Evidence map›Paper›PMID 41108412›Full record

ArticleArchives of microbiology2025

Uncovering therapeutic targets in Leishmania donovani through comparative network topology analysis.

Upasana Hazarika, Anupam Nath Jha

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Article in Archives of microbiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Upasana HazarikaComputational Biophysics Laboratory, Department of Molecular Biology and Biotechnology, Tezpur University, Napam, Sonitpur, Tezpur, Assam, 784028, India.
Anupam Nath JhaComputational Biophysics Laboratory, Department of Molecular Biology and Biotechnology, Tezpur University, Napam, Sonitpur, Tezpur, Assam, 784028, India. anjha@tezu.ac.in.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Leishmaniasis is a significant vector-borne health problem affecting 88 countries, transmitted by the bite of female sandflies (phlebotomine). It manifests in various clinical forms, such as diffuse cutaneous leishmaniasis, mucocutaneous leishmaniasis, and visceral leishmaniasis (VL). The most chronic and possibly fatal form, VL, is caused by Leishmania donovani (L. donovani). The critical need for novel, effective therapeutic targets is highlighted by the escalating costs, toxicity, and side effects of existing treatments, as well as the increasing occurrence of drug-resistant parasites. The emergence of computational biology domains, including Bioinformatics and systems biology, has opened up a plethora of opportunities for researching and discovering therapeutic compounds for illnesses using in-silico techniques. Enzymes involved in key metabolic pathways of L. donovani were used to construct a PPI network, followed by topological analysis employing centrality measures such as degree, closeness, bottleneck, and betweenness centrality (BC). Gene Ontology (GO) enrichment indicated that the most connected proteins are involved in essential biological processes, including redox regulation, energy metabolism, and nucleic acid synthesis. MCODE clustering identified high-degree hub proteins within top modules. From this integrative network-based analysis, we propose four proteins that recur across multiple pathways and exhibit high centrality and modularity scores, underscoring their functional significance. These proteins likely serve as chokepoints or regulatory hubs, making them promising multi-pathway drug targets. Their inhibition may disrupt multiple vital cellular functions simultaneously, offering an effective strategy against multidrug-resistant L. donovani strains. This study provides a computational framework to prioritize and validate novel therapeutic targets in L. donovani, contributing to the development of more effective treatments for visceral leishmaniasis.

Indexed as

Antiprotozoal AgentsLeishmania donovaniProtozoan ProteinsComputational BiologyHumansLeishmaniasis, VisceralMetabolic Networks and PathwaysProtein Interaction MapsAntiprotozoal AgentsProtozoan ProteinsDrug targetHub proteinLeishmania donovani (L. donovani)Molecular complex detection (MCODE)Protein-protein interaction (PPI)Topological analysis

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.