Biology subjects

Suresh, H.

Publications and source records attributed to Suresh, H..

11 recordsLinked to original sources

bioRxiv2025

The misplaced mouse Pax6 interneuron subclass: A cross-species transcriptomic reassignment

Subclass cell-types are strongly conserved across mammalian brains, making the systemic absence of a caudal ganglionic eminence (CGE) Pax6 interneuron subclass in mouse transcriptional atlases unexpected. Through cross-species transcriptomic analysis, we identify a Pax6 subclass homolog in mouse and uncover primate-specific divergence in the Sncg subclass. Our results highlight the pitfalls of relying on single marker genes and provide evolution-aware annotations and marker sets to support robust cross-mammal interneuron comparisons.

neuroscience↗

bioRxiv2025

A Bioelectrochemical Crossbar Architecture Screening Platform (BiCASP) for Extracellular Electron Transfer

Electroactive microbes can be used as components in electrical devices to leverage their unique behavior for biotechnology, but they remain challenging to engineer because the bioelectrochemical systems (BES) used for characterization are low-throughput. To overcome this challenge, we describe the development of the Bioelectrochemical Crossbar Architecture Screening Platform (BiCASP), which allows for samples to be arrayed and characterized in individually addressable microwells. This device reliably reports on the current generated by electroactive bacteria on the minute time scale, decreasing the time for data acquisition by several orders of magnitude compared to conventional BES. Also, this device increased the throughput of screening engineered biological components in cells, quickly identifying mutants of the membrane protein wire MtrA in Shewanella oneidensis that retain the ability to support extracellular electron transfer (EET). BiCASP is expected to enable the design of new components for bioelectronics by supporting directed evolution of electroactive proteins. The bigger pictureDevices that interface microbes and materials, known as bioelectronics, can be used to sense environmental chemicals in real time, generate energy from sugars, and synthesize chemicals. While these devices leverage the unique capabilities of living systems as components in devices, such as their ability to convert chemical information in the environment into electrical information at the cell surface, it remains challenging to engineer these cellular components and their biomolecules for new applications, largely because commercially available bioelectrochemical systems for monitoring current generated by electroactive microbes are costly and require large culture volumes, needs continuous monitoring for days to obtain stable signals, and multichannel potentiostats to monitor multiple microbes in parallel. To overcome these challenges, we created the Bioelectrochemical Crossbar Architecture Screening Platform or BiCASP that is easy to fabricate, enables parallel analysis of microbial samples in flexible arrayed formats, and yields a stable signal on the minute time scale. This device is expected to enable the application of combinatorial protein engineering methods, such as directed evolution, to proteins that control microbial current production, by allowing for fast screening of cells expressing protein mutant libraries. As a proof-of-concept, we demonstrate that this device can screen for cells that express mutants of decaheme cytochromes that retain the ability to electrically connect cells to electrodes. This device will simplify the engineering of cells and proteins that function as electrical switches as well as the diversification of bioelectronic devices for real-time sensing of chemicals in the environment. Furthermore, BiCASP is promising as a high-throughput screening (HTS) platform, enabling rapid, parallel analysis of cellular and molecular interactions of diverse biological systems through label-free electrochemical methods. Such capabilities could transform drug discovery, personalized medicine, and functional genomics, supporting systematic genetic and chemical screens even at single-cell resolution. HighlightsO_LIA high-throughput screening platform with individual addressability C_LIO_LIA device with a flexible crossbar architecture that simplifies current analysis C_LIO_LIReproducible detection of real-time cellular current on the minute time scale C_LIO_LIThe device can be used to screen a library for cells with functional protein wires C_LI Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=187 HEIGHT=200 SRC="FIGDIR/small/663982v1_ufig1.gif" ALT="Figure 1"> View larger version (57K): org.highwire.dtl.DTLVardef@c451d1org.highwire.dtl.DTLVardef@d7a50corg.highwire.dtl.DTLVardef@194e986org.highwire.dtl.DTLVardef@16f7af9_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology↗

bioRxiv2025

Cluster replicability in single-cell and single-nucleus atlases of the mouse brain

Single-cell RNA sequencing has advanced our understanding of cellular heterogeneity. Ensuring the replicability of identified cell clusters across studies is essential for determining their biological robustness. We assess the replicability of cell clusters identified in two large mouse brain atlases, one generated using single-cell RNA sequencing and the other with single nuclei. Both profile over 4 million cells and group them into over 5000 clusters. Using transcriptome-wide neighbor voting, we identify 2009 reciprocally matched cluster pairs with consistent spatial localization and coordinated gene expression, which were also observed in datasets from multiple species. Reciprocal clusters are enriched in the cerebellum, where lower diversity aids replicability, while the hypothalamuss heterogeneity limits agreement. Distinguishing close clusters is much more challenging than differentiating a cluster from most others, especially when using marker genes. By incorporating replicability data, we provide a stronger foundation for investigating the newly identified clusters and their biological meaning.

neuroscience↗

bioRxiv2024

Solanum pan-genomics and pan-genetics reveal paralogs as contingencies in crop engineering

Pan-genomics and genome editing technologies are revolutionizing the breeding of globally cultivated crops. A transformative opportunity lies in the reciprocal exchange of genotype-to-phenotype knowledge of agricultural traits between these major crops and hundreds of locally cultivated indigenous crops, thereby enhancing the diversity and resilience of our food system. However, species-specific genetic variants and their interactions with desired natural or engineered mutations pose barriers to achieving predictable phenotypic effects, even between closely related crops or genotypes. Here, by establishing a pan-genome of the crop-rich genus Solanum and integrating functional genomics and genetics, we show that gene duplication and subsequent paralog diversification are a major obstacle to genotype-phenotype predictability. Despite broad conservation of gene macrosynteny among chromosome-scale references for 22 species, including 13 indigenous crops, hundreds of global and lineage-specific gene duplications exhibited dynamic evolutionary trajectories in paralog sequence, expression, and function, including among members of key domestication gene families. Extending our pan-genome with 10 cultivars of African eggplant and leveraging quantitative genetics and genome editing, we uncovered an intricate history of paralog emergence and evolution within this indigenous crop. The loss of an ancient redundant paralog of the classical regulator of stem cell proliferation and fruit organ number, CLAVATA3 (CLV3), was compensated by a lineage-specific tandem duplication. Subsequent pseudogenization of the derived copy followed by a cultivar-specific structural variant resulted in a single fused functional copy of CLV3 that modifies locule number alongside a newly identified gene controlling the same trait. Our findings demonstrate that paralog diversifications over short evolutionary periods are critical yet underexplored contingencies in trait evolvability and independent crop domestication histories. Unraveling these contingencies is crucial for translating genotype-to-phenotype relationships across related species.

genetics↗

bioRxiv2024

The wide spectrum anti-inflammatory activity of andrographolide in comparison to NSAIDs: a promising therapeutic compound against the cytokine storm

The challenges of the COVID-19 pandemic have highlighted an increasing clinical demand for safe and effective treatment options against an overzealous immune defence response, also known as the "cytokine storm". Andrographolide is a naturally derived bioactive compound with promising anti-inflammatory activity in many clinical studies. However, its cytokine-inhibiting activity, in direct comparison to commonly used nonsteroidal anti-inflammatory drugs (NSAIDs), has not been extensively investigated in existing literature. The anti-inflammatory activities of andrographolide and common NSAIDs, such as diclofenac, aspirin, paracetamol and ibuprofen were measured on lipopolysaccharide (LPS) and interferon-{gamma} induced RAW264.7 cells. The levels of PGE2, nitric oxide (NO), TNF- & LPS-induced release of pro-inflammatory cytokines on differentiated human macrophage THP-1 cells were measured against increasing concentrations of andrographolide and aforementioned NSAIDs. The associated mechanistic pathway was examined on NF{kappa}B using flow cytometry on the human endothelial-leukocyte adhesion molecule (ELAM9) (E-selectin) transfected RAW264.7 cells with green fluorescent protein (GFP). Andrographolide exhibited broad and potent anti-inflammatory and cytokine-inhibiting activity in both cell lines by inhibiting the release of IL-6, TNF- and IFN-{gamma}, which are known to play a key role in the etiology of cytokine storm and the pathogenesis of inflammation. In comparison, the tested NSAIDs demonstrated weak or no activity against proinflammatory mediators except for PGE2, where the activity of andrographolide (IC50 = 8.8 {micro}M, 95% CI= 7.4 to 10.4 {micro}M) was comparable to that of paracetamol (IC50 = 7.73 {micro}M, 95% CI = 6.14 to 9.73 {micro}M). The anti-inflammatory action of andrographolide was associated with its potent downregulation of NF{kappa}B. The wide-spectrum anti-inflammatory activity of andrographolide demonstrates its therapeutic potential against cytokine storms as an alternative to NSAIDs.

pharmacology and toxicology↗

bioRxiv2022

Conserved coexpression at single cell resolution across primate brains

Enhanced cognitive function in humans is hypothesized to result from cortical expansion and increased cellular diversity. However, the mechanisms that drive these phenotypic differences remain poorly understood, in part due to the lack of high-quality cellular resolution data in human and non-human primates. Here, we take advantage of single cell expression data from the middle temporal gyrus of five primates (human, chimp, gorilla, macaque and marmoset) to identify 57 homologous cell types and generate cell-type specific gene coexpression networks for comparative analysis. While ortholog expression patterns are generally well conserved, we find 24% of genes with extensive differences between human and non-human primates (3383/14,131), which are also associated with multiple brain disorders. To validate these observations, we perform a meta-analysis of coexpression networks across 19 animals, and find that a subset of these genes have deeply conserved coexpression across all non-human animals, and strongly divergent coexpression relationships in humans (139/3383, <1% of primate orthologs). Genes with human-specific cellular expression and coexpression networks (like NHEJ1, GTF2H2, C2 and BBS5) typically evolve under relaxed selective constraints and may drive rapid evolutionary change in brain function. One Sentence SummaryCross-primate middle temporal gyrus single cell expression data reveals patterns of conservation and divergence that can be validated with population coexpression networks.

systems biology↗

bioRxiv2022

Comparative transcriptomics reveals human-specific cortical features

Humans have unique cognitive abilities among primates, including language, but their molecular, cellular, and circuit substrates are poorly understood. We used comparative single nucleus transcriptomics in adult humans, chimpanzees, gorillas, rhesus macaques, and common marmosets from the middle temporal gyrus (MTG) to understand human-specific features of cellular and molecular organization. Human, chimpanzee, and gorilla MTG showed highly similar cell type composition and laminar organization, and a large shift in proportions of deep layer intratelencephalic-projecting neurons compared to macaque and marmoset. Species differences in gene expression generally mirrored evolutionary distance and were seen in all cell types, although chimpanzees were more similar to gorillas than humans, consistent with faster divergence along the human lineage. Microglia, astrocytes, and oligodendrocytes showed accelerated gene expression changes compared to neurons or oligodendrocyte precursor cells, indicating either relaxed evolutionary constraints or positive selection in these cell types. Only a few hundred genes showed human-specific patterning in all or specific cell types, and were significantly enriched near human accelerated regions (HARs) and conserved deletions (hCONDELS) and in cell adhesion and intercellular signaling pathways. These results suggest that relatively few cellular and molecular changes uniquely define adult human cortical structure, particularly by affecting circuit connectivity and glial cell function.

neuroscience↗

bioRxiv2022

Dissociable default-mode subnetworks subserve childhood attention and cognitive flexibility: evidence from deep learning and stereotaxic electroencephalography

BackgroundCognitive flexibility encompasses the ability to efficiently shift focus and forms a critical component of goal-directed attention. The neural substrates of this process are incompletely understood in part due to difficulties in sampling the involved circuitry. MethodsStereotactic intracranial recordings that permit direct resolution of local-field potentials from otherwise inaccessible structures were employed to study moment-to-moment attentional activity in children with epilepsy during the performance of an attentional set-shifting task. A combined deep learning and model-agnostic feature explanation approach was used to analyze these data and decode attentionally-relevant neural features. Connectomic profiling of highly predictive attentional nodes was further employed to examine task-related engagement of large-scale functional networks. ResultsThrough this approach, we show that beta/gamma power within executive control, salience, and default mode networks accurately predicts single-trial attentional performance. Connectomic profiling reveals that key attentional nodes exclusively recruit dorsal default mode subsystems during attentional shifts. ConclusionsThe identification of distinct substreams within the default mode system supports a key role for this network in cognitive flexibility and attention in children. Furthermore, convergence of our results onto consistent functional networks despite significant inter-subject variability in electrode implantations supports a broader role for deep learning applied to intracranial electrodes in the study of human attention. FundingNo funds supported this specific investigation. Awards and grants supporting authors include: Canadian Institutes of Health Research (CIHR) Vanier Scholarship (NMW, HY); CIHR Frederick Banting and Charles Best Canada Graduate Scholarship Doctoral Award (SMW); CIHR Canada Graduate Scholarship Masters Award (ONA); and a CIHR project grant (GMI).

neuroscience↗

bioRxiv2020

Coexpression reveals conserved mechanisms of transcriptional cell identity

What makes a mouse a mouse, and not a hamster? The answer lies in the genome, and more specifically, in differences in gene regulation between the two organisms: where and when each gene is expressed. To quantify differences, a typical study will either compare functional genomics data from homologous tissues, limiting the approach to closely related species; or compare gene repertoires, limiting the resolution of the analysis to gross correlations between phenotypes and gene family size. As an alternative, gene coexpression networks provide a basis for studying the evolution of gene regulation without these constraints. By incorporating data from hundreds of independent experiments, meta-analytic coexpression networks reflect the convergent output of species-specific transcriptional regulation. In this work, we develop a measure of regulatory evolution based on gene coexpression. Comparing data from 14 species, we quantify the conservation of coexpression patterns 1) as a function of evolutionary time, 2) across orthology prediction algorithms, and 3) with reference to cell- and tissue-specificity. Strikingly, we uncover deeply conserved patterns of gradient-like expression across cell types from both the animal and plant kingdoms. These results suggest that ancient genes contribute to transcriptional cell identity through mechanisms that are independent of duplication and divergence.

bioinformatics↗

bioRxiv2019

Free-energy-based framework for early forecasting of stem cell differentiation

Commitment of stem cells to different lineages is inherently stochastic but regulated by a range of environmental bio/chemo/mechanical cues. Here we develop an integrated stochastic modelling framework for predicting the differentiation of hMSCs in response to a range of environmental cues including sizes of adhesive islands, stiffness of substrates and treatment with ROCK inhibitors in both growth and mixed media. The statistical framework analyses the fluctuations of cell morphologies over around a 24-hour period after seeding the cells in the specific environment and uses the distribution of their cytoskeletal free-energy to forecast the lineage the hMSCs will commit to. The cytoskeletal free-energy which succinctly parameterises the biochemical state of the cell is shown to capture hMSC commitment over a range of environments while simple morphological factors such as cell shape, tractions on their own are unable to correlate with lineages hMSCs adopt.

biophysics↗

bioRxiv2018

Entropic forces drive cellular contact guidance

Contact guidance--the widely-known phenomenon of cell alignment induced by anisotropic environmental features--is an essential step in the organization of adherent cells, but the mechanisms by which cells achieve this orientational ordering remain unclear. Here we seeded myofibroblasts on substrates micropatterned with stripes of fibronectin and observed that contact guidance emerges at stripe widths much greater than the cell size. To understand the origins of this surprising observation, we combined morphometric analysis of cells and their subcellular components with a novel statistical framework for modelling non-thermal fluctuations of living cells. This modelling framework is shown to predict not only the trends but also the statistical variability of a wide range of biological observables including cell (and nucleus) shapes, sizes and orientations, as well as stress-fibre arrangements within the cells with remarkable fidelity. By comparing observations and theory, we identified two regimes of contact guidance: (i) guidance on stripe widths smaller than the cell size (w [≤] 160 m), which is accompanied by biochemical changes within the cells, including increasing stress-fibre polarisation and cell elongation, and (ii) entropic guidance on larger stripe widths, which is governed by fluctuations in the cell morphology. Overall, our findings suggest an entropy-mediated mechanism for contact guidance associated with the tendency of cells to maximise their morphological entropy through shape fluctuations.

biophysics↗