Biology subjects

Lakhani, A.

Publications and source records attributed to Lakhani, A..

7 recordsLinked to original sources

bioRxiv2024

Whisker deprivation triggers a distinct form of cortical homeostatic plasticity that is impaired in the Fmr1 KO

Mouse models of Fragile X Syndrome (FXS) have demonstrated impairments in excitatory and inhibitory sensory-evoked neuronal firing. Homeostatic plasticity, which encompasses a set of mechanisms to stabilize baseline activity levels, does not compensate for these changes in activity. Previous work has shown that impairments in homeostatic plasticity are observed in FXS, including deficits in synaptic scaling and intrinsic excitability. Here, we aimed to examine how homeostatic plasticity is altered in vivo in an Fmr1 KO mouse model following unilateral whisker deprivation (WD). We show that WD in the wild type leads to an increase in the proportion of L5/6 somatosensory neurons that are recruited, but this does not occur in the KO. In addition, we observed a change in the threshold of excitatory neurons at a later developmental stage in the KO. Compromised homeostatic plasticity in development could influence sensory processing and long-term cortical organization.

neuroscience↗

bioRxiv2024

Effectiveness of a network Open House model to recruit trainees to post-baccalaureate STEM programs

Post-baccalaureate (post-bac) programs can be instrumental in strengthening science training and expanding STEM career opportunities for junior trainees. Many of these sponsored programs are designed to increase research exposure for underrepresented minorities, including African American, Hispanic, Native American, and first-generation college students, among others. Recruiting trainees to post-bac programs can be challenging for reasons including a lack of awareness about available programs. To address this gap, an Open House event was created with the goal of raising awareness and generating interest among potential students for future post-bac programs. Students were recruited from partnering Minority Serving Institutions (MSIs) to attend a two-day event at a Primarily Undergraduate Institution (PUI) and a Research-Intensive (R1) institution. During the visit, students toured both campuses, learned about various post-bac programs and research opportunities, and interacted with faculty, current graduate students, and a former post-bac scholar. Transportation, lodging, and meals were provided. Participants completed voluntary pre- and post-surveys. Results indicated that attendees, the majority of whom were underrepresented minorities in STEM, left with a stronger understanding of post-bac programs and how these experiences could support their future careers in STEM and that students attendance at the event made it more likely they would apply to available post-bac programs. Mentor and MSI faculty survey responses highlighted their strong support for participating in future recruitment events. These findings demonstrate that in-person Open House events, built on collaborative partnerships across institutions, are an effective strategy for increasing awareness and encouraging participation in post-bac training programs-- particularly among underrepresented student populations.

scientific communication and education↗

bioRxiv2023

Determination of Metabolic Fluxes by Deep Learning of Isotope Labeling Patterns

All life forms operate metabolism in constant flux. Metabolic fluxes offer a direct readout of cellular state, detailing the rates and driving forces of metabolic pathways. However, indirect, iterative solvers for mapping isotope patterns from tracing experiments onto metabolic fluxes leave much of cellular state uncharted. Here, we streamline metabolic flux quantitation by innovating a machine learning framework, ML-Flux, that deciphers complex isotope labeling patterns. We train neural networks using isotope pattern-flux pairs across central carbon metabolism from 26 key 13C-glucose, 2H-glucose, and 13C-glutamine tracers. ML-Flux takes variable-size isotope labeling patterns as input, imputes missing isotope patterns, and outputs mass-balanced metabolic fluxes. Computation of fluxes using ML-Flux is more accurate and faster than that of leading metabolic flux analysis software employing a least-squares method. Our biochemical networks and machine learning models constitute a curated and growing online knowledgebase of metabolic flux and free energy to democratize quantitative metabolic profiling.

systems biology↗

bioRxiv2023

Extracellular Domains of CAR Reprogram T-Cell Metabolism Without Antigen Stimulation

Metabolism is an indispensable part of T-cell proliferation, activation, and exhaustion, yet the metabolism of chimeric antigen receptor (CAR)-T cells remains incompletely understood. CARs are comprised of extracellular domains that determine cancer specificity, often using single-chain variable fragments (scFvs), and intracellular domains that trigger signaling upon antigen binding. Here we show that CARs differing only in the scFv reprogram T-cell metabolism differently. Even in the absence of antigens, some CARs increase proliferation and nutrient uptake in T cells. Using stable isotope tracers and mass spectrometry, we observe basal metabolic fluxes through glycolysis doubling and amino acid uptake overtaking anaplerosis in CAR-T cells harboring rituximab scFv, unlike other similar anti-CD20 scFvs. Disparate rituximab and 14g2a-based anti-GD2 CAR-T cells are similarly hypermetabolic and channel excess nutrients to nitrogen overflow metabolism. Since CAR-dependent metabolic reprogramming alters cellular energetics, nutrient utilization, and proliferation, metabolic profiling should be an integral part of CAR-T cell development.

systems biology↗

bioRxiv2021

Dicer promotes genome stability via the bromodomain transcriptional co-activator Brd4.

RNA interference is essential for transcriptional silencing and genome stability, but conservation of this role in mammals has been difficult to demonstrate. Dicer1-/- mouse embryonic stem cells have microRNA-independent proliferation defects, and we conducted a CRISPR-Cas9 screen to restore viability. We identified suppressor mutations in transcriptional activators, H3K9 methyltransferases, and chromosome segregation factors, strongly resembling Dicer suppressors in fission yeast. Suppressors rescued chromosomal defects, and reversed strand-specific transcription of major satellite repeats in Dicer1-/-. The strongest suppressors were in Brd4, and in the transcriptional elongator/histone acetyltransferase Elp3. Using viable mutants and pharmaceutical inhibitors, we demonstrate that deletion of specific residues in Brd4 rescue genome instability defects of Dicer1-/- in both mammalian cells and fission yeast, implicating Dicer in coordinating transcription and replication of satellite repeats. SummaryReplication and segregation defects in Dicer1-/- stem cells depend on centromeric transcription by Brd4, and are deeply conserved in fission yeast.

genetics↗

bioRxiv2020

Rational Tuning of CAR Tonic Signaling Yields Superior T-Cell Therapy for Cancer

Chimeric antigen receptors (CARs) are modular proteins capable of redirecting immune cells toward a wide variety of disease-associated antigens. Here, we explore the effects of CAR protein sequence and structure on CAR-T cell function. Based on the empirical observation that CD20 CARs with similar sequences exhibit divergent tonic-signaling and anti-tumor activities, we devised engineering strategies that aimed to improve CAR-T cell function by tuning the intensity of tonic signaling. We found that CARs designed to exhibit low but non-zero levels of tonic signaling show robust effector function upon antigen stimulation while avoiding premature functional exhaustion by CAR-T cells. Through alterations of the CARs ligand-binding domain and overall protein conformation, we generated CD20 CAR variants that outperform the CD19 CAR in mouse models of human lymphoma. We further demonstrate that rational modification of protein confirmation can be generalized to improve GD2 CAR-T cell efficacy against neuroblastoma. These findings point to tonic signaling and basal T-cell activation as informative parameters to guide the rational design of next-generation CARs for cancer therapy.

bioengineering↗

bioRxiv2019

Noninvasive Sleep Scoring in Mice using Electric Field Sensors

BackgroundRodent sleep scoring in principally reliant on electroencephalogram (EEG) and electromyogram (EMG), but this approach is invasive, can be expensive, and requires expertise and specialized equipment. Affordable, simple to use, and noninvasive ways to accurately quantify rodent sleep are needed.\n\nNew methodWe developed and validated a new method for sleep-wake staging in mice using cost-effective, noninvasive electric field (EF) sensors that detect respiration and other movements. We validated recordings from EF sensors attached to the exterior of specialty chambers used to continuously capture sleep with EEG/EMG, then compared this to EF sensors attached to vivarium home-cages.\n\nResultsEF sensors quantified 3-state sleep architecture (wake, rapid eye movement - REM - sleep, and non-REM sleep) with high agreement (>93%) and comparable inter- and intra-scorer error as expert EEG/EMG scoring. Novices given an instruction document with examples were able to score sleep comparable to expert scorers (>91% agreement). Additionally, EF sensors were able to quantify 3-state sleep scoring in traditional mouse home cages.\n\nComparison with existing methodMost noninvasive sleep assessment technology requires animal contact, altered cage environments, and/or can only discern 2 states of arousal (wake or asleep). The EF sensors are able to discriminate REM from non-REM sleep accurately and from outside the animals home cage.\n\nConclusionsEF sensors provide a simple and reliable method to accurately score 3-state sleep architecture; (i) from outside the typical home cage, (ii) where noninvasive approaches are preferred, or (iii) which EEG/EMG is not possible.\n\nGraphical Abstract\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=110 SRC=\"FIGDIR/small/794552v1_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (43K):\norg.highwire.dtl.DTLVardef@1b36cf3org.highwire.dtl.DTLVardef@b5fdc9org.highwire.dtl.DTLVardef@28a6dforg.highwire.dtl.DTLVardef@e39a5a_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗