Updated Database Research Articles

DNAproDB: an updated database for the automated and interactive analysis of protein-DNA complexes.

DNAproDB (https://dnaprodb.usc.edu/) is a database, visualization tool, and processing pipeline for analyzing structural features of protein-DNA interactions. Here, we present a substantially updated version of the database through additional structural annotations, search, and user interface functionalities. The update expands the number of pre-analyzed protein-DNA structures, which are automatically updated weekly. The analysis pipeline identifies water-mediated hydrogen bonds that are incorporated into the visualizations of protein-DNA complexes. Tertiary structure-aware nucleotide layouts are now available. New file formats and external database annotations are supported. The website has been redesigned, and interacting with graphs and data is more intuitive. We also present a statistical analysis on the updated collection of structures revealing salient patterns in protein-DNA interactions.

VISTA Enhancer browser: an updated database of tissue-specific developmental enhancers.

Regulatory elements (enhancers) are major drivers of gene expression in mammals and harbor many genetic variants associated with human diseases. Here, we present an updated VISTA Enhancer Browser (https://enhancer.lbl.gov), a database of transgenic enhancer assays conducted in developing mouse embryos in vivo. Since the original publication in 2007, the database grew nearly 20-fold from 250 to over 4500 experiments and currently harbors over 23500 images. The updated database provides structured information on experiments conducted at different stages of embryonic development, including enhancer activities of human pathogenic and synthetic variants and sequences derived from a variety of species. In addition to manually curated results of thousands of individual experiments, the new database also features hundreds of manually curated comparisons between alleles. The VISTA Enhancer Browser provides a crucial resource for study of human genetic variation, gene regulation and developmental biology.

RPS 2.0: an updated database of RNAs involved in liquid-liquid phase separation.

Liquid-liquid phase separation (LLPS) is a crucial process for the formation of biomolecular condensates such as coacervate droplets, P-bodies and stress granules, which play critical roles in many physiological and pathological processes. Increasing studies have shown that not only proteins but also RNAs play a critical role in LLPS. To host LLPS-associated RNAs, we previously developed a database named 'RPS' in 2021. In this study, we present an updated version RPS 2.0 (https://rps.renlab.cn/) to incorporate the newly generated data and to host new LLPS-associated RNAs driven by post-transcriptional regulatory mechanisms. Currently, RPS 2.0 hosts 171 301 entries of LLPS-associated RNAs in 24 different biomolecular condensates with four evidence types, including 'Reviewed', 'High-throughput (LLPS enrichment)', 'High-throughput (LLPS perturbation)' and 'Predicted', and five event types, including 'Expression', 'APA', 'AS', 'A-to-I' and 'Modification'. Additionally, extensive annotations of LLPS-associated RNAs are provided in RPS 2.0, including RNA sequence and structure features, RNA-protein/RNA-RNA interactions, RNA modifications, as well as diseases related annotations. We expect that RPS 2.0 will further promote research of LLPS-associated RNAs and deepen our understanding of the biological functions and regulatory mechanisms of LLPS.

CovalentInDB 2.0: an updated comprehensive database for structure-based and ligand-based covalent inhibitor design and screening.

The rational design of targeted covalent inhibitors (TCIs) has emerged as a powerful strategy in drug discovery, known for its ability to achieve strong binding affinity and prolonged target engagement. However, the development of covalent drugs is often challenged by the need to optimize both covalent warhead and non-covalent interactions, alongside the limitations of existing compound libraries. To address these challenges, we present CovalentInDB 2.0, an updated online database designed to support covalent drug discovery. This updated version includes 8303 inhibitors and 368 targets, supplemented by 3445 newly added cocrystal structures, providing detailed analyses of non-covalent interactions. Furthermore, we have employed an AI-based model to profile the ligandability of 144 864 cysteines across the human proteome. CovalentInDB 2.0 also features the largest covalent virtual screening library with 2 030 192 commercially available compounds and a natural product library with 105 901 molecules, crucial for covalent drug screening and discovery. To enhance the utility of these compounds, we performed structural similarity analysis and drug-likeness predictions. Additionally, a new user data upload feature enables efficient data contribution and continuous updates. CovalentInDB 2.0 is freely accessible at http://cadd.zju.edu.cn/cidb/.

Phytochemical Riches, Bioactivities, and Clinical Potential of Prinsepia utilis Royle: A Comprehensive Review

Prinsepia utilis Royle, widely recognized in Chinese and Indian traditional medicine, has attracted significant interest in the realms of phytochemistry and bioactivity research. This deciduous shrub from the Rosaceae family thrives in the Himalayan region at altitudes between 1000 and 3000 meters. The current paper aims to consolidate existing knowledge on P. utilis, providing an updated database of its phytochemical constituents and related bioactivities. Investigations have revealed a wealth of secondary metabolites, including alkaloids, flavonoids, phenolics, and terpenoids. Research has shown that extracts, fractions, and isolated compounds of P. utilis exhibit notable antioxidant, anti‐inflammatory, antimicrobial, enzymatic inhibitory, immunosuppressive, and cytotoxic properties. In vivo studies confirm its traditional uses, demonstrating anti‐inflammatory, anti‐benign prostatic hyperplasia, osteoprotective, and hypoglycemic effects. Effective formulations using P. utilis extract have been developed for atrophic dermatitis and acne vulgaris. These findings underscore the plant's potential as a source of new therapeutic agents and functional additives. Nonetheless, further research is essential to advance pharmacological assessments, clinical trials, and sustainable cultivation methods to fully harness its pharmaceutical and industrial applications.

IMCMK-CNN: A lightweight convolutional neural network with Multi-scale Kernels for Image-based Malware Classification

Rapid and accurate identification of unknown malware and its variants is the premise and basis for the effective prevention of malicious attacks. However, with the explosive growth of malware variants, the efficiency of manual updating of the sample database is getting worse and worse. It is difficult for the traditional identification methods to effectively capture the sample feature information operated by the confusion method only based on the delayed database information. The research into the direction of malware detection is dedicated to surmounting the limitations of conventional detection methodologies, and delves deeply into the application of cutting-edge technologies such as data visualization, machine learning, and hybrid detection within the realm of malware detection. Through these investigations, our goal is to construct a detection system that is both more precise and efficient, capable of addressing the ever-evolving threats to cybersecurity. Pursuing research in this direction is not only vital for enhancing network security defenses and safeguarding user data, but it will also foster the advancement of related state-of-the-art technologies and further mitigate the economic and societal repercussions of malware attacks. In light of this issue, this paper proposes the Image-based Malware Classification with Multi-scale Kernels (IMCMK), a Convolutional Neural Network (CNN) architecture using multi-scale convolution kernels mixing action to improve malware variants detection capabilities. First, we propose the Multi-scale Kernels (MK) block combining deep large kernel convolution and standard small kernel convolution with shortcuts to improve the accuracy. Furthermore, we propose Multi-scale Kernel Fusion (MKF) to reduce the number of parameters that come with the large kernels. The improved Squeeze-and-Excitation (SE) block can obtain the correlation between different channels to further increase the model performance. Experimental results show that IMCMK outperforms the state-of-the-art methods in malware family classification accuracy, which has achieved 99.25 %.

The 2024 IPD-MHC database update: a comprehensive resource for major histocompatibility complex studies.

The IPD-MHC Database project (http://www.ebi.ac.uk/ipd/mhc/) serves as a comprehensive and expertly curated repository for major histocompatibility complex (MHC) sequences from non-human species, providing the necessary infrastructure and tools to study the function and evolution of this highly polymorphic genomic region. In its latest version, the IPD-MHC database has expanded both in content and in the tools for data visualization and comparison. The database now hosts over 18 000 MHC alleles from 125 species, organized into eleven taxonomic groups, all manually curated and named by the Comparative MHC Nomenclature Committee. A cetacean section has recently been included, offering researchers valuable data to study the immune system of whales, dolphins, and porpoises, as well establishing the official nomenclature platform for the Cetacea Leukocyte Antigens (CeLA). In response to user demand and reflecting broader trends in bioinformatics and immunogenetics, IPD-MHC now includes the predicted tertiary structure of over 8000 alleles and allows comparison and visualisation of allele variation within and between species at single residue resolution. These latest developments maintain the critically important link between official nomenclature of curated alleles and the ability to analyse this complex polymorphism using the most up to date methods within a single repository.

Revisiting a Drag Partition Model For Canopy-Like Roughness Elements

Turbulent flows over a large surface area (S) covered by n obstacles experience an overall drag due to the presence of the ground and the protruding obstacles into the flow. The drag partition between the roughness obstacles and the ground is analyzed using an analytical model proposed by Raupach (Boundary-Layer Meteorol 60:375-395, 1992) and is hereafter referred to as R92. The R92 is based on the premise that the wake behind an isolated roughness element can be described by a shelter area A and a shelter volume V. The individual sizes of A and V without any interference from other obstacles can be determined from scaling analysis for the spread of wakes. To upscale from an individual roughness element to n/S elements where wakes may interact, R92 adopted a background stress re-normalizing instead of reducing A or V with each element addition. This work demonstrates that R92’s approach results in a linear background stress reduction in A and V only when the ratio of n/S is small, due to a low probability of wake interactions. This probabilistic nature suggests that up-scaling from individual to multiple roughness elements can be re-formulated using stochastic averaging methods proposed here. The two approaches are shown to recover R92 under plausible conditions. An alternative scaling for the shelter volume is also proposed here using thermodynamic arguments of work and dissipation though the final outcome remains similar to R92. Comparisons between R92 and available data spanning more than two decades after R92 on blocks and vegetation-like roughness elements confirm the practical utility of R92. The agreement between R92 and this updated databases of experiments and simulations confirm the potential use of R92 in large-scale models provided that the relevant parameters accommodate certain features of the roughness element type (cube versus vegetation-like) and, to a lesser extent, their configuration throughout S. Last, a comparison between R92 and models based on first-order closure principles with constant mixing length suggests that R92 can outperform such models when evaluated across a wide range of roughness densities.

Reexamining the Mycovirome of Botrytis spp.

Botrytis species cause gray mold disease in more than 200 crops worldwide. To control this disease, chemical fungicides are usually applied. However, more sustainable control alternatives should be explored, such as the use of hypovirulent mycovirus-infected fungal strains. To determine the mycovirome of two Botrytis species, B. cinerea and B. prunorum, we reanalyzed RNA-Seq and small RNA-Seq data using different assembly programs and an updated viral database, aiming to identify new mycoviruses that were previously not described in the same dataset. New mycoviruses were identified, including those previously reported to infect or be associated with B. cinerea and Plasmopara viticola, such as Botrytis cinerea alpha-like virus 1 and Plasmopara viticola lesion-associated ourmia-like virus 80. Additionally, two novel narnaviruses, not previously identified infecting Botrytis species, have been characterized, tentatively named Botrytis cinerea narnavirus 1 and Botrytis narnavirus 1. The analysis of small RNAs suggested that all identified mycoviruses were targeted by the antiviral fungal mechanism, regardless of the viral genome type. In conclusion, the enlarged list of newly found viruses and the application of different bioinformatics approaches have enabled the identification of novel mycoviruses not previously described in Botrytis species, expanding the already extensive list.

Monitoring Coastal Evolution and Geomorphological Processes Using Time-Series Remote Sensing and Geospatial Analysis: Application Between Cape Serrat and Kef Abbed, Northern Tunisia

The monitoring of coastal evolution (coastline and associated geomorphological features) caused by episodic and persistent processes associated with climatic and anthropic activities is required for coastal management decisions. The availability of open access, remotely sensed data with increasing spatial, temporal, and spectral resolutions, is promising in this context. The coastline of Northern Tunisia is currently showing geomorphic process, such as increasing erosion associated with lateral sedimentation. This study aims to investigate the potential of time-series optical data, namely Landsat (from 1985–2019) and Google Earth® satellite imagery (from 2007 to 2023), to analyze shoreline changes and morphosedimentary and geomorphological processes between Cape Serrat and Kef Abbed, Northern Tunisia. The Digital Shoreline Analysis System (DSAS) was used to quantify the multitemporal rates of shoreline using two metrics: the net shoreline movement (NSM) and the end-point rate (EPR). Erosion was observed around the tombolo and near river mouths, exacerbated by the presence of surrounding dams, where the NSM is up to −8.31 m/year. Despite a total NSM of −15 m, seasonal dynamics revealed a maximum erosion in winter (71% negative NSM) and accretion in spring (57% positive NSM). The effects of currents, winds, and dams on dune dynamics were studied using historical images of Google Earth®. In the period from 1994 to 2023, the area is marked by dune face retreat and removal in more than 40% of the site, showing the increasing erosion. At finer spatial resolution and according to the synergy of field observations and photointerpretation, four key geomorphic processes shaping the coastline were identified: wave/tide action, wind transport, pedogenesis, and deposition. Given the frequent changes in coastal areas, this method facilitates the maintenance and updating of coastline databases, which are essential for analyzing the impacts of the sea level rise in the southern Mediterranean region. Furthermore, the developed approach could be implemented with a range of forecast scenarios to simulate the impacts of a higher future sea-level enhanced climate change.

DeOri 10.0: An Updated Database of Experimentally Identified Eukaryotic Replication Origins.

DNA replication is a complex and crucial biological process in eukaryotes. To facilitate the study of eukaryotic replication events, we present a database of eukaryotic DNA replication origins (DeOri), which collects scattered data and integrates extensive sequencing data on eukaryotic DNA replication origins. With continuous updates of DeOri, the number of datasets in the new release increased from 10 to 151 and the number of sequences increased from 16,145 to 9,742,396. Besides nucleotide sequences and bed files, corresponding annotation files, such as coding sequences (CDS), mRNA, and other biological elements within replication origins, are also provided. The experimental techniques used for each dataset, as well as other statistical data, are also presented on web page. Differences in experimental methods, cell lines, and sequencing technologies have resulted in distinct replication origins, making it challenging to differentiate between cell-specific and non-specific replication. We combined multiple replication origins at the species level, scored them, and screened them. The screened regions were considered as species-conservative origins. They are integrated and presented as reference replication origins (rORIs), including Homo sapiens, Gallus gallus, Mus musculus, Drosophila melanogaster, and Caenorhabditis elegans. Additionally, we analyzed the distribution of relevant genomic elements associated with replication origins at the genome level, such as CpG island (CGI), transcription start site (TSS), and G-quadruplex (G4). These analysis results allow users to select the desired data based on it. DeOri is available at http://tubic.tju.edu.cn/deori/.

The O-GlcNAc database: introducing new features and tools developed from community feedback.

O-GlcNAc is a reversible post-translational modification found on serine and threonine residues of nucleocytoplasmic proteins. Four years ago, we released the O-GlcNAc Database( oglcnac.mcw.edu ), a comprehensive catalog of O-GlcNAcylated proteins that has become one of the most cited resources in the field, with hundreds of unique users per month. We are now presenting an updated O-GlcNAc Database, which includes nearly 20,000 O-GlcNAcylated proteins and 48 species, marking substantial growth in data volume and scope. This paper presents the most noteworthy features implemented over the last year, often originating from feedback from the O-GlcNAc community. Among these features, we provide a brief overview of the database content, introduce our new protein viewer mode, and discuss the implementation of subcellular localization information and its applications in the O-GlcNAc score. We also provide an interface to use CytOVS, a tool designed to evaluate and sort O-GlcNAcome datasets derived from MS experiments. In conclusion, this new and improved O-GlcNAc Database represents a significant advancement in providing a comprehensive and expanded resource for researchers in the field of O-GlcNAc biology.

Key role for inflammation-related signaling in the pathogenesis of myopia based on evidence from proteomics analysis

The mechanisms underlying myopia pathogenesis are not well understood. Using publicly-available human and animal datasets, we expound on the roles of known, implicated proteins, and new myopia-related signaling pathways were hypothesized. Proteins identified from human serum or ocular fluids, and from ocular tissues in myopic animal models, were uploaded and analyzed with the QIAGEN Ingenuity Pathway Analysis (IPA) software (March 2023). With each IPA database update, more potentially-relevant proteins and signaling pathways previously unavailable during data acquisition are added, allowing extraction of novel conclusions from existing data. Canonical pathway analysis was used to analyze these data and calculate an IPA activation z-score—which indicates not only whether an association is significant, but also whether the pathway is likely activated or inhibited. Cellular immune response and cytokine signaling were frequently found to be affected in both human and animal myopia studies. Analysis of two publicly-available proteomic datasets highlighted a potential role of the innate immune system and inflammation in myopia development, detailing specific signaling pathways involved such as Granzyme A (GzmA) and S100 family signaling in the retina, and activation of myofibroblast trans-differentiation in the sclera. This perspective in myopia research may facilitate development of more effective and targeted therapeutic agents.

Coarse-grained modeling of high-enthalpy air flows based on the updated vibrational state-to-state kinetics

The state-to-state (StS) model can accurately describe high-temperature thermochemical nonequilibrium flows. For the five-species air gas mixture, we develop a comprehensive database for the state-specific rate coefficients for temperatures 300–25 000 K in this paper. The database incorporates recent molecular dynamics simulations (based on the ab initio potential energy surfaces) in the literature, and theoretical methods, including the forced harmonic oscillator model and the Marrone–Treanor model, are employed to complement the rate coefficients that are unavailable from molecular dynamics calculations. The post-shock StS simulations using the present database agree with the experimental NO infrared radiation. Based on this updated StS kinetics database, we investigate the post-shock high-enthalpy air flows by employing both the StS and coarse-grained models (CGM). The CGM, which lumps molecular vibrational states into groups, shows results that align with the StS model, even utilizing only two groups for each molecule. However, the CGM-1G model, with only one group per molecule and belonging to the multi-temperature model (but uses StS kinetics), fails to reproduce the StS results. Analysis of vibrational energy source terms for different kinetic processes and fractions of vibrational groups reveals that the deficiency of the CGM-1G model stems from the overestimation of high-lying vibrational states, leading to higher dissociation rates and increased consumption of vibrational energy in dissociation. Furthermore, the presence of the Zeldovich-exchange processes indirectly facilitates energy transfer in N2 and O2, a phenomenon not observed in binary gas systems. These findings have important implications for developing the reduced-order model based on coarse-grained treatment.

Experimental modal identification of a pedestrian bridge through drive-by monitoring integrated with shared-mobility vehicles

In the context of improving resilience in transport infrastructure, an emerging approach of indirect structural health monitoring is gaining attention, known as drive-by monitoring, instrumenting a vehicle with sensors to evaluate the bridges it crosses. However, their effectiveness has predominantly been investigated in ideal scenarios, with actual real-world applications being quite scarce. The research presented in this paper explores the feasibility of combining two routes: (1) fleet emissions reduction and (2) transport resilience enhancement, through drive-by monitoring integrated with shared mobility, including electric mobility scooter and bicycle. The information from drive-by data collected from shared mobility can give valuable information on critical transport infrastructure (e.g., bridges) and such a drive-by database has the potential for network level infrastructure condition assessment. In this paper, two different drive-by roadmaps are investigated subject to the flexibility of the bridges, namely partially or fully drive-by monitoring. To validate the proposed roadmaps, a full-scale pedestrian bridge was chosen for drive-by testing, where smartphone sensors and specialised accelerometers are mounted on shared mobility for data acquisition. Experimental results demonstrate that (i) smartphone sensing can provide data with similar accuracy compared to specialised accelerometers, (ii) bridge frequencies can be easily obtained from temporarily parked shared mobility, with a maximum relative error of 1.05%, (iii) both the bridge frequencies and operational deflection shapes are successfully extracted from the moving shared mobility by using variational mode decomposition and filtering techniques, and shared mobility’s GPS data along with moving speeds are collected for potential vehicle positioning and drive-by database updating.

Evaluation of age estimation using alveolar bone images

ObjectiveThe purpose of this study is to examine the time-related changes of alveolar bone in 2D images quantitatively and to estimate age groups based on the change index. Materials and methodsThe 238 panoramic X-ray images and 140 CT panoramic reconstructed images of the permanent dentition period were used to examine age-related changes. Comparisons between the younger age group and each of the other age groups were calculated using the landmark method of Procrustes analysis. As aging changes were observed in each age group, age estimation was performed using antemortem panoramic X-ray images and postmortem CT images so that they could be used in practice. The CT images used in the age estimation were performed using forty-two postmortem CT panoramic reconstructed images of known age submitted to the judicial autopsy. ResultsBoth panoramic and CT images showed changes in the alveolar bone over time. Age estimation using postmortem CT images provided a certain assessment. ConclusionIn this study, clinically observed changes in alveolar bone over time were quantified on the images. Furthermore, the possibility of age estimation by alveolar bone was also suggested. The use of an updatable clinical database that can be stored in coordinate values offers the potential for age estimation in line with the times.

Development and implementation of effective Web Scraping methods for automated data collection and processing using Python

With each passing year, the process of digitalization in society is accelerating, leading to a significant increase in demand for processed and analyzed information. In today's world, data has become a valuable resource, and the ability to quickly find and analyze large amounts of information is a key competitive advantage for companies, researchers, and analysts. In this context, web scraping has become an important tool, enabling the efficient collection of data from various online sources for further analysis and informed decision-making. This paper examines the latest advancements in the development and implementation of effective web scraping methods for automatic data collection and processing using Python. The use of the latest Python libraries, such as BeautifulSoup, Selenium, and Scrapy, allows for high-speed and accurate data collection from various web sources, particularly in secondary markets. The proposed algorithms reduce the risk of site blocking, ensuring the stability and reliability of data collection in various situations. Additionally, the paper places great emphasis on automating the data collection process through the development of automated scripts and the implementation of job scheduling programs, such as cron jobs. This ensures continuous database updates and the collection of new information without manual intervention. Special attention is given to the processing and cleaning of collected data, particularly in the development of methods for filtering out unnecessary information, duplicates, and noise, which enhances data quality. The efficient use of the collected data demonstrates its value for market analysis, demand assessment, and quality forecasting, highlighting the importance of the proposed method. The research includes examples of real-world data use cases in various fields such as marketing, economics, and business analysis. A comparative analysis of different data collection methods is also provided, allowing for the assessment of the effectiveness and reliability of the proposed solutions.

PTMD 2.0: an updated database of disease-associated post-translational modifications.

Various post-translational modifications (PTMs) participate in nearly all aspects of biological processes by regulating protein functions, and aberrant states of PTMs are frequently associated with human diseases. Here, we present a comprehensive database of PTMs associated with diseases (PTMD 2.0), including 342 624 PTM-disease associations (PDAs) in 15 105 proteins for 93 types of PTMs and 2083 diseases.Based on the distinct PTM states in diseases, we classified all PDAs into six categories: upregulation (U) or downregulation (D) of PTM levels, absence (A) or presence (P) of PTMs, and creation (C) or disruption (N) of PTM sites. We provided detailed annotations for each PDA and carefully annotated disease-associated proteins by integrating the knowledge from 101 additional resources that covered 13 aspects, including disease-associated information, variation and mutation, protein-protein interaction, protein functional annotation, DNA and RNA element, protein structure, chemical-target relationship, mRNA expression, protein expression/proteomics, subcellular localization, biological pathway annotation, functional domain annotation and physicochemical property. With a data volume of ∼8 GB, we anticipate that PTMD 2.0 will serve as a fundamental resource for further analysing the relationships between PTMs and diseases. The online service of PTMD 2.0 is freely available at https://ptmd.biocuckoo.cn/.

FlavorDB2: An updated database of flavor molecules.

Flavor is expressed through the interaction of molecules via gustatory and olfactory mechanisms. Knowing the pivotal role of flavor molecules in food and fragrances, the existence of a comprehensive repository becomes valuable. Such a repository encompasses the information of flavor molecules characterizing their flavor profile, chemical properties, regulatory status, consumption statistics, taste/aroma threshold values, reported uses in food categories, and synthesis. FlavorDB2 (https://cosylab.iiitd.edu.in/flavordb2/) is an updated database of flavor molecules with a user-friendly interface consisting of 25,595 flavor molecules. Among these, 2254 flavor molecules are associated with 936 natural ingredients from 34 categories. This repository simplifies the search for flavor molecules and their associated attributes. This platform offers multifaceted applications, including food pairing. FlavorDB2 serves as a standard repository of flavor compounds for researchers, chefs, and fragrance industry.

Long-term monitoring of water quality and phytoplankton community structure of Lake Manzala, Mediterranean Coast of Egypt

Coastal wetlands play a crucial role in supporting biodiversity, providing valuable ecosystem services, and contributing to the resilience of coastal ecosystems, making the preservation and restoration of these wetlands essential for sustainable development in coastal regions. This study focuses on Lake Manzala, a coastal wetland located on the Mediterranean Coast of Egypt, highlighting the significance of conserving and managing this unique environment. The objective of this research was to evaluate the water quality and phytoplankton structure of Lake Manzala and establish an updated long-term ecological database for the region, specifically evaluating the effectiveness of development plans that were carried out in 2017. Surface water samples were collected seasonally at eleven sites between 2010 to 2022. The findings revealed that prior to the development plans, the phytoplankton abundance in Lake Manzala exhibited high levels of eutrophication, characterized by increased abundance and species richness. The dominant phytoplankton classes in Lake Manzala were Bacillariophyceae, Chlorophyceae, and Cyanophyceae. Prior to development plans, they accounted for 46.5% and 45.0% respectively. Post-development, Bacillariophyceae increased to 62.8%, while Chlorophyceae decreased to 25.1%. Dinophyceae increased from 1.3% to 9.04%, while Cyanophyceae decreased from 6.1% to 1.6%. Based on the Trophic State Index for chlorophyll a, Lake Manzala underwent a shift from predominantly hypertrophic to eutrophic conditions. The study explored the relationship between biological factors and environmental conditions using principal component analysis, cluster analysis, and the modified water quality index (WQI). The results indicated positive signs of improvement in Lake Manzala during the post-development phase, as it transitioned from a poor to a moderate state. This research emphasizes the need for integrated land and water management approaches. By informing policy direction and development, this research underscores the importance of preserving and restoring ecosystems for the long-term well-being of both local communities and the global environment.