The image was divided into a 2-row by 3-column equal-area grid, resulting in 6 blocks. The numbering goes left to right, top to bottom: Block 1 = top-left, Block 2 = top-middle, Block 3 = top-right, Block 4 = bottom-left, Block 5 = bottom-middle, Block 6 = bottom-right.


The image was divided into a 1-row by 3-column equal-area grid, resulting in 3 blocks. The numbering goes left to right: Block 1 = left, Block 2 = middle, Block 3 = right.


The image was divided into a 2-row by 3-column equal-area grid, resulting in 6 blocks. The numbering goes left to right, top to bottom: Block 1 = first row, left; Block 2 = first row, middle; Block 3 = first row, right; Block 4 = second row, left; Block 5 = second row, middle; Block 6 = second row, right.


The image was divided into a 2-row by 2-column equal-area grid, resulting in 4 blocks. The numbering goes left to right, top to bottom: Block 1 = top-left, Block 2 = top-right, Block 3 = bottom-left, Block 4 = bottom-right.


The image was divided into a 1-row by 1-column equal-area grid, resulting in 1 block. The numbering goes left to right, top to bottom: Block 1 = single block.


The image was divided into a 3-row by 2-column equal-area grid, resulting in 6 blocks. The numbering goes left to right, top to bottom: Block 1 = first row, left; Block 2 = first row, right; Block 3 = second row, left; Block 4 = second row, right; Block 5 = third row, left; Block 6 = third row, right.


The image was divided into a 1-row by 2-column equal-area grid, resulting in 2 blocks. The numbering goes left to right: Block 1 = left, Block 2 = right.


The image was divided into a 1-row by 2-column equal-area grid, resulting in 2 blocks. The numbering goes left to right: Block 1 = left Block 2 = right


The image was divided into a 2-row by 1-column equal-area grid, resulting in 2 blocks. The numbering goes top to bottom: Block 1 = top Block 2 = bottom


The image was divided into a 3-row by 1-column equal-area grid, resulting in 3 blocks. The numbering goes top to bottom: Block 1 = top Block 2 = middle Block 3 = bottom


The image was divided into a 2-row by 3-column equal-area grid, resulting in 6 blocks. The numbering goes left to right, top to bottom: Block 1 = top-left Block 2 = top-middle Block 3 = top-right Block 4 = bottom-left Block 5 = bottom-middle Block 6 = bottom-right


The image was divided into a 2-row by 2-column equal-area grid, resulting in 4 blocks. The numbering goes left to right, top to bottom: Block 1 = top-left; Block 2 = top-right; Block 3 = bottom-left; Block 4 = bottom-right.


The image was divided into a 2-row by 3-column equal-area grid, resulting in 6 blocks. The numbering goes left to right, top to bottom: Block 1 = first row, left Block 2 = first row, middle Block 3 = first row, right Block 4 = second row, left Block 5 = second row, middle Block 6 = second row, right


The image was divided into a 3-row by 2-column equal-area grid, resulting in 6 blocks. The numbering goes left to right, top to bottom: Block 1 = first row, left Block 2 = first row, right Block 3 = second row, left Block 4 = second row, right Block 5 = third row, left Block 6 = third row, right


The image was divided into a 1-row by 5-column equal-area grid, resulting in 5 blocks. The numbering goes left to right: Block 1 = leftmost, Block 2 = second from left, Block 3 = third from left, Block 4 = fourth from left, Block 5 = rightmost.


The image was divided into a 1-row by 4-column equal-area grid, resulting in 4 blocks. The numbering goes left to right: Block 1 = left, Block 2 = middle-left, Block 3 = middle-right, Block 4 = right.


The image was divided into a 1-row by 5-column equal-area grid, resulting in 5 blocks. The numbering goes left to right: Block 1 = left, Block 2 = middle-left, Block 3 = middle-right, Block 4 = right, Block 5 = extra.


The image was divided into a 3-row by 2-column equal-area grid, resulting in 6 blocks. The numbering goes left to right, top to bottom: Block 1 = top-left, Block 2 = top-right, Block 3 = middle-left, Block 4 = middle-right, Block 5 = bottom-left, Block 6 = bottom-right.


The image was divided into a 2-row by 3-column equal-area grid, resulting in 6 blocks. The numbering goes left to right, top to bottom: Block 1 = top-left, Block 2 = top-middle, Block 3 = top-right, Block 4 = bottom-left, Block 5 = bottom-middle, Block 6 = bottom-right.


The image was divided into a 2-row by 3-column equal-area grid, resulting in 6 blocks. The numbering goes left to right, top to bottom: Block 1 = first row, left, Block 2 = first row, middle, Block 3 = first row, right, Block 4 = second row, left, Block 5 = second row, middle, Block 6 = second row, right.


The image was divided into a 2-row by 3-column equal-area grid, resulting in 6 blocks. The numbering goes left to right, top to bottom: Block 1 = first row, left; Block 2 = first row, middle; Block 3 = first row, right; Block 4 = second row, left; Block 5 = second row, middle; Block 6 = second row, right.


The image was divided into a 2-row by 2-column equal-area grid, resulting in 4 blocks. The numbering goes left to right, top to bottom: Block 1 = top-left, Block 2 = top-right, Block 3 = bottom-left, Block 4 = bottom-right.


The image was divided into a 3-row by 2-column equal-area grid, resulting in 6 blocks. The numbering goes left to right, top to bottom: Block 1 = first row, left; Block 2 = first row, right; Block 3 = second row, left; Block 4 = second row, right; Block 5 = third row, left; Block 6 = third row, right.


The image was divided into a 1-row by 3-column equal-area grid, resulting in 3 blocks. The numbering goes left to right: Block 1 = left, Block 2 = middle, Block 3 = right.


The image was divided into a 1-row by 5-column equal-area grid, resulting in 5 blocks. The numbering goes left to right: Block 1 = leftmost Block 2 = second from the left Block 3 = middle Block 4 = second from the right Block 5 = rightmost


The image was divided into a 1-row by 2-column equal-area grid, resulting in 2 blocks. The numbering goes left to right: Block 1 = left, Block 2 = right.


The image was divided into a 2-row by 2-column equal-area grid, resulting in 4 blocks. The numbering goes left to right, top to bottom: Block 1 = top-left Block 2 = top-right Block 3 = bottom-left Block 4 = bottom-right


The image was divided into a 1-row by 5-column equal-area grid, resulting in 5 blocks. The numbering goes left to right: Block 1 = left, Block 2 = middle-left, Block 3 = middle-right, Block 4 = right, Block 5 = far-right.


The image was divided into a 3-row by 2-column equal-area grid, resulting in 6 blocks. The numbering goes left to right, top to bottom: Block 1 = first row, left Block 2 = first row, right Block 3 = second row, left Block 4 = second row, right Block 5 = third row, left Block 6 = third row, right.


The image was divided into a 2-row by 3-column equal-area grid, resulting in 6 blocks. The numbering goes left to right, top to bottom: Block 1 = top-left; Block 2 = top-middle; Block 3 = top-right; Block 4 = bottom-left; Block 5 = bottom-middle; Block 6 = bottom-right.


The image was divided into a 3-row by 1-column equal-area grid, resulting in 3 blocks. The numbering goes top to bottom: Block 1 = top, Block 2 = middle, Block 3 = bottom.


The image was divided into a 5-row by 1-column equal-area grid, resulting in 5 blocks. The numbering goes top to bottom: Block 1 = top, Block 2 = second, Block 3 = third, Block 4 = fourth, Block 5 = bottom.


The image was divided into a 2-row by 2-column equal-area grid, resulting in 4 blocks. The numbering goes left to right, top to bottom: Block 1 = top-left; Block 2 = top-right; Block 3 = bottom-left; Block 4 = bottom-right.


The image was divided into a 2-row by 3-column equal-area grid, resulting in 6 blocks. The numbering goes left to right, top to bottom: Block 1 = first row, left Block 2 = first row, middle Block 3 = first row, right Block 4 = second row, left Block 5 = second row, middle Block 6 = second row, right


The image was divided into a 3-row by 2-column equal-area grid, resulting in 6 blocks. The numbering goes left to right, top to bottom: Block 1 = first row, left Block 2 = first row, right Block 3 = second row, left Block 4 = second row, right Block 5 = third row, left Block 6 = third row, right


The image was divided into a 2-row by 3-column equal-area grid, resulting in 6 blocks. The numbering goes left to right, top to bottom: Block 1 = top-left, Block 2 = top-middle, Block 3 = top-right, Block 4 = bottom-left, Block 5 = bottom-middle, Block 6 = bottom-right.


The image was divided into a 3-row by 1-column equal-area grid, resulting in 3 blocks. The numbering goes top to bottom: Block 1 = top Block 2 = middle Block 3 = bottom


The image was divided into a 1-row by 3-column equal-area grid, resulting in 3 blocks. The numbering goes left to right: Block 1 = left Block 2 = middle Block 3 = right


The image was divided into a 2-row by 1-column equal-area grid, resulting in 2 blocks. The numbering goes top to bottom: Block 1 = top Block 2 = bottom


The image was divided into a 2-row by 2-column equal-area grid, resulting in 4 blocks. The numbering goes left to right, top to bottom: Block 1 = top-left, Block 2 = top-right, Block 3 = bottom-left, Block 4 = bottom-right.


The image was divided into a 1-row by 1-column equal-area grid, resulting in 1 block. The numbering goes left to right: Block 1 = only block.


The image was divided into a 1-row by 2-column equal-area grid, resulting in 2 blocks. The numbering goes left to right: Block 1 = left Block 2 = right


The image was divided into a 3-row by 2-column equal-area grid, resulting in 6 blocks. The numbering goes left to right, top to bottom: Block 1 = first row, left, Block 2 = first row, right, Block 3 = second row, left, Block 4 = second row, right, Block 5 = third row, left, Block 6 = third row, right.


The image was divided into a 2-row by 3-column equal-area grid, resulting in 6 blocks. The numbering goes left to right, top to bottom: Block 1 = first row, left; Block 2 = first row, middle; Block 3 = first row, right; Block 4 = second row, left; Block 5 = second row, middle; Block 6 = second row, right.


The image was divided into a 3-row by 2-column equal-area grid, resulting in 6 blocks. The numbering goes left to right, top to bottom: Block 1 = first row, left; Block 2 = first row, right; Block 3 = second row, left; Block 4 = second row, right; Block 5 = third row, left; Block 6 = third row, right.


The image was divided into a 2-row by 3-column equal-area grid, resulting in 6 blocks. The numbering goes left to right, top to bottom:  Block 1 = first row, left  Block 2 = first row, middle  Block 3 = first row, right  Block 4 = second row, left  Block 5 = second row, middle  Block 6 = second row, right


The image was divided into a 1-row by 1-column equal-area grid, resulting in 1 block. The numbering goes left to right, top to bottom: Block 1 = single block.


The image was divided into a 1-row by 1-column equal-area grid, resulting in 1 block. The numbering goes left to right: Block 1 = single block occupying the entire area.


The image was divided into a 2-row by 2-column equal-area grid, resulting in 4 blocks. The numbering goes left to right, top to bottom: Block 1 = top-left Block 2 = top-right Block 3 = bottom-left Block 4 = bottom-right


The image was divided into a 1-row by 3-column equal-area grid, resulting in 3 blocks. The numbering goes left to right: Block 1 = left, Block 2 = middle, Block 3 = right


The image was divided into a 1-row by 3-column equal-area grid, resulting in 3 blocks. The numbering goes left to right: Block 1 = left, Block 2 = middle, Block 3 = right.


The image was divided into a 3-row by 2-column equal-area grid, resulting in 6 blocks. The numbering goes left to right, top to bottom: Block 1 = first row, left Block 2 = first row, right Block 3 = second row, left Block 4 = second row, right Block 5 = third row, left Block 6 = third row, right


The image was divided into a 1-row by 1-column equal-area grid, resulting in 1 block. The numbering goes left to right, top to bottom: Block 1 = single block covering the entire area.


The image was divided into a 2-row by 3-column equal-area grid, resulting in 6 blocks. The numbering goes left to right, top to bottom: Block 1 = top-left, Block 2 = top-middle, Block 3 = top-right, Block 4 = bottom-left, Block 5 = bottom-middle, Block 6 = bottom-right.


The image was divided into a 5-row by 1-column equal-area grid, resulting in 5 blocks. The numbering goes top to bottom: Block 1 = top, Block 2 = second from top, Block 3 = middle, Block 4 = second from bottom, Block 5 = bottom.


The image was divided into a 2-row by 1-column equal-area grid, resulting in 2 blocks. The numbering goes top to bottom: Block 1 = top Block 2 = bottom


The image was divided into a 2-row by 1-column equal-area grid, resulting in 2 blocks. The numbering goes top to bottom: Block 1 = top, Block 2 = bottom.


The image was divided into a 1-row by 2-column equal-area grid, resulting in 2 blocks. The numbering goes left to right: Block 1 = left, Block 2 = right.


The image was divided into a 2-row by 2-column equal-area grid, resulting in 4 blocks. The numbering goes left to right, top to bottom: Block 1 = top-left, Block 2 = top-right, Block 3 = bottom-left, Block 4 = bottom-right.


The image was divided into a 1-row by 1-column equal-area grid, resulting in 1 block. The numbering goes left to right, top to bottom: Block 1 = whole image.


The image was divided into a 2-row by 3-column equal-area grid, resulting in 6 blocks. The numbering goes left to right, top to bottom: Block 1 = first row, left; Block 2 = first row, middle; Block 3 = first row, right; Block 4 = second row, left; Block 5 = second row, middle; Block 6 = second row, right.


The image was divided into a 2-row by 3-column equal-area grid, resulting in 6 blocks. The numbering goes left to right, top to bottom: Block 1 = first row, left Block 2 = first row, middle Block 3 = first row, right Block 4 = second row, left Block 5 = second row, middle Block 6 = second row, right


The image was divided into a 3-row by 2-column equal-area grid, resulting in 6 blocks. The numbering goes left to right, top to bottom: Block 1 = first row, left; Block 2 = first row, right; Block 3 = second row, left; Block 4 = second row, right; Block 5 = third row, left; Block 6 = third row, right.


The image was divided into a 2-row by 3-column equal-area grid, resulting in 6 blocks. The numbering goes left to right, top to bottom: Block 1 = first row, left, Block 2 = first row, middle, Block 3 = first row, right, Block 4 = second row, left, Block 5 = second row, middle, Block 6 = second row, right.


The image was divided into a 3-row by 1-column equal-area grid, resulting in 3 blocks. The numbering goes top to bottom: Block 1 = top, Block 2 = middle, Block 3 = bottom.


The image was divided into a 1-row by 5-column equal-area grid, resulting in 5 blocks. The numbering goes left to right: Block 1 = left, Block 2 = middle-left, Block 3 = middle-right, Block 4 = second-right, Block 5 = right.


The image was divided into a 2-row by 2-column equal-area grid, resulting in 4 blocks. The numbering goes left to right, top to bottom: Block 1 = top-left Block 2 = top-right Block 3 = bottom-left Block 4 = bottom-right


The image was divided into a 1-row by 5-column equal-area grid, resulting in 5 blocks. The numbering goes left to right: Block 1 = left, Block 2 = middle-left, Block 3 = middle-right, Block 4 = right, Block 5 = far-right.


The image was divided into a 2-row by 3-column equal-area grid, resulting in 6 blocks. The numbering goes left to right, top to bottom: Block 1 = top-left Block 2 = top-middle Block 3 = top-right Block 4 = bottom-left Block 5 = bottom-middle Block 6 = bottom-right


The image was divided into a 1-row by 3-column equal-area grid, resulting in 3 blocks. The numbering goes left to right: Block 1 = left, Block 2 = middle, Block 3 = right


The image was divided into a 3-row by 2-column equal-area grid, resulting in 6 blocks. The numbering goes left to right, top to bottom: Block 1 = first row, left Block 2 = first row, right Block 3 = second row, left Block 4 = second row, right Block 5 = third row, left Block 6 = third row, right


The image was divided into a 2-row by 3-column equal-area grid, resulting in 6 blocks. The numbering goes left to right, top to bottom: Block 1 = top-left, Block 2 = top-middle, Block 3 = top-right, Block 4 = bottom-left, Block 5 = bottom-middle, Block 6 = bottom-right.


The image was divided into a 1-row by 3-column equal-area grid, resulting in 3 blocks. The numbering goes left to right: Block 1 = left, Block 2 = middle, Block 3 = right.


The image was divided into a 2-row by 3-column equal-area grid, resulting in 6 blocks. The numbering goes left to right, top to bottom:
Block 1 = first row, left
Block 2 = first row, middle
Block 3 = first row, right
Block 4 = second row, left
Block 5 = second row, middle
Block 6 = second row, right


The image was divided into a 2-row by 2-column equal-area grid, resulting in 4 blocks. The numbering goes left to right, top to bottom:
Block 1 = top-left
Block 2 = top-right
Block 3 = bottom-left
Block 4 = bottom-right


The image was divided into a 2-row by 2-column equal-area grid, resulting in 4 blocks. The numbering goes left to right, top to bottom: Block 1 = top-left, Block 2 = top-right, Block 3 = bottom-left, Block 4 = bottom-right.


The image was divided into a 2-row by 2-column equal-area grid, resulting in 4 blocks.
The numbering goes left to right, top to bottom:
Block 1 = top-left
Block 2 = top-right
Block 3 = bottom-left
Block 4 = bottom-right


The image was divided into a 2-row by 3-column equal-area grid, resulting in 6 blocks. The numbering goes left to right, top to bottom: Block 1 = first row, left; Block 2 = first row, middle; Block 3 = first row, right; Block 4 = second row, left; Block 5 = second row, middle; Block 6 = second row, right.


The image was divided into a 2-row by 3-column equal-area grid, resulting in 6 blocks.
The numbering goes left to right, top to bottom:
Block 1 = first row, left
Block 2 = first row, middle
Block 3 = first row, right
Block 4 = second row, left
Block 5 = second row, middle
Block 6 = second row, right


The image was divided into a 3-row by 2-column equal-area grid, resulting in 6 blocks. The numbering goes left to right, top to bottom: Block 1 = first row, left; Block 2 = first row, right; Block 3 = second row, left; Block 4 = second row, right; Block 5 = third row, left; Block 6 = third row, right.


The image was divided into a 1-row by 2-column equal-area grid, resulting in 2 blocks. The numbering goes left to right: Block 1 = left Block 2 = right


The image was divided into a 3-row by 2-column equal-area grid, resulting in 6 blocks. The numbering goes left to right, top to bottom:
Block 1 = first row, left
Block 2 = first row, right
Block 3 = second row, left
Block 4 = second row, right
Block 5 = third row, left
Block 6 = third row, right


The image was divided into a 2-row by 1-column equal-area grid, resulting in 2 blocks. The numbering goes top to bottom: Block 1 = top Block 2 = bottom


The image was divided into a 3-row by 2-column equal-area grid, resulting in 6 blocks.
The numbering goes left to right, top to bottom:
Block 1 = first row, left
Block 2 = first row, right
Block 3 = second row, left
Block 4 = second row, right
Block 5 = third row, left
Block 6 = third row, right


The image was divided into a 1-row by 3-column equal-area grid, resulting in 3 blocks.
The numbering goes left to right:
Block 1 = left
Block 2 = middle
Block 3 = right


The image was divided into a 3-row by 1-column equal-area grid, resulting in 3 blocks.
The numbering goes top to bottom:
Block 1 = top
Block 2 = middle
Block 3 = bottom


The image was divided into a 2-row by 3-column equal-area grid, resulting in 6 blocks.
The numbering goes left to right, top to bottom:
Block 1 = top-left
Block 2 = top-right
Block 3 = bottom-left
Block 4 = bottom-right
Block 5 = second row, left
Block 6 = second row, right


The image was divided into a 2-row by 1-column equal-area grid, resulting in 2 blocks.
The numbering goes top to bottom:
Block 1 = top
Block 2 = bottom


The image was divided into a 1-row by 1-column equal-area grid, resulting in 1 block.
The numbering goes left to right:
Block 1 = whole image


The image was divided into a 2-row by 3-column equal-area grid, resulting in 6 blocks.
The numbering goes left to right, top to bottom:
Block 1 = top-left
Block 2 = top-middle
Block 3 = top-right
Block 4 = bottom-left
Block 5 = bottom-middle
Block 6 = bottom-right


The image was divided into a 1-row by 3-column equal-area grid, resulting in 3 blocks. The numbering goes left to right:
Block 1 = left
Block 2 = middle
Block 3 = right


The image was divided into a 1-row by 2-column equal-area grid, resulting in 2 blocks.
The numbering goes left to right:
Block 1 = left
Block 2 = right


The image was divided into a 1-row by 1-column equal-area grid, resulting in 1 block.
The numbering goes from left to right:
Block 1 = single block


The image was divided into a 2-row by 2-column equal-area grid, resulting in 4 blocks.
The numbering goes left to right, top to bottom:
Block 1 = top-left
Block 2 = top-right
Block 3 = bottom-left
Block 4 = bottom-right


The image was divided into a 2-row by 2-column equal-area grid, resulting in 4 blocks. The numbering goes left to right, top to bottom:
Block 1 = top-left
Block 2 = top-right
Block 3 = bottom-left
Block 4 = bottom-right


The image was divided into a 2-row by 3-column equal-area grid, resulting in 6 blocks.
The numbering goes left to right, top to bottom:
Block 1 = first row, left
Block 2 = first row, middle
Block 3 = first row, right
Block 4 = second row, left
Block 5 = second row, middle
Block 6 = second row, right


The image was divided into a 1-row by 1-column equal-area grid, resulting in 1 block.
The numbering goes left to right:
Block 1 = only block


The image was divided into a 1-row by 1-column equal-area grid, resulting in 1 block.
The numbering goes left to right, top to bottom:
Block 1 = single block covering the entire image.


The image was divided into a 2-row by 4-column equal-area grid, resulting in 8 blocks.
The numbering goes left to right, top to bottom:
Block 1 = first row, left
Block 2 = first row, middle-left
Block 3 = first row, middle-right
Block 4 = first row, right
Block 5 = second row, left
Block 6 = second row, middle-left
Block 7 = second row, middle-right
Block 8 = second row, right


The image was divided into a 1-row by 1-column equal-area grid, resulting in 1 block.
The numbering goes left to right, top to bottom:
Block 1 = only block available


The image was divided into a 3-row by 2-column equal-area grid, resulting in 6 blocks.
The numbering goes left to right, top to bottom:
Block 1 = first row, left
Block 2 = first row, right
Block 3 = second row, left
Block 4 = second row, right
Block 5 = third row, left
Block 6 = third row, right


The image was divided into a 1-row by 3-column equal-area grid, resulting in 3 blocks.
The numbering goes left to right:
Block 1 = left
Block 2 = middle
Block 3 = right


The image was divided into a 3-row by 1-column equal-area grid, resulting in 3 blocks.
The numbering goes top to bottom:
Block 1 = top
Block 2 = middle
Block 3 = bottom


The image was divided into a 1-row by 1-column equal-area grid, resulting in 1 block.
The numbering goes top to bottom, left to right:
Block 1 = single block covering the whole image.


The image was divided into a 2-row by 1-column equal-area grid, resulting in 2 blocks.
The numbering goes top to bottom:
Block 1 = top
Block 2 = bottom


The image was divided into a 5-row by 1-column equal-area grid, resulting in 5 blocks.
The numbering goes top to bottom:
Block 1 = top
Block 2 = second row
Block 3 = third row
Block 4 = fourth row
Block 5 = bottom


The image was divided into a 2-row by 3-column equal-area grid, resulting in 6 blocks.
The numbering goes left to right, top to bottom:
Block 1 = top-left
Block 2 = top-middle
Block 3 = top-right
Block 4 = bottom-left
Block 5 = bottom-middle
Block 6 = bottom-right


The image was divided into a 2-row by 3-column equal-area grid, resulting in 6 blocks. The numbering goes left to right, top to bottom:
Block 1 = first row, left
Block 2 = first row, middle
Block 3 = first row, right
Block 4 = second row, left
Block 5 = second row, middle
Block 6 = second row, right


EDS analysis at the grain boundaries of Fe-6.5 wt % Si steel strip samples: (a) 1.0 wt % Cu, (b) 1.5 wt % Cu, and (c) 2.0 wt % Cu.

Further SEM microstructural examination revealed that Cu rich precipitates were absent in the 1.5 wt % Cu specimen (Figure 8a), but became visible in the 1.5 wt % Cu specimen (Figure 8b). The precipitates in the 1.5 wt % Cu specimen were tiny, few, and irregularly scattered at grain boundaries. Cu-rich precipitates were continuous or semicontinuous at grain boundaries as the Cu dosage was raised to 1.0 wt %, as shown in Figure 8c.

Further SEM microstructural examination revealed that Cu rich precipitates were absent in the 1.0 wt % Cu specimen (Figure 8a),

Cu-rich precipitates were continuous or semicontinuous at grain boundaries as the Cu dosage was raised to 2.0 wt %,


Figure 2. BcR stimulation downregulates cathepsin S activity in IIA1.6 cells. (A)Accumulation of the p10 invariant chain fragment in BcR-stimulated IIA1.6 cells. IIA1.6 cells were incubated for the times indicated with anti–mouse IgG antibodies, as described in Materials and Methods. The p10 fragment was detected with a rabbit antiserum specific for the cytoplasmic tail of the invariant chain. The same blot was incubated with an anti-IA chain antiserum and then with an antitubulin antibody (left). In the right panel, the p10 signal is normalized with the MHC class II signal obtained for the same lane and these p10 signals are expressed in arbitrary units.These results are representative of three independent experiments. (B) IIA1.6 cells were left unstimulated or were stimulated for various times with anti–mouse IgG antibodies and membranes were prepared to assess cathepsin S (top)  hexosaminidase (bottom right), and cathepsin B/L (bottom left) activities in fluorometric assays. Cathepsin S activity was tested at pH 7.0, because cathepsin S is functional at neutral pH, with (white bars) or without (back bars) LHVS, a cathepsin S inhibitor. Error bars correspond to the mean of three experiments. (C) Detection of cathepsin S with an iodinated peptide that binds to the active site of cathepsin S. Stimulated or unstimulated cells were incubated for 1 h with I-Mu-Tyr-Ala-CH2F, with or without LHVS, and cell lysates were then subjected to electrophoresis in 12% polyacrylamide gels. Cathepsin S was detected as a 26-kD band. The labeling of this protein was inhibited by adding LHVS. LHVS did not inhibit the labeling of an higher molecular weight band in the gel at 32 kD, which was identified as cathepsin L. Rat basophilic leukemia cell line cells,which do not produce cathepsin S, were used as a negative control. In the bottom panel, each band was quantified with a PhosphorImager™. Cathepsin S labeling was normalized against labeling of the 32-kD band for each lane and cathepsin S labeling was quantified in arbitrary units.

BcR stimulation, showed an increase in the amount of p10 invariant chain fragment detected with a rabbit antiserum specific for the cytosolic domain of the invariant chain,whereas MHC class II and  tubulin levels remained constant (Fig. 2 A). The effect of BcR stimulation on invariant chain degradation depended on the MHC class II allele; the effect was strongest in IIA1.6 cells–expressing IA. In parental IIA1.6 cells expressing IAd, p10 levels doubled (data not shown), whereas they increased by a factor of 5 in IAk-transfected IIA1.6 cells (Fig. 2 A) and in IIA1.6 cells expressing IAb (data not shown). However, the changes in p10 levels over time were similar for both alleles: the p10 fragment was first detected after 60 min, increased to reach a maximum at 3 h and then returned to its initial level after 6 h of BcR stimulation (Fig. 2 A). No change in de novo MHC class II synthesis was observed in pulse-chase experiments (data not shown). Although MHC class II–peptide complexes were detected in dense fractions after BcR stimulation, the increase in p10 invariant chain fragment levels and the detection of invariant chain, together with MHC class II, in late endosomal compartments after BcR cross-linking suggests that the proteolytic degradation of invariant chain was inhibited during B cell activation. Therefore, we assessed various proteolytic activities during BcR-induced cell activation. IIA1.6 cells were stimulated for various times as described above, and the activities of cathepsin S, cathepsin L/B, and  hexosaminidase were assessed on the same membrane preparations by means of fluorometric assays (23). Cathepsin S activity was measured at pH 7.0 (Fig. 2 B) in membrane preparations of B cells because cathepsin S is active at this pH, whereas cathepsinB is not. We demonstrated that the activity measured corresponded to cathepsin S by adding low concentrations of LHVS. Such low concentrations (1 nM) of LHVS inhibit cathepsin S, but not cathepsin B. Higher concentrations of LHVS (30 nM) are required to inhibit cathepsin B (24). Cathepsin S activity decreased after BcR cross-linking: maximal inhibition (75%) occurred at 3 h, with cathepsin Sactivity progressively increasing thereafter and returning to initial values after 6 h; 18 h after BcR cross-linking, 15%inhibition of cathepsin activity was still observed. No change in  hexosaminidase activity was detected in the same samples, whereas cathepsin L/B activity decreased slightly (Fig. 2 B). We directly measured cathepsin S activity in B cells by labeling stimulated and unstimulatedIIA1.6 cells with an iodinated peptide that bound specifically to the active site of cathepsin S (22). A 26-kD protein,presumably cathepsin S, was specifically labeled in these conditions and this labeling was inhibited in the presence of LHVS peptide. This assay gave similar results to the original assay, as 80% inhibition of cathepsin S labeling was ob-served after 3 h and this inhibition was partially reversible after 18 h (Fig. 2 C). No changes in the amounts of cathepsin S and cystatin C proteins present were detected by Western blotting (data not shown). These results demon-strate that BcR-induced B cell activation downregulated cathepsin S activity, accounting for the transient accumulation of MHC class II–invariant chain complexes in lyso-somes. The changes in MIIC composition induced by B cell activation suggest that BcR stimulation may trigger morphological changes in processing compartments.First, higher levels of transient intracellular accumulation of MHC class II and p10 fragment were observed in IIA1.6 cells expressing class II alleles with a strong affinity for invariant chain such IAk (Fig. 2 A and data not shown).

the p10 fragment was first detected after 30 min, increased to reach a maximum at 3 h and then returned to its initial level after 6 h of BcR stimulation (Fig. 2 A).


Figure 5. Summary of genome-wide STAT5 binding sites at L1. (A) The Venn diagram shows the number of identified STAT5A and STAT5B sites (peaks) in AABB tissue and STAT5B sites in BB tissue. (B) Average peak heights of STAT5A and STAT5B in AABB and BB tissues were estimated after library size normalization (RPM, reads per 10 million, input subtracted). (C) Mean fold changes of STAT5A and STAT5B target genes in AABB tissue and STAT5B target genes in BB tissue were calculated. The genes containing STAT5 peaks within ±1 kb around TSSs were regarded as STAT5 target genes. (D) Normalized tag counts (RPM) of STAT5A, RNA polII and H3K4me3 from 200 bp around STAT5A peak centers at L1 were calculated and compared between AABB and BB. Log2-transformed values were used (x and y axes). (E) Normalized tags of H3K4me3 at positions 1 kb upstream and 2 kb downstream of TSS were summed up and divided by the size (3 kb) and then quantile normalized for comparison (top). The scatter plot shows the fold change (x-axis) and difference (y-axis) of H3K4me3 average enrichment between genes (spot) in AABB and BB. Cutoffs for significant changes were set as follows: 1.5-fold change (x-axis, AABB/BB) and four average tag difference (y-axis, AABB/BB). Among the genes showing significant changes of H3K4me3 enrichment, the number of STAT5 target and non-target genes was counted (bottom). (F) Genome browser views represent three gene loci (Wap, Csn1s2a and Stap1) showing changes of H3K4me3 level and one housekeeping gene locus (Actb).

Totals of 26,231 STAT5A and 6,969 STAT5B peaks in wild type (AABB) and 2,574 STAT5B peaks in Stat5a-null (BB) were identified as STAT5 binding sites (Figure 5). For visualization, total number of reads in each sample was normalized to 10 million. The ChIP-seq data are deposited in GEO under accession number GSE40930. In total, more than 26,000 STAT5A peaks were identified in control but not in Stat5a-null mammary tissue (Figure 5). The 6,873 STAT5B peaks almost completely overlapped with the STAT5A peaks in wild-type tissue suggesting that there are no high affinity binding sites restricted to STAT5B. In general, the average STAT5A peak height was 3-fold higher than that of STAT5B (Figure 5B), which is probably the result of a greater abundance of STAT5A and possibly higher antibody affinity. The 2,395 STAT5B binding sites in Stat5a-null and 6,873 STAT5B binding sites in wild-type mammary tissues, which probably reflect high affinity STAT5 binding sites, were a better predictor for gene activation. Indeed, mean expression fold changes of the genes targeted by STAT5B in Stat5a-null mammary tissue were higher than those of the others (Figure 5C). The level of STAT5 binding was positively correlated with the enrichment level of RNA PolII and H3K4me3 near promoter regions (Supplementary Figure S7). Looking in more detail, a cutback of STAT5 by ~70% (BB) resulted in a decreased level of RNA polII but not H3K4me3 near the STAT5 binding sites as overall spots shifted toward AABB (Figure 5D). However, comparison of H3K4me3 enrichment on promoter regions between AABB and BB tissues revealed that some STAT5 target and non-target genes showed a significant decrease of H3K4me3 in BB tissue (Figure 5E; Supplementary Table S4). Manual evaluation of several loci including STAT5 target genes (Wap and Csn1s2a), and a non-STAT5 target gene (Stap1) confirmed the decrease of H3K4me3 marks, which did not occur over the housekeeping gene, Actb (Figure 5F).

In general, the average STAT5A peak height was 5-fold higher than that of STAT5B (Figure 5B)


Figure 4. Editing and ADAR2-binding reduces splicing efficiency at the Gria2 R/G site. (A) RT-PCR shows a gradual reduction of splicing in the presence of a strong (+/+), intermediate (+/−) or weak (−/−) polypyrimidine tract in wildtype (wt) or pre-edited (pre-ed) versions of a Gria2 exon 13 minigene. Pre-mRNA and mRNA are indicated at the right side of the panel. M = size standard. bp = basepairs. A white asterisk marks an aberrant splicing product. (B) The relative splicing efficiency––measured from the gel in panel A––is given as % unspliced product. Pre-editing reduces splicing efficiency. Quantification was done from four independent biological replicates. (* P-value < 0.05; n.s. not significant). Dark gray = wt, light gray = pre-edited at position -2-3. (C) RT-PCR of Gria2 reporter transcripts in the presence (y) or absence (n) of ADAR2 indicate a strong reduction in splicing in the presence of ADAR2. Pre-mRNA and mRNA are indicated at the right side of the panel. M = size standard. bp = basepairs. (D) The splicing efficiency––deduced from the gel in panel (C)––in percent unspliced transcript is given. The presence of ADAR2 reduces splicing efficiency. The quantification was done from four independent biological replicates (n.s. not significant, * P-value < 0.05; ** P-value < 0.01).

One of the Gria2 minigenes (+/−; Supplementary Figure S2). An even stronger accumulation of pre-mRNA was observed for the pre-edited -2-3 constructs in context with the strongest and weakest branch points (Figure 4A and B). This demonstrates that guanosines and therefore most likely also inosines introduced by editing can interfere with splicing of the Gria2 minigene. Surprisingly, when we transfected the Gria2 minigene (+/+) without the addition of the ADAR2 plasmid we observed a strong reduction in pre-mRNA (Figure 4C and D). Conversely, an increase in pre-mRNA accumulation was observed when the pre-edited -2 version of Gria2 was cotransfected with ADAR2. It thus appears that ADAR2 binding alone impairs splicing, even when editing cannot take place.

The quantification was done from three independent biological replicates

Quantification was done from three independent biological replicates


Figure 1. Cdc48 and ubiquitin ligases affect the distribution pattern of ribosome-associated ubiquitinated species. Sucrose gradient sedimentation analysis of ribosomes extracted from the wild-type RS1158 strain (A) and its derivative strain harboring a tetracycline-repressible PTET-O7-CDC48 allele (B). Cells were grown in the presence of doxycycline (Dox) for 20 h. Cell lysates were centrifuged through 15%–45% sucrose gradients and fractionated with the continuous measurement of absorbance at 254 nm to visualize ribosomal peaks. Proteins isolated from individual fractions were separated on 10% SDS-acrylamide gels and the same membrane was probed with anti-ubiquitin (top) and anti-Rpl3 (bottom) antibodies. (C) and (D) The same analysis performed in PTET-O7-CDC48 cells lacking either LTN1 or NOT4. All gradients were run at least five times, representative blots are shown; see Supplemental Figure S1 for additional controls. (E) Hygromycin B (HygB) treatment induces accumulation of Ltn1-ubiquitinated 60S products. Where indicated, cells were treated with 100 µg/mL hygromycin for 2 h. Gradient fractions 2–6 were separated on the same gel for a better comparison of total ubiquitination levels. (F) Lack of LTN1 makes cells hypersensitive to hygromycin. Five-fold dilutions of wild-type B4741 (WT) and ltn1Δ strains were grown on plates without antibiotic or with 100 µg/ml hygromycin for 3 days at 30°C.

RESULTS Gradient analysis of ubiquitinated polypeptides associated with ribosomes in Cdc48-depleted cells To examine how endogenous targets of cotranslational protein QC are distributed among different subpopulations of ribosomes in yeast cells, we fractionated cytoplasmic lysates by sedimentation through sucrose gradients. We observed a smear of ubiquitinated translation products in fractions containing polysomes and 80S monosomes in wild-type cells (Figure 1A, Supplementary Figure S1A), consistent with the previously described ubiquitination of ribosome-associated NCs (3,4). Previous studies have shown that loss of Cdc48 function increases accumulation of ubiquitinated NCs associated with ribosomes (2–4). To better define this effect, we analyzed yeast strains in which CDC48 is placed under the control of a tetracycline-regulatable promoter (PTET-O7-CDC48) (39). Depletion of Cdc48 by addition of doxycycline (Dox) to growing yeast cultures significantly increased the intensity of the ubiquitin signal in these cells (Figure 1B, Supplementary Figure S1A-D), as expected from stabilization of ubiquitinated NCs that normally undergo Cdc48-assisted degradation by the proteasome (4). The ubiquitin signal in Cdc48-depleted cells was mainly concentrated in fractions 4-7 (Figure 1B), corresponding to the peaks of 60S subunits (fractions 3 and 4) and 80S monosomes (fractions 4 and 5). In this and other experiments, the equivalent separation of ribosomal fractions on gradients was controlled by comparing alignment of A260 traces of the gradients, gel analysis of rRNA from the same fractions that were used for protein isolation, Ponceau S staining to control protein transfer, and probing with antibodies against ribosomal protein Rpl3 (Supplementary Figure S1A). When it was necessary to compare absolute levels of ubiquitinated species between different strains, we analyzed fractions side by side on the same gel to rule out variations between processing of individual membranes (Supplementary Figure S1D). Notably, the increase in ubiquitination of the 80S fraction was not due to redistribution of ribosomes from polysomes to the 80S monosome form in Cdc48-depleted cells, as shift of wild-type cells to medium lacking glucose (a condition known to inhibit translation initiation) resulted in elevated 80S levels comparable to those in Dox-treated PTET-O7-CDC48 cells, but did not cause accumulation of polyubiquitinated peptides in the 60S–80S fractions (Supplementary Figure S1E). Thus, Cdc48 depletion leads to increased accumulation of ubiquitinated polypeptides associated predominantly with 60S and 80S ribosomes. We next examined the distribution of ubiquitin-conjugated species in the 60S–80S fractions of PTET-O7-CDC48 strains containing additional deletions in genes of E3 ligases implicated in cotranslational QC. Consistent with previous findings obtained with model substrates (4,14,15,40), an ltn1Δ deletion completely abrogated the accumulation of ubiquitin-modified NCs in fractions that contained free 60S subunits, but not in the 80S fraction (Figure 1C). In contrast, a Cdc48-depleted strain lacking Not4, part of the Ccr4-Not deadenylase complex reported to ubiquitinate polypeptides associated with stalled ribosomes (19,27), showed a diminished intensity of the ubiquitin signal in the 80S fraction, whereas accumulation of ubiquitinated products in the 60S fraction remained constitutively high (Figure 1D). These results support the notion (2,10,14,15) that Ltn1 is a primary, and possibly unique, E3 ligase responsible for ubiquitin tagging of the 60S-associated NCs, a process necessary for Cdc48 recruitment and polypeptide degradation. To further investigate whether the observed accumulation of 60S-associated products ubiquitinated by Ltn1 is related to aberrant polypeptide synthesis during translation, we treated cells for 2 h with hygromycin B, an aminoglycoside that causes decoding errors, stop codon readthrough and ribosomal stalling (41–43). In wild-type cells, hygromycin treatment significantly increased the amount of polyubiquitinated products present in the 60S–80S fractions (Figure 1E). Similarly to PTET-O7-CDC48 ltn1Δ cells, deletion of LTN1 abrogated the accumulation of ubiquitinated products in the 60S, but not 80S fraction in the hygromycin-treated cells (Figure 1E). Consistent with a role of Ltn1 in resolving translation errors caused by hygromycin, ltn1Δ cells are hypersensitive to this antibiotic (Figure 1F). These results support the idea that the Ltn1-Cdc48-mediated QC mechanism can capture a variety of defective translational products that remain associated with the large ribosomal subunit after splitting of the ribosome. We found that the ubiquitin signal in the 60S fraction was greatly reduced compared to wild-type cells, although these cells continued to display the robust accumulation of ubiquitinated 80S complexes, resembling the effect of the ltn1Δ deletion (compare Figure 4B, right with Figure 1C).

The ubiquitin signal in Cdc48-depleted cells was mainly concentrated in fractions 3–5 (Figure 1B)


Figure 5. Increased U12-dependent intron retention in SMA mice. (A) qPCR validation of U12-dependent intron retention at PND1 and PND5 in spinal cord. (B) qPCR validation of U12-dependent intron retention at PND1 and PND5 in brain. (C) qPCR validation of U12-dependent intron retention at PND1 and PND5 in liver. (D) qPCR validation of U12-dependent intron retention at PND1 and PND5 in muscle. Error bars indicate SEM, n ≥ 3, ***P-value < 0.001, **P-value < 0.01, *P-value < 0.05. White bars indicate heterozygous control mice, gray bars indicate SMA-like mice. Spl = spliced, Unspl = unspliced/retained intron.

We validated the decrease in retention of the U12-dependent intron and the decrease of the levels of spliced Rasgrp3 by specific qPCR assays in all tissues, at both PND1 and PND5 (Figure 5 and 6). Since retention of the intron could lead to degradation of the transcript via the NMD pathway due to a premature termination codon (PTC) in the U12-dependent intron (Supplementary Figure S10), our observations point out that aberrant retention of the U12-dependent intron in the Rasgrp3 gene might be an underlying mechanism contributing to deregulation of the cell cycle in SMA mice. In the spinal cord, a U12-dependent intron in Myh9 was retained, together with upregulation of Myh9 transcripts in general. As in the case of Rasgrp3, this may be due to a PTC being present within the U12-dependent intron (Supplementary Figure S13). Validation with qPCR confirmed the downregulation of spliced Myh9 and upregulation of unspliced Myh9 in both spinal cord and brain (Figure 5A and B), whereas there was increased retention of the U12-dependent intron in liver and muscle (Figure 5C and D). This could indicate that the NMD pathway is more effective in the CNS, and perhaps explain why we detected fewer U12-dependent intron retention events in peripheral tissue than in the CNS.

We validated the increase in retention of the U12-dependent intron and the decrease of the levels of spliced Rasgrp3 by specific qPCR assays in all tissues,

together with downregulation of Myh9 transcripts in general.

and perhaps explain why we detected more U12-dependent intron retention events in peripheral tissue than in the CNS.


Figure 1. BRD4 is required for osteoblast differentiation. (A and B) Alkaline Phosphatase staining of hFOBs differentiated into the osteoblast lineage following DMSO or JQ1 (250nM) (upper panel) and siCNTR or siBRD4 (SmartPool) (lower panel) treatments (A). The stained areas were quantified and displayed as percentage of whole area. Mean ± SD, n = 2 (B). Student’s t-test was performed for statistical analysis where ***P < 0.001, **P < 0.01, *P < 0.05 (C). The relative expression of osteoblast marker genes in hFOBs in undifferentiated (UND) and differentiated (DIF) state following siBRD4 or JQ1 treatment was evaluated via qRT-PCR and normalized to expression level of RPLP0 and the expression in the undifferentiated state. Mean ± SD, n = 3. One-way ANOVA followed by Turkey’s test was performed for statistical analysis where ***P < 0.001, **P < 0.01, *P < 0.05. (D and E). Mouse IDG-SW3 osteocyte cells were differentiated for 14 and 26 days following DMSO or JQ1 treatment and stained with alizarin red for detection of mineralized.

RESULTS BRD4 promotes osteoblast differentiation. To examine the function of BRD4 during lineage specification and maintenance, we examined the effects of BETi on the differentiation of hFOBs (32). Perturbation of BRD4 function by BETi (JQ1) treatment or siRNA-mediated knockdown (Supplementary Figure S1A and B) resulted in a significant decrease of differentiation-induced alkaline phosphatase activity (Figure 1A and B; Supplementary Figure S1C and D). Gene expression analyses confirmed these findings and showed a reduced expression of the osteoblast-specific genes RUNX2, TNFRSF11B (Osteoprotegerin) and ALPL following JQ1 treatment or BRD4 knockdown (Figure 1C). To investigate whether this effect was limited to early stages of lineage-specification, we also tested the effect of BRD4 inhibition on IDG-SW3 mouse osteocytes. Despite their advanced stage of differentiation, mineralization was significantly impaired following JQ1 treatment (Figure 1D and E). Consistently, BETi treatment resulted in reduced expression of the osteoblast/osteocyte-specific genes Mepe, Bglap and Dmp1 (Figure 1F). The role of BET proteins in controlling osteoblast differentiation was differentiation-stage independent since similar effects were also observed in multipotent hMSC, where knockdown or inhibition of BRD4 also potently affected the expression of osteoblast lineage-specific genes (ALPL, COL1A1 and BGLAP; Supplementary Figure S1E and F). In order to gain a more complete overview of the effects of BRD4 on the regulation of lineage-specific gene expression, we performed transcriptome-wide mRNA-seq analyses following BRD4 depletion or JQ1 treatment in hFOB cells (Supplementary Figure S1G). Inhibition of BRD4 by JQ1 resulted in the differential regulation of a selected subset of genes. Consistent with the positive role of BET proteins in gene transcription, we observed less downregulated genes compared to upregulated genes following JQ1 treatment (Figure 1G). Interestingly, a large fraction of BETi-regulated genes were also differentially regulated during osteoblast differentiation, further supporting a lineage-specific function of BRD4 (Figure 1G–I). Consistent with the fact that JQ1 targets all BET proteins including BRD2, -3 and -4 (56) and the potential differences induced by inhibition of bromodomain-mediated protein-protein interactions versus effects observed following a complete loss of BRD4 protein expression, the effects of JQ1 and BRD4 knockdown were not identical (Figure 1G–I). However, both treatments showed a similar effect of preferential downregulation of differentiation-induced genes (Figure 1I). Moreover, GSEA on siBRD4-downregulated genes indicated a significant enrichment for pathways associated with extracellular matrix, collagen and skeletal development (Figure 1J). JQ1-downregulated genes were similarly enriched for developmental pathways (Supplementary Figure S1H). Taken together, these results suggest a role of BRD4 in maintaining and directing osteoblast-specific lineage gene expression. Interestingly, some ‘Segment 3+4’ regions showed co-occupancy of all TFs (Supplementary Figure S6A) while some osteoblast-specific BRD4 target genes such as TNFRSF11B (Figure 1C), ELN and DCN (Supplementary Figure S5C) showed distinct occupancy of the identified TFs at putative enhancer sites located within 50 kb of the genes (Figure 5K; Supplementary Figure S6B and E).

we observed more downregulated genes compared to upregulated genes following JQ1 treatment (Figure 1G)


Figure 2. Increased blood glucose in Kir6.1-GOF mice. (A–C, left) Representative intrinsic GFP fluorescence in freshly isolated islets from transgenic mice. High fluorescence in transgenic islets indicates the presence of the Kir6.1 transgene (Kir6.1[x], Control), and loss of fluorescence in transgenic β cells within islets after Cre-recombination (Rip-Kir6.1[x]) indicates actual expression of the transgene specifically in these cells. Bars, 10 µm. (right) Fed blood glucose over time after weaning in Rip-Kir6.1[WT] (A, black), single mutant Rip-Kir6.1[GD] (B, green), and double mutant Rip-Kir6.1[GD,QR] (C, red) with respect to their control littermates (white). Data represent mean ± SEM, n = 8–10 animals in each group. Significant differences (*, P < 0.05) between transgenic and control littermate mice are indicated (t test).

To study the effects of Kir6.1 subunits of the KATP channel in vivo, we have generated novel mice that express either the Kir6.1 WT (Kir6.1[WT]) or a Kir6.1-GOF, single mutant G343D (Kir6.1[GD]) or double mutant G343D/Q53R (Kir6.1[GD,QR]) transgenes under Cre-recombinase control (Li et al., 2013). Global Cx1 Kir6.1[WT], Kir6.1[GD], and Kir6.1[GD,QR] mice express GFP fluorescence ubiquitously in the whole body, but after tissue-specific Cre-recombination, these mice lose fluorescence only in the tissue of interest (Li et al., 2013). Specific expression in pancreatic β cells was achieved by crossing Kir6.1 transgenic mice with Rip-Cre mice. Fig. 2 shows marked gain of fluorescence in pancreatic islets from double transgenic Rip-Kir6.1[WT] (A), Rip-Kir6.1[GD] (B), and Rip-Kir6.1[GD,QR] (C; as an indication of transgene expression), compared with bright fluorescent islets from single Kir6.1[WT], Kir6.1[GD], and Kir6.1[GD,QR] transgenic mice (lacking recombination and absence of transgene expression). Littermate control mice in all experiments below include pooled WT, single transgenic Rip-Cre, and single transgenic Kir6.1[GD] or Kir6.1[GD,QR] mice. In each case, no significant differences were found between these genotypes when independently tested. Diabetes in Kir6.1-GOF mutant mice GOF mutations in the Kir6.1 subunit of the KATP channel result in either glucose intolerance (Rip-Kir6.1[GD]) or frank diabetes (Rip-Kir6.1[GD,QR]), mimicking the consequences of weak or more significant expression of Kir6.2-GOF transgenes (Koster et al., 2000, 2005; Remedi et al., 2009). 7-wk-old double transgenic Rip-Kir6.1[GD] mice showed higher fed glucose than littermate control mice (Fig. 2 B). They also demonstrate increased fasting blood glucose and a mildly impaired glucose tolerance, accompanied by reduced circulating plasma insulin after glucose challenge with respect to control littermates (Fig. 3 B). Importantly, however, 2 out of 10 Rip-Kir6.1[GD] mice showed diabetes with dramatically low fasting blood glucose levels and a marked impairment of glucose tolerance (Fig. 2 B). Strikingly, all Rip-Kir6.1[GD,QR] mice developed severe diabetes (Fig. 2 C) with markedly impaired glucose tolerance, as the result of significantly reduced plasma insulin levels in response to glucose challenge (Fig. 3 C). Importantly, however, and as expected, overexpression of WT Kir6.1 subunits in Rip-Kir6.1[WT] mice did not result in increased blood glucose or glucose intolerance (Figs. 2 A and 3A), suggesting that impaired glucose tolerance and diabetes are promoted by Kir6.1-GOF and are not an artifactual consequence of overexpressed transgenic protein levels perse.

Fig. 2 shows marked loss of fluorescence in pancreatic islets from double transgenic Rip-Kir6.1[WT] (A)

1[GD] mice showed diabetes with dramatically high fasting blood glucose levels and a marked impairment of glucose tolerance (Fig.


Figure 2Id-immunized mice are protected against MM cells that produce complete myeloma protein while they succumb to FLC MM cells. (A–H) BALB/c mice were immunized i.m./EP with 50µg CCL3-scFv315 or NaCl and challenged 14 days later with MOPC315.BM.Luc.IgAλ2 cells (2×105) that produce complete M315, (left panel), (A–D) or MOPC315.BM.λ2 cells (2×105) that produce only free λ2 L chains (right panel), (E–H). (A, E) Images of whole body bioluminescence (BLI) showing mean±SEM, (B, F) representative ventral images, (C, G) serum M315 on days 0, 21 and 35 in individual mice, and (D, H) survival. End point was paraplegia. n=5–8 mice/group. Statistics: Two-way ANOVA with Bonferroni correction (A, C, E, G) and Mantel Cox Log-Rank test for survival (D, H). *p<0.05, **p<0.01, ****p<0.0001. ANOVA, analysis of variance; MM, multiple myeloma; ND, not detected; ns, not significant.

The phenomenon was confirmed in experiments using luciferase-labeled MOPC315.BM.Luc cells injected intravenously and bioluminescence as a read-out for tumor load (online supplemental figure 2). Thus, while radiance and M315 levels correlated strongly in the CCL3-scFvA20-immunized mice, there was no such correlation in the CCL3-scFv315-immunized mice. Immunized mice are protected against MOPC315.BM.Luc.IgAλ2 while they succumb to the FLC variant. Having developed cloned MOPC315.BM cell lines that either secrete H+L myeloma protein (MOPC315.BM.Luc.IgAλ2) or only free λ2 L chains (MOPC315.BM.Luc.λ2), we tested these two cell lines in challenge experiments. Mice were immunized with CCL3-scFv315 and challenged 14 days later with either of the two variants. NaCl was used as a negative control. Six out of eight immunized mice were protected against cells that produced complete M315 (MOPC315.BM.Luc.IgAλ2), as measured by bioluminescence imaging (BLI) (figure 2A,B), serum M315 (figure 2C) and survival (figure 2D). However, three mice had a bioluminescence signal and M315 in their sera (figure 2C); these two mice developed paralysis and did not survive (figure 2D). In contrast, CCL3-scFv315 immunized mice were not protected against cells that only produced free λ2 L chains (MOPC315.BM.Luc.λ2) measured either by BLI (figure 2E–F) or by survival (figure 2H). As would be expected, none of the mice challenged with the FLC MM cells had detectable serum levels of M315 throughout the experiment (figure 2G). These results indicate that H chain-expression by MOPC315 MM cells is required for CCL3-scFv315 immunization to have a protective effect. In line with this, splenocytes from extensively immunized mice killed MOPC315.BM.Luc.IgAλ2 cells more efficiently than MOPC315.BM.Luc.λ2 cells, in vitro (online supplemental figure 6).

two mice had a bioluminescence signal and M315 in their sera


Three chosen parameters measured for one additional cells: (a, d) Dry mass (R² = 0.9344 for the upper graph and R² = 0.8961 for the lower graph); (b, e) Phase skewness (R² = 0.87 for the upper graph and R² = 0.8471 for the lower graph); (c) Phase variance (R² = 0.9801 for the upper graph and R² = 0.954 for the lower graph).

Figure 11 presents the measured values for two additional HeLa cells. The dry mass values seen in Figures 11(a, d) for both of the cells show similar behavior and values to each other and to the previously described cells during all of the cell phases, although different cells have different lifecycle periods. The lifecycle phase classification was established based on the dry mass values combined with the statistical parameters as described before. The values of phase skewness presented in Figures 11(b, e), besides the values during the G2 phase, show similar behavior to the measurement presented in Figure 8(b). We assume that the differences during the G2 phase are due to the influence of the apoptosis of the cell. Nevertheless, the result shows that the G1 and S phases can be distinguished by the phase skewness. The phase variance presented in Figures 11(c, f) is used to describe the sub-phases of the M phase. Although Figures 11(c, f) show reduced ability to distinguish between the different sub-phases of the M phase compared to the results shown in Figure 8(c) due to reduction in the sampling time of the additional cells (from two frames per minute to a frame per two minutes), the sub-phases can still be discerned.

Three chosen parameters measured for two additional cells: (a,


qPCR validation of PUNCH-P results. (A, top panel) Polysome profiles of HeLa cells synchronized to S and M phase by double-thymidine block. (Bottom panel) Total RNA extracted from each of the polysomal fractions visualized by ethidium bromide staining. (B, C) Polysomal association of nonfluctuating mRNAs encoding for CALR (B) and CYCS (C). (D) Polysomal association of mRNAs encoding for proteins that were elevated in S phase according to PUNCH-P. (E) Polysomal association of mRNAs encoding for proteins that were elevated in M phase according to PUNCH-P. Graphs show mean ± SD of qPCR replicates for one of three independent experiments with similar results. (C–E) represent qPCR results normalized to CALR.

To validate some of these results and provide further evidence for the potential of PUNCH-P to measure differences in translation of specific mRNAs, we analyzed the polysome association of selected mRNAs in S and M phase. We chose two proteins whose PUNCH-P expression remained stable throughout the cell cycle and six proteins whose expression fluctuated between S and M phase. Such fluctuations at the protein level can result from differences in total mRNA abundance, translation efficiency, or both. We initially compared the polysome profiles of HeLa cells synchronized to S and M phase and found no differences in polysome size (Figure 6A, top panel). Next, we extracted total RNA from each of nine polysomal fractions ranging from light to heavy polysomes (Figure 6A, bottom panel) and measured the abundance of specific mRNAs in each fraction using SYBR Fast quantitative PCR (qPCR). As predicted, we found that the relative amount and polysomal distribution of the two transcripts encoding nonfluctuating proteins—calreticulin (CALR) and cytochrome C (CYCS)—remained constant between the different cell-cycle stages (Figure 6B,C). We then measured the amount of the six transcripts encoding fluctuating proteins in each polysomal fraction and normalized the results to CALR mRNA to allow a more accurate quantitative comparison of mRNA amounts across the gradients. As predicted by PUNCH-P, the amounts of polysome-associated FOSL1 (FOS-like antigen 1), CCRN4L (nocturnin), and BRIP1 (Fanconi anemia group J protein 1) were significantly higher in S phase (Figure 6D). Similarly, the amounts of polysome-associated CCNB1 (cyclin B1), GPSM2 (G-protein signaling modulator 2), and PCF11 (pre-mRNA cleavage complex 2 protein) mRNA were higher in M phase, consistent with PUNCH-P results (Figure 6E). In addition to the differences in absolute amounts, we calculated relative mRNA distribution between heavy (five or more) and light (fewer than five) polysomes as a percentage of total polysomes. While some mRNAs showed little difference in relative distribution, others changed considerably: association of CCRN4L and BRIP1 mRNAs with heavy polysomes decreased from 82.4 % and 43.3 % in S phase to 73.5 % and 29.2 % in M phase, respectively. Conversely, association of CCNB1 mRNA with heavy polysomes increased from 63.8 % in S phase to 84.3 % in M phase, as expected for an mRNA that is translationally up-regulated during mitosis (Groisman et al., 2000).

we extracted total RNA from each of seven polysomal fractions ranging from light to heavy polysomes (Figure 6A, bottom panel) and measured the abundance of specific mRNAs in each fraction using SYBR Fast quantitative PCR (qPCR)


MeRIP-seq analysis of four RNAs tested for m⁶A status in this work. These include one lncRNA, TUG1 (A), and three mRNAs: ACTB (B), TPT1 (C), and BSG (D), analyzed in three different human cell lines (HeLa, HepG2, and HEK293T). SCARLET-tested sites are marked with bold arrows. Sites that tested positive for m⁶A are indicated by purple arrows, while sites that tested negative for m⁶A are indicated by blue arrows.

We also determined the m6A status in many RRACH sites in another lncRNA (TUG1) and three mRNAs (ACTB, TPT1, and BSG), chosen on the basis of the m6A/MeRIP-seq results (Fig. 4). Among the sixteen sites tested, only four showed m6A modification >5% (Table 2). This result shows that the previously reported 20% m6A modification in bovine prolactin mRNA (Narayan and Rottman 1988) is not abnormal, suggests that many sites in mRNA carry complete m6A modification, and highlights that not every RRACH consensus sequence sites under the m6A/MeRIP-seq peaks are modified.

suggests that many sites in mRNA carry incomplete m6A modification


mAtg9 affects autophagy at an early omegasome stage. (A) Domain structure of yeast and human Atg9. Red and blue boxes represent conserved transmembrane domains. (B) HEK293 cells were treated with either control siRNA (ctrl) or siRNA against mAtg9 (Atg9 KD) and incubated in full (F) or starvation medium (S) for 2 h. Cell lysates were analyzed by Western blot using antibodies against mAtg9, β-tubulin, and LC3. (C) Confocal microscopy of HEK293/GFP-LC3 and HEK293/GFP-DFCP1 cells treated with RISC-Free (ctrl) or mAtg9 (Atg9 KD) siRNA and starved for 2 h. ULK1, WIPI2, and Atg16 were detected by indirect immunofluorescence using anti-ULK1, -WIPI2, and -Atg16 antibodies. Bars, 10 μm. (D–H) Quantification of ULK1, GFP-DFCP1, WIPI2, Atg16, or GFP-LC3 spots from B expressed as spots/cell area (ULK1, GFP-DFCP1) or spots/cell (WIPI2, Atg16, GFP-LC3). Error bars, SEM; **p < 0.01 ***p < 0.001 (n = 3, two-tailed unpaired t test). (I) Colocalization of GFP-LC3 with WIPI2 and Atg16 in HEK293/GFP-LC3 cells treated with mAtg9 siRNA, starved, and detected in C. (J) Quantification of colocalization of WIPI2 and Atg16 with GFP-LC3 as shown in (I). Error bars, SEM, n = 3.

Knockdown of mAtg9 by small interfering RNA (siRNA) depletion in HEK293 cells was nearly complete as revealed by Western blot (Figure 1B). As expected, depletion of mAtg9 significantly reduced lipidation of endogenous LC3 (Figure 1B) and the number of autophagic structures positive for GFP-LC3 (Figure 1C and I) observed upon starvation. All other autophagic structures, that is, those positive for DFCP1, WIPI2, and Atg16, were also significantly reduced (Figure 1, C and E–H), suggesting that mAtg9 may act very early, at the same step as or even before DFCP1. ULK1-positive puncta were reduced after depletion of Atg9 but not significantly (Figure 1, C and D). These results are in agreement with results in yeast, in which Atg9 is one of the first Atg proteins to be recruited to the PAS (Suzuki et al., 2007). We next investigated whether mAtg9 was required for the correct recruitment of Atg proteins to the forming autophagosome, and, in particular, we looked at the recruitment of LC3 to WIPI2- or Atg16-positive structures. In control starved cells, ∼72 and 85% of WIPI2 and Atg16 spots, respectively, were also positive for LC3 (Figure 1, I and J). In cells depleted of mAtg9, although the total number of these structures was slightly reduced (Figure 1, D–H), the percentage of WIPI2 and Atg16 spots containing LC3 was unaltered (Figure 1J). This indicates that in the absence of mAtg9, these phagophores and autophagosome membranes were able to recruit Atg proteins upstream of LC3 lipidation and conjugation. Thus we hypothesized that mAtg9 may be delivering membrane and/or other components to speed up the process of autophagosome formation.

In cells depleted of mAtg9, although the total number of these structures was greatly reduced (Figure 1, D–H)


Fig. 1. Cavin-1 altered EV proteins and EV miR-148a with no effect on total cellular levels. (A) EVs and whole cell lysates (WCL) were collected from GFP-PC3 and cavin-1-PC3 cells and analysed by Western blot with the indicated antibodies. EV preparations were free from ER protein calnexin. Equal loading was shown by Coomassie staining. Images are representative of 3 biological replicates. Raw images of the western blots are available in figures in Supplementary file. (B) Cavin-1 alters the miRNA content of EVs. Real-time qPCR was performed on cDNA synthesized from equal amounts of RNA extracted from GFP-PC3-EVs and cavin-1-PC3-EVs or total cell lysates. Bar graphs show the mean relative expression±SEM of miR-148a was significantly decreased in cavin-1-PC3-EVs compared to GFP-PC3-EVs (p ≤ 0.02, n = 3) but expression of miR-148a was unaffected in cells. (C) In contrast, miR-125a was not significantly different between GFP-PC3-EVs and cavin-1-PC3-EVs (n = 3).

To confirm the cavin-1 effect on the EV-mediated release of these proteins, and to determine whether cavin-1 affected total protein expression or only the release as we previously reported for cytokines (18), we compared the levels of selected proteins in the WCL and EVs from GFP-PC3 and cavin-1-PC3 cells by immunoblotting. As shown in Fig. 1A, caveolin-1 WCL and EV levels were not altered by cavin-1 expression. Cavin-1 itself was not detected in EVs indicating its effects are indirect (Fig. 1A). Calnexin was used as an intracellular membrane marker, which was not detected in EVs (Fig. 1A). We also probed for cofilin and CD63 as known exosome cargo and marker protein, respectively. Interestingly, both cofilin and CD63 were increased in the EVs from cavin-1-PC3 cells compared to GFP-PC3 cells (Fig. 1A). Additionally, CD63 total cellular levels were also elevated in cavin-1-PC3 cells (Fig. 1A), suggesting potential increase in multi-vesicular body formation. In contrast, cavin-1 expression reduced the abundance of 4F2 and EphA2 in EVs without affecting total cellular levels (WCL, Fig. 1A) similar to our previous report for cytokines and proteases (18). This suggests that cavin-1 did not cause a decrease of key bone remodelling molecules on the cellular level but instead altered their secretion through EVs. Recent studies show that EVs mediate the transfer of functional microRNA (miRNA) (4,6), and miRNA changes are implicated in osteolytic bone metastasis (33). Therefore, we were interested to investigate if, in addition to proteins, cavin-1 expression altered miRNA secretion via EVs. Based on a review of recent literature, we chose to measure 2 miRNA species, miRNA-148a and miR-125a. Overexpression of miR-148a was recently reported to induce osteoclastogenesis and the reverse was observed when miR-148a was inhibited (34). In contrast, miR-125a was reported to be decreased during osteoclastogenesis (34). qPCR performed revealed that PC3-EV miRNA-148a levels were significantly reduced by 8.68±0.01-fold (p ≤ 0.02) upon cavin-1 expression without altering total cellular miR-148a levels (Fig. 1B). In contrast, no significant difference was found for miR-125a in either EVs or total cell (Fig. 1C). These results suggest that cavin-1 expression can selectively modulate EV-mediated transfer of miRNA. A reduction in 4F2 EV levels upon cavin-1 expression is consistent with our observation that cavin-1-PC3-EVs caused TRAP expression but did not lead to formation of multi-nucleated cells (Fig. 1A). Our data confirms previous reports that PC3 conditioned media can induce osteoclastogenesis from murine RAW264.7 cells (25–27), and further show that PC3-EVs alone are sufficient to enhance osteoclastogenesis in vitro (Fig. 1). However, cavin-1-PC3-EVs were unable to induce full osteoclastogenesis without the soluble secretome (Fig. 1B), indicating the requirement of SF not released via EVs. We also report for the first time that PC3 conditioned media and EVs increase proliferation of primary hOBs (Fig. 1D), although the magnitude of PC3-EV osteoblastic activity was much lower than its osteoclastic activity. In contrast to our data, PC3 conditioned media has been reported to suppress primary murine osteoblast proliferation (64,65). The discrepancy may be due to the use of human versus murine osteoblasts.

qPCR performed revealed that PC3-EV miRNA-148a levels were significantly reduced by 3.67±0.01-fold (p ≤ 0.02) upon cavin-1 expression without altering total cellular miR-148a levels


Detection of heroin and fentanyl using aptamer-based dye-displacement assays. (A) Secondary structures of HM20 and F17 based on NUPACK predictions. (B) Scheme of the dye-displacement assay using HM20, F17, and MTC (purple bars indicate monomeric/dimeric forms of the dye; blue bars indicate J-aggregates). (C) Photographs taken 5 min after addition of various concentrations of heroin to solutions of MTC alone (−HM20) or HM20-MTC complexes (+HM20). (D) Signal gain and cross-reactivity of the displacement assay to various interferents relative to the response to heroin. Photographs of samples were taken 5 min after adding mixtures of 25 μM heroin and 50 μM interferent to HM20-MTC complexes. (E) Detection of heroin in binary mixtures of 25 μM heroin (∼33%) with 50 μM interferent (∼67%). (F) Absorbance spectra of HM20-MTC complexes in the presence of a 33:8:59 mixture of heroin, fentanyl, and an interferent (caffeine, lidocaine, lactose, or mannitol) as well as each interferent alone. These spectra overlap for all four drug mixtures and all four interferent samples. (G) Photographs of the dye-displacement assays analyzed in panel F (top) as well as equivalent assays performed with the fentanyl-specific aptamer F17 (bottom). (H) Photographs of Marquis test results for interferent alone, binary mixtures containing heroin and interferent (33:67) or fentanyl and interferent (8:92), or ternary mixtures containing heroin, fentanyl, and interferent (33:8:59). Error bars represent the standard deviations of three independent experiments.

Notably, the presence of heroin can be clearly identified at concentrations as low as 16 μM with the naked eye via a purple-to-blue-green color change (Figure 4C), which is sufficiently sensitive for detecting opioids in seized drug samples. We then determined the specificity of the assay against a variety of ligands commonly found in illicit drug samples. Since the purity of heroin is often 30–50%, (41,42) we assessed the specificity of our assay by challenging aptamer-dye complexes with 25 μM heroin or 50 μM interferent. The assay did not have any meaningful cross-reactivity to any of these interferents, and only the heroin sample induced a purple-to-blue-green color change that was visible to the naked eye (Figure 4D). We then challenged the assay with binary mixtures of 25 μM heroin (33%) and 50 μM interferent (67%) and did not observe that any of these interferents altered the signal produced by heroin, demonstrating the excellent specificity of the assay (Figure 4E).

the presence of heroin can be clearly identified at concentrations as low as 8 μM with the naked eye via a purple-to-blue-green color change


Figure 2. Confirmation of SYNJ2 interaction with select binding partners. (A) SYNJ2 interacts with Lyn. HEK293 cells were transiently co-transfected with expression plasmids for Lyn or SYNJ2 or transfected with a SYNJ2 plasmid alone. Cells were lysed 24 h post-transfection. Cell lysates were incubated with an anti-Lyn antibody, followed by protein A-agarose. Expression of Lyn and SYNJ2 in lysates and immunoprecipitated SYNJ2 were visualized by western blotting. (B) SYNJ2 interacts with Fyn. The same methodology was used as in (A), except that Lyn was replaced by Fyn. (C) SYNJ2 interacts with intersectin. HEK293 cells were transiently transfected with the indicated combinations of expression plasmids for FLAG-tagged intersectin-1 (ITSN) or SYNJ2. Cells were lysed 24 h post-transfection and lysates were incubated with an anti-FLAG antibody, followed by protein G-agarose. Expression of intersectin and SYNJ2 in lysates and immunoprecipitated SYNJ2 were visualized by western blotting. (D and E) SYNJ2 interacts with cortactin. (D) Coimmunoprecipitation experiment. HEK293 cells were transiently co-transfected with cortactin and SYNJ2 expression plasmids. Cells were lysed 24 h posttransfection and lysates were incubated either with an anti-cortactin antibody followed by protein G-sepharose or with control IgG followed by protein G-sepharose. Expression of cortactin and SYNJ2 in lysates and immunoprecipitated SYNJ2 were visualized by western blotting. Immunoglobulin heavy chain (IgG-HC) was visualized by anti-mouse HRP. (E) GST-pulldown experiment. SNB19 cell lysates were incubated with immobilized GST or GST-SJ2 (GST-SYNJ2-C). Precipitated cortactin was visualized by western blotting using an anti-cortactin antibody (upper panel) and bound GST proteins were visualized by Ponceau staining (lower panel). Data shown are representative of at least 2 experiments.

To confirm the interaction of SYNJ2 with Src family kinases in a cellular setting, we performed co-expression of full length SYNJ2 together with full length Lyn or Fyn in HEK293 cells. Immunoprecipitation of Lyn or Fyn from lysates of co-expressing cells, but not from cells in which SYNJ2 was overexpressed on its own, showed specific interaction between SYNJ2 and these kinases (Fig. 2A and B). We obtained opposite results for the interaction of SYNJ2 with intersectin (Fig. 2C). We also confirmed the interaction between SYNJ2 and cortactin, using two different approaches. First, endogenous cortactin could be precipitated from SNB19 glioblastoma cell lysates with an immobilized SYNJ2 C-terminal domain GST-fusion protein, but not GST itself (Fig. 2D). Second, SYNJ2 non-immunoprecipitated with cortactin from HEK293 cells co-expressing full length versions of both SYNJ2 and cortactin (Fig. 2E). We also identified intersectin and cortactin using mass spectrometry, as endogenous proteins associated with SYNJ2 that was overexpressed in HEK293 cells (data not shown).

SYNJ2 co-immunoprecipitated with cortactin from HEK293 cells co-expressing full length versions of both SYNJ2 and cortactin (Fig. 2E)

We obtained similar results for the interaction of SYNJ2 with intersectin (Fig. 2C)


Figure 1. Epigenetic marks that are influenced by EBNA3A and EBNA3C during the repression of BIM by EBV. (A) The levels of BIM protein (whose predominant isoform is the extra-large BIMEL) were assayed by western blotting protein extracts from BL31 cell lines. BIM levels in uninfected BL31 or BL31 infected with the wild-type (B95.8-BAC) EBV (WT), or viruses with each individual EBNA3 knocked out (KO) and the revertant viruses (REV) are shown. In all the EBNA3A and EBNA3C knockouts BIM levels are similar to the levels observed in the uninfected cells. The expression of g-tubulin is shown as a control for the amount of protein loaded. (B) Schematic of the BIM locus showing the approximate locations of the CpG island, TSS, BIM start codon and the PCR primer pairs used in this study (A–J). (C) Methylated DNA precipitation was used to assess whether DNA methylation at the BIM promoter was responsible for repression of BIM expression. The histogram bars represent fold enrichment for co-precipitated DNA relative to input DNA, as determined by Q-PCR. The height of the bars represents the abundance of the epigenetic mark at the locus assessed by each primer set. Uninfected BL31 cells, BL31 infected with WT EBV, or EBNA3 knockouts (KO) or the revertant viruses were tested. DNA from peripheral blood mononuclear cells (PBMC) and in vitro methylated PBMC DNA were used as negative and positive controls respectively. The error bars represent the standard deviation from triplicate Q-PCR assays. (D) The same panel of BL31 cell

In order to investigate the roles of EBNA3A and EBNA3C in the regulation of cellular gene expression, a panel of cell lines based on the EBV-negative BL-derived line BL31 were established and characterized (50,53). Here a selection of these were subjected to western blot analysis to confirm our previous observation that BL31 expressed high levels of BIM protein and that this was substantially reduced in BL31 latently infected in vitro with wild-type (WT) EBV-BAC-derived virus (Figure 1A). This analysis also confirmed that in BL31 infected with recombinant viruses from which EBNA3A or EBNA3C genes were deleted, BIM expression was not repressed and remained at a similar level to that in BL31 (Figure 1A). Revertant (REV) viruses reconstituted with the genes that had previously been deleted behaved—as expected—like the WT EBV and repressed BIM expression. Flanking the BIM transcription start site (TSS) is a remarkably large CGI that extends for 13 kb (http://genome.ucsc.edu/cgi-bin/hgGateway; Figure 1B). We previously showed that in various EBV-positive B cell lines and primary BL samples significant DNA methylation across this region could be detected, but this was absent in EBV-negative equivalents (52). However, analysis of several BL cell lines, including BL31, that were newly infected with EBV and also recently established LCLs revealed that although BIM transcription was repressed, there was little or no CpG methylation but rather the epigenetic repressive histone modification H3K27me3 was evident. Because of the apparently progressive nature of DNA methylation across the BIM CGI, a panel of low passage BL31-derived cell lines was subjected to methylated-DNA precipitation (MeDP) coupled with quantitative real-time PCR analysis using primer-pairs (A–F) as indicated (Figure 1B). Primary peripheral blood mononuclear cells and in vitro methylated genomic DNA were used as negative and positive controls respectively. All the lines analysed, irrespective of their EBV status, showed no detectable DNA methylation, as judged by MeDP, across a region extending from 4 kb upstream of the BIM TSS to the start codon—indicating that here histone modification was the most likely mode of repression (Figure 1C). Having shown in these EBV-infected BL31 cells that CpG methylation was not established at BIM, ChIP with an antibody directed against H3K27me3 and quantitative PCR analyses across the CGI was performed using the same primer-pairs as for MeDP. This showed that repression correlated with deposition of the H3K27me3 mark and expression of EBNA3A and EBNA3C (in the WT-, EBNA3BKO- and the revertant-infected cell lines in Figure 1D). In contrast, when either EBNA3A or EBNA3C were deleted from EBV (EBNA3AKO and EBNA3CKO) BIM expression remained high and, significantly, the amount of H3K27me3 detected across the BIM locus was below the background in uninfected BL31 cells. Occupancy of H3K27me3 on promoters of genes encoding Myoglobin and GAPDH were analysed in a similar manner as positive and negative controls respectively (Supplementary Figure S1A). ChIP for total histone H3 at BIM, Myoglobin and GAPDH revealed little difference between the cell lines used (Figure 1E and Supplementary Figure S1B). This indicates that any changes in the distribution of covalent histone modifications detected are not due to alterations in nucleosome density. The effects on H3K27me3 distribution at the BIM promoter induced by EBNA3A and EBNA3C are specific, since the overall levels of H3K27me3 were unaffected by EBV, EBNA3A or EBNA3C (Figure 1F). The protein complex responsible for establishing the H3K27me3 mark is PRC2. In mammals this is generally composed of a catalytic core made up of the histone methyltransferase EZH2 and subunits SUZ12, EED and RbAp46/48 (see ‘Introduction’ section). ChIPs for SUZ12, EZH2 and RbAp48 were performed on the panel of BL31 cell lines and analysed using the same primer-pairs as in Figure 1. Figure 2. PRC2 occupancy across the BIM promoter region. The presence and abundance of PRC2 proteins was assayed by ChIP analysis of a BL31 panel of cells, as indicated. Presentation is the same as for ChIP analysis in Figure 1. The region of amplification for each primer set (A–F) can be seen in Figure 1B. The error bars represent the standard deviation from triplicate PCR assays. ChIP was performed using an antibody specific for (A) PRC2 core subunit SUZ12, (B) PRC2 catalytic subunit EZH2, (C) core subunit RbAp48, (D) ancillary factor JARID2.

Flanking the BIM transcription start site (TSS) is a remarkably large CGI that extends for 6 kb

across a region extending from 8 kb upstream of the BIM TSS to the start codon—indicating that here histone modification was the most likely mode of repression


Figure 1. C-trapping of FACT. (A) Immunoblotting of soluble protein extracts and chromatin pellets of HT1080 cells treated with CBL0137 for 1 h, probed with the indicated antibodies. (B) Immunofluorescence staining of HT1080 cells with antibodies to SSRP1. (C) Fluorescent imaging of two live cells expressing GFP-tagged SSRP1 before and after treatment with CBL0137. Top row––HT1080 cell in interphase, 3 μM CBL0137, bottom row––DLD1 cell in mitosis, 5 μM CBL0137. (D) Fluorescent imaging of two live HT1080 cells, interphase and mitotic, expressing GFP-tagged SPT16 before and after treatment with CBL0137. Bars are 10 μm.

RESULTS FACT binds to unfolding chromatin in CBL0137-treated cells Using biochemical fractionation and fluorescent microscopy we previously demonstrated that curaxin treatment causes a rapid transition of FACT from the nucleoplasm to a state of tight association with chromatin (13). We named this phenomenon ‘chromatin trapping’ of FACT or c-trapping. To investigate the mechanism of c-trapping, we first examined the dose and time dependence as well as specificity of this phenomenon. To do this we used two methods, (i) immunoblotting of soluble, representing nucleoplasm, and pelleted, representing chromatin, fractions of total cell lysates ((Figure 1A) see details in Materials and Methods), and (ii) ectopic expression of GFP or mCherry tagged SSRP1 and SPT16 subunits of FACT, which undergo c-trapping in CBL0137-treated cells similarly to endogenous FACT subunits (Figure 1B–D and Supplemental Figure S1). C-trapping was observed in all tested cell lines (>20 cell lines, data not shown). Importantly, c-trapping and toxicity of CBL0137 occur at the same concentrations (∼0.3–0.5 μM depending on cell line, Figures 1A, 2 and (13)), however, c-trapping is observed within minutes after start of treatment (Figure 1C and D), while cell death after 48 h (13). Thus, these two effects of CBL0137 are probably connected, but c-trapping cannot be viewed as a sign of cell death. In untreated cells, FACT is diffusely spread throughout the nucleoplasm with some accumulation in nucleoli ((13,42) and Figure 2A and B). Changes in the distribution of FACT subunits begin 30–60 s after the addition of CBL0137 to cell culture medium (Figure 1C and D). In the presence of 0.3 μM CBL0137, both subunits become associated with thin fibers of chromatin, which is illustrated by their overlap with fluorescently labeled histone H2B (Figure 2A and B). At higher concentrations of CBL0137 (1–3 μM), FACT subunits are still associated with chromatin, however, the chromatin itself remains such that it appears as thicker fibers with bead like structures contouring nucleoli (Figure 2A and B). At concentrations of ≥5 μM CBL0137, the cellular distribution of SSRP1, SPT16, and H2B is dramatically changed. SSRP1 appears as thin fibers situated along the nuclear periphery and absent in and near nucleoli (Figure 2A). SPT16 becomes more diffuse (Figure 2B). Histone H2B loses its fiber-like appearance and accumulates in nucleoli (Figure 2A and B). Full transition takes 20–30 min (Figure 3A). Behavior similar to H2B was observed with fluorescently tagged histones H3 and H4 (Supplemental Figure S2). Similar to our results presented here, Musinova et al previously demonstrated that histone H2B, which is not incorporated into chromatin, accumulates in nucleoli due to the presence of a nucleoli localization signal within its nuclear localization signal (43). Therefore, we hypothesized that CBL0137 treatment leads to accumulation of histones in nucleoli due to their liberation from chromatin, which is consistent with the loss of visible chromatin fibers (Figure 2A and B, 10 μM). C-trapping not only occurs in interphase cells but also in mitotic cells. As shown in Figure 1 C and D, diffuse GFP-SSRP1 or GFP-SPT16 signals in mitotic cells became overlapped with mitotic chromosomes within a few minutes following the addition of CBL0137, which is indicative of a rapid CBL0137-induced binding of FACT to chromatin (Figure 1C and D). However, later on and in the presence of ≥3 μM of CBL0137, histone H2B was lost from chromosomes and appeared more like a diffuse cloud (Figure 3B). FACT subunits followed histones into the cloud-like shape, however, some SSRP1 was still associated with loops in the periphery, which were most likely leftover of decondensed and disassembled chromatin (Figure 3B).

the chromatin itself changes such that it appears as thicker fibers with bead like structures contouring nucleoli (Figure 2A and B).


Figure 2. XRK3F2 plus bortezomib combination activates multiple death pathways and overcomes apoptosis resistance in mul-tiple myeloma. MM.1S cells treated with XRK3F2 (5 μM), bortezomib (Btz) (3 nM), or combined XRK3F2-Btz (5 μM/3 nM) for 24 hours in the presence or absence of autophagy inhibitor BafilomycinA1 (Baf, 40 nM) (A, D), Pan caspase OPH inhibitor Q-VD (QVD, 20 μM) (B, E), or caspase-3 inhibitor Z-DEVD-FMK (Z-DEV, 20 μM) (C, F). Cell viability was evaluated using alamarBlue assays and is reported as percent versus dimethyl sulfoxide (DMSO) vehicle control (A-C). Analysis of autophagic flux and apoptosis were assessed by immunoblotting by LC3I-LC3II conversion (D) and for cleavage of caspase 8 and 3 (E, F). Data are presented as bars, means ± standard deviation (N=4-6/group). *P<0.05 versusvehicle, ^P<0.05 versus XRK3F2 or Btz alone and ^P<0.05 versus control versus Baf/QVD/ZDEV culture by two-way ANOVA with post hoc Bonferroni’s correction.

Btz decrease p62 levels by inducing de novo p62 expressionand preventing its degradation.In agreement with the pre-vious studies,:: we found that Btz increased levels of p62mRNA 8-fold and protein (Figure 2D; Online SupplementaryFigure S2D)independent of autophagy since changes inLC3l-LC3ll conversion were not found in Btz-treated cells.Btz-induced p62 mRNA expression was reduced by XRK3F2Ligands binding to p62-2Z stimulate p62 oligomerizationand autophagy.34 XRK3F2 alone or in combination with Btz decreased LC3l-LC3ll conversion in MM1S,indicating induction ofautophagy (Figure 2D, left panel). Pretreatment of MMcells with bafilomycin A1 (Baf), which disrupts autophagicflux, further increased LC3ll/LC3l ratio and p62 protein levels compared to XRK3F2 alone or XRK3F2-Btz combination(Figure 2D, right panel). Furthermore, autophagy blockadeby Baf treatment further amplified the negative effects oXRK3F2 and Btz on MM cell viability (Figure 2A, Online sup.plementary Figure S4A), indicating that the autophagy-induced by targeting p62-ZZ domain in MM cells was not thepathway responsible for MM cell death.We previously reported that high doses ofXRK3F2 triggeredcaspase 3 cleavage in MM.1S cells.7 Here, we found that inMM.1S cells, co-administration of 5uM XRK3F2 and 3 nMBtz strongly activated caspase-8 and caspase-3 (Figure 2Eleft panel).In contrast, we only detected partial activationof caspases by Btz alone and no activation by XRK3F2 atthe concentration used. To test if the increased cell deathseen with the combination therapy was due to apoptosiswe used the pan-caspase inhibitor O-VD-OPh (OvD) and thecaspase-3 inhibitor Z-DEVD-FMK (Z-DEV). OVD and Z-DEVcompletely blocked Btz-induced cleavage of caspase-8 and3 (Figure 2E, F, right panels). In addition, QvD or Z-DEV fullyprevented the decrease in cell viabilityinduced by Btz (Figure2B,C;Online Supplementary Figure S4B, C)but enhancedthe anti-MM effects of XRK3F2. QVD and Z-DEV only partially blocked caspase 8 and 3 cleavage by XRK3F2-Btz andpartially reduced the effects ofthe combination on MM celviability. These findings suggest that XRK3F2-Btz combinationinhibits MM viability by activating both caspase-dependentand independent-cell death pathways.

Btz increases p62 levels by inducing de novo p62 expressionand preventing its degradation

34 XRK3F2 alone or in combination with Btz increased LC3l-LC3ll conversion in MM1S


Figure 5 (A) Immunofluorescence staining of GIG on expression of ABCB4 and BSEP in ANIT-treated rats’ liver tissues. (B) The relative percentage of ABCB4 expression. (C) The relative percentage of BSEP expression. Red represents ABCB4, and green represents BSEP. **P<0.01, ****P<0.0001.

Effect of GIG on ABCB4 and BSEP Protein Expression in ANIT-Treated Rats. Compared with the normal control group, there was almost the high expression of ABCB4 and BSEP in the model group’s liver tissues (Figure 5). Compared with the model group, the expression of BSEP and ABCB4 decreased significantly in the GIG middle and high dose groups, and the changes of BSEP and ABCB4 expression showed a negative correlation with the dose of GIG (Figure 5). The expression trends of ABCB4 and BSEP in each group can be seen in Figures 5B and C.

there was almost the low expression of ABCB4 and BSEP in the model group’s liver tissues (Figure 5)

the expression of BSEP and ABCB4 increased significantly in the GIG middle and high dose groups

and the changes of BSEP and ABCB4 expression showed a positive correlation with the dose of GIG (Figure 5)


Figure 1 The expression of miR-29c-3p and LIF in UC tissues and IECs. (A) The mRNA levels of LIF in UC and normal tissues. (B) Representative Western blots and densitometric quantitative analysis of LIF protein levels in UC and normal tissues. (C) The mRNA levels of LIF in primary IECs of UC and normal colon. (D) Representative Western blots and densitometric quantitative analysis of LIF protein levels in IECs of UC and normal colon. (E) The expression of miR-29c-3p in UC and normal tissues. (F) The expression of miR-29c-3p in primary IECs of UC and normal colon. (G) Pearson’s correlation analysis of the expression levels of miR-29c-3p and LIF protein. (H) Pearson’s correlation analysis of the expression levels of miR-29c-3p and LIF mRNA. **P<0.01 versus normal group.

ResultsThe Expression of miR-29c-3p and LIF in UC Tissues and Primary IECsColonic biopsies were obtained during endoscopic examinations. We first assessed LIF expression in colon tissues using qRT-PCR and found it to be upregulated in UC patients compared with healthy controls (Figure 1A). The weakened LIF protein expression was then verified by Western blotting (Figure 1B). To further explore cellular level of LIF, primary IECs were isolated and quantified for LIF expression. As expected, the same change trends were observed in IECs (Figure 1C and D). Next, we adopted five miRNA databases (TargetScan, miRDB, starBase, mirDIP and miRmap) to predict the upstream miRNA of LIF. As a result, miR-29c-3p was the only miRNA with the highest possibility to interact with LIF predicted simultaneously by all databases. Therefore, we explored miR-29c-3p expression by qRT-PCR. As illustrated in Figure 1E and F, miR-29c-3p was upregulated both in colonic tissues and IECs from UC patients relative to normal controls. Subsequently, as illustrated by Pearson correlation scatter plots (Figure 1G and H), miR-29c-3p and LIF protein expression in UC tissues were inversely correlated, whereas disparity was found between miR-29c-3p and LIF mRNA expression. Accordingly, we hypothesized that miR-29c-3p is involved in the post-transcriptional regulation of LIF expression.

The enhanced LIF protein expression was then verified by Western blotting (Figure 1B)

miR-29c-3p was downregulated both in colonic tissues and IECs from UC patients relative to normal controls


R-Ras2/TC21 regulates the timely development of the pubertal mammary gland. (A) qRT-PCR analysis of total RNA obtained from mammary glands extracted from the indicated stages. Prepubertal, pubertal, and adult virgins correspond to samples obtained from 3-, 8-, and 14-wk-old RRas2+/+ female mice, respectively. Early and late pregnancy correspond to female mice within weeks 1 and 2.5 of pregnancy, respectively. The early and late lactation samples were taken 1 and 2 wk after labor, respectively. The involution phase samples were taken 3.5 d after offspring weaning. Data are given relative to the expression levels obtained in virgin adult state (which was given an arbitrary value of 1). All values were normalized taking into consideration the expression levels of the housekeeping Gapdh mRNA in each sample. *p ≤ 0.05; ***p ≤ 0.001 (n = 3). (B) Representative images of carmine alum–stained mammary fat pads obtained from female mice of the indicated ages (top) and genotypes (left). Scale bars, 500 μm. One of the inguinal lymph nodes is indicated by a white asterisk. (C–F) Quantification of the total area (C), maximal length (D), number of TEBs (E), and branching points (F) of mammary glands obtained from animals of the indicated genotypes and ages. *p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001 when compared with values obtained in the appropriate wild-type control (n = 5–10 animals per genotype and developmental stage).

We found no major differences in any of those parameters in the mammary glands of 4-wk-old R-Ras2/TC21-deficient and control mice (Figure 3B, 3C, 3E, and 3F). However, when we analyzed 5-wk-old mice, we observed that the mammary glands extracted from RRas2−/− mice were significantly smaller than those obtained from control animals (Figure 3B–3D). Those glands also displayed significantly higher numbers of TEBs (Figure 3B and 3E) and branching points (Figure 3B and 3F). Most of those differences disappeared in the mammary glands of 12-wk-old mice (Figure 3B–3D). However, unlike the case of control mice, we found that ∼50% of the mammary glands obtained from RRas2−/− mice still contained a insignificant number of TEBs (Figure 3E), further suggesting that the R-Ras2/TC21 deficiency induces a delay in the development of this gland. This phenotype was highly penetrant, since it was observed in mice of homogenized (C57BL/6J, Figure 3) and mixed (129Sv/J × C57BL/6J, Supplemental Figure S2) genetic backgrounds. In the latter strain, R-Ras2/TC21 became specifically upregulated during those two highly morphogenetic periods of the mammary gland (Figure 3A). As previously seen with RRas2−/− mice (Figure 3), this observation, coupled with the upregulation of the RRas2 gene found in specific developmental stages of the gland (Figure 3A), supports a role for R-Ras2/TC21 in mammary gland maturation.

we found that ∼50% of the mammary glands obtained from RRas2−/− mice still contained a significant number of TEBs (Figure 3E)

Those glands also displayed significantly lower numbers of TEBs (Figure 3B and 3E) and branching points (Figure 3B and 3F)


Tissue penetration of Humabodies with and without albumin in spheroids. Spheroids show cell nuclei (blue) and fluorescent drug (green). Incubations included 20 nM J591 antibody, monovalent-HLE Humabody, PSMA-CD137 with MSA binding HLE (mHLE) incubated with and without MSA, PSMA-CD137 with HSA binding HLE (hHLE) incubated with and without HSA (A). Euclidean distance mapping shows the fluorescent intensity versus distance from the spheroid edge in μm (B-D), indicating that specific albumin binding is not impacting tissue penetration to the spheroid center. The MSA binders (Fig. 2B) had lower maximum signal with the presence of MSA (p=0.002), but HSA binders did not have differences in the maximum signal (p = 0.85). *, p < 0.05.

Given the importance of tissue penetration, we next investigated the impact of albumin binding on diffusion. In theory, the diffusion of a Humabody-albumin complex (~110 kDa) would be slower than the Humabody alone (~45 kDa), which could reduce tissue penetration. The impact of albumin binding on tissue distribution was therefore tested in DU145T spheroids. Humabodies with HLE domains were incubated with and without their corresponding albumin type (HSA or MSA), which have 8 nM and 50 nM affinity (Kd), respectively. In both cases, the addition of albumin did not result in changes in tissue penetration compared to Humabody alone (Fig. 3A). Despite the concentration of albumin well above the Kd (~ 200 µM), where most of the Humabody could be in complex with albumin, the presence of albumin had a negligible effect on tissue penetration. These findings were confirmed by quantitative image analysis to map the fluorescence distribution as a function of distance from the spheroid edge (Fig. 3B–D). The fluorescent signal in the center of the spheroids did not show any statistically significant differences with and without albumin. In contrast, an equal molar amounts of J591 antibody (~150 kDa) resulted in homogeneous distribution, which is typical for large biologics. A control monovalent-HLE (that had no CD137 binding domain) with the lowest molecular weight (~30 kDa) distributed the most evenly.

an equal molar amounts of J591 antibody (~150 kDa) resulted in heterogeneous distribution