Matteo Di Bernardo

Functional genetics reveals a role for the proteasome in regulating centromere assembly

The paper identifies the proteasome's role in regulating CENP-T levels to prevent non-centromere kinetochore assembly.

Systematic functional genetic analysis of cell-substrate adhesion

This study identifies gene targets involved in cell-substrate adhesion through functional genetic analyses in human cells, revealing dynamic requirements and novel regulators.

Brieflow: An Integrated Computational Pipeline for High-Throughput Analysis of Optical Pooled Screening Data

Brieflow is a computational pipeline for analyzing optical pooled screening data, demonstrating its ability to recover biological modules missed by existing methods.

A forward genetic screen identifies Sirtuin1 as a driver of neuroendocrine prostate cancer

The paper uses a forward genetic screen in a mouse model to identify Sirtuin1 as a driver of neuroendocrine prostate cancer, showing its role in promoting NEPC and potential as a therapeutic target.

The one-week automated genome-wide optical pooled screen

The paper introduces OttoSeq, an automated optical pooled screen platform that completes a genome-wide cell painting screen in eight days, analyzing over 5 million cells across 21,732 gene knockouts.

5' UTR length regulates alternative N-terminal protein isoform production in health and disease

This study shows that 5' UTR length influences alternative N-terminal protein isoform production through start codon selection and demonstrates this in health and disease.

TMPO promotes cellular dissemination and metastasis in circulating tumor cells

This study identifies TMPO as a driver of CTC heterogeneity and metastasis in prostate cancer by enhancing survival under metabolic stress.

Alternative start codon selection shapes mitochondrial function during evolution, homeostasis, and disease

The paper identifies differentially-localized protein isoforms generated by alternative start codon selection and shows their role in mitochondrial function, evolution, and disease.

Rapid protein evolution by few-shot learning with a protein language model

This paper introduces EVOLVEpro, a few-shot learning framework using protein language models to rapidly improve protein activity with minimal experimental data.