable) compliance criteria to evaluate and encourage reproducibility and shareability of research outcomes. Although shortcomings in their application remain, data mining and machine learning have significant potential to break new ground in understanding fundamental aspects of vector-host-pathogen relationships and their application in this field should be encouraged. In particular, while predictive modeling, feature engineering and supervised machine learning are already being used in the field, other data mining and machine learning methods such as deep learning and association rules analysis lag behind and should be implemented in combination with established methods to accelerate hypothesis and knowledge generation in the domain.
Central nervous system atypical teratoid/rhabdoid tumors (ATRTs) are rare and aggressive tumors with a very poor prognosis. Current treatments for ATRT include resection of the tumor, followed by ...systemic chemotherapy and radiation therapy, which have toxic side effects for young children. Gene expression analyses of human ATRTs and normal brain samples indicate that ATRTs have aberrant expression of epigenetic markers including class I histone deacetylases (HDAC's) and lysine demethylase (LSD1). Here, we investigate the effect of a small molecule epigenetic modulator known as Domatinostat (4SC-202), which inhibits both class I HDAC's and Lysine Demethylase (LSD1), on ATRT cell survival and single cell heterogeneity. Our findings suggest that 4SC-202 is both cytotoxic and cytostatic to ATRT in 2D and 3D scaffold cell culture models and may target cancer stem cells. Single-cell RNA sequencing data from ATRT-06 spheroids treated with 4SC-202 have a reduced population of cells overexpressing stem cell-related genes, including
. Flow cytometry and immunofluorescence on 3D ATRT-06 scaffold models support these results suggesting that 4SC-202 reduces expression of cancer stem cell markers SOX2, CD133, and FOXM1. Drug-induced changes to the systems biology landscape are also explored by multi-omics enrichment analyses. In summary, our data indicate that 4SC-202 has both cytotoxic and cytostatic effects on ATRT, targets specific cell sub-populations, including those with cancer stem-like features, and is an important potential cancer therapeutic to be investigated in vivo.
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