Characterization of the Sediment Bacterial Community Structure and Composition in Najafgarh Lake and Adjoining Dhansa Barrage.

This study was undertaken to assess the changes in the community structure, diversity, and composition of sediment bacteria in a shallow lake, Najafgarh Lake (NL), that receives untreated sewage effluent through drains connected to it. These changes were analyzed by comparing the sediment bacterial community structure of NL to the sediment bacterial community structure of Dhansa Barrage (DB), which receives no such effluents. 16S rRNA amplicon was used for bacterial community analysis. Water and sediment samples were also analyzed and compared revealing high conductivity, ammonia, nitrite content, and low dissolved oxygen in NL. The organic matter content is also higher in the sediments of NL. Proteobacteria, Firmicutes, Bacteroidetes, and Actinobacteria are the predominant phyla in both sites and account for 91% of total bacterial abundance in DB and only 77% in the case of NL. Proteobacteria have the highest relative abundance, accounting for around 42% of the total bacterial population in the case of DB and Firmicutes has the highest relative abundance in Najafgarh at 30%. The diversity analysis found significant differences in the community structure at the two sites. The variation in the bacterial communities in the two wetlands is significantly associated with two water parameters (conductivity and temperature) and two sediment parameters (Sediment Nitrogen and Sediment Organic Matter). Correlation Analysis showed that high ammonia, nitrite, and conductance in NL resulted in bacterial communities shifting towards phyla abundant in degraded ecosystems like Acidobacteria, Choloroflexi, Caldiserica, Aminicenantes, Thaumarchaeota, and Planctomycetes.

Intersection of network medicine and machine learning towards investigating the key biomarkers and pathways underlying amyotrophic lateral sclerosis: a systematic review.

BACKGROUND: Network medicine is an emerging area of research that focuses on delving into the molecular complexity of the disease, leading to the discovery of network biomarkers and therapeutic target discovery. Amyotrophic lateral sclerosis (ALS) is a complicated rare disease with unknown pathogenesis and no available treatment. In ALS, network properties appear to be potential biomarkers that can be beneficial in disease-related applications when explored independently or in tandem with machine learning (ML) techniques. OBJECTIVE: This systematic literature review explores recent trends in network medicine and implementations of network-based ML algorithms in ALS. We aim to provide an overview of the identified primary studies and gather details on identifying the potential biomarkers and delineated pathways. METHODS: The current study consists of searching for and investigating primary studies from PubMed and Dimensions.ai, published between 2018 and 2022 that reported network medicine perspectives and the coupling of ML techniques. Each abstract and full-text study was individually evaluated, and the relevant studies were finally included in the review for discussion once they met the inclusion and exclusion criteria. RESULTS: We identified 109 eligible publications from primary studies representing this systematic review. The data coalesced into two themes: application of network science to identify disease modules and promising biomarkers in ALS, along with network-based ML approaches. Conclusion This systematic review gives an overview of the network medicine approaches and implementations of network-based ML algorithms in ALS to determine new disease genes, and identify critical pathways and therapeutic target discovery for personalized treatment.

Mapping the FtsQBL divisome components in bacterial NTD pathogens as potential drug targets.

Cytokinesis is an essential process in bacterial cell division, and it involves more than 25 essential/non-essential cell division proteins that form a protein complex known as a divisome. Central to the divisome are the proteins FtsB and FtsL binding to FtsQ to form a complex FtsQBL, which helps link the early proteins with late proteins. The FtsQBL complex is highly conserved as a component across bacteria. Pathogens like Vibrio cholerae, Mycobacterium ulcerans, Mycobacterium leprae, and Chlamydia trachomatis are the causative agents of the bacterial Neglected Tropical Diseases Cholera, Buruli ulcer, Leprosy, and Trachoma, respectively, some of which seemingly lack known homologs for some of the FtsQBL complex proteins. In the absence of experimental characterization, either due to insufficient resources or the massive increase in novel sequences generated from genomics, functional annotation is traditionally inferred by sequence similarity to a known homolog. With the advent of accurate protein structure prediction methods, features both at the fold level and at the protein interaction level can be used to identify orthologs that cannot be unambiguously identified using sequence similarity methods. Using the FtsQBL complex proteins as a case study, we report potential remote homologs using Profile Hidden Markov models and structures predicted using AlphaFold. Predicted ortholog structures show conformational similarity with corresponding E. coli proteins irrespective of their level of sequence similarity. Alphafold multimer was used to characterize remote homologs as FtsB or FtsL, when they were not sufficiently distinguishable at both the sequence or structure level, as their interactions with FtsQ and FtsW play a crucial role in their function. The structures were then analyzed to identify functionally critical regions of the proteins consistent with their homologs and delineate regions potentially useful for inhibitor discovery.