Biology subjects

Syed, R.

Publications and source records attributed to Syed, R..

7 recordsLinked to original sources

bioRxiv2025

Knockout of Rab27b exacerbates neuropathology in alpha-synuclein mouse models

Parkinsons Disease (PD) and other synucleinopathies are characterized by the formation of inclusions comprised of alpha-synuclein (syn) among other proteins, but the mechanisms by which these inclusions form and cause toxicity are not well understood. We have previously reported that the small GTPase Rab27b modulates autophagic-lysosomal function in neurons and supports lysosomal degradation of syn across multiple syn cellular models. Knockout (KO) and knockdown of Rab27b damage lysosomal degradative capacity and exacerbate syn pathology, while Rab27b overexpression is conversely protective in cellular syn models. Elevations of Rab27b seen in human synucleinopathies suggest a compensatory role for Rab27b in these disorders. Here, we examined the role Rab27b plays in vivo in the context of both A53T genetic syn overexpression and viral AAV syn overexpression mouse models. Rab27b knockout in A53T+ mice did not alter motor behavior or survival. However, Rab27b knockout increased proteinase-K resistant syn in the cortex, striatum, and substantia nigra of A53T mice starting as early as six months of age. Additionally, Rab27b KO increased phosphorylated S129 syn in the cortex and nigra. Astrocyte and microglial activation were also observed upon Rab27b KO in the A53T model. In the AAV syn model, Rab27b KO resulted in accelerated dopaminergic cell loss in the nigra. Collectively, we report that loss of Rab27b results in elevated neuropathology in PD-relevant brain regions, validating its role as a therapeutic target in synucleinopathies.

neuroscience↗

bioRxiv2024

Intraguild predation among snails drives human schistosome amplification despite susceptible host regulation

Competitors and predators of hosts have the potential to alter transmission dynamics within host- parasite systems. Biocontrol aims to harness these effects to mitigate diseases of human or agricultural importance, but without a detailed understanding of the mechanisms of how hosts and non-hosts interact over time these attempts may backfire. We investigated the role that two resource competitors that have been employed in biocontrol, Melanoides tuberculata and Physa acuta, play in the Biomphalaria glabrata-Schistosoma mansoni system. We created experimental communities with differing compositions in mesocosms and tracked resource availability, host snail abundance, size, reproduction, and parasite production over 16 weeks. We found that Melanoides acted as a typical, albeit weak, resource competitor of Biomphalaria, reducing host body size and per capita cercarial production but with no appreciable influence on host abundance. Physa, however, reduced host abundance but increased resource availability and cercarial production, which represents biocontrol failure and is inconsistent with solely acting as a resource competitor. In follow-up experiments, we determined that Physa is a voracious consumer of host eggs. We then built a model representing this intraguild predation effect, which was able to explain these initially counterintuitive results. These divergent results from two putative resource competitors of host snails underscore the importance of establishing the mechanisms through which hosts, non-hosts, and parasites interact, especially for anticipating the potential outcomes of biocontrol interventions.

ecology↗

bioRxiv2024

Sleep-wake states are encoded across emotion-regulation regions of the mouse brain

Emotional dysregulation is highly comorbid with sleep disturbances. Sleep is comprised of unique physiological states that are reflected by conserved brain oscillations. Though the role of these state-dependent oscillations in cognitive function has been well established, less is known regarding the nature of state-dependent oscillations across brain regions that strongly contribute to emotional function. To characterize these dynamics, we recorded local field potentials simultaneously from multiple cortical and subcortical regions implicated in sleep and emotion-regulation and characterize widespread patterns of spectral power and synchrony between brain regions during sleep/wake states. First, we showed that these brain regions encode sleep state, albeit to various degrees of accuracy. We then identified network-based classifiers of sleep based on the combination of features from all recorded brain regions. Spectral power and synchrony from brain networks allowed for automatic, accurate and rapid discrimination of wake, non-REM sleep (NREM) and rapid eye movement (REM) sleep. When we examined the impact of commonly prescribed sleep promoting medications on neural dynamics across these regions, we found disparate alterations to both cortical and subcortical activity across all three states. Finally, a we found that a stress manipulation that disrupts circadian rhythm produced increased sleep fragmentation without altering the underlying average brain dynamics across sleep-wake states. Thus, we characterized state dependent brain dynamics across regions canonically associated with emotions. Significance StatementSleep and emotion regulation are known to be intertwined at the level of behavior and in neuropsychiatric illnesses. Here, we examined how brain regions involved in emotion regulation encode wake and sleep states by performing multi-site electrophysiological recordings in mice. We developed sleep-wake state classifiers that rapidly labeled sleep-wake states from brain activity alone. We then identified how commonly prescribed sleep-inducing medications have unique impacts on brain activity throughout these emotion-regulation regions. Finally, we explored the impact of circadian rhythm disruption on sleep architecture and brain activity. Together, these data shed light on how brain regions which regulate emotion behave during sleep so that one day, treatments to improve both sleep and emotional well-being may be developed.

neuroscience↗

bioRxiv2024

Identification of type 2 diabetes- and obesity-associated human β-cells using deep transfer learning

Diabetes affects >10% of adults worldwide and is caused by impaired production or response to insulin, resulting in chronic hyperglycemia. Pancreatic islet {beta}-cells are the sole source of endogenous insulin and our understanding of {beta}-cell dysfunction and death in type 2 diabetes (T2D) is incomplete. Single-cell RNA-seq data supports heterogeneity as an important factor in {beta}-cell function and survival. However, it is difficult to identify which {beta}-cell phenotypes are critical for T2D etiology and progression. Our goal was to prioritize specific disease-related {beta}-cell subpopulations to better understand T2D pathogenesis and identify relevant genes for targeted therapeutics. To address this, we applied a deep transfer learning tool, DEGAS, which maps disease associations onto single-cell RNA-seq data from bulk expression data. Independent runs of DEGAS using T2D or obesity status identified distinct {beta}-cell subpopulations. A singular cluster of T2D-associated {beta}-cells was identified; however, {beta}-cells with high obese-DEGAS scores contained two subpopulations derived largely from either non-diabetic or T2D donors. The obesity-associated non-diabetic cells were enriched for translation and unfolded protein response genes compared to T2D cells. We selected CDKN1C and DLK1 for validation by immunostaining in human pancreas sections from healthy and T2D donors. Both CDKN1C and DLK1 were heterogeneously expressed among {beta}-cells. CDKN1C was increased in {beta}-cells from T2D donors, in agreement with the DEGAS predictions, while DLK1 appeared depleted from T2D islets of some donors. In conclusion, DEGAS has the potential to advance our holistic understanding of the {beta}-cell transcriptomic phenotypes, including features that distinguish {beta}-cells in obese non-diabetic or lean T2D states. Future work will expand this approach to additional human islet omics datasets to reveal the complex multicellular interactions driving T2D.

bioinformatics↗

bioRxiv2022

Parasite transmission in size-structured populations

Host heterogeneity can impact parasite transmission, but determining underlying traits and incorporating them into transmission models remains challenging. Body size is easily measured and influences numerous ecological interactions, including transmission. In the snail-schistosome system, larger snails have a higher exposure to parasites but lower susceptibility to infection per parasite. We quantified the impact of size-based heterogeneity on population-level transmission by conducting transmission trials in differently size-structured snail populations and competing size-dependent transmission models. Populations with greater proportions of large snails had lower prevalence, and small snails were shielded from infection by co-occurring large conspecifics. Using the winning size-explicit model, we then estimated that schistosome transmission varies dramatically across time due to seasonal changes in snail population size structure. Thus, incorporating traits such as body size, which are impacted by and directly affect host ecology, into transmission models could yield insights for natural dynamics and disease mitigation in many systems. Open Research StatementAll experimental data and novel code used for data analysis are currently available on Figshare via this private link: https://figshare.com/s/7d70b88220b912e7eec5. Data will be made public on Figshare upon acceptance. Data set utilized for Figure 4 are from the following source: Rumi, A., D. Gutierrez Gregoric, and A. Roche. 2009. Tendencias Del Crecimiento Individual en Poblaciones Naturales de Biomphalaria spp. (Gastropoda, Planorbidae) en la Cuenca del Plata, Argentina. Comunicaciones de la Sociedad Malacologica del Uruguay. URL: https://www.redalyc.org/articulo.oa?id=52414008002 O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=151 SRC="FIGDIR/small/519457v2_fig4.gif" ALT="Figure 4"> View larger version (22K): org.highwire.dtl.DTLVardef@18be9forg.highwire.dtl.DTLVardef@8ad57eorg.highwire.dtl.DTLVardef@c674b6org.highwire.dtl.DTLVardef@11876df_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 4.C_FLOATNO Predicted infection success of a single schistosome parasite given exposure of a snail in a natural population with varying body size structure over time. A) Distribution of body sizes from a population of Biomphalaria tenagophilia in del Plata Basin, Argentina from 2000-2001(Rumi et al. 2009). Number of snails sampled at each timepoint represented as n=. B) Points are the mean {+/-} 95% confidence interval of the predicted infection success of a single schistosome introduced into this snail population from simulated transmission trials using the fully size-dependent model. Grey line is the predicted successful infection from a transmission model that ignores size-based exposure and susceptibility. Substantial changes in snail population size structure cause month-to-month estimates of schistosome success to diverge substantially from predictions that ignore size dependence. In particular, the size-null prediction tends to overestimate schistosome success except at the times in which the population is heavily dominated by small snails. C_FIG

ecology↗

bioRxiv2019

Benchmarking Database Systems for Genomic Selection Implementation

MotivationWith high-throughput genotyping systems now available, it has become feasible to fully integration genotyping information into breeding programs [22]. To make use of this information effectively requires DNA extraction facilities and marker production facilities that can efficiently deploy the desired set of markers across samples with a rapid turnaround time that allows for selection before crosses needed to be made. In reality, breeders often have a short window of time to make decisions by the time they are able collect all their phenotyping data and receive corresponding genotyping data. This presents a challenge to organize information and utilize them in downstream analyses to support decisions made by breeders. In order to implement genomic selection routinely as part of breeding programs one would need an efficient genotype data storage system. We selected and benchmarked six popular open-source data storage systems, including relational database management and columnar storage systems.\n\nResultsWe found that data extract times are greatly influenced by the orientation in which genotype data is stored in a system. HDF5 consistently performed best, in part because it can more efficiently work with both orientations of the allele matrix.\n\nAvailabilityhttp://gobiinx1.bti.cornell.edu:6083/projects/GBM/repos/benchmarking/browse\n\nContactyn259@cornell.edu

genomics↗

bioRxiv2017

The Habc Domain of Syntaxin 3 is a Ubiquitin Binding Domain with Specificity for K63-Linked Poly-Ubiquitin Chains

Syntaxins are a family of membrane-anchored SNARE proteins that are essential components required for membrane fusion in all eukaryotic intracellular membrane trafficking pathways. Syntaxins contain an N-terminal regulatory domain, termed the Habc domain, that is not highly conserved at the primary sequence level but folds into a three-helix bundle that is structurally conserved among family members. The syntaxin Habc domain has previously been found to be structurally very similar to the GAT domain present in GGA family members and related proteins that are otherwise completely unrelated to syntaxins. Because the GAT domain has been found to be a ubiquitin binding domain we hypothesized that the Habc domain of syntaxins may also bind to ubiquitin. Here, we report that the Habc domain of syntaxin 3 (Stx3) indeed binds to monomeric ubiquitin with low affinity. This domain binds efficiently to K63-linked poly-ubiquitin chains within a narrow range of chain lengths but not to K48-linked poly-ubiquitin chains. Other syntaxin family members also bind to K63-linked polyubiquitin chains but with different chain length specificities. Molecular modeling suggests that residues of the GGA3-GAT domain known to be important for ionic and hydrophobic interactions with ubiquitin have equivalent, conserved residues within the Habc domain of Stx3. We conclude that the syntaxin Habc domain and the GAT domain are both structurally and functionally related, and likely share a common ancestry despite sequence divergence. Binding of K63-linked poly-ubiquitin chains to the Habc domain may regulate the function of syntaxins in membrane fusion or may suggest additional functions of this protein family.

cell biology↗