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Associated data (v1.0.0) for the pending publication of: Liu Z & Samee MAH. Structural Underpinnings of Mutation Rate Variations in the Human Genome. Nucleic Acids Research. The directory is compressed as a zipped tarball (.tar.gz). Run `tar -xzf` or `tar -xvzf` to extract the contents. Please review the README.md file for a more detailed description., Please refer to the Codeberg repository (https://codeberg.org/sameelab/mutprediction-with-shape) for up-to-date changes and correspondences., {"references":["Aggarwala V, Voight BF. An expanded sequence context model broadly explains variability in polymorphism levels across the human genome. https://doi.org/10.1038/ng.3511","Kheradpour P, Kellis M. Systematic discovery and characterization of regulatory motifs in ENCODE TF binding experiments. https://doi.org/10.1093/nar/gkt1249"]}

Antibiotics resistant pathogenic bacteria are one of the emerging challenges of 21st century. Recently, researchers are looking for antibacterials (therapeutic agents) from ethnomedicinal indigenous plants to be considered new sources to meet the emerging challenges. These antibacterials could be used as an alternative to conventional antibiotics against infectious human diseases. The antibacterial properties of ethnomedicinal plants used by the local people of Brahmanbaria were determined. Ethnomedicinal data was collected in between June 2015 and June 2017 from 467 local people using mainly key informant’s interviews followed by group discussions, filed interviews and plant interviews. A total of 243 ethnomedicinal plants were recorded. In order to identify the most important ethnomedicinal plants Factor of informant consensus (Fic) was calculated and 8 medicinal plants were identified based on the higher consensus of informants. These are Litseaglutinosa (Lour.), Robinson, Scopariadulcis (L.), Dalbergiasissoo (Miq.), Clerodendrumviscosum (Vent.), Holarrhena antidysenterica (L.) Wall.exDecne., Phyllanthus reticulatus (Poir.), Paederia foetida (L.) and Stephania japonica (Thunb.) Miers. Both the aqueous and ethanol extracts of leaves were screened for antibacterial activity against 8 clinical strains through disc diffusion assays. The experimental results revealed that maximum (85%) plant extracts were potentially effective in inhibiting growth of pathogenic bacteria with variable potency. Litseaglutinosa was the most effective plant species retarding microbial growth of eight tested pathogenic bacteria. This plant can be evaluated for production of potential herbal antimicrobials alternative to antibiotics. Bangladesh J. Bot. 51(4): 779-786, 2022 (December)

Detailed taxonomic description of Najas marina L. including information on flowering and fruiting time, autecology, and distribution in Bangladesh are provided. The diagnostic characters with illustrations and behavioural patterns of different habitats are also provided. Presence of Najas marina in Joydia baor indicates that the water quality of this baor is fairly good and minor human interference of the habitats. Bangladesh J. Plant Taxon. 29(2): 183-191, 2022 (December)

An ethnomedicinal investigation was carried out in Sherpur Sadar and Sreebardi upazilas of Sherpur district to record, and document the traditional knowledge alongside with determining the consensus factor, citation frequency and fidelity level among the folklore medicinal practitioners. A total 51 plant species belonging to 49 genera and 38 families were cited with their mode of application for treating different ailments. The most frequently used plant species were represented by herbs (35.94%) followed by trees (33.33%), shrubs (19.61) and climbers (11.76). Leaves were found to be the most utilized part (50%) followed by root (14%), fruit (10%), flower (10%), stem (10%), bark (4%) and seed (2%). The reported ailments were categorized into 14 diseases and the maximum species were employed to treat digestive and gastrointestinal disorders. Leaves of Eclipta alba (L.) Hassk. are used in treatment of cataract without applying in eyes at the initial stage which is the first report for Bangladesh, and this species could be further screened for bioactive compound which can lead to discovery of new and potential drugs. Many species reported in the current study were found to be very rare which need to be conserved to maximize the sustainable uses of these vital resources in the study area. Bangladesh J. Plant Taxon. 29(2): 269-282, 2022 (December)

Haploinsufficiency of TGF-beta-activated kinase 1 (MAP3K7) binding protein 2 (TAB2) has been associated with congenital heart disease and more recently multiorgan structural abnormalities. Missense variant represents a major proportion of non-synonymous TAB2 variants reported in gnomAD (295/576) and Clinvar (16/73), most of which are variants of uncertain significance (VUSs). However, interpretation of TAB2 missense variants remains challenging because of lack of functional assays. To address this issue, we established a cell-based luciferase assay that enables high-throughput screening of TAB2 variants to assess the functional consequence for predicting variant pathogenicity. Using this platform, we screened 47 TAB2 variants including five pathogenic controls and one benign control, and the results showed that the transcriptional activity of activator protein 1 (AP-1) but not nuclear factor kappa B predicts the TAB2 variant pathogenicity. This assay provides accurate functional readout for both loss-of-function (LOF) and gain-of-function variants, which are associated with distinct phenotypes. In all, 22 out of 32 tested VUSs were reclassified. Genotype–Phenotype association showed that most patients with partial LOF variants do not exhibit congenital heart disease but high frequency of developmental delay, hypotonia and dysmorphic features, which suggests that genetic testing for TAB2 is needed for a broader spectrum of patients with more diverse phenotypes. Molecular modeling with Npl4 zinc finger (NZF) domain variants revealed that the stability of the NZF domain in TAB2 protein is crucial for AP-1 activation. In conclusion, we developed a highly effective functional assay for TAB2 variant prediction and interpretation.

Cancer has been one of the most dominant causes of mortality globally over the last few decades. In cancer treatment, the selective targeting of tumor cells is indispensable, making it a better replacement for conventional chemotherapies by diminishing their adverse side effects. While designing a drug to be delivered selectively in the target organ, the drug development scientists should focus on various factors such as the type of cancer they are dealing with according to which drug, targeting moieties, and pharmaceutical carriers should be targeted. All published articles have been collected regarding cancer and drug-targeting approaches from well reputed databases including MEDLINE, Embase, Cochrane Library, CENTRAL and ClinicalTrials.gov, Science Direct, PubMed, Scopus, Wiley, and Springer. The articles published between January 2010 and December 2020 were considered. Due to the existence of various mechanisms, it is challenging to choose which one is appropriate for a specific case. Moreover, a combination of more than one approach is often utilized to achieve optimal drug effects. In this review, we have summarized and highlighted central mechanisms of how the targeted drug delivery system works in the specific diseased microenvironment, along with the strategies to make an approach more effective. We have also included some pictorial illustrations to have a precise idea about different types of drug targeting. The core contribution of this work includes providing a cancer drug development scientist with a broad preliminary idea to choose the appropriate approach among the various targeted drug delivery mechanisms. Also, the study will contribute to improving anticancer treatment approaches by providing a pathway for lesser side effects observed in conventional chemotherapeutic techniques.

The genus Asparagus Tourn. ex Linn. represented by five species in Bangladesh, viz., Asparagus adscendens Roxb., A. densiflorus (Kunth) J.P. Jessop, A. officinalis L., A. racemosus Willd. and A. setaceus (Kunth) J.P. Jessop is revised. Each species is described with updated nomenclature, important synonyms, phenology, specimens examined, chromosome number, habitat, distribution, economic value and mode of propagation. A dichotomous bracketed key to the species and illustrations along with photographs of four species are provided. Bangladesh J. Plant Taxon. 29(1): 97-107, 2022 (June)

A new variety Abrus precatorius L. var. albo-spermum Hassan, Rahman et Afroz, var. nov. is described and illustrated from Bangladesh. The new variety is closest to Abrus preactorius L., but differs by its white coloured oval shaped seeds, short gynoecium which is half the length of longer filaments and with a short style. Bangladesh J. Plant Taxon. 28(2): 289-294, 2021 (December)

Hypoxia is a hallmark of many diseases, including cancer, arthritis, heart and kidney diseases, and diabetes, and it is often associated with disease aggressiveness and poor prognosis. Consequently, there is a critical need for imaging hypoxia in a noninvasive and direct way to diagnose, stage, and monitor the treatment and development of new therapies for these diseases. Eu-containing contrast agents for magnetic resonance imaging have demonstrated potential for in vivo imaging of hypoxia via changes in metal oxidation state from +2 to +3, but rapid oxidation in blood limits Eu

not available Bangladesh J. Plant Taxon. 29(1): 157-159, 2022 (June)

Spatial transcriptomics (ST) is an emerging technology that quantifies gene expression at spatial resolution from intact tissue sections. Although ST is enabling unprecedented studies on spatial gene expression, it has posed new challenges to biological data science. A typical ST dataset contains information of ~20K genes from 50K-100K cells. It is challenging to design efficient and scalable algorithms that generate new biological insights from these datasets. Here we feature an efficient and scalable non-negative matrix factorization (NMF) algorithm for identifying “spatial gene modules” (spatial-gems), i.e., groups of genes that express at spatially adjacent locations, in ST data. Spatial-gems are fundamental aspects of multi-cellular organisms. NMF is suitable for this problem since, in theory, NMF can identify the “informative parts” constituting a dataset, e.g., lips and eyes in human facial images and spatial-gems in ST data. The basic NMF formulation, however, can give sub-optimal results for spatial datasets – it ignores spatial locations of data points and thus does not guarantee informative parts that are spatially coherent. Graph-regularized NMF (GNMF) overcomes this issue by constraining the informative parts to comprise spatially adjacent data points. We introduce three changes to tailor the state-of-the-art GNMF algorithm for ST data. First, we statistically determine the optimal number of spatial-gems in an ST dataset. Secondly, we introduce regularizations that minimize the number of genes common between spatial-gems. Finally, we leverage numerical libraries and efficient data structures to obtain a scalable implementation. We benchmarked our GNMF against alternative algorithms on a brain ST dataset. Our algorithm comprehensively charted the spatial-gems in this dataset with a 20x speedup in execution time, making this an attractive tool for large-scale ST consortia like HuBMAP (Human BioMolecular Atlas Program). This tool and our multifaceted approach to enhance efficiency and scalability will be of major interest to the broad userbase of TACC.

A fundamental limitation of the emerging single-cell spatial transcriptomics (sc-ST) technologies is their panel size. Being based on fluorescence in situ hybridization, an sc-ST dataset can profile only a pre-determined panel of a few hundred genes. This often forces biologists to build panels from only the marker genes of different cell types and forgo other genes of interest, e.g., genes encoding ligand-receptor complexes or genes in specific pathways. We propose scGIST– a deep neural network that designs sc-ST panels through constrained feature selection. On four datasets, scGIST outperformed alternative methods in terms of cell type detection accuracy. Moreover, unlike other methods, scGIST allows genes of interest to be prioritized for inclusion in the panel while staying within the its size constraint. We demonstrate through diverse use cases that scGIST includes large fractions of prioritized genes without compromising cell type prediction efficacy making it a valuable addition to sc-ST’s algorithmic toolbox.

Spatial transcriptomics (ST) holds the promise to identify the existence and extent of spatial variation of gene expression in complex tissues. Such analyses could help identify gene expression signatures that distinguish between healthy and disease samples. Existing tools to detect spatially variable genes assume a constant noise variance across location. This assumption might miss important biological signals when the variance could change across spatial locations, e.g., in the tumor microenvironment. In this paper, we propose NoVaTeST, a framework to identify genes with location-dependent noise variance in ST data. NoVaTeST can model gene expression as a function of spatial location with a spatially variable noise. We then compare the model to one with constant noise to detect genes that show significant spatial variation in noise. Our results show genes detected by NoVaTeST provide complimentary information to existing tools while providing important biological insights.

Viral transcription factors (vTFs) are known to bind the human genome and impact critical biological processes. However, a vTF can infect multiple cell-types, and the extent to which they deploy the same mechanisms for DNA binding across different cell-types is poorly understood. We used the Epstein-Barr virus Nuclear Antigen Proteins 2 and 3 (EBNA2 and EBNA3) to address this gap, the two widely studied vTFs associated with cancers, as case studies. We analyze multiple ChIP-seq datasets of these vTFs from different human cell-lines using a state-of-the-art convolutional neural network model. We show that each vTF uses both cell-type specific and cell-type independent cofactors to bind the human genome. Interestingly, each vTF requires ∼10 cofactors consistently across all binding peaks. Such dependency on a large number of cofactors is in contrast to human TFs since human TFs typically co-bind with 2-3 cofactors. Our de novo motif finding approach also reveals novel sequence motifs at the ChIP peaks of these vTFs. These critical signals were missed by previous studies doing enrichment analysis using known human TFs. Finally, we find that although a vTF impacts similar biological processes across cell-types, it also impacts distinct biological processes in a cell-type specific manner. Our study provides a roadmap for integrative analysis of vTF binding and pinpointing their diverse mechanisms of targeting the human genome across different human cell-types.

The bendability of genomic DNA impacts chromatin packaging and protein-DNA binding. However, beyond a handful of known sequence motifs, such as certain dinucleotides and poly(A)/poly(T) sequences, we do not have a comprehensive understanding of the motifs influencing DNA bendability. Recent high-throughput technologies like Loop-Seq offer an opportunity to address this gap but the lack of accurate and interpretable machine learning models still poses a significant challenge. Here we introduce DeepBend, a convolutional neural network model built as a visible neural network where we designed the convolutions to directly capture the motifs underlying DNA bendability and how their periodic occurrences or relative arrangements modulate bendability. Through extensive benchmarking on Loop-Seq data, we show that DeepBend consistently performs on par with alternative machine learning and deep learning models while giving an extra edge through mechanistic interpretations. Besides confirming the known motifs of DNA bendability, DeepBend also revealed several novel motifs and showed how the spatial patterns of motif occurrences influence bendability. DeepBend’s genome-wide prediction of bendability further showed how bendability is linked to chromatin conformation and revealed the motifs controlling bendability of topologically associated domains and their boundaries.